Big_data.ipynb

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        "outputId": "cbea73ae-15c6-49bf-f58c-2bed3de807e9"
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      "outputs": [
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          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.6-py3-none-any.whl (8.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.6\n"
          ]
        }
      ],
      "source": [
        "!pip install ucimlrepo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "5584275c-f38f-4ead-9668-86ce3b78bdf5"
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "{'uci_id': 352, 'name': 'Online Retail', 'repository_url': 'https://archive.ics.uci.edu/dataset/352/online+retail', 'data_url': 'https://archive.ics.uci.edu/static/public/352/data.csv', 'abstract': 'This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.', 'area': 'Business', 'tasks': ['Classification', 'Clustering'], 'characteristics': ['Multivariate', 'Sequential', 'Time-Series'], 'num_instances': 541909, 'num_features': 6, 'feature_types': ['Integer', 'Real'], 'demographics': [], 'target_col': None, 'index_col': ['InvoiceNo', 'StockCode'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2015, 'last_updated': 'Fri Jan 05 2024', 'dataset_doi': '10.24432/C5BW33', 'creators': ['Daqing Chen'], 'intro_paper': {'title': 'Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining', 'authors': 'Daqing Chen, Sai Laing Sain, Kun Guo', 'published_in': 'Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3', 'year': 2012, 'url': 'https://www.semanticscholar.org/paper/e43a5a90fa33d419df42e485099f8f08badf2149', 'doi': '10.1057/dbm.2012.17'}, 'additional_info': {'summary': 'This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': \"InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. \\nStockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.\\nDescription: Product (item) name. Nominal.\\nQuantity: The quantities of each product (item) per transaction. Numeric.\\t\\nInvoiceDate: Invoice Date and time. Numeric, the day and time when each transaction was generated.\\nUnitPrice: Unit price. Numeric, Product price per unit in sterling.\\nCustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.\\nCountry: Country name. Nominal, the name of the country where each customer resides. \", 'citation': None}}\n",
            "          name     role         type demographic  \\\n",
            "0    InvoiceNo       ID  Categorical        None   \n",
            "1    StockCode       ID  Categorical        None   \n",
            "2  Description  Feature  Categorical        None   \n",
            "3     Quantity  Feature      Integer        None   \n",
            "4  InvoiceDate  Feature         Date        None   \n",
            "5    UnitPrice  Feature   Continuous        None   \n",
            "6   CustomerID  Feature  Categorical        None   \n",
            "7      Country  Feature  Categorical        None   \n",
            "\n",
            "                                         description     units missing_values  \n",
            "0  a 6-digit integral number uniquely assigned to...      None             no  \n",
            "1  a 5-digit integral number uniquely assigned to...      None             no  \n",
            "2                                       product name      None             no  \n",
            "3  the quantities of each product (item) per tran...      None             no  \n",
            "4  the day and time when each transaction was gen...      None             no  \n",
            "5                             product price per unit  sterling             no  \n",
            "6  a 5-digit integral number uniquely assigned to...      None             no  \n",
            "7  the name of the country where each customer re...      None             no  \n"
          ]
        }
      ],
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "online_retail = fetch_ucirepo(id=352)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = online_retail.data.features\n",
        "y = online_retail.data.targets\n",
        "\n",
        "# metadata\n",
        "print(online_retail.metadata)\n",
        "print(online_retail.variables)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "outputId": "9ee9647b-9be5-4253-d4d2-3a19a3b69177"
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      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   Description  540455 non-null  object \n",
            " 1   Quantity     541909 non-null  int64  \n",
            " 2   InvoiceDate  541909 non-null  object \n",
            " 3   UnitPrice    541909 non-null  float64\n",
            " 4   CustomerID   406829 non-null  float64\n",
            " 5   Country      541909 non-null  object \n",
            "dtypes: float64(2), int64(1), object(3)\n",
            "memory usage: 24.8+ MB\n"
          ]
        }
      ],
      "source": [
        "X.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "0Dtt197I1DPg",
        "outputId": "f63892f7-120d-41ee-932e-18da227fd2c3"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Description  Quantity     InvoiceDate  UnitPrice  \\\n",
              "0   WHITE HANGING HEART T-LIGHT HOLDER         6  12/1/2010 8:26       2.55   \n",
              "1                  WHITE METAL LANTERN         6  12/1/2010 8:26       3.39   \n",
              "2       CREAM CUPID HEARTS COAT HANGER         8  12/1/2010 8:26       2.75   \n",
              "3  KNITTED UNION FLAG HOT WATER BOTTLE         6  12/1/2010 8:26       3.39   \n",
              "4       RED WOOLLY HOTTIE WHITE HEART.         6  12/1/2010 8:26       3.39   \n",
              "\n",
              "   CustomerID         Country  \n",
              "0     17850.0  United Kingdom  \n",
              "1     17850.0  United Kingdom  \n",
              "2     17850.0  United Kingdom  \n",
              "3     17850.0  United Kingdom  \n",
              "4     17850.0  United Kingdom  "
            ],
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          "name": "stdout",
          "text": [
            "Collecting pyspark\n",
            "  Downloading pyspark-3.5.1.tar.gz (317.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.0/317.0 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
            "Building wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built pyspark\n",
            "Installing collected packages: pyspark\n",
            "Successfully installed pyspark-3.5.1\n"
          ]
        }
      ],
      "source": [
        "!pip install pyspark\n",
        "from pyspark import SparkContext, SparkConf\n",
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql import Row\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "from pyspark.ml.regression import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3co_q4Bb1DPm"
      },
      "outputs": [],
      "source": [
        "spark = SparkSession.builder \\\n",
        "      .master(\"local[*]\") \\\n",
        "      .appName(\"SPARK-TUT\") \\\n",
        "      .getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PuPoOuAB1DPm"
      },
      "outputs": [],
      "source": [
        "df_spark = spark.createDataFrame(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umb87Wl81DPn",
        "outputId": "c2700ff7-c6bd-4868-9204-d0f27db87cc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "|         Description|Quantity|   InvoiceDate|UnitPrice|CustomerID|       Country|\n",
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "|WHITE HANGING HEA...|       6|12/1/2010 8:26|     2.55|   17850.0|United Kingdom|\n",
            "| WHITE METAL LANTERN|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|CREAM CUPID HEART...|       8|12/1/2010 8:26|     2.75|   17850.0|United Kingdom|\n",
            "|KNITTED UNION FLA...|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|RED WOOLLY HOTTIE...|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|SET 7 BABUSHKA NE...|       2|12/1/2010 8:26|     7.65|   17850.0|United Kingdom|\n",
            "|GLASS STAR FROSTE...|       6|12/1/2010 8:26|     4.25|   17850.0|United Kingdom|\n",
            "|HAND WARMER UNION...|       6|12/1/2010 8:28|     1.85|   17850.0|United Kingdom|\n",
            "|HAND WARMER RED P...|       6|12/1/2010 8:28|     1.85|   17850.0|United Kingdom|\n",
            "|ASSORTED COLOUR B...|      32|12/1/2010 8:34|     1.69|   13047.0|United Kingdom|\n",
            "|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|      2.1|   13047.0|United Kingdom|\n",
            "|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|      2.1|   13047.0|United Kingdom|\n",
            "|FELTCRAFT PRINCES...|       8|12/1/2010 8:34|     3.75|   13047.0|United Kingdom|\n",
            "|IVORY KNITTED MUG...|       6|12/1/2010 8:34|     1.65|   13047.0|United Kingdom|\n",
            "|BOX OF 6 ASSORTED...|       6|12/1/2010 8:34|     4.25|   13047.0|United Kingdom|\n",
            "|BOX OF VINTAGE JI...|       3|12/1/2010 8:34|     4.95|   13047.0|United Kingdom|\n",
            "|BOX OF VINTAGE AL...|       2|12/1/2010 8:34|     9.95|   13047.0|United Kingdom|\n",
            "|HOME BUILDING BLO...|       3|12/1/2010 8:34|     5.95|   13047.0|United Kingdom|\n",
            "|LOVE BUILDING BLO...|       3|12/1/2010 8:34|     5.95|   13047.0|United Kingdom|\n",
            "|RECIPE BOX WITH M...|       4|12/1/2010 8:34|     7.95|   13047.0|United Kingdom|\n",
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_spark = df_spark.na.drop(subset=\"Description\")\n",
        "df_spark.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kp0W5Tq11DPr",
        "outputId": "5ad0613c-54f1-4d70-d7da-e535333fff48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+\n",
            "|         Description|\n",
            "+--------------------+\n",
            "|WHITE HANGING HEA...|\n",
            "| WHITE METAL LANTERN|\n",
            "|CREAM CUPID HEART...|\n",
            "|KNITTED UNION FLA...|\n",
            "|RED WOOLLY HOTTIE...|\n",
            "|SET 7 BABUSHKA NE...|\n",
            "|GLASS STAR FROSTE...|\n",
            "|HAND WARMER UNION...|\n",
            "|HAND WARMER RED P...|\n",
            "|ASSORTED COLOUR B...|\n",
            "|POPPY'S PLAYHOUSE...|\n",
            "|POPPY'S PLAYHOUSE...|\n",
            "|FELTCRAFT PRINCES...|\n",
            "|IVORY KNITTED MUG...|\n",
            "|BOX OF 6 ASSORTED...|\n",
            "|BOX OF VINTAGE JI...|\n",
            "|BOX OF VINTAGE AL...|\n",
            "|HOME BUILDING BLO...|\n",
            "|LOVE BUILDING BLO...|\n",
            "|RECIPE BOX WITH M...|\n",
            "+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_df = df_spark.select('Description')\n",
        "data_df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7rl4VGcb1DPs"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql.functions import lower\n",
        "df_spark = df_spark.withColumn(\"Description\", lower(df_spark[\"Description\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3Thy6j21DPt",
        "outputId": "974d75f6-9fe9-4bad-db1c-1d34c1f1c838"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "|         Description|Quantity|   InvoiceDate|UnitPrice|CustomerID|       Country|\n",
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "|white hanging hea...|       6|12/1/2010 8:26|     2.55|   17850.0|United Kingdom|\n",
            "| white metal lantern|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|cream cupid heart...|       8|12/1/2010 8:26|     2.75|   17850.0|United Kingdom|\n",
            "|knitted union fla...|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|red woolly hottie...|       6|12/1/2010 8:26|     3.39|   17850.0|United Kingdom|\n",
            "|set 7 babushka ne...|       2|12/1/2010 8:26|     7.65|   17850.0|United Kingdom|\n",
            "|glass star froste...|       6|12/1/2010 8:26|     4.25|   17850.0|United Kingdom|\n",
            "|hand warmer union...|       6|12/1/2010 8:28|     1.85|   17850.0|United Kingdom|\n",
            "|hand warmer red p...|       6|12/1/2010 8:28|     1.85|   17850.0|United Kingdom|\n",
            "|assorted colour b...|      32|12/1/2010 8:34|     1.69|   13047.0|United Kingdom|\n",
            "|poppy's playhouse...|       6|12/1/2010 8:34|      2.1|   13047.0|United Kingdom|\n",
            "|poppy's playhouse...|       6|12/1/2010 8:34|      2.1|   13047.0|United Kingdom|\n",
            "|feltcraft princes...|       8|12/1/2010 8:34|     3.75|   13047.0|United Kingdom|\n",
            "|ivory knitted mug...|       6|12/1/2010 8:34|     1.65|   13047.0|United Kingdom|\n",
            "|box of 6 assorted...|       6|12/1/2010 8:34|     4.25|   13047.0|United Kingdom|\n",
            "|box of vintage ji...|       3|12/1/2010 8:34|     4.95|   13047.0|United Kingdom|\n",
            "|box of vintage al...|       2|12/1/2010 8:34|     9.95|   13047.0|United Kingdom|\n",
            "|home building blo...|       3|12/1/2010 8:34|     5.95|   13047.0|United Kingdom|\n",
            "|love building blo...|       3|12/1/2010 8:34|     5.95|   13047.0|United Kingdom|\n",
            "|recipe box with m...|       4|12/1/2010 8:34|     7.95|   13047.0|United Kingdom|\n",
            "+--------------------+--------+--------------+---------+----------+--------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_spark.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmM-UnJo1DPw",
        "outputId": "ec2fa538-83fa-4725-d60b-dd911719b9da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row(Description='white hanging heart t-light holder', words=['white', 'hanging', 'heart', 't-light', 'holder'])\n"
          ]
        }
      ],
      "source": [
        "text_data = df_spark.select(\"Description\")\n",
        "from pyspark.ml.feature import Tokenizer\n",
        "\n",
        "tokenizer = Tokenizer(inputCol=\"Description\", outputCol=\"words\")\n",
        "tokenized_data = tokenizer.transform(text_data)\n",
        "first_row = tokenized_data.head()\n",
        "print(first_row)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0x3BpFKj1DPx",
        "outputId": "63e621af-a964-4a87-b8c1-f97d16bbf957"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------------------+\n",
            "|         Description|               words|\n",
            "+--------------------+--------------------+\n",
            "|white hanging hea...|[white, hanging, ...|\n",
            "| white metal lantern|[white, metal, la...|\n",
            "|cream cupid heart...|[cream, cupid, he...|\n",
            "|knitted union fla...|[knitted, union, ...|\n",
            "|red woolly hottie...|[red, woolly, hot...|\n",
            "|set 7 babushka ne...|[set, 7, babushka...|\n",
            "|glass star froste...|[glass, star, fro...|\n",
            "|hand warmer union...|[hand, warmer, un...|\n",
            "|hand warmer red p...|[hand, warmer, re...|\n",
            "|assorted colour b...|[assorted, colour...|\n",
            "|poppy's playhouse...|[poppy's, playhou...|\n",
            "|poppy's playhouse...|[poppy's, playhou...|\n",
            "|feltcraft princes...|[feltcraft, princ...|\n",
            "|ivory knitted mug...|[ivory, knitted, ...|\n",
            "|box of 6 assorted...|[box, of, 6, asso...|\n",
            "|box of vintage ji...|[box, of, vintage...|\n",
            "|box of vintage al...|[box, of, vintage...|\n",
            "|home building blo...|[home, building, ...|\n",
            "|love building blo...|[love, building, ...|\n",
            "|recipe box with m...|[recipe, box, wit...|\n",
            "+--------------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "tokenized_data.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BPY88mH1DPy",
        "outputId": "f6c0284a-4050-4b49-d5d7-905aec0276b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of different unique products: 4207\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import col\n",
        "unique_products_count = df_spark.select(col(\"Description\")).distinct().count()\n",
        "print(\"Number of different unique products:\", unique_products_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2I4Y0ZDt1DP0",
        "outputId": "e72ffdb5-6298-497e-caee-d905d3c79ed3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+-------------+\n",
            "|         Description|TotalQuantity|\n",
            "+--------------------+-------------+\n",
            "|world war 2 glide...|        53847|\n",
            "|jumbo bag red ret...|        47363|\n",
            "|assorted colour b...|        36381|\n",
            "|      popcorn holder|        36334|\n",
            "|pack of 72 retros...|        36039|\n",
            "|white hanging hea...|        35317|\n",
            "|  rabbit night light|        30680|\n",
            "|mini paint set vi...|        26437|\n",
            "|pack of 12 london...|        26315|\n",
            "|pack of 60 pink p...|        24753|\n",
            "|victorian glass h...|        23854|\n",
            "|assorted colours ...|        23082|\n",
            "| brocade ring purse |        23053|\n",
            "|red  harmonica in...|        21866|\n",
            "|jumbo bag pink po...|        21009|\n",
            "|small popcorn holder|        20116|\n",
            "|paper chain kit 5...|        18902|\n",
            "|lunch bag red ret...|        18779|\n",
            "|60 teatime fairy ...|        18040|\n",
            "|       party bunting|        18022|\n",
            "+--------------------+-------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import sum\n",
        "\n",
        "product_quantity_df = df_spark.groupBy(\"Description\").agg(sum(\"Quantity\").alias(\"TotalQuantity\"))\n",
        "product_quantity_ordered_df = product_quantity_df.orderBy(\"TotalQuantity\", ascending=False)\n",
        "product_quantity_ordered_df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m5oLRn_T1DP1"
      },
      "outputs": [],
      "source": [
        "top_15_products = product_quantity_ordered_df.limit(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EOvG2nYW1DP2"
      },
      "outputs": [],
      "source": [
        "top_15_products_pd = top_15_products.toPandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "R7uiOkDJ1DP3",
        "outputId": "b71b415c-bd57-4e86-e2f0-27cf5d181f44"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.bar(top_15_products_pd[\"Description\"], top_15_products_pd[\"TotalQuantity\"], color='skyblue')\n",
        "plt.xlabel(\"Product\")\n",
        "plt.ylabel(\"Total Quantity\")\n",
        "plt.title(\"Top 15 Products by Total Quantity\")\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjNcmGGd1DP4",
        "outputId": "bd255418-ca14-4941-e909-b7396fbcc11d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Unique Customer: 4373\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import col\n",
        "cust = df_spark.select(col(\"CustomerID\")).distinct().count()\n",
        "print(\"Number of Unique Customer:\", cust)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYiH-0yc1DP5",
        "outputId": "fcdecfd1-2e21-4618-e64e-9113c98172be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of NaNs in ClientID column in df_spark: 135080\n"
          ]
        }
      ],
      "source": [
        "import pyspark.sql.functions as F\n",
        "nan_count = df_spark.filter(F.isnan(\"CustomerID\")).count()\n",
        "print(\"Number of NaNs in ClientID column in df_spark:\", nan_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZcThbY31DP5",
        "outputId": "adc24650-75e2-4a91-b6c0-28a008dc6f55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Unique Customer: 4372\n"
          ]
        }
      ],
      "source": [
        "df_spark = df_spark.dropna(subset=[\"CustomerID\"])\n",
        "\n",
        "from pyspark.sql.functions import col\n",
        "cust = df_spark.select(col(\"CustomerID\")).distinct().count()\n",
        "print(\"Number of Unique Customer:\", cust)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6fFQFkT1DP6",
        "outputId": "81903c61-8e5e-439b-adbc-fb20d94959b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+------------------+\n",
            "|CustomerID|      TotalPayment|\n",
            "+----------+------------------+\n",
            "|   14646.0| 279489.0199999992|\n",
            "|   18102.0|256438.48999999993|\n",
            "|   17450.0|187482.16999999998|\n",
            "|   14911.0|132572.62000000002|\n",
            "|   12415.0| 123725.4499999999|\n",
            "|   14156.0|113384.14000000004|\n",
            "|   17511.0| 88125.37999999996|\n",
            "|   16684.0| 65892.07999999999|\n",
            "|   13694.0|62653.100000000006|\n",
            "|   15311.0|59419.340000000084|\n",
            "|   13089.0| 57385.88000000011|\n",
            "|   14096.0| 57120.90999999968|\n",
            "|   15061.0| 54228.73999999999|\n",
            "|   17949.0|52750.840000000004|\n",
            "|   15769.0|          51823.72|\n",
            "|   16029.0|50992.609999999964|\n",
            "|   14298.0|  50862.4399999999|\n",
            "|   14088.0| 50415.48999999999|\n",
            "|   17841.0| 40340.78000000022|\n",
            "|   13798.0|36351.419999999984|\n",
            "+----------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "df_spark = df_spark.withColumn(\"TotalPrice\", F.col(\"Quantity\") * F.col(\"UnitPrice\"))\n",
        "customer_total_payment = df_spark.groupBy(\"CustomerID\").agg(F.sum(\"TotalPrice\").alias(\"TotalPayment\"))\n",
        "customer_total_payment_ordered = customer_total_payment.orderBy(\"TotalPayment\", ascending=False)\n",
        "customer_total_payment_ordered.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbkdvN-F1DP7",
        "outputId": "94f36cb6-9879-4c09-c1f0-ef1c70d4a9c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4372"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "customer_total_payment_ordered.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4j_PO3Ns1DP9"
      },
      "outputs": [],
      "source": [
        "df_spark.createOrReplaceTempView(\"retail_data\")\n",
        "\n",
        "top_products_by_country = spark.sql(\"\"\"\n",
        "    SELECT Country, Description, COUNT(*) AS Count\n",
        "    FROM retail_data\n",
        "    GROUP BY Country, Description\n",
        "    ORDER BY Country, Count DESC\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dqz8I-S1DP-",
        "outputId": "61e2d505-e3cb-417c-c138-79cdd4b56446"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19370"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "top_products_by_country.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nRK7V7g1DP_",
        "outputId": "93b1bcaa-fa00-4cbb-b6ab-bac3bcb5285f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------------+\n",
            "|           Country|\n",
            "+------------------+\n",
            "|            Sweden|\n",
            "|         Singapore|\n",
            "|           Germany|\n",
            "|               RSA|\n",
            "|            France|\n",
            "|            Greece|\n",
            "|European Community|\n",
            "|           Belgium|\n",
            "|           Finland|\n",
            "|             Malta|\n",
            "|       Unspecified|\n",
            "|             Italy|\n",
            "|              EIRE|\n",
            "|         Lithuania|\n",
            "|            Norway|\n",
            "|             Spain|\n",
            "|           Denmark|\n",
            "|            Israel|\n",
            "|           Iceland|\n",
            "|   Channel Islands|\n",
            "+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "unique_countries = top_products_by_country.select(\"Country\").distinct()\n",
        "unique_countries.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WKayBtIC1DQA"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql.window import Window\n",
        "import pyspark.sql.functions as F\n",
        "\n",
        "window_spec = Window.partitionBy(\"Country\").orderBy(F.desc(\"Count\"))\n",
        "top_products_by_country_ranked = top_products_by_country.withColumn(\"Rank\", F.rank().over(window_spec))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXNCZiSu1DQA",
        "outputId": "bfac7ed9-04ea-49d3-9e93-bdfc02250be0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------+--------------------+-----+----+\n",
            "|  Country|         Description|Count|Rank|\n",
            "+---------+--------------------+-----+----+\n",
            "|Australia|set of 3 cake tin...|   10|   1|\n",
            "|  Austria|             postage|   14|   1|\n",
            "|  Bahrain|novelty biscuits ...|    2|   1|\n",
            "|  Belgium|             postage|   98|   1|\n",
            "|   Brazil|recycled acapulco...|    1|   1|\n",
            "|   Brazil|natural slate rec...|    1|   1|\n",
            "|   Brazil|edwardian parasol...|    1|   1|\n",
            "|   Brazil|small heart flowe...|    1|   1|\n",
            "|   Brazil| spaceboy lunch box |    1|   1|\n",
            "|   Brazil|green regency tea...|    1|   1|\n",
            "|   Brazil|lunch bag spacebo...|    1|   1|\n",
            "|   Brazil|recycled acapulco...|    1|   1|\n",
            "|   Brazil|roses regency tea...|    1|   1|\n",
            "|   Brazil|cook with wine me...|    1|   1|\n",
            "|   Brazil|pink regency teac...|    1|   1|\n",
            "|   Brazil|recycled acapulco...|    1|   1|\n",
            "|   Brazil|set of 6 spice ti...|    1|   1|\n",
            "|   Brazil|set of 4 pantry j...|    1|   1|\n",
            "|   Brazil|    jam clock magnet|    1|   1|\n",
            "|   Brazil|recycled acapulco...|    1|   1|\n",
            "+---------+--------------------+-----+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "top_1_product_by_country = top_products_by_country_ranked.filter(F.col(\"Rank\") == 1)\n",
        "top_1_product_by_country.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VU-0WuZw1DQB"
      },
      "outputs": [],
      "source": [
        "top_10_countries = top_1_product_by_country.groupBy(\"Country\").agg(F.sum(\"Count\").alias(\"TotalCount\")) \\\n",
        "                                            .orderBy(F.desc(\"TotalCount\")).limit(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wp_fet5q1DQB",
        "outputId": "67a3ebd5-907b-4239-8515-b2fa438d440d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+\n",
            "|             Country|TotalCount|\n",
            "+--------------------+----------+\n",
            "|      United Kingdom|      1980|\n",
            "|             Germany|       383|\n",
            "|              France|       311|\n",
            "|                EIRE|        99|\n",
            "|             Belgium|        98|\n",
            "|United Arab Emirates|        68|\n",
            "|               Spain|        62|\n",
            "|                 RSA|        58|\n",
            "|             Lebanon|        45|\n",
            "|             Finland|        41|\n",
            "+--------------------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "top_10_countries.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "kzp72pVf1DQC",
        "outputId": "0dc8e306-d5d2-40bb-e8b1-efe2708ba06b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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UqVN6/fXXJUm1a9eWt7e33n33Xbf37rRp0xQWFhbrfbNhwwZFRkY6bYsXL9bRo0fv+LWmSZPmtk+9+LfSpUsrX758evPNN3XhwoVYy2MubxcVFRXrufz9/RUYGBjnUH4AgDuGlwMA/pPGjRurZs2aGjRokA4dOqTixYtr6dKlWrBggXr16uUcTZOubeQvX75c48ePV2BgoPLkyaPy5cvrscce0yeffKJ06dKpcOHCCg4O1vLly5UpU6Y7qilfvnyaPXu2/ve//6lQoUJq06aNHnnkEUVGRmr9+vXO5bQkqXjx4mrbtq2mTJmic+fOqXr16vrll180a9YsNW3aVDVr1nQet2PHjurSpYuaNWumOnXqaOvWrfrhhx+UOXPmO15/pUuX1uTJk/X6668rf/788vf316OPPnpbj9G5c2d9+OGHateunTZt2qTcuXPryy+/1Lp16zRhwoR4Tw52u8aNG6cGDRqoYsWK6tChg3PJsHTp0sV5PfODBw+qSZMmql+/voKDg/Xpp5+qVatWKl68+E2fJywszAnoMdePfu+995Q+fXqlT59e3bt3d6upSZMmqlu3rlq2bKkdO3bovffeU8eOHZ1z3G8kQ4YM+uabb9SwYUOVKFFCzz77rEqXLi1J2rx5sz7//HNVrFhR0rVLtQ0cOFDDhg1T/fr11aRJE+3Zs0fvv/++ypYtq2effdZ53I4dO+rLL79U/fr11aJFC+3fv1+ffvqp22fjdpUuXVpz585Vnz59VLZsWaVNm1aNGze+rcfw8vLS1KlT1aBBAxUpUkTt27dX9uzZ9ccff2jVqlXy8/PTokWLdP78eeXIkUPNmzdX8eLFlTZtWi1fvly//vqr3nrrrTt+DQDwwEjUudMBAEnOvy8ZZmZ2/vx56927twUGBlqKFCmsQIECNm7cOLfLEJmZ7d6926pVq2apUqUySc5llM6ePWvt27e3zJkzW9q0aa1evXq2e/fuWJdais8lw673+++/W6dOnSx37tzm7e1tvr6+VrlyZZs4caJdvnzZ6XflyhUbNmyY5cmTx1KkSGFBQUE2cOBAtz5mZlFRUTZgwADLnDmzpU6d2urVq2f79u274SXDfv31V7f7x1V/aGioNWrUyHx9fU3SLS8fFtclw8zMTp486axDb29vK1q0qM2YMcOtT8wlw8aNG3fzFReHuC4ZZma2fPlyq1y5sqVKlcr8/PyscePGtmvXLrc+MZcM27VrlzVv3tx8fX0tQ4YM1r17d7t06dItnzum7rj+4rps1jfffGMlSpQwHx8fy5Ejh7366qsWGRkZ79d6/Phx6927tz300EOWMmVKS506tZUuXdpGjhxpYWFhbn3fe+89e/jhhy1FihSWNWtW69q1q509ezbWY7711luWPXt28/HxscqVK9vGjRtveMmwefPmxfn6r//3vHDhgrVq1crSp0/vth5u9BjXL/v352fLli325JNPWqZMmczHx8dy5cplLVq0sBUrVpiZWUREhPXv39+KFy9uvr6+liZNGitevLi9//778V6nAPAgc5kl8mwuAADgvjZ06FANGzZMp0+f/k+jAgAASIo4pxsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPIRzugEAAAAA8BCOdAMAAAAA4CGEbgAAAAAAPCR5YheQFERHR+v48ePy9fWVy+VK7HIAAAAAAInMzHT+/HkFBgbKy+vGx7MJ3fFw/PhxBQUFJXYZAAAAAIB7zNGjR5UjR44bLid0x4Ovr6+kayvTz88vkasBAAAAACS28PBwBQUFOXnxRgjd8RAzpNzPz4/QDQAAAABw3OoUZCZSAwAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHpKooXv06NEqW7asfH195e/vr6ZNm2rPnj1ufS5fvqxu3bopU6ZMSps2rZo1a6aTJ0+69Tly5IgaNWqk1KlTy9/fX/3799fVq1fd+qxevVqlSpWSj4+P8ufPr5kzZ3r65QEAAAAAHnCJGrrXrFmjbt26acOGDVq2bJmuXLmiunXr6uLFi06f3r17a9GiRZo3b57WrFmj48eP68knn3SWR0VFqVGjRoqMjNT69es1a9YszZw5U4MHD3b6HDx4UI0aNVLNmjUVEhKiXr16qWPHjvrhhx/u6usFAAAAADxYXGZmiV1EjNOnT8vf319r1qxRtWrVFBYWpixZsmj27Nlq3ry5JGn37t0qVKiQgoODVaFCBX3//fd67LHHdPz4cWXNmlWS9MEHH2jAgAE6ffq0vL29NWDAAH377bfasWOH81wtW7bUuXPntGTJklvWFR4ernTp0iksLEx+fn6eefEAAAAAgCQjvjnxnjqnOywsTJKUMWNGSdKmTZt05coV1a5d2+nz8MMPK2fOnAoODpYkBQcHq2jRok7glqR69eopPDxcO3fudPpc/xgxfWIeAwAAAAAAT0ie2AXEiI6OVq9evVS5cmU98sgjkqTQ0FB5e3srffr0bn2zZs2q0NBQp8/1gTtmecyym/UJDw/XpUuXlCpVKrdlERERioiIcG6Hh4f/9xcIAAAAAHjg3DNHurt166YdO3Zozpw5iV2KRo8erXTp0jl/QUFBiV0SAAAAACAJuidCd/fu3bV48WKtWrVKOXLkcNoDAgIUGRmpc+fOufU/efKkAgICnD7/ns085vat+vj5+cU6yi1JAwcOVFhYmPN39OjR//waAQAAAAAPnkQN3Wam7t2765tvvtHKlSuVJ08et+WlS5dWihQptGLFCqdtz549OnLkiCpWrChJqlixorZv365Tp045fZYtWyY/Pz8VLlzY6XP9Y8T0iXmMf/Px8ZGfn5/bHwAAAAAAtytRZy9/4YUXNHv2bC1YsEAFCxZ02tOlS+ccge7atau+++47zZw5U35+furRo4ckaf369ZKuXTKsRIkSCgwM1NixYxUaGqrWrVurY8eOGjVqlKRrlwx75JFH1K1bNz333HNauXKlevbsqW+//Vb16tW7ZZ3MXg4AAAAAuF58c2Kihm6XyxVn+4wZM9SuXTtJ0uXLl9W3b199/vnnioiIUL169fT+++87Q8cl6fDhw+ratatWr16tNGnSqG3bthozZoySJ/9nnrjVq1erd+/e2rVrl3LkyKHXXnvNeY5bSVKhe3bc6/SB1OqeuRoeAAAAgPtMkgjdSQWhO4kidAMAAADwkCR5nW4AAAAAAO4nhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQxI1dK9du1aNGzdWYGCgXC6X5s+f77bc5XLF+Tdu3DinT+7cuWMtHzNmjNvjbNu2TVWrVlXKlCkVFBSksWPH3o2XBwAAAAB4wCVq6L548aKKFy+uSZMmxbn8xIkTbn/Tp0+Xy+VSs2bN3PoNHz7crV+PHj2cZeHh4apbt65y5cqlTZs2ady4cRo6dKimTJni0dcGAAAAAEDyxHzyBg0aqEGDBjdcHhAQ4HZ7wYIFqlmzpvLmzevW7uvrG6tvjM8++0yRkZGaPn26vL29VaRIEYWEhGj8+PHq3Lnzf38RAAAAAADcQJI5p/vkyZP69ttv1aFDh1jLxowZo0yZMqlkyZIaN26crl696iwLDg5WtWrV5O3t7bTVq1dPe/bs0dmzZ+N8roiICIWHh7v9AQAAAABwuxL1SPftmDVrlnx9ffXkk0+6tffs2VOlSpVSxowZtX79eg0cOFAnTpzQ+PHjJUmhoaHKkyeP232yZs3qLMuQIUOs5xo9erSGDRvmoVcCAAAAAHhQJJnQPX36dD3zzDNKmTKlW3ufPn2c/y9WrJi8vb31/PPPa/To0fLx8bmj5xo4cKDb44aHhysoKOjOCgcAAAAAPLCSROj+8ccftWfPHs2dO/eWfcuXL6+rV6/q0KFDKliwoAICAnTy5Em3PjG3b3QeuI+Pzx0HdgAAAAAAYiSJc7qnTZum0qVLq3jx4rfsGxISIi8vL/n7+0uSKlasqLVr1+rKlStOn2XLlqlgwYJxDi0HAAAAACChJGrovnDhgkJCQhQSEiJJOnjwoEJCQnTkyBGnT3h4uObNm6eOHTvGun9wcLAmTJigrVu36sCBA/rss8/Uu3dvPfvss06gbtWqlby9vdWhQwft3LlTc+fO1TvvvOM2fBwAAAAAAE9I1OHlGzduVM2aNZ3bMUG4bdu2mjlzpiRpzpw5MjM9/fTTse7v4+OjOXPmaOjQoYqIiFCePHnUu3dvt0CdLl06LV26VN26dVPp0qWVOXNmDR48mMuFAQAAAAA8zmVmlthF3OvCw8OVLl06hYWFyc/PL7HLubnZrsSu4N7Rirc2AAAAAM+Ib05MEud0AwAAAACQFBG6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHhIoobutWvXqnHjxgoMDJTL5dL8+fPdlrdr104ul8vtr379+m59zpw5o2eeeUZ+fn5Knz69OnTooAsXLrj12bZtm6pWraqUKVMqKChIY8eO9fRLAwAAAAAgcUP3xYsXVbx4cU2aNOmGferXr68TJ044f59//rnb8meeeUY7d+7UsmXLtHjxYq1du1adO3d2loeHh6tu3brKlSuXNm3apHHjxmno0KGaMmWKx14XAAAAAACSlDwxn7xBgwZq0KDBTfv4+PgoICAgzmW//fablixZol9//VVlypSRJE2cOFENGzbUm2++qcDAQH322WeKjIzU9OnT5e3trSJFiigkJETjx493C+cAAAAAACS0e/6c7tWrV8vf318FCxZU165d9ddffznLgoODlT59eidwS1Lt2rXl5eWln3/+2elTrVo1eXt7O33q1aunPXv26OzZs3fvhQAAAAAAHjiJeqT7VurXr68nn3xSefLk0f79+/XKK6+oQYMGCg4OVrJkyRQaGip/f3+3+yRPnlwZM2ZUaGioJCk0NFR58uRx65M1a1ZnWYYMGWI9b0REhCIiIpzb4eHhCf3SAAAAAAAPgHs6dLds2dL5/6JFi6pYsWLKly+fVq9erVq1annseUePHq1hw4Z57PEBAAAAAA+Ge354+fXy5s2rzJkza9++fZKkgIAAnTp1yq3P1atXdebMGec88ICAAJ08edKtT8ztG50rPnDgQIWFhTl/R48eTeiXAgAAAAB4ACSp0H3s2DH99ddfypYtmySpYsWKOnfunDZt2uT0WblypaKjo1W+fHmnz9q1a3XlyhWnz7Jly1SwYME4h5ZL1yZv8/Pzc/sDAAAAAOB2JWrovnDhgkJCQhQSEiJJOnjwoEJCQnTkyBFduHBB/fv314YNG3To0CGtWLFCjz/+uPLnz6969epJkgoVKqT69eurU6dO+uWXX7Ru3Tp1795dLVu2VGBgoCSpVatW8vb2VocOHbRz507NnTtX77zzjvr06ZNYLxsAAAAA8IBwmZkl1pOvXr1aNWvWjNXetm1bTZ48WU2bNtWWLVt07tw5BQYGqm7duhoxYoQzEZoknTlzRt27d9eiRYvk5eWlZs2a6d1331XatGmdPtu2bVO3bt3066+/KnPmzOrRo4cGDBgQ7zrDw8OVLl06hYWF3ftHvWe7EruCe0erRHtrAwAAALjPxTcnJmroTioI3UkUoRsAAACAh8Q3Jyapc7oBAAAAAEhKCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhyRq6F67dq0aN26swMBAuVwuzZ8/31l25coVDRgwQEWLFlWaNGkUGBioNm3a6Pjx426PkTt3brlcLre/MWPGuPXZtm2bqlatqpQpUyooKEhjx469Gy8PAAAAAPCAS9TQffHiRRUvXlyTJk2Ktezvv//W5s2b9dprr2nz5s36+uuvtWfPHjVp0iRW3+HDh+vEiRPOX48ePZxl4eHhqlu3rnLlyqVNmzZp3LhxGjp0qKZMmeLR1wYAAAAAQPLEfPIGDRqoQYMGcS5Lly6dli1b5tb23nvvqVy5cjpy5Ihy5szptPv6+iogICDOx/nss88UGRmp6dOny9vbW0WKFFFISIjGjx+vzp07J9yLAQAAAADgX5LUOd1hYWFyuVxKnz69W/uYMWOUKVMmlSxZUuPGjdPVq1edZcHBwapWrZq8vb2dtnr16mnPnj06e/ZsnM8TERGh8PBwtz8AAAAAAG5Xoh7pvh2XL1/WgAED9PTTT8vPz89p79mzp0qVKqWMGTNq/fr1GjhwoE6cOKHx48dLkkJDQ5UnTx63x8qaNauzLEOGDLGea/To0Ro2bJgHXw0AAAAA4EGQJEL3lStX1KJFC5mZJk+e7LasT58+zv8XK1ZM3t7eev755zV69Gj5+Pjc0fMNHDjQ7XHDw8MVFBR0Z8UDAAAAAB5Y93zojgnchw8f1sqVK92OcselfPnyunr1qg4dOqSCBQsqICBAJ0+edOsTc/tG54H7+PjccWAHAAAAACDGbZ/TnTdvXv3111+x2s+dO6e8efMmSFExYgL33r17tXz5cmXKlOmW9wkJCZGXl5f8/f0lSRUrVtTatWt15coVp8+yZctUsGDBOIeWAwAAAACQUG77SPehQ4cUFRUVqz0iIkJ//PHHbT3WhQsXtG/fPuf2wYMHFRISoowZMypbtmxq3ry5Nm/erMWLFysqKkqhoaGSpIwZM8rb21vBwcH6+eefVbNmTfn6+io4OFi9e/fWs88+6wTqVq1aadiwYerQoYMGDBigHTt26J133tHbb799uy8dAAAAAIDbEu/QvXDhQuf/f/jhB6VLl865HRUVpRUrVih37ty39eQbN25UzZo1ndsx51G3bdtWQ4cOdZ6zRIkSbvdbtWqVatSoIR8fH82ZM0dDhw5VRESE8uTJo969e7udj50uXTotXbpU3bp1U+nSpZU5c2YNHjyYy4UBAAAAADzOZWYWn45eXtdGortcLv37LilSpFDu3Ln11ltv6bHHHkv4KhNZeHi40qVLp7CwsFueU57oZrsSu4J7R6t4vbUBAAAA4LbFNyfG+0h3dHS0JClPnjz69ddflTlz5v9eJQAAAAAA97HbPqf74MGDnqgDAAAAAID7zh1dMmzFihVasWKFTp065RwBjzF9+vQEKQwAAAAAgKTutkP3sGHDNHz4cJUpU0bZsmWTy8U5xAAAAAAAxOW2Q/cHH3ygmTNnqnXr1p6oBwAAAACA+4bX7d4hMjJSlSpV8kQtAAAAAADcV247dHfs2FGzZ8/2RC0AAAAAANxXbnt4+eXLlzVlyhQtX75cxYoVU4oUKdyWjx8/PsGKAwAAAAAgKbvt0L1t2zaVKFFCkrRjxw63ZUyqBgAAAADAP247dK9atcoTdQAAAAAAcN+57XO6AQAAAABA/Nz2ke6aNWvedBj5ypUr/1NBAAAAAADcL247dMeczx3jypUrCgkJ0Y4dO9S2bduEqgsAAAAAgCTvtkP322+/HWf70KFDdeHChf9cEAAAAAAA94sEO6f72Wef1fTp0xPq4QAAAAAASPISLHQHBwcrZcqUCfVwAAAAAAAkebc9vPzJJ590u21mOnHihDZu3KjXXnstwQoDAAAAACCpu+3QnS5dOrfbXl5eKliwoIYPH666desmWGEAAAAAACR1tx26Z8yY4Yk6AAAAAAC479x26I6xadMm/fbbb5KkIkWKqGTJkglWFAAAAAAA94PbDt2nTp1Sy5YttXr1aqVPn16SdO7cOdWsWVNz5sxRlixZErpGAAAAAACSpNuevbxHjx46f/68du7cqTNnzujMmTPasWOHwsPD1bNnT0/UCAAAAABAknTbR7qXLFmi5cuXq1ChQk5b4cKFNWnSJCZSAwAAAADgOrd9pDs6OlopUqSI1Z4iRQpFR0cnSFEAAAAAANwPbjt0P/roo3rxxRd1/Phxp+2PP/5Q7969VatWrQQtDgAAAACApOy2Q/d7772n8PBw5c6dW/ny5VO+fPmUJ08ehYeHa+LEiZ6oEQAAAACAJOm2z+kOCgrS5s2btXz5cu3evVuSVKhQIdWuXTvBiwMAAAAAICm7o+t0u1wu1alTR3Xq1EnoegAAAAAAuG/Ee3j5ypUrVbhwYYWHh8daFhYWpiJFiujHH39M0OIAAAAAAEjK4h26J0yYoE6dOsnPzy/WsnTp0un555/X+PHjE7Q4AAAAAACSsniH7q1bt6p+/fo3XF63bl1t2rQpQYoCAAAAAOB+EO/QffLkyTivzx0jefLkOn36dIIUBQAAAADA/SDeoTt79uzasWPHDZdv27ZN2bJlS5CiAAAAAAC4H8Q7dDds2FCvvfaaLl++HGvZpUuXNGTIED322GMJWhwAAAAAAEmZy8wsPh1PnjypUqVKKVmyZOrevbsKFiwoSdq9e7cmTZqkqKgobd68WVmzZvVowYkhPDxc6dKlU1hYWJwTyd1TZrsSu4J7R6t4vbUBAAAA4LbFNyfG+zrdWbNm1fr169W1a1cNHDhQMVnd5XKpXr16mjRp0n0ZuAEAAAAAuFPxDt2SlCtXLn333Xc6e/as9u3bJzNTgQIFlCFDBk/VBwAAAABAkhXvc7qvlyFDBpUtW1blypX7T4F77dq1aty4sQIDA+VyuTR//ny35WamwYMHK1u2bEqVKpVq166tvXv3uvU5c+aMnnnmGfn5+Sl9+vTq0KGDLly44NZn27Ztqlq1qlKmTKmgoCCNHTv2jmsGAAAAACC+7ih0J5SLFy+qePHimjRpUpzLx44dq3fffVcffPCBfv75Z6VJk0b16tVzm8ztmWee0c6dO7Vs2TItXrxYa9euVefOnZ3l4eHhqlu3rnLlyqVNmzZp3LhxGjp0qKZMmeLx1wcAAAAAeLDFeyI1T3O5XPrmm2/UtGlTSdeOcgcGBqpv377q16+fJCksLExZs2bVzJkz1bJlS/32228qXLiwfv31V5UpU0aStGTJEjVs2FDHjh1TYGCgJk+erEGDBik0NFTe3t6SpJdfflnz58/X7t2741UbE6klUUykBgAAAMBD4psTE/VI980cPHhQoaGhql27ttOWLl06lS9fXsHBwZKk4OBgpU+f3gncklS7dm15eXnp559/dvpUq1bNCdySVK9ePe3Zs0dnz569S68GAAAAAPAguq2J1O6m0NBQSYo1I3rWrFmdZaGhofL393dbnjx5cmXMmNGtT548eWI9RsyyuM5Jj4iIUEREhHM7PDz8P74aAAAAAMCDKF6he+HChfF+wCZNmtxxMfeK0aNHa9iwYYldBgAAAAAgiYtX6I45z/pWXC6XoqKi/ks9joCAAEnSyZMnlS1bNqf95MmTKlGihNPn1KlTbve7evWqzpw549w/ICBAJ0+edOsTczumz78NHDhQffr0cW6Hh4crKCjov70gAAAAAMADJ17ndEdHR8frL6ECtyTlyZNHAQEBWrFihdMWHh6un3/+WRUrVpQkVaxYUefOndOmTZucPitXrlR0dLTKly/v9Fm7dq2uXLni9Fm2bJkKFix4w8ud+fj4yM/Pz+0PAAAAAIDblagTqV24cEEhISEKCQmRdG3ytJCQEB05ckQul0u9evXS66+/roULF2r79u1q06aNAgMDnSPvhQoVUv369dWpUyf98ssvWrdunbp3766WLVsqMDBQktSqVSt5e3urQ4cO2rlzp+bOnat33nnH7Ug2AAAAAACecEcTqV28eFFr1qzRkSNHFBkZ6basZ8+e8X6cjRs3qmbNms7tmCDctm1bzZw5Uy+99JIuXryozp0769y5c6pSpYqWLFmilClTOvf57LPP1L17d9WqVUteXl5q1qyZ3n33XWd5unTptHTpUnXr1k2lS5dW5syZNXjwYLdreQMAAAAA4Am3fZ3uLVu2qGHDhvr777918eJFZcyYUX/++adSp04tf39/HThwwFO1Jhqu051EcZ1uAAAAAB7iset09+7dW40bN9bZs2eVKlUqbdiwQYcPH1bp0qX15ptv/qeiAQAAAAC4n9x26A4JCVHfvn3l5eWlZMmSKSIiQkFBQRo7dqxeeeUVT9QIAAAAAECSdNuhO0WKFPLyunY3f39/HTlyRNK1c6ePHj2asNUBAAAAAJCE3fZEaiVLltSvv/6qAgUKqHr16ho8eLD+/PNPffLJJ3rkkUc8USMAAAAAAEnSbR/pHjVqlLJlyyZJGjlypDJkyKCuXbvq9OnT+vDDDxO8QAAAAAAAkqrbPtJdpkwZ5//9/f21ZMmSBC0IAAAAAID7xW0f6X700Ud17ty5WO3h4eF69NFHE6ImAAAAAADuC7cdulevXq3IyMhY7ZcvX9aPP/6YIEUBAAAAAHA/iPfw8m3btjn/v2vXLoWGhjq3o6KitGTJEmXPnj1hqwMAAAAAIAmLd+guUaKEXC6XXC5XnMPIU6VKpYkTJyZocQAAAAAAJGXxDt0HDx6UmSlv3rz65ZdflCVLFmeZt7e3/P39lSxZMo8UCQAAAABAUhTv0J0rVy5JUnR0tMeKAQAAAADgfnLblwyTpP3792vChAn67bffJEmFCxfWiy++qHz58iVocQAAAAAAJGW3PXv5Dz/8oMKFC+uXX35RsWLFVKxYMf38888qUqSIli1b5okaAQAAAABIklxmZrdzh5IlS6pevXoaM2aMW/vLL7+spUuXavPmzQla4L0gPDxc6dKlU1hYmPz8/BK7nJub7UrsCu4drW7rrQ0AAAAA8RbfnHjbR7p/++03dejQIVb7c889p127dt3uwwEAAAAAcN+67dCdJUsWhYSExGoPCQmRv79/QtQEAAAAAMB9Id4TqQ0fPlz9+vVTp06d1LlzZx04cECVKlWSJK1bt05vvPGG+vTp47FCAQAAAABIauJ9TneyZMl04sQJZcmSRRMmTNBbb72l48ePS5ICAwPVv39/9ezZUy7X/XdOMed0J1Gc0w0AAADAQ+KbE+Mdur28vBQaGuo2hPz8+fOSJF9f3/9Y7r2N0J1EEboBAAAAeEh8c+JtXaf730ex7/ewDQAAAADAf3Fbofuhhx665fDxM2fO/KeCAAAAAAC4X9xW6B42bJjSpUvnqVoAAAAAALiv3FbobtmyJZcFAwAAAAAgnuJ9ne77cVZyAAAAAAA8Kd6hO56TnAMAAAAAgP8X7+Hl0dHRnqwDAAAAAID7TryPdAMAAAAAgNtD6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeMg9H7pz584tl8sV669bt26SpBo1asRa1qVLF7fHOHLkiBo1aqTUqVPL399f/fv319WrVxPj5QAAAAAAHiDJE7uAW/n1118VFRXl3N6xY4fq1Kmjp556ymnr1KmThg8f7txOnTq18/9RUVFq1KiRAgICtH79ep04cUJt2rRRihQpNGrUqLvzIgAAAAAAD6R7PnRnyZLF7faYMWOUL18+Va9e3WlLnTq1AgIC4rz/0qVLtWvXLi1fvlxZs2ZViRIlNGLECA0YMEBDhw6Vt7e3R+sHAAAAADy47vnh5deLjIzUp59+queee04ul8tp/+yzz5Q5c2Y98sgjGjhwoP7++29nWXBwsIoWLaqsWbM6bfXq1VN4eLh27twZ5/NEREQoPDzc7Q8AAAAAgNt1zx/pvt78+fN17tw5tWvXzmlr1aqVcuXKpcDAQG3btk0DBgzQnj179PXXX0uSQkND3QK3JOd2aGhonM8zevRoDRs2zDMvAgAAAADwwEhSoXvatGlq0KCBAgMDnbbOnTs7/1+0aFFly5ZNtWrV0v79+5UvX747ep6BAweqT58+zu3w8HAFBQXdeeEAAAAAgAdSkgndhw8f1vLly50j2DdSvnx5SdK+ffuUL18+BQQE6JdffnHrc/LkSUm64XngPj4+8vHxSYCqAQAAAAAPsiRzTveMGTPk7++vRo0a3bRfSEiIJClbtmySpIoVK2r79u06deqU02fZsmXy8/NT4cKFPVYvAAAAAABJ4kh3dHS0ZsyYobZt2yp58n9K3r9/v2bPnq2GDRsqU6ZM2rZtm3r37q1q1aqpWLFikqS6deuqcOHCat26tcaOHavQ0FC9+uqr6tatG0ezAQAAAAAelSRC9/Lly3XkyBE999xzbu3e3t5avny5JkyYoIsXLyooKEjNmjXTq6++6vRJliyZFi9erK5du6pixYpKkyaN2rZt63ZdbwAAAAAAPMFlZpbYRdzrwsPDlS5dOoWFhcnPzy+xy7m52a5b93lQtOKtDQAAAMAz4psTk8w53QAAAAAAJDWEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeck+H7qFDh8rlcrn9Pfzww87yy5cvq1u3bsqUKZPSpk2rZs2a6eTJk26PceTIETVq1EipU6eWv7+/+vfvr6tXr97tlwIAAAAAeAAlT+wCbqVIkSJavny5czt58n9K7t27t7799lvNmzdP6dKlU/fu3fXkk09q3bp1kqSoqCg1atRIAQEBWr9+vU6cOKE2bdooRYoUGjVq1F1/LQAAAACAB8s9H7qTJ0+ugICAWO1hYWGaNm2aZs+erUcffVSSNGPGDBUqVEgbNmxQhQoVtHTpUu3atUvLly9X1qxZVaJECY0YMUIDBgzQ0KFD5e3tfbdfDgAAAADgAXJPDy+XpL179yowMFB58+bVM888oyNHjkiSNm3apCtXrqh27dpO34cfflg5c+ZUcHCwJCk4OFhFixZV1qxZnT716tVTeHi4du7ceXdfCAAAAADggXNPH+kuX768Zs6cqYIFC+rEiRMaNmyYqlatqh07dig0NFTe3t5Knz69232yZs2q0NBQSVJoaKhb4I5ZHrPsRiIiIhQREeHcDg8PT6BXBAAAAAB4kNzTobtBgwbO/xcrVkzly5dXrly59MUXXyhVqlQee97Ro0dr2LBhHnt8AAAAAMCD4Z4fXn699OnT66GHHtK+ffsUEBCgyMhInTt3zq3PyZMnnXPAAwICYs1mHnM7rvPEYwwcOFBhYWHO39GjRxP2hQAAAAAAHghJKnRfuHBB+/fvV7Zs2VS6dGmlSJFCK1ascJbv2bNHR44cUcWKFSVJFStW1Pbt23Xq1Cmnz7Jly+Tn56fChQvf8Hl8fHzk5+fn9gcAAAAAwO26p4eX9+vXT40bN1auXLl0/PhxDRkyRMmSJdPTTz+tdOnSqUOHDurTp48yZswoPz8/9ejRQxUrVlSFChUkSXXr1lXhwoXVunVrjR07VqGhoXr11VfVrVs3+fj4JPKrAwAAAADc7+7p0H3s2DE9/fTT+uuvv5QlSxZVqVJFGzZsUJYsWSRJb7/9try8vNSsWTNFRESoXr16ev/99537J0uWTIsXL1bXrl1VsWJFpUmTRm3bttXw4cMT6yUBAAAAAB4gLjOzxC7iXhceHq506dIpLCzs3h9qPtuV2BXcO1rx1gYAAADgGfHNiUnqnG4AAAAAAJISQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHJE/sAoB72mxXYldw72hliV0BAAAAkORwpBsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQugGAAAAAMBDCN0AAAAAAHgIoRsAAAAAAA8hdAMAAAAA4CGEbgAAAAAAPITQDQAAAACAhxC6AQAAAADwkOSJXQCAB8hsV2JXcO9oZYldAQAAAO4CjnQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4yD0dukePHq2yZcvK19dX/v7+atq0qfbs2ePWp0aNGnK5XG5/Xbp0cetz5MgRNWrUSKlTp5a/v7/69++vq1ev3s2XAgAAAAB4ACVP7AJuZs2aNerWrZvKli2rq1ev6pVXXlHdunW1a9cupUmTxunXqVMnDR8+3LmdOnVq5/+joqLUqFEjBQQEaP369Tpx4oTatGmjFClSaNSoUXf19QAAAAAAHiz3dOhesmSJ2+2ZM2fK399fmzZtUrVq1Zz21KlTKyAgIM7HWLp0qXbt2qXly5cra9asKlGihEaMGKEBAwZo6NCh8vb29uhrAAAAAAA8uO7p4eX/FhYWJknKmDGjW/tnn32mzJkz65FHHtHAgQP1999/O8uCg4NVtGhRZc2a1WmrV6+ewsPDtXPnzjifJyIiQuHh4W5/AAAAAADcrnv6SPf1oqOj1atXL1WuXFmPPPKI096qVSvlypVLgYGB2rZtmwYMGKA9e/bo66+/liSFhoa6BW5Jzu3Q0NA4n2v06NEaNmyYh14JAAAAAOBBkWRCd7du3bRjxw799NNPbu2dO3d2/r9o0aLKli2batWqpf379ytfvnx39FwDBw5Unz59nNvh4eEKCgq6s8IBAAAAAA+sJDG8vHv37lq8eLFWrVqlHDly3LRv+fLlJUn79u2TJAUEBOjkyZNufWJu3+g8cB8fH/n5+bn9AQAAAABwu+7p0G1m6t69u7755hutXLlSefLkueV9QkJCJEnZsmWTJFWsWFHbt2/XqVOnnD7Lli2Tn5+fChcu7JG6AQAAAACQ7vHh5d26ddPs2bO1YMEC+fr6Oudgp0uXTqlSpdL+/fs1e/ZsNWzYUJkyZdK2bdvUu3dvVatWTcWKFZMk1a1bV4ULF1br1q01duxYhYaG6tVXX1W3bt3k4+OTmC8PAAAAAHCfu6ePdE+ePFlhYWGqUaOGsmXL5vzNnTtXkuTt7a3ly5erbt26evjhh9W3b181a9ZMixYtch4jWbJkWrx4sZIlS6aKFSvq2WefVZs2bdyu6w0AAAAAgCfc00e6zeymy4OCgrRmzZpbPk6uXLn03XffJVRZAAAAAADEyz19pBsAAAAAgKSM0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4SPLELgAAcIdmuxK7gntHK0vsCgAAAOJE6AYAQGInxvXYiQEAQIJheDkAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAAD2EiNQAAkPCYmO4fTEwHAA80jnQDAAAAAOAhHOkGAAC41zFy4B8JMXKA9fkPRmIAHseRbgAAAAAAPITQDQAAAACAhzC8HAAAAMCdY7j+PxiujzgQugEAAADgXsFOjH/cJzsxGF4OAAAAAICHELoBAAAAAPCQByp0T5o0Sblz51bKlClVvnx5/fLLL4ldEgAAAADgPvbAhO65c+eqT58+GjJkiDZv3qzixYurXr16OnXqVGKXBgAAAAC4Tz0woXv8+PHq1KmT2rdvr8KFC+uDDz5Q6tSpNX369MQuDQAAAABwn3ogQndkZKQ2bdqk2rVrO21eXl6qXbu2goODE7EyAAAAAMD97IG4ZNiff/6pqKgoZc2a1a09a9as2r17d6z+ERERioiIcG6HhYVJksLDwz1baEL4O7ELuIckxL8X6/MfrM+ExfpMWKzPhMX6TFisz4TF+kxYrM+ExfpMWPd4/orJh2Y3v7TZAxG6b9fo0aM1bNiwWO1BQUGJUA3uWKd0iV3B/YX1mbBYnwmL9ZmwWJ8Ji/WZsFifCYv1mbBYnwkriazP8+fPK126G9f6QITuzJkzK1myZDp58qRb+8mTJxUQEBCr/8CBA9WnTx/ndnR0tM6cOaNMmTLJ5eJi9TcTHh6uoKAgHT16VH5+foldTpLH+kxYrM+ExfpMWKzPhMX6TFisz4TF+kxYrM+ExfqMPzPT+fPnFRgYeNN+D0To9vb2VunSpbVixQo1bdpU0rUgvWLFCnXv3j1Wfx8fH/n4+Li1pU+f/i5Uev/w8/PjQ5qAWJ8Ji/WZsFifCYv1mbBYnwmL9ZmwWJ8Ji/WZsFif8XOzI9wxHojQLUl9+vRR27ZtVaZMGZUrV04TJkzQxYsX1b59+8QuDQAAAABwn3pgQvf//vc/nT59WoMHD1ZoaKhKlCihJUuWxJpcDQAAAACAhPLAhG5J6t69e5zDyZFwfHx8NGTIkFjD83FnWJ8Ji/WZsFifCYv1mbBYnwmL9ZmwWJ8Ji/WZsFifCc9lt5rfHAAAAAAA3BGvxC4AAAAAAID7FaEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAeAAwnVfiIHQDiSA6OjqxSwCA+wIbkABwa3v27FFkZKRcLhffm4mA0I2biiscEhj/Oy+vax+9FStW6PLly3z54Z6xfft2rVmzJrHLAOIU8125ceNGLViwQJLkcrkSsyQAuOfNmTNHDRo00IIFC3TlyhWCdyIgdOOGoqOjnXC4du1arV69WkePHnXacOfMTL/99pvq1KmjdevWsdGIe8K2bdtUvHhxQjfuSWYml8ulr7/+Wo0bN9aWLVv0+++/uy3H7WEnumfwXvQM3q937vHHH1fevHk1btw4LVy4kCPeiYDrdOOWBg4cqEmTJilz5sw6e/asPvnkEz322GOJXdZ94dlnn1WyZMn0/vvvK02aNIldTpIUsyFuZrp69apSpEgRaxlubevWrapYsaL69u2rESNGuC27fgcc4u9G64335Z1bsWKFmjZtqnHjxqlTp05KliyZ23LWbfxd//784Ycf9Pfffyt37twqWbJkIleWNB0+fFinTp1SwYIF5efn57TznrwzMevt+PHjunDhgh566KHELinJioyMlLe3tySpfv36Cg8PV58+ffT4448rRYoUvEfvEraiEEvMfhgz086dO7V06VItWbJE8+fP13PPPacnn3xSn332WSJXmbT8e+9szO0qVapo3bp1On/+fJz9cHMxPxRLlixR27ZtVblyZQ0aNEg//vijJIadxtfWrVtVuXJlvfjii26Be/r06Tpw4ACB+w6YmbPevvzyS02bNk3Lli3T1atX5XK5+KzfJjNTVFSUPv/8c7Vq1UpdunTRxYsX9fPPP6tfv37q2rWrzp49y5GbeLr+/dm3b1+1a9dOHTt2VKdOnfTKK6+wDm/TX3/9pTJlymjAgAGqW7euVqxYoWPHjkn653eIdXp7XC6XvvrqK1WsWFG1a9dWiRIltHr1akVERCR2aUlOzMGIffv2qVWrVtqxY4dGjRqlb7/9lqHmdxFbUnATHR3t/EDEfLE1atRIlSpVUrFixfTWW2+pb9++at++vWbPnp2YpSYpMRs3GzZs0B9//OHc7tKli/z8/DRw4EC3fogfl8ulBQsWqHnz5sqSJYu6du2qefPmqX///tqxY0dil5cknD59WhUrVtTjjz+u0aNHOz+8b7zxhl588UWdOnUqkStMmmK+R19++WV17dpVw4cPV79+/dSlSxdFRkbKy8uL4B1PMTvXkiVLJl9fX23dulVr1qxRt27dNGTIEP34449at26datWqJYmdbTdjZm5HtbZs2aJffvlF3377rX7++Wc1bNhQy5cv14svvshG+G24fPmy0qZNqwYNGqht27Z66aWX9Nxzz2ns2LE6c+aMs875zN9azPtu586d6t+/v7p3766ZM2cqc+bMat++vRYvXqzLly8ncpVJS8y2UpEiRbRv3z4988wzunjxol5++WUtWrSI4H23GBCHIUOGWJ06dSwoKMhq1qxpoaGhbstffvllS5UqlX300UeJVGHS88MPP1j+/PktV65cNnXqVNuyZYuZmc2YMcNq1KhhBw4cMDOz6OjoRKwy6YiOjrZTp05ZhQoVbMKECWZmduXKFcuSJYv17t07katLOo4fP27NmjWzzJkz28aNG83MbNSoUZYxY0ZbunRpIleX9ERFRZnZtffn6dOnrWHDhrZ9+3Y7deqUvffee1a2bFn73//+ZxEREW79EVvMd+H134nffvut1alTx1KmTGlPP/20LVy40K5cuWLz5s2zihUrWnh4eGKVe887cuSI2+05c+bYE088YZ07d3bawsPDbdSoUVamTBnr2bMnv0e34aOPPrIqVarYlStXbMuWLfbFF1/Yww8/7KzL06dP29WrVxO7zCTh559/tlmzZln//v3d2ps2bWp58uSxr776yi5fvpxI1SUt0dHRdubMGStRooQNHTrUab906ZJVrVrVHnroIfv6668tMjLS6Q/PIHTDzNw3/D788EPLkiWLDRo0yJ555hlzuVw2fvz4WBszL7zwglWrVu1ul5pkxLUx/csvv9jIkSMtf/78Vq5cOevbt6+tWrXKfH19bcqUKYlQZdJ27tw5K1OmjJ04ccIOHDhggYGB1qlTJ2f56tWr7dSpU4lYYdIQGhpqLVq0sPTp01vXrl0ta9as9v3338fqt2XLFjZ0buL6z/zx48dtz549Vq9ePTt9+rSZXdvImTp1qpUpU8ZatmzpBG82cmKLWSerVq2yV155xTp06GBTpkyxyMhIi4qKsq1bt7r179mzp9WqVcsuXLiQGOXe83r16mVt2rQxM7OrV69aeHi4tWvXzrJlyxbrdzw8PNxGjx5t5cuXt9atWydGuUnG9Z/dHTt2WJ06dWzlypVOW7NmzSxPnjxWpkwZy5Ytmz3xxBO2Zs2axCg1SSlZsqS5XC5r2LChXblyxW1Z06ZNrUCBAjZ79mx+j+Lp8uXLVrJkSXv33Xed22bXfpPy5s1rFSpUsE8//dQJ3vAMQjfcbNq0yXr06GELFixw2oYPH25eXl72zjvv2Pnz5936s7EYt+s3vrdu3Wrr1693W759+3b7+uuvLX/+/PbEE0+Yy+WywoUL2+HDh+92qUlKzPstZv0eO3bMcufObdOnT7cCBQpYp06dnCMJ+/btsyeeeMJWrFiRaPXeq/69EWNmduLECWvXrp25XC7nh/n6ozIvvfSSZcyY0f7666+7VmdS9corr1hQUJCVLl3a8ufP77Yhc/nyZZs6daqVL1/e6tSpw0bOTXz11VeWNm1ae+GFF6x9+/ZWokQJq1Spkts62759u7344ouWPn36WEEc/1i7dq2z3v78808zM/vjjz+sT58+liNHDhsxYoRb//DwcBs4cKB16NCB0RhxCAsLi7O9TZs2VqlSJTMzZ6dGzKi28ePHW/Pmze3333+/W2UmaXXq1LFMmTLZqlWrYo0QqFmzphUvXpyRLfEQFRVlkZGRVr58eWvVqpXTHrPTt2XLlpY8eXKrW7cu69PDCN1w/PTTT5YyZUrz8/OzTz75xG3ZsGHDLFmyZDZx4sRYPzYE7xvr16+f5cyZ01KmTGm1a9e2BQsWuP14XL582b788kt74YUXzNvb27777jszY8hpXGLeZ6tXr7bhw4fbmTNnzMxs8ODBlixZMqtfv75b/0GDBlnx4sXt6NGjd73We9mePXusW7duVq9ePRs1apRt377dWXbs2DFr3bq1+fn52c8//+y0v/baa5Y2bVq3Nvzj+s/rV199ZQEBATZr1iwbMGCAZc+e3apXr+7W//Lly/bOO+8QaG7i8OHDVrhwYZs0aZKZmR08eNAyZ85s3bp1c/qEhIRYp06drHz58gTuG/j37/Nnn31mhQoVsl27dpnZtREZPXr0sAoVKtioUaPc+l68eDHWjk6Y7d692x577DF79dVXY62fo0ePWp06daxw4cKWLVs2+/XXX93ue+nSpbte770uZh1evnzZGckSo1SpUvbQQw/ZunXrYr0H+W2PW8z6PH36tEVERNjFixfNzGzZsmXm4+Njr732mlv/Xr162TfffBPr9BMkPEI33EyYMMF8fX2tY8eOsb7QRowYYS6Xy+bNm5dI1d37rv9RWLBggRUqVMi+//57++mnn6xq1apWpUoVmz17dpwbMM8995yVK1cuzqOQuObLL7+0dOnSWb9+/ZwAuGvXLnv22Wcta9as9sEHH9iUKVOse/fu5uvrayEhIYlc8b0lJCTEMmXKZE899ZQ1btzYUqVKZS1atHAbgn/y5En73//+Z76+vrZz504bO3aspUyZ0jnfGzf26aef2vTp023q1Klmdu1IwuLFi+3hhx+2WrVqufWNjIwk0PzL9QFx48aNVqBAAYuMjLTDhw9bUFCQ27nHq1evNjOzbdu22cmTJ+96rUnV119/bY8++qhVrVrVfvvtNzO7trMtJniPGTMm1n3Ysf6Pbdu2mb+/v73wwgs2a9asWMvDw8PtySeftDRp0rhtQ7EO4xazXr799lt7+umnrXTp0ta/f3/7+uuvnT4lS5a0hx56yIKDg/mujKf58+dbuXLlrGjRotavXz9np+R7771n3t7e1qxZMxs2bJh17NjRUqdOTeC+SwjdD6ibfXGNHj3asmfPbsOGDbM//vjDbdmMGTMIhfGwaNEie+mll2zs2LFO259//mkNGza0ypUr2+eff+78G8QM+Vu4cKFVqlTJzp07lyg13+u2bt1q/v7+9v7778e57JVXXrHAwEArU6aMNW3a1LZt25YIVd67duzYYWnSpLHhw4c7Gzrdu3c3b29vZ+M7xokTJ+zpp582l8tlyZMnJ3DHQ2hoqAUEBJjL5XI7YhgTvAsXLmx16tSJdb8HeWM85jswKirKWQ/79u1z/lurVi1bvXq1E7hjfnt27NhhnTp14uj2LdzovbVo0SKrW7euVapUyS14v/jii5Y3b177+OOP72aZScbBgwctV65c9sorr9x0G+rXX381Pz8/W7hw4V2sLulasGCBeXt7W//+/a1Lly722GOPWc6cOe2DDz5w+pQvX94yZ87MaKt42Lp1q/n5+dmoUaOsR48e9uijj1rNmjVt8+bNZnZth2XNmjWtWrVqVqNGDQ5O3EWE7gfQ9T8WU6dOteeff966d+9u7733ntM+YsSIGwZvs7jPCcU1Z8+etVy5cpnL5XKb1Mvsn+BdrVo1mzZtmttGUf/+/S1LlizOsGm4+/zzz61ChQpu5xz9+314+vRpi46Otr///vtul3dPO3v2rAUEBFjZsmXt7NmzTnv37t3N5XLZkiVL7MSJE27r7dSpU9a3b1+34eeIW8zn+Ndff7Vy5cpZyZIl3Sb0ioyMtO+++84yZsxoPXv2TKwy70l79uyxl19+2czM5s6da7ly5bK9e/fauXPnrEiRIuZyuaxDhw5u9+nbt69VqlSJI9w3cf3v/O7du23v3r22f/9+p23BggWxgvfhw4dt/PjxzLB9A9OnT7cGDRpYeHi4s373799vS5cutZdfftm+//57O3HihJmZPf3009ahQwcm9ruFsLAwq127tg0fPtxp27t3rw0cONBy585tixcvdtofffRRZ6cc4rZjxw4bM2aMDRs2zGn77rvvrFGjRlatWjVnp8WVK1csKiqKbaW7jND9AHvppZcsc+bM1qFDB6tVq5YFBQVZgwYNnOUjR460XLlyWd++fZ3ZdxE/R44csYoVK1rx4sVjXXbpzz//tHLlylmXLl2ctvPnz9ugQYNinf+Ff0yZMsVy584d54b2ypUr3XZWPMhHD29k0KBBljdvXhs5cqRFR0fbW2+9ZT4+Pla8eHHr2LGjZciQwerXr28jR450Jp9jPd7aqFGjbOzYsc4OoE2bNln+/PmtUqVKbjPrRkRE2Pr16wk0//Ldd9+Zy+Wyxo0bm8vlspkzZzrL9u7da/7+/ta4cWNbvHixrVixwl588UXz8/PjKPdNXP+5HTx4sJUoUcICAgKsRo0aNnnyZGfZggULrF69ela1atVYI4N4n8bWr18/K1SokHP7888/t8cff9wCAwMtW7ZsFhAQ4JwvO3r0aMuaNesNJ1zDNWFhYZYnTx63UYFm1z77tWvXjjXBH27s2LFjVqtWLcuSJYuzIzNGTPB+9NFHmT0/ERG6HyDX7/kODg62HDlyOOfFRURE2A8//GA5cuSwJ5980un36quvWtOmTdn4voGbDTE7cOCAFS9e3OrUqRNrBu2wsLBY92Uj5+YWL15svr6+bkPzY3Ts2NHGjx/P+/Rfjh8/bps2bXJuDxkyxHLlymV169a1jBkz2k8//eQEw+XLl9vEiRMtS5YsVqpUKeeIDW7u1VdfNZfLZZMmTYoVvCtXruzMEHs9PuvuevXqZS6Xy+rWreu0xayjTZs2WbFixSxv3rxWsGBBq1atGsMh42nIkCGWJUsW++GHH2zr1q327LPPWrJkyezNN990+ixatMhKly5tzz//vJmxo+1mVq9ebbly5bLmzZtbmzZtLF26dNarVy/nEmF9+vSxnDlzOnNkxDVKEO7+/vtva968uXXt2tVtFJbZtdECderU4T15G6ZOnWqlSpWyIkWKxJolf8mSJVa1alVr1KgRE/olEkL3A6B9+/a2Y8cOM/snJM6fP9+yZcvm9iUXGRlp8+bNs4IFC9qPP/7otMd84fHF5+764Ddp0iR74YUXrEmTJrZ06VLnaOy+ffusePHiVrduXVu1atVNHwPXxLzPNm/ebN99950tW7bMWda+fXtLmzatffLJJ3bw4EE7efKkDRgwwPz9/bkMy79ERkZa6dKlrUGDBvbLL7847SNHjjQfHx9r165dnKcynD592kJDQ+9mqUnGjT6vY8aMMS8vL5s4caJb8C5YsKAVKFCA03Fu4fXXX7fnn3/efHx8rHv37s4GYcz6vnDhgh05csQOHTrEJW3iacOGDVahQgXnqNaSJUvM19fXGjdubGnSpLG3337b6fvjjz/yWxSHI0eO2NSpU+3DDz90Lvv54YcfWu3ata1mzZq2fPlyt22ohQsXWsGCBbn05w3E/LaHh4e7hb633nrLMmfObB999JHb+nz22Weta9eu7KS8TR9//LFVrlzZWrRoYXv27HFbtmzZMiZNS0SE7vtczFDmgIAAtw/fzp07LXv27PbNN9+49d+/f7+lT5/evvrqK7d2AveNDRgwwLJkyWL9+vWzJ554wgoXLmyDBw92Zi7dt2+flSpVykqWLOl21BE3Nm/ePEufPr3lzJnTgoKC7H//+5+zrGvXrubv72/+/v5WsmRJCwoKciYIwTV79uyxnTt3WnBwsD300EPWsmVL27Bhg7N8+PDhFhQUZCNHjnQ7os3GTfzEtYNn1KhR5uXlZe+9954zOeKGDRusRYsWrNd4mj9/vhO8rx8hwPfm7fvrr79s+PDhdunSJVu+fLkFBATYhx9+aKdPn7Zq1aqZy+WyIUOGuN2H4P2PrVu3Wq5cuaxcuXKWKVMmy5s3ry1atMhZHtcIll69elmdOnXYMXQTCxYssLJly1qVKlXc5mro16+fZcmSxdq2bWuDBg2y559/3nx9fZlT5AZitsk3btxoH374oc2cOdPtN37q1KlWvXp1rgt/jyF03+eio6Pt2LFj1qBBA8uSJYsTvP/44w+rXbu2PfXUU84eXLNrIb1EiRK2YMGCxCo5SZk5c6blyZPHbVZIl8tlDz/8sA0cONCOHz9uZtcmsmnbti0bNTcR8yNy8eJFq127tn388cf2+++/25w5cywgIMDtOtw//vijzZ8/3xYtWmTHjh1LrJLvSVu2bLHUqVPbu+++a2bX1lXevHntf//7n9uP8pAhQyxHjhw2evRohkHeRN++fd1C3/fff28ulyvWDkuza9czT5kypU2bNs3tfG4zdmjEiPmcb9q0yb766iubMmWKs5PC7FrwTpkypXXr1s0OHDhgQ4cOtfz58zOvyE3c6HclZpKktm3bWq9evZz13KlTJ6tQoYI1bNjQoqOj2an+L1u3brXUqVPbyy+/bBcvXrRly5ZZYGCgNWrUyMLDwy06OtptnZ86dcpefvlly5gxIyHxJjZs2GBp0qSxl156yQYMGGDZsmWzKlWqONeRfv/9961t27ZWsmRJa968OfM23EDM5/Wrr76yzJkzW7Vq1axYsWJWpUoVmzZtmtNv2rRpVqtWLatXrx4T0N0jCN33seuHNG7dutXKly9v+fPnd/Z6rVmzxooXL27169e3ESNG2MKFC6127dpWokQJNhDjISoqymbOnOlMAPL1119b+vTpbcqUKda/f3/z9fW1QYMG2cGDB2PdD3FbtWqVPfnkk9a6dWtniP6VK1dsyZIlljVrVqtXr14iV3hvCwkJcTYWzf75cd6wYUOcwXv48OGWOnVqe+utt/jMx+HQoUP2xBNPxBoe3qlTJ/P19bX58+eb2T/refv27ZY6dWpzuVz25Zdf3vV673Ux6+nrr7+2gIAAK1WqlOXMmdOKFi1q69atc0Lhd999Z8mTJ7ciRYpYpkyZuGTdTVz/e/Lrr7/asmXLLDQ01M6fP29m14bmFytWzJk1/8KFC9a8eXObO3eucz9C9z+OHDlimTNntqeeesqtvWzZsvbQQw/FuqTnBx98YA0aNLCHHnrItmzZchcrTVp27Nhhy5cvd64DHx0dbTt27HAmnYwJ3lFRUXbp0qVYOy3hbs2aNRYQEOBMjLhy5Urz8/OznDlz2jvvvOP0mzRpkj322GMcnLhHELofAK+++qrVrl3bKlasaC6Xy7Jly2a7du0yM7P169dbly5dLEeOHFamTBlr2LChs+HDRri7uDZMjh49aqGhoXbs2DErWbKkvfXWW2Z2bc+3v7+/5ciRw7muNBs2N3f16lWbNm2as96uFxO8s2fPbpUrV06kCu9tMUdnXnnlFbf277//3i5cuGDr16+3vHnzxhpqPmbMGIafxeHfO8c+//xzW758uXO7S5culipVKid4m10b1j9o0CCbNWsW53HfwMqVKy1Tpkw2ffp0M7u2Y8PlclnRokVt5cqVzu/PwYMH7bvvvnNO08HNxQzPTZ8+veXJk8dat25tu3fvNrNro1qyZ89uzz//vFWuXNlKlSrl/L7zu+Tu4MGDVrZsWWvSpIn99NNPZnbt1BGXy2XlypWzxo0bW/v27W3ixIm2fft2mzJlik2ePNntcmxw99dff1nGjBnN5XLZwIED3Zbt2LHD8uXLZzVq1Ii1QwPXTJo0yW2HzpUrV2zgwIH2wgsvmNm1S/3lyZPHWrRoYe3bt7fAwEC3I96s13sHofs+98EHH1jatGlt3bp1duTIEVu5cqXVqFHDsmTJ4lybMzIy0s6fP2+nTp1yfoDZYHR3/Qb4hQsXnL2yMdavX2/58+d3hqFu2bLF2rZta++88w47L27h+o2+M2fO2KxZs8zX19fat2/v1u/KlSu2aNEiK1iwIBOB/EvM0ZkWLVq4tY8YMcJy5MjhTKQYM9T8mWeecTYoEbeY9+XVq1ft9OnTFhAQYHXr1rW1a9c6fbp06WLe3t42duxYW7hwoTVu3NieeOIJZznfo+6f76tXr9qrr77qXFbpwIEDlidPHuvatatVqVLF8uXLZytWrGBm3Xi4fr0uWrTIChQoYMuXL7djx47ZxIkTrU6dOlanTh07cuSI/fHHHzZs2DB79NFHrW3btuxYv4Xff//d6tevb02aNLGOHTtalixZbN68eXb48GH75ptv7PXXXzd/f38rUKCA1a9fP87zu/GPqKgo+/777+3hhx+2WrVqOe0x7+GdO3dahgwZrFGjRuwE+peIiAgrUaKEBQUFuZ26cPLkSVu3bp1dvHjRypcvb88995yZXdsWTZs2raVJk8bee++9xCobN0Dovs/17dvXnn76abe233//3cqVK2e5cuWK8zwPvvRubNiwYVaxYkWrVauWffDBB0770qVLrUCBAjZx4kTbvHmzPfbYY9amTRtnORs3scW8z86fP2/R0dHOhsvZs2dtxowZ5u/vb506dXK7z5UrV+zChQt3vdZ7XVxHZ0aPHm2ZM2e277//3sz+eQ/+9NNPlj59emvXrh3h5gau/w6Meb/t2LHDihUrZg0aNHC7zungwYMtICDAuUTY9ecn4591GXNZpeDgYNuxY4eFhYVZxYoVrWPHjmZ2baRGsmTJLCgoyG3HBmK7fujttGnTbMiQIbGOIH799ddWoUIFGz58uNN2/c5jdgjd3J49e6xOnTqWMmVKGzduXKzlf/75p33xxRe2d+/eRKgu6bl8+bItXbrUMmXK5LZzOOb74bfffmNd3kB4eLjVrl3bcufObdu2bXNb9uOPP1qJEiWc0Wrbt2+3hg0b2rBhw+zAgQOJUS5ugtB9H4nrXOEXX3zRChQoEKv93XffNZfLZSlSpODyFvE0efJkCwwMtOHDh9tzzz1nKVKksEGDBjnL27VrZ7lz57bs2bNbuXLlnI1vdmLEFrNOlixZYo0bN7ZHH33Umjdv7sykfe7cOZsxY4ZlzZrVunTpkpilJhnXH53p1KmTc33efwsLC7N9+/axgXMD13+Pfvrpp9arVy/nfblz504rXLiwNWjQwFavXu30+/333+3QoUPOfQk07lauXBlr8rnVq1dbyZIlnaM369evt1atWln9+vU53eEmfvjhBxs7dqwFBwebmVmhQoXM5XJZ48aNY20DdOnSxR555JFY7fwmxc++ffusbt261qBBA7fLqLJj7caun1X7k08+sffff9/++usvM7v23bp06VLLmDFjnMEbNxYeHm41a9aMFbzXrl1r6dOnty+++MLMzF555RVr0aJFnJcDReIjdN+Hrp+QZs2aNVasWDEbPXq021GtRYsWWfv27W3QoEEchb2Bf2+ofPTRR87kSBcvXrRp06ZZihQp7KWXXnL6/PLLLxYcHOysUza+b2z+/PmWNm1aGzhwoL333ntWrVo1y5cvn7PBfe7cOZs1a5YlT57cXnzxxcQtNomIOTqTKlUqe/PNN83M3GYnHjRokGXLls2ZZAnurv/Mb9u2zWrXrm158uSxYcOGORP7xQTvRo0auQXvuB7jQXX9996+ffts0qRJbpP7mF27lmzGjBntt99+c4adP//886y/m5g+fbplz57dunbt6jYvQ/369c3X19e+//57t6HOn376qZUuXdoJPbh9MTsz69Wrxyk5t3D9rNqBgYFWqlQpK1WqlGXNmtV+/vlnp9/SpUsta9asblckwa2Fh4dbjRo13IL3kSNH7JlnnrGAgAArXry4+fn5WUhISCJXihshdN8Hrt9I2b59u7lcLpswYYKZXQuH3bt3typVqtjAgQPt9OnTdvjwYXvsscesW7duzv0I3u6u3/M6d+5cmzFjhpUvX95mzJjhtEdERNj06dPN29vbmS36eqzTG/vtt9+sRIkSNmnSJDO79sORM2dOy5Ahg/n7+zsTAJ05c8Y+++wzt2vM4+auPzpz/TDdmMtZ/frrr4lYXdLQq1cvq1Klij355JNWtGhRy5gxo7322mtuR7yLFi1q5cuXZwPnOm+++abb6R+///67FSlSxLJmzWoff/yxmZnbDskiRYpY5syZrXTp0pYuXTrn0ouI7fPPP7fUqVPb3LlzLSwszMzcf2OqVq1qOXLksDlz5tjx48ft5MmTVr16datfvz5HEv+j33//3R577DGrUKGCM8IAcVu9erVlzJjRpk6dambXLpfqcrksR44ctmLFCqff4sWLLW/evMyqfQMxn9lz5865TYR24cIFq169ulvw3rVrl82ePdveeOMNRrDd4wjdSdz1P6ZvvPGGvf766+bj42MpUqSwUaNGmdm1vWMDBgyw4sWLW7JkyaxAgQJWtGhRhkjdwPXr9JVXXrEUKVJY2bJlzdvb25599lm3jcqIiAibMWOGuVwu+/DDDxOj3CQjZr1GRETYgQMHrFevXnb16lU7evSoFShQwDp27Gi7du2yhx56yAoWLOjMsM8G4+27/ujM5s2b7Y033rCUKVNy6aWbuP5yVhkyZLDNmzc735E9e/a0Rx55xAYPHmynTp0ys2uXZ3vmmWc4Mvv/9u7da9WqVXPbQbZnzx7r1auXZciQwW20SszR2EuXLtnw4cPtzTffdHa0IbZTp05ZjRo1Yk2MdP78efvpp5+cdde4cWNzuVyWP39++9///mc1atRw1jXfo//Nb7/9Zs2bN+d0vJu4dOmSjRgxwoYOHWpm/+xM79y5szVr1syyZMniNh/Gvyekhbv58+dbjRo1rGDBgjZy5EjbuXOnmV1bb9WrV7ecOXNyXfgkhtB9nxg6dKhlzpzZFi5caHPmzLGXX37ZvLy8bMSIEWZ27Ryks2fP2tdff23Lli1j+HM87N6922rVqmWbNm2y48eP25dffmne3t724osvuk1kExERYYsXL2ZdxsO8efOsRYsWFh0d7Uzi165dO2vevLmzcdi0aVNzuVyWL18+i4iIYGPxDsUcnfH397cUKVIQuOMwdOhQZ6K5GDNmzLD8+fPb6dOn3d57HTt2tLRp09rgwYOdI94xywne19ZBzA7Jn376yTmF4cCBA/bSSy9ZtmzZbOzYsU5/ZnyOv1OnTlnhwoXdzol///33rXnz5uZyuSxLlizWpEkTMzNr3ry5pUiRwhYtWuTsNGIHe8LgPRu3678n165da5s2bbKwsDCrUKGCde7c2cyuXUPe5XJZqlSp3II34rZhwwbLkCGDvfzyy9anTx/Lli2bPfvss85Ii4sXL1qtWrXM19fXCeO49xG67wPnz5+3SpUquW3QmJm988475nK5bOzYsXEGQoY/u7t+w3nMmDFWpUoVe/zxx92ObM+fP9+8vb2tV69ebsE7BsHb3fXnDv/+++/2yCOP2Icffuj8SP/9999WqVIle/fdd51+Xbp0scWLF9vx48fver33m927d1uTJk2cS4bhH1u3brUKFSpY3bp1nZm1za6daxwUFGSHDh0ys382tP/44w/LnDmzlSlTxt544w37+++/E6Xue91ff/1lJUuWtIceesj5/O/fv98GDBhgBQsWdJsJmt+g+Dl16pTlyJHDOnbsaCtWrLBmzZpZ0aJFrWvXrrZ06VKbN2+eBQUF2cSJE83MrHTp0pY/f35bv349QREecfXq1ZvuEN+wYYOVKlXKtm7dambXRgY9/fTT1qFDB+dytYjboUOHbMyYMTZ69GinbenSpVa0aFFr1aqVM5/DhQsXrFGjRgwpT0II3feBs2fPWvbs2Z2NmejoaIuKirLIyEjnqGHMOd4cNYzb9etl8+bNtmPHDkuePLllyZIlVmCZP3++pU6d2tq1a8cRhJvYuHGj5c2b186fP28hISE2cOBAa9OmjUVGRrrtnGjUqJEVKlTIVq5caT169LCgoCCG8CUg3qM3tmzZMmvcuLHVrl3bOd/wypUrli9fPqtbt67butu+fbs988wz1r59e8ubNy/Xir+Bq1ev2tKlS61ChQpWqlQpJ3jv3bvXBgwYYEWKFHG7jBXiZ/ny5ZYuXTrLmzevFS9e3FasWGF//vmnmV2b+6JEiRJulw2rUqWKZciQwW3CNeC/itkZGWPNmjU2aNAgGzNmjH311VdO+9y5c83Ly8t+//13u3r1qr322mv2+OOPsxPoX9555x23+S6OHTtmgYGBlj59envllVfc+i5ZssSKFClirVu3dptNH0kHoTuJudEwxh49eljhwoVjnQfbt29fq1mzprlcLps3b95dqzMpuX6dvvzyy+ZyuezKlSsWHBxsKVKksNatW8ea7GPOnDlWvXp1hpXeQEhIiPn6+lrPnj3NzOzxxx+3NGnSWOnSpZ0+McF748aNVqVKFQsKCrLChQszmRI87vqdbMuWLbNGjRpZ7dq1nUusbdmyxbJnz25VqlSx+fPn28qVK61+/frWoUMHi4qKslSpUsU6v/ZBFbMur1y54mxQX7lyxVauXGmlS5d2C9779u2z7t27W9myZZlR+w6cOnUqzmvvnjlzxqpWrWoffvih2w7N2rVrcxQMCWbWrFn26KOP2rJly8zM7NtvvzUvLy+rU6eOFS5c2HLkyGHPPvus079atWqWIkUKK1OmjPn6+tqWLVsSqfJ7T8zn9PHHH7eDBw+6Lfv444/N39/fGjRoEGuui6VLl1pgYKB16tTJ7YpESBoI3UnI9QFvy5YtbkPHgoODrXbt2vb44487l1y6dOmSPf7447Zo0SLr2rWrlSpVys6cOcPR7hvYvn279erVy1atWuW0rV692lKkSGHt2rW74SybBG93W7dutdSpU7vtpb18+bI99dRTlj9/fpswYUKsdXblyhXbs2cPG+LwuJjvv+uHNn///ffWsGFDq127tjPU/MCBA1apUiXLmzevBQUFWZUqVezvv/+2ixcvWpEiRezbb79NlPrvJTHrcvHixda8eXNr2rSpc1nFqKgoW7VqVazgfeDAAefya/jvTp06ZY0aNbLy5cs772lGt8ATli1bZuXLl7emTZvaggULrHXr1s4VSP766y/78ssvLVOmTE7wvnLlik2YMMHeffddZ7sU/2wzHjhwwHLmzGlRUVEWHBzsNhnvp59+aoGBgfbiiy/GWncrVqyw/fv339WakTAI3UlQv379LCgoyFKmTGlVq1a1RYsWmZnZwoULrXbt2pYhQwarX7++FS5c2B555BEzMxs5cqSVLVuWgHgDX3/9tQUGBtpDDz1kBw4csKioKGdP5Jo1a8zb29uee+45hj3fwpEjRyxz5szWokULt/aZM2da69atrWnTplalShWbNm2as4zzOnG3XP/9d+rUKbch4qtXr7YGDRpYrVq1bPny5U773r17bf/+/U7AfPXVVy137tx8F/y/NWvWWLp06ax169b2+OOPm8vlspEjR5rZP8G7fPnylidPHrf5MfDfnD592kaPHm2NGjWysmXLOkGb71N40tq1a61y5crWsmVLK1eunP3yyy/OsoiICJs7d67lypUr1gSVuCbmN2jLli2WNm1a69Gjh5mZPfHEE1aiRAn76KOPnL4zZ8607NmzW8+ePRmxcp/wEu550dHRzv8vXrxY33//vaZOnaoff/xR3t7eGjFihObOnavGjRtr+vTpGjVqlPLly6eWLVtq06ZNkqTDhw8rZ86cioiIkJkl1ku5Z6VMmVJly5bVoUOHdPToUXl5ecnlcunq1auqVq2ali9frhkzZuiTTz5J7FLvaVFRUcqTJ48uX76sdevWSZJGjx6tF154Qf369dPUqVOVNWtWzZw5UzNnzpQkJUuWLBErxoPCzOTlde0nb8SIEWrUqJGqV6+ucuXKaeHChapevbr69eunlClT6o033tDy5cslSfnz51fevHn122+/qXXr1vrggw/0zTffKGfOnIn5cu4ZZ86c0aBBg/Txxx/rm2++0YcffqjBgwdrxIgR8vLyUrVq1TRixAjlypVLp06dSuxy7xvHjh3TunXrlD9/fq1fv14pUqTQ1atX+T6FR1WtWlUjRozQ4cOH9euvv2rr1q3OMm9vb1WtWlUul0sHDx5MxCrvTdHR0fLy8tK2bdtUuXJl9ezZU++++64k6bPPPlOuXLn0zTffaMqUKZKktm3bauTIkVq4cKHGjBmjAwcOJGb5SAiJnfpxYzHXg40xf/58e+mll9xmNAwLC7PHH3/cypYta7Nnz441rOzkyZPWu3dvS58+PTMY/78bHe0PDg62Rx991AoWLOhMPnP9Ee+QkBBmJ4+HmGtEN2nSxDp16mT+/v7OubJmZidOnLAWLVpY0aJF7dNPP03ESvEgGjp0qGXNmtW++OIL+/PPP61IkSJWuHBh51zZmMnVSpYsaZs2bXLud+jQIZs0adIDP/NuzBH/zZs327fffmutW7e2d955x63PRx99ZF5eXvb666+b2bXvUWZ7T3hnz56N83QJwNPWrVtn5cuXt6pVq8Y6ql2hQgV78803zYzJe//tRqMBP/74Y3vmmWesSZMmVqVKFZsyZYqz7IMPPrDChQtbaGjo3S4XCYzQfY9q166dvfXWW2Z2bYMlLCzM8uXLZy6Xy9q1a+fWNyZ4V65c2SZPnuz8+J4+fdqGDRtmVapUYQKL/3d94J4zZ45NmDDBBg0a5Jwf88svv9jjjz9upUqVsp9//tnMrm3MXL9BQ/C+tT179lidOnUsVapUzo+v2T/r7o8//rA2bdrEmgkV8JSrV6/ayZMnrUKFCs55x8uXLzdfX1+3c+nMzBYtWmT9+vWLtYOODchr5s+fb8mSJbNixYqZy+WyJ598MtYl/qZNm+ZcshKexfsSnhLz3tq0aZN98cUX9t577znhb/369VapUiWrWLGivffee7Zs2TJ7+eWXLVWqVJzDfQMHDx60smXLWpMmTeynn34yM7NRo0ZZ6tSpbevWrfbnn39as2bNrGrVqjZ16lTnfufOnUuskpGACN33qBkzZjgzE8Z82I4fP27VqlWzRx55xL777ju3H9qwsDCrUqWKde7c2e1xjh8/bqdPn757hScR/fv3t2zZstkzzzxjpUqVsoceesjZs7hq1Sp74oknrEyZMlyW4T/Yt2+f1a1b1xo0aOC2Hjn3EHdLRESEXbx40bl96NAhK1CggF2+fNmWLFliadOmtcmTJ5vZtWueTp48OdbGDfNgXBPze3P06FFr1KiRffTRR3b48GH7+OOPzeVyWf/+/WONzvr444+dK2oASJq+/PJLy549u1WsWNEqVqxoadOmtblz55qZ2Y8//mjly5c3Ly8vq1ChgnXu3Nm2bduWyBXf225nNOCsWbPMjB1r9wtC9z3o2LFjTiD56KOPrEuXLs7Qx2PHjlmZMmWsZs2abh9SM7OLFy86G4hsKN7YnDlzLEeOHBYSEmJm12bedblc9vXXXzt9fvzxR6tataq1b98+scq8L8T8uNSrV8/ZqwvcDV9++aU9+eSTVrJkSRsxYoTTXq5cOWvWrJn5+vq6TVqzd+9eq1KlijMx5YMurt+QNWvWWJcuXaxJkyZuM5DPmTPHXC6X9evXj528wH1k48aNljlzZps+fbqZXTvt0eVy2ZgxY5wgGBwcbIUKFbLnn3/ezpw5k5jlJhmMBnwwEbrvMV999ZUVL17cwsLCzOzaTLlFixa1l156ybmW39GjR6106dJWs2ZN53qJ1yNwu/v3HsLx48dby5Ytzcxs9uzZ5ufnZ++//76ZmYWHh9vRo0fN7No5i6zL/+7333+3xx57zCpUqGDBwcGJXQ4eAB988IH5+flZ7969rVevXpYsWTLnutrvvvuuZcuWzZo2ber0//vvv61Ro0ZWp04dRmDYP78hISEh1r59e+c79PPPP7eUKVOan59frJ1oc+fOtRQpUliXLl3szz//vOs1A0h4X3/9tT3xxBNmdu23PGfOnG4jKmNGZK5evZqAeJsYDfjgYfbye8wff/yhnDlzys/PT9K1WXafeeYZLVu2TJMmTdKhQ4eUI0cOzZ8/XxcvXlTv3r3166+/uj1GzAy9kNauXavx48dr/PjxOnHihKRrs76mTp1amzZtUufOnTVmzBh17dpVkjR79mzNnDlTV69eVcmSJeXl5eU2ezxuX4ECBTRu3DjlyJFDgYGBiV0O7nNTp05Vjx49NGvWLI0fP15vv/22WrRooaioKJ0/f15NmzZV06ZNtW/fPjVs2FDPP/+86tWrpyNHjujbb79VsmTJHujPfMwMu1u3blXFihWVLVs2uVwuSVLLli01b948pU6dWtOnT9fu3bud+7Vo0ULTpk3T3LlzFRUVlVjlA0hA+/fvV2hoqEJDQ1WnTh3Vr19fkydPliR98cUX6t27tyIjI1W9enXlypUrkatNWvLly6f33ntPZqbXX3/dueJLihQpJHFll/sR6ewec+LECWeDL2bDZcCAAWrRooVWrFih9957zwne8+bNU/ny5VWqVKnELPme9fHHH6tTp046duyY0qZNq2zZskmSnn32WS1atEhly5bVBx984ATuS5cuacGCBTp16pSSJ0/uPA47Mf67hx9+WJ999hmXWYJHrV69Wp07d9arr76qpk2bOu27du3S1KlTFRgYqC5duiggIECDBw9WihQpFBkZqZo1a2rz5s3OZZce1M98TOAOCQlRpUqV1Lt3b40cOdJZbmZ67LHH9O6772rp0qV69913tWfPHmd569atdfjwYfn7+ydG+QBuU8z25uXLl+Nc3rhxYyVLlkwFChRQrVq19OGHHzrLfvnlF504cUKXLl26K7XejwoUKKB3331XKVKkUL9+/bRhw4bELgkelPzWXeBpJ06ccALhuXPnnKMKMUdcvLy89PLLL0uS5s2bJy8vL3Xu3Fn58+fX1KlTJV0L6OwV+8cnn3yiLl266JNPPtFjjz0mHx8fSdLbb7+tzJkzq3///po8ebKOHTum06dP69ChQxoyZIiOHz+uhQsXSrq2gRnzb4H/ztvbO7FLwH0ue/bsqlKlijZt2qSNGzeqTJkyatasmS5evKiRI0fKz89Pffv21fHjx9WhQwc99dRTbvePiopy2+H2oPHy8tKOHTtUqVIlDRw4UK+99pqzbMqUKYqMjFT37t2d9danTx8lS5ZMXbp0UZEiRSRJadOmTZTaAdw+Ly8v/fHHH+rdu7e6du2qmjVrui0PDAxUmTJldPLkSeXPn1+SdPToUU2ZMkUzZ87UmjVrlC5dusQo/b4RMxrwtddeYzTg/S6Rh7c/8NatW2cVKlSwL774wszMXnzxRevYsaOZ/XM+R8w5M2bXLi2QLVs2mzhxopkxo2Fcdu3aZUWLFrUPPvjArb158+bmcrmsadOmNn78eBs7dqxlyZLFMmbMaMWKFbM6depwLg2QxMVM3teoUSOrXLmylSpVypkPw+zapW9cLpd98803iVbjvers2bNWoUIFy5kzp1v7qFGjzNfXN9Z53F9++aWlSpXK+vbt63x3Akha9u/fbxUrVrRGjRq5fcZjtoNOnTplzz33nD388MOWJk0aK1OmjOXLl882b96cWCXflyIiIhK7BHjYgzmG7h4SHR2tDBky6P3339eSJUsUFRXlDM2LOXKdMmVKp3///v317rvvOkOiORIb29GjR3X+/HlVr17dGTrVrVs3hYSEaNGiRbpw4YLWrVunfPny6ffff9f8+fM1Z84cLVmyxBleyqgBIGmKGa4XERGhHTt26OWXX1bu3LkVHR0tM5MkFSpUSFmyZEnkSu89yZMn1xNPPKEsWbKoU6dOkqQJEyborbfe0pdffqnKlStLkrMemzVrpo8//ljPP/+8cx4igKQlb968mjVrlqKiojRixAjn3OJkyZLpypUrypIli9555x2tXbtWgwcP1qxZs7R27VqVLFkykSu/vzAa8P7nsphfTySaDRs2aOzYsYqMjNTGjRslSTlz5nTCX8y53WfPnlWLFi30xhtvSGJI+Y2MHDlSb7/9tv7880+n7cSJE4qKilKOHDm0a9cude7cWVeuXNG3336rzJkzO/1ihvMDSNr279+vbt26ycvLSwMHDlTVqlUlXTtH8cKFC1qxYgWf9evY/59Oc/78ec2cOVPTpk2Ty+XSkSNHtHDhQidwx5g6daratGnDhiJwn9i7d6969uwpM9Nrr73mfOajoqIUFRWlwYMHa//+/frss8/43AN3gC2ORBSzv6NChQrq06ePvL29ZWby9/fXc889p0aNGqlWrVp66qmn1KxZMz377LNuk9oQuOOWP39+Xbp0ScuWLXPasmXLphw5cig6OlqFCxdWkyZNlDlzZqVOndrtvmyEA/eHfPnyaeLEiTIzjRkzRj/99JOaNWum33//XUuXLuXKBP/icrlkZvL19VW7du3UoUMHXb58WWXKlHHb+JakoUOHqnPnzjp48GBilgwgAcWMEnK5XG5HvKOiotS3b1+NGzdOr7zyCoEbuEMc6b6LbnUU9eeff9abb76pP//8U0OHDlX16tXj7Hf16tUHerKfWzlw4IBKliypOnXq6K233op1GYvz58+rVatWKlCggMaPH59IVQK4G/bu3avevXtr6dKlyps3r7Zv3+6cRsL3aGxxHfEuU6aMM2nnq6++qvHjx+unn37iyhnAfej6I94vv/yyvv/+e02cOFHr1q1jSDnwHxC675IOHTqoadOmatCgwU039NatW6e33npLZ8+eVe/evdWkSZO7WOX94/PPP1f79u3VrFkz9e/fXyVKlJAkHT58WJ06ddKpU6e0ceNGJU+enFnKgfvc7t279f7772v8+PFKnjw5gfsW4gre1apVU8aMGfXGG2/op59+UunSpRO7TAAesnfvXvXp00fr1q3TxYsXFRwczE424D8idN8lZcuW1fHjxzVr1izVqFHjpht869ev19tvv63du3dr8uTJqlKlyl2s9P4QFRWlGTNm6IUXXlDWrFn1yCOP6OrVqzp//rwk6ccff1SKFCk4Lx54wBC44+f64P3xxx9r1KhROnXqlDZs2EDgBh4Ae/bs0UsvvaRRo0Y5lwQEcOcI3R52/ZDyhg0basuWLU7wvtl5MatXr9bSpUs1YsQIQuF/EBISoqlTp+r3339Xzpw5VapUKT3//PNKliwZG98AHkgxOxvPnz8vLy8vpUmTxll2/W9WTPAODw/XnDlzVKtWLeXLly+xygZwl125coUrEwAJhNDtYddvwPz999+qUKGCkiVLpjfeeEOPPvpovEIfR2MTHusUwINk9erVypkzp/LmzStJ+uabbzRp0iQdPXpUjz76qKpVq6ann35aUtzBm9NwAAC4c0zV7GExGy69e/fW008/rQwZMujAgQPq1KmTVq1apatXr97yMQiH/01c+5VYpwAeBGamTZs2qUGDBvrwww91+vRpbdy4UW3atFGFChX01FNP6a+//tKAAQOciSWvn/AzJmgTuAEAuHMc6b4Lpk+frj59+mjlypUKCAiQmal169bas2ePZs6cqRo1ajB8BwDgMe+++67eeustPffcc4qOjpaZafjw4ZKko0eP6uOPP9ZHH32kN998U82bN0/kagEAuL9wpPsu+OOPP1SmTBmVKFFC2bJlU/bs2bVy5Urly5dP3bp10+rVqxUZGZnYZQIA7jMx1yLv2bOn+vXrp2nTpmnatGm6cuWK0ycoKEht2rRRsWLFtH79eklxjxACAAB3htDtQTEbOxcuXNCRI0fk5eUll8uly5cvS5L69u2rffv26amnntLWrVsTs1QAwH3Iy8vL+S3q0aOHhgwZojNnzig4OFh79+51+gUFBSl37tz66aefFBkZyXByAAASEKE7AcVs2MSIOS+uc+fOCg8PV8+ePSVJKVOmlCSlSZNGvXv3Vrt27bj+IQDAI64/R7tDhw7OJSmnTJmi33//3VkWHh6u7Nmzc5QbAIAExjndCeT62V4/++wzbdu2TV5eXipTpoyaNWum999/X++8846qVq2qESNG6Ny5c+rTp48KFy6st956SxIzagMAEkbMbOMhISE6dOiQIiMjVaNGDfn7+0v/196dR0V13n8cfw97JqiIYl2KioobwR1I1EZUMI3RikdEo3E9LDZYtdpQe1yrRoMcVzTuG+ISECouccEFTxONSz2m1hUDMcYlWnEBRLaZ3x+W+UlsExdwjH5e/3CGuffOF/6587nP83wfHqzxnj59Oh4eHvj4+GAymdiwYQP79u2jefPmVq5eRETk5aJNistISeCOiopiw4YNdOrUCWdnZ3r37s2iRYsYMGAAzs7OTJ48maSkJCpUqICbmxtbtmyxXEOBW0REnlVJ4E5OTiYiIoKGDRty6tQp/P39GTBgAL169WLEiBE4OjoSFRXFlStXiIyM5MiRI9qHW0REpBwodJehHTt2sHHjRhISEnjrrbfYtGkTixYtwmAwUKFCBQYOHEi/fv1IS0vDaDTi5+eHra0tRUVFj7Vft4iIyM8xGAzs37+fYcOGMX36dMLCwvjyyy/p2LEjt2/fJj8/n379+hEREUFeXh4rV64kLCyMypUrW7t0ERGRl5Kmlz+DkinlJT8XL17M9u3b2bp1K8nJyQwaNIhZs2YRHh7OnTt3OHv2LH5+fqWuoSnlIiLyrEpGtwEKCgqYOnUq9+7dY9asWWRkZNClSxdatmzJlStXyMnJYdy4cYSEhACQlZWFq6urNcsXERF5qWl49SkVFhZa9ta+efMmbm5uVKxYEaPRyLp16xg2bBgxMTGEh4cDcODAAXbu3ImHh4dlTR1oSrmIiJSdY8eOUa1aNYKDg3FwcCA7O5v333+fDh06sGLFCo4dO0bHjh2ZOXMmJpOJvn37aoRbRESknKl7+VNITk4mKSkJgJEjRxIUFERhYSE1a9bk2LFjDB06lKlTpzJs2DAAcnNzWbx4MWazGTc3N2uWLiIiL5GSyWoGg4EdO3bg6+tLZmYmDRs2pEmTJhw8eJCCggLGjRsHPLgftWjRgnr16tGuXTvLuSIiIlJ+FLqfQkpKCv369aN79+6sXbuWJUuWYG9vj7+/P5GRkRQWFpKTk8OePXv44osv6NmzJ1euXCE2NhaDwaDtWEREpEyUBOYbN25w+fJlYmJi6NChg2Vrynv37pGdnc3FixcB2LdvHz4+PqxcuRJ3d3er1S0iIvIq0ZruxzRlyhRCQkJo3LgxAF5eXpw/f57p06fz0UcfPXLstm3b+Prrr2ndujUuLi6kpKRgb2+vNdwiIlKmzp07R5MmTahVqxZTpkxhyJAhlvfS09Pp06ePZUlURkYGaWlptGjRwnoFi4iIvGIUuh/DP/7xD8aPH8/WrVuxs7OjsLCQ4OBgzGYz+/fvZ9myZfTs2RNHR0fLOdeuXeP69etUqVKFmjVrYjAY1KVcRETKREkDT4A7d+4wY8YMZs+ezcSJExk/fjwmkwl4sJ3lyZMnSU1NJTc3l5CQEBo1amTN0kVERF45Ct2P6eF9T0vWwwEMGTKExMREli9fXip4X758mVq1alnOf/gLkoiIyJMouYfk5OTg7Oz8yPu3bt0iOjqamJgYNm7cSO/evUsFbxEREbEeDbs+gYsXLzJ48GC6du3KmDFj8PHxYdWqVdjY2BAeHk5+fj7+/v4MHz4cgK1bt1rCur70iIjI07KxseHMmTP07NkTPz8/xo0bR5UqVahSpQoAlStXZuzYsRQXF9O3b18MBoNlRpaIiIhYl0L3T3h431ODwUCdOnVITk7m97//PfPmzWPkyJH4+PiwYsUK7OzsGD16NG5ubjg5OXH06FHLeSIiIs/q2LFjODg4kJGRwZ/+9CdycnKIioqiTZs2VK1aFRcXFyZMmIDJZGLAgAHk5+fTv39/a5ctIiLyytP08v/h4engN27coHLlypjNZuzt7dmzZw9hYWG0a9fOErwBdu3aRVFREb/97W+xtbXVGm4RESkzx48fZ+LEicTExODg4MCKFSvYuHEjfn5++Pr6EhkZiZ2dHTY2NkRERJCUlERmZiYVKlSwdukiIiKvNIXunzFt2jRSUlJ47bXXeO+994iIiMDFxYXU1FTCw8Np3749I0eOpE2bNqXOU5dyEREpa4MGDeLq1ats3boVR0dHMjMz6dSpExcvXsTf3x9fX1+GDh3Kr3/9a3JycqhWrZq1SxYREXnlaaHxjzz8DGLlypXMnTuXoUOHUr16dbZs2cLw4cPJysoiMDCQZcuWcejQISZNmsT58+dLXUeBW0REykpJU7SoqChMJhNff/018ODBsK2tLV988QXt27dn9+7ddOnSBZPJpMAtIiLygtBI9388vH4bYN++faSmptK6dWuCg4MBWLhwIevWraNu3bosWLAAV1dXtm3bxurVq0lISFCzNBERKVd5eXkEBQXh7e1Nbm4uKSkpbN68GV9fX8xmM3fu3CE3N7fU7hkiIiJiXQrd/3Ht2jWqV68OwP79+xkxYgQ3btxg7dq1BAYGAlBUVMTSpUtZt24d9erVY86cOVStWtVyDW0LJiIiz+LHD4AfVnKPOXLkCG+++SbVq1dn165deHt7P+cqRURE5EkoIfKgI2ydOnXYtm0bAK1bt6ZHjx7Y2NiwevVq8vPzAbCzsyMiIoIBAwZw+PBhFixYAPz/lHQFbhEReVrz58/n888/p6io6L++X3KPadCgAe+++y7BwcF4e3tTXFz8PMsUERGRJ6SUCLi4uNCnTx+GDh3Ktm3bqFixImPHjiUsLIzz588zYcIECgoKgAdrtUNDQ4mOjmbChAmAtgUTEZFnt2HDBgYPHkxaWtr/DN4Arq6uBAUFsXLlSi5cuKAeIiIiIi84TS//j4yMDD755BMSExNZu3Yt3bp1Izs7m+joaPbs2cPbb7/NtGnTcHBwKHWeupSLiMizeHhK+Xvvvcfx48dZs2YN/v7+j9xzSo4tLCykZcuWdOzYkXnz5mmmlYiIyAvslQ/dD4fmb775hujoaBISEoiPj7cE75kzZ7Jv3z7eeOMNFi5cqL23RUSkTBUUFFgCdtu2bcnNzSUmJoZOnTr9z3vO9OnTCQkJoUGDBs+zVBEREXlCr2ToTktL49KlSwwYMAAoHbxLRry3b9/O2rVr6dSpE7m5uYwbN4579+6xZMkSTScXEZEyUzJ6vXHjRjZt2sT9+/fZuXMn7u7uLF26lI4dO5YK3j/VbE1ERERePK9U6DabzeTl5dGjRw9ycnIYOXIkffv2BUoH71OnTjFp0iRMJhOrV6+mYsWK3L9/H0dHRwwGg77wiIhImTp06BABAQHExsbStm1bTCYTkZGRnD9/ntWrVz8SvEVEROSX45VaBGYwGDAajSxevJhf/epXLF26lPXr1wMPGqSVdID18vKic+fOHDp0yNK53MnJSYFbRETKxdmzZ/H09CQkJITGjRvTtGlT9u/fj6enJxEREezfv9/S0FNERER+WV6p0F2ifv36zJkzB6PRyPLly9mwYQPwIHiXfKmpX78+np6ejzSnUeAWEZGyUjLZLDs7m+vXr+Ps7AxAXl4eADNnzuTixYsMGjSIL7/80mp1ioiIyNN7JUM3gIeHB7GxsRiNRpYtW8aqVasAcHBw4P79+8ybN4/q1avj6upq5UpFRORlVfIgt2/fvpjNZiIjIwF47bXXALC3t6dPnz60atWKWrVqWa1OEREReXqv1Jru/yYzM5OoqCgyMjJo3LgxrVq1Yvfu3dy8eZOvvvoKOzs7TSkXEZEyUXI/OXr0KIcPH8ZkMuHp6cm7777L0qVLmT17Nh07dmT+/Pncvn2befPmkZGRQVxcnNZ0i4iI/EK98qEb4MqVKyQmJpKQkEDVqlWpXbs2c+bMwc7OjqKiIn3RERGRMpOUlMSwYcNo06YNTk5O7Ny5kxkzZhAWFsaGDRuYMmUKeXl5uLq68u9//5vU1FRatWpl7bJFRETkKSl0/4SHO5qLiIg8q9OnTxMQEMD48eP58MMPOXXqFC1btiQ8PJwFCxZQXFxMTk4Of/vb36hUqRItWrTAw8PD2mWLiIjIM1Do/o8fTyHXlHIRESkrJpMJGxsbdu3axYwZM0hLS+PixYu0b9+e7t278+mnnwJw8uRJvL29rVytiIiIlKVXtpHaj/04YCtwi4jI0zCZTKV+Aty7dw948EC3qKiIo0eP8vbbb9O1a1diY2OBB3t1x8bGcunSpedftIiIiJQbhW4REZEyZGNjQ3p6Ops3bwYgMTGR9u3bc/fuXWrUqEFOTg6BgYEEBASwZMkSyzKmhIQErl69SoUKFaxYvYiIiJQ1hW4REZEyFhsbS3BwMKNHj+b999/nj3/8IxUrVqR58+YMHjyYu3fvUr9+fU6ePMmFCxf46KOPWLNmDTNmzMDFxcXa5YuIiEgZ0ppuERGRMhAfH0+zZs1o1qwZAJ07d+bAgQOEh4db1myXmDhxIps2beK7776jUaNG5OfnEx8fT4sWLaxQuYiIiJQnhW4REZFnYDabuXDhAkFBQezYsYPatWsDD0J3Xl4eJ0+eZNmyZQQFBeHk5GQ579y5c1y5cgVXV1dq1qyJm5ubtf4EERERKUcK3SIiImUgJycHZ2dnTpw4QZUqVXB3dwdgyJAhJCYmsnz5cnr27ImjoyMAWVlZuLq6WrNkEREReQ60pltERKQMGI1GsrKy+N3vfseIESM4evQoAKtWraJPnz6EhYWRnJzMnTt3+Pjjj+nSpQv5+fno2beIiMjLTSPdIiIiz8hsNlu2mty9ezfDhw/H19eXkSNH4uPjA0B4eDgbN26kadOmnDt3jtTUVNq0aWPNskVEROQ5UOgWERF5SiVh+969exiNRoqLi7G1tWXPnj2EhYXRrl27UsF7/fr15OXl0aFDBxo0aGDl6kVEROR5UOgWERF5Bp9//jmffvop9vb2BAQE8MEHH1CpUiVSU1MJDw+nXbt2jBo1SqPaIiIiryit6RYREXlKBw8epGfPnjRs2JDs7Gzi4+MZPnw4WVlZBAYGsnTpUo4cOcLUqVP55z//ae1yRURExAoUukVERB5DycQwk8kEQHp6OocOHWLGjBnMnj2b3bt388EHH5CRkVEqeM+dO5eLFy9StWpVa5YvIiIiVqLQLSIi8hNKwnZeXh4ANjY2nDt3jtDQUObOnUulSpUsvw8PD6d///58++23jBo1ips3b9K1a1cOHjxIzZo1rfY3iIiIiPUodIuIiPwEg8HADz/8gLe3N1u2bAGgRo0a+Pn5YTab2b59u2X0297enoiICAYOHMixY8cYO3YsJpMJJycna/4JIiIiYkV21i5ARETkRZeXl4evry+hoaGsXLmSbt26MXHiRIxGIykpKYwdO5Zp06bh4OCAra0toaGh2Nvb07lzZ2xs9HxbRETkVabu5SIiIo8hIyODTz75hMTERNauXUu3bt3Izs4mOjqaPXv28Jvf/IaPP/4YBwcHa5cqIiIiLxCFbhERkYeYTKZSo9NFRUXY2T2YGPbNN98QHR1NQkIC8fHxpYJ3WloazZo1Y+7cuQreIiIiYqE5byIiIg+xsbHh0qVLJCUlAWBnZ0dxcTEA9evX589//jMhISGEhoayd+9eKlSowF/+8hf8/PxIT0/n9u3bVqxeREREXjQa6RYREXlIUVERAwcO5OzZs0RFRdG3b18AiouLsbW1BeDMmTP89a9/5fr163z22We4ublx7949cnNzcXNzs2b5IiIi8oLRSLeIiMhD7OzsmDJlCrVr12bp0qWsX78eAFtbW8uId5MmTQgODub06dPcvXsXAKPRqMAtIiIij1DoFhER+ZEGDRowZ84cjEYjy5cvZ8OGDcCD4F1YWAhAw4YNqVatGpowJiIiIj9FoVtEROS/8PDwIDY2FqPRyLJly1izZg3wYC9ugHXr1mE0Gqlatao1yxQREZEXnNZ0i4iI/ITMzEzGjBnD5cuXefPNN2nbti1///vfSUxMJDU1lWbNmlm7RBEREXmBKXSLiIj8jO+//54VK1aQnJyMra0t7u7uTJ8+HS8vL2uXJiIiIi84hW4REZHHZDKZyMvLw9bWFicnJ2uXIyIiIr8ACt0iIiKPwWw2YzAYrF2GiIiI/MKokZqIiMhjUOAWERGRp6HQLSIiIiIiIlJOFLpFREREREREyolCt4iIiIiIiEg5UegWERERERERKScK3SIiIiIiIiLlRKFbREREREREpJwodIuIiIiIiIiUE4VuERERERERkXKi0C0iIvISuXbtGn/4wx+oV68ejo6OuLu70717d/bu3ftc6zAYDGzevPm5fqaIiMiLyM7aBYiIiEjZ+Pbbb2nXrh0uLi7ExMTg7e1NYWEhu3btIjIykrNnz1q7xFIKCgpwcHCwdhkiIiLlSiPdIiIiL4kPP/wQg8HAkSNH6NWrFw0bNsTLy4vRo0fz1VdfAfDdd9/Ro0cPnJ2dqVixIiEhIfzwww+WawwePJigoKBS1x01ahT+/v6W1/7+/owYMYKoqChcXV2pXr06kydPtrxft25dAHr27InBYLC8njx5Mi1atGD58uV4eHjg5OREXFwcVapUIT8/v9RnBgUFMWDAgDL734iIiFiLQreIiMhLICsri507dxIZGcnrr7/+yPsuLi6YTCZ69OhBVlYWBw4cIDU1lYyMDPr06fPEn7dmzRpef/11Dh8+zMyZM5kyZQqpqakAHD16FIBVq1Zx9epVy2uACxcukJSURHJyMidOnKB3794UFxezZcsWyzHXr19n+/btDB069InrEhERedFoermIiMhL4MKFC5jNZho3bvw/j9m7dy8nT54kMzMTd3d3AOLi4vDy8uLo0aP4+Pg89uc1a9aMSZMmAeDp6cmCBQvYu3cvgYGBuLm5AQ+CfvXq1UudV1BQQFxcnOUYgH79+rFq1Sp69+4NQHx8PLVr1y41ui4iIvJLpZFuERGRl4DZbP7ZY86cOYO7u7slcAM0bdoUFxcXzpw580Sf16xZs1Kva9SowfXr13/2vDp16pQK3ABhYWHs3r2by5cvA7B69WoGDx6MwWB4oppEREReRBrpFhEReQl4enpiMBieuVmajY3NIwG+sLDwkePs7e1LvTYYDJhMpp+9/n+b+t6yZUuaN29OXFwcXbp04dSpU2zfvv0JKxcREXkxaaRbRETkJeDq6so777zDwoULyc3NfeT927dv06RJEy5dusSlS5csvz99+jS3b9+madOmALi5uXH16tVS5544ceKJ67G3t6e4uPixjw8NDWX16tWsWrWKgICAUqPxIiIiv2QK3SIiIi+JhQsXUlxcjK+vL0lJSaSnp3PmzBnmz5/PW2+9RUBAAN7e3vTv35/jx49z5MgRBg4cSIcOHWjTpg0AnTp14tixY8TFxZGens6kSZP417/+9cS11K1bl71793Lt2jVu3br1s8f369eP77//nmXLlqmBmoiIvFQUukVERF4S9erV4/jx43Ts2JExY8bwxhtvEBgYyN69e1m0aBEGg4GUlBQqV67M22+/TUBAAPXq1eOzzz6zXOOdd95hwoQJREVF4ePjQ3Z2NgMHDnziWmbNmkVqairu7u60bNnyZ4+vVKkSvXr1wtnZ+ZEty0RERH7JDObH6bwiIiIiUs46d+6Ml5cX8+fPt3YpIiIiZUahW0RERKzq1q1bpKWlERwczOnTp2nUqJG1SxIRESkz6l4uIiIiVtWyZUtu3bpFdHS0AreIiLx0NNItIiIiIiIiUk7USE1ERERERESknCh0i4iIiIiIiJQThW4RERERERGRcqLQLSIiIiIiIlJOFLpFREREREREyolCt4iIiIiIiEg5UegWERERERERKScK3SIiIiIiIiLlRKFbREREREREpJz8H0HAWGFNEgWYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "top_10_countries_pd = top_10_countries.toPandas()\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(top_10_countries_pd[\"Country\"], top_10_countries_pd[\"TotalCount\"], color='orange')\n",
        "plt.xlabel(\"Country\")\n",
        "plt.ylabel(\"Total Count\")\n",
        "plt.title(\"Total Count for Top 10 Countries\")\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ABQb4a41DQD",
        "outputId": "7f6c0186-7032-494a-9675-fc1604c61c33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+\n",
            "|         Description|Quantity|        InvoiceDate|UnitPrice|CustomerID|       Country|        TotalPrice|      date|   month|     week|time|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+\n",
            "|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|   17850.0|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|\n",
            "| white metal lantern|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|\n",
            "|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|   17850.0|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|\n",
            "|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|\n",
            "|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|\n",
            "|set 7 babushka ne...|       2|2010-12-01 08:26:00|     7.65|   17850.0|United Kingdom|              15.3|12/01/2010|December|Wednesday|  08|\n",
            "|glass star froste...|       6|2010-12-01 08:26:00|     4.25|   17850.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|\n",
            "|hand warmer union...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|\n",
            "|hand warmer red p...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|\n",
            "|assorted colour b...|      32|2010-12-01 08:34:00|     1.69|   13047.0|United Kingdom|             54.08|12/01/2010|December|Wednesday|  08|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|\n",
            "|feltcraft princes...|       8|2010-12-01 08:34:00|     3.75|   13047.0|United Kingdom|              30.0|12/01/2010|December|Wednesday|  08|\n",
            "|ivory knitted mug...|       6|2010-12-01 08:34:00|     1.65|   13047.0|United Kingdom| 9.899999999999999|12/01/2010|December|Wednesday|  08|\n",
            "|box of 6 assorted...|       6|2010-12-01 08:34:00|     4.25|   13047.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|\n",
            "|box of vintage ji...|       3|2010-12-01 08:34:00|     4.95|   13047.0|United Kingdom|14.850000000000001|12/01/2010|December|Wednesday|  08|\n",
            "|box of vintage al...|       2|2010-12-01 08:34:00|     9.95|   13047.0|United Kingdom|              19.9|12/01/2010|December|Wednesday|  08|\n",
            "|home building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|\n",
            "|love building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|\n",
            "|recipe box with m...|       4|2010-12-01 08:34:00|     7.95|   13047.0|United Kingdom|              31.8|12/01/2010|December|Wednesday|  08|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql.functions import date_format\n",
        "spark = SparkSession.builder \\\n",
        "    .appName(\"DateTimeConversion\") \\\n",
        "    .config(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\") \\\n",
        "    .getOrCreate()\n",
        "df_spark = df_spark.withColumn(\"InvoiceDate\", F.to_timestamp(\"InvoiceDate\", \"MM/dd/yyyy HH:mm\"))\n",
        "df_spark = df_spark.withColumn(\"date\", date_format(\"InvoiceDate\", \"MM/dd/yyyy\")) \\\n",
        "                   .withColumn(\"month\", date_format(\"InvoiceDate\", \"MMMM\")) \\\n",
        "                   .withColumn(\"week\", date_format(\"InvoiceDate\", \"EEEE\")) \\\n",
        "                   .withColumn(\"time\", date_format(\"InvoiceDate\", \"HH\"))\n",
        "df_spark.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jmHaoko1DQF",
        "outputId": "82c307c1-4916-4735-d1c3-87dda79fd502"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+------------------+\n",
            "|time|        TotalSales|\n",
            "+----+------------------+\n",
            "|  06|-497.3499999999998|\n",
            "|  07|31009.319999999985|\n",
            "|  08|280103.26999999915|\n",
            "|  09| 655114.8309999981|\n",
            "|  10| 1150556.961000019|\n",
            "|  11|1054638.9100000344|\n",
            "|  12|  1337091.77000002|\n",
            "|  13|1122675.4300000356|\n",
            "|  14| 948603.8210000372|\n",
            "|  15| 898958.8600000264|\n",
            "|  16| 447390.8899999974|\n",
            "|  17|212975.73099999907|\n",
            "|  18|100734.56000000011|\n",
            "|  19| 44688.44000000018|\n",
            "|  20|16020.370000000014|\n",
            "+----+------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sales_by_hour = df_spark.groupBy(\"time\").agg(F.sum(\"TotalPrice\").alias(\"TotalSales\"))\n",
        "sales_by_hour = sales_by_hour.orderBy(\"time\")\n",
        "sales_by_hour.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yDsk6T5V1DQH"
      },
      "outputs": [],
      "source": [
        "negative_values_df = df_spark.filter((col(\"UnitPrice\").contains(\"-\")) | (col(\"Quantity\").contains(\"-\")))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcJ28_Y31DQI",
        "outputId": "30923ee3-2e09-4122-b4c2-57619303cc02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+-------------------+---------+----------+--------------+-------------------+----------+--------+---------+----+\n",
            "|         Description|Quantity|        InvoiceDate|UnitPrice|CustomerID|       Country|         TotalPrice|      date|   month|     week|time|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+-------------------+----------+--------+---------+----+\n",
            "|            discount|      -1|2010-12-01 09:41:00|     27.5|   14527.0|United Kingdom|              -27.5|12/01/2010|December|Wednesday|  09|\n",
            "|set of 3 coloured...|      -1|2010-12-01 09:49:00|     4.65|   15311.0|United Kingdom|              -4.65|12/01/2010|December|Wednesday|  09|\n",
            "|plasters in tin c...|     -12|2010-12-01 10:24:00|     1.65|   17548.0|United Kingdom|-19.799999999999997|12/01/2010|December|Wednesday|  10|\n",
            "|pack of 12 pink p...|     -24|2010-12-01 10:24:00|     0.29|   17548.0|United Kingdom| -6.959999999999999|12/01/2010|December|Wednesday|  10|\n",
            "|pack of 12 blue p...|     -24|2010-12-01 10:24:00|     0.29|   17548.0|United Kingdom| -6.959999999999999|12/01/2010|December|Wednesday|  10|\n",
            "|pack of 12 red re...|     -24|2010-12-01 10:24:00|     0.29|   17548.0|United Kingdom| -6.959999999999999|12/01/2010|December|Wednesday|  10|\n",
            "|chick grey hot wa...|     -12|2010-12-01 10:24:00|     3.45|   17548.0|United Kingdom|-41.400000000000006|12/01/2010|December|Wednesday|  10|\n",
            "|plasters in tin v...|     -12|2010-12-01 10:24:00|     1.65|   17548.0|United Kingdom|-19.799999999999997|12/01/2010|December|Wednesday|  10|\n",
            "|plasters in tin s...|     -24|2010-12-01 10:24:00|     1.65|   17548.0|United Kingdom|-39.599999999999994|12/01/2010|December|Wednesday|  10|\n",
            "|jam making set wi...|      -6|2010-12-01 12:38:00|     4.25|   17897.0|United Kingdom|              -25.5|12/01/2010|December|Wednesday|  12|\n",
            "|hand warmer red r...|      -1|2010-12-01 14:30:00|      2.1|   17841.0|United Kingdom|               -2.1|12/01/2010|December|Wednesday|  14|\n",
            "|charlotte bag suk...|      -2|2010-12-01 14:30:00|     0.85|   17841.0|United Kingdom|               -1.7|12/01/2010|December|Wednesday|  14|\n",
            "|3 hook hanger mag...|      -4|2010-12-01 14:33:00|     1.95|   12472.0|       Germany|               -7.8|12/01/2010|December|Wednesday|  14|\n",
            "|5 hook hanger mag...|      -5|2010-12-01 14:33:00|     1.65|   12472.0|       Germany|              -8.25|12/01/2010|December|Wednesday|  14|\n",
            "|set/5 red retrosp...|      -1|2010-12-01 14:33:00|     2.95|   12472.0|       Germany|              -2.95|12/01/2010|December|Wednesday|  14|\n",
            "|set of salt and p...|      -7|2010-12-01 14:33:00|     1.25|   12472.0|       Germany|              -8.75|12/01/2010|December|Wednesday|  14|\n",
            "|  deluxe sewing kit |      -1|2010-12-01 14:33:00|     5.95|   12472.0|       Germany|              -5.95|12/01/2010|December|Wednesday|  14|\n",
            "|triple photo fram...|      -2|2010-12-01 14:33:00|     9.95|   12472.0|       Germany|              -19.9|12/01/2010|December|Wednesday|  14|\n",
            "|retrospot party b...|      -1|2010-12-01 14:33:00|     1.65|   12472.0|       Germany|              -1.65|12/01/2010|December|Wednesday|  14|\n",
            "|hook, 1 hanger ,m...|      -2|2010-12-01 14:33:00|     0.85|   12472.0|       Germany|               -1.7|12/01/2010|December|Wednesday|  14|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+-------------------+----------+--------+---------+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "negative_values_df.show() #We can see that they are discounts and returns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "WsoFC49R1DQI",
        "outputId": "8442e701-e1fe-4a98-e65b-9432865264b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "sales_by_hour_pd = sales_by_hour.toPandas()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(sales_by_hour_pd['time'], sales_by_hour_pd['TotalSales'], marker='o', color='b')\n",
        "plt.xlabel('Hour')\n",
        "plt.ylabel('Total Sales')\n",
        "plt.title('Total Sales by Hour of The Day')\n",
        "plt.xticks(rotation=35)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UcTCBlV71DQJ"
      },
      "outputs": [],
      "source": [
        "def get_season(month):\n",
        "    if month in [\"December\", \"January\", \"February\"]:\n",
        "        return \"Winter\"\n",
        "    elif month in [\"March\", \"April\", \"May\"]:\n",
        "        return \"Spring\"\n",
        "    elif month in [\"June\", \"July\", \"August\"]:\n",
        "        return \"Summer\"\n",
        "    else:\n",
        "        return \"Autumn\"\n",
        "get_season_udf = F.udf(get_season)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dRfU72Kd1DQK"
      },
      "outputs": [],
      "source": [
        "df_spark = df_spark.withColumn(\"season\", get_season_udf(df_spark[\"month\"]))\n",
        "sales_by_season = df_spark.groupBy(\"season\").agg(F.sum(\"Quantity\").alias(\"TotalSales\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "dVv17SsP1DQK",
        "outputId": "c27dd562-a2d7-4559-f65f-f4f661e36e81"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "sales_by_season_pd = sales_by_season.toPandas()\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(sales_by_season_pd['season'], sales_by_season_pd['TotalSales'], color='skyblue')\n",
        "plt.xlabel('Season')\n",
        "plt.ylabel('Total Sales')\n",
        "plt.title('Total Sales by Season')\n",
        "plt.grid(axis='y')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oEm3ZCW61DQL"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.feature import Tokenizer, HashingTF, IDF\n",
        "from pyspark.ml.linalg import Vector\n",
        "\n",
        "tokenizer = Tokenizer(inputCol=\"Description\", outputCol=\"words\")\n",
        "wordsData = tokenizer.transform(df_spark)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYcsOn6xAjS1",
        "outputId": "370a7aeb-3922-4138-e831-a758ed7f611d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+\n",
            "|         Description|Quantity|        InvoiceDate|UnitPrice|CustomerID|       Country|        TotalPrice|      date|   month|     week|time|season|               words|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+\n",
            "|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|   17850.0|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|Winter|[white, hanging, ...|\n",
            "| white metal lantern|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[white, metal, la...|\n",
            "|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|   17850.0|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|Winter|[cream, cupid, he...|\n",
            "|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[knitted, union, ...|\n",
            "|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[red, woolly, hot...|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "wordsData.show(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wf5y-I74BENk"
      },
      "outputs": [],
      "source": [
        "hashingTF = HashingTF(inputCol=\"words\", outputCol=\"rawFeatures\", numFeatures=10000)\n",
        "featurizedData = hashingTF.transform(wordsData)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70HncrN1BY4K",
        "outputId": "c6849b86-8453-42a7-f94c-4ceb767628fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------------------------------------------------------------+\n",
            "|rawFeatures                                                      |\n",
            "+-----------------------------------------------------------------+\n",
            "|(10000,[4618,4667,5843,8889,9808],[1.0,1.0,1.0,1.0,1.0])         |\n",
            "|(10000,[426,671,5843],[1.0,1.0,1.0])                             |\n",
            "|(10000,[477,6575,7369,7815,9002],[1.0,1.0,1.0,1.0,1.0])          |\n",
            "|(10000,[2352,2589,3932,4508,6606,9693],[1.0,1.0,1.0,1.0,1.0,1.0])|\n",
            "|(10000,[52,388,691,5843,9470],[1.0,1.0,1.0,1.0,1.0])             |\n",
            "|(10000,[405,946,5015,5342,7153],[1.0,1.0,1.0,1.0,1.0])           |\n",
            "|(10000,[4667,5170,6932,8819,8889],[1.0,1.0,1.0,1.0,1.0])         |\n",
            "|(10000,[3423,4467,4508,8944],[1.0,1.0,1.0,1.0])                  |\n",
            "|(10000,[52,3197,3423,8151,8944],[1.0,1.0,1.0,1.0,1.0])           |\n",
            "|(10000,[2134,5894,8855,9363],[1.0,1.0,1.0,1.0])                  |\n",
            "|(10000,[7058,7842,9737],[1.0,1.0,1.0])                           |\n",
            "|(10000,[756,7058,9737],[1.0,1.0,1.0])                            |\n",
            "|(10000,[50,2126,4175,4685],[1.0,1.0,1.0,1.0])                    |\n",
            "|(10000,[2352,3322,6996,9056],[1.0,1.0,1.0,1.0])                  |\n",
            "|(10000,[3666,3774,4495,5894,7922,9363],[1.0,1.0,1.0,1.0,1.0,1.0])|\n",
            "|(10000,[2056,3189,4495,7922,7960],[1.0,1.0,1.0,1.0,1.0])         |\n",
            "|(10000,[2023,2056,3189,4495,7922],[1.0,1.0,1.0,1.0,1.0])         |\n",
            "|(10000,[3365,3519,5852,8454],[1.0,1.0,1.0,1.0])                  |\n",
            "|(10000,[3519,5852,6240,8454],[1.0,1.0,1.0,1.0])                  |\n",
            "|(10000,[426,7650,7922,9700,9808],[1.0,1.0,1.0,1.0,1.0])          |\n",
            "+-----------------------------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "featurizedData.select('rawFeatures').show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HZLpbdXAAhl3"
      },
      "outputs": [],
      "source": [
        "idf = IDF(inputCol=\"rawFeatures\", outputCol=\"features\")\n",
        "idfModel = idf.fit(featurizedData)\n",
        "rescaledData = idfModel.transform(featurizedData)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mSU1pP3B4Ja",
        "outputId": "dcaefcb8-1743-4080-efbb-58fa9c62932d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+--------------------+--------------------+\n",
            "|         Description|Quantity|        InvoiceDate|UnitPrice|CustomerID|       Country|        TotalPrice|      date|   month|     week|time|season|               words|         rawFeatures|            features|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+--------------------+--------------------+\n",
            "|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|   17850.0|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|Winter|[white, hanging, ...|(10000,[4618,4667...|(10000,[4618,4667...|\n",
            "| white metal lantern|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[white, metal, la...|(10000,[426,671,5...|(10000,[426,671,5...|\n",
            "|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|   17850.0|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|Winter|[cream, cupid, he...|(10000,[477,6575,...|(10000,[477,6575,...|\n",
            "|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[knitted, union, ...|(10000,[2352,2589...|(10000,[2352,2589...|\n",
            "|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|[red, woolly, hot...|(10000,[52,388,69...|(10000,[52,388,69...|\n",
            "|set 7 babushka ne...|       2|2010-12-01 08:26:00|     7.65|   17850.0|United Kingdom|              15.3|12/01/2010|December|Wednesday|  08|Winter|[set, 7, babushka...|(10000,[405,946,5...|(10000,[405,946,5...|\n",
            "|glass star froste...|       6|2010-12-01 08:26:00|     4.25|   17850.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|[glass, star, fro...|(10000,[4667,5170...|(10000,[4667,5170...|\n",
            "|hand warmer union...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|[hand, warmer, un...|(10000,[3423,4467...|(10000,[3423,4467...|\n",
            "|hand warmer red p...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|[hand, warmer, re...|(10000,[52,3197,3...|(10000,[52,3197,3...|\n",
            "|assorted colour b...|      32|2010-12-01 08:34:00|     1.69|   13047.0|United Kingdom|             54.08|12/01/2010|December|Wednesday|  08|Winter|[assorted, colour...|(10000,[2134,5894...|(10000,[2134,5894...|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|[poppy's, playhou...|(10000,[7058,7842...|(10000,[7058,7842...|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|[poppy's, playhou...|(10000,[756,7058,...|(10000,[756,7058,...|\n",
            "|feltcraft princes...|       8|2010-12-01 08:34:00|     3.75|   13047.0|United Kingdom|              30.0|12/01/2010|December|Wednesday|  08|Winter|[feltcraft, princ...|(10000,[50,2126,4...|(10000,[50,2126,4...|\n",
            "|ivory knitted mug...|       6|2010-12-01 08:34:00|     1.65|   13047.0|United Kingdom| 9.899999999999999|12/01/2010|December|Wednesday|  08|Winter|[ivory, knitted, ...|(10000,[2352,3322...|(10000,[2352,3322...|\n",
            "|box of 6 assorted...|       6|2010-12-01 08:34:00|     4.25|   13047.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|[box, of, 6, asso...|(10000,[3666,3774...|(10000,[3666,3774...|\n",
            "|box of vintage ji...|       3|2010-12-01 08:34:00|     4.95|   13047.0|United Kingdom|14.850000000000001|12/01/2010|December|Wednesday|  08|Winter|[box, of, vintage...|(10000,[2056,3189...|(10000,[2056,3189...|\n",
            "|box of vintage al...|       2|2010-12-01 08:34:00|     9.95|   13047.0|United Kingdom|              19.9|12/01/2010|December|Wednesday|  08|Winter|[box, of, vintage...|(10000,[2023,2056...|(10000,[2023,2056...|\n",
            "|home building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|[home, building, ...|(10000,[3365,3519...|(10000,[3365,3519...|\n",
            "|love building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|[love, building, ...|(10000,[3519,5852...|(10000,[3519,5852...|\n",
            "|recipe box with m...|       4|2010-12-01 08:34:00|     7.95|   13047.0|United Kingdom|              31.8|12/01/2010|December|Wednesday|  08|Winter|[recipe, box, wit...|(10000,[426,7650,...|(10000,[426,7650,...|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+--------------------+--------------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "rescaledData.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iS3-yjxtCJjV",
        "outputId": "8c2e8a41-58dd-4809-bc02-954fe5396b7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|features                                                                                                                                               |\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|(10000,[4618,4667,5843,8889,9808],[3.426588933240004,3.563269627991808,3.2134949537670243,3.4452939103949065,2.625905253571504])                       |\n",
            "|(10000,[426,671,5843],[3.2422020926170427,5.32479364005155,3.2134949537670243])                                                                        |\n",
            "|(10000,[477,6575,7369,7815,9002],[5.7217138356497665,4.601563395430525,5.480122870398253,4.126642900013204,6.073467404511678])                         |\n",
            "|(10000,[2352,2589,3932,4508,6606,9693],[6.759171701164545,3.9768319463325916,5.786052176624523,4.562653588004653,3.844612717654377,3.9368600376592253])|\n",
            "|(10000,[52,388,691,5843,9470],[2.5243592311302705,7.061078764547673,7.1050096937734,3.2134949537670243,7.102020154925034])                             |\n",
            "|(10000,[405,946,5015,5342,7153],[5.0889097849972895,7.044032897274685,6.727886563667511,2.279454248080181,5.173748664934319])                          |\n",
            "|(10000,[4667,5170,6932,8819,8889],[3.563269627991808,4.461258521531243,3.811393077348983,7.925718099971364,3.4452939103949065])                        |\n",
            "|(10000,[3423,4467,4508,8944],[4.565484446229177,4.9147956609233985,4.562653588004653,4.683444676889125])                                               |\n",
            "|(10000,[52,3197,3423,8151,8944],[2.5243592311302705,7.8162842589259025,4.565484446229177,8.39436210970106,4.683444676889125])                          |\n",
            "|(10000,[2134,5894,8855,9363],[4.522934675153574,4.1836848384002225,5.656331076386914,4.37037003848195])                                                |\n",
            "|(10000,[7058,7842,9737],[5.8923917320116574,7.117058032289575,5.8923917320116574])                                                                     |\n",
            "|(10000,[756,7058,9737],[4.925235223658776,5.8923917320116574,5.8923917320116574])                                                                      |\n",
            "|(10000,[50,2126,4175,4685],[3.964192518180837,5.077019038475767,4.485387223332251,5.320260769031562])                                                  |\n",
            "|(10000,[2352,3322,6996,9056],[6.759171701164545,6.197137532364841,4.109427132024464,4.167052438351081])                                                |\n",
            "|(10000,[3666,3774,4495,5894,7922,9363],[3.7277492222926716,8.58541734646377,2.283184024406637,4.1836848384002225,3.0982210225756193,4.37037003848195]) |\n",
            "|(10000,[2056,3189,4495,7922,7960],[2.748607992393348,6.778423632663866,2.283184024406637,3.0982210225756193,5.376060366604776])                        |\n",
            "|(10000,[2023,2056,3189,4495,7922],[4.852772864383074,2.748607992393348,6.778423632663866,2.283184024406637,3.0982210225756193])                        |\n",
            "|(10000,[3365,3519,5852,8454],[4.4734655475089244,5.564350817692324,5.564350817692324,5.295935916175646])                                               |\n",
            "|(10000,[3519,5852,6240,8454],[5.564350817692324,5.564350817692324,4.2214809902031085,5.295935916175646])                                               |\n",
            "|(10000,[426,7650,7922,9700,9808],[3.2422020926170427,4.041842648166745,3.0982210225756193,5.309266155530471,2.625905253571504])                        |\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "rescaledData.select('features').show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCLGbnk0CY1f",
        "outputId": "b736c9e6-4f56-4d00-a0dc-56b1ae7988f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|features                                                                                                                                               |\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "|(10000,[4618,4667,5843,8889,9808],[3.426588933240004,3.563269627991808,3.2134949537670243,3.4452939103949065,2.625905253571504])                       |\n",
            "|(10000,[426,671,5843],[3.2422020926170427,5.32479364005155,3.2134949537670243])                                                                        |\n",
            "|(10000,[477,6575,7369,7815,9002],[5.7217138356497665,4.601563395430525,5.480122870398253,4.126642900013204,6.073467404511678])                         |\n",
            "|(10000,[2352,2589,3932,4508,6606,9693],[6.759171701164545,3.9768319463325916,5.786052176624523,4.562653588004653,3.844612717654377,3.9368600376592253])|\n",
            "|(10000,[52,388,691,5843,9470],[2.5243592311302705,7.061078764547673,7.1050096937734,3.2134949537670243,7.102020154925034])                             |\n",
            "|(10000,[405,946,5015,5342,7153],[5.0889097849972895,7.044032897274685,6.727886563667511,2.279454248080181,5.173748664934319])                          |\n",
            "|(10000,[4667,5170,6932,8819,8889],[3.563269627991808,4.461258521531243,3.811393077348983,7.925718099971364,3.4452939103949065])                        |\n",
            "|(10000,[3423,4467,4508,8944],[4.565484446229177,4.9147956609233985,4.562653588004653,4.683444676889125])                                               |\n",
            "|(10000,[52,3197,3423,8151,8944],[2.5243592311302705,7.8162842589259025,4.565484446229177,8.39436210970106,4.683444676889125])                          |\n",
            "|(10000,[2134,5894,8855,9363],[4.522934675153574,4.1836848384002225,5.656331076386914,4.37037003848195])                                                |\n",
            "|(10000,[7058,7842,9737],[5.8923917320116574,7.117058032289575,5.8923917320116574])                                                                     |\n",
            "|(10000,[756,7058,9737],[4.925235223658776,5.8923917320116574,5.8923917320116574])                                                                      |\n",
            "|(10000,[50,2126,4175,4685],[3.964192518180837,5.077019038475767,4.485387223332251,5.320260769031562])                                                  |\n",
            "|(10000,[2352,3322,6996,9056],[6.759171701164545,6.197137532364841,4.109427132024464,4.167052438351081])                                                |\n",
            "|(10000,[3666,3774,4495,5894,7922,9363],[3.7277492222926716,8.58541734646377,2.283184024406637,4.1836848384002225,3.0982210225756193,4.37037003848195]) |\n",
            "|(10000,[2056,3189,4495,7922,7960],[2.748607992393348,6.778423632663866,2.283184024406637,3.0982210225756193,5.376060366604776])                        |\n",
            "|(10000,[2023,2056,3189,4495,7922],[4.852772864383074,2.748607992393348,6.778423632663866,2.283184024406637,3.0982210225756193])                        |\n",
            "|(10000,[3365,3519,5852,8454],[4.4734655475089244,5.564350817692324,5.564350817692324,5.295935916175646])                                               |\n",
            "|(10000,[3519,5852,6240,8454],[5.564350817692324,5.564350817692324,4.2214809902031085,5.295935916175646])                                               |\n",
            "|(10000,[426,7650,7922,9700,9808],[3.2422020926170427,4.041842648166745,3.0982210225756193,5.309266155530471,2.625905253571504])                        |\n",
            "+-------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "vectorized_df = rescaledData.select(\"features\")\n",
        "vectorized_df.show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFfJr21yCoF5",
        "outputId": "eb7c53c8-6fa9-4c39-ef76-4f4451ac186e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+\n",
            "|         Description|Quantity|        InvoiceDate|UnitPrice|CustomerID|       Country|        TotalPrice|      date|   month|     week|time|season|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+\n",
            "|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|   17850.0|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|Winter|\n",
            "| white metal lantern|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|   17850.0|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|   17850.0|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|set 7 babushka ne...|       2|2010-12-01 08:26:00|     7.65|   17850.0|United Kingdom|              15.3|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|glass star froste...|       6|2010-12-01 08:26:00|     4.25|   17850.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|hand warmer union...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|hand warmer red p...|       6|2010-12-01 08:28:00|     1.85|   17850.0|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|assorted colour b...|      32|2010-12-01 08:34:00|     1.69|   13047.0|United Kingdom|             54.08|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|   13047.0|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|feltcraft princes...|       8|2010-12-01 08:34:00|     3.75|   13047.0|United Kingdom|              30.0|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|ivory knitted mug...|       6|2010-12-01 08:34:00|     1.65|   13047.0|United Kingdom| 9.899999999999999|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|box of 6 assorted...|       6|2010-12-01 08:34:00|     4.25|   13047.0|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|box of vintage ji...|       3|2010-12-01 08:34:00|     4.95|   13047.0|United Kingdom|14.850000000000001|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|box of vintage al...|       2|2010-12-01 08:34:00|     9.95|   13047.0|United Kingdom|              19.9|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|home building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|love building blo...|       3|2010-12-01 08:34:00|     5.95|   13047.0|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|\n",
            "|recipe box with m...|       4|2010-12-01 08:34:00|     7.95|   13047.0|United Kingdom|              31.8|12/01/2010|December|Wednesday|  08|Winter|\n",
            "+--------------------+--------+-------------------+---------+----------+--------------+------------------+----------+--------+---------+----+------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_spark.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X1nlLFfYEtOy"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.sql.functions import monotonically_increasing_id\n",
        "from pyspark.ml.feature import StringIndexer\n",
        "\n",
        "df_spark = df_spark.withColumn(\"row_id\", monotonically_increasing_id())\n",
        "vectorized_df = vectorized_df.withColumn(\"row_id\", monotonically_increasing_id())\n",
        "\n",
        "joined_df = df_spark.join(vectorized_df.select(\"row_id\", \"features\"), \"row_id\", \"inner\")\n",
        "\n",
        "joined_df = joined_df.drop(\"row_id\", \"Description\", \"InvoiceDate\", \"date\")\n",
        "\n",
        "indexers = [\n",
        "    StringIndexer(inputCol=column, outputCol=column+\"_index\").fit(joined_df)\n",
        "    for column in [\"Country\", \"month\", \"week\", \"season\"]\n",
        "]\n",
        "for indexer in indexers:\n",
        "    joined_df = indexer.transform(joined_df)\n",
        "assembler = VectorAssembler(\n",
        "    inputCols=[\"Quantity\", \"UnitPrice\", \"CustomerID\", \"TotalPrice\", \"time\",\n",
        "               \"Country_index\", \"month_index\", \"week_index\", \"season_index\", \"features\"],\n",
        "    outputCol=\"features_combined\"\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHwQY5enF1y6",
        "outputId": "f1626026-c32b-47e3-c96a-b28d5cc55a58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---------+----------+--------------+------------------+--------+---------+----+------+--------------------+-------------+-----------+----------+------------+--------------------+\n",
            "|Quantity|UnitPrice|CustomerID|       Country|        TotalPrice|   month|     week|time|season|            features|Country_index|month_index|week_index|season_index|   features_combined|\n",
            "+--------+---------+----------+--------------+------------------+--------+---------+----+------+--------------------+-------------+-----------+----------+------------+--------------------+\n",
            "|       6|     2.55|   17850.0|United Kingdom|15.299999999999999|December|Wednesday|   8|Winter|(10000,[4618,4667...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       6|     3.39|   17850.0|United Kingdom|             20.34|December|Wednesday|   8|Winter|(10000,[426,671,5...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       2|     7.65|   17850.0|United Kingdom|              15.3|December|Wednesday|   8|Winter|(10000,[405,946,5...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       6|     4.25|   17850.0|United Kingdom|              25.5|December|Wednesday|   8|Winter|(10000,[4667,5170...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       6|     1.85|   17850.0|United Kingdom|11.100000000000001|December|Wednesday|   8|Winter|(10000,[3423,4467...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      32|     1.69|   13047.0|United Kingdom|             54.08|December|Wednesday|   8|Winter|(10000,[2134,5894...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       6|      2.1|   13047.0|United Kingdom|12.600000000000001|December|Wednesday|   8|Winter|(10000,[7058,7842...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       3|     5.95|   13047.0|United Kingdom|             17.85|December|Wednesday|   8|Winter|(10000,[3365,3519...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       4|     7.95|   13047.0|United Kingdom|              31.8|December|Wednesday|   8|Winter|(10000,[426,7650,...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       3|     4.95|   13047.0|United Kingdom|14.850000000000001|December|Wednesday|   8|Winter|(10000,[52,477,20...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|       3|     5.95|   13047.0|United Kingdom|             17.85|December|Wednesday|   8|Winter|(10000,[3519,5852...|          0.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      24|     3.75|   12583.0|        France|              90.0|December|Wednesday|   8|Winter|(10000,[4995,7689...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      24|     3.75|   12583.0|        France|              90.0|December|Wednesday|   8|Winter|(10000,[52,4995,8...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      12|     3.75|   12583.0|        France|              45.0|December|Wednesday|   8|Winter|(10000,[551,4995,...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      12|     0.85|   12583.0|        France|              10.2|December|Wednesday|   8|Winter|(10000,[591,2443,...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      48|     0.85|   12583.0|        France|              40.8|December|Wednesday|   8|Winter|(10000,[2058,3842...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      24|     1.25|   12583.0|        France|              30.0|December|Wednesday|   8|Winter|(10000,[2056,2144...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      18|     2.95|   12583.0|        France|              53.1|December|Wednesday|   8|Winter|(10000,[52,129,29...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      24|     2.95|   12583.0|        France| 70.80000000000001|December|Wednesday|   8|Winter|(10000,[405,4686,...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "|      24|     1.65|   12583.0|        France|39.599999999999994|December|Wednesday|   8|Winter|(10000,[52,773,17...|          2.0|        2.0|       1.0|         1.0|(10009,[0,1,2,3,4...|\n",
            "+--------+---------+----------+--------------+------------------+--------+---------+----+------+--------------------+-------------+-----------+----------+------------+--------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import col\n",
        "\n",
        "joined_df = joined_df.withColumn(\"time\", col(\"time\").cast(\"int\"))\n",
        "\n",
        "assembler = VectorAssembler(\n",
        "    inputCols=[\"Quantity\", \"UnitPrice\", \"CustomerID\", \"TotalPrice\", \"time\",\n",
        "               \"Country_index\", \"month_index\", \"week_index\", \"season_index\", \"features\"],\n",
        "    outputCol=\"features_combined\"\n",
        ")\n",
        "\n",
        "output = assembler.transform(joined_df)\n",
        "output.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYY5HpK-GPIX",
        "outputId": "1c3d861c-0ff8-4d8a-c3cf-71d1184b6572"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---------+----------+------------------+----+--------------------+-------------+-----------+----------+------------+\n",
            "|Quantity|UnitPrice|CustomerID|        TotalPrice|time|            features|Country_index|month_index|week_index|season_index|\n",
            "+--------+---------+----------+------------------+----+--------------------+-------------+-----------+----------+------------+\n",
            "|       6|     2.55|   17850.0|15.299999999999999|   8|(10000,[4618,4667...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       6|     3.39|   17850.0|             20.34|   8|(10000,[426,671,5...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       2|     7.65|   17850.0|              15.3|   8|(10000,[405,946,5...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       6|     4.25|   17850.0|              25.5|   8|(10000,[4667,5170...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       6|     1.85|   17850.0|11.100000000000001|   8|(10000,[3423,4467...|          0.0|        2.0|       1.0|         1.0|\n",
            "|      32|     1.69|   13047.0|             54.08|   8|(10000,[2134,5894...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       6|      2.1|   13047.0|12.600000000000001|   8|(10000,[7058,7842...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       3|     5.95|   13047.0|             17.85|   8|(10000,[3365,3519...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       4|     7.95|   13047.0|              31.8|   8|(10000,[426,7650,...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       3|     4.95|   13047.0|14.850000000000001|   8|(10000,[52,477,20...|          0.0|        2.0|       1.0|         1.0|\n",
            "|       3|     5.95|   13047.0|             17.85|   8|(10000,[3519,5852...|          0.0|        2.0|       1.0|         1.0|\n",
            "|      24|     3.75|   12583.0|              90.0|   8|(10000,[4995,7689...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      24|     3.75|   12583.0|              90.0|   8|(10000,[52,4995,8...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      12|     3.75|   12583.0|              45.0|   8|(10000,[551,4995,...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      12|     0.85|   12583.0|              10.2|   8|(10000,[591,2443,...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      48|     0.85|   12583.0|              40.8|   8|(10000,[2058,3842...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      24|     1.25|   12583.0|              30.0|   8|(10000,[2056,2144...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      18|     2.95|   12583.0|              53.1|   8|(10000,[52,129,29...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      24|     2.95|   12583.0| 70.80000000000001|   8|(10000,[405,4686,...|          2.0|        2.0|       1.0|         1.0|\n",
            "|      24|     1.65|   12583.0|39.599999999999994|   8|(10000,[52,773,17...|          2.0|        2.0|       1.0|         1.0|\n",
            "+--------+---------+----------+------------------+----+--------------------+-------------+-----------+----------+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "output = output.drop(\"Country\", \"month\", \"week\", \"season\", \"features_combined\")\n",
        "output.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OYTcxo4PTK-e"
      },
      "outputs": [],
      "source": [
        "output = output.drop('features')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "zcPDI7lYSIHE",
        "outputId": "47dab108-7e9a-4eab-fe30-2f098f2e06cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Quantity  UnitPrice  CustomerID  TotalPrice  time  Country_index  month_index  week_index  season_index\n",
              "0        24       3.75     12583.0       90.00     8            2.0          2.0         1.0           1.0\n",
              "1        12       0.85     12583.0       10.20     8            2.0          2.0         1.0           1.0\n",
              "2        12       0.42     14307.0        5.04    11            0.0          2.0         1.0           1.0\n",
              "3         1       1.65     14729.0        1.65    12            0.0          2.0         1.0           1.0\n",
              "4         1       2.55     16274.0        2.55    15            0.0          2.0         1.0           1.0"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>TotalPrice</th>\n",
              "      <th>time</th>\n",
              "      <th>Country_index</th>\n",
              "      <th>month_index</th>\n",
              "      <th>week_index</th>\n",
              "      <th>season_index</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>3.75</td>\n",
              "      <td>12583.0</td>\n",
              "      <td>90.00</td>\n",
              "      <td>8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12</td>\n",
              "      <td>0.85</td>\n",
              "      <td>12583.0</td>\n",
              "      <td>10.20</td>\n",
              "      <td>8</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>0.42</td>\n",
              "      <td>14307.0</td>\n",
              "      <td>5.04</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14729.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2.55</td>\n",
              "      <td>16274.0</td>\n",
              "      <td>2.55</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "import pyspark.pandas as ps\n",
        "df_pandas = ps.DataFrame(output)\n",
        "df_pandas.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "uns31EH4190n",
        "outputId": "9cc87320-21c5-4773-84ca-7715f201d05a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Quantity  UnitPrice  CustomerID  TotalPrice  time  Country_index  month_index  week_index  season_index\n",
              "0        24       3.75     12583.0       90.00     8            2.0          2.0         1.0           1.0\n",
              "1        12       0.85     12583.0       10.20     8            2.0          2.0         1.0           1.0\n",
              "2        12       0.42     14307.0        5.04    11            0.0          2.0         1.0           1.0\n",
              "3         1       1.65     14729.0        1.65    12            0.0          2.0         1.0           1.0\n",
              "4         1       2.55     16274.0        2.55    15            0.0          2.0         1.0           1.0\n",
              "5         1       1.25     16274.0        1.25    15            0.0          2.0         1.0           1.0\n",
              "6        12       1.25     12947.0       15.00    16            0.0          2.0         1.0           1.0\n",
              "7         1       3.25     18011.0        3.25    17            0.0          2.0         1.0           1.0\n",
              "8         2       7.95     17732.0       15.90     9            0.0          2.0         0.0           1.0"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>TotalPrice</th>\n",
              "      <th>time</th>\n",
              "      <th>Country_index</th>\n",
              "      <th>month_index</th>\n",
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              "      <th>season_index</th>\n",
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              "      <td>24</td>\n",
              "      <td>3.75</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>0.42</td>\n",
              "      <td>14307.0</td>\n",
              "      <td>5.04</td>\n",
              "      <td>11</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14729.0</td>\n",
              "      <td>1.65</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "      <td>16274.0</td>\n",
              "      <td>2.55</td>\n",
              "      <td>15</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1.25</td>\n",
              "      <td>16274.0</td>\n",
              "      <td>1.25</td>\n",
              "      <td>15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>1.25</td>\n",
              "      <td>12947.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>3.25</td>\n",
              "      <td>18011.0</td>\n",
              "      <td>3.25</td>\n",
              "      <td>17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>7.95</td>\n",
              "      <td>17732.0</td>\n",
              "      <td>15.90</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df_pandas.head(9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blhkYfMS44_5",
        "outputId": "1036f22f-592d-4918-9893-168c5c518ec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pyspark.pandas.frame.DataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print(type(df_pandas))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtP0m5wm7KtF",
        "outputId": "bb019792-74e2-4cb5-9786-d8ec83141c2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/utils.py:1016: PandasAPIOnSparkAdviceWarning: `to_pandas` loads all data into the driver's memory. It should only be used if the resulting pandas DataFrame is expected to be small.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)\n"
          ]
        }
      ],
      "source": [
        "df_pandas_pd = df_pandas.to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "YF40Q9Mu4uSc",
        "outputId": "1a2a1d41-b0f0-418b-86e7-e9400025173f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(16, 6))\n",
        "heatmap = sns.heatmap(df_pandas_pd.corr(), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
        "heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=12);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3rfOqqvhJ8e",
        "outputId": "eba1ccb8-d468-4042-d55f-2afa62800389"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- Description: string (nullable = true)\n",
            " |-- Quantity: long (nullable = true)\n",
            " |-- InvoiceDate: timestamp (nullable = true)\n",
            " |-- UnitPrice: double (nullable = true)\n",
            " |-- CustomerID: double (nullable = true)\n",
            " |-- Country: string (nullable = true)\n",
            " |-- TotalPrice: double (nullable = true)\n",
            " |-- date: string (nullable = true)\n",
            " |-- month: string (nullable = true)\n",
            " |-- week: string (nullable = true)\n",
            " |-- time: string (nullable = true)\n",
            " |-- season: string (nullable = true)\n",
            " |-- row_id: long (nullable = false)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_spark.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn_ddk3ciwJp",
        "outputId": "b7dcca0d-88a0-46cc-d4be-19659a40fcd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+------------------+\n",
            "|CustomerID|      TotalPayment|\n",
            "+----------+------------------+\n",
            "|   14646.0| 279489.0199999992|\n",
            "|   18102.0|256438.48999999993|\n",
            "|   17450.0|187482.16999999998|\n",
            "|   14911.0|132572.62000000002|\n",
            "|   12415.0| 123725.4499999999|\n",
            "|   14156.0|113384.14000000004|\n",
            "|   17511.0| 88125.37999999996|\n",
            "|   16684.0| 65892.07999999999|\n",
            "|   13694.0|62653.100000000006|\n",
            "|   15311.0|59419.340000000084|\n",
            "|   13089.0| 57385.88000000011|\n",
            "|   14096.0| 57120.90999999968|\n",
            "|   15061.0| 54228.73999999999|\n",
            "|   17949.0|52750.840000000004|\n",
            "|   15769.0|          51823.72|\n",
            "|   16029.0|50992.609999999964|\n",
            "|   14298.0|  50862.4399999999|\n",
            "|   14088.0| 50415.48999999999|\n",
            "|   17841.0| 40340.78000000022|\n",
            "|   13798.0|36351.419999999984|\n",
            "+----------+------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pyspark.sql.functions as F\n",
        "\n",
        "df_spark = df_spark.withColumn(\"TotalPrice\", F.col(\"Quantity\") * F.col(\"UnitPrice\"))\n",
        "customer_total_payment = df_spark.groupBy(\"CustomerID\").agg(F.sum(\"TotalPrice\").alias(\"TotalPayment\"))\n",
        "customer_total_payment_ordered = customer_total_payment.orderBy(\"TotalPayment\", ascending=False)\n",
        "customer_total_payment_ordered.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UcmtKZePi0rl"
      },
      "outputs": [],
      "source": [
        "df_spark = df_spark.na.drop(subset=\"InvoiceDate\")\n",
        "df_spark = df_spark.na.drop(subset=\"CustomerID\")\n",
        "df_spark = df_spark.na.drop(subset=\"UnitPrice\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p2qjes2YjXPg"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import functions as F\n",
        "from pyspark.sql.window import Window\n",
        "import datetime as dt\n",
        "\n",
        "today_date = dt.datetime(2012, 4, 22).date()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEYZY5Lxje_L",
        "outputId": "e6659afc-ecee-497f-a776-8d14b86c7431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-------------------+\n",
            "|CustomerID|  last_invoice_date|\n",
            "+----------+-------------------+\n",
            "|   16916.0|2011-11-16 12:08:00|\n",
            "|   17884.0|2011-12-06 11:33:00|\n",
            "|   13094.0|2011-11-18 14:07:00|\n",
            "|   16596.0|2011-11-24 10:02:00|\n",
            "|   17633.0|2011-11-08 13:25:00|\n",
            "|   18114.0|2011-02-22 13:20:00|\n",
            "|   13973.0|2011-02-25 15:21:00|\n",
            "|   14473.0|2011-09-26 15:10:00|\n",
            "|   13956.0|2011-12-04 13:20:00|\n",
            "|   13533.0|2011-06-10 11:54:00|\n",
            "|   13918.0|2011-10-21 12:25:00|\n",
            "|   12493.0|2011-06-27 10:17:00|\n",
            "|   14285.0|2011-11-18 14:50:00|\n",
            "|   16858.0|2010-12-08 14:52:00|\n",
            "|   13649.0|2011-03-28 11:51:00|\n",
            "|   16656.0|2011-11-17 14:43:00|\n",
            "|   15160.0|2010-12-17 14:15:00|\n",
            "|   18277.0|2011-10-12 15:22:00|\n",
            "|   14760.0|2011-02-18 14:36:00|\n",
            "|   15397.0|2011-02-23 09:25:00|\n",
            "+----------+-------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql import functions as F\n",
        "\n",
        "df_last_invoice = df_spark.groupBy(\"CustomerID\").agg(F.max(\"InvoiceDate\").alias(\"last_invoice_date\"))\n",
        "df_last_invoice.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wsDxDxJj8cH",
        "outputId": "0b81f0e3-da18-4658-e9c0-161cb8830657"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "406829"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "df_spark = df_spark.join(df_last_invoice, on=\"CustomerID\", how=\"left\")\n",
        "df_spark.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fe1L_OuSkZh1",
        "outputId": "b2bc7fb6-4ed9-41b2-8133-5d534b00dc52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- CustomerID: double (nullable = true)\n",
            " |-- Description: string (nullable = true)\n",
            " |-- Quantity: long (nullable = true)\n",
            " |-- InvoiceDate: timestamp (nullable = true)\n",
            " |-- UnitPrice: double (nullable = true)\n",
            " |-- Country: string (nullable = true)\n",
            " |-- TotalPrice: double (nullable = true)\n",
            " |-- date: string (nullable = true)\n",
            " |-- month: string (nullable = true)\n",
            " |-- week: string (nullable = true)\n",
            " |-- time: string (nullable = true)\n",
            " |-- season: string (nullable = true)\n",
            " |-- row_id: long (nullable = false)\n",
            " |-- last_invoice_date: timestamp (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_spark.printSchema()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qZX8sjrqS_N"
      },
      "source": [
        "we will segment clients into two groups based on their purchasing preferences—one group preferring to buy big quantities for low prices and the other group preferring higher quality with less quantity at higher prices.\n",
        "Assigning each customer to either \"Group 1\" or \"Group 2\" based on the segmentation criteria. Where Group 2 represent the customers who prefer high quality products."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gLGpNXAlf-d",
        "outputId": "6c8873c3-719f-4ea0-da86-78107572e6c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+------------------+------------------+------------------+\n",
            "|summary|        CustomerID|      avg_quantity|    avg_unit_price|\n",
            "+-------+------------------+------------------+------------------+\n",
            "|  count|              4372|              4372|              4372|\n",
            "|   mean|15299.677721866423|22.389287841455918| 7.978607822561685|\n",
            "| stddev|1722.3907054276908| 213.2574664023018|144.96331549059562|\n",
            "|    min|           12346.0|            -144.0|               0.0|\n",
            "|    max|           18287.0|           12540.0| 8055.779999999999|\n",
            "+-------+------------------+------------------+------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql.functions import avg, when\n",
        "\n",
        "customer_stats = df_spark.groupBy('CustomerID').agg(\n",
        "    avg('Quantity').alias('avg_quantity'),\n",
        "    avg('UnitPrice').alias('avg_unit_price')\n",
        ")\n",
        "customer_stats.describe().show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lkCxcz5PoLxz"
      },
      "outputs": [],
      "source": [
        "low_price_threshold = 100\n",
        "high_quality_threshold = 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oVGIXE51p1G9"
      },
      "outputs": [],
      "source": [
        "customer_stats = customer_stats.withColumn(\n",
        "    'Segment',\n",
        "    when((customer_stats['avg_quantity'] > high_quality_threshold) &\n",
        "         (customer_stats['avg_unit_price'] > low_price_threshold), 'Group 2').otherwise('Group 1')\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MF6BQeaNrlp0"
      },
      "outputs": [],
      "source": [
        "df_with_segment = df_spark.join(customer_stats, on='CustomerID', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wBpfxuYrrIe",
        "outputId": "bf1a0fad-8078-453d-c533-2acdae78604b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-------------------+-----------------+------------------+-------+\n",
            "|CustomerID|         Description|Quantity|        InvoiceDate|UnitPrice|       Country|        TotalPrice|      date|   month|     week|time|season|row_id|  last_invoice_date|     avg_quantity|    avg_unit_price|Segment|\n",
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-------------------+-----------------+------------------+-------+\n",
            "|   17850.0|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|Winter|     0|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0| white metal lantern|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     1|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|Winter|     2|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     3|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     4|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|set 7 babushka ne...|       2|2010-12-01 08:26:00|     7.65|United Kingdom|              15.3|12/01/2010|December|Wednesday|  08|Winter|     5|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|glass star froste...|       6|2010-12-01 08:26:00|     4.25|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|     6|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|hand warmer union...|       6|2010-12-01 08:28:00|     1.85|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|     7|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   17850.0|hand warmer red p...|       6|2010-12-01 08:28:00|     1.85|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|     8|2011-02-10 14:38:00|5.426282051282051|3.9247115384615325|Group 1|\n",
            "|   13047.0|assorted colour b...|      32|2010-12-01 08:34:00|     1.69|United Kingdom|             54.08|12/01/2010|December|Wednesday|  08|Winter|     9|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|    10|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|    11|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|feltcraft princes...|       8|2010-12-01 08:34:00|     3.75|United Kingdom|              30.0|12/01/2010|December|Wednesday|  08|Winter|    12|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|ivory knitted mug...|       6|2010-12-01 08:34:00|     1.65|United Kingdom| 9.899999999999999|12/01/2010|December|Wednesday|  08|Winter|    13|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|box of 6 assorted...|       6|2010-12-01 08:34:00|     4.25|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|    14|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|box of vintage ji...|       3|2010-12-01 08:34:00|     4.95|United Kingdom|14.850000000000001|12/01/2010|December|Wednesday|  08|Winter|    15|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|box of vintage al...|       2|2010-12-01 08:34:00|     9.95|United Kingdom|              19.9|12/01/2010|December|Wednesday|  08|Winter|    16|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|home building blo...|       3|2010-12-01 08:34:00|     5.95|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|    17|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|love building blo...|       3|2010-12-01 08:34:00|     5.95|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|    18|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "|   13047.0|recipe box with m...|       4|2010-12-01 08:34:00|     7.95|United Kingdom|              31.8|12/01/2010|December|Wednesday|  08|Winter|    19|2011-11-08 12:10:00|6.913265306122449| 4.072959183673467|Group 1|\n",
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-------------------+-----------------+------------------+-------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_with_segment.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6rQ8hoXEyDVo"
      },
      "outputs": [],
      "source": [
        "if \"last_invoice_date\" in df_with_segment.columns:\n",
        "  df = df_with_segment.drop(\"last_invoice_date\")  # Remove the duplicate column\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scDvFAvmyaD5",
        "outputId": "1af07ec4-3768-4671-879b-d1685ad72a9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "406829"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "df = df.join(df_last_invoice, on=\"CustomerID\", how=\"left\")\n",
        "df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HBgM_jQyM-T",
        "outputId": "ba87c5fb-a5be-44ad-f640-50a2f97ddab8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-----------------+------------------+-------+-------------------+\n",
            "|CustomerID|         Description|Quantity|        InvoiceDate|UnitPrice|       Country|        TotalPrice|      date|   month|     week|time|season|row_id|     avg_quantity|    avg_unit_price|Segment|  last_invoice_date|\n",
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-----------------+------------------+-------+-------------------+\n",
            "|   17850.0|white hanging hea...|       6|2010-12-01 08:26:00|     2.55|United Kingdom|15.299999999999999|12/01/2010|December|Wednesday|  08|Winter|     0|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0| white metal lantern|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     1|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|cream cupid heart...|       8|2010-12-01 08:26:00|     2.75|United Kingdom|              22.0|12/01/2010|December|Wednesday|  08|Winter|     2|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|knitted union fla...|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     3|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|red woolly hottie...|       6|2010-12-01 08:26:00|     3.39|United Kingdom|             20.34|12/01/2010|December|Wednesday|  08|Winter|     4|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|set 7 babushka ne...|       2|2010-12-01 08:26:00|     7.65|United Kingdom|              15.3|12/01/2010|December|Wednesday|  08|Winter|     5|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|glass star froste...|       6|2010-12-01 08:26:00|     4.25|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|     6|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|hand warmer union...|       6|2010-12-01 08:28:00|     1.85|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|     7|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   17850.0|hand warmer red p...|       6|2010-12-01 08:28:00|     1.85|United Kingdom|11.100000000000001|12/01/2010|December|Wednesday|  08|Winter|     8|5.426282051282051|3.9247115384615325|Group 1|2011-02-10 14:38:00|\n",
            "|   13047.0|assorted colour b...|      32|2010-12-01 08:34:00|     1.69|United Kingdom|             54.08|12/01/2010|December|Wednesday|  08|Winter|     9|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|    10|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|poppy's playhouse...|       6|2010-12-01 08:34:00|      2.1|United Kingdom|12.600000000000001|12/01/2010|December|Wednesday|  08|Winter|    11|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|feltcraft princes...|       8|2010-12-01 08:34:00|     3.75|United Kingdom|              30.0|12/01/2010|December|Wednesday|  08|Winter|    12|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|ivory knitted mug...|       6|2010-12-01 08:34:00|     1.65|United Kingdom| 9.899999999999999|12/01/2010|December|Wednesday|  08|Winter|    13|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|box of 6 assorted...|       6|2010-12-01 08:34:00|     4.25|United Kingdom|              25.5|12/01/2010|December|Wednesday|  08|Winter|    14|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|box of vintage ji...|       3|2010-12-01 08:34:00|     4.95|United Kingdom|14.850000000000001|12/01/2010|December|Wednesday|  08|Winter|    15|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|box of vintage al...|       2|2010-12-01 08:34:00|     9.95|United Kingdom|              19.9|12/01/2010|December|Wednesday|  08|Winter|    16|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|home building blo...|       3|2010-12-01 08:34:00|     5.95|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|    17|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|love building blo...|       3|2010-12-01 08:34:00|     5.95|United Kingdom|             17.85|12/01/2010|December|Wednesday|  08|Winter|    18|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "|   13047.0|recipe box with m...|       4|2010-12-01 08:34:00|     7.95|United Kingdom|              31.8|12/01/2010|December|Wednesday|  08|Winter|    19|6.913265306122449| 4.072959183673467|Group 1|2011-11-08 12:10:00|\n",
            "+----------+--------------------+--------+-------------------+---------+--------------+------------------+----------+--------+---------+----+------+------+-----------------+------------------+-------+-------------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "id": "CuTmYm8fvmAb",
        "outputId": "c8b59d5d-29ae-4ece-9c7e-855d0d3877cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting h2o==3.40.0.2\n",
            "  Downloading http://h2o-release.s3.amazonaws.com/h2o/rel-zz_kurka/2/Python/h2o-3.40.0.2-py2.py3-none-any.whl (177.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.7/177.7 MB\u001b[0m \u001b[31m57.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from h2o==3.40.0.2) (2.31.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from h2o==3.40.0.2) (0.9.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from h2o==3.40.0.2) (0.18.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->h2o==3.40.0.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->h2o==3.40.0.2) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->h2o==3.40.0.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->h2o==3.40.0.2) (2024.2.2)\n",
            "Installing collected packages: h2o\n",
            "Successfully installed h2o-3.40.0.2\n",
            "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.22\" 2024-01-16; OpenJDK Runtime Environment (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1); OpenJDK 64-Bit Server VM (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.10/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmp0y2nlwsr\n",
            "  JVM stdout: /tmp/tmp0y2nlwsr/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmp0y2nlwsr/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n",
            "Warning: Your H2O cluster version is (1 year and 2 months) old.  There may be a newer version available.\n",
            "Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.html\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "--------------------------  -----------------------------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         07 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.40.0.2\n",
              "H2O_cluster_version_age:    1 year and 2 months\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_2mokac\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    8 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         locked, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null, \"colab_language_server\": \"/usr/colab/bin/language_service\"}\n",
              "H2O_internal_security:      False\n",
              "Python_version:             3.10.12 final\n",
              "--------------------------  -----------------------------------------------------------------------------------------"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "\n",
              "#h2o-table-1.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-1 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-1 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-1 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-1 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-1 .h2o-table th,\n",
              "#h2o-table-1 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-1 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-1\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption></caption>\n",
              "    <thead></thead>\n",
              "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>07 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.40.0.2</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>1 year and 2 months</td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_2mokac</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>8 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>locked, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null, \"colab_language_server\": \"/usr/colab/bin/language_service\"}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.10.12 final</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip3 install http://h2o-release.s3.amazonaws.com/h2o/rel-zz_kurka/2/Python/h2o-3.40.0.2-py2.py3-none-any.whl\n",
        "import h2o\n",
        "h2o.init(nthreads = -1, max_mem_size = 8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rx18f2kGvs_F"
      },
      "outputs": [],
      "source": [
        "\n",
        "df.toPandas().to_csv(\"Edited_file.csv\", index=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Lb7CENXwNmf",
        "outputId": "142598de-4ecc-4e6b-d4b9-cfe79c6addc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ],
      "source": [
        "data = h2o.import_file(\"/content/Edited_file.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "O726TzZMzQAu",
        "outputId": "27a2744b-bf73-4ba7-8c2c-0535a36d97d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  CustomerID  Description                           Quantity  InvoiceDate            UnitPrice  Country           TotalPrice  date                 month     week        time  season      row_id    avg_quantity    avg_unit_price  Segment    last_invoice_date\n",
              "------------  ----------------------------------  ----------  -------------------  -----------  --------------  ------------  -------------------  --------  --------  ------  --------  --------  --------------  ----------------  ---------  -------------------\n",
              "       16916  english rose spirit level                    1  2010-12-02 13:14:00         1.95  United Kingdom          1.95  2010-12-02 00:00:00  December  Thursday      13  Winter        2810         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  small square cut glass candlestick           2  2010-12-02 13:14:00         4.95  United Kingdom          9.9   2010-12-02 00:00:00  December  Thursday      13  Winter        2811         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  ladybird + bee raffia food cover             1  2010-12-02 13:14:00         3.75  United Kingdom          3.75  2010-12-02 00:00:00  December  Thursday      13  Winter        2812         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  small heart measuring spoons                 1  2010-12-02 13:14:00         0.85  United Kingdom          0.85  2010-12-02 00:00:00  December  Thursday      13  Winter        2813         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  paper chain kit 50's christmas               1  2010-12-02 13:14:00         2.95  United Kingdom          2.95  2010-12-02 00:00:00  December  Thursday      13  Winter        2814         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  box of 24 cocktail parasols                  1  2010-12-02 13:14:00         0.42  United Kingdom          0.42  2010-12-02 00:00:00  December  Thursday      13  Winter        2815         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  set of 3 butterfly cookie cutters            1  2010-12-02 13:14:00         1.25  United Kingdom          1.25  2010-12-02 00:00:00  December  Thursday      13  Winter        2816         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  36 pencils tube skulls                       1  2010-12-02 13:14:00         1.25  United Kingdom          1.25  2010-12-02 00:00:00  December  Thursday      13  Winter        2817         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  camouflage led torch                         6  2010-12-02 13:14:00         1.69  United Kingdom         10.14  2010-12-02 00:00:00  December  Thursday      13  Winter        2818         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "       16916  plasters in tin skulls                       1  2010-12-02 13:14:00         1.65  United Kingdom          1.65  2010-12-02 00:00:00  December  Thursday      13  Winter        2819         3.07692           2.22042  Group 1    2011-11-16 12:08:00\n",
              "[10 rows x 17 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  CustomerID</th><th>Description                       </th><th style=\"text-align: right;\">  Quantity</th><th>InvoiceDate        </th><th style=\"text-align: right;\">  UnitPrice</th><th>Country       </th><th style=\"text-align: right;\">  TotalPrice</th><th>date               </th><th>month   </th><th>week    </th><th style=\"text-align: right;\">  time</th><th>season  </th><th style=\"text-align: right;\">  row_id</th><th style=\"text-align: right;\">  avg_quantity</th><th style=\"text-align: right;\">  avg_unit_price</th><th>Segment  </th><th>last_invoice_date  </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>english rose spirit level         </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       1.95</td><td>United Kingdom</td><td style=\"text-align: right;\">        1.95</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2810</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>small square cut glass candlestick</td><td style=\"text-align: right;\">         2</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       4.95</td><td>United Kingdom</td><td style=\"text-align: right;\">        9.9 </td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2811</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>ladybird + bee raffia food cover  </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       3.75</td><td>United Kingdom</td><td style=\"text-align: right;\">        3.75</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2812</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>small heart measuring spoons      </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       0.85</td><td>United Kingdom</td><td style=\"text-align: right;\">        0.85</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2813</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>paper chain kit 50&#x27;s christmas    </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       2.95</td><td>United Kingdom</td><td style=\"text-align: right;\">        2.95</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2814</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>box of 24 cocktail parasols       </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       0.42</td><td>United Kingdom</td><td style=\"text-align: right;\">        0.42</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2815</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>set of 3 butterfly cookie cutters </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       1.25</td><td>United Kingdom</td><td style=\"text-align: right;\">        1.25</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2816</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>36 pencils tube skulls            </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       1.25</td><td>United Kingdom</td><td style=\"text-align: right;\">        1.25</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2817</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>camouflage led torch              </td><td style=\"text-align: right;\">         6</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       1.69</td><td>United Kingdom</td><td style=\"text-align: right;\">       10.14</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2818</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "<tr><td style=\"text-align: right;\">       16916</td><td>plasters in tin skulls            </td><td style=\"text-align: right;\">         1</td><td>2010-12-02 13:14:00</td><td style=\"text-align: right;\">       1.65</td><td>United Kingdom</td><td style=\"text-align: right;\">        1.65</td><td>2010-12-02 00:00:00</td><td>December</td><td>Thursday</td><td style=\"text-align: right;\">    13</td><td>Winter  </td><td style=\"text-align: right;\">    2819</td><td style=\"text-align: right;\">       3.07692</td><td style=\"text-align: right;\">         2.22042</td><td>Group 1  </td><td>2011-11-16 12:08:00</td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[10 rows x 17 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3tFm4rTxcsX"
      },
      "outputs": [],
      "source": [
        "y = \"Segment\"\n",
        "x = data.names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K4sdKaukzTDy"
      },
      "outputs": [],
      "source": [
        "\n",
        "x.remove(y)\n",
        "train, test = data.split_frame([0.8])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QPSo92Ep1GdX",
        "outputId": "edc7439c-da46-458f-f28f-a0ce70d81d6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "deeplearning Model Build progress: |█████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model Details\n",
              "=============\n",
              "H2ODeepLearningEstimator : Deep Learning\n",
              "Model Key: DeepLearning_model_python_1715346154206_1\n",
              "\n",
              "\n",
              "Status of Neuron Layers: predicting Segment, 2-class classification, bernoulli distribution, CrossEntropy loss, 834,602 weights/biases, 9.7 MB, 245,203 training samples, mini-batch size 1\n",
              "    layer    units    type       dropout    l1    l2    mean_rate           rate_rms             momentum    mean_weight              weight_rms            mean_bias                bias_rms\n",
              "--  -------  -------  ---------  ---------  ----  ----  ------------------  -------------------  ----------  -----------------------  --------------------  -----------------------  ---------------------\n",
              "    1        3969     Input      0.0\n",
              "    2        200      Rectifier  0.0        0.0   0.0   1.0013330722014595  0.05022810399532318  0.0         -1.1864479245706432e-05  0.022721610963344574  0.5005427490546438       0.02346912771463394\n",
              "    3        200      Rectifier  0.0        0.0   0.0   0.9738662054221174  0.16970902681350708  0.0         -0.0007085113136752795   0.06920942664146423   0.9992820240608249       0.005631601437926292\n",
              "    4        2        Softmax               0.0   0.0   0.9739034733886365  0.16352880001068115  0.0         0.03221767878290848      0.3949671983718872    -1.5917358351880617e-05  0.0007136252243071795\n",
              "\n",
              "ModelMetricsBinomial: deeplearning\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 0.00021584687906339017\n",
              "RMSE: 0.01469172825311543\n",
              "LogLoss: 0.00144181163296338\n",
              "Mean Per-Class Error: 0.00010036130068245685\n",
              "AUC: 0.9997992773986351\n",
              "AUCPR: 0.3068528194400547\n",
              "Gini: 0.9995985547972701\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0009037294438483626\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  ------------\n",
              "Group 1  9962       2          0.0002   (2.0/9964.0)\n",
              "Group 2  0          2          0        (0.0/2.0)\n",
              "Total    9962       4          0.0002   (2.0/9966.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.000903729  0.666667  3\n",
              "max f2                       0.000903729  0.833333  3\n",
              "max f0point5                 0.000903729  0.555556  3\n",
              "max accuracy                 0.000903729  0.999799  3\n",
              "max precision                0.000903729  0.5       3\n",
              "max recall                   0.000903729  1         3\n",
              "max specificity              0.393725     0.9999    0\n",
              "max absolute_mcc             0.000903729  0.707036  3\n",
              "max min_per_class_accuracy   0.000903729  0.999799  3\n",
              "max mean_per_class_accuracy  0.000903729  0.9999    3\n",
              "max tns                      0.393725     9963      0\n",
              "max fns                      0.393725     2         0\n",
              "max fps                      2.0198e-09   9964      399\n",
              "max tps                      0.000903729  2         3\n",
              "max tnr                      0.393725     0.9999    0\n",
              "max fnr                      0.393725     1         0\n",
              "max fpr                      2.0198e-09   1         399\n",
              "max tpr                      0.000903729  1         3\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.02 %, avg score:  0.00 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
              "1        0.0100341                   3.36406e-07        99.66   99.66              0.02             0.00401188   0.02                        0.00401188          1               1                          9866    9866               0.990165\n",
              "2        0.0200682                   2.45622e-07        0       49.83              0                2.81069e-07  0.01                        0.00200608          0               1                          -100    4883               0.980128\n",
              "3        0.030002                    2.02405e-07        0       33.3311            0                2.20836e-07  0.00668896                  0.00134193          0               1                          -100    3233.11            0.970193\n",
              "4        0.0400361                   1.8149e-07         0       24.9774            0                1.92422e-07  0.00501253                  0.00100566          0               1                          -100    2397.74            0.960157\n",
              "5        0.0500702                   1.62902e-07        0       19.9719            0                1.71417e-07  0.00400802                  0.000804157         0               1                          -100    1897.19            0.95012\n",
              "6        0.10004                     1.06459e-07        0       9.99599            0                1.30783e-07  0.00200602                  0.000402547         0               1                          -100    899.599            0.900141\n",
              "7        0.15001                     7.50611e-08        0       6.66622            0                8.98333e-08  0.00133779                  0.000268485         0               1                          -100    566.622            0.850161\n",
              "8        0.20008                     5.64535e-08        0       4.99799            0                6.48864e-08  0.00100301                  0.000201312         0               1                          -100    399.799            0.80008\n",
              "9        0.30002                     3.74631e-08        0       3.33311            0                4.60093e-08  0.000668896                 0.000134268         0               1                          -100    233.311            0.70012\n",
              "10       0.40006                     2.59813e-08        0       2.49962            0                3.12237e-08  0.00050163                  0.000100701         0               1                          -100    149.962            0.60006\n",
              "11       0.5                         1.96311e-08        0       2                  0                2.24742e-08  0.000401365                 8.05772e-05         0               1                          -100    100                0.5001\n",
              "12       0.60004                     1.5557e-08         0       1.66656            0                1.75658e-08  0.000334448                 6.71461e-05         0               1                          -100    66.6555            0.40004\n",
              "13       0.69998                     1.21457e-08        0       1.42861            0                1.38079e-08  0.000286697                 5.75613e-05         0               1                          -100    42.8612            0.30008\n",
              "14       0.80002                     9.27989e-09        0       1.24997            0                1.07215e-08  0.000250847                 5.03647e-05         0               1                          -100    24.9969            0.20002\n",
              "15       0.89996                     6.34832e-09        0       1.11116            0                7.79568e-09  0.00022299                  4.47726e-05         0               1                          -100    11.1161            0.10006\n",
              "16       1                           1.80877e-09        0       1                  0                4.79386e-09  0.000200682                 4.02941e-05         0               1                          -100    0                  0\n",
              "\n",
              "Scoring History: \n",
              "    timestamp            duration           training_speed    epochs       iterations    samples    training_rmse    training_logloss    training_r2    training_auc    training_pr_auc    training_lift    training_classification_error\n",
              "--  -------------------  -----------------  ----------------  -----------  ------------  ---------  ---------------  ------------------  -------------  --------------  -----------------  ---------------  -------------------------------\n",
              "    2024-05-10 13:04:44  0.000 sec                            0            0             0          nan              nan                 nan            nan             nan                nan              nan\n",
              "    2024-05-10 13:04:48  22.432 sec         62 obs/sec        0.000399181  1             130        0.0141662        0.00365103          -0.000200697   0.999523        0.164473           99.66            0.000702388\n",
              "    2024-05-10 13:07:56  3 min 21.906 sec   224 obs/sec       0.11825      321           38510      0.0173484        0.00291217          -0.500019      0.999799        0.306853           99.66            0.000200682\n",
              "    2024-05-10 13:09:36  4 min 59.660 sec   245 obs/sec       0.19741      536           64290      0.0146917        0.00144181          -0.0757809     0.999799        0.306853           99.66            0.000200682\n",
              "    2024-05-10 13:10:53  6 min 18.278 sec   259 obs/sec       0.26454      720           86152      0.0173331        0.00477502          -0.497367      0.999799        0.306853           99.66            0.000200682\n",
              "    2024-05-10 13:12:20  7 min 43.052 sec   272 obs/sec       0.342356     931           111494     0.0172462        0.00442099          -0.482402      0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:13:26  8 min 51.171 sec   280 obs/sec       0.404103     1098          131603     0.0108954        0.00358276          0.408353       0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:14:52  10 min 17.420 sec  284 obs/sec       0.478384     1299          155794     0.0171567        0.00445157          -0.467055      0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:16:21  11 min 44.614 sec  287 obs/sec       0.552782     1502          180023     0.0104056        0.00350715          0.460345       0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:17:39  13 min  2.296 sec  288 obs/sec       0.616455     1674          200759     0.0148187        0.0039328           -0.0944582     0.999799        0.306853           99.66            0.000200682\n",
              "    2024-05-10 13:18:51  14 min 15.869 sec  288 obs/sec       0.675104     1834          219859     0.0152229        0.00416009          -0.154979      0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:20:21  15 min 45.257 sec  291 obs/sec       0.752926     2045          245203     0.0108918        0.00352853          0.408744       0.9999          0.450694           99.66            0.000100341\n",
              "    2024-05-10 13:20:29  15 min 53.696 sec  291 obs/sec       0.752926     2045          245203     0.0146917        0.00144181          -0.0757809     0.999799        0.306853           99.66            0.000200682\n",
              "\n",
              "Variable Importances: \n",
              "variable                                       relative_importance    scaled_importance    percentage\n",
              "---------------------------------------------  ---------------------  -------------------  ----------------------\n",
              "Description.mirrored wall art photo frames     1.0                    1.0                  0.0002925862509906997\n",
              "Description.ribbon reel christmas sock bauble  0.9940683245658875     0.9940683245658875   0.00029085072431333907\n",
              "Description.laurel heart antique gold          0.9889955520629883     0.9889955520629883   0.0002893665008245871\n",
              "Description.gold/m.o.p pendant orbit necklace  0.9852657914161682     0.9852657914161682   0.00028827522413984134\n",
              "Description.filigree heart bird white          0.9850196242332458     0.9850196242332458   0.0002882031990066731\n",
              "Description.doughnut lip gloss                 0.9707045555114746     0.9707045555114746   0.00028401480671669585\n",
              "Description.natural slate chalkboard large     0.9694697856903076     0.9694697856903076   0.00028365353004388414\n",
              "Description.rex cash+carry jumbo shopper       0.969100296497345      0.969100296497345    0.00028354542258613363\n",
              "Description.blue polkadot cup                  0.967805802822113      0.967805802822113    0.0002831666715347664\n",
              "Description.embossed heart trinket box         0.9660957455635071     0.9660957455635071   0.0002826663322924914\n",
              "---                                            ---                    ---                  ---\n",
              "Description.dotcomgiftshop tea towel           0.7515444159507751     0.7515444159507751   0.0002198915631160323\n",
              "Description.memo board cottage design          0.7439258098602295     0.7439258098602295   0.00021766246372222463\n",
              "Description.bunting , spotty                   0.7438026666641235     0.7438026666641235   0.00021762643371614097\n",
              "Description.floral bathroom set                0.7419683337211609     0.7419683337211609   0.0002170897331172908\n",
              "Description.wrap carousel                      0.7322932481765747     0.7322932481765747   0.000214258936109786\n",
              "Description.missing(NA)                        0.0                    0.0                  0.0\n",
              "Country.missing(NA)                            0.0                    0.0                  0.0\n",
              "month.missing(NA)                              0.0                    0.0                  0.0\n",
              "week.missing(NA)                               0.0                    0.0                  0.0\n",
              "season.missing(NA)                             0.0                    0.0                  0.0\n",
              "[3969 rows x 4 columns]\n",
              "\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
            ],
            "text/html": [
              "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
              "=============\n",
              "H2ODeepLearningEstimator : Deep Learning\n",
              "Model Key: DeepLearning_model_python_1715346154206_1\n",
              "</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-2.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-2 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-2 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-2 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-2 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-2 .h2o-table th,\n",
              "#h2o-table-2 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-2 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-2\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Status of Neuron Layers: predicting Segment, 2-class classification, bernoulli distribution, CrossEntropy loss, 834,602 weights/biases, 9.7 MB, 245,203 training samples, mini-batch size 1</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>layer</th>\n",
              "<th>units</th>\n",
              "<th>type</th>\n",
              "<th>dropout</th>\n",
              "<th>l1</th>\n",
              "<th>l2</th>\n",
              "<th>mean_rate</th>\n",
              "<th>rate_rms</th>\n",
              "<th>momentum</th>\n",
              "<th>mean_weight</th>\n",
              "<th>weight_rms</th>\n",
              "<th>mean_bias</th>\n",
              "<th>bias_rms</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>1</td>\n",
              "<td>3969</td>\n",
              "<td>Input</td>\n",
              "<td>0.0</td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td>\n",
              "<td></td></tr>\n",
              "<tr><td></td>\n",
              "<td>2</td>\n",
              "<td>200</td>\n",
              "<td>Rectifier</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0013331</td>\n",
              "<td>0.0502281</td>\n",
              "<td>0.0</td>\n",
              "<td>-0.0000119</td>\n",
              "<td>0.0227216</td>\n",
              "<td>0.5005427</td>\n",
              "<td>0.0234691</td></tr>\n",
              "<tr><td></td>\n",
              "<td>3</td>\n",
              "<td>200</td>\n",
              "<td>Rectifier</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.9738662</td>\n",
              "<td>0.1697090</td>\n",
              "<td>0.0</td>\n",
              "<td>-0.0007085</td>\n",
              "<td>0.0692094</td>\n",
              "<td>0.9992820</td>\n",
              "<td>0.0056316</td></tr>\n",
              "<tr><td></td>\n",
              "<td>4</td>\n",
              "<td>2</td>\n",
              "<td>Softmax</td>\n",
              "<td></td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.9739035</td>\n",
              "<td>0.1635288</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0322177</td>\n",
              "<td>0.3949672</td>\n",
              "<td>-0.0000159</td>\n",
              "<td>0.0007136</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: deeplearning\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 0.00021584687906339017\n",
              "RMSE: 0.01469172825311543\n",
              "LogLoss: 0.00144181163296338\n",
              "Mean Per-Class Error: 0.00010036130068245685\n",
              "AUC: 0.9997992773986351\n",
              "AUCPR: 0.3068528194400547\n",
              "Gini: 0.9995985547972701</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-3.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-3 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-3 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-3 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-3 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-3 .h2o-table th,\n",
              "#h2o-table-3 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-3 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-3\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0009037294438483626</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>9962.0</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0002</td>\n",
              "<td> (2.0/9964.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/2.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>9962.0</td>\n",
              "<td>4.0</td>\n",
              "<td>0.0002</td>\n",
              "<td> (2.0/9966.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-4.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-4 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-4 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-4 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-4 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-4 .h2o-table th,\n",
              "#h2o-table-4 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-4 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-4\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.6666667</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.8333333</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.5555556</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.9997993</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.5</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.0009037</td>\n",
              "<td>1.0</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>0.3937247</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.7070358</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.9997993</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.0009037</td>\n",
              "<td>0.9998996</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>0.3937247</td>\n",
              "<td>9963.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>0.3937247</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0000000</td>\n",
              "<td>9964.0</td>\n",
              "<td>399.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>0.0009037</td>\n",
              "<td>2.0</td>\n",
              "<td>3.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>0.3937247</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>0.3937247</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0000000</td>\n",
              "<td>1.0</td>\n",
              "<td>399.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>0.0009037</td>\n",
              "<td>1.0</td>\n",
              "<td>3.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-5.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-5 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-5 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-5 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-5 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-5 .h2o-table th,\n",
              "#h2o-table-5 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-5 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-5\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.02 %, avg score:  0.00 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>0.0100341</td>\n",
              "<td>0.0000003</td>\n",
              "<td>99.66</td>\n",
              "<td>99.66</td>\n",
              "<td>0.02</td>\n",
              "<td>0.0040119</td>\n",
              "<td>0.02</td>\n",
              "<td>0.0040119</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>9866.0</td>\n",
              "<td>9866.0</td>\n",
              "<td>0.9901646</td></tr>\n",
              "<tr><td>2</td>\n",
              "<td>0.0200682</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0</td>\n",
              "<td>49.83</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000003</td>\n",
              "<td>0.01</td>\n",
              "<td>0.0020061</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>4883.0</td>\n",
              "<td>0.9801285</td></tr>\n",
              "<tr><td>3</td>\n",
              "<td>0.0300020</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0</td>\n",
              "<td>33.3311037</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0066890</td>\n",
              "<td>0.0013419</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>3233.1103679</td>\n",
              "<td>0.9701927</td></tr>\n",
              "<tr><td>4</td>\n",
              "<td>0.0400361</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0</td>\n",
              "<td>24.9774436</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0050125</td>\n",
              "<td>0.0010057</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>2397.7443609</td>\n",
              "<td>0.9601566</td></tr>\n",
              "<tr><td>5</td>\n",
              "<td>0.0500702</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0</td>\n",
              "<td>19.9719439</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000002</td>\n",
              "<td>0.0040080</td>\n",
              "<td>0.0008042</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>1897.1943888</td>\n",
              "<td>0.9501204</td></tr>\n",
              "<tr><td>6</td>\n",
              "<td>0.1000401</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0</td>\n",
              "<td>9.9959880</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0020060</td>\n",
              "<td>0.0004025</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>899.5987964</td>\n",
              "<td>0.9001405</td></tr>\n",
              "<tr><td>7</td>\n",
              "<td>0.1500100</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0</td>\n",
              "<td>6.6662207</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0013378</td>\n",
              "<td>0.0002685</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>566.6220736</td>\n",
              "<td>0.8501606</td></tr>\n",
              "<tr><td>8</td>\n",
              "<td>0.2000803</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0</td>\n",
              "<td>4.9979940</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0010030</td>\n",
              "<td>0.0002013</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>399.7993982</td>\n",
              "<td>0.8000803</td></tr>\n",
              "<tr><td>9</td>\n",
              "<td>0.3000201</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>3.3331104</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0006689</td>\n",
              "<td>0.0001343</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>233.3110368</td>\n",
              "<td>0.7001204</td></tr>\n",
              "<tr><td>10</td>\n",
              "<td>0.4000602</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>2.4996238</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0005016</td>\n",
              "<td>0.0001007</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>149.9623777</td>\n",
              "<td>0.6000602</td></tr>\n",
              "<tr><td>11</td>\n",
              "<td>0.5</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0004014</td>\n",
              "<td>0.0000806</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>100.0</td>\n",
              "<td>0.5001004</td></tr>\n",
              "<tr><td>12</td>\n",
              "<td>0.6000401</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>1.6665552</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0003344</td>\n",
              "<td>0.0000671</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>66.6555184</td>\n",
              "<td>0.4000401</td></tr>\n",
              "<tr><td>13</td>\n",
              "<td>0.6999799</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>1.4286124</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0002867</td>\n",
              "<td>0.0000576</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>42.8612385</td>\n",
              "<td>0.3000803</td></tr>\n",
              "<tr><td>14</td>\n",
              "<td>0.8000201</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>1.2499686</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0002508</td>\n",
              "<td>0.0000504</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>24.9968644</td>\n",
              "<td>0.2000201</td></tr>\n",
              "<tr><td>15</td>\n",
              "<td>0.8999599</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>1.1111607</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0002230</td>\n",
              "<td>0.0000448</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>11.1160665</td>\n",
              "<td>0.1000602</td></tr>\n",
              "<tr><td>16</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000000</td>\n",
              "<td>0.0002007</td>\n",
              "<td>0.0000403</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-6.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-6 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-6 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-6 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-6 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-6 .h2o-table th,\n",
              "#h2o-table-6 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-6 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-6\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Scoring History: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>timestamp</th>\n",
              "<th>duration</th>\n",
              "<th>training_speed</th>\n",
              "<th>epochs</th>\n",
              "<th>iterations</th>\n",
              "<th>samples</th>\n",
              "<th>training_rmse</th>\n",
              "<th>training_logloss</th>\n",
              "<th>training_r2</th>\n",
              "<th>training_auc</th>\n",
              "<th>training_pr_auc</th>\n",
              "<th>training_lift</th>\n",
              "<th>training_classification_error</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>2024-05-10 13:04:44</td>\n",
              "<td> 0.000 sec</td>\n",
              "<td>None</td>\n",
              "<td>0.0</td>\n",
              "<td>0</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:04:48</td>\n",
              "<td>22.432 sec</td>\n",
              "<td>62 obs/sec</td>\n",
              "<td>0.0003992</td>\n",
              "<td>1</td>\n",
              "<td>130.0</td>\n",
              "<td>0.0141662</td>\n",
              "<td>0.0036510</td>\n",
              "<td>-0.0002007</td>\n",
              "<td>0.9995233</td>\n",
              "<td>0.1644726</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0007024</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:07:56</td>\n",
              "<td> 3 min 21.906 sec</td>\n",
              "<td>224 obs/sec</td>\n",
              "<td>0.1182496</td>\n",
              "<td>321</td>\n",
              "<td>38510.0</td>\n",
              "<td>0.0173484</td>\n",
              "<td>0.0029122</td>\n",
              "<td>-0.5000193</td>\n",
              "<td>0.9997993</td>\n",
              "<td>0.3068528</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0002007</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:09:36</td>\n",
              "<td> 4 min 59.660 sec</td>\n",
              "<td>245 obs/sec</td>\n",
              "<td>0.1974102</td>\n",
              "<td>536</td>\n",
              "<td>64290.0</td>\n",
              "<td>0.0146917</td>\n",
              "<td>0.0014418</td>\n",
              "<td>-0.0757809</td>\n",
              "<td>0.9997993</td>\n",
              "<td>0.3068528</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0002007</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:10:53</td>\n",
              "<td> 6 min 18.278 sec</td>\n",
              "<td>259 obs/sec</td>\n",
              "<td>0.2645402</td>\n",
              "<td>720</td>\n",
              "<td>86152.0</td>\n",
              "<td>0.0173331</td>\n",
              "<td>0.0047750</td>\n",
              "<td>-0.4973670</td>\n",
              "<td>0.9997993</td>\n",
              "<td>0.3068528</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0002007</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:12:20</td>\n",
              "<td> 7 min 43.052 sec</td>\n",
              "<td>272 obs/sec</td>\n",
              "<td>0.3423558</td>\n",
              "<td>931</td>\n",
              "<td>111494.0</td>\n",
              "<td>0.0172462</td>\n",
              "<td>0.0044210</td>\n",
              "<td>-0.4824016</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:13:26</td>\n",
              "<td> 8 min 51.171 sec</td>\n",
              "<td>280 obs/sec</td>\n",
              "<td>0.4041030</td>\n",
              "<td>1098</td>\n",
              "<td>131603.0</td>\n",
              "<td>0.0108954</td>\n",
              "<td>0.0035828</td>\n",
              "<td>0.4083530</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:14:52</td>\n",
              "<td>10 min 17.420 sec</td>\n",
              "<td>284 obs/sec</td>\n",
              "<td>0.4783844</td>\n",
              "<td>1299</td>\n",
              "<td>155794.0</td>\n",
              "<td>0.0171567</td>\n",
              "<td>0.0044516</td>\n",
              "<td>-0.4670549</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:16:21</td>\n",
              "<td>11 min 44.614 sec</td>\n",
              "<td>287 obs/sec</td>\n",
              "<td>0.5527824</td>\n",
              "<td>1502</td>\n",
              "<td>180023.0</td>\n",
              "<td>0.0104056</td>\n",
              "<td>0.0035072</td>\n",
              "<td>0.4603447</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:17:39</td>\n",
              "<td>13 min  2.296 sec</td>\n",
              "<td>288 obs/sec</td>\n",
              "<td>0.6164548</td>\n",
              "<td>1674</td>\n",
              "<td>200759.0</td>\n",
              "<td>0.0148187</td>\n",
              "<td>0.0039328</td>\n",
              "<td>-0.0944582</td>\n",
              "<td>0.9997993</td>\n",
              "<td>0.3068528</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0002007</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:18:51</td>\n",
              "<td>14 min 15.869 sec</td>\n",
              "<td>288 obs/sec</td>\n",
              "<td>0.6751037</td>\n",
              "<td>1834</td>\n",
              "<td>219859.0</td>\n",
              "<td>0.0152229</td>\n",
              "<td>0.0041601</td>\n",
              "<td>-0.1549794</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:20:21</td>\n",
              "<td>15 min 45.257 sec</td>\n",
              "<td>291 obs/sec</td>\n",
              "<td>0.7529255</td>\n",
              "<td>2045</td>\n",
              "<td>245203.0</td>\n",
              "<td>0.0108918</td>\n",
              "<td>0.0035285</td>\n",
              "<td>0.4087440</td>\n",
              "<td>0.9998996</td>\n",
              "<td>0.4506939</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0001003</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:20:29</td>\n",
              "<td>15 min 53.696 sec</td>\n",
              "<td>291 obs/sec</td>\n",
              "<td>0.7529255</td>\n",
              "<td>2045</td>\n",
              "<td>245203.0</td>\n",
              "<td>0.0146917</td>\n",
              "<td>0.0014418</td>\n",
              "<td>-0.0757809</td>\n",
              "<td>0.9997993</td>\n",
              "<td>0.3068528</td>\n",
              "<td>99.66</td>\n",
              "<td>0.0002007</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-7.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-7 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-7 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-7 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-7 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-7 .h2o-table th,\n",
              "#h2o-table-7 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-7 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-7\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Variable Importances: </caption>\n",
              "    <thead><tr><th>variable</th>\n",
              "<th>relative_importance</th>\n",
              "<th>scaled_importance</th>\n",
              "<th>percentage</th></tr></thead>\n",
              "    <tbody><tr><td>Description.mirrored wall art photo frames</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0002926</td></tr>\n",
              "<tr><td>Description.ribbon reel christmas sock bauble</td>\n",
              "<td>0.9940683</td>\n",
              "<td>0.9940683</td>\n",
              "<td>0.0002909</td></tr>\n",
              "<tr><td>Description.laurel heart antique gold</td>\n",
              "<td>0.9889956</td>\n",
              "<td>0.9889956</td>\n",
              "<td>0.0002894</td></tr>\n",
              "<tr><td>Description.gold/m.o.p pendant orbit necklace</td>\n",
              "<td>0.9852658</td>\n",
              "<td>0.9852658</td>\n",
              "<td>0.0002883</td></tr>\n",
              "<tr><td>Description.filigree heart bird white </td>\n",
              "<td>0.9850196</td>\n",
              "<td>0.9850196</td>\n",
              "<td>0.0002882</td></tr>\n",
              "<tr><td>Description.doughnut lip gloss </td>\n",
              "<td>0.9707046</td>\n",
              "<td>0.9707046</td>\n",
              "<td>0.0002840</td></tr>\n",
              "<tr><td>Description.natural slate chalkboard large </td>\n",
              "<td>0.9694698</td>\n",
              "<td>0.9694698</td>\n",
              "<td>0.0002837</td></tr>\n",
              "<tr><td>Description.rex cash+carry jumbo shopper</td>\n",
              "<td>0.9691003</td>\n",
              "<td>0.9691003</td>\n",
              "<td>0.0002835</td></tr>\n",
              "<tr><td>Description.blue polkadot cup</td>\n",
              "<td>0.9678058</td>\n",
              "<td>0.9678058</td>\n",
              "<td>0.0002832</td></tr>\n",
              "<tr><td>Description.embossed heart trinket box</td>\n",
              "<td>0.9660957</td>\n",
              "<td>0.9660957</td>\n",
              "<td>0.0002827</td></tr>\n",
              "<tr><td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td></tr>\n",
              "<tr><td>Description.dotcomgiftshop tea towel</td>\n",
              "<td>0.7515444</td>\n",
              "<td>0.7515444</td>\n",
              "<td>0.0002199</td></tr>\n",
              "<tr><td>Description.memo board cottage design</td>\n",
              "<td>0.7439258</td>\n",
              "<td>0.7439258</td>\n",
              "<td>0.0002177</td></tr>\n",
              "<tr><td>Description.bunting , spotty </td>\n",
              "<td>0.7438027</td>\n",
              "<td>0.7438027</td>\n",
              "<td>0.0002176</td></tr>\n",
              "<tr><td>Description.floral bathroom set</td>\n",
              "<td>0.7419683</td>\n",
              "<td>0.7419683</td>\n",
              "<td>0.0002171</td></tr>\n",
              "<tr><td>Description.wrap carousel</td>\n",
              "<td>0.7322932</td>\n",
              "<td>0.7322932</td>\n",
              "<td>0.0002143</td></tr>\n",
              "<tr><td>Description.missing(NA)</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Country.missing(NA)</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>month.missing(NA)</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>week.missing(NA)</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>season.missing(NA)</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "<pre style='font-size: smaller; margin-bottom: 1em;'>[3969 rows x 4 columns]</pre></div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "\n",
        "m = h2o.estimators.deeplearning.H2ODeepLearningEstimator(auc_type = \"MACRO_OVR\" )\n",
        "m.train(x, y, train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "F540uX8m1aQL",
        "outputId": "e181eee1-eff9-45e7-f316-21c1816d7d2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "predict      Group 1      Group 2\n",
              "---------  ---------  -----------\n",
              "Group 1            1  2.43761e-08\n",
              "Group 1            1  3.24415e-08\n",
              "Group 1            1  3.12588e-08\n",
              "Group 1            1  2.74102e-08\n",
              "Group 1            1  3.33112e-08\n",
              "Group 1            1  3.68135e-08\n",
              "Group 1            1  2.55857e-08\n",
              "Group 1            1  3.04575e-08\n",
              "Group 1            1  2.45461e-08\n",
              "Group 1            1  3.23648e-08\n",
              "[10 rows x 3 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th>predict  </th><th style=\"text-align: right;\">  Group 1</th><th style=\"text-align: right;\">    Group 2</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">2.43761e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.24415e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.12588e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">2.74102e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.33112e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.68135e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">2.55857e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.04575e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">2.45461e-08</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">3.23648e-08</td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[10 rows x 3 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "p = m.predict(test)\n",
        "p.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pq_0x4tn1ibn",
        "outputId": "5a6cbf0d-18d1-422e-f713-4449053f1131"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ModelMetricsBinomial: deeplearning\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 6.153406014658903e-05\n",
            "RMSE: 0.007844364865722975\n",
            "LogLoss: 0.00048821661376542024\n",
            "Mean Per-Class Error: 0.2000061608980125\n",
            "AUC: 0.9999556415343099\n",
            "AUCPR: 0.4612385437723089\n",
            "Gini: 0.9999112830686199\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0005106600906483747\n",
            "         Group 1    Group 2    Error    Rate\n",
            "-------  ---------  ---------  -------  -------------\n",
            "Group 1  81156      1          0        (1.0/81157.0)\n",
            "Group 2  2          3          0.4      (2.0/5.0)\n",
            "Total    81158      4          0        (3.0/81162.0)\n",
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "metric                       threshold    value     idx\n",
            "---------------------------  -----------  --------  -----\n",
            "max f1                       0.00051066   0.666667  3\n",
            "max f2                       0.000101114  0.757576  12\n",
            "max f0point5                 0.00051066   0.714286  3\n",
            "max accuracy                 0.00051066   0.999963  3\n",
            "max precision                0.00051066   0.75      3\n",
            "max recall                   0.000101114  1         12\n",
            "max specificity              0.00234815   0.999988  0\n",
            "max absolute_mcc             0.00051066   0.670802  3\n",
            "max min_per_class_accuracy   0.000101114  0.999901  12\n",
            "max mean_per_class_accuracy  0.000101114  0.999951  12\n",
            "max tns                      0.00234815   81156     0\n",
            "max fns                      0.00234815   5         0\n",
            "max fps                      2.62597e-09  81157     399\n",
            "max tps                      0.000101114  5         12\n",
            "max tnr                      0.00234815   0.999988  0\n",
            "max fnr                      0.00234815   1         0\n",
            "max fpr                      2.62597e-09  1         399\n",
            "max tpr                      0.000101114  1         12\n",
            "\n",
            "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.00 %\n",
            "group    cumulative_data_fraction    lower_threshold    lift     cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain     cumulative_gain    kolmogorov_smirnov\n",
            "-------  --------------------------  -----------------  -------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  -------  -----------------  --------------------\n",
            "1        0.0100047                   3.65238e-07        99.9532  99.9532            0.00615764       1.01663e-05  0.00615764                  1.01663e-05         1               1                          9895.32  9895.32            0.990056\n",
            "2        0.0200094                   2.55222e-07        0        49.9766            0                3.01289e-07  0.00307882                  5.23381e-06         0               1                          -100     4897.66            0.980051\n",
            "3        0.0300017                   2.08462e-07        0        33.3314            0                2.30008e-07  0.00205339                  3.56725e-06         0               1                          -100     3233.14            0.970058\n",
            "4        0.0400064                   1.81357e-07        0        24.996             0                1.94451e-07  0.00153988                  2.72379e-06         0               1                          -100     2399.6             0.960053\n",
            "5        0.0500111                   1.60957e-07        0        19.9956            0                1.70726e-07  0.00123183                  2.21305e-06         0               1                          -100     1899.56            0.950047\n",
            "6        0.10001                     1.04326e-07        0        9.99901            0                1.28902e-07  0.000615991                 1.1711e-06          0               1                          -100     899.901            0.900046\n",
            "7        0.150009                    7.36396e-08        0        6.66628            0                8.75796e-08  0.000410678                 8.09959e-07         0               1                          -100     566.628            0.850044\n",
            "8        0.200007                    5.65855e-08        0        4.99982            0                6.4369e-08   0.000308015                 6.23573e-07         0               1                          -100     399.982            0.800042\n",
            "9        0.300005                    3.77107e-08        0        3.33328            0                4.60929e-08  0.000205347                 4.31088e-07         0               1                          -100     233.328            0.700038\n",
            "10       0.400002                    2.67846e-08        0        2.49998            0                3.17931e-08  0.000154012                 3.31267e-07         0               1                          -100     149.998            0.600035\n",
            "11       0.5                         2.0052e-08         0        2                  0                2.3203e-08   0.00012321                  2.69656e-07         0               1                          -100     100                0.500031\n",
            "12       0.599998                    1.56093e-08        0        1.66667            0                1.77194e-08  0.000102676                 2.27667e-07         0               1                          -100     66.6674            0.400027\n",
            "13       0.699995                    1.21551e-08        0        1.42858            0                1.38302e-08  8.8008e-05                  1.9712e-07          0               1                          -100     42.8581            0.300023\n",
            "14       0.799993                    9.1527e-09         0        1.25001            0                1.06369e-08  7.70072e-05                 1.7381e-07          0               1                          -100     25.0012            0.20002\n",
            "15       0.89999                     6.33553e-09        0        1.11112            0                7.73693e-09  6.8451e-05                  1.55357e-07         0               1                          -100     11.1123            0.100016\n",
            "16       1                           9.87379e-55        0        1                  0                4.71911e-09  6.16052e-05                 1.40292e-07         0               1                          -100     0                  0\n"
          ]
        }
      ],
      "source": [
        "print(m.model_performance(test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K-9Brsn71v33",
        "outputId": "0f5ea8c4-8049-4712-9a41-1391bb409e1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drf Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model Details\n",
              "=============\n",
              "H2ORandomForestEstimator : Distributed Random Forest\n",
              "Model Key: rf_fit1\n",
              "\n",
              "\n",
              "Model Summary: \n",
              "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
              "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
              "    50                 50                          10284                  2            11           5.76          3             33            11.58\n",
              "\n",
              "ModelMetricsBinomial: drf\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 3.839108293652223e-06\n",
              "RMSE: 0.001959364257521358\n",
              "LogLoss: 2.6566109014115046e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.6666666666666667\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  --------------\n",
              "Group 1  325630     0          0        (0.0/325630.0)\n",
              "Group 2  0          37         0        (0.0/37.0)\n",
              "Total    325630     37         0        (0.0/325667.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.666667     1         25\n",
              "max f2                       0.666667     1         25\n",
              "max f0point5                 0.666667     1         25\n",
              "max accuracy                 0.666667     1         25\n",
              "max precision                1            1         0\n",
              "max recall                   0.666667     1         25\n",
              "max specificity              1            1         0\n",
              "max absolute_mcc             0.666667     1         25\n",
              "max min_per_class_accuracy   0.666667     1         25\n",
              "max mean_per_class_accuracy  0.666667     1         25\n",
              "max tns                      1            325630    0\n",
              "max fns                      1            31        0\n",
              "max fps                      0            325630    51\n",
              "max tps                      0.666667     37        25\n",
              "max tnr                      1            1         0\n",
              "max fnr                      1            0.837838  0\n",
              "max fpr                      0            1         51\n",
              "max tpr                      0.666667     1         25\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
              "1        1                           0                  1       1                  0.000113613      0.000109736  0.000113613                 0.000109736         1               1                          0       0                  0\n",
              "\n",
              "ModelMetricsBinomial: drf\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 4.6329130133052375e-06\n",
              "RMSE: 0.0021524202687452183\n",
              "LogLoss: 3.224495481023348e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.7\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  --------------\n",
              "Group 1  325630     0          0        (0.0/325630.0)\n",
              "Group 2  0          37         0        (0.0/37.0)\n",
              "Total    325630     37         0        (0.0/325667.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.7          1         19\n",
              "max f2                       0.7          1         19\n",
              "max f0point5                 0.7          1         19\n",
              "max accuracy                 0.7          1         19\n",
              "max precision                1            1         0\n",
              "max recall                   0.7          1         19\n",
              "max specificity              1            1         0\n",
              "max absolute_mcc             0.7          1         19\n",
              "max min_per_class_accuracy   0.7          1         19\n",
              "max mean_per_class_accuracy  0.7          1         19\n",
              "max tns                      1            325630    0\n",
              "max fns                      1            36        0\n",
              "max fps                      0            325630    62\n",
              "max tps                      0.7          37        19\n",
              "max tnr                      1            1         0\n",
              "max fnr                      1            0.972973  0\n",
              "max fpr                      0            1         62\n",
              "max tpr                      0.7          1         19\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift     cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain     cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  -------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  -------  -----------------  --------------------\n",
              "1        0.0248567                   9.7e-07            40.2306  40.2306            0.00457072       0.00461981   0.00457072                  0.00461981          1               1                          3923.06  3923.06            0.975254\n",
              "2        0.224653                    7.3e-07            0        4.45131            0                7.3e-07      0.000505727                 0.000511807         0               1                          -100     345.131            0.775435\n",
              "3        0.345525                    3e-07              0        2.89415            0                4.14471e-07  0.000328813                 0.000332911         0               1                          -100     189.415            0.65455\n",
              "4        0.659923                    1.2e-07            0        1.51533            0                1.20041e-07  0.000172161                 0.000174364         0               1                          -100     51.5329            0.340116\n",
              "5        1                           0                  0        1                  0                0            0.000113613                 0.000115067         0               1                          -100     0                  0\n",
              "\n",
              "Cross-Validation Metrics Summary: \n",
              "                         mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
              "-----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
              "accuracy                 1            0            1             1             1             1             1\n",
              "auc                      1            0            1             1             1             1             1\n",
              "err                      0            0            0             0             0             0             0\n",
              "err_count                0            0            0             0             0             0             0\n",
              "f0point5                 1            0            1             1             1             1             1\n",
              "f1                       1            0            1             1             1             1             1\n",
              "f2                       1            0            1             1             1             1             1\n",
              "lift_top_group           2.7019       3.09908      1.0029        1.00214       1             8.14503       2.35945\n",
              "logloss                  3.22486e-05  1.1645e-05   3.54934e-05   4.30003e-05   1.47746e-05   2.67447e-05   4.123e-05\n",
              "max_per_class_error      0            0            0             0             0             0             0\n",
              "mcc                      1            0            1             1             1             1             1\n",
              "mean_per_class_accuracy  1            0            1             1             1             1             1\n",
              "mean_per_class_error     0            0            0             0             0             0             0\n",
              "mse                      4.6333e-06   2.56588e-06  5.07947e-06   7.39349e-06   1.05431e-06   3.14056e-06   6.4987e-06\n",
              "pr_auc                   1            0            1             1             1             1             1\n",
              "precision                1            0            1             1             1             1             1\n",
              "r2                       0.95919      0.0164857    0.963149      0.931093      0.965577      0.974532      0.961602\n",
              "recall                   1            0            1             1             1             1             1\n",
              "rmse                     0.00206422   0.000682201  0.00225377    0.0027191     0.0010268     0.00177216    0.00254925\n",
              "specificity              1            0            1             1             1             1             1\n",
              "\n",
              "Scoring History: \n",
              "    timestamp            duration          number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error\n",
              "--  -------------------  ----------------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------\n",
              "    2024-05-10 13:24:21  1 min 34.777 sec  0                  nan              nan                 nan             nan                nan              nan\n",
              "    2024-05-10 13:24:21  1 min 35.300 sec  1                  0.00288982       0.000288436         0.999996        0.923077           1                8.35108e-06\n",
              "    2024-05-10 13:24:22  1 min 35.735 sec  2                  0.00277082       0.000183876         0.999997        0.952575           1                5.11831e-06\n",
              "    2024-05-10 13:24:22  1 min 36.129 sec  3                  0.00378861       0.000430617         0.982138        0.89308            1                1.2303e-05\n",
              "    2024-05-10 13:24:23  1 min 36.535 sec  4                  0.00305764       0.000258134         0.982757        0.929312           1                1.09454e-05\n",
              "    2024-05-10 13:24:23  1 min 36.809 sec  5                  0.00228737       0.000123855         0.999998        0.964464           1                3.40912e-06\n",
              "    2024-05-10 13:24:23  1 min 37.168 sec  6                  0.00152409       7.41703e-06         1               0.999517           1                3.27931e-06\n",
              "    2024-05-10 13:24:24  1 min 37.693 sec  7                  0.00141389       6.49443e-06         1               1                  1                0\n",
              "    2024-05-10 13:24:24  1 min 38.023 sec  8                  0.00139086       6.28103e-06         1               1                  1                0\n",
              "    2024-05-10 13:24:25  1 min 38.769 sec  9                  0.00196632       1.46649e-05         1               0.999618           1                3.12457e-06\n",
              "    2024-05-10 13:24:29  1 min 42.936 sec  26                 0.00225133       2.80585e-05         1               1                  1                0\n",
              "    2024-05-10 13:24:33  1 min 47.051 sec  50                 0.00195936       2.65661e-05         1               1                  1                0\n",
              "\n",
              "Variable Importances: \n",
              "variable           relative_importance    scaled_importance    percentage\n",
              "-----------------  ---------------------  -------------------  ------------\n",
              "avg_unit_price     762.816                1                    0.56\n",
              "avg_quantity       272.175                0.356803             0.19981\n",
              "CustomerID         75.3935                0.0988357            0.055348\n",
              "last_invoice_date  70.569                 0.0925112            0.0518063\n",
              "month              27.8427                0.0364999            0.02044\n",
              "Quantity           22.1795                0.0290759            0.0162825\n",
              "date               21.9963                0.0288357            0.016148\n",
              "week               19.4947                0.0255563            0.0143115\n",
              "TotalPrice         18.2909                0.0239781            0.0134278\n",
              "UnitPrice          17.3415                0.0227335            0.0127308\n",
              "time               16.9947                0.0222789            0.0124762\n",
              "InvoiceDate        14.6885                0.0192556            0.0107831\n",
              "Description        13.687                 0.0179428            0.010048\n",
              "season             5.16075                0.00676539           0.00378862\n",
              "row_id             3.54016                0.00464091           0.00259891\n",
              "Country            0                      0                    0\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
            ],
            "text/html": [
              "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
              "=============\n",
              "H2ORandomForestEstimator : Distributed Random Forest\n",
              "Model Key: rf_fit1\n",
              "</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-8.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-8 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-8 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-8 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-8 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-8 .h2o-table th,\n",
              "#h2o-table-8 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-8 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-8\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Model Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>number_of_trees</th>\n",
              "<th>number_of_internal_trees</th>\n",
              "<th>model_size_in_bytes</th>\n",
              "<th>min_depth</th>\n",
              "<th>max_depth</th>\n",
              "<th>mean_depth</th>\n",
              "<th>min_leaves</th>\n",
              "<th>max_leaves</th>\n",
              "<th>mean_leaves</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>50.0</td>\n",
              "<td>50.0</td>\n",
              "<td>10284.0</td>\n",
              "<td>2.0</td>\n",
              "<td>11.0</td>\n",
              "<td>5.76</td>\n",
              "<td>3.0</td>\n",
              "<td>33.0</td>\n",
              "<td>11.58</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 3.839108293652223e-06\n",
              "RMSE: 0.001959364257521358\n",
              "LogLoss: 2.6566109014115046e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-9.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-9 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-9 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-9 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-9 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-9 .h2o-table th,\n",
              "#h2o-table-9 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-9 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-9\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.6666666666666667</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325630.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/37.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>325630.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325667.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-10.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-10 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-10 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-10 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-10 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-10 .h2o-table th,\n",
              "#h2o-table-10 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-10 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-10\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>1.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>1.0</td>\n",
              "<td>31.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>51.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>0.6666667</td>\n",
              "<td>37.0</td>\n",
              "<td>25.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>1.0</td>\n",
              "<td>0.8378378</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>51.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>0.6666667</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-11.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-11 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-11 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-11 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-11 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-11 .h2o-table th,\n",
              "#h2o-table-11 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-11 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-11\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001097</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001097</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 4.6329130133052375e-06\n",
              "RMSE: 0.0021524202687452183\n",
              "LogLoss: 3.224495481023348e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-12.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-12 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-12 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-12 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-12 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-12 .h2o-table th,\n",
              "#h2o-table-12 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-12 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-12\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.7</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325630.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/37.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>325630.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325667.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-13.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-13 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-13 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-13 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-13 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-13 .h2o-table th,\n",
              "#h2o-table-13 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-13 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-13\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>1.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>1.0</td>\n",
              "<td>36.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>62.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>0.7</td>\n",
              "<td>37.0</td>\n",
              "<td>19.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9729730</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>62.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>0.7</td>\n",
              "<td>1.0</td>\n",
              "<td>19.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-14.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-14 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-14 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-14 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-14 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-14 .h2o-table th,\n",
              "#h2o-table-14 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-14 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-14\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>0.0248567</td>\n",
              "<td>9.7e-07</td>\n",
              "<td>40.2306362</td>\n",
              "<td>40.2306362</td>\n",
              "<td>0.0045707</td>\n",
              "<td>0.0046198</td>\n",
              "<td>0.0045707</td>\n",
              "<td>0.0046198</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>3923.0636195</td>\n",
              "<td>3923.0636195</td>\n",
              "<td>0.9752541</td></tr>\n",
              "<tr><td>2</td>\n",
              "<td>0.2246528</td>\n",
              "<td>7.3e-07</td>\n",
              "<td>0.0</td>\n",
              "<td>4.4513135</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000007</td>\n",
              "<td>0.0005057</td>\n",
              "<td>0.0005118</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>345.1313523</td>\n",
              "<td>0.7754353</td></tr>\n",
              "<tr><td>3</td>\n",
              "<td>0.3455247</td>\n",
              "<td>3e-07</td>\n",
              "<td>0.0</td>\n",
              "<td>2.8941489</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000004</td>\n",
              "<td>0.0003288</td>\n",
              "<td>0.0003329</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>189.4148908</td>\n",
              "<td>0.6545496</td></tr>\n",
              "<tr><td>4</td>\n",
              "<td>0.6599226</td>\n",
              "<td>1.2e-07</td>\n",
              "<td>0.0</td>\n",
              "<td>1.5153293</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000001</td>\n",
              "<td>0.0001722</td>\n",
              "<td>0.0001744</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>51.5329316</td>\n",
              "<td>0.3401161</td></tr>\n",
              "<tr><td>5</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001151</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-15.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-15 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-15 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-15 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-15 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-15 .h2o-table th,\n",
              "#h2o-table-15 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-15 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-15\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Cross-Validation Metrics Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>mean</th>\n",
              "<th>sd</th>\n",
              "<th>cv_1_valid</th>\n",
              "<th>cv_2_valid</th>\n",
              "<th>cv_3_valid</th>\n",
              "<th>cv_4_valid</th>\n",
              "<th>cv_5_valid</th></tr></thead>\n",
              "    <tbody><tr><td>accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>auc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>err</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>err_count</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>f0point5</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>f1</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>f2</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>lift_top_group</td>\n",
              "<td>2.7019036</td>\n",
              "<td>3.099079</td>\n",
              "<td>1.0029035</td>\n",
              "<td>1.0021354</td>\n",
              "<td>1.0</td>\n",
              "<td>8.145027</td>\n",
              "<td>2.359451</td></tr>\n",
              "<tr><td>logloss</td>\n",
              "<td>0.0000322</td>\n",
              "<td>0.0000116</td>\n",
              "<td>0.0000355</td>\n",
              "<td>0.0000430</td>\n",
              "<td>0.0000148</td>\n",
              "<td>0.0000267</td>\n",
              "<td>0.0000412</td></tr>\n",
              "<tr><td>max_per_class_error</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>mcc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>mean_per_class_accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>mean_per_class_error</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>mse</td>\n",
              "<td>0.0000046</td>\n",
              "<td>0.0000026</td>\n",
              "<td>0.0000051</td>\n",
              "<td>0.0000074</td>\n",
              "<td>0.0000011</td>\n",
              "<td>0.0000031</td>\n",
              "<td>0.0000065</td></tr>\n",
              "<tr><td>pr_auc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>precision</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>r2</td>\n",
              "<td>0.9591904</td>\n",
              "<td>0.0164857</td>\n",
              "<td>0.9631491</td>\n",
              "<td>0.9310926</td>\n",
              "<td>0.9655767</td>\n",
              "<td>0.974532</td>\n",
              "<td>0.9616016</td></tr>\n",
              "<tr><td>recall</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>rmse</td>\n",
              "<td>0.0020642</td>\n",
              "<td>0.0006822</td>\n",
              "<td>0.0022538</td>\n",
              "<td>0.0027191</td>\n",
              "<td>0.0010268</td>\n",
              "<td>0.0017722</td>\n",
              "<td>0.0025493</td></tr>\n",
              "<tr><td>specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-16.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-16 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-16 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-16 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-16 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-16 .h2o-table th,\n",
              "#h2o-table-16 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-16 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-16\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Scoring History: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>timestamp</th>\n",
              "<th>duration</th>\n",
              "<th>number_of_trees</th>\n",
              "<th>training_rmse</th>\n",
              "<th>training_logloss</th>\n",
              "<th>training_auc</th>\n",
              "<th>training_pr_auc</th>\n",
              "<th>training_lift</th>\n",
              "<th>training_classification_error</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>2024-05-10 13:24:21</td>\n",
              "<td> 1 min 34.777 sec</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:21</td>\n",
              "<td> 1 min 35.300 sec</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0028898</td>\n",
              "<td>0.0002884</td>\n",
              "<td>0.9999958</td>\n",
              "<td>0.9230769</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000084</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:22</td>\n",
              "<td> 1 min 35.735 sec</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0027708</td>\n",
              "<td>0.0001839</td>\n",
              "<td>0.9999972</td>\n",
              "<td>0.9525749</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000051</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:22</td>\n",
              "<td> 1 min 36.129 sec</td>\n",
              "<td>3.0</td>\n",
              "<td>0.0037886</td>\n",
              "<td>0.0004306</td>\n",
              "<td>0.9821385</td>\n",
              "<td>0.8930798</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000123</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:23</td>\n",
              "<td> 1 min 36.535 sec</td>\n",
              "<td>4.0</td>\n",
              "<td>0.0030576</td>\n",
              "<td>0.0002581</td>\n",
              "<td>0.9827565</td>\n",
              "<td>0.9293124</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000109</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:23</td>\n",
              "<td> 1 min 36.809 sec</td>\n",
              "<td>5.0</td>\n",
              "<td>0.0022874</td>\n",
              "<td>0.0001239</td>\n",
              "<td>0.9999981</td>\n",
              "<td>0.9644644</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000034</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:23</td>\n",
              "<td> 1 min 37.168 sec</td>\n",
              "<td>6.0</td>\n",
              "<td>0.0015241</td>\n",
              "<td>0.0000074</td>\n",
              "<td>0.9999999</td>\n",
              "<td>0.9995166</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000033</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:24</td>\n",
              "<td> 1 min 37.693 sec</td>\n",
              "<td>7.0</td>\n",
              "<td>0.0014139</td>\n",
              "<td>0.0000065</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:24</td>\n",
              "<td> 1 min 38.023 sec</td>\n",
              "<td>8.0</td>\n",
              "<td>0.0013909</td>\n",
              "<td>0.0000063</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:25</td>\n",
              "<td> 1 min 38.769 sec</td>\n",
              "<td>9.0</td>\n",
              "<td>0.0019663</td>\n",
              "<td>0.0000147</td>\n",
              "<td>1.0000000</td>\n",
              "<td>0.9996177</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000031</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:29</td>\n",
              "<td> 1 min 42.936 sec</td>\n",
              "<td>26.0</td>\n",
              "<td>0.0022513</td>\n",
              "<td>0.0000281</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:24:33</td>\n",
              "<td> 1 min 47.051 sec</td>\n",
              "<td>50.0</td>\n",
              "<td>0.0019594</td>\n",
              "<td>0.0000266</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-17.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-17 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-17 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-17 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-17 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-17 .h2o-table th,\n",
              "#h2o-table-17 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-17 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-17\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Variable Importances: </caption>\n",
              "    <thead><tr><th>variable</th>\n",
              "<th>relative_importance</th>\n",
              "<th>scaled_importance</th>\n",
              "<th>percentage</th></tr></thead>\n",
              "    <tbody><tr><td>avg_unit_price</td>\n",
              "<td>762.8159180</td>\n",
              "<td>1.0</td>\n",
              "<td>0.5600004</td></tr>\n",
              "<tr><td>avg_quantity</td>\n",
              "<td>272.1752930</td>\n",
              "<td>0.3568034</td>\n",
              "<td>0.1998100</td></tr>\n",
              "<tr><td>CustomerID</td>\n",
              "<td>75.3934631</td>\n",
              "<td>0.0988357</td>\n",
              "<td>0.0553480</td></tr>\n",
              "<tr><td>last_invoice_date</td>\n",
              "<td>70.5689850</td>\n",
              "<td>0.0925112</td>\n",
              "<td>0.0518063</td></tr>\n",
              "<tr><td>month</td>\n",
              "<td>27.8426971</td>\n",
              "<td>0.0364999</td>\n",
              "<td>0.0204400</td></tr>\n",
              "<tr><td>Quantity</td>\n",
              "<td>22.1795235</td>\n",
              "<td>0.0290759</td>\n",
              "<td>0.0162825</td></tr>\n",
              "<tr><td>date</td>\n",
              "<td>21.9963264</td>\n",
              "<td>0.0288357</td>\n",
              "<td>0.0161480</td></tr>\n",
              "<tr><td>week</td>\n",
              "<td>19.4947357</td>\n",
              "<td>0.0255563</td>\n",
              "<td>0.0143115</td></tr>\n",
              "<tr><td>TotalPrice</td>\n",
              "<td>18.2908916</td>\n",
              "<td>0.0239781</td>\n",
              "<td>0.0134278</td></tr>\n",
              "<tr><td>UnitPrice</td>\n",
              "<td>17.3414669</td>\n",
              "<td>0.0227335</td>\n",
              "<td>0.0127308</td></tr>\n",
              "<tr><td>time</td>\n",
              "<td>16.9946880</td>\n",
              "<td>0.0222789</td>\n",
              "<td>0.0124762</td></tr>\n",
              "<tr><td>InvoiceDate</td>\n",
              "<td>14.6884651</td>\n",
              "<td>0.0192556</td>\n",
              "<td>0.0107831</td></tr>\n",
              "<tr><td>Description</td>\n",
              "<td>13.6870241</td>\n",
              "<td>0.0179428</td>\n",
              "<td>0.0100480</td></tr>\n",
              "<tr><td>season</td>\n",
              "<td>5.1607485</td>\n",
              "<td>0.0067654</td>\n",
              "<td>0.0037886</td></tr>\n",
              "<tr><td>row_id</td>\n",
              "<td>3.5401573</td>\n",
              "<td>0.0046409</td>\n",
              "<td>0.0025989</td></tr>\n",
              "<tr><td>Country</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "rf_fit1 = h2o.estimators.random_forest.H2ORandomForestEstimator(model_id='rf_fit1',keep_cross_validation_predictions=True,nfolds = 5, auc_type =\"MACRO_OVR\")\n",
        "rf_fit1.train(x, y, train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "GkgACVId3Iu-",
        "outputId": "c07bb8a4-6275-4749-e110-f25e4d8bd278"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             variable  relative_importance  scaled_importance  percentage\n",
              "0      avg_unit_price           762.815918           1.000000    0.560000\n",
              "1        avg_quantity           272.175293           0.356803    0.199810\n",
              "2          CustomerID            75.393463           0.098836    0.055348\n",
              "3   last_invoice_date            70.568985           0.092511    0.051806\n",
              "4               month            27.842697           0.036500    0.020440\n",
              "5            Quantity            22.179523           0.029076    0.016282\n",
              "6                date            21.996326           0.028836    0.016148\n",
              "7                week            19.494736           0.025556    0.014312\n",
              "8          TotalPrice            18.290892           0.023978    0.013428\n",
              "9           UnitPrice            17.341467           0.022733    0.012731\n",
              "10               time            16.994688           0.022279    0.012476\n",
              "11        InvoiceDate            14.688465           0.019256    0.010783\n",
              "12        Description            13.687024           0.017943    0.010048\n",
              "13             season             5.160748           0.006765    0.003789\n",
              "14             row_id             3.540157           0.004641    0.002599\n",
              "15            Country             0.000000           0.000000    0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02a89134-0761-4d9f-badc-bb4d662e1d1d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variable</th>\n",
              "      <th>relative_importance</th>\n",
              "      <th>scaled_importance</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>avg_unit_price</td>\n",
              "      <td>762.815918</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>avg_quantity</td>\n",
              "      <td>272.175293</td>\n",
              "      <td>0.356803</td>\n",
              "      <td>0.199810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CustomerID</td>\n",
              "      <td>75.393463</td>\n",
              "      <td>0.098836</td>\n",
              "      <td>0.055348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>last_invoice_date</td>\n",
              "      <td>70.568985</td>\n",
              "      <td>0.092511</td>\n",
              "      <td>0.051806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>month</td>\n",
              "      <td>27.842697</td>\n",
              "      <td>0.036500</td>\n",
              "      <td>0.020440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Quantity</td>\n",
              "      <td>22.179523</td>\n",
              "      <td>0.029076</td>\n",
              "      <td>0.016282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>date</td>\n",
              "      <td>21.996326</td>\n",
              "      <td>0.028836</td>\n",
              "      <td>0.016148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>week</td>\n",
              "      <td>19.494736</td>\n",
              "      <td>0.025556</td>\n",
              "      <td>0.014312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>TotalPrice</td>\n",
              "      <td>18.290892</td>\n",
              "      <td>0.023978</td>\n",
              "      <td>0.013428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>UnitPrice</td>\n",
              "      <td>17.341467</td>\n",
              "      <td>0.022733</td>\n",
              "      <td>0.012731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>time</td>\n",
              "      <td>16.994688</td>\n",
              "      <td>0.022279</td>\n",
              "      <td>0.012476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>InvoiceDate</td>\n",
              "      <td>14.688465</td>\n",
              "      <td>0.019256</td>\n",
              "      <td>0.010783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Description</td>\n",
              "      <td>13.687024</td>\n",
              "      <td>0.017943</td>\n",
              "      <td>0.010048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>season</td>\n",
              "      <td>5.160748</td>\n",
              "      <td>0.006765</td>\n",
              "      <td>0.003789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>row_id</td>\n",
              "      <td>3.540157</td>\n",
              "      <td>0.004641</td>\n",
              "      <td>0.002599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Country</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rf_fit1\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"variable\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"avg_unit_price\",\n          \"avg_quantity\",\n          \"Quantity\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relative_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 192.2081849746743,\n        \"min\": 0.0,\n        \"max\": 762.81591796875,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          762.81591796875,\n          272.17529296875,\n          22.179523468017578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scaled_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2519719115019155,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          1.0,\n          0.35680337360225367,\n          0.029075852962111626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1411043635285836,\n        \"min\": 0.0,\n        \"max\": 0.5600003694360628,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.5600003694360628,\n          0.1998100210332956,\n          0.01628248840045115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "\n",
        "rf_fit1.varimp(use_pandas=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3em04as3Myy",
        "outputId": "e3aaa760-2e47-4f6d-9f37-856b30225a17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[predict      Group 1      Group 2\n",
              "---------  ---------  -----------\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            1  1.21593e-07\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            1  1.21593e-07\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "[325667 rows x 3 columns]\n",
              ",\n",
              " predict      Group 1      Group 2\n",
              "---------  ---------  -----------\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0.999999  7.29561e-07\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0         0\n",
              "Group 1     0.999999  7.29561e-07\n",
              "[325667 rows x 3 columns]\n",
              ",\n",
              " predict      Group 1    Group 2\n",
              "---------  ---------  ---------\n",
              "Group 1            1          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            1          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "[325667 rows x 3 columns]\n",
              ",\n",
              " predict      Group 1    Group 2\n",
              "---------  ---------  ---------\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            0          0\n",
              "Group 1            1          0\n",
              "Group 1            0          0\n",
              "Group 1            1          0\n",
              "Group 1            0          0\n",
              "[325667 rows x 3 columns]\n",
              ",\n",
              " predict      Group 1      Group 2\n",
              "---------  ---------  -----------\n",
              "Group 1            0  0\n",
              "Group 1            1  1.21593e-07\n",
              "Group 1            0  0\n",
              "Group 1            1  1.21593e-07\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "Group 1            0  0\n",
              "[325667 rows x 3 columns]\n",
              "]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "\n",
        "rf_fit1.cross_validation_predictions()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "lcbuC7Wt3QW7",
        "outputId": "514bf2b2-7e3c-4e7b-863d-f3e73b7aa877"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "predict      Group 1    Group 2\n",
              "---------  ---------  ---------\n",
              "Group 1     1           0\n",
              "Group 1     1           1.2e-07\n",
              "Group 1     1           1.2e-07\n",
              "Group 1     1           1.2e-07\n",
              "Group 1     0.999999    7.3e-07\n",
              "Group 1     1           0\n",
              "Group 1     1           0\n",
              "Group 1     1           1.2e-07\n",
              "Group 1     1           0\n",
              "Group 1     0.999999    7.3e-07\n",
              "[325667 rows x 3 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th>predict  </th><th style=\"text-align: right;\">  Group 1</th><th style=\"text-align: right;\">  Group 2</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  0      </td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  1.2e-07</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  1.2e-07</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  1.2e-07</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999999</td><td style=\"text-align: right;\">  7.3e-07</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  0      </td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  0      </td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  1.2e-07</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 1       </td><td style=\"text-align: right;\">  0      </td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999999</td><td style=\"text-align: right;\">  7.3e-07</td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[325667 rows x 3 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "\n",
        "rf_fit1.cross_validation_holdout_predictions()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "oLMOIwrk3Sm3",
        "outputId": "79f3e3a7-ffe1-424c-ff72-7c6e3b311e87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "predict      Group 1    Group 2\n",
              "---------  ---------  ---------\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "Group 1            1          0\n",
              "[81162 rows x 3 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th>predict  </th><th style=\"text-align: right;\">  Group 1</th><th style=\"text-align: right;\">  Group 2</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">        0</td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[81162 rows x 3 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "predictions = rf_fit1.predict(test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rf_fit1.model_performance(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L82PE6oxGQch",
        "outputId": "9e629e42-0e08-45d9-c046-7751566b4180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ModelMetricsBinomial: drf\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 1.1350139228892434e-06\n",
            "RMSE: 0.0010653703219487781\n",
            "LogLoss: 1.713049673131749e-05\n",
            "Mean Per-Class Error: 0.0\n",
            "AUC: 1.0\n",
            "AUCPR: 1.0\n",
            "Gini: 1.0\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.8\n",
            "         Group 1    Group 2    Error    Rate\n",
            "-------  ---------  ---------  -------  -------------\n",
            "Group 1  81157      0          0        (0.0/81157.0)\n",
            "Group 2  0          5          0        (0.0/5.0)\n",
            "Total    81157      5          0        (0.0/81162.0)\n",
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "metric                       threshold    value    idx\n",
            "---------------------------  -----------  -------  -----\n",
            "max f1                       0.8          1        3\n",
            "max f2                       0.8          1        3\n",
            "max f0point5                 0.8          1        3\n",
            "max accuracy                 0.8          1        3\n",
            "max precision                0.98         1        0\n",
            "max recall                   0.8          1        3\n",
            "max specificity              0.98         1        0\n",
            "max absolute_mcc             0.8          1        3\n",
            "max min_per_class_accuracy   0.8          1        3\n",
            "max mean_per_class_accuracy  0.8          1        3\n",
            "max tns                      0.98         81157    0\n",
            "max fns                      0.98         3        0\n",
            "max fps                      0            81157    11\n",
            "max tps                      0.8          5        3\n",
            "max tnr                      0.98         1        0\n",
            "max fnr                      0.98         0.6      0\n",
            "max fpr                      0            1        11\n",
            "max tpr                      0.8          1        3\n",
            "\n",
            "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
            "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
            "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
            "1        1                           0                  1       1                  6.16052e-05      6.97371e-05  6.16052e-05                 6.97371e-05         1               1                          0       0                  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J-5TCPa63UZz"
      },
      "outputs": [],
      "source": [
        "from h2o.grid.grid_search import H2OGridSearch\n",
        "from h2o.estimators import H2OGradientBoostingEstimator\n",
        "\n",
        "gbm_params2 = {'learn_rate': [i * 0.01 for i in range(1, 11)],\n",
        "                'max_depth': list(range(2, 11)),\n",
        "                'sample_rate': [i * 0.1 for i in range(5, 11)],\n",
        "                'col_sample_rate': [i * 0.1 for i in range(1, 11)]}\n",
        "\n",
        "# Search criteria\n",
        "search_criteria = {'strategy': 'RandomDiscrete', 'max_models': 36, 'seed': 1}\n",
        "\n",
        "# Train and validate a random grid of GBMs\n",
        "gbm_grid2 = H2OGridSearch(model=H2OGradientBoostingEstimator(auc_type =\"MACRO_OVR\"),\n",
        "                          grid_id='gbm_grid2',\n",
        "                          hyper_params=gbm_params2,\n",
        "                          search_criteria=search_criteria)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "CVSIPWbv3hDj",
        "outputId": "b01ce2a4-2f4a-4134-f8eb-8c74499a2ad5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gbm Grid Build progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hyper-Parameter Search Summary: ordered by increasing logloss\n",
              "     col_sample_rate     learn_rate    max_depth    sample_rate         model_ids           logloss\n",
              "---  ------------------  ------------  -----------  ------------------  ------------------  ----------------------\n",
              "     0.6000000000000001  0.1           4.0          1.0                 gbm_grid2_model_22  5.1573007947370495e-09\n",
              "     0.5                 0.1           9.0          1.0                 gbm_grid2_model_7   5.1573007947370495e-09\n",
              "     0.9                 0.1           4.0          0.7000000000000001  gbm_grid2_model_15  5.1573024604319344e-09\n",
              "     0.5                 0.1           4.0          0.8                 gbm_grid2_model_33  5.157321852985381e-09\n",
              "     0.6000000000000001  0.09          3.0          0.9                 gbm_grid2_model_18  1.4019214948062868e-08\n",
              "     0.9                 0.09          8.0          0.9                 gbm_grid2_model_36  1.4019219002066339e-08\n",
              "     0.6000000000000001  0.09          2.0          0.8                 gbm_grid2_model_19  1.401923765825924e-08\n",
              "     0.6000000000000001  0.09          3.0          0.8                 gbm_grid2_model_1   1.401925639743891e-08\n",
              "     0.9                 0.09          9.0          0.6000000000000001  gbm_grid2_model_3   1.4019366815533238e-08\n",
              "     0.4                 0.08          6.0          0.9                 gbm_grid2_model_28  3.611130900613647e-08\n",
              "---  ---                 ---           ---          ---                 ---                 ---\n",
              "     0.6000000000000001  0.01          9.0          0.8                 gbm_grid2_model_11  4.17921647448921e-05\n",
              "     0.2                 0.02          10.0         0.5                 gbm_grid2_model_25  7.658143180997399e-05\n",
              "     0.9                 0.03          10.0         0.5                 gbm_grid2_model_21  0.00011170273823413487\n",
              "     0.1                 0.07          7.0          0.8                 gbm_grid2_model_9   0.0005302774981025202\n",
              "     0.8                 0.03          5.0          0.5                 gbm_grid2_model_12  0.0005358952192141599\n",
              "     0.1                 0.06          8.0          0.9                 gbm_grid2_model_34  0.0007423884973435282\n",
              "     0.1                 0.08          3.0          0.8                 gbm_grid2_model_13  0.0014847769946870564\n",
              "     1.0                 0.01          2.0          0.5                 gbm_grid2_model_23  0.0014868641832315752\n",
              "     0.1                 0.02          9.0          0.8                 gbm_grid2_model_16  0.0015490206184638456\n",
              "     0.2                 0.04          5.0          0.9                 gbm_grid2_model_29  0.02626081696486671\n",
              "[36 rows x 7 columns]\n"
            ],
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              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-18\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Hyper-Parameter Search Summary: ordered by increasing logloss</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>col_sample_rate</th>\n",
              "<th>learn_rate</th>\n",
              "<th>max_depth</th>\n",
              "<th>sample_rate</th>\n",
              "<th>model_ids</th>\n",
              "<th>logloss</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_22</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.1</td>\n",
              "<td>9.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_7</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>0.7000000</td>\n",
              "<td>gbm_grid2_model_15</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_33</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_18</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.09</td>\n",
              "<td>8.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_36</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>2.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_19</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_1</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.09</td>\n",
              "<td>9.0</td>\n",
              "<td>0.6000000</td>\n",
              "<td>gbm_grid2_model_3</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.4</td>\n",
              "<td>0.08</td>\n",
              "<td>6.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_28</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.01</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_11</td>\n",
              "<td>0.0000418</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.2</td>\n",
              "<td>0.02</td>\n",
              "<td>10.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_25</td>\n",
              "<td>0.0000766</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.03</td>\n",
              "<td>10.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_21</td>\n",
              "<td>0.0001117</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.07</td>\n",
              "<td>7.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_9</td>\n",
              "<td>0.0005303</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.8</td>\n",
              "<td>0.03</td>\n",
              "<td>5.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_12</td>\n",
              "<td>0.0005359</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.06</td>\n",
              "<td>8.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_34</td>\n",
              "<td>0.0007424</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.08</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_13</td>\n",
              "<td>0.0014848</td></tr>\n",
              "<tr><td></td>\n",
              "<td>1.0</td>\n",
              "<td>0.01</td>\n",
              "<td>2.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_23</td>\n",
              "<td>0.0014869</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.02</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_16</td>\n",
              "<td>0.0015490</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.2</td>\n",
              "<td>0.04</td>\n",
              "<td>5.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_29</td>\n",
              "<td>0.0262608</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "<pre style='font-size: smaller; margin-bottom: 1em;'>[36 rows x 7 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "gbm_grid2.train(x=x, y=y,\n",
        "                training_frame=train,\n",
        "                ntrees=100,\n",
        "                seed=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "apaL4QRb3ivg",
        "outputId": "97234011-9c8c-4085-dbcc-35f6bb6599ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hyper-Parameter Search Summary: ordered by decreasing auc\n",
              "     col_sample_rate      learn_rate    max_depth    sample_rate         model_ids           auc\n",
              "---  -------------------  ------------  -----------  ------------------  ------------------  ------------------\n",
              "     0.6000000000000001   0.09          3.0          0.8                 gbm_grid2_model_1   1.0\n",
              "     1.0                  0.01          2.0          0.7000000000000001  gbm_grid2_model_10  1.0\n",
              "     0.6000000000000001   0.01          9.0          0.8                 gbm_grid2_model_11  1.0\n",
              "     0.7000000000000001   0.06          5.0          0.6000000000000001  gbm_grid2_model_14  1.0\n",
              "     0.9                  0.1           4.0          0.7000000000000001  gbm_grid2_model_15  1.0\n",
              "     0.9                  0.07          4.0          0.9                 gbm_grid2_model_17  1.0\n",
              "     0.6000000000000001   0.09          3.0          0.9                 gbm_grid2_model_18  1.0\n",
              "     0.6000000000000001   0.09          2.0          0.8                 gbm_grid2_model_19  1.0\n",
              "     0.5                  0.05          7.0          0.9                 gbm_grid2_model_2   1.0\n",
              "     0.30000000000000004  0.02          2.0          1.0                 gbm_grid2_model_20  1.0\n",
              "---  ---                  ---           ---          ---                 ---                 ---\n",
              "     0.5                  0.1           9.0          1.0                 gbm_grid2_model_7   1.0\n",
              "     0.8                  0.03          9.0          0.9                 gbm_grid2_model_8   1.0\n",
              "     0.9                  0.03          10.0         0.5                 gbm_grid2_model_21  0.9999984645149402\n",
              "     1.0                  0.01          2.0          0.5                 gbm_grid2_model_23  0.9999723612689249\n",
              "     0.1                  0.06          8.0          0.9                 gbm_grid2_model_34  0.9864772735761281\n",
              "     0.1                  0.08          3.0          0.8                 gbm_grid2_model_13  0.9864665251807101\n",
              "     0.1                  0.07          7.0          0.8                 gbm_grid2_model_9   0.9729683665177937\n",
              "     0.2                  0.04          5.0          0.9                 gbm_grid2_model_29  0.9295906645828336\n",
              "     0.8                  0.03          5.0          0.5                 gbm_grid2_model_12  0.9189951536771547\n",
              "     0.1                  0.02          9.0          0.8                 gbm_grid2_model_16  0.9059582630261007\n",
              "[36 rows x 7 columns]\n"
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              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-19 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-19 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-19 .h2o-table th,\n",
              "#h2o-table-19 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-19 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-19\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Hyper-Parameter Search Summary: ordered by decreasing auc</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>col_sample_rate</th>\n",
              "<th>learn_rate</th>\n",
              "<th>max_depth</th>\n",
              "<th>sample_rate</th>\n",
              "<th>model_ids</th>\n",
              "<th>auc</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_1</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>1.0</td>\n",
              "<td>0.01</td>\n",
              "<td>2.0</td>\n",
              "<td>0.7000000</td>\n",
              "<td>gbm_grid2_model_10</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.01</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_11</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.7000000</td>\n",
              "<td>0.06</td>\n",
              "<td>5.0</td>\n",
              "<td>0.6000000</td>\n",
              "<td>gbm_grid2_model_14</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>0.7000000</td>\n",
              "<td>gbm_grid2_model_15</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.07</td>\n",
              "<td>4.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_17</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_18</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>2.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_19</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.05</td>\n",
              "<td>7.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_2</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.3000000</td>\n",
              "<td>0.02</td>\n",
              "<td>2.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_20</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.1</td>\n",
              "<td>9.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_7</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.8</td>\n",
              "<td>0.03</td>\n",
              "<td>9.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_8</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.03</td>\n",
              "<td>10.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_21</td>\n",
              "<td>0.9999985</td></tr>\n",
              "<tr><td></td>\n",
              "<td>1.0</td>\n",
              "<td>0.01</td>\n",
              "<td>2.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_23</td>\n",
              "<td>0.9999724</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.06</td>\n",
              "<td>8.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_34</td>\n",
              "<td>0.9864773</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.08</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_13</td>\n",
              "<td>0.9864665</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.07</td>\n",
              "<td>7.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_9</td>\n",
              "<td>0.9729684</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.2</td>\n",
              "<td>0.04</td>\n",
              "<td>5.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_29</td>\n",
              "<td>0.9295907</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.8</td>\n",
              "<td>0.03</td>\n",
              "<td>5.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_12</td>\n",
              "<td>0.9189952</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.02</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_16</td>\n",
              "<td>0.9059583</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "<pre style='font-size: smaller; margin-bottom: 1em;'>[36 rows x 7 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "gbm_gridperf2 = gbm_grid2.get_grid(sort_by='auc', decreasing=True)\n",
        "gbm_gridperf2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbm_grid2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "Orrw81730WhF",
        "outputId": "def007d5-7cfc-4013-f7da-e18584483763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hyper-Parameter Search Summary: ordered by increasing logloss\n",
              "     col_sample_rate     learn_rate    max_depth    sample_rate         model_ids           logloss\n",
              "---  ------------------  ------------  -----------  ------------------  ------------------  ----------------------\n",
              "     0.6000000000000001  0.1           4.0          1.0                 gbm_grid2_model_22  5.1573007947370495e-09\n",
              "     0.5                 0.1           9.0          1.0                 gbm_grid2_model_7   5.1573007947370495e-09\n",
              "     0.9                 0.1           4.0          0.7000000000000001  gbm_grid2_model_15  5.1573024604319344e-09\n",
              "     0.5                 0.1           4.0          0.8                 gbm_grid2_model_33  5.157321852985381e-09\n",
              "     0.6000000000000001  0.09          3.0          0.9                 gbm_grid2_model_18  1.4019214948062868e-08\n",
              "     0.9                 0.09          8.0          0.9                 gbm_grid2_model_36  1.4019219002066339e-08\n",
              "     0.6000000000000001  0.09          2.0          0.8                 gbm_grid2_model_19  1.401923765825924e-08\n",
              "     0.6000000000000001  0.09          3.0          0.8                 gbm_grid2_model_1   1.401925639743891e-08\n",
              "     0.9                 0.09          9.0          0.6000000000000001  gbm_grid2_model_3   1.4019366815533238e-08\n",
              "     0.4                 0.08          6.0          0.9                 gbm_grid2_model_28  3.611130900613647e-08\n",
              "---  ---                 ---           ---          ---                 ---                 ---\n",
              "     0.6000000000000001  0.01          9.0          0.8                 gbm_grid2_model_11  4.17921647448921e-05\n",
              "     0.2                 0.02          10.0         0.5                 gbm_grid2_model_25  7.658143180997399e-05\n",
              "     0.9                 0.03          10.0         0.5                 gbm_grid2_model_21  0.00011170273823413487\n",
              "     0.1                 0.07          7.0          0.8                 gbm_grid2_model_9   0.0005302774981025202\n",
              "     0.8                 0.03          5.0          0.5                 gbm_grid2_model_12  0.0005358952192141599\n",
              "     0.1                 0.06          8.0          0.9                 gbm_grid2_model_34  0.0007423884973435282\n",
              "     0.1                 0.08          3.0          0.8                 gbm_grid2_model_13  0.0014847769946870564\n",
              "     1.0                 0.01          2.0          0.5                 gbm_grid2_model_23  0.0014868641832315752\n",
              "     0.1                 0.02          9.0          0.8                 gbm_grid2_model_16  0.0015490206184638456\n",
              "     0.2                 0.04          5.0          0.9                 gbm_grid2_model_29  0.02626081696486671\n",
              "[36 rows x 7 columns]\n"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "\n",
              "#h2o-table-30.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-30 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
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              "#h2o-table-30 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
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              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-30 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
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              "#h2o-table-30 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-30 .h2o-table th,\n",
              "#h2o-table-30 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-30 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-30\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Hyper-Parameter Search Summary: ordered by increasing logloss</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>col_sample_rate</th>\n",
              "<th>learn_rate</th>\n",
              "<th>max_depth</th>\n",
              "<th>sample_rate</th>\n",
              "<th>model_ids</th>\n",
              "<th>logloss</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_22</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.1</td>\n",
              "<td>9.0</td>\n",
              "<td>1.0</td>\n",
              "<td>gbm_grid2_model_7</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>0.7000000</td>\n",
              "<td>gbm_grid2_model_15</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.5</td>\n",
              "<td>0.1</td>\n",
              "<td>4.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_33</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_18</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.09</td>\n",
              "<td>8.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_36</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>2.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_19</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.09</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_1</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.09</td>\n",
              "<td>9.0</td>\n",
              "<td>0.6000000</td>\n",
              "<td>gbm_grid2_model_3</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.4</td>\n",
              "<td>0.08</td>\n",
              "<td>6.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_28</td>\n",
              "<td>0.0000000</td></tr>\n",
              "<tr><td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td>\n",
              "<td>---</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.6000000</td>\n",
              "<td>0.01</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_11</td>\n",
              "<td>0.0000418</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.2</td>\n",
              "<td>0.02</td>\n",
              "<td>10.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_25</td>\n",
              "<td>0.0000766</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.9</td>\n",
              "<td>0.03</td>\n",
              "<td>10.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_21</td>\n",
              "<td>0.0001117</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.07</td>\n",
              "<td>7.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_9</td>\n",
              "<td>0.0005303</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.8</td>\n",
              "<td>0.03</td>\n",
              "<td>5.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_12</td>\n",
              "<td>0.0005359</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.06</td>\n",
              "<td>8.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_34</td>\n",
              "<td>0.0007424</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.08</td>\n",
              "<td>3.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_13</td>\n",
              "<td>0.0014848</td></tr>\n",
              "<tr><td></td>\n",
              "<td>1.0</td>\n",
              "<td>0.01</td>\n",
              "<td>2.0</td>\n",
              "<td>0.5</td>\n",
              "<td>gbm_grid2_model_23</td>\n",
              "<td>0.0014869</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.1</td>\n",
              "<td>0.02</td>\n",
              "<td>9.0</td>\n",
              "<td>0.8</td>\n",
              "<td>gbm_grid2_model_16</td>\n",
              "<td>0.0015490</td></tr>\n",
              "<tr><td></td>\n",
              "<td>0.2</td>\n",
              "<td>0.04</td>\n",
              "<td>5.0</td>\n",
              "<td>0.9</td>\n",
              "<td>gbm_grid2_model_29</td>\n",
              "<td>0.0262608</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "<pre style='font-size: smaller; margin-bottom: 1em;'>[36 rows x 7 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(gbm_grid2.model_performance(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saBS4EfvGS6S",
        "outputId": "959f20b9-de31-412d-8adb-a0fa02c2a909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'gbm_grid2_model_22': H2OBinomialModelMetrics({'_metric_json': {'__meta': {'schema_version': 3, 'schema_name': 'ModelMetricsBinomialV3', 'schema_type': 'ModelMetricsBinomial'}, 'model': {'__meta': {'schema_version': 3, 'schema_name': 'ModelKeyV3', 'schema_type': 'Key<Model>'}, 'name': 'gbm_grid2_model_22', 'type': 'Key<Model>', 'URL': '/3/Models/gbm_grid2_model_22'}, 'model_checksum': 6077863589086177841, 'frame': {'__meta': {'schema_version': 3, 'schema_name': 'FrameKeyV3', 'schema_type': 'Key<Frame>'}, 'name': 'py_4_sid_8f14', 'type': 'Key<Frame>', 'URL': '/3/Frames/py_4_sid_8f14'}, 'frame_checksum': -7387182095329455119, 'description': None, 'model_category': 'Binomial', 'scoring_time': 1715354560378, 'predictions': None, 'MSE': 2.660323938317903e-17, 'RMSE': 5.157832818459613e-09, 'nobs': 81162, 'custom_metric_name': None, 'custom_metric_value': 0.0, 'r2': 0.9999999999995681, 'logloss': 5.1576739546916006e-09, 'AUC': 1.0, 'pr_auc': 1.0, 'Gini': 1.0, 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-385489325581965124, 'frame': {'__meta': {'schema_version': 3, 'schema_name': 'FrameKeyV3', 'schema_type': 'Key<Frame>'}, 'name': 'py_4_sid_8f14', 'type': 'Key<Frame>', 'URL': '/3/Frames/py_4_sid_8f14'}, 'frame_checksum': -7387182095329455119, 'description': None, 'model_category': 'Binomial', 'scoring_time': 1715354614180, 'predictions': None, 'MSE': 0.00013650406161221099, 'RMSE': 0.011683495265211135, 'nobs': 81162, 'custom_metric_name': None, 'custom_metric_value': 0.0, 'r2': -1.2159250421855416, 'logloss': 0.0043245570528980355, 'AUC': 0.6515938243158323, 'pr_auc': 0.16365888327548797, 'Gini': 0.3031876486316647, 'mean_per_class_error': 0.20004928718409998, 'domain': ['Group 1', 'Group 2'], 'cm': {'__meta': {'schema_version': 3, 'schema_name': 'ConfusionMatrixV3', 'schema_type': 'ConfusionMatrix'}, 'table': H2OTwoDimTable({'_table_header': 'Confusion Matrix', '_col_header': ['Group 1', 'Group 2', 'Error', 'Rate']})}, 'thresholds_and_metric_scores': H2OTwoDimTable({'_table_header': 'Metrics for Thresholds', '_col_header': ['threshold', 'f1', 'f2', 'f0point5', 'accuracy', 'precision', 'recall', 'specificity', 'absolute_mcc', 'min_per_class_accuracy', 'mean_per_class_accuracy', 'tns', 'fns', 'fps', 'tps', 'tnr', 'fnr', 'fpr', 'tpr', 'idx']}), 'max_criteria_and_metric_scores': H2OTwoDimTable({'_table_header': 'Maximum Metrics', '_col_header': ['metric', 'threshold', 'value', 'idx']}), 'gains_lift_table': H2OTwoDimTable({'_table_header': 'Gains/Lift Table', '_col_header': ['group', 'cumulative_data_fraction', 'lower_threshold', 'lift', 'cumulative_lift', 'response_rate', 'score', 'cumulative_response_rate', 'cumulative_score', 'capture_rate', 'cumulative_capture_rate', 'gain', 'cumulative_gain', 'kolmogorov_smirnov']})}, '_on': 'test', '_algo': 'gbm', '_bci': {'giniCoef': <bound method <lambda> of H2OBinomialModelMetrics({...})>}}), 'gbm_grid2_model_29': H2OBinomialModelMetrics({'_metric_json': {'__meta': {'schema_version': 3, 'schema_name': 'ModelMetricsBinomialV3', 'schema_type': 'ModelMetricsBinomial'}, 'model': {'__meta': {'schema_version': 3, 'schema_name': 'ModelKeyV3', 'schema_type': 'Key<Model>'}, 'name': 'gbm_grid2_model_29', 'type': 'Key<Model>', 'URL': '/3/Models/gbm_grid2_model_29'}, 'model_checksum': -7492319108284940682, 'frame': {'__meta': {'schema_version': 3, 'schema_name': 'FrameKeyV3', 'schema_type': 'Key<Frame>'}, 'name': 'py_4_sid_8f14', 'type': 'Key<Frame>', 'URL': '/3/Frames/py_4_sid_8f14'}, 'frame_checksum': -7387182095329455119, 'description': None, 'model_category': 'Binomial', 'scoring_time': 1715354616301, 'predictions': None, 'MSE': 0.0009070409051185637, 'RMSE': 0.030117119801178923, 'nobs': 81162, 'custom_metric_name': None, 'custom_metric_value': 0.0, 'r2': -13.724357885033555, 'logloss': 0.02518630695649434, 'AUC': 0.6541789371218748, 'pr_auc': 0.03053117794445543, 'Gini': 0.3083578742437496, 'mean_per_class_error': 0.2003450102886997, 'domain': ['Group 1', 'Group 2'], 'cm': {'__meta': {'schema_version': 3, 'schema_name': 'ConfusionMatrixV3', 'schema_type': 'ConfusionMatrix'}, 'table': H2OTwoDimTable({'_table_header': 'Confusion Matrix', '_col_header': ['Group 1', 'Group 2', 'Error', 'Rate']})}, 'thresholds_and_metric_scores': H2OTwoDimTable({'_table_header': 'Metrics for Thresholds', '_col_header': ['threshold', 'f1', 'f2', 'f0point5', 'accuracy', 'precision', 'recall', 'specificity', 'absolute_mcc', 'min_per_class_accuracy', 'mean_per_class_accuracy', 'tns', 'fns', 'fps', 'tps', 'tnr', 'fnr', 'fpr', 'tpr', 'idx']}), 'max_criteria_and_metric_scores': H2OTwoDimTable({'_table_header': 'Maximum Metrics', '_col_header': ['metric', 'threshold', 'value', 'idx']}), 'gains_lift_table': H2OTwoDimTable({'_table_header': 'Gains/Lift Table', '_col_header': ['group', 'cumulative_data_fraction', 'lower_threshold', 'lift', 'cumulative_lift', 'response_rate', 'score', 'cumulative_response_rate', 'cumulative_score', 'capture_rate', 'cumulative_capture_rate', 'gain', 'cumulative_gain', 'kolmogorov_smirnov']})}, '_on': 'test', '_algo': 'gbm', '_bci': {'giniCoef': <bound method <lambda> of H2OBinomialModelMetrics({...})>}})}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-f0-uSFv3mkQ"
      },
      "outputs": [],
      "source": [
        "from h2o.automl import H2OAutoML\n",
        "from h2o.frame import H2OFrame\n",
        "\n",
        "\n",
        "aml = H2OAutoML(max_models=5, seed=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aml.train(x, y, train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RlWtXnFfsyGM",
        "outputId": "41cd0372-a618-43e8-b3b6-edb2bf9e38f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AutoML progress: |██████\n",
            "13:45:33.810: GLM_1_AutoML_1_20240510_134044 [GLM def_1] failed: java.lang.ArrayIndexOutOfBoundsException: Index 3861 out of bounds for length 3861\n",
            "\n",
            "█████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model Details\n",
              "=============\n",
              "H2ORandomForestEstimator : Distributed Random Forest\n",
              "Model Key: DRF_1_AutoML_1_20240510_134044\n",
              "\n",
              "\n",
              "Model Summary: \n",
              "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
              "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
              "    22                 22                          4505                   2            13           5.90909       3             33            11.6818\n",
              "\n",
              "ModelMetricsBinomial: drf\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 5.077478969531889e-06\n",
              "RMSE: 0.002253326201314823\n",
              "LogLoss: 2.824413262030226e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.5714285714285714\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  --------------\n",
              "Group 1  325618     0          0        (0.0/325618.0)\n",
              "Group 2  0          37         0        (0.0/37.0)\n",
              "Total    325618     37         0        (0.0/325655.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.571429     1         13\n",
              "max f2                       0.571429     1         13\n",
              "max f0point5                 0.571429     1         13\n",
              "max accuracy                 0.571429     1         13\n",
              "max precision                1            1         0\n",
              "max recall                   0.571429     1         13\n",
              "max specificity              1            1         0\n",
              "max absolute_mcc             0.571429     1         13\n",
              "max min_per_class_accuracy   0.571429     1         13\n",
              "max mean_per_class_accuracy  0.571429     1         13\n",
              "max tns                      1            325618    0\n",
              "max fns                      1            16        0\n",
              "max fps                      0            325618    58\n",
              "max tps                      0.571429     37        13\n",
              "max tnr                      1            1         0\n",
              "max fnr                      1            0.432432  0\n",
              "max fpr                      0            1         58\n",
              "max tpr                      0.571429     1         13\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
              "1        1                           0                  1       1                  0.000113613      0.000117787  0.000113613                 0.000117787         1               1                          0       0                  0\n",
              "\n",
              "ModelMetricsBinomial: drf\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 2.6342450573073925e-06\n",
              "RMSE: 0.0016230357535517795\n",
              "LogLoss: 2.088508310345583e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.72\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  --------------\n",
              "Group 1  325630     0          0        (0.0/325630.0)\n",
              "Group 2  0          37         0        (0.0/37.0)\n",
              "Total    325630     37         0        (0.0/325667.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value     idx\n",
              "---------------------------  -----------  --------  -----\n",
              "max f1                       0.72         1         12\n",
              "max f2                       0.72         1         12\n",
              "max f0point5                 0.72         1         12\n",
              "max accuracy                 0.72         1         12\n",
              "max precision                1            1         0\n",
              "max recall                   0.72         1         12\n",
              "max specificity              1            1         0\n",
              "max absolute_mcc             0.72         1         12\n",
              "max min_per_class_accuracy   0.72         1         12\n",
              "max mean_per_class_accuracy  0.72         1         12\n",
              "max tns                      1            325630    0\n",
              "max fns                      1            25        0\n",
              "max fps                      0            325630    40\n",
              "max tps                      0.72         37        12\n",
              "max tnr                      1            1         0\n",
              "max fnr                      1            0.675676  0\n",
              "max fpr                      0            1         40\n",
              "max tpr                      0.72         1         12\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift     cumulative_lift    response_rate    score       cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain     cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  -------  -----------------  ---------------  ----------  --------------------------  ------------------  --------------  -------------------------  -------  -----------------  --------------------\n",
              "1        0.109194                    1.4e-06            9.15798  9.15798            0.00104047       0.00104639  0.00104047                  0.00104639          1               1                          815.798  815.798            0.890907\n",
              "2        0.232274                    4.9e-07            0        4.30526            0                4.9e-07     0.000489133                 0.000492177         0               1                          -100     330.526            0.767813\n",
              "3        1                           0                  0        1                  0                0           0.000113613                 0.00011432          0               1                          -100     0                  0\n",
              "\n",
              "Cross-Validation Metrics Summary: \n",
              "                         mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
              "-----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
              "accuracy                 1            0            1             1             1             1             1\n",
              "auc                      1            0            1             1             1             1             1\n",
              "err                      0            0            0             0             0             0             0\n",
              "err_count                0            0            0             0             0             0             0\n",
              "f0point5                 1            0            1             1             1             1             1\n",
              "f1                       1            0            1             1             1             1             1\n",
              "f2                       1            0            1             1             1             1             1\n",
              "lift_top_group           44.5705      39.514       1             72.2106       72.37         1.62366       75.6481\n",
              "logloss                  2.43275e-05  7.34313e-06  2.17071e-05   1.31654e-05   2.82911e-05   3.24237e-05   2.60503e-05\n",
              "max_per_class_error      0            0            0             0             0             0             0\n",
              "mcc                      1            0            1             1             1             1             1\n",
              "mean_per_class_accuracy  1            0            1             1             1             1             1\n",
              "mean_per_class_error     0            0            0             0             0             0             0\n",
              "mse                      3.08832e-06  1.33495e-06  2.06617e-06   1.60157e-06   4.02652e-06   4.81158e-06   2.93574e-06\n",
              "pr_auc                   1            0            1             1             1             1             1\n",
              "precision                1            0            1             1             1             1             1\n",
              "r2                       0.972575     0.0116185    0.977568      0.985096      0.956286      0.965174      0.978751\n",
              "recall                   1            0            1             1             1             1             1\n",
              "rmse                     0.0017233    0.000384956  0.00143742    0.00126553    0.00200662    0.00219353    0.0017134\n",
              "specificity              1            0            1             1             1             1             1\n",
              "\n",
              "Scoring History: \n",
              "    timestamp            duration          number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error\n",
              "--  -------------------  ----------------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------\n",
              "    2024-05-10 13:53:03  58.743 sec        0                  nan              nan                 nan             nan                nan              nan\n",
              "    2024-05-10 13:53:04  59.779 sec        5                  0.00500634       0.000614539         0.970581        0.851593           1                2.0487e-05\n",
              "    2024-05-10 13:53:04  1 min  0.644 sec  10                 0.00285704       0.00012759          0.999998        0.968342           1                3.10302e-06\n",
              "    2024-05-10 13:53:05  1 min  1.608 sec  15                 0.00215156       2.17002e-05         1               0.999279           1                3.07337e-06\n",
              "    2024-05-10 13:53:06  1 min  2.604 sec  20                 0.00213619       2.58568e-05         1               1                  1                0\n",
              "    2024-05-10 13:53:07  1 min  3.172 sec  22                 0.00225333       2.82441e-05         1               1                  1                0\n",
              "\n",
              "Variable Importances: \n",
              "variable           relative_importance    scaled_importance    percentage\n",
              "-----------------  ---------------------  -------------------  ------------\n",
              "avg_unit_price     327.75                 1                    0.558806\n",
              "avg_quantity       124.265                0.379147             0.21187\n",
              "last_invoice_date  35.2889                0.10767              0.0601667\n",
              "CustomerID         30.1876                0.0921057            0.0514693\n",
              "date               14.3766                0.0438645            0.0245118\n",
              "month              12.3384                0.0376458            0.0210367\n",
              "InvoiceDate        7.88564                0.0240599            0.0134448\n",
              "time               7.58615                0.0231462            0.0129342\n",
              "Description        6.97807                0.0212908            0.0118975\n",
              "UnitPrice          5.24389                0.0159997            0.00894072\n",
              "TotalPrice         4.58419                0.0139869            0.00781595\n",
              "Quantity           4.14937                0.0126602            0.00707458\n",
              "season             3.0721                 0.0093733            0.00523786\n",
              "week               2.73285                0.00833821           0.00465944\n",
              "Country            0.0444444              0.000135605          7.57768e-05\n",
              "row_id             0.034546               0.000105403          5.89001e-05\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
            ],
            "text/html": [
              "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
              "=============\n",
              "H2ORandomForestEstimator : Distributed Random Forest\n",
              "Model Key: DRF_1_AutoML_1_20240510_134044\n",
              "</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-20.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-20 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-20 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-20 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-20 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-20 .h2o-table th,\n",
              "#h2o-table-20 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-20 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-20\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Model Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>number_of_trees</th>\n",
              "<th>number_of_internal_trees</th>\n",
              "<th>model_size_in_bytes</th>\n",
              "<th>min_depth</th>\n",
              "<th>max_depth</th>\n",
              "<th>mean_depth</th>\n",
              "<th>min_leaves</th>\n",
              "<th>max_leaves</th>\n",
              "<th>mean_leaves</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>22.0</td>\n",
              "<td>22.0</td>\n",
              "<td>4505.0</td>\n",
              "<td>2.0</td>\n",
              "<td>13.0</td>\n",
              "<td>5.909091</td>\n",
              "<td>3.0</td>\n",
              "<td>33.0</td>\n",
              "<td>11.681818</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 5.077478969531889e-06\n",
              "RMSE: 0.002253326201314823\n",
              "LogLoss: 2.824413262030226e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-21.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-21 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-21 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-21 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-21 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-21 .h2o-table th,\n",
              "#h2o-table-21 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-21 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-21\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.5714285714285714</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>325618.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325618.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/37.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>325618.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325655.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-22.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-22 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-22 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-22 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-22 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-22 .h2o-table th,\n",
              "#h2o-table-22 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-22 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-22\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>1.0</td>\n",
              "<td>325618.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>1.0</td>\n",
              "<td>16.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0</td>\n",
              "<td>325618.0</td>\n",
              "<td>58.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>0.5714286</td>\n",
              "<td>37.0</td>\n",
              "<td>13.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>1.0</td>\n",
              "<td>0.4324324</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>58.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>0.5714286</td>\n",
              "<td>1.0</td>\n",
              "<td>13.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-23.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-23 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-23 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-23 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-23 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-23 .h2o-table th,\n",
              "#h2o-table-23 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-23 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-23\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001178</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001178</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
              "** Reported on cross-validation data. **\n",
              "\n",
              "MSE: 2.6342450573073925e-06\n",
              "RMSE: 0.0016230357535517795\n",
              "LogLoss: 2.088508310345583e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-24.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-24 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-24 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-24 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-24 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-24 .h2o-table th,\n",
              "#h2o-table-24 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-24 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-24\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.72</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325630.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/37.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>325630.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325667.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-25.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-25 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-25 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-25 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-25 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-25 .h2o-table th,\n",
              "#h2o-table-25 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-25 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-25\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>1.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>1.0</td>\n",
              "<td>25.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>40.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>0.72</td>\n",
              "<td>37.0</td>\n",
              "<td>12.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>1.0</td>\n",
              "<td>0.6756757</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>40.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>0.72</td>\n",
              "<td>1.0</td>\n",
              "<td>12.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-26.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-26 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-26 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-26 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-26 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-26 .h2o-table th,\n",
              "#h2o-table-26 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-26 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-26\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>0.1091944</td>\n",
              "<td>1.4e-06</td>\n",
              "<td>9.1579821</td>\n",
              "<td>9.1579821</td>\n",
              "<td>0.0010405</td>\n",
              "<td>0.0010464</td>\n",
              "<td>0.0010405</td>\n",
              "<td>0.0010464</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>815.7982059</td>\n",
              "<td>815.7982059</td>\n",
              "<td>0.8909069</td></tr>\n",
              "<tr><td>2</td>\n",
              "<td>0.2322741</td>\n",
              "<td>4.9e-07</td>\n",
              "<td>0.0</td>\n",
              "<td>4.3052588</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0000005</td>\n",
              "<td>0.0004891</td>\n",
              "<td>0.0004922</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>330.5258844</td>\n",
              "<td>0.7678132</td></tr>\n",
              "<tr><td>3</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001143</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>-100.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-27.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-27 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-27 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-27 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-27 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-27 .h2o-table th,\n",
              "#h2o-table-27 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-27 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-27\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Cross-Validation Metrics Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>mean</th>\n",
              "<th>sd</th>\n",
              "<th>cv_1_valid</th>\n",
              "<th>cv_2_valid</th>\n",
              "<th>cv_3_valid</th>\n",
              "<th>cv_4_valid</th>\n",
              "<th>cv_5_valid</th></tr></thead>\n",
              "    <tbody><tr><td>accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>auc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>err</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>err_count</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>f0point5</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>f1</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>f2</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>lift_top_group</td>\n",
              "<td>44.570477</td>\n",
              "<td>39.514004</td>\n",
              "<td>1.0</td>\n",
              "<td>72.21064</td>\n",
              "<td>72.37</td>\n",
              "<td>1.623657</td>\n",
              "<td>75.64809</td></tr>\n",
              "<tr><td>logloss</td>\n",
              "<td>0.0000243</td>\n",
              "<td>0.0000073</td>\n",
              "<td>0.0000217</td>\n",
              "<td>0.0000132</td>\n",
              "<td>0.0000283</td>\n",
              "<td>0.0000324</td>\n",
              "<td>0.0000261</td></tr>\n",
              "<tr><td>max_per_class_error</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>mcc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>mean_per_class_accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>mean_per_class_error</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>mse</td>\n",
              "<td>0.0000031</td>\n",
              "<td>0.0000013</td>\n",
              "<td>0.0000021</td>\n",
              "<td>0.0000016</td>\n",
              "<td>0.0000040</td>\n",
              "<td>0.0000048</td>\n",
              "<td>0.0000029</td></tr>\n",
              "<tr><td>pr_auc</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>precision</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>r2</td>\n",
              "<td>0.9725751</td>\n",
              "<td>0.0116185</td>\n",
              "<td>0.9775683</td>\n",
              "<td>0.985096</td>\n",
              "<td>0.9562861</td>\n",
              "<td>0.9651738</td>\n",
              "<td>0.9787511</td></tr>\n",
              "<tr><td>recall</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>rmse</td>\n",
              "<td>0.0017233</td>\n",
              "<td>0.0003850</td>\n",
              "<td>0.0014374</td>\n",
              "<td>0.0012655</td>\n",
              "<td>0.0020066</td>\n",
              "<td>0.0021935</td>\n",
              "<td>0.0017134</td></tr>\n",
              "<tr><td>specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-28.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-28 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-28 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-28 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-28 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-28 .h2o-table th,\n",
              "#h2o-table-28 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-28 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-28\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Scoring History: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>timestamp</th>\n",
              "<th>duration</th>\n",
              "<th>number_of_trees</th>\n",
              "<th>training_rmse</th>\n",
              "<th>training_logloss</th>\n",
              "<th>training_auc</th>\n",
              "<th>training_pr_auc</th>\n",
              "<th>training_lift</th>\n",
              "<th>training_classification_error</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>2024-05-10 13:53:03</td>\n",
              "<td>58.743 sec</td>\n",
              "<td>0.0</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td>\n",
              "<td>nan</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:53:04</td>\n",
              "<td>59.779 sec</td>\n",
              "<td>5.0</td>\n",
              "<td>0.0050063</td>\n",
              "<td>0.0006145</td>\n",
              "<td>0.9705813</td>\n",
              "<td>0.8515930</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000205</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:53:04</td>\n",
              "<td> 1 min  0.644 sec</td>\n",
              "<td>10.0</td>\n",
              "<td>0.0028570</td>\n",
              "<td>0.0001276</td>\n",
              "<td>0.9999982</td>\n",
              "<td>0.9683421</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000031</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:53:05</td>\n",
              "<td> 1 min  1.608 sec</td>\n",
              "<td>15.0</td>\n",
              "<td>0.0021516</td>\n",
              "<td>0.0000217</td>\n",
              "<td>0.9999999</td>\n",
              "<td>0.9992792</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000031</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:53:06</td>\n",
              "<td> 1 min  2.604 sec</td>\n",
              "<td>20.0</td>\n",
              "<td>0.0021362</td>\n",
              "<td>0.0000259</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 13:53:07</td>\n",
              "<td> 1 min  3.172 sec</td>\n",
              "<td>22.0</td>\n",
              "<td>0.0022533</td>\n",
              "<td>0.0000282</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-29.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-29 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-29 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-29 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-29 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-29 .h2o-table th,\n",
              "#h2o-table-29 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-29 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-29\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Variable Importances: </caption>\n",
              "    <thead><tr><th>variable</th>\n",
              "<th>relative_importance</th>\n",
              "<th>scaled_importance</th>\n",
              "<th>percentage</th></tr></thead>\n",
              "    <tbody><tr><td>avg_unit_price</td>\n",
              "<td>327.7498474</td>\n",
              "<td>1.0</td>\n",
              "<td>0.5588062</td></tr>\n",
              "<tr><td>avg_quantity</td>\n",
              "<td>124.2653122</td>\n",
              "<td>0.3791468</td>\n",
              "<td>0.2118696</td></tr>\n",
              "<tr><td>last_invoice_date</td>\n",
              "<td>35.2888718</td>\n",
              "<td>0.1076701</td>\n",
              "<td>0.0601667</td></tr>\n",
              "<tr><td>CustomerID</td>\n",
              "<td>30.1876392</td>\n",
              "<td>0.0921057</td>\n",
              "<td>0.0514693</td></tr>\n",
              "<tr><td>date</td>\n",
              "<td>14.3765984</td>\n",
              "<td>0.0438645</td>\n",
              "<td>0.0245118</td></tr>\n",
              "<tr><td>month</td>\n",
              "<td>12.3384142</td>\n",
              "<td>0.0376458</td>\n",
              "<td>0.0210367</td></tr>\n",
              "<tr><td>InvoiceDate</td>\n",
              "<td>7.8856354</td>\n",
              "<td>0.0240599</td>\n",
              "<td>0.0134448</td></tr>\n",
              "<tr><td>time</td>\n",
              "<td>7.5861497</td>\n",
              "<td>0.0231462</td>\n",
              "<td>0.0129342</td></tr>\n",
              "<tr><td>Description</td>\n",
              "<td>6.9780726</td>\n",
              "<td>0.0212908</td>\n",
              "<td>0.0118975</td></tr>\n",
              "<tr><td>UnitPrice</td>\n",
              "<td>5.2438941</td>\n",
              "<td>0.0159997</td>\n",
              "<td>0.0089407</td></tr>\n",
              "<tr><td>TotalPrice</td>\n",
              "<td>4.5841923</td>\n",
              "<td>0.0139869</td>\n",
              "<td>0.0078159</td></tr>\n",
              "<tr><td>Quantity</td>\n",
              "<td>4.1493683</td>\n",
              "<td>0.0126602</td>\n",
              "<td>0.0070746</td></tr>\n",
              "<tr><td>season</td>\n",
              "<td>3.0720983</td>\n",
              "<td>0.0093733</td>\n",
              "<td>0.0052379</td></tr>\n",
              "<tr><td>week</td>\n",
              "<td>2.7328463</td>\n",
              "<td>0.0083382</td>\n",
              "<td>0.0046594</td></tr>\n",
              "<tr><td>Country</td>\n",
              "<td>0.0444444</td>\n",
              "<td>0.0001356</td>\n",
              "<td>0.0000758</td></tr>\n",
              "<tr><td>row_id</td>\n",
              "<td>0.0345460</td>\n",
              "<td>0.0001054</td>\n",
              "<td>0.0000589</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbm_regressor = H2OGradientBoostingEstimator(distribution=\"bernoulli\",\n",
        "          ntrees=10, max_depth=3, min_rows=2, learn_rate=0.2)"
      ],
      "metadata": {
        "id": "fnq9ikAH_7D7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbm_regressor.train(x, y,train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "15C82_Kw_9Qr",
        "outputId": "314f415e-0eaa-41e3-e1e7-b7a861c9ef72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gbm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model Details\n",
              "=============\n",
              "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
              "Model Key: GBM_model_python_1715346154206_4339\n",
              "\n",
              "\n",
              "Model Summary: \n",
              "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
              "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
              "    10                 10                          948                    2            2            2             3             3             3\n",
              "\n",
              "ModelMetricsBinomial: gbm\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 2.3638502433481347e-10\n",
              "RMSE: 1.537481786346796e-05\n",
              "LogLoss: 1.5374062642902088e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0\n",
              "\n",
              "Confusion Matrix (Act/Pred) for max f1 @ threshold = 1.0\n",
              "         Group 1    Group 2    Error    Rate\n",
              "-------  ---------  ---------  -------  --------------\n",
              "Group 1  325630     0          0        (0.0/325630.0)\n",
              "Group 2  0          37         0        (0.0/37.0)\n",
              "Total    325630     37         0        (0.0/325667.0)\n",
              "\n",
              "Maximum Metrics: Maximum metrics at their respective thresholds\n",
              "metric                       threshold    value    idx\n",
              "---------------------------  -----------  -------  -----\n",
              "max f1                       1            1        0\n",
              "max f2                       1            1        0\n",
              "max f0point5                 1            1        0\n",
              "max accuracy                 1            1        0\n",
              "max precision                1            1        0\n",
              "max recall                   1            1        0\n",
              "max specificity              1            1        0\n",
              "max absolute_mcc             1            1        0\n",
              "max min_per_class_accuracy   1            1        0\n",
              "max mean_per_class_accuracy  1            1        0\n",
              "max tns                      1            325630   0\n",
              "max fns                      1            0        0\n",
              "max fps                      1.53757e-05  325630   1\n",
              "max tps                      1            37       0\n",
              "max tnr                      1            1        0\n",
              "max fnr                      1            0        0\n",
              "max fpr                      1.53757e-05  1        1\n",
              "max tpr                      1            1        0\n",
              "\n",
              "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
              "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
              "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
              "1        1                           1.53757e-05        1       1                  0.000113613      0.000128987  0.000113613                 0.000128987         1               1                          0       0                  0\n",
              "\n",
              "Scoring History: \n",
              "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_pr_auc    training_lift    training_classification_error\n",
              "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  -----------------  ---------------  -------------------------------\n",
              "    2024-05-10 14:58:43  0.053 sec   0                  0.0106583        0.00114552          0.5             0.000113613        1                0.999886\n",
              "    2024-05-10 14:58:44  1.347 sec   1                  9.30129e-05      9.3012e-05          1               1                  1                0\n",
              "    2024-05-10 14:58:45  2.331 sec   2                  7.61524e-05      7.6151e-05          1               1                  1                0\n",
              "    2024-05-10 14:58:46  3.441 sec   3                  6.23482e-05      6.23466e-05         1               1                  1                0\n",
              "    2024-05-10 14:58:49  6.475 sec   10                 1.53748e-05      1.53741e-05         1               1                  1                0\n",
              "\n",
              "Variable Importances: \n",
              "variable           relative_importance    scaled_importance    percentage\n",
              "-----------------  ---------------------  -------------------  ------------\n",
              "avg_unit_price     24.8867                1                    0.67269\n",
              "avg_quantity       12.1091                0.486569             0.32731\n",
              "CustomerID         0                      0                    0\n",
              "Description        0                      0                    0\n",
              "Quantity           0                      0                    0\n",
              "InvoiceDate        0                      0                    0\n",
              "UnitPrice          0                      0                    0\n",
              "Country            0                      0                    0\n",
              "TotalPrice         0                      0                    0\n",
              "date               0                      0                    0\n",
              "month              0                      0                    0\n",
              "week               0                      0                    0\n",
              "time               0                      0                    0\n",
              "season             0                      0                    0\n",
              "row_id             0                      0                    0\n",
              "last_invoice_date  0                      0                    0\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
            ],
            "text/html": [
              "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
              "=============\n",
              "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
              "Model Key: GBM_model_python_1715346154206_4339\n",
              "</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-31.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-31 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-31 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-31 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-31 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-31 .h2o-table th,\n",
              "#h2o-table-31 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-31 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-31\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Model Summary: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>number_of_trees</th>\n",
              "<th>number_of_internal_trees</th>\n",
              "<th>model_size_in_bytes</th>\n",
              "<th>min_depth</th>\n",
              "<th>max_depth</th>\n",
              "<th>mean_depth</th>\n",
              "<th>min_leaves</th>\n",
              "<th>max_leaves</th>\n",
              "<th>mean_leaves</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>10.0</td>\n",
              "<td>10.0</td>\n",
              "<td>948.0</td>\n",
              "<td>2.0</td>\n",
              "<td>2.0</td>\n",
              "<td>2.0</td>\n",
              "<td>3.0</td>\n",
              "<td>3.0</td>\n",
              "<td>3.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: gbm\n",
              "** Reported on train data. **\n",
              "\n",
              "MSE: 2.3638502433481347e-10\n",
              "RMSE: 1.537481786346796e-05\n",
              "LogLoss: 1.5374062642902088e-05\n",
              "Mean Per-Class Error: 0.0\n",
              "AUC: 1.0\n",
              "AUCPR: 1.0\n",
              "Gini: 1.0</pre>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-32.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-32 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-32 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-32 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-32 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-32 .h2o-table th,\n",
              "#h2o-table-32 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-32 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-32\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 1.0</caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>Group 1</th>\n",
              "<th>Group 2</th>\n",
              "<th>Error</th>\n",
              "<th>Rate</th></tr></thead>\n",
              "    <tbody><tr><td>Group 1</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325630.0)</td></tr>\n",
              "<tr><td>Group 2</td>\n",
              "<td>0.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/37.0)</td></tr>\n",
              "<tr><td>Total</td>\n",
              "<td>325630.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td>\n",
              "<td> (0.0/325667.0)</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-33.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-33 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-33 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-33 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-33 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-33 .h2o-table th,\n",
              "#h2o-table-33 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-33 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-33\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
              "    <thead><tr><th>metric</th>\n",
              "<th>threshold</th>\n",
              "<th>value</th>\n",
              "<th>idx</th></tr></thead>\n",
              "    <tbody><tr><td>max f1</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max f2</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max f0point5</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max precision</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max recall</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max specificity</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max absolute_mcc</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max min_per_class_accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max mean_per_class_accuracy</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max tns</td>\n",
              "<td>1.0</td>\n",
              "<td>325630.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fns</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fps</td>\n",
              "<td>0.0000154</td>\n",
              "<td>325630.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>max tps</td>\n",
              "<td>1.0</td>\n",
              "<td>37.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max tnr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fnr</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>max fpr</td>\n",
              "<td>0.0000154</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td></tr>\n",
              "<tr><td>max tpr</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-34.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-34 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-34 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-34 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-34 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-34 .h2o-table th,\n",
              "#h2o-table-34 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-34 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-34\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %</caption>\n",
              "    <thead><tr><th>group</th>\n",
              "<th>cumulative_data_fraction</th>\n",
              "<th>lower_threshold</th>\n",
              "<th>lift</th>\n",
              "<th>cumulative_lift</th>\n",
              "<th>response_rate</th>\n",
              "<th>score</th>\n",
              "<th>cumulative_response_rate</th>\n",
              "<th>cumulative_score</th>\n",
              "<th>capture_rate</th>\n",
              "<th>cumulative_capture_rate</th>\n",
              "<th>gain</th>\n",
              "<th>cumulative_gain</th>\n",
              "<th>kolmogorov_smirnov</th></tr></thead>\n",
              "    <tbody><tr><td>1</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000154</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001290</td>\n",
              "<td>0.0001136</td>\n",
              "<td>0.0001290</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div></div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-35.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-35 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-35 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-35 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-35 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-35 .h2o-table th,\n",
              "#h2o-table-35 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-35 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-35\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Scoring History: </caption>\n",
              "    <thead><tr><th></th>\n",
              "<th>timestamp</th>\n",
              "<th>duration</th>\n",
              "<th>number_of_trees</th>\n",
              "<th>training_rmse</th>\n",
              "<th>training_logloss</th>\n",
              "<th>training_auc</th>\n",
              "<th>training_pr_auc</th>\n",
              "<th>training_lift</th>\n",
              "<th>training_classification_error</th></tr></thead>\n",
              "    <tbody><tr><td></td>\n",
              "<td>2024-05-10 14:58:43</td>\n",
              "<td> 0.053 sec</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0106583</td>\n",
              "<td>0.0011455</td>\n",
              "<td>0.5</td>\n",
              "<td>0.0001136</td>\n",
              "<td>1.0</td>\n",
              "<td>0.9998864</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 14:58:44</td>\n",
              "<td> 1.347 sec</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0000930</td>\n",
              "<td>0.0000930</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 14:58:45</td>\n",
              "<td> 2.331 sec</td>\n",
              "<td>2.0</td>\n",
              "<td>0.0000762</td>\n",
              "<td>0.0000762</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 14:58:46</td>\n",
              "<td> 3.441 sec</td>\n",
              "<td>3.0</td>\n",
              "<td>0.0000623</td>\n",
              "<td>0.0000623</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td></td>\n",
              "<td>2024-05-10 14:58:49</td>\n",
              "<td> 6.475 sec</td>\n",
              "<td>10.0</td>\n",
              "<td>0.0000154</td>\n",
              "<td>0.0000154</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>1.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div>\n",
              "<div style='margin: 1em 0 1em 0;'>\n",
              "<style>\n",
              "\n",
              "#h2o-table-36.h2o-container {\n",
              "  overflow-x: auto;\n",
              "}\n",
              "#h2o-table-36 .h2o-table {\n",
              "  /* width: 100%; */\n",
              "  margin-top: 1em;\n",
              "  margin-bottom: 1em;\n",
              "}\n",
              "#h2o-table-36 .h2o-table caption {\n",
              "  white-space: nowrap;\n",
              "  caption-side: top;\n",
              "  text-align: left;\n",
              "  /* margin-left: 1em; */\n",
              "  margin: 0;\n",
              "  font-size: larger;\n",
              "}\n",
              "#h2o-table-36 .h2o-table thead {\n",
              "  white-space: nowrap; \n",
              "  position: sticky;\n",
              "  top: 0;\n",
              "  box-shadow: 0 -1px inset;\n",
              "}\n",
              "#h2o-table-36 .h2o-table tbody {\n",
              "  overflow: auto;\n",
              "}\n",
              "#h2o-table-36 .h2o-table th,\n",
              "#h2o-table-36 .h2o-table td {\n",
              "  text-align: right;\n",
              "  /* border: 1px solid; */\n",
              "}\n",
              "#h2o-table-36 .h2o-table tr:nth-child(even) {\n",
              "  /* background: #F5F5F5 */\n",
              "}\n",
              "\n",
              "</style>      \n",
              "<div id=\"h2o-table-36\" class=\"h2o-container\">\n",
              "  <table class=\"h2o-table\">\n",
              "    <caption>Variable Importances: </caption>\n",
              "    <thead><tr><th>variable</th>\n",
              "<th>relative_importance</th>\n",
              "<th>scaled_importance</th>\n",
              "<th>percentage</th></tr></thead>\n",
              "    <tbody><tr><td>avg_unit_price</td>\n",
              "<td>24.8867035</td>\n",
              "<td>1.0</td>\n",
              "<td>0.6726901</td></tr>\n",
              "<tr><td>avg_quantity</td>\n",
              "<td>12.1090899</td>\n",
              "<td>0.4865687</td>\n",
              "<td>0.3273099</td></tr>\n",
              "<tr><td>CustomerID</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Description</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Quantity</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>InvoiceDate</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>UnitPrice</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>Country</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>TotalPrice</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>date</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>month</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>week</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>time</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>season</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>row_id</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr>\n",
              "<tr><td>last_invoice_date</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td>\n",
              "<td>0.0</td></tr></tbody>\n",
              "  </table>\n",
              "</div>\n",
              "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
              "\n",
              "[tips]\n",
              "Use `model.explain()` to inspect the model.\n",
              "--\n",
              "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions3 = gbm_regressor.predict(test)"
      ],
      "metadata": {
        "id": "cZCHWvWdAHzQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcc04b92-0208-4297-aa9b-44ed9bf406bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions3\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "0tvO-KFRCCgw",
        "outputId": "2b92ba44-cf59-42df-c49f-d5ff7148e3a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "predict      Group 1      Group 2\n",
              "---------  ---------  -----------\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "Group 1     0.999985  1.53757e-05\n",
              "[81162 rows x 3 columns]\n"
            ],
            "text/html": [
              "<table class='dataframe'>\n",
              "<thead>\n",
              "<tr><th>predict  </th><th style=\"text-align: right;\">  Group 1</th><th style=\"text-align: right;\">    Group 2</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "<tr><td>Group 1  </td><td style=\"text-align: right;\"> 0.999985</td><td style=\"text-align: right;\">1.53757e-05</td></tr>\n",
              "</tbody>\n",
              "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[81162 rows x 3 columns]</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(gbm_regressor.model_performance(test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhMCbfmeDFex",
        "outputId": "2cea7a59-cf90-4aa7-a8d2-d7c5a920c116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ModelMetricsBinomial: gbm\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 2.3639725592792584e-10\n",
            "RMSE: 1.5375215638420356e-05\n",
            "LogLoss: 1.5374860234465057e-05\n",
            "Mean Per-Class Error: 0.0\n",
            "AUC: 1.0\n",
            "AUCPR: 1.0\n",
            "Gini: 1.0\n",
            "\n",
            "Confusion Matrix (Act/Pred) for max f1 @ threshold = 1.0\n",
            "         Group 1    Group 2    Error    Rate\n",
            "-------  ---------  ---------  -------  -------------\n",
            "Group 1  81157      0          0        (0.0/81157.0)\n",
            "Group 2  0          5          0        (0.0/5.0)\n",
            "Total    81157      5          0        (0.0/81162.0)\n",
            "\n",
            "Maximum Metrics: Maximum metrics at their respective thresholds\n",
            "metric                       threshold    value    idx\n",
            "---------------------------  -----------  -------  -----\n",
            "max f1                       1            1        0\n",
            "max f2                       1            1        0\n",
            "max f0point5                 1            1        0\n",
            "max accuracy                 1            1        0\n",
            "max precision                1            1        0\n",
            "max recall                   1            1        0\n",
            "max specificity              1            1        0\n",
            "max absolute_mcc             1            1        0\n",
            "max min_per_class_accuracy   1            1        0\n",
            "max mean_per_class_accuracy  1            1        0\n",
            "max tns                      1            81157    0\n",
            "max fns                      1            0        0\n",
            "max fps                      1.53757e-05  81157    1\n",
            "max tps                      1            5        0\n",
            "max tnr                      1            1        0\n",
            "max fnr                      1            0        0\n",
            "max fpr                      1.53757e-05  1        1\n",
            "max tpr                      1            1        0\n",
            "\n",
            "Gains/Lift Table: Avg response rate:  0.01 %, avg score:  0.01 %\n",
            "group    cumulative_data_fraction    lower_threshold    lift    cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain    cumulative_gain    kolmogorov_smirnov\n",
            "-------  --------------------------  -----------------  ------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  ------  -----------------  --------------------\n",
            "1        1                           1.53757e-05        1       1                  6.16052e-05      7.69799e-05  6.16052e-05                 7.69799e-05         1               1                          0       0                  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KO3y63HqE_V0"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "datalore": {
      "base_environment": "default",
      "computation_mode": "JUPYTER",
      "package_manager": "pip",
      "packages": [],
      "report_row_ids": [],
      "version": 3
    },
    "kernelspec": {
      "display_name": "Python",
      "language": "python",
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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