Lasso Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "885c237a",
   "metadata": {},
   "source": [
    "## Standard libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "428dd2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b23deff",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adc26055",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>date</th>\n",
       "      <th>away_score</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_AB</th>\n",
       "      <th>away_H</th>\n",
       "      <th>away_2B</th>\n",
       "      <th>away_3B</th>\n",
       "      <th>away_HR</th>\n",
       "      <th>away_RBI</th>\n",
       "      <th>away_SH</th>\n",
       "      <th>...</th>\n",
       "      <th>home_SF</th>\n",
       "      <th>home_HBP</th>\n",
       "      <th>home_BB</th>\n",
       "      <th>home_IBB</th>\n",
       "      <th>home_K</th>\n",
       "      <th>home_SB</th>\n",
       "      <th>home_CS</th>\n",
       "      <th>home_GIDP</th>\n",
       "      <th>home_CI</th>\n",
       "      <th>home_LOB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000403</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>8</td>\n",
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       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  away_score  home_score  away_AB  away_H  away_2B  away_3B  \\\n",
       "0  20000329           5           3       33      12        1        0   \n",
       "1  20000330           5           1       37       6        2        0   \n",
       "2  20000403           0           2       31       6        2        0   \n",
       "3  20000403           3           3       22       7        1        0   \n",
       "4  20000403           4           6       35      10        2        2   \n",
       "\n",
       "   away_HR  away_RBI  away_SH  ...  home_SF  home_HBP  home_BB  home_IBB  \\\n",
       "0        2         5        1  ...        1         0        3         0   \n",
       "1        1         5        1  ...        0         0        6         1   \n",
       "2        0         0        1  ...        0         1        1         0   \n",
       "3        0         2        0  ...        0         0        1         0   \n",
       "4        1         4        0  ...        0         1        1         0   \n",
       "\n",
       "   home_K  home_SB  home_CS  home_GIDP  home_CI  home_LOB  \n",
       "0       4        0        0          1        0         8  \n",
       "1       9        0        0          2        0        10  \n",
       "2       6        1        0          1        0         6  \n",
       "3       4        0        0          0        0         2  \n",
       "4       7        1        0          1        0         8  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the full dataset \n",
    "full_data = pd.read_excel(\"C:/Users/bodin/School/STAT 5001/project/gl_project_dataset.xlsx\")\n",
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3de21887",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce data to include only games from the year 2000+\n",
    "data_reduced = full_data[full_data['date'] > 20000101]     #yyyymmdd\n",
    "\n",
    "# Only include variables specified in write-up\n",
    "data = data_reduced[['away_AB', 'away_H', 'away_2B', 'away_3B',\n",
    "             'away_HR', 'away_SH', 'away_SF', 'away_HBP', \n",
    "             'away_BB', 'away_IBB', 'away_K', 'away_SB', 'away_CS', \n",
    "             'away_GIDP', 'away_CI', 'away_score', \n",
    "             'home_AB', 'home_H', 'home_2B', 'home_3B', \n",
    "             'home_HR', 'home_SH', 'home_SF', 'home_HBP', \n",
    "             'home_BB', 'home_IBB', 'home_K', 'home_SB', 'home_CS', \n",
    "             'home_GIDP', 'home_CI', 'home_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1175c289",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure no 'na' values\n",
    "data = data.dropna()\n",
    "\n",
    "# Add singles column as specified in our write-up\n",
    "data['away_1B'] = data['away_H'] - data['away_2B'] - data['away_3B'] - data['away_HR']\n",
    "data['home_1B'] = data['home_H'] - data['home_2B'] - data['home_3B'] - data['home_HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8aa9fcd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine away and home data columns\n",
    "data2 = pd.DataFrame()\n",
    "\n",
    "data2['AB'] = pd.concat([data['away_AB'], data['home_AB']], ignore_index=True)\n",
    "data2['H'] = pd.concat([data['away_H'], data['home_H']], ignore_index=True)\n",
    "data2['1B'] = pd.concat([data['away_1B'], data['home_1B']], ignore_index=True)\n",
    "data2['2B'] = pd.concat([data['away_2B'], data['home_2B']], ignore_index=True)\n",
    "data2['3B'] = pd.concat([data['away_3B'], data['home_3B']], ignore_index=True)\n",
    "data2['HR'] = pd.concat([data['away_HR'], data['home_HR']], ignore_index=True)\n",
    "data2['SH'] = pd.concat([data['away_SH'], data['home_SH']], ignore_index=True)\n",
    "data2['SF'] = pd.concat([data['away_SF'], data['home_SF']], ignore_index=True)\n",
    "data2['HBP'] = pd.concat([data['away_HBP'], data['home_HBP']], ignore_index=True)\n",
    "data2['BB'] = pd.concat([data['away_BB'], data['home_BB']], ignore_index=True)\n",
    "data2['IBB'] = pd.concat([data['away_IBB'], data['home_IBB']], ignore_index=True)\n",
    "data2['K'] = pd.concat([data['away_K'], data['home_K']], ignore_index=True)\n",
    "data2['SB'] = pd.concat([data['away_SB'], data['home_SB']], ignore_index=True)\n",
    "data2['CS'] = pd.concat([data['away_CS'], data['home_CS']], ignore_index=True)\n",
    "data2['GIDP'] = pd.concat([data['away_GIDP'], data['home_GIDP']], ignore_index=True)\n",
    "data2['CI'] = pd.concat([data['away_CI'], data['home_CI']], ignore_index=True)\n",
    "data2['SCORE'] = pd.concat([data['away_score'], data['home_score']], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac81a703",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into input and output variables\n",
    "X = data2.drop('SCORE', axis=1)\n",
    "y = data2['SCORE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8fe01fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "np.random.seed(42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1a793ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c008e670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Lasso\n",
    "from sklearn.linear_model import Lasso\n",
    "a = 0.001\n",
    "lasso_model = Lasso(alpha = a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9987b192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model\n",
    "lasso_model.fit(X_train_scaled, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9145a311",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions using model\n",
    "y_pred = lasso_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b6d0fb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.273573255102066\n",
      "Score: 0.7762801640597561\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "score = lasso_model.score(X_test_scaled, y_test)     #r-squared\n",
    "print(f\"MSE: {mse}\")\n",
    "print(f\"Score: {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "28d8d95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Feature  Coefficient\n",
      "0         1    -0.413964\n",
      "1         2     1.993855\n",
      "2         3     0.000000\n",
      "3         4     0.346810\n",
      "4         5     0.209442\n",
      "5         6     1.002085\n",
      "6         7    -0.039145\n",
      "7         8     0.263394\n",
      "8         9     0.234061\n",
      "9        10     0.710455\n",
      "10       11    -0.094752\n",
      "11       12    -0.064230\n",
      "12       13     0.052333\n",
      "13       14    -0.165515\n",
      "14       15    -0.327413\n",
      "15       16     0.030305\n"
     ]
    }
   ],
   "source": [
    "# Display coefficients\n",
    "coefficients = pd.DataFrame({'Feature': range(1, X.shape[1] + 1), 'Coefficient': lasso_model.coef_})\n",
    "print(coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6af39c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted vs actual values\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Lasso Regression: Actual vs Predicted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a56fb0",
   "metadata": {},
   "source": [
    "### Use cross val to find best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c433e6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, KFold\n",
    "\n",
    "lasso_model = Lasso()\n",
    "cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "param_grid = {\n",
    "    'alpha': [.001, .01, .1, 1]\n",
    "}\n",
    "\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "39c3b8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bodin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.351e+04, tolerance: 1.102e+02\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             estimator=Lasso(), n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.001, 0.01, 0.1, 1]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             estimator=Lasso(), n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.001, 0.01, 0.1, 1]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
       "             estimator=Lasso(), n_jobs=-1,\n",
       "             param_grid={'alpha': [0.001, 0.01, 0.1, 1]})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(lasso_model, param_grid, cv=kf, n_jobs=-1)\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd9899e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'alpha': 0.001}\n",
      "Best Cross-Validated RMSE: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bodin\\AppData\\Local\\Temp\\ipykernel_26832\\1905331825.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
      "  best_rmse = np.sqrt(-grid_search.best_score_)\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_rmse = np.sqrt(-grid_search.best_score_)\n",
    "print(f'Best Parameters: {best_params}')\n",
    "print(f'Best Cross-Validated RMSE: {best_rmse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce431543",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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