ml_club_noIntelex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7f9c0b9",
   "metadata": {},
   "source": [
    "## Exploring the speed-up in Scikit-Learn ML algorithms from Intel® Extension (without extension activated)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b52a9b",
   "metadata": {},
   "source": [
    "Begin in the usual way by importing a load of packages and methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0943d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np, time, warnings\n",
    "\n",
    "#from sklearnex import patch_sklearn\n",
    "#patch_sklearn()\n",
    "\n",
    "import sklearn\n",
    "from sklearn import neighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8016830",
   "metadata": {},
   "source": [
    "Create a synthetic dataset (100k rows x 50 columns) with binary class labels to use for these tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dcee9eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>feature0</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>feature9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature41</th>\n",
       "      <th>feature42</th>\n",
       "      <th>feature43</th>\n",
       "      <th>feature44</th>\n",
       "      <th>feature45</th>\n",
       "      <th>feature46</th>\n",
       "      <th>feature47</th>\n",
       "      <th>feature48</th>\n",
       "      <th>feature49</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.422800</td>\n",
       "      <td>-0.584662</td>\n",
       "      <td>0.462237</td>\n",
       "      <td>0.989699</td>\n",
       "      <td>-1.121211</td>\n",
       "      <td>-0.635441</td>\n",
       "      <td>-0.548132</td>\n",
       "      <td>-0.530651</td>\n",
       "      <td>2.172797</td>\n",
       "      <td>0.603010</td>\n",
       "      <td>...</td>\n",
       "      <td>2.159485</td>\n",
       "      <td>0.901096</td>\n",
       "      <td>0.018292</td>\n",
       "      <td>-0.265369</td>\n",
       "      <td>-1.119584</td>\n",
       "      <td>-0.070546</td>\n",
       "      <td>-1.168910</td>\n",
       "      <td>0.262057</td>\n",
       "      <td>2.160698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.041315</td>\n",
       "      <td>-1.206425</td>\n",
       "      <td>-0.309431</td>\n",
       "      <td>-1.920550</td>\n",
       "      <td>-0.906647</td>\n",
       "      <td>0.392948</td>\n",
       "      <td>-0.447752</td>\n",
       "      <td>0.539187</td>\n",
       "      <td>0.657150</td>\n",
       "      <td>1.960977</td>\n",
       "      <td>...</td>\n",
       "      <td>1.013742</td>\n",
       "      <td>0.237744</td>\n",
       "      <td>-0.231629</td>\n",
       "      <td>-0.665971</td>\n",
       "      <td>-0.252495</td>\n",
       "      <td>-1.359173</td>\n",
       "      <td>-1.141313</td>\n",
       "      <td>0.290716</td>\n",
       "      <td>-1.033460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.541894</td>\n",
       "      <td>-1.919632</td>\n",
       "      <td>-0.461707</td>\n",
       "      <td>1.108704</td>\n",
       "      <td>-1.494200</td>\n",
       "      <td>2.202662</td>\n",
       "      <td>-0.115268</td>\n",
       "      <td>-0.338679</td>\n",
       "      <td>-1.450074</td>\n",
       "      <td>-1.471831</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.796161</td>\n",
       "      <td>-0.753390</td>\n",
       "      <td>-0.501331</td>\n",
       "      <td>-2.269483</td>\n",
       "      <td>0.027072</td>\n",
       "      <td>0.404530</td>\n",
       "      <td>-1.253471</td>\n",
       "      <td>-0.707580</td>\n",
       "      <td>1.068665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.426785</td>\n",
       "      <td>0.312948</td>\n",
       "      <td>0.216396</td>\n",
       "      <td>-2.001491</td>\n",
       "      <td>-0.597713</td>\n",
       "      <td>-0.285472</td>\n",
       "      <td>-0.119522</td>\n",
       "      <td>-1.817114</td>\n",
       "      <td>-0.629797</td>\n",
       "      <td>0.584540</td>\n",
       "      <td>...</td>\n",
       "      <td>1.751154</td>\n",
       "      <td>-0.686781</td>\n",
       "      <td>0.532474</td>\n",
       "      <td>-1.448586</td>\n",
       "      <td>2.279289</td>\n",
       "      <td>-0.628497</td>\n",
       "      <td>0.632469</td>\n",
       "      <td>-1.242756</td>\n",
       "      <td>0.573785</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.639737</td>\n",
       "      <td>0.219193</td>\n",
       "      <td>-1.287882</td>\n",
       "      <td>1.297504</td>\n",
       "      <td>1.459814</td>\n",
       "      <td>0.942885</td>\n",
       "      <td>-0.221012</td>\n",
       "      <td>1.396323</td>\n",
       "      <td>0.412051</td>\n",
       "      <td>0.473779</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.351119</td>\n",
       "      <td>-1.195827</td>\n",
       "      <td>0.019775</td>\n",
       "      <td>-2.226484</td>\n",
       "      <td>-0.198740</td>\n",
       "      <td>-0.085489</td>\n",
       "      <td>-0.479642</td>\n",
       "      <td>-0.533513</td>\n",
       "      <td>-1.782120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>\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>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>-0.211418</td>\n",
       "      <td>1.032209</td>\n",
       "      <td>1.541046</td>\n",
       "      <td>1.829190</td>\n",
       "      <td>1.429372</td>\n",
       "      <td>-0.265037</td>\n",
       "      <td>-1.317229</td>\n",
       "      <td>0.381129</td>\n",
       "      <td>0.582187</td>\n",
       "      <td>1.970145</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.192479</td>\n",
       "      <td>0.430262</td>\n",
       "      <td>0.260991</td>\n",
       "      <td>1.347965</td>\n",
       "      <td>-1.540521</td>\n",
       "      <td>-1.290673</td>\n",
       "      <td>0.099129</td>\n",
       "      <td>0.577689</td>\n",
       "      <td>-1.616334</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>0.863530</td>\n",
       "      <td>0.301672</td>\n",
       "      <td>0.108365</td>\n",
       "      <td>-0.405745</td>\n",
       "      <td>0.401090</td>\n",
       "      <td>1.047433</td>\n",
       "      <td>-0.887619</td>\n",
       "      <td>0.456457</td>\n",
       "      <td>1.008957</td>\n",
       "      <td>-1.073167</td>\n",
       "      <td>...</td>\n",
       "      <td>1.025543</td>\n",
       "      <td>-1.595556</td>\n",
       "      <td>-0.920994</td>\n",
       "      <td>-0.462491</td>\n",
       "      <td>-1.073571</td>\n",
       "      <td>-1.703985</td>\n",
       "      <td>0.660477</td>\n",
       "      <td>0.563899</td>\n",
       "      <td>1.615149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>-0.046031</td>\n",
       "      <td>-0.643225</td>\n",
       "      <td>-1.621285</td>\n",
       "      <td>1.478438</td>\n",
       "      <td>-0.339183</td>\n",
       "      <td>0.025995</td>\n",
       "      <td>1.083149</td>\n",
       "      <td>-0.233827</td>\n",
       "      <td>0.707580</td>\n",
       "      <td>-0.835927</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.527178</td>\n",
       "      <td>-1.871331</td>\n",
       "      <td>-0.422885</td>\n",
       "      <td>-1.404959</td>\n",
       "      <td>-0.535566</td>\n",
       "      <td>-1.344177</td>\n",
       "      <td>0.103058</td>\n",
       "      <td>1.189367</td>\n",
       "      <td>0.966756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>-0.311873</td>\n",
       "      <td>0.635279</td>\n",
       "      <td>-1.035370</td>\n",
       "      <td>-1.170977</td>\n",
       "      <td>-0.189695</td>\n",
       "      <td>-0.468615</td>\n",
       "      <td>-0.697571</td>\n",
       "      <td>0.835783</td>\n",
       "      <td>-0.939901</td>\n",
       "      <td>-0.950801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203080</td>\n",
       "      <td>1.306455</td>\n",
       "      <td>-1.275350</td>\n",
       "      <td>1.070224</td>\n",
       "      <td>-0.956421</td>\n",
       "      <td>0.750739</td>\n",
       "      <td>-0.952765</td>\n",
       "      <td>0.513875</td>\n",
       "      <td>0.854141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>0.781748</td>\n",
       "      <td>1.185038</td>\n",
       "      <td>1.320383</td>\n",
       "      <td>1.416354</td>\n",
       "      <td>0.174423</td>\n",
       "      <td>0.076463</td>\n",
       "      <td>-0.518874</td>\n",
       "      <td>-0.285564</td>\n",
       "      <td>-0.386359</td>\n",
       "      <td>-0.396245</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.600905</td>\n",
       "      <td>0.366728</td>\n",
       "      <td>-0.055505</td>\n",
       "      <td>0.462156</td>\n",
       "      <td>-1.359377</td>\n",
       "      <td>-1.472841</td>\n",
       "      <td>0.626962</td>\n",
       "      <td>-0.572210</td>\n",
       "      <td>0.190561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        feature0  feature1  feature2  feature3  feature4  feature5  feature6  \\\n",
       "0      -0.422800 -0.584662  0.462237  0.989699 -1.121211 -0.635441 -0.548132   \n",
       "1      -1.041315 -1.206425 -0.309431 -1.920550 -0.906647  0.392948 -0.447752   \n",
       "2      -1.541894 -1.919632 -0.461707  1.108704 -1.494200  2.202662 -0.115268   \n",
       "3       0.426785  0.312948  0.216396 -2.001491 -0.597713 -0.285472 -0.119522   \n",
       "4       0.639737  0.219193 -1.287882  1.297504  1.459814  0.942885 -0.221012   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "999995 -0.211418  1.032209  1.541046  1.829190  1.429372 -0.265037 -1.317229   \n",
       "999996  0.863530  0.301672  0.108365 -0.405745  0.401090  1.047433 -0.887619   \n",
       "999997 -0.046031 -0.643225 -1.621285  1.478438 -0.339183  0.025995  1.083149   \n",
       "999998 -0.311873  0.635279 -1.035370 -1.170977 -0.189695 -0.468615 -0.697571   \n",
       "999999  0.781748  1.185038  1.320383  1.416354  0.174423  0.076463 -0.518874   \n",
       "\n",
       "        feature7  feature8  feature9  ...  feature41  feature42  feature43  \\\n",
       "0      -0.530651  2.172797  0.603010  ...   2.159485   0.901096   0.018292   \n",
       "1       0.539187  0.657150  1.960977  ...   1.013742   0.237744  -0.231629   \n",
       "2      -0.338679 -1.450074 -1.471831  ...  -3.796161  -0.753390  -0.501331   \n",
       "3      -1.817114 -0.629797  0.584540  ...   1.751154  -0.686781   0.532474   \n",
       "4       1.396323  0.412051  0.473779  ...  -1.351119  -1.195827   0.019775   \n",
       "...          ...       ...       ...  ...        ...        ...        ...   \n",
       "999995  0.381129  0.582187  1.970145  ...  -1.192479   0.430262   0.260991   \n",
       "999996  0.456457  1.008957 -1.073167  ...   1.025543  -1.595556  -0.920994   \n",
       "999997 -0.233827  0.707580 -0.835927  ...  -1.527178  -1.871331  -0.422885   \n",
       "999998  0.835783 -0.939901 -0.950801  ...   0.203080   1.306455  -1.275350   \n",
       "999999 -0.285564 -0.386359 -0.396245  ...  -0.600905   0.366728  -0.055505   \n",
       "\n",
       "        feature44  feature45  feature46  feature47  feature48  feature49  \\\n",
       "0       -0.265369  -1.119584  -0.070546  -1.168910   0.262057   2.160698   \n",
       "1       -0.665971  -0.252495  -1.359173  -1.141313   0.290716  -1.033460   \n",
       "2       -2.269483   0.027072   0.404530  -1.253471  -0.707580   1.068665   \n",
       "3       -1.448586   2.279289  -0.628497   0.632469  -1.242756   0.573785   \n",
       "4       -2.226484  -0.198740  -0.085489  -0.479642  -0.533513  -1.782120   \n",
       "...           ...        ...        ...        ...        ...        ...   \n",
       "999995   1.347965  -1.540521  -1.290673   0.099129   0.577689  -1.616334   \n",
       "999996  -0.462491  -1.073571  -1.703985   0.660477   0.563899   1.615149   \n",
       "999997  -1.404959  -0.535566  -1.344177   0.103058   1.189367   0.966756   \n",
       "999998   1.070224  -0.956421   0.750739  -0.952765   0.513875   0.854141   \n",
       "999999   0.462156  -1.359377  -1.472841   0.626962  -0.572210   0.190561   \n",
       "\n",
       "        Target  \n",
       "0            0  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  \n",
       "...        ...  \n",
       "999995       1  \n",
       "999996       1  \n",
       "999997       1  \n",
       "999998       0  \n",
       "999999       1  \n",
       "\n",
       "[1000000 rows x 51 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features,labels = sklearn.datasets.make_classification(n_samples=1000_000, n_features=50)\n",
    "\n",
    "cols_ml = ['feature'+str(x) for x in range(50)]\n",
    "\n",
    "# for convenience let's put it all together in a pandas dataframe:\n",
    "dataset = pd.DataFrame(data=features, columns=cols_ml)\n",
    "dataset['Target'] = labels\n",
    "\n",
    "# inspect the resulting dataframe\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80070e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d9e4f82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.91 s ± 34.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "dataset[cols_ml] = scaler.fit_transform(dataset[cols_ml])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04436cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# when using timeit I do each operation a second time to get the output\n",
    "dataset[cols_ml] = scaler.fit_transform(dataset[cols_ml])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3e8901",
   "metadata": {},
   "source": [
    "Calculate and apply a PCA transform:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d38b037d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%timeit\n",
    "#dimReducer = PCA(n_components=10)\n",
    "#result = dimReducer.fit_transform(dataset[cols_ml].to_numpy())\n",
    "# the result isn't used for anything in this notebook, but the test ins included for interest\n",
    "# commented out due to heavy memory requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9908ccb7",
   "metadata": {},
   "source": [
    "Do a train / test split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17cf9a09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276 ms ± 2.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset[cols_ml].to_numpy(),dataset['Target'].to_numpy(),test_size=0.3,random_state=1984)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac6f1e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to avoid waiting too long for the classifiers to train, let's downsample the data from 1m to 20k rows\n",
    "dataset = dataset.sample(n=20_000)\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset[cols_ml],dataset['Target'],test_size=0.3,random_state=1984)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530bf0aa",
   "metadata": {},
   "source": [
    "Fit a random forest classifier and predict classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bc26304",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=1000,n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68e60072",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.9 s ± 417 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ea18a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=1000, n_jobs=4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "713e52f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "321 ms ± 49.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "preds = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fda4b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dffdf36",
   "metadata": {},
   "source": [
    "Now let's try the KNeighborsClassifier (without the intel extension):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73a644d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier(n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f721853b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8 ms ± 27.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c5fa328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07c27aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.36 s ± 814 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "preds = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f4c8615",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8537243",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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