autox/autox_interpreter/interpreter_demo/lr_interpret.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/pandas/compat/_optional.py:138: UserWarning: Pandas requires version '2.7.0' or newer of 'numexpr' (version '2.6.9' currently installed).\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from mlinterpreter.util import preprocess_data\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读入数据 和 schema\n",
    "data_path = \"./demo_data/adult.data\"\n",
    "schema_path = \"./demo_data/schema.json\"\n",
    "df = pd.read_csv(data_path, header=None)\n",
    "df.columns = [\"age\",\"workclass\",\"fnlwgt\",\"education\",\"education-num\",\"marital-status\",\"occupation\",\"relationship\",\"race\",\"sex\",\n",
    "                 \"capital-gain\",\"capital-loss\",\"hours-per-week\",\"native-country\",\"income\"]\n",
    "with open(schema_path, \"r\") as f:\n",
    "    schema = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in schema['features']:\n",
    "    if col['type'] == 'Numeric':\n",
    "        df[col['name']] = pd.to_numeric(df[col['name']]).fillna(0)\n",
    "    elif col['type'] == 'Category':\n",
    "        df[col['name']] = df[col['name']].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 编码label\n",
    "le = preprocessing.LabelEncoder()\n",
    "y = le.fit_transform(df[schema['label']])\n",
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:110: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_int = np.zeros((n_samples, n_features), dtype=np.int)\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:111: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  X_mask = np.ones((n_samples, n_features), dtype=np.bool)\n"
     ]
    }
   ],
   "source": [
    "ct = ColumnTransformer(\n",
    "    [[col['name'], preprocessing.StandardScaler(), [col['name']]] for col in  schema['features'] if col['type'] =='Numeric' and col['name'] != schema['label']] +\n",
    "    [[col['name'], OneHotEncoder(handle_unknown='ignore'), [col['name']]] for col in schema['features'] if col['type'] =='Category' and col['name'] != schema['label']]\n",
    "    )\n",
    "train_x = ct.fit_transform(df)\n",
    "columns = []\n",
    "for ins in ct.transformers_:\n",
    "    if isinstance(ins[1], preprocessing.StandardScaler):\n",
    "        columns.append(ins[0])\n",
    "    elif isinstance(ins[1], OneHotEncoder):\n",
    "        columns += [\"{}.{}\".format(ins[0], i) for i in ins[1].categories_[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:30: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  method='lar', copy_X=True, eps=np.finfo(np.float).eps,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:167: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  method='lar', copy_X=True, eps=np.finfo(np.float).eps,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:284: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_Gram=True, verbose=0,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1101: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1127: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, positive=False):\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1362: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1602: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/home/caihengxing/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1738: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(train_x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_imp = pd.DataFrame(columns)\n",
    "lr_imp.columns = ['column']\n",
    "lr_imp['importance'] = lr.coef_\n",
    "lr_imp['importance_abs'] = abs(lr.coef_)\n",
    "lr_imp = lr_imp.sort_values(by = 'importance_abs', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>importance</th>\n",
       "      <th>importance_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>relationship. Wife</td>\n",
       "      <td>0.190654</td>\n",
       "      <td>0.190654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>native-country. Cambodia</td>\n",
       "      <td>0.167092</td>\n",
       "      <td>0.167092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>native-country. Holand-Netherlands</td>\n",
       "      <td>-0.143475</td>\n",
       "      <td>0.143475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>workclass. Without-pay</td>\n",
       "      <td>-0.134732</td>\n",
       "      <td>0.134732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>native-country. Outlying-US(Guam-USVI-etc)</td>\n",
       "      <td>-0.132514</td>\n",
       "      <td>0.132514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>occupation. Adm-clerical</td>\n",
       "      <td>0.002638</td>\n",
       "      <td>0.002638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>native-country. Ecuador</td>\n",
       "      <td>-0.002461</td>\n",
       "      <td>0.002461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>occupation. ?</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.002039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>native-country. Scotland</td>\n",
       "      <td>-0.001939</td>\n",
       "      <td>0.001939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>native-country. Haiti</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>0.001650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         column  importance  importance_abs\n",
       "58                           relationship. Wife    0.190654        0.190654\n",
       "67                     native-country. Cambodia    0.167092        0.167092\n",
       "81           native-country. Holand-Netherlands   -0.143475        0.143475\n",
       "14                       workclass. Without-pay   -0.134732        0.134732\n",
       "94   native-country. Outlying-US(Guam-USVI-etc)   -0.132514        0.132514\n",
       "..                                          ...         ...             ...\n",
       "39                     occupation. Adm-clerical    0.002638        0.002638\n",
       "73                      native-country. Ecuador   -0.002461        0.002461\n",
       "38                                occupation. ?    0.002039        0.002039\n",
       "100                    native-country. Scotland   -0.001939        0.001939\n",
       "80                        native-country. Haiti    0.001650        0.001650\n",
       "\n",
       "[108 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 108 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(columns, lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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