notebooks/third_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting useful features\n",
    "\n",
    "When looking at the second model's feature importances, we saw that the TF-IDF vectorization features were suspiciously absent from the most important features we displayed. To verify that a model can perform well without these features, let's omit them and train a third model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/haesun/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "import joblib\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from ml_editor.data_processing import (\n",
    "    format_raw_df,\n",
    "    get_split_by_author\n",
    ")\n",
    "from ml_editor.model_v2 import (\n",
    "    add_char_count_features,\n",
    "    get_word_stats,\n",
    "    get_sentiment_score,\n",
    "    POS_NAMES,\n",
    "    get_question_score_from_input\n",
    ")\n",
    "from ml_editor.model_evaluation import (\n",
    "    get_feature_importance,\n",
    "    get_roc_plot,\n",
    "    get_confusion_matrix_plot,\n",
    "    get_calibration_plot\n",
    ")\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "np.random.seed(35)\n",
    "\n",
    "data_path = Path('../data/writers.csv')\n",
    "df = pd.read_csv(data_path)\n",
    "df = format_raw_df(df.copy())\n",
    "\n",
    "df = df.loc[df[\"is_question\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"full_text\"] = df[\"Title\"].str.cat(df[\"body_text\"], sep=\" \", na_rep=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below add features. Feel free to check out the ml_editor source code to see more about what these functions are doing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7971/7971 [02:16<00:00, 58.48it/s]\n",
      "100%|██████████| 7971/7971 [00:09<00:00, 803.91it/s]\n"
     ]
    }
   ],
   "source": [
    "df = add_char_count_features(df.copy())\n",
    "df = get_word_stats(df.copy())\n",
    "df = get_sentiment_score(df.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_arr = [\"num_questions\", \n",
    "               \"num_periods\",\n",
    "               \"num_commas\",\n",
    "               \"num_exclam\",\n",
    "               \"num_quotes\",\n",
    "               \"num_colon\",\n",
    "               \"num_stops\",\n",
    "               \"num_semicolon\",\n",
    "               \"num_words\",\n",
    "               \"num_chars\",\n",
    "               \"num_diff_words\",\n",
    "               \"avg_word_len\",\n",
    "               \"polarity\"\n",
    "              ]\n",
    "feature_arr.extend(POS_NAMES.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n",
    "\n",
    "Now that we've added our new features, let's train a new model. We'll use the same architecture as before, but with new features. You can visualize the new features below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We split again since we have now added all features. \n",
    "train_df, test_df = get_split_by_author(df, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_questions</th>\n",
       "      <th>num_periods</th>\n",
       "      <th>num_commas</th>\n",
       "      <th>num_exclam</th>\n",
       "      <th>num_quotes</th>\n",
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       "      <th>...</th>\n",
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       "      <th>NUM</th>\n",
       "      <th>PART</th>\n",
       "      <th>PRON</th>\n",
       "      <th>PROPN</th>\n",
       "      <th>PUNCT</th>\n",
       "      <th>SCONJ</th>\n",
       "      <th>SYM</th>\n",
       "      <th>VERB</th>\n",
       "      <th>X</th>\n",
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       "      <th>Id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.818182</td>\n",
       "      <td>56.818182</td>\n",
       "      <td>28.409091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1022.727273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2073.863636</td>\n",
       "      <td>35200</td>\n",
       "      <td>...</td>\n",
       "      <td>3.409091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.988636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.409091</td>\n",
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       "      <th>2</th>\n",
       "      <td>90.634441</td>\n",
       "      <td>30.211480</td>\n",
       "      <td>60.422961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.422961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1087.613293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2024.169184</td>\n",
       "      <td>33100</td>\n",
       "      <td>...</td>\n",
       "      <td>4.229607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.510574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.416918</td>\n",
       "      <td>0.302115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.510574</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>175.438596</td>\n",
       "      <td>87.719298</td>\n",
       "      <td>87.719298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1491.228070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2719.298246</td>\n",
       "      <td>11400</td>\n",
       "      <td>...</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>0.877193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.140351</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.508772</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70.257611</td>\n",
       "      <td>46.838407</td>\n",
       "      <td>117.096019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>843.091335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2154.566745</td>\n",
       "      <td>42700</td>\n",
       "      <td>...</td>\n",
       "      <td>3.981265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.170960</td>\n",
       "      <td>0.702576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.810304</td>\n",
       "      <td>0.234192</td>\n",
       "      <td>0.234192</td>\n",
       "      <td>3.278689</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>135.135135</td>\n",
       "      <td>67.567568</td>\n",
       "      <td>67.567568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>945.945946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2027.027027</td>\n",
       "      <td>14800</td>\n",
       "      <td>...</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>1.351351</td>\n",
       "      <td>2.702703</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.027027</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "    num_questions  num_periods  num_commas  num_exclam  num_quotes  num_colon  \\\n",
       "Id                                                                              \n",
       "1       56.818182    56.818182   28.409091         0.0    0.000000        0.0   \n",
       "2       90.634441    30.211480   60.422961         0.0   60.422961        0.0   \n",
       "3      175.438596    87.719298   87.719298         0.0    0.000000        0.0   \n",
       "5       70.257611    46.838407  117.096019         0.0    0.000000        0.0   \n",
       "7      135.135135    67.567568   67.567568         0.0    0.000000        0.0   \n",
       "\n",
       "      num_stops  num_semicolon    num_words  num_chars  ...      NOUN  \\\n",
       "Id                                                      ...             \n",
       "1   1022.727273            0.0  2073.863636      35200  ...  3.409091   \n",
       "2   1087.613293            0.0  2024.169184      33100  ...  4.229607   \n",
       "3   1491.228070            0.0  2719.298246      11400  ...  2.631579   \n",
       "5    843.091335            0.0  2154.566745      42700  ...  3.981265   \n",
       "7    945.945946            0.0  2027.027027      14800  ...  2.702703   \n",
       "\n",
       "         NUM      PART      PRON     PROPN     PUNCT     SCONJ       SYM  \\\n",
       "Id                                                                         \n",
       "1   0.000000  1.136364  2.272727  0.000000  1.988636  0.000000  0.000000   \n",
       "2   0.000000  0.000000  1.510574  0.000000  2.416918  0.302115  0.000000   \n",
       "3   0.877193  0.000000  6.140351  0.000000  3.508772  0.000000  0.000000   \n",
       "5   0.000000  1.170960  0.702576  0.000000  2.810304  0.234192  0.234192   \n",
       "7   0.000000  0.000000  2.702703  1.351351  2.702703  0.675676  0.000000   \n",
       "\n",
       "        VERB    X  \n",
       "Id                 \n",
       "1   3.409091  0.0  \n",
       "2   1.510574  0.0  \n",
       "3   5.263158  0.0  \n",
       "5   3.278689  0.0  \n",
       "7   2.027027  0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[feature_arr].head()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_vector_and_label(df, feature_names):\n",
    "    \"\"\"\n",
    "    Generate input and output vectors using the vectors feature and\n",
    "     the given feature names\n",
    "    :param df: input DataFrame\n",
    "    :param feature_names: names of feature columns (other than vectors)\n",
    "    :return: feature array and label array\n",
    "    \"\"\"\n",
    "    features = df[feature_names].astype(float)\n",
    "    labels = df[\"Score\"] > df[\"Score\"].median()\n",
    "    return features, labels\n",
    "\n",
    "X_train, y_train = get_feature_vector_and_label(train_df, feature_arr)\n",
    "X_test, y_test = get_feature_vector_and_label(test_df, feature_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3483\n",
       "True     2959\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1529, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train a model using `sklearn`, and measure its performance using the methods we've covered before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, class_weight='balanced', oob_score=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_predicted = clf.predict(X_test)\n",
    "y_predicted_proba = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy = 0.577, precision = 0.550, recall = 0.438, f1 = 0.488\n"
     ]
    }
   ],
   "source": [
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=True,\n",
    "                                    average='binary')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=True,\n",
    "                              average='binary')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=True, average='binary')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "\n",
    "\n",
    "# Training accuracy\n",
    "# Thanks to https://datascience.stackexchange.com/questions/13151/randomforestclassifier-oob-scoring-method\n",
    "y_train_pred = np.argmax(clf.oob_decision_function_,axis=1)\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_train, y_train_pred)\n",
    "print(\"Training accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy = 0.593, precision = 0.605, recall = 0.483, f1 = 0.537\n"
     ]
    }
   ],
   "source": [
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted)\n",
    "print(\"Validation accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save our new model and vectorizer to disk so we can use it later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/model_3.pkl']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = Path(\"../models/model_3.pkl\")\n",
    "joblib.dump(clf, model_path) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating that features are useful\n",
    "\n",
    "Let's look at feature importances to validate that our new features are being used by the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_feature_names = np.array(feature_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 importances:\n",
      "\n",
      "num_questions: 0.048\n",
      "num_chars: 0.048\n",
      "num_periods: 0.048\n",
      "num_diff_words: 0.047\n",
      "ADJ: 0.047\n",
      "AUX: 0.044\n",
      "DET: 0.043\n",
      "ADV: 0.043\n",
      "NOUN: 0.043\n",
      "PUNCT: 0.042\n",
      "num_commas: 0.042\n",
      "PART: 0.042\n",
      "ADP: 0.041\n",
      "VERB: 0.041\n",
      "num_stops: 0.041\n",
      "PRON: 0.04\n",
      "polarity: 0.04\n",
      "avg_word_len: 0.039\n",
      "num_words: 0.038\n",
      "SCONJ: 0.034\n",
      "\n",
      "Bottom 20 importances:\n",
      "\n",
      "num_commas: 0.042\n",
      "PART: 0.042\n",
      "ADP: 0.041\n",
      "VERB: 0.041\n",
      "num_stops: 0.041\n",
      "PRON: 0.04\n",
      "polarity: 0.04\n",
      "avg_word_len: 0.039\n",
      "num_words: 0.038\n",
      "SCONJ: 0.034\n",
      "PROPN: 0.029\n",
      "NUM: 0.026\n",
      "num_colon: 0.022\n",
      "num_quotes: 0.019\n",
      "INTJ: 0.014\n",
      "SYM: 0.014\n",
      "X: 0.01\n",
      "num_exclam: 0.0072\n",
      "num_semicolon: 0.007\n",
      "CONJ: 0\n"
     ]
    }
   ],
   "source": [
    "k = 20\n",
    "print(\"Top %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[:k]]))\n",
    "\n",
    "print(\"\\nBottom %s importances:\\n\" % k)\n",
    "print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, all_feature_names)[-k:]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing predictions to data\n",
    "\n",
    "This section uses the evaluation methods described in the Comparing Data To Predictions [notebook](https://github.com/hundredblocks/ml-powered-applications/blob/master/notebooks/comparing_data_to_predictions.ipynb) on this third model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_roc_plot(y_predicted_proba[:,1], y_test, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix_plot(y_predicted, y_test, figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(y_predicted_proba[:,1], y_test, figsize=(9,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This third model only uses features that can be easily interpreted, and is better calibrated than previous models. This makes it a very good candidate for our application (feel free to look at `Comparing Models` for a more detailed comparison)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Inference\n",
    "\n",
    "Just like for our first two models, we define an inference function that takes in an arbitrary question and outputs an estimated probability of it receiving a high score according to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 173.88it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 1769.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.38 probability of the question receiving a high score according to our model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "pos_prob = get_question_score_from_input(\"\"\"\n",
    "When quoting a person's informal speech, how much liberty do you have to make changes to what they say?\n",
    "\"\"\")\n",
    "\n",
    "print(\"%s probability of the question receiving a high score according to our model\" % (pos_prob))"
   ]
  }
 ],
 "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",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}