From e127de87f53e417ed1f29e0e76eb4b0dc60100b3 Mon Sep 17 00:00:00 2001
From: ibidyouadu <60790401+ibidyouadu@users.noreply.github.com>
Date: Sun, 10 May 2020 23:22:31 -0400
Subject: [PATCH] Delete BERT_neighbors_classification.ipynb

---
 BERT_neighbors_classification.ipynb | 719 ----------------------------
 1 file changed, 719 deletions(-)
 delete mode 100644 BERT_neighbors_classification.ipynb

diff --git a/BERT_neighbors_classification.ipynb b/BERT_neighbors_classification.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first few cells will import some libraries and a script. For the most part this is identical to [xhlulu's Kaggle code.](https://www.kaggle.com/xhlulu/disaster-nlp-keras-bert-using-tfhub) Only difference is the first cell, which you only need to run if using google colab, and the importing of re and nltk in cell 3. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 196
-    },
-    "colab_type": "code",
-    "id": "z2EOfe64FcUh",
-    "outputId": "fb8d8809-4d89-40b4-833f-7836feff4577"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting sentencepiece\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/2c/8df20f3ac6c22ac224fff307ebc102818206c53fc454ecd37d8ac2060df5/sentencepiece-0.1.86-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
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-      "\u001b[?25hInstalling collected packages: sentencepiece\n",
-      "Successfully installed sentencepiece-0.1.86\n",
-      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
-      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
-      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
-      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use the following if you need the libraries. If working in google colab, you will need these lines.\n",
-    "\n",
-    "# !pip install sentencepiece\n",
-    "# import nltk\n",
-    "# nltk.download('wordnet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gR6-jumvFhr9"
-   },
-   "outputs": [],
-   "source": [
-    "!wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 35
-    },
-    "colab_type": "code",
-    "id": "75PqQCwcFlji",
-    "outputId": "41863890-3b6e-4b60-9b60-e4d5158a73dd"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DONE\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.layers import Dense, Input\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
-    "import tensorflow_hub as hub\n",
-    "import re\n",
-    "from nltk.stem import WordNetLemmatizer\n",
-    "from nltk.corpus import stopwords\n",
-    "\n",
-    "\n",
-    "import tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following four cells are unchanged from the Kaggle code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ULdRsYGtFnro"
-   },
-   "outputs": [],
-   "source": [
-    "def bert_encode(texts, tokenizer, max_len=512):\n",
-    "    all_tokens = []\n",
-    "    all_masks = []\n",
-    "    all_segments = []\n",
-    "\n",
-    "\n",
-    "    for text in texts:\n",
-    "        text = tokenizer.tokenize(text)\n",
-    "           \n",
-    "        text = text[:max_len-2]\n",
-    "        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n",
-    "        pad_len = max_len - len(input_sequence)\n",
-    "        \n",
-    "        tokens = tokenizer.convert_tokens_to_ids(input_sequence)\n",
-    "        tokens += [0] * pad_len\n",
-    "        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n",
-    "        segment_ids = [0] * max_len\n",
-    "        \n",
-    "        all_tokens.append(tokens)\n",
-    "        all_masks.append(pad_masks)\n",
-    "        all_segments.append(segment_ids)\n",
-    "    \n",
-    "    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "mo7LXhGLFrCz"
-   },
-   "outputs": [],
-   "source": [
-    "def build_model(bert_layer, max_len=512):\n",
-    "    input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n",
-    "    input_mask = Input(shape=(max_len,), dtype=tf.int32, name=\"input_mask\")\n",
-    "    segment_ids = Input(shape=(max_len,), dtype=tf.int32, name=\"segment_ids\")\n",
-    "\n",
-    "    _, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n",
-    "    clf_output = sequence_output[:, 0, :]\n",
-    "    out = Dense(1, activation='sigmoid')(clf_output)\n",
-    "    \n",
-    "    model = Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n",
-    "    model.compile(Adam(lr=2e-6), loss='binary_crossentropy', metrics=['accuracy'])\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 53
-    },
-    "colab_type": "code",
-    "id": "ukl7WfAiFtqL",
-    "outputId": "94997e5e-b150-4d00-ba59-ba515d56a169"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 9.67 s, sys: 2.01 s, total: 11.7 s\n",
-      "Wall time: 13 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "module_url = \"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1\"\n",
-    "bert_layer = hub.KerasLayer(module_url, trainable=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "l-N0Z4J_62Nh"
-   },
-   "outputs": [],
-   "source": [
-    "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
-    "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
-    "tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This next cell is what separates this notebook from the Kaggle code. The function used to generated neighborhoods is defined. A small lemmatization function is defined and used in the generator function in order to extract a keyword index. The basic BERT tokenizer in conjunction with some regex filtering is used to clean and tokenize the text, again to find the keyword index. Once the keyword index is found, the neighborhood is generated by finding how many tokens to the left and how many tokens to the right to use as the neighborhood. These tokens are joined together with spaces and returned as output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "x66XjPOMFvMd"
-   },
-   "outputs": [],
-   "source": [
-    "def lemmatize(x):\n",
-    "  lemmatizer = WordNetLemmatizer()\n",
-    "  return lemmatizer.lemmatize(lemmatizer.lemmatize(x,pos='v'))\n",
-    "\n",
-    "kwds = 'blackout duke dukeenergy electric electricity fpl outage power'.split()\n",
-    "basic_tok = tokenization.BasicTokenizer()\n",
-    "\n",
-    "def neighborhood_generator(text,kwd):\n",
-    "    parse_seq = r'[-_+<=>\\[\\]{}`&;\\/()#!@,.\\n?]|\\x80|\\:\\/\\/.*$'\n",
-    "    split_tokens = text.lower().split()\n",
-    "    stop_words = set(stopwords.words('english'))\n",
-    "    kwd_idx = 0\n",
-    "\n",
-    "    def tok_process(tok): #remove special chars, apply basic BERT tokenizer, lemmatize, and remove stop words\n",
-    "        parse_tok = re.sub(parse_seq, ' ', tok)\n",
-    "        tok_tokens = basic_tok.tokenize(parse_tok) #tokens of the token!\n",
-    "        tok_lemmatized_tokens = [lemmatize(tok_tok) for tok_tok in tok_tokens]\n",
-    "        # tok_lemmatized_tokens = [tok_tok for tok_tok in tok_lemmatized_tokens if tok_tok not in stop_words]\n",
-    "        return tok_lemmatized_tokens\n",
-    "\n",
-    "    if kwd in split_tokens: #see if we can get away without going through the text processing\n",
-    "        kwd_idx = [idx for idx in range(len(split_tokens)) if split_tokens[idx]==kwd][0]\n",
-    "    else:\n",
-    "        for idx in range(len(split_tokens)):\n",
-    "            tok = split_tokens[idx]\n",
-    "            tok_lemmatized_tokens = tok_process(tok)\n",
-    "            if kwd in tok_lemmatized_tokens:\n",
-    "                kwd_idx = idx\n",
-    "                break\n",
-    "\n",
-    "    neighborhood_radius = 3\n",
-    "    before_kwd = split_tokens[:kwd_idx]\n",
-    "    after_kwd = split_tokens[kwd_idx+1:]\n",
-    "\n",
-    "    before_idx = [idx for idx in range(kwd_idx)][::-1]\n",
-    "    after_idx = [idx for idx in range(kwd_idx+1,len(split_tokens))]\n",
-    "    start_tok_idx = 0 #if search extends beyond startin index, use the first token\n",
-    "    end_tok_idx = len(split_tokens) #if search extends beyond last index, use the last index\n",
-    "    before_sig_toks = 0\n",
-    "    after_sig_toks = 0\n",
-    "\n",
-    "    for idx in before_idx:\n",
-    "        tok = split_tokens[idx]\n",
-    "        tok_lemmatized_tokens = tok_process(tok)\n",
-    "        if len(tok_lemmatized_tokens) > 0: # significant token\n",
-    "            before_sig_toks += 1\n",
-    "            if before_sig_toks == neighborhood_radius:\n",
-    "                start_tok_idx = idx\n",
-    "                break\n",
-    "    \n",
-    "    for idx in after_idx:\n",
-    "        tok = split_tokens[idx]\n",
-    "        tok_lemmatized_tokens = tok_process(tok)\n",
-    "        if len(tok_lemmatized_tokens) > 0:\n",
-    "            after_sig_toks += 1\n",
-    "            if after_sig_toks == neighborhood_radius:\n",
-    "                end_tok_idx = idx\n",
-    "                break\n",
-    "                \n",
-    "    neighborhood = split_tokens[start_tok_idx:end_tok_idx+1]\n",
-    "    neighborhood = ' '.join(neighborhood)\n",
-    "    \n",
-    "    return neighborhood"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Iterate through all the tweets to generate the neighborhoods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "UBn2jMFuBC27"
-   },
-   "outputs": [],
-   "source": [
-    "town = pd.DataFrame(columns=['text']) # a town is a collection of neighborhoods\n",
-    "neighborhood_vals = []\n",
-    "\n",
-    "data = pd.read_csv('irma_power_tweets.csv')\n",
-    "\n",
-    "for idx in data.index:\n",
-    "  row = data.iloc[idx]\n",
-    "  text = row.text\n",
-    "  kwd = row.kwd\n",
-    "  new_row = neighborhood_generator(text,kwd)\n",
-    "  neighborhood_vals.append(new_row)\n",
-    "town.text = neighborhood_vals\n",
-    "town['original_twt'] = data.text\n",
-    "town = town[['original_twt', 'text']]\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "train, test, train_labels, test_labels = train_test_split(town.text, data.topic_related,\n",
-    "                                                          test_size=0.2,\n",
-    "                                                          random_state=42)\n",
-    "\n",
-    "train_input = bert_encode(train, tokenizer, max_len=160)\n",
-    "test_input = bert_encode(test, tokenizer, max_len=160)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gu5PX-saIhSQ"
-   },
-   "source": [
-    "The next three cells are unchanged from the Kaggle code. These blocks build the model and predict the outputs for the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 410
-    },
-    "colab_type": "code",
-    "id": "OYjF80BvIjId",
-    "outputId": "327fa3e6-2fd1-4f53-bca1-8bc2456ca391"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_1\"\n",
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "input_word_ids (InputLayer)     [(None, 160)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "input_mask (InputLayer)         [(None, 160)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "segment_ids (InputLayer)        [(None, 160)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "keras_layer (KerasLayer)        [(None, 768), (None, 109482241   input_word_ids[0][0]             \n",
-      "                                                                 input_mask[0][0]                 \n",
-      "                                                                 segment_ids[0][0]                \n",
-      "__________________________________________________________________________________________________\n",
-      "tf_op_layer_strided_slice_1 (Te [(None, 768)]        0           keras_layer[1][1]                \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_1 (Dense)                 (None, 1)            769         tf_op_layer_strided_slice_1[0][0]\n",
-      "==================================================================================================\n",
-      "Total params: 109,483,010\n",
-      "Trainable params: 109,483,009\n",
-      "Non-trainable params: 1\n",
-      "__________________________________________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = build_model(bert_layer, max_len=160)\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 124
-    },
-    "colab_type": "code",
-    "id": "g3JP8ENtIry4",
-    "outputId": "6360a6df-3da4-4087-ed43-23fc52a2c20e"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/3\n",
-      "94/94 [==============================] - 947s 10s/step - loss: 0.3609 - accuracy: 0.8203 - val_loss: 0.2471 - val_accuracy: 0.9200\n",
-      "Epoch 2/3\n",
-      "94/94 [==============================] - 942s 10s/step - loss: 0.1991 - accuracy: 0.9332 - val_loss: 0.2080 - val_accuracy: 0.9227\n",
-      "Epoch 3/3\n",
-      "94/94 [==============================] - 910s 10s/step - loss: 0.1244 - accuracy: 0.9639 - val_loss: 0.2133 - val_accuracy: 0.9253\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_history = model.fit(\n",
-    "    train_input, train_labels,\n",
-    "    validation_split=0.2,\n",
-    "    epochs=3,\n",
-    "    batch_size=16\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "S3KrM3vnUaru"
-   },
-   "outputs": [],
-   "source": [
-    "test_pred = model.predict(test_input)\n",
-    "train_pred = model.predict(train_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After producing outputs, we're interested in seeing how well the model has done. So the following block defines a scoring function to output some metrics for both the training and test sets. Then a function for drawing a color map is defined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "l9C1RyC0mYLF"
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import itertools\n",
-    "from matplotlib import pyplot as plt\n",
-    "plt.rcParams['figure.figsize'] = [10,10]\n",
-    "\n",
-    "\n",
-    "def scoring(Ytrain, Ytrain_pred, Ytest, Ypred):\n",
-    "    \n",
-    "\n",
-    "    acc_train = accuracy_score(Ytrain, Ytrain_pred)\n",
-    "    prec_train = precision_score(Ytrain, Ytrain_pred)\n",
-    "    rec_train = recall_score(Ytrain, Ytrain_pred)\n",
-    "    f1_train = f1_score(Ytrain, Ytrain_pred)\n",
-    "    \n",
-    "    acc = accuracy_score(Ytest, Ypred)\n",
-    "    prec = precision_score(Ytest, Ypred)\n",
-    "    rec = recall_score(Ytest, Ypred)\n",
-    "    f1 = f1_score(Ytest, Ypred)\n",
-    "\n",
-    "    scores = {'accuracy': acc,\n",
-    "              'precision': prec,\n",
-    "             'recall': rec,\n",
-    "             'f1 score': f1}\n",
-    "\n",
-    "    scores_train = {'accuracy': acc_train,\n",
-    "              'precision': prec_train,\n",
-    "             'recall': rec_train,\n",
-    "             'f1 score': f1_train}\n",
-    "\n",
-    "    print('Ytrain vs Ytrain_pred')\n",
-    "    for metric in scores_train.keys():\n",
-    "        print(f'{metric}: {scores_train[metric]:.2f}')\n",
-    "    \n",
-    "    print('\\nYtest vs Ypred')\n",
-    "    for metric in scores.keys():\n",
-    "        print(f'{metric}: {scores[metric]:.2f}')\n",
-    "        \n",
-    "\n",
-    "\n",
-    "def plot_confusion_matrix(cm, labels: list, normalize=False, title='Confusion Matrix', cmap=plt.cm.Oranges):\n",
-    "    if normalize:\n",
-    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
-    "    plt.figure(figsize=(10,10))\n",
-    "    plt.imshow(cm, interpolation='nearest',cmap=cmap)\n",
-    "    plt.title(title,size=24)\n",
-    "    plt.colorbar(aspect=4)\n",
-    "    tick_marks = np.arange(len(labels))\n",
-    "    plt.xticks(tick_marks, labels, size=14)\n",
-    "    plt.yticks(tick_marks, labels, size=14)\n",
-    "    \n",
-    "    fmt = '.2f' if normalize else 'd' # format of decimal precision to display\n",
-    "    thresh = cm.max()/2 # threshold to change color of text depending on color of cell\n",
-    "    for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): # realistically, cm.shape[0] = cm.shape[1] = len(labels)\n",
-    "        plt.text(j, i, format(cm[i,j], fmt), fontsize=20, # i,j rev bc of diff btwn matrix and list of lists indexing\n",
-    "                 horizontalalignment='center',\n",
-    "                 color='white' if cm[i,j] > thresh else 'black') # if the cell color is too dark, make the text white\n",
-    "    plt.grid(False)\n",
-    "    plt.tight_layout()\n",
-    "    plt.ylabel('True label', size=16)\n",
-    "    plt.xlabel('Predicted label', size=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 759
-    },
-    "colab_type": "code",
-    "id": "YoE6nWXFquEK",
-    "outputId": "8c0d739f-b7fd-40c2-dd16-5e90dae6061f"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ytrain vs Ytrain_pred\n",
-      "accuracy: 0.97\n",
-      "precision: 0.97\n",
-      "recall: 0.99\n",
-      "f1 score: 0.98\n",
-      "\n",
-      "Ytest vs Ypred\n",
-      "accuracy: 0.94\n",
-      "precision: 0.94\n",
-      "recall: 0.98\n",
-      "f1 score: 0.96\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "round_train_pred = np.round(train_pred)\n",
-    "round_test_pred = np.round(test_pred)\n",
-    "scoring(train_labels, round_train_pred, test_labels, round_test_pred)\n",
-    "cm = confusion_matrix(test_labels, round_test_pred)\n",
-    "cm_labels = ['Not topic-related', 'Topic-related']\n",
-    "plot_confusion_matrix(cm, cm_labels, normalize=False,title=f'BERT Confusion Matrix\\nn={len(test_labels)}')"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "machine_shape": "hm",
-   "name": "2nd Copy of BERT final.ipynb",
-   "provenance": []
-  },
-  "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
-- 
2.43.0