train-word2vec-bigru.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom gensim.models import Word2Vec\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport time\nimport nltk, re, string\nfrom sklearn.model_selection import train_test_split\nimport gc\nfrom keras.layers import *\nfrom keras.models import *\nfrom sklearn import metrics\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"max_features = 95000 # how many unique words to use (i.e num rows in embedding vector)\nmaxlen = 70 # max number of words in a question to use","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"ff4c9c49bfd826714f42da6fbca6f27af3cc4dba"},"cell_type":"code","source":"puncts = [',', '.', '\"', ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\\\', '•',  '~', '@', '£', \n '·', '_', '{', '}', '©', '^', '®', '`',  '<', '→', '°', '€', '™', '›',  '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', \n '“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', \n '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', \n '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]\ndef clean_text(x):\n    x = str(x)\n    for punct in puncts:\n        x = x.replace(punct, f' {punct} ')\n    return x\n\nmispell_dict = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \n                \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \n                \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \n                \"how's\": \"how is\",  \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \n                \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \n                \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\", \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\n                \"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\n                \"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \n                \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \n                \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \n                \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\", \"should've\": \"should have\", \"shouldn't\": \"should not\", \n                \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\", \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \n                \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \n                \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \n                \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \n                \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",  \"what's\": \"what is\", \"what've\": \"what have\", \n                \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \n                \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\", \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\", 'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', \"mastrubating\": 'masturbating', 'pennis': 'penis', 'Etherium': 'Ethereum', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}\n\ndef _get_mispell(mispell_dict):\n    mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))\n    return mispell_dict, mispell_re\n\nmispellings, mispellings_re = _get_mispell(mispell_dict)\ndef replace_typical_misspell(text):\n    def replace(match):\n        return mispellings[match.group(0)]\n    return mispellings_re.sub(replace, text)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"af0ca46678691d39970fdb25e62b1ac1a6c9bd00"},"cell_type":"code","source":"train_df = pd.read_csv('../input/train.csv')\ntest_df = pd.read_csv('../input/test.csv')\n\ntrain_df[\"question_text\"] = train_df[\"question_text\"].str.lower()\ntest_df[\"question_text\"] = test_df[\"question_text\"].str.lower()\n\ntrain_df[\"question_text\"] = train_df[\"question_text\"].apply(lambda x: clean_text(x))\ntest_df[\"question_text\"] = test_df[\"question_text\"].apply(lambda x: clean_text(x))\n\ntrain_df[\"question_text\"] = train_df[\"question_text\"].apply(lambda x: replace_typical_misspell(x))\ntest_df[\"question_text\"] = test_df[\"question_text\"].apply(lambda x: replace_typical_misspell(x))\n\ntrain_df, val_df = train_test_split(train_df, stratify=train_df[\"target\"], test_size=0.005, random_state=2018)\n\ntrain_X = train_df[\"question_text\"].fillna(\"_##_\").values\nval_X = val_df[\"question_text\"].fillna(\"_##_\").values\ntest_X = test_df[\"question_text\"].fillna(\"_##_\").values\n\ntrain_y = train_df['target'].values\nval_y = val_df['target'].values \n\n#shuffling the data\nnp.random.seed(2018)\ntrn_idx = np.random.permutation(len(train_X))\nval_idx = np.random.permutation(len(val_X))\n\ntrain_X = train_X[trn_idx]\nval_X = val_X[val_idx]\ntrain_y = train_y[trn_idx]\nval_y = val_y[val_idx]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b66afc3081c83db2ee6479a8a23c22b579462cab"},"cell_type":"code","source":"tokenizer = Tokenizer(num_words=max_features)\ntokenizer.fit_on_texts(list(train_X)+list(test_X)+list(val_X))\ntrain_X = tokenizer.texts_to_sequences(train_X)\nval_X = tokenizer.texts_to_sequences(val_X)\ntest_X = tokenizer.texts_to_sequences(test_X)\n\ntrain_X = pad_sequences(train_X, maxlen=maxlen)\nval_X = pad_sequences(val_X, maxlen=maxlen)\ntest_X = pad_sequences(test_X, maxlen=maxlen)\n\nword_index = tokenizer.word_index","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"10b03f2ee9cab878764f57ca30a95e8dab2ec57b"},"cell_type":"code","source":"count = 0\nnb_words = len(word_index)  # 200593\nprint(nb_words)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8eae74349cfd613015e9b3b460924065a103ba9e"},"cell_type":"code","source":"start =time.clock()\nall_data=pd.concat([train_df[\"question_text\"],test_df[\"question_text\"]])\n# file_name = './word2vec.model'\n# if not os.path.exists(file_name):\nmodel = Word2Vec([[word for word in sentence.split(' ')] for sentence in all_data.values],\n                     size=200, window=5, iter=10, workers=11, seed=2018, min_count=2)\n#     model.save(file_name)\n# else:\n#     model = Word2Vec.load(file_name)\nprint(\"add word2vec finished....\")  \nend = time.clock()\nprint('Running time: %s Seconds'%(end-start))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b95fe0bf3f42a8e48f7b977065100806a2e16c4e"},"cell_type":"code","source":"# req_count = 10\n# for key in model.wv.similar_by_word('tony', topn =100):\n#     if len(key[0])==3:\n#         req_count -= 1\n#         print(key[0], key[1])\n#         if req_count == 0:\n#             break;","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"508140df74e679c71277be0b2861edd6e25e8b43"},"cell_type":"code","source":"# del train_df; gc.collect()\n# del test_df; gc.collect()\n# del all_data; gc.collect()\ndel tokenizer; gc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5a43c3328242d7616b3e4a17276348d6da5fe70c"},"cell_type":"code","source":"nb_words = min(max_features, len(word_index))\nembedding_word2vec_matrix = np.zeros((nb_words, 200))  # (95000, 200)\nfor word, i in word_index.items():\n    if i >= max_features: continue\n    embedding_vector = model[word] if word in model else None\n    if embedding_vector is not None:\n        count += 1\n        embedding_word2vec_matrix[i] = embedding_vector\n    else:\n        unk_vec = np.random.random(200) * 0.5\n        unk_vec = unk_vec - unk_vec.mean()\n        embedding_word2vec_matrix[i] = unk_vec","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7b3c9307e37da6202ac066a202d74da08ea9222b"},"cell_type":"code","source":"print(embedding_word2vec_matrix.shape)\ndel model; gc.collect()  # biyao","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2588d34f4c12dfe661251c77c9869d472c516c77"},"cell_type":"code","source":"EMBEDDING_FILE = '../input/embeddings/glove.840B.300d/glove.840B.300d.txt'\ndef get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\nembeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE))\n\nall_embs = np.stack(embeddings_index.values())\nemb_mean,emb_std = -0.005838499,0.48782197\nembed_size = all_embs.shape[1]\n\n# word_index = tokenizer.word_index\nnb_words = min(max_features, len(word_index))\nembedding_glove_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\nfor word, i in word_index.items():\n    if i >= max_features: continue\n    embedding_vector = embeddings_index.get(word)\n    if embedding_vector is not None: embedding_glove_matrix[i] = embedding_vector","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d7546ee9894b9126abd75cefa1fa40d5e686323e"},"cell_type":"code","source":"embedding_matrix=np.concatenate((embedding_word2vec_matrix,embedding_glove_matrix),axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"dc1fbedf498ea8c7c9fbd305572d8cbb512ad7f4"},"cell_type":"code","source":"# https://www.kaggle.com/suicaokhoailang/lstm-attention-baseline-0-652-lb\n\nclass Attention(Layer):\n    def __init__(self, step_dim,\n                 W_regularizer=None, b_regularizer=None,\n                 W_constraint=None, b_constraint=None,\n                 bias=True, **kwargs):\n        self.supports_masking = True\n        self.init = initializers.get('glorot_uniform')\n\n        self.W_regularizer = regularizers.get(W_regularizer)\n        self.b_regularizer = regularizers.get(b_regularizer)\n\n        self.W_constraint = constraints.get(W_constraint)\n        self.b_constraint = constraints.get(b_constraint)\n\n        self.bias = bias\n        self.step_dim = step_dim\n        self.features_dim = 0\n        super(Attention, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        assert len(input_shape) == 3\n\n        self.W = self.add_weight((input_shape[-1],),\n                                 initializer=self.init,\n                                 name='{}_W'.format(self.name),\n                                 regularizer=self.W_regularizer,\n                                 constraint=self.W_constraint)\n        self.features_dim = input_shape[-1]\n\n        if self.bias:\n            self.b = self.add_weight((input_shape[1],),\n                                     initializer='zero',\n                                     name='{}_b'.format(self.name),\n                                     regularizer=self.b_regularizer,\n                                     constraint=self.b_constraint)\n        else:\n            self.b = None\n\n        self.built = True\n\n    def compute_mask(self, input, input_mask=None):\n        return None\n\n    def call(self, x, mask=None):\n        features_dim = self.features_dim\n        step_dim = self.step_dim\n\n        eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),\n                        K.reshape(self.W, (features_dim, 1))), (-1, step_dim))\n\n        if self.bias:\n            eij += self.b\n\n        eij = K.tanh(eij)\n\n        a = K.exp(eij)\n\n        if mask is not None:\n            a *= K.cast(mask, K.floatx())\n\n        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())\n\n        a = K.expand_dims(a)\n        weighted_input = x * a\n        return K.sum(weighted_input, axis=1)\n\n    def compute_output_shape(self, input_shape):\n        return input_shape[0],  self.features_dim\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"554257682289a7a870623035af90464d5a2e1ad6"},"cell_type":"code","source":"def bi_gru_model(embeddings_weight):\n    content = Input(shape=(maxlen,))\n    embedding = Embedding(\n        name=\"word_embedding\",\n        input_dim=embeddings_weight.shape[0],\n        weights=[embeddings_weight],\n        output_dim=embeddings_weight.shape[1],\n        trainable=False)\n#     embedding = Embedding(max_features, 500, weights=[embeddings_weight], trainable=False)(content)\n    x = SpatialDropout1D(0.2)(embedding(content))\n    x = Bidirectional(CuDNNGRU(200, return_sequences=True))(x)\n    x = Bidirectional(CuDNNGRU(200, return_sequences=True))(x)\n    avg_pool = GlobalAveragePooling1D()(x)\n    max_pool = GlobalMaxPooling1D()(x)\n    conc = concatenate([avg_pool, max_pool])\n    print(\"conc==\", conc.shape)\n    x = Dropout(0.2)(Activation(activation=\"relu\")(BatchNormalization()(Dense(1000)(conc))))\n    print(\"x shape==\", x.shape)\n    x = Activation(activation=\"relu\")(BatchNormalization()(Dense(500)(x)))\n\n    output = Dense(1, activation=\"sigmoid\")(x)\n\n    model = Model(inputs=content, outputs=output)\n#     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"629f528df0111f4f86b827fb34a069f30a28601e"},"cell_type":"code","source":"# https://www.kaggle.com/strideradu/word2vec-and-gensim-go-go-go\ndef train_pred(model, epochs=2):\n    for e in range(epochs):\n        model.fit(train_X, train_y, batch_size=512, epochs=1, validation_data=(val_X, val_y))\n        pred_val_y = model.predict([val_X], batch_size=1024, verbose=0)\n\n        best_thresh = 0.5\n        best_score = 0.0\n        for thresh in np.arange(0.1, 0.501, 0.01):\n            thresh = np.round(thresh, 2)\n            score = metrics.f1_score(val_y, (pred_val_y > thresh).astype(int))\n            if score > best_score:\n                best_thresh = thresh\n                best_score = score\n\n        print(\"Val F1 Score: {:.4f}\".format(best_score))\n\n    pred_test_y = model.predict([test_X], batch_size=1024, verbose=0)\n    return pred_val_y, pred_test_y, best_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2e915dff1a44248ea4fbf92fea705a7889ffb7ff"},"cell_type":"code","source":"outputs = []","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7bafba6199132665caac3b5c6b0288a060511335"},"cell_type":"code","source":"pred_val_y, pred_test_y, best_score = train_pred(bi_gru_model(embedding_matrix), epochs = 1)\noutputs.append([pred_val_y, pred_test_y, best_score, 'bi_gru_model'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"92551dd3876a0666b8803b87ccfd43d88a26635f"},"cell_type":"code","source":"for output in outputs:\n    print(output[2], output[3])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9a42d0863c1dad5f7fdc08abfb5f37c176a2acfa"},"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nX = np.asarray([outputs[i][0] for i in range(len(outputs))])\nX = X[...,0]\nreg = LinearRegression().fit(X.T, val_y)\nprint(reg.score(X.T, val_y),reg.coef_)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3f0ad603119759485bbeb7e528c6083e53415539"},"cell_type":"code","source":"pred_val_y = np.sum([outputs[i][0] * reg.coef_[i] for i in range(len(outputs))], axis = 0)\n# pred_val_y = np.mean([outputs[i][0] for i in range(len(outputs))], axis = 0) # to avoid overfitting, just take average\n\nthresholds = []\nfor thresh in np.arange(0.1, 0.501, 0.01):\n    thresh = np.round(thresh, 2)\n    res = metrics.f1_score(val_y, (pred_val_y > thresh).astype(int))\n    thresholds.append([thresh, res])\n    print(\"F1 score at threshold {0} is {1}\".format(thresh, res))\n    \nthresholds.sort(key=lambda x: x[1], reverse=True)\nbest_thresh = thresholds[0][0]\nprint(\"Best threshold: \", best_thresh)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"71a307879f4371ea1024be1cbe7f58355285e93b"},"cell_type":"code","source":"pred_test_y = np.sum([outputs[i][1] * reg.coef_[i] for i in range(len(outputs))], axis = 0)\n# pred_test_y = np.mean([outputs[i][1] for i in range(len(outputs))], axis = 0)\n\npred_test_y = (pred_test_y > best_thresh).astype(int)\ntest_df = pd.read_csv(\"../input/test.csv\", usecols=[\"qid\"])\nout_df = pd.DataFrame({\"qid\":test_df[\"qid\"].values})\nout_df['prediction'] = pred_test_y\nout_df.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c206e484ac710b209fd60c2c039fcd9393739844"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d2e5ee14d0f85c09fd64d91e3ed2cfde0764b6bd"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"e979918a60393d47edc34f4baed2e47495c91b78"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"91399cc7f56b43c835eca21df841e4d7b658368c"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0cab039061f99fa2a55e851b86bcfd9f36c19500"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0bea2fa07c6a7fe4187f297670182630004241c7"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bdfa47226fba33b27a64e2c15a006d4371620235"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}