{"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)\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\"))\n\n# Any results you write to the current directory are saved as output.\n\nimport re\nimport string\nimport operator\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.preprocessing.text import Tokenizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nfrom sklearn.linear_model import LinearRegression\nfrom keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D\nfrom keras.layers import Bidirectional, GlobalMaxPool1D, GlobalMaxPooling1D, GlobalAveragePooling1D\nfrom keras.layers import Input, Embedding, Dense, Conv2D, MaxPool2D, concatenate, Lambda\nfrom keras.layers import Reshape, Flatten, Concatenate, Dropout, SpatialDropout1D\nfrom keras.optimizers import Adam\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras.engine.topology import Layer\nfrom keras import initializers, regularizers, constraints, optimizers, layers\nfrom keras.layers import concatenate\nimport nltk\nfrom nltk.corpus import stopwords","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/train.csv\")\ntest = pd.read_csv(\"../input/test.csv\")\n\nprint(\"Train shape : \",train.shape)\nprint(\"Test shape : \",test.shape)","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# Tamaño del vector palabra\nEMBED_SIZE = 300\n# Palabras únicas\nMAX_FEATURES = 100000 \n# Longitud máxima de la pregunta\nMAXLEN = 40 ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Seguir el vocabulario de entrenamiento, que recorre todo el texto y cuenta los casos de las palabras contenidas\ndef build_vocab(texts):\n   \n    sentences = texts.apply(lambda x: x.split()).values\n    vocab = {}\n    for sentence in sentences:\n        for word in sentence:\n            try:\n                vocab[word] += 1\n            except KeyError:\n                vocab[word] = 1\n    return vocab","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Completar vocabulario\ndf = pd.concat([train ,test], sort=False)\nvocab = build_vocab(df['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def load_embed(file):\n    def get_coefs(word,*arr): \n        return word, np.asarray(arr, dtype='float32')\n    \n    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(file, encoding='latin'))\n    \n    return embeddings_index","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"glove = '../input/embeddings/glove.840B.300d/glove.840B.300d.txt'\n\nembed_glove = load_embed(glove)\n\nlen(embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def load_glove_matrix(word_index, embeddings_index):\n\n    all_embs = np.stack(embeddings_index.values())\n    emb_mean, emb_std = all_embs.mean(), all_embs.std()\n    EMBED_SIZE = all_embs.shape[1]\n    \n    nb_words = min(MAX_FEATURES, len(word_index))\n    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, EMBED_SIZE))\n\n    for word, i in word_index.items():\n        if i >= MAX_FEATURES:\n            continue\n        embedding_vector = embeddings_index.get(word)\n        if embedding_vector is not None:\n            embedding_matrix[i] = embedding_vector\n\n    return embedding_matrix","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndef check_coverage(vocab, embeddings_index):\n    known_words = {}\n    unknown_words = {}\n    nb_known_words = 0\n    nb_unknown_words = 0\n    for word in vocab.keys():\n        try:\n            known_words[word] = embeddings_index[word]\n            nb_known_words += vocab[word]\n        except:\n            unknown_words[word] = vocab[word]\n            nb_unknown_words += vocab[word]\n            pass\n\n    print('Found embeddings for {:.3%} of vocabulary'.format(len(known_words)/len(vocab)))\n    print('Found embeddings for {:.3%} of all text'.format(nb_known_words/(nb_known_words + nb_unknown_words)))\n    unknown_words = sorted(unknown_words.items(), key=operator.itemgetter(1))[::-1]\n\n    return unknown_words","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Glove: \")\noov_glove = check_coverage(vocab, embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def add_lower(embedding, vocab):\n    count = 0\n    for word in vocab:\n        if word in embedding and word.lower() not in embedding:  \n            embedding[word.lower()] = embedding[word]\n            count += 1\n    print(f\"Added {count} words to embedding\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['question_text'] = train['question_text'].apply(lambda x: x.lower())\ntest['question_text'] = test['question_text'].apply(lambda x: x.lower())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Glove: \")\noov_glove = check_coverage(vocab, embed_glove)\nadd_lower(embed_glove, vocab) # mismo vocabulario\noov_glove = check_coverage(vocab, embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"contraction_mapping = {\n    \"ain't\": \"is not\",\n    \"aren't\": \"are not\",\n    \"can't\": \"cannot\",\n    \"'cause\": \"because\",\n    \"could've\": \"could have\",\n    \"couldn't\": \"could not\",\n    \"didn't\": \"did not\",\n    \"doesn't\": \"does not\",\n    \"don't\": \"do not\",\n    \"hadn't\": \"had not\",\n    \"hasn't\": \"has not\",\n    \"haven't\": \"have not\",\n    \"he'd\": \"he would\",\n    \"he'll\": \"he will\",\n    \"he's\": \"he is\",\n    \"how'd\": \"how did\",\n    \"how'd'y\": \"how do you\",\n    \"how'll\": \"how will\",\n    \"how's\": \"how is\",\n    \"I'd\": \"I would\",\n    \"I'd've\": \"I would have\",\n    \"I'll\": \"I will\",\n    \"I'll've\": \"I will have\",\n    \"I'm\": \"I am\",\n    \"I've\": \"I have\",\n    \"i'd\": \"i would\",\n    \"i'd've\": \"i would have\",\n    \"i'll\": \"i will\",\n    \"i'll've\": \"i will have\",\n    \"i'm\": \"i am\",\n    \"i've\": \"i have\",\n    \"isn't\": \"is not\",\n    \"it'd\": \"it would\",\n    \"it'd've\": \"it would have\",\n    \"it'll\": \"it will\",\n    \"it'll've\": \"it will have\",\n    \"it's\": \"it is\",\n    \"let's\": \"let us\",\n    \"ma'am\": \"madam\",\n    \"mayn't\": \"may not\",\n    \"might've\": \"might have\",\n    \"mightn't\": \"might not\",\n    \"mightn't've\": \"might not have\",\n    \"must've\": \"must have\",\n    \"mustn't\": \"must not\",\n    \"mustn't've\": \"must not have\",\n    \"needn't\": \"need not\",\n    \"needn't've\": \"need not have\",\n    \"o'clock\": \"of the clock\",\n    \"oughtn't\": \"ought not\",\n    \"oughtn't've\": \"ought not have\",\n    \"shan't\": \"shall not\",\n    \"sha'n't\": \"shall not\",\n    \"shan't've\": \"shall not have\",\n    \"she'd\": \"she would\",\n    \"she'd've\": \"she would have\",\n    \"she'll\": \"she will\",\n    \"she'll've\": \"she will have\",\n    \"she's\": \"she is\",\n    \"should've\": \"should have\",\n    \"shouldn't\": \"should not\",\n    \"shouldn't've\": \"should not have\",\n    \"so've\": \"so have\",\n    \"so's\": \"so as\",\n    \"this's\": \"this is\",\n    \"that'd\": \"that would\",\n    \"that'd've\": \"that would have\",\n    \"that's\": \"that is\",\n    \"there'd\": \"there would\",\n    \"there'd've\": \"there would have\",\n    \"there's\": \"there is\",\n    \"here's\": \"here is\",\n    \"they'd\": \"they would\",\n    \"they'd've\": \"they would have\",\n    \"they'll\": \"they will\",\n    \"they'll've\": \"they will have\",\n    \"they're\": \"they are\",\n    \"they've\": \"they have\",\n    \"to've\": \"to have\",\n    \"wasn't\": \"was not\",\n    \"we'd\": \"we would\",\n    \"we'd've\": \"we would have\",\n    \"we'll\": \"we will\",\n    \"we'll've\": \"we will have\",\n    \"we're\": \"we are\",\n    \"we've\": \"we have\",\n    \"weren't\": \"were not\",\n    \"what'll\": \"what will\",\n    \"what'll've\": \"what will have\",\n    \"what're\": \"what are\",\n    \"what's\": \"what is\",\n    \"what've\": \"what have\",\n    \"when's\": \"when is\",\n    \"when've\": \"when have\",\n    \"where'd\": \"where did\",\n    \"where's\": \"where is\",\n    \"where've\": \"where have\",\n    \"who'll\": \"who will\",\n    \"who'll've\": \"who will have\",\n    \"who's\": \"who is\",\n    \"who've\": \"who have\",\n    \"why's\": \"why is\",\n    \"why've\": \"why have\",\n    \"will've\": \"will have\",\n    \"won't\": \"will not\",\n    \"won't've\": \"will not have\",\n    \"would've\": \"would have\",\n    \"wouldn't\": \"would not\",\n    \"wouldn't've\": \"would not have\",\n    \"y'all\": \"you all\",\n    \"y'all'd\": \"you all would\",\n    \"y'all'd've\": \"you all would have\",\n    \"y'all're\": \"you all are\",\n    \"y'all've\": \"you all have\",\n    \"you'd\": \"you would\",\n    \"you'd've\": \"you would have\",\n    \"you'll\": \"you will\",\n    \"you'll've\": \"you will have\",\n    \"you're\": \"you are\",\n    \"you've\": \"you have\",\n    \"who'd\": \"who would\",\n    \"who're\": \"who are\",\n    \"'re\": \" are\",\n    \"tryin'\": \"trying\",\n    \"doesn'\": \"does not\",\n    'howdo': 'how do',\n    'whatare': 'what are',\n    'howcan': 'how can',\n    'howmuch': 'how much',\n    'howmany': 'how many',\n    'whydo': 'why do',\n    'doI': 'do I',\n    'theBest': 'the best',\n    'howdoes': 'how does',\n}\nlen(contraction_mapping)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def known_contractions(embed):\n    known = []\n    for contract in contraction_mapping:\n        if contract in embed:\n            known.append(contract)\n    return known\n#Contracciones conocidas de glove\nprint(known_contractions(embed_glove))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def clean_contractions(text, mapping):\n    specials = [\"’\", \"‘\", \"´\", \"`\"]\n    for s in specials:\n        text = text.replace(s, \"'\")\n    text = ' '.join([mapping[t] if t in mapping else t for t in text.split(\" \")])\n    return text","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['question_text'] = train['question_text'].apply(lambda x: clean_contractions(x, contraction_mapping))\ntest['question_text'] = test['question_text'].apply(lambda x: clean_contractions(x, contraction_mapping))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.concat([train ,test], sort=False)\nvocab = build_vocab(df['question_text']) #crear vocabulario con nuevas preguntas tratadas\nprint(\"Glove: \")\noov_glove = check_coverage(vocab, embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"punct_mapping = \"/-'?!.,#$%\\'()*+-/:;<=>@[\\\\]^_`{|}~\" + '\"\"“”’' + '∞θ÷α•à−β∅³π‘₹´°£€\\×™√²—–&'\npunct_mapping += '©^®` <→°€™› ♥←ק″′Â█½à…“★”–●â►−¢²¬░¶↑±¿▾═¦║―¥▓—‹─▒：¼⊕▼▪†■’▀¨▄♫☆é¯♦¤▲踾Ã⋅‘∞∙）↓、│（»，♪╩╚³・╦╣╔╗▬❤ïع≤‡√'\n\ndef unknown_punct(embed, punct):\n    unknown = ''\n    for p in punct:\n        if p not in embed:\n            unknown += p\n            unknown += ' '\n    return unknown\n\nprint(unknown_punct(embed_glove, punct_mapping))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"puncts = {\"‘\": \"'\", \"´\": \"'\", \"°\": \"\", \"€\": \"e\", \"—\": \"-\", \"–\": \"-\", \"’\": \"'\", \"_\": \"-\", \"`\": \"'\", '“': '\"', '”': '\"', '“': '\"', \"£\": \"e\", '∞': 'infinity', 'θ': 'theta', '÷': '/', 'α': 'alpha', '•': '.', 'à': 'a', '−': '-', 'β': 'beta', '∅': '', '³': '3', 'π': 'pi', '…': ' '}\n\ndef clean_special_chars(text, punct, mapping):\n    for p in mapping:\n        text = text.replace(p, mapping[p])\n    \n    for p in punct:\n        text = text.replace(p, f' {p} ')\n    \n    return text","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['question_text'] = train['question_text'].apply(lambda x: clean_special_chars(x, punct_mapping, puncts))\ntest['question_text'] = test['question_text'].apply(lambda x: clean_special_chars(x, punct_mapping, puncts))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.concat([train ,test], sort=False)\nvocab = build_vocab(df['question_text'])\nprint(\"Glove: \")\noov_glove = check_coverage(vocab, embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head(5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train, val = train_test_split(train, test_size=0.2, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Completar los valores que faltan en caso de que existan Na\nxtrain = train['question_text'].fillna('_na_').values\nxval = val['question_text'].fillna('_na_').values\nxtest = test['question_text'].fillna('_na_').values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Tokenizar oraciones\ntokenizer = Tokenizer(num_words=MAX_FEATURES)\ntokenizer.fit_on_texts(list(xtrain))\n\nxtrain = tokenizer.texts_to_sequences(xtrain)\nxval = tokenizer.texts_to_sequences(xval)\nxtest = tokenizer.texts_to_sequences(xtest)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"xtrain = pad_sequences(xtrain, maxlen=MAXLEN)\nxval = pad_sequences(xval, maxlen=MAXLEN)\nxtest = pad_sequences(xtest, maxlen=MAXLEN)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ytrain = train['target'].values\nyval = val['target'].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.random.seed(42)\n\ntrn_idx = np.random.permutation(len(xtrain))\nval_idx = np.random.permutation(len(xval))\n\nxtrain = xtrain[trn_idx]\nytrain = ytrain[trn_idx]\nxval = xval[val_idx]\nyval = yval[val_idx]\n\nembedding_matrix_glove = load_glove_matrix(tokenizer.word_index, embed_glove)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class 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        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},"cell_type":"code","source":"def f1(y_true, y_pred):\n\n    def recall(y_true, y_pred):\n        \n        true_positives = K.sum(K.round(K.clip(y_true*y_pred, 0, 1)))\n        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n        recall = true_positives/(possible_positives + K.epsilon())\n        return recall\n\n    def precision(y_true, y_pred):\n        \n        true_positives = K.sum(K.round(K.clip(y_true*y_pred, 0, 1)))\n        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n        precision = true_positives/(predicted_positives + K.epsilon())\n        return precision\n\n    precision = precision(y_true, y_pred)\n    recall = recall(y_true, y_pred)\n\n    return 2*((precision*recall)/(precision+recall+K.epsilon()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def model_lstm_att(embedding_matrix):\n    \n    inp = Input(shape=(MAXLEN,))\n    x = Embedding(MAX_FEATURES, EMBED_SIZE, weights=[embedding_matrix], trainable=False)(inp)\n    x = Bidirectional(CuDNNLSTM(64, return_sequences=True))(x)\n    x = Bidirectional(CuDNNLSTM(32, return_sequences=True))(x)\n    \n    att = Attention(MAXLEN)(x)\n    \n    y = Dense(32, activation='relu')(att)\n    y = Dropout(0.1)(y)\n    outp = Dense(1, activation='sigmoid')(y)    \n\n    model = Model(inputs=inp, outputs=outp)\n    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])\n    \n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def train_pred(model, epochs=2):\n    \n    for e in range(epochs):\n        model.fit(xtrain, ytrain, batch_size=512, epochs=3, validation_data=(xval, yval))\n        pred_val_y = model.predict([xval], batch_size=1024, verbose=1)\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(yval, (pred_val_y > thresh).astype(int))\n            if score > best_score:\n                best_thresh = thresh\n                best_score = score\n\n        print(\"Val puntuacion F1: {:.4f}\".format(best_score))\n\n    pred_test_y = model.predict([xtest], batch_size=1024, verbose=1)\n\n    return pred_val_y, pred_test_y, best_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"paragram = '../input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'\nembedding_matrix_para = load_glove_matrix(tokenizer.word_index, load_embed(paragram))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedding_matrix = np.mean([embedding_matrix_glove, embedding_matrix_para], axis=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_lstm = model_lstm_att(embedding_matrix)\nmodel_lstm.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"outputs = []\npred_val_y, pred_test_y, best_score = train_pred(model_lstm, epochs=3)\noutputs.append([pred_val_y, pred_test_y, best_score, 'model_lstm_att only Glove'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"outputs.sort(key=lambda x: x[2]) \nweights = [i for i in range(1, len(outputs) + 1)]\nweights = [float(i) / sum(weights) for i in weights] \n\npred_val_y = np.mean([outputs[i][0] for i in range(len(outputs))], axis = 0)\n\nthresholds = []\nfor thresh in np.arange(0.1, 0.501, 0.01):\n    thresh = np.round(thresh, 2)\n    res = metrics.f1_score(yval, (pred_val_y > thresh).astype(int))\n    thresholds.append([thresh, res])\n    print(\"La puntuación F1 en los limites {0} y {1}\".format(thresh, res))\n    \nthresholds.sort(key=lambda x: x[1], reverse=True)\nbest_thresh = thresholds[0][0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Mejor limite:\", best_thresh, \"y puntuacion F1 \", thresholds[0][1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_test_y = np.mean([outputs[i][1] for i in range(len(outputs))], axis = 0)\npred_test_y = (pred_test_y > best_thresh).astype(int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.read_csv('../input/sample_submission.csv')\nout_df = pd.DataFrame({\"qid\":sub[\"qid\"].values})\nout_df['prediction'] = pred_test_y\nout_df.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}