{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"markdown","source":"OK, the title is just for fun. Basically I am mixing a lot of awesome kernels together.\n\nhttps://www.kaggle.com/tunguz/lstm-gru-why-not-both \n\nhttps://www.kaggle.com/shujian/single-rnn-with-4-folds-clr\n\nhttps://www.kaggle.com/gmhost/gru-capsule\n\nhttps://www.kaggle.com/nikhilroxtomar/gru-with-kfold-lb-0-689\n\nhttps://www.kaggle.com/theoviel/improve-your-score-with-text-preprocessing-v2\n\n......\n"},{"metadata":{"trusted":true,"_uuid":"151f67fbe66328d3e4fa0732d8c7b78168949052"},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nfrom tqdm import tqdm\nimport math\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nfrom sklearn.model_selection import GridSearchCV, StratifiedKFold\nfrom sklearn.metrics import f1_score, roc_auc_score\n\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\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, add, BatchNormalization\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.initializers import glorot_normal, orthogonal\nfrom keras.layers import concatenate\nfrom keras.callbacks import *\n\nimport tensorflow as tf\n\nimport re\nimport time\nimport os\nprint(os.listdir(\"../input\"))\n\nimport gc","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"## some config values \nembed_size = 300 # how big is each word vector\nmax_features = 95000 # how many unique words to use (i.e num rows in embedding vector)\nmaxlen = 72 # max number of words in a question to use\n\ntrain_batch_size = 448\ntrain_epochs = 6\n\nSEED = 2018","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2595e4c6cb2932380d91a136dc251445d1a95a06"},"cell_type":"code","source":"puncts = [',', '.', '\"', ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\\\', '•',  '~', '@', '£', \n '·', '_', '{', '}', '©', '^', '®', '`',  '<', '→', '°', '€', '™', '›',  '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', \n '“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', \n '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', \n '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]\n\ndef clean_text(x):\n    x = str(x)\n    for punct in puncts:\n        x = x.replace(punct, f' {punct} ')\n    return x\n\ndef clean_numbers(x):\n    x = re.sub('[0-9]{5,}', '#####', x)\n    x = re.sub('[0-9]{4}', '####', x)\n    x = re.sub('[0-9]{3}', '###', x)\n    x = re.sub('[0-9]{2}', '##', x)\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\", \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"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\", \"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\", \"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\", \"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\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"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\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\", \"should've\": \"should have\", \"shouldn't\": \"should not\", \"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\", \"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\", \"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\", \"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\", \"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\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"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":"78cb2dbb8d2e3c02be48fd36e26328c8c255db14"},"cell_type":"code","source":"def load_and_prec():\n    train_df = pd.read_csv(\"../input/train.csv\")\n    test_df = pd.read_csv(\"../input/test.csv\")\n    print(\"Train shape : \",train_df.shape)\n    print(\"Test shape : \",test_df.shape)\n    \n    # lower\n    train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: x.lower())\n    test_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: x.lower())\n    \n    # Clean the text\n    train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: clean_text(x))\n    test_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: clean_text(x))\n    \n    # Clean numbers\n    train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: clean_numbers(x))\n    test_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: clean_numbers(x))\n    \n    # Clean speelings\n    train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: replace_typical_misspell(x))\n    test_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: replace_typical_misspell(x))\n    \n    ## fill up the missing values\n    train_X = train_df[\"question_text\"].fillna(\"_##_\").values\n    test_X = test_df[\"question_text\"].fillna(\"_##_\").values\n\n    ## Tokenize the sentences\n    tokenizer = Tokenizer(num_words=max_features)\n    tokenizer.fit_on_texts(list(train_X))\n    train_X = tokenizer.texts_to_sequences(train_X)\n    test_X = tokenizer.texts_to_sequences(test_X)\n\n    ## Pad the sentences \n    train_X = pad_sequences(train_X, maxlen=maxlen)\n    test_X = pad_sequences(test_X, maxlen=maxlen)\n\n    ## Get the target values\n    train_y = train_df['target'].values\n    \n    #shuffling the data\n    np.random.seed(SEED)\n    trn_idx = np.random.permutation(len(train_X))\n\n    train_X = train_X[trn_idx]\n    train_y = train_y[trn_idx]\n    \n    return train_X, test_X, train_y, tokenizer.word_index","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8911163d4cf6c5d420b7e0fcf9cbc54e911682d5"},"cell_type":"code","source":"def load_glove(word_index):\n    EMBEDDING_FILE = '../input/embeddings/glove.840B.300d/glove.840B.300d.txt'\n    def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE))\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    # word_index = tokenizer.word_index\n    nb_words = min(max_features, len(word_index))\n    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\n    for 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_matrix[i] = embedding_vector\n            \n    return embedding_matrix \n\ndef load_para(word_index):\n    EMBEDDING_FILE = '../input/embeddings/paragram_300_sl999/paragram_300_sl999.txt'\n    def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE, encoding=\"utf8\", errors='ignore') if len(o)>100)\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    # word_index = tokenizer.word_index\n    nb_words = min(max_features, len(word_index))\n    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\n    for 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_matrix[i] = embedding_vector\n    \n    return embedding_matrix","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6a5043ae8abe8dfd2e922af615f5f668931f5e8d"},"cell_type":"code","source":"from tqdm import tqdm\ntqdm.pandas()\n\nstart_time = time.time()\n\ntrain_X, test_X, train_y, word_index = load_and_prec()\nembedding_matrix_1 = load_glove(word_index)\nembedding_matrix_2 = load_para(word_index)\n\ntotal_time = (time.time() - start_time) / 60\nprint(\"Took {:.2f} minutes\".format(total_time))\n\nembedding_matrix = np.mean([embedding_matrix_1, embedding_matrix_2], axis=0)\n# embedding_matrix = np.concatenate((embedding_matrix_1, embedding_matrix_2), axis=1)\nprint(np.shape(embedding_matrix))\n\ndel embedding_matrix_1, embedding_matrix_2\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0d11d9fbf074e8891d2d9c954894db35e5dea334"},"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        self.W_regularizer = regularizers.get(W_regularizer)\n        self.b_regularizer = regularizers.get(b_regularizer)\n        self.W_constraint = constraints.get(W_constraint)\n        self.b_constraint = constraints.get(b_constraint)\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        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        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        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        eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),\n                        K.reshape(self.W, (features_dim, 1))), (-1, step_dim))\n        if self.bias:\n            eij += self.b\n        eij = K.tanh(eij)\n        a = K.exp(eij)\n        if mask is not None:\n            a *= K.cast(mask, K.floatx())\n        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())\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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a24ecd02c2b5aafc7fd7eb40c16b25f6c901559b"},"cell_type":"code","source":"def squash(x, axis=-1):\n    s_squared_norm = K.sum(K.square(x), axis, keepdims=True)\n    scale = K.sqrt(s_squared_norm + K.epsilon())\n    return x / scale\n\nclass Capsule(Layer):\n    def __init__(self, num_capsule, dim_capsule, routings=3, kernel_size=(9, 1), share_weights=True,\n                 activation='default', **kwargs):\n        super(Capsule, self).__init__(**kwargs)\n        self.num_capsule = num_capsule\n        self.dim_capsule = dim_capsule\n        self.routings = routings\n        self.kernel_size = kernel_size\n        self.share_weights = share_weights\n        if activation == 'default':\n            self.activation = squash\n        else:\n            self.activation = Activation(activation)\n\n    def build(self, input_shape):\n        super(Capsule, self).build(input_shape)\n        input_dim_capsule = input_shape[-1]\n        if self.share_weights:\n            self.W = self.add_weight(name='capsule_kernel',\n                                     shape=(1, input_dim_capsule,\n                                            self.num_capsule * self.dim_capsule),\n                                     # shape=self.kernel_size,\n                                     initializer='glorot_uniform',\n                                     trainable=True)\n        else:\n            input_num_capsule = input_shape[-2]\n            self.W = self.add_weight(name='capsule_kernel',\n                                     shape=(input_num_capsule,\n                                            input_dim_capsule,\n                                            self.num_capsule * self.dim_capsule),\n                                     initializer='glorot_uniform',\n                                     trainable=True)\n\n    def call(self, u_vecs):\n        if self.share_weights:\n            u_hat_vecs = K.conv1d(u_vecs, self.W)\n        else:\n            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])\n\n        batch_size = K.shape(u_vecs)[0]\n        input_num_capsule = K.shape(u_vecs)[1]\n        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,\n                                            self.num_capsule, self.dim_capsule))\n        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))\n        # final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]\n\n        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]\n        for i in range(self.routings):\n            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]\n            c = K.softmax(b)\n            c = K.permute_dimensions(c, (0, 2, 1))\n            b = K.permute_dimensions(b, (0, 2, 1))\n            outputs = self.activation(tf.keras.backend.batch_dot(c, u_hat_vecs, [2, 2]))\n            if i < self.routings - 1:\n                b = tf.keras.backend.batch_dot(outputs, u_hat_vecs, [2, 3])\n\n        return outputs\n\n    def compute_output_shape(self, input_shape):\n        return (None, self.num_capsule, self.dim_capsule)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"392c19396a9d4b4f955780a502ea2ef00e315324"},"cell_type":"code","source":"# DropConnect\n# https://github.com/andry9454/KerasDropconnect\n\nfrom keras.layers import Wrapper\n\nclass DropConnect(Wrapper):\n    def __init__(self, layer, prob=1., **kwargs):\n        self.prob = prob\n        self.layer = layer\n        super(DropConnect, self).__init__(layer, **kwargs)\n        if 0. < self.prob < 1.:\n            self.uses_learning_phase = True\n\n    def build(self, input_shape):\n        if not self.layer.built:\n            self.layer.build(input_shape)\n            self.layer.built = True\n        super(DropConnect, self).build()\n\n    def compute_output_shape(self, input_shape):\n        return self.layer.compute_output_shape(input_shape)\n\n    def call(self, x):\n        if 0. < self.prob < 1.:\n            self.layer.kernel = K.in_train_phase(K.dropout(self.layer.kernel, self.prob), self.layer.kernel)\n            self.layer.bias = K.in_train_phase(K.dropout(self.layer.bias, self.prob), self.layer.bias)\n        return self.layer.call(x)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"68eb9452eb3ae65be361d807a0d1b2db5e48876a"},"cell_type":"code","source":"def f1(y_true, y_pred):\n    '''\n    metric from here \n    https://stackoverflow.com/questions/43547402/how-to-calculate-f1-macro-in-keras\n    '''\n    def recall(y_true, y_pred):\n        \"\"\"Recall metric.\n\n        Only computes a batch-wise average of recall.\n\n        Computes the recall, a metric for multi-label classification of\n        how many relevant items are selected.\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        \"\"\"Precision metric.\n\n        Only computes a batch-wise average of precision.\n\n        Computes the precision, a metric for multi-label classification of\n        how many selected items are relevant.\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    precision = precision(y_true, y_pred)\n    recall = recall(y_true, y_pred)\n    return 2*((precision*recall)/(precision+recall+K.epsilon()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"360ff57cbfb0fa25e0ee0954cd0e9e59e00f43da"},"cell_type":"code","source":"def build_my_model(embedding_matrix):\n    inp = Input(shape=(maxlen,))\n    x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)\n    x = SpatialDropout1D(rate=0.24)(x)\n    x = Bidirectional(CuDNNLSTM(80, \n                                return_sequences=True, \n                                kernel_initializer=glorot_normal(seed=1029), \n                                recurrent_initializer=orthogonal(gain=1.0, seed=1029)))(x)\n\n    x_1 = Attention(maxlen)(x)\n    x_1 = DropConnect(Dense(32, activation=\"relu\"), prob=0.2)(x_1)\n    \n    x_2 = Capsule(num_capsule=10, dim_capsule=10, routings=4, share_weights=True)(x)\n    x_2 = Flatten()(x_2)\n    x_2 = DropConnect(Dense(32, activation=\"relu\"), prob=0.2)(x_2)\n\n    conc = concatenate([x_1, x_2])\n    # conc = add([x_1, x_2])\n    outp = Dense(1, activation=\"sigmoid\")(conc)\n    model = Model(inputs=inp, outputs=outp)\n    model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=[f1])\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"e4604039412de89bd3bdafae2154e931ff34599d"},"cell_type":"code","source":"def train_pred(model, train_X, train_y, val_X, val_y, epochs=2, callback=None):\n    for e in range(epochs):\n        start_time = time.time()\n        model.fit(train_X, train_y, batch_size=train_batch_size, epochs=1, callbacks=callback, verbose=0)\n        pred_val_y = model.predict([val_X], batch_size=2048, verbose=0)\n        pred_test_y = model.predict([test_X], batch_size=2048, verbose=0)      \n        total_time = (time.time() - start_time) / 60.0\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(\"Epoch: \", e, \"-    Val F1 Score: {:.4f} Took {:.2f} minutes\".format(best_score, total_time))\n    \n    print('='*80)\n    return pred_val_y, pred_test_y, best_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"32be67c09aab6a7d9644fcafddaed67969fa6e33"},"cell_type":"code","source":"count = 0\n\ntrain_meta = np.zeros(train_y.shape)\ntest_meta = np.zeros(test_X.shape[0])\nsplits = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED).split(train_X, train_y))\nfor idx, (train_idx, valid_idx) in enumerate(splits):\n        X_train = train_X[train_idx]\n        y_train = train_y[train_idx]\n        X_val = train_X[valid_idx]\n        y_val = train_y[valid_idx]\n        model = build_my_model(embedding_matrix)\n        pred_val_y, pred_test_y, best_score = train_pred(model, X_train, y_train, X_val, y_val, epochs=train_epochs)\n        if best_score > 0.68:\n            train_meta[valid_idx] = pred_val_y.reshape(-1)\n            test_meta += pred_test_y.reshape(-1)\n            count += 1\nif count > 0:\n    test_meta = test_meta / count ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"48c69d8c47ab2d19352bd2a1faaa9030a9379c5f"},"cell_type":"code","source":"def threshold_search(y_true, y_proba):\n    best_threshold = 0\n    best_score = 0\n    for threshold in [i * 0.01 for i in range(100)]:\n        score = f1_score(y_true=y_true, y_pred=y_proba > threshold)\n        if score > best_score:\n            best_threshold = threshold\n            best_score = score\n    search_result = {'threshold': best_threshold, 'f1': best_score}\n    return search_result","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"daad326e20208bbab2f295f1e58c350ff80f9460"},"cell_type":"code","source":"search_result = threshold_search(train_y, train_meta)\nprint(search_result)\n\nsub = pd.read_csv('../input/sample_submission.csv')\nsub.prediction = test_meta > search_result['threshold']\nsub.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"f14495f0cae43d9fd24617052acd1a64e58207a0"},"cell_type":"code","source":"!head submission.csv","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}