processed-embeddings-sampled-lstm.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)\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.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import nltk, re, string\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, Embedding, concatenate\nfrom keras.layers import CuDNNGRU, Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D, Conv1D\nfrom keras.layers import Add, BatchNormalization, Activation, CuDNNLSTM, Dropout\nfrom keras.layers import *\nfrom keras.models import *\nfrom keras.preprocessing import text, sequence\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nimport gc\nfrom sklearn import metrics\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\ncolor = sns.color_palette()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8e7dff66276cb5ef1ca17d098c212964de473a4f"},"cell_type":"code","source":"train_df = pd.read_csv('../input/train.csv')\n#train_df[\"question_text\"] = train_df[\"question_text\"].map(lambda x: clean_text(x))\n\ntest_df = pd.read_csv('../input/test.csv')\n#test_df[\"question_text\"] = test_df[\"question_text\"].map(lambda x: clean_text(x))\n\ntrain_ques_lens = train_df['question_text'].map(lambda x: len(x.split(' ')))\ntest_ques_lens = test_df['question_text'].map(lambda x: len(x.split(' ')))\nprint('Train text max len:', train_ques_lens.max())\nprint('Test text max len:', test_ques_lens.max())\nplt.figure(figsize=(10, 4))\nsns.kdeplot(train_ques_lens)\nsns.kdeplot(test_ques_lens)\nplt.legend(('train', 'test'))\nplt.show()\ndel train_ques_lens; del test_ques_lens\ngc.collect()\npass","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0aa54bdb0cf52989cf898cb2d6fd099463248395"},"cell_type":"code","source":"puncts = [',', '.', '\"', ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\\\', '•',  '~', '@', '£', \n '·', '_', '{', '}', '©', '^', '®', '`',  '<', '→', '°', '€', '™', '›',  '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', \n '“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', \n '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', \n '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]\n\npunct_mapping = {\"‘\": \"'\", \"₹\": \"e\", \"´\": \"'\", \"°\": \"\", \"€\": \"e\", \"™\": \"tm\", \"√\": \" sqrt \", \"×\": \"x\", \"²\": \"2\", \"—\": \"-\", \"–\": \"-\", \"’\": \"'\", \"_\": \"-\", \"`\": \"'\", \n                 '“': '\"', '”': '\"', '“': '\"', \"£\": \"e\", '∞': 'infinity', 'θ': 'theta', '÷': '/', 'α': 'alpha', '•': '.', 'à': 'a', '−': '-', 'β': 'beta', '∅': '', \n                 '³': '3', 'π': 'pi', }\n\ndef clean_text(x):\n    x = str(x)\n    for p in punct_mapping:\n        x = x.replace(p, punct_mapping[p])\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\", \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":"39f9890e9f3d1e10feff77be24d083458af1931d"},"cell_type":"code","source":"# lower\ntrain_df[\"question_text\"] = train_df[\"question_text\"].apply(lambda x: x.lower())\ntest_df[\"question_text\"] = test_df[\"question_text\"].apply(lambda x: x.lower())\n\n# Clean the text\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\n# Clean numbers\ntrain_df[\"question_text\"] = train_df[\"question_text\"].apply(lambda x: clean_numbers(x))\ntest_df[\"question_text\"] = test_df[\"question_text\"].apply(lambda x: clean_numbers(x))\n\n# Clean speelings\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\nX_train = train_df[\"question_text\"].fillna(\"na\").values\nX_test = test_df[\"question_text\"].fillna(\"na\").values\ny = train_df[\"target\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"64f39f9dfcb3aac0b65d486573a8fac09d9472f4"},"cell_type":"code","source":"maxlen = 70\nmax_features = 90000  # capsule 950000\nembed_size = 300","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"31e032f9281f9dabdadf39ffc942fddaefddc597"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6a30c7fac8c49bc1ebb73ec0f52e67bb806ec5df"},"cell_type":"code","source":"\n\ntokenizer = text.Tokenizer(num_words=max_features)\ntokenizer.fit_on_texts(list(X_train) + list(X_test))\n\nX_train = tokenizer.texts_to_sequences(X_train)\nX_test = tokenizer.texts_to_sequences(X_test)\n\nx_train = sequence.pad_sequences(X_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(X_test, maxlen=maxlen)\nprint(\"finished!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"86a3a6a7f975235b09546b511c94b0ac8ae52f85"},"cell_type":"code","source":"def attention_3d_block(inputs):\n    # inputs.shape = (batch_size, time_steps, input_dim)\n    TIME_STEPS = inputs.shape[1].value\n    SINGLE_ATTENTION_VECTOR = False\n    \n    input_dim = int(inputs.shape[2])\n    a = Permute((2, 1))(inputs)\n    a = Reshape((input_dim, TIME_STEPS))(a) # this line is not useful. It's just to know which dimension is what.\n    a = Dense(TIME_STEPS, activation='softmax')(a)\n    if SINGLE_ATTENTION_VECTOR:\n        a = Lambda(lambda x: K.mean(x, axis=1))(a)\n        a = RepeatVector(input_dim)(a)\n    a_probs = Permute((2, 1))(a)\n    output_attention_mul = Multiply()([inputs, a_probs])\n    return output_attention_mul","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"818745e98414714b6872012a309a48a10898f9bb"},"cell_type":"code","source":"from keras import backend as K\nfrom keras.engine.topology import Layer, InputSpec\nfrom keras import initializers\n\nclass AttLayer(Layer):\n    def __init__(self, attention_dim):\n        self.init = initializers.get('normal')\n        self.supports_masking = True\n        self.attention_dim = attention_dim\n        super(AttLayer, self).__init__()\n\n    def build(self, input_shape):\n        assert len(input_shape) == 3\n        self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))\n        self.b = K.variable(self.init((self.attention_dim, )))\n        self.u = K.variable(self.init((self.attention_dim, 1)))\n        self.trainable_weights = [self.W, self.b, self.u]\n        super(AttLayer, self).build(input_shape)\n\n    def compute_mask(self, inputs, mask=None):\n        return mask\n\n    def call(self, x, mask=None):\n        # size of x :[batch_size, sel_len, attention_dim]\n        # size of u :[batch_size, attention_dim]\n        # uit = tanh(xW+b)\n        uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b))\n        ait = K.dot(uit, self.u)\n        ait = K.squeeze(ait, -1)\n\n        ait = K.exp(ait)\n\n        if mask is not None:\n            # Cast the mask to floatX to avoid float64 upcasting in theano\n            ait *= K.cast(mask, K.floatx())\n        ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())\n        ait = K.expand_dims(ait)\n        weighted_input = x * ait\n        output = K.sum(weighted_input, axis=1)\n\n        return output\n\n    def compute_output_shape(self, input_shape):\n        return (input_shape[0], input_shape[-1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"4522884fcba20bd2294db6e2113ce1d0b8cae748"},"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()) # 二维数组 2096016*300\nprint(all_embs.shape[0])\nemb_mean,emb_std = all_embs.mean(), all_embs.std()  # scalar 均值，标准差\nprint(\"mean=\",emb_mean)\nembed_size = all_embs.shape[1]  # 300\n\nword_index = tokenizer.word_index  # 单词对应的整数编号形成的列表\nnb_words = min(max_features, len(word_index))  # 只取两者中较小者的单词数量\n# 结合下面的代码。对于embeddings中没有的单词，使用随机初始化的词向量\nembedding_matrix_1 = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\nfor 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_1[i] = embedding_vector\n\ndel embeddings_index; gc.collect() # 内存空间清理\nprint(\"finished!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a891d77ae958018831c8620d08c6802fb47fa541"},"cell_type":"code","source":"EMBEDDING_FILE = '../input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec'\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) if len(o)>100)\n\nall_embs = np.stack(embeddings_index.values())  # 999994*300\nprint(all_embs.shape[0])\nemb_mean,emb_std = all_embs.mean(), all_embs.std()\nembed_size = all_embs.shape[1]\n\nword_index = tokenizer.word_index\nnb_words = min(max_features, len(word_index))\nembedding_matrix_2 = 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_matrix_2[i] = embedding_vector\ndel embeddings_index; gc.collect()\nprint(\"finished!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"38a1801470507394d54086358df95176db1a578c"},"cell_type":"code","source":"EMBEDDING_FILE = '../input/embeddings/paragram_300_sl999/paragram_300_sl999.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, encoding=\"utf8\", errors='ignore') if len(o)>100)\n\nall_embs = np.stack(embeddings_index.values()) # 1703755*300\nprint(all_embs.shape[0])\nemb_mean,emb_std = all_embs.mean(), all_embs.std()\nembed_size = all_embs.shape[1]\n\nword_index = tokenizer.word_index\nnb_words = min(max_features, len(word_index))\nembedding_matrix_3 = 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_matrix_3[i] = embedding_vector\n        \ndel embeddings_index; gc.collect()   \nprint(\"finished!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a622c4d6abe9be689c776bef676d8cc0ec15558e"},"cell_type":"code","source":"embedding_matrix = np.mean([embedding_matrix_1, embedding_matrix_2, embedding_matrix_3], axis=0)  \n# embedding_matrix = np.concatenate((embedding_matrix_1, embedding_matrix_2, embedding_matrix_3), axis=1) \ndel embedding_matrix_1, embedding_matrix_2, embedding_matrix_3\ngc.collect()\nprint(np.shape(embedding_matrix))\nprint(\"finished!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6d68801ae521944a750ff66c6b351bda3c6df30b"},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_tra, X_val, y_tra, y_val = train_test_split(x_train, y, test_size = 0.1, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"466e193c584d8efee50191d551612ef71874de73"},"cell_type":"code","source":"def model2():\n    inp = Input(shape=(maxlen, ))\n    embed = Embedding(max_features, embed_size * 1, weights=[embedding_matrix], trainable=False)(inp)\n    x = embed\n    print(\"x_embed shape: \", x.shape)  # (?, 70, 900)\n    \n    x = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x)\n    print(\"x shape: \", x.shape)  # (?, 70, 256)\n    x = attention_3d_block(x)\n    print(\"x shape after attention: \", x.shape)  # (?, 70, 256)\n    x = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x)\n    print(\"x shape: \", x.shape)  # (?, 70, 256)\n    x = AttLayer(64)(x)\n    print(\"x shape after attention: \", x.shape)  # (?, 256)\n    x = Dropout(0.3)(x)\n    x = Dense(128, activation='relu')(x)\n    outp = Dense(1, activation=\"sigmoid\")(x)\n    model = Model(inputs=inp, outputs=outp)\n    model.compile(loss='binary_crossentropy',\n                  optimizer='adam',\n                  metrics=['accuracy'])    \n\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"41256efbd46f5695dcb8643263b481bf1b6bd55f"},"cell_type":"code","source":"MODEL2 = model2()\nMODEL2.summary()\n\nbatch_size = 2048\nepochs = 3\n\nearly_stopping = EarlyStopping(patience=3, verbose=1, monitor='val_loss', mode='min')\nmodel_checkpoint = ModelCheckpoint('./model2.model', save_best_only=True, verbose=1, monitor='val_loss', mode='min')\nreduce_lr = ReduceLROnPlateau(factor=0.5, patience=3, min_lr=0.0001, verbose=1)\n\nhist = MODEL2.fit(X_tra, y_tra, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val), verbose=True)\nMODEL2.save('./model2.h5')\nprint(\"finished!!\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"69ec3454804c29f7d3e02fcb6252ab346e9973d0"},"cell_type":"code","source":"pred_val_y_2 = MODEL2.predict([X_val], batch_size=1024, verbose=1)\nthresholds = []\nfor thresh in np.arange(0.1, 0.501, 0.01):\n    thresh = np.round(thresh, 2)\n    acc = metrics.accuracy_score(y_val, (pred_val_y_2 > thresh).astype(int))\n    f1_score = metrics.f1_score(y_val, (pred_val_y_2 > thresh).astype(int))\n    pre = metrics.precision_score(y_val, (pred_val_y_2 > thresh).astype(int))\n    recall = metrics.recall_score(y_val, (pred_val_y_2 > thresh).astype(int))\n    thresholds.append([thresh, f1_score])\n    print(\"acc score at threshold {0} is {1}\".format(thresh, acc))\n    print(\"F1 score at threshold {0} is {1}\".format(thresh, f1_score))\n    print(\"precision at threshold {0} is {1}\".format(thresh, pre))\n    print(\"recall at threshold {0} is {1}\".format(thresh, recall))\n    \nthresholds.sort(key=lambda x: x[1], reverse=True)\nbest_thresh_2 = str(thresholds[0][0])\nbest_thresh_2_f1 = str(thresholds[0][1])\nprint(\"Best threshold:{0} ， f1:{1}\".format(best_thresh_2, best_thresh_2_f1))\n# print(\"Best threshold:{0} ， f1:{1}\".format(best_thresh_2, best_thresh_2_f1))\n\n# metrics.roc_curve(pred_val_y_2, y_val, pos_label=None, sample_weight=None, drop_intermediate=True)\n\ny_pred_2 = MODEL2.predict(x_test, batch_size=1024, verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0c02e024e479fe10a3eaa7fc2cbf3829347d55ab"},"cell_type":"code","source":"pred_val_y = pred_val_y_2\nthresholds = []\nfor thresh in np.arange(0.1, 0.501, 0.01):\n    thresh = np.round(thresh, 2)\n    res = metrics.f1_score(y_val, (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":"59505530701fe57d98050a9dea1951d96715dc3f"},"cell_type":"code","source":"\ny_pred = y_pred_2\ny_te = (y_pred[:,0] > best_thresh).astype(np.int)\n\nsubmit_df = pd.DataFrame({\"qid\": test_df[\"qid\"], \"prediction\": y_te})\nsubmit_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":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}