# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory import os print(os.listdir("../input")) # Any results you write to the current directory are saved as output. # Baseline from https://www.kaggle.com/johnkyon/single-rnn-with-4-folds-clr ## some config values embed_size = 300 # how big is each word vector max_features = 95000 # how many unique words to use (i.e num rows in embedding vector) maxlen = 70 # max number of words in a question to use # Data loading import os import time import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) from tqdm import tqdm import math from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.model_selection import GridSearchCV, StratifiedKFold from sklearn.metrics import f1_score, roc_auc_score from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D from keras.layers import Bidirectional, GlobalMaxPool1D, GlobalMaxPooling1D, GlobalAveragePooling1D from keras.layers import Input, Embedding, Dense, Conv2D, MaxPool2D, concatenate from keras.layers import Reshape, Flatten, Concatenate, Dropout, SpatialDropout1D from keras.optimizers import Adam from keras.models import Model from keras import backend as K from keras.engine.topology import Layer from keras import initializers, regularizers, constraints, optimizers, layers from keras.layers import concatenate from keras.callbacks import * # Transform contractions contraction_mapping = {"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" } def clean_contractions(text, mapping): specials = ["’", "‘", "´", "`"] for s in specials: text = text.replace(s, "'") text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")]) return text # Deal with special characters punct_mapping = {"‘": "'", "₹": "e", "´": "'", "°": "", "€": "e", "™": "tm", "√": " sqrt ", "×": "x", "²": "2", "—": "-", "–": "-", "’": "'", "_": "-","`": "'", '“': '"', '”': '"', '“': '"', "£": "e", '∞': 'infinity', 'θ': 'theta','÷': '/', 'α': 'alpha', '•': '.', 'à': 'a', '−': '-', 'β': 'beta', '∅': '', '³': '3', 'π': 'pi'} puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\', '•', '~', '@', '£', '·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', '“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ] puncts = puncts + list("/-'?!.,#$%\'()*+-/:;<=>@[\\]^_`{|}~" + '""“”’' + '∞θ÷α•à−β∅³π‘₹´°£€\×™√²—–&') def map_special_chars(text, punct, mapping): for p in mapping: text = text.replace(p, mapping[p]) for p in punct: text = text.replace(p, f' {p} ') specials = {'\u200b': ' ', '…': ' ... ', '\ufeff': '', 'करना': '', 'है': ''} # Other special characters that I have to deal with in last for s in specials: text = text.replace(s, specials[s]) return text typos = {'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', 'pokémon': 'pokemon'} def correct_spelling(x, dic): for word in dic.keys(): x = x.replace(word, dic[word]) return x def load_and_prec(): """ Simple data reading the preprocessing (both train data and test data). This function does the following job: Data reading, missing value filling, tokenizing, padding and data shuffling. :return: pd.DataFrame * 3, tokenizer.word_index. """ train_df = pd.read_csv("../input/train.csv") test_df = pd.read_csv("../input/test.csv") print("Train shape : ",train_df.shape) print("Test shape : ",test_df.shape) train_df['question_text'] = train_df['question_text'].apply(lambda x: clean_contractions(x, contraction_mapping)) test_df['question_text'] = test_df['question_text'].apply(lambda x: clean_contractions(x, contraction_mapping)) train_df['question_text'] = train_df['question_text'].apply(lambda x: map_special_chars(x, puncts, punct_mapping)) test_df['question_text'] = test_df['question_text'].apply(lambda x: map_special_chars(x, puncts, punct_mapping)) train_df['question_text'] = train_df['question_text'].apply(lambda x: correct_spelling(x, typos)) test_df['question_text'] = test_df['question_text'].apply(lambda x: correct_spelling(x, typos)) ## fill up the missing values train_X = train_df["question_text"].fillna("_##_").values test_X = test_df["question_text"].fillna("_##_").values ## Tokenize the sentences tokenizer = Tokenizer(num_words=max_features) tokenizer.fit_on_texts(list(train_X)) train_X = tokenizer.texts_to_sequences(train_X) test_X = tokenizer.texts_to_sequences(test_X) ## Pad the sentences train_X = pad_sequences(train_X, maxlen=maxlen) test_X = pad_sequences(test_X, maxlen=maxlen) ## Get the target values train_y = train_df['target'].values #shuffling the data np.random.seed(2018) trn_idx = np.random.permutation(len(train_X)) train_X = train_X[trn_idx] train_y = train_y[trn_idx] return train_X, test_X, train_y, tokenizer.word_index def load_glove(word_index): """Load glove embedding set, construct embedding matrix for words in word_index.""" EMBEDDING_FILE = '../input/embeddings/glove.840B.300d/glove.840B.300d.txt' def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32') embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE)) all_embs = np.stack(embeddings_index.values()) emb_mean,emb_std = all_embs.mean(), all_embs.std() embed_size = all_embs.shape[1] # word_index = tokenizer.word_index nb_words = min(max_features, len(word_index)) embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size)) for word, i in word_index.items(): if i >= max_features: continue embedding_vector = embeddings_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector return embedding_matrix def load_fasttext(word_index): """ Load wiki-news embedding set, construct embedding matrix for words in word_index. """ EMBEDDING_FILE = '../input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec' def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32') embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE) if len(o)>100) all_embs = np.stack(embeddings_index.values()) emb_mean,emb_std = all_embs.mean(), all_embs.std() embed_size = all_embs.shape[1] # word_index = tokenizer.word_index nb_words = min(max_features, len(word_index)) embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size)) for word, i in word_index.items(): if i >= max_features: continue embedding_vector = embeddings_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector return embedding_matrix def load_para(word_index): """Load paragram embedding set, construct embedding matrix for words in word_index.""" EMBEDDING_FILE = '../input/embeddings/paragram_300_sl999/paragram_300_sl999.txt' def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32') embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE, encoding="utf8", errors='ignore') if len(o)>100) all_embs = np.stack(embeddings_index.values()) emb_mean,emb_std = all_embs.mean(), all_embs.std() embed_size = all_embs.shape[1] # word_index = tokenizer.word_index nb_words = min(max_features, len(word_index)) embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size)) for word, i in word_index.items(): if i >= max_features: continue embedding_vector = embeddings_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector return embedding_matrix # Attention layer # https://www.kaggle.com/suicaokhoailang/lstm-attention-baseline-0-652-lb class Attention(Layer): def __init__(self, step_dim, W_regularizer=None, b_regularizer=None, W_constraint=None, b_constraint=None, bias=True, **kwargs): self.supports_masking = True self.init = initializers.get('glorot_uniform') self.W_regularizer = regularizers.get(W_regularizer) self.b_regularizer = regularizers.get(b_regularizer) self.W_constraint = constraints.get(W_constraint) self.b_constraint = constraints.get(b_constraint) self.bias = bias self.step_dim = step_dim self.features_dim = 0 super(Attention, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 self.W = self.add_weight((input_shape[-1],), initializer=self.init, name='{}_W'.format(self.name), regularizer=self.W_regularizer, constraint=self.W_constraint) self.features_dim = input_shape[-1] if self.bias: self.b = self.add_weight((input_shape[1],), initializer='zero', name='{}_b'.format(self.name), regularizer=self.b_regularizer, constraint=self.b_constraint) else: self.b = None self.built = True def compute_mask(self, input, input_mask=None): return None def call(self, x, mask=None): features_dim = self.features_dim step_dim = self.step_dim eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)), K.reshape(self.W, (features_dim, 1))), (-1, step_dim)) if self.bias: eij += self.b eij = K.tanh(eij) a = K.exp(eij) if mask is not None: a *= K.cast(mask, K.floatx()) a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx()) a = K.expand_dims(a) weighted_input = x * a return K.sum(weighted_input, axis=1) def compute_output_shape(self, input_shape): return input_shape[0], self.features_dim # F1 score and CLR # https://www.kaggle.com/hireme/fun-api-keras-f1-metric-cyclical-learning-rate/code class CyclicLR(Callback): """This callback implements a cyclical learning rate policy (CLR). The method cycles the learning rate between two boundaries with some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186). The amplitude of the cycle can be scaled on a per-iteration or per-cycle basis. This class has three built-in policies, as put forth in the paper. "triangular": A basic triangular cycle w/ no amplitude scaling. "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. "exp_range": A cycle that scales initial amplitude by gamma**(cycle iterations) at each cycle iteration. For more detail, please see paper. # Example ```python clr = CyclicLR(base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular') model.fit(X_train, Y_train, callbacks=[clr]) ``` Class also supports custom scaling functions: ```python clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.)) clr = CyclicLR(base_lr=0.001, max_lr=0.006, step_size=2000., scale_fn=clr_fn, scale_mode='cycle') model.fit(X_train, Y_train, callbacks=[clr]) ``` # Arguments base_lr: initial learning rate which is the lower boundary in the cycle. max_lr: upper boundary in the cycle. Functionally, it defines the cycle amplitude (max_lr - base_lr). The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore max_lr may not actually be reached depending on scaling function. step_size: number of training iterations per half cycle. Authors suggest setting step_size 2-8 x training iterations in epoch. mode: one of {triangular, triangular2, exp_range}. Default 'triangular'. Values correspond to policies detailed above. If scale_fn is not None, this argument is ignored. gamma: constant in 'exp_range' scaling function: gamma**(cycle iterations) scale_fn: Custom scaling policy defined by a single argument lambda function, where 0 <= scale_fn(x) <= 1 for all x >= 0. mode paramater is ignored scale_mode: {'cycle', 'iterations'}. Defines whether scale_fn is evaluated on cycle number or cycle iterations (training iterations since start of cycle). Default is 'cycle'. """ def __init__(self, base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular', gamma=1., scale_fn=None, scale_mode='cycle'): super(CyclicLR, self).__init__() self.base_lr = base_lr self.max_lr = max_lr self.step_size = step_size self.mode = mode self.gamma = gamma if scale_fn == None: if self.mode == 'triangular': self.scale_fn = lambda x: 1. self.scale_mode = 'cycle' elif self.mode == 'triangular2': self.scale_fn = lambda x: 1/(2.**(x-1)) self.scale_mode = 'cycle' elif self.mode == 'exp_range': self.scale_fn = lambda x: gamma**(x) self.scale_mode = 'iterations' else: self.scale_fn = scale_fn self.scale_mode = scale_mode self.clr_iterations = 0. self.trn_iterations = 0. self.history = {} self._reset() def _reset(self, new_base_lr=None, new_max_lr=None, new_step_size=None): """Resets cycle iterations. Optional boundary/step size adjustment. """ if new_base_lr != None: self.base_lr = new_base_lr if new_max_lr != None: self.max_lr = new_max_lr if new_step_size != None: self.step_size = new_step_size self.clr_iterations = 0. def clr(self): cycle = np.floor(1+self.clr_iterations/(2*self.step_size)) x = np.abs(self.clr_iterations/self.step_size - 2*cycle + 1) if self.scale_mode == 'cycle': return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(cycle) else: return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(self.clr_iterations) def on_train_begin(self, logs={}): logs = logs or {} if self.clr_iterations == 0: K.set_value(self.model.optimizer.lr, self.base_lr) else: K.set_value(self.model.optimizer.lr, self.clr()) def on_batch_end(self, epoch, logs=None): logs = logs or {} self.trn_iterations += 1 self.clr_iterations += 1 self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr)) self.history.setdefault('iterations', []).append(self.trn_iterations) for k, v in logs.items(): self.history.setdefault(k, []).append(v) K.set_value(self.model.optimizer.lr, self.clr()) def f1(y_true, y_pred): ''' metric from here https://stackoverflow.com/questions/43547402/how-to-calculate-f1-macro-in-keras ''' def recall(y_true, y_pred): """Recall metric. Only computes a batch-wise average of recall. Computes the recall, a metric for multi-label classification of how many relevant items are selected. """ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recall def precision(y_true, y_pred): """Precision metric. Only computes a batch-wise average of precision. Computes the precision, a metric for multi-label classification of how many selected items are relevant. """ true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision precision = precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) # LSTM Models def model_lstm_atten(embedding_matrix): inp = Input(shape=(maxlen,)) x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp) x = SpatialDropout1D(0.1)(x) x = Bidirectional(CuDNNLSTM(40, return_sequences=True))(x) y = Bidirectional(CuDNNGRU(40, return_sequences=True))(x) atten_1 = Attention(maxlen)(x) # skip connect atten_2 = Attention(maxlen)(y) avg_pool = GlobalAveragePooling1D()(y) max_pool = GlobalMaxPooling1D()(y) conc = concatenate([atten_1, atten_2, avg_pool, max_pool]) conc = Dense(16, activation="relu")(conc) conc = Dropout(0.1)(conc) outp = Dense(1, activation="sigmoid")(conc) model = Model(inputs=inp, outputs=outp) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[f1]) return model # Train and Predict # https://www.kaggle.com/strideradu/word2vec-and-gensim-go-go-go def train_pred(model, train_X, train_y, val_X, val_y, epochs=2, callback=None): for e in range(epochs): model.fit(train_X, train_y, batch_size=512, epochs=1, validation_data=(val_X, val_y), callbacks = callback, verbose=0) pred_val_y = model.predict([val_X], batch_size=1024, verbose=0) best_score = metrics.f1_score(val_y, (pred_val_y > 0.33).astype(int)) print("Epoch: ", e, "- Val F1 Score: {:.4f}".format(best_score)) pred_test_y = model.predict([test_X], batch_size=1024, verbose=0) print('=' * 60) return pred_val_y, pred_test_y, best_score # Main script train_X, test_X, train_y, word_index = load_and_prec() embedding_matrix_1 = load_glove(word_index) # embedding_matrix_2 = load_fasttext(word_index) embedding_matrix_3 = load_para(word_index) ## Simple average: http://aclweb.org/anthology/N18-2031 # We have presented an argument for averaging as # a valid meta-embedding technique, and found experimental # performance to be close to, or in some cases # better than that of concatenation, with the # additional benefit of reduced dimensionality ## Unweighted DME in https://arxiv.org/pdf/1804.07983.pdf # “The downside of concatenating embeddings and # giving that as input to an RNN encoder, however, # is that the network then quickly becomes inefficient # as we combine more and more embeddings.” embedding_matrix = np.mean([embedding_matrix_1, embedding_matrix_3], axis = 0) np.shape(embedding_matrix) # https://www.kaggle.com/ryanzhang/tfidf-naivebayes-logreg-baseline def threshold_search(y_true, y_proba): best_threshold = 0 best_score = 0 for threshold in [i * 0.01 for i in range(100)]: score = f1_score(y_true=y_true, y_pred=y_proba > threshold) if score > best_score: best_threshold = threshold best_score = score search_result = {'threshold': best_threshold, 'f1': best_score} return search_result DATA_SPLIT_SEED = 2018 clr = CyclicLR(base_lr=0.001, max_lr=0.002, step_size=300., mode='exp_range', gamma=0.99994) train_meta = np.zeros(train_y.shape) test_meta = np.zeros(test_X.shape[0]) splits = list(StratifiedKFold(n_splits=4, shuffle=True, random_state=DATA_SPLIT_SEED).split(train_X, train_y)) for idx, (train_idx, valid_idx) in enumerate(splits): X_train = train_X[train_idx] y_train = train_y[train_idx] X_val = train_X[valid_idx] y_val = train_y[valid_idx] model = model_lstm_atten(embedding_matrix) pred_val_y, pred_test_y, best_score = train_pred(model, X_train, y_train, X_val, y_val, epochs = 8, callback = [clr,]) train_meta[valid_idx] = pred_val_y.reshape(-1) test_meta += pred_test_y.reshape(-1) / len(splits) sub = pd.read_csv('../input/sample_submission.csv') search_result = threshold_search(train_y, train_meta) sub.prediction = test_meta > search_result['threshold'] sub.to_csv("submission.csv", index=False) print("Best score: %f， \t Threshold: %f" % (search_result["threshold"], search_result["f1"])) f1_score(y_true=train_y, y_pred=train_meta > search_result['threshold'])