{"cells":[{"metadata":{},"cell_type":"markdown","source":"<center>\n    <h1>Toxic Comment Classification</h1>\n    <h5>Detect toxic content to improve online conversations</h5>\n    <h5>Dataset - Quora Insincere Questions Classification</h5>\n\n</center>"},{"metadata":{},"cell_type":"markdown","source":"<h3> Brief Problem Statement </h3>\n<h5>\n    An existential issue for any major website nowadays is how to handle toxic and divisive content.\n    A key challenge is to get rid of toxic/insincere comments/text -- those founded upon false premises, or that intend to make a statement rather than look for helpful answers.\n</h5>"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')\ntest = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.shape, test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Check for missing values"},{"metadata":{"trusted":true},"cell_type":"code","source":"train.isnull().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.isnull().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['target'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['target'].value_counts().plot.bar()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"By analyzing above data, we can say that **Data is Imbalanced** "},{"metadata":{"trusted":true},"cell_type":"code","source":"toxic = train[train['target'] == 1]\nnon_toxic = train[train['target'] == 0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"non_toxic.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"toxic.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Building n-grams for analyzing data"},{"metadata":{"trusted":true},"cell_type":"code","source":"import nltk\nfrom nltk.util import ngrams\nfrom nltk.corpus import stopwords\nimport re\n\nREPLACE_BY_SPACE_RE = re.compile('[/(){}\\[\\]\\|@,;]')\nBAD_SYMBOLS_RE = re.compile('[^0-9a-z #+_]')\nSTOPWORDS = set(stopwords.words('english'))\n\ndef pre_process(text):\n    text = text.lower()\n    text = REPLACE_BY_SPACE_RE.sub(' ', text)\n    text = BAD_SYMBOLS_RE.sub('', text)\n    text = ' '.join(word for word in text.split() if word not in STOPWORDS)\n    return text\n\ndef build_ngrams(text, ngram):\n    text = text.lower()\n    text = REPLACE_BY_SPACE_RE.sub(' ', text)\n    text = BAD_SYMBOLS_RE.sub('', text)\n    text = ' '.join(word for word in text.split() if word not in STOPWORDS) \n    tokenize = nltk.word_tokenize(text)\n    return list(ngrams(tokenize, ngram))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['question_text'] = train['question_text'].apply(lambda x: pre_process(x))\ntest['question_text'] = test['question_text'].apply(lambda x: pre_process(x))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(build_ngrams('I am testing ngrams', 1))\nprint(build_ngrams('I am testing ngrams', 2))\nprint(build_ngrams('I am testing ngrams', 3))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plot_horizontal_bar(data, title):\n    plt.figure(figsize=(20, 20))\n    freq = pd.DataFrame(sorted(data.items(), key=lambda x: x[1]), columns=['Word','Count'])\n    sns.barplot(x='Count', y='Word', data=freq.sort_values(by=\"Count\", ascending=True).tail(30))\n    plt.title(title)\n    plt.tight_layout()\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Creating One-grams, Bigrams, Trigrams"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nfrom collections import defaultdict\n\nonegram_nontoxic_freq = defaultdict(int)\nfor sentence in non_toxic['question_text']:\n    for word in build_ngrams(sentence, 1):\n        onegram_nontoxic_freq[word] += 1\n        \n\nonegram_toxic_freq = defaultdict(int)\nfor sentence in toxic['question_text']:\n    for word in build_ngrams(sentence, 1):\n        onegram_toxic_freq[word] += 1\n        \n        \nbigram_nontoxic_freq = defaultdict(int)\nfor sentence in non_toxic['question_text']:\n    for word in build_ngrams(sentence, 2):\n        bigram_nontoxic_freq[word] += 1\n        \n\nbigram_toxic_freq = defaultdict(int)\nfor sentence in toxic['question_text']:\n    for word in build_ngrams(sentence, 2):\n        bigram_toxic_freq[word] += 1\n        \n        \n\ntrigram_nontoxic_freq = defaultdict(int)\nfor sentence in non_toxic['question_text']:\n    for word in build_ngrams(sentence, 3):\n        trigram_nontoxic_freq[word] += 1\n        \n\ntrigram_toxic_freq = defaultdict(int)\nfor sentence in toxic['question_text']:\n    for word in build_ngrams(sentence, 3):\n        trigram_toxic_freq[word] += 1","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Plotting Top Frequency N-grams"},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(onegram_toxic_freq, 'Top Toxic Words')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(onegram_nontoxic_freq, 'Top Non-Toxic Words')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(bigram_toxic_freq, 'Top Toxic Bigrams')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(bigram_nontoxic_freq, 'Top Non-Toxic Bigrams')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(trigram_toxic_freq, 'Top Toxic Trigrams')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_horizontal_bar(trigram_nontoxic_freq, 'Top Non-Toxic Trigrams')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['target'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.pie(train['target'].value_counts().to_list(), labels=['Non Toxic', 'Toxic'], autopct='%1.1f%%')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"After analyzing above, we can say that data is highly imbalanced. Imbalanced data is a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other classes. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets.\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.utils import resample\nnon_toxic = resample(non_toxic, replace=False, n_samples=1000001, random_state=25)\ntoxic = resample(toxic, replace=True, n_samples=120001, random_state=25)\ntrain = pd.concat([non_toxic, toxic])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.pie(train['target'].value_counts().to_list(), labels=['Non Toxic', 'Toxic'], autopct='%1.1f%%')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[\"num_chars\"] = train[\"question_text\"].apply(lambda x: len(str(x)))\ntest[\"num_chars\"] = test[\"question_text\"].apply(lambda x: len(str(x)))\n\ntrain[\"num_words\"] = train[\"question_text\"].apply(lambda x: len(str(x).split()))\ntest[\"num_words\"] = test[\"question_text\"].apply(lambda x: len(str(x).split()))\n\ntrain[\"num_unique_words\"] = train[\"question_text\"].apply(lambda x: len(set(str(x).split())))\ntest[\"num_unique_words\"] = test[\"question_text\"].apply(lambda x: len(set(str(x).split())))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"axes= sns.boxplot(x='target', y='num_words', data=train)\naxes.set_xlabel('Target')\naxes.set_title(\"Number of words in each class\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"axes= sns.boxplot(x='target', y='num_chars', data=train)\naxes.set_xlabel('Target')\naxes.set_title(\"Number of num_chars in each class\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain, validation = train_test_split(train, test_size=0.15, random_state=25)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\n\nembed_size = 300\nmax_features = 50000\nmaxlen = 100\n\ntokenizer = Tokenizer(num_words=max_features)\ntokenizer.fit_on_texts(list(train['question_text']))\n\ntrain_X = tokenizer.texts_to_sequences(train['question_text'])\ntest_X = tokenizer.texts_to_sequences(test['question_text'])\nvalidation_X = tokenizer.texts_to_sequences(validation['question_text'])\n\ntrain_X = pad_sequences(train_X, maxlen=maxlen)\ntest_X = pad_sequences(test_X, maxlen=maxlen)\nvalidation_X = pad_sequences(validation_X, maxlen=maxlen)\n\n\nword_index = tokenizer.word_index\nunq_len = min(max_features, len(word_index))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pickle\nwith open('tokenizer.pickle', 'wb') as handle:\n    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def loadGlove(file_path):\n    f = open(file_path, 'r')\n    glove_embeddings_index = {}\n    \n    for line in f:\n        lines = line.split(' ')\n        word, coef = lines[0], np.asarray(lines[1: ], dtype='float32')\n        glove_embeddings_index[word] = coef\n        \n    all_embs = np.stack(glove_embeddings_index.values())\n    emb_mean,emb_std = all_embs.mean(), all_embs.std()\n    embed_size = all_embs.shape[1]\n        \n    embedding_matrix = np.random.normal(emb_mean, emb_std, (min(max_features, len(word_index)), embed_size))\n\n    for word, i in word_index.items():\n        if i < max_features: \n            embedding_vector = glove_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":"import io\nfrom tqdm import tqdm\n\ndef loadWiki(file_path):\n    \n    fin = open(file_path, 'r', encoding='utf-8', newline='\\n', errors='ignore')\n    n, d = map(int, fin.readline().split())\n    data = {}\n    cnt = 0\n    for line in tqdm(fin):\n#         if(cnt == 500000):\n#             break\n        tokens = line.rstrip().split(' ')\n        data[tokens[0]] =  np.asarray(tokens[1: ], dtype='float32')\n        cnt += 1\n        \n#     def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n#     data = dict(get_coefs(*o.split(\" \")) for o in open('../input/quora-insincere-questions-classification/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec') if len(o)>100)\n\n        \n    all_embs = np.stack(data.values())\n    emb_mean,emb_std = all_embs.mean(), all_embs.std()\n    embed_size = all_embs.shape[1]\n        \n    embedding_matrix = np.random.normal(emb_mean, emb_std, (min(max_features, len(word_index)), embed_size))\n\n    for word, i in word_index.items():\n        if i < max_features: \n            embedding_vector = data.get(word)\n            if embedding_vector is not None: \n                embedding_matrix[i] = embedding_vector\n    \n    return embedding_matrix\n\n\n# %%time\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nembedding_matrix = loadGlove('../input/glove840b300dtxt/glove.840B.300d.txt')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input/fasttext-wikinews/wiki-news-300d-1M.vec","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Input, LSTM, Embedding, Dropout, Activation, CuDNNGRU, CuDNNLSTM, Dense, Bidirectional, GlobalMaxPool1D\nfrom keras.models import Model\n\ninput_12 = Input(shape=(maxlen,))\nlayer = Embedding(max_features, embed_size, weights=[embedding_matrix])(input_12)\nlayer = Bidirectional(CuDNNLSTM(64, return_sequences=True))(layer)\nlayer = Bidirectional(CuDNNLSTM(64))(layer)\nlayer = Dense(1, activation=\"sigmoid\")(layer)\nmodel = Model(inputs = input_12, output = layer)\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = train['target']\nvalidation_y = validation['target']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nmodel.fit(train_X, y, epochs = 1, validation_data=(validation_X, validation_y))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\npredict = model.predict(test_X)\npredict_val = model.predict(validation_X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict_bool = (np.array(predict) > 0.5).astype(np.int)\n\npredict_val_bool = (np.array(predict_val) > 0.4).astype(np.int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import metrics\nmetrics.accuracy_score(validation_y, predict_val_bool)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"metrics.f1_score(validation_y, predict_val_bool)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(metrics.classification_report(validation_y, predict_val_bool))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit = pd.DataFrame({'qid': test['qid'].values, 'prediction': predict_bool.flatten()})\nsubmit.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save('saved.h5')\n# json_string = model.to_json()\n# print(json_string)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save_weights('weights.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# import pickle\nwith open('model.pickle', 'wb') as handle:\n    pickle.dump(model, handle, protocol=pickle.HIGHEST_PROTOCOL)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedding_matrix_wiki","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# del embedding_matrix_\nimport gc\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n# embedding_matrix_wiki = loadWiki('../input/embeddings-glove-crawl-torch-cached/wiki-news-300d-1M.vec')\nembedding_matrix_wiki = loadWiki('../input/fasttext-wikinews/wiki-news-300d-1M.vec')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\n\ninput_12_w = Input(shape=(100,))\nlayer_w = Embedding(max_features, embed_size, weights=[embedding_matrix_wiki])(input_12_w)\nlayer_w = Bidirectional(CuDNNLSTM(64, return_sequences=True))(layer_w)\nlayer_w = Bidirectional(CuDNNLSTM(64))(layer_w)\nlayer_w = Dense(1, activation=\"sigmoid\")(layer_w)\nmodel_w = Model(inputs = input_12_w, output = layer_w)\nmodel_w.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n\nmodel_w.fit(train_X, y, epochs = 1, validation_data=(validation_X, validation_y))\n\npredict_val_w = model_w.predict(validation_X)\npredict_val_bool_w = (np.array(predict_val_w) > 0.4).astype(np.int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(metrics.classification_report(validation_y, predict_val_bool_w))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}