{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","colab":{},"colab_type":"code","id":"vwxu6ddj5piO","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)\nimport time\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 all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\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","colab":{},"colab_type":"code","id":"tQlmoOLv5pil","trusted":true},"cell_type":"code","source":"# add progress bar\n# !pip install tqdm --upgrade\nfrom tqdm._tqdm_notebook import tqdm_notebook as tqdm\ntqdm.pandas()","execution_count":null,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"HLnkuqqI5pjG","trusted":true},"cell_type":"code","source":"# Global Constants\nemb_size = 300\nmax_features = 200000\nmaxlen = 100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def clean_memory(*args):\n    for arg in args:\n        del arg\n    import gc\n    gc.collect()\n    time.sleep(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\n\ndef plot_it(x, y, data):\n    sns.boxplot(x=x, y=y, data=data)\n#     plt.show()","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"colab_type":"code","id":"APffiMod5pjL","outputId":"e98053bd-4480-482b-c0fa-ed44e97725cf","trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\n\npath = '/kaggle/input/quora-insincere-questions-classification'\n# path = '/content'\ntrain_df = pd.read_csv(path + \"/train.csv\")\ntest_df = pd.read_csv(path + \"/test.csv\")\n\ntrain_df[\"question_text\"].fillna(\"_na_\", inplace=True)\ntest_df[\"question_text\"].fillna(\"_na_\", inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df[\"num_words\"] = train_df[\"question_text\"].progress_apply(lambda x: len(str(x).split()))\ntrain_df[\"num_unique_words\"] = train_df[\"question_text\"].progress_apply(lambda x: len(set(str(x).split())))\ntrain_df[\"num_chars\"] = train_df[\"question_text\"].progress_apply(lambda x: len(str(x)))\n# stat_data[\"num_words_upper\"] = train_df[\"question_text\"].progress_apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n# stat_data[\"num_words_title\"] = train_df[\"question_text\"].progress_apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n# stat_data[\"mean_word_len\"] = train_df[\"question_text\"].progress_apply(lambda x: np.mean([len(w) for w in str(x).split()]))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_df[\"num_words\"] = test_df[\"question_text\"].progress_apply(lambda x: len(str(x).split()))\ntest_df[\"num_unique_words\"] = test_df[\"question_text\"].progress_apply(lambda x: len(set(str(x).split())))\ntest_df[\"num_chars\"] = test_df[\"question_text\"].progress_apply(lambda x: len(str(x)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'num_words', stat_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'num_unique_words', stat_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'num_chars', stat_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'num_words_upper', stat_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'num_words_title', stat_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# plot_it('target', 'mean_word_len', stat_data)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Increase Coverage"},{"metadata":{"colab":{},"colab_type":"code","id":"3JLVZ1255pjk","trusted":true},"cell_type":"code","source":"# Vocabulary build with count\ndef build_vocab(sentences, verbose =  True):\n    vocab = {}\n    for sentence in tqdm(sentences, disable = (not verbose)):\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":"emb_path = '/kaggle/input/quora-insincere-questions-classification/embeddings'\n\ndef load_all():\n    word2vec_format = {}\n    glove = [o.split(\" \")[0] for o in tqdm(open(emb_path + '/glove.840B.300d/glove.840B.300d.txt'))]\n    for word in tqdm(glove):\n        word2vec_format[word] = 1\n    clean_memory(glove)\n\n    paragram = [o.split(\" \")[0] for o in open(emb_path + '/paragram_300_sl999/paragram_300_sl999.txt', encoding=\"utf8\", errors='ignore') if len(o)>100]\n    para = 0\n    for word in tqdm(paragram):\n        word2vec_format[word] = 1\n    clean_memory(paragram)\n    \n    return word2vec_format\n\nemb_all = load_all()","execution_count":null,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"TmQummN95pkH","trusted":true},"cell_type":"code","source":"# Coverage check for current embedding\nimport operator \n\ndef check_coverage(vocab, embeddings_index):\n    a = {}\n    oov = {}\n    k = 0\n    i = 0\n    for word in tqdm(vocab):\n        try:\n            a[word] = embeddings_index[word]\n            k += vocab[word]\n        except:\n            oov[word] = vocab[word]\n            i += vocab[word]\n            pass\n\n    print('Found embeddings for {:.2%} of vocab'.format(len(a) / len(vocab)))\n    print('Found embeddings for  {:.2%} of all text'.format(k / (k + i)))\n    sorted_x = sorted(oov.items(), key=operator.itemgetter(1))[::-1]\n\n    return sorted_x","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":71},"colab_type":"code","id":"TDFsk_gs5pkM","outputId":"58d7b157-a9a4-4f83-c0d5-69035538fc27","trusted":true},"cell_type":"code","source":"sentences = train_df[\"question_text\"].progress_apply(lambda x: x.split())\nvocab = build_vocab(sentences)\noov = check_coverage(vocab, emb_all)\noov[:10]","execution_count":null,"outputs":[]},{"metadata":{"colab_type":"text","id":"cEPQmU7C5pkf"},"cell_type":"markdown","source":"# Preprocessing"},{"metadata":{"colab_type":"text","id":"4ehbee6kLF84"},"cell_type":"markdown","source":"## Text clean from punctuation"},{"metadata":{"colab":{},"colab_type":"code","id":"HuRHskEh5pkh","trusted":true},"cell_type":"code","source":"punct = \"/-'?!.,#$%\\'()*+-/:;<=>@[\\\\]^_`{|}~\" + '\"\"“”’' + '∞θ÷α•à−β∅³π‘₹´°£€\\×™√²—–&'\npunct_mapping = {\"‘\": \"'\", \"₹\": \"e\", \"´\": \"'\", \"°\": \"\", \"€\": \"e\", \"™\": \"tm\", \"√\": \" sqrt \", \"×\": \"x\", \"²\": \"2\", \"—\": \"-\", \"–\": \"-\", \"’\": \"'\", \"_\": \"-\", \"`\": \"'\", '“': '\"', '”': '\"', '“': '\"', \"£\": \"e\", '∞': 'infinity', 'θ': 'theta', '÷': '/', 'α': 'alpha', '•': '.', 'à': 'a', '−': '-', 'β': 'beta', '∅': '', '³': '3', 'π': 'pi', }\n\ndef clean_special_chars(text, punct=punct, mapping=punct_mapping):\n    for p in mapping:\n        if p in text:\n            text = text.replace(p, mapping[p])\n    \n    for p in punct:\n        if p in text:\n            text = text.replace(p, f' {p} ')\n    \n    specials = {'\\u200b': ' ', '…': ' ... ', '\\ufeff': '', 'करना': '', 'है': ''}  # Other special characters that I have to deal with in last\n    for s in specials:\n        if s in text:\n            text = text.replace(s, specials[s])\n    \n    return text","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"colab_type":"code","id":"HvPlAPJr5pkt","outputId":"d1115fe0-ccdc-45aa-8bc7-f05571cd63a0","trusted":true},"cell_type":"code","source":"train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: clean_special_chars(x))\ntest_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: clean_special_chars(x))\nsentences = train_df[\"question_text\"].progress_apply(lambda x: x.split())\nvocab = build_vocab(sentences)\noov = check_coverage(vocab,emb_all)\noov[:10]","execution_count":null,"outputs":[]},{"metadata":{"colab_type":"text","id":"B1x3o1YgLT8O"},"cell_type":"markdown","source":"## Contaction Remove (don't need for glove)"},{"metadata":{"colab":{},"colab_type":"code","id":"3Pu9B8z4ITci","trusted":true},"cell_type":"code","source":"# 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\" }\n\n# 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":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","id":"4EUUjK8KIYC7","outputId":"1b2cb9a4-dfc3-4f5e-9207-6f25af2f2656","trusted":true},"cell_type":"code","source":"# train[\"question_text\"] = train[\"question_text\"].progress_apply(lambda x: clean_contractions(x, contraction_mapping))\n# sentences = train[\"question_text\"].apply(lambda x: x.split())\n# vocab = build_vocab(sentences)\n# oov = check_coverage(vocab,glove_emb)\n# oov[:10]","execution_count":null,"outputs":[]},{"metadata":{"colab_type":"text","id":"hevk6Y0XNcO9"},"cell_type":"markdown","source":"## Mis-spell clean"},{"metadata":{"colab":{},"colab_type":"code","id":"K1aHNbhBJwnZ","trusted":true},"cell_type":"code","source":"mispell_dict = {'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'}\n\ndef correct_spelling(x, dic):\n    for word in dic.keys():\n        if word in x:\n            x = x.replace(word, dic[word])\n    return x","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","id":"gUsm3pd7J0AZ","outputId":"84b4f477-3df5-43ab-ce18-453edfbea9a7","trusted":true},"cell_type":"code","source":"train_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: correct_spelling(x, mispell_dict))\ntest_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: correct_spelling(x, mispell_dict))\nsentences = train_df[\"question_text\"].apply(lambda x: x.split())\nvocab = build_vocab(sentences)\noov = check_coverage(vocab, emb_all)\noov[:10]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Common words replace"},{"metadata":{"trusted":true},"cell_type":"code","source":"go_to_more_common_words = {\n    'Redmi': 'Mobile',\n    'OnePlus': 'Mobile',\n    'Quorans': 'Quoran',\n    'cryptocurrencies': 'technology',\n    'Cryptocurrency': 'Technology',\n    'Blockchain': 'Technology',\n    'Upwork': 'Technology',\n    'HackerRank': 'Programming',\n}\n\ntrain_df[\"question_text\"] = train_df[\"question_text\"].progress_apply(lambda x: correct_spelling(x, go_to_more_common_words))\ntest_df[\"question_text\"] = test_df[\"question_text\"].progress_apply(lambda x: correct_spelling(x, go_to_more_common_words))\nsentences = train_df[\"question_text\"].apply(lambda x: x.split())\nvocab = build_vocab(sentences)\noov = check_coverage(vocab, emb_all)\noov[:10]","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"colab_type":"code","id":"ZfsGjZGqKwM4","outputId":"65bb9d7e-ebda-4d5c-da68-a7abe5148df6","trusted":true},"cell_type":"code","source":"clean_memory(oov, vocab, sentences, mispell_dict, go_to_more_common_words, punct, punct_mapping, emb_all)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Get embeddings"},{"metadata":{"trusted":true},"cell_type":"code","source":"def load_all_emb(word_index):\n    def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n    glove = dict(get_coefs(*o.split(\" \")) for o in tqdm(open(emb_path + '/glove.840B.300d/glove.840B.300d.txt')) if o.split(\" \")[0] in word_index)\n    paragram = dict(get_coefs(*o.split(\" \")) for o in open(emb_path + '/paragram_300_sl999/paragram_300_sl999.txt', encoding=\"utf8\", errors='ignore') if len(o)>100 and o.split(\" \")[0] in word_index)\n\n    global max_features\n    all_embs = np.stack(glove.values())\n    emb_mean, emb_std = all_embs.mean(), all_embs.std()\n#     emb_mean, emb_std = -0.005838499, 0.48782197\n    clean_memory(all_embs)\n\n    nb_words = min(max_features, len(word_index))\n    max_features = nb_words\n    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, emb_size))\n    for word, i in tqdm(word_index.items()):\n        if i >= nb_words: continue\n        glove_vector = glove.get(word)\n        paragram_vector = paragram.get(word)\n        if glove_vector is not None and paragram_vector is not None:\n            embedding_matrix[i] = 0.7 * glove_vector + 0.3 * paragram_vector\n        elif glove_vector is not None:\n            embedding_matrix[i] = glove_vector\n        elif paragram_vector is not None:\n            embedding_matrix[i] = paragram_vector\n    clean_memory(glove_vector, paragram_vector)\n            \n    return embedding_matrix","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"emb_path = '/kaggle/input/quora-insincere-questions-classification/embeddings'\n# emb_path = '/content'\ntokenizer = Tokenizer(num_words=max_features, filters=\"\")\ntokenizer.fit_on_texts(np.concatenate((train_df[\"question_text\"].values, test_df[\"question_text\"].values)))\nembeddings = load_all_emb(tokenizer.word_index)","execution_count":null,"outputs":[]},{"metadata":{"colab_type":"text","id":"3cNZ2swrF84k"},"cell_type":"markdown","source":"# Dataset Preparation"},{"metadata":{"colab":{},"colab_type":"code","id":"LOsWAHRo5pky","trusted":true},"cell_type":"code","source":"## split to train and val\ntrain_df, val_df = train_test_split(train_df, test_size=0.1, random_state=47)\n\n## fill up the missing values\ntrain_X = train_df[\"question_text\"].values\nval_X = val_df[\"question_text\"].values\ntest_X = test_df[\"question_text\"].values\n\n## 2nd type input\ntrain_X2 = train_df[['num_words', 'num_unique_words', 'num_chars']]\nval_X2 = val_df[['num_words', 'num_unique_words', 'num_chars']]\ntest_X2 = test_df[['num_words', 'num_unique_words', 'num_chars']]\n\n## Tokenize the sentences\ntrain_X = tokenizer.texts_to_sequences(train_X)\nval_X = tokenizer.texts_to_sequences(val_X)\ntest_X = tokenizer.texts_to_sequences(test_X)\n\n## Pad the sentences \ntrain_X = pad_sequences(train_X, maxlen=maxlen)\nval_X = pad_sequences(val_X, maxlen=maxlen)\ntest_X = pad_sequences(test_X, maxlen=maxlen)\n\n## Get the target values\ntrain_y = train_df['target'].values\nval_y = val_df['target'].values\n\nprint('Train shape: {}'.format(train_X.shape))\nprint('Validation shape: {}'.format(val_X.shape))\nprint('Test shape: {}'.format(test_X.shape))\n\nclean_memory(train_df, val_df, test_df)","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"colab_type":"code","id":"qsP47hqM5pk8","outputId":"3bec266a-a186-4464-85df-6c9ebe0a16c5","trusted":true},"cell_type":"code","source":"print(\"Train shape: {}\".format(train_X.shape))\nprint(\"Validation shape: {}\".format(val_X.shape))","execution_count":null,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"phm3cTVcYEgN","trusted":true},"cell_type":"code","source":"from keras.callbacks import EarlyStopping, ReduceLROnPlateau\nearly_stopping = EarlyStopping(monitor=\"val_f1\", patience=2, restore_best_weights=True, mode=\"max\")\nreduce_lr = ReduceLROnPlateau(monitor=\"val_f1\", mode=\"max\")\ncallbacks = [early_stopping, reduce_lr]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras import backend as K\n\ndef 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        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        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},"cell_type":"code","source":"train_X2.shape, train_X.shape, train_y.shape\n# train_X2.head()","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":271},"colab_type":"code","id":"Tycz7Wfy5ply","outputId":"2c497861-ae6f-4771-8b35-83dcd555287a","trusted":true},"cell_type":"code","source":"from tensorflow.python.keras.models import Sequential\nfrom tensorflow.python.keras.layers import Input, Embedding, LSTM, Reshape, Flatten, BatchNormalization, GlobalAveragePooling1D, SpatialDropout1D, CuDNNGRU, CuDNNLSTM, Dense, Bidirectional, GlobalMaxPooling1D, Dropout, Conv1D, concatenate\nfrom tensorflow.python.keras.models import Model\n\n# 1st Input\ninp1 = Input(shape=(maxlen, ))\nx = Embedding(max_features, emb_size, weights=[embeddings], trainable=False)(inp1)\nx = SpatialDropout1D(0.4)(x)\nx = Bidirectional(CuDNNLSTM(128, return_sequences=True))(x)\n# x = Bidirectional(CuDNNGRU(40, return_sequences=True))(x)\nx = Conv1D(64, 1)(x)\n\n# avg_pool = GlobalAveragePooling1D()(x)\nmax_pool = GlobalMaxPooling1D()(x)\n\n# 2nd Input\ninp2 = Input(shape=(3, ))\ny = Dense(64)(inp2)\n\nxy = concatenate([max_pool, y], axis=1)\n# xy = Flatten()(xy)\nxy = Dense(128, activation=\"relu\")(xy)\nxy = Dropout(0.1)(xy)\nxy = BatchNormalization()(xy)\noutp = Dense(1, activation=\"sigmoid\")(xy)\n\nmodel = Model(inputs=[inp1, inp2], outputs=outp)\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=[f1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"colab_type":"code","id":"qanOFXqX5pl3","outputId":"cf506ed8-0576-43c1-d555-0c645ae09b60","trusted":true},"cell_type":"code","source":"model.fit([train_X, train_X2], train_y, batch_size=512, epochs=20, validation_data=([val_X, val_X2], val_y), callbacks=callbacks, verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"4Cwcl8PFWO2P","trusted":false},"cell_type":"code","source":"clean_memory(train_X, train_y)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Simple Threshhold finding"},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":755},"colab_type":"code","id":"Wbv833OtSuVl","outputId":"b2db3e81-9e80-42da-c27d-282a0ee1dc6a","trusted":false},"cell_type":"code","source":"# from sklearn.metrics import f1_score\n# optimal = -100\n# optimal_point = 0\n# pred_val_y = model.predict(test_val_x)\n# for thresh in np.arange(0.1, 0.501, 0.01):\n#     thresh = np.round(thresh, 2)\n#     now_f1 = f1_score(test_val_y, (pred_val_y > thresh).astype(int))\n#     if now_f1 > optimal:\n#       optimal_point = thresh\n#       optimal = now_f1\n#     print(\"F1 score at threshold {0} is {1}\".format(thresh, now_f1))","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"colab_type":"code","id":"81wmd2xoSuuW","outputId":"261122ab-c78f-4fe8-fbfa-e1ce65d88b0d","trusted":false},"cell_type":"code","source":"# print(\"Optimal F1 {} on threshhold {}\".format(optimal, optimal_point))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# More complex threshold finding"},{"metadata":{"trusted":false},"cell_type":"code","source":"pred_val_y = model.predict([val_X, val_X2], batch_size=512, verbose=1)\n\ndef scoring(y_true, y_proba, verbose=True):\n    from sklearn.metrics import roc_curve, precision_recall_curve, f1_score\n    from sklearn.model_selection import RepeatedStratifiedKFold\n\n    def threshold_search(y_true, y_proba):\n        precision , recall, thresholds = precision_recall_curve(y_true, y_proba)\n        thresholds = np.append(thresholds, 1.001) \n        F = 2 / (1/precision + 1/recall)\n        best_score = np.max(F)\n        best_th = thresholds[np.argmax(F)]\n        return best_th \n\n\n    rkf = RepeatedStratifiedKFold(n_splits=5, n_repeats=10)\n\n    scores = []\n    ths = []\n    for train_index, test_index in rkf.split(y_true, y_true):\n        y_prob_train, y_prob_test = y_proba[train_index], y_proba[test_index]\n        y_true_train, y_true_test = y_true[train_index], y_true[test_index]\n\n        # determine best threshold on 'train' part \n        best_threshold = threshold_search(y_true_train, y_prob_train)\n\n        # use this threshold on 'test' part for score \n        sc = f1_score(y_true_test, (y_prob_test >= best_threshold).astype(int))\n        scores.append(sc)\n        ths.append(best_threshold)\n\n    best_th = np.mean(ths)\n    score = np.mean(scores)\n\n    if verbose: print(f'Best threshold: {np.round(best_th, 4)}, Score: {np.round(score,5)}')\n\n    return best_th, score\n\noptimal_point1, optimal1 = scoring(val_y, pred_val_y)\nprint(\"Optimal F1 {} on threshhold {}\".format(optimal1, optimal_point1))","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"clean_memory(val_X, val_y, pred_val_y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"test_df.head()","execution_count":null,"outputs":[]},{"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"colab_type":"code","id":"WholyhjVSvN8","outputId":"9812bca6-ddf3-459f-861d-7fad157f5e9d","trusted":false},"cell_type":"code","source":"all_preds = model.predict([test_X, test_X2], batch_size=512, verbose=1)\npred_test_y = (np.array(all_preds) > optimal_point1).astype(np.int)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"pred_test_y1 = np.asarray([y[0] for y in pred_test_y])\npred_test_y1.shape\npred_test_y1[:10]","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"submit_df = pd.DataFrame({\"qid\": test_df[\"qid\"], \"prediction\": pred_test_y1})\nsubmit_df.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"name":"1234.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 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