Kaggle competition: Quora Insincere Questions Classification
<SOS> metric: FScore_1 Kaggle data source: quora-insincere-questions-classification<set_options>
import time
import random
import pandas as pd
import numpy as np
import gc
import re
import torch
from torchtext import data
import spacy
from tqdm import tqdm_notebook, tnrange
from tqdm.auto import tqdm
from collections import Counter
from textblob import TextBlob
from nltk import word_tokenize
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch.autograd import Variable
from torchtext.data import Example
from sklearn.metrics import f1_score
import torchtext
import os
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score
from torch.optim.optimizer import Optimizer
from unidecode import unidecode
def seed_everything(seed=0):
np.random.seed(seed)
 tf.random.set_seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
 warnings.filterwarnings("ignore" )<train_on_grid>
embed_size = 300
max_features = 120000
maxlen = 72
batch_size = 512
n_epochs = 5
n_splits = 5
SEED = 1029
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print("Running on TPU ", tpu.master())
 except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 else:
strategy = tf.distribute.get_strategy()
print("REPLICAS : ", strategy.num_replicas_in_sync )<define_variables>
def seed_everything(seed=1029):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything()
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
WARMUP_EPOCHS = 3
WARMUP_LEARNING_RATE = 1e-4 * strategy.num_replicas_in_sync
EPOCHS = 20
LEARNING_RATE = 3e-5 * strategy.num_replicas_in_sync
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 104
ES_PATIENCE = 6
RLROP_PATIENCE = 3
DECAY_DROP = 0.3<define_variables>
def load_glove(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')[:300]
embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(EMBEDDING_FILE))
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = -0.005838499,0.48782197
embed_size = all_embs.shape[1]
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):
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]
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):
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 = -0.0053247833,0.49346462
embed_size = all_embs.shape[1]
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
model_path = 'DenseNet201_%sx%s.h5' %(HEIGHT, WIDTH)
 GCS_PATH = KaggleDatasets().get_gcs_path() + '/tfrecords-jpeg-%sx%s' %(HEIGHT, WIDTH)
 TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
 VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec' )<define_variables>
df_train = pd.read_csv(".. /input/train.csv")
 df_test = pd.read_csv(".. /input/test.csv")
 df = pd.concat([df_train ,df_test],sort=True )
CLASSES = [
'pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',
'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood',
'globe thistle', 'snapdragon', "colt's foot", 'king protea', 'spear thistle',
'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily',
'balloon flower', 'giant white arum lily', 'fire lily', 'pincushion flower',
'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy',
'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william',
'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly',
'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip',
'lenten rose', 'barberton daisy', 'daffodil', 'sword lily', 'poinsettia',
'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy',
'common dandelion', 'petunia', 'wild pansy', 'primula', 'sunflower',
'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia',
'pink-yellow dahlia', 'cautleya spicata', 'japanese anemone', 'black-eyed susan',
'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris',
'windflower', 'tree poppy', 'gazania', 'azalea', 'water lily', 'rose',
'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily',
'anthurium', 'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose',
'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea',
'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower',
'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose'
]<normalization>
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
vocab = build_vocab(df['question_text'] )
AUTO = tf.data.experimental.AUTOTUNE
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels = 3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [HEIGHT, WIDTH, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
'image' : tf.io.FixedLenFeature([], tf.string),
'class' : tf.io.FixedLenFeature([], tf.int64)
 }
example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 return image, label
def read_unlabeled_tfrecord(example):
UNLABELED_TREC_FORMAT = {
'image' : tf.io.FixedLenFeature([], tf.string),
'id' : tf.io.FixedLenFeature([], tf.string)
 }
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum<create_dataframe>
sin = len(df_train[df_train["target"]==0])
 insin = len(df_train[df_train["target"]==1])
 persin =(sin/(sin+insin)) *100
perinsin =(insin/(sin+insin)) *100
print("
print("
def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset<define_variables>
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def known_contractions(embed):
known = []
for contract in contraction_mapping:
if contract in embed:
known.append(contract)
 return known
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
def correct_spelling(x, dic):
for word in dic.keys() :
x = x.replace(word, dic[word])
 return x
def unknown_punct(embed, punct):
unknown = ''
for p in punct:
if p not in embed:
unknown += p
unknown += ' '
return unknown
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
def clean_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': ''}
for s in specials:
text = text.replace(s, specials[s])
 return text
def add_lower(embedding, vocab):
count = 0
for word in vocab:
if word in embedding and word.lower() not in embedding:
embedding[word.lower() ] = embedding[word]
count += 1
print(f"Added {count} words to embedding" )
def get_training_dataset() :
dataset = load_dataset(TRAINING_FILENAMES, labeled = True)
 dataset = dataset.map(data_augment, num_parallel_calls = AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset<create_dataframe>
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]
def clean_text(x):
x = str(x)
 for punct in puncts:
x = x.replace(punct, f' {punct} ')
 return x
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
mispell_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'}
def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
return mispellings_re.sub(replace, text )
def get_training_dataset_preview(ordered=True):
dataset = load_dataset(TRAINING_FILENAMES, labeled=True, ordered = ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset<create_dataframe>
def add_features(df):
df['question_text'] = df['question_text'].apply(lambda x:str(x))
df['total_length'] = df['question_text'].apply(len)
 df['capitals'] = df['question_text'].apply(lambda comment: sum(1 for c in comment if c.isupper()))
df['caps_vs_length'] = df.apply(lambda row: float(row['capitals'])/float(row['total_length']),
axis=1)
 df['num_words'] = df.question_text.str.count('\S+')
 df['num_unique_words'] = df['question_text'].apply(lambda comment: len(set(w for w in comment.split())))
 df['words_vs_unique'] = df['num_unique_words'] / df['num_words']
return df
def load_and_prec() :
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: x.lower())
 test_df["question_text"] = test_df["question_text"].apply(lambda x: x.lower())
 train_df["question_text"] = train_df["question_text"].apply(lambda x: clean_text(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_text(x))
train_df["question_text"] = train_df["question_text"].apply(lambda x: clean_numbers(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: clean_numbers(x))
train_df["question_text"] = train_df["question_text"].apply(lambda x: replace_typical_misspell(x))
test_df["question_text"] = test_df["question_text"].apply(lambda x: replace_typical_misspell(x))
train_X = train_df["question_text"].fillna("_
test_X = test_df["question_text"].fillna("_
train = add_features(train_df)
 test = add_features(test_df)
 features = train[['caps_vs_length', 'words_vs_unique']].fillna(0)
 test_features = test[['caps_vs_length', 'words_vs_unique']].fillna(0)
 ss = StandardScaler()
ss.fit(np.vstack(( features, test_features)))
 features = ss.transform(features)
 test_features = ss.transform(test_features)
 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)
 train_X = pad_sequences(train_X, maxlen=maxlen)
 test_X = pad_sequences(test_X, maxlen=maxlen)
 train_y = train_df['target'].values
np.random.seed(SEED)
 trn_idx = np.random.permutation(len(train_X))
train_X = train_X[trn_idx]
train_y = train_y[trn_idx]
features = features[trn_idx]
return train_X, test_X, train_y, features, test_features, tokenizer.word_index
def get_validation_dataset(ordered=True):
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset<create_dataframe>
x_train, x_test, y_train, features, test_features, word_index = load_and_prec()
def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset<groupby>
np.save("x_train",x_train)
 np.save("x_test",x_test)
 np.save("y_train",y_train)
 np.save("features",features)
 np.save("test_features",test_features)
 np.save("word_index.npy",word_index )
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n )<categorify>
x_train = np.load("x_train.npy")
 x_test = np.load("x_test.npy")
 y_train = np.load("y_train.npy")
 features = np.load("features.npy")
 test_features = np.load("test_features.npy")
 word_index = np.load("word_index.npy", allow_pickle=True ).item()
np.set_printoptions(threshold=15, linewidth=80)
 def batch_to_numpy_images_and_labels(data):
images, labels = data
numpy_images = images.numpy()
numpy_labels = labels.numpy()
if numpy_labels.dtype == object:
numpy_labels = [None for _ in enumerate(numpy_images)]
return numpy_images, numpy_labels
def title_from_label_and_target(label, correct_label):
if correct_label is None:
return CLASSES[label], True
correct =(label == correct_label)
 return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
CLASSES[correct_label] if not correct else ''), correct
def display_one_flower(image, title, subplot, red=False, titlesize=16):
plt.subplot(*subplot)
 plt.axis('off')
 plt.imshow(image)
 if len(title)> 0:
plt.title(title, fontsize=int(titlesize)if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
return(subplot[0], subplot[1], subplot[2]+1)
 def display_batch_of_images(databatch, predictions=None):

 images, labels = batch_to_numpy_images_and_labels(databatch)
 if labels is None:
labels = [None for _ in enumerate(images)]
rows = int(math.sqrt(len(images)))
 cols = len(images)//rows
FIGSIZE = 13.0
SPACING = 0.1
subplot=(rows,cols,1)
 if rows < cols:
plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
else:
plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
for i,(image, label)in enumerate(zip(images[:rows*cols], labels[:rows*cols])) :
title = '' if label is None else CLASSES[label]
correct = True
if predictions is not None:
title, correct = title_from_label_and_target(predictions[i], label)
 dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3
subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
 plt.tight_layout()
if label is None and predictions is None:
plt.subplots_adjust(wspace=0, hspace=0)
 else:
plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
 plt.show()
def dataset_to_numpy_util(dataset, N):
dataset = dataset.unbatch().batch(N)
 for images, labels in dataset:
numpy_images = images.numpy()
numpy_labels = labels.numpy()
break;
return numpy_images, numpy_labels
def title_from_label_and_target(label, correct_label):
label = np.argmax(label, axis=-1)
 correct =(label == correct_label)
 return "{} [{}{}{}]".format(CLASSES[label], str(correct), ', shoud be ' if not correct else '',
CLASSES[correct_label] if not correct else ''), correct
def display_one_flower_eval(image, title, subplot, red=False):
plt.subplot(subplot)
 plt.axis('off')
 plt.imshow(image)
 plt.title(title, fontsize=14, color='red' if red else 'black')
 return subplot+1
def display_9_images_with_predictions(images, predictions, labels):
subplot=331
plt.figure(figsize=(13,13))
for i, image in enumerate(images):
title, correct = title_from_label_and_target(predictions[i], labels[i])
 subplot = display_one_flower_eval(image, title, subplot, not correct)
 if i >= 8:
break;
plt.tight_layout()
plt.subplots_adjust(wspace=0.1, hspace=0.1)
 plt.show()<categorify>
seed_everything()
glove_embeddings = load_glove(word_index)
 paragram_embeddings = load_para(word_index)
 fasttext_embeddings = load_fasttext(word_index)
 embedding_matrix = np.mean([glove_embeddings, paragram_embeddings, fasttext_embeddings], axis=0)
 del glove_embeddings, paragram_embeddings, fasttext_embeddings
gc.collect()
np.shape(embedding_matrix )
AUTO = tf.data.experimental.AUTOTUNE
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [HEIGHT, WIDTH, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
}
example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 return image, label
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def data_augment(image, label):
image = tf.image.random_flip_left_right(image, seed=seed)
 image = tf.image.random_flip_up_down(image, seed=seed)
 image = tf.image.random_saturation(image, lower=0, upper=2, seed=seed)
 image = tf.image.random_crop(image, size=[int(HEIGHT*.8), int(WIDTH*.8), CHANNELS], seed=seed)
 return image, label
def get_training_dataset() :
dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_training_dataset_preview(ordered=True):
dataset = load_dataset(TRAINING_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset(ordered=False):
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n )<prepare_x_and_y>
splits = list(StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED ).split(x_train, y_train))
splits[:3]
NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 train_dataset = get_training_dataset_preview(ordered=True)
 y_train = next(iter(train_dataset.unbatch().map(lambda image, label: label ).batch(NUM_TRAINING_IMAGES)) ).numpy()
print('Number of training images %d' % NUM_TRAINING_IMAGES )<prepare_x_and_y>
class CyclicLR(object):
def __init__(self, optimizer, base_lr=1e-3, max_lr=6e-3,
step_size=2000, mode='triangular', gamma=1.,
scale_fn=None, scale_mode='cycle', last_batch_iteration=-1):
if not isinstance(optimizer, Optimizer):
raise TypeError('{} is not an Optimizer'.format(
 type(optimizer ).__name__))
self.optimizer = optimizer
if isinstance(base_lr, list)or isinstance(base_lr, tuple):
if len(base_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} base_lr, got {}".format(
 len(optimizer.param_groups), len(base_lr)))
 self.base_lrs = list(base_lr)
 else:
self.base_lrs = [base_lr] * len(optimizer.param_groups)
 if isinstance(max_lr, list)or isinstance(max_lr, tuple):
if len(max_lr)!= len(optimizer.param_groups):
raise ValueError("expected {} max_lr, got {}".format(
 len(optimizer.param_groups), len(max_lr)))
 self.max_lrs = list(max_lr)
 else:
self.max_lrs = [max_lr] * len(optimizer.param_groups)
 self.step_size = step_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
 self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.batch_step(last_batch_iteration + 1)
 self.last_batch_iteration = last_batch_iteration
def batch_step(self, batch_iteration=None):
if batch_iteration is None:
batch_iteration = self.last_batch_iteration + 1
self.last_batch_iteration = batch_iteration
for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
param_group['lr'] = lr
def _triangular_scale_fn(self, x):
return 1.
 def _triangular2_scale_fn(self, x):
return 1 /(2.**(x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
 def get_lr(self):
step_size = float(self.step_size)
 cycle = np.floor(1 + self.last_batch_iteration /(2 * step_size))
x = np.abs(self.last_batch_iteration / step_size - 2 * cycle + 1)
 lrs = []
param_lrs = zip(self.optimizer.param_groups, self.base_lrs, self.max_lrs)
 for param_group, base_lr, max_lr in param_lrs:
base_height =(max_lr - base_lr)* np.maximum(0,(1 - x))
if self.scale_mode == 'cycle':
lr = base_lr + base_height * self.scale_fn(cycle)
 else:
lr = base_lr + base_height * self.scale_fn(self.last_batch_iteration)
 lrs.append(lr)
 return lrs
NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
 valid_dataset = get_validation_dataset(ordered=True)
 y_valid = next(iter(valid_dataset.unbatch().map(lambda image, label: label ).batch(NUM_VALIDATION_IMAGES)) ).numpy()
print('Number of validation images %d' % NUM_VALIDATION_IMAGES )<define_variables>
embedding_dim = 300
embedding_path = '.. /save/embedding_matrix.npy'
use_pretrained_embedding = True
hidden_size = 60
gru_len = hidden_size
Routings = 4
Num_capsule = 5
Dim_capsule = 5
dropout_p = 0.25
rate_drop_dense = 0.28
LR = 0.001
T_epsilon = 1e-7
num_classes = 30
class Embed_Layer(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None, embedding_dim=300):
super(Embed_Layer, self ).__init__()
self.encoder = nn.Embedding(vocab_size + 1, embedding_dim)
 if use_pretrained_embedding:
self.encoder.weight.data.copy_(t.from_numpy(embedding_matrix))
def forward(self, x, dropout_p=0.25):
return nn.Dropout(p=dropout_p )(self.encoder(x))
class GRU_Layer(nn.Module):
def __init__(self):
super(GRU_Layer, self ).__init__()
self.gru = nn.GRU(input_size=300,
hidden_size=gru_len,
bidirectional=True)
 def init_weights(self):
ih =(param.data for name, param in self.named_parameters() if 'weight_ih' in name)
 hh =(param.data for name, param in self.named_parameters() if 'weight_hh' in name)
 b =(param.data for name, param in self.named_parameters() if 'bias' in name)
 for k in ih:
nn.init.xavier_uniform_(k)
 for k in hh:
nn.init.orthogonal_(k)
 for k in b:
nn.init.constant_(k, 0)
 def forward(self, x):
return self.gru(x)
 class Caps_Layer(nn.Module):
def __init__(self, input_dim_capsule=gru_len * 2, num_capsule=Num_capsule, dim_capsule=Dim_capsule, \
routings=Routings, kernel_size=(9, 1), share_weights=True,
activation='default', **kwargs):
super(Caps_Layer, self ).__init__(**kwargs)
 self.num_capsule = num_capsule
self.dim_capsule = dim_capsule
self.routings = routings
self.kernel_size = kernel_size
self.share_weights = share_weights
if activation == 'default':
self.activation = self.squash
else:
self.activation = nn.ReLU(inplace=True)
 if self.share_weights:
self.W = nn.Parameter(
 nn.init.xavier_normal_(t.empty(1, input_dim_capsule, self.num_capsule * self.dim_capsule)))
 else:
self.W = nn.Parameter(
 t.randn(BATCH_SIZE, input_dim_capsule, self.num_capsule * self.dim_capsule))
def forward(self, x):
if self.share_weights:
u_hat_vecs = t.matmul(x, self.W)
 else:
print('add later')
 batch_size = x.size(0)
 input_num_capsule = x.size(1)
 u_hat_vecs = u_hat_vecs.view(( batch_size, input_num_capsule,
self.num_capsule, self.dim_capsule))
u_hat_vecs = u_hat_vecs.permute(0, 2, 1, 3)
 b = t.zeros_like(u_hat_vecs[:, :, :, 0])
 for i in range(self.routings):
b = b.permute(0, 2, 1)
 c = F.softmax(b, dim=2)
 c = c.permute(0, 2, 1)
 b = b.permute(0, 2, 1)
 outputs = self.activation(t.einsum('bij,bijk->bik',(c, u_hat_vecs)))
 if i < self.routings - 1:
b = t.einsum('bik,bijk->bij',(outputs, u_hat_vecs))
return outputs
def squash(self, x, axis=-1):
s_squared_norm =(x ** 2 ).sum(axis, keepdim=True)
 scale = t.sqrt(s_squared_norm + T_epsilon)
 return x / scale
class Capsule_Main(nn.Module):
def __init__(self, embedding_matrix=None, vocab_size=None):
super(Capsule_Main, self ).__init__()
self.embed_layer = Embed_Layer(embedding_matrix, vocab_size)
 self.gru_layer = GRU_Layer()
self.gru_layer.init_weights()
self.caps_layer = Caps_Layer()
self.dense_layer = Dense_Layer()
def forward(self, content):
content1 = self.embed_layer(content)
 content2, _ = self.gru_layer(
 content1)
 content3 = self.caps_layer(content2)
 output = self.dense_layer(content3)
 return output
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 print('Number of test images %d' % NUM_TEST_IMAGES)
 test_dataset = get_test_dataset(ordered=True )<define_variables>
class Attention(nn.Module):
def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
super(Attention, self ).__init__(**kwargs)
 self.supports_masking = True
self.bias = bias
self.feature_dim = feature_dim
self.step_dim = step_dim
self.features_dim = 0
weight = torch.zeros(feature_dim, 1)
 nn.init.xavier_uniform_(weight)
 self.weight = nn.Parameter(weight)
 if bias:
self.b = nn.Parameter(torch.zeros(step_dim))
def forward(self, x, mask=None):
feature_dim = self.feature_dim
step_dim = self.step_dim
eij = torch.mm(
 x.contiguous().view(-1, feature_dim),
self.weight
).view(-1, step_dim)
 if self.bias:
eij = eij + self.b
eij = torch.tanh(eij)
 a = torch.exp(eij)
 if mask is not None:
a = a * mask
a = a / torch.sum(a, 1, keepdim=True)+ 1e-10
weighted_input = x * torch.unsqueeze(a, -1)
 return torch.sum(weighted_input, 1)
 class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self ).__init__()
fc_layer = 16
fc_layer1 = 16
self.embedding = nn.Embedding(max_features, embed_size)
 self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
 self.lstm = nn.LSTM(embed_size, hidden_size, bidirectional=True, batch_first=True)
 self.gru = nn.GRU(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm2 = nn.LSTM(hidden_size * 2, hidden_size, bidirectional=True, batch_first=True)
 self.lstm_attention = Attention(hidden_size * 2, maxlen)
 self.gru_attention = Attention(hidden_size * 2, maxlen)
 self.bn = nn.BatchNorm1d(16, momentum=0.5)
 self.linear = nn.Linear(hidden_size*8+3, fc_layer1)
 self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.1)
 self.fc = nn.Linear(fc_layer**2,fc_layer)
 self.out = nn.Linear(fc_layer, 1)
 self.lincaps = nn.Linear(Num_capsule * Dim_capsule, 1)
 self.caps_layer = Caps_Layer()
def forward(self, x):
h_embedding = self.embedding(x[0])
 h_embedding = torch.squeeze(
 self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))
 h_lstm, _ = self.lstm(h_embedding)
 h_gru, _ = self.gru(h_lstm)
 content3 = self.caps_layer(h_gru)
 content3 = self.dropout(content3)
 batch_size = content3.size(0)
 content3 = content3.view(batch_size, -1)
 content3 = self.relu(self.lincaps(content3))
h_lstm_atten = self.lstm_attention(h_lstm)
 h_gru_atten = self.gru_attention(h_gru)
 avg_pool = torch.mean(h_gru, 1)
 max_pool, _ = torch.max(h_gru, 1)
 f = torch.tensor(x[1], dtype=torch.float ).cuda()
conc = torch.cat(( h_lstm_atten, h_gru_atten, content3, avg_pool, max_pool, f), 1)
 conc = self.relu(self.linear(conc))
conc = self.bn(conc)
 conc = self.dropout(conc)
 out = self.out(conc)
 return out
display_batch_of_images(next(iter(train_dataset.unbatch().batch(20))))<define_variables>
class MyDataset(Dataset):
def __init__(self,dataset):
self.dataset = dataset
def __getitem__(self, index):
data, target = self.dataset[index]
return data, target, index
def __len__(self):
return len(self.dataset )
display_batch_of_images(next(iter(valid_dataset.unbatch().batch(20))))<define_variables>
def sigmoid(x):
return 1 /(1 + np.exp(-x))
train_preds = np.zeros(( len(x_train)))
 test_preds = np.zeros(( len(df_test)))
 seed_everything()
x_test_cuda = torch.tensor(x_test, dtype=torch.long ).cuda()
test = torch.utils.data.TensorDataset(x_test_cuda)
 test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)
 avg_losses_f = []
avg_val_losses_f = []
display_batch_of_images(next(iter(test_dataset.unbatch().batch(20))))<choose_model_class>
for i,(train_idx, valid_idx)in enumerate(splits):
x_train = np.array(x_train)
 y_train = np.array(y_train)
 features = np.array(features)
 x_train_fold = torch.tensor(x_train[train_idx.astype(int)], dtype=torch.long ).cuda()
y_train_fold = torch.tensor(y_train[train_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
kfold_X_features = features[train_idx.astype(int)]
kfold_X_valid_features = features[valid_idx.astype(int)]
x_val_fold = torch.tensor(x_train[valid_idx.astype(int)], dtype=torch.long ).cuda()
y_val_fold = torch.tensor(y_train[valid_idx.astype(int), np.newaxis], dtype=torch.float32 ).cuda()
model = NeuralNet()
model.cuda()
loss_fn = torch.nn.BCEWithLogitsLoss(reduction='sum')
 step_size = 300
base_lr, max_lr = 0.001, 0.003
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=max_lr)
 scheduler = CyclicLR(optimizer, base_lr=base_lr, max_lr=max_lr,
step_size=step_size, mode='exp_range',
gamma=0.99994)
 train = torch.utils.data.TensorDataset(x_train_fold, y_train_fold)
 valid = torch.utils.data.TensorDataset(x_val_fold, y_val_fold)
 train = MyDataset(train)
 valid = MyDataset(valid)
 train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
 valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size, shuffle=False)
 print(f'Fold {i + 1}')
 for epoch in range(n_epochs):
start_time = time.time()
model.train()
avg_loss = 0.
 for i,(x_batch, y_batch, index)in enumerate(train_loader):
f = kfold_X_features[index]
y_pred = model([x_batch,f])
 if scheduler:
scheduler.batch_step()
loss = loss_fn(y_pred, y_batch)
 optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
 model.eval()
valid_preds_fold = np.zeros(( x_val_fold.size(0)))
 test_preds_fold = np.zeros(( len(df_test)))
 avg_val_loss = 0.
 for i,(x_batch, y_batch, index)in enumerate(valid_loader):
f = kfold_X_valid_features[index]
y_pred = model([x_batch,f] ).detach()
avg_val_loss += loss_fn(y_pred, y_batch ).item() / len(valid_loader)
 valid_preds_fold[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
elapsed_time = time.time() - start_time
print('Epoch {}/{} \t loss={:.4f} \t val_loss={:.4f} \t time={:.2f}s'.format(
 epoch + 1, n_epochs, avg_loss, avg_val_loss, elapsed_time))
avg_losses_f.append(avg_loss)
 avg_val_losses_f.append(avg_val_loss)
 for i,(x_batch,)in enumerate(test_loader):
f = test_features[i * batch_size:(i+1)* batch_size]
y_pred = model([x_batch,f] ).detach()
test_preds_fold[i * batch_size:(i+1)* batch_size] = sigmoid(y_pred.cpu().numpy())[:, 0]
train_preds[valid_idx] = valid_preds_fold
test_preds += test_preds_fold / len(splits)
 print('All \t loss={:.4f} \t val_loss={:.4f} \t '.format(np.average(avg_losses_f),np.average(avg_val_losses_f)))
def create_model(input_shape, N_CLASSES):
base_model = applications.DenseNet201(weights = 'imagenet',
include_top = False,
input_shape = input_shape)
 base_model.trainable = False
model = tf.keras.Sequential([
base_model,
layers.GlobalAveragePooling2D() ,
layers.Dense(N_CLASSES, activation='softmax')
 ])
 return model<choose_model_class>
def bestThresshold(y_train,train_preds):
tmp = [0,0,0]
delta = 0
for tmp[0] in tqdm(np.arange(0.1, 0.501, 0.01)) :
tmp[1] = f1_score(y_train, np.array(train_preds)>tmp[0])
 if tmp[1] > tmp[2]:
delta = tmp[0]
tmp[2] = tmp[1]
print('best threshold is {:.4f} with F1 score: {:.4f}'.format(delta, tmp[2]))
return delta
delta = bestThresshold(y_train,train_preds )
with strategy.scope() :
model = create_model(( None, None, CHANNELS), N_CLASSES)
 metric_list = ['sparse_categorical_accuracy']
optimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)
 model.compile(optimizer=optimizer,
loss = 'sparse_categorical_crossentropy',
metrics = metric_list)
 model.summary()<train_model>
submission = df_test[['qid']].copy()
submission['prediction'] =(test_preds > delta ).astype(int)
 submission.to_csv('submission.csv', index=False )