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run_experiment_english_task_1.py
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run_experiment_english_task_1.py
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import configparser
import numpy as np
import pandas as pd
from bert_text import run_on_dfs
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras_preprocessing.sequence import pad_sequences
from keras_preprocessing.text import Tokenizer
from numpy.random import seed
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, recall_score, precision_score
from sklearn.model_selection import StratifiedKFold, train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow import set_random_seed
import tensorflow as tf
from algo.nn.__keras.models import capsule, attention_capsule, cnn_2d, pooled_gru, lstm_attention, lstm_gru_attention
from algo.nn.__keras.utility import f1_smart
from embeddings import get_emb_matrix
from preprocessing import clean_text, remove_names, entity_recognizing, remove_url
def run_keras_experiment():
print('Reading files')
# Reading File Section - This should change
full = pd.read_csv("data/english_dataset.tsv", sep='\t',
names=['text_id', 'text', 'task_1', 'task_2', 'task_3'])
test_2019 = pd.read_csv("data/hasoc2019_en_test.tsv", sep="\t", names=['text_id', 'text'])
train, test = train_test_split(full, test_size=0.2)
print('Completed reading')
#############
print("Train shape : ", train.shape)
print("Test shape : ", test.shape)
print("Test 2019 shape : ", test_2019.shape)
# Variables
TEXT_COLUMN = "text"
LABEL_COLUMN = "task_1"
configParser = configparser.RawConfigParser()
configFilePath = "config.txt"
configParser.read(configFilePath)
EMBEDDING_FILE = configParser.get('english_task_1_model-config', 'EMBEDDING_FILE')
MODEL_PATH = configParser.get('english_task_1_model-config', 'MODEL_PATH')
PREDICTION_FILE = configParser.get('english_task_1_model-config', 'PREDICTION_FILE')
print(train.head())
# print("Removing URLs")
# train[TEXT_COLUMN] = train[TEXT_COLUMN].apply(lambda x: remove_url(x))
# test[TEXT_COLUMN] = test[TEXT_COLUMN].apply(lambda x: remove_url(x))
# print(train.head())
#
print("Removing usernames")
train[TEXT_COLUMN] = train[TEXT_COLUMN].apply(lambda x: remove_names(x))
test[TEXT_COLUMN] = test[TEXT_COLUMN].apply(lambda x: remove_names(x))
test_2019[TEXT_COLUMN] = test_2019[TEXT_COLUMN].apply(lambda x: remove_names(x))
print(train.head())
#
# print("Identifying names")
#
# train[TEXT_COLUMN] = train[TEXT_COLUMN].apply(lambda x: entity_recognizing(x))
# test[TEXT_COLUMN] = test[TEXT_COLUMN].apply(lambda x: entity_recognizing(x))
# print(train.head())
print("Converting to lower-case")
train[TEXT_COLUMN] = train[TEXT_COLUMN].str.lower()
test[TEXT_COLUMN] = test[TEXT_COLUMN].str.lower()
test_2019[TEXT_COLUMN] = test_2019[TEXT_COLUMN].str.lower()
print(train.head())
print("Cleaning punctuation marks")
train[TEXT_COLUMN] = train[TEXT_COLUMN].apply(lambda x: clean_text(x))
test[TEXT_COLUMN] = test[TEXT_COLUMN].apply(lambda x: clean_text(x))
test_2019[TEXT_COLUMN] = test_2019[TEXT_COLUMN].apply(lambda x: clean_text(x))
print(train.head())
train['doc_len'] = train[TEXT_COLUMN].apply(lambda words: len(words.split(" ")))
max_seq_len = np.round(train['doc_len'].mean() + train['doc_len'].std()).astype(int)
embed_size = 300 # how big is each word vector
max_features = None # how many unique words to use (i.e num rows in embedding vector)
maxlen = max_seq_len # max number of words in a question to use #99.99%
# fill up the missing values
X = train[TEXT_COLUMN].fillna("_na_").values
X_test = test[TEXT_COLUMN].fillna("_na_").values
X_test_2019 = test_2019[TEXT_COLUMN].fillna("_na_").values
# Tokenize the sentences
tokenizer = Tokenizer(num_words=max_features, filters='')
tokenizer.fit_on_texts(list(X))
X = tokenizer.texts_to_sequences(X)
X_test = tokenizer.texts_to_sequences(X_test)
X_test_2019 = tokenizer.texts_to_sequences(X_test_2019)
# Pad the sentences
X = pad_sequences(X, maxlen=maxlen)
X_test = pad_sequences(X_test, maxlen=maxlen)
X_test_2019 = pad_sequences(X_test_2019, maxlen=maxlen)
# Get the target values
Y = train[LABEL_COLUMN].values
le = LabelEncoder()
le.fit(Y)
encoded_Y = le.transform(Y)
word_index = tokenizer.word_index
max_features = len(word_index) + 1
print('Loading Embeddings')
embedding_matrix = get_emb_matrix(word_index, max_features, EMBEDDING_FILE)
print('Finished loading Embeddings')
print('Start Training')
kfold = StratifiedKFold(n_splits=5, random_state=10, shuffle=True)
bestscore = []
y_test = np.zeros((X_test.shape[0],))
y_test_2019 = np.zeros((X_test_2019.shape[0],))
for i, (train_index, valid_index) in enumerate(kfold.split(X, encoded_Y)):
X_train, X_val, Y_train, Y_val = X[train_index], X[valid_index], encoded_Y[train_index], encoded_Y[valid_index]
filepath = MODEL_PATH
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=2, save_best_only=True, mode='min')
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.6, patience=1, min_lr=0.0001, verbose=2)
earlystopping = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=2, verbose=2, mode='auto')
callbacks = [checkpoint, reduce_lr]
model = attention_capsule(maxlen, max_features, embed_size, embedding_matrix, 1)
if i == 0: print(model.summary())
model.fit(X_train, Y_train, batch_size=64, epochs=20, validation_data=(X_val, Y_val), verbose=2,
callbacks=callbacks,
)
model.load_weights(filepath)
y_pred = model.predict([X_val], batch_size=64, verbose=2)
y_test += np.squeeze(model.predict([X_test], batch_size=64, verbose=2)) / 5
y_test_2019 += np.squeeze(model.predict([X_test_2019], batch_size=64, verbose=2)) / 5
f1, threshold = f1_smart(np.squeeze(Y_val), np.squeeze(y_pred))
print('Optimal F1: {:.4f} at threshold: {:.4f}'.format(f1, threshold))
bestscore.append(threshold)
print('Finished Training')
y_test = y_test.reshape((-1, 1))
pred_test_y = (y_test > np.mean(bestscore)).astype(int)
test['predictions'] = le.inverse_transform(pred_test_y)
y_test_2019 = y_test_2019.reshape((-1, 1))
pred_test_y_2019 = (y_test_2019 > np.mean(bestscore)).astype(int)
test_2019['task_1'] = le.inverse_transform(pred_test_y_2019)
test_2019 = test_2019[['text_id', 'task_1']]
# save predictions
file_path = PREDICTION_FILE
test_2019.to_csv(file_path, sep='\t', encoding='utf-8')
print('Saved Predictions')
# post analysis
tn, fp, fn, tp = confusion_matrix(test[LABEL_COLUMN], test['predictions']).ravel()
weighted_f1 = f1_score(test[LABEL_COLUMN], test['predictions'], average='weighted')
accuracy = accuracy_score(test[LABEL_COLUMN], test['predictions'])
weighted_recall = recall_score(test[LABEL_COLUMN], test['predictions'], average='weighted')
weighted_precision = precision_score(test[LABEL_COLUMN], test['predictions'], average='weighted')
print("Confusion Matrix (tn, fp, fn, tp) {} {} {} {}".format(tn, fp, fn, tp))
print("Accuracy ", accuracy)
print("Weighted F1 ", weighted_f1)
print("Weighted Recall ", weighted_recall)
print("Weighted Precision ", weighted_precision)
def run_pytorch_experiment():
seed(726)
set_random_seed(726)
print('Reading files')
# Reading File Section - This should change
full = pd.read_csv("data/english_dataset.tsv", sep='\t',
names=['text_id', 'text', 'task_1', 'task_2', 'task_3'])
train, test = train_test_split(full, test_size=0.2)
print('Completed reading')
#############
print("Train shape : ", train.shape)
print("Test shape : ", test.shape)
myparam = {
"DATA_COLUMN": "text",
"LABEL_COLUMN": "task_1",
"LEARNING_RATE": 2e-5,
"NUM_TRAIN_EPOCHS": 10
}
tf.logging.set_verbosity(tf.logging.INFO)
result, estimator = run_on_dfs(train, test, **myparam)
print(result)
if __name__ == "__main__":
seed(726)
set_random_seed(726)
run_keras_experiment()