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fastText_Ktrain.py
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fastText_Ktrain.py
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!pip3 install ktrain
import pandas as pd
import numpy as np
import ktrain
from ktrain import text
def shuffle(df, n=1, axis=0):
df = df.copy()
for _ in range(n):
df.apply(np.random.shuffle, axis=axis)
return df
data = pd.read_csv('data/dataset.csv', encoding='utf-8', sep=';')
data.sort_values(by='Emotion', axis=0, inplace=True)
data.set_index(keys=['Emotion'], drop=False,inplace=True)
emotions=data['Emotion'].unique().tolist()
joys = shuffle(data.loc[data.Emotion=='joy'])
fears = shuffle(data.loc[data.Emotion=='fear'])
angers = shuffle(data.loc[data.Emotion=='anger'])
sadnesss = shuffle(data.loc[data.Emotion=='sadness'])
neutrals = shuffle(data.loc[data.Emotion=='neutral'])
joys_train = joys.iloc[0:int(joys.shape[0]*0.8)]
joys_test = joys.iloc[int(joys.shape[0]*0.8)+1:joys.shape[0]]
fears_train = fears.iloc[0:int(fears.shape[0]*0.8)]
fears_test = fears.iloc[int(fears.shape[0]*0.8)+1:fears.shape[0]]
angers_train = angers.iloc[0:int(angers.shape[0]*0.8)]
angers_test = angers.iloc[int(angers.shape[0]*0.8)+1:angers.shape[0]]
sadnesss_train = sadnesss.iloc[0:int(sadnesss.shape[0]*0.8)]
sadnesss_test = sadnesss.iloc[int(sadnesss.shape[0]*0.8)+1:sadnesss.shape[0]]
neutrals_train = neutrals.iloc[0:int(neutrals.shape[0]*0.8)]
neutrals_test = neutrals.iloc[int(neutrals.shape[0]*0.8)+1:neutrals.shape[0]]
data_train = pd.concat([joys_train, fears_train, angers_train, sadnesss_train, neutrals_train])
data_test = pd.concat([joys_test, fears_test, angers_test, sadnesss_test, neutrals_test])
print(data_train.shape)
print(data_test.shape)
X_train = data_train.Text.tolist()
X_test = data_test.Text.tolist()
y_train = data_train.Emotion.tolist()
y_test = data_test.Emotion.tolist()
data = data_train.append(data_test, ignore_index=True)
class_names = ['joy', 'sadness', 'fear', 'anger', 'neutral']
print('size of training set: %s' % (len(data_train['Text'])))
print('size of validation set: %s' % (len(data_test['Text'])))
print(data.Emotion.value_counts())
data.head(10)
encoding = {
'joy': 0,
'sadness': 1,
'fear': 2,
'anger': 3,
'neutral': 4
}
y_train = [encoding[x] for x in y_train]
y_test = [encoding[x] for x in y_test]
(x_train, y_train), (x_test, y_test), preproc = text.texts_from_array(x_train=X_train, y_train=y_train,
x_test=X_test, y_test=y_test,
class_names=class_names,
preprocess_mode='standard',
maxlen=350,
max_features=135000)
model = text.text_classifier('fasttext', train_data=(x_train, y_train), preproc=preproc)
learner = ktrain.get_learner(model, train_data=(x_train, y_train),
val_data=(x_test, y_test),
batch_size=32)
import tensorflow as tf
from datetime import datetime
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, CSVLogger, TensorBoard
import os
basedir = "logs/"
logdir = os.path.join("logs", datetime.now().strftime("%Y%m%d-%H%M%S"))
tf.debugging.experimental.enable_dump_debug_info(logdir)
callbacks = [
ModelCheckpoint(filepath=basedir+'checkpoint1-{epoch:02d}.hdf5', verbose=2, save_best_only=True, monitor='accuracy',mode='max'),
CSVLogger(basedir+'model_1trainanalysis1.csv',separator=',', append=False),
EarlyStopping(monitor='val_loss', min_delta=1e-6, patience=1, verbose=2, mode='auto'),
TensorBoard(log_dir=logdir,histogram_freq=1)]
learner.lr_find(show_plot=True, max_epochs=10)
learner.fit_onecycle(6e-3, 20, callbacks = callbacks)
predictor = ktrain.get_predictor(learner.model, preproc)
predictor.get_classes()
from sklearn.metrics import precision_recall_fscore_support
predictions = learner.model.predict(x_test)
predictions = np.argmax(predictions, axis=1)
predictions = [class_names[pred] for pred in predictions]
print(precision_recall_fscore_support(data_test.Emotion, predictions, average='weighted'))
import time
message = 'Test!'
start_time = time.time()
prediction = predictor.predict(message)
print('predicted: {} ({:.2f})'.format(prediction, (time.time() - start_time)))
predictor.save("models/fastText_Ktrain")