85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
import pandas as pd
|
|
from tensorflow.keras.preprocessing.text import Tokenizer
|
|
from tensorflow.keras.preprocessing.sequence import pad_sequences
|
|
from tensorflow.keras.models import Sequential
|
|
from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
|
|
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
|
|
from sacred import Experiment
|
|
from sacred.observers import MongoObserver, FileStorageObserver
|
|
from math import sqrt
|
|
|
|
ex = Experiment('464979')
|
|
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
|
|
ex.observers.append(FileStorageObserver('sacred_runs'))
|
|
|
|
@ex.config
|
|
def my_config():
|
|
epochs = 10
|
|
batch_size = 32
|
|
|
|
@ex.automain
|
|
def run_experiment(epochs, batch_size, _run):
|
|
train_data = pd.read_csv('beer_reviews_train.csv')
|
|
X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
|
|
y_train = train_data['review_overall']
|
|
|
|
tokenizer = Tokenizer(num_words=10000)
|
|
tokenizer.fit_on_texts(X_train)
|
|
X_train_seq = tokenizer.texts_to_sequences(X_train)
|
|
|
|
X_train_pad = pad_sequences(X_train_seq, maxlen=100)
|
|
|
|
model = Sequential([
|
|
Embedding(input_dim=10000, output_dim=16, input_length=100),
|
|
GlobalAveragePooling1D(),
|
|
Dense(16, activation='relu'),
|
|
Dense(1, activation='sigmoid')
|
|
])
|
|
|
|
model.compile(optimizer='adam',
|
|
loss='binary_crossentropy',
|
|
metrics=['accuracy'])
|
|
|
|
model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
|
|
|
|
model.save('beer_review_sentiment_model.keras')
|
|
_run.add_artifact('beer_review_sentiment_model.keras')
|
|
|
|
test_data = pd.read_csv('beer_reviews_test.csv')
|
|
X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
|
|
y_test = test_data['review_overall']
|
|
|
|
tokenizer = Tokenizer(num_words=10000)
|
|
tokenizer.fit_on_texts(X_test)
|
|
|
|
X_test_text = X_test.astype(str).agg(' '.join, axis=1)
|
|
X_test_seq = tokenizer.texts_to_sequences(X_test_text)
|
|
X_test_pad = pad_sequences(X_test_seq, maxlen=100)
|
|
|
|
predictions = model.predict(X_test_pad)
|
|
|
|
if len(predictions.shape) > 1:
|
|
predictions = predictions[:, 0]
|
|
|
|
results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
|
|
results.to_csv('beer_review_sentiment_predictions.csv', index=False)
|
|
|
|
y_pred = results['Predictions']
|
|
y_test = results['Actual']
|
|
y_test_binary = (y_test >= 3).astype(int)
|
|
|
|
accuracy = accuracy_score(y_test_binary, y_pred.round())
|
|
precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
|
|
rmse = sqrt(mean_squared_error(y_test, y_pred))
|
|
|
|
print(f'Accuracy: {accuracy}')
|
|
print(f'Micro-avg Precision: {precision}')
|
|
print(f'Micro-avg Recall: {recall}')
|
|
print(f'F1 Score: {f1}')
|
|
print(f'RMSE: {rmse}')
|
|
|
|
_run.add_resource('./beer_reviews_train.csv')
|
|
_run.add_resource('./beer_reviews_test.csv')
|
|
|
|
return accuracy
|