ium_464979/mlflow_project/mlflow_training_model.py
AWieczarek d739f275e8 IUM_08
2024-05-13 20:25:54 +02:00

54 lines
2.0 KiB
Python

import pandas as pd
import tensorflow as tf
import sys
import mlflow
from sklearn.metrics import accuracy_score
mlflow.set_tracking_uri("http://localhost:5000")
def main():
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 = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_train)
X_train_seq = tokenizer.texts_to_sequences(X_train)
X_train_pad = tf.keras.preprocessing.sequence.pad_sequences(X_train_seq, maxlen=100)
with mlflow.start_run() as run:
print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
model = tf.keras.Sequential([
tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=100),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(16, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
print(sys.argv[1])
print(sys.argv[2])
model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
mlflow.log_param("epochs", int(sys.argv[1]))
mlflow.log_param("batch_size", int(sys.argv[2]))
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']
predictions = model.predict(X_test).flatten()
y_test_binary = (y_test >= 3).astype(int)
accuracy = accuracy_score(y_test_binary, predictions.round())
mlflow.log_metric("accuracy", accuracy)
if __name__ == '__main__':
main()