EmbeddingMLP.py

import tensorflow as tf
from matplotlib import pyplot as plt


# 导入相关包和数据集
#TRAIN_DATA_URL = "file:///Users/zhewang/Workspace/SparrowRecSys/src/main/resources/webroot/sampledata/modelSamples.csv"
samples_file_path = ".\modelSamples.csv"


# load sample as tf dataset
def get_dataset(file_path):
    dataset = tf.data.experimental.make_csv_dataset(
        file_path,
        batch_size=12,
        label_name='label',
        na_value="0",
        num_epochs=1,
        ignore_errors=True)
    return dataset


# sample dataset size 110830/12(batch_size) = 9235
raw_samples_data = get_dataset(samples_file_path)
print(raw_samples_data)

# split as test dataset and training dataset
test_dataset = raw_samples_data.take(1000)
train_dataset = raw_samples_data.skip(1000)

# genre features vocabulary
genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary',
               'Sci-Fi', 'Drama', 'Thriller',
               'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical']

GENRE_FEATURES = {
    'userGenre1': genre_vocab,
    'userGenre2': genre_vocab,
    'userGenre3': genre_vocab,
    'userGenre4': genre_vocab,
    'userGenre5': genre_vocab,
    'movieGenre1': genre_vocab,
    'movieGenre2': genre_vocab,
    'movieGenre3': genre_vocab
}

# all categorical features
categorical_columns = []
for feature, vocab in GENRE_FEATURES.items():
    cat_col = tf.feature_column.categorical_column_with_vocabulary_list(
        key=feature, vocabulary_list=vocab)
    emb_col = tf.feature_column.embedding_column(cat_col, 10)
    categorical_columns.append(emb_col)
# movie id embedding feature
movie_col = tf.feature_column.categorical_column_with_identity(key='movieId', num_buckets=1001)
movie_emb_col = tf.feature_column.embedding_column(movie_col, 10)
categorical_columns.append(movie_emb_col)

# user id embedding feature
user_col = tf.feature_column.categorical_column_with_identity(key='userId', num_buckets=30001)
user_emb_col = tf.feature_column.embedding_column(user_col, 10)
categorical_columns.append(user_emb_col)

# all numerical features
numerical_columns = [tf.feature_column.numeric_column('releaseYear'),
                     tf.feature_column.numeric_column('movieRatingCount'),
                     tf.feature_column.numeric_column('movieAvgRating'),
                     tf.feature_column.numeric_column('movieRatingStddev'),
                     tf.feature_column.numeric_column('userRatingCount'),
                     tf.feature_column.numeric_column('userAvgRating'),
                     tf.feature_column.numeric_column('userRatingStddev')]

# embedding + MLP model architecture
model = tf.keras.Sequential([
    tf.keras.layers.DenseFeatures(numerical_columns + categorical_columns),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid'),
])

# compile the model, set loss function, optimizer and evaluation metrics
model.compile(
    loss='binary_crossentropy',
    optimizer='adam',
    metrics=['accuracy'])

# train the model
history = model.fit(train_dataset, epochs=5)


acc = history.history['accuracy']
#val_acc = history.history['val_sparse_categorical_accuracy']


plt.subplot(1, 2, 1)
plt.plot(acc, label='CTR accuracy')
#plt.plot(val_acc, label='Validation Accuracy')
plt.title('CTR accuracy training curve')
plt.legend()
plt.show()


# evaluate the model
test_loss, test_accuracy = model.evaluate(test_dataset)
print('\n\nTest Loss {}, Test Accuracy {}'.format(test_loss, test_accuracy))

# print some predict results
predictions = model.predict(test_dataset)
for prediction, goodRating in zip(predictions[:12], list(test_dataset)[0][1][:12]):
    print("Predicted good rating: {:.2%}".format(prediction[0]),
          " | Actual rating label: ",
          ("Good Rating" if bool(goodRating) else "Bad Rating"))