Datasets:
HenryAI
/
KerasCodeExamples.txt

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Prepare the data
Download and process data
urlretrieve(\"http://files.grouplens.org/datasets/movielens/ml-1m.zip\", \"movielens.zip\")
ZipFile(\"movielens.zip\", \"r\").extractall()
ratings_data = pd.read_csv(
\"ml-1m/ratings.dat\",
sep=\"::\",
names=[\"user_id\", \"movie_id\", \"rating\", \"unix_timestamp\"],
)
ratings_data[\"movie_id\"] = ratings_data[\"movie_id\"].apply(lambda x: f\"movie_{x}\")
ratings_data[\"user_id\"] = ratings_data[\"user_id\"].apply(lambda x: f\"user_{x}\")
ratings_data[\"rating\"] = ratings_data[\"rating\"].apply(lambda x: float(x))
del ratings_data[\"unix_timestamp\"]
print(f\"Number of users: {len(ratings_data.user_id.unique())}\")
print(f\"Number of movies: {len(ratings_data.movie_id.unique())}\")
print(f\"Number of ratings: {len(ratings_data.index)}\")
Number of users: 6040
Number of movies: 3706
Number of ratings: 1000209
Create train and eval data splits
random_selection = np.random.rand(len(ratings_data.index)) <= 0.85
train_data = ratings_data[random_selection]
eval_data = ratings_data[~random_selection]
train_data.to_csv(\"train_data.csv\", index=False, sep=\"|\", header=False)
eval_data.to_csv(\"eval_data.csv\", index=False, sep=\"|\", header=False)
print(f\"Train data split: {len(train_data.index)}\")
print(f\"Eval data split: {len(eval_data.index)}\")
print(\"Train and eval data files are saved.\")
Train data split: 850361
Eval data split: 149848
Train and eval data files are saved.
Define dataset metadata and hyperparameters
csv_header = list(ratings_data.columns)
user_vocabulary = list(ratings_data.user_id.unique())
movie_vocabulary = list(ratings_data.movie_id.unique())
target_feature_name = \"rating\"
learning_rate = 0.001
batch_size = 128
num_epochs = 3
base_embedding_dim = 64
Train and evaluate the model
def get_dataset_from_csv(csv_file_path, batch_size=128, shuffle=True):
return tf.data.experimental.make_csv_dataset(
csv_file_path,
batch_size=batch_size,
column_names=csv_header,
label_name=target_feature_name,
num_epochs=1,
header=False,
field_delim=\"|\",
shuffle=shuffle,
)
def run_experiment(model):
# Compile the model.
model.compile(
optimizer=keras.optimizers.Adam(learning_rate),
loss=tf.keras.losses.MeanSquaredError(),
metrics=[keras.metrics.MeanAbsoluteError(name=\"mae\")],
)
# Read the training data.
train_dataset = get_dataset_from_csv(\"train_data.csv\", batch_size)
# Read the test data.
eval_dataset = get_dataset_from_csv(\"eval_data.csv\", batch_size, shuffle=False)
# Fit the model with the training data.
history = model.fit(train_dataset, epochs=num_epochs, validation_data=eval_dataset,)
return history
Experiment 1: baseline collaborative filtering model
Implement embedding encoder
def embedding_encoder(vocabulary, embedding_dim, num_oov_indices=0, name=None):
return keras.Sequential(
[
StringLookup(
vocabulary=vocabulary, mask_token=None, num_oov_indices=num_oov_indices
),
layers.Embedding(
input_dim=len(vocabulary) + num_oov_indices, output_dim=embedding_dim
),
],
name=f\"{name}_embedding\" if name else None,
)
Implement the baseline model
def create_baseline_model():
# Receive the user as an input.
user_input = layers.Input(name=\"user_id\", shape=(), dtype=tf.string)
# Get user embedding.
user_embedding = embedding_encoder(
vocabulary=user_vocabulary, embedding_dim=base_embedding_dim, name=\"user\"
)(user_input)
# Receive the movie as an input.
movie_input = layers.Input(name=\"movie_id\", shape=(), dtype=tf.string)
# Get embedding.
movie_embedding = embedding_encoder(
vocabulary=movie_vocabulary, embedding_dim=base_embedding_dim, name=\"movie\"