def test_estimator_graph_fit_dataset(self):
import zoo.orca.data.pandas
tf.reset_default_graph()
model = SimpleModel()
file_path = os.path.join(resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy(), df['item'].to_numpy()),
"y": df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
dataset = Dataset.from_tensor_slices(data_shard)
est = Estimator.from_graph(
inputs=[model.user, model.item],
labels=[model.label],
loss=model.loss,
optimizer=tf.train.AdamOptimizer(),
metrics={"loss": model.loss})
est.fit(data=dataset,
batch_size=8,
epochs=10,
validation_data=dataset)
result = est.evaluate(dataset, batch_size=4)
assert 'loss' in result
def read_as_tf(path):
"""
return a orca.data.tf.data.Dataset
:param path:
:return:
"""
from zoo.orca.data.tf.data import Dataset
xshards = ParquetDataset._read_as_xshards(path)
return Dataset.from_tensor_slices(xshards)
def test_estimator_graph_predict_dataset(self):
tf.reset_default_graph()
model = SimpleModel()
file_path = os.path.join(resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
est = Estimator.from_graph(inputs=[model.user, model.item],
outputs=[model.logits])
def transform(df):
result = {
"x": (df['user'].to_numpy(), df['item'].to_numpy()),
}
return result
data_shard = data_shard.transform_shard(transform)
dataset = Dataset.from_tensor_slices(data_shard)
predictions = est.predict(dataset).collect()
assert len(predictions) == 10
