def test_predict_dataframe_with_feature_columns():
predictor = TensorflowPredictor(model_definition=build_model,
model_weights=weights)
data = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
predictions = predictor.predict(data, feature_columns=["A"])
assert len(predictions) == 2
assert predictions.to_numpy().flatten().tolist() == [1, 3]
def test_predict_array_with_preprocessor():
preprocessor = DummyPreprocessor()
predictor = TensorflowPredictor(model_definition=build_model,
preprocessor=preprocessor,
model_weights=weights)
data_batch = np.array([[1], [2], [3]])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
assert predictions.to_numpy().flatten().tolist() == [2, 4, 6]
assert hasattr(predictor.preprocessor, "_batch_transformed")
def test_predict_feature_columns():
preprocessor = DummyPreprocessor()
predictor = TensorflowPredictor(
model_definition=build_model, preprocessor=preprocessor, model_weights=weights
)
data_batch = np.array([[1, 4], [2, 5], [3, 6]])
predictions = predictor.predict(data_batch, feature_columns=[0])
assert len(predictions) == 3
assert predictions.to_numpy().flatten().round().tolist() == [2, 4, 6]
assert hasattr(predictor.preprocessor, "_batch_transformed")
def test_predict_array_with_input_shape_unspecified():
def model_definition():
return tf.keras.models.Sequential(
tf.keras.layers.Lambda(lambda tensor: tensor))
predictor = TensorflowPredictor(model_definition=model_definition,
model_weights=[])
data_batch = np.array([[1], [2], [3]])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
assert predictions.to_numpy().flatten().tolist() == [1, 2, 3]
def test_init():
preprocessor = DummyPreprocessor()
predictor = TensorflowPredictor(model_definition=build_model,
preprocessor=preprocessor,
model_weights=weights)
checkpoint = {MODEL_KEY: weights, PREPROCESSOR_KEY: preprocessor}
checkpoint_predictor = TensorflowPredictor.from_checkpoint(
Checkpoint.from_dict(checkpoint), build_model)
assert checkpoint_predictor.model_definition == predictor.model_definition
assert checkpoint_predictor.model_weights == predictor.model_weights
assert checkpoint_predictor.preprocessor == predictor.preprocessor
def test_predict_array():
checkpoint = {MODEL_KEY: weights}
predictor = TensorflowPredictor.from_checkpoint(
Checkpoint.from_dict(checkpoint), build_model)
data_batch = np.array([[1], [2], [3]])
predictions = predictor.predict(data_batch)
assert len(predictions) == 3
assert predictions.to_numpy().flatten().tolist() == [1, 2, 3]
def __init__(self):
self.pred = TensorflowPredictor.from_checkpoint(
result.checkpoint, build_model)
def __init__(self):
self.predictor = TensorflowPredictor.from_checkpoint(
Checkpoint.from_object_ref(checkpoint_object_ref),
model_definition=build_model,
)