def test_multiple_metrics_default(self):
modelBuilder_keras = KerasModelBuilder(model_creator_multiple_metrics)
model = modelBuilder_keras.build(config={
"lr": 1e-2,
"batch_size": 32,
})
with pytest.raises(ValueError):
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
epochs=20)
def test_unaligned_metric_value(self):
modelBuilder_keras = KerasModelBuilder(model_creator_keras)
model = modelBuilder_keras.build(config={
"lr": 1e-2,
"batch_size": 32,
})
with pytest.raises(ValueError):
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
metric='mae',
epochs=20)
def test_fit_evaluate(self):
modelBuilder_keras = KerasModelBuilder(model_creator_keras)
model = modelBuilder_keras.build(config={
"lr": 1e-2,
"batch_size": 32,
"metric": "mse"
})
val_result = model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
epochs=20)
assert val_result is not None
def test_fit_eval_default_metric(self):
modelBuilder_keras = KerasModelBuilder(model_creator_keras)
model = modelBuilder_keras.build(config={
"lr": 1e-2,
"batch_size": 32,
})
val_result = model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
epochs=20)
hist_metric_name = tf.keras.metrics.get("mse").__name__
assert val_result.get(hist_metric_name)
def test_uncompiled_model(self):
def model_creator(config):
"""Returns a tf.keras model"""
model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])
return model
modelBuilder_keras = KerasModelBuilder(model_creator)
with pytest.raises(ValueError):
model = modelBuilder_keras.build(config={
"lr": 1e-2,
"batch_size": 32,
"metric": "mse"
})
model.fit_eval(data=(self.data["x"], self.data["y"]),
validation_data=(self.data["val_x"],
self.data["val_y"]),
epochs=20)
def from_keras(*,
model_creator,
logs_dir="/tmp/auto_estimator_logs",
resources_per_trial=None,
name=None,
):
"""
Create an AutoEstimator for tensorflow keras.
:param model_creator: Tensorflow keras model creator function.
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_estimator_logs"
:param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
:param name: Name of the auto estimator.
:return: an AutoEstimator object.
"""
from zoo.automl.model.base_keras_model import KerasModelBuilder
model_builder = KerasModelBuilder(model_creator=model_creator)
return AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
name=name)