def test_call_data_regression(AutoMLFit): context = an.AutoMLPipeline(an.DataRegression()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() assert an.AutoMLFit.is_called
def test_runtime_dataclassifier(): train_file_path = tf.keras.utils.get_file( "train.csv", "https://storage.googleapis.com/tf-datasets/titanic/train.csv") test_file_path = tf.keras.utils.get_file( "test.csv", "https://storage.googleapis.com/tf-datasets/titanic/eval.csv") data_train = pd.read_csv(train_file_path) data_test = pd.read_csv(test_file_path) x_train = data_train.drop(columns="survived") y_train = data_train["survived"] x_test = data_test.drop(columns="survived") y_test = data_test["survived"] context = an.AutoMLPipeline( an.DataClassification(max_trials=5, overwrite=True, loss="mean_squared_error")) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=100) context.run_automl() context.train = an.AutoMLEvaluate(x_test, y_test, batch_size=32) context.run_automl() context.train = an.AutoMLPredict(x_train, batch_size=32) context.run_automl() assert context.return_automl["model"] != None assert isinstance(context.return_automl["prediction"], np.ndarray) assert isinstance(context.return_automl["evaluation"], list)
def test_call_timeseries_forecast(AutoMLFit): context = an.AutoMLPipeline(an.TimeseriesForecaster()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() assert an.AutoMLFit.is_called
def test_call_text_classification(AutoMLFit): context = an.AutoMLPipeline(an.TextClassification()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() assert an.AutoMLFit.is_called
def test_call_save(AutoMLFit, AutoMLSave): context = an.AutoMLPipeline(an.DataClassification()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() context.train = an.AutoMLSave("dummy") context.run_automl() assert an.AutoMLSave.is_called
def test_call_evaluation(AutoMLFit, AutoMLEvaluate): context = an.AutoMLPipeline(an.DataClassification()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() context.train = an.AutoMLEvaluate(x_test, y_test, batch_size=32) context.run_automl() assert an.AutoMLEvaluate.is_called
def test_call_prediction(AutoMLFit, AutoMLPredict): context = an.AutoMLPipeline(an.DataClassification()) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() context.train = an.AutoMLPredict(x_train, batch_size=32) context.run_automl() assert an.AutoMLPredict.is_called
def test_multi_model(): context = an.AutoMLPipeline( an.MultiModel( inputs=[ak.ImageInput(), ak.StructuredDataInput()], outputs=[ ak.RegressionHead(metrics=["mae"]), ak.ClassificationHead(loss="categorical_crossentropy", metrics=["accuracy"]), ], overwrite=True, max_trials=2, )) context.run_automl() assert context.return_automl["model"] != None
def test_save_load(): data = fetch_california_housing() x_train, _, y_train, _ = train_test_split( data.data, data.target, test_size=0.33, random_state=42, ) context = an.AutoMLPipeline( an.DataRegression(max_trials=3, overwrite=True, loss="mean_squared_error")) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=10) context.run_automl() context.train = an.AutoMLSave(model_name="model_autokeras") context.run_automl() model = an.AutoMLModels().load_model(model_name="model_autokeras") assert model != None
def test_runtime_dataregression(): data = fetch_california_housing() x_train, x_test, y_train, y_test = train_test_split( data.data, data.target, test_size=0.33, random_state=42, ) context = an.AutoMLPipeline( an.DataRegression(max_trials=3, overwrite=True, loss="mean_squared_error")) context.run_automl() context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=10) context.run_automl() context.train = an.AutoMLEvaluate(x_test, y_test, batch_size=32) context.run_automl() context.train = an.AutoMLPredict(x_train, batch_size=32) context.run_automl() assert context.return_automl["model"] != None assert isinstance(context.return_automl["prediction"], np.ndarray) assert isinstance(context.return_automl["evaluation"], list)
def test_return_train(): model = an.DataRegression(max_trials=4) context = an.AutoMLPipeline(model) assert context.train == model
from sklearn.model_selection import train_test_split from ai2business.ai_engines import automl_neural_network as an """ ### Setup the Timeseries Forecaster. """ x_train, y_train, x_test, y_test = train_test_split( dataset.iloc[:, 0:2].values, dataset.iloc[:, 3].values, test_size=0.33, random_state=42, ) context = an.AutoMLPipeline(an.TimeseriesForecaster()) context.run_automl() """ ### Fitting the Timeseries Forecaster. """ context.train = an.AutoMLFit(x_train, y_train, batch_size=32, epochs=1) context.run_automl() """ ### Evaluate the Timeseries Forecaster. """ context.train = an.AutoMLEvaluate(x_test, y_test, batch_size=32) context.run_automl()