def test_config_variations_3():
df = pd.DataFrame([range(300), range(1000, 1300)]).T
df["time"] = pd.date_range("2018-01-01", periods=len(df), freq="H")
config.update({
"data": df,
"datetime_column": "time",
"freq": "",
"predicted_column": False,
"datalength": 100,
"other_columns": True,
"analyzeit": 2,
"default_other_columns_length": 5,
"data_transform": None,
"repeatit": 10,
"error_criterion": "max_error",
"multiprocessing": None,
"remove_outliers": None,
"remove_nans_threshold": 0.8,
"remove_nans_or_replace": "mean",
"optimization": True,
"optimization_variable": "default_n_steps_in",
"optimization_values": [3, 6, 9],
"plot_all_optimized_models": True,
"correlation_threshold": 0.4,
"standardizeit": "standardize",
"predicts": 7,
"smoothit": None,
})
assert validate_result(predictit.predict().predictions)
def test_presets():
config.update({
"data":
"https://www.ncdc.noaa.gov/cag/global/time-series/globe/land_ocean/ytd/12/1880-2016.json",
"request_datatype_suffix": ".json",
"predicted_table": "data",
"data_orientation": "index",
"predicted_column": 0,
"datalength": 100,
})
config.update({"use_config_preset": "fast"})
preset_result = predictit.predict().predictions
assert validate_result(preset_result)
config.update({"use_config_preset": "normal"})
assert validate_result(preset_result)
def test_config_variations_2():
config.update({
"data": "test_random",
"predicted_column": "",
"predicts": 4,
"analyzeit": 1,
"datetime_column": "",
"freq": "M",
"datalength": 100,
"default_n_steps_in": 3,
"data_transform": "difference",
"error_criterion": "rmse",
"remove_outliers": 3,
"print_number_of_models": None,
"print_table": "detailed",
"plot_library": "matplotlib",
"show_plot": True,
"print_time_table": True,
"correlation_threshold": 0.2,
"data_extension": None,
"optimizeit": True,
"optimizeit_limit": 0.1,
"optimizeit_details": 2,
"optimizeit_plot": True,
"standardizeit": None,
"multiprocessing": "pool",
"confidence_interval": None,
"trace_processes_memory": True,
"used_models": ["Bayes ridge regression"],
"models_parameters": {
"Bayes ridge regression": {
"model": "BayesianRidge",
"n_iter": 300,
"alpha_1": 1.0e-6,
"alpha_2": 1.0e-6,
"lambda_1": 1.0e-6,
"lambda_2": 1.0e-6,
}
},
"fragments": 4,
"iterations": 2,
"models_parameters_limits": {
"Bayes ridge regression": {
"alpha_1": [0.1e-6, 3e-6],
"model":
["BayesianRidge", "LassoRegression", "LinearRegression"],
}
},
})
config.hyperparameter_optimization.optimizeit_limit = 10
assert validate_result(predictit.predict().predictions)
def test_default_config_and_outputs():
config.reset()
results_default = predictit.predict()
# Validate tables
validate_tables = True
for i in [
results_default.tables.detailed,
results_default.tables.simple,
results_default.tables.time,
]:
if not isinstance(i, str) or len(i) < 20:
validate_tables = False
assert validate_result(results_default.predictions)
assert validate_result(results_default.best_prediction)
assert validate_result(results_default.results_df)
assert validate_tables
assert np.isnan(results_default.with_history["Predicted column"].iloc[-1])
assert not np.isnan((results_default.with_history.iloc[-1, -1]))
def test_config_variations_1():
config.update({
"embedding": "label",
"plot_library": "plotly",
"data":
"https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv",
"predicted_column": "Temp",
"datetime_column": "Date",
"freq": "D",
"error_criterion": "dtw",
"max_imported_length": 300,
"remove_nans_threshold": 0.85,
"remove_nans_or_replace": "neighbor",
"trace_processes_memory": False,
"print_number_of_models": 10,
"add_fft_columns": 16,
"data_extension": {
"differences": True,
"second_differences": True,
"multiplications": True,
"rolling_means": 10,
"rolling_stds": 10,
"mean_distances": True,
},
"analyzeit": 3,
"correlation_threshold": 0.2,
"optimizeit": False,
"standardizeit": "01",
"multiprocessing": "process",
"smoothit": (19, 2),
"power_transformed": True,
"analyze_seasonal_decompose": {
"period": 32,
"model": "additive"
},
"confidence_interval": 0.6,
})
assert validate_result(predictit.predict().predictions)
def test_main_multiple():
data = pd.read_csv(
"https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv"
).iloc[:120]
data["Another col"] = data["Temp"] + 16
config_test = config.copy()
config_test.update({
"datetime_column": "Date",
"freqs": ["D", "H"],
"data": data,
"predicted_columns": ["*"],
"remove_nans_threshold": 0.9,
"remove_nans_or_replace": 2,
"optimization": False,
})
result = predictit.predict_multiple_columns(config=config_test)
result_dataframe = next(iter(result.best_predictions_dataframes.values()))
assert (result.results and len(result.results) == 4
and validate_result(result_dataframe)
and len(result_dataframe.columns) == 2)
def test_config_inputs():
predict_with_params = predictit.predict(predicts=3)
data = [
pd.DataFrame(np.random.randn(100, 3), columns=["a", "b", "c"]),
pd.DataFrame(np.random.randn(80, 3), columns=["e", "b", "c"]),
]
predict_with_positional_params = predictit.predict(data, "b")
config_test = config.copy()
config_test.output.predicts = 4
predict_config_as_param = predictit.predict(config=config_test)
cli_args_str = (
"python predictit/main.py --used_function predict --data "
"'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv' "
"--predicted_column 'Temp' ")
result_cli = subprocess.check_output(cli_args_str.split(" "))
assert len(predict_with_params.best_prediction) == 3
assert len(predict_config_as_param.best_prediction) == 4
assert validate_result(predict_with_positional_params.predictions)
assert "Best model is" in str(result_cli)