def test_setup(params):
"""Tests __init__ and attributes set during fit"""
coverage = 0.90
silverkite = SimpleSilverkiteForecast()
model = SimpleSilverkiteEstimator(silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params)
assert model.silverkite == silverkite
assert model.score_func == mean_squared_error
assert model.coverage == coverage
assert model.null_model_params is None
# set_params must be able to replicate the init
model2 = SimpleSilverkiteEstimator()
model2.set_params(**dict(silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params))
assert model2.__dict__ == model.__dict__
initialized_params = model.__dict__
initialized_params_subset = {
k: v
for k, v in initialized_params.items() if k in params.keys()
}
assert_equal(initialized_params_subset, params)
assert model.model_dict is None
assert model.pred_cols is None
assert model.feature_cols is None
assert model.coef_ is None
train_df = daily_data_reg().get("train_df").copy()
model.fit(train_df)
assert model.fit_algorithm_dict == {
"fit_algorithm": "sgd",
"fit_algorithm_params": {
"alpha": 0.1
}
}
assert model.model_dict is not None
assert type(model.model_dict["ml_model"]) == SGDRegressor
assert model.model_dict["ml_model"].alpha == (
params["fit_algorithm_dict"]["fit_algorithm_params"]["alpha"])
assert model.model_dict["training_evaluation"] is not None
assert model.model_dict["test_evaluation"] is None
assert model.pred_cols is not None
assert model.feature_cols is not None
assert_frame_equal(model.df, train_df)
assert model.coef_ is not None
def test_summary(daily_data):
"""Checks summary function returns without error"""
model = SimpleSilverkiteEstimator(
fit_algorithm_dict={"fit_algorithm_params": {
"cv": 3
}})
train_df = daily_data["train_df"]
model.summary()
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.fit_algorithm_dict == {
"fit_algorithm": "ridge",
"fit_algorithm_params": {
"cv": 3
}
}
model.summary()
def test_uncertainty(daily_data):
"""Runs a basic model with uncertainty intervals
and checks coverage"""
uncertainty_dict = {
"uncertainty_method": "simple_conditional_residuals",
"params": {
"conditional_cols": ["dow_hr"],
"quantiles": [0.025, 0.975],
"quantile_estimation_method": "normal_fit",
"sample_size_thresh": 10,
"small_sample_size_method": "std_quantiles",
"small_sample_size_quantile": 0.98
}
}
model = SimpleSilverkiteEstimator(
uncertainty_dict=uncertainty_dict,
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data["train_df"]
test_df = daily_data["test_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.forecast is None
predictions = model.predict(test_df)
expected_forecast_cols = \
{"ts", "y", "y_quantile_summary", "err_std", "forecast_lower", "forecast_upper"}
assert expected_forecast_cols.issubset(list(model.forecast.columns))
actual = daily_data["test_df"][cst.VALUE_COL]
forecast_lower = predictions[cst.PREDICTED_LOWER_COL]
forecast_upper = predictions[cst.PREDICTED_UPPER_COL]
calc_pred_coverage = 100 * ((actual <= forecast_upper)
& (actual >= forecast_lower)).mean()
assert round(calc_pred_coverage) == 95, "forecast coverage is incorrect"
def test_score_function_null(daily_data):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy with null model
"""
model = SimpleSilverkiteEstimator(
null_model_params={"strategy": "mean"},
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data["train_df"]
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.fit_algorithm_dict == {
"fit_algorithm": "linear",
"fit_algorithm_params": None
}
score = model.score(daily_data["test_df"],
daily_data["test_df"][cst.VALUE_COL])
assert score == pytest.approx(0.90, rel=1e-2)
def test_score_function(daily_data_with_reg):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy without null model
"""
model = SimpleSilverkiteEstimator(
extra_pred_cols=["ct1", "regressor1", "regressor2"],
fit_algorithm_dict={"fit_algorithm": "linear"},
holidays_to_model_separately=[],
yearly_seasonality=5,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=3,
feature_sets_enabled=False)
train_df = daily_data_with_reg["train_df"]
test_df = daily_data_with_reg["test_df"]
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
score = model.score(test_df, test_df[cst.VALUE_COL])
pred_df = model.predict(test_df)
assert list(pred_df.columns) == [cst.TIME_COL, cst.PREDICTED_COL]
assert score == pytest.approx(
mean_squared_error(pred_df[cst.PREDICTED_COL], test_df[cst.VALUE_COL]))
assert score == pytest.approx(4.39, rel=1e-2)
def test_plot_components():
"""Tests plot_components.
Because component plots are implemented in `base_silverkite_estimator.py,` the bulk of
the testing is done there. This file only tests inheritance and compatibility of the
trained_model generated by this estimator's fit.
"""
daily_data = generate_df_with_reg_for_tests(
freq="D",
periods=20,
train_start_date=datetime.datetime(2018, 1, 1),
conti_year_origin=2018)
train_df = daily_data.get("train_df").copy()
model = SimpleSilverkiteEstimator(
fit_algorithm_dict={"fit_algorithm": "linear"},
yearly_seasonality=True,
quarterly_seasonality=False,
monthly_seasonality=False,
weekly_seasonality=True,
daily_seasonality=False,
)
model.fit(train_df)
# Test plot_components
with pytest.warns(Warning) as record:
title = "Custom component plot"
fig = model.plot_components(
names=["trend", "YEARLY_SEASONALITY", "DUMMY"], title=title)
expected_rows = 3
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "trend", "YEARLY_SEASONALITY"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == cst.TIME_COL
assert fig.layout.xaxis3.title["text"] == "Time of year"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "trend"
assert fig.layout.yaxis3.title["text"] == "yearly"
assert fig.layout.title["text"] == title
assert f"The following components have not been specified in the model: " \
f"{{'DUMMY'}}, plotting the rest." in record[0].message.args[0]
# Test plot_trend
title = "Custom trend plot"
fig = model.plot_trend(title=title)
expected_rows = 2
assert len(fig.data) == expected_rows
assert [fig.data[i].name
for i in range(expected_rows)] == [cst.VALUE_COL, "trend"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == cst.TIME_COL
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "trend"
assert fig.layout.title["text"] == title
# Test plot_seasonalities
with pytest.warns(Warning):
# suppresses the warning on seasonalities removed
title = "Custom seasonality plot"
fig = model.plot_seasonalities(title=title)
expected_rows = 3
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "WEEKLY_SEASONALITY", "YEARLY_SEASONALITY"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == "Day of week"
assert fig.layout.xaxis3.title["text"] == "Time of year"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "weekly"
assert fig.layout.yaxis3.title["text"] == "yearly"
assert fig.layout.title["text"] == title
