def test_silverkite_with_components_hourly_data():
"""Tests get_components, plot_components, plot_trend,
plot_seasonalities with hourly data
"""
hourly_data = generate_df_with_reg_for_tests(
freq="H",
periods=24 * 4,
train_start_date=datetime.datetime(2018, 1, 1),
conti_year_origin=2018)
train_df = hourly_data.get("train_df").copy()
params_hourly = params_components()
# converts into parameters for `forecast_silverkite`
coverage = params_hourly.pop("coverage")
model = BaseSilverkiteEstimator(
coverage=coverage, uncertainty_dict=params_hourly["uncertainty_dict"])
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
silverkite = SilverkiteForecast()
model.model_dict = silverkite.forecast(df=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**params_hourly)
model.finish_fit()
# Test plot_components
with pytest.warns(Warning) as record:
title = "Custom component plot"
fig = model.plot_components(
names=["trend", "DAILY_SEASONALITY", "DUMMY"], title=title)
expected_rows = 3 + 1 # includes changepoints
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "trend", "DAILY_SEASONALITY", "trend change point"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == cst.TIME_COL
assert fig.layout.xaxis3.title["text"] == "Hour of day"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "trend"
assert fig.layout.yaxis3.title["text"] == "daily"
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 + 1 # includes changepoints
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 = 4
assert len(fig.data) == expected_rows
assert [fig.data[i].name for i in range(expected_rows)] == \
[cst.VALUE_COL, "DAILY_SEASONALITY", "WEEKLY_SEASONALITY", "YEARLY_SEASONALITY"]
assert fig.layout.xaxis.title["text"] == cst.TIME_COL
assert fig.layout.xaxis2.title["text"] == "Hour of day"
assert fig.layout.xaxis3.title["text"] == "Day of week"
assert fig.layout.xaxis4.title["text"] == "Time of year"
assert fig.layout.yaxis.title["text"] == cst.VALUE_COL
assert fig.layout.yaxis2.title["text"] == "daily"
assert fig.layout.yaxis3.title["text"] == "weekly"
assert fig.layout.yaxis4.title["text"] == "yearly"
assert fig.layout.title["text"] == title
def test_silverkite_with_components_daily_data():
"""Tests get_components, plot_components, plot_trend,
plot_seasonalities with daily data and missing input values.
"""
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["train_df"].copy()
train_df.loc[[2, 4, 7], cst.VALUE_COL] = np.nan # creates missing values
params_daily = params_components() # SilverkiteEstimator parameters
# converts into parameters for `forecast_silverkite`
coverage = params_daily.pop("coverage")
# removes daily seasonality terms
params_daily["fs_components_df"] = pd.DataFrame({
"name": ["tow", "ct1"],
"period": [7.0, 1.0],
"order": [4, 5],
"seas_names": ["weekly", "yearly"]
})
model = BaseSilverkiteEstimator(
coverage=coverage, uncertainty_dict=params_daily["uncertainty_dict"])
with pytest.raises(NotFittedError,
match="Call `fit` before calling `plot_components`."):
model.plot_components()
with pytest.warns(Warning):
# suppress warnings from conf_interval.py and sklearn
# a subclass's fit() method will have these steps
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
silverkite = SilverkiteForecast()
model.model_dict = silverkite.forecast(df=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**params_daily)
model.finish_fit()
# Tests plot_components
with pytest.warns(Warning) as record:
title = "Custom component plot"
model._set_silverkite_diagnostics_params()
fig = model.plot_components(
names=["trend", "YEARLY_SEASONALITY", "DUMMY"], title=title)
expected_rows = 3
assert len(fig.data) == expected_rows + 1 # includes changepoints
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]
# Missing component error
with pytest.raises(
ValueError,
match=
"None of the provided components have been specified in the model."
):
model.plot_components(names=["DUMMY"])
# Tests plot_trend
title = "Custom trend plot"
fig = model.plot_trend(title=title)
expected_rows = 2
assert len(fig.data) == expected_rows + 1 # includes changepoints
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
# Tests 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
# Component plot error if `fit_algorithm` is "rf" or "gradient_boosting"
params_daily["fit_algorithm"] = "rf"
model = BaseSilverkiteEstimator(
coverage=coverage, uncertainty_dict=params_daily["uncertainty_dict"])
with pytest.warns(Warning):
# suppress warnings from conf_interval.py and sklearn
# a subclass's fit() method will have these steps
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
model.model_dict = silverkite.forecast(df=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**params_daily)
model.finish_fit()
assert model.coef_ is None
with pytest.raises(
NotImplementedError,
match=
"Component plot has only been implemented for additive linear models."
):
model.plot_components()
with pytest.raises(
NotImplementedError,
match=
"Component plot has only been implemented for additive linear models."
):
model.plot_trend()
with pytest.raises(
NotImplementedError,
match=
"Component plot has only been implemented for additive linear models."
):
model.plot_seasonalities()