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[fix] Pytest for fix ValueError when plotting events components #1434

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merged 2 commits into from
Oct 2, 2023
Merged

[fix] Pytest for fix ValueError when plotting events components #1434

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818efa1
pytest for plotting components
Jul 6, 2023
ad45b49
Merge branch 'main' into pytest-plotting
ourownstory Oct 2, 2023
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58 changes: 32 additions & 26 deletions neuralprophet/plot_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,7 +193,7 @@
if "lagged_regressors" in components and m.config_lagged_regressors is None:
components.remove("lagged_regressors")
invalid_components.append("lagged_regressors")
if "events" in components and (m.config_events and m.config_country_holidays) is None:
if "events" in components and (m.config_events is None and m.config_country_holidays is None):
components.remove("events")
invalid_components.append("events")
if "future_regressors" in components and m.config_regressors is None:
Expand Down Expand Up @@ -419,31 +419,37 @@
if "events" in components:
additive_events_flag = False
muliplicative_events_flag = False
for event, configs in m.config_events.items():
if validator == "plot_components" and configs.mode == "additive":
additive_events_flag = True
elif validator == "plot_components" and configs.mode == "multiplicative":
muliplicative_events_flag = True
elif validator == "plot_parameters":
event_params = m.model.get_event_weights(event)
weight_list = [(key, param.detach().numpy()[quantile_index, :]) for key, param in event_params.items()]
if configs.mode == "additive":
additive_events = additive_events + weight_list
elif configs.mode == "multiplicative":
multiplicative_events = multiplicative_events + weight_list

for country_holiday in m.config_country_holidays.holiday_names:
if validator == "plot_components" and m.config_country_holidays.mode == "additive":
additive_events_flag = True
elif validator == "plot_components" and m.config_country_holidays.mode == "multiplicative":
muliplicative_events_flag = True
elif validator == "plot_parameters":
event_params = m.model.get_event_weights(country_holiday)
weight_list = [(key, param.detach().numpy()[quantile_index, :]) for key, param in event_params.items()]
if m.config_country_holidays.mode == "additive":
additive_events = additive_events + weight_list
elif m.config_country_holidays.mode == "multiplicative":
multiplicative_events = multiplicative_events + weight_list
if m.config_events is not None:
for event, configs in m.config_events.items():
if validator == "plot_components" and configs.mode == "additive":
additive_events_flag = True
elif validator == "plot_components" and configs.mode == "multiplicative":
muliplicative_events_flag = True
elif validator == "plot_parameters":
event_params = m.model.get_event_weights(event)
weight_list = [
(key, param.detach().numpy()[quantile_index, :]) for key, param in event_params.items()
]
if configs.mode == "additive":
additive_events = additive_events + weight_list
elif configs.mode == "multiplicative":
multiplicative_events = multiplicative_events + weight_list

if m.config_country_holidays is not None:
for country_holiday in m.config_country_holidays.holiday_names:
if validator == "plot_components" and m.config_country_holidays.mode == "additive":
additive_events_flag = True
elif validator == "plot_components" and m.config_country_holidays.mode == "multiplicative":
muliplicative_events_flag = True

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elif validator == "plot_parameters":
event_params = m.model.get_event_weights(country_holiday)
weight_list = [
(key, param.detach().numpy()[quantile_index, :]) for key, param in event_params.items()
]
if m.config_country_holidays.mode == "additive":
additive_events = additive_events + weight_list
elif m.config_country_holidays.mode == "multiplicative":
multiplicative_events = multiplicative_events + weight_list

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if additive_events_flag:
plot_components.append(
Expand Down
105 changes: 105 additions & 0 deletions tests/test_plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -271,6 +271,73 @@ def test_plot_events(plotting_backend):
["superbowl", "playoff"], lower_window=-1, upper_window=1, mode="multiplicative", regularization=0.5
)
# add the country specific holidays
m = m.add_country_holidays("US", mode="multiplicative", regularization=0.5)
m.add_country_holidays("Indonesia")
m.add_country_holidays("Thailand")
m.add_country_holidays("Philippines")
m.add_country_holidays("Pakistan")
m.add_country_holidays("Belarus")
history_df = m.create_df_with_events(df, events_df)
m.fit(history_df, freq="D")
future = m.make_future_dataframe(df=history_df, events_df=events_df, periods=30, n_historic_predictions=90)
forecast = m.predict(df=future)
log.debug(f"Event Parameters:: {m.model.event_params}")

fig1 = m.plot_components(forecast, plotting_backend=plotting_backend)
fig2 = m.plot(forecast, plotting_backend=plotting_backend)
fig3 = m.plot_parameters(plotting_backend=plotting_backend)

if PLOT:
fig1.show()
fig2.show()
fig3.show()


@pytest.mark.parametrize(*decorator_input)
def test_plot_events_additive(plotting_backend):
log.info(f"testing: Plotting with events with {plotting_backend}")
df = pd.read_csv(PEYTON_FILE)[-NROWS:]
playoffs = pd.DataFrame(
{
"event": "playoff",
"ds": pd.to_datetime(
[
"2008-01-13",
"2009-01-03",
"2010-01-16",
"2010-01-24",
"2010-02-07",
"2011-01-08",
"2013-01-12",
"2014-01-12",
"2014-01-19",
"2014-02-02",
"2015-01-11",
"2016-01-17",
"2016-01-24",
"2016-02-07",
]
),
}
)
superbowls = pd.DataFrame(
{
"event": "superbowl",
"ds": pd.to_datetime(["2010-02-07", "2014-02-02", "2016-02-07"]),
}
)
events_df = pd.concat((playoffs, superbowls))
m = NeuralProphet(
epochs=EPOCHS,
batch_size=BATCH_SIZE,
learning_rate=LR,
n_lags=2,
n_forecasts=30,
daily_seasonality=False,
)
# set event windows
m = m.add_events(["superbowl", "playoff"], lower_window=-1, upper_window=1, mode="additive", regularization=0.5)
# add the country specific holidays
m = m.add_country_holidays("US", mode="additive", regularization=0.5)
m.add_country_holidays("Indonesia")
m.add_country_holidays("Thailand")
Expand All @@ -293,6 +360,44 @@ def test_plot_events(plotting_backend):
fig3.show()


@pytest.mark.parametrize(*decorator_input)
def test_plot_events_components(plotting_backend):
log.info(f"testing: Plotting components with events with {plotting_backend}")
df = pd.read_csv(PEYTON_FILE)[-NROWS:]

events_df = pd.DataFrame(
{
"event": "superbowl",
"ds": pd.to_datetime(["2010-02-07", "2014-02-02", "2016-02-07"]),
}
)
m = NeuralProphet(
epochs=EPOCHS,
batch_size=BATCH_SIZE,
learning_rate=LR,
n_forecasts=7,
n_lags=14,
yearly_seasonality=True,
weekly_seasonality=True,
)
# set event windows
m = m.add_events(["superbowl"], lower_window=-1, upper_window=1, mode="additive", regularization=0.5)
history_df = m.create_df_with_events(df, events_df)
m.fit(history_df, freq="D")
future = m.make_future_dataframe(df=history_df, events_df=events_df, periods=30, n_historic_predictions=90)
forecast = m.predict(df=future)
log.debug(f"Event Parameters:: {m.model.event_params}")

fig1 = m.plot_components(forecast, plotting_backend=plotting_backend)
fig2 = m.plot(forecast, plotting_backend=plotting_backend)
fig3 = m.plot_parameters(plotting_backend=plotting_backend)

if PLOT:
fig1.show()
fig2.show()
fig3.show()


@pytest.mark.parametrize(*decorator_input)
def test_plot_trend(plotting_backend):
log.info(f"testing: Plotting linear trend with {plotting_backend}")
Expand Down
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