def basic_arima(data):
arima = ARIMA(out_of_sample_size=30, order=(2, 0, 3))
return GroupedPmdarima(arima).fit(
data.df,
data.key_columns,
"y",
"ds",
)
def test_grouped_pmdarima_save_load_predict(model):
save_path = "/tmp/pmdarima/test.pmd"
forecast = model.predict(30, return_conf_int=True)
model.save(save_path)
loaded = GroupedPmdarima.load(save_path)
loaded_forecast = loaded.predict(30, return_conf_int=True)
assert_frame_equal(forecast, loaded_forecast)
def basic_pmdarima(data):
arima = AutoARIMA(out_of_sample_size=30)
return GroupedPmdarima(arima).fit(
data.df,
data.key_columns,
"y",
"ds",
)
def test_grouped_model_cross_validate():
metrics = ["smape", "mean_squared_error", "mean_absolute_error"]
expected_columns = (
[f"{met}_mean" for met in metrics]
+ [f"{met}_stddev" for met in metrics]
+ ["grouping_key_columns", "key0"]
)
train = data_generator.generate_test_data(1, 2, 765, "2019-01-01")
grouped_model = GroupedPmdarima(
model_template=AutoARIMA(max_order=5, out_of_sample_size=30),
).fit(train.df, train.key_columns, "y", "ds", silence_warnings=True)
cross_validator = RollingForecastCV(h=90, step=120, initial=365)
cv_metrics = grouped_model.cross_validate(train.df, metrics, cross_validator)
assert len(cv_metrics) == 2
assert set(cv_metrics.columns).issubset(set(expected_columns))
def basic_pipeline(data):
pipeline = Pipeline(steps=[
("fourier", FourierFeaturizer(k=3, m=7)),
("arima", AutoARIMA(out_of_sample_size=60)),
])
return GroupedPmdarima(pipeline).fit(
data.df,
data.key_columns,
"y",
"ds",
)
def grouped_pmdarima(diviner_data):
from pmdarima.arima.auto import AutoARIMA
base_model = AutoARIMA(out_of_sample_size=60, maxiter=30)
return GroupedPmdarima(model_template=base_model).fit(
df=diviner_data.df,
group_key_columns=diviner_data.key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
def model(data):
arima = GroupedPmdarima(model_template=Pipeline(
steps=[("arima",
AutoARIMA(out_of_sample_size=60, max_order=7))]), ).fit(
df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
return arima
def test_pmdarima_default_arima_fit_attribute_extraction(data):
arima_model = GroupedPmdarima(model_template=AutoARIMA(
out_of_sample_size=30)).fit(data.df, data.key_columns, "y", "ds")
for group in arima_model.model.keys():
pipeline = arima_model._extract_individual_model(group)
instance_model = _extract_arima_model(pipeline)
group_metrics = _get_arima_training_metrics(instance_model)
for key, value in group_metrics.items():
assert value > 0
assert key in _PMDARIMA_MODEL_METRICS
for item in _PMDARIMA_MODEL_METRICS:
assert item in group_metrics.keys()
group_params = _get_arima_params(instance_model)
for item in {"P", "D", "Q", "s"}: # this isn't a seasonality model
assert group_params[item] == 0
def test_grouped_pmdarima_save_and_load(model):
orig_params = model.get_model_params()
orig_metrics = model.get_metrics()
save_path = "/tmp/pmdarima/test.pmd"
model.save(save_path)
loaded = GroupedPmdarima.load(save_path)
loaded_params = loaded.get_model_params()
loaded_metrics = loaded.get_metrics()
assert_frame_equal(orig_params, loaded_params)
assert_frame_equal(orig_metrics, loaded_metrics)
def test_pmdarima_ndiffs_override_class_args(data):
ndiffs = PmdarimaAnalyzer(df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds").calculate_ndiffs(alpha=0.4,
max_d=4)
base_template = AutoARIMA(d=10, out_of_sample_size=7)
model = GroupedPmdarima(base_template).fit(
df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds",
ndiffs=ndiffs,
silence_warnings=True,
)
params = model.get_model_params()
for _, row in params.iterrows():
assert row["d"] <= 4
def pipeline_override_d(data):
pipeline = Pipeline(steps=[("arima", AutoARIMA(out_of_sample_size=30))])
util = PmdarimaAnalyzer(df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds")
ndiffs = util.calculate_ndiffs(alpha=0.2, test="kpss", max_d=7)
nsdiffs = util.calculate_nsdiffs(m=7, test="ocsb", max_D=7)
return GroupedPmdarima(pipeline).fit(
df=data.df,
group_key_columns=data.key_columns,
y_col="y",
datetime_col="ds",
ndiffs=ndiffs,
nsdiffs=nsdiffs,
silence_warnings=True,
)
column_count=3,
series_count=4,
series_size=365 * 4,
start_dt="2019-01-01",
days_period=1,
)
training_data = generated_data.df
group_key_columns = generated_data.key_columns
# Build a GroupedPmdarima model by specifying an ARIMA model
arima_obj = ARIMA(order=(2, 1, 3), out_of_sample_size=60)
base_arima = GroupedPmdarima(model_template=arima_obj).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
# Save to local directory
save_dir = "/tmp/group_pmdarima/arima.gpmd"
base_arima.save(save_dir)
# Load from saved model
loaded_model = GroupedPmdarima.load(save_dir)
print("\nARIMA results:\n", "-" * 40)
get_and_print_model_metrics_params(loaded_model)
prediction = loaded_model.predict(
diff_analyzer = PmdarimaAnalyzer(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
)
ndiff = diff_analyzer.calculate_ndiffs(
alpha=0.05,
test="kpss",
max_d=4,
)
grouped_model = GroupedPmdarima(model_template=pipeline).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
ndiffs=ndiff,
silence_warnings=True,
)
# Save to local directory
save_dir = "/tmp/group_pmdarima/pipeline_override.gpmd"
grouped_model.save(save_dir)
# Load from saved model
loaded_model = GroupedPmdarima.load(save_dir)
print("\nAutoARIMA results:\n", "-" * 40)
get_and_print_model_metrics_params(loaded_model)
print("\nPredictions:\n", "-" * 40)
generated_data = generate_example_data(
column_count=2,
series_count=6,
series_size=365 * 4,
start_dt="2019-01-01",
days_period=1,
)
training_data = generated_data.df
group_key_columns = generated_data.key_columns
arima_obj = ARIMA(order=(2, 1, 3), out_of_sample_size=60)
base_arima = GroupedPmdarima(model_template=arima_obj).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
# Get a subset of group keys to generate forecasts for
group_df = training_data.copy()
group_df["groups"] = list(zip(*[group_df[c] for c in group_key_columns]))
distinct_groups = group_df["groups"].unique()
groups_to_predict = list(distinct_groups[:3])
print("-" * 65)
print(f"Unique groups that have been modeled: {distinct_groups}")
print(f"Subset of groups to generate predictions for: {groups_to_predict}")
print("-" * 65)