def test_transformer(self):
model_name = "transformer"
model = Model(
model_name,
model_parameters={
"activated": True,
"kwargs": {
"model_dim": 16
}
},
frequency="D",
prediction_length=self.prediction_length,
epoch=1,
use_external_features=True,
batch_size=32,
num_batches_per_epoch=50,
)
metrics, identifiers_columns, forecasts_df = model.train_evaluate(
self.train_list_dataset,
self.test_list_dataset,
make_forecasts=True)
TestModel.metrics_assertions(metrics, model_name)
TestModel.forecasts_assertions(
forecasts_df, model_name, prediction_length=self.prediction_length)
def test_minute_frequency():
prediction_length = 1
timeseries = {
TIMESERIES_KEYS.START: "2021-01-15 12:40:00",
TIMESERIES_KEYS.TARGET: np.array([12, 13]),
TIMESERIES_KEYS.TARGET_NAME: "target",
TIMESERIES_KEYS.TIME_COLUMN_NAME: "date",
}
frequency = "20min"
gluon_dataset = ListDataset([timeseries], freq=frequency)
model = Model(
"simplefeedforward",
model_parameters={"activated": True, "kwargs": {}},
frequency=frequency,
prediction_length=prediction_length,
epoch=1,
batch_size=8,
num_batches_per_epoch=5,
)
evaluation_forecasts_df = model.train_evaluate(gluon_dataset, gluon_dataset, make_forecasts=True, retrain=True)[2]
assert evaluation_forecasts_df["index"].iloc[0] == pd.Timestamp("2021-01-15 13:00:00")
trained_model = TrainedModel(
predictor=model.predictor,
gluon_dataset=gluon_dataset,
prediction_length=prediction_length,
quantiles=[0.5],
include_history=True,
)
trained_model.predict()
forecasts_df = trained_model.get_forecasts_df(session="2021-01-01", model_label="TEST")
assert forecasts_df["date"].iloc[0] == pd.Timestamp("2021-01-15 13:20:00")
def test_mqcnn(self):
model_name = "mqcnn"
model = Model(
model_name,
model_parameters={"activated": True, "kwargs": {}},
frequency="D",
prediction_length=self.prediction_length,
epoch=1,
use_external_features=False,
batch_size=32,
num_batches_per_epoch=50,
)
model.train(self.test_list_dataset)
assert model.predictor is not None
def setup_class(self):
df = pd.DataFrame(
{
"date": ["2018-01-06", "2018-01-07", "2018-01-08", "2018-01-09", "2018-01-08", "2018-01-09", "2018-01-10", "2018-01-11", "2018-01-12"],
"target": [2, 4, 2, 2, 5, 2, 3, 2, 3],
"key": [1, 1, 1, 1, 2, 2, 2, 2, 2],
"ext_feat": [0, 0, 0, 0, 0, 1, 0, 1, 1],
}
)
df["date"] = pd.to_datetime(df["date"]).dt.tz_localize(tz=None)
self.frequency = "D"
gluon_dataset = GluonDataset(
dataframe=df,
time_column_name="date",
frequency=self.frequency,
target_columns_names=["target"],
timeseries_identifiers_names=["key"],
external_features_columns_names=["ext_feat"],
min_length=2,
)
self.prediction_length = 2
gluon_list_datasets = gluon_dataset.create_list_datasets(cut_lengths=[self.prediction_length, 0])
self.train_list_dataset = gluon_list_datasets[0]
self.test_list_dataset = gluon_list_datasets[1]
self.model_name = "simplefeedforward"
self.model = Model(
self.model_name,
model_parameters={"activated": True, "kwargs": {}},
frequency=self.frequency,
prediction_length=self.prediction_length,
epoch=1,
use_external_features=True,
batch_size=32,
num_batches_per_epoch=50,
)
def instantiate_models(self):
"""Instantiate all the selected models. """
for model_name, model_parameters in self.models_parameters.items():
self.models.append(
Model(
model_name,
model_parameters=model_parameters,
frequency=self.frequency,
prediction_length=self.prediction_length,
epoch=self.epoch,
use_external_features=self.use_external_features,
batch_size=self.batch_size,
num_batches_per_epoch=self.num_batches_per_epoch,
season_length=self.season_length,
mxnet_context=self.mxnet_context,
))
class TestExternalFeaturesSimpleFeedForward:
"""Test simple feed forward with external features on multiple timeseries of different lengths"""
def setup_class(self):
df = pd.DataFrame(
{
"date": ["2018-01-06", "2018-01-07", "2018-01-08", "2018-01-09", "2018-01-08", "2018-01-09", "2018-01-10", "2018-01-11", "2018-01-12"],
"target": [2, 4, 2, 2, 5, 2, 3, 2, 3],
"key": [1, 1, 1, 1, 2, 2, 2, 2, 2],
"ext_feat": [0, 0, 0, 0, 0, 1, 0, 1, 1],
}
)
df["date"] = pd.to_datetime(df["date"]).dt.tz_localize(tz=None)
self.frequency = "D"
gluon_dataset = GluonDataset(
dataframe=df,
time_column_name="date",
frequency=self.frequency,
target_columns_names=["target"],
timeseries_identifiers_names=["key"],
external_features_columns_names=["ext_feat"],
min_length=2,
)
self.prediction_length = 2
gluon_list_datasets = gluon_dataset.create_list_datasets(cut_lengths=[self.prediction_length, 0])
self.train_list_dataset = gluon_list_datasets[0]
self.test_list_dataset = gluon_list_datasets[1]
self.model_name = "simplefeedforward"
self.model = Model(
self.model_name,
model_parameters={"activated": True, "kwargs": {}},
frequency=self.frequency,
prediction_length=self.prediction_length,
epoch=1,
use_external_features=True,
batch_size=32,
num_batches_per_epoch=50,
)
def test_simplefeedforward_external_features_training(self):
metrics, identifiers_columns, forecasts_df = self.model.train_evaluate(
self.train_list_dataset, self.test_list_dataset, make_forecasts=True, retrain=True
)
assert len(forecasts_df.index) == 4
def test_simplefeedforward_external_features_predictions(self):
external_features_future_df = pd.DataFrame(
{
"date": ["2018-01-10", "2018-01-11", "2018-01-13", "2018-01-14"],
"key": [1, 1, 2, 2],
"ext_feat": [0, 0, 0, 1],
}
)
external_features_future_df["date"] = pd.to_datetime(external_features_future_df["date"]).dt.tz_localize(tz=None)
gluon_dataset = add_future_external_features(self.test_list_dataset, external_features_future_df, self.prediction_length, self.frequency)
trained_model = TrainedModel(
model_name=self.model_name,
predictor=self.model.predictor,
gluon_dataset=gluon_dataset,
prediction_length=self.prediction_length,
quantiles=[0.1, 0.5, 0.9],
include_history=True,
)
trained_model.predict()
forecasts_df = trained_model.get_forecasts_df(session="1234")
assert len(forecasts_df.index) == 13
