def build_from_ckpt(self, checkpoint_filename):
"""
Build Prophet Model from checkpoint
:param checkpoint_filename: model checkpoint filename
"""
from zoo.chronos.model.prophet import ProphetModel
model = ProphetModel(config=self.model_config)
model.restore(checkpoint_filename)
return model
def build(self, config):
"""
Build Prophet Model
:param config: Other prophet hyperparameters. You may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
for the parameter names to specify.
"""
from zoo.chronos.model.prophet import ProphetModel
model = ProphetModel(config=self.model_config)
model._build(**config)
return model
def test_save_restore(self):
self.model.fit_eval(data=self.data, **self.config)
result_save = self.model.predict(x=None, horizon=self.horizon)
model_file = "tmp.json"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = ProphetModel()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(horizon=self.horizon)
assert_array_almost_equal(result_save['yhat'], result_restore['yhat'], decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)
def setup_method(self, method):
self.seq_len = 480
self.config = {
"changepoint_prior_scale": np.exp(np.random.uniform(np.log(0.001), np.log(0.5))),
"seasonality_prior_scale": np.exp(np.random.uniform(np.log(0.01), np.log(10))),
"holidays_prior_scale": np.exp(np.random.uniform(np.log(0.01), np.log(10))),
"seasonality_mode": np.random.choice(['additive', 'multiplicative']),
"changepoint_range": np.random.uniform(0.8, 0.95)
}
self.model = ProphetModel()
self.x = pd.DataFrame(pd.date_range('20130101', periods=self.seq_len), columns=['ds'])
self.x.insert(1, 'y', np.random.rand(self.seq_len))
self.horizon = np.random.randint(2, 50)
self.target = pd.DataFrame(pd.date_range('20140426', periods=self.horizon), columns=['ds'])
self.target.insert(1, 'y', np.random.rand(self.horizon))
self.data = {'x': self.x, 'y': None, 'val_x': None, 'val_y': self.target}
def __init__(
self,
changepoint_prior_scale=0.05,
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
seasonality_mode='additive',
changepoint_range=0.8,
metric="mse",
):
"""
Build a Prophet Forecast Model.
:param changepoint_prior_scale: hyperparameter changepoint_prior_scale for the
Prophet model, for details you may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
:param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
Prophet model, for details you may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
:param holidays_prior_scale: hyperparameter holidays_prior_scale for the
Prophet model, for details you may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
:param seasonality_mode: hyperparameter seasonality_mode for the
Prophet model, for details you may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
:param changepoint_range: hyperparameter changepoint_range for the
Prophet model, for details you may refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning
:param metric: the metric for validation and evaluation. For regression, we support
Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
Cosine Proximity: ("cosine_proximity", "cosine")
"""
self.model_config = {
"changepoint_prior_scale": changepoint_prior_scale,
"seasonality_prior_scale": seasonality_prior_scale,
"holidays_prior_scale": holidays_prior_scale,
"seasonality_mode": seasonality_mode,
"changepoint_range": changepoint_range,
"metric": metric
}
self.internal = ProphetModel()
super().__init__()
class ProphetForecaster(Forecaster):
"""
Example:
>>> #The dataset is split into data, validation_data
>>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
>>> model.fit(data, validation_data)
>>> predict_result = model.predict(horizon=24)
"""
def __init__(
self,
changepoint_prior_scale=0.05,
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
seasonality_mode='additive',
changepoint_range=0.8,
metric="mse",
):
"""
Build a Prophet Forecast Model.
User can customize changepoint_prior_scale, seasonality_prior_scale,
holidays_prior_scale, seasonality_mode, changepoint_range and metric
of the Prophet model, for details of the Prophet model hyperparameters, refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
:param changepoint_prior_scale: hyperparameter changepoint_prior_scale for the
Prophet model.
:param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
Prophet model.
:param holidays_prior_scale: hyperparameter holidays_prior_scale for the
Prophet model.
:param seasonality_mode: hyperparameter seasonality_mode for the
Prophet model.
:param changepoint_range: hyperparameter changepoint_range for the
Prophet model.
:param metric: the metric for validation and evaluation. For regression, we support
Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
Cosine Proximity: ("cosine_proximity", "cosine")
"""
self.model_config = {
"changepoint_prior_scale": changepoint_prior_scale,
"seasonality_prior_scale": seasonality_prior_scale,
"holidays_prior_scale": holidays_prior_scale,
"seasonality_mode": seasonality_mode,
"changepoint_range": changepoint_range,
"metric": metric
}
self.internal = ProphetModel()
super().__init__()
def fit(self, data, validation_data):
"""
Fit(Train) the forecaster.
:param data: training data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param validation_data: evaluation data, should be the same type as x
"""
self._check_data(data, validation_data)
data_dict = {
'x': data,
'y': None,
'val_x': None,
'val_y': validation_data
}
return self.internal.fit_eval(data=data_dict, **self.model_config)
def _check_data(self, data, validation_data):
assert 'ds' in data.columns and 'y' in data.columns, \
"data should be a pandas dataframe that has at least 2 columns 'ds' and 'y'."
assert 'ds' in validation_data.columns and 'y' in validation_data.columns, \
"validation_data should be a dataframe that has at least 2 columns 'ds' and 'y'."
def predict(self, horizon):
"""
Predict using a trained forecaster.
:param horizon: the number of steps forward to predict
"""
if self.internal.model is None:
raise RuntimeError(
"You must call fit or restore first before calling predict!")
return self.internal.predict(horizon=horizon)
def evaluate(self, validation_data, metrics=['mse']):
"""
Evaluate using a trained forecaster.
:param validation_data: evaluation data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param data: We don't support input data currently.
:param metrics: A list contains metrics for test/valid data.
"""
if validation_data is None:
raise ValueError("Input invalid validation_data of None")
if self.internal.model is None:
raise RuntimeError(
"You must call fit or restore first before calling evaluate!")
return self.internal.evaluate(None, validation_data, metrics=metrics)
def save(self, checkpoint_file):
"""
Save the forecaster.
:param checkpoint_file: The location you want to save the forecaster, should be a json file
"""
if self.internal.model is None:
raise RuntimeError(
"You must call fit or restore first before calling save!")
self.internal.save(checkpoint_file)
def restore(self, checkpoint_file):
"""
Restore the forecaster.
:param checkpoint_file: The checkpoint file location you want to load the forecaster.
"""
self.internal.restore(checkpoint_file)
class TestProphetModel(ZooTestCase):
def setup_method(self, method):
self.seq_len = 480
self.config = {
"changepoint_prior_scale":
np.exp(np.random.uniform(np.log(0.001), np.log(0.5))),
"seasonality_prior_scale":
np.exp(np.random.uniform(np.log(0.01), np.log(10))),
"holidays_prior_scale":
np.exp(np.random.uniform(np.log(0.01), np.log(10))),
"seasonality_mode":
np.random.choice(['additive', 'multiplicative']),
"changepoint_range":
np.random.uniform(0.8, 0.95)
}
self.model = ProphetModel()
self.data = pd.DataFrame(pd.date_range('20130101',
periods=self.seq_len),
columns=['ds'])
self.data.insert(1, 'y', np.random.rand(self.seq_len))
self.horizon = np.random.randint(2, 50)
self.validation_data = pd.DataFrame(pd.date_range(
'20140426', periods=self.horizon),
columns=['ds'])
self.validation_data.insert(1, 'y', np.random.rand(self.horizon))
def teardown_method(self, method):
del self.model
del self.data
del self.validation_data
def test_prophet(self):
# test fit_eval
evaluate_result = self.model.fit_eval(
data=self.data,
validation_data=self.validation_data,
**self.config)
# test predict
result = self.model.predict(horizon=self.horizon)
assert result.shape[0] == self.horizon
# test evaluate
evaluate_result = self.model.evaluate(target=self.validation_data,
metrics=['mae', 'smape'])
assert len(evaluate_result) == 2
def test_error(self):
with pytest.raises(ValueError,
match="We don't support input data currently"):
self.model.predict(data=1)
with pytest.raises(ValueError,
match="We don't support input data currently"):
self.model.evaluate(target=self.validation_data, data=1)
with pytest.raises(ValueError, match="Input invalid target of None"):
self.model.evaluate(target=None)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling predict"
):
self.model.predict()
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling evaluate"
):
self.model.evaluate(target=self.validation_data)
with pytest.raises(
Exception,
match=
"Needs to call fit_eval or restore first before calling save"):
model_file = "tmp.json"
self.model.save(model_file)
def test_save_restore(self):
self.model.fit_eval(data=self.data,
validation_data=self.validation_data,
**self.config)
result_save = self.model.predict(horizon=self.horizon)
model_file = "tmp.json"
self.model.save(model_file)
assert os.path.isfile(model_file)
new_model = ProphetModel()
new_model.restore(model_file)
assert new_model.model
result_restore = new_model.predict(horizon=self.horizon)
assert_array_almost_equal(result_save['yhat'], result_restore['yhat'], decimal=2), \
"Prediction values are not the same after restore: " \
"predict before is {}, and predict after is {}".format(result_save, result_restore)
os.remove(model_file)