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Merge pull request #247 from winedarksea/dev
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0.6.15
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@winedarksea
winedarksea authored Jul 22, 2024
2 parents 995c748 + 9297587 commit 6e473e8
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Showing 58 changed files with 1,879 additions and 616 deletions.
16 changes: 11 additions & 5 deletions TODO.md
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Expand Up @@ -13,11 +13,17 @@
* Forecasts are desired for the future immediately following the most recent data.
* trimmed_mean to AverageValueNaive

# 0.6.14 🇺🇦 🇺🇦 🇺🇦
* prevent excessive use of 'CenterSplit' and other macro_micro style transformers
* added ElasticNetwork as subsidiary regression model option
* KalmanSmoothing, BKBandpassFilter added on_inverse option
* add threshold arg to AlignLastValue
# 0.6.15 🇺🇦 🇺🇦 🇺🇦
* Constraint transformer added
* historical_growth constraint method added
* fft as multivariate_feature for Cassandra
* None trend_window now searched as part of Cassandra
* "quarterlydayofweek" method added for datepart
* threshold_method arg to AlignLastValue
* general tempate updated
* slight change to MATSE metric, now only abs values for scaling
* additional args to DatepartRegression
* bug fixes

### Unstable Upstream Pacakges (those that are frequently broken by maintainers)
* Pytorch-Forecasting
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2 changes: 1 addition & 1 deletion autots/__init__.py
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Expand Up @@ -27,7 +27,7 @@
from autots.models.cassandra import Cassandra


__version__ = '0.6.14'
__version__ = '0.6.15'

TransformTS = GeneralTransformer

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2 changes: 1 addition & 1 deletion autots/evaluator/anomaly_detector.py
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Expand Up @@ -221,7 +221,7 @@ def get_new_params(method="random"):
method_choice, method_params, transform_dict = anomaly_new_params(method=method)
if transform_dict == "random":
transform_dict = RandomTransform(
transformer_list='fast', transformer_max_depth=2
transformer_list='scalable', transformer_max_depth=2
)
if method == "fast":
preforecast = False
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38 changes: 35 additions & 3 deletions autots/evaluator/auto_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,6 +616,7 @@ def ModelMonster(
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
forecast_length=forecast_length,
**parameters,
)
elif model == "PreprocessingRegression":
Expand Down Expand Up @@ -810,6 +811,7 @@ def __init__(
holiday_country=self.holiday_country,
verbose=self.verbose,
random_seed=self.random_seed,
forecast_length=self.forecast_length,
)
self.name = "ModelPrediction"
self._fit_complete = False
Expand Down Expand Up @@ -912,7 +914,7 @@ def predict(self, forecast_length=None, future_regressor=None):

if df_forecast.forecast.shape[1] != self.df.shape[1]:
raise ValueError(
f"Model failed to return correct number of series. Returned {df_forecast.forecast.shape[1]} and requested: {self.df.shape[1]}"
f"{self.model.name} with {self.transformer_object.transformations} failed to return correct number of series. Returned {df_forecast.forecast.shape[1]} and requested: {self.df.shape[1]}"
)

df_forecast.transformation_parameters = self.transformation_dict
Expand Down Expand Up @@ -2489,7 +2491,10 @@ def validation_aggregation(


def generate_score(
model_results, metric_weighting: dict = {}, prediction_interval: float = 0.9
model_results,
metric_weighting: dict = {},
prediction_interval: float = 0.9,
return_score_dict: bool = False,
):
"""Generate score based on relative accuracies.
Expand Down Expand Up @@ -2528,6 +2533,8 @@ def generate_score(
wasserstein_weighting = metric_weighting.get('wasserstein_weighting', 0)
dwd_weighting = metric_weighting.get('dwd_weighting', 0)
matse_weighting = metric_weighting.get('matse_weighting', 0)

score_dict = {"ID": model_results["ID"]}
# handle various runtime information records
if 'TotalRuntimeSeconds' in model_results.columns:
model_results['TotalRuntimeSeconds'] = np.where(
Expand Down Expand Up @@ -2556,23 +2563,27 @@ def generate_score(
].min()
smape_score = model_results['smape_weighted'] / smape_scaler
overall_score = smape_score * smape_weighting
score_dict['smape'] = smape_score * smape_weighting
if mae_weighting != 0:
mae_scaler = model_results['mae_weighted'][
model_results['mae_weighted'] != 0
].min()
mae_score = model_results['mae_weighted'] / mae_scaler
score_dict['mae'] = mae_score * mae_weighting
overall_score = overall_score + (mae_score * mae_weighting)
if rmse_weighting != 0:
rmse_scaler = model_results['rmse_weighted'][
model_results['rmse_weighted'] != 0
].min()
rmse_score = model_results['rmse_weighted'] / rmse_scaler
score_dict['rmse'] = rmse_score * rmse_weighting
overall_score = overall_score + (rmse_score * rmse_weighting)
if made_weighting != 0:
made_scaler = model_results['made_weighted'][
model_results['made_weighted'] != 0
].min()
made_score = model_results['made_weighted'] / made_scaler
score_dict['made'] = made_score * made_weighting
# fillna, but only if all are nan (forecast_length = 1)
# if pd.isnull(made_score.max()):
# made_score.fillna(0, inplace=True)
Expand All @@ -2582,54 +2593,63 @@ def generate_score(
model_results['mage_weighted'] != 0
].min()
mage_score = model_results['mage_weighted'] / mage_scaler
score_dict['mage'] = mage_score * mage_weighting
overall_score = overall_score + (mage_score * mage_weighting)
if mle_weighting != 0:
mle_scaler = model_results['mle_weighted'][
model_results['mle_weighted'] != 0
].min()
mle_score = model_results['mle_weighted'] / mle_scaler
score_dict['mle'] = mle_score * mle_weighting
overall_score = overall_score + (mle_score * mle_weighting)
if imle_weighting != 0:
imle_scaler = model_results['imle_weighted'][
model_results['imle_weighted'] != 0
].min()
imle_score = model_results['imle_weighted'] / imle_scaler
score_dict['imle'] = imle_score * imle_weighting
overall_score = overall_score + (imle_score * imle_weighting)
if maxe_weighting != 0:
maxe_scaler = model_results['maxe_weighted'][
model_results['maxe_weighted'] != 0
].min()
maxe_score = model_results['maxe_weighted'] / maxe_scaler
score_dict['maxe'] = maxe_score * maxe_weighting
overall_score = overall_score + (maxe_score * maxe_weighting)
if mqae_weighting != 0:
mqae_scaler = model_results['mqae_weighted'][
model_results['mqae_weighted'] != 0
].min()
mqae_score = model_results['mqae_weighted'] / mqae_scaler
score_dict['mqae'] = mqae_score * mqae_weighting
overall_score = overall_score + (mqae_score * mqae_weighting)
if dwae_weighting != 0:
dwae_scaler = model_results['dwae_weighted'][
model_results['dwae_weighted'] != 0
].min()
dwae_score = model_results['dwae_weighted'] / dwae_scaler
score_dict['dwae'] = dwae_score * dwae_weighting
overall_score = overall_score + (dwae_score * dwae_weighting)
if ewmae_weighting != 0:
ewmae_scaler = model_results['ewmae_weighted'][
model_results['ewmae_weighted'] != 0
].min()
ewmae_score = model_results['ewmae_weighted'] / ewmae_scaler
score_dict['ewmae'] = ewmae_score * ewmae_weighting
overall_score = overall_score + (ewmae_score * ewmae_weighting)
if uwmse_weighting != 0:
uwmse_scaler = model_results['uwmse_weighted'][
model_results['uwmse_weighted'] != 0
].min()
uwmse_score = model_results['uwmse_weighted'] / uwmse_scaler
score_dict['uwmse'] = uwmse_score * uwmse_weighting
overall_score = overall_score + (uwmse_score * uwmse_weighting)
if mate_weighting != 0:
mate_scaler = model_results['mate_weighted'][
model_results['mate_weighted'] != 0
].min()
mate_score = model_results['mate_weighted'] / mate_scaler
score_dict['mate'] = mate_score * mate_weighting
overall_score = overall_score + (mate_score * mate_weighting)
if wasserstein_weighting != 0:
wasserstein_scaler = model_results['wasserstein_weighted'][
Expand All @@ -2638,30 +2658,35 @@ def generate_score(
wasserstein_score = (
model_results['wasserstein_weighted'] / wasserstein_scaler
)
score_dict['wasserstein'] = wasserstein_score * wasserstein_weighting
overall_score = overall_score + (wasserstein_score * wasserstein_weighting)
if dwd_weighting != 0:
dwd_scaler = model_results['dwd_weighted'][
model_results['dwd_weighted'] != 0
].min()
dwd_score = model_results['dwd_weighted'] / dwd_scaler
score_dict['dwd'] = dwd_score * dwd_weighting
overall_score = overall_score + (dwd_score * dwd_weighting)
if matse_weighting != 0:
matse_scaler = model_results['matse_weighted'][
model_results['matse_weighted'] != 0
].min()
matse_score = model_results['matse_weighted'] / matse_scaler
score_dict['matse'] = matse_score * matse_weighting
overall_score = overall_score + (matse_score * matse_weighting)
if smoothness_weighting != 0:
smoothness_scaler = model_results['smoothness_weighted'][
model_results['smoothness_weighted'] != 0
].mean()
smoothness_score = model_results['smoothness_weighted'] / smoothness_scaler
score_dict['smoothness'] = smoothness_score * smoothness_weighting
overall_score = overall_score + (smoothness_score * smoothness_weighting)
if spl_weighting != 0:
spl_scaler = model_results['spl_weighted'][
model_results['spl_weighted'] != 0
].min()
spl_score = model_results['spl_weighted'] / spl_scaler
score_dict['spl'] = spl_score * spl_weighting
overall_score = overall_score + (spl_score * spl_weighting)
smape_median = smape_score.median()
if runtime_weighting != 0:
Expand All @@ -2670,18 +2695,22 @@ def generate_score(
runtime_score = runtime / runtime_scaler
# this scales it into a similar range as SMAPE
runtime_score = runtime_score * (smape_median / runtime_score.median())
score_dict['runtime'] = runtime_score * runtime_weighting
overall_score = overall_score + (runtime_score * runtime_weighting)
# these have values in the range 0 to 1
if contour_weighting != 0:
contour_score = (2 - model_results['contour_weighted']) * smape_median
score_dict['contour'] = contour_score * contour_weighting
overall_score = overall_score + (contour_score * contour_weighting)
if oda_weighting != 0:
oda_score = (2 - model_results['oda_weighted']) * smape_median
score_dict['oda'] = oda_score * oda_weighting
overall_score = overall_score + (oda_score * oda_weighting)
if containment_weighting != 0:
containment_score = (
1 + abs(prediction_interval - model_results['containment_weighted'])
) * smape_median
score_dict['contaiment'] = containment_score * containment_weighting
overall_score = overall_score + (containment_score * containment_weighting)

except Exception as e:
Expand All @@ -2693,7 +2722,10 @@ def generate_score(
A new starting template may also help. {repr(e)}"""
)

return overall_score.astype(float) # need to handle complex values (!)
if return_score_dict:
return overall_score.astype(float), score_dict
else:
return overall_score.astype(float) # need to handle complex values (!)


def generate_score_per_series(
Expand Down
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