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etna 2.1.0

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@Mr-Geekman Mr-Geekman released this 03 Jul 15:27
· 22 commits to master since this release
2.1.0
7aa1f0b
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Learn about vigilant mode.

Highlights

  • Add class etna.auto.Tune for tuning hyperparameters
  • Extend functionality of class etna.auto.Auto to include a tuning stage
  • Add notebook about AutoML
  • Add utilities for estimating number of folds for backtesting and forecasting and integrate them into CLI
  • Add parameter for setting the start of prediction into CLI
  • Add etna.transforms.ExogShiftTransform to shift all exogenous variables
  • Add etna.models.DeepStateModel
  • Update requirements for holidays, scipy, ruptures, sqlalchemy, tsfresh
  • Optimize make_samples of etna.models.RNNNet and etna.models.MLPNet
  • Add parameter fast_redundancy in etna.analysis.feature_selection.mrmm and etna.transforms.MRMRFeatureSelectionTransform to speed it up

Full changelog

Added

  • Notebook forecast_interpretation.ipynb with forecast decomposition (#1220)
  • Exogenous variables shift transform ExogShiftTransform(#1254)
  • Parameter start_timestamp to forecast CLI command (#1265)
  • DeepStateModel (#1253)
  • Function estimate_max_n_folds for folds number estimation (#1279)
  • Parameters estimate_n_folds and context_size to forecast and backtest CLI commands (#1284)
  • Class Tune for hyperparameter optimization within existing pipeline (#1200)
  • Add etna.distributions for using it instead of using optuna.distributions (#1292)

Changed

  • Set the default value of final_model to LinearRegression(positive=True) in the constructor of StackingEnsemble (#1238)
  • Add microseconds to FileLogger's directory name (#1264)
  • Inherit SaveMixin from AbstractSaveable for mypy checker (#1261)
  • Update requirements for holidays and scipy, change saving library from pickle to dill in SaveMixin (#1268)
  • Update requirement for ruptures, add requirement for sqlalchemy (#1276)
  • Optimize make_samples of RNNNet and MLPNet (#1281)
  • Remove to_be_fixed from inference tests on SpecialDaysTransform (#1283)
  • Rewrite TimeSeriesImputerTransform to work without per-segment wrapper (#1293)
  • Add default params_to_tune for catboost models (#1185)
  • Add default params_to_tune for ProphetModel (#1203)
  • Add default params_to_tune for SARIMAXModel, change default parameters for the model (#1206)
  • Add default params_to_tune for linear models (#1204)
  • Add default params_to_tune for SeasonalMovingAverageModel, MovingAverageModel, NaiveModel and DeadlineMovingAverageModel (#1208)
  • Add default params_to_tune for DeepARModel and TFTModel (#1210)
  • Add default params_to_tune for HoltWintersModel, HoltModel and SimpleExpSmoothingModel (#1209)
  • Add default params_to_tune for RNNModel and MLPModel (#1218)
  • Add default params_to_tune for DateFlagsTransform, TimeFlagsTransform, SpecialDaysTransform and FourierTransform (#1228)
  • Add default params_to_tune for MedianOutliersTransform, DensityOutliersTransform and PredictionIntervalOutliersTransform (#1231)
  • Add default params_to_tune for TimeSeriesImputerTransform (#1232)
  • Add default params_to_tune for DifferencingTransform, MedianTransform, MaxTransform, MinTransform, QuantileTransform, StdTransform, MeanTransform, MADTransform, MinMaxDifferenceTransform, SumTransform, BoxCoxTransform, YeoJohnsonTransform, MaxAbsScalerTransform, MinMaxScalerTransform, RobustScalerTransform and StandardScalerTransform (#1233)
  • Add default params_to_tune for LabelEncoderTransform (#1242)
  • Add default params_to_tune for ChangePointsSegmentationTransform, ChangePointsTrendTransform, ChangePointsLevelTransform, TrendTransform, LinearTrendTransform, TheilSenTrendTransform and STLTransform (#1243)
  • Add default params_to_tune for TreeFeatureSelectionTransform, MRMRFeatureSelectionTransform and GaleShapleyFeatureSelectionTransform (#1250)
  • Add tuning stage into Auto.fit (#1272)
  • Add params_to_tune into Tune init (#1282)
  • Skip duplicates during Tune.fit, skip duplicates in top_k, add AutoML notebook (#1285)
  • Add parameter fast_redundancy in mrmm, fix relevance calculation in get_model_relevance_table (#1294)

Fixed

  • Fix plot_backtest and plot_backtest_interactive on one-step forecast (1260)
  • Fix BaseReconciliator to work on pandas==1.1.5 (#1229)
  • Fix TSDataset.make_future to handle hierarchy, quantiles, target components (#1248)
  • Fix warning during creation of ResampleWithDistributionTransform (#1230)
  • Add deep copy for copying attributes of TSDataset (#1241)
  • Add tsfresh into optional dependencies, remove instruction about pip install tsfresh (#1246)
  • Fix DeepARModel and TFTModel to work with changed prediction_size (#1251)
  • Fix problems with flake8 B023 (#1252)
  • Fix problem with swapped forecast methods in HierarchicalPipeline (#1259)
  • Fix problem with segment name "target" in StackingEnsemble (#1262)
  • Fix BasePipeline.forecast when prediction intervals are estimated on history data with presence of NaNs (#1291)
  • Teach BaseMixin.set_params to work with nested list and tuple (#1201)
  • Fix get_anomalies_prediction_interval to work when segments have different start date (#1296)
  • Fix classification notebook to download FordA dataset without error (#1299)
  • Fix signature of Auto.fit, Tune.fit to not have a breaking change (#1300)
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