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test_inverse_transform.py
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test_inverse_transform.py
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from copy import deepcopy
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from ruptures import Binseg
from sklearn.tree import DecisionTreeRegressor
from etna.analysis import StatisticsRelevanceTable
from etna.models import ProphetModel
from etna.transforms import AddConstTransform
from etna.transforms import BinaryOperationTransform
from etna.transforms import BoxCoxTransform
from etna.transforms import ChangePointsLevelTransform
from etna.transforms import ChangePointsSegmentationTransform
from etna.transforms import ChangePointsTrendTransform
from etna.transforms import DateFlagsTransform
from etna.transforms import DensityOutliersTransform
from etna.transforms import DeseasonalityTransform
from etna.transforms import DifferencingTransform
from etna.transforms import EventTransform
from etna.transforms import ExogShiftTransform
from etna.transforms import FilterFeaturesTransform
from etna.transforms import FourierTransform
from etna.transforms import GaleShapleyFeatureSelectionTransform
from etna.transforms import HolidayTransform
from etna.transforms import LabelEncoderTransform
from etna.transforms import LagTransform
from etna.transforms import LambdaTransform
from etna.transforms import LimitTransform
from etna.transforms import LinearTrendTransform
from etna.transforms import LogTransform
from etna.transforms import MADTransform
from etna.transforms import MaxAbsScalerTransform
from etna.transforms import MaxTransform
from etna.transforms import MeanSegmentEncoderTransform
from etna.transforms import MeanTransform
from etna.transforms import MedianOutliersTransform
from etna.transforms import MedianTransform
from etna.transforms import MinMaxDifferenceTransform
from etna.transforms import MinMaxScalerTransform
from etna.transforms import MinTransform
from etna.transforms import MRMRFeatureSelectionTransform
from etna.transforms import OneHotEncoderTransform
from etna.transforms import PredictionIntervalOutliersTransform
from etna.transforms import QuantileTransform
from etna.transforms import ResampleWithDistributionTransform
from etna.transforms import RobustScalerTransform
from etna.transforms import SegmentEncoderTransform
from etna.transforms import SpecialDaysTransform
from etna.transforms import StandardScalerTransform
from etna.transforms import StdTransform
from etna.transforms import STLTransform
from etna.transforms import SumTransform
from etna.transforms import TheilSenTrendTransform
from etna.transforms import TimeFlagsTransform
from etna.transforms import TimeSeriesImputerTransform
from etna.transforms import TreeFeatureSelectionTransform
from etna.transforms import TrendTransform
from etna.transforms import YeoJohnsonTransform
from etna.transforms.decomposition import RupturesChangePointsModel
from tests.test_transforms.utils import assert_column_changes
from tests.test_transforms.utils import find_columns_diff
from tests.utils import convert_ts_to_int_timestamp
from tests.utils import select_segments_subset
from tests.utils import to_be_fixed
class TestInverseTransformTrain:
"""Test inverse transform on train dataset.
Expected that inverse transformation creates columns, removes columns and changes values.
"""
def _test_inverse_transform_train(self, ts, transform, expected_changes):
# prepare data
train_ts = deepcopy(ts)
test_ts = deepcopy(ts)
# fit
transform.fit(train_ts)
# transform
transformed_test_ts = transform.transform(deepcopy(test_ts))
# inverse transform
inverse_transformed_test_ts = transform.inverse_transform(deepcopy(transformed_test_ts))
# check
assert_column_changes(
ts_1=transformed_test_ts, ts_2=inverse_transformed_test_ts, expected_changes=expected_changes
)
flat_test_df = test_ts.to_pandas(flatten=True)
flat_transformed_test_df = transformed_test_ts.to_pandas(flatten=True)
flat_inverse_transformed_test_df = inverse_transformed_test_ts.to_pandas(flatten=True)
created_columns, removed_columns, changed_columns = find_columns_diff(
flat_transformed_test_df, flat_inverse_transformed_test_df
)
pd.testing.assert_frame_equal(
flat_test_df[list(changed_columns)], flat_inverse_transformed_test_df[list(changed_columns)]
)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
[
# decomposition
(
ChangePointsSegmentationTransform(
in_column="target",
change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
out_column="res",
),
"regular_ts",
{},
),
(
ChangePointsTrendTransform(in_column="target"),
"regular_ts",
{"change": {"target"}},
),
(
ChangePointsLevelTransform(in_column="target"),
"regular_ts",
{"change": {"target"}},
),
(LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
(TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
(STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
(DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
(
TrendTransform(
in_column="target",
change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
out_column="res",
),
"regular_ts",
{},
),
# encoders
(LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {}),
(
OneHotEncoderTransform(in_column="weekday", out_column="res"),
"ts_with_exog",
{},
),
(MeanSegmentEncoderTransform(), "regular_ts", {}),
(SegmentEncoderTransform(), "regular_ts", {}),
# feature_selection
(FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {}),
(FilterFeaturesTransform(exclude=["year"], return_features=True), "ts_with_exog", {"create": {"year"}}),
(
GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
"ts_with_exog",
{},
),
(
GaleShapleyFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True
),
"ts_with_exog",
{"create": {"month", "year", "weekday"}},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
),
"ts_with_exog",
{},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
),
"ts_with_exog",
{},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(),
top_k=2,
return_features=True,
fast_redundancy=True,
),
"ts_with_exog",
{"create": {"weekday", "monthday", "positive"}},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True, fast_redundancy=False
),
"ts_with_exog",
{"create": {"weekday", "monthday", "positive"}},
),
(
TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
"ts_with_exog",
{},
),
(
TreeFeatureSelectionTransform(
model=DecisionTreeRegressor(random_state=42), top_k=2, return_features=True
),
"ts_with_exog",
{"create": {"year", "month", "monthday"}},
),
# math
(
AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
"regular_ts",
{},
),
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
(
BinaryOperationTransform(
left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
{"change": {"target"}},
),
(
BinaryOperationTransform(
left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
{},
),
(
LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
"regular_ts",
{},
),
(
ExogShiftTransform(lag="auto", horizon=7),
"ts_with_exog_to_shift",
{},
),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
{},
),
(
LambdaTransform(
in_column="target",
transform_func=lambda x: x + 1,
inverse_transform_func=lambda x: x - 1,
inplace=True,
),
"regular_ts",
{"change": {"target"}},
),
(LimitTransform(in_column="target"), "regular_ts", {}),
(LimitTransform(in_column="target", lower_bound=-50, upper_bound=50), "regular_ts", {"change": {"target"}}),
(LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {}),
(LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
(
DifferencingTransform(in_column="target", inplace=False, out_column="res"),
"regular_ts",
{},
),
(DifferencingTransform(in_column="target", inplace=True), "regular_ts", {"change": {"target"}}),
(MADTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MaxTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MeanTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MedianTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
MinMaxDifferenceTransform(in_column="target", window=7, out_column="res"),
"regular_ts",
{},
),
(MinTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
QuantileTransform(in_column="target", quantile=0.9, window=7, out_column="res"),
"regular_ts",
{},
),
(StdTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(SumTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"positive_ts",
{},
),
(
BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
"positive_ts",
{"change": {"target"}},
),
(
BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"positive_ts",
{},
),
(BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
(
MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
# setting clip=False is important
MinMaxScalerTransform(in_column="target", mode="per-segment", clip=False, inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
# setting clip=False is important
MinMaxScalerTransform(in_column="target", mode="macro", clip=False, inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
RobustScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
StandardScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(YeoJohnsonTransform(in_column="target", mode="macro", inplace=True), "regular_ts", {"change": {"target"}}),
# missing_values
(
ResampleWithDistributionTransform(
in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
),
"ts_to_resample",
{},
),
(TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
(
TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
"ts_to_fill",
{"change": {"target"}},
),
(TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
(TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
(
TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
"ts_to_fill",
{"change": {"target"}},
),
(
TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
"ts_to_fill",
{"change": {"target"}},
),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
(
PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
"ts_with_outliers",
{"change": {"target"}},
),
# timestamp
(
DateFlagsTransform(out_column="res"),
"regular_ts",
{},
),
(
DateFlagsTransform(out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
(
FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
"ts_with_external_int_timestamp",
{},
),
(HolidayTransform(out_column="res", mode="binary"), "regular_ts", {}),
(
HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(HolidayTransform(out_column="res", mode="category"), "regular_ts", {}),
(
HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(HolidayTransform(out_column="res", mode="days_count"), "regular_ts_one_month", {}),
(
HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
"ts_with_external_timestamp_one_month",
{},
),
(SpecialDaysTransform(), "regular_ts", {}),
(
SpecialDaysTransform(in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
TimeFlagsTransform(out_column="res"),
"regular_ts",
{},
),
(
TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
{},
),
],
)
def test_inverse_transform_train_datetime_timestamp(self, transform, dataset_name, expected_changes, request):
ts = request.getfixturevalue(dataset_name)
self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
# It is the only transform that doesn't change values back during `inverse_transform`
@to_be_fixed(raises=AssertionError)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
[
(
ResampleWithDistributionTransform(
in_column="regressor_exog", distribution_column="target", inplace=True
),
"ts_to_resample",
{"change": {"regressor_exog"}},
),
(
ResampleWithDistributionTransform(
in_column="regressor_exog", distribution_column="target", inplace=True
),
"ts_to_resample_int_timestamp",
{"change": {"regressor_exog"}},
),
],
)
def test_inverse_transform_train_fail_resample(self, transform, dataset_name, expected_changes, request):
ts = request.getfixturevalue(dataset_name)
self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
[
# decomposition
(
ChangePointsSegmentationTransform(
in_column="target",
change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
out_column="res",
),
"regular_ts",
{},
),
(
ChangePointsTrendTransform(in_column="target"),
"regular_ts",
{"change": {"target"}},
),
(
ChangePointsLevelTransform(in_column="target"),
"regular_ts",
{"change": {"target"}},
),
(LinearTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
(TheilSenTrendTransform(in_column="target"), "regular_ts", {"change": {"target"}}),
(STLTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
(DeseasonalityTransform(in_column="target", period=7), "regular_ts", {"change": {"target"}}),
(
TrendTransform(
in_column="target",
change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
out_column="res",
),
"regular_ts",
{},
),
# encoders
(LabelEncoderTransform(in_column="weekday", out_column="res"), "ts_with_exog", {}),
(
OneHotEncoderTransform(in_column="weekday", out_column="res"),
"ts_with_exog",
{},
),
(MeanSegmentEncoderTransform(), "regular_ts", {}),
(SegmentEncoderTransform(), "regular_ts", {}),
# feature_selection
(FilterFeaturesTransform(exclude=["year"]), "ts_with_exog", {}),
(FilterFeaturesTransform(exclude=["year"], return_features=True), "ts_with_exog", {"create": {"year"}}),
(
GaleShapleyFeatureSelectionTransform(relevance_table=StatisticsRelevanceTable(), top_k=2),
"ts_with_exog",
{},
),
(
GaleShapleyFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True
),
"ts_with_exog",
{"create": {"month", "year", "weekday"}},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=True
),
"ts_with_exog",
{},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, fast_redundancy=False
),
"ts_with_exog",
{},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(),
top_k=2,
return_features=True,
fast_redundancy=True,
),
"ts_with_exog",
{"create": {"weekday", "monthday", "positive"}},
),
(
MRMRFeatureSelectionTransform(
relevance_table=StatisticsRelevanceTable(), top_k=2, return_features=True, fast_redundancy=False
),
"ts_with_exog",
{"create": {"weekday", "monthday", "positive"}},
),
(
TreeFeatureSelectionTransform(model=DecisionTreeRegressor(random_state=42), top_k=2),
"ts_with_exog",
{},
),
(
TreeFeatureSelectionTransform(
model=DecisionTreeRegressor(random_state=42), top_k=2, return_features=True
),
"ts_with_exog",
{"create": {"year", "month", "monthday"}},
),
# math
(
AddConstTransform(in_column="target", value=1, inplace=False, out_column="res"),
"regular_ts",
{},
),
(AddConstTransform(in_column="target", value=1, inplace=True), "regular_ts", {"change": {"target"}}),
(
BinaryOperationTransform(
left_column="positive", right_column="target", operator="+", out_column="target"
),
"ts_with_exog",
{"change": {"target"}},
),
(
BinaryOperationTransform(
left_column="positive", right_column="target", operator="+", out_column="new_col"
),
"ts_with_exog",
{},
),
(
LagTransform(in_column="target", lags=[1, 2, 3], out_column="res"),
"regular_ts",
{},
),
(
ExogShiftTransform(lag="auto", horizon=7),
"ts_with_exog_to_shift",
{},
),
(
LambdaTransform(in_column="target", transform_func=lambda x: x + 1, inplace=False, out_column="res"),
"regular_ts",
{},
),
(
LambdaTransform(
in_column="target",
transform_func=lambda x: x + 1,
inverse_transform_func=lambda x: x - 1,
inplace=True,
),
"regular_ts",
{"change": {"target"}},
),
(LimitTransform(in_column="target"), "regular_ts", {}),
(LimitTransform(in_column="target", lower_bound=-50, upper_bound=50), "regular_ts", {"change": {"target"}}),
(LogTransform(in_column="target", inplace=False, out_column="res"), "positive_ts", {}),
(LogTransform(in_column="target", inplace=True), "positive_ts", {"change": {"target"}}),
(
DifferencingTransform(in_column="target", inplace=False, out_column="res"),
"regular_ts",
{},
),
(DifferencingTransform(in_column="target", inplace=True), "regular_ts", {"change": {"target"}}),
(MADTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MaxTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MeanTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(MedianTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
MinMaxDifferenceTransform(in_column="target", window=7, out_column="res"),
"regular_ts",
{},
),
(MinTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
QuantileTransform(in_column="target", quantile=0.9, window=7, out_column="res"),
"regular_ts",
{},
),
(StdTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(SumTransform(in_column="target", window=7, out_column="res"), "regular_ts", {}),
(
BoxCoxTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"positive_ts",
{},
),
(
BoxCoxTransform(in_column="target", mode="per-segment", inplace=True),
"positive_ts",
{"change": {"target"}},
),
(
BoxCoxTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"positive_ts",
{},
),
(BoxCoxTransform(in_column="target", mode="macro", inplace=True), "positive_ts", {"change": {"target"}}),
(
MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
MaxAbsScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MaxAbsScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
MaxAbsScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MinMaxScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
# setting clip=False is important
MinMaxScalerTransform(in_column="target", mode="per-segment", clip=False, inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
MinMaxScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
# setting clip=False is important
MinMaxScalerTransform(in_column="target", mode="macro", clip=False, inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
RobustScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
RobustScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
RobustScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
RobustScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
StandardScalerTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
StandardScalerTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
StandardScalerTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
StandardScalerTransform(in_column="target", mode="macro", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=False, out_column="res"),
"regular_ts",
{},
),
(
YeoJohnsonTransform(in_column="target", mode="per-segment", inplace=True),
"regular_ts",
{"change": {"target"}},
),
(
YeoJohnsonTransform(in_column="target", mode="macro", inplace=False, out_column="res"),
"regular_ts",
{},
),
(YeoJohnsonTransform(in_column="target", mode="macro", inplace=True), "regular_ts", {"change": {"target"}}),
# missing_values
(TimeSeriesImputerTransform(in_column="target", strategy="constant"), "ts_to_fill", {"change": {"target"}}),
(
TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),
"ts_to_fill",
{"change": {"target"}},
),
(TimeSeriesImputerTransform(in_column="target", strategy="mean"), "ts_to_fill", {"change": {"target"}}),
(TimeSeriesImputerTransform(in_column="target", strategy="seasonal"), "ts_to_fill", {"change": {"target"}}),
(
TimeSeriesImputerTransform(in_column="target", strategy="running_mean"),
"ts_to_fill",
{"change": {"target"}},
),
(
TimeSeriesImputerTransform(in_column="target", strategy="seasonal_nonautoreg"),
"ts_to_fill",
{"change": {"target"}},
),
# outliers
(DensityOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
(MedianOutliersTransform(in_column="target"), "ts_with_outliers", {"change": {"target"}}),
# timestamp
(
DateFlagsTransform(out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
FourierTransform(period=7, order=2, out_column="res"),
"regular_ts",
{},
),
(
FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
FourierTransform(period=7, order=2, out_column="res", in_column="external_timestamp"),
"ts_with_external_int_timestamp",
{},
),
(
HolidayTransform(out_column="res", mode="binary", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
HolidayTransform(out_column="res", mode="category", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
HolidayTransform(out_column="res", mode="days_count", in_column="external_timestamp"),
"ts_with_external_timestamp_one_month",
{},
),
(
SpecialDaysTransform(in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(
TimeFlagsTransform(out_column="res", in_column="external_timestamp"),
"ts_with_external_timestamp",
{},
),
(EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1), "ts_with_binary_exog", {}),
(
EventTransform(in_column="holiday", out_column="holiday", n_pre=1, n_post=1, mode="distance"),
"ts_with_binary_exog",
{},
),
],
)
def test_inverse_transform_train_int_timestamp(self, transform, dataset_name, expected_changes, request):
ts = request.getfixturevalue(dataset_name)
ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
self._test_inverse_transform_train(ts_int_timestamp, transform, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, expected_changes",
[
(
ResampleWithDistributionTransform(
in_column="regressor_exog", distribution_column="target", inplace=False, out_column="res"
),
"ts_to_resample_int_timestamp",
{},
),
],
)
def test_inverse_transform_train_int_timestamp_non_inplace_resample(
self, transform, dataset_name, expected_changes, request
):
ts = request.getfixturevalue(dataset_name)
self._test_inverse_transform_train(ts, transform, expected_changes=expected_changes)
@pytest.mark.parametrize(
"transform, dataset_name, error_match",
[
# outliers
(
PredictionIntervalOutliersTransform(in_column="target", model=ProphetModel),
"ts_with_outliers",
"Invalid timestamp! Only datetime type is supported",
),
# timestamp
(DateFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
(
HolidayTransform(out_column="res", mode="binary"),
"regular_ts",
"Transform can't work with integer index",
),
(
HolidayTransform(out_column="res", mode="category"),
"regular_ts",
"Transform can't work with integer index",
),
(
HolidayTransform(out_column="res", mode="days_count"),
"regular_ts_one_month",
"Transform can't work with integer index",
),
(TimeFlagsTransform(out_column="res"), "regular_ts", "Transform can't work with integer index"),
(SpecialDaysTransform(), "regular_ts", "Transform can't work with integer index"),
],
)
def test_inverse_transform_train_int_timestamp_not_supported(self, transform, dataset_name, error_match, request):
ts = request.getfixturevalue(dataset_name)
ts_int_timestamp = convert_ts_to_int_timestamp(ts, shift=10)
with pytest.raises(ValueError, match=error_match):
self._test_inverse_transform_train(ts_int_timestamp, transform, expected_changes={})
class TestInverseTransformTrainSubsetSegments:
"""Test inverse transform on train part of subset of segments.
Expected that inverse transformation on subset of segments match subset of inverse transformation on full dataset.
"""
def _test_inverse_transform_train_subset_segments(self, ts, transform, segments):
# prepare data
segments = list(set(segments))
subset_ts = select_segments_subset(ts=ts, segments=segments)
# fit
transform.fit(ts)
# transform full
transformed_ts = transform.transform(ts)
inverse_transformed_df = transform.inverse_transform(transformed_ts).to_pandas()
# transform subset of segments
transformed_subset_ts = transform.transform(subset_ts)
inverse_transformed_subset_df = transform.inverse_transform(transformed_subset_ts).to_pandas()
# check
assert_frame_equal(inverse_transformed_subset_df, inverse_transformed_df.loc[:, pd.IndexSlice[segments, :]])
@pytest.mark.parametrize(
"transform, dataset_name",
[
# decomposition
(
ChangePointsSegmentationTransform(
in_column="target",
change_points_model=RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=5),
),
"regular_ts",
),
(
ChangePointsTrendTransform(
in_column="target",
),
"regular_ts",
),
(
ChangePointsLevelTransform(
in_column="target",
),
"regular_ts",
),
(LinearTrendTransform(in_column="target"), "regular_ts"),
(TheilSenTrendTransform(in_column="target"), "regular_ts"),
(STLTransform(in_column="target", period=7), "regular_ts"),