Target transforms

import pandas as pd
from fastcore.test import test_fail
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PowerTransformer
from utilsforecast.processing import counts_by_id

from mlforecast import MLForecast
from mlforecast.utils import generate_daily_series

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BaseTargetTransform

 BaseTargetTransform ()

Base class used for target transformations.

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Differences

 Differences (differences:Iterable[int])

Subtracts previous values of the serie. Can be used to remove trend or seasonalities.

series = generate_daily_series(10, min_length=50, max_length=100)

diffs = Differences([1, 2, 5])
id_counts = counts_by_id(series, 'unique_id')
indptr = np.append(0, id_counts['counts'].cumsum())
ga = GroupedArray(series['y'].values, indptr)

# differences are applied correctly
transformed = diffs.fit_transform(ga)
assert diffs.fitted_ == []
expected = series.copy()
for d in diffs.differences:
    expected['y'] -= expected.groupby('unique_id', observed=True)['y'].shift(d)
np.testing.assert_allclose(transformed.data, expected['y'].values)

# fitted differences are restored correctly
diffs.store_fitted = True
transformed = diffs.fit_transform(ga)
keep_mask = ~np.isnan(transformed.data)
restored = diffs.inverse_transform_fitted(transformed)
np.testing.assert_allclose(ga.data[keep_mask], restored.data[keep_mask])

# test transform
new_ga = GroupedArray(np.random.rand(10), np.arange(11))
prev_orig = [diffs.scalers_[i].tails_[::d].copy() for i, d in enumerate(diffs.differences)]
expected = new_ga.data - np.add.reduce(prev_orig)
updates = diffs.update(new_ga)
np.testing.assert_allclose(expected, updates.data)
np.testing.assert_allclose(diffs.scalers_[0].tails_, new_ga.data)
np.testing.assert_allclose(diffs.scalers_[1].tails_[1::2], new_ga.data - prev_orig[0])
np.testing.assert_allclose(diffs.scalers_[2].tails_[4::5], new_ga.data - np.add.reduce(prev_orig[:2]))
# variable sizes
diff1 = Differences([1])
ga = GroupedArray(np.arange(10), np.array([0, 3, 10]))
diff1.fit_transform(ga)
new_ga = GroupedArray(np.arange(4), np.array([0, 1, 4]))
updates = diff1.update(new_ga)
np.testing.assert_allclose(updates.data, np.array([0 - 2, 1 - 9, 2 - 1, 3 - 2]))
np.testing.assert_allclose(diff1.scalers_[0].tails_, np.array([0, 3]))

# short series
ga = GroupedArray(np.arange(20), np.array([0, 2, 20]))
test_fail(lambda: diffs.fit_transform(ga), contains="[0]")

# stack
diffs = Differences([1, 2, 5])
ga = GroupedArray(series['y'].values, indptr)
diffs.fit_transform(ga)
stacked = Differences.stack([diffs, diffs])
for i in range(len(diffs.differences)):
    np.testing.assert_allclose(
        stacked.scalers_[i].tails_,
        np.tile(diffs.scalers_[i].tails_, 2)
    )

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AutoDifferences

 AutoDifferences (max_diffs:int)

Find and apply the optimal number of differences to each serie.

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AutoSeasonalDifferences

 AutoSeasonalDifferences (season_length:int, max_diffs:int,
                          n_seasons:Optional[int]=10)

Find and apply the optimal number of seasonal differences to each group.

	Type	Default	Details
season_length	int		Length of the seasonal period.
max_diffs	int		Maximum number of differences to apply.
n_seasons	Optional	10	Number of seasons to use to determine the number of differences. Defaults to 10. If `None` will use all samples, otherwise `season_length` * `n_seasons samples` will be used for the test. Smaller values will be faster but could be less accurate.

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AutoSeasonalityAndDifferences

 AutoSeasonalityAndDifferences (max_season_length:int, max_diffs:int,
                                n_seasons:Optional[int]=10)

Find the length of the seasonal period and apply the optimal number of differences to each group.

	Type	Default	Details
max_season_length	int		Maximum length of the seasonal period.
max_diffs	int		Maximum number of differences to apply.
n_seasons	Optional	10	Number of seasons to use to determine the number of differences. Defaults to 10. If `None` will use all samples, otherwise `max_season_length` * `n_seasons samples` will be used for the test. Smaller values will be faster but could be less accurate.

def test_scaler(sc, series):
    id_counts = counts_by_id(series, 'unique_id')
    indptr = np.append(0, id_counts['counts'].cumsum())
    ga = GroupedArray(series['y'].values, indptr)
    transformed = sc.fit_transform(ga)
    np.testing.assert_allclose(
        sc.inverse_transform(transformed).data,
        ga.data,
    )
    transformed2 = sc.update(ga)
    np.testing.assert_allclose(transformed.data, transformed2.data)
    
    idxs = [0, 7]
    subset = ga.take(idxs)
    transformed_subset = transformed.take(idxs)
    subsc = sc.take(idxs)
    np.testing.assert_allclose(
        subsc.inverse_transform(transformed_subset).data,
        subset.data,
    )

    stacked = sc.stack([sc, sc])
    stacked_stats = stacked.scaler_.stats_
    np.testing.assert_allclose(
        stacked_stats,
        np.tile(sc.scaler_.stats_, (2, 1)),
    )

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LocalStandardScaler

 LocalStandardScaler ()

Standardizes each serie by subtracting its mean and dividing by its standard deviation.

test_scaler(LocalStandardScaler(), series)

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LocalMinMaxScaler

 LocalMinMaxScaler ()

Scales each serie to be in the [0, 1] interval.

test_scaler(LocalMinMaxScaler(), series)

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LocalRobustScaler

 LocalRobustScaler (scale:str)

Scaler robust to outliers.

	Type	Details
scale	str	Statistic to use for scaling. Can be either ‘iqr’ (Inter Quartile Range) or ‘mad’ (Median Asbolute Deviation)

test_scaler(LocalRobustScaler(scale='iqr'), series)

test_scaler(LocalRobustScaler(scale='mad'), series)

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LocalBoxCox

 LocalBoxCox ()

Finds the optimum lambda for each serie and applies the Box-Cox transformation

test_scaler(LocalBoxCox(), series)

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GlobalSklearnTransformer

 GlobalSklearnTransformer (transformer:sklearn.base.TransformerMixin)

Applies the same scikit-learn transformer to all series.

# need this import in order for isinstance to work
from mlforecast.target_transforms import Differences as ExportedDifferences

sk_boxcox = PowerTransformer(method='box-cox', standardize=False)
boxcox_global = GlobalSklearnTransformer(sk_boxcox)
single_difference = ExportedDifferences([1])
series = generate_daily_series(10)
fcst = MLForecast(
    models=[LinearRegression(), HistGradientBoostingRegressor()],
    freq='D',
    lags=[1, 2],
    target_transforms=[boxcox_global, single_difference]
)
prep = fcst.preprocess(series, dropna=False)
expected = (
    pd.Series(
        sk_boxcox.fit_transform(series[['y']])[:, 0], index=series['unique_id']
    ).groupby('unique_id', observed=True)
    .diff()
    .dropna()
    .values
)
np.testing.assert_allclose(prep['y'].values, expected)
preds = fcst.fit(series).predict(5)

import pandas as pd from fastcore.test import test_fail from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PowerTransformer from utilsforecast.processing import counts_by_id from mlforecast import MLForecast from mlforecast.utils import generate_daily_series

diffs = Differences([1, 2, 5]) id_counts = counts_by_id(series, 'unique_id') indptr = np.append(0, id_counts['counts'].cumsum()) ga = GroupedArray(series['y'].values, indptr) # differences are applied correctly transformed = diffs.fit_transform(ga) assert diffs.fitted_ == [] expected = series.copy() for d in diffs.differences: expected['y'] -= expected.groupby('unique_id', observed=True)['y'].shift(d) np.testing.assert_allclose(transformed.data, expected['y'].values) # fitted differences are restored correctly diffs.store_fitted = True transformed = diffs.fit_transform(ga) keep_mask = ~np.isnan(transformed.data) restored = diffs.inverse_transform_fitted(transformed) np.testing.assert_allclose(ga.data[keep_mask], restored.data[keep_mask]) # test transform new_ga = GroupedArray(np.random.rand(10), np.arange(11)) prev_orig = [diffs.scalers_[i].tails_[::d].copy() for i, d in enumerate(diffs.differences)] expected = new_ga.data - np.add.reduce(prev_orig) updates = diffs.update(new_ga) np.testing.assert_allclose(expected, updates.data) np.testing.assert_allclose(diffs.scalers_[0].tails_, new_ga.data) np.testing.assert_allclose(diffs.scalers_[1].tails_[1::2], new_ga.data - prev_orig[0]) np.testing.assert_allclose(diffs.scalers_[2].tails_[4::5], new_ga.data - np.add.reduce(prev_orig[:2])) # variable sizes diff1 = Differences([1]) ga = GroupedArray(np.arange(10), np.array([0, 3, 10])) diff1.fit_transform(ga) new_ga = GroupedArray(np.arange(4), np.array([0, 1, 4])) updates = diff1.update(new_ga) np.testing.assert_allclose(updates.data, np.array([0 - 2, 1 - 9, 2 - 1, 3 - 2])) np.testing.assert_allclose(diff1.scalers_[0].tails_, np.array([0, 3])) # short series ga = GroupedArray(np.arange(20), np.array([0, 2, 20])) test_fail(lambda: diffs.fit_transform(ga), contains="[0]") # stack diffs = Differences([1, 2, 5]) ga = GroupedArray(series['y'].values, indptr) diffs.fit_transform(ga) stacked = Differences.stack([diffs, diffs]) for i in range(len(diffs.differences)): np.testing.assert_allclose( stacked.scalers_[i].tails_, np.tile(diffs.scalers_[i].tails_, 2) )

Type

Default

Details

season_length

int

Length of the seasonal period.

max_diffs

int

Maximum number of differences to apply.

n_seasons

Optional

Number of seasons to use to determine the number of differences. Defaults to 10.
If None will use all samples, otherwise season_length * n_seasons samples will be used for the test.
Smaller values will be faster but could be less accurate.

Type

Default

Details

max_season_length

int

Maximum length of the seasonal period.

max_diffs

int

Maximum number of differences to apply.

n_seasons

Optional

Number of seasons to use to determine the number of differences. Defaults to 10.
If None will use all samples, otherwise max_season_length * n_seasons samples will be used for the test.
Smaller values will be faster but could be less accurate.

def test_scaler(sc, series): id_counts = counts_by_id(series, 'unique_id') indptr = np.append(0, id_counts['counts'].cumsum()) ga = GroupedArray(series['y'].values, indptr) transformed = sc.fit_transform(ga) np.testing.assert_allclose( sc.inverse_transform(transformed).data, ga.data, ) transformed2 = sc.update(ga) np.testing.assert_allclose(transformed.data, transformed2.data) idxs = [0, 7] subset = ga.take(idxs) transformed_subset = transformed.take(idxs) subsc = sc.take(idxs) np.testing.assert_allclose( subsc.inverse_transform(transformed_subset).data, subset.data, ) stacked = sc.stack([sc, sc]) stacked_stats = stacked.scaler_.stats_ np.testing.assert_allclose( stacked_stats, np.tile(sc.scaler_.stats_, (2, 1)), )

Type

Details

scale

str

Statistic to use for scaling. Can be either ‘iqr’ (Inter Quartile Range) or ‘mad’ (Median Asbolute Deviation)

sk_boxcox = PowerTransformer(method='box-cox', standardize=False) boxcox_global = GlobalSklearnTransformer(sk_boxcox) single_difference = ExportedDifferences([1]) series = generate_daily_series(10) fcst = MLForecast( models=[LinearRegression(), HistGradientBoostingRegressor()], freq='D', lags=[1, 2], target_transforms=[boxcox_global, single_difference] ) prep = fcst.preprocess(series, dropna=False) expected = ( pd.Series( sk_boxcox.fit_transform(series[['y']])[:, 0], index=series['unique_id'] ).groupby('unique_id', observed=True) .diff() .dropna() .values ) np.testing.assert_allclose(prep['y'].values, expected) preds = fcst.fit(series).predict(5)

​BaseTargetTransform

​Differences

​AutoDifferences

​AutoSeasonalDifferences

​AutoSeasonalityAndDifferences

​LocalStandardScaler

​LocalMinMaxScaler

​LocalRobustScaler

​LocalBoxCox

​GlobalSklearnTransformer

​BaseTargetTransform

​Differences

​AutoDifferences

​AutoSeasonalDifferences

​AutoSeasonalityAndDifferences

​LocalStandardScaler

​LocalMinMaxScaler

​LocalRobustScaler

​LocalBoxCox

​GlobalSklearnTransformer

BaseTargetTransform

Differences

AutoDifferences

AutoSeasonalDifferences

AutoSeasonalityAndDifferences

LocalStandardScaler

LocalMinMaxScaler

LocalRobustScaler

LocalBoxCox

GlobalSklearnTransformer

BaseTargetTransform

Differences

AutoDifferences

AutoSeasonalDifferences

AutoSeasonalityAndDifferences

LocalStandardScaler

LocalMinMaxScaler

LocalRobustScaler

LocalBoxCox

GlobalSklearnTransformer