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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,196 @@ | ||
| from enum import Enum | ||
| from typing import Dict | ||
| from typing import List | ||
| from typing import Optional | ||
|
|
||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| from etna.datasets import TSDataset | ||
| from etna.distributions import BaseDistribution | ||
| from etna.distributions import CategoricalDistribution | ||
| from etna.distributions import IntDistribution | ||
| from etna.transforms.base import IrreversibleTransform | ||
|
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||
|
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| class ImputerMode(str, Enum): | ||
| """Enum for different imputation strategy.""" | ||
|
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| binary = "binary" | ||
| distance = "distance" | ||
|
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| @classmethod | ||
| def _missing_(cls, value): | ||
| raise NotImplementedError( | ||
| f"{value} is not a valid {cls.__name__}. Supported modes: {', '.join([repr(m.value) for m in cls])}" | ||
| ) | ||
|
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|
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| class EventTransform(IrreversibleTransform): | ||
| """EventTransform marks days before and after event depending on ``mode``. | ||
| It creates two columns for future and past. | ||
| * In `'binary'` mode shows whether there will be or were events regarding current date. | ||
| * In `'distance'` mode shows distance to the previous and future events regarding current date. Computed as :math:`1 / x`, where x is a distance to the nearest event. | ||
| Examples | ||
| -------- | ||
| >>> from copy import deepcopy | ||
| >>> import numpy as np | ||
| >>> import pandas as pd | ||
| >>> from etna.datasets import generate_const_df | ||
| >>> from etna.datasets import TSDataset | ||
| >>> from etna.transforms import EventTransform | ||
| >>> | ||
| >>> df = generate_const_df(start_time="2020-01-01", periods=5, freq="D", scale=1, n_segments=1) | ||
| >>> df_exog = generate_const_df(start_time="2020-01-01", periods=10, freq="D", scale=1, n_segments=1) | ||
| >>> df_exog.rename(columns={"target": "holiday"}, inplace=True) | ||
| >>> df_exog["holiday"] = np.array([0, 0, 1, 0, 0, 0, 0, 1, 1, 0]) | ||
| >>> df = TSDataset.to_dataset(df) | ||
| >>> df_exog = TSDataset.to_dataset(df_exog) | ||
| >>> ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all") | ||
| >>> transform = EventTransform(in_column='holiday', out_column='holiday', n_pre=1, n_post=1) | ||
| >>> transform.fit_transform(deepcopy(ts)) | ||
| segment segment_0 | ||
| feature holiday holiday_post holiday_pre target | ||
| timestamp | ||
| 2020-01-01 0 0.0 0.0 1.0 | ||
| 2020-01-02 0 0.0 1.0 1.0 | ||
| 2020-01-03 1 0.0 0.0 1.0 | ||
| 2020-01-04 0 1.0 0.0 1.0 | ||
| 2020-01-05 0 0.0 0.0 1.0 | ||
| >>> transform = EventTransform(in_column='holiday', out_column='holiday', n_pre=2, n_post=2, mode='distance') | ||
| >>> transform.fit_transform(deepcopy(ts)) | ||
| segment segment_0 | ||
| feature holiday holiday_post holiday_pre target | ||
| timestamp | ||
| 2020-01-01 0 0.0 0.5 1.0 | ||
| 2020-01-02 0 0.0 1.0 1.0 | ||
| 2020-01-03 1 0.0 0.0 1.0 | ||
| 2020-01-04 0 1.0 0.0 1.0 | ||
| 2020-01-05 0 0.5 0.0 1.0 | ||
| """ | ||
|
|
||
| def __init__(self, in_column: str, out_column: str, n_pre: int, n_post: int, mode: str = ImputerMode.binary): | ||
| """ | ||
| Init EventTransform. | ||
| Parameters | ||
| ---------- | ||
| in_column: | ||
| binary column with event indicator. | ||
| out_column: | ||
| base for creating out columns names for future and past - '{out_column}_pre' and '{out_column}_post' | ||
| n_pre: | ||
| number of days before the event to react. | ||
| n_post: | ||
| number of days after the event to react. | ||
| mode: | ||
| mode of marking events: | ||
| - `'binary'`: whether there will be or were events regarding current date in binary type; | ||
| - `'distance'`: distance to the previous and future events regarding current date; | ||
| Raises | ||
| ------ | ||
| ValueError: | ||
| Some ``in_column`` features are not binary. | ||
| ValueError: | ||
| ``n_pre`` or ``n_post`` values are less than one. | ||
| NotImplementedError: | ||
| Given ``mode`` value is not supported. | ||
| """ | ||
| if n_pre < 1 or n_post < 1: | ||
| raise ValueError(f"`n_pre` and `n_post` must be greater than zero, given {n_pre} and {n_post}") | ||
| super().__init__(required_features=[in_column]) | ||
| self.in_column = in_column | ||
| self.out_column = out_column | ||
| self.n_pre = n_pre | ||
| self.n_post = n_post | ||
| self.mode = ImputerMode(mode) | ||
| self.in_column_regressor: Optional[bool] = None | ||
|
|
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| def fit(self, ts: TSDataset) -> "EventTransform": | ||
| """Fit the transform.""" | ||
| self.in_column_regressor = self.in_column in ts.regressors | ||
| super().fit(ts) | ||
| return self | ||
|
|
||
| def _fit(self, df: pd.DataFrame): | ||
| """Fit method does nothing and is kept for compatibility. | ||
| Parameters | ||
| ---------- | ||
| df: | ||
| dataframe with data. | ||
| """ | ||
| pass | ||
|
|
||
| def _compute_event_column(self, df: pd.DataFrame, column: str, max_distance: int) -> pd.DataFrame: | ||
| """Compute event column.""" | ||
| indexes = df.copy() | ||
| indexes[:] = np.repeat((np.arange(len(indexes)) + 1).reshape(-1, 1), len(indexes.columns), axis=1) | ||
|
|
||
| col = indexes.copy() | ||
| col.mask(df != 1, None, inplace=True) | ||
| col = (col.bfill() if column == "pre" else col.ffill()).fillna(indexes) | ||
| col = (col - indexes).abs() | ||
| distance = 1 if self.mode == "binary" else 1 / col | ||
| col.mask(col > max_distance, 0, inplace=True) | ||
| col = col.mask((col >= 1) & (col <= max_distance), distance).astype(float) | ||
|
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| col.rename(columns={self.in_column: f"{self.out_column}_{column}"}, inplace=True, level="feature") | ||
| return col | ||
|
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| def _transform(self, df: pd.DataFrame) -> pd.DataFrame: | ||
| """Add marked days before and after event to dataset. | ||
| Parameters | ||
| ---------- | ||
| df: | ||
| dataframe with data to transform. | ||
| Returns | ||
| ------- | ||
| : | ||
| transformed dataframe | ||
| """ | ||
| if not set(df.values.reshape(-1)).issubset({0, 1}): | ||
| raise ValueError("Input columns must be binary") | ||
|
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||
| pre = self._compute_event_column(df, column="pre", max_distance=self.n_pre) | ||
| post = self._compute_event_column(df, column="post", max_distance=self.n_post) | ||
|
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| df = pd.concat([df, pre, post], axis=1) | ||
|
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| return df | ||
|
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| def get_regressors_info(self) -> List[str]: | ||
| """Return the list with regressors created by the transform.""" | ||
| if self.in_column_regressor is None: | ||
| raise ValueError("Fit the transform to get the correct regressors info!") | ||
| return [self.out_column + "_pre", self.out_column + "_post"] if self.in_column_regressor else [] | ||
|
|
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| def params_to_tune(self) -> Dict[str, BaseDistribution]: | ||
| """Get default grid for tuning hyperparameters. | ||
| This grid tunes parameters: ``n_pre``, ``n_post``. | ||
| Other parameters are expected to be set by the user. | ||
| Returns | ||
| ------- | ||
| : | ||
| Grid to tune. | ||
| """ | ||
| return { | ||
| "n_pre": IntDistribution(low=1, high=self.n_pre), | ||
| "n_post": IntDistribution(low=1, high=self.n_post), | ||
| "mode": CategoricalDistribution(["binary", "distance"]), | ||
| } | ||
|
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|
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| __all__ = ["EventTransform"] |
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