Skip to content

timebasedcv.sklearn

timebasedcv.sklearn.TimeBasedCVSplitter

Bases: _BaseKFold

The TimeBasedCVSplitter is a scikit-learn compatible CV Splitter that generates splits based on time values.

The number of sample in each split is independent of the number of splits but based purely on the timestamp of the sample.

In order to achieve such behaviour we include the arguments of TimeBasedSplit.split() method (namely time_series, start_dt and end_dt) in the constructor (a.k.a. __init__ method) and store the for future use in its split and get_n_splits methods.

In this way we can restrict the arguments of split and get_n_splits to the arrays to split (i.e. X, y and groups), which are the only arguments required by scikit-learn CV Splitters.

Parameters:

Name Type Description Default
frequency FrequencyUnit

The frequency (or time unit) of the time series. Must be one of "days", "seconds", "microseconds", "milliseconds", "minutes", "hours", "weeks". These are the only valid values for the unit argument of timedelta from python datetime standard library.

 required
train_size int

Defines the minimum number of time units required to be in the train set.

 required
forecast_horizon int

Specifies the number of time units to forecast.

 required
time_series Union[SeriesLike[date], SeriesLike[datetime], SeriesLike[Datetime]]

The time series used to create boolean mask for splits. It is not required to be sorted, but it must support:

  • comparison operators (with other date-like objects).
  • bitwise operators (with other boolean arrays).
  • .min() and .max() methods.
  • .shape attribute.
 required
gap int

Sets the number of time units to skip between the end of the train set and the start of the forecast set.

 0
stride Union[int, None]

How many time unit to move forward after each split. If None (or set to 0), the stride is equal to the forecast_horizon quantity.

 None
window WindowType

The type of window to use, either "rolling" or "expanding".

 'rolling'
mode ModeType

Determines in which orders the splits are generated, either "forward" (start to end) or "backward" (end to start).

 'forward'
start_dt NullableDatetime

The start of the time period. If provided, it is used in place of the time_series.min().

 None
end_dt NullableDatetime

The end of the time period. If provided,it is used in place of the time_series.max().

 None

Raises:

Type Description
ValueError
  • If frequency is not one of "days", "seconds", "microseconds", "milliseconds", "minutes", "hours", "weeks".
  • If window is not one of "rolling" or "expanding".
  • If mode is not one of "forward" or "backward"
  • If train_size, forecast_horizon, gap or stride are not strictly positive.
TypeError

If train_size, forecast_horizon, gap or stride are not of type int.

Examples:

import pandas as pd
import numpy as np

from sklearn.linear_model import Ridge
from sklearn.model_selection import RandomizedSearchCV

from timebasedcv.sklearn import TimeBasedCVSplitter

start_dt = pd.Timestamp(2023, 1, 1)
end_dt = pd.Timestamp(2023, 1, 31)

time_series = pd.Series(pd.date_range(start_dt, end_dt, freq="D"))
size = len(time_series)

df = pd.DataFrame(data=np.random.randn(size, 2), columns=["a", "b"])

X, y = df[["a", "b"]], df[["a", "b"]].sum(axis=1)

cv = TimeBasedCVSplitter(
    frequency="days",
    train_size=7,
    forecast_horizon=11,
    gap=0,
    stride=1,
    window="rolling",
    time_series=time_series,
    start_dt=start_dt,
    end_dt=end_dt,
)

param_grid = {
    "alpha": np.linspace(0.1, 2, 10),
    "fit_intercept": [True, False],
    "positive": [True, False],
}

random_search_cv = RandomizedSearchCV(
    estimator=Ridge(),
    param_distributions=param_grid,
    cv=cv,
    n_jobs=-1,
).fit(X, y)
Source code in timebasedcv/sklearn.py 
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
class TimeBasedCVSplitter(_BaseKFold):
    """The `TimeBasedCVSplitter` is a scikit-learn compatible CV Splitter that generates splits based on time values.

    The number of sample in each split is independent of the number of splits but based purely on the timestamp of the
    sample.

    In order to achieve such behaviour we include the arguments of
    [`TimeBasedSplit.split()`][timebasedcv.core.TimeBasedSplit.split] method (namely `time_series`, `start_dt` and
    `end_dt`) in the constructor (a.k.a. `__init__` method) and store the for future use in its `split` and
    `get_n_splits` methods.

    In this way we can restrict the arguments of `split` and `get_n_splits` to the arrays to split (i.e. `X`, `y` and
    `groups`), which are the only arguments required by scikit-learn CV Splitters.

    Arguments:
        frequency: The frequency (or time unit) of the time series. Must be one of "days", "seconds", "microseconds",
            "milliseconds", "minutes", "hours", "weeks". These are the only valid values for the `unit` argument of
            `timedelta` from python `datetime` standard library.
        train_size: Defines the minimum number of time units required to be in the train set.
        forecast_horizon: Specifies the number of time units to forecast.
        time_series: The time series used to create boolean mask for splits. It is not required to be sorted, but it
            must support:

            - comparison operators (with other date-like objects).
            - bitwise operators (with other boolean arrays).
            - `.min()` and `.max()` methods.
            - `.shape` attribute.
        gap: Sets the number of time units to skip between the end of the train set and the start of the forecast set.
        stride: How many time unit to move forward after each split. If `None` (or set to 0), the stride is equal to the
            `forecast_horizon` quantity.
        window: The type of window to use, either "rolling" or "expanding".
        mode: Determines in which orders the splits are generated, either "forward" (start to end) or "backward"
            (end to start).
        start_dt: The start of the time period. If provided, it is used in place of the `time_series.min()`.
        end_dt: The end of the time period. If provided,it is used in place of the `time_series.max()`.

    Raises:
        ValueError:
            - If `frequency` is not one of "days", "seconds", "microseconds", "milliseconds", "minutes", "hours",
            "weeks".
            - If `window` is not one of "rolling" or "expanding".
            - If `mode` is not one of "forward" or "backward"
            - If `train_size`, `forecast_horizon`, `gap` or `stride` are not strictly positive.
        TypeError: If `train_size`, `forecast_horizon`, `gap` or `stride` are not of type `int`.

    Examples:
        ```python
        import pandas as pd
        import numpy as np

        from sklearn.linear_model import Ridge
        from sklearn.model_selection import RandomizedSearchCV

        from timebasedcv.sklearn import TimeBasedCVSplitter

        start_dt = pd.Timestamp(2023, 1, 1)
        end_dt = pd.Timestamp(2023, 1, 31)

        time_series = pd.Series(pd.date_range(start_dt, end_dt, freq="D"))
        size = len(time_series)

        df = pd.DataFrame(data=np.random.randn(size, 2), columns=["a", "b"])

        X, y = df[["a", "b"]], df[["a", "b"]].sum(axis=1)

        cv = TimeBasedCVSplitter(
            frequency="days",
            train_size=7,
            forecast_horizon=11,
            gap=0,
            stride=1,
            window="rolling",
            time_series=time_series,
            start_dt=start_dt,
            end_dt=end_dt,
        )

        param_grid = {
            "alpha": np.linspace(0.1, 2, 10),
            "fit_intercept": [True, False],
            "positive": [True, False],
        }

        random_search_cv = RandomizedSearchCV(
            estimator=Ridge(),
            param_distributions=param_grid,
            cv=cv,
            n_jobs=-1,
        ).fit(X, y)
        ```
    """

    def __init__(  # noqa: PLR0913
        self: Self,
        *,
        frequency: FrequencyUnit,
        train_size: int,
        forecast_horizon: int,
        time_series: Union[SeriesLike[date], SeriesLike[datetime], SeriesLike[pd.Datetime]],
        gap: int = 0,
        stride: Union[int, None] = None,
        window: WindowType = "rolling",
        mode: ModeType = "forward",
        start_dt: NullableDatetime = None,
        end_dt: NullableDatetime = None,
    ) -> None:
        self.splitter = TimeBasedSplit(
            frequency=frequency,
            train_size=train_size,
            forecast_horizon=forecast_horizon,
            gap=gap,
            stride=stride,
            window=window,
            mode=mode,
        )

        self.time_series_ = time_series
        self.start_dt_ = start_dt
        self.end_dt_ = end_dt

        self.n_splits = self._compute_n_splits()
        self.size_ = time_series.shape[0]

    def split(
        self: Self,
        X: Union[NDArray, None] = None,
        y: Union[NDArray, None] = None,
        groups: Union[NDArray, None] = None,
    ) -> Generator[Tuple[NDArray[np.int_], NDArray[np.int_]], None, None]:
        """Generates integer indices corresponding to train and test sets.

        Arguments:
            X: Optional input features array.
            y: Optional target variable array.
            groups: Optional array containing group labels for the samples.

        Returns:
            A generator that yields tuples of train and test indices.

        Raises:
            ValueError: If the input arrays have incompatible lengths with reference `time_series`.

        """
        self._validate_split_args(self.size_, X, y, groups)

        _indexes = np.arange(self.size_)

        yield from self.splitter.split(  # type: ignore[call-overload]
            _indexes,
            time_series=self.time_series_,
            start_dt=self.start_dt_,
            end_dt=self.end_dt_,
            return_splitstate=False,
        )

    def get_n_splits(
        self: Self,
        X: Union[NDArray, None] = None,
        y: Union[NDArray, None] = None,
        groups: Union[NDArray, None] = None,
    ) -> int:
        """Returns the number of splits that can be generated from the instance.

        Arguments:
            X: Unused, exists for compatibility, checked if not None.
            y: Unused, exists for compatibility, checked if not None.
            groups: Unused, exists for compatibility, checked if not None.

        Returns:
            The number of splits that can be generated from `time_series`.
        """
        self._validate_split_args(self.size_, X, y, groups)
        return self.n_splits

    def _compute_n_splits(self: Self) -> int:
        """Computes number of splits just once in the init."""
        time_start = self.start_dt_ or self.time_series_.min()
        time_end = self.end_dt_ or self.time_series_.max()

        return len(tuple(self.splitter._splits_from_period(time_start, time_end)))  # noqa: SLF001

    @staticmethod
    def _validate_split_args(
        size: int,
        X: Union[NDArray, None] = None,
        y: Union[NDArray, None] = None,
        groups: Union[NDArray, None] = None,
    ) -> None:
        """Validates the arguments passed to the `split` and `get_n_splits` methods."""
        if X is not None and X.shape[0] != size:
            msg = f"Invalid shape: {X.shape[0]=} doesn't match time_series.shape[0]={size}"
            raise ValueError(msg)

        if y is not None and y.shape[0] != size:
            msg = f"Invalid shape: {y.shape[0]=} doesn't match time_series.shape[0]={size}"
            raise ValueError(msg)

        if groups is not None and groups.shape[0] != size:
            msg = f"Invalid shape: {groups.shape[0]=} doesn't match time_series.shape[0]={size}"
            raise ValueError(msg)

split(X=None, y=None, groups=None)

Generates integer indices corresponding to train and test sets.

Parameters:

Name Type Description Default
X Union[NDArray, None]

Optional input features array.

 None
y Union[NDArray, None]

Optional target variable array.

 None
groups Union[NDArray, None]

Optional array containing group labels for the samples.

 None

Returns:

Type Description
Generator[Tuple[NDArray[int_], NDArray[int_]], None, None]

A generator that yields tuples of train and test indices.

Raises:

Type Description
ValueError

If the input arrays have incompatible lengths with reference time_series.
Source code in timebasedcv/sklearn.py 
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
def split(
    self: Self,
    X: Union[NDArray, None] = None,
    y: Union[NDArray, None] = None,
    groups: Union[NDArray, None] = None,
) -> Generator[Tuple[NDArray[np.int_], NDArray[np.int_]], None, None]:
    """Generates integer indices corresponding to train and test sets.

    Arguments:
        X: Optional input features array.
        y: Optional target variable array.
        groups: Optional array containing group labels for the samples.

    Returns:
        A generator that yields tuples of train and test indices.

    Raises:
        ValueError: If the input arrays have incompatible lengths with reference `time_series`.

    """
    self._validate_split_args(self.size_, X, y, groups)

    _indexes = np.arange(self.size_)

    yield from self.splitter.split(  # type: ignore[call-overload]
        _indexes,
        time_series=self.time_series_,
        start_dt=self.start_dt_,
        end_dt=self.end_dt_,
        return_splitstate=False,
    )

get_n_splits(X=None, y=None, groups=None)

Returns the number of splits that can be generated from the instance.

Parameters:

Name Type Description Default
X Union[NDArray, None]

Unused, exists for compatibility, checked if not None.

 None
y Union[NDArray, None]

Unused, exists for compatibility, checked if not None.

 None
groups Union[NDArray, None]

Unused, exists for compatibility, checked if not None.

 None

Returns:

Type Description
int

The number of splits that can be generated from time_series.
Source code in timebasedcv/sklearn.py 
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
def get_n_splits(
    self: Self,
    X: Union[NDArray, None] = None,
    y: Union[NDArray, None] = None,
    groups: Union[NDArray, None] = None,
) -> int:
    """Returns the number of splits that can be generated from the instance.

    Arguments:
        X: Unused, exists for compatibility, checked if not None.
        y: Unused, exists for compatibility, checked if not None.
        groups: Unused, exists for compatibility, checked if not None.

    Returns:
        The number of splits that can be generated from `time_series`.
    """
    self._validate_split_args(self.size_, X, y, groups)
    return self.n_splits