data_splitters

Data splitter classes.

Submodules

• no_split
• sk_splitters
• time_series_split
• training_validation_split

Package Contents

Classes Summary

KFold	Wrapper class for sklearn's KFold splitter.
NoSplit	Does not split the training data into training and validation sets.
StratifiedKFold	Wrapper class for sklearn's Stratified KFold splitter.
TimeSeriesSplit	Rolling Origin Cross Validation for time series problems.
TrainingValidationSplit	Split the training data into training and validation sets.

Contents

class evalml.preprocessing.data_splitters.KFold(n_splits=5, *, shuffle=False, random_state=None)

Wrapper class for sklearn’s KFold splitter.

Methods

get_n_splits	Returns the number of splitting iterations in the cross-validator
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Generate indices to split data into training and test set.

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

Returns the number of splitting iterations in the cross-validator

Parameters

• X (object) – Always ignored, exists for compatibility.

• y (object) – Always ignored, exists for compatibility.

• groups (object) – Always ignored, exists for compatibility.

Returns

n_splits – Returns the number of splitting iterations in the cross-validator.

Return type

int

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

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

Generate indices to split data into training and test set.

Parameters

• X (array-like of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape (n_samples,), default=None) – The target variable for supervised learning problems.

• groups (array-like of shape (n_samples,), default=None) – Group labels for the samples used while splitting the dataset into train/test set.

Yields

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.

class evalml.preprocessing.data_splitters.NoSplit(random_seed=0)

Does not split the training data into training and validation sets.

All data is passed as the training set, test data is simply an array of None. To be used for future unsupervised learning, should not be used in any of the currently supported pipelines.

Parameters

random_seed (int) – The seed to use for random sampling. Defaults to 0. Not used.

Methods

get_n_splits	Return the number of splits of this object.
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Divide the data into training and testing sets, where the testing set is empty.

static get_n_splits()

Return the number of splits of this object.

Returns

Always returns 0.

Return type

int

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

split(self, X, y=None)

Divide the data into training and testing sets, where the testing set is empty.

Parameters

• X (pd.DataFrame) – Dataframe of points to split

• y (pd.Series) – Series of points to split

Returns

Indices to split data into training and test set

Return type

list

class evalml.preprocessing.data_splitters.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None)

Wrapper class for sklearn’s Stratified KFold splitter.

Methods

get_n_splits	Returns the number of splitting iterations in the cross-validator
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Generate indices to split data into training and test set.

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

Returns the number of splitting iterations in the cross-validator

Parameters

• X (object) – Always ignored, exists for compatibility.

• y (object) – Always ignored, exists for compatibility.

• groups (object) – Always ignored, exists for compatibility.

Returns

n_splits – Returns the number of splitting iterations in the cross-validator.

Return type

int

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

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

Generate indices to split data into training and test set.

Parameters

• X (array-like of shape (n_samples, n_features)) –

  Training data, where n_samples is the number of samples and n_features is the number of features.

  Note that providing y is sufficient to generate the splits and hence np.zeros(n_samples) may be used as a placeholder for X instead of actual training data.

• y (array-like of shape (n_samples,)) – The target variable for supervised learning problems. Stratification is done based on the y labels.

• groups (object) – Always ignored, exists for compatibility.

Yields

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.

Notes

Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

class evalml.preprocessing.data_splitters.TimeSeriesSplit(max_delay=0, gap=0, forecast_horizon=None, time_index=None, n_splits=3)

Rolling Origin Cross Validation for time series problems.

The max_delay, gap, and forecast_horizon parameters are only used to validate that the requested split size is not too small given these parameters.

Parameters

• max_delay (int) – Max delay value for feature engineering. Time series pipelines create delayed features from existing features. This process will introduce NaNs into the first max_delay number of rows. The splitter uses the last max_delay number of rows from the previous split as the first max_delay number of rows of the current split to avoid “throwing out” more data than in necessary. Defaults to 0.

• gap (int) – Number of time units separating the data used to generate features and the data to forecast on. Defaults to 0.

• forecast_horizon (int, None) – Number of time units to forecast. Used for parameter validation. If an integer, will set the size of the cv splits. Defaults to None.

• time_index (str) – Name of the column containing the datetime information used to order the data. Defaults to None.

• n_splits (int) – number of data splits to make. Defaults to 3.

Example

>>> import numpy as np
>>> import pandas as pd
...
>>> X = pd.DataFrame([i for i in range(10)], columns=["First"])
>>> y = pd.Series([i for i in range(10)])
...
>>> ts_split = TimeSeriesSplit(n_splits=4)
>>> generator_ = ts_split.split(X, y)
...
>>> first_split = next(generator_)
>>> assert (first_split[0] == np.array([0, 1])).all()
>>> assert (first_split[1] == np.array([2, 3])).all()
...
...
>>> second_split = next(generator_)
>>> assert (second_split[0] == np.array([0, 1, 2, 3])).all()
>>> assert (second_split[1] == np.array([4, 5])).all()
...
...
>>> third_split = next(generator_)
>>> assert (third_split[0] == np.array([0, 1, 2, 3, 4, 5])).all()
>>> assert (third_split[1] == np.array([6, 7])).all()
...
...
>>> fourth_split = next(generator_)
>>> assert (fourth_split[0] == np.array([0, 1, 2, 3, 4, 5, 6, 7])).all()
>>> assert (fourth_split[1] == np.array([8, 9])).all()

Methods

get_n_splits	Get the number of data splits.
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Get the time series splits.

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

Get the number of data splits.

Parameters

• X (pd.DataFrame, None) – Features to split.

• y (pd.DataFrame, None) – Target variable to split. Defaults to None.

• groups – Ignored but kept for compatibility with sklearn API. Defaults to None.

Returns

Number of splits.

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

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

Get the time series splits.

X and y are assumed to be sorted in ascending time order. This method can handle passing in empty or None X and y data but note that X and y cannot be None or empty at the same time.

Parameters

• X (pd.DataFrame, None) – Features to split.

• y (pd.DataFrame, None) – Target variable to split. Defaults to None.

• groups – Ignored but kept for compatibility with sklearn API. Defaults to None.

Yields

Iterator of (train, test) indices tuples.

Raises

ValueError – If one of the proposed splits would be empty.

class evalml.preprocessing.data_splitters.TrainingValidationSplit(test_size=None, train_size=None, shuffle=False, stratify=None, random_seed=0)

Split the training data into training and validation sets.

Parameters

• test_size (float) – What percentage of data points should be included in the validation set. Defalts to the complement of train_size if train_size is set, and 0.25 otherwise.

• train_size (float) – What percentage of data points should be included in the training set. Defaults to the complement of test_size

• shuffle (boolean) – Whether to shuffle the data before splitting. Defaults to False.

• stratify (list) – Splits the data in a stratified fashion, using this argument as class labels. Defaults to None.

• random_seed (int) – The seed to use for random sampling. Defaults to 0.

Examples

>>> import numpy as np
>>> import pandas as pd
...
>>> X = pd.DataFrame([i for i in range(10)], columns=["First"])
>>> y = pd.Series([i for i in range(10)])
...
>>> tv_split = TrainingValidationSplit()
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([0, 1, 2, 3, 4, 5, 6])).all()
>>> assert (split_[1] == np.array([7, 8, 9])).all()
...
...
>>> tv_split = TrainingValidationSplit(test_size=0.5)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([0, 1, 2, 3, 4])).all()
>>> assert (split_[1] == np.array([5, 6, 7, 8, 9])).all()
...
...
>>> tv_split = TrainingValidationSplit(shuffle=True)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([9, 1, 6, 7, 3, 0, 5])).all()
>>> assert (split_[1] == np.array([2, 8, 4])).all()
...
...
>>> y = pd.Series([i % 3 for i in range(10)])
>>> tv_split = TrainingValidationSplit(shuffle=True, stratify=y)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([1, 9, 3, 2, 8, 6, 7])).all()
>>> assert (split_[1] == np.array([0, 4, 5])).all()

Methods

get_n_splits	Return the number of splits of this object.
is_cv	Returns whether or not the data splitter is a cross-validation data splitter.
split	Divide the data into training and testing sets.

static get_n_splits()

Return the number of splits of this object.

Returns

Always returns 1.

Return type

int

property is_cv(self)

Returns whether or not the data splitter is a cross-validation data splitter.

Returns

If the splitter is a cross-validation data splitter

Return type

bool

split(self, X, y=None)

Divide the data into training and testing sets.

Parameters

• X (pd.DataFrame) – Dataframe of points to split

• y (pd.Series) – Series of points to split

Returns

Indices to split data into training and test set

Return type

list