utils

Module Contents

Functions

drop_nan_target_rows	Drops rows in X and y when row in the target y has a value of NaN.
load_data	Load features and target from file.
number_of_features	Get the number of features of each specific dtype in a DataFrame.
split_data	Splits data into train and test sets.
target_distribution	Get the target distributions.

Contents

evalml.preprocessing.utils.drop_nan_target_rows(X, y)

Drops rows in X and y when row in the target y has a value of NaN.

Parameters

• X (pd.DataFrame, np.ndarray) – Data to transform

• y (pd.Series, np.ndarray) – Target data

Returns

Transformed X (and y, if passed in) with rows that had a NaN value removed.

Return type

pd.DataFrame, pd.DataFrame

evalml.preprocessing.utils.load_data(path, index, target, n_rows=None, drop=None, verbose=True, **kwargs)

Load features and target from file.

Parameters

• path (str) – Path to file or a http/ftp/s3 URL

• index (str) – Column for index

• target (str) – Column for target

• n_rows (int) – Number of rows to return

• drop (list) – List of columns to drop

• verbose (bool) – If True, prints information about features and target

Returns

Features matrix and target

Return type

pd.DataFrame, pd.Series

evalml.preprocessing.utils.number_of_features(dtypes)

Get the number of features of each specific dtype in a DataFrame.

Parameters

dtypes (pd.Series) – DataFrame.dtypes to get the number of features for

Returns

dtypes and the number of features for each input type

Return type

pd.Series

evalml.preprocessing.utils.split_data(X, y, problem_type, problem_configuration=None, test_size=0.2, random_seed=0)

Splits data into train and test sets.

Parameters

• X (pd.DataFrame or np.ndarray) – data of shape [n_samples, n_features]

• y (pd.Series, or np.ndarray) – target data of length [n_samples]

• problem_type (str or ProblemTypes) – type of supervised learning problem. see evalml.problem_types.problemtype.all_problem_types for a full list.

• problem_configuration (dict) – Additional parameters needed to configure the search. For example, in time series problems, values should be passed in for the date_index, gap, and max_delay variables.

• test_size (float) – What percentage of data points should be included in the test set. Defaults to 0.2 (20%).

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Returns

Feature and target data each split into train and test sets

Return type

pd.DataFrame, pd.DataFrame, pd.Series, pd.Series

evalml.preprocessing.utils.target_distribution(targets)

Get the target distributions.

Parameters

targets (pd.Series) – Target data

Returns

Target data and their frequency distribution as percentages.

Return type

pd.Series

training_validation_split Problem Types