utils¶
Helpful preprocessing utilities.
Module Contents¶
Functions¶
Load features and target from file. |
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Get the number of features of each specific dtype in a DataFrame. |
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Split data into train and test sets. |
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Get the target distributions. |
Contents¶
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evalml.preprocessing.utils.load_data(path, index, target, n_rows=None, drop=None, verbose=True, **kwargs)[source]¶ 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. Defaults to None.
drop (list) – List of columns to drop. Defaults to None.
verbose (bool) – If True, prints information about features and target. Defaults to True.
**kwargs – Other keyword arguments that should be passed to panda’s read_csv method.
- Returns
Features matrix and target.
- Return type
pd.DataFrame, pd.Series
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evalml.preprocessing.utils.number_of_features(dtypes)[source]¶ 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
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evalml.preprocessing.utils.split_data(X, y, problem_type, problem_configuration=None, test_size=0.2, random_seed=0)[source]¶ Split 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