Preprocessing¶
Preprocessing utilities.
Package Contents¶
Classes Summary¶
Does not split the training data into training and validation sets. |
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Rolling Origin Cross Validation for time series problems. |
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Split the training data into training and validation sets. |
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.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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class
evalml.preprocessing.NoSplit(random_seed=0)[source]¶ 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
Return the number of splits of this object.
Divide the data into training and testing sets, where the testing set is empty.
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evalml.preprocessing.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
Example
>>> X = pd.DataFrame() >>> X["integers"] = [i for i in range(10)] >>> X["floats"] = [float(i) for i in range(10)] >>> X["strings"] = [str(i) for i in range(10)] >>> X["booleans"] = [bool(i%2) for i in range(10)] >>> number_of_features(X.dtypes) Number of Features Boolean 1 Categorical 1 Numeric 2
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evalml.preprocessing.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
Examples
>>> X = pd.DataFrame([1, 2, 3, 4, 5, 6], columns=["First"]) >>> y = pd.Series([8, 9, 10, 11, 12, 13]) ... >>> X_train, X_validation, y_train, y_validation = split_data(X, y, "regression", random_seed=42) >>> X_train First 5 6 2 3 4 5 3 4 >>> X_validation First 0 1 1 2 >>> y_train 5 13 2 10 4 12 3 11 dtype: int64 >>> y_validation 0 8 1 9 dtype: int64
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evalml.preprocessing.target_distribution(targets)[source]¶ Get the target distributions.
- Parameters
targets (pd.Series) – Target data.
- Returns
Target data and their frequency distribution as percentages.
- Return type
pd.Series
Examples
>>> y = pd.Series([1, 2, 4, 1, 3, 3, 1, 2]) >>> target_distribution(y) Targets 1 37.50% 2 25.00% 3 25.00% 4 12.50% dtype: object >>> y = pd.Series([True, False, False, False, True]) >>> target_distribution(y) Targets False 60.00% True 40.00% dtype: object
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class
evalml.preprocessing.TimeSeriesSplit(max_delay=0, gap=0, forecast_horizon=1, date_index=None, n_splits=3)[source]¶ 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) – Number of time units to forecast. Defaults to 1.
date_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 the number of data splits.
Get the time series splits.
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get_n_splits(self, X=None, y=None, groups=None)[source]¶ 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.
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split(self, X, y=None, groups=None)[source]¶ 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.
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class
evalml.preprocessing.TrainingValidationSplit(test_size=None, train_size=None, shuffle=False, stratify=None, random_seed=0)[source]¶ 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
Return the number of splits of this object.
Divide the data into training and testing sets.