Source code for evalml.preprocessing.utils

"""Helpful preprocessing utilities."""
import pandas as pd
from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit

from evalml.pipelines.utils import stack_data, stack_X, unstack_multiseries
from evalml.preprocessing.data_splitters import TrainingValidationSplit
from evalml.problem_types import is_classification, is_regression, is_time_series
from evalml.utils import infer_feature_types


[docs]def load_data(path, index, target, n_rows=None, drop=None, verbose=True, **kwargs): """Load features and target from file. Args: 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: pd.DataFrame, pd.Series: Features matrix and target. """ feature_matrix = pd.read_csv(path, index_col=index, nrows=n_rows, **kwargs) targets = [target] + (drop or []) y = feature_matrix[target] X = feature_matrix.drop(columns=targets) if verbose: # number of features print(number_of_features(X.dtypes), end="\n\n") # number of total training examples info = "Number of training examples: {}" print(info.format(len(X)), end="\n") # target distribution print(target_distribution(y)) return infer_feature_types(X), infer_feature_types(y)
[docs]def split_multiseries_data(X, y, series_id, time_index, **kwargs): """Split stacked multiseries data into train and test sets. Unstacked data can use `split_data`. Args: X (pd.DataFrame): The input training data of shape [n_samples*n_series, n_features]. y (pd.Series): The target training targets of length [n_samples*n_series]. series_id (str): Name of column containing series id. time_index (str): Name of column containing time index. **kwargs: Additional keyword arguments to pass to the split_data function. Returns: pd.DataFrame, pd.DataFrame, pd.Series, pd.Series: Feature and target data each split into train and test sets. """ X_unstacked, y_unstacked = unstack_multiseries( X, y, series_id, time_index, y.name, ) ( X_train_unstacked, X_holdout_unstacked, y_train_unstacked, y_holdout_unstacked, ) = split_data( X_unstacked, y_unstacked, problem_type="time series regression", **kwargs ) X_train = stack_X(X_train_unstacked, series_id, time_index) X_holdout = stack_X( X_holdout_unstacked, series_id, time_index, starting_index=X_train.index[-1] + 1, ) y_train = stack_data(y_train_unstacked) y_holdout = stack_data(y_holdout_unstacked, starting_index=y_train.index[-1] + 1) return X_train, X_holdout, y_train, y_holdout
[docs]def split_data( X, y, problem_type, problem_configuration=None, test_size=None, random_seed=0, ): """Split data into train and test sets. Args: 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 time_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%) for non-timeseries problems and 0.1 (10%) for timeseries problems. random_seed (int): Seed for the random number generator. Defaults to 0. Returns: pd.DataFrame, pd.DataFrame, pd.Series, pd.Series: Feature and target data each split into train and test sets. 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 """ X = infer_feature_types(X) y = infer_feature_types(y) data_splitter = None if is_time_series(problem_type): if test_size is None: test_size = 0.1 if ( problem_configuration is not None and "forecast_horizon" in problem_configuration ): fh_pct = problem_configuration["forecast_horizon"] / len(X) test_size = max(test_size, fh_pct) data_splitter = TrainingValidationSplit( test_size=test_size, shuffle=False, stratify=None, random_seed=random_seed, ) else: if test_size is None: test_size = 0.2 if is_regression(problem_type): data_splitter = ShuffleSplit( n_splits=1, test_size=test_size, random_state=random_seed, ) elif is_classification(problem_type): data_splitter = StratifiedShuffleSplit( n_splits=1, test_size=test_size, random_state=random_seed, ) train, test = next(data_splitter.split(X, y)) X_train = X.ww.iloc[train] X_test = X.ww.iloc[test] y_train = y.ww.iloc[train] y_test = y.ww.iloc[test] return X_train, X_test, y_train, y_test
[docs]def number_of_features(dtypes): """Get the number of features of each specific dtype in a DataFrame. Args: dtypes (pd.Series): DataFrame.dtypes to get the number of features for. Returns: pd.Series: dtypes and the number of features for each input type. 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)] Lists the number of columns corresponding to each dtype. >>> number_of_features(X.dtypes) Number of Features Boolean 1 Categorical 1 Numeric 2 """ dtype_to_vtype = { "bool": "Boolean", "int32": "Numeric", "int64": "Numeric", "float64": "Numeric", "object": "Categorical", "datetime64[ns]": "Datetime", } vtypes = dtypes.astype(str).map(dtype_to_vtype).value_counts() return vtypes.sort_index().to_frame("Number of Features")
[docs]def target_distribution(targets): """Get the target distributions. Args: targets (pd.Series): Target data. Returns: pd.Series: Target data and their frequency distribution as percentages. Examples: >>> y = pd.Series([1, 2, 4, 1, 3, 3, 1, 2]) >>> print(target_distribution(y).to_string()) Targets 1 37.50% 2 25.00% 3 25.00% 4 12.50% >>> y = pd.Series([True, False, False, False, True]) >>> print(target_distribution(y).to_string()) Targets False 60.00% True 40.00% """ distribution = targets.value_counts() / len(targets) return distribution.mul(100).apply("{:.2f}%".format).rename_axis("Targets")