Source code for evalml.data_checks.outliers_data_check

import pandas as pd
from sklearn.ensemble import IsolationForest

from .data_check import DataCheck
from .data_check_message import DataCheckWarning

from evalml.utils import get_random_state


class OutliersDataCheck(DataCheck):
    """Checks if there are any outliers in input data by using an Isolation Forest to obtain the anomaly score
        of each index and then using IQR to determine score anomalies. Indices with score anomalies are considered outliers."""

    def __init__(self, random_state=0):
        """Checks if there are any outliers in the input data.

        Arguments:
            random_state (int, np.random.RandomState): The random seed/state. Defaults to 0.
        """
        self.random_state = get_random_state(random_state)

    def validate(self, X, y=None):
        """Checks if there are any outliers in a dataframe by using an Isolation Forest to obtain the anomaly score
        of each index and then using IQR to determine score anomalies. Indices with score anomalies are considered outliers.

        Arguments:
            X (pd.DataFrame): features
            y: Ignored.

        Returns:
            A set of indices that may have outlier data.

        Example:
            >>> df = pd.DataFrame({
            ...     'x': [1, 2, 3, 40, 5],
            ...     'y': [6, 7, 8, 990, 10],
            ...     'z': [-1, -2, -3, -1201, -4]
            ... })
            >>> outliers_check = OutliersDataCheck()
            >>> assert outliers_check.validate(df) == [DataCheckWarning("Row '3' is likely to have outlier data", "OutliersDataCheck")]
        """

        if not isinstance(X, pd.DataFrame):
            X = pd.DataFrame(X)

        # only select numeric
        numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
        X = X.select_dtypes(include=numerics)

        if len(X.columns) == 0:
            return []

        def get_IQR(df, k=2.0):
            q1 = df.quantile(0.25)
            q3 = df.quantile(0.75)
            iqr = q3 - q1
            lower_bound = q1 - (k * iqr)
            upper_bound = q3 + (k * iqr)
            return (lower_bound, upper_bound)

        clf = IsolationForest(random_state=self.random_state)
        clf.fit(X)
        scores = pd.Series(clf.decision_function(X))
        lower_bound, upper_bound = get_IQR(scores, k=2)
        outliers = (scores < lower_bound) | (scores > upper_bound)
        outliers_indices = outliers[outliers].index.values.tolist()
        warning_msg = "Row '{}' is likely to have outlier data"
        return [DataCheckWarning(warning_msg.format(row_index), self.name) for row_index in outliers_indices]