Source code for evalml.data_checks.invalid_targets_data_check

import pandas as pd

from .data_check import DataCheck
from .data_check_message import DataCheckError


[docs]class InvalidTargetDataCheck(DataCheck): """Checks if the target labels contain missing or invalid data."""
[docs] def validate(self, X, y): """Checks if the target labels contain missing or invalid data. Arguments: X (pd.DataFrame, pd.Series, np.array, list) : Features. Ignored. y : Target labels to check for invalid data. Returns: list (DataCheckError): list with DataCheckErrors if any invalid data is found in target labels. Example: >>> X = pd.DataFrame({}) >>> y = pd.Series([0, 1, None, None]) >>> target_check = InvalidTargetDataCheck() >>> assert target_check.validate(X, y) == [DataCheckError("2 row(s) (50.0%) of target values are null", "InvalidTargetDataCheck")] """ if not isinstance(y, pd.Series): y = pd.Series(y) messages = [] null_rows = y.isnull() if null_rows.any(): messages.append(DataCheckError("{} row(s) ({}%) of target values are null".format(null_rows.sum(), null_rows.mean() * 100), self.name)) numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64', 'bool'] valid_target_types = numerics + ['object', 'category'] if y.dtype.name not in valid_target_types: messages.append(DataCheckError("Target is unsupported {} type. Valid target types include: {}".format(y.dtype, ", ".join(valid_target_types)), self.name)) value_counts = y.value_counts() if len(value_counts) == 2 and y.dtype in numerics: unique_values = value_counts.index.tolist() if set(unique_values) != set([0, 1]): messages.append(DataCheckError("Numerical binary classification target classes must be [0, 1], got [{}] instead".format(", ".join([str(val) for val in unique_values])), self.name)) return messages