Source code for evalml.data_checks.id_columns_data_check

from evalml.data_checks import (
    DataCheck,
    DataCheckAction,
    DataCheckActionCode,
    DataCheckMessageCode,
    DataCheckWarning,
)
from evalml.utils import infer_feature_types


[docs]class IDColumnsDataCheck(DataCheck): """Check if any of the features are likely to be ID columns."""
[docs] def __init__(self, id_threshold=1.0): """Check if any of the features are likely to be ID columns. Arguments: id_threshold (float): The probability threshold to be considered an ID column. Defaults to 1.0. """ if id_threshold < 0 or id_threshold > 1: raise ValueError("id_threshold must be a float between 0 and 1, inclusive.") self.id_threshold = id_threshold
[docs] def validate(self, X, y=None): """Check if any of the features are likely to be ID columns. Currently performs these simple checks: - column name is "id" - column name ends in "_id" - column contains all unique values (and is categorical / integer type) Arguments: X (pd.DataFrame, np.ndarray): The input features to check Returns: dict: A dictionary of features with column name or index and their probability of being ID columns Example: >>> import pandas as pd >>> df = pd.DataFrame({ ... 'df_id': [0, 1, 2, 3, 4], ... 'x': [10, 42, 31, 51, 61], ... 'y': [42, 54, 12, 64, 12] ... }) >>> id_col_check = IDColumnsDataCheck() >>> assert id_col_check.validate(df) == {"errors": [],\ "warnings": [{"message": "Column 'df_id' is 100.0% or more likely to be an ID column",\ "data_check_name": "IDColumnsDataCheck",\ "level": "warning",\ "code": "HAS_ID_COLUMN",\ "details": {"column": "df_id"}}],\ "actions": [{"code": "DROP_COL",\ "metadata": {"column": "df_id"}}]} """ results = {"warnings": [], "errors": [], "actions": []} X = infer_feature_types(X) col_names = [col for col in X.columns] cols_named_id = [ col for col in col_names if (str(col).lower() == "id") ] # columns whose name is "id" id_cols = {col: 0.95 for col in cols_named_id} X = X.ww.select(include=["Integer", "Categorical"]) check_all_unique = X.nunique() == len(X) cols_with_all_unique = check_all_unique[ check_all_unique ].index.tolist() # columns whose values are all unique id_cols.update( [ (col, 1.0) if col in id_cols else (col, 0.95) for col in cols_with_all_unique ] ) col_ends_with_id = [ col for col in col_names if str(col).lower().endswith("_id") ] # columns whose name ends with "_id" id_cols.update( [ (col, 1.0) if str(col) in id_cols else (col, 0.95) for col in col_ends_with_id ] ) id_cols_above_threshold = { key: value for key, value in id_cols.items() if value >= self.id_threshold } warning_msg = "Column '{}' is {}% or more likely to be an ID column" results["warnings"].extend( [ DataCheckWarning( message=warning_msg.format(col_name, self.id_threshold * 100), data_check_name=self.name, message_code=DataCheckMessageCode.HAS_ID_COLUMN, details={"column": col_name}, ).to_dict() for col_name in id_cols_above_threshold ] ) results["actions"].extend( [ DataCheckAction( DataCheckActionCode.DROP_COL, metadata={"column": col_name} ).to_dict() for col_name in id_cols_above_threshold ] ) return results