"""Data check that checks if any of the features are likely to be ID columns."""
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.
Args:
id_threshold (float): The probability threshold to be considered an ID column. Defaults to 1.0.
"""
def __init__(self, id_threshold=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 a number of simple checks.
Checks performed are:
- column name is "id"
- column name ends in "_id"
- column contains all unique values (and is categorical / integer type)
Args:
X (pd.DataFrame, np.ndarray): The input features to check.
y (pd.Series): The target. Defaults to None. Ignored.
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": "Columns 'df_id' are 100.0% or more likely to be an ID column",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "code": "HAS_ID_COLUMN",
... "details": {"columns": ["df_id"], "rows": None}}],
... "actions": [{"code": "DROP_COL",
... "metadata": {"columns": ["df_id"], "rows": None}}]}
"""
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
}
if id_cols_above_threshold:
warning_msg = "Columns {} are {}% or more likely to be an ID column"
results["warnings"].append(
DataCheckWarning(
message=warning_msg.format(
(", ").join(
["'{}'".format(str(col)) for col in id_cols_above_threshold]
),
self.id_threshold * 100,
),
data_check_name=self.name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": list(id_cols_above_threshold)},
).to_dict()
)
results["actions"].append(
DataCheckAction(
DataCheckActionCode.DROP_COL,
metadata={"columns": list(id_cols_above_threshold)},
).to_dict()
)
return results