"""Data check that checks if any of the features are likely to be ID columns."""
from evalml.data_checks import (
DataCheck,
DataCheckActionCode,
DataCheckActionOption,
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.
exclude_time_index (bool): If True, the column set as the time index will not be
included in the data check. Default is True.
"""
def __init__(self, id_threshold=1.0, exclude_time_index=True):
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
self.exclude_time_index = exclude_time_index
[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
Examples:
>>> import pandas as pd
Columns that end in "_id" and are completely unique are likely to be ID columns.
>>> df = pd.DataFrame({
... "profits": [25, 15, 15, 31, 19],
... "customer_id": [123, 124, 125, 126, 127],
... "Sales": [10, 42, 31, 51, 61]
... })
...
>>> id_col_check = IDColumnsDataCheck()
>>> assert id_col_check.validate(df) == [
... {
... "message": "Columns 'customer_id' are 100.0% or more likely to be an ID column",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "code": "HAS_ID_COLUMN",
... "details": {"columns": ["customer_id"], "rows": None},
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "IDColumnsDataCheck",
... "parameters": {},
... "metadata": {"columns": ["customer_id"], "rows": None}
... }
... ]
... }
... ]
Columns named "ID" with all unique values will also be identified as ID columns.
>>> df = df.rename(columns={"customer_id": "ID"})
>>> id_col_check = IDColumnsDataCheck()
>>> assert id_col_check.validate(df) == [
... {
... "message": "Columns 'ID' are 100.0% or more likely to be an ID column",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "code": "HAS_ID_COLUMN",
... "details": {"columns": ["ID"], "rows": None},
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "IDColumnsDataCheck",
... "parameters": {},
... "metadata": {"columns": ["ID"], "rows": None}
... }
... ]
... }
... ]
Despite being all unique, "Country_Rank" will not be identified as an ID column as id_threshold is set to 1.0
by default and its name doesn't indicate that it's an ID.
>>> df = pd.DataFrame({
... "humidity": ["high", "very high", "low", "low", "high"],
... "Country_Rank": [1, 2, 3, 4, 5],
... "Sales": ["very high", "high", "high", "medium", "very low"]
... })
...
>>> id_col_check = IDColumnsDataCheck()
>>> assert id_col_check.validate(df) == []
However lowering the threshold will cause this column to be identified as an ID.
>>> id_col_check = IDColumnsDataCheck()
>>> id_col_check = IDColumnsDataCheck(id_threshold=0.95)
>>> assert id_col_check.validate(df) == [
... {
... "message": "Columns 'Country_Rank' are 95.0% or more likely to be an ID column",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "details": {"columns": ["Country_Rank"], "rows": None},
... "code": "HAS_ID_COLUMN",
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "IDColumnsDataCheck",
... "parameters": {},
... "metadata": {"columns": ["Country_Rank"], "rows": None}
... }
... ]
... }
... ]
If the first column of the dataframe has all unique values and is named either 'ID' or a name that ends with '_id', it is probably the primary key.
The other ID columns should be dropped.
>>> df = pd.DataFrame({
... "sales_id": [0, 1, 2, 3, 4],
... "customer_id": [123, 124, 125, 126, 127],
... "Sales": [10, 42, 31, 51, 61]
... })
...
>>> id_col_check = IDColumnsDataCheck()
>>> assert id_col_check.validate(df) == [
... {
... "message": "The first column 'sales_id' is likely to be the primary key",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "code": "HAS_ID_FIRST_COLUMN",
... "details": {"columns": ["sales_id"], "rows": None},
... "action_options": [
... {
... "code": "SET_FIRST_COL_ID",
... "data_check_name": "IDColumnsDataCheck",
... "parameters": {},
... "metadata": {"columns": ["sales_id"], "rows": None}
... }
... ]
... },
... {
... "message": "Columns 'customer_id' are 100.0% or more likely to be an ID column",
... "data_check_name": "IDColumnsDataCheck",
... "level": "warning",
... "code": "HAS_ID_COLUMN",
... "details": {"columns": ["customer_id"], "rows": None},
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "IDColumnsDataCheck",
... "parameters": {},
... "metadata": {"columns": ["customer_id"], "rows": None}
... }
... ]
... }
... ]
"""
messages = []
X = infer_feature_types(X)
if X.ww.time_index and self.exclude_time_index:
X = X.ww.select(exclude="time_index")
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}
for dtypes in [["Double"], ["Integer", "IntegerNullable", "Categorical"]]:
X_temp = X.ww.select(include=dtypes)
check_all_unique = X_temp.nunique() == len(X_temp)
cols_with_all_unique = check_all_unique[
check_all_unique
].index.tolist() # columns whose values are all unique
# Temporary solution for downstream instances of integers being mapped to doubles.
# Will be removed when resolved.
if dtypes == ["Double"]:
cols_with_all_unique = [
col
for col in cols_with_all_unique
if all(
X_temp[col].mod(1).eq(0),
) # Parse out columns that contain all `integer` values
]
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:
if (
col_names[0] in id_cols_above_threshold
and id_cols_above_threshold[col_names[0]] == 1.0
):
del id_cols_above_threshold[col_names[0]]
warning_msg = "The first column '{}' is likely to be the primary key"
warning_msg = warning_msg.format(
col_names[0],
)
messages.append(
DataCheckWarning(
message=warning_msg,
data_check_name=self.name,
message_code=DataCheckMessageCode.HAS_ID_FIRST_COLUMN,
details={"columns": [col_names[0]]},
action_options=[
DataCheckActionOption(
DataCheckActionCode.SET_FIRST_COL_ID,
data_check_name=self.name,
metadata={"columns": [col_names[0]]},
),
],
).to_dict(),
)
if id_cols_above_threshold:
warning_msg = "Columns {} are {}% or more likely to be an ID column"
warning_msg = warning_msg.format(
(", ").join(
["'{}'".format(str(col)) for col in id_cols_above_threshold],
),
self.id_threshold * 100,
)
messages.append(
DataCheckWarning(
message=warning_msg,
data_check_name=self.name,
message_code=DataCheckMessageCode.HAS_ID_COLUMN,
details={"columns": list(id_cols_above_threshold)},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=self.name,
metadata={"columns": list(id_cols_above_threshold)},
),
],
).to_dict(),
)
return messages