"""Data check that checks if there are any highly-null columns and rows in the input."""
from evalml.data_checks import (
DataCheck,
DataCheckActionCode,
DataCheckActionOption,
DataCheckMessageCode,
DataCheckWarning,
)
from evalml.utils import infer_feature_types
[docs]class NullDataCheck(DataCheck):
"""Check if there are any highly-null numerical, boolean, categorical, natural language, and unknown columns and rows in the input.
Args:
pct_null_col_threshold(float): If the percentage of NaN values in an input feature exceeds this amount,
that column will be considered highly-null. Defaults to 0.95.
pct_moderately_null_col_threshold(float): If the percentage of NaN values in an input feature exceeds this amount
but is less than the percentage specified in pct_null_col_threshold, that column will be considered
moderately-null. Defaults to 0.20.
pct_null_row_threshold(float): If the percentage of NaN values in an input row exceeds this amount,
that row will be considered highly-null. Defaults to 0.95.
"""
def __init__(
self,
pct_null_col_threshold=0.95,
pct_moderately_null_col_threshold=0.20,
pct_null_row_threshold=0.95,
):
if not 0 <= pct_null_col_threshold <= 1:
raise ValueError(
"`pct_null_col_threshold` must be a float between 0 and 1, inclusive.",
)
if not 0 <= pct_moderately_null_col_threshold <= pct_null_col_threshold <= 1:
raise ValueError(
"`pct_moderately_null_col_threshold` must be a float between 0 and 1, inclusive, and must be less than or equal to `pct_null_col_threshold`.",
)
if not 0 <= pct_null_row_threshold <= 1:
raise ValueError(
"`pct_null_row_threshold` must be a float between 0 and 1, inclusive.",
)
self.pct_null_col_threshold = pct_null_col_threshold
self.pct_moderately_null_col_threshold = pct_moderately_null_col_threshold
self.pct_null_row_threshold = pct_null_row_threshold
[docs] def validate(self, X, y=None):
"""Check if there are any highly-null columns or rows in the input.
Args:
X (pd.DataFrame, np.ndarray): Features.
y (pd.Series, np.ndarray): Ignored. Defaults to None.
Returns:
dict: dict with a DataCheckWarning if there are any highly-null columns or rows.
Examples:
>>> import pandas as pd
...
>>> class SeriesWrap():
... def __init__(self, series):
... self.series = series
...
... def __eq__(self, series_2):
... return all(self.series.eq(series_2.series))
With pct_null_col_threshold set to 0.50, any column that has 50% or more of its observations set to null will be
included in the warning, as well as the percentage of null values identified ("all_null": 1.0, "lots_of_null": 0.8).
>>> df = pd.DataFrame({
... "all_null": [None, pd.NA, None, None, None],
... "lots_of_null": [None, None, None, None, 5],
... "few_null": [1, 2, None, 2, 3],
... "no_null": [1, 2, 3, 4, 5]
... })
...
>>> highly_null_dc = NullDataCheck(pct_null_col_threshold=0.50)
>>> assert highly_null_dc.validate(df) == [
... {
... "message": "Column(s) 'all_null', 'lots_of_null' are 50.0% or more null",
... "data_check_name": "NullDataCheck",
... "level": "warning",
... "details": {
... "columns": ["all_null", "lots_of_null"],
... "rows": None,
... "pct_null_rows": {"all_null": 1.0, "lots_of_null": 0.8}
... },
... "code": "HIGHLY_NULL_COLS",
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "NullDataCheck",
... "parameters": {},
... "metadata": {"columns": ["all_null", "lots_of_null"], "rows": None}
... }
... ]
... },
... {
... "message": "Column(s) 'few_null' have between 20.0% and 50.0% null values",
... "data_check_name": "NullDataCheck",
... "level": "warning",
... "details": {"columns": ["few_null"], "rows": None},
... "code": "COLS_WITH_NULL",
... "action_options": [
... {
... "code": "IMPUTE_COL",
... "data_check_name": "NullDataCheck",
... "metadata": {"columns": ["few_null"], "rows": None, "is_target": False},
... "parameters": {
... "impute_strategies": {
... "parameter_type": "column",
... "columns": {
... "few_null": {
... "impute_strategy": {"categories": ["mean", "most_frequent"], "type": "category", "default_value": "mean"}
... }
... }
... }
... }
... }
... ]
... }
... ]
With pct_null_row_threshold set to 0.50, any row with 50% or more of its respective column values set to null will
included in the warning, as well as the offending rows ("rows": [0, 1, 2, 3]).
Since the default value for pct_null_col_threshold is 0.95, "all_null" is also included in the warnings since
the percentage of null values in that row is over 95%.
Since the default value for pct_moderately_null_col_threshold is 0.20, "few_null" is included as a "moderately null"
column as it has a null column percentage of 20%.
>>> highly_null_dc = NullDataCheck(pct_null_row_threshold=0.50)
>>> validation_messages = highly_null_dc.validate(df)
>>> validation_messages[0]["details"]["pct_null_cols"] = SeriesWrap(validation_messages[0]["details"]["pct_null_cols"])
>>> highly_null_rows = SeriesWrap(pd.Series([0.5, 0.5, 0.75, 0.5]))
>>> assert validation_messages == [
... {
... "message": "4 out of 5 rows are 50.0% or more null",
... "data_check_name": "NullDataCheck",
... "level": "warning",
... "details": {
... "columns": None,
... "rows": [0, 1, 2, 3],
... "pct_null_cols": highly_null_rows
... },
... "code": "HIGHLY_NULL_ROWS",
... "action_options": [
... {
... "code": "DROP_ROWS",
... "data_check_name": "NullDataCheck",
... "parameters": {},
... "metadata": {"columns": None, "rows": [0, 1, 2, 3]}
... }
... ]
... },
... {
... "message": "Column(s) 'all_null' are 95.0% or more null",
... "data_check_name": "NullDataCheck",
... "level": "warning",
... "details": {
... "columns": ["all_null"],
... "rows": None,
... "pct_null_rows": {"all_null": 1.0}
... },
... "code": "HIGHLY_NULL_COLS",
... "action_options": [
... {
... "code": "DROP_COL",
... "data_check_name": "NullDataCheck",
... "metadata": {"columns": ["all_null"], "rows": None},
... "parameters": {}
... }
... ]
... },
... {
... "message": "Column(s) 'lots_of_null', 'few_null' have between 20.0% and 95.0% null values",
... "data_check_name": "NullDataCheck",
... "level": "warning",
... "details": {"columns": ["lots_of_null", "few_null"], "rows": None},
... "code": "COLS_WITH_NULL",
... "action_options": [
... {
... "code": "IMPUTE_COL",
... "data_check_name": "NullDataCheck",
... "metadata": {"columns": ["lots_of_null", "few_null"], "rows": None, "is_target": False},
... "parameters": {
... "impute_strategies": {
... "parameter_type": "column",
... "columns": {
... "lots_of_null": {"impute_strategy": {"categories": ["mean", "most_frequent"], "type": "category", "default_value": "mean"}},
... "few_null": {"impute_strategy": {"categories": ["mean", "most_frequent"], "type": "category", "default_value": "mean"}}
... }
... }
... }
... }
... ]
... }
... ]
"""
messages = []
X = infer_feature_types(X)
highly_null_rows = NullDataCheck.get_null_row_information(
X,
pct_null_row_threshold=self.pct_null_row_threshold,
)
if len(highly_null_rows) > 0:
warning_msg = f"{len(highly_null_rows)} out of {len(X)} rows are {self.pct_null_row_threshold*100}% or more null"
rows_to_drop = highly_null_rows.index.tolist()
messages.append(
DataCheckWarning(
message=warning_msg,
data_check_name=self.name,
message_code=DataCheckMessageCode.HIGHLY_NULL_ROWS,
details={
"rows": highly_null_rows.index.tolist(),
"pct_null_cols": highly_null_rows,
},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_ROWS,
data_check_name=self.name,
metadata={"rows": rows_to_drop},
),
],
).to_dict(),
)
highly_null_cols, _ = NullDataCheck.get_null_column_information(
X,
pct_null_col_threshold=self.pct_null_col_threshold,
)
X_to_check_for_any_null = X.ww.select(
[
"category",
"boolean",
"numeric",
"IntegerNullable",
"BooleanNullable",
],
)
cols_at_least_moderately_null, _ = NullDataCheck.get_null_column_information(
X_to_check_for_any_null,
pct_null_col_threshold=self.pct_moderately_null_col_threshold,
)
moderately_null_cols = [
col for col in cols_at_least_moderately_null if col not in highly_null_cols
]
warning_msg = "Column(s) {} are {}% or more null"
if highly_null_cols:
messages.append(
DataCheckWarning(
message=warning_msg.format(
(", ").join(
["'{}'".format(str(col)) for col in highly_null_cols],
),
self.pct_null_col_threshold * 100,
),
data_check_name=self.name,
message_code=DataCheckMessageCode.HIGHLY_NULL_COLS,
details={
"columns": list(highly_null_cols),
"pct_null_rows": highly_null_cols,
},
action_options=[
DataCheckActionOption(
DataCheckActionCode.DROP_COL,
data_check_name=self.name,
metadata={"columns": list(highly_null_cols)},
),
],
).to_dict(),
)
if moderately_null_cols:
impute_strategies_dict = {}
for col in moderately_null_cols:
col_in_df = X.ww[col]
categories = (
["mean", "most_frequent"]
if col_in_df.ww.schema.is_numeric
else ["most_frequent"]
)
default_value = (
"mean" if col_in_df.ww.schema.is_numeric else "most_frequent"
)
impute_strategies_dict[col] = {
"impute_strategy": {
"categories": categories,
"type": "category",
"default_value": default_value,
},
}
messages.append(
DataCheckWarning(
message="Column(s) {} have between {}% and {}% null values".format(
(", ").join(
["'{}'".format(str(col)) for col in moderately_null_cols],
),
self.pct_moderately_null_col_threshold * 100,
self.pct_null_col_threshold * 100,
),
data_check_name=self.name,
message_code=DataCheckMessageCode.COLS_WITH_NULL,
details={
"columns": list(moderately_null_cols),
},
action_options=[
DataCheckActionOption(
DataCheckActionCode.IMPUTE_COL,
data_check_name=self.name,
parameters={
"impute_strategies": {
"parameter_type": "column",
"columns": impute_strategies_dict,
},
},
metadata={
"columns": list(moderately_null_cols),
"is_target": False,
},
),
],
).to_dict(),
)
return messages
