#!/usr/bin/env python
# -*- coding: utf-8 -*--
# Copyright (c) 2021, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
"""
The module that represents the ADS Feature Types Mixin class that extends
Pandas Series and Dataframe accessors.
Classes
-------
ADSFeatureTypesMixin
ADS Feature Types Mixin class that extends Pandas Series and Dataframe accessors.
"""
import inspect
from typing import Union
import pandas as pd
import tabulate
from ads.feature_engineering.feature_type.base import FeatureType
from ads.feature_engineering.feature_type_manager import (
FeatureTypeManager as feature_type_manager,
)
from ads.feature_engineering.feature_type_manager import _feature_type_by_dtype
[docs]class ADSFeatureTypesMixin:
"""ADS Feature Types Mixin class that extends Pandas Series and DataFrame accessors.
Methods
-------
warning_registered(cls) -> pd.DataFrame
Lists registered warnings for registered feature types.
validator_registered(cls) -> pd.DataFrame
Lists registered validators for registered feature types.
help(self, prop: str = None) -> None
Help method that prints either a table of available properties or, given a property,
returns its docstring.
"""
@staticmethod
def _feature_type_by_dtype(dtype) -> FeatureType:
"""Determines feature type by DataFrame dtype.
Parameters
----------
dtype: pd.DataFrame.dtypes
The Pandas series data type.
Returns
-------
FeatureType
The subclass of FeatureType.
"""
return _feature_type_by_dtype(dtype)
@staticmethod
def _is_type_registered(feature_type: Union[FeatureType, str]) -> bool:
"""Checks if provided feature type is registered in the system.
Parameters
----------
feature_type: Union[FeatureType, str]
The FeatureType subclass or a str indicating feature type.
Returns
-------
bool
True if provided feature type registered, False otherwise.
"""
return feature_type_manager.is_type_registered(feature_type)
@staticmethod
def _get_type(feature_type: Union[FeatureType, str]) -> FeatureType:
"""Gets a feature type by class object or name.
Parameters
----------
feature_type: Union[FeatureType, str]
The FeatureType subclass or a str indicating feature type.
Returns
-------
FeatureType
Found feature type.
"""
return feature_type_manager.feature_type_object(feature_type)
[docs] def warning_registered(self) -> pd.DataFrame:
"""Lists registered warnings for all registered feature types.
Returns
-------
:class:`pandas.DataFrame`
The list of registered warnings for registered feature types.
Examples
--------
>>> df.ads.warning_registered()
Column Feature Type Warning Handler
-------------------------------------------------------------------------
0 Age continuous zeros zeros_handler
1 Age continuous high_cardinality high_cardinality_handler
>>> df["Age"].ads.warning_registered()
Feature Type Warning Handler
---------------------------------------------------------------
0 continuous zeros zeros_handler
1 continuous high_cardinality high_cardinality_handler
"""
common_columns = ["Feature Type", "Warning", "Handler"]
if isinstance(self._obj, pd.DataFrame):
result_df = pd.DataFrame((), columns=["Column"] + common_columns)
for col in self._obj.columns:
feature_type_df = self._obj[col].ads.warning_registered()
feature_type_df.insert(0, "Column", col)
result_df = result_df.append(feature_type_df)
else:
result_df = pd.DataFrame((), columns=common_columns)
for feature_type in self._feature_type:
feature_type_df = feature_type.warning.registered()
feature_type_df.insert(0, "Feature Type", feature_type.name)
feature_type_df = feature_type_df.rename(columns={"Name": "Warning"})
result_df = result_df.append(feature_type_df)
result_df.reset_index(drop=True, inplace=True)
return result_df
[docs] def validator_registered(self) -> pd.DataFrame:
"""Lists registered validators for registered feature types.
Returns
-------
:class:`pandas.DataFrame`
The list of registered validators for registered feature types
Examples
--------
>>> df.ads.validator_registered()
Column Feature Type Validator Condition Handler
------------------------------------------------------------------------------------------------------
0 PhoneNumber phone_number is_phone_number () default_handler
1 PhoneNumber phone_number is_phone_number {'country_code': '+7'} specific_country_handler
2 CreditCard credit_card is_credit_card () default_handler
>>> df['PhoneNumber'].ads.validator_registered()
Feature Type Validator Condition Handler
-------------------------------------------------------------------------------------------
0 phone_number is_phone_number () default_handler
1 phone_number is_phone_number {'country_code': '+7'} specific_country_handler
"""
common_columns = ["Feature Type", "Validator", "Condition", "Handler"]
if isinstance(self._obj, pd.DataFrame):
result_df = pd.DataFrame((), columns=["Column"] + common_columns)
for col in self._obj.columns:
feature_type_df = self._obj[col].ads.validator_registered()
feature_type_df.insert(0, "Column", col)
result_df = result_df.append(feature_type_df)
else:
result_df = pd.DataFrame((), columns=common_columns)
for feature_type in self._feature_type:
feature_type_df = feature_type.validator.registered()
feature_type_df.insert(0, "Feature Type", feature_type.name)
feature_type_df = feature_type_df.rename(columns={"Name": "Validator"})
result_df = result_df.append(feature_type_df)
result_df.reset_index(drop=True, inplace=True)
return result_df
[docs] def help(self, prop: str = None) -> None:
"""Help method that prints either a table of available properties or, given an individual property,
returns its docstring.
Parameters
----------
prop : str
The Name of property.
Returns
-------
None
Nothing.
"""
if prop:
if hasattr(self, prop):
print(inspect.getdoc(getattr(self, prop)))
return
print(f"Property {prop} not found.")
return
methods = set()
attrs = set()
def get_attr(c):
for attr in dir(c):
if not attr.startswith("__") and not attr.startswith("_"):
attr_obj = getattr(c, attr)
if callable(attr_obj):
doc = inspect.getdoc(attr_obj)
if doc and len(doc.split(".")) > 0:
methods.add((attr, doc.split(".")[0]))
else:
methods.add((attr, "method"))
else:
if hasattr(c.__class__, attr):
attr_obj = getattr(c.__class__, attr)
doc = inspect.getdoc(attr_obj)
attrs.add((attr, doc.split(".")[0]))
get_attr(self)
props = sorted(list(methods) + list(attrs))
print(tabulate.tabulate(props, headers=("Property", "Description")))