ads.feature_engineering.feature_type.handler package#
Submodules#
ads.feature_engineering.feature_type.handler.feature_validator module#
The module that helps to register custom validators for the feature types and extending registered validators with dispatching based on the specific arguments.
Classes#
- FeatureValidator
The Feature Validator class to manage custom validators.
- FeatureValidatorMethod
The Feature Validator Method class. Extends methods which requires dispatching based on the specific arguments.
- class ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator[source]#
Bases:
objectThe Feature Validator class to manage custom validators.
- register(self, name: str, handler: Callable, condition: Tuple | Dict[str, Any] = None, replace: bool = False) None[source]#
Registers new validator.
- unregister(self, name: str, condition: Tuple | Dict[str, Any] = None) None[source]#
Unregisters validator.
Examples
>>> series = pd.Series(['+1-202-555-0141', '+1-202-555-0142'], name='Phone Number')
>>> def phone_number_validator(data: pd.Series) -> pd.Series: ... print("phone_number_validator") ... return data
>>> def universal_phone_number_validator(data: pd.Series, country_code) -> pd.Series: ... print("universal_phone_number_validator") ... return data
>>> def us_phone_number_validator(data: pd.Series, country_code) -> pd.Series: ... print("us_phone_number_validator") ... return data
>>> PhoneNumber.validator.register(name="is_phone_number", handler=phone_number_validator, replace=True) >>> PhoneNumber.validator.register(name="is_phone_number", handler=universal_phone_number_validator, condition = ('country_code',)) >>> PhoneNumber.validator.register(name="is_phone_number", handler=us_phone_number_validator, condition = {'country_code':'+1'})
>>> PhoneNumber.validator.is_phone_number(series) phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
>>> PhoneNumber.validator.is_phone_number(series, country_code = '+7') universal_phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
>>> PhoneNumber.validator.is_phone_number(series, country_code = '+1') us_phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
>>> PhoneNumber.validator.registered() Validator Condition Handler --------------------------------------------------------------------------------- 0 is_phone_number () phone_number_validator 1 is_phone_number ('country_code') universal_phone_number_validator 2 is_phone_number {'country_code': '+1'} us_phone_number_validator
>>> series.ads.validator.is_phone_number() phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
>>> series.ads.validator.is_phone_number(country_code = '+7') universal_phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
>>> series.ads.validator.is_phone_number(country_code = '+1') us_phone_number_validator 0 +1-202-555-0141 1 +1-202-555-0142
Initializes the FeatureValidator.
- register(name: str, handler: Callable, condition: Tuple | Dict[str, Any] | None = None, replace: bool = False) None[source]#
Registers new validator.
- Parameters:
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – The name is empty or handler is not provided.
TypeError – The handler is not callable. The name of the validator is not a string.
ValidatorAlreadyExists – The validator is already registered.
- registered() DataFrame[source]#
Gets the list of registered validators.
- Returns:
The list of registerd validators.
- Return type:
pd.DataFrame
- unregister(name: str, condition: Tuple | Dict[str, Any] | None = None) None[source]#
Unregisters validator.
- Parameters:
- Returns:
Nothing.
- Return type:
None
- Raises:
TypeError – The name of the validator is not a string.
ValidatorNotFound – The validator not found.
ValidatorWIthConditionNotFound – The validator with provided condition not found.
- class ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidatorMethod(handler: Callable)[source]#
Bases:
objectThe Feature Validator Method class.
Extends methods which requires dispatching based on the specific arguments.
- register(self, condition: Tuple | Dict[str, Any], handler: Callable) None[source]#
Registers new handler.
Initializes the Feature Validator Method.
- Parameters:
handler (Callable) – The handler that will be called by default if suitable one not found.
- register(condition: Tuple | Dict[str, Any], handler: Callable) None[source]#
Registers new handler.
- Parameters:
condition (Union[Tuple, Dict[str, Any]]) – The condition which will be used to register a new handler.
handler (Callable) – The handler to be registered.
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – If condition not provided or provided in the wrong format. If handler not provided or has wrong format.
- registered() DataFrame[source]#
Gets the list of registered handlers.
- Returns:
The list of registerd handlers.
- Return type:
pd.DataFrame
- unregister(condition: Tuple | Dict[str, Any]) None[source]#
Unregisters existing handler.
- Parameters:
condition (Union[Tuple, Dict[str, Any]]) – The condition which will be used to unregister a handler.
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – If condition not provided or provided in the wrong format. If condition not registered.
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorAlreadyExists(name: str)[source]#
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorNotFound(name: str)[source]#
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorWithConditionAlreadyExists(name: str)[source]#
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorWithConditionNotFound(name: str)[source]#
Bases:
ValueError
ads.feature_engineering.feature_type.handler.feature_warning module#
The module that helps to register custom warnings for the feature types.
Classes#
- FeatureWarning
The Feature Warning class. Provides functionality to register warning handlers and invoke them.
Examples
>>> warning = FeatureWarning()
>>> def warning_handler_zeros_count(data):
... return pd.DataFrame(
... [['Zeros', 'Age has 38 zeros', 'Count', 38]],
... columns=['Warning', 'Message', 'Metric', 'Value'])
>>> def warning_handler_zeros_percentage(data):
... return pd.DataFrame(
... [['Zeros', 'Age has 12.2% zeros', 'Percentage', '12.2%']],
... columns=['Warning', 'Message', 'Metric', 'Value'])
>>> warning.register(name="zeros_count", handler=warning_handler_zeros_count)
>>> warning.register(name="zeros_percentage", handler=warning_handler_percentage)
>>> warning.registered()
Name Handler
----------------------------------------------------------
0 zeros_count warning_handler_zeros_count
1 zeros_percentage warning_handler_zeros_percentage
>>> warning.zeros_percentage(data_series)
Warning Message Metric Value
----------------------------------------------------------------
0 Zeros Age has 38 zeros Count 38
>>> warning.zeros_count(data_series)
Warning Message Metric Value
----------------------------------------------------------------
1 Zeros Age has 12.2% zeros Percentage 12.2%
>>> warning(data_series)
Warning Message Metric Value
----------------------------------------------------------------
0 Zeros Age has 38 zeros Count 38
1 Zeros Age has 12.2% zeros Percentage 12.2%
>>> warning.unregister('zeros_count')
>>> warning(data_series)
Warning Message Metric Value
----------------------------------------------------------------
0 Zeros Age has 12.2% zeros Percentage 12.2%
- class ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning[source]#
Bases:
objectThe Feature Warning class.
Provides functionality to register warning handlers and invoke them.
- register(self, name: str, handler: Callable) None[source]#
Registers a new warning for the feature type.
Examples
>>> warning = FeatureWarning() >>> def warning_handler_zeros_count(data): ... return pd.DataFrame( ... [['Zeros', 'Age has 38 zeros', 'Count', 38]], ... columns=['Warning', 'Message', 'Metric', 'Value']) >>> def warning_handler_zeros_percentage(data): ... return pd.DataFrame( ... [['Zeros', 'Age has 12.2% zeros', 'Percentage', '12.2%']], ... columns=['Warning', 'Message', 'Metric', 'Value']) >>> warning.register(name="zeros_count", handler=warning_handler_zeros_count) >>> warning.register(name="zeros_percentage", handler=warning_handler_percentage) >>> warning.registered() Warning Handler ---------------------------------------------------------- 0 zeros_count warning_handler_zeros_count 1 zeros_percentage warning_handler_zeros_percentage
>>> warning.zeros_percentage(data_series) Warning Message Metric Value ---------------------------------------------------------------- 0 Zeros Age has 38 zeros Count 38
>>> warning.zeros_count(data_series) Warning Message Metric Value ---------------------------------------------------------------- 1 Zeros Age has 12.2% zeros Percentage 12.2%
>>> warning(data_series) Warning Message Metric Value ---------------------------------------------------------------- 0 Zeros Age has 38 zeros Count 38 1 Zeros Age has 12.2% zeros Percentage 12.2%
>>> warning.unregister('zeros_count') >>> warning(data_series) Warning Message Metric Value ---------------------------------------------------------------- 0 Zeros Age has 12.2% zeros Percentage 12.2%
Initializes the FeatureWarning.
- register(name: str, handler: Callable, replace: bool = False) None[source]#
Registers a new warning.
- Parameters:
- Returns:
Nothing
- Return type:
None
- Raises:
ValueError – If warning name is empty or handler not defined.
TypeError – If handler is not callable.
WarningAlreadyExists – If warning is already registered.
- registered() DataFrame[source]#
Gets the list of registered warnings.
- Return type:
pd.DataFrame
Examples
>>> The list of registerd warnings in DataFrame format. Name Handler ----------------------------------------------------------- 0 zeros_count warning_handler_zeros_count 1 zeros_percentage warning_handler_zeros_percentage
- unregister(name: str) None[source]#
Unregisters warning.
- Parameters:
name (str) – The name of warning to be unregistered.
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – If warning name is not provided or empty.
WarningNotFound – If warning not found.
ads.feature_engineering.feature_type.handler.warnings module#
The module with all default warnings provided to user. These are registered to relevant feature types directly in the feature type files themselves.
- ads.feature_engineering.feature_type.handler.warnings.high_cardinality_handler(s: Series) DataFrame[source]#
Warning if number of unique values (including Nan) in series is greater than or equal to 15.
- Parameters:
s (pd.Series) – Pandas series - column of some feature type.
- Returns:
Dataframe with 4 columns ‘Warning’, ‘Message’, ‘Metric’, ‘Value’ and 1 rows, which lists count of unique values.
- Return type:
pd.Dataframe
- ads.feature_engineering.feature_type.handler.warnings.missing_values_handler(s: Series) DataFrame[source]#
Warning for > 5 percent missing values (Nans) in series.
- Parameters:
s (pd.Series) – Pandas series - column of some feature type.
- Returns:
Dataframe with 4 columns ‘Warning’, ‘Message’, ‘Metric’, ‘Value’ and 2 rows, where first row is count of missing values and second is percentage of missing values.
- Return type:
pd.Dataframe
- ads.feature_engineering.feature_type.handler.warnings.skew_handler(s: Series) DataFrame[source]#
Warning if absolute value of skew is greater than 1.
- Parameters:
s (pd.Series) – Pandas series - column of some feature type, expects continuous values.
- Returns:
Dataframe with 4 columns ‘Warning’, ‘Message’, ‘Metric’, ‘Value’ and 1 rows, which lists skew value of that column.
- Return type:
pd.Dataframe
- ads.feature_engineering.feature_type.handler.warnings.zeros_handler(s: Series) DataFrame[source]#
Warning for greater than 10 percent zeros in series.
- Parameters:
s (pd.Series) – Pandas series - column of some feature type.
- Returns:
Dataframe with 4 columns ‘Warning’, ‘Message’, ‘Metric’, ‘Value’ and 2 rows, where first row is count of zero values and second is percentage of zero values.
- Return type:
pd.Dataframe