ads.feature_engineering package
Submodules
ads.feature_engineering.exceptions module
- exception ads.feature_engineering.exceptions.InvalidFeatureType(tname: str)
Bases:
TypeError
- exception ads.feature_engineering.exceptions.NameAlreadyRegistered(name: str)
Bases:
NameError
- exception ads.feature_engineering.exceptions.TypeAlreadyAdded(tname: str)
Bases:
TypeError
- exception ads.feature_engineering.exceptions.TypeAlreadyRegistered(tname: str)
Bases:
TypeError
- exception ads.feature_engineering.exceptions.TypeNotFound(tname: str)
Bases:
TypeError
- exception ads.feature_engineering.exceptions.WarningAlreadyExists(name: str)
Bases:
ValueError
- exception ads.feature_engineering.exceptions.WarningNotFound(name: str)
Bases:
ValueError
ads.feature_engineering.feature_type_manager module
The module that helps to manage feature types. Provides functionalities to register, unregister, list feature types.
Classes
- FeatureTypeManager
Feature Types Manager class that manages feature types.
Examples
>>> from ads.feature_engineering.feature_type.base import FeatureType
>>> class NewType(FeatureType):
... description="My personal type."
... pass
>>> FeatureTypeManager.feature_type_register(NewType)
>>> FeatureTypeManager.feature_type_registered()
Name Feature Type Description
---------------------------------------------------------------------------------
0 Continuous continuous Type representing continuous values.
1 DateTime date_time Type representing date and/or time.
2 Category category Type representing discrete unordered values.
3 Ordinal ordinal Type representing ordered values.
4 NewType new_type My personal type.
>>> FeatureTypeManager.warning_registered()
Feature Type Warning Handler
----------------------------------------------------------------------
0 continuous zeros zeros_handler
1 continuous high_cardinality high_cardinality_handler
>>> FeatureTypeManager.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
2 credit_card is_credit_card () default_handler
>>> FeatureTypeManager.feature_type_unregister(NewType)
>>> FeatureTypeManager.feature_type_reset()
>>> FeatureTypeManager.feature_type_object('continuous')
Continuous
- class ads.feature_engineering.feature_type_manager.FeatureTypeManager
Bases:
objectFeature Types Manager class that manages feature types.
Provides functionalities to register, unregister, list feature types.
- feature_type_object(cls, feature_type: Union[FeatureType, str]) FeatureType
Gets a feature type by class object or name.
- feature_type_register(cls, feature_type_cls: FeatureType) None
Registers a feature type.
- feature_type_unregister(cls, feature_type_cls: Union[FeatureType, str]) None
Unregisters a feature type.
- feature_type_reset(cls) None
Resets feature types to be default.
- feature_type_registered(cls) pd.DataFrame
Lists all registered feature types as a DataFrame.
- warning_registered(cls) pd.DataFrame
Lists registered warnings for all registered feature types.
- validator_registered(cls) pd.DataFrame
Lists registered validators for all registered feature types.
Examples
>>> from ads.feature_engineering.feature_type.base import FeatureType >>> class NewType(FeatureType): ... pass >>> FeatureTypeManager.register_feature_type(NewType) >>> FeatureTypeManager.feature_type_registered() Name Feature Type Description ------------------------------------------------------------------------------- 0 Continuous continuous Type representing continuous values. 1 DateTime date_time Type representing date and/or time. 2 Category category Type representing discrete unordered values. 3 Ordinal ordinal Type representing ordered values.
>>> FeatureTypeManager.warning_registered() Feature Type Warning Handler ---------------------------------------------------------------------- 0 continuous zeros zeros_handler 1 continuous high_cardinality high_cardinality_handler
>>> FeatureTypeManager.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 2 credit_card is_credit_card () default_handler
>>> FeatureTypeManager.feature_type_unregister(NewType) >>> FeatureTypeManager.feature_type_reset() >>> FeatureTypeManager.feature_type_object('continuous') Continuous
- classmethod feature_type_object(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:
Found feature type.
- Return type:
- Raises:
TypeNotFound – If provided feature type not registered.
TypeError – If provided feature type not a subclass of FeatureType.
- classmethod feature_type_register(feature_type_cls: FeatureType) None
Registers new feature type.
- Parameters:
feature_type (FeatureType) – Subclass of FeatureType to be registered.
- Returns:
Nothing.
- Return type:
None
- Raises:
TypeError – Type is not a subclass of FeatureType.
TypeError – Type has already been registered.
NameError – Name has already been used.
- classmethod feature_type_registered() DataFrame
Lists all registered feature types as a DataFrame.
- Returns:
The list of feature types in a DataFrame format.
- Return type:
pd.DataFrame
- classmethod feature_type_reset() None
Resets feature types to be default.
- Returns:
Nothing.
- Return type:
None
- classmethod feature_type_unregister(feature_type: Union[FeatureType, str]) None
Unregisters a feature type.
- Parameters:
feature_type ((FeatureType | str)) – The FeatureType subclass or a str indicating feature type.
- Returns:
Nothing.
- Return type:
None
- Raises:
TypeError – In attempt to unregister a default feature type.
- classmethod is_type_registered(feature_type: Union[FeatureType, str]) bool
Checks if provided feature type registered in the system.
- Parameters:
feature_type (Union[FeatureType, str]) – The FeatureType subclass or a str indicating feature type.
- Returns:
True if provided feature type registered, False otherwise.
- Return type:
bool
- classmethod validator_registered() DataFrame
Lists registered validators for registered feature types.
- Returns:
The list of registered validators for registered feature types in a DataFrame format.
- Return type:
pd.DataFrame
Examples
>>> FeatureTypeManager.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 2 credit_card is_credit_card () default_handler
- classmethod warning_registered() DataFrame
Lists registered warnings for all registered feature types.
- Returns:
The list of registered warnings for registered feature types in a DataFrame format.
- Return type:
pd.DataFrame
Examples
>>> FeatureTypeManager.warning_registered() Feature Type Warning Handler ---------------------------------------------------------------------- 0 continuous zeros zeros_handler 1 continuous high_cardinality high_cardinality_handler
ads.feature_engineering.accessor.dataframe_accessor module
The ADS accessor for the Pandas DataFrame. The accessor will be initialized with the pandas object the user is interacting with.
Examples
>>> from ads.feature_engineering.accessor.dataframe_accessor import ADSDataFrameAccessor
>>> from ads.feature_engineering.feature_type.continuous import Continuous
>>> from ads.feature_engineering.feature_type.creditcard import CreditCard
>>> from ads.feature_engineering.feature_type.string import String
>>> from ads.feature_engineering.feature_type.base import Tag
>>> df = pd.DataFrame({'Name': ['Alex'], 'CreditCard': ["4532640527811543"]})
>>> df.ads.feature_type
{'Name': ['string'], 'Credit Card': ['string']}
>>> df.ads.feature_type_description
Column Feature Type Description
------------------------------------------------------------------
0 Name string Type representing string values.
1 Credit Card string Type representing string values.
>>> df.ads.default_type
{'Name': 'string', 'Credit Card': 'string'}
>>> df.ads.feature_type = {'Name':['string', Tag('abc')]}
>>> df.ads.tags
{'Name': ['abc']}
>>> df.ads.feature_type = {'Credit Card':['credit_card']}
>>> df.ads.feature_select(include=['credit_card'])
Credit Card
-------------------------------
0 4532640527811543
- class ads.feature_engineering.accessor.dataframe_accessor.ADSDataFrameAccessor(pandas_obj)
Bases:
ADSFeatureTypesMixin,EDAMixin,DBAccessMixin,DataLabelingAccessMixinADS accessor for the Pandas DataFrame.
- columns
The column labels of the DataFrame.
- Type:
List[str]
- tags(self) Dict[str, str]
Gets the dictionary of user defined tags for the dataframe.
- default_type(self) Dict[str, str]
Gets the map of columns and associated default feature type names.
- feature_type(self) Dict[str, List[str]]
Gets the list of registered feature types.
- feature_type_description(self) pd.DataFrame
Gets the list of registered feature types in a DataFrame format.
- sync(self, src: Union[pd.DataFrame, pd.Series]) pd.DataFrame
Syncs feature types of current DataFrame with that from src.
- feature_select(self, include: List[Union[FeatureType, str]] = None, exclude: List[Union[FeatureType, str]] = None) pd.DataFrame
Gets the list of registered feature types in a DataFrame format.
- help(self, prop: str = None) None
Provids docstring for affordable methods and properties.
Examples
>>> from ads.feature_engineering.accessor.dataframe_accessor import ADSDataFrameAccessor >>> from ads.feature_engineering.feature_type.continuous import Continuous >>> from ads.feature_engineering.feature_type.creditcard import CreditCard >>> from ads.feature_engineering.feature_type.string import String >>> from ads.feature_engineering.feature_type.base import Tag df = pd.DataFrame({'Name': ['Alex'], 'CreditCard': ["4532640527811543"]}) >>> df.ads.feature_type {'Name': ['string'], 'Credit Card': ['string']} >>> df.ads.feature_type_description Column Feature Type Description ------------------------------------------------------------------- 0 Name string Type representing string values. 1 Credit Card string Type representing string values. >>> df.ads.default_type {'Name': 'string', 'Credit Card': 'string'} >>> df.ads.feature_type = {'Name':['string', Tag('abc')]} >>> df.ads.tags {'Name': ['abc']} >>> df.ads.feature_type = {'Credit Card':['credit_card']} >>> df.ads.feature_select(include=['credit_card']) Credit Card ------------------------------ 0 4532640527811543
Initializes ADS Pandas DataFrame Accessor.
- Parameters:
pandas_obj (pandas.DataFrame) – Pandas dataframe
- Raises:
ValueError – If provided DataFrame has duplicate columns.
- property default_type: Dict[str, str]
Gets the map of columns and associated default feature type names.
- Returns:
The dictionary where key is column name and value is the name of default feature type.
- Return type:
Dict[str, str]
- feature_select(include: Optional[List[Union[FeatureType, str]]] = None, exclude: Optional[List[Union[FeatureType, str]]] = None) DataFrame
Returns a subset of the DataFrame’s columns based on the column feature_types.
- Parameters:
include (List[Union[FeatureType, str]], optional) – Defaults to None. A list of FeatureType subclass or str to be included.
exclude (List[Union[FeatureType, str]], optional) – Defaults to None. A list of FeatureType subclass or str to be excluded.
- Raises:
ValueError – If both of include and exclude are empty
ValueError – If include and exclude are used simultaneously
- Returns:
The subset of the frame including the feature types in include and excluding the feature types in exclude.
- Return type:
pandas.DataFrame
- property feature_type: Dict[str, List[str]]
Gets the list of registered feature types.
- Returns:
The dictionary where key is column name and value is list of associated feature type names.
- Return type:
Dict[str, List[str]]
- property feature_type_description: DataFrame
Gets the list of registered feature types in a DataFrame format.
- Return type:
pandas.DataFrame
Examples
>>> df.ads.feature_type_description() Column Feature Type Description ------------------------------------------------------------------- 0 City string Type representing string values. 1 Phone Number string Type representing string values.
- info() Any
Gets information about the dataframe.
- Returns:
The information about the dataframe.
- Return type:
Any
- model_schema(max_col_num: int = 2000)
Generates schema from the dataframe.
- Parameters:
max_col_num (int, optional. Defaults to 1000) – The maximum column size of the data that allows to auto generate schema.
Examples
>>> df = pd.read_csv('./orcl_attrition.csv', usecols=['Age', 'Attrition']) >>> schema = df.ads.model_schema() >>> schema Schema: - description: Attrition domain: constraints: [] stats: count: 1470 unique: 2 values: String dtype: object feature_type: String name: Attrition required: true - description: Age domain: constraints: [] stats: 25%: 31.0 50%: 37.0 75%: 44.0 count: 1470.0 max: 61.0 mean: 37.923809523809524 min: 19.0 std: 9.135373489136732 values: Integer dtype: int64 feature_type: Integer name: Age required: true >>> schema.to_dict() {'Schema': [{'dtype': 'object', 'feature_type': 'String', 'name': 'Attrition', 'domain': {'values': 'String', 'stats': {'count': 1470, 'unique': 2}, 'constraints': []}, 'required': True, 'description': 'Attrition'}, {'dtype': 'int64', 'feature_type': 'Integer', 'name': 'Age', 'domain': {'values': 'Integer', 'stats': {'count': 1470.0, 'mean': 37.923809523809524, 'std': 9.135373489136732, 'min': 19.0, '25%': 31.0, '50%': 37.0, '75%': 44.0, 'max': 61.0}, 'constraints': []}, 'required': True, 'description': 'Age'}]}
- Returns:
data schema.
- Return type:
ads.feature_engineering.schema.Schema
- Raises:
ads.feature_engineering.schema.DataSizeTooWide – If the number of columns of input data exceeds max_col_num.
- sync(src: Union[DataFrame, Series]) DataFrame
Syncs feature types of current DataFrame with that from src.
Syncs feature types of current dataframe with that from src, where src can be a dataframe or a series. In either case, only columns with matched names are synced.
- Parameters:
src (pd.DataFrame | pd.Series) – The source to sync from.
- Returns:
Synced dataframe.
- Return type:
pandas.DataFrame
- property tags: Dict[str, List[str]]
Gets the dictionary of user defined tags for the dataframe. Key is column name and value is list of tag names.
- Returns:
The map of columns and associated default tags.
- Return type:
Dict[str, List[str]]
ads.feature_engineering.accessor.series_accessor module
The ADS accessor for the Pandas Series. The accessor will be initialized with the pandas object the user is interacting with.
Examples
>>> from ads.feature_engineering.accessor.series_accessor import ADSSeriesAccessor
>>> from ads.feature_engineering.feature_type.string import String
>>> from ads.feature_engineering.feature_type.ordinal import Ordinal
>>> from ads.feature_engineering.feature_type.base import Tag
>>> series = pd.Series(['name1', 'name2', 'name3'])
>>> series.ads.default_type
'string'
>>> series.ads.feature_type
['string']
>>> series.ads.feature_type_description
Feature Type Description
----------------------------------------------------
0 string Type representing string values.
>>> series.ads.feature_type = ['string', Ordinal, Tag('abc')]
>>> series.ads.feature_type
['string', 'ordinal', 'abc']
>>> series1 = series.dropna()
>>> series1.ads.sync(series)
>>> series1.ads.feature_type
['string', 'ordinal', 'abc']
- class ads.feature_engineering.accessor.series_accessor.ADSSeriesAccessor(pandas_obj: Series)
Bases:
ADSFeatureTypesMixin,EDAMixinSeriesADS accessor for Pandas Series.
- name
The name of Series.
- Type:
str
- tags
The list of tags for the Series.
- Type:
List[str]
- help(self, prop: str = None) None
Provids docstring for affordable methods and properties.
- sync(self, src: Union[pd.DataFrame, pd.Series]) None
Syncs feature types of current series with that from src.
- default_type(self) str
Gets the name of default feature type for the series.
- feature_type(self) List[str]
Gets the list of registered feature types for the series.
- feature_type_description(self) pd.DataFrame
Gets the list of registered feature types in a DataFrame format.
Examples
>>> from ads.feature_engineering.accessor.series_accessor import ADSSeriesAccessor >>> from ads.feature_engineering.feature_type.string import String >>> from ads.feature_engineering.feature_type.ordinal import Ordinal >>> from ads.feature_engineering.feature_type.base import Tag >>> series = pd.Series(['name1', 'name2', 'name3']) >>> series.ads.default_type 'string' >>> series.ads.feature_type ['string'] >>> series.ads.feature_type_description Feature Type Description ---------------------------------------------------- 0 string Type representing string values. >>> series.ads.feature_type = ['string', Ordinal, Tag('abc')] >>> series.ads.feature_type ['string', 'ordinal', 'abc'] >>> series1 = series.dropna() >>> series1.ads.sync(series) >>> series1.ads.feature_type ['string', 'ordinal', 'abc']
Initializes ADS Pandas Series Accessor.
- Parameters:
pandas_obj (pd.Series) – The pandas series
- property default_type: str
Gets the name of default feature type for the series.
- Returns:
The name of default feature type.
- Return type:
str
- property feature_type: List[str]
Gets the list of registered feature types for the series.
- Returns:
Names of feature types.
- Return type:
List[str]
Examples
>>> series = pd.Series(['name1']) >>> series.ads.feature_type = ['name', 'string', Tag('tag for name')] >>> series.ads.feature_type ['name', 'string', 'tag for name']
- property feature_type_description: DataFrame
Gets the list of registered feature types in a DataFrame format.
- Returns:
The DataFrame with feature types for this series.
- Return type:
pd.DataFrame
Examples
>>> series = pd.Series(['name1']) >>> series.ads.feature_type = ['name', 'string', Tag('Name tag')] >>> series.ads.feature_type_description Feature Type Description ---------------------------------------------------------- 0 name Type representing name values. 1 string Type representing string values. 2 Name tag Tag.
- sync(src: Union[DataFrame, Series]) None
Syncs feature types of current series with that from src.
The src could be a dataframe or a series. In either case, only columns with matched names are synced.
- Parameters:
src ((pd.DataFrame | pd.Series)) – The source to sync from.
- Returns:
Nothing.
- Return type:
None
Examples
>>> series = pd.Series(['name1', 'name2', 'name3', None]) >>> series.ads.feature_type = ['name'] >>> series.ads.feature_type ['name', string] >>> series.dropna().ads.feature_type ['string'] >>> series1 = series.dropna() >>> series1.ads.sync(series) >>> series1.ads.feature_type ['name', 'string']
- class ads.feature_engineering.accessor.series_accessor.ADSSeriesValidator(feature_type_list: List[FeatureType], series: Series)
Bases:
objectClass helper to invoke registerred validator on a series level.
Initializes ADS series validator.
- Parameters:
feature_type_list (List[FeatureType]) – The list of feature types.
series (pd.Series) – The pandas series.
ads.feature_engineering.accessor.mixin.correlation module
- ads.feature_engineering.accessor.mixin.correlation.cat_vs_cat(df: DataFrame, normal_form: bool = True) DataFrame
Calculates the correlation of all pairs of categorical features and categorical features.
- ads.feature_engineering.accessor.mixin.correlation.cat_vs_cont(df: DataFrame, categorical_columns, continuous_columns, normal_form: bool = True) DataFrame
Calculates the correlation of all pairs of categorical features and continuous features.
- ads.feature_engineering.accessor.mixin.correlation.cont_vs_cont(df: DataFrame, normal_form: bool = True) DataFrame
Calculates the Pearson correlation between two columns of the DataFrame.
ads.feature_engineering.accessor.mixin.eda_mixin module
This exploratory data analysis (EDA) Mixin is used in the ADS accessor for the Pandas Dataframe. The series of purpose-driven methods enable the data scientist to complete analysis on the dataframe.
From the accessor we have access to the pandas object the user is interacting with as well as corresponding lists of feature types per column.
- class ads.feature_engineering.accessor.mixin.eda_mixin.EDAMixin
Bases:
object- correlation_ratio() DataFrame
Generate a Correlation Ratio data frame for all categorical-continuous variable pairs.
- Returns:
pandas.DataFrameCorrelation Ratio correlation data frame with the following 3 columns –
Column 1 (name of the first categorical/continuous column)
Column 2 (name of the second categorical/continuous column)
Value (correlation value)
Note
Pairs will be replicated. For example for variables x and y, we would have (x,y), (y,x) both with same correlation value. We will also have (x,x) and (y,y) with value 1.0.
- correlation_ratio_plot() Axes
Generate a heatmap of the Correlation Ratio correlation for all categorical-continuous variable pairs.
- Returns:
Correlation Ratio correlation plot object that can be updated by the customer
- Return type:
Plot object
- cramersv() DataFrame
Generate a Cramer’s V correlation data frame for all categorical variable pairs.
Gives a warning for dropped non-categorical columns.
- Returns:
- Cramer’s V correlation data frame with the following 3 columns:
Column 1 (name of the first categorical column)
Column 2 (name of the second categorical column)
Value (correlation value)
- Return type:
pandas.DataFrame
Note
Pairs will be replicated. For example for variables x and y, we would have (x,y), (y,x) both with same correlation value. We will also have (x,x) and (y,y) with value 1.0.
- cramersv_plot() Axes
Generate a heatmap of the Cramer’s V correlation for all categorical variable pairs.
Gives a warning for dropped non-categorical columns.
- Returns:
Cramer’s V correlation plot object that can be updated by the customer
- Return type:
Plot object
- feature_count() DataFrame
Counts the number of columns for each feature type and each primary feature. The column of primary is the number of primary feature types that is assigned to the column.
- Returns:
The number of columns for each feature type The number of columns for each primary feature
- Return type:
Dataframe with
Examples
>>> df.ads.feature_type {'PassengerId': ['ordinal', 'category'], 'Survived': ['ordinal'], 'Pclass': ['ordinal'], 'Name': ['category'], 'Sex': ['category']} >>> df.ads.feature_count() Feature Type Count Primary 0 category 3 2 1 ordinal 3 3
- feature_plot() DataFrame
For every column in the dataframe plot generate a list of summary plots based on the most relevant feature type.
- Returns:
Dataframe with 2 columns: 1. Column - feature name 2. Plot - plot object
- Return type:
pandas.DataFrame
- feature_stat() DataFrame
Summary statistics Dataframe provided.
This returns feature stats on each column using FeatureType summary method.
Examples
>>> df = pd.read_csv('~/advanced-ds/tests/vor_datasets/vor_titanic.csv') >>> df.ads.feature_stat().head() Column Metric Value 0 PassengerId count 891.000 1 PassengerId mean 446.000 2 PassengerId standard deviation 257.354 3 PassengerId sample minimum 1.000 4 PassengerId lower quartile 223.500
- Returns:
Dataframe with 3 columns: name, metric, value
- Return type:
pandas.DataFrame
- pearson() DataFrame
Generate a Pearson correlation data frame for all continuous variable pairs.
Gives a warning for dropped non-numerical columns.
- Returns:
pandas.DataFramePearson correlation data frame with the following 3 columns –
Column 1 (name of the first continuous column)
Column 2 (name of the second continuous column)
Value (correlation value)
Note
Pairs will be replicated. For example for variables x and y, we’d have (x,y), (y,x) both with same correlation value. We’ll also have (x,x) and (y,y) with value 1.0.
- pearson_plot() Axes
Generate a heatmap of the Pearson correlation for all continuous variable pairs.
- Returns:
Pearson correlation plot object that can be updated by the customer
- Return type:
Plot object
- warning() DataFrame
Generates a data frame that lists feature specific warnings.
- Returns:
The list of feature specific warnings.
- Return type:
pandas.DataFrame
Examples
>>> df.ads.warning() Column Feature Type Warning Message Metric Value -------------------------------------------------------------------------------------- 0 Age continuous Zeros Age has 38 zeros Count 38 1 Age continuous Zeros Age has 12.2% zeros Percentage 12.2%
ads.feature_engineering.accessor.mixin.eda_mixin_series module
This exploratory data analysis (EDA) Mixin is used in the ADS accessor for the Pandas Series. The series of purpose-driven methods enable the data scientist to complete univariate analysis.
From the accessor we have access to the pandas object the user is interacting with as well as corresponding list of feature types.
- class ads.feature_engineering.accessor.mixin.eda_mixin_series.EDAMixinSeries
Bases:
object- feature_plot() Axes
For the series generate a summary plot based on the most relevant feature type.
- Returns:
Plot object for the series based on the most relevant feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- feature_stat() DataFrame
Summary statistics Dataframe provided.
This returns feature stats on series using FeatureType summary method.
Examples
>>> df = pd.read_csv('~/advanced-ds/tests/vor_datasets/vor_titanic.csv') >>> df['Cabin'].ads.feature_stat() Metric Value 0 count 891 1 unqiue 147 2 missing 687
- Returns:
Dataframe with 2 columns and rows for different metric values
- Return type:
pandas.DataFrame
- warning() DataFrame
Generates a data frame that lists feature specific warnings.
- Returns:
The list of feature specific warnings.
- Return type:
pandas.DataFrame
Examples
>>> df["Age"].ads.warning() Feature Type Warning Message Metric Value --------------------------------------------------------------------------- 0 continuous Zeros Age has 38 zeros Count 38 1 continuous Zeros Age has 12.2% zeros Percentage 12.2%
ads.feature_engineering.accessor.mixin.feature_types_mixin module
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.
- class ads.feature_engineering.accessor.mixin.feature_types_mixin.ADSFeatureTypesMixin
Bases:
objectADS Feature Types Mixin class that extends Pandas Series and DataFrame accessors.
- 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.
- help(prop: Optional[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:
Nothing.
- Return type:
None
- validator_registered() DataFrame
Lists registered validators for registered feature types.
- Returns:
The list of registered validators for registered feature types
- Return type:
pandas.DataFrame
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
- warning_registered() DataFrame
Lists registered warnings for all registered feature types.
- Returns:
The list of registered warnings for registered feature types.
- Return type:
pandas.DataFrame
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
ads.feature_engineering.adsstring.common_regex_mixin module
- class ads.feature_engineering.adsstring.common_regex_mixin.CommonRegexMixin
Bases:
object- property address
- property credit_card
- property date
- property email
- property ip
- property link
- property phone_number_US
- property price
- redact(fields: Union[List[str], Dict[str, str]]) str
Remove personal information in a string. For example, “Jane’s phone number is 123-456-7890” is turned into “Jane’s phone number is [phone_number_US].”
- Parameters:
fields ((list(str) | dict)) – either a list of fields to redact, e.g. [‘email’, ‘phone_number_US’], in which case the redacted text is replaced with capitalized word like [EMAIL] or [PHONE_NUMBER_US_WITH_EXT], or a dictionary where key is a field to redact and value is the replacement text, e.g., {‘email’: ‘HIDDEN_EMAIL’}.
- Returns:
redacted string
- Return type:
str
- redact_map = {'address': '[ADDRESS]', 'address_with_zip': '[ADDRESS_WITH_ZIP]', 'credit_card': '[CREDIT_CARD]', 'date': '[DATE]', 'email': '[EMAIL]', 'ip': '[IP]', 'ipv6': '[IPV6]', 'link': '[LINK]', 'phone_number_US': '[PHONE_NUMBER_US]', 'phone_number_US_with_ext': '[PHONE_NUMBER_US_WITH_EXT]', 'po_box': '[PO_BOX]', 'price': '[PRICE]', 'ssn': '[SSN]', 'time': '[TIME]', 'zip_code': '[ZIP_CODE]'}
- property ssn
- property time
- property zip_code
ads.feature_engineering.adsstring.oci_language module
ads.feature_engineering.adsstring.string module
ads.feature_engineering.feature_type.address module
The module that represents an Address feature type.
- Classes:
- Address
The Address feature type.
- class ads.feature_engineering.feature_type.address.Address
Bases:
StringType representing address.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the location of given address on map base on zip code.
Example
>>> from ads.feature_engineering.feature_type.address import Address >>> import pandas as pd >>> address = pd.Series(['1 Miller Drive, New York, NY 12345', '1 Berkeley Street, Boston, MA 67891', '54305 Oxford Street, Seattle, WA 95132', '']) >>> Address.validator.is_address(address) 0 True 1 True 2 True 3 False dtype: bool
- description = 'Type representing address.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> address = pd.Series(['1 Miller Drive, New York, NY 12345', '1 Berkeley Street, Boston, MA 67891', '54305 Oxford Street, Seattle, WA 95132', ''], name='address') >>> address.ads.feature_type = ['address'] >>> address.ads.feature_domain() constraints: [] stats: count: 4 missing: 1 unique: 3 values: Address
- Returns:
Domain based on the Address feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the location of given address on map base on zip code.
Examples
>>> address = pd.Series(['1 Miller Drive, New York, NY 12345', '1 Berkeley Street, Boston, MA 67891', '54305 Oxford Street, Seattle, WA 95132', ''], name='address') >>> address.ads.feature_type = ['address'] >>> address.ads.feature_plot()
- Returns:
Plot object for the series based on the Address feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> address = pd.Series(['1 Miller Drive, New York, NY 12345', '1 Berkeley Street, Boston, MA 67891', '54305 Oxford Street, Seattle, WA 95132', ''], name='address') >>> address.ads.feature_type = ['address'] >>> address.ads.feature_stat() Metric Value 0 count 4 1 unique 3 2 missing 1
- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.address.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (pd.Series) – The data to process.
- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.base module
- class ads.feature_engineering.feature_type.base.FeatureBaseType(classname, bases, dictionary)
Bases:
typeThe helper metaclass to extend fucntionality of FeatureType class.
- class ads.feature_engineering.feature_type.base.FeatureBaseTypeMeta(classname, bases, dictionary)
Bases:
FeatureBaseType,ABCMetaThe class to provide compatibility between ABC and FeatureBaseType metaclass.
- class ads.feature_engineering.feature_type.base.FeatureType
Bases:
ABCAbstract case for feature types. Default class attribute include name and description. Name is auto generated using camel to snake conversion unless specified.
- description = 'Base feature type.'
- name = 'feature_type'
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- class ads.feature_engineering.feature_type.base.Name
Bases:
object
- class ads.feature_engineering.feature_type.base.Tag(name: str)
Bases:
objectClass for free form tags. Name must be specified.
Initialize a tag instance.
- Parameters:
name (str) – The name of the tag.
ads.feature_engineering.feature_type.boolean module
The module that represents a Boolean feature type.
- Classes:
- Boolean
The feature type that represents binary values True/False.
- Functions:
- default_handler(data: pd.Series) -> pd.Series
Processes given data and indicates if the data matches requirements.
- class ads.feature_engineering.feature_type.boolean.Boolean
Bases:
FeatureTypeType representing binary values True/False.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Show the counts of observations in True/False using bars.
Examples
>>> from ads.feature_engineering.feature_type.boolean import Boolean >>> import pandas as pd >>> import numpy as np >>> s = pd.Series([True, False, True, False, np.NaN, None], name='bool') >>> s.ads.feature_type = ['boolean'] >>> Boolean.validator.is_boolean(s) 0 True 1 True 2 True 3 True 4 False 5 False dtype: bool
- description = 'Type representing binary values True/False.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series([True, False, True, False, np.NaN, None], name='bool') >>> s.ads.feature_type = ['boolean'] >>> s.ads.feature_domain() constraints: - expression: $x in [True, False] language: python stats: count: 6 missing: 2 unique: 2 values: Boolean
- Returns:
Domain based on the Boolean feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the counts of observations in True/False using bars.
- Parameters:
x (
pandas.Series) – The feature being evaluated.- Returns:
Plot object for the series based on the Boolean feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
Examples
>>> s = pd.Series([True, False, True, False, np.NaN, None], name='bool') >>> s.ads.feature_type = ['boolean'] >>> s.ads.feature_plot()
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
- Parameters:
x (
pandas.Series) – The feature being evaluated.- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
Examples
>>> s = pd.Series([True, False, True, False, np.NaN, None], name='bool') >>> s.ads.feature_type = ['boolean'] >>> s.ads.feature_stat() Metric Value 0 count 6 1 unique 2 2 missing 2
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.boolean.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.category module
The module that represents a Category feature type.
- Classes:
- Category
The Category feature type.
- class ads.feature_engineering.feature_type.category.Category
Bases:
FeatureTypeType representing discrete unordered values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the counts of observations in each categorical bin using bar chart.
- description = 'Type representing discrete unordered values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> cat = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='category') >>> cat.ads.feature_type = ['category'] >>> cat.ads.feature_domain() constraints: - expression: $x in ['S', 'C', 'Q', ''] language: python stats: count: 22 missing: 3 unique: 3 values: Category
- Returns:
Domain based on the Category feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the counts of observations in each categorical bin using bar chart.
- Parameters:
x (
pandas.Series) – The feature being evaluated.- Returns:
Plot object for the series based on the Category feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
Examples
>>> cat = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='сategory') >>> cat.ads.feature_type = ['сategory'] >>> cat.ads.feature_plot()
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count) if there are any.
- Parameters:
x (
pandas.Series) – The feature being evaluated.- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
Examples
>>> cat = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='сategory') >>> cat.ads.feature_type = ['сategory'] >>> cat.ads.feature_stat() Metric Value 0 count 22 1 unique 3 2 missing 3
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.constant module
The module that represents a Constant feature type.
- Classes:
- Constant
The Constant feature type.
- class ads.feature_engineering.feature_type.constant.Constant
Bases:
FeatureTypeType representing constant values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the counts of observations in bars.
- description = 'Type representing constant values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type. .. rubric:: Example
>>> s = pd.Series([1, 1, 1, 1, 1], name='constant') >>> s.ads.feature_type = ['constant'] >>> s.ads.feature_domain() constraints: [] stats: count: 5 unique: 1 values: Constant
- Returns:
Domain based on the Constant feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the counts of observations in bars.
- Parameters:
x (
pandas.Series) – The feature being shown.
Examples
>>> s = pd.Series([1, 1, 1, 1, 1], name='constant') >>> s.ads.feature_type = ['constant'] >>> s.ads.feature_plot()
- Returns:
Plot object for the series based on the Constant feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
- Parameters:
x (
pandas.Series) – The feature being evaluated.- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
Examples
>>> s = pd.Series([1, 1, 1, 1, 1], name='constant') >>> s.ads.feature_type = ['constant'] >>> s.ads.feature_stat() Metric Value 0 count 5 1 unique 1
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.continuous module
The module that represents a Continuous feature type.
- Classes:
- Continuous
The Continuous feature type.
- class ads.feature_engineering.feature_type.continuous.Continuous
Bases:
FeatureTypeType representing continuous values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datasets using box plot.
- description = 'Type representing continuous values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> cts = pd.Series([13.32, 3.32, 4.3, 2.45, 6.34, 2.25, 4.43, 3.26, np.NaN, None], name='continuous') >>> cts.ads.feature_type = ['continuous'] >>> cts.ads.feature_domain() constraints: [] stats: count: 10.0 lower quartile: 3.058 mean: 4.959 median: 3.81 missing: 2.0 sample maximum: 13.32 sample minimum: 2.25 skew: 2.175 standard deviation: 3.62 upper quartile: 4.908 values: Continuous
- Returns:
Domain based on the Continuous feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows distributions of datasets using box plot.
Examples
>>> cts = pd.Series([13.32, 3.32, 4.3, 2.45, 6.34, 2.25, 4.43, 3.26, np.NaN, None], name='continuous') >>> cts.ads.feature_type = ['continuous'] >>> cts.ads.feture_plot()
- Returns:
Plot object for the series based on the Continuous feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, mean, standard deviation, sample minimum, lower quartile, median, 75%, upper quartile, skew and missing(count).
Examples
>>> cts = pd.Series([13.32, 3.32, 4.3, 2.45, 6.34, 2.25, 4.43, 3.26, np.NaN, None], name='continuous') >>> cts.ads.feature_type = ['continuous'] >>> cts.ads.feature_stat() Metric Value 0 count 10.000 1 mean 4.959 2 standard deviation 3.620 3 sample minimum 2.250 4 lower quartile 3.058 5 median 3.810 6 upper quartile 4.908 7 sample maximum 13.320 8 skew 2.175 9 missing 2.000
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.creditcard module
The module that represents a CreditCard feature type.
- Classes:
- CreditCard
The CreditCard feature type.
- Functions:
- default_handler(data: pd.Series) -> pd.Series
Processes given data and indicates if the data matches requirements.
- _luhn_checksum(card_number: str) -> float
Implements Luhn algorithm to validate a credit card number.
- class ads.feature_engineering.feature_type.creditcard.CreditCard
Bases:
StringType representing credit card numbers.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the counts of observations in each credit card type using bar chart.
Examples
>>> from ads.feature_engineering.feature_type.creditcard import CreditCard >>> import pandas as pd >>> s = pd.Series(["4532640527811543", None, "4556929308150929", "4539944650919740", "4485348152450846", "4556593717607190"], name='credit_card') >>> s.ads.feature_type = ['credit_card'] >>> CreditCard.validator.is_credit_card(s) 0 True 1 False 2 True 3 True 4 True 5 True Name: credit_card, dtype: bool
- description = 'Type representing credit card numbers.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> visa = [ "4532640527811543", None, "4556929308150929", "4539944650919740", "4485348152450846", "4556593717607190", ] >>> mastercard = [ "5334180299390324", "5111466404826446", "5273114895302717", "5430972152222336", "5536426859893306", ] >>> amex = [ "371025944923273", "374745112042294", "340984902710890", "375767928645325", "370720852891659", ] >>> creditcard_list = visa + mastercard + amex >>> creditcard_series = pd.Series(creditcard_list,name='card') >>> creditcard_series.ads.feature_type = ['credit_card'] >>> creditcard_series.ads.feature_domain() constraints: [] stats: count: 16 count_Amex: 5 count_Diners Club: 2 count_MasterCard: 3 count_Visa: 5 count_missing: 1 missing: 1 unique: 15 values: CreditCard
- Returns:
Domain based on the CreditCard feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the counts of observations in each credit card type using bar chart.
Examples
>>> visa = [ "4532640527811543", None, "4556929308150929", "4539944650919740", "4485348152450846", "4556593717607190", ] >>> mastercard = [ "5334180299390324", "5111466404826446", "5273114895302717", "5430972152222336", "5536426859893306", ] >>> amex = [ "371025944923273", "374745112042294", "340984902710890", "375767928645325", "370720852891659", ] >>> creditcard_list = visa + mastercard + amex >>> creditcard_series = pd.Series(creditcard_list,name='card') >>> creditcard_series.ads.feature_type = ['credit_card'] >>> creditcard_series.ads.feature_plot()
- Returns:
Plot object for the series based on the CreditCard feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series)
Generates feature statistics.
- Feature statistics include (total)count, unique(count), missing(count) and
count of each credit card type.
Examples
>>> visa = [ "4532640527811543", None, "4556929308150929", "4539944650919740", "4485348152450846", "4556593717607190", ] >>> mastercard = [ "5334180299390324", "5111466404826446", "5273114895302717", "5430972152222336", "5536426859893306", ] >>> amex = [ "371025944923273", "374745112042294", "340984902710890", "375767928645325", "370720852891659", ] >>> creditcard_list = visa + mastercard + amex >>> creditcard_series = pd.Series(creditcard_list,name='card') >>> creditcard_series.ads.feature_type = ['credit_card'] >>> creditcard_series.ads.feature_stat() Metric Value 0 count 16 1 unique 15 2 missing 1 3 count_Amex 5 4 count_Visa 5 5 count_MasterCard 3 6 count_Diners Club 2 7 count_missing 1
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.creditcard.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.datetime module
The module that represents a DateTime feature type.
- Classes:
- DateTime
The DateTime feature type.
- class ads.feature_engineering.feature_type.datetime.DateTime
Bases:
FeatureTypeType representing date and/or time.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datetime datasets using histograms.
Example
>>> from ads.feature_engineering.feature_type.datetime import DateTime >>> import pandas as pd >>> s = pd.Series(["12/12/12", "12/12/13", None, "12/12/14"], name='datetime') >>> s.ads.feature_type = ['date_time'] >>> DateTime.validator.is_datetime(s) 0 True 1 True 2 False 3 True Name: datetime, dtype: bool
- description = 'Type representing date and/or time.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000', '', None, np.nan, 'April/13/2011', 'April/15/11'], name='datetime') >>> s.ads.feature_type = ['date_time'] >>> s.ads.feature_domain() constraints: [] stats: count: 8 missing: 3 sample maximum: April/15/11 sample minimum: 3/11/2000 values: DateTime
- Returns:
Domain based on the DateTime feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows distributions of datetime datasets using histograms.
Examples
>>> x = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000', '', None, np.nan, 'April/13/2011', 'April/15/11'], name='datetime') >>> x.ads.feature_type = ['date_time'] >>> x.ads.feature_plot()
- Returns:
Plot object for the series based on the DateTime feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, sample maximum, sample minimum, and missing(count) if there is any.
Examples
>>> x = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000', '', None, np.nan, 'April/13/2011', 'April/15/11'], name='datetime') >>> x.ads.feature_type = ['date_time'] >>> x.ads.feature_stat() Metric Value 0 count 8 1 sample maximum April/15/11 2 sample minimum 3/11/2000 3 missing 3
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.datetime.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.discrete module
The module that represents a Discrete feature type.
- Classes:
- Discrete
The Discrete feature type.
- class ads.feature_engineering.feature_type.discrete.Discrete
Bases:
FeatureTypeType representing discrete values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datasets using box plot.
- description = 'Type representing discrete values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> discrete_numbers = pd.Series([35, 25, 13, 42], name='discrete') >>> discrete_numbers.ads.feature_type = ['discrete'] >>> discrete_numbers.ads.feature_domain() constraints: [] stats: count: 4 unique: 4 values: Discrete
- Returns:
Domain based on the Discrete feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows distributions of datasets using box plot.
Examples
>>> discrete_numbers = pd.Series([35, 25, 13, 42], name='discrete') >>> discrete_numbers.ads.feature_type = ['discrete'] >>> discrete_numbers.ads.feature_stat() Metric Value 0 count 4 1 unique 4
- Returns:
Plot object for the series based on the Discrete feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> discrete_numbers = pd.Series([35, 25, 13, 42], name='discrete') >>> discrete_numbers.ads.feature_type = ['discrete'] >>> discrete_numbers.ads.feature_stat() discrete count 4 unique 4
- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.document module
The module that represents a Document feature type.
- Classes:
- Document
The Document feature type.
- class ads.feature_engineering.feature_type.document.Document
Bases:
FeatureTypeType representing document values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- description = 'Type representing document values.'
- classmethod feature_domain()
- Returns:
Nothing.
- Return type:
None
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.gis module
The module that represents a GIS feature type.
- Classes:
- GIS
The GIS feature type.
- class ads.feature_engineering.feature_type.gis.GIS
Bases:
FeatureTypeType representing geographic information.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the location of given address on map base on longitude and latitute.
Example
>>> from ads.feature_engineering.feature_type.gis import GIS >>> import pandas as pd >>> s = pd.Series(["-18.2193965, -93.587285", "-21.0255305, -122.478584", "85.103913, 19.405744", "82.913736, 178.225672", "62.9795085,-66.989705", "54.5604395,95.235090", "24.2811855,-162.380403", "-1.818319,-80.681214", None, "(51.816119, 175.979008)", "(54.3392995,-11.801615)"], name='gis') >>> s.ads.feature_type = ['gis'] >>> GIS.validator.is_gis(s) 0 True 1 True 2 True 3 True 4 True 5 True 6 True 7 True 8 False 9 True 10 True Name: gis, dtype: bool
- description = 'Type representing geographic information.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> gis = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='gis' ) >>> gis.ads.feature_type = ['gis'] >>> gis.ads.feature_domain() constraints: [] stats: count: 13 missing: 3 unique: 10 values: GIS
- Returns:
Domain based on the GIS feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the location of given address on map base on longitude and latitute.
Examples
>>> gis = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='gis' ) >>> gis.ads.feature_type = ['gis'] >>> gis.ads.feature_plot()
- Returns:
Plot object for the series based on the GIS feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> gis = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='gis' ) >>> gis.ads.feature_type = ['gis'] >>> gis.ads.feature_stat() gis count 13 unique 10 missing 3
- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.gis.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.integer module
The module that represents an Integer feature type.
- Classes:
- Integer
The Integer feature type.
- class ads.feature_engineering.feature_type.integer.Integer
Bases:
FeatureTypeType representing integer values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datasets using box plot.
- description = 'Type representing integer values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series([True, False, True, False, np.NaN, None], name='integer') >>> s.ads.feature_type = ['integer'] >>> s.ads.feature_domain() constraints: [] stats: count: 6 freq: 2 missing: 2 top: true unique: 2 values: Integer
- Returns:
Domain based on the Integer feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows distributions of datasets using box plot.
Examples
>>> x = pd.Series([1, 0, 1, 2, 3, 4, np.nan], name='integer') >>> x.ads.feature_type = ['integer'] >>> x.ads.feature_plot()
- Returns:
Plot object for the series based on the Integer feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, mean, standard deviation, sample minimum, lower quartile, median, 75%, upper quartile, max and missing(count) if there is any.
Examples
>>> x = pd.Series([1, 0, 1, 2, 3, 4, np.nan], name='integer') >>> x.ads.feature_type = ['integer'] >>> x.ads.feature_stat() Metric Value 0 count 7 1 mean 1 2 standard deviation 1 3 sample minimum 0 4 lower quartile 1 5 median 1 6 upper quartile 2 7 sample maximum 4 8 missing 1
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.ip_address module
The module that represents an IpAddress feature type.
- Classes:
- IpAddress
The IpAddress feature type.
- class ads.feature_engineering.feature_type.ip_address.IpAddress
Bases:
FeatureTypeType representing IP Address.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
Example
>>> from ads.feature_engineering.feature_type.ip_address import IpAddress >>> import pandas as pd >>> import numpy as np >>> s = pd.Series(['192.168.0.1', '2001:db8::', '', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address'] >>> IpAddress.validator.is_ip_address(s) 0 True 1 True 2 False 3 False 4 False Name: ip_address, dtype: bool
- description = 'Type representing IP Address.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series(['2002:db8::', '192.168.0.1', '2001:db8::', '2002:db8::', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address'] >>> s.ads.feature_domain() constraints: [] stats: count: 6 missing: 2 unique: 3 values: IpAddress
- Returns:
Domain based on the IpAddress feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> s = pd.Series(['2002:db8::', '192.168.0.1', '2001:db8::', '2002:db8::', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address'] >>> s.ads.feature_stat() Metric Value 0 count 6 1 unique 2 2 missing 2
- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.ip_address.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.ip_address_v4 module
The module that represents an IpAddressV4 feature type.
- Classes:
- IpAddressV4
The IpAddressV4 feature type.
- class ads.feature_engineering.feature_type.ip_address_v4.IpAddressV4
Bases:
FeatureTypeType representing IP Address V4.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
Example
>>> from ads.feature_engineering.feature_type.ip_address_v4 import IpAddressV4 >>> import pandas as pd >>> import numpy as np >>> s = pd.Series(['192.168.0.1', '2001:db8::', '', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address_v4'] >>> IpAddressV4.validator.is_ip_address_v4(s) 0 True 1 False 2 False 3 False 4 False Name: ip_address, dtype: bool
- description = 'Type representing IP Address V4.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series(['192.168.0.1', '192.168.0.2', '192.168.0.3', '192.168.0.4', np.NaN, None], name='ip_address_v4') >>> s.ads.feature_type = ['ip_address_v4'] >>> s.ads.feature_domain() constraints: [] stats: count: 6 missing: 2 unique: 4 values: IpAddressV4
- Returns:
Domain based on the IpAddressV4 feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> s = pd.Series(['192.168.0.1', '192.168.0.2', '192.168.0.3', '192.168.0.4', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address_v4'] >>> s.ads.feature_stat() Metric Value 0 count 6 1 unique 4 2 missing 2
- Returns:
Summary statistics of the Series provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.ip_address_v4.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.ip_address_v6 module
The module that represents an IpAddressV6 feature type.
- Classes:
- IpAddressV6
The IpAddressV6 feature type.
- class ads.feature_engineering.feature_type.ip_address_v6.IpAddressV6
Bases:
FeatureTypeType representing IP Address V6.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
Example
>>> from ads.feature_engineering.feature_type.ip_address_v6 import IpAddressV6 >>> import pandas as pd >>> import numpy as np >>> s = pd.Series(['192.168.0.1', '2001:db8::', '', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address_v6'] >>> IpAddressV6.validator.is_ip_address_v6(s) 0 False 1 True 2 False 3 False 4 False Name: ip_address, dtype: bool
- description = 'Type representing IP Address V6.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series(['2002:db8::', '2001:db8::', '2001:db8::', '2002:db8::', np.NaN, None], name='ip_address_v6') >>> s.ads.feature_type = ['ip_address_v6'] >>> s.ads.feature_domain() constraints: [] stats: count: 6 missing: 2 unique: 2 values: IpAddressV6
- Returns:
Domain based on the IpAddressV6 feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> s = pd.Series(['2002:db8::', '2001:db8::', '2001:db8::', '2002:db8::', np.NaN, None], name='ip_address') >>> s.ads.feature_type = ['ip_address_v6'] >>> s.ads.feature_stat() Metric Value 0 count 6 1 unique 2 2 missing 2
- Returns:
Summary statistics of the Series provided.
- Return type:
Pandas Dataframe
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.ip_address_v6.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.lat_long module
The module that represents a LatLong feature type.
- Classes:
- LatLong
The LatLong feature type.
- Functions:
- default_handler(data: pd.Series) -> pd.Series
Processes given data and indicates if the data matches requirements.
- class ads.feature_engineering.feature_type.lat_long.LatLong
Bases:
StringType representing longitude and latitute.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the location of given address on map base on longitude and latitute.
Example
>>> from ads.feature_engineering.feature_type.lat_long import LatLong >>> import pandas as pd >>> s = pd.Series(["-18.2193965, -93.587285", "-21.0255305, -122.478584", "85.103913, 19.405744", "82.913736, 178.225672", "62.9795085,-66.989705", "54.5604395,95.235090", "24.2811855,-162.380403", "-1.818319,-80.681214", None, "(51.816119, 175.979008)", "(54.3392995,-11.801615)"], name='latlong') >>> s.ads.feature_type = ['lat_long'] >>> LatLong.validator.is_lat_long(s) 0 True 1 True 2 True 3 True 4 True 5 True 6 True 7 True 8 False 9 True 10 True Name: latlong, dtype: bool
- description = 'Type representing longitude and latitute.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> latlong_series = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='latlong' ) >>> latlong_series.ads.feature_type = ['lat_long'] >>> latlong_series.ads.feature_domain() constraints: [] stats: count: 13 missing: 3 unique: 10 values: LatLong
- Returns:
Domain based on the LatLong feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the location of given address on map base on longitude and latitute.
Examples
>>> latlong_series = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='latlong' ) >>> latlong_series.ads.feature_type = ['lat_long'] >>> latlong_series.ads.feature_plot()
- Returns:
Plot object for the series based on the LatLong feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generate feature statistics.
Feature statistics include (total)count, unique(count) and missing(count) if there is any.
Examples
>>> latlong_series = pd.Series([ "69.196241,-125.017615", "5.2272595,-143.465712", "-33.9855425,-153.445155", "43.340610,86.460554", "24.2811855,-162.380403", "2.7849025,-7.328156", "45.033805,157.490179", "-1.818319,-80.681214", "-44.510428,-169.269477", "-56.3344375,-166.407038", "", np.NaN, None ], name='latlong' ) >>> latlong_series.ads.feature_type = ['lat_long'] >>> latlong_series.ads.feature_stat() Metric Value 0 count 13 1 unique 10 2 missing 3
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.lat_long.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.object module
The module that represents an Object feature type.
- Classes:
- Object
The Object feature type.
- class ads.feature_engineering.feature_type.object.Object
Bases:
FeatureTypeType representing object.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- description = 'Type representing object.'
- classmethod feature_domain()
- Returns:
Nothing.
- Return type:
None
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.ordinal module
The module that represents an Ordinal feature type.
- Classes:
- Ordinal
The Ordinal feature type.
- class ads.feature_engineering.feature_type.ordinal.Ordinal
Bases:
FeatureTypeType representing ordered values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the counts of observations in each categorical bin using bar chart.
- description = 'Type representing ordered values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal') >>> x.ads.feature_type = ['ordinal'] >>> x.ads.feature_domain() constraints: - expression: $x in [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] language: python stats: count: 10 missing: 1 unique: 9 values: Ordinal
- Returns:
Domain based on the Ordinal feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the counts of observations in each categorical bin using bar chart.
Examples
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal') >>> x.ads.feature_type = ['ordinal'] >>> x.ads.feature_plot()
- Returns:
The bart chart plot object for the series based on the Continuous feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count), and missing(count) if there is any.
Examples
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal') >>> x.ads.feature_type = ['ordinal'] >>> x.ads.feature_stat() Metric Value 0 count 10 1 unique 9 2 missing 1
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.phone_number module
The module that represents a Phone Number feature type.
- Classes:
- PhoneNumber
The Phone Number feature type.
- Functions:
- default_handler(data: pd.Series) -> pd.Series
Processes given data and indicates if the data matches requirements.
- class ads.feature_engineering.feature_type.phone_number.PhoneNumber
Bases:
StringType representing phone numbers.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
Examples
>>> from ads.feature_engineering.feature_type.phone_number import PhoneNumber >>> import pandas as pd >>> s = pd.Series([None, "1-640-124-5367", "1-573-916-4412"]) >>> PhoneNumber.validator.is_phone_number(s) 0 False 1 True 2 True dtype: bool
- description = 'Type representing phone numbers.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> s = pd.Series(['2068866666', '6508866666', '2068866666', '', np.NaN, np.nan, None], name='phone') >>> s.ads.feature_type = ['phone_number'] >>> s.ads.feature_domain() constraints: [] stats: count: 7 missing: 4 unique: 2 values: PhoneNumber
- Returns:
Domain based on the PhoneNumber feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count) if there is any.
Examples
>>> s = pd.Series(['2068866666', '6508866666', '2068866666', '', np.NaN, np.nan, None], name='phone') >>> s.ads.feature_type = ['phone_number'] >>> s.ads.feature_stat() Metric Value 1 count 7 2 unique 2 3 missing 4
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
pandas.DataFrame
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.phone_number.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (
pandas.Series) – The data to process.- Returns:
The logical list indicating if the data matches requirements.
- Return type:
pandas.Series
ads.feature_engineering.feature_type.string module
The module that represents a String feature type.
- Classes:
- String
The feature type that represents string values.
- class ads.feature_engineering.feature_type.string.String
Bases:
FeatureTypeType representing string values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datasets using wordcloud.
Example
>>> from ads.feature_engineering.feature_type.string import String >>> import pandas as pd >>> s = pd.Series(["Hello", "world", None], name='string') >>> String.validator.is_string(s) 0 True 1 True 2 False Name: string, dtype: bool
- description = 'Type representing string values.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string') >>> string.ads.feature_type = ['string'] >>> string.ads.feature_domain() constraints: [] stats: count: 22 missing: 3 unique: 3 values: String
- Returns:
Domain based on the String feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows distributions of datasets using wordcloud.
Examples
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string') >>> string.ads.feature_type = ['string'] >>> string.ads.feature_plot()
- Returns:
Plot object for the series based on the String feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count) if there is any.
Examples
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string') >>> string.ads.feature_type = ['string'] >>> string.ads.feature_stat() Metric Value 0 count 22 1 unique 3 2 missing 3
- Returns:
Summary statistics of the Series or Dataframe provided.
- Return type:
Pandas Dataframe
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.string.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (pd.Series) – The data to process.
- Returns:
pd.Series
- Return type:
The logical list indicating if the data matches requirements.
ads.feature_engineering.feature_type.text module
The module that represents a Text feature type.
- Classes:
- Text
The Text feature type.
- class ads.feature_engineering.feature_type.text.Text
Bases:
StringType representing text values.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_plot(x: pd.Series) plt.Axes
Shows distributions of datasets using wordcloud.
- description = 'Type representing text values.'
- classmethod feature_domain()
- Returns:
Nothing.
- Return type:
None
- static feature_plot(x: Series) Axes
Shows distributions of datasets using wordcloud.
Examples
>>> text = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S', 'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='text') >>> text.ads.feature_type = ['text'] >>> text.ads.feature_plot()
- Returns:
Plot object for the series based on the Text feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.unknown module
The module that represents an Unknown feature type.
- Classes:
- Text
The Unknown feature type.
- class ads.feature_engineering.feature_type.unknown.Unknown
Bases:
FeatureTypeType representing third-party dtypes.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- description = 'Type representing unknown type.'
- classmethod feature_domain()
- Returns:
Nothing.
- Return type:
None
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
ads.feature_engineering.feature_type.zip_code module
The module that represents a ZipCode feature type.
- Classes:
- ZipCode
The ZipCode feature type.
- Functions:
- default_handler(data: pd.Series) -> pd.Series
Processes given data and indicates if the data matches requirements.
- class ads.feature_engineering.feature_type.zip_code.ZipCode
Bases:
StringType representing postal code.
- description
The feature type description.
- Type:
str
- name
The feature type name.
- Type:
str
- warning
Provides functionality to register warnings and invoke them.
- Type:
- validator
Provides functionality to register validators and invoke them.
- feature_stat(x: pd.Series) pd.DataFrame
Generates feature statistics.
- feature_plot(x: pd.Series) plt.Axes
Shows the geometry distribution base on location of zipcode.
Example
>>> from ads.feature_engineering.feature_type.zip_code import ZipCode >>> import pandas as pd >>> import numpy as np >>> s = pd.Series(["94065", "90210", np.NaN, None], name='zipcode') >>> ZipCode.validator.is_zip_code(s) 0 True 1 True 2 False 3 False Name: zipcode, dtype: bool
- description = 'Type representing postal code.'
- classmethod feature_domain(x: Series) Domain
Generate the domain of the data of this feature type.
Examples
>>> zipcode = pd.Series([94065, 90210, np.NaN, None], name='zipcode') >>> zipcode.ads.feature_type = ['zip_code'] >>> zipcode.ads.feature_domain() constraints: [] stats: count: 4 missing: 2 unique: 2 values: ZipCode
- Returns:
Domain based on the ZipCode feature type.
- Return type:
ads.feature_engineering.schema.Domain
- static feature_plot(x: Series) Axes
Shows the geometry distribution base on location of zipcode.
Examples
>>> zipcode = pd.Series([94065, 90210, np.NaN, None], name='zipcode') >>> zipcode.ads.feature_type = ['zip_code'] >>> zipcode.ads.feature_plot()
- Returns:
Plot object for the series based on the ZipCode feature type.
- Return type:
matplotlib.axes._subplots.AxesSubplot
- static feature_stat(x: Series) DataFrame
Generates feature statistics.
Feature statistics include (total)count, unique(count) and missing(count).
Examples
>>> zipcode = pd.Series([94065, 90210, np.NaN, None], name='zipcode') >>> zipcode.ads.feature_type = ['zip_code'] >>> zipcode.ads.feature_stat() Metric Value 0 count 4 1 unique 2 2 missing 2
- Returns:
Summary statistics of the Series provided.
- Return type:
Pandas Dataframe
- validator = <ads.feature_engineering.feature_type.handler.feature_validator.FeatureValidator object>
- warning = <ads.feature_engineering.feature_type.handler.feature_warning.FeatureWarning object>
- ads.feature_engineering.feature_type.zip_code.default_handler(data: Series, *args, **kwargs) Series
Processes given data and indicates if the data matches requirements.
- Parameters:
data (pd.Series) – The data to process.
- Returns:
pd.Series
- Return type:
The logical list indicating if the data matches requirements.
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
Bases:
objectThe Feature Validator class to manage custom validators.
- register(self, name: str, handler: Callable, condition: Union[Tuple, Dict[str, Any]] = None, replace: bool = False) None
Registers new validator.
- unregister(self, name: str, condition: Union[Tuple, Dict[str, Any]] = None) None
Unregisters validator.
- registered(self) pd.DataFrame
Gets the list of registered validators.
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: Optional[Union[Tuple, Dict[str, Any]]] = None, replace: bool = False) None
Registers new validator.
- Parameters:
name (str) – The validator name.
handler (callable) – The handler.
condition (Union[Tuple, Dict[str, Any]]) – The condition for the validator.
replace (bool) – The flag indicating if the registered validator should be replaced with the new one.
- 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
Gets the list of registered validators.
- Returns:
The list of registerd validators.
- Return type:
pd.DataFrame
- unregister(name: str, condition: Optional[Union[Tuple, Dict[str, Any]]] = None) None
Unregisters validator.
- Parameters:
name (str) – The name of the validator to be unregistered.
condition (Union[Tuple, Dict[str, Any]]) – The condition for the validator to be unregistered.
- 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)
Bases:
objectThe Feature Validator Method class.
Extends methods which requires dispatching based on the specific arguments.
- register(self, condition: Union[Tuple, Dict[str, Any]], handler: Callable) None
Registers new handler.
- unregister(self, condition: Union[Tuple, Dict[str, Any]]) None
Unregisters existing handler.
- registered(self) pd.DataFrame
Gets the list of registered handlers.
Initializes the Feature Validator Method.
- Parameters:
handler (Callable) – The handler that will be called by default if suitable one not found.
- register(condition: Union[Tuple, Dict[str, Any]], handler: Callable) None
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
Gets the list of registered handlers.
- Returns:
The list of registerd handlers.
- Return type:
pd.DataFrame
- unregister(condition: Union[Tuple, Dict[str, Any]]) None
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)
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorNotFound(name: str)
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorWithConditionAlreadyExists(name: str)
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.ValidatorWithConditionNotFound(name: str)
Bases:
ValueError
- exception ads.feature_engineering.feature_type.handler.feature_validator.WrongHandlerMethodSignature(handler_name: str, condition: str, handler_signature: str)
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
Bases:
objectThe Feature Warning class.
Provides functionality to register warning handlers and invoke them.
- register(self, name: str, handler: Callable) None
Registers a new warning for the feature type.
- unregister(self, name: str) None
Unregisters warning.
- registered(self) pd.DataFrame
Gets the list of registered warnings.
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
Registers a new warning.
- Parameters:
name (str) – The warning name.
handler (callable) – The handler associated with the warning.
replace (bool) – The flag indicating if the registered warning should be replaced with the new one.
- 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
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
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
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
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
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
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