ads.feature_engineering.accessor package¶

Subpackages¶

ads.feature_engineering.accessor.mixin package

Submodules¶

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)[source]¶

Bases: ADSFeatureTypesMixin, EDAMixin, DBAccessMixin, DataLabelingAccessMixin, ADSDatasetAccessMixin

ADS 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: pd.DataFrame | pd.Series) → pd.DataFrame[source]¶: Syncs feature types of current DataFrame with that from src.

feature_select(self, include: List[FeatureType | str] = None, exclude: List[FeatureType | str] = None) → pd.DataFrame[source]¶: 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: List[FeatureType | str] | None = None, exclude: List[FeatureType | str] | None = None) → DataFrame[source]¶

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[source]¶

Gets information about the dataframe.

Returns:: The information about the dataframe.
Return type:: Any

model_schema(max_col_num: int = 2000)[source]¶

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: DataFrame | Series) → DataFrame[source]¶

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)[source]¶

Bases: ADSFeatureTypesMixin, EDAMixinSeries

ADS 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: pd.DataFrame | pd.Series) → None[source]¶: 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: DataFrame | Series) → None[source]¶

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)[source]¶

Bases: object

Class 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 package¶

Subpackages¶

Submodules¶

ads.feature_engineering.accessor.dataframe_accessor module¶

ads.feature_engineering.accessor.series_accessor module¶

Module contents¶