pdstools.explanations.ExplanationsUtils¶

Attributes¶

ContextInfo

Classes¶

`_PREDICTOR_TYPE`	Create a collection of name/value pairs.
`_TABLE_NAME`	Create a collection of name/value pairs.
`_CONTRIBUTION_TYPE`	Create a collection of name/value pairs.
`_COL`	Create a collection of name/value pairs.
`_SPECIAL`	Create a collection of name/value pairs.
`_DEFAULT`	Create a collection of name/value pairs.
`ContextOperations`	Context related operations such as to filter unique contexts.

Module Contents¶

class _PREDICTOR_TYPE(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

NUMERIC = 'NUMERIC'¶

SYMBOLIC = 'SYMBOLIC'¶

class _TABLE_NAME(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

NUMERIC = 'numeric'¶

SYMBOLIC = 'symbolic'¶

NUMERIC_OVERALL = 'numeric_overall'¶

SYMBOLIC_OVERALL = 'symbolic_overall'¶

CREATE = 'create'¶

class _CONTRIBUTION_TYPE(default, alt)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

alt¶

classmethod validate_and_get_type(val)¶: get the accepted contribution type which is validated against user input

CONTRIBUTION = ('contribution', 'contribution')¶

CONTRIBUTION_ABS = ('contribution_abs', '|contribution|')¶

CONTRIBUTION_WEIGHTED = ('contribution_weighted', 'contribution weighted')¶

CONTRIBUTION_WEIGHTED_ABS = ('contribution_weighted_abs', '|contribution weighted|')¶

FREQUENCY = ('frequency', 'frequency')¶

CONTRIBUTION_MIN = ('contribution_min', 'contribution min')¶

CONTRIBUTION_MAX = ('contribution_max', 'contribution max')¶

class _COL(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

PARTITON = 'partition'¶

PREDICTOR_NAME = 'predictor_name'¶

PREDICTOR_TYPE = 'predictor_type'¶

BIN_CONTENTS = 'bin_contents'¶

BIN_ORDER = 'bin_order'¶

CONTRIBUTION = 'contribution'¶

CONTRIBUTION_ABS = 'contribution_abs'¶

CONTRIBUTION_MIN = 'contribution_min'¶

CONTRIBUTION_MAX = 'contribution_max'¶

CONTRIBUTION_WEIGHTED = 'contribution_weighted'¶

CONTRIBUTION_WEIGHTED_ABS = 'contribution_weighted_abs'¶

FREQUENCY = 'frequency'¶

class _SPECIAL(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

REMAINING = 'remaining'¶

TOTAL_FREQUENCY = 'total_frequency'¶

MISSING = 'missing'¶

class _DEFAULT(*args, **kwds)¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:

>>> Color.RED
<Color.RED: 1>

value lookup:

>>> Color(1)
<Color.RED: 1>

name lookup:

>>> Color['RED']
<Color.RED: 1>

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

TOP_N = 10¶

TOP_K = 10¶

DESCENDING = True¶

MISSING = True¶

REMAINING = True¶

ContextInfo¶

class ContextOperations(aggregate: pdstools.explanations.Aggregate.Aggregate)¶

Bases: pdstools.utils.namespaces.LazyNamespace

Context related operations such as to filter unique contexts. Parameters:

aggregate (Aggregate): The aggregate object to operate on.

Attributes:

aggregate (Aggregate): The aggregate object. _df (Optional[pl.DataFrame]): DataFrame containing context information. _context_keys (Optional[List[str]]): List of context keys. initialized (bool): Flag indicating if the context operations have been initialized.

Methods:

get_context_keys():

Returns the list of context keys from loaded data. Eg. [‘pyChannel’, ‘pyDirection’, …]

get_df(context_infos=None, with_partition_col=False):

Returns a DataFrame containing unique contexts If with_partition_col is True, includes the partition column. If context_infos is None, returns the full unique contexts, else filtered by the context Eg. with partition column: | pyChannel | pyDirection | … | partition | |-----------|————-|-----|———–| | channel1 | direction1 | … | {“partition”: {“pyChannel”: “channel1”, “pyDirection”: “direction1”}} | | channel1 | direction2 | … | {“partition”: {“pyChannel”: “channel1”, “pyDirection”: “direction2”}} |

get_list(context_infos=None, with_partition_col=False):

Returns a List[ContextInfo] containing unique contexts If with_partition_col is True, includes the partition column. If context_infos is None, returns the full unique contexts, else filtered by the context Eg. without partition column: [

{“pyChannel”: “channel1”, “pyDirection”: “direction1”, …}, {“pyChannel”: “channel1”, “pyDirection”: “direction2”, …},

]

get_context_info_str(context_info, sep=”-“):

Returns a string representation of a single context information. Eg. channel1-direction1-…

Parameters:: aggregate (pdstools.explanations.Aggregate.Aggregate)

dependencies = ['polars']¶

dependency_group = 'explanations'¶

aggregate¶

_df: polars.DataFrame | None = None¶

_context_keys: List[str] | None = None¶

initialized = False¶

_load()¶

get_context_keys()¶

get_df(context_infos: List[ContextInfo] | None = None, with_partition_col: bool = False) → polars.DataFrame¶

Get the DataFrame filtered by the provided context information.

Parameters:

context_infos (Optional[List[ContextInfo]])
with_partition_col (bool)

Return type:

polars.DataFrame

get_list(context_infos: List[ContextInfo] | None = None, with_partition_col: bool = False) → List[ContextInfo]¶

Get the list of context information filtered by the provided context information.

Parameters:

context_infos (Optional[List[ContextInfo]])
with_partition_col (bool)

Return type:

List[ContextInfo]

_filter_df_by_context_infos(df, context_infos)¶

static _get_filter_expression(context_infos)¶

static _get_clean_df(df: polars.DataFrame) → polars.DataFrame¶

Parameters:: df (polars.DataFrame)
Return type:: polars.DataFrame

static get_context_info_str(context_info: ContextInfo, sep: str = '-') → str¶

Parameters:

context_info (ContextInfo)
sep (str)

Return type:

str