matrices

This module contains all functions related to matrices.

class ColumnView(matrix)

Bases: Mapping[Any, CommensurableValues[S]], Generic[S]

This class defines a view of a value matrix per column.

Parameters: matrix (IValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class IAdjacencyMatrix(data, vertices=None, **kwargs)

Bases: IMatrix, ABC

This class implements graphs as an adjacency matrix.

The adjacency matrix is represented internally by a pandas.DataFrame with vertices as the indexes and columns.

Parameters

data – adjacency matrix in an array-like or dict-structure
vertices (Union[List, None]) –

Raises

KeyError –

if columns and rows have different sets of labels
if some vertices are duplicated

Note

the cells of the matrix can be of any type (not just numerics)

property cell: Mapping[Tuple[Any, Any], Any]: Return matrix cell accessor.

Note

actually returns DataFrame.at() from data

property columns: List: Return column labels.

property rows: List: Return row labels.

property vertices: List: Return list of vertices

class IMatrix(data, **kwargs)

Bases: ABC

This class implements a wrapper on pandas.DataFrame.

It adds a method to check if two such objects are equals. It is meant to be use for any class that needs a DataFrame as its internal data representation in this package.

Parameters

data – dataframe containing the matrix

Raises

KeyError –

if some indexes are duplicated
if some columns are duplicated

property cell: Mapping[Tuple[Any, Any], Any]: Return matrix cell accessor.

Note

actually returns DataFrame.at() from data

property columns: List: Return column labels.

property rows: List: Return row labels.

class IValueMatrix(data, scales=None, stype=None, **kwargs)

Bases: IMatrix, Generic[S], ABC

This class defines a matrix with associated scales (one per column).

Parameters

data –
scales (Union[S, Mapping[Any, S], None]) – data scale(s) (one per column or one shared, will be inferred from data if absent using Scale.fit())
stype (Union[Type[S], None]) – scales type used

Raises

KeyError –
- if some indexes are duplicated
- if some columns are duplicated
TypeError – if stype and scales types mismatch

Todo

remove this class completely, put its code in current subclasses

property bounds: Mapping[Any, S]: Return scales inferred from values.

property cell: Mapping[Tuple[Any, Any], Any]: Return matrix cell accessor.

Note

actually returns DataFrame.at() from data

property columns: List: Return column labels.

property columns_values: Mapping[Any, CommensurableValues[S]]: Iterator on the table criteria values

copy()

Return a copy of the object

Return type: Self

property is_numeric: bool

Check whether table is numeric.

Returns
Return type: bool

property is_ordinal: bool

Check whether scales are all ordinal.

Returns

property is_within_scales: bool

Check whether all values are within their respective scales.

Returns

property rows: List: Return row labels.

property rows_values: Mapping[Any, Values[S]]: Iterator on the table alternatives values

sum(axis: None = None) → float

sum(axis: Literal[0, 1]) → CommensurableValues[QuantitativeScale]

Return the sum of the data (all table, row or column-wise).

Parameters

axis –

0: column-wise sum
1: row-wise sum
None: complete sum

Returns

Warning

it will raise a TypeError if data contains numeric and non-numeric values

abstract property to_numeric: IValueMatrix[QuantitativeScale]

Return numeric conversion of ordinal values.

Raises: TypeError – if scales are not ordinal

property union_bounds: S: Return one scale for whole matrix inferred from values.

property within_scales: DataFrame

Return a dataframe indicating which values are within their respective scale.

Returns

OutrankingMatrix

This type alias represents an outranking matrix

alias of AdjacencyValueMatrix[DiscreteQuantitativeScale]

class PartialColumnView(matrix)

Bases: Mapping[Any, PerformanceTable[S]], Generic[S]

This class defines a view of a partial value matrix per column.

Parameters: matrix (PartialValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class PartialCriterionView(matrix)

Bases: Mapping[Any, AdjacencyValueMatrix[S]], Generic[S]

This class defines a view of a partial value matrix per criterion.

Parameters: matrix (PartialValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class PartialRowView(matrix)

Bases: Mapping[Any, PerformanceTable[S]], Generic[S]

This class defines a view of a partial value matrix per row.

Parameters: matrix (PartialValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class RowView(matrix)

Bases: Mapping[Any, Values[S]], Generic[S]

This class defines a view of a value matrix per row.

Parameters: matrix (IValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class VertexColumnView(matrix)

Bases: ColumnView[S], Generic[S]

This class defines a view of an adjacency value matrix per column.

Parameters: matrix (AdjacencyValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

class VertexRowView(matrix)

Bases: Mapping[Any, CommensurableValues[S]], Generic[S]

This class defines a view of an adjacency value matrix per row.

Parameters: matrix (AdjacencyValueMatrix[S]) –

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

values() → an object providing a view on D's values

create_outranking_matrix(data, vertices=None, **kwargs)

Create an outranking matrix.

Parameters

data – adjacency matrix in an array-like or dict-structure
vertices (Optional[List]) –

Raises

KeyError –
- if columns and rows have different sets of labels
- if some vertices are duplicated
TypeError – if data is not binary

Return type

AdjacencyValueMatrix[DiscreteQuantitativeScale]

Note

This function is just an easier way to create AdjacencyValueMatrix with binary scale

dataframe_equals(df1, df2)

Check if two dataframes have the same values.

It will realign the indexes and columns if they are ordered differently.

Parameters

df1 (DataFrame) –
df2 (DataFrame) –

Returns

Return type

bool

Todo

integrate into mcda/core.adjacency_matrix.Matrix

requires_graphviz(func)

Wrap a class or function that requires graphviz to function.

Parameters: obj – either a class or function
Raises: ImportError – if graphviz is not installed
Returns: wrapped class or function