Linear Algebra¶

evalica.eigen(xs, ys, winners, index=None, weights=None, win_weight=1.0, tie_weight=0.5, solver='pyo3', tolerance=1e-06, limit=100, **kwargs)[source]¶

Compute the eigenvalue-based scores.

Parameters:

xs (Collection[T_contra]) – The left-hand side elements.
ys (Collection[T_contra]) – The right-hand side elements.
winners (Collection[Winner]) – The winner elements.
index (Index | None) – The index.
weights (Collection[float] | None) – The example weights.
win_weight (float) – The win weight.
tie_weight (float) – The tie weight.
solver (Literal['naive', 'pyo3']) – The solver.
tolerance (float) – The convergence tolerance.
limit (int) – The maximum number of iterations.
**kwargs (Any) – The additional arguments.

Returns:

The eigenvalue result.

Return type:

EigenResult

class evalica.EigenResult(scores, index, win_weight, tie_weight, solver, tolerance, iterations, limit)[source]¶

The eigenvalue result.

Parameters:

scores (Series)
index (Index)
win_weight (float)
tie_weight (float)
solver (Literal['naive', 'pyo3'])
tolerance (float)
iterations (int)
limit (int)

scores¶

The element scores.

Type:: pandas.Series

index¶

The index.

Type:: pandas.Index

win_weight¶

The win weight.

Type:: float

tie_weight¶

The tie weight.

Type:: float

solver¶

The solver.

Type:: Literal[‘naive’, ‘pyo3’]

tolerance¶

The convergence tolerance.

Type:: float

iterations¶

The actual number of iterations.

Type:: int

limit¶

The maximum number of iterations.

Type:: int

evalica.pagerank(xs, ys, winners, index=None, damping=0.85, weights=None, win_weight=1.0, tie_weight=0.5, solver='pyo3', tolerance=1e-06, limit=100, **kwargs)[source]¶

Compute the PageRank scores.

Note

Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems. 30, 107–117 (1998). <https://doi.org/10.1016/S0169-7552(98)00110-X>.

Parameters:

xs (Collection[T_contra]) – The left-hand side elements.
ys (Collection[T_contra]) – The right-hand side elements.
winners (Collection[Winner]) – The winner elements.
index (Index | None) – The index.
damping (float) – The damping (alpha) factor.
weights (Collection[float] | None) – The example weights.
win_weight (float) – The win weight.
tie_weight (float) – The tie weight.
solver (Literal['naive', 'pyo3']) – The solver.
tolerance (float) – The convergence tolerance.
limit (int) – The maximum number of iterations.
**kwargs (Any) – The additional arguments.

Returns:

The PageRank result.

Return type:

PageRankResult

class evalica.PageRankResult(scores, index, damping, win_weight, tie_weight, solver, tolerance, iterations, limit)[source]¶

The PageRank result.

Parameters:

scores (Series)
index (Index)
damping (float)
win_weight (float)
tie_weight (float)
solver (Literal['naive', 'pyo3'])
tolerance (float)
iterations (int)
limit (int)

scores¶

The element scores.

Type:: pandas.Series

index¶

The index.

Type:: pandas.Index

damping¶

The damping (alpha) factor.

Type:: float

win_weight¶

The win weight.

Type:: float

tie_weight¶

The tie weight.

Type:: float

solver¶

The solver.

Type:: Literal[‘naive’, ‘pyo3’]

tolerance¶

The convergence tolerance.

Type:: float

iterations¶

The actual number of iterations.

Type:: int

limit¶

The maximum number of iterations.

Type:: int