import numpy as np
from typing import Callable, Union, Tuple
from .ensemble import Ensemble, EnsemblePrediction
from .utils import get_prediction_dataset
try:
import sklearn.decomposition
from sklearn.preprocessing import StandardScaler
import umap
import umap.plot
except ImportError:
pass
[docs]def get_umap_representation_parameters(
ens: Ensemble,
n_components: int = 2,
normalize_data: bool = False,
**kwargs) -> Tuple:
"""
Compute the representation with reduced dimensionality via umap
(with a given number of umap components) of the parameter ensemble.
Allows to pass on additional keyword arguments to the umap routine.
Parameters
==========
ens:
Ensemble objects containing a set of parameter vectors
n_components:
number of components for the dimension reduction
normalize_data:
flag indicating whether the parameter ensemble should be rescaled with
mean and standard deviation
Returns
=======
umap_components:
first components of the umap embedding
umap_object:
returned fitted umap object from umap.UMAP()
"""
# call lowlevel routine using the parameter vector ensemble
return _get_umap_representation_lowlevel(
dataset=ens.x_vectors.transpose(),
n_components=n_components,
normalize_data=normalize_data,
**kwargs
)
[docs]def get_umap_representation_predictions(
ens: Union[Ensemble, EnsemblePrediction],
prediction_index: int = 0,
n_components: int = 2,
normalize_data: bool = False,
**kwargs) -> Tuple:
"""
Compute the representation with reduced dimensionality via umap
(with a given number of umap components) of the ensemble predictions.
Allows to pass on additional keyword arguments to the umap routine.
Parameters
==========
ens:
Ensemble objects containing a set of parameter vectors and a set of
predictions or EnsemblePrediction object containing only predictions
prediction_index:
index telling which prediction from the list should be analyzed
n_components:
number of components for the dimension reduction
normalize_data:
flag indicating whether the parameter ensemble should be rescaled with
mean and standard deviation
Returns
=======
umap_components:
first components of the umap embedding
umap_object:
returned fitted umap object from umap.UMAP()
"""
# extract the an array of predictions from either an Ensemble object or an
# EnsemblePrediction object
dataset = get_prediction_dataset(ens, prediction_index)
# call lowlevel routine using the prediction ensemble
return _get_umap_representation_lowlevel(
dataset=dataset,
n_components=n_components,
normalize_data=normalize_data,
**kwargs
)
[docs]def get_pca_representation_parameters(
ens: Ensemble,
n_components: int = 2,
rescale_data: bool = True,
rescaler: Union[Callable, None] = None
) -> Tuple:
"""
Compute the representation with reduced dimensionality via principal
component analysis (with a given number of principal components) of the
parameter ensemble.
Parameters
==========
ens:
Ensemble objects containing a set of parameter vectors
n_components:
number of components for the dimension reduction
rescale_data:
flag indicating whether the principal components should be rescaled
using a rescaler function (e.g., an arcsinh function)
rescaler:
callable function to rescale the output of the PCA (defaults to
numpy.arcsinh)
Returns
=======
principal_components:
principal components of the parameter vector ensemble
pca_object:
returned fitted pca object from sklearn.decomposition.PCA()
"""
return _get_pca_representation_lowlevel(
dataset=ens.x_vectors.transpose(),
n_components=n_components,
rescale_data=rescale_data,
rescaler=rescaler
)
[docs]def get_pca_representation_predictions(
ens: Union[Ensemble, EnsemblePrediction],
prediction_index: int = 0,
n_components: int = 2,
rescale_data: bool = True,
rescaler: Union[Callable, None] = None
) -> Tuple:
"""
Compute the representation with reduced dimensionality via principal
component analysis (with a given number of principal components) of the
ensemble prediction.
Parameters
==========
ens:
Ensemble objects containing a set of parameter vectors and a set of
predictions or EnsemblePrediction object containing only predictions
prediction_index:
index telling which prediction from the list should be analyzed
n_components:
number of components for the dimension reduction
rescale_data:
flag indicating whether the principal components should be rescaled
using a rescaler function (e.g., an arcsinh function)
rescaler:
callable function to rescale the output of the PCA (defaults to
numpy.arcsinh)
Returns
=======
principal_components:
principal components of the parameter vector ensemble
pca_object:
returned fitted pca object from sklearn.decomposition.PCA()
"""
# extract the an array of predictions from either an Ensemble object or an
# EnsemblePrediction object
dataset = get_prediction_dataset(ens, prediction_index)
# call lowlevel routine using the prediction ensemble
return _get_pca_representation_lowlevel(
dataset=dataset,
n_components=n_components,
rescale_data=rescale_data,
rescaler=rescaler
)
def _get_umap_representation_lowlevel(
dataset: np.ndarray,
n_components: int = 2,
normalize_data: bool = False,
**kwargs) -> Tuple:
"""
Compute the representation with reduced dimensionality via principal
component analysis (with a given number of principal components) of the
parameter ensemble.
Parameters
==========
dataset:
numpy array containing either the ensemble predictions or the parameter
ensemble itself
n_components:
number of components for the dimension reduction
rescale_data:
flag indicating whether the principal components should be rescaled
using a rescaler function (e.g., an arcsinh function)
rescaler:
callable function to rescale the output of the PCA (defaults to
numpy.arcsinh)
Returns
=======
umap_components:
first components of the umap embedding
umap_object:
returned fitted umap object from umap.UMAP()
"""
# create a umap object
umap_object = umap.UMAP(n_components=n_components, **kwargs)
# normalize data with mean and standard deviation if wanted
if normalize_data:
dataset = StandardScaler().fit_transform(dataset)
# perform the manifold fitting and transform the dataset
umap_components = umap_object.fit_transform(dataset)
return umap_components, umap_object
def _get_pca_representation_lowlevel(
dataset: np.ndarray,
n_components: int = 2,
rescale_data: bool = True,
rescaler: Union[Callable, None] = None
) -> Tuple:
"""
Compute the representation with reduced dimensionality via principal
component analysis (with a given number of principal components) of the
parameter ensemble.
Parameters
==========
dataset:
numpy array containing either the ensemble predictions or the parameter
ensemble itself
n_components:
number of components for the dimension reduction
rescale_data:
flag indicating whether the principal components should be rescaled
using a rescaler function (e.g., an arcsinh function)
rescaler:
callable function to rescale the output of the PCA (defaults to
numpy.arcsinh)
Returns
=======
principal_components:
principal components of the parameter vector ensemble
pca_object:
returned fitted pca object from sklearn.decomposition.PCA()
"""
# create a PCA object and decompose the dataset
pca_object = sklearn.decomposition.PCA(n_components=n_components)
pca_object.fit(dataset)
# get the projection down to the first components
principal_components = pca_object.transform(dataset)
# rescale the principal components with a non-linear function, if wanted
if rescale_data:
if rescaler is None:
# use arcsinh as default
principal_components = np.arcsinh(principal_components)
else:
# use provided funcation for rescaling
principal_components = rescaler(principal_components)
return principal_components, pca_object