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Updates to decomposition module and to fluctsca module.
Signed-off-by: Timothy Click <tcthepoet@yahoo.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,52 +1,41 @@ | ||
| # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*- | ||
| # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # pysca --- https://github.com/tclick/python-pysca | ||
| # Copyright (c) 2015-2017 The pySCA Development Team and contributors | ||
| # (see the file AUTHORS for the full list of names) | ||
| # python-fluctmatch - | ||
| # Copyright (c) 2019 Timothy H. Click, Ph.D. | ||
| # | ||
| # Released under the New BSD license. | ||
| # All rights reserved. | ||
| # | ||
| # Please cite your use of fluctmatch in published work: | ||
| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions are met: | ||
| # | ||
| # Timothy H. Click, Nixon Raj, and Jhih-Wei Chu. | ||
| # Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics | ||
| # Simulation. Meth Enzymology. 578 (2016), 327-342, | ||
| # doi:10.1016/bs.mie.2016.05.024. | ||
| # Redistributions of source code must retain the above copyright notice, this | ||
| # list of conditions and the following disclaimer. | ||
| # | ||
| from __future__ import ( | ||
| absolute_import, | ||
| division, | ||
| print_function, | ||
| unicode_literals, | ||
| ) | ||
|
|
||
| from future.utils import ( | ||
| native_str, | ||
| raise_from, | ||
| with_metaclass, | ||
| ) | ||
| from future.builtins import ( | ||
| ascii, | ||
| bytes, | ||
| chr, | ||
| dict, | ||
| filter, | ||
| hex, | ||
| input, | ||
| map, | ||
| next, | ||
| oct, | ||
| open, | ||
| pow, | ||
| range, | ||
| round, | ||
| str, | ||
| super, | ||
| zip, | ||
| ) | ||
|
|
||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| # Redistributions in binary form must reproduce the above copyright notice, | ||
| # this list of conditions and the following disclaimer in the documentation | ||
| # and/or other materials provided with the distribution. | ||
| # | ||
| # Neither the name of the author nor the names of its contributors may be used | ||
| # to endorse or promote products derived from this software without specific | ||
| # prior written permission. | ||
| # | ||
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” | ||
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | ||
| # ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR | ||
| # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| # | ||
| # Timothy H. Click, Nixon Raj, and Jhih-Wei Chu. | ||
| # Simulation. Meth Enzymology. 578 (2016), 327-342, | ||
| # Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics | ||
| # doi:10.1016/bs.mie.2016.05.024. | ||
|
|
||
| from .eigh import Eigh | ||
| from .ica import ICA | ||
| from .ipca import IPCA | ||
| from .svd import SVD |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,146 @@ | ||
| # python-fluctmatch - | ||
| # Copyright (c) 2019 Timothy H. Click, Ph.D. | ||
| # | ||
| # All rights reserved. | ||
| # | ||
| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions are met: | ||
| # | ||
| # Redistributions of source code must retain the above copyright notice, this | ||
| # list of conditions and the following disclaimer. | ||
| # | ||
| # Redistributions in binary form must reproduce the above copyright notice, | ||
| # this list of conditions and the following disclaimer in the documentation | ||
| # and/or other materials provided with the distribution. | ||
| # | ||
| # Neither the name of the author nor the names of its contributors may be used | ||
| # to endorse or promote products derived from this software without specific | ||
| # prior written permission. | ||
| # | ||
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” | ||
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | ||
| # ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR | ||
| # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| # | ||
| """Performs independent principal component analysis (see [1]. | ||
| References | ||
| ---------- | ||
| .. [1] Yao, F.; Coquery, J.; Le Cao, K.-A. 2012. Independent Principal | ||
| Component Analysis for biologically meaningful dimension reduction of | ||
| large biological data sets. BMC Bioinformatics 13 (1). | ||
| """ | ||
|
|
||
| from typing import Union | ||
|
|
||
| import numpy as np | ||
| from scipy import linalg, stats | ||
| from sklearn.decomposition.base import BaseEstimator, TransformerMixin | ||
| from sklearn.decomposition import PCA | ||
| from sklearn.preprocessing import StandardScaler | ||
| from sklearn.pipeline import make_pipeline, Pipeline | ||
| from sklearn.utils.validation import check_array, check_is_fitted | ||
|
|
||
| from .ica import ICA | ||
|
|
||
|
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| class IPCA(BaseEstimator, TransformerMixin): | ||
| """Signal decomposition using Independent Principal Component Analysis (IPCA). | ||
| This object can be used to estimate ICA components and then remove some | ||
| from Raw or Epochs for data exploration or artifact correction. | ||
| .. note:: Methods currently implemented are FastICA (default), Infomax, | ||
| Extended Infomax. Infomax can be quite sensitive to differences in | ||
| floating point arithmetic. Extended Infomax seems to be more | ||
| stable in this respect enhancing reproducibility and stability of | ||
| results. | ||
| Parameters | ||
| ---------- | ||
| n_components : int | float | None | ||
| Number of components to extract. If None no dimension reduction | ||
| is performed. | ||
| whiten : boolean, optional | ||
| If whiten is false, the data is already considered to be | ||
| whitened, and no whitening is performed. | ||
| random_state : None | int | instance of np.random.RandomState | ||
| Random state to initialize ICA estimation for reproducible results. | ||
| method : {'fastica', 'infomax', 'extended-infomax'} | ||
| The ICA method to use. Defaults to 'fastica'. For reference, see [2]_, | ||
| [3]_, and [4] . | ||
| max_iter : int | ||
| The maximum number of iterations. Defaults to 200. | ||
| random_state : int | np.random.RandomState | ||
| If random_state is an int, use random_state to seed the random number | ||
| generator. If random_state is already a np.random.RandomState instance, | ||
| use random_state as random number generator. | ||
| Attributes | ||
| ---------- | ||
| components_ : ndarray, shape (`n_samples`, `n_components`) | ||
| If fit, the matrix to unmix observed data. | ||
| References | ||
| ---------- | ||
| .. [2] Hyvärinen, A., 1999. Fast and robust fixed-point algorithms for | ||
| independent component analysis. IEEE transactions on Neural | ||
| Networks, 10(3), pp.626-634. | ||
| .. [3] Bell, A.J., Sejnowski, T.J., 1995. An information-maximization | ||
| approach to blind separation and blind deconvolution. Neural | ||
| computation, 7(6), pp.1129-1159. | ||
| .. [4] Lee, T.W., Girolami, M., Sejnowski, T.J., 1999. Independent | ||
| component analysis using an extended infomax algorithm for mixed | ||
| subgaussian and supergaussian sources. Neural computation, 11(2), | ||
| pp.417-441. | ||
| """ | ||
| def __init__(self, n_components: Union[int, float, str]=None, | ||
| whiten: bool=True, max_iter: int=1000, copy=True, | ||
| method: str= "fastica", | ||
| random_state: np.random.RandomState=None): | ||
| self.n_components: Union[int, float, str] = n_components | ||
| self.whiten: bool = whiten | ||
| self.max_iter: int = max_iter | ||
| self.copy: bool = copy | ||
| self.method: str = method | ||
| self.random_state: np.random.RandomState = random_state | ||
|
|
||
| def fit(self, X: np.ndarray, y=None) -> "IPCA": | ||
| scale: StandardScaler = StandardScaler(with_std=self.whiten) | ||
| pca: PCA = PCA(n_components=self.n_components, svd_solver="full", | ||
| copy=self.copy) | ||
| pca_pipeline: Pipeline = make_pipeline(scale, pca) | ||
| self.pca_projection_: np.ndarray = pca_pipeline.fit_transform(X) | ||
| self.components_: np.ndarray = pca.components_ | ||
| self.singular_values_: np.ndarray = pca.singular_values_ | ||
| self.explained_variance_: np.ndarray = pca.explained_variance_ | ||
| self.explained_variance_ratio_: np.ndarray = pca.explained_variance_ratio_ | ||
| return self | ||
|
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||
| def transform(self, X: np.ndarray) -> np.ndarray: | ||
| check_is_fitted(self, "components_") | ||
|
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| X: np.ndarray = check_array(X, copy=self.copy) | ||
| X = StandardScaler().fit_transform(X) | ||
| scale: StandardScaler = StandardScaler() | ||
| ica: ICA = ICA(whiten=False, method=self.method, max_iter=self.max_iter, | ||
| random_state=self.random_state) | ||
| ica_pipeline: Pipeline = make_pipeline(scale, ica) | ||
| S: np.ndarray = ica_pipeline.fit_transform(self.components_.T) | ||
|
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||
| # Sort signals by kurtosis and reduce dimensions. | ||
| kurtosis: np.ndarray = stats.kurtosis(S) | ||
| idx: np.ndarray = np.argsort(-kurtosis) | ||
| self.kurtosis_: np.ndarray = kurtosis[idx] | ||
| S: np.ndarray = S[:, idx][:, np.where(np.abs(self.kurtosis_) >= 1.)[0]] | ||
| S /= linalg.norm(S, ord=2) | ||
| self.signal_ = S.copy() | ||
| return S |
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