Spaces:
Runtime error
Runtime error
File size: 8,790 Bytes
ba5dcdc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
# Copyright 2020 Erik Härkönen. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
# OF ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
from sklearn.decomposition import FastICA, PCA, IncrementalPCA, MiniBatchSparsePCA, SparsePCA, KernelPCA
import fbpca
import numpy as np
import itertools
from types import SimpleNamespace
# ICA
class ICAEstimator():
def __init__(self, n_components):
self.n_components = n_components
self.maxiter = 10000
self.whiten = True # ICA: whitening is essential, should not be skipped
self.transformer = FastICA(n_components, random_state=0, whiten=self.whiten, max_iter=self.maxiter)
self.batch_support = False
self.stdev = np.zeros((n_components,))
self.total_var = 0.0
def get_param_str(self):
return "ica_c{}{}".format(self.n_components, '_w' if self.whiten else '')
def fit(self, X):
self.transformer.fit(X)
if self.transformer.n_iter_ >= self.maxiter:
raise RuntimeError(f'FastICA did not converge (N={X.shape[0]}, it={self.maxiter})')
# Normalize components
self.transformer.components_ /= np.sqrt(np.sum(self.transformer.components_**2, axis=-1, keepdims=True))
# Save variance for later
self.total_var = X.var(axis=0).sum()
# Compute projected standard deviations
self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
# Sort components based on explained variance
idx = np.argsort(self.stdev)[::-1]
self.stdev = self.stdev[idx]
self.transformer.components_[:] = self.transformer.components_[idx]
def get_components(self):
var_ratio = self.stdev**2 / self.total_var
return self.transformer.components_, self.stdev, var_ratio # ICA outputs are not normalized
# Incremental PCA
class IPCAEstimator():
def __init__(self, n_components):
self.n_components = n_components
self.whiten = False
self.transformer = IncrementalPCA(n_components, whiten=self.whiten, batch_size=max(100, 2*n_components))
self.batch_support = True
def get_param_str(self):
return "ipca_c{}{}".format(self.n_components, '_w' if self.whiten else '')
def fit(self, X):
self.transformer.fit(X)
def fit_partial(self, X):
try:
self.transformer.partial_fit(X)
self.transformer.n_samples_seen_ = \
self.transformer.n_samples_seen_.astype(np.int64) # avoid overflow
return True
except ValueError as e:
print(f'\nIPCA error:', e)
return False
def get_components(self):
stdev = np.sqrt(self.transformer.explained_variance_) # already sorted
var_ratio = self.transformer.explained_variance_ratio_
return self.transformer.components_, stdev, var_ratio # PCA outputs are normalized
# Standard PCA
class PCAEstimator():
def __init__(self, n_components):
self.n_components = n_components
self.solver = 'full'
self.transformer = PCA(n_components, svd_solver=self.solver)
self.batch_support = False
def get_param_str(self):
return f"pca-{self.solver}_c{self.n_components}"
def fit(self, X):
self.transformer.fit(X)
# Save variance for later
self.total_var = X.var(axis=0).sum()
# Compute projected standard deviations
self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
# Sort components based on explained variance
idx = np.argsort(self.stdev)[::-1]
self.stdev = self.stdev[idx]
self.transformer.components_[:] = self.transformer.components_[idx]
# Check orthogonality
dotps = [np.dot(*self.transformer.components_[[i, j]])
for (i, j) in itertools.combinations(range(self.n_components), 2)]
if not np.allclose(dotps, 0, atol=1e-4):
print('IPCA components not orghogonal, max dot', np.abs(dotps).max())
self.transformer.mean_ = X.mean(axis=0, keepdims=True)
def get_components(self):
var_ratio = self.stdev**2 / self.total_var
return self.transformer.components_, self.stdev, var_ratio
# Facebook's PCA
# Good default choice: very fast and accurate.
# Very high sample counts won't fit into RAM,
# in which case IncrementalPCA must be used.
class FacebookPCAEstimator():
def __init__(self, n_components):
self.n_components = n_components
self.transformer = SimpleNamespace()
self.batch_support = False
self.n_iter = 2
self.l = 2*self.n_components
def get_param_str(self):
return "fbpca_c{}_it{}_l{}".format(self.n_components, self.n_iter, self.l)
def fit(self, X):
U, s, Va = fbpca.pca(X, k=self.n_components, n_iter=self.n_iter, raw=True, l=self.l)
self.transformer.components_ = Va
# Save variance for later
self.total_var = X.var(axis=0).sum()
# Compute projected standard deviations
self.stdev = np.dot(self.transformer.components_, X.T).std(axis=1)
# Sort components based on explained variance
idx = np.argsort(self.stdev)[::-1]
self.stdev = self.stdev[idx]
self.transformer.components_[:] = self.transformer.components_[idx]
# Check orthogonality
dotps = [np.dot(*self.transformer.components_[[i, j]])
for (i, j) in itertools.combinations(range(self.n_components), 2)]
if not np.allclose(dotps, 0, atol=1e-4):
print('FBPCA components not orghogonal, max dot', np.abs(dotps).max())
self.transformer.mean_ = X.mean(axis=0, keepdims=True)
def get_components(self):
var_ratio = self.stdev**2 / self.total_var
return self.transformer.components_, self.stdev, var_ratio
# Sparse PCA
# The algorithm is online along the features direction, not the samples direction
# => no partial_fit
class SPCAEstimator():
def __init__(self, n_components, alpha=10.0):
self.n_components = n_components
self.whiten = False
self.alpha = alpha # higher alpha => sparser components
#self.transformer = MiniBatchSparsePCA(n_components, alpha=alpha, n_iter=100,
# batch_size=max(20, n_components//5), random_state=0, normalize_components=True)
self.transformer = SparsePCA(n_components, alpha=alpha, ridge_alpha=0.01,
max_iter=100, random_state=0, n_jobs=-1, normalize_components=True) # TODO: warm start using PCA result?
self.batch_support = False # maybe through memmap and HDD-stored tensor
self.stdev = np.zeros((n_components,))
self.total_var = 0.0
def get_param_str(self):
return "spca_c{}_a{}{}".format(self.n_components, self.alpha, '_w' if self.whiten else '')
def fit(self, X):
self.transformer.fit(X)
# Save variance for later
self.total_var = X.var(axis=0).sum()
# Compute projected standard deviations
# NB: cannot simply project with dot product!
self.stdev = self.transformer.transform(X).std(axis=0) # X = (n_samples, n_features)
# Sort components based on explained variance
idx = np.argsort(self.stdev)[::-1]
self.stdev = self.stdev[idx]
self.transformer.components_[:] = self.transformer.components_[idx]
# Check orthogonality
dotps = [np.dot(*self.transformer.components_[[i, j]])
for (i, j) in itertools.combinations(range(self.n_components), 2)]
if not np.allclose(dotps, 0, atol=1e-4):
print('SPCA components not orghogonal, max dot', np.abs(dotps).max())
def get_components(self):
var_ratio = self.stdev**2 / self.total_var
return self.transformer.components_, self.stdev, var_ratio # SPCA outputs are normalized
def get_estimator(name, n_components, alpha):
if name == 'pca':
return PCAEstimator(n_components)
if name == 'ipca':
return IPCAEstimator(n_components)
elif name == 'fbpca':
return FacebookPCAEstimator(n_components)
elif name == 'ica':
return ICAEstimator(n_components)
elif name == 'spca':
return SPCAEstimator(n_components, alpha)
else:
raise RuntimeError('Unknown estimator') |