class PCAImputer:
def __init__(self, n_dimension):
self._q = n_dimension
def fit_transform(self,
data,
method='eig',
probabilistic=False,
n_iteration=100):
"""fitting a PCA to the original data by iterativly filling the missing entries
with value generated from PCA. Each missing entries are initialized with the
row mean."""
self._data = data.copy()
self._missing = np.isnan(data)
self._observed = ~self._missing
self._pca = PPCA(n_dimension=self._q)
row_defau = np.zeros(self._data.shape[0])
row_means = np.repeat(np.nanmean(self._data, axis=1, out=row_defau).reshape(-1, 1), \
self._data.shape[1], axis=1)
self._data[self._missing] = row_means[self._missing]
for i in range(n_iteration):
self._pca.fit(self._data, method=method)
self._data[self._missing] = self._pca.inverse_transform(self._pca.transform(self._data, \
probabilistic), probabilistic)[self._missing]
return self._data
def run_factorization(self, N, S, X, Z, I, K, k, n):
# Smart initialization
#rat = k/n
rat = filter_lowly_expressed_sites(k, n, 3)
nans = np.isnan(rat)
scaled_rat = scale_allelic_ratios(rat)
scaled_residual_rat = regress_out_cell_line(scaled_rat, Z)
rescaled_residual_rat = scale_allelic_ratios(scaled_residual_rat)
ppca = PPCA()
ppca.fit(data=np.transpose(rescaled_residual_rat),
d=K,
verbose=True,
tol=1e-6)
U = ppca.C
V = ppca.transform()
pickle.dump(ppca, open(self.output_root + '_model', 'wb'))
np.savetxt(self.output_root + '_temper_U.txt',
U,
fmt="%s",
delimiter='\t')
np.savetxt(self.output_root + '_temper_V.txt',
V.T,
fmt="%s",
delimiter='\t')
def initialize_variables(self):
self.N = self.allelic_counts.shape[0]
self.T = self.allelic_counts.shape[1]
self.num_cov = self.cov.shape[1]
# Randomly initialize (only U matters)
self.U = np.random.randn(self.N, self.K)
self.V = np.random.randn(self.K, self.T)
mom_conc_init = 1.0 / np.nanvar(
self.allelic_counts / self.total_counts)
self.conc = np.ones(self.T) * mom_conc_init
self.C = np.random.randn(self.num_cov, self.T)
ppca_init = False
if ppca_init == True:
rat = self.allelic_counts / self.total_counts
nans = np.isnan(rat)
scaled_rat = scale_allelic_ratios(rat)
scaled_residual_rat = regress_out_cell_line(
scaled_rat, self.cov[:, 1:])
rescaled_residual_rat = scale_allelic_ratios(scaled_residual_rat)
ppca = PPCA()
ppca.fit(data=np.transpose(rescaled_residual_rat),
d=self.K,
verbose=True,
tol=1e-6)
self.U = ppca.C / np.std(ppca.C)
def probPCA(self):
"""
Do probabilistic PCA
:return: result 2d array X
"""
# Normalizing X
sc = StandardScaler()
X_normalized = sc.fit_transform(self.X)
ppca = PPCA()
ppca.fit(data=X_normalized, d=2)
result = ppca.transform()
return result
def __init__(self, method='pca', n_dimension=0):
self._q = n_dimension
self._method = method
if method == 'pca':
self._pca = PCA(n_components=self._q)
elif method == 'ppca':
self._pca = PPCA(n_dimension=self._q)
else:
self._pca = BPCA()
def run_factorization(self, N, S, X, Z, I, K, k, n):
# Smart initialization
rat = k / n
nans = np.isnan(rat)
scaled_rat = scale_allelic_ratios(rat)
ppca = PPCA()
ppca.fit(data=np.transpose(scaled_rat), d=K, verbose=True)
U = ppca.C
V = ppca.transform()
pickle.dump(ppca, open(self.output_root + '_model', 'wb'))
np.savetxt(self.output_root + '_temper_U.txt',
U,
fmt="%s",
delimiter='\t')
np.savetxt(self.output_root + '_temper_V.txt',
V.T,
fmt="%s",
delimiter='\t')
def remove_nan_test(self):
N = 101
k = 23
p_nan = 0.02
n_components = 3
data = np.random.random((N, k))
for n in range(N):
for _k in range(k):
if random.random() < p_nan:
data[n, _k] = np.nan
pca = PPCA()
pca.fit(data, n_components)
self.assertEqual(pca.data[np.isnan(pca.data)].shape, (0, ))
self.assertEqual(pca.C.shape, (k, n_components))
self.assertEqual(pca.transform().shape, (N, n_components))
def run_ppca_initialization(self):
print('Starting PPCA initialization')
rat = self.allelic_counts/self.total_counts
nans = np.isnan(rat)
scaled_rat = scale_allelic_ratios(rat)
scaled_residual_rat = regress_out_cell_line(scaled_rat, self.Z)
rescaled_residual_rat = scale_allelic_ratios(scaled_residual_rat)
ppca = PPCA()
ppca.fit(data=np.transpose(rescaled_residual_rat), d=self.K, verbose=True, tol=1e-6)
self.U_init = ppca.C/np.std(ppca.C)
# Run bb-mf
with pm.Model() as bb_glm_init:
CONC = pm.HalfCauchy('CONC', beta=5, shape=(1,self.S), testval=self.conc_init)
BETA = pm.Normal('BETA', mu=0, tau=(1/1000000.0), shape=(self.S, self.num_cov), testval=self.beta_init)
#U = pm.Normal('U', mu=0, tau=(1.0/1.0), shape=(N, K), testval=self.U_init)
V = pm.Normal('V', mu=0, tau=(1.0/1.0), shape=(self.S, self.K), testval=np.zeros(self.V_init.shape))
MU_A = pm.Normal("MU_A", mu=0., sd=100**2, shape=(1,self.S), testval=self.mu_a_init)
SIGMA_A = pm.HalfCauchy("SIGMA_A", beta=5.0, shape=(1,self.S), testval=self.sigma_a_init)
mu_a_mat = pm.math.dot(np.ones((self.I,1)), MU_A)
sigma_a_mat = pm.math.dot(np.ones((self.I,1)), SIGMA_A)
A = pm.Normal('A', mu=mu_a_mat, sigma=sigma_a_mat, shape=(self.I,self.S), testval=self.A_init)
p = pm.math.invlogit(pm.math.dot(self.cov, BETA.T) + pm.math.dot(self.U_init,V.T) + A[self.Z,:])
conc_mat = pm.math.dot(np.ones((self.N,1)), CONC)
R = pm.BetaBinomial('like',alpha=(p*conc_mat)[~nans], beta=((1.0-p)*conc_mat)[~nans], n=self.total_counts[~nans], observed=self.allelic_counts[~nans])
approx_init = pm.fit(method='advi', n=2000)
pickle.dump(approx_init, open(self.output_root + '_model_init', 'wb'))
init_dict = approx_init.bij.rmap(approx_init.params[0].eval())
self.beta_init = init_dict['BETA']
self.A_init = init_dict['A']
self.sigma_a_init = np.exp(init_dict['SIGMA_A_log__'])
self.mu_a_init = init_dict['MU_A']
self.conc_init = np.exp(init_dict['CONC_log__'])
self.V_init = init_dict['V']
print('Smart PPCA complete')
def reducePCA(x, ndim):
# if there are any nans in any of the lists, use ppca
if np.isnan(np.vstack(x)).any():
warnings.warn(
'Missing data: Inexact solution computed with PPCA (see https://github.com/allentran/pca-magic for details)'
)
# ppca if missing data
m = PPCA(np.vstack(x))
m.fit(d=ndim)
x_pca = m.transform()
# if the whole row is missing, return nans
all_missing = [
idx for idx, a in enumerate(np.vstack(x))
if all([type(b) == np.nan for b in a])
]
if len(all_missing) > 0:
for i in all_missing:
x_pca[i, :] = np.nan
# get the original lists back
if len(x) > 1:
x_split = np.cumsum([i.shape[0] for i in x][:-1])
return list(np.split(x_pca, x_split, axis=0))
else:
return [x_pca]
else:
m = PCA(n_components=ndim, whiten=True)
m.fit(np.vstack(x))
if len(x) > 1:
return [m.transform(i) for i in x]
else:
return [m.transform(x[0])]
def run_ppca(data, features, ncomponents = None, min_obs = 0.1, use_corr = False):
X = data[features]
if ncomponents is None:
ncomponents=len(features)
ppca = PPCA(X,d = ncomponents, min_obs = min_obs)
ppca.standardize()
ppca.fit()
scores, loadings = ppca.transform()
explained_variance = ppca.var_exp
pca_columns = []
for i in range(scores.shape[1]):
pca_columns.append('pc_{}'.format(i))
data['pc_{}'.format(i)] = scores[:,i]
return list([data,explained_variance])
def compute_pca(data,
predictors,
how='pca',
what='factors',
n_components=1,
use_corr=True):
data[predictors] = data[predictors].astype('float64')
X = data[predictors].values
if (use_corr == True):
## If PCA are computed using the correlation matrix --> standardize the data (zero mean, unit std.)
scaler = preprocessing.StandardScaler()
X_std = scaler.fit_transform(X)
else:
## If PCA are computed using the covariance matrix --> center the data only (zero mean)
X_mean = np.mean(X, axis=0)
X_std = X - X_mean
if (how == 'pca'):
pca = PCA(n_components)
pca.fit(X_std)
factors = pca.transform(X_std)
explained_variance = pca.explained_variance_ratio_
Xhat_std = pca.inverse_transform(factors)
if (use_corr == True):
Xhat = scaler.inverse_transform(Xhat_std)
else:
Xhat = Xhat_std + X_mean
if (how == 'ppca'):
ppca = PPCA()
ppca.fit(X_std, n_components)
factors = ppca.transform()
explained_variance = ppca.var_exp
Xhat_std = ppca.reconstruct()
if (use_corr == True):
Xhat = scaler.inverse_transform(Xhat_std)
else:
Xhat = Xhat_std + X_mean
if (what != 'recon'):
pca_columns = []
for i in range(factors.shape[1]):
pca_columns.append('pca_{}'.format(i))
data['pca_{}'.format(i)] = factors[:, i]
return list([data, explained_variance])
else:
rec_data = pd.DataFrame(Xhat)
return rec_data
E = []
for i in range(len(X)):
Y = np.fromstring(X[i], dtype=int, sep = ' ')
Y = np.reshape(Y,(48, 48))
E.append(Y)
X_inp = np.array(E)
#X_train = X_inp.reshape(-1,X_inp.shape[1], X_inp.shape[2],1)
X_train = X_inp.astype('float32')
print X_inp
inp_img = X_train[0,:,:]
ppca = PPCA(inp_img)
ppca.fit(d=20, verbose=False)
component_mat = ppca.transform()
E_y = component_mat.reshape(1,component_mat.shape[0],component_mat.shape[1])
for i in range(1,len(X_train)):
print i
inp_img = X_train[i,:,:]
ppca = PPCA(inp_img)
try:
ppca.fit(d=20, verbose=False)
component_mat = ppca.transform()
shape = component_mat.shape
component_mat = component_mat.reshape(1,component_mat.shape[0],component_mat.shape[1])
if shape[1] == 20:
E_y = concatenate((E_y,component_mat))
def do_probabilistic_PCA(df, var, output):
""" Perform probabilistic PCA (PPCA) on scaled values for the whole screen
Args:
df: Existing combined dictionary
var: Minimum explained variance required
output: Output filenames
Return:
df: Updated with added 'DataPCA' values
exp_var: List of explained variance with each added PC
num_PCs: Number of PCs to explain var
PCA_loadings: Principal axes in feature space (n_components, n_features)
"""
print('Feature selection using probabilistic PCA...')
log_write(output['log'], 'Feature selection using probabilistic PCA...\n')
# Initialize parameters
exp_var = [0]
exp_var_ratio = []
num_PCs = 0
ppca = PPCA()
ppca.fit(df['DataScaled'], d=2)
exp_var.append(ppca.var_exp[0])
exp_var_ratio.append(ppca.var_exp[0])
# Do PPCA with number of components iteratively (max is the number of features, min is 2)
for i in range(2, df['DataScaled'].shape[1]):
num_PCs = i
ppca = PPCA()
ppca.fit(df['DataScaled'], d=i)
total_var = ppca.var_exp[i - 1]
exp_var.append(total_var)
exp_var_ratio.append(ppca.var_exp[i - 1] - ppca.var_exp[i - 2])
# End PCA if the total variance passes the minimum variance required
if total_var > var:
num_PCs = i
np.savetxt(output['PCAExplainedVariance'],
exp_var_ratio,
fmt='%0.4f')
break
# Do the final PCA with num_PCs
ppca = PPCA()
ppca.fit(df['Data'], d=num_PCs)
df['DataPCA'] = ppca.transform()
PPCA_loadings = np.transpose(ppca.C)
return df, exp_var, num_PCs, PPCA_loadings
SelectedImage = showImagesRandomImages(
3) #select and image randomly from MNSIT dataset
missingPercentage = 0.2 # missing rate percentage
missingImage = generateMissingFig(
SelectedImage,
missingPercentage) #inserting missing values to the original image
imputer = KNNImputer(n_neighbors=2, weights="uniform")
imputed_by_KNN = imputer.fit_transform(missingImage)
KNNImputed_RMSE = mean_squared_error(SelectedImage, imputed_by_KNN)
#plt.imshow(imputed_by_KNN, cmap='gray', vmin=0, vmax=1)
#plt.show()
imputer = MissForest()
MissForest_imputed = imputer.fit_transform(missingImage)
MissForest_RMSE = mean_squared_error(SelectedImage, MissForest_imputed)
#plt.imshow(MissForest_imputed, cmap='gray', vmin=0, vmax=1)
#plt.show()
imputer = IterativeImputer()
MICE_imputed = imputer.fit_transform(missingImage)
MICE_RMSE = mean_squared_error(SelectedImage, MICE_imputed)
#plt.imshow(MICE_imputed, cmap='gray', vmin=0, vmax=1)
#plt.show()
ppca = PPCA()
ppca.fit(data=SelectedImage, d=100, verbose=True)
PPCA_imputed = ppca.transform(missingImage)
PPCA_RMSE = mean_squared_error(SelectedImage, PPCA_imputed)
#plt.imshow(PPCA_imputed, cmap='gray', vmin=0, vmax=1)
#plt.show()
# @Date: 2020-07-07 11:22:28
# @Last Modified by: ashayaan
# @Last Modified time: 2020-07-07 12:44:33
import torch
import pandas as pd
import numpy as np
import json
from ppca import PPCA
import pickle
if __name__ == '__main__':
roberta_features = pd.read_csv('../data/roberta.csv')
roberta_features = roberta_features.set_index('idx')
mca_features = pd.read_csv('../data/mca.csv')
mca_features = mca_features.set_index('idx')
pca_features = pd.read_csv('../data/pca.csv')
pca_features = pca_features.set_index('idx')
links = pd.read_csv('../data/ml-20m/links.csv')
df = pd.concat([roberta_features, mca_features, pca_features], axis=1)
ppca = PPCA()
ppca.fit(data=df.values.astype(float),d=128,verbose=True)
print(ppca.var_exp)
transformed = ppca.transform()
films_dict = dict([(k, torch.tensor(transformed[i]).float()) for k, i in zip(df.index, range(transformed.shape[0]))])
pickle.dump(films_dict, open('../data//ml20_pca128.pkl', 'wb'))
import numpy as np
import matplotlib.pyplot as plt
from numpy.random import multivariate_normal
from ppca import PPCA
if __name__ == '__main__':
cov = np.diag([10, 9, 8, 7] + [1]*28 + [6, 5, 4, 3] + [1]*28)**2
data = multivariate_normal(np.zeros(64), cov, 256)
ppca1 = PPCA(n_dimension=4)
ppca1.fit(data, method='EM')
ppca2 = PPCA(n_dimension=4)
ppca2.fit(data, method='eig')
print('\n\n\n\n**TEST FITTING THE COVARIANCE MATRIX**')
plt.matshow(cov);
print('\n\noriginal covariance matrix')
plt.show()
plt.matshow(ppca1._C);
print('\n\nfitted covariance matrix (fitted by EM)')
plt.show()
plt.matshow(ppca2._C);
print('\n\nfitted covariance matrix (fitted by eigen)')
plt.show()
print('\n\n\n\n**TEST GENERATING DATA**')
plt.scatter(data[:, 0], data[:, 1], alpha=0.2);
print('\n\noriginal data (first 2 dimensions)')
plt.show()
gene = ppca1.generate(256)