def btnConvert_click(self):
msgBox = QMessageBox()
# Kernel
Kernel = ui.cbKernel.currentText()
# Gamma
try:
Gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Degree
try:
Degree = np.int32(ui.txtDegree.text())
except:
msgBox.setText("Degree is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coef0
try:
Coef0 = np.float(ui.txtCoef0.text())
except:
msgBox.setText("Coef0 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Alpha
try:
Alpha = np.int32(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter <= 0:
MaxIter = None
# Number of Job
try:
NJob = np.int32(ui.txtJobs.text())
except:
msgBox.setText("The number of parallel jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NJob < -1 or NJob == 0:
msgBox.setText(
"The number of parallel jobs must be -1 or greater than 0!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.rbScale.isChecked() == True and ui.rbALScale.isChecked(
) == False:
msgBox.setText(
"Subject Level Normalization is just available for Subject Level Analysis!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = io.loadmat(InFile)
OutData = dict()
OutData["imgShape"] = InData["imgShape"]
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked() and (not ui.rbScale.isChecked()):
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(X)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = Subject
except:
print("Cannot load Subject ID")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = InData[ui.txtLabel.currentText()]
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = InData[ui.txtTask.currentText()]
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = InData[ui.txtRun.currentText()]
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = InData[
ui.txtCounter.currentText()]
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = InData[ui.txtScan.currentText()]
Models = dict()
Models["Name"] = "KPCA"
if ui.rbALScale.isChecked():
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
X_Sub.append(
preprocessing.scale(
X[np.where(Subject == subj)[1], :]))
print("Data in subject level is scaled, X_" + str(subj) +
"~N(0,1).")
else:
X_Sub.append(X[np.where(Subject == subj)[1], :])
print("Subject ", subj, " is extracted from data.")
print("Running KPCA in subject level ...")
X_Sub_PCA = list()
lenPCA = len(X_Sub)
for xsubindx, xsub in enumerate(X_Sub):
model = KernelPCA(n_components=NumFea,kernel=Kernel,gamma=Gamma,degree=Degree,\
coef0=Coef0, alpha=Alpha, tol=Tol, max_iter=MaxIter, n_jobs=NJob)
X_Sub_PCA.append(model.fit_transform(xsub))
Models["Model" + str(xsubindx + 1)] = str(
model.get_params(deep=True))
print("KPCA: ", xsubindx + 1, " of ", lenPCA, " is done.")
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub_PCA):
X_new = np.concatenate(
(X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA,
" is done.")
OutData[ui.txtOData.text()] = X_new
else:
print("Running KPCA ...")
model = KernelPCA(n_components=NumFea,kernel=Kernel,gamma=Gamma,degree=Degree,\
coef0=Coef0, alpha=Alpha, tol=Tol, max_iter=MaxIter, n_jobs=NJob)
OutData[ui.txtOData.text()] = model.fit_transform(X)
Models["Model"] = str(model.get_params(deep=True))
OutData["ModelParameter"] = Models
print("Saving ...")
io.savemat(ui.txtOutFile.text(), mdict=OutData)
print("DONE.")
msgBox.setText("Kernel PCA is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
#ETAPA XX: APLICAÇÃO DO KERNEL PCA
#Bloco 01: Aplicação do KernelPCA
#Variance Caused by Each of the Principal Components
from sklearn.decomposition import KernelPCA
kpca_linear = KernelPCA(kernel='linear')
kpca_linear.get_params().keys()
X_train_kpca_new = kpca_linear.fit_transform(X_train_kpca)
X_test_kpca_new = kpca_linear.transform(X_test_kpca)
print('Original Number of Features:', X_train_kpca.shape[1])
print('Reduced Number of Features:', X_train_kpca_new.shape[1])
print('Original Number of Features:', X_test_kpca.shape[1])
print('Reduced Number of Features:', X_test_kpca_new.shape[1])
from pprint import pprint
print('Parameters Currently In Use:\n')
pprint(kpca_linear.get_params())
explained_variance_kpca_linear = kpca_linear.explained_variance_ratio_
kpca_rbf = KernelPCA(kernel='rbf')
#kpca = KernelPCA(kernel = "rbf", gamma = 15, n_components = 1)
kpca_rbf.get_params().keys()
def main():
print("Enter main()")
#==========================================================================
# カーネル主成分分析 [kernelPCA] による教師なしデータの次元削除、特徴抽出
# scikit-learn ライブラリでのカーネル主成分分析使用
#==========================================================================
#====================================================
# Data Preprocessing（前処理）
#====================================================
#----------------------------------------------------
# read & set data
#----------------------------------------------------
# 検証用サンプルデータセットの生成
#dat_X, dat_y = DataPreProcess.DataPreProcess.generateMoonsDataSet() # 半月状のデータセット
dat_X, dat_y = DataPreProcess.DataPreProcess.generateCirclesDataSet() # 同心円状のデータセット
plot_numUp = 500 #
plot_numDown = 500 #
#========================================================================
# Learning Process
#========================================================================
# scikit-learn ライブラリでの PCA
pca1 = PCA( n_components = 2 ) # PC1, PC2
pca2 = PCA( n_components = None) # 主成分（固有値）解析用
X_pca1 = pca1.fit_transform( dat_X )
X_pca2 = pca2.fit_transform( dat_X )
# pca2 オブジェクトの内容確認
print( "pca2.explained_variance_ : \n", pca2.explained_variance_ )
print( "pca2.explained_variance_ratio_ : \n", pca2.explained_variance_ratio_ ) # 寄与率（分散比）
print( "numpy.cumsum( pca2.explained_variance_ratio_ ) : \n", numpy.cumsum( pca2.explained_variance_ratio_ ) ) # 累積寄与率
print( "pca2.components_ : \n", pca2.components_ ) # 主成分ベクトル（固有ベクトル）
print( "pca2.get_covariance() : \n", pca2.get_covariance() ) # 共分散分散行列
print( "numpy.linalg.eig( pca2.get_covariance() )[0] : \n" , numpy.linalg.eig( pca2.get_covariance() )[0] ) # 固有値のリスト
# scikit-learn ライブラリでの kernelPCA
scikit_kpca1 = KernelPCA(
n_components = 2,
kernel = 'rbf', # カーネル関数として, RBF カーネルを指定
gamma = 15, # カーネル関数のパラメータ
n_jobs = -1 # CPUの並列処理 (default=1)
)
scikit_kpca2 = KernelPCA(
n_components = None,
kernel = 'rbf', # カーネル関数として, RBF カーネルを指定
gamma = 15, # カーネル関数のパラメータ
remove_zero_eig = True, # カーネル行列の固有値 0 となるものを削除
n_jobs = -1 # CPUの並列処理 (default=1)
)
X_scikit_kpca1 = scikit_kpca1.fit_transform( dat_X )
X_scikit_kpca2 = scikit_kpca2.fit_transform( dat_X )
# scikit_kpca2 オブジェクトの内容確認
print( "scikit_kpca2.get_params() : \n", scikit_kpca2.get_params() )
print( "scikit_kpca2.coef0 : \n", scikit_kpca2.coef0 )
print( "scikit_kpca2.lambdas_ : \n", scikit_kpca2.lambdas_ ) # カーネル行列の固有値
print( "scikit_kpca2.lambdas_[0:40] : \n", scikit_kpca2.lambdas_[0:40] ) # カーネル行列の固有値
#====================================================
# 汎化性能の評価
#====================================================
#==========================================================
# 検証用サンプルデータ（半月）での図を plot（通常のPCA）
#==========================================================
#------------------------------------
# サンプルデータの散布図 plot
#------------------------------------
# 現在の図をクリア
plt.clf()
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 1)
plt.grid()
# サンプルデータの散布図を plot
plt.scatter(
dat_X[ dat_y == 0, 0 ], dat_X[ dat_y == 0, 1 ],
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
dat_X[ dat_y == 1, 0 ], dat_X[ dat_y == 1, 1 ],
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title( "verification data \n sklearn.datasets.make_moons() dataset" )
plt.legend( loc = 'best' )
#plt.tight_layout()
#----------------------------------------
# 変換した主成分空間での散布図 plot
#----------------------------------------
# x_axis = PC1, y_axis = PC2 (２次元→２次元で次元削除を行わない)
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 2)
plt.grid()
# サンプルデータの散布図を plot
plt.scatter(
X_pca1[ dat_y == 0, 0 ], X_pca1[ dat_y == 0, 1 ],
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
dat_X[ dat_y == 1, 0 ], X_pca1[ dat_y == 1, 1 ],
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title("transformed data (PCA) \n dimension is not deleted")
plt.xlabel( "PC1" )
plt.ylabel( "PC2" )
plt.legend( loc = 'best' )
#plt.tight_layout()
# x_axis = PC1 (２次元→１次元で次元削除)
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 3)
# サンプルデータの散布図を plot
plt.scatter(
X_pca1[ dat_y == 0, 0 ], numpy.zeros( (len(X_pca1)/2,1) ) + 0.02,
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
dat_X[ dat_y == 1, 0 ], numpy.zeros( (len(X_pca1)/2,1) ) - 0.02,
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title("transformed data (PCA) \n dimension is deleted")
plt.xlabel( "PC1" )
plt.ylim( [-1,1] )
plt.axhline( 0.0, color = 'gray', linestyle = '--', linewidth = 1 )
plt.yticks( [] )
plt.legend( loc = 'best' )
#plt.tight_layout()
#plt.show()
#------------------------------------
# 第 k 主成分の固有値の図 plot
#------------------------------------
# 現在の図をクリア
#plt.clf()
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 4)
# 棒グラフ（第１主成分, 第２主成分）
plt.bar(
range(1, 3), numpy.linalg.eig( pca2.get_covariance() )[0],
alpha = 1.0,
align = 'center',
label = 'Eigenvalues',
color = "blue"
)
plt.axhline( 0.2, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.4, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.6, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.8, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 1.0, color = 'gray', linestyle = '--', linewidth = 1 )
plt.xticks(
range(1, 3),
[ "lamda_1", "lamda_2", "" ],
rotation = 90
)
plt.title("Principal components - Eigenvalues (PCA)")
plt.xlabel('Principal components')
plt.ylabel('Eigenvalues')
plt.legend( loc = 'best' )
plt.tight_layout()
#----------------------------------------
# 第 k 主成分の寄与率＆累積寄与率の plot
#----------------------------------------
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 5)
# 棒グラフ（第１主成分, 第２主成分）赤棒
plt.bar(
range(1, 3), pca2.explained_variance_ratio_,
alpha = 1.0,
align = 'center',
label = ' explained variance ratio(principal component 1 and 2)',
color = "blue"
)
# 累積寄与率の階段グラフ
plt.step(
range(1, 3), numpy.cumsum( pca2.explained_variance_ratio_ ),
where = 'mid',
label='cumulative proportion of the variance',
color = "red"
)
plt.axhline( 0.1, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.2, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.3, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.4, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.5, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.6, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.7, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.8, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 0.9, color = 'gray', linestyle = '--', linewidth = 1 )
plt.axhline( 1.0, color = 'gray', linestyle = '--', linewidth = 1 )
plt.xticks( range(1, 3), range(1, 3) )
plt.title("Principal components - Proportion of the variance (PCA)")
plt.xlabel('Principal components')
plt.ylabel('Proportion of the variance \n individual explained variance')
plt.legend( loc = 'best' )
plt.tight_layout()
plt.savefig("./kernelPCA_scikit-learn_1.png", dpi = 300, bbox_inches = 'tight' )
plt.show()
#===========================================================
# 検証用サンプルデータ（半月）での図を plot（カーネルPCA）
#===========================================================
#------------------------------------
# サンプルデータの散布図 plot
#------------------------------------
# 現在の図をクリア
plt.clf()
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 1)
plt.grid()
# サンプルデータの散布図を plot
plt.scatter(
dat_X[ dat_y == 0, 0 ], dat_X[ dat_y == 0, 1 ],
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
dat_X[ dat_y == 1, 0 ], dat_X[ dat_y == 1, 1 ],
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title( "verification data \n sklearn.datasets.make_moons() dataset" )
plt.legend( loc = 'best' )
#plt.tight_layout()
#----------------------------------------
# 変換した主成分空間での散布図 plot
#----------------------------------------
# x_axis = PC1, y_axis = PC2 (２次元→２次元で次元削除を行わない)
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 2)
plt.grid()
# サンプルデータの散布図を plot
plt.scatter(
X_scikit_kpca1[ dat_y == 0, 0 ], X_scikit_kpca1[ dat_y == 0, 1 ],
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
X_scikit_kpca1[ dat_y == 1, 0 ], X_scikit_kpca1[ dat_y == 1, 1 ],
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title("transformed data (RBF-kernel PCA) \n dimension is not deleted")
plt.xlabel( "PC1" )
plt.ylabel( "PC2" )
plt.legend( loc = 'best' )
#plt.tight_layout()
# x_axis = PC1 (２次元→１次元で次元削除)
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 3)
# サンプルデータの散布図を plot
plt.scatter(
X_scikit_kpca1[ dat_y == 0, 0 ], numpy.zeros( (len(X_scikit_kpca1)/2,1) ) + 0.02,
color = 'red',
marker = '^',
label = '0',
alpha = 0.5
)
plt.scatter(
X_scikit_kpca1[ dat_y == 1, 0 ], numpy.zeros( (len(X_scikit_kpca1)/2,1) ) - 0.02,
color = 'blue',
marker = 'o',
label = '1',
alpha = 0.5
)
plt.title("transformed data (RBF-kernelPCA) \n dimension is deleted")
plt.xlabel( "PC1" )
plt.ylim( [-1,1] )
plt.axhline( 0.0, color = 'gray', linestyle = '--', linewidth = 1 )
plt.yticks( [] )
plt.legend( loc = 'best' )
#plt.tight_layout()
#--------------------------
# 固有値の図
#--------------------------
#plt.clf()
# plt.subplot(行数, 列数, 何番目のプロットか)
plt.subplot(2, 3, 4)
# 棒グラフ（固有値の値が上位のものを表示 ）
plt.bar(
range( 1, 41 ), scikit_kpca2.lambdas_[0:40],
alpha = 1.0,
align = 'center',
color = "blue"
)
plt.xticks(
range( 1, len(scikit_kpca2.lambdas_[0:40]) ),
rotation = 90
)
plt.title("Eigenvalues (RBF-kernelPCA)")
plt.xlabel('index of Eigenvalues')
plt.ylabel('Eigenvalues')
plt.legend( loc = 'best' )
plt.tight_layout()
plt.savefig("./kernelPCA_scikit-learn_2.png", dpi = 300, bbox_inches = 'tight' )
plt.show()
print("Finish main()")
return
