#Import toy data and target X = pd.read_csv('../../data/simulated/mvnsim/mvnsim' + dataset + '.csv', sep=',', header=0, index_col=0).as_matrix() y = np.load('../../data/simulated/mvnsim/target' + dataset + '.npy') print(y) print(y.shape) print(X.shape) #Plot initial data plot_scatter( X, y, 'Initial data', x_label='x coordinate', y_label='y coordinate', #output='save', output='show', #path='../../figs/out/%s/%s/initial.png' % (scriptname, dataset) ) ## PREPROCESSING ## #Scale initial data to centre data X_scaled = scale(X) plot_scatter( X_scaled, y, 'Scaled data',
kpca_kernels = [] for kernel, abbreviation, kpca in kpcas: if kernel == 'Laplacian KPCA': X_kpca = kpca.fit_transform(K_lap) else: X_kpca = kpca.fit_transform(X_scaled) p2.plot_scatter( X_kpca, y, 'First 2 principal components after %s' % kernel, gamma=gamma, x_label='Principal component 1', y_label='Principal component 2', #output = 'show', #output='save', #path='%s%s_%spca_gamma%s.png' % (filepath, nowtime, abbreviation, gamma) ) #print('\nScatter plot of first two principal components after %sPCA for dataset %s saved.' % (kernel, dataset)) kpca_kernels.append(kernel) # Declare names of models deployed and ROC AUC for each model mdl_names = [] mean_aucs = [] for model_name, model_abv, model in models:
cv = StratifiedKFold(n_splits=10, random_state=10) # Declare KPCA kernels deployed kpca_kernels = [] for kernel, abbreviation, kpca in kpcas: X_kpca = kpca.fit_transform(X_scaled) plot_scatter( X_kpca, y, 'First 2 principal components after %sPCA' % kernel, gamma=gamma, x_label='Principal component 1', y_label='Principal component 2', #output = 'show', output='save', path='../../figs/out/%s/%s/%spca_gamma%s.png' % (scriptname, dataset, abbreviation, gamma)) print( '\nScatter plot of first two principal components after %sPCA for dataset %s saved.' % (kernel, dataset)) X_kpca = kpca.fit_transform(X) kpca_kernels.append(kernel) # Declare names of models deployed mdl_names = []
# Name of script to trace where images came from scriptname = 'gs_tune_1_2' #Select current toy dataset dataset = '013' #Import toy data and target X = pd.read_csv('../../data/simulated/mvnsim/mvnsim' + dataset + '.csv', sep=',', header=0, index_col=0).as_matrix() y = np.load('../../data/simulated/mvnsim/target' + dataset + '.npy') #Plot initial data plot_scatter(X, y, 'Initial data', x_label='x coordinate', y_label='y coordinate', output='save', path='../../figs/out/%s/%s/initial.png' % (scriptname, dataset) ) ## PREPROCESSING ## #Scale initial data to centre data X_scaled = scale(X) plot_scatter(X_scaled, y, 'Scaled data', x_label='x coordinate', y_label='y coordinate',
#Create directory if directory does not exist filepath = '../../figs/out/%s/%s/' % (scriptname, dataset) if not os.path.exists(filepath): os.makedirs(filepath) #Import toy data and target X = pd.read_csv('../../data/simulated/mvnsim/mvnsim' + dataset + '.csv', sep=',', header=0, index_col=0).as_matrix() y = np.load('../../data/simulated/mvnsim/target' + dataset + '.npy') #Plot initial data plot_scatter(X, y, 'Initial data', x_label='x coordinate', y_label='y coordinate', #output='save', #path='%sinitial.png' % filepath output = 'show', ) ## PREPROCESSING ## #Scale initial data to centre data X_scaled = scale(X) plot_scatter(X_scaled, y, 'Scaled data', x_label='x coordinate',
# To utilise precomputed kernel(s) if kernel == 'Laplacian KPCA': X_kpca = kpca.fit_transform(kpca_lap) #elif kernel == 'Chi Squared KPCA': # X_kpca = kpca.fit_transform(kpca_chi) else: X_kpca = kpca.fit_transform(X_scaled) p2f.plot_scatter( X_kpca, y, 'First 2 principal components after %s' % kernel, gamma=gamma, x_label='Principal component 1', y_label='Principal component 2', #output = 'show', output='save', path='%s%s_%s_gamma%s.png' % (filepath, nowtime, abbreviation, gamma), writepath='%s%s_%s_%s_plottingdata.txt' % (plotpath, nowtime, scriptname, dataset), dataset=dataset, kernel=kernel, ) print( '\nScatter plot of first two principal components after %s for dataset %s saved.' % (kernel, dataset)) kpca_kernels.append(kernel) # Declare names of models deployed
X, y = make_circles(n_samples=1000, factor=.3, noise=.05, random_state=12) gamma = 5 x_kerns = [("linear", linear_kernel(X)), ("gaussian", rbf_kernel(X, gamma=gamma)), ("laplacian", laplacian_kernel(X, gamma=gamma)), ("cosine", cosine_similarity(X)), ("sigmoid", sigmoid_kernel(X))] fig = plt.figure(figsize=(8, 6)) p2f.plot_scatter(X, y, '', gamma=gamma, x_label='x coordinate', y_label='y coordinate', cat1='Category 1', cat0='Category 0', output='save', path='%sinit_scatter.png' % imgpath, jspath='%sinit_scatter.js' % jspath) for k_lab, k_X in x_kerns: p2f.plot_scatter(k_X, y, '', gamma=gamma, x_label='x coordinate', y_label='y coordinate', cat1='Category 1',