def experiment_schizophrenia_data(data_path='data', n_folds=5, iterations=10000, verbose=True, plot=True, random_state=None): """Run the experiments on the Schizophrenia dataset Parameters: ---------- data_path: string Path to the folder containing the dataset. n_folds: int The number of folds in a StratifiedKFold cross-validation iterations: int Number of iterations to compute the null distribution of balanced_accuracy and MMD^2_u verbose: bool plot: bool Whether to plot the results of the statistical tests. """ name = 'Schizophrenia' if verbose: print '\nWorking on %s dataset...' % name print '-----------------------' X, y = load_schizophrenia_data(data_path, verbose=verbose) # DCE + RBF if verbose: print '\n### Results for DCE_Embedding ###' gk_dce = GK_DCE(kernel_vector_space='rbf') K_dce = gk_dce.compare_pairwise(X) simple_experiment(K_dce, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dce', plot=plot, random_state=random_state) # DRE + RBF if verbose: print '\n### Results for DR_Embedding ###' gk_dre = GK_DRE(kernel_vector_space='rbf') K_dre = gk_dre.compare_pairwise(X) simple_experiment(K_dre, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_dr',plot=plot, random_state=random_state) # WL Kernel if verbose: print '\n### Results for WL_K_Embedding ###' gk_wl = GK_WL(th=0.2) K_wl = gk_wl.compare_pairwise(X) simple_experiment(K_wl, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_wl', plot=plot, random_state=random_state) # SP Kernel if verbose: print '\n### Results for SP_K_Embedding ###' gk_sp = GK_SP(th=0.2) K_sp = gk_sp.compare_pairwise(X) simple_experiment(K_sp, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_sp', plot=plot, random_state=random_state) # # NBS th = 0.5 mxcmp, mxcmp_null, p_value = apply_nbs(X, y, th, iterations, verbose) if plot: plot_statistic(mxcmp, mxcmp_null, p_value, data_name=name+'_nbs', stats_name='Max_comp_size')
def experiment_schizophrenia_data(data_path='data', n_folds=5, iterations=10000, verbose=True, plot=True, random_state=None): """Run the experiments on the Schizophrenia dataset Parameters: ---------- data_path: string Path to the folder containing the dataset. n_folds: int The number of folds in a StratifiedKFold cross-validation iterations: int Number of iterations to compute the null distribution of balanced_accuracy and MMD^2_u verbose: bool plot: bool Whether to plot the results of the statistical tests. """ name = 'Schizophrenia' if verbose: print '\nWorking on %s dataset...' % name print '-----------------------' X, y = load_schizophrenia_data(data_path, verbose=verbose) # DCE Embedding if verbose: print '\n### Results for DCE_Embedding ###' X_dce = DCE_embedding(X) K_dce = compute_rbf_kernel_matrix(X_dce) simple_experiment(K_dce, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dce', plot=plot, random_state=random_state) # DR Embedding if verbose: print '\n### Results for DR_Embedding ###' X_dr = DR_embedding(X) K_dr = compute_rbf_kernel_matrix(X_dr) simple_experiment(K_dr, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dr', plot=plot, random_state=random_state) # WL Kernel based Embedding if verbose: print '\n### Results for WL_K_Embedding ###' th = 0.2 K_wl = WL_K_embedding(X, th) simple_experiment(K_wl, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_wl', plot=plot, random_state=random_state) # SP Kernel based Embedding if verbose: print '\n### Results for SP_K_Embedding ###' th = 0.2 K_sp = SP_K_embedding(X, th) simple_experiment(K_sp, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_sp', plot=plot, random_state=random_state)
# for name in locs: # if verbose: # print '\nWorking on %s dataset...' % name # print '-----------------------' # X, y = load_1000_funct_connectome(data_path, name, # verbose=verbose) # check_instability_classification(X, y, location=name, # n_folds=n_folds, # iterations=iterations, # verbose=verbose, # reps=repetitions, seed=seed) # Working on the schizophrenia data if verbose: print '\nWorking on Schizophrenia dataset...' X, y = load_schizophrenia_data(data_path, verbose=verbose) check_instability_classification(X, y, location='Schizophrenia', n_folds=n_folds, iterations=iterations, verbose=verbose, reps=repetitions, seed=seed) # # Working on URI data # X, y = load_kernel_matrix() # check_instability_classification(X, y, n_folds=n_folds, # iterations=iterations, # verbose=verbose, # reps=repetitions, seed=seed)
def experiment_schizophrenia_data(data_path='data', n_folds=5, iterations=10000, verbose=True, plot=True, random_state=None): """Run the experiments on the Schizophrenia dataset Parameters: ---------- data_path: string Path to the folder containing the dataset. n_folds: int The number of folds in a StratifiedKFold cross-validation iterations: int Number of iterations to compute the null distribution of balanced_accuracy and MMD^2_u verbose: bool plot: bool Whether to plot the results of the statistical tests. """ name = 'Schizophrenia' if verbose: print '\nWorking on %s dataset...' % name print '-----------------------' X, y = load_schizophrenia_data(data_path, verbose=verbose) # DCE Embedding if verbose: print '\n### Results for DCE_Embedding ###' X_dce = DCE_embedding(X) K_dce = compute_rbf_kernel_matrix(X_dce) simple_experiment(K_dce, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dce', plot=plot, random_state=random_state) # DR Embedding if verbose: print '\n### Results for DR_Embedding ###' X_dr = DR_embedding(X) K_dr = compute_rbf_kernel_matrix(X_dr) simple_experiment(K_dr, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_dr',plot=plot, random_state=random_state) # WL Kernel based Embedding if verbose: print '\n### Results for WL_K_Embedding ###' th = 0.2 K_wl = WL_K_embedding(X, th) simple_experiment(K_wl, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_wl', plot=plot, random_state=random_state) # SP Kernel based Embedding if verbose: print '\n### Results for SP_K_Embedding ###' th = 0.2 K_sp = SP_K_embedding(X, th) simple_experiment(K_sp, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name+'_sp', plot=plot, random_state=random_state)
# # Working on the functional connectome data # for name in locs: # if verbose: # print '\nWorking on %s dataset...' % name # print '-----------------------' # X, y = load_1000_funct_connectome(data_path, name, # verbose=verbose) # check_instability_classification(X, y, location=name, # n_folds=n_folds, # iterations=iterations, # verbose=verbose, # reps=repetitions, seed=seed) # Working on the schizophrenia data if verbose: print '\nWorking on Schizophrenia dataset...' X, y = load_schizophrenia_data(data_path, verbose=verbose) check_instability_classification(X, y, location='Schizophrenia', n_folds=n_folds, iterations=iterations, verbose=verbose, reps=repetitions, seed=seed) # # Working on URI data # X, y = load_kernel_matrix() # check_instability_classification(X, y, n_folds=n_folds, # iterations=iterations, # verbose=verbose, # reps=repetitions, seed=seed)
def experiment_schizophrenia_data(data_path='data', n_folds=5, iterations=10000, verbose=True, plot=True, random_state=None): """Run the experiments on the Schizophrenia dataset Parameters: ---------- data_path: string Path to the folder containing the dataset. n_folds: int The number of folds in a StratifiedKFold cross-validation iterations: int Number of iterations to compute the null distribution of balanced_accuracy and MMD^2_u verbose: bool plot: bool Whether to plot the results of the statistical tests. """ name = 'Schizophrenia' if verbose: print '\nWorking on %s dataset...' % name print '-----------------------' X, y = load_schizophrenia_data(data_path, verbose=verbose) # DCE + RBF if verbose: print '\n### Results for DCE_Embedding ###' gk_dce = GK_DCE(kernel_vector_space='rbf') K_dce = gk_dce.compare_pairwise(X) simple_experiment(K_dce, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dce', plot=plot, random_state=random_state) # DRE + RBF if verbose: print '\n### Results for DR_Embedding ###' gk_dre = GK_DRE(kernel_vector_space='rbf') K_dre = gk_dre.compare_pairwise(X) simple_experiment(K_dre, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_dr', plot=plot, random_state=random_state) # WL Kernel if verbose: print '\n### Results for WL_K_Embedding ###' gk_wl = GK_WL(th=0.2) K_wl = gk_wl.compare_pairwise(X) simple_experiment(K_wl, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_wl', plot=plot, random_state=random_state) # SP Kernel if verbose: print '\n### Results for SP_K_Embedding ###' gk_sp = GK_SP(th=0.2) K_sp = gk_sp.compare_pairwise(X) simple_experiment(K_sp, y, n_folds=n_folds, iterations=iterations, verbose=verbose, data_name=name + '_sp', plot=plot, random_state=random_state) # # NBS th = 0.5 mxcmp, mxcmp_null, p_value = apply_nbs(X, y, th, iterations, verbose) if plot: plot_statistic(mxcmp, mxcmp_null, p_value, data_name=name + '_nbs', stats_name='Max_comp_size')