reliability = clf.predict(X_test) # outliers = -1 data = [] output = 'test2.csv' for i in range(len(X_test)): satom1 = periodic_table.get_el_sp(int(X_test[i][0])) satom2 = periodic_table.get_el_sp(int(X_test[i][1])) natom1 = int(X_test[i][2]) natom2 = int(X_test[i][3]) str_mat = str(satom1) + str(natom1) + str(satom2) + str(natom2) formula = Composition(str_mat).reduced_formula temp = (formula, int(X_test[i][4]), int(y_pred[i]), reliability[i]) data.append(temp) properties=['formula','P', 'Tc', 'AD'] df = pd.DataFrame(data, columns=properties) df.sort_values('Tc', ascending=False, inplace=True) df.to_csv(output, index=False) #df_in_ = df[df.AD == 1] #df_in_.to_csv(output, index=False) print('Predicted Tc is written in file {}'.format(output)) #%% param_grid = [ {'kernel': ['rbf'], 'gamma': range_g, 'C': range_c,'epsilon': range_e}, ] for i in range(10): dcv(X_train, y_train, model, param_grid) print('{:.2f} seconds '.format(time() - start))
y_pred = gscv.predict(X_train) print('train data: ', end="") print_score(y_train, y_pred) y_pred = gscv.predict(X_test) print('test data: ', end="") print_score(y_test, y_pred) y_pred = gscv.predict(X_train) fig = yyplot(y_train, y_pred) y_pred = gscv.predict(X_test) fig = yyplot(y_test, y_pred) #%% # Novelty detection by One Class SVM with optimized hyperparameter clf = OneClassSVM(nu=0.003, kernel=gscv.best_params_['kernel'], gamma=gscv.best_params_['gamma']) clf.fit(X_train) y_pred = gscv.predict(X_test) # prediction reliability = clf.predict(X_test) # outliers = -1 results = np.c_[y_pred, y_test, reliability] columns = ['predicted y', 'observed y', 'reliability'] df = pd.DataFrame(results, columns=columns) print(df) #%% for i in range(10): dcv(X, y, mod, param_grid) print('{:.2f} seconds '.format(time() - start))