results = { 'realization': [], 'ACCURACY': [], # 'MCC': [], 'f1_score': [], 'precision': [], 'recall': [], 'cf': [], 'alphas': [] } # carregar a base base = load_base(path='iris.data', type='csv') # normalizar a base base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']] = normalization( base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']], type='min-max') N, M = base.shape C = len(base['Species'].unique()) y_out_of_c = pd.get_dummies(base['Species']) base = base.drop(['Species'], axis=1) base = concatenate([base[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']], y_out_of_c], axis=1) for realization in range(20): train, test = split_random(base, train_percentage=.8) train, train_val = split_random(train, train_percentage=.8) x_train = train[:, :4] y_train = train[:, 4:]
# carregar a base base = load_base(path='column_3C_weka.arff', type='arff') # features features = ['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis'] print(base.info()) # ----------------------------- Clean the data ---------------------------------------------------------------- # -------------------------- Normalization ------------------------------------------------------------------ # normalizar a base base[features] = normalization(base[features], type='min-max') base = base.drop(['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope'], axis=1) # ------------------------------------------------------------------------------------------------------------ base['class'][base['class'] == b'Abnormal'] = 1 base['class'][base['class'] == b'Normal'] = 0 for one_versus_others in ['Hernia_vs_OT', 'Spondylolisthesis_vs_OT', 'Normal_vs_OT']: final_result = { 'ACCURACY': [], 'std ACCURACY': [], 'f1_score': [], 'std f1_score': [], 'precision': [], 'std precision': [], 'recall': [],
df = df.iloc[:100000] nRow, nCol = df.shape print(f'There are {nRow} rows and {nCol} columns') df.info() features = ['u_q', 'coolant', 'u_d', 'motor_speed', 'i_d', 'i_q', 'ambient', 'torque'] # 'profile_id' targets = ['stator_yoke', 'pm', 'stator_winding', 'stator_tooth'] # -------------------- Realiztions --------------------------------------------- # normalizar a base df[features] = normalization(df[features], type='min-max') N, M = df.shape C = 1 # Problema de regressão for different_target in targets: print('Target: ' + different_target) final_result = { 'MSE': [], 'std MSE': [], 'RMSE': [], 'std RMSE': [], 'alphas': [] } results = {
} results = { 'realization': [], 'ACCURACY': [], # 'MCC': [], 'f1_score': [], 'precision': [], 'recall': [], 'cf': [], 'alphas': [] } base = load_mock(type='TRIANGLE_CLASSES') # normalizar a base base[['x1', 'x2']] = normalization(base[['x1', 'x2']], type='min-max') x = array(base[['x1', 'x2']]) y = array(base[['y']]) classe0 = x[np.where(y == 0)[0]] classe1 = x[np.where(y == 1)[0]] classe2 = x[np.where(y == 2)[0]] plt.plot(classe0[:, 0], classe0[:, 1], 'b^') plt.plot(classe1[:, 0], classe1[:, 1], 'go') plt.plot(classe2[:, 0], classe2[:, 1], 'm*') plt.xlabel("X1") plt.ylabel("X2") plt.savefig(get_project_root() + '/run/TR-03/ARTIFICIAL/results/' + 'dataset_artificial.png')
# ----------------------------- Clean the data ---------------------------------------------------------------- # The Age has values ? for unique_value in base['x33'].unique(): if unique_value != '?': base['x33'][base['x33'] == unique_value] = int(unique_value) # ? -> mean of column base['x33'][base['x33'] == '?'] = int( np.mean(base['x33'][base['x33'] != '?'])) # -------------------------- Normalization ------------------------------------------------------------------ # normalizar a base base[features] = (normalization(base[features], type='min-max')).to_numpy(dtype=np.float) # ------------------------------------------------------------------------------------------------------------ N, M = base.shape C = len(base['y'].unique()) y_out_of_c = pd.get_dummies(base['y']) base = base.drop(['y'], axis=1) base = concatenate([base[features], y_out_of_c], axis=1) for realization in range(1): train, test = split_random(base, train_percentage=.8) train, train_val = split_random(train, train_percentage=.8)