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)
