def return_prediction(data, clf_filename='clf_rf'):
"""
Returns the predicted category of an object by using a trained network.
Parameters:
data (list): List of the 4 parameters (float or integer) of the object. These parameters
are the height, the width, the depth and the weight of the concerned object.
Raise a Value_Error exception if the 4 parameters describe abnormal object dimensions.
clf_filename (str): The file name of a trained classifier without its extension.
Default extension is a joblib file. (Default : 'clf_rf')
Returns:
flatten_list (str): Predicted category of the object. Can be either 'mlp', 'deco' or 'meuble'.
"""
# We put the data into a nested list
inputs = [data]
# We scale the data by retrieving the scaler computed on the training dataset
print("directory", os.getcwd())
try:
test_scaled = scale_test(inputs, filename='src/scaler_mdm.joblib')
except FileNotFoundError:
test_scaled = scale_test(inputs, filename='../src/scaler_mdm.joblib')
# We load the trained classifier and make the prediction on the data
try:
clf = load('src/' + clf_filename + '.joblib')
except FileNotFoundError:
clf = load('../src/' + clf_filename + '.joblib')
print(test_scaled)
prediction = clf.predict(test_scaled)
# We load the encoder to decode the prediction into a string
load_onehot = OneHotEncoder()
load_onehot.drop_idx_ = None
try:
load_onehot.categories_ = np.load('src/classes_onehot.npy',
allow_pickle=True)
except FileNotFoundError:
load_onehot.categories_ = np.load('../src/classes_onehot.npy',
allow_pickle=True)
# Raises ValueError Exception if the values are outliers
try:
str_pred = load_onehot.inverse_transform(prediction)
except ValueError:
return "Values are too different from training dataset"
# Return the category predicted
flatten_list = list(chain.from_iterable(str_pred))
print("Catégorie prédite : ", flatten_list[0])
return flatten_list[0]
def encoder_for(field):
if field.get('optype') != 'categorical':
return 'passthrough'
encoder = OneHotEncoder()
encoder.categories_ = np.array(
[self.field_mapping[field.get('name')][1].categories])
encoder.drop_idx_ = np.array([None for x in encoder.categories_])
encoder._legacy_mode = False
return encoder
