def encode(variable, variable_encoder_info):
"""
Encoding each variable of test set as it was done in the training set.
Parameters
----------
data : numpy.array
Data as numpy array.
encoding_info : dict
Variable encoding info from training data.
Returns
-------
numpy.array
Encoded variable.
"""
variable = np.array(variable).reshape(len(variable), 1)
encoding_type = variable_encoder_info['type']
if encoding_type in VariableEncoding.get_available_encoder_types():
variable_str = []
for elem in variable:
if type(elem) is not str:
variable_str.append(str(elem[0]))
else:
variable_str.append(elem)
variable_str = np.array(variable_str).reshape(len(variable_str), 1)
#variable_str.dtype = str
if encoding_type == "OneHot":
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = OneHotEncoder(categories=[training_input_categories])
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
encoded_variable = enc.transform(variable_str).toarray()
elif encoding_type == "Dummy":
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = OneHotEncoder(categories=[training_input_categories],
drop='first')
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
encoded_variable = enc.transform(variable_str).toarray()
elif encoding_type == "Target":
training_input_mapping = variable_encoder_info['mapping']
variable_str = pd.Series(
variable_str.reshape(len(variable_str), ))
encoded_variable = variable_str.map(training_input_mapping)
encoded_variable = np.array(encoded_variable).reshape(
len(encoded_variable), 1)
elif encoding_type == "Effect":
variable_str = list(variable_str.reshape(len(variable_str), ))
encoded_variable = np.array([
variable_encoder_info['mapping'][elem]
for elem in variable_str
])
elif encoding_type == "CountOfFrequency":
variable_str = pd.Series(
variable_str.reshape(len(variable_str), ))
encoded_variable = variable_str.map(
variable_encoder_info['mapping'])
encoded_variable = np.array(encoded_variable).reshape(
len(encoded_variable), 1)
elif encoding_type == "Label":
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = LabelEncoder()
enc.classes_ = training_input_categories
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
encoded_variable = enc.transform(variable_str)
encoded_variable = np.array(encoded_variable).reshape(
len(encoded_variable), 1)
else:
variable_str = pd.Series(
variable_str.reshape(len(variable_str), ))
encoded_variable = variable_str.map(
variable_encoder_info['mapping'])
encoded_variable = np.array(encoded_variable).reshape(
len(encoded_variable), 1)
return encoded_variable
else:
raise Exception("Encoding type " +
str(variable_encoder_info['type']) +
" is not suported.")
def decode(variable, variable_encoder_info, threshold):
"""
Decode variable in relation to the encode method used in the optimization process.
Parameters
----------
variable : numpy.array
Data as numpy array.
variable_encoder_info : dict
Variable encoding info from training input data.
variable_encoder_info : float
The threshold that determines the binary classes.
Returns
-------
numpy.array
Decoded variable.
"""
decoding_type = variable_encoder_info['type']
if decoding_type in VariableEncoding.get_available_decoder_types():
if decoding_type == "OneHot":
predicted_var = np.array(variable)
predicted_var = np.where(
predicted_var == predicted_var.max(axis=1).reshape(
len(predicted_var), 1), 1, 0)
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = OneHotEncoder(categories=[training_input_categories])
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
decoded_variable = enc.inverse_transform(predicted_var)
decoded_variable = np.array([
elem[0] for elem in decoded_variable
]).reshape(len(decoded_variable), 1)
elif decoding_type == "Label":
predicted_var = np.array(variable)
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = LabelEncoder()
enc.classes_ = training_input_categories
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
decoded_variable = enc.inverse_transform(predicted_var)
decoded_variable = np.array([
elem for elem in decoded_variable
]).reshape(len(decoded_variable), 1)
else:
predicted_var = np.array(variable).reshape(len(variable), 1)
predicted_var = np.where(predicted_var > threshold, 1, 0)
training_input_categories = np.array(
variable_encoder_info['categories'])
enc = OneHotEncoder(categories=[training_input_categories],
drop='first')
enc.fit(
training_input_categories.reshape(
len(training_input_categories), 1))
decoded_variable = enc.inverse_transform(predicted_var)
try:
decoded_variable = np.array([
int(float(elem[0])) for elem in decoded_variable
]).reshape(len(decoded_variable), 1)
return decoded_variable
except:
return decoded_variable
else:
raise Exception("Decoding type " +
str(variable_encoder_info['type']) +
" is not suported.")