def data(root=''):
"""
Data providing function:
This function is separated from create_hyperparam_model() so that hyperopt
won't reload data for each evaluation run.
"""
labels, class_names = get_merged_labels_three(root='../')
x = get_doc_vec_ticketing_message(root='../')
y = labels
n_values = len(class_names)
y = np.eye(n_values)[y]
data_set = DataSet.from_np_array(x,
y,
class_names=class_names,
p_train=0.8,
p_val=0.1)
x_train = data_set.x_train
y_train = data_set.y_train
x_val = data_set.x_val
y_val = data_set.y_val
x_test = data_set.x_test
y_test = data_set.y_test
return x_train, y_train, x_val, y_val, x_test, y_test
n = words.shape[0]
if n > max:
max = n
new_x = np.zeros((x.shape[0], max, x[0].shape[1]))
i = 0
for words in x:
new_x[i, :words.shape[0], :] = words
i += 1
x = new_x
y = keras.utils.to_categorical(y)
data_set = DataSet.from_np_array(x, y, class_names=class_names)
path = "../classification/RNN/saved_model/rnn.model"
with Logger("rnn", root='../') as l:
l.log_and_print(data_set)
l.log("")
if os.path.isfile(path):
classifier = RnnClassifier.load(path, data_set, logger=l)
else:
classifier = RnnClassifier(data_set, logger=l)
classifier.fit(path, epochs=20)
classifier.validate()
from classification.data_set import DataSet
from sklearn import preprocessing
from classification.ticketing_data import *
from classification.util.logger import Logger
labels, class_names = get_merged_labels_three(root='../')
x = get_doc_vec_ticketing_message(root='../')
y = labels
n_values = len(class_names)
y = np.eye(n_values)[y]
data_set = DataSet.from_np_array(x,
y,
class_names=class_names,
p_train=0.8,
p_val=0.1)
with Logger("multilayer_perceptron", root='../') as l:
l.log_and_print(data_set)
l.log("")
# classifier = multilayer_perceptron.MultilayerPerceptron(data_set, num_classes=len(class_names), epoch=50, verbose=1,
# logger=l)
# classifier.fit()
# classifier.validate()
# classifier.metrics()
# classifier.plot_confusion_matrix()
model = hyperparameter_tuning.fit_hyper(root='../')