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main.py
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main.py
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from utils.utils import generate_results_csv
from utils.utils import transform_labels
from utils.utils import create_directory
from utils.utils import read_dataset
from utils.utils import transform_mts_to_ucr_format
from utils.utils import visualize_filter
from utils.utils import viz_for_survey_paper
from utils.utils import viz_cam
import numpy as np
import sys
import sklearn
def fit_classifier():
x_train = datasets_dict[dataset_name][0]
y_train = datasets_dict[dataset_name][1]
x_test = datasets_dict[dataset_name][2]
y_test = datasets_dict[dataset_name][3]
nb_classes = len(np.unique(np.concatenate((y_train,y_test),axis =0)))
# make the min to zero of labels
y_train,y_test = transform_labels(y_train,y_test)
# save orignal y because later we will use binary
y_true = y_test.astype(np.int64)
# transform the labels from integers to one hot vectors
enc = sklearn.preprocessing.OneHotEncoder()
enc.fit(np.concatenate((y_train,y_test),axis =0).reshape(-1,1))
y_train = enc.transform(y_train.reshape(-1,1)).toarray()
y_test = enc.transform(y_test.reshape(-1,1)).toarray()
if len(x_train.shape) == 2: # if univariate
# add a dimension to make it multivariate with one dimension
x_train = x_train.reshape((x_train.shape[0],x_train.shape[1],1))
x_test = x_test.reshape((x_test.shape[0],x_test.shape[1],1))
input_shape = x_train.shape[1:]
classifier = create_classifier(classifier_name,input_shape, nb_classes, output_directory)
classifier.fit(x_train,y_train,x_test,y_test, y_true)
def create_classifier(classifier_name, input_shape, nb_classes, output_directory, verbose = False):
if classifier_name=='fcn':
from classifiers import fcn
return fcn.Classifier_FCN(output_directory,input_shape, nb_classes, verbose)
if classifier_name=='mlp':
from classifiers import mlp
return mlp.Classifier_MLP(output_directory,input_shape, nb_classes, verbose)
if classifier_name=='resnet':
from classifiers import resnet
return resnet.Classifier_RESNET(output_directory,input_shape, nb_classes, verbose)
if classifier_name=='mcnn':
from classifiers import mcnn
return mcnn.Classifier_MCNN(output_directory,verbose)
if classifier_name=='tlenet':
from classifiers import tlenet
return tlenet.Classifier_TLENET(output_directory,verbose)
if classifier_name=='twiesn':
from classifiers import twiesn
return twiesn.Classifier_TWIESN(output_directory,verbose)
if classifier_name=='encoder':
from classifiers import encoder
return encoder.Classifier_ENCODER(output_directory,input_shape, nb_classes, verbose)
if classifier_name=='mcdcnn':
from classifiers import mcdcnn
return mcdcnn.Classifier_MCDCNN(output_directory,input_shape, nb_classes, verbose)
if classifier_name=='cnn': # Time-CNN
from classifiers import cnn
return cnn.Classifier_CNN(output_directory,input_shape, nb_classes, verbose)
############################################### main
# change this directory for your machine
# it should contain the archive folder containing both univariate and multivariate archives
root_dir = '/mnt/nfs/casimir/'
if sys.argv[1]=='transform_mts_to_ucr_format':
transform_mts_to_ucr_format()
elif sys.argv[1]=='visualize_filter':
visualize_filter(root_dir)
elif sys.argv[1]=='viz_for_survey_paper':
viz_for_survey_paper(root_dir)
elif sys.argv[1]=='viz_cam':
viz_cam(root_dir)
elif sys.argv[1]=='generate_results_csv':
res = generate_results_csv('results.csv',root_dir)
print(res)
else:
# this is the code used to launch an experiment on a dataset
archive_name = sys.argv[1]
dataset_name = sys.argv[2]
classifier_name=sys.argv[3]
itr = sys.argv[4]
if itr == '_itr_0':
itr = ''
output_directory = root_dir+'/results/'+classifier_name+'/'+archive_name+itr+'/'+\
dataset_name+'/'
output_directory = create_directory(output_directory)
print('Method: ',archive_name, dataset_name, classifier_name, itr)
if output_directory is None:
print('Already done')
else:
datasets_dict = read_dataset(root_dir,archive_name,dataset_name)
fit_classifier()
print('DONE')
# the creation of this directory means
create_directory(output_directory+'/DONE')