/
combine_uea.py
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/
combine_uea.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import json
import numpy
import torch
import sklearn
import argparse
import uea
import scikit_wrappers
def load_classifier(save_path, dataset, cuda, gpu):
"""
Loads and returns classifier from the given parameters.
@param save_path Path where the model is located.
@param dataset Name of the dataset.
@param cuda If True, enables computations on the GPU.
@param gpu GPU to use if CUDA is enabled.
"""
classifier = scikit_wrappers.CausalCNNEncoderClassifier()
hf = open(
os.path.join(
save_path,
dataset + '_hyperparameters.json'
), 'r'
)
hp_dict = json.load(hf)
hf.close()
hp_dict['cuda'] = cuda
hp_dict['gpu'] = gpu
classifier.set_params(**hp_dict)
classifier.load(os.path.join(save_path, dataset))
return classifier
def parse_arguments():
parser = argparse.ArgumentParser(
description='Classification tests for UEA repository datasets, ' +
'using the features of several precomputed encoders, ' +
'possibly with different hyperparameters, and combining ' +
'their computed representations to train an SVM on top ' +
'of them.'
)
parser.add_argument('--dataset', type=str, metavar='D', required=True,
help='dataset name')
parser.add_argument('--path', type=str, metavar='PATH', required=True,
help='path where the dataset is located')
parser.add_argument('--model_path', type=str, metavar='PATH',
required=True,
help='path where the folders containing models for ' +
'different hyperparameters are located')
parser.add_argument('--folders', type=str, metavar='FOLDERS',
required=True, nargs='+',
help='list of folders, each one containing a model ' +
'for the chosen dataset')
parser.add_argument('--save_path', type=str, metavar='PATH', required=True,
help='path where the classifier is/should be saved')
parser.add_argument('--load', action='store_true', default=False,
help='activate to load the classifier instead of ' +
'training it')
parser.add_argument('--cuda', action='store_true',
help='activate to use CUDA')
parser.add_argument('--gpu', type=int, default=0, metavar='GPU',
help='index of GPU used for computations (default: 0)')
return parser.parse_args()
if __name__ == '__main__':
args = parse_arguments()
if args.cuda and not torch.cuda.is_available():
print("CUDA is not available, proceeding without it...")
args.cuda = False
# Train, test datasets
train, train_labels, test, test_labels = uea.load_UEA_dataset(
args.path, args.dataset
)
# List of classifiers
classifiers = [
load_classifier(
os.path.join(args.model_path, folder), args.dataset, args.cuda,
args.gpu
) for folder in args.folders
]
train_representations = numpy.concatenate([
c.encode(train) for c in classifiers
], axis=1)
test_representations = numpy.concatenate([
c.encode(test) for c in classifiers
], axis=1)
classifier = sklearn.svm.SVC(C=numpy.inf, gamma='scale')
if not args.load:
nb_classes = numpy.shape(
numpy.unique(train_labels, return_counts=True)[1]
)[0]
train_size = numpy.shape(train_representations)[0]
if train_size // nb_classes < 5 or train_size < 50:
classifier.fit(train_representations, train_labels)
else:
grid_search = sklearn.model_selection.GridSearchCV(
classifier, {
'C': [
0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000,
numpy.inf
],
'kernel': ['rbf'],
'degree': [3],
'gamma': ['scale'],
'coef0': [0],
'shrinking': [True],
'probability': [False],
'tol': [0.001],
'cache_size': [200],
'class_weight': [None],
'verbose': [False],
'max_iter': [10000000],
'decision_function_shape': ['ovr'],
'random_state': [None]
},
cv=5, iid=False, n_jobs=5
)
if train_size <= 10000:
grid_search.fit(train_representations, train_labels)
else:
# If the training set is too large, subsample 10000 train
# examples
split = sklearn.model_selection.train_test_split(
train_representations, train_labels,
train_size=10000, random_state=0, stratify=train_labels
)
grid_search.fit(split[0], split[2])
classifier = grid_search.best_estimator_
sklearn.externals.joblib.dump(
classifier, os.path.join(
args.save_path, args.dataset + '_CausalCNN_classifier.pkl'
)
)
else:
classifier = sklearn.externals.joblib.load(os.path.join(
args.save_path, args.dataset + '_CausalCNN_classifier.pkl'
))
# Testing
print("Test accuracy: " + str(
classifier.score(test_representations, test_labels)
))