def get(dataset_name):
# List of datasets that works with the current model ?
datasets = ['adult',
'binarized_mnist',
'connect4',
'dna',
'mushrooms',
'nips',
'ocr_letters',
'rcv1',
'rcv2_russ',
'web']
# Setup dataset env
if dataset_name not in datasets:
raise ValueError('Dataset unknown: ' + dataset_name)
# mldataset = __import__('mlpython.datasets.' + dataset_name, globals(), locals(), [dataset_name], -1)
datadir = os.path.join(os.getenv("MLPYTHON_DATASET_REPO"), dataset_name)
# Verify if dataset exist and if not, download it
if(not os.path.exists(datadir)):
dataset_store.download(dataset_name)
print('### Loading dataset [{0}] ...'.format(dataset_name))
start_time = t.time()
all_data = mldataset.load(datadir, load_to_memory=True)
train_data, train_metadata = all_data['train']
if dataset_name == 'binarized_mnist' or dataset_name == 'nips':
trainset = mlpb.MLProblem(train_data, train_metadata)
else:
trainset = mlpb.SubsetFieldsProblem(train_data, train_metadata)
trainset.setup()
valid_data, valid_metadata = all_data['valid']
validset = trainset.apply_on(valid_data, valid_metadata)
test_data, test_metadata = all_data['test']
testset = trainset.apply_on(test_data, test_metadata)
# Cleaning up, packaging and theanized
full_dataset = {'input_size': trainset.metadata['input_size']}
trainset_theano = theano.shared(value=Dataset._clean(trainset), borrow=True)
validset_theano = theano.shared(value=Dataset._clean(validset), borrow=True)
testset_theano = theano.shared(value=Dataset._clean(testset), borrow=True)
full_dataset['train'] = {'data': trainset_theano, 'length': all_data['train'][1]['length']}
full_dataset['valid'] = {'data': validset_theano, 'length': all_data['valid'][1]['length']}
full_dataset['test'] = {'data': testset_theano, 'length': all_data['test'][1]['length']}
print("(Dim:{0} Train:{1} Valid:{2} Test:{3})".format(trainset.metadata['input_size'], full_dataset['train']['length'], full_dataset['valid']['length'], full_dataset['test']['length']))
print(get_done_text(start_time), "###")
return full_dataset
def get(dataset_name):
# List of datasets that works with the current model ?
datasets = ['adult',
'binarized_mnist',
'connect4',
'dna',
'mushrooms',
'nips',
'ocr_letters',
'rcv1',
'rcv2_russ',
'web']
# Setup dataset env
if dataset_name not in datasets:
raise ValueError('Dataset unknown: ' + dataset_name)
mldataset = __import__('mlpython.datasets.' + dataset_name, globals(), locals(), [dataset_name], -1)
datadir = os.path.join(os.getenv("MLPYTHON_DATASET_REPO"), dataset_name)
# Verify if dataset exist and if not, download it
if(not os.path.exists(datadir)):
dataset_store.download(dataset_name)
print '### Loading dataset [{0}] ...'.format(dataset_name),
start_time = t.time()
all_data = mldataset.load(datadir, load_to_memory=True)
train_data, train_metadata = all_data['train']
if dataset_name == 'binarized_mnist' or dataset_name == 'nips':
trainset = mlpb.MLProblem(train_data, train_metadata)
else:
trainset = mlpb.SubsetFieldsProblem(train_data, train_metadata)
trainset.setup()
valid_data, valid_metadata = all_data['valid']
validset = trainset.apply_on(valid_data, valid_metadata)
test_data, test_metadata = all_data['test']
testset = trainset.apply_on(test_data, test_metadata)
# Cleaning up, packaging and theanized
full_dataset = {'input_size': trainset.metadata['input_size']}
trainset_theano = theano.shared(value=Dataset._clean(trainset), borrow=True)
validset_theano = theano.shared(value=Dataset._clean(validset), borrow=True)
testset_theano = theano.shared(value=Dataset._clean(testset), borrow=True)
full_dataset['train'] = {'data': trainset_theano, 'length': all_data['train'][1]['length']}
full_dataset['valid'] = {'data': validset_theano, 'length': all_data['valid'][1]['length']}
full_dataset['test'] = {'data': testset_theano, 'length': all_data['test'][1]['length']}
print "(Dim:{0} Train:{1} Valid:{2} Test:{3})".format(trainset.metadata['input_size'], full_dataset['train']['length'], full_dataset['valid']['length'], full_dataset['test']['length']),
print get_done_text(start_time), "###"
return full_dataset
def sklearn_convex(classifier,
algorithm,
max_evals=100,
seed=1,
filename='none',
preproc=[],
loss=None):
global suppress_output
if suppress_output:
dump_file = None
else:
dump_file = filename + '.dump'
estim = hyperopt_estimator(classifier=classifier,
algo=algorithm,
preprocessing=preproc,
max_evals=max_evals,
trial_timeout=240,
fit_increment_dump_filename=dump_file,
loss_fn=loss)
filename = filename + '.out'
dataset_store.download('convex')
trainset, validset, testset = dataset_store.get_classification_problem(
'convex')
X_train = trainset.data.mem_data[0]
y_train = trainset.data.mem_data[1]
X_valid = validset.data.mem_data[0]
y_valid = validset.data.mem_data[1]
X_test = testset.data.mem_data[0]
y_test = testset.data.mem_data[1]
X_fulltrain = np.concatenate((X_train, X_valid))
y_fulltrain = np.concatenate((y_train, y_valid))
print(y_train.shape)
print(y_valid.shape)
print(y_test.shape)
#find_model( X_train, y_train, X_test, y_test, estim, filename )
find_model(X_fulltrain, y_fulltrain, X_test, y_test, estim, filename)
def convex():
dataset_store.download('convex')
trainset,validset,testset = dataset_store.get_classification_problem('convex')
X_train = trainset.data.mem_data[0]
y_train = trainset.data.mem_data[1]
X_valid = validset.data.mem_data[0]
y_valid = validset.data.mem_data[1]
X_test = testset.data.mem_data[0]
y_test = testset.data.mem_data[1]
X_fulltrain = np.concatenate((X_train, X_valid))
y_fulltrain = np.concatenate((y_train, y_valid))
pca = PCA()
X_train_pca = pca.fit_transform( X_fulltrain )
X_test_pca = pca.fit_transform( X_test )
clfs = [ MultinomialNB(), SVC(),
KNeighborsClassifier(),
SGDClassifier() ]
pca_clfs = [ SVC(),
KNeighborsClassifier(),
SGDClassifier() ]
print("Convex\n")
with open( "convex_baselines.txt", 'w' ) as f:
for clf in clfs:
clf.fit( X_fulltrain, y_fulltrain )
pred = clf.predict( X_test )
score = metrics.f1_score( y_test, pred )
print( "Classifier: %s\nScore: %f\n" % (clf, score) )
f.write("Classifier: %s\nScore: %f\n\n" % (clf, score))
for clf in pca_clfs:
clf.fit( X_train_pca, y_fulltrain )
pred = clf.predict( X_test_pca )
score = metrics.f1_score( y_test, pred )
print( "Classifier: PCA + %s\nScore: %f\n" % (clf, score) )
f.write("Classifier: PCA + %s\nScore: %f\n\n" % (clf, score))
def convex():
dataset_store.download('convex')
trainset, validset, testset = dataset_store.get_classification_problem(
'convex')
X_train = trainset.data.mem_data[0]
y_train = trainset.data.mem_data[1]
X_valid = validset.data.mem_data[0]
y_valid = validset.data.mem_data[1]
X_test = testset.data.mem_data[0]
y_test = testset.data.mem_data[1]
X_fulltrain = np.concatenate((X_train, X_valid))
y_fulltrain = np.concatenate((y_train, y_valid))
pca = PCA()
X_train_pca = pca.fit_transform(X_fulltrain)
X_test_pca = pca.fit_transform(X_test)
clfs = [MultinomialNB(), SVC(), KNeighborsClassifier(), SGDClassifier()]
pca_clfs = [SVC(), KNeighborsClassifier(), SGDClassifier()]
print("Convex\n")
with open("convex_baselines.txt", 'w') as f:
for clf in clfs:
clf.fit(X_fulltrain, y_fulltrain)
pred = clf.predict(X_test)
score = metrics.f1_score(y_test, pred)
print("Classifier: %s\nScore: %f\n" % (clf, score))
f.write("Classifier: %s\nScore: %f\n\n" % (clf, score))
for clf in pca_clfs:
clf.fit(X_train_pca, y_fulltrain)
pred = clf.predict(X_test_pca)
score = metrics.f1_score(y_test, pred)
print("Classifier: PCA + %s\nScore: %f\n" % (clf, score))
f.write("Classifier: PCA + %s\nScore: %f\n\n" % (clf, score))
def sklearn_convex( classifier, algorithm, max_evals=100, seed=1,
filename = 'none', preproc=[], loss=None ):
global suppress_output
if suppress_output:
dump_file = None
else:
dump_file = filename+'.dump'
estim = hyperopt_estimator( classifier=classifier, algo=algorithm,
preprocessing=preproc,
max_evals=max_evals, trial_timeout=240,
fit_increment_dump_filename=dump_file,
loss_fn=loss)
filename = filename + '.out'
dataset_store.download('convex')
trainset,validset,testset = dataset_store.get_classification_problem('convex')
X_train = trainset.data.mem_data[0]
y_train = trainset.data.mem_data[1]
X_valid = validset.data.mem_data[0]
y_valid = validset.data.mem_data[1]
X_test = testset.data.mem_data[0]
y_test = testset.data.mem_data[1]
X_fulltrain = np.concatenate((X_train, X_valid))
y_fulltrain = np.concatenate((y_train, y_valid))
print(y_train.shape)
print(y_valid.shape)
print(y_test.shape)
#find_model( X_train, y_train, X_test, y_test, estim, filename )
find_model( X_fulltrain, y_fulltrain, X_test, y_test, estim, filename )
def setUp():
try:
dataset_store.download('mnist_rotated_background_images')
except:
print 'Could not download the dataset : ', 'mnist_rotated_background_images'
assert False
def setUp():
try:
dataset_store.download('binarized_mnist')
except:
print 'Could not download the dataset : ', 'binarized_mnist'
assert False
def setUp():
try:
dataset_store.download('letor_mq2007')
except:
print 'Could not download the dataset : ', 'letor_mq2007'
assert False
def setUp():
try:
dataset_store.download('face_completion_lfw')
except:
print 'Could not download the dataset : ', 'face_completion_lfw'
assert False
def setUp():
try:
dataset_store.download("cadata")
except:
print "Could not download the dataset : ", "cadata"
assert False
def setUp():
try:
dataset_store.download('bibtex')
except:
print 'Could not download the dataset : ', 'bibtex'
assert False
def setUp():
try:
dataset_store.download('occluded_faces_lfw')
except:
print 'Could not download the dataset : ', 'occluded_faces_lfw'
assert False
def setUp():
try:
dataset_store.download('newsgroups')
except:
print 'Could not download the dataset : ', 'newsgroups'
assert False
def setUp():
try:
dataset_store.download('housing')
except:
print 'Could not download the dataset : ', 'housing'
assert False
def setUp():
try:
dataset_store.download('medical')
except:
print 'Could not download the dataset : ', 'medical'
assert False
def setUp():
try:
dataset_store.download('ocr_letters')
except:
print 'Could not download the dataset : ', 'ocr_letters'
assert False
import mlpython.datasets.store as store
store.download('adult')
store.download('connect4')
store.download('dna')
store.download('mushrooms')
store.download('nips')
store.download('ocr_letters')
store.download('rcv1')
store.download('web')
def setUp():
try:
dataset_store.download('corrupted_ocr_letters')
except:
print 'Could not download the dataset : ', 'corrupted_ocr_letters'
assert False
def setUp():
try:
dataset_store.download('mnist_basic')
except:
print 'Could not download the dataset : ', 'mnist_basic'
assert False
def setUp():
try:
dataset_store.download('mnist_background_random')
except:
print 'Could not download the dataset : ', 'mnist_background_random'
assert False
def setUp():
try:
dataset_store.download('connect4')
except:
print 'Could not download the dataset : ', 'connect4'
assert False
def setUp():
try:
dataset_store.download('housing')
except:
print 'Could not download the dataset : ', 'housing'
assert False
def setUp():
try:
dataset_store.download('rectangles')
except:
print 'Could not download the dataset : ', 'rectangles'
assert False
def setUp():
try:
dataset_store.download('newsgroups')
except:
print 'Could not download the dataset : ', 'newsgroups'
assert False
def setUp():
try:
dataset_store.download('mediamill')
except:
print 'Could not download the dataset : ', 'mediamill'
assert False
def setUp():
try:
dataset_store.download('rectangles')
except:
print 'Could not download the dataset : ', 'rectangles'
assert False
def setUp():
try:
dataset_store.download('adult')
except:
print 'Could not download the dataset : ', 'adult'
assert False
def setUp():
try:
dataset_store.download('occluded_mnist')
except:
print 'Could not download the dataset : ', 'occluded_mnist'
assert False
def setUp():
try:
dataset_store.download('mushrooms')
except:
print 'Could not download the dataset : ', 'mushrooms'
assert False
def setUp():
try:
dataset_store.download('connect4')
except:
print 'Could not download the dataset : ', 'connect4'
assert False
def setUp():
try:
dataset_store.download('letor_mq2008')
except:
print 'Could not download the dataset : ', 'letor_mq2008'
assert False
def setUp():
try:
dataset_store.download('face_completion_lfw')
except:
print 'Could not download the dataset : ', 'face_completion_lfw'
assert False
def setUp():
try:
dataset_store.download('mnist_basic')
except:
print 'Could not download the dataset : ', 'mnist_basic'
assert False
def setUp():
try:
dataset_store.download('abalone')
except:
print 'Could not download the dataset : ', 'abalone'
assert False
