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Commit 0af6d2cf authored by Dominique Benielli's avatar Dominique Benielli
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Pipeline #3387 failed
Stage: test
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multiview_platform/execute.py
+ 0
− 6
View file @ 0af6d2cf
"""This is the execution module, used to execute the code"""
<<<<<<< HEAD
def execute():
import multiview_platform.versions as vs
vs.test_versions()
=======
def exec():
import multiview_platform.versions as versions
versions.test_versions()
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
import sys
from multiview_platform.mono_multi_view_classifiers import exec_classif
......
multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
+ 2
− 12
View file @ 0af6d2cf
......@@ -90,13 +90,8 @@ def makeMeNoisy(viewData, random_state, percentage=5):
return noisyViewData
<<<<<<< HEAD
def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="",
random_state=None, full=True, add_noise=False,
=======
def get_plausible_db_hdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="",
randomState=None, full=True, add_noise=False,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
noise_std=0.15, nbView=3,
nbClass=2, datasetLength=100, randomStateInt=42, nbFeatures = 10):
"""Used to generate a plausible dataset to test the algorithms"""
......@@ -472,13 +467,8 @@ def add_gaussian_noise(dataset_file, random_state, path_f, dataset_name,
return noisy_dataset, dataset_name + "_noised"
<<<<<<< HEAD
def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
random_state, full=False, add_noise=False, noise_std=0.15,
=======
def get_classic_db_csv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
randomState, full=False, add_noise=False, noise_std=0.15,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delimiter=","):
# TODO : Update this one
labels_names = np.genfromtxt(pathF + nameDB + "-labels-names.csv",
......
multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+ 0
− 30
View file @ 0af6d2cf
......@@ -108,35 +108,6 @@ def randomized_search_x(X, y, framework, random_state, output_file_name, classif
min_list = np.array(
[min(nb_possible_combination, n_iter) for nb_possible_combination in
nb_possible_combinations])
<<<<<<< HEAD
random_search = MultiviewCompatibleRandomizedSearchCV(
estimator,
n_iter=int(np.sum(min_list)),
param_distributions=params_dict,
refit=True,
n_jobs=nb_cores, scoring=scorer,
cv=folds, random_state=random_state,
learning_indices=learning_indices,
view_indices=view_indices,
framework=framework)
detector = random_search.fit(X, y)
best_params = dict((key, value) for key, value in
estimator.genBestParams(detector).items() if
key is not "random_state")
scores_array = detector.cv_results_['mean_test_score']
params = estimator.genParamsFromDetector(detector)
gen_heat_maps(params, scores_array, output_file_name)
best_estimator = detector.best_estimator_
else:
best_estimator = estimator
best_params = {}
test_folds_preds = get_test_folds_preds(X, y, folds, best_estimator,
framework, learning_indices)
return best_params, test_folds_preds
=======
random_search = MultiviewCompatibleRandomizedSearchCV(estimator,
n_iter=int(np.sum(min_list)),
param_distributions=params_dict,
......@@ -161,7 +132,6 @@ def randomized_search_x(X, y, framework, random_state, output_file_name, classif
testFoldsPreds = get_test_folds_preds(X, y, folds, best_estimator,
framework, learning_indices)
return best_params, testFoldsPreds
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
from sklearn.base import clone
......
multiview_platform/tests/test_ExecClassif.py
+ 1
− 72
View file @ 0af6d2cf
......@@ -130,11 +130,6 @@ class Test_InitArgumentDictionaries(unittest.TestCase):
]
self.assertEqual(arguments["multiview"][0], expected_output[0])
<<<<<<< HEAD
def fakeBenchmarkExec(core_index=-1, a=7, args=1):
return [core_index, a]
=======
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
def test_init_argument_dictionaries_multiview_complex(self):
self.multiview_classifier_arg_value = {"fake_value_2":"plif", "plaf":"plouf"}
......@@ -213,11 +208,6 @@ def fakeBenchmarkExec(core_index=-1, a=7, args=1):
def fakeBenchmarkExec(core_index=-1, a=7, args=1):
return [core_index, a]
<<<<<<< HEAD
def fakeBenchmarkExec_mutlicore(nb_cores=-1, a=6, args=1):
return [nb_cores, a]
=======
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
def fakeBenchmarkExec_mutlicore(nb_cores=-1, a=6, args=1):
return [nb_cores, a]
......@@ -270,17 +260,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 1, 2, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
=======
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
......@@ -292,17 +275,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 2, 2, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_monoCore=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
=======
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
......@@ -310,17 +286,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 1, 1, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_oneBenchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
=======
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
......@@ -333,26 +302,15 @@ class Test_execBenchmark(unittest.TestCase):
os.rmdir(path)
<<<<<<< HEAD
def fakeExecMono(directory, name, labels_names, classification_indices, k_folds,
coreIndex, type, pathF, random_state, labels,
=======
def fakeExecMono(directory, name, labelsNames, classificationIndices, kFolds,
coreIndex, type, pathF, randomState, labels,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
hyper_param_search="try", metrics="try", nIter=1, **arguments):
return ["Mono", arguments]
<<<<<<< HEAD
def fakeExecMulti(directory, coreIndex, name, classification_indices, k_folds,
type, pathF, labels_dictionary,
random_state, labels, hyper_param_search="", metrics=None,
=======
def fakeExecMulti(directory, coreIndex, name, classificationIndices, kFolds,
type, pathF, LABELS_DICTIONARY,
randomState, labels, hyper_param_search="", metrics=None,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
nIter=1, **arguments):
return ["Multi", arguments]
......@@ -387,21 +345,6 @@ class Test_execOneBenchmark(unittest.TestCase):
def test_simple(cls):
flag, results = exec_classif.exec_one_benchmark(core_index=10,
<<<<<<< HEAD
labels_dictionary={
0: "a",
1: "b"},
directory="multiview_platform/tests/tmp_tests/",
classification_indices=(
[1, 2, 3, 4],
[0, 5, 6, 7, 8]),
args=cls.args,
k_folds=FakeKfold(),
random_state="try",
hyper_param_search="try",
metrics="try",
argument_dictionaries={
=======
labels_dictionary={
0: "a",
1: "b"},
......@@ -415,7 +358,6 @@ class Test_execOneBenchmark(unittest.TestCase):
hyper_param_search="try",
metrics="try",
argument_dictionaries={
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
"Monoview": [
{
"try": 0},
......@@ -427,26 +369,16 @@ class Test_execOneBenchmark(unittest.TestCase):
"try4": 10}]},
benchmark="try",
views="try",
<<<<<<< HEAD
views_indices="try",
=======
views_indices="try",
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
flag=None,
labels=np.array(
[0, 1, 2, 1,
2, 2, 2, 12,
1, 2, 1, 1,
2, 1, 21]),
<<<<<<< HEAD
exec_monoview_multicore=fakeExecMono,
exec_multiview_multicore=fakeExecMulti,
init_multiview_arguments=fakeInitMulti)
=======
exec_monoview_multicore=fakeExecMono,
exec_multiview_multicore=fakeExecMulti,
init_multiview_arguments=fakeInitMulti)
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
cls.assertEqual(flag, None)
cls.assertEqual(results ,
......@@ -480,11 +412,8 @@ class Test_execOneBenchmark_multicore(unittest.TestCase):
def test_simple(cls):
flag, results = exec_classif.exec_one_benchmark_multicore(
<<<<<<< HEAD
nbCores=2,
=======
nb_cores=2,
>>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
labels_dictionary={0: "a", 1: "b"},
directory="multiview_platform/tests/tmp_tests/",
classification_indices=([1, 2, 3, 4], [0, 10, 20, 30, 40]),
......
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