import os
import unittest
import h5py
import numpy as np
from sklearn.model_selection import StratifiedKFold
from ..utils import rm_tmp, tmp_path
from ...mono_multi_view_classifiers.monoview import exec_classif_mono_view
from ...mono_multi_view_classifiers.monoview_classifiers import decision_tree
class Test_initConstants(unittest.TestCase):
@classmethod
def setUpClass(cls):
rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.view_name=test_dataset
cls.datasetFile = h5py.File(
tmp_path+"test.hdf5", "w")
cls.random_state = np.random.RandomState(42)
cls.args = {"classifier_name": "test_clf"}
cls.X_value = cls.random_state.randint(0, 500, (10, 20))
cls.X = cls.datasetFile.create_dataset("View0", data=cls.X_value)
cls.X.attrs["name"] = "test_dataset"
cls.X.attrs["sparse"] = False
cls.classification_indices = [np.array([0, 2, 4, 6, 8]),
np.array([1, 3, 5, 7, 9]),
np.array([1, 3, 5, 7, 9])]
cls.labels_names = ["test_true", "test_false"]
cls.name = "test"
cls.directory = tmp_path+"test_dir/"
def test_simple(cls):
kwargs, \
t_start, \
feat, \
CL_type, \
X, \
learningRate, \
labelsString, \
outputFileName = exec_classif_mono_view.initConstants(cls.args,
cls.X,
cls.classification_indices,
cls.labels_names,
cls.name,
cls.directory,
cls.view_name)
cls.assertEqual(kwargs, cls.args)
cls.assertEqual(feat, "test_dataset")
cls.assertEqual(CL_type, "test_clf")
np.testing.assert_array_equal(X, cls.X_value)
cls.assertEqual(learningRate, 0.5)
cls.assertEqual(labelsString, "test_true-test_false")
# cls.assertEqual(outputFileName, "Code/tests/temp_tests/test_dir/test_clf/test_dataset/results-test_clf-test_true-test_false-learnRate0.5-test-test_dataset-")
@classmethod
def tearDownClass(cls):
os.remove(tmp_path+"test.hdf5")
os.rmdir(
tmp_path+"test_dir/test_clf/test_dataset")
os.rmdir(tmp_path+"test_dir/test_clf")
os.rmdir(tmp_path+"test_dir")
os.rmdir("multiview_platform/tests/tmp_tests")
class Test_initTrainTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
rm_tmp()
cls.random_state = np.random.RandomState(42)
cls.X = cls.random_state.randint(0, 500, (10, 5))
cls.Y = cls.random_state.randint(0, 2, 10)
cls.classification_indices = [np.array([0, 2, 4, 6, 8]),
np.array([1, 3, 5, 7, 9]),
np.array([1, 3, 5, 7, 9])]
def test_simple(cls):
X_train, y_train, X_test, y_test, X_test_multiclass = exec_classif_mono_view.init_train_test(
cls.X, cls.Y, cls.classification_indices)
np.testing.assert_array_equal(X_train, np.array(
[np.array([102, 435, 348, 270, 106]),
np.array([466, 214, 330, 458, 87]),
np.array([149, 308, 257, 343, 491]),
np.array([276, 160, 459, 313, 21]),
np.array([58, 169, 475, 187, 463])]))
np.testing.assert_array_equal(X_test, np.array(
[np.array([71, 188, 20, 102, 121]),
np.array([372, 99, 359, 151, 130]),
np.array([413, 293, 385, 191, 443]),
np.array([252, 235, 344, 48, 474]),
np.array([270, 189, 445, 174, 445])]))
np.testing.assert_array_equal(y_train, np.array([0, 0, 1, 0, 0]))
np.testing.assert_array_equal(y_test, np.array([1, 1, 0, 0, 0]))
class Test_getHPs(unittest.TestCase):
@classmethod
def setUpClass(cls):
rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.classifierModule = decision_tree
cls.hyper_param_search = "randomized_search"
cls.n_iter = 2
cls.classifier_name = "decision_tree"
cls.random_state = np.random.RandomState(42)
cls.X = cls.random_state.randint(0,10,size=(10,5))
cls.y = cls.random_state.randint(0,2,size=10)
cls.output_file_name = tmp_path
cls.cv = StratifiedKFold(n_splits=2, random_state=cls.random_state)
cls.nb_cores = 1
cls.metrics = [["accuracy_score", None]]
cls.kwargs = {"decision_tree" : {"max_depth": 1,
"criterion": "gini",
"splitter": "best"}}
cls.classifier_class_name = "DecisionTree"
@classmethod
def tearDownClass(cls):
for file_name in os.listdir("multiview_platform/tests/tmp_tests"):
os.remove(
os.path.join("multiview_platform/tests/tmp_tests", file_name))
os.rmdir("multiview_platform/tests/tmp_tests")
def test_simple(self):
kwargs, test_folds_predictions = exec_classif_mono_view.getHPs(self.classifierModule,
self.hyper_param_search,
self.n_iter,
self.classifier_name,
self.classifier_class_name,
self.X,
self.y,
self.random_state,
self.output_file_name,
self.cv,
self.nb_cores,
self.metrics,
self.kwargs)
# class Test_getKWARGS(unittest.TestCase):
#
# @classmethod
# def setUpClass(cls):
# cls.classifierModule = None
# cls.hyper_param_search = "None"
# cls.nIter = 2
# cls.CL_type = "string"
# cls.X_train = np.zeros((10,20))
# cls.y_train = np.zeros((10))
# cls.random_state = np.random.RandomState(42)
# cls.outputFileName = "test_file"
# cls.KFolds = None
# cls.nbCores = 1
# cls.metrics = {"accuracy_score":""}
# cls.kwargs = {}
#
# def test_simple(cls):
# clKWARGS = ExecClassifMonoView.getHPs(cls.classifierModule,
# cls.hyper_param_search,
# cls.nIter,
# cls.CL_type,
# cls.X_train,
# cls.y_train,
# cls.random_state,
# cls.outputFileName,
# cls.KFolds,
# cls.nbCores,
# cls.metrics,
# cls.kwargs)
# pass
#
# class Test_saveResults(unittest.TestCase):
#
# @classmethod
# def setUpClass(cls):
# cls.stringAnalysis = "string analysis"
# cls.outputFileName = "test_file"
# cls.full_labels_pred = np.zeros(10)
# cls.y_train_pred = np.ones(5)
# cls.y_train = np.zeros(5)
# cls.imagesAnalysis = {}
#
# def test_simple(cls):
# ExecClassifMonoView.saveResults(cls.stringAnalysis,
# cls.outputFileName,
# cls.full_labels_pred,
# cls.y_train_pred,
# cls.y_train,
# cls.imagesAnalysis)
# # Test if the files are created with the right content
#
# def test_with_image_analysis(cls):
# cls.imagesAnalysis = {"test_image":"image.png"} # Image to gen
# ExecClassifMonoView.saveResults(cls.stringAnalysis,
# cls.outputFileName,
# cls.full_labels_pred,
# cls.y_train_pred,
# cls.y_train,
# cls.imagesAnalysis)
# # Test if the files are created with the right content
#