diff --git a/Code/Exec.py b/Code/Exec.py
index 8bf6c5769aafc5fb9a13fb4cb60c19c075fabdb1..72b121adc212294cf140c873f5adefbf3f278561 100644
--- a/Code/Exec.py
+++ b/Code/Exec.py
@@ -1,8 +1,9 @@
-from Versions import testVersions
-testVersions()
-import sys
+if __name__=="__main__":
-from MonoMultiViewClassifiers import ExecClassif
-ExecClassif.execClassif(sys.argv[1:])
+ import sys
+
+ from MonoMultiViewClassifiers import ExecClassif
+ ExecClassif.execClassif(sys.argv[1:])
+
diff --git a/Code/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py b/Code/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
index b5f75f49cb1194e52b364edfa889643bc633b7e4..1fa0b546c8af374e91b0badd0b42115aed9bbb8c 100644
--- a/Code/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/Code/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -22,8 +22,9 @@ def canProbas():
def fit(DATASET, CLASS_LABELS, randomState, NB_CORES=1, **kwargs):
+ """Used to fit the monoview classifier with the args stored in kwargs"""
num_estimators = int(kwargs['0'])
- base_estimators = DecisionTreeClassifier() # kwargs['1']
+ base_estimators = DecisionTreeClassifier()
classifier = AdaBoostClassifier(n_estimators=num_estimators, base_estimator=base_estimators,
random_state=randomState)
classifier.fit(DATASET, CLASS_LABELS)
@@ -31,6 +32,7 @@ def fit(DATASET, CLASS_LABELS, randomState, NB_CORES=1, **kwargs):
def paramsToSet(nIter, randomState):
+ """Used for weighted linear early fusion to generate random search sets"""
paramsSet = []
for _ in range(nIter):
paramsSet.append([randomState.randint(1, 15), DecisionTreeClassifier()])
@@ -38,12 +40,15 @@ def paramsToSet(nIter, randomState):
def getKWARGS(kwargsList):
+ """Used to format kwargs for the parsed args"""
kwargsDict = {}
for (kwargName, kwargValue) in kwargsList:
if kwargName == "CL_Adaboost_n_est":
kwargsDict['0'] = int(kwargValue)
elif kwargName == "CL_Adaboost_b_est":
kwargsDict['1'] = kwargValue
+ else:
+ raise(ValueError, "Wrong arguments served to Adaboost")
return kwargsDict
@@ -74,7 +79,7 @@ def randomizedSearch(X_train, y_train, randomState, outputFileName, KFolds=4, me
def getConfig(config):
- if type(config) not in [list, dict]:
+ if type(config) not in [list, dict]: # Used in late fusion when config is a classifier
return "\n\t\t- Adaboost with num_esimators : " + str(config.n_estimators) + ", base_estimators : " + str(
config.base_estimator)
else:
@@ -84,6 +89,7 @@ def getConfig(config):
return "\n\t\t- Adaboost with num_esimators : " + str(config["0"]) + ", base_estimators : " + str(
config["1"])
+
def getInterpret(classifier, directory):
interpretString = getFeatureImportance(classifier, directory)
return interpretString
\ No newline at end of file
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/__init__.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/__init__.py
index 3f1a1d3e7fa1952ecfbbcdb7da47057e05e1fcdf..2f08d4c626a5f86381ca7217f6320107a4f9d53c 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/__init__.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/__init__.py
@@ -3,7 +3,6 @@ import os
for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
if module == '__init__.py' or module[-3:] == '.py' or module[-4:] == '.pyc' or module == '__pycache__' :
continue
- print(module)
__import__(module, locals(), globals(), [], 1)
del module
del os
diff --git a/Code/MonoMultiViewClassifiers/utils/Dataset.py b/Code/MonoMultiViewClassifiers/utils/Dataset.py
index 75866388d0b1f2210a8396b64fb52385bbdbb435..9b58de26f74565a432540792476283d69c2d3d2b 100644
--- a/Code/MonoMultiViewClassifiers/utils/Dataset.py
+++ b/Code/MonoMultiViewClassifiers/utils/Dataset.py
@@ -107,7 +107,7 @@ def confirm(resp=True, timeout=15):
def input_(timeout=15):
"""used as a UI to stop if too much HDD space will be used"""
- print("You have " + str(timeout) + " seconds to stop the script by typing n")
+ print("You have " + str(timeout) + " seconds to stop the dataset copy by typing n")
i, o, e = select.select([sys.stdin], [], [], timeout)
if i:
return sys.stdin.readline().strip()
diff --git a/Code/Tests.py b/Code/Tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..318166e2d0d4599c263796eca66eeaca1e283a52
--- /dev/null
+++ b/Code/Tests.py
@@ -0,0 +1,6 @@
+# if __name__=="__main__":
+# import unittest
+# from .Tests.test_ExecClassif import suite
+#
+# runner = unittest.TextTestRunner()
+# runner.run(suite())
\ No newline at end of file
diff --git a/Code/Tests/Test_MonoviewClassifiers/__init__.py b/Code/Tests/Test_MonoviewClassifiers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Code/Tests/Test_MonoviewClassifiers/test_Adaboost.py b/Code/Tests/Test_MonoviewClassifiers/test_Adaboost.py
new file mode 100644
index 0000000000000000000000000000000000000000..0241cc3d830ac2a116f56c0bce9070e21b0ccdf2
--- /dev/null
+++ b/Code/Tests/Test_MonoviewClassifiers/test_Adaboost.py
@@ -0,0 +1,28 @@
+import unittest
+import numpy as np
+
+from ...MonoMultiViewClassifiers.MonoviewClassifiers import Adaboost
+
+
+class Test_fit(unittest.TestCase):
+
+ def setUp(self):
+ self.random_state = np.random.RandomState(42)
+ self.dataset = self.random_state.randint(0,100,(10,5))
+ self.labels = self.random_state.randint(0,2,10)
+ self.kwargs = {"0":5}
+ self.classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
+
+ def test_fit_kwargs_string(self):
+ self.kwargs = {"0":"5"}
+ classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
+ self.assertEqual(classifier.n_estimators, 5)
+
+ def test_fit_kwargs_int(self):
+ self.kwargs = {"0":5}
+ classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
+ self.assertEqual(classifier.n_estimators, 5)
+
+ def test_fit_labels(self):
+ predicted_labels = self.classifier.predict(self.dataset)
+ np.testing.assert_array_equal(predicted_labels, self.labels)
\ No newline at end of file
diff --git a/Code/Tests/Test_utils/test_execution.py b/Code/Tests/Test_utils/test_execution.py
index 39984b898abdaf9149b9c886258c917319b04b18..c26700e2b0444db26835492e63e7e7b03f259e91 100644
--- a/Code/Tests/Test_utils/test_execution.py
+++ b/Code/Tests/Test_utils/test_execution.py
@@ -6,7 +6,7 @@ import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit
-from MonoMultiViewClassifiers.utils import execution
+from ...MonoMultiViewClassifiers.utils import execution
class Test_parseTheArgs(unittest.TestCase):
@@ -23,16 +23,16 @@ class Test_initRandomState(unittest.TestCase):
def test_random_state_42(self):
randomState_42 = np.random.RandomState(42)
- randomState = execution.initRandomState("42", "Tests/temp_tests/")
- os.remove("Tests/temp_tests/randomState.pickle")
+ randomState = execution.initRandomState("42", "Code/Tests/temp_tests/")
+ os.remove("Code/Tests/temp_tests/randomState.pickle")
np.testing.assert_array_equal(randomState.beta(1,100,100),
randomState_42.beta(1,100,100))
def test_random_state_pickle(self):
- randomState_to_pickle = execution.initRandomState(None, "Tests/temp_tests/")
- pickled_randomState = execution.initRandomState("Tests/temp_tests/randomState.pickle",
- "Tests/temp_tests/")
- os.remove("Tests/temp_tests/randomState.pickle")
+ randomState_to_pickle = execution.initRandomState(None, "Code/Tests/temp_tests/")
+ pickled_randomState = execution.initRandomState("Code/Tests/temp_tests/randomState.pickle",
+ "Code/Tests/temp_tests/")
+ os.remove("Code/Tests/temp_tests/randomState.pickle")
np.testing.assert_array_equal(randomState_to_pickle.beta(1,100,100),
pickled_randomState.beta(1,100,100))
diff --git a/Code/Tests/test_ExecClassif.py b/Code/Tests/test_ExecClassif.py
index 8b4cd14b2e09d3794305979c0fe4b24000447aea..fdec1b59507927fc3ecb6f9fdcc30dd6e5034d28 100644
--- a/Code/Tests/test_ExecClassif.py
+++ b/Code/Tests/test_ExecClassif.py
@@ -1,7 +1,7 @@
import unittest
import argparse
-from MonoMultiViewClassifiers import ExecClassif
+from ..MonoMultiViewClassifiers import ExecClassif
class Test_initBenchmark(unittest.TestCase):
@@ -23,12 +23,12 @@ class Test_initMonoviewArguments(unittest.TestCase):
def test_initMonoviewArguments_no_monoview(self):
benchmark = {"Monoview":{}, "Multiview":{}}
- arguments = ExecClassif.initMonoviewArguments(benchmark, {}, [], [], None, 0, {})
+ arguments = ExecClassif.initMonoviewExps(benchmark, {}, [], None, 0, {})
self.assertEqual(arguments, {})
def test_initMonoviewArguments_empty(self):
benchmark = {"Monoview":{}, "Multiview":{}}
- arguments = ExecClassif.initMonoviewArguments(benchmark, {}, [], [], None, 0, {})
+ arguments = ExecClassif.initMonoviewExps(benchmark, {}, [], None, 0, {})
class Essai(unittest.TestCase):
@@ -203,3 +203,10 @@ class Essai(unittest.TestCase):
help='Determine which method to use to select the monoview classifiers',
default="intersect")
self.args = parser.parse_args([])
+
+
+def suite():
+ suite = unittest.TestSuite()
+ suite.addTest(Test_initBenchmark('test_initKWARGSFunc_no_monoview'))
+ # suite.addTest(WidgetTestCase('test_widget_resize'))
+ return suite
\ No newline at end of file
diff --git a/Code/__init__.py b/Code/__init__.py
index 27925d1c65ac8c0c097fe0a5e4efdaa4cee6b060..4ed6671084a85435bcfbf14f6adde84ea506e7b0 100644
--- a/Code/__init__.py
+++ b/Code/__init__.py
@@ -1,2 +1,2 @@
from . import MonoMultiViewClassifiers, Tests, Exec
-import pdb;pdb.set_trace()
\ No newline at end of file
+# import pdb;pdb.set_trace()
\ No newline at end of file
diff --git a/Code/Versions.py b/Versions.py
similarity index 100%
rename from Code/Versions.py
rename to Versions.py