diff --git a/config_files/config.yml b/config_files/config.yml
index 5c3f48fc24d18b5e334592eea9446d6045da8efe..bcd99f24eea4b96b91abe4e562cb82304a02aaa9 100644
--- a/config_files/config.yml
+++ b/config_files/config.yml
@@ -137,4 +137,9 @@ min_cq:
weighted_linear_early_fusion:
view_weights: [None]
- monoview_classifier: ["decision_tree"]
+ monoview_classifier_name: ["decision_tree"]
+ monoview_classifier_config:
+ decision_tree:
+ max_depth: [1]
+ criterion: ["gini"]
+ splitter: ["best"]
diff --git a/ipynb/FeatureExtraction-All_unix.ipynb b/ipynb/FeatureExtraction-All_unix.ipynb
index 42b4e2e64409c15503e9af8034c09798f00a90f1..fd972a80342a04b04fbd8a6b243fd211667c6e5e 100644
--- a/ipynb/FeatureExtraction-All_unix.ipynb
+++ b/ipynb/FeatureExtraction-All_unix.ipynb
@@ -480,7 +480,6 @@
{
"ename": "ValueError",
"evalue": "all the input array dimensions except for the concatenation axis must match exactly",
- "output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
@@ -488,7 +487,8 @@
"\u001b[1;32m<ipython-input-13-c8d37ffc0446>\u001b[0m in \u001b[0;36mcalcSurfHisto\u001b[1;34m(dfImages_, k_)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdescriptor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdes_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[0mdescriptors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdescriptors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescriptor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;31m#### Bag of Words Approach\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/doob/anaconda2/lib/python2.7/site-packages/numpy/core/shape_base.pyc\u001b[0m in \u001b[0;36mvstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 229\u001b[0m \"\"\"\n\u001b[1;32m--> 230\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
- ]
+ ],
+ "output_type": "error"
}
],
"source": [
diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
index cd066e7274fbda67eeffe406339c0decae445a55..8f8ebb7ece1cb8b2e67b744d8eae4725adae17e7 100644
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
@@ -200,7 +200,7 @@ def gen_single_multiview_arg_dictionary(classifier_name,arguments,nb_class,
"nb_class": nb_class,
"labels_names": None,
classifier_name: dict((key, value[0]) for key, value in arguments[
- classifier_name].items())
+ classifier_name].items() if isinstance(value, list))
}
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index 3b2952e4481e0ca2bf1a4510751c742f7ef2699e..e079dae7b54fda07d3e036bf21d0d762f6f47d30 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
@@ -84,12 +84,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
np.array([self.train_time, self.pred_time]), delimiter=',')
return interpretString
-#
-# def formatCmdArgs(args):
-# """Used to format kwargs for the parsed args"""
-# kwargsDict = {'n_estimators': args.Ada_n_est,
-# 'base_estimator': [DecisionTreeClassifier(max_depth=1)]}
-# return kwargsDict
def paramsToSet(nIter, random_state):
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
index 51a9a5686f1ccb3ac3f9d6b05546eadfeb10859b..dd4767d557d8b05d4d1d8a3676d9d162e1e12364 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
@@ -97,21 +97,20 @@ def get_train_views_indices(dataset, train_indices, view_indices,):
class ConfigGenerator():
- def __init__(self):
+ def __init__(self, classifier_names):
self.distribs = {}
- for name in dir(monoview_classifiers):
- if not name.startswith("__"):
- module = getattr(monoview_classifiers, name)
- classifier_class = getattr(module,
- module.classifier_class_name)()
- self.distribs[name] = dict((param_name, param_distrib)
- for param_name, param_distrib in
- zip(classifier_class().param_names,
- classifier_class().distribs))
+ for classifier_name in classifier_names:
+ classifier_module = getattr(monoview_classifiers, classifier_name)
+ classifier_class = getattr(classifier_module, classifier_module.classifier_class_name)
+ self.distribs[classifier_name] = dict((param_name, param_distrib)
+ for param_name, param_distrib in
+ zip(classifier_class().param_names,
+ classifier_class().distribs))
def rvs(self, random_state=None):
config_sample = {}
for classifier_name, classifier_config in self.distribs.items():
+ config_sample[classifier_name] = {}
for param_name, param_distrib in classifier_config.items():
if hasattr(param_distrib, "rvs"):
config_sample[classifier_name][param_name]=param_distrib.rvs(random_state=random_state)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
index 303f52157a858dde1d1f0a6769a9d139de0edc90..25539a9f90982fb05de991587693f7f123bb29bb 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
@@ -1,5 +1,5 @@
import numpy as np
-import pkgutil
+import inspect
from ..utils.dataset import getV
from ..multiview.multiview_utils import BaseMultiviewClassifier, get_train_views_indices, ConfigGenerator
@@ -11,39 +11,46 @@ classifier_class_name = "WeightedLinearEarlyFusion"
class WeightedLinearEarlyFusion(BaseMultiviewClassifier):
def __init__(self, random_state=None, view_weights=None,
- monoview_classifier="decision_tree",
+ monoview_classifier_name="decision_tree",
monoview_classifier_config={}):
super(WeightedLinearEarlyFusion, self).__init__(random_state=random_state)
self.view_weights = view_weights
- if isinstance(monoview_classifier, str):
- self.short_name = "early fusion "+monoview_classifier
- monoview_classifier_module = getattr(monoview_classifiers,
- monoview_classifier)
- monoview_classifier_class = getattr(monoview_classifier_module,
- monoview_classifier_module.classifier_class_name)
- self.monoview_classifier = monoview_classifier_class(random_state=random_state,
- **monoview_classifier_config)
- else:
- self.monoview_classifier = monoview_classifier(monoview_classifier_config)
- self.short_name = "early fusion "+self.monoview_classifier.__class__.__name__
-
- self.param_names = ["monoview_classifier","random_state", "monoview_classifier_config"]
- classifier_classes = []
- for name in dir(monoview_classifiers):
- if not name.startswith("__"):
- module = getattr(monoview_classifiers, name)
- classifier_class = getattr(module, module.classifier_class_name)
- classifier_classes.append(classifier_class)
- self.distribs = [classifier_classes, [self.random_state], ConfigGenerator()]
- self.classed_params = ["monoview_classifier"]
- self.weird_strings={"monoview_classifier":["class_name", "config"]}
-
- def set_params(self, monoview_classifier=None, monoview_classifier_config=None, **params):
- monoview_classifier_name = monoview_classifier.__module__
- self.monoview_classifier = monoview_classifier()
+ self.monoview_classifier_name = monoview_classifier_name
+ self.short_name = "early fusion " + monoview_classifier_name
+ self.monoview_classifier_config = monoview_classifier_config
+
+ monoview_classifier_module = getattr(monoview_classifiers,
+ self.monoview_classifier_name)
+ monoview_classifier_class = getattr(monoview_classifier_module,
+ monoview_classifier_module.classifier_class_name)
+ self.monoview_classifier = monoview_classifier_class(random_state=random_state,
+ **self.monoview_classifier_config)
+
+ self.param_names = ["monoview_classifier_name", "monoview_classifier_config"]
+ classifier_names = []
+ for module_name in dir(monoview_classifiers):
+ if not module_name.startswith("__"):
+ classifier_names.append(module_name)
+ self.distribs = [classifier_names, ConfigGenerator(classifier_names)]
+ self.classed_params = []
+ self.weird_strings={}
+
+ def set_params(self, monoview_classifier_name=None, monoview_classifier_config=None, **params):
+ self.monoview_classifier_name = monoview_classifier_name
+ monoview_classifier_module = getattr(monoview_classifiers,
+ self.monoview_classifier_name)
+ monoview_classifier_class = getattr(monoview_classifier_module,
+ monoview_classifier_module.classifier_class_name)
+ self.monoview_classifier = monoview_classifier_class()
self.set_monoview_classifier_config(monoview_classifier_name,
monoview_classifier_config)
+ return self
+ def get_params(self, deep=True):
+ return {"random_state":self.random_state,
+ "view_weights":self.view_weights,
+ "monoview_classifier_name":self.monoview_classifier_name,
+ "monoview_classifier_config":self.monoview_classifier_config}
def fit(self, X, y, train_indices=None, view_indices=None):
train_indices, X = self.transform_data_to_monoview(X, train_indices, view_indices)
@@ -80,9 +87,9 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier):
def set_monoview_classifier_config(self, monoview_classifier_name, monoview_classifier_config):
if monoview_classifier_name in monoview_classifier_config:
- self.monoview_classifier.set_params(monoview_classifier_config[monoview_classifier_name])
+ self.monoview_classifier.set_params(**monoview_classifier_config[monoview_classifier_name])
else:
- self.monoview_classifier.set_params(monoview_classifier_config)
+ self.monoview_classifier.set_params(**monoview_classifier_config)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py b/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
index 4534c685529b9978ba135ad54121f3b42340b737..72318b2a1cdac0f246193e5e33e39bdf120f2487 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
@@ -1,4 +1,3 @@
-import configparser
import builtins
from distutils.util import strtobool as tobool
import yaml
@@ -9,34 +8,3 @@ def get_the_args(path_to_config_file="../config_files/config.yml"):
with open(path_to_config_file, 'r') as stream:
yaml_config = yaml.safe_load(stream)
return yaml_config
-
- # config_parser = configparser.ConfigParser(comment_prefixes=('#'))
- # config_parser.read(path_to_config_file)
- # config_dict = {}
- # for section in config_parser:
- # config_dict[section] = {}
- # for key in config_parser[section]:
- # value = format_raw_arg(config_parser[section][key])
- # config_dict[section][key] = value
- # return config_dict
-
-
-def format_raw_arg(raw_arg):
- """This function is used to convert the raw arg in a types value.
- For example, 'list_int ; 10 20' will be formatted in [10,20]"""
- function_name, raw_value = raw_arg.split(" ; ")
- if function_name.startswith("list"):
- function_name = function_name.split("_")[1]
- raw_values = raw_value.split(" ")
- value = [getattr(builtins, function_name)(raw_value)
- if function_name != "bool" else bool(tobool(raw_value))
- for raw_value in raw_values]
- else:
- if raw_value == "None":
- value = None
- else:
- if function_name=="bool":
- value = bool(tobool(raw_value))
- else:
- value = getattr(builtins, function_name)(raw_value)
- return value
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
index 6a19674c653148ab554121ba41f8f31c66cf9904..2a37b3d12408777f022309c12706d174ea2abcc1 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
@@ -10,6 +10,8 @@ from scipy import sparse
from . import get_multiview_db as DB
+
+
def getV(DATASET, viewIndex, usedIndices=None):
"""Used to extract a view as a numpy array or a sparse mat from the HDF5 dataset"""
if usedIndices is None:
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index 6c066e1cdcf6ff4753179e2f138278f13c152185..6bbf2eb4acb77a67d069bcbb25fa86710ea640d6 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -107,7 +107,7 @@ def randomized_search(X, y, framework, random_state, output_file_name, classifie
min_list = np.array(
[min(nb_possible_combination, n_iter) for nb_possible_combination in
nb_possible_combinations])
- randomSearch = MultiviewCompatibleRandomizedSearchCV(estimator,
+ random_search = MultiviewCompatibleRandomizedSearchCV(estimator,
n_iter=int(np.sum(min_list)),
param_distributions=params_dict,
refit=True,
@@ -116,23 +116,25 @@ def randomized_search(X, y, framework, random_state, output_file_name, classifie
learning_indices=learning_indices,
view_indices=view_indices,
framework = framework)
- detector = randomSearch.fit(X, y)
-
- bestParams = dict((key, value) for key, value in
- estimator.genBestParams(detector).items() if
- key is not "random_state")
-
- scoresArray = detector.cv_results_['mean_test_score']
- params = estimator.genParamsFromDetector(detector)
-
- genHeatMaps(params, scoresArray, output_file_name)
- best_estimator = detector.best_estimator_
+ random_search.fit(X, y)
+ best_params = random_search.best_params_
+ if "random_state" in best_params:
+ best_params.pop("random_state")
+
+ # bestParams = dict((key, value) for key, value in
+ # estimator.genBestParams(detector).items() if
+ # key is not "random_state")
+
+ scoresArray = random_search.cv_results_['mean_test_score']
+ params = [(key[6:], value ) for key, value in random_search.cv_results_.items() if key.startswith("param_")]
+ # genHeatMaps(params, scoresArray, output_file_name)
+ best_estimator = random_search.best_estimator_
else:
best_estimator = estimator
- bestParams = {}
+ best_params = {}
testFoldsPreds = get_test_folds_preds(X, y, folds, best_estimator,
framework, learning_indices)
- return bestParams, testFoldsPreds
+ return best_params, testFoldsPreds
from sklearn.base import clone
@@ -192,6 +194,7 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
self.best_score_ = cross_validation_score
if self.refit:
self.best_estimator_ = clone(base_estimator).set_params(**self.best_params_)
+ self.best_estimator_.fit(X, y, **fit_params)
self.n_splits_ = n_splits
return self
diff --git a/multiview_platform/tests/test_ExecClassif.py b/multiview_platform/tests/test_ExecClassif.py
index cd9545cb9371f1b61afa9e709a919fb0f758f12b..1af0dc200460ac4b1b3ef60549df5a6969424438 100644
--- a/multiview_platform/tests/test_ExecClassif.py
+++ b/multiview_platform/tests/test_ExecClassif.py
@@ -4,6 +4,8 @@ import unittest
import h5py
import numpy as np
+from .utils import rm_tmp
+
from ..mono_multi_view_classifiers import exec_classif
@@ -25,6 +27,7 @@ class Test_initKWARGS(unittest.TestCase):
class Test_init_argument_dictionaries(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
cls.benchmark = {"monoview": ["fake_monoview_classifier"], "multiview": {}}
cls.views_dictionnary = {'test_view_0': 0, 'test_view': 1}
cls.nb_class = 2
@@ -113,6 +116,7 @@ class Test_execBenchmark(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.Dataset = h5py.File(
"multiview_platform/tests/tmp_tests/test_file.hdf5", "w")
@@ -216,6 +220,7 @@ class Test_execOneBenchmark(unittest.TestCase):
@classmethod
def setUp(cls):
+ rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.args = {
"Base": {"name": "chicken_is_heaven", "type": "type",
@@ -282,6 +287,7 @@ class Test_execOneBenchmark_multicore(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.args = {
"Base": {"name": "chicken_is_heaven", "type": "type",
diff --git a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
index 78a4bdb2a570e48aadef94a4b9138dcbd74bc7f4..c5e70b74c7aadddf7b7d93bef5030e2911d1f647 100644
--- a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
+++ b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
@@ -5,6 +5,8 @@ import h5py
import numpy as np
from sklearn.model_selection import StratifiedKFold
+from ..utils import rm_tmp
+
from ...mono_multi_view_classifiers.monoview import exec_classif_mono_view
from ...mono_multi_view_classifiers.monoview_classifiers import decision_tree
@@ -13,6 +15,7 @@ class Test_initConstants(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
os.mkdir("multiview_platform/tests/temp_tests")
cls.datasetFile = h5py.File(
"multiview_platform/tests/temp_tests/test.hdf5", "w")
@@ -65,6 +68,7 @@ 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)
@@ -95,6 +99,7 @@ class Test_getHPs(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
os.mkdir("multiview_platform/tests/tmp_tests")
cls.classifierModule = decision_tree
cls.hyperParamSearch = "randomized_search"
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py b/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
index 1b70609141096f5d6d3870393cdffb2334a0a19b..11b74a75abc59b18e5d2dd8a80537a3811fd9155 100644
--- a/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
+++ b/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
@@ -2,6 +2,7 @@ import unittest
import numpy as np
+from ..utils import rm_tmp
from multiview_platform.mono_multi_view_classifiers.multiview.additions import \
diversity_utils
@@ -14,6 +15,7 @@ class Test_global_div_measure(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
cls.randomState = np.random.RandomState(42)
cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
cls.viewsIndices = np.array([0, 1])
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
index d78acf8020081205e42e36d79cc936d8511be72c..3fb36b314d67184ed43c801ab8e8e355d9ff24d3 100644
--- a/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
+++ b/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
@@ -4,6 +4,8 @@ import numpy as np
import h5py
import os
+from ..utils import rm_tmp
+
from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import \
weighted_linear_early_fusion
@@ -11,6 +13,7 @@ class Test_WeightedLinearEarlyFusion(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
cls.random_state = np.random.RandomState(42)
cls.view_weights = [0.5, 0.5]
os.mkdir("multiview_platform/tests/tmp_tests")
@@ -32,7 +35,7 @@ class Test_WeightedLinearEarlyFusion(unittest.TestCase):
cls.monoview_classifier_config = {"max_depth":1, "criterion": "gini", "splitter": "best"}
cls.classifier = weighted_linear_early_fusion.WeightedLinearEarlyFusion(
random_state=cls.random_state, view_weights=cls.view_weights,
- monoview_classifier=cls.monoview_classifier_name,
+ monoview_classifier_name=cls.monoview_classifier_name,
monoview_classifier_config=cls.monoview_classifier_config)
@classmethod
diff --git a/multiview_platform/tests/test_utils/test_configuration.py b/multiview_platform/tests/test_utils/test_configuration.py
index c1e8c3b47125380c120e8516c6880f115b6f6bc4..bc922be1e50733cc8dd61e343e2cd3f73d35ac45 100644
--- a/multiview_platform/tests/test_utils/test_configuration.py
+++ b/multiview_platform/tests/test_utils/test_configuration.py
@@ -3,11 +3,14 @@ import unittest
import yaml
import numpy as np
+from ..utils import rm_tmp
+
from multiview_platform.mono_multi_view_classifiers.utils import configuration
class Test_get_the_args(unittest.TestCase):
def setUp(self):
+ rm_tmp()
self.path_to_config_file = "multiview_platform/tests/tmp_tests/config_temp.yml"
os.mkdir("multiview_platform/tests/tmp_tests")
data = {"Base":{"first_arg": 10, "second_arg":[12.5, 1e-06]}, "Classification":{"third_arg":True}}
diff --git a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
index 0024a1427a85b07adbbd4f4ebee038fcf75cc28d..6b207372e6bf3fee23b5c1cf005b427d77ab0044 100644
--- a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
+++ b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
@@ -5,6 +5,8 @@ import h5py
import numpy as np
from sklearn.model_selection import StratifiedKFold
+from ..utils import rm_tmp
+
from ...mono_multi_view_classifiers.utils import hyper_parameter_search
from ...mono_multi_view_classifiers.multiview_classifiers import weighted_linear_early_fusion
@@ -12,6 +14,7 @@ class Test_randomized_search(unittest.TestCase):
@classmethod
def setUpClass(cls):
+ rm_tmp()
cls.random_state = np.random.RandomState(42)
cls.view_weights = [0.5, 0.5]
os.mkdir("multiview_platform/tests/tmp_tests")
diff --git a/multiview_platform/tests/utils.py b/multiview_platform/tests/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5766b68885035b216a141caa8e273c0df74583ae
--- /dev/null
+++ b/multiview_platform/tests/utils.py
@@ -0,0 +1,9 @@
+import os
+
+def rm_tmp():
+ try:
+ 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")
+ except:
+ pass