Last modifs

multiview-machine-learning-omis.iml

@@ -2,7 +2,6 @@
 <module type="WEB_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
     <orderEntry type="module" module-name="multiview_generator" />
     <orderEntry type="module" module-name="short_projects" />

multiview_platform/examples/config_files/config_example_1.yml

@@ -3,15 +3,15 @@
 # Enable logging
 log: True
 # The name of each dataset in the directory on which the benchmark should be run
-name: ["doc_summit"]
+name: ["tuto"]
 # A label for the resul directory
-label: "example_1"
+label:
 # The type of dataset, currently supported ".hdf5", and ".csv"
 file_type: ".hdf5"
 # The views to use in the banchmark, an empty value will result in using all the views
 views:
 # The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
+pathf: "."
 # The niceness of the processes, useful to lower their priority
 nice: 0
 # The random state of the benchmark, useful for reproducibility
@@ -23,7 +23,7 @@ full: True
 # Used to be able to run more than one benchmark per minute
 debug: False
 # The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_1/"
+res_dir: "."
 # If an error occurs in a classifier, if track_tracebacks is set to True, the
 # benchmark saves the traceback and continues, if it is set to False, it will
 # stop the benchmark and raise the error
@@ -34,25 +34,23 @@ track_tracebacks: True
 # The ratio of test examples/number of train examples
 split: 0.35
 # The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
+nb_folds: 5
 # The number of classes to select in the dataset
 nb_class:
 # The name of the classes to select in the dataset
 classes:
 # The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
+type: ["monoview"]
 # The name of the monoview algorithms to run, ["all"] to run all the available classifiers
 algos_monoview: ["decision_tree"]
 # The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion",]
+algos_multiview: []
 # The number of times the benchamrk is repeated with different train/test
 # split, to have more statistically significant results
-stats_iter: 1
+stats_iter: 5
 # The metrics that will be use din the result analysis
 metrics:
   accuracy_score: {}
-  f1_score:
-    average: "micro"
 # The metric that will be used in the hyper-parameter optimization process
 metric_princ: "accuracy_score"
 # The type of hyper-parameter optimization method
@@ -65,14 +63,3 @@ hps_args: {}
 decision_tree:
   max_depth: 3
 
-weighted_linear_early_fusion:
-  monoview_classifier_name: "decision_tree"
-  monoview_classifier_config:
-    decision_tree:
-      max_depth: 6
-
-weighted_linear_late_fusion:
-  classifiers_names: "decision_tree"
-  classifier_configs:
-    decision_tree:
-      max_depth: 3

multiview_platform/execute.py

@@ -2,13 +2,13 @@
 
 import os
 
-def execute(config_path=os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_1.yml")):
+def execute(config_path=None):
     from multiview_platform import versions as vs
     vs.test_versions()
     import sys
 
     from multiview_platform.mono_multi_view_classifiers import exec_classif
-    if sys.argv[1:]:
+    if config_path is None:
         exec_classif.exec_classif(sys.argv[1:])
     else:
         if config_path == "example 0":

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py

@@ -26,7 +26,7 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
     ----------
 
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     n_estimators : int number of estimators

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py

@@ -15,7 +15,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     n_estimators : int (default : 10) number of estimators

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py

@@ -15,7 +15,7 @@ class SGD(SGDClassifier, BaseMonoviewClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     loss : str , (default = "hinge")

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py

@@ -15,7 +15,7 @@ class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
 

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py

@@ -17,7 +17,7 @@ class SVMPoly(SVCClassifier, BaseMonoviewClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
 

multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py

@@ -16,7 +16,7 @@ class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     C :

multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py

@@ -152,7 +152,7 @@ def exec_multiview_multicore(directory, core_index, name, learning_rate,
     labels_dictionary
 
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     labels :
@@ -217,7 +217,7 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
     labels_dictionary : dict dictionary of labels
 
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
 
     labels

multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py

@@ -20,7 +20,7 @@ class BaseMultiviewClassifier(BaseClassifier):
     Parameters
     ----------
     random_state : int seed, RandomState instance, or None (default=None)
-        The seed of the pseudo random number generator to use when
+        The seed of the pseudo random number multiview_generator to use when
         shuffling the data.
     """
 

multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py

@@ -226,7 +226,7 @@ def plot_2d(data, classifiers_names, nb_classifiers, file_name, labels=None,
         fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                           plot_bgcolor='rgba(0,0,0,0)')
         fig.update_xaxes(showticklabels=True, )
-        plotly.offline.plot(fig, filename=file_name + "err.html",
+        plotly.offline.plot(fig, filename=file_name + "error_analysis_2D.html",
                             auto_open=False)
         del fig
 

multiview_platform/mono_multi_view_classifiers/utils/execution.py

@@ -153,9 +153,9 @@ def init_log_file(name, views, cl_type, log, debug, label,
     """
     if views is None:
         views = []
-    result_directory = os.path.join(os.path.dirname(
-        os.path.dirname(os.path.dirname(os.path.realpath(__file__)))),
-                                    result_directory)
+    # result_directory = os.path.join(os.path.dirname(
+    #     os.path.dirname(os.path.dirname(os.path.realpath(__file__)))),
+    #                                 result_directory)
     if debug:
         result_directory = os.path.join(result_directory, name,
                                         "debug_started_" + time.strftime(