diff --git a/README.rst b/README.rst
index ae56f162e10417d9b9f3aa4cd8eb932cc2463e89..0b6e662707f208275b93470713473e6324e8659b 100644
--- a/README.rst
+++ b/README.rst
@@ -1,16 +1,18 @@
.. |pipeline| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg
:alt: Pipeline status
-.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
+.. |license| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
:target: http://www.gnu.org/licenses/gpl-3.0
:alt: License: GPL v3
-|pipeline|
-
-.. image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
+.. |coverage| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
:target: http://baptiste.bauvin.pages.lis-lab.fr/summit/coverage/index.html
:alt: Coverage
+|pipeline| |license| |coverage|
+
+
+
Supervised MultiModal Integration Tool's Readme
===============================================
@@ -109,7 +111,7 @@ For your first go with SuMMIT, you can run it on simulated data with
This will run the benchmark of `documentation's Example 1 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example1.html>`_.
-For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/>`_.
+For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html>`_.
Results will, by default, be stored in the results directory of the installation path :
``path/to/summit/multiview_platform/examples/results``.
diff --git a/docs/source/classifiers.rst b/docs/source/classifiers.rst
deleted file mode 100644
index f14e5f968a17a2a7409418a6eb8642223b95ef34..0000000000000000000000000000000000000000
--- a/docs/source/classifiers.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-.. toctree::
- :maxdepth: 1
-
- autoapi/summit/multiview_platform/monoview_classifiers/index
- autoapi/summit/multiview_platform/multiview_classifiers/index
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 97c2a905eefc85d6343ec6580cdceb643da9ae0d..0709f0de5e4ec9e36c29a10ed1f7e7aea8da496c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -20,7 +20,8 @@ This documentation consists in a short read me, with instructions to install and
.. toctree::
:maxdepth: 2
- classifiers
+ autoapi/summit/multiview_platform/monoview_classifiers/index
+ autoapi/summit/multiview_platform/multiview_classifiers/index
Read me
diff --git a/summit/multiview_platform/monoview_classifiers/adaboost.py b/summit/multiview_platform/monoview_classifiers/adaboost.py
index 605041556c5b0f488d0c1474a45fa031e57792e9..82b380f7c93198128064cd2b290c2d7690bcaf17 100644
--- a/summit/multiview_platform/monoview_classifiers/adaboost.py
+++ b/summit/multiview_platform/monoview_classifiers/adaboost.py
@@ -1,4 +1,3 @@
-""" Ada"""
import os
import time
@@ -19,38 +18,7 @@ classifier_class_name = "Adaboost"
class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
"""
- This class implement a Classifier with adaboost algorithm inherit from sklearn
- AdaBoostClassifier
-
- Parameters
- ----------
-
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- n_estimators : int number of estimators
-
- base_estimator :
-
- kwargs : others arguments
-
-
- Attributes
- ----------
- param_name :
-
- classed_params :
-
- distribs :
-
- weird_strings :
-
- plotted_metric : selection of metric to plot
-
- plotted_metric_name : name of the metric to plot
-
- step_predictions :
+ This class is an adaptation of scikit-learn's `AdaBoostClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_
"""
@@ -75,23 +43,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
self.step_predictions = None
def fit(self, X, y, sample_weight=None):
- """
- Fit adaboost model
-
- Parameters
- ----------
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
-
- y : { array-like, shape (n_samples,)
- Target values class labels in classification
-
- sample_weight :
-
- Returns
- -------
- self : object
- Returns self.
- """
begin = time.time()
AdaBoostClassifier.fit(self, X, y, sample_weight=sample_weight)
end = time.time()
@@ -104,21 +55,6 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
return self
def predict(self, X):
- """
-
- Parameters
- ----------
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- Training vectors, where n_samples is the number of samples
- and n_features is the number of features.
- For kernel="precomputed", the expected shape of X is
- (n_samples, n_samples).
-
- Returns
- -------
- predictions : ndarray of shape (n_samples, )
- The estimated labels.
- """
begin = time.time()
pred = AdaBoostClassifier.predict(self, X)
end = time.time()
diff --git a/summit/multiview_platform/monoview_classifiers/decision_tree.py b/summit/multiview_platform/monoview_classifiers/decision_tree.py
index 6a6a3b2f7dbc0a205c72b51d7b0a8c118e6d26ac..6b309dc3e11f09e165bab1d5c30d9082681de300 100644
--- a/summit/multiview_platform/monoview_classifiers/decision_tree.py
+++ b/summit/multiview_platform/monoview_classifiers/decision_tree.py
@@ -11,9 +11,15 @@ classifier_class_name = "DecisionTree"
class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
+ """
+ This class is an adaptation of scikit-learn's `DecisionTreeClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_
+
+
+ """
def __init__(self, random_state=None, max_depth=None,
criterion='gini', splitter='best', **kwargs):
+
DecisionTreeClassifier.__init__(self,
max_depth=max_depth,
criterion=criterion,
diff --git a/summit/multiview_platform/monoview_classifiers/gradient_boosting.py b/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
index 8651dbc676241a76b30206eb6593d129f550de8d..e242dee80c6c1ef76daacd2c43d4c178b8f4c495 100644
--- a/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
+++ b/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
@@ -24,6 +24,11 @@ class CustomDecisionTreeGB(DecisionTreeClassifier):
class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
+ """
+ This class is an adaptation of scikit-learn's `GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html>`_
+
+
+ """
def __init__(self, random_state=None, loss="exponential", max_depth=1.0,
n_estimators=100,
diff --git a/summit/multiview_platform/monoview_classifiers/knn.py b/summit/multiview_platform/monoview_classifiers/knn.py
index 0aeb093f5cd59c99f6469d1bcc6aab49142508dd..c23d4d3f910449d4abefc810bf64a110f58b05ae 100644
--- a/summit/multiview_platform/monoview_classifiers/knn.py
+++ b/summit/multiview_platform/monoview_classifiers/knn.py
@@ -12,18 +12,10 @@ classifier_class_name = "KNN"
class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
"""
- Implement extention of KNeighborsClassifier of sklearn
- for the usage of the summit.
-
- Parameters
- ----------
- random_state
- n_neighbors
- weights
- algorithm
- p
- kwargs
- """
+ This class is an adaptation of scikit-learn's `KNeighborsClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html>`_
+
+
+ """
def __init__(self, random_state=None, n_neighbors=5,
weights='uniform', algorithm='auto', p=2, **kwargs):
diff --git a/summit/multiview_platform/monoview_classifiers/lasso.py b/summit/multiview_platform/monoview_classifiers/lasso.py
index 9359a3009c72fec13dd4fcd69ace06cfdce190ea..288bf1dbf75b9bf5d5990c41d240fefd956015d9 100644
--- a/summit/multiview_platform/monoview_classifiers/lasso.py
+++ b/summit/multiview_platform/monoview_classifiers/lasso.py
@@ -13,38 +13,10 @@ classifier_class_name = "Lasso"
class Lasso(LassoSK, BaseMonoviewClassifier):
"""
+ This class is an adaptation of scikit-learn's `Lasso <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html>`_
- Parameters
- ----------
- random_state :
- alpha : float, optional
- Constant that multiplies the L1 term. Defaults to 1.0.
- ``alpha = 0`` is equivalent to an ordinary least square, solved
- by the :class:`LinearRegression` object. For numerical
- reasons, using ``alpha = 0`` is with the Lasso object is
- not advised
- and you should prefer the LinearRegression object. (default( : 10)
-
- max_iter : int The maximum number of iterations (default : 10)
-
- warm_start : bool, optional
- When set to True, reuse the solution of the previous call to fit as
- initialization, otherwise, just erase the previous solution.
-
- kwargs : others arguments
-
- Attributes
- ----------
- param_name :
-
- classed_params :
-
- distribs :
-
- weird_strings :
-
- """
+ """
def __init__(self, random_state=None, alpha=1.0,
max_iter=10, warm_start=False, **kwargs):
diff --git a/summit/multiview_platform/monoview_classifiers/random_forest.py b/summit/multiview_platform/monoview_classifiers/random_forest.py
index d39943a4c0c0e36386e9f7a63edeec5e85baa4ca..c0ebaaa570e33e6d0fa2a92944a16b7f7ccecb99 100644
--- a/summit/multiview_platform/monoview_classifiers/random_forest.py
+++ b/summit/multiview_platform/monoview_classifiers/random_forest.py
@@ -11,47 +11,15 @@ classifier_class_name = "RandomForest"
class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
- """RandomForest Classifier Class
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- n_estimators : int (default : 10) number of estimators
-
- max_depth : int , optional (default : None) maximum of depth
-
- criterion : criteria (default : 'gini')
-
- kwargs : others arguments
-
-
- Attributes
- ----------
- param_names :
-
- distribs :
-
- classed_params :
+ """
+ This class is an adaptation of scikit-learn's `RandomForestClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_
- weird_strings :
"""
def __init__(self, random_state=None, n_estimators=10,
max_depth=None, criterion='gini', **kwargs):
- """
- Parameters
- ----------
- random_state
- n_estimators
- max_depth
- criterion
- kwargs
- """
RandomForestClassifier.__init__(self,
n_estimators=n_estimators,
max_depth=max_depth,
@@ -68,17 +36,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
def get_interpretation(self, directory, base_file_name, y_test,
multiclass=False):
- """
-
- Parameters
- ----------
- directory
- y_test
- Returns
- -------
- string for interpretation interpret_string
- """
interpret_string = ""
interpret_string += self.get_feature_importance(directory,
base_file_name)
diff --git a/summit/multiview_platform/monoview_classifiers/sgd.py b/summit/multiview_platform/monoview_classifiers/sgd.py
index e5f01a95b0cb4cecc6520c056811ba5db3626230..1b1d3375c39527152c767d7a9f86f0e0c0611b00 100644
--- a/summit/multiview_platform/monoview_classifiers/sgd.py
+++ b/summit/multiview_platform/monoview_classifiers/sgd.py
@@ -12,30 +12,8 @@ classifier_class_name = "SGD"
class SGD(SGDClassifier, BaseMonoviewClassifier):
"""
+ This class is an adaptation of scikit-learn's `SGDClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html>`_
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- loss : str , (default = "hinge")
- penalty : str, (default = "l2")
-
- alpha : float, (default = 0.0001)
-
- kwargs : other arguments
-
-
- Attributes
- ----------
- param_names :
-
- distribs :
-
- classed_params :
-
- weird_strings :
"""
diff --git a/summit/multiview_platform/monoview_classifiers/svm_linear.py b/summit/multiview_platform/monoview_classifiers/svm_linear.py
index 40eaa483edb9985219980d416e7ff145dd0f77db..e6f7fb1e2d9f05cd44f41e2db151c9ab27dfdf68 100644
--- a/summit/multiview_platform/monoview_classifiers/svm_linear.py
+++ b/summit/multiview_platform/monoview_classifiers/svm_linear.py
@@ -11,20 +11,10 @@ classifier_class_name = "SVMLinear"
class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
- """SVMLinear
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
-
- C : float, optional (default=1.0)
- Penalty parameter C of the error term.
-
- kwargs : others arguments
+ """
+ This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
+ Here, it is the linear kernel version
"""
def __init__(self, random_state=None, C=1.0, **kwargs):
diff --git a/summit/multiview_platform/monoview_classifiers/svm_poly.py b/summit/multiview_platform/monoview_classifiers/svm_poly.py
index 86f93db5767159bb2aa259bf89fc66c8a08dc115..a5200f82aa656d760a0eb7ddaf094dbcb52238df 100644
--- a/summit/multiview_platform/monoview_classifiers/svm_poly.py
+++ b/summit/multiview_platform/monoview_classifiers/svm_poly.py
@@ -13,30 +13,9 @@ classifier_class_name = "SVMPoly"
class SVMPoly(SVCClassifier, BaseMonoviewClassifier):
"""
- Class of SVMPoly for SVC Classifier
+ This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
-
- C : float, optional (default=1.0)
- Penalty parameter C of the error term.
-
-
- degree :
-
- kwargs : others arguments
-
-
- Attributes
- ----------
-
- param_names : list of parameters names
-
- distribs : list of random_state distribution
+ Here, it is the polynomial kernel version
"""
def __init__(self, random_state=None, C=1.0, degree=3, **kwargs):
diff --git a/summit/multiview_platform/monoview_classifiers/svm_rbf.py b/summit/multiview_platform/monoview_classifiers/svm_rbf.py
index 450ed6305458d48d9926f0f92133a24a90fb6004..8e75a3c798a2eaa7a4a8fc211d7594a2d5a8f644 100644
--- a/summit/multiview_platform/monoview_classifiers/svm_rbf.py
+++ b/summit/multiview_platform/monoview_classifiers/svm_rbf.py
@@ -12,24 +12,9 @@ classifier_class_name = "SVMRBF"
class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
"""
- class SVMRBF for classifier SVCC
+ This class is an adaptation of scikit-learn's `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- C :
-
- kwargs : others arguments
-
- Attributes
- ----------
-
- param_names : list of parameters names
-
- distribs : list of random_state distribution
+ Here, it is the RBF kernel version
"""
def __init__(self, random_state=None, C=1.0, **kwargs):
diff --git a/summit/multiview_platform/multiview_classifiers/bayesian_inference_fusion.py b/summit/multiview_platform/multiview_classifiers/bayesian_inference_fusion.py
index bca25f2b11fb650422ce52247a7efe281ac8a947..7abaacaed2b0decebe5bd33be15a794a91bbd87e 100644
--- a/summit/multiview_platform/multiview_classifiers/bayesian_inference_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/bayesian_inference_fusion.py
@@ -8,6 +8,11 @@ classifier_class_name = "BayesianInferenceClassifier"
class BayesianInferenceClassifier(LateFusionClassifier):
+
+ """
+
+ """
+
def __init__(self, random_state, classifiers_names=None,
classifier_configs=None, nb_cores=1, weights=None,
rs=None):
diff --git a/summit/multiview_platform/multiview_classifiers/difficulty_fusion.py b/summit/multiview_platform/multiview_classifiers/difficulty_fusion.py
index 47dad295d481f12bf66cec0638e3a534ec3905b7..c2531be566a0b6359f5eeb5e9efbaac8bb12e83f 100644
--- a/summit/multiview_platform/multiview_classifiers/difficulty_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/difficulty_fusion.py
@@ -8,6 +8,11 @@ classifier_class_name = "DifficultyFusion"
class DifficultyFusion(GlobalDiversityFusionClassifier):
+ """
+ This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+ It find the subset of monoview classifiers with the best difficulty
+ """
+
def diversity_measure(self, classifiers_decisions, combination, y):
_, nb_view, nb_samples = classifiers_decisions.shape
scores = np.zeros((nb_view, nb_samples), dtype=int)
diff --git a/summit/multiview_platform/multiview_classifiers/disagree_fusion.py b/summit/multiview_platform/multiview_classifiers/disagree_fusion.py
index abaa19014c1d6193880d62dc9d25db5ca088ac17..dbe295b8fe7246ac298c61c5a38d6f37dc3581e0 100644
--- a/summit/multiview_platform/multiview_classifiers/disagree_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/disagree_fusion.py
@@ -8,6 +8,11 @@ classifier_class_name = "DisagreeFusion"
class DisagreeFusion(CoupleDiversityFusionClassifier):
+ """
+ This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+ It find the subset of monoview classifiers with the best disagreement
+ """
+
def diversity_measure(self, first_classifier_decision,
second_classifier_decision, _):
return np.logical_xor(first_classifier_decision,
diff --git a/summit/multiview_platform/multiview_classifiers/double_fault_fusion.py b/summit/multiview_platform/multiview_classifiers/double_fault_fusion.py
index c33a19740185af03ddac8311b8fa6c1c794c70d8..d3b5347abe814010c812951425aaccc3f378ff42 100644
--- a/summit/multiview_platform/multiview_classifiers/double_fault_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/double_fault_fusion.py
@@ -8,6 +8,13 @@ classifier_class_name = "DoubleFaultFusion"
class DoubleFaultFusion(CoupleDiversityFusionClassifier):
+ """
+ This classifier is inspired by
+ Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in
+ Classifier Ensembles.
+ It find the subset of monoview classifiers with the best double fault
+ """
+
def diversity_measure(self, first_classifier_decision,
second_classifier_decision, y):
return np.logical_and(np.logical_xor(first_classifier_decision, y),
diff --git a/summit/multiview_platform/multiview_classifiers/entropy_fusion.py b/summit/multiview_platform/multiview_classifiers/entropy_fusion.py
index 56b0e458467f1102f11ad11da6f5e2db652d66f6..6a30b56906e40182e238a7688bda540b75e6a7e6 100644
--- a/summit/multiview_platform/multiview_classifiers/entropy_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/entropy_fusion.py
@@ -8,6 +8,11 @@ classifier_class_name = "EntropyFusion"
class EntropyFusion(GlobalDiversityFusionClassifier):
+ """
+ This classifier is inspired by Kuncheva, Ludmila & Whitaker, Chris. (2000). Measures of Diversity in Classifier Ensembles.
+ It find the subset of monoview classifiers with the best entropy
+ """
+
def diversity_measure(self, classifiers_decisions, combination, y):
_, nb_view, nb_samples = classifiers_decisions.shape
scores = np.zeros((nb_view, nb_samples), dtype=int)
diff --git a/summit/multiview_platform/multiview_classifiers/majority_voting_fusion.py b/summit/multiview_platform/multiview_classifiers/majority_voting_fusion.py
index 1afed35767fd456d6ce039c3e2b9a010d5b8e688..82e37356ce9f378050ecbd3dcc7b7c3b01ecbb64 100644
--- a/summit/multiview_platform/multiview_classifiers/majority_voting_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/majority_voting_fusion.py
@@ -12,6 +12,11 @@ class VotingIndecision(Exception):
class MajorityVoting(LateFusionClassifier):
+
+ """
+ This classifier is a late fusion that builds a majority vote between the views
+ """
+
def __init__(self, random_state, classifiers_names=None,
classifier_configs=None, weights=None, nb_cores=1, rs=None):
self.need_probas = False
diff --git a/summit/multiview_platform/multiview_classifiers/svm_jumbo_fusion.py b/summit/multiview_platform/multiview_classifiers/svm_jumbo_fusion.py
index 4d826efe4b27cd9e00db2f538f8c7595011d3191..f0a7bd6218d45a627cccdde6c36d6fa2da9ff268 100644
--- a/summit/multiview_platform/multiview_classifiers/svm_jumbo_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/svm_jumbo_fusion.py
@@ -8,6 +8,11 @@ classifier_class_name = "SVMJumboFusion"
class SVMJumboFusion(BaseJumboFusion):
+ """
+ This classifier learns monoview classifiers on each view and then uses an
+ SVM on their decisions to aggregate them.
+ """
+
def __init__(self, random_state=None, classifiers_names=None,
classifier_configs=None, nb_cores=1, weights=None,
nb_monoview_per_view=1, C=1.0, kernel="rbf", degree=2,
diff --git a/summit/multiview_platform/multiview_classifiers/weighted_linear_early_fusion.py b/summit/multiview_platform/multiview_classifiers/weighted_linear_early_fusion.py
index ec86f9b9a9d6f5abc9ccb401fcb0012bb7c52752..9af0183658e2ebbba32f4c894d1d6fffb4bcf762 100644
--- a/summit/multiview_platform/multiview_classifiers/weighted_linear_early_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/weighted_linear_early_fusion.py
@@ -13,17 +13,8 @@ classifier_class_name = "WeightedLinearEarlyFusion"
class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
"""
- WeightedLinearEarlyFusion
-
- Parameters
- ----------
- random_state
- view_weights
- monoview_classifier_name
- monoview_classifier_config
-
- Attributes
- ----------
+ Builds a monoview dataset by concatenating the views (with a weight if
+ needed) and learns a monoview classifier on the concatenation
"""
def __init__(self, random_state=None, view_weights=None,
@@ -37,10 +28,6 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
self.monoview_classifier_config = monoview_classifier_config[
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 = self.init_monoview_estimator(
monoview_classifier_name, monoview_classifier_config)
self.param_names = ["monoview_classifier_name",
diff --git a/summit/multiview_platform/multiview_classifiers/weighted_linear_late_fusion.py b/summit/multiview_platform/multiview_classifiers/weighted_linear_late_fusion.py
index 1b7b4c2ff4ae82e5d8b09272debd01b10ef6e0dd..a7f488a1bcac6ffa5c98cf2f98403433344fd84b 100644
--- a/summit/multiview_platform/multiview_classifiers/weighted_linear_late_fusion.py
+++ b/summit/multiview_platform/multiview_classifiers/weighted_linear_late_fusion.py
@@ -8,6 +8,10 @@ classifier_class_name = "WeightedLinearLateFusion"
class WeightedLinearLateFusion(LateFusionClassifier):
+
+ """
+ Similar to the majority voting fusion.
+ """
def __init__(self, random_state, classifiers_names=None,
classifier_configs=None, weights=None, nb_cores=1, rs=None):
self.need_probas = True