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Commit 65b27dd5 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Added check view in develop

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with 75 additions and 62 deletions
multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+ 6
− 1
View file @ 65b27dd5
......@@ -4,7 +4,7 @@ import numpy as np
from .. import monoview_classifiers
from ..utils.base import BaseClassifier, ResultAnalyser
from ..utils.dataset import RAMDataset
from ..utils.dataset import RAMDataset, get_examples_views_indices
class FakeEstimator():
......@@ -29,6 +29,7 @@ class BaseMultiviewClassifier(BaseClassifier):
self.random_state = random_state
self.short_name = self.__module__.split(".")[-1]
self.weird_strings = {}
self.used_views = None
@abstractmethod
def fit(self, X, y, train_indices=None, view_indices=None):
......@@ -38,6 +39,10 @@ class BaseMultiviewClassifier(BaseClassifier):
def predict(self, X, example_indices=None, view_indices=None):
pass
def _check_views(self, view_indices):
if self.used_views is not None and not np.array_equal(np.sort(self.used_views), np.sort(view_indices)):
raise ValueError('Used {} views to fit, and trying to predict on {}'.format(self.used_views, view_indices))
def to_str(self, param_name):
if param_name in self.weird_strings:
string = ""
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
+ 2
− 0
View file @ 65b27dd5
......@@ -30,6 +30,7 @@ class DiversityFusionClassifier(BaseMultiviewClassifier,
train_indices, view_indices = get_examples_views_indices(X,
train_indices,
view_indices)
self.used_views = view_indices
# TODO : Finer analysis, may support a bit of mutliclass
if np.unique(y[train_indices]).shape[0] > 2:
raise ValueError(
......@@ -56,6 +57,7 @@ class DiversityFusionClassifier(BaseMultiviewClassifier,
example_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
self._check_views(view_indices)
nb_class = X.get_nb_class()
if nb_class > 2:
nb_class = 3
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
+ 2
− 0
View file @ 65b27dd5
......@@ -27,6 +27,7 @@ class BaseJumboFusion(LateFusionClassifier):
example_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
self._check_views(view_indices)
monoview_decisions = self.predict_monoview(X,
example_indices=example_indices,
view_indices=view_indices)
......@@ -36,6 +37,7 @@ class BaseJumboFusion(LateFusionClassifier):
train_indices, view_indices = get_examples_views_indices(X,
train_indices,
view_indices)
self.used_views = view_indices
self.init_classifiers(len(view_indices),
nb_monoview_per_view=self.nb_monoview_per_view)
self.fit_monoview_estimators(X, y, train_indices=train_indices,
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
+ 1
− 0
View file @ 65b27dd5
......@@ -97,6 +97,7 @@ class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
train_indices, view_indices = get_examples_views_indices(X,
train_indices,
view_indices)
self.used_views = view_indices
if np.unique(y).shape[0] > 2:
multiclass = True
else:
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
+ 55
− 55
View file @ 65b27dd5
......@@ -6,59 +6,59 @@ def get_names(classed_list):
return np.array([object_.__class__.__name__ for object_ in classed_list])
class BaseMultiviewClassifier(BaseEstimator, ClassifierMixin):
# class BaseMultiviewClassifier(BaseEstimator, ClassifierMixin):
#
# def __init__(self, random_state):
# self.random_state = random_state
#
# def genBestParams(self, detector):
# return dict((param_name, detector.best_params_[param_name])
# for param_name in self.param_names)
#
# def genParamsFromDetector(self, detector):
# if self.classed_params:
# classed_dict = dict((classed_param, get_names(
# detector.cv_results_["param_" + classed_param]))
# for classed_param in self.classed_params)
# if self.param_names:
# return [(param_name,
# np.array(detector.cv_results_["param_" + param_name]))
# if param_name not in self.classed_params else (
# param_name, classed_dict[param_name])
# for param_name in self.param_names]
# else:
# return [()]
#
# def genDistribs(self):
# return dict((param_name, distrib) for param_name, distrib in
# zip(self.param_names, self.distribs))
#
# def getConfig(self):
# if self.param_names:
# return "\n\t\t- " + self.__class__.__name__ + "with " + ", ".join(
# [param_name + " : " + self.to_str(param_name) for param_name in
# self.param_names])
# else:
# return "\n\t\t- " + self.__class__.__name__ + "with no config."
#
# def to_str(self, param_name):
# if param_name in self.weird_strings:
# if self.weird_strings[param_name] == "class_name":
# return self.get_params()[param_name].__class__.__name__
# else:
# return self.weird_strings[param_name](
# self.get_params()[param_name])
# else:
# return str(self.get_params()[param_name])
#
# def get_interpretation(self):
# return "No detailed interpretation function"
def __init__(self, random_state):
self.random_state = random_state
def genBestParams(self, detector):
return dict((param_name, detector.best_params_[param_name])
for param_name in self.param_names)
def genParamsFromDetector(self, detector):
if self.classed_params:
classed_dict = dict((classed_param, get_names(
detector.cv_results_["param_" + classed_param]))
for classed_param in self.classed_params)
if self.param_names:
return [(param_name,
np.array(detector.cv_results_["param_" + param_name]))
if param_name not in self.classed_params else (
param_name, classed_dict[param_name])
for param_name in self.param_names]
else:
return [()]
def genDistribs(self):
return dict((param_name, distrib) for param_name, distrib in
zip(self.param_names, self.distribs))
def getConfig(self):
if self.param_names:
return "\n\t\t- " + self.__class__.__name__ + "with " + ", ".join(
[param_name + " : " + self.to_str(param_name) for param_name in
self.param_names])
else:
return "\n\t\t- " + self.__class__.__name__ + "with no config."
def to_str(self, param_name):
if param_name in self.weird_strings:
if self.weird_strings[param_name] == "class_name":
return self.get_params()[param_name].__class__.__name__
else:
return self.weird_strings[param_name](
self.get_params()[param_name])
else:
return str(self.get_params()[param_name])
def get_interpretation(self):
return "No detailed interpretation function"
def get_train_views_indices(dataset, train_indices, view_indices, ):
"""This function is used to get all the examples indices and view indices if needed"""
if view_indices is None:
view_indices = np.arange(dataset.nb_view)
if train_indices is None:
train_indices = range(dataset.get_nb_examples())
return train_indices, view_indices
#
# def get_train_views_indices(dataset, train_indices, view_indices, ):
# """This function is used to get all the examples indices and view indices if needed"""
# if view_indices is None:
# view_indices = np.arange(dataset.nb_view)
# if train_indices is None:
# train_indices = range(dataset.get_nb_examples())
# return train_indices, view_indices
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
+ 1
− 1
View file @ 65b27dd5
......@@ -23,7 +23,7 @@ class BayesianInferenceClassifier(LateFusionClassifier):
example_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
self._check_views(view_indices)
if sum(self.weights) != 1.0:
self.weights = self.weights / sum(self.weights)
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
+ 3
− 3
View file @ 65b27dd5
......@@ -23,16 +23,16 @@ class MajorityVoting(LateFusionClassifier):
rs=rs)
def predict(self, X, example_indices=None, view_indices=None):
examples_indices, views_indices = get_examples_views_indices(X,
examples_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
self._check_views(view_indices)
n_examples = len(examples_indices)
votes = np.zeros((n_examples, X.get_nb_class(example_indices)),
dtype=float)
monoview_decisions = np.zeros((len(examples_indices), X.nb_view),
dtype=int)
for index, view_index in enumerate(views_indices):
for index, view_index in enumerate(view_indices):
monoview_decisions[:, index] = self.monoview_estimators[
index].predict(
X.get_v(view_index, examples_indices))
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
+ 2
− 0
View file @ 65b27dd5
......@@ -69,6 +69,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
def fit(self, X, y, train_indices=None, view_indices=None):
train_indices, X = self.transform_data_to_monoview(X, train_indices,
view_indices)
self.used_views = view_indices
if np.unique(y[train_indices]).shape[0] > 2 and \
not (isinstance(self.monoview_classifier, MultiClassWrapper)):
self.monoview_classifier = get_mc_estim(self.monoview_classifier,
......@@ -81,6 +82,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
def predict(self, X, example_indices=None, view_indices=None):
_, X = self.transform_data_to_monoview(X, example_indices, view_indices)
self._check_views(self.view_indices)
predicted_labels = self.monoview_classifier.predict(X)
return predicted_labels
......
multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
+ 3
− 2
View file @ 65b27dd5
......@@ -17,11 +17,12 @@ class WeightedLinearLateFusion(LateFusionClassifier):
nb_cores=nb_cores, weights=weights, rs=rs)
def predict(self, X, example_indices=None, view_indices=None):
example_indices, views_indices = get_examples_views_indices(X,
example_indices, view_indices = get_examples_views_indices(X,
example_indices,
view_indices)
self._check_views(view_indices)
view_scores = []
for index, viewIndex in enumerate(views_indices):
for index, viewIndex in enumerate(view_indices):
view_scores.append(
np.array(self.monoview_estimators[index].predict_proba(
X.get_v(viewIndex, example_indices))) * self.weights[index])
......
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