From 65b27dd5e9df2d93554006136e12fd5f3de31f23 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Thu, 27 Feb 2020 20:10:36 +0100
Subject: [PATCH] Added check view in develop
---
.../multiview/multiview_utils.py | 7 +-
.../additions/diversity_utils.py | 2 +
.../additions/jumbo_fusion_utils.py | 2 +
.../additions/late_fusion_utils.py | 1 +
.../multiview_classifiers/additions/utils.py | 110 +++++++++---------
.../bayesian_inference_fusion.py | 2 +-
.../majority_voting_fusion.py | 6 +-
.../weighted_linear_early_fusion.py | 2 +
.../weighted_linear_late_fusion.py | 5 +-
9 files changed, 75 insertions(+), 62 deletions(-)
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 3d78d991..31e2546b 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
@@ -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 = ""
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
index 05e4cd05..a4984519 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
@@ -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
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
index f657c6c2..e9cbac4c 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
@@ -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,
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
index e2e8da5d..d6ff8b4c 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
@@ -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:
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
index 6aa2a7be..5fbd4d56 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
@@ -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
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
index 5c5ae1c2..b1cd5f9e 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
@@ -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)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
index 23c102b6..53a255c7 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
@@ -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))
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 83b4c555..eaa8ce34 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
@@ -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
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
index 32f4a710..403791ce 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
@@ -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])
--
GitLab