From e1d410a58885908249a32af666b8f815bcfe606b Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Tue, 6 Sep 2022 10:13:11 -0400
Subject: [PATCH] Working order
---
.../multiview/multiview_utils.py | 1 -
.../multiview_classifiers/mucombo.py | 47 ++++++++++-
.../multiview_classifiers/mumbo.py | 2 +-
.../multiview_classifiers/spkm_pw.py | 79 +++++++++++++++++++
.../result_analysis/feature_importances.py | 7 +-
5 files changed, 130 insertions(+), 6 deletions(-)
create mode 100644 summit/multiview_platform/multiview_classifiers/spkm_pw.py
diff --git a/summit/multiview_platform/multiview/multiview_utils.py b/summit/multiview_platform/multiview/multiview_utils.py
index 032f181a..93a56639 100644
--- a/summit/multiview_platform/multiview/multiview_utils.py
+++ b/summit/multiview_platform/multiview/multiview_utils.py
@@ -28,7 +28,6 @@ class BaseMultiviewClassifier(BaseClassifier):
self.used_views = None
def set_base_estim_from_dict(self, base_estim_dict, **kwargs):
- print(base_estim_dict)
if base_estim_dict is None:
base_estimator = DecisionTreeClassifier()
elif isinstance(base_estim_dict, str) and kwargs is not None:
diff --git a/summit/multiview_platform/multiview_classifiers/mucombo.py b/summit/multiview_platform/multiview_classifiers/mucombo.py
index ad5268e6..784b34f8 100644
--- a/summit/multiview_platform/multiview_classifiers/mucombo.py
+++ b/summit/multiview_platform/multiview_classifiers/mucombo.py
@@ -1,16 +1,17 @@
from sklearn.tree import DecisionTreeClassifier
+from sklearn.base import BaseEstimator
-
+import numpy as np
from multimodal.boosting.combo import MuComboClassifier
from ..multiview.multiview_utils import BaseMultiviewClassifier
from ..utils.hyper_parameter_search import CustomRandint
from ..utils.dataset import get_samples_views_indices
from ..utils.base import base_boosting_estimators
-classifier_class_name = "MuCumbo"
+classifier_class_name = "MuCombo"
-class MuCumbo(BaseMultiviewClassifier, MuComboClassifier):
+class MuCombo(BaseMultiviewClassifier, MuComboClassifier):
def __init__(self, base_estimator=None,
n_estimators=50,
@@ -31,9 +32,33 @@ class MuCumbo(BaseMultiviewClassifier, MuComboClassifier):
self.used_views = view_indices
numpy_X, view_limits = X.to_numpy_array(sample_indices=train_indices,
view_indices=view_indices)
+ self.view_shapes = [view_limits[ind + 1] - view_limits[ind]
+ for ind in range(len(self.used_views))]
return MuComboClassifier.fit(self, numpy_X, y[train_indices],
view_limits)
+ def set_params(self, base_estimator=None, **params):
+ """
+ Sets the base estimator from a dict.
+ :param base_estimator:
+ :param params:
+ :return:
+ """
+ if base_estimator is None:
+ self.base_estimator = DecisionTreeClassifier()
+ elif type(base_estimator) is list:
+ if type(base_estimator[0]) is dict:
+ self.base_estimator = [self.set_base_estim_from_dict(estim) for estim in base_estimator]
+ elif isinstance(base_estimator[0], BaseEstimator):
+ self.base_estimator = base_estimator
+ else:
+ raise ValueError("base_estimator should ba a list of dict or a sklearn classifier list")
+ elif isinstance(base_estimator, dict):
+ self.base_estimator = self.set_base_estim_from_dict(base_estimator)
+ MuComboClassifier.set_params(self, **params)
+ else:
+ MuComboClassifier.set_params(self, base_estimator=base_estimator, **params)
+
def predict(self, X, sample_indices=None, view_indices=None):
sample_indices, view_indices = get_samples_views_indices(X,
sample_indices,
@@ -43,6 +68,22 @@ class MuCumbo(BaseMultiviewClassifier, MuComboClassifier):
view_indices=view_indices)
return MuComboClassifier.predict(self, numpy_X)
+ def accepts_multi_class(self, random_state, n_samples=10, dim=2,
+ n_classes=3, n_views=2):
+ return True
+
def get_interpretation(self, directory, base_file_name, labels,
multiclass=False):
+ self.feature_importances_ = [np.zeros(view_shape)
+ for view_shape in self.view_shapes]
+ # for best_view, estimator_weight, estimator in zip(self.best_views_, self.estimator_weights_, self.estimators_):
+ # self.view_importances[best_view] += estimator_weight
+ # if hasattr(estimator, "feature_importances_"):
+ # self.feature_importances_[best_view] += estimator.feature_importances_
+ # importances_sum = sum([np.sum(feature_importances)
+ # for feature_importances
+ # in self.feature_importances_])
+ # self.feature_importances_ = [feature_importances/importances_sum
+ # for feature_importances
+ # in self.feature_importances_]
return ""
diff --git a/summit/multiview_platform/multiview_classifiers/mumbo.py b/summit/multiview_platform/multiview_classifiers/mumbo.py
index 203228ba..1bb2f3db 100644
--- a/summit/multiview_platform/multiview_classifiers/mumbo.py
+++ b/summit/multiview_platform/multiview_classifiers/mumbo.py
@@ -72,7 +72,6 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
view_indices=view_indices)
self.view_shapes = [view_limits[ind+1]-view_limits[ind]
for ind in range(len(self.used_views)) ]
-
return MumboClassifier.fit(self, numpy_X, y[train_indices],
view_limits)
@@ -118,6 +117,7 @@ class Mumbo(BaseMultiviewClassifier, MumboClassifier):
self.view_importances[view_index])
interpret_string +="\n The boosting process selected views : \n" + ", ".join(map(str, self.best_views_))
interpret_string+="\n\n With estimator weights : \n"+ "\n".join(map(str,self.estimator_weights_/np.sum(self.estimator_weights_)))
+ print(self.feature_importances_)
return interpret_string
def accepts_multi_class(self, random_state, n_samples=10, dim=2,
diff --git a/summit/multiview_platform/multiview_classifiers/spkm_pw.py b/summit/multiview_platform/multiview_classifiers/spkm_pw.py
new file mode 100644
index 00000000..4c5a51ad
--- /dev/null
+++ b/summit/multiview_platform/multiview_classifiers/spkm_pw.py
@@ -0,0 +1,79 @@
+import numpy as np
+from sklearn.preprocessing import LabelBinarizer
+
+from spkm.spkm_wrapper import pairwiseSPKMlikeSklearn
+from spkm.kernels_and_gradients import RBFKernel, PolyKernel
+
+from ..multiview.multiview_utils import BaseMultiviewClassifier
+from ..utils.hyper_parameter_search import CustomRandint
+from ..utils.dataset import get_samples_views_indices
+
+classifier_class_name = "PWSPKM"
+
+class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMlikeSklearn):
+
+ def __init__(self, random_state=42, n_u=2, kernel1=RBFKernel(0.5),
+ kernel2=RBFKernel(0.5), spkmregP=1, spkminit="randn",
+ nspkminits=10, preprocessinglist=[0,1,2], **kwargs):
+ BaseMultiviewClassifier.__init__(self, random_state)
+
+ pairwiseSPKMlikeSklearn.__init__(self, random_state=random_state,
+ n_u=n_u,
+ kernel1=kernel1,
+ kernel2=kernel2,
+ spkmregP=spkmregP,
+ spkminit=spkminit,
+ nspkminits=nspkminits,
+ preprocessinglist=preprocessinglist)
+ self.param_names = ["n_u", "kernel1", "kernel2", "spkmregP",
+ "spkminit", "nspkminits", "preprocessinglist",
+ "random_state"]
+ self.distribs = [[2], [PolyKernel({"d":3, "r":1})], [PolyKernel({"d":3, "r":1})], CustomRandint(1,15),
+ ["data", "randn"], CustomRandint(1,30),
+ [[], [0], [0,1], [0,1,2]], [random_state],]
+ self.more_than_two_views = False
+ self.random_state = random_state
+
+ def fit(self, X, y, train_indices=None, view_indices=None):
+
+ self.lb = LabelBinarizer(pos_label=1, neg_label=-1)
+ y = self.lb.fit_transform(y)
+ train_indices, view_indices = get_samples_views_indices(X,
+ train_indices,
+ view_indices)
+ if len(view_indices)>2:
+ self.more_than_two_views = True
+ self.label_set = np.unique(y)
+ return self
+ self.used_views = view_indices
+ self.view_names = [X.get_view_name(view_index)
+ for view_index in view_indices]
+ view_list = [X.get_v(view_index)[train_indices, :]
+ for view_index in view_indices]
+
+ return pairwiseSPKMlikeSklearn.fit(self, view_list, y[train_indices,0],)
+
+ def predict(self, X, sample_indices=None, view_indices=None):
+ if self.more_than_two_views:
+ return self.random_state.choice(self.label_set, replace=True, size=X.shape[0])
+ sample_indices, view_indices = get_samples_views_indices(X,
+ sample_indices,
+ view_indices)
+ view_list = [X.get_v(view_index)[sample_indices, :]
+ for view_index in view_indices]
+ self._check_views(view_indices)
+
+ view_list = [X.get_v(view_index)[sample_indices, :]
+ for view_index in view_indices]
+ print(self.lb.inverse_transform(np.sign(pairwiseSPKMlikeSklearn.predict(self, view_list))))
+ return self.lb.inverse_transform(np.sign(pairwiseSPKMlikeSklearn.predict(self, view_list)))
+
+ def get_interpretation(self, directory, base_file_name, labels, multiclass=False):
+ u, v = self.feature_interpretability()
+ importances_sum = np.sum(u+v)
+ self.feature_importances_ = [u/importances_sum, v/importances_sum]
+ return ""
+
+ def accepts_multi_class(self, random_state, n_samples=10, dim=2,
+ n_classes=3, n_views=2):
+ return False
diff --git a/summit/multiview_platform/result_analysis/feature_importances.py b/summit/multiview_platform/result_analysis/feature_importances.py
index b7fe1186..b40161da 100644
--- a/summit/multiview_platform/result_analysis/feature_importances.py
+++ b/summit/multiview_platform/result_analysis/feature_importances.py
@@ -30,6 +30,7 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
feature_importances[classifier_result.view_name] = pd.DataFrame(
index=feature_ids[classifier_result.view_index])
if hasattr(classifier_result.clf, 'feature_importances_'):
+ print(classifier_result.classifier_name, classifier_result.view_name)
feature_importances[classifier_result.view_name][
classifier_result.classifier_name] = classifier_result.clf.feature_importances_
else:
@@ -44,7 +45,11 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
v_feature_id]
feature_importances["mv"] = pd.DataFrame(index=feat_ids)
if hasattr(classifier_result.clf, 'feature_importances_'):
- feature_importances["mv"][classifier_result.classifier_name] = classifier_result.clf.feature_importances_
+ if len(classifier_result.clf.feature_importances_)==len(feature_ids):
+ concat = np.concatenate(classifier_result.clf.feature_importances_, axis=0)
+ feature_importances["mv"][classifier_result.classifier_name] = concat/np.sum(concat)
+ else:
+ feature_importances["mv"][classifier_result.classifier_name] = classifier_result.clf.feature_importances_
return feature_importances
--
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