diff --git a/config_files/config.yml b/config_files/config.yml
index 4baa1dae8537a94d62db23c4acb26a5dd1623c97..bcd321ed418dff029bdeda2adde64812b4aa3369 100644
--- a/config_files/config.yml
+++ b/config_files/config.yml
@@ -197,4 +197,9 @@ weighted_linear_late_fusion:
decision_tree:
max_depth: [1]
criterion: ["gini"]
- splitter: ["best"]
\ No newline at end of file
+ splitter: ["best"]
+
+mumbo:
+ base_estimator: [None]
+ n_estimators: [10]
+ best_view_mode: ["edge"]
\ No newline at end of file
diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 5a4d59df8fc4edc1066c69fb6d7d99b427f66f15..c2d2d87bfe8cb7dda8052dd6516aba82f45f1c40 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -24,7 +24,7 @@ Classification:
classes:
type: ["multiview"]
algos_monoview: ["all"]
- algos_multiview: ["mumbo"]
+ algos_multiview: ["mumbo", "easy_mkl"]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
metric_princ: "f1_score"
@@ -202,4 +202,8 @@ weighted_linear_late_fusion:
mumbo:
base_estimator: [None]
n_estimators: [10]
- best_view_mode: ["edge"]
\ No newline at end of file
+ best_view_mode: ["edge"]
+
+easy_mkl:
+ degrees: [1]
+ lam: [0.1]
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/easy_mkl.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/easy_mkl.py
new file mode 100644
index 0000000000000000000000000000000000000000..18c4a0ab1939d0d58720998c6a949364e7b7ceae
--- /dev/null
+++ b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/easy_mkl.py
@@ -0,0 +1,62 @@
+from MKLpy.algorithms import EasyMKL
+from MKLpy.metrics import pairwise
+import numpy as np
+
+from ..multiview.multiview_utils import BaseMultiviewClassifier, get_examples_views_indices
+from ..utils.hyper_parameter_search import CustomUniform
+
+
+classifier_class_name = "EasyMKLClassifier"
+
+class EasyMKLClassifier(BaseMultiviewClassifier, EasyMKL):
+
+ def __init__(self, random_state=None, degrees=1, lam=0.1):
+ super().__init__(random_state)
+ super(BaseMultiviewClassifier, self).__init__(lam=lam)
+ self.degrees = degrees
+ self.param_names = ["lam", "degrees"]
+ self.distribs = [CustomUniform(), DegreesGenerator()]
+
+ def fit(self, X, y, train_indices=None, views_indices=None ):
+ train_indices, views_indices = get_examples_views_indices(X,
+ train_indices,
+ views_indices)
+ if isinstance(self.degrees, DegreesDistribution):
+ self.degrees = self.degrees.draw(len(views_indices))
+ elif isinstance(int, self.degrees):
+ self.degrees = [self.degrees for _ in range(len(views_indices))]
+
+ kernels = [pairwise.homogeneous_polynomial_kernel(X.get_V(views_indices[index],
+ train_indices),
+ degree=degree)
+ for index, degree in enumerate(self.degrees)]
+ return super(EasyMKLClassifier, self).fit(kernels, y[train_indices])
+
+ def predict(self, X, example_indices=None, views_indices=None):
+ example_indices, views_indices = get_examples_views_indices(X,
+ example_indices,
+ views_indices)
+ kernels = [
+ pairwise.homogeneous_polynomial_kernel(X.get_V(views_indices[index],
+ example_indices),
+ degree=degree)
+ for index, degree in enumerate(self.degrees)]
+ return super(EasyMKLClassifier, self).predict(kernels,)
+
+
+class DegreesGenerator:
+
+ def __init__(self):
+ pass
+
+ def rvs(self, random_state=None):
+ return DegreesDistribution(seed=random_state.randint(1))
+
+
+class DegreesDistribution:
+
+ def __init__(self, seed=42):
+ self.random_state=np.random.RandomState(seed)
+
+ def draw(self, nb_view):
+ return self.random_state.randint(low=1,high=10,size=nb_view)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index 8e3c104268599f2a528e48244ca4aab50eea9f05..d1ad6272f0c55dad2c14a54972b0388f9415dbb4 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -3,7 +3,7 @@ import sys
import matplotlib.pyplot as plt
import numpy as np
-from scipy.stats import randint
+from scipy.stats import randint, uniform
from sklearn.model_selection import RandomizedSearchCV
@@ -38,6 +38,22 @@ def grid_search(dataset, classifier_name, views_indices=None, k_folds=None, n_it
"""Used to perfom gridsearch on the classifiers"""
pass
+class CustomUniform:
+ """Used as a distribution returning a float between loc and loc + scale..
+ It can be used with a multiplier agrument to be able to perform more complex generation
+ for example 10 e -(float)"""
+
+ def __init__(self, loc=0, state=1, multiplier=""):
+ self.uniform = uniform(loc, state)
+ self.multiplier = multiplier
+
+ def rvs(self, random_state=None):
+ unif = self.uniform.rvs(random_state=random_state)
+ if self.multiplier == 'e-':
+ return 10 ** -unif
+ else:
+ return unif
+
class CustomRandint:
"""Used as a distribution returning a integer between low and high-1.
It can be used with a multiplier agrument to be able to perform more complex generation
diff --git a/requirements.txt b/requirements.txt
index a99989254c35ff3dfa00bfb97997f888a40caa89..3899b3fa4a24155369d0b13c09a4f8639428e4c1 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,5 @@ pandas==0.23.3
m2r==0.2.1
docutils==0.12
pyyaml==3.12
-cvxopt==1.2.0
\ No newline at end of file
+cvxopt==1.2.0
+-e git+https://github.com/IvanoLauriola/MKLpy.git#egg=MKLpy
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 3bc02d0f1fa3a6d2d666ac213262ba4f34a77a6d..f715ce8708c8a718f3229a381ddd929108cc226e 100644
--- a/setup.py
+++ b/setup.py
@@ -55,7 +55,7 @@ def setup_package():
install_requires=['numpy>=1.16', 'scipy>=0.16','scikit-learn==0.19',
'matplotlib', 'h5py', 'joblib',
'pandas', 'm2r', 'pyyaml', 'pyscm @ git+https://github.com/aldro61/pyscm',
- 'cvxopt'],
+ 'cvxopt', 'MKLpy @ git+https://github.com/IvanoLauriola/MKLpy'],
# Il est d'usage de mettre quelques metadata à propos de sa lib
# Pour que les robots puissent facilement la classer.