diff --git a/summit/multiview_platform/exec_classif.py b/summit/multiview_platform/exec_classif.py
index 11697f4b998b3548093484c6af638cbb4d7ed94d..089f0c9478b9057f40ae89f5bc47504368dbcc6f 100644
--- a/summit/multiview_platform/exec_classif.py
+++ b/summit/multiview_platform/exec_classif.py
@@ -51,7 +51,6 @@ def init_benchmark(cl_type, monoview_algos, multiview_algos):
Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
"""
benchmark = {"monoview": {}, "multiview": {}}
-
if "monoview" in cl_type:
if monoview_algos == ['all']: # pragma: no cover
benchmark["monoview"] = [name for _, name, isPackage in
@@ -306,8 +305,9 @@ def init_kwargs(args, classifiers_names, framework="monoview"):
For example, for Adaboost, the KWARGS will be `{"n_estimators":<value>, "base_estimator":<value>}`"""
- logging.info("Start:\t Initializing monoview classifiers arguments")
+ logging.info("Start:\t Initializing {} classifiers arguments".format(framework))
kwargs = {}
+
for classifiers_name in classifiers_names:
try:
if framework == "monoview":
@@ -316,13 +316,13 @@ def init_kwargs(args, classifiers_names, framework="monoview"):
getattr(multiview_classifiers, classifiers_name)
except AttributeError:
raise AttributeError(
- classifiers_name + " is not implemented in monoview_classifiers, "
- "please specify the name of the file in monoview_classifiers")
+ classifiers_name + " is not implemented in {}_classifiers, "
+ "please specify the name of the file in monoview_classifiers".format(framework))
if classifiers_name in args:
kwargs[classifiers_name] = args[classifiers_name]
else:
kwargs[classifiers_name] = {}
- logging.info("Done:\t Initializing monoview classifiers arguments")
+ logging.info("Done:\t Initializing {} classifiers arguments".format(framework))
return kwargs
diff --git a/summit/multiview_platform/monoview_classifiers/ib_svm_rbf.py b/summit/multiview_platform/monoview_classifiers/ib_svm_rbf.py
new file mode 100644
index 0000000000000000000000000000000000000000..3aaf35de2b0f55cebf5d6459ffd22399c68d26d7
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/ib_svm_rbf.py
@@ -0,0 +1,39 @@
+from imblearn.ensemble import BalancedBaggingClassifier
+import numpy as np
+from sklearn.svm import SVC
+
+
+from ..monoview.monoview_utils import BaseMonoviewClassifier
+from ..utils.base import base_boosting_estimators
+from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+classifier_class_name = "ImbalanceBaggingSVMRBF"
+
+class ImbalanceBaggingSVMRBF(BaseMonoviewClassifier, BalancedBaggingClassifier):
+
+ def __init__(self, random_state=None, base_estimator=SVC(),
+ n_estimators=10, sampling_strategy="auto",
+ replacement=False, base_estimator_config=None, **kwargs):
+ base_estimator = self.get_base_estimator(base_estimator, base_estimator_config, **kwargs)
+ super(ImbalanceBaggingSVMRBF, self).__init__(random_state=random_state, base_estimator=base_estimator,
+ n_estimators=n_estimators,
+ sampling_strategy=sampling_strategy,
+ replacement=replacement)
+
+ self.param_names = ["n_estimators", "sampling_strategy",]
+ self.distribs = [CustomRandint(low=1, high=50),
+ ["auto"]]
+ self.weird_strings = {}
+ self.base_estimator_config = base_estimator_config
+
+ def fit(self, X, y):
+ BalancedBaggingClassifier.fit(self, X, y)
+ self.feature_importances_ = np.zeros(X.shape[1])
+ for estim in self.estimators_:
+ if hasattr(estim['classifier'], 'feature_importances_'):
+ self.feature_importances_ += estim['classifier'].feature_importances_
+ self.feature_importances_ /= np.sum(self.feature_importances_)
+ return self
+
+
+
diff --git a/summit/multiview_platform/monoview_classifiers/scm_bagging_mincq.py b/summit/multiview_platform/monoview_classifiers/scm_bagging_mincq.py
new file mode 100644
index 0000000000000000000000000000000000000000..92c7d95047042e481cc6acf339d97e9cc308d3ec
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/scm_bagging_mincq.py
@@ -0,0 +1,112 @@
+from randomscm.randomscm import RandomScmClassifier
+
+
+
+from ..monoview.monoview_utils import BaseMonoviewClassifier
+from summit.multiview_platform.utils.hyper_parameter_search import CustomUniform, CustomRandint
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+classifier_class_name = "ScmBaggingMinCq"
+
+import numpy as np
+from six import iteritems
+
+MAX_INT = np.iinfo(np.int32).max
+
+
+class ScmBaggingMinCq(RandomScmClassifier, BaseMonoviewClassifier):
+
+ """A Bagging classifier. for SetCoveringMachineClassifier()
+ The base estimators are built on subsets of both samples
+ and features.
+ Parameters
+ ----------
+ n_estimators : int, default=10
+ The number of base estimators in the ensemble.
+ max_samples : int or float, default=1.0
+ The number of samples to draw from X to train each base estimator with
+ replacement.
+ - If int, then draw `max_samples` samples.
+ - If float, then draw `max_samples * X.shape[0]` samples.
+ max_features : int or float, default=1.0
+ The number of features to draw from X to train each base estimator (
+ without replacement.
+ - If int, then draw `max_features` features.
+ - If float, then draw `max_features * X.shape[1]` features.
+ p_options : list of float with len =< n_estimators, default=[1.0]
+ The estimators will be fitted with values of p found in p_options
+ let k be k = n_estimators/len(p_options),
+ the k first estimators will have p=p_options[0],
+ the next k estimators will have p=p_options[1] and so on...
+ random_state : int or RandomState, default=None
+ Controls the random resampling of the original dataset
+ (sample wise and feature wise).
+ If the base estimator accepts a `random_state` attribute, a different
+ seed is generated for each instance in the ensemble.
+ Pass an int for reproducible output across multiple function calls.
+ See :term:`Glossary <random_state>`.
+
+ Attributes
+ ----------
+ n_features_ : int
+ The number of features when :meth:`fit` is performed.
+ estimators_ : list of estimators
+ The collection of fitted base estimators.
+ estim_features : list of arrays
+ The subset of drawn features for each base estimator.
+
+ Examples
+ --------
+ >>> @TODO
+
+ References
+ ----------
+ .. [1] L. Breiman, "Pasting small votes for classification in large
+ databases and on-line", Machine Learning, 36(1), 85-103, 1999.
+ .. [2] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine
+ Learning and Knowledge Discovery in Databases, 346-361, 2012.
+ """
+
+ def __init__(self,
+ n_estimators=50,
+ max_samples=1.0,
+ max_features=1.0,
+ max_rules=10,
+ p_options=[0.316],
+ model_type="conjunction",
+ min_cq_combination=True,
+ min_cq_mu=10e-3,
+ random_state=None):
+ if isinstance(p_options, float):
+ p_options = [p_options]
+ RandomScmClassifier.__init__(self, n_estimators=n_estimators,
+ max_samples=max_samples,
+ max_features=max_features,
+ max_rules=max_rules,
+ p_options=p_options,
+ model_type=model_type,
+ min_cq_combination=min_cq_combination,
+ min_cq_mu=min_cq_mu,
+ random_state=random_state)
+ self.param_names = ["n_estimators", "max_rules", "max_samples", "max_features", "model_type", "p_options", "random_state"]
+ self.classed_params = []
+ self.distribs = [CustomRandint(low=1, high=300), CustomRandint(low=1, high=20),
+ CustomUniform(), CustomUniform(), ["conjunction", "disjunction"], CustomUniform(), [random_state]]
+ self.weird_strings = {}
+
+ def set_params(self, p_options=[0.316], **kwargs):
+ if not isinstance(p_options, list):
+ p_options = [p_options]
+ kwargs["p_options"] = p_options
+ for parameter, value in iteritems(kwargs):
+ setattr(self, parameter, value)
+ return self
+
+ def get_interpretation(self, directory, base_file_name, y_test,
+ multi_class=False):
+ self.features_importance()
+ interpret_string = self.get_feature_importance(directory, base_file_name)
+ return interpret_string
diff --git a/summit/multiview_platform/monoview_classifiers/svm_rbf.py b/summit/multiview_platform/monoview_classifiers/svm_rbf.py
index 8e75a3c798a2eaa7a4a8fc211d7594a2d5a8f644..5869c19ed7806f4ea866fd28d1908ab6988dc4da 100644
--- a/summit/multiview_platform/monoview_classifiers/svm_rbf.py
+++ b/summit/multiview_platform/monoview_classifiers/svm_rbf.py
@@ -1,5 +1,7 @@
from summit.multiview_platform.monoview_classifiers.additions.SVCClassifier import \
SVCClassifier
+from sklearn.preprocessing import RobustScaler
+
from ..monoview.monoview_utils import BaseMonoviewClassifier
from summit.multiview_platform.utils.hyper_parameter_search import CustomUniform
@@ -17,11 +19,27 @@ class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
Here, it is the RBF kernel version
"""
- def __init__(self, random_state=None, C=1.0, **kwargs):
+ def __init__(self, random_state=None, C=1.0, scaler=None ,gamma="scale",**kwargs):
SVCClassifier.__init__(self,
C=C,
kernel='rbf',
+ gamma=gamma,
random_state=random_state
)
- self.param_names = ["C", "random_state"]
- self.distribs = [CustomUniform(loc=0, state=1), [random_state]]
+ self.scaler=scaler
+ self.param_names = ["C", "gamma", "scaler", "random_state"]
+ self.distribs = [CustomUniform(loc=0, state=1),
+ CustomUniform(loc=0, state=1),
+ [None, RobustScaler()],
+ [random_state]]
+
+ def fit(self, X, y, sample_weight=None):
+ if self.scaler is not None:
+ X = self.scaler.fit_transform(X)
+ return SVCClassifier.fit(self, X, y, sample_weight=sample_weight)
+
+ def predict(self, X):
+ if self.scaler is not None:
+ X = self.scaler.transform(X)
+ return SVCClassifier.predict(self, X)
+
diff --git a/summit/multiview_platform/utils/dataset.py b/summit/multiview_platform/utils/dataset.py
index 600a06618169570e18e400901742f303c53f3402..11a9f317e7261731c16ca67d1bc7b19b609b74b5 100644
--- a/summit/multiview_platform/utils/dataset.py
+++ b/summit/multiview_platform/utils/dataset.py
@@ -503,6 +503,7 @@ class HDF5Dataset(Dataset):
seleted labels' names
"""
selected_labels = self.get_labels(sample_indices)
+
if type(self.dataset["Labels"].attrs["names"][0]) == bytes:
return [label_name.decode("utf-8")
for label, label_name in