Merge pull request #2 from thibgo/randomscm

added random_scm classifier

summit/multiview_platform/monoview_classifiers/random_scm.py

+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 = "ScmBagging"
+
+import numpy as np
+from six import iteritems
+
+MAX_INT = np.iinfo(np.int32).max
+
+
+class ScmBagging(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=100,
+                 max_samples=0.5,
+                 max_features=0.5,
+                 max_rules=10,
+                 p_options=[1.0],
+                 model_type="conjunction",
+                 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,
+                 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, feature_ids,
+                           multi_class=False):
+        self.features_importance()
+        interpret_string = self.get_feature_importance(directory,
+                                                       base_file_name,
+                                                       feature_ids)
+        return interpret_string