pre_cluster

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py

@@ -14,7 +14,7 @@ from ... import Metrics
 
 
 class ColumnGenerationClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
-    def __init__(self, mu=0.01, epsilon=1e-06, n_max_iterations=100, estimators_generator=None, dual_constraint_rhs=0, save_iteration_as_hyperparameter_each=None, random_state=None):
+    def __init__(self, mu=0.01, epsilon=1e-06, n_max_iterations=None, estimators_generator=None, dual_constraint_rhs=0, save_iteration_as_hyperparameter_each=None, random_state=None):
         super(ColumnGenerationClassifier, self).__init__()
         self.epsilon = epsilon
         self.n_max_iterations = n_max_iterations

multiview_platform/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py

@@ -80,6 +80,7 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
     logging.debug("Start:\t Predicting")
     y_train_pred = classifier.predict(X_train)
     y_test_pred = classifier.predict(X_test)
+    print(np.unique(y_test_pred))
     full_labels_pred = np.zeros(Y.shape, dtype=int)-100
     for trainIndex, index in enumerate(classificationIndices[0]):
         full_labels_pred[index] = y_train_pred[trainIndex]

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Lasso.py

+from sklearn.linear_model import Lasso
+import numpy as np
+
+from ..Monoview.MonoviewUtils import CustomRandint, CustomUniform, BaseMonoviewClassifier
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype"  # Production, Development, Prototype
+
+
+class Lasso(Lasso, BaseMonoviewClassifier):
+
+    def __init__(self, random_state=None, alpha=1.0,
+                 max_iter=10, warm_start=False, **kwargs):
+        super(Lasso, self).__init__(
+            alpha=alpha,
+            max_iter=max_iter,
+            warm_start=warm_start,
+            random_state=random_state
+            )
+        self.param_names = ["max_iter", "alpha",]
+        self.classed_params = []
+        self.distribs = [CustomRandint(low=1, high=300),
+                         CustomUniform(),]
+        self.weird_strings = {}
+
+    def fit(self, X, y, check_input=True):
+        neg_y = np.copy(y)
+        neg_y[np.where(neg_y==0)] = -1
+        super(Lasso, self).fit(X, neg_y)
+        return self
+
+    def predict(self, X):
+        prediction = super(Lasso, self).predict(X)
+        signed = np.sign(prediction)
+        signed[np.where(signed==-1)] = 0
+        return signed
+
+
+    def canProbas(self):
+        """Used to know if the classifier can return label probabilities"""
+        return False
+
+    def getInterpret(self, directory, y_test):
+        interpretString = ""
+        return interpretString
+
+
+def formatCmdArgs(args):
+    """Used to format kwargs for the parsed args"""
+    kwargsDict = {"alpha": args.LA_alpha,
+                  "max_iter": args.LA_n_iter}
+    return kwargsDict
+
+
+def paramsToSet(nIter, randomState):
+    paramsSet = []
+    for _ in range(nIter):
+        paramsSet.append({"max_iter": randomState.randint(1, 300),
+                          "alpha": randomState.uniform(0,1.0),})
+    return paramsSet
\ No newline at end of file

multiview_platform/MonoMultiViewClassifiers/utils/execution.py

@@ -200,6 +200,16 @@ def parseTheArgs(arguments):
                              help='Set the n_max_iterations parameter for CGreed',
                              default=100)
 
+    groupLasso = parser.add_argument_group('Lasso arguments')
+    groupLasso.add_argument('--LA_n_iter', metavar='INT', type=int,
+                             action='store',
+                             help='Set the max_iter parameter for Lasso',
+                             default=1)
+    groupLasso.add_argument('--LA_alpha', metavar='FLOAT', type=float,
+                             action='store',
+                             help='Set the alpha parameter for Lasso',
+                             default=1.0)
+
     groupGradientBoosting = parser.add_argument_group('Gradient Boosting arguments')
     groupGradientBoosting.add_argument('--GB_n_est', metavar='INT', type=int,
                              action='store',