Suppress warnings called by sklearn.

cml/ports/ml_adapter.py

 from collections import Counter
 from abc import abstractmethod
+import warnings
 
 from numpy import array, linspace, less, greater, std, argsort
 from scipy.signal import argrelextrema
@@ -21,8 +22,14 @@ SCIKIT_CLUSTERING_TABLE = {
 }
 
 
-class MachineLearningModel:
+def log_warning(func):
+    def wrapper(*args, **kwargs):
+        warnings.filterwarnings("ignore")
+        return func(*args, **kwargs)
+    return wrapper
+
 
+class MachineLearningModel:
     @abstractmethod
     def train(self, data, *args, **kwargs):
         pass
@@ -32,6 +39,7 @@ class FilterMethod(MachineLearningModel):
     def __init__(self, model):
         self.__model = model
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         self.__model = self.__model.fit(data)
         return self
@@ -48,6 +56,7 @@ class EmbeddedMethod(MachineLearningModel):
     def __init__(self, model):
         self.__model = model
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         labels = data.get_column_values("Z")
         self.__model = self.__model.fit(data, labels)
@@ -91,6 +100,7 @@ class ConstructionClusteringMLModel(MachineLearningModel):
         )
         return Counter(labels)
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         self.__model.fit(data)
         return self
@@ -103,6 +113,7 @@ class ReconstructionConceptualMLModel(MachineLearningModel):
         self.accuracy = None
         self.subject = model.__class__.__name__
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         # TODO (dmt): Improve signature of this function!
         labels = args[0]
@@ -121,6 +132,7 @@ class ReconstructionProceduralMLModel(MachineLearningModel):
         self.max_error = None
         self.subject = model.__class__.__name__
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         # TODO (dmt): Provide a better way dealing with
         # zero values as max_abs_label!
@@ -149,6 +161,7 @@ class KernelDensityEstimator(MachineLearningModel):
         self.bandwidth = bandwidth
         self.gridsize = gridsize
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         reshaped_data = array(data).reshape(-1, 1)
         if not self.__model:
@@ -173,6 +186,7 @@ class Autoencoder(MachineLearningModel):
         self.__model = None
         self.__hidden_outputter = None
 
+    @log_warning
     def train(self, data, *args, **kwargs):
         inputer = Input(shape=(self.io_shape, ))
         hidden = Dense(units=self.target_number,