Update demo file test_construction.

tests/func/test_construction.py

 import cml
+from cml import load_knowledge
 
 from sklearn.cluster import KMeans
 from sklearn.cluster import SpectralClustering
@@ -6,26 +7,45 @@ from sklearn.feature_selection import VarianceThreshold
 from sklearn.ensemble import ExtraTreesClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.svm import SVC
-from pprint import pprint
 
 
 def demo():
-    cml.load_settings()
-    source = cml.get_data_source("/home/dmt/.cml/data/double_toyset.csv")
+    source = cml.get_source("/home/dmt/.cml/data/double_toyset.csv")
     kmeans = KMeans(precompute_distances=True, algorithm="elkan")
     spectral = SpectralClustering(affinity='rbf')
-    constructor = cml.construction("conceptual", kmeans, spectral)
+    algorithms = [("Kme", kmeans), ("Spe", spectral)]
+    constructor = cml.construction("conceptual", algorithms)
 
     variance = VarianceThreshold(1000)
     forest = ExtraTreesClassifier(n_estimators=250, random_state=1)
-    selector = cml.feature_selection(variance, forest)
+    selector = cml.feature_selection(filter_method=variance,
+                                     embedded_method=forest)
 
     svc = SVC(gamma=2, C=1)
     tree = DecisionTreeClassifier(max_depth=5)
-    reconstructor = cml.reconstruction("conceptual", svc, tree)
-
-    result = cml.search_knowledge(constructor, selector, reconstructor, source,
-                                  stdout=True)
+    algos = [("Svc", svc), ("Tre", tree)]
+
+    reconstructor = cml.reconstruction("conceptual", algos)
+    deconstructor = cml.deconstruction("conceptual", source)
+
+    result = cml.search_knowledge(constructor,
+                                  selector,
+                                  reconstructor,
+                                  deconstructor,
+                                  stdout=True,
+                                  parallel=True)
+    result.dump("./lol")
+    knowledge = load_knowledge("./lol")
+
+    source = cml.get_source("/home/dmt/.cml/data/double_toyset.csv")
+    deconstructor = cml.deconstruction("conceptual", source, knowledge=knowledge)
+    print("Starting Next Deconstruction")
+    input()
+    result_2 = cml.search_knowledge(constructor,
+                                    selector,
+                                    reconstructor,
+                                    deconstructor,
+                                    stdout=True)
 
 
 if __name__ == '__main__':