updated examples

examples/fanova_example.py

@@ -27,7 +27,7 @@
 f = fANOVA(X = features, Y = responses, config_space=cs)
 
 # marginal of particular parameter:
-dims = list([1])
+dims = (1, )
 res = f.quantify_importance(dims)
 print(res)
 
@@ -39,6 +39,6 @@
 # first create an instance of the visualizer with fanova object and configspace
 vis = visualizer.Visualizer(f, cs)
 # creating the plot of pairwise marginal:
-vis.plot_pairwise_marginal(list([0,2]), resolution=20)
+vis.plot_pairwise_marginal((0,2), resolution=20)
 # creating all plots in the directory
 vis.create_all_plots(plot_dir)

examples/onlineLDA_example.py

@@ -1,7 +1,6 @@
 import numpy as np
 from smac.configspace import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformFloatHyperparameter
-import csv
 from fanova import fANOVA
 import fanova.visualizer
 
@@ -17,17 +16,22 @@
 f = open(param_file, 'rb')
 cs = ConfigurationSpace()
 for row in f:
-    cs.add_hyperparameter(UniformFloatHyperparameter("%s" %row[0:4], np.float(row[6:9]), np.float(row[10:13]),np.float(row[18:21])))
+    cs.add_hyperparameter(UniformFloatHyperparameter("%s" %row[0:4].decode('utf-8'), np.float(row[6:9]), np.float(row[10:13]),np.float(row[18:21])))
 param = cs.get_hyperparameters()
 
 # create an instance of fanova with data for the random forest and the configSpace
-f = fANOVA(X = X, Y = Y)
+f = fANOVA(X = X, Y = Y, config_space = cs)
 
 # marginal for first parameter
-p_list = (0, )
+p_list = (1, )
 res = f.quantify_importance(p_list)
 print(res)
 
+print(cs)
+p2_list = ('Col1', 'Col2')
+res2 = f.quantify_importance(p2_list)
+print(res2)
+
 # getting the most important pairwise marginals sorted by importance
 best_margs = f.get_most_important_pairwise_marginals(n=3)
 print(best_margs)