Miha Stajdohar avatar Miha Stajdohar committed 0e7bdd3

Bug fixes.

Comments (0)

Files changed (3)

examples/ensemble/build_rf.py

 
     build_map = mm.BuildModelMap(fname)
 
-    trees = 300
+    trees = 200
     depth = 1000
 
     print "build models..."
-    models, rf_classifier = build_map.build_rf_models(trees=trees, max_depth=depth)
+    models, models_2, rf_classifier, _ = build_map.build_rf_models(trees=trees, max_depth=depth)
 
     print "build model data..."
     table = build_map.build_model_data(models)
+    table_2 = build_map.build_model_data(models_2)
 
     print "build matrix..."
     smx = build_map.build_model_matrix(models)
-    mm.save(os.path.join(ROOT, "_ensemble_", "rf_%s_%d_depth_None_%s" % (DATASET, trees, sys.platform)), smx, table, build_map.data())
+    smx_2 = build_map.build_model_matrix(models_2)
+    mm.save(os.path.join(ROOT, "_ensemble_", "rf_%s_%d_depth_None_%s" % (DATASET, len(models), sys.platform)), smx, table, build_map.data())
+    mm.save(os.path.join(ROOT, "_ensemble_", "rf_%s_%d_depth_None_%s" % (DATASET, len(models_2), sys.platform)), smx_2, table_2, build_map.data())
 
-build_rd_map("zoo")
-build_rd_map("marketing")
-build_rd_map("vehicle")
-build_rd_map("iris")
-build_rd_map("voting")
+#build_rd_map("zoo")
+#build_rd_map("marketing")
+#build_rd_map("vehicle")
+#build_rd_map("iris")
+#build_rd_map("voting")
+
+DO = ["iris", "breast-cancer-wisconsin", "voting", "zoo", "mushroom", "adult_sample", "glass", "marketing", "primary-tumor", "vehicle", "wdbc", "dermatology"]
+DO = ["marketing"]
+
+for d in DO:
+    build_rd_map(d)

examples/ensemble/play_with_rf.py

 import matplotlib
 matplotlib.use('Agg')
 
-import os.path
+import math, os.path, random, re, sys
 import cPickle as pickle
 import matplotlib.pyplot as plt
+import numpy as np
 
-from Orange import data, ensemble, evaluation, utils
+from itertools import groupby
+from operator import itemgetter
+
+from Orange import clustering, data, distance, ensemble, evaluation, network, utils
 from Orange.classification.tree import SimpleTreeLearner
 
+import _modelmaps as mm
+
 ROOT = "/home/miha/work/res/modelmaps"
 #ROOT = "C:\\Users\\Miha\\work\\res\\modelmaps"
 #ROOT = "/Network/Servers/xgridcontroller.private/lab/mihas/modelmaps"
+

examples/ensemble/trees_score.py

     cas, aucs, briers = pickle.load(open(os.path.join(ROOT, "_ensemble_", "scores_%s_%d_to_%d_depth_%s_instances_%d.pkl" % (DATASET, a, b, depth, min_instances)), "rb"))
 
     fig = plt.figure(figsize=(3, 6), dpi=300)
+    fig.subplots_adjust(wspace=0.3, hspace=0.6, top=0.9, bottom=0.05, left=0.1, right=0.95)
 
     ax = fig.add_subplot(3, 1, 1)
 
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