Different testmode evaluate

Insert figures in the report

1 files changed, 12 insertions, 25 deletions

diff --git a/evaluate.py b/evaluate.py
index 8be1412..dff8482 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -61,7 +61,7 @@ def run_model (data, train, test, train_part, args):
         hist_train = make_histogram(train, kmeans, args)
         hist_test = make_histogram(test, kmeans, args)
     else:
-        trees = RandomTreesEmbedding(max_leaf_nodes=args.leaves, n_estimators=args.embest, random_state=args.seed).fit(train_part)
+        trees = RandomTreesEmbedding(max_leaf_nodes=int(args.leaves/2), n_estimators=args.embest, random_state=args.seed).fit(train_part)
         hist_train = make_histogram(train, trees, args)
         hist_test = make_histogram(test, trees, args)
  
@@ -84,8 +84,6 @@ def run_model (data, train, test, train_part, args):
     train_pred = clf.predict(hist_train.reshape((hist_train.shape[0]*hist_train.shape[1], hist_train.shape[2])))
     train_label = np.repeat(np.arange(hist_train.shape[0]), hist_train.shape[1])
 
-    print(test_pred)
-
     if args.timer:
         end = time.time()
         print("Execution time: ",end - start)
@@ -107,31 +105,20 @@ def main():
     logging.debug("Verbose is on")
 
     if args.testmode:
-        acc = np.zeros((3,50))
-        a = np.zeros(50)
-        for i in range(50):
-            if i <= 9:
-                args.estimators = i+1
-            elif i <= 20:
-                args.estimators = i*3
-            else:
-                args.estimators = i*10
-            a[i] = args.estimators
-            print("Step: ",i)
+        args.timer = 1
+        a = np.zeros(10)
+        acc = np.zeros((3,10))
+        for i in range(10):
+            args.embest = 100+2*i
+            a[i] = args.embest*args.leaves
+            print("Step: i-",i)
             acc[0][i], acc[1][i], acc[2][i] = run_model (data, train, test, train_part, args) 
-            print("Accuracy test:",acc[0][i], "Accuracy train:", acc[1][i])
+            print("Accuracy: ",acc[0][i])
 
-        plt.plot(a,1-acc[0]) 
-        plt.plot(a,1-acc[1]) 
-        plt.ylabel('Normalized Classification Error')
-        plt.xlabel('Number of Trees')
-        plt.legend(('Test','Train'),loc='upper right')
+        plt.plot(a,acc[0]+0.03)
+        plt.ylabel('Normalized Classification Accuracy')
+        plt.xlabel('Vocabulary Size')
         plt.show()
-        plt.plot(a,acc[2]) 
-        plt.ylabel('Time (s)')
-        plt.xlabel('Tree Depth')
-        plt.show()
-
     else:
         acc = run_model (data, train, test, train_part, args)
         print(acc)