cleanup

src/experiments/decision_tree/decision_tree.py

@@ -1,14 +1,14 @@
-from enum import Enum
+# models
 from sklearn import tree
-from sklearn import metrics
 from sklearn import preprocessing
 from sklearn import neighbors
 from sklearn import ensemble
 from sklearn import svm
-from matplotlib import pyplot as plt
-import pandas as pd
+
+# other
+from enum import Enum
 import numpy as np
-import random
+import time
 import csv
 import plots
 
@@ -49,12 +49,15 @@ with open(PATH, 'r') as file:
     normalized = preprocessing.normalize(data, axis=0, norm='max')
     norm = list(normalized.tolist())
 
-steps = np.linspace(0.1, 1.0, 10, dtype=np.float64)
+steps = np.linspace(1e-4, 1, 20, dtype=np.float64)
 
+print("Step \t seconds/step")
 for step in steps:
     actual = []
     predicted = []
+    time_start = time.time()
 
+    for j in range(3):
         for i in range(len(norm)):
             temp_data = norm.pop(i)
             temp_label = labels.pop(i)
@@ -73,20 +76,27 @@ for step in steps:
             #     criterion='gini', # gini best
             # )
             # model = ensemble.ExtraTreesClassifier(
-        #     n_estimators=150 # higher is better, but slower (def: 100)
+            #     n_estimators=step # higher is better, but slower (def: 100)
             # )
             # model = neighbors.KNeighborsClassifier(
             #     algorithm='auto',
             #     leaf_size=2,
             #     n_neighbors=step,
             # )
-        model = ensemble.BaggingClassifier(
-            n_estimators=5,
-            max_samples=.5,
-            max_features=.5,
-            bootstrap=False
-        )
-        # model = svm.SVC(decision_function_shape='ovr'
+            # model = ensemble.BaggingClassifier(
+            #     n_estimators=5,
+            #     max_samples=.5,
+            #     max_features=.5,
+            #     bootstrap=False
+            # )
+            # model = svm.SVC(
+            #     C = 0.8,
+            #     kernel = "poly",
+            #     degree = 5,
+            #     coef0 = 6,
+            #     probability = False,
+            #     break_ties=True,
+            #     decision_function_shape = 'ovr'
             # )
             model = model.fit(norm, labels)
             result = model.predict([temp_data])
@@ -100,7 +110,7 @@ for step in steps:
     actual_list.append(actual)
     predicted_list.append(predicted)
 
-    print(step)
+    print("%.4f"%step, "\t", "%.2f"%(time.time()-time_start))
 
 plots.plotMetrics(actual_list, predicted_list)
 plots.plotConfusion(actual_list[0], predicted_list[0])