Visualise benchmark_results.cv with vis.py

mini_proj/benchmark_results.csv

@@ -0,0 +1,5 @@
+name,time,accuracy
+svm,7.871559143066406,0.8446601941747572
+tree,0.25446152687072754,0.7087378640776699
+naive_bayes,0.12949371337890625,0.8252427184466019
+forest,0.2792677879333496,0.9514563106796117

mini_proj/traditionals.py

@@ -3,19 +3,27 @@ import time as t
 from sklearn import svm, ensemble, naive_bayes, neighbors
 from _image_classifier import ImageClassifier
 
+def accuracy(y_true, y_pred):
+    """returns the accuracy"""
+    y_pred = np.round(y_pred)
+    return (y_true == y_pred).mean()
+
 def precision(y_true, y_pred):
+    """returns the precision"""
     y_pred = np.round(y_pred)
     num = np.sum(np.logical_and(y_true, y_pred))
     den = np.sum(y_pred)
     return np.divide(num, den)
 
 def recall(y_true, y_pred):
+    """returns the recall"""
     y_pred = np.round(y_pred)
     num = np.sum(np.logical_and(y_true, y_pred))
     den = np.sum(y_true)
     return np.divide(num, den)
 
 def f_measure(y_true, y_pred):
+    """returns the F1 measure"""
     p = precision(y_true, y_pred)
     r = recall(y_true, y_pred)
     return 2 * p * r / (p + r)
@@ -39,18 +47,40 @@ my_metric_test = lambda iclf, f: metric_test(iclf, f, im_test, lbl_test)
 svm_iclf = ImageClassifier(svm.SVC)
 tree_iclf = ImageClassifier(neighbors.KNeighborsClassifier)
 naive_bayes_iclf = ImageClassifier(naive_bayes.GaussianNB)
-ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
+forest_iclf = ImageClassifier(ensemble.RandomForestClassifier)
 
 classifiers = [
     svm_iclf,
     tree_iclf,
     naive_bayes_iclf,
-    ensemble_iclf,
+    forest_iclf,
 ]
 
-for clf in classifiers:
+classifier_names = [
+    "svm",
+    "tree",
+    "naive_bayes",
+    "forest",
+]
+
+# print("name,time,accuracy,precision,recall,f_measure")
+print("name,time,accuracy")
+for clf, name in zip(classifiers, classifier_names):
     start = t.time()                            # Records time before training
     clf.fit(im_train, lbl_train)
     end = t.time()                              # Records time after tranining
-    print("training time:", end-start)
-    print(clf.score(im_test, lbl_test))
+    y_pred = clf.predict(im_test)
+    print(
+        name,
+        end-start,
+        clf.score(im_test, lbl_test),
+        # precision(lbl_test, y_pred),
+        # recall(lbl_test, y_pred),
+        # f_measure(lbl_test, y_pred),
+        sep=","
+        )
+    # print("training time:\t", end-start)
+    # print("Accuracy:\t", clf.score(im_test, lbl_test))
+    # print("Precision:\t", precision(lbl_test, y_pred))
+    # print("Recall:\t\t", recall(lbl_test, y_pred))
+    # print("F1-measure:\t", f_measure(lbl_test, y_pred))

mini_proj/vis.py

@@ -0,0 +1,20 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv("benchmark_results.csv")
+names = df.name
+df = df.drop("name", axis=1)
+
+plt.figure()
+plt.bar(names, df.time)
+plt.title("Training times")
+plt.xlabel("Classifier")
+plt.ylabel("Time (s)")
+
+plt.figure()
+plt.bar(names, df.accuracy)
+plt.title("Prediction Accuracy")
+plt.xlabel("Classifier")
+plt.ylabel("Accuracy")
+
+plt.show()