Train test ratio (#43)

* KNN model trained and tested on generated.csv dataset

* Effect of split ratio on performance

* Hyperparameter tuning and cross validation implemented

* Black formatting applied

Co-authored-by: mlopatka <[email protected]>

dev/tab1tha/k_nn.py

@@ -35,9 +35,12 @@ def k_nearest(data_train, target_train, data_test, target_test):
     for use as a parameter of the classifier."""
     knn = KNeighborsClassifier(n_neighbors=n)
     knn.fit(data_train, target_train)
+
+    # predict labels of the test data
+    target_pred = knn.predict(data_test.values)
     # The held out test set is used to evaluate the accuracy of the model
     acc = knn.score(data_test, target_test)
-    return acc
+    return (acc, target_pred)
 
 
 def visual_compare(data_train, target_train, data_test, target_test):

dev/tab1tha/test_size_vary.py

@@ -0,0 +1,36 @@
+# Import necessary modules
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+from sklearn.model_selection import train_test_split
+import k_nn
+
+
+def vary_size(X, y):
+    """This function trains tests and evaluates the performance of a dataset in comparison
+    with a changing train-to-test size ratio. It returns two lists of values; accuracy and test_ratio which
+    containing the values of accuracy and test_size respectively"""
+    accuracy = []
+    test_ratio = []
+    performance = "\n PERFORMANCE \n "
+    """starting the range iterator from zero  or ending at 101 raises a ValueError: 
+    The test_size = 0 (1.0) should be greater or equal to the number of classes = 4"""
+    for i in range(5, 100, 5):
+        size = i / 100
+        # split data set into train and test sets
+        data_train, data_test, target_train, target_test = train_test_split(
+            X, y, test_size=size, random_state=10, stratify=y
+        )
+        # Evaluation of the  performance of the K-nearest neighbors prediction model
+        kn_accuracy, target_pred = k_nn.k_nearest(
+            data_train, target_train, data_test, target_test
+        )
+        # generate classification report to observe values of precision, recall, f1_score and support
+        class_report = classification_report(target_test, target_pred)
+        # separator string demarcates results of one iteration from the other
+        separator = ("+" * 100) + " \n"
+        performance = performance + separator + class_report + " \n"
+        # update lists
+        accuracy.append(kn_accuracy)
+        test_ratio.append(size)
+
+    return (test_ratio, accuracy, performance)