Feat: Added KNN

BECK/KNN/test1.py

@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import classification_report, confusion_matrix, f1_score
+def train_val_test_split(df, rstate=42, shuffle=True, stratify=None):
+    strat = df[stratify] if stratify else None
+    train_set, test_set = train_test_split(
+        df, test_size=0.3, random_state=rstate, shuffle=shuffle, stratify=strat)
+    strat = test_set[stratify] if stratify else None
+    val_set, test_set = train_test_split(
+        test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)
+    return (train_set, val_set, test_set)
+df = pd.read_csv('../datasets/dataset_entrenamiento_coseno.csv')
+
+train_set, val_set, test_set = train_val_test_split(
+    df, stratify='Clase')
+
+x_train = train_set.drop(labels='Clase', axis=1)
+y_train = train_set['Clase']
+x_test = test_set.drop(labels='Clase', axis=1)
+y_test = test_set['Clase']
+x_val = val_set.drop(labels='Clase', axis=1)
+y_val = val_set['Clase']
+print(train_set['Clase'].value_counts())
+print('################################')
+print(val_set['Clase'].value_counts())
+print('################################')
+print(test_set['Clase'].value_counts())
+print("Longitud del Training Set:", len(train_set))
+print("Longitud del Validation Set:", len(val_set))
+print("Longitud del Test Set:", len(test_set))
+
+#? KNN
+classifier = KNeighborsClassifier(n_neighbors=5)
+classifier.fit(x_train, y_train)
+
+#Test
+print(' TEST '.center(50, '#'))
+y_pred = classifier.predict(x_test)
+print(classification_report(y_test, y_pred))
+print(confusion_matrix(y_test, y_pred))
+print(f1_score(y_test, y_pred))
+
+print(' VALIDATION '.center(50, '#'))
+y_pred2 = classifier.predict(x_val)
+print(classification_report(y_val, y_pred2))
+print(confusion_matrix(y_val, y_pred2))
+print(f1_score(y_val, y_pred2))