pre

Author: Y1ran

Diff for: Unsup_learn/Preprocessing.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Aug 12 14:28:52 2018
+
+@author: Administrator
+"""
+
+import mglearn
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.svm import LinearSVC, SVC
+from sklearn.datasets import load_breast_cancer
+
+mglearn.plots.plot_scaling()
+
+def test_prep():
+    cancer = load_breast_cancer()
+
+    X_train, X_test, y_train, y_test = train_test_split(
+                cancer.data, cancer.target, stratify=cancer.target
+                , random_state=1)
+    
+    scale = MinMaxScaler()
+    scale.fit(X_train)
+    
+    X_scaled = scale.transform(X_train)
+    print(X_scaled)
+    
+def test_SVC():
+
+    cancer = load_breast_cancer()
+
+    X_train, X_test, y_train, y_test = train_test_split(
+                cancer.data, cancer.target, stratify=cancer.target
+                , random_state=42)
+    
+    svc = SVC(C=100)
+    svc.fit(X_train,y_train)
+    
+    print(svc.score(X_train,y_train))
+    print("the test score is {:.2f}".format(svc.score(X_test
+                    ,y_test)))
+    scale = MinMaxScaler()
+    scale.fit(X_train)
+    X_test_scaled = scale.transform(X_test)
+    X_scaled = scale.transform(X_train)
+    
+    svc.fit(X_scaled, y_train)
+    print("the test score scaled is {:.2f}".format(svc.score(X_test_scaled
+                    ,y_test)))
+    
+