cv

Author: Y1ran

Diff for: Para_tunning/CV_fold.py

@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 22 18:05:44 2018
+
+@author: Administrator
+"""
+
+from sklearn.model_selection import cross_val_score, train_test_split
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import GridSearchCV
+
+iris = load_iris()
+log_reg = LogisticRegression()
+
+score = cross_val_score(log_reg, iris.data, iris.target,cv=10)
+print("cross-vali score is: {}".format(score.mean()))
+
+import mglearn
+#mglearn.plots.plot_stratified_cross_validation()
+
+kfold = StratifiedKFold(n_splits=5, shuffle=True)
+for train_index, test_index in kfold.split(iris.data, iris.target):
+    print(train_index, test_index)
+    
+from sklearn.svm import SVC
+
+def simple_grid(iris, kfold):
+    X_train,X_test, y_train, y_test = train_test_split(
+            iris.data, iris.target, test_size=0.3,random_state = 0)
+    best_score = 0
+    para_list = [0.001, 0.01, 0.1, 1, 10]
+    for  gamma in para_list:
+        for C in para_list:
+            svm = SVC(gamma=gamma, C=C)
+            #svm.fit(X_train, y_train)
+            scores = cross_val_score(svm, iris.data, iris.target,cv=kfold)
+            score = scores.mean()
+            
+            if score > best_score:
+                best_score = score
+                best_para = {'C':C, 'gamma':gamma}
+    print("best score is {:.2f}".format(best_score))
+    print("best parameters is {}".format(best_para))
+    score = cross_val_score(svm, iris.data, iris.target,cv=kfold)
+    
+    print("CV-score is {}".format(score.mean(0)))
+    return best_para
+
+para = simple_grid(iris, kfold)
+
+para_grid = {"C":[0.001, 0.01, 0.1, 1, 10],
+                     'gamma':[0.001, 0.01, 0.1, 1, 10]}
+grid_search = GridSearchCV(SVC(), para_grid, cv = kfold)
+X_train,X_test, y_train, y_test = train_test_split(
+            iris.data, iris.target, test_size=0.3,random_state = 0)
+
+grid_search.fit(X_train, y_train)
+print("best grid score is {:.2f}".format(grid_search.score(X_test,
+              y_test)))
+
+import pandas as pd
+results = pd.DataFrame(grid_search.cv_results_)
+display(results.head())
+
+print(cross_val_score(GridSearchCV(SVC(), para_grid, cv = kfold),
+                     X_train,y_train, cv = kfold).mean())
+y_pred = grid_search.predict(X_test,y_test)
+
+from sklearn.metrics import classification_report
+print(classification_report(y_test, y_pred))

Diff for: Para_tunning/__main__.py

@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 22 15:12:49 2018
+
+@author: Administrator
+"""
+