Python3入门机器学习经典算法与应用——knn算法网格搜索

叁歲伎倆 2022-12-20 11:29 145阅读 0赞

# knn算法网格搜索 #

## 06 网格搜索和更多kNN中的超参数 ##

import numpy as np
    from sklearn import datasets

digits = datasets.load_digits()
    X = digits.data
    y = digits.target

from sklearn.model_selection import train_test_split
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)

from sklearn.neighbors import KNeighborsClassifier
    
    sk_knn_clf = KNeighborsClassifier(n_neighbors=4, weights="uniform")
    sk_knn_clf.fit(X_train, y_train)
    sk_knn_clf.score(X_test, y_test)

0.9916666666666667

### Grid Search ###

param_grid = [
        { 
            'weights': ['uniform'], 
            'n_neighbors': [i for i in range(1, 11)]
        },
        { 
            'weights': ['distance'],
            'n_neighbors': [i for i in range(1, 11)], 
            'p': [i for i in range(1, 6)]
        }
    ]

knn_clf = KNeighborsClassifier()

from sklearn.model_selection import GridSearchCV
    
    grid_search = GridSearchCV(knn_clf, param_grid)

%%time
    grid_search.fit(X_train, y_train)

Wall time: 1min 27s
    
    
    
    
    
    GridSearchCV(cv=None, error_score=nan,
                 estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                                metric='minkowski',
                                                metric_params=None, n_jobs=None,
                                                n_neighbors=5, p=2,
                                                weights='uniform'),
                 iid='deprecated', n_jobs=None,
                 param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                              'weights': ['uniform']},
                             {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                              'p': [1, 2, 3, 4, 5], 'weights': ['distance']}],
                 pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
                 scoring=None, verbose=0)

grid_search.best_estimator_

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                         metric_params=None, n_jobs=None, n_neighbors=1, p=2,
                         weights='uniform')

grid_search.best_score_

0.9860820751064653

%%time
    grid_search = GridSearchCV(knn_clf, param_grid, n_jobs=-1, verbose=2)
    grid_search.fit(X_train, y_train)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
    
    
    [Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
    [Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.3s
    [Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:    7.6s
    
    
    Wall time: 16.8 s
    
    
    [Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   16.7s finished
    
    
    
    
    
    GridSearchCV(cv=None, error_score=nan,
                 estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                                metric='minkowski',
                                                metric_params=None, n_jobs=None,
                                                n_neighbors=5, p=2,
                                                weights='uniform'),
                 iid='deprecated', n_jobs=-1,
                 param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                              'weights': ['uniform']},
                             {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                              'p': [1, 2, 3, 4, 5], 'weights': ['distance']}],
                 pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
                 scoring=None, verbose=2)

### 其他超参数 ###

metrics: [http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html][http_scikit-learn.org_stable_modules_generated_sklearn.neighbors.DistanceMetric.html]

[http_scikit-learn.org_stable_modules_generated_sklearn.neighbors.DistanceMetric.html]: http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html