03 測(cè)試我們的算法

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import numpy as np import matplotlib.pyplot as plt from sklearn import datasets iris = datasets.load_iris() iris.keys() dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names']) X = iris.data y = iris.target X.shape (150, 4) y.shape (150,)

train_test_split

分離出一部分?jǐn)?shù)據(jù)做訓(xùn)練，另外一部分?jǐn)?shù)據(jù)做測(cè)試。

y array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) shuffled_indexes = np.random.permutation(len(X)) shuffled_indexes array([ 69, 11, 5, 9, 12, 148, 111, 34, 73, 104, 108, 47, 60,102, 95, 75, 139, 110, 55, 77, 49, 6, 92, 32, 124, 21,127, 130, 66, 58, 63, 0, 80, 144, 97, 1, 8, 79, 123,101, 50, 23, 90, 56, 19, 149, 142, 57, 72, 147, 86, 145,15, 91, 4, 62, 41, 39, 135, 112, 115, 38, 138, 33, 125,134, 35, 87, 26, 114, 61, 143, 103, 43, 82, 140, 141, 30,78, 146, 10, 22, 51, 122, 18, 31, 113, 131, 129, 7, 65,59, 36, 85, 81, 28, 116, 118, 83, 67, 52, 109, 119, 16,25, 2, 136, 94, 93, 45, 132, 76, 71, 99, 54, 20, 96,3, 117, 64, 128, 120, 48, 46, 13, 126, 88, 53, 68, 107,121, 40, 29, 133, 89, 44, 42, 98, 137, 17, 100, 84, 24,27, 74, 106, 37, 14, 70, 105]) test_ratio = 0.2 test_size = int(len(X) * test_ratio) test_indexes = shuffled_indexes[:test_size] train_indexes = shuffled_indexes[test_size:] X_train = X[train_indexes] y_train = y[train_indexes]X_test = X[test_indexes] y_test = y[test_indexes] print(X_train.shape) print(y_train.shape) (120, 4) (120,) print(X_test.shape) print(y_test.shape) (30, 4) (30,)

封裝

from playML.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y) print(X_train.shape) print(y_train.shape) (120, 4) (120,) print(X_test.shape) print(y_test.shape) (30, 4) (30,)

測(cè)試我們的算法

from playML.kNN import KNNClassifiermy_knn_clf = KNNClassifier(k=3) my_knn_clf.fit(X_train, y_train) y_predict = my_knn_clf.predict(X_test) y_predict array([2, 1, 0, 0, 0, 0, 1, 1, 2, 2, 0, 0, 0, 2, 2, 0, 1, 2, 1, 1, 0, 1, 0,1, 1, 2, 2, 0, 0, 1]) y_test array([2, 1, 0, 0, 0, 0, 1, 1, 1, 2, 0, 0, 0, 2, 2, 0, 1, 2, 1, 1, 0, 1, 0,1, 1, 2, 2, 0, 0, 1]) sum(y_predict == y_test) 29 sum(y_predict == y_test) / len(y_test) 0.96666666666666667

sklearn中的train_test_split

from sklearn.model_selection import train_test_splittrain_test_split <function sklearn.model_selection._split.train_test_split> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666) print(X_train.shape) print(y_train.shape) (120, 4) (120,) print(X_test.shape) print(y_test.shape) (30, 4) (30,)

總結(jié)

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