場景：需要將模型保存到內存，或磁盤。

代碼：

# -*- coding: utf-8 -*-import pandas as pd import pickle as pkl from sklearn.externals import joblib from sklearn import svm #加載數據 label_ds=pd.read_csv(r"D:\\tmp\\sam_11.csv",sep=',',encoding='utf8',\names=['u_spu_num','u_brand_num','u_cat_num','u_cat_spu','u_brand_spu','u_spu_date','action_type']) print "訓練集，有", label_ds.shape[0], "行", label_ds.shape[1], "列" #模型訓練 label_X = label_ds[['u_spu_num','u_brand_num','u_cat_num','u_cat_spu','u_brand_spu','u_spu_date']] label_y = label_ds['action_type']#類別 model = svm.SVC() model.fit(label_X, label_y) print model #模型導出導入磁盤 joblib.dump(model, 'D:\\tmp\\model.pkl') model2 = joblib.load('D:\\tmp\\model.pkl') print model2 #模型保存 s = pkl.dumps(model) model3 = pkl.loads(s) print model3數據集：

0,0,6,6,0,0,1 0,0,2,2,0,0,1 0,0,3,3,0,0,1 0,0,2,2,0,0,1 0,0,0,0,0,0,1 0,0,1,1,0,0,0 0,0,9,9,0,0,0 0,0,1,1,0,0,0 0,0,3,3,0,0,0

執行結果：

訓練集，有 9 行 7 列 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False)

1）采用Python內建的持久性模型 pickle 保存scikit的模型，字符串形式；

2）用joblib（joblib.dump&joblib.load）保存到磁盤，文件形式；

總結

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