Python scikit-学习JSON

问题内容：

我有一个使用Python scikit-
learn构建的模型。我知道可以将模型保存为Pickle或Joblib格式。是否有现有方法将作业保存为JSON格式？请参阅下面的模型构建代码以供参考：

import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
import pickle
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names =['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
# Fit the model on 33%
model = LogisticRegression()
model.fit(X_train, Y_train)
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))

问题答案：

您将必须准备自己的序列化/反序列化方法。幸运的是，逻辑回归基本上可以通过系数和截距来捕获。但是，该LogisticRegression对象还保留了一些其他元数据，我们也可以围绕这些元数据进行捕获。我汇集了以下功能来完成工作。请记住，这仍然很粗糙：

import numpy as np
import json
from sklearn.linear_model import LogisticRegression

def logistic_regression_to_json(lrmodel, file=None):
    if file is not None:
        serialize = lambda x: json.dump(x, file)
    else:
        serialize = json.dumps
    data = {}
    data['init_params'] = lrmodel.get_params()
    data['model_params'] = mp = {}
    for p in ('coef_', 'intercept_','classes_', 'n_iter_'):
        mp[p] = getattr(lrmodel, p).tolist()
    return serialize(data)

def logistic_regression_from_json(jstring):
    data = json.loads(jstring)
    model = LogisticRegression(**data['init_params'])
    for name, p in data['model_params'].items():
        setattr(model, name, np.array(p))
    return model

注意，'coef_', 'intercept_','classes_'由于逻辑回归是直接线性模型，因此您可以自己进行预测，因此它只是矩阵乘法。

Python scikit-学习JSON

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