image_model.py

#-*- coding:utf-8 -*


import os
from os.path import join
from PIL import Image
import os
# import ConfigParser
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.externals import joblib
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_predict
from sklearn import metrics
from sklearn import svm
from captcha_ml.config import *
import configparser



#全局变量
# config = configparser.ConfigParser()
# config.read("./config.ini")
# model_path = config.get("global", "model_path") #模型存放路径


#训练模型
def trainModel(data, label):
    print("trainning process >>>>>>>>>>>>>>>>>>>>>>")
    rbf = svm.SVC(decision_function_shape='ovo',kernel='rbf')
    scores = cross_val_score(rbf, data, label,cv=10)
    print("rbf: ",scores.mean())

    linear = svm.SVC(decision_function_shape='ovo',kernel='linear')
    scores = cross_val_score(linear, data, label,cv=10)
    print("linear: ",scores.mean())
    linear.fit(data, label)

    rf = RandomForestClassifier(n_estimators=100, max_depth=None,min_samples_split=2, random_state=0)
    scores = cross_val_score(rf, data, label,cv=10)
    print("rf: ",scores.mean())
    rf.fit(data, label)


    predict = rf.predict(data)
    acc = 0
    for num in range(len(label)):
        if predict[num] == label[num]:
            acc += 1
            # print("predict:", predict[num], "\tlabel: ", label[num])
    print("model acc: ", acc/len(label))

    #持久化
    joblib.dump(rf, model_path)
    print("model save success!")

    return rbf



#测试模型
def testModel(data, label):
    #读取模型
    model = joblib.load(model_path)
    #预测
    predict_list = model.predict(data)
    #print classification_report(label, predict_list)#按类别分类的各种指标
    print("\ntest process >>>>>>>>>>>>>>>>>>>>>>>>")
    print("test precision: ",metrics.precision_score(label, predict_list))#precision
    print("test recall: ",metrics.recall_score(label, predict_list))#recall
    print("test f1 score: ",metrics.f1_score(label, predict_list))#f1 score
    print("confusion matrix:")
    print(confusion_matrix(label, predict_list))#混淆矩阵