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model_svm.py
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model_svm.py
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# -*- coding: utf-8 -*-
"""
Created on Sun May 5 11:16:16 2019
@author: WellenWoo
"""
import numpy as np
from sklearn.svm import SVC
from sklearn.externals import joblib
from sklearn.metrics import confusion_matrix, classification_report
import time
from utils import Preprocessor
class Trainer(object):
'''训练器;'''
def svc(self, x_train, y_train):
'''构建分类器'''
model = SVC(kernel = 'poly',degree = 4,probability= True)
model.fit(x_train, y_train)
return model
def save_model(self, model, output_name):
'''保存模型'''
joblib.dump(model,output_name, compress = 1)
def load_model(self, model_path):
'''加载模型'''
clf = joblib.load(model_path)
return clf
class Tester(object):
'''测试器;'''
def __init__(self, model_path):
tr = Trainer()
self.clf = tr.load_model(model_path)
def clf_quality(self,X_test,y_test):
"""评估分类器效果"""
pred = self.clf.predict(X_test)
cnf_matrix = confusion_matrix(y_test, pred)
score = self.clf.score(X_test, y_test)
clf_repo = classification_report(y_test, pred)
return cnf_matrix,score,clf_repo
def predict(self, fn):
'''样本预测;'''
pt = Preprocessor()
tmp = pt.img2vec(fn)
X_test = tmp.reshape(1, -1)
ans = self.clf.predict(X_test)
return ans
def run_train():
t0 = time.time()
pt = Preprocessor()
tr = Trainer()
X_train, y_train = pt.get_data_labels()
X_test, y_test = pt.get_data_labels("test")
t1 = time.time()
print(t1 - t0)
clf = tr.svc(X_train, y_train)
print(time.time() - t1)
tr.save_model(clf, "mnist_svm.m")
tester = Tester("mnist_svm.m")
mt, score, repo = tester.clf_quality(X_test, y_test)
print(mt, score, repo)
return clf