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save_and_load_model.py
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save_and_load_model.py
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import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction import DictVectorizer
from sklearn import svm
from sklearn.externals import joblib
import pickle
from datetime import datetime
from os import listdir
from os.path import isfile, join
import re
PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
now = datetime.now()
date_time = now.strftime("_%m_%d_%Y_%H_%M_%S")
def save_model(model, model_name):
save_path = PROJECT_DIR + '/models/classification/' + model_name + date_time + '.pkl'
f = open(save_path, 'wb')
pickle.dump(model, f)
f.close()
print('Saved model :', save_path)
def load_model(model_name):
train_path = PROJECT_DIR + '/models/classification/' + model_name + '.joblib'
return joblib.load(train_path)
def load_latest_model():
train_path = PROJECT_DIR + '/models/classification/'
onlyfiles = [f for f in listdir(train_path) if isfile(join(train_path, f))]
r = [(f, datetime.strptime(re.findall(r'([\d]+_[\d]+_[\d]+_[\d]+_[\d]+_[\d]+)', f)[0], '%m_%d_%Y_%H_%M_%S')) for f in onlyfiles]
r = sorted(r, key=lambda x: x[1])
model_path = r[-1][0]
return joblib.load(train_path + model_path)