class_saver.py

import pandas as pd
import numpy as np
from sklearn.metrics import *
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network.multilayer_perceptron import MLPClassifier
from sklearn.svm import SVC
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
import pickle
import os, sys, re, time

proj_path = "/home/jwales/eclipse-workspace/crowdnews/"
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "crowdnews.settings")
sys.path.append(proj_path)
os.chdir(proj_path)
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
from django.contrib.gis.views import feed
from newsbot.models import *
from newsbot.util import *

print("Setting up..")
cDict = loadCanonDict()
qs_Examples = ArticleExample.objects.filter(quality_class__lt = 5)
print("Processing examples")
(Y_vector, examplesMatrix) = processExamples(qs_Examples, cDict)
X_train, X_test, y_train, y_test = train_test_split(examplesMatrix, Y_vector, test_size=0.2)
chosen_models = {}
chosen_models['newsbot/MLPC_model.sav'] = MLPClassifier(hidden_layer_sizes=(128,64,32,16,8), max_iter=2500)
chosen_models['newsbot/svc_model.sav'] = SVC(gamma='scale', probability = True)
chosen_models['newsbot/log_model.sav'] = LogisticRegression()

for fname, model in chosen_models.items():
    print("Working on " + fname)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print("Classification report: ")
    print(classification_report(predictions, y_test))
    print("***************")
    dosave = input("Save " + fname + "? ")
    if(dosave == 'y' or dosave == 'Y'):
        print("Saving...")
        pickle.dump(model, open(fname, 'wb'))
        print("Saved!")
    else:
        print("Not saved!")