def testProcessingPipeline(filename):
jsonData = getJSONData(filename)
ingredient_list = getIngredientList(jsonData)
tfidf = ClassificationUtils.load_classifier("tfidf.pickle")
bag_of_words = ClassificationUtils.load_classifier("bag_of_words.pickle")
adaboost = ClassificationUtils.load_classifier("ada_idf_cook.pickle")
randomfor = ClassificationUtils.load_classifier("rf_idf_cook.pickle")
bagging = ClassificationUtils.load_classifier("bagging_idf_cook.pickle")
test_data_tfidf = tfidf.transform(ingredient_list)
test_data_bag = bag_of_words.transform(ingredient_list)
adaboost.predict(test_data_bag)
adaboost.predict(test_data_tfidf)
randomfor.predict(test_data_bag)
randomfor.predict(test_data_tfidf)
bagging.predict(test_data_bag)
bagging.predict(test_data_tfidf)
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 25 18:18:43 2015
Test Data for the Kaggle Email Spam Test Competition
@author: Rupak Chakraborty
"""
import numpy as np
import ClassificationUtils
test_filename = "test.csv"
num_features = 100
test_file = open(test_filename, "r")
test_data = test_file.read()
test_data = test_data.split()
test_set = np.zeros((len(test_data), num_features))
ClassificationUtils.populateNumpyData(test_filename, test_set)
svm = ClassificationUtils.load_classifier("svm_email.pickle")
rf = ClassificationUtils.load_classifier("rf_email.pickle")
bnb = ClassificationUtils.load_classifier("bnb_email.pickle")
gnb = ClassificationUtils.load_classifier("gnb_email.pickle")
mnb = ClassificationUtils.load_classifier("mnb_email.pickle")
svm_predict = svm.predict(test_set)
rf_predict = rf.predict(test_set)
bnb_predict = bnb.predict(test_set)
gnb_predict = gnb.predict(test_set)
mnb_predict = mnb.predict(test_set)