# Importing classes

from baseline import BaseLine

from logreg import LogReg

from naivebayes import NaiveBayes

from review import Review

from KNN import knn

#from topic2 import TopicModel

import argparse

import json

import sys

# Create command line arguments.

parser = argparse.ArgumentParser(description="Uses NLP models to predict the season of a Yelp review.") # TODO: revise so-called project title.

parser.add_argument("-m", required=True, default="baseline", help="the NLP model to be used", action="store", dest="model")

#parser.add_argument("-c", required=True, help="the classifier for datasets, i.e. city name", action="store", dest="classifier")

parser.add_argument("-i", required=False, default = "False", help="indicator for whether training and test data should be inverted", action="store", dest="invert")

args = parser.parse_args()

reviews = [] # creating a list of reviews to classify

#classifier = args.classifier.lower() # stores file classifier (i.e. "pittsburgh")

# Reading training data into reviews list

#categories = {"food":0, "shopping":0, "hotels-travel":0, "nightlife":0, "health":0}

#categories = {"chinese":0, "japanese": 0}

#categories = {"mexican":0, "italian":0}

categories = {"japanese":0, "chinese": 0, "thai":0, "korean":0, "indian":0, "middle-eastern":0, "greek":0, "american-traditional":0, "italian":0, "mexican":0}

if args.invert == "False":

for classifier in categories:

with open("spring-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("summer-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("fall-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("winter-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

else:

for classifier in categories:

with open("spring-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("summer-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("fall-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("winter-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

# Creating model objects

model = args.model

if (model == "baseline"):

model_obj = BaseLine(reviews, categories)

elif (model == "logreg"):

model_obj = LogReg(reviews)

elif (model == "multinomialNB"):

model_obj = NaiveBayes(reviews, "multinomial")

elif (model == "lda"):

model_obj = TopicModel(reviews)

elif (model == "kNearestNeighbors"):

model_obj = knn(reviews,target)

else: # put additional models here.

print("Argument Error: invalid model specified")

sys.exit()

model_classified = [] # classifications stored here

reviews = [] # resetting reviews list to save memory

# Reading test data into reviews list

if args.invert == "False":

for classifier in categories:

with open("spring-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("summer-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("fall-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("winter-"+classifier+".json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

else:

for classifier in categories:

with open("spring-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("summer-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("fall-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

with open("winter-"+classifier+"-training.json") as json_file:

for line in json_file:

json_obj = json.loads(line)

reviews += [(classifier,json_obj)]

"""

for __, json_obj in reviews:

model_classified += [(model_obj.classify(json_obj['text']), json_obj)]

"""

#Made more sense in my case to just pass all the test reviews at once,

#since the LogisiticRegression classify method expects all of the test corpus

#at once. I imagine the other models will work in a similar manner, but if not

#we can add more logic.

model_classified = model_obj.classify_all(reviews)

correct = 0

index = 0

# Going through every classification baseline made

for classification in model_classified:

# if the tuple the baseline predicted is correct...

if classification == reviews[index][0]:

correct = correct + 1

index = index + 1

print (float(correct) / len(reviews)) # print accuracy