import pandas as pd # our main data management package
import matplotlib.pyplot as plt # our main display package
import string # used for preprocessing
import re # used for preprocessing
import nltk # the Natural Language Toolkit, used for preprocessing
import numpy as np # used for managing NaNs
import nltk
from prefect import task, Flow, Parameter
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords # used for preprocessing
from nltk.stem import WordNetLemmatizer # used for preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression # our model
from sklearn.model_selection import train_test_split
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')

train = pd.read_csv('train.csv')
train_count=train.count()
train = train.drop(['keyword', 'location'], axis = 1)

test = pd.read_csv('test.csv')

stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

pp_text_train = [] # our preprocessed text column
pp_text_test = [] # our preprocessed text column


@task(log_stdout=True)
def describe(file):
	file.describe()
	
with Flow("Prepare") as prepare:
	file = Parameter('file')
	describe(file)
	
prepare.run(file=train)
prepare.run(file=test)

	
# remove urls, handles, and the hashtag from hashtags (taken from https://stackoverflow.com/questions/8376691/how-to-remove-hashtag-user-link-of-a-tweet-using-regular-expression)
def remove_urls(text):
	new_text = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",text).split())
	return new_text

# make all text lowercase
def text_lowercase(text):
	return text.lower()

# remove numbers
def remove_numbers(text):
	result = re.sub(r'\d+', '', text)
	return result

# remove punctuation
def remove_punctuation(text):
	translator = str.maketrans('', '', string.punctuation)
	return text.translate(translator)

# tokenize
def tokenize(text):
	text = word_tokenize(text)
	return text

# remove stopwords
def remove_stopwords(text):
	text = [i for i in text if not i in stop_words]
	return text
    
# lemmatize
def lemmatize(text):
	text = [lemmatizer.lemmatize(token) for token in text]
	return text

def preprocessing(text):
	text = text_lowercase(text)
	text = remove_urls(text)
	text = remove_numbers(text)
	text = remove_punctuation(text)
	text = tokenize(text)
	text = remove_stopwords(text)
	text = lemmatize(text)
	text = ' '.join(text)
	return text	
	


@task(log_stdout=True)
def preprocess(file, pp_text):
	for text_data in file['text']:
		pp_text_data = preprocessing(text_data)
		pp_text.append(pp_text_data)
	file['pp_text'] = pp_text # add the preprocessed text as a column	
	
	
with Flow("Preprocess") as preprocessData:
	file = Parameter('file')
	pp_text = Parameter('pp_text')
	preprocess(file, pp_text)

preprocessData.run(file=train, pp_text=pp_text_train)
preprocessData.run(file=test, pp_text=pp_text_test)



train_text_data = list(train['pp_text'])
test_text_data = list(test['pp_text'])
corpus = train_text_data + test_text_data
tf=TfidfVectorizer()
fitted_vectorizer = tf.fit(corpus)

# train
train_transform = fitted_vectorizer.transform(train['pp_text'])
y = train['target']
# test
test_transform = fitted_vectorizer.transform(test['pp_text'])

X=train_transform
print(X)
X_train, X_test, y_train, y_test = train_test_split(X, y)

scikit_log_reg = LogisticRegression()
model=scikit_log_reg.fit(X_train, y_train)
predictions = model.predict(X_test)
count = 0
for guess, answer in zip(predictions, y_test):
	if guess == answer:
		count += 1
print(count/len(y_test))