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from datetime import datetime, timedelta
import time
import random
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from pprint import pprint


default_args = {
    "owner": "airflow",

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for batch in dp(train_data).preprocess().iterate(batch_size=32, epoch=10):
    model.train_on_batch(batch["review"], batch["polarity"])

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from chariot.dataset_preprocessor import DatasetPreprocessor
from chariot.transformer.formatter import Padding


dp = DatasetPreprocessor()
dp.process("review")\
    .by(ct.text.UnicodeNormalizer())\
    .by(ct.Tokenizer("en"))\
    .by(ct.token.StopwordFilter("en"))\
    .by(ct.Vocabulary(min_df=5, max_df=0.5))\

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import chariot.transformer as ct
from chariot.preprocessor import Preprocessor


preprocessor = Preprocessor()
preprocessor\
    .stack(ct.text.UnicodeNormalizer())\
    .stack(ct.Tokenizer("en"))\
    .stack(ct.token.StopwordFilter("en"))\
    .stack(ct.Vocabulary(min_df=5, max_df=0.5))\

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import os
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from chariot.storage import Storage


class SimilarityGraph():

    def __init__(self, vocabulary, nearest_neighbor=4, mode="connectivity",
                 representation="GloVe.6B.200d", root=""):

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import numpy as np
import spacy


class DependencyGraph():

    def __init__(self, lang, vocabulary):
        self.lang = lang
        self._parser = spacy.load(lang, disable=["ner", "textcat"])
        self.vocabulary = vocabulary

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import os
import argparse
import random
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib
import tensorflow as tf
from tensorflow.python import keras as K
import gym
from fn_framework import FNAgent, Trainer, Observer

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import gym
import itertools
import matplotlib
import numpy as np
import sys
import tensorflow as tf
import collections
import sklearn.pipeline
import sklearn.preprocessing
from sklearn.kernel_approximation import RBFSampler

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import gym
import numpy as np
from tensorflow.python import keras as K
import tensorflow as tf

import random
from collections import deque


class ActorCritic:

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import csv
import requests


ISSUES_URL = "https://api.github.com/repos/arXivTimes/arXivTimes/issues?page=1&per_page=100"


def write_issues(response):
    "output a list of issues to csv"
    if not r.status_code == 200: