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from io import StringIO
import pandas as pd
import numpy as np
import sklearn
def parse_classification_report(classification_report):
    """Parses sklearn classification report into a pandas dataframe."""
    return pd.read_fwf(StringIO(classification_report),lineterminator='\n', index_col=0, colspecs=[(0,12),(12,22),(22,32),(32,42),(42,52)]).dropna()

  target_names['leak','no leak']

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/*
Importing natural into webpack (the node nlp toolkit)

This is just copy of natural's index.js with some exports commented out. 
This way it works in webpack, however I haven't full tested it.
*/

exports.SoundEx = require('natural/lib/natural/phonetics/soundex');
exports.Metaphone = require('natural/lib/natural/phonetics/metaphone');
exports.DoubleMetaphone = require('natural/lib/natural/phonetics/double_metaphone');

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from rl.callbacks import TrainIntervalLogger
from tqdm import tqdm_notebook
import timeit

class TrainIntervalLoggerTQDMNotebook(TrainIntervalLogger):
    """TrainIntervalLogger using tqdm_notebook for jupyter-notebook."""
    def reset(self):
        self.interval_start = timeit.default_timer()
        self.metrics = []
        self.infos = []

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from rl.callbacks import Callback
from rl.callbacks import TrainIntervalLogger
from keras import backend as K
import warnings
from tqdm import tqdm_notebook
import timeit
import numpy as np

class TrainIntervalLoggerTQDMNotebook(TrainIntervalLogger):
    """TrainIntervalLogger for keras-rl using tqdm_notebook for jupyter-notebook."""

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import numpy as np
from matplotlib import pyplot as plt
def subimshow(image, title=''):
    """
    Show each band seperately.
    
    The shape si a square or rectangle of images
    
    image: array of shape (channel, height, width)
    

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"""
mod to Allow the ImageDataGenerator to have multiple channels instead of just 1,3,4

modified from https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py
"""

from keras.preprocessing.image import Iterator
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator as _ImageDataGenerator
from path import Path