Orbifold

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Orbifold

require(mxnet)
batch.size = 32
seq.len = 32
num.hidden = 16
num.embed = 16
num.lstm.layer = 1
num.round = 1
learning.rate= 0.1
wd=0.00001
clip_gradient=1

Orbifold

# http://nlpforhackers.io/training-ner-large-dataset/
# http://gmb.let.rug.nl/data.php
import os
from nltk import conlltags2tree


def to_conll_iob(annotated_sentence):
    """
    `annotated_sentence` = list of triplets [(w1, t1, iob1), ...]
    Transform a pseudo-IOB notation: O, PERSON, PERSON, O, O, LOCATION, O

Orbifold

from gensim.utils import simple_preprocess

tokenize = lambda x: simple_preprocess(x)
# tokenize("We can load the vocabulary from the JSON file, and generate a reverse mapping (from index to word, so that we can decode an encoded string if we want)?!")

import os
import json
import numpy as np
from gensim.models import Word2Vec

Orbifold

Orbifold

import argparse

import matplotlib.pyplot as plt
import numpy as np
from keras.layers.core import Dense
from keras.models import Sequential
from numpy import array
from scipy import signal

Orbifold

# The [Anki repository](http://www.manythings.org/anki/) has a lot of sentence-pairs to learn a language and they are ideal to train a translation network.
# To judge the quality of a translation it helps to understand a bit both languages so in my case
#  the [Dutch-English](http://www.manythings.org/anki/nld-eng.zip),
# [French-English](http://www.manythings.org/anki/fra-eng.zip)
# and [German-English](http://www.manythings.org/anki/deu-eng.zip) were perfect.

import string
import re
from pickle import dump
from unicodedata import normalize

Orbifold

Orbifold

Orbifold

Intro

Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.For example, you may have a 2-class (binary) classification problem with 100 instances (rows). A total of 80 instances are labeled with Class-1 and the remaining 20 instances are labeled with Class-2. This is an imbalanced dataset and the ratio of Class-1 to Class-2 instances is 80:20 or more concisely 4:1. You can have a class imbalance problem on two-class classification problems as well as multi-class classification problems. Most techniques can be used on either.

Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter.

There are problems where a class imbalance is not just common, it is expected. For example, in datasets like those that characterize fraudulent transactions are imbalanced. The vast majority of the transactions will be in the “Not-Fraud” class and a very small minority will be