reason 0.6.1

Easy-to-use NLP toolbox

Reason

Python easy-to-use natural language processing toolbox with powerful integrated machine learning packages.

Packages

• classify
  Naive bayes classifier
• cluster
  Kmeans++ and DBSCAN clusterer, elbow method
• metrics
  Confusion matrix, accuracy
• tag
  POS tagger, regex, lookup and default tagging tools
• tokenize
  Regex word and sentence tokenizer
• stem
  Porter and regex stemmer
• analysis
  Frequency distribution
• util
  Bigrams, trigrams and ngrams

Install

Install latest stable version using pip:

pip install reason

Quick Start

Classification:

>>> from reason.classify import NaiveBayesClassifier
>>> classifier = NaiveBayesClassifier()
>>> classifier.fit(x, y)
>>> y_pred = classifier.predict(new_data)

>>> from reason.metrics import accuracy
>>> accuracy(y_true, y_pred)
0.9358

Clustering:

>>> from reason.cluster import KMeansClusterer
>>> from reason.cluster import elbow_method
>>> elbow_method(x, clusterer=KMeansClusterer, max_k=10)
5

>>> clusterer = KMeansClusterer()
>>> labels = clusterer.fit(x, k=5)
>>> pred = clusterer.predict(new_data)

>>> from reason.cluster import DBSCAN
>>> clusterer = DBSCAN()
>>> labels = clusterer.fit(x, eps=0.21)

Confusion matrix:

>>> from reason.metrics import ConfusionMatrix
>>> cm = ConfusionMatrix(y_true, y_pred)

>>> cm
68 21 13
16 70 11
14 10 77

>>> cm[actual, predicted]
16

>>> from reason.metrics import BinaryConfusionMatrix
>>> bcm = BinaryConfusionMatrix(b_y_true, b_y_pred)

>>> bcm.precision()
0.7837
>>> bcm.recall()
0.8055
>>> bcm.f1_score()
0.7944

Part-of-speech tagging:

>>> from reason.tag import POSTagger

>>> text = "10 tools from the file"
>>> tagger = POSTagger()
>>> tagger.tag(text)
[('10', 'CD'), ('tools', 'NNS'), ('from', 'IN'), ('the', 'AT'), ('file', 'NN')]

Word tokenization:

>>> from reason.tokenize import word_tokenize

>>> text = "Testing reason0.1.0, (on: 127.0.0.1). Cool stuff..."
>>> word_tokenize(text, 'alphanumeric')
['Testing', 'reason0.1.0', 'on', '127.0.0.1', 'Cool', 'stuff']

Sentence tokenization:

>>> from reason.tokenize import sent_tokenize

>>> text = "Hey, what's up? I love using Reason library!"
>>> sents = sent_tokenize(text)
>>> for sent in sents:
...     print(sent)
Hey, what's up?
I love using Reason library!

Lemmatization:

>>> from reason.stem import PorterStemmer

>>> text = "watched birds flying"
>>> stemmer = PorterStemmer()
>>> stemmer.stem(text)
['watch', 'bird', 'fly']

>>> from reason.stem import regex_stem

>>> regex_pattern = r'^(.*?)(ous)?$'
>>> regex_stem('dangerous', regex_pattern)
danger

Preprocess text (tokenizing + stemming):

>>> from reason import preprocess

>>> text = "What's up? I love using Reason library!"
>>> preprocess(text)
[["what's", 'up', '?'], ['i', 'love', 'us', 'reason', 'librari', '!']]

Frequency distribution:

>>> from reason.analysis import FreqDist

>>> words = ['hey', 'hey', 'oh', 'oh', 'oh', 'yeah']
>>> fd = FreqDist(words)

>>> fd
Frequency Distribution
Most-Common: [('oh', 3), ('hey', 2), ('yeah', 1)]
>>> fd.most_common(2)
[('oh', 3), ('hey', 2)]
>>> fd['yeah']
1

N-grams:

>>> sent = "Reason is easy to use"

>>> from reason.util import bigrams
>>> bigrams(sent)
[('Reason', 'is'), ('is', 'easy'), ('easy', 'to'), ('to', 'use')]

>>> from reason.util import trigrams
>>> trigrams(sent)
[('Reason', 'is', 'easy'), ('is', 'easy', 'to'), ('easy', 'to', 'use')]

>>> from reason.util import ngrams
>>> ngrams(sent, 4)
[('Reason', 'is', 'easy', 'to'), ('is', 'easy', 'to', 'use')]

Dependencies

• NumPy
  Used to handle data
• Pandas
  Used in classify and cluster packages

Keep in mind NumPy will be automatically installed with Reason.

License

MIT -- See LICENSE for details.