Skip to main content

Text visualization python package

Project description

Titulus

Text visualization python package

from titulus import color, print_

test = "Nous sommes le 12/24/2018 aujourd'hui. Mon numéro de tel est le (301)227-1340"
tokens = test.split()
weights = np.random.randint(low=0, high=10, size=len(tokens))

print_(' '.join(color(tokens, weights, n=10)))

alt text

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline

categories = ['alt.atheism', 'talk.religion.misc']
newsgroups_train = fetch_20newsgroups(subset='train',
                                      categories=categories)
newsgroups_test = fetch_20newsgroups(subset='test',
                                     categories=categories)

X_train, X_test = newsgroups_train.data, newsgroups_test.data
y_train, y_test = newsgroups_train.target, newsgroups_test.target
idx = np.random.randint(len(X_vec_list))

tokens = tokenizer(X_train[idx])
token_idx = [voc.index(t) if t in voc else -1 for t in tokens]

weights = [X_vec_arr[idx, :][i] if i>0 else 0 for i in token_idx]

print_(' '.join(color(tokens, weights, start_hex="#FEFEFE", finish_hex="#00a4e4", n=20)))

alt text

text_clf = Pipeline([('vect', vectorizer),
                     ('clf', SGDClassifier(loss='hinge', penalty='l2', tol=0.2,
                                               alpha=1e-3, max_iter=15, random_state=42)),

                    ])

_ = text_clf.fit(X_train, y_train)

X_vec = vectorizer.transform(X_train)
X_vec_arr = X_vec.toarray()
X_vec_list = [list(x) for x in X_vec_arr]
voc = vectorizer.get_feature_names()

idx = np.random.randint(len(X_vec_list))

tokens = tokenizer(X_train[idx])
token_idx = [voc.index(t) if t in voc else -1 for t in tokens]

weights_ = np.multiply(X_vec_arr[idx, :], text_clf.named_steps['clf'].coef_[0, :])
weights = [weights_[i] if i>0 else 0 for i in token_idx]

print_(' '.join(color(tokens, weights, start_hex="#34BF49", finish_hex="#BE0027", middle_hex="#FEFEFE", n=20)))

alt text

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

titulus-0.0.1.tar.gz (3.4 kB view hashes)

Uploaded source

Built Distribution

titulus-0.0.1-py3-none-any.whl (7.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page