{
    "info": {
        "author": "Antoine Simoulin",
        "author_email": "antoine.simoulin@gmail.com",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "# Titulus\n\nText visualization python package\n\n```python\nfrom titulus import color, print_\n\ntest = \"Nous sommes le 12/24/2018 aujourd'hui. Mon num\u00e9ro de tel est le (301)227-1340\"\ntokens = test.split()\nweights = np.random.randint(low=0, high=10, size=len(tokens))\n\nprint_(' '.join(color(tokens, weights, n=10)))\n```\n\n![alt text](./imgs/demo.png \"color demo\")\n\n```python\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.pipeline import Pipeline\n\ncategories = ['alt.atheism', 'talk.religion.misc']\nnewsgroups_train = fetch_20newsgroups(subset='train',\n                                      categories=categories)\nnewsgroups_test = fetch_20newsgroups(subset='test',\n                                     categories=categories)\n\nX_train, X_test = newsgroups_train.data, newsgroups_test.data\ny_train, y_test = newsgroups_train.target, newsgroups_test.target\n```\n\n```python\nidx = np.random.randint(len(X_vec_list))\n\ntokens = tokenizer(X_train[idx])\ntoken_idx = [voc.index(t) if t in voc else -1 for t in tokens]\n\nweights = [X_vec_arr[idx, :][i] if i>0 else 0 for i in token_idx]\n\nprint_(' '.join(color(tokens, weights, start_hex=\"#FEFEFE\", finish_hex=\"#00a4e4\", n=20)))\n```\n\n![alt text](./imgs/demo2.png \"color demo\")\n\n\n```python\ntext_clf = Pipeline([('vect', vectorizer),\n                     ('clf', SGDClassifier(loss='hinge', penalty='l2', tol=0.2,\n                                               alpha=1e-3, max_iter=15, random_state=42)),\n\n                    ])\n\n_ = text_clf.fit(X_train, y_train)\n\nX_vec = vectorizer.transform(X_train)\nX_vec_arr = X_vec.toarray()\nX_vec_list = [list(x) for x in X_vec_arr]\nvoc = vectorizer.get_feature_names()\n\nidx = np.random.randint(len(X_vec_list))\n\ntokens = tokenizer(X_train[idx])\ntoken_idx = [voc.index(t) if t in voc else -1 for t in tokens]\n\nweights_ = np.multiply(X_vec_arr[idx, :], text_clf.named_steps['clf'].coef_[0, :])\nweights = [weights_[i] if i>0 else 0 for i in token_idx]\n\nprint_(' '.join(color(tokens, weights, start_hex=\"#34BF49\", finish_hex=\"#BE0027\", middle_hex=\"#FEFEFE\", n=20)))\n```\n\n![alt text](./imgs/demo3.png \"color demo\")\n\n\n",
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