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fit topic models

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Matthew Gaughan 2025-02-02 15:42:09 -06:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "e09a84d6-cbd4-4a12-8e96-3775f734a262",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import numpy as np\n",
"import pandas as pd\n",
"import glob\n",
"import copy\n",
"import csv\n",
"from statistics import mean, median\n",
"from strip_markdown import strip_markdown\n",
"import joblib"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9483091c-ac72-415c-932d-ac7cf7970789",
"metadata": {},
"outputs": [],
"source": [
"import gensim\n",
"import gensim.corpora as corpora\n",
"from gensim.utils import simple_preprocess\n",
"from gensim.models import CoherenceModel\n",
"from gensim.models.phrases import Phrases\n",
"\n",
"from sklearn.decomposition import LatentDirichletAllocation\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"\n",
"from statistics import mode"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "196abd6a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] /home/SOC.NORTHWESTERN.EDU/nws8519/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('wordnet')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3da6b590-875d-478d-aaaa-de020039c519",
"metadata": {},
"outputs": [],
"source": [
"# spacy and nltk for lemmatization\n",
"import nltk \n",
"#nltk.download('stopwords')\n",
"import spacy\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem.wordnet import WordNetLemmatizer\n",
"\n",
"stopwords = stopwords.words('english')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "60c137ae-6fe9-4b03-b899-6141b1645d6b",
"metadata": {},
"outputs": [],
"source": [
"def metadata_for_file(file):\n",
" word_list = file.split()\n",
" word_count = len(word_list)\n",
" #print(word_list)\n",
" if word_count == 0:\n",
" avg_word_length = 0\n",
" else: \n",
" avg_word_length = sum(map(len, word_list)) / len(word_list)\n",
" #return number of paragraphs\n",
" return word_count, avg_word_length"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2e674fef-adb4-48c9-86a0-a655c41a95f3",
"metadata": {},
"outputs": [],
"source": [
"def get_data_from_dir(directory):\n",
" files = glob.glob(f\"{directory}/*\")\n",
" data_list = []\n",
" word_counts = []\n",
" avg_word_lengths = []\n",
" file_list = []\n",
" for file in files:\n",
" text = open(file, encoding='utf-8').read()\n",
" #here's some of the descriptive text analysis\n",
" word_count, avg_word_length = metadata_for_file(text)\n",
" word_counts.append(word_count)\n",
" avg_word_lengths.append(avg_word_length)\n",
" #adding the data to the list of text\n",
" data_list.append(text)\n",
" #adding filename\n",
" file_list.append(file)\n",
" return data_list, word_counts, avg_word_lengths, file_list"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2b332b10-bfc8-4566-8c52-19a8a334af00",
"metadata": {},
"outputs": [],
"source": [
"#preprocessing text data\n",
"def preprocess(corpus_list):\n",
" #extending stopwords \n",
" specific_stopwords = [\"http\", \"com\", \"www\", \"org\", \"file\", \"code\", \"time\", \"software\", \"use\", \"user\", \"set\", \"line\", \"run\", \"source\", \"github\",\n",
" \"lineno\", \"python\", \"php\", \"ruby\", \"api\"]\n",
" stopwords.extend(specific_stopwords)\n",
" D = copy.copy(corpus_list)\n",
" #stripping markdown from documents\n",
" D = [strip_markdown(doc) for doc in D]\n",
" #strip html \n",
" D = [re.sub(r'<!--.*?-->', '', doc, flags=re.DOTALL) for doc in D]\n",
" #mvp right now, can certainly be expanded as iterations of text analysis are done\n",
" D = [[token for token in simple_preprocess(doc) if token not in stopwords and len(token) > 2]for doc in D]\n",
" lemmatizer = WordNetLemmatizer()\n",
" D_lemma = [\" \".join([lemmatizer.lemmatize(token) for token in doc]) for doc in D]\n",
" return D_lemma"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7",
"metadata": {},
"outputs": [],
"source": [
"#preparing processed data for model usage\n",
"def text_preparation(lemmatized_text):\n",
" #bigrams\n",
" D_bigrams = copy.copy(lemmatized_text)\n",
" bigram = Phrases(D_bigrams, min_count=2)\n",
" for i in range(len(lemmatized_text)):\n",
" for token in bigram[D_bigrams[i]]:\n",
" if '_' in token:\n",
" D_bigrams[i].append(token)\n",
" #id2word\n",
" id2word = corpora.Dictionary(D_bigrams)\n",
" id2word.filter_extremes(no_below=5, no_above=0.5)\n",
" #bow representation \n",
" bag_of_words = [id2word.doc2bow(doc) for doc in D_bigrams]\n",
" return bag_of_words, id2word"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "24799e25-2c0c-4e16-b503-68296f604f52",
"metadata": {},
"outputs": [],
"source": [
"def lda_model_identification(data_vectorized):\n",
" lda = LatentDirichletAllocation()\n",
" search_params = {'n_components': [5], 'learning_decay': [.5, .7, .9], 'batch_size' : [128, 256] }\n",
" model = GridSearchCV(lda, param_grid=search_params, verbose=10)\n",
" model.fit(data_vectorized)\n",
" best_lda_model = model.best_estimator_\n",
" print(\"Best Model's Params: \", model.best_params_)\n",
" print(\"Best Log Likelihood Score: \", model.best_score_)\n",
" print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5",
"metadata": {},
"outputs": [],
"source": [
"def best_lda_model(data_vectorized, vocab):\n",
" lda = LatentDirichletAllocation(n_components=5, learning_decay = 0.7, batch_size = 256, max_iter = 50)\n",
" id_topic = lda.fit_transform(data_vectorized)\n",
" topic_words = {}\n",
" for topic, comp in enumerate(lda.components_):\n",
" word_idx = np.argsort(comp)[::-1][:10]\n",
" topic_words[topic] = [vocab[i] for i in word_idx]\n",
" for topic, words in topic_words.items():\n",
" print('Topic: %d' % topic)\n",
" print(' %s' % ', '.join(words))\n",
" #lda.print_topics(num_words=10)\n",
" joblib.dump(lda, '020125_CONTRIBUTING_lda.jl')\n",
" #lda = joblib.load('0509_lda.jl')\n",
" return id_topic"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "80bcdc6c-8a3d-4738-87b5-15e6c2db3a27",
"metadata": {},
"outputs": [],
"source": [
"def get_most_prevalent(vect_documents, documents):\n",
" lda = joblib.load('020125_CONTRIBUTING_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n",
" most_prevalent = {0: [0, \"\"],1: [0, \"\"], 2: [0, \"\"], 3: [0, \"\"], 4: [0, \"\"]}\n",
" for i, topic_distribution in enumerate(distributions):\n",
" for j in range(5):\n",
" if topic_distribution[j] > most_prevalent[j][0]:\n",
" most_prevalent[j] = [topic_distribution[j], documents[i]]\n",
" print(most_prevalent)\n",
" return most_prevalent\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3afd27af-8e8f-43c0-8610-06f7a68d5aec",
"metadata": {},
"outputs": [],
"source": [
"def prevalent_topics(vect_documents, file_list):\n",
" lda = joblib.load('020125_CONTRIBUTING_lda.jl')\n",
" #lda = joblib.load('0514_contrib_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n",
" #figuring out what the max distribution is and then figuring out the mode\n",
" top_topic = []\n",
" count_of_multiple = 0\n",
" topic_arrays = []\n",
" for i, topic_distribution in enumerate(distributions):\n",
" max_dist = max(topic_distribution)\n",
" indexes = np.where(topic_distribution == max_dist)[0]\n",
" if len(indexes) == 1:\n",
" top_topic.append(indexes[0])\n",
" else:\n",
" count_of_multiple += 1\n",
" topic_arrays.append(topic_distribution)\n",
" #most_frequent(top_topic)\n",
" print(count_of_multiple)\n",
" df = pd.DataFrame(topic_arrays)\n",
" #finding the distribution values for all documents\n",
" with open('020125_CONTRIBUTING_file_topic_distributions.csv', 'w', newline='') as csvfile:\n",
" fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4']\n",
" writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
" writer.writeheader()\n",
" for i, row in df.iterrows():\n",
" project_dir = {}\n",
" project_dir['filename'] = file_list[i].split(\"/\")[-1]\n",
" array_row = df.iloc[i].to_numpy()\n",
" for j in range(5):\n",
" project_dir[\"t\" + str(j)] = array_row[j]\n",
" writer.writerow(project_dir)\n",
" #print(df.sort_values(by=['0']).head(5))\n",
" for i in range(5):\n",
" print(\"-----------------------Topic \" + str(i) + \" --------------------------------\")\n",
" top5 = df.nlargest(10, i)\n",
" top_indices = top5.index.to_list()\n",
" print(top5)\n",
" for index in top_indices:\n",
" print(file_list[index])\n",
" bottom5 = df.nsmallest(10, i)\n",
" bottom_indices = bottom5.index.to_list()\n",
" print(bottom5)\n",
" for index in bottom_indices:\n",
" print(file_list[index])\n",
" averages = df.mean()\n",
" print(averages)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5aefbafc-0c1b-409a-afbc-655e4cef91e3",
"metadata": {},
"outputs": [],
"source": [
"def most_frequent(topic_prevalence):\n",
" most_frequent_array = []\n",
" for j in range(5):\n",
" topic = mode(topic_prevalence)\n",
" most_frequent_array.append(topic)\n",
" topic_prevalence = [i for i in topic_prevalence if i != topic]\n",
" print(most_frequent_array)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "69d606fd",
"metadata": {},
"outputs": [],
"source": [
"contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1f937c2e-2714-475d-b670-602164c46642",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean wordcount: 357.8979020979021\n",
"Median wordcount: 225\n",
"Mean wordlength: 6.345704522542385\n",
"Median wordlength: 5.778551532033426\n"
]
}
],
"source": [
"listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(contributing_directory)\n",
"print(\"Mean wordcount: \", mean(wordcounts))\n",
"print(\"Median wordcount: \", median(wordcounts))\n",
"print(\"Mean wordlength: \", mean(wordlengths))\n",
"print(\"Median wordlength: \", median(wordlengths))\n",
"lemmatized_corpus = preprocess(listed_corpus)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e90e236f-8db5-40cc-88a3-60e674b9d1de",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['020125_CONTRIBUTING_vectorizer.joblib']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"vectorizer = CountVectorizer(analyzer='word', \n",
" min_df=2, \n",
" stop_words='english', \n",
" lowercase=True, \n",
" token_pattern='[a-zA-Z0-9]{2,}', \n",
" )\n",
"data_vectorized = vectorizer.fit_transform(lemmatized_corpus)\n",
"joblib.dump(vectorizer, '020125_CONTRIBUTING_vectorizer.joblib')\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d68aaf7b",
"metadata": {},
"outputs": [],
"source": [
"vectorizer = joblib.load('020125_CONTRIBUTING_vectorizer.joblib')\n",
"data_vectorized = vectorizer.transform(lemmatized_corpus) "
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "dd1a70c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
"[CV 1/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\n",
"[CV 1/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-196851.911 total time= 2.1s\n",
"[CV 2/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\n",
"[CV 2/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-168250.194 total time= 2.0s\n",
"[CV 3/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\n",
"[CV 3/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-180223.622 total time= 2.0s\n",
"[CV 4/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\n",
"[CV 4/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-183729.380 total time= 2.0s\n",
"[CV 5/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\n",
"[CV 5/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-174617.480 total time= 2.0s\n",
"[CV 1/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\n",
"[CV 1/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-197553.780 total time= 2.0s\n",
"[CV 2/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\n",
"[CV 2/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-168410.434 total time= 1.9s\n",
"[CV 3/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\n",
"[CV 3/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-180343.045 total time= 2.0s\n",
"[CV 4/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\n",
"[CV 4/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-182641.089 total time= 1.9s\n",
"[CV 5/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\n",
"[CV 5/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-174709.936 total time= 1.9s\n",
"[CV 1/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\n",
"[CV 1/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-196647.084 total time= 1.9s\n",
"[CV 2/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\n",
"[CV 2/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-168704.239 total time= 1.9s\n",
"[CV 3/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\n",
"[CV 3/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-180953.203 total time= 2.0s\n",
"[CV 4/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\n",
"[CV 4/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-183528.875 total time= 2.0s\n",
"[CV 5/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\n",
"[CV 5/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-174199.789 total time= 1.9s\n",
"[CV 1/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\n",
"[CV 1/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-197018.748 total time= 2.0s\n",
"[CV 2/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\n",
"[CV 2/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-169232.243 total time= 2.1s\n",
"[CV 3/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\n",
"[CV 3/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-179211.983 total time= 1.8s\n",
"[CV 4/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\n",
"[CV 4/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-184133.365 total time= 2.0s\n",
"[CV 5/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\n",
"[CV 5/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-173599.255 total time= 1.9s\n",
"[CV 1/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\n",
"[CV 1/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-196318.238 total time= 2.0s\n",
"[CV 2/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\n",
"[CV 2/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-168220.802 total time= 2.0s\n",
"[CV 3/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\n",
"[CV 3/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-179868.443 total time= 2.0s\n",
"[CV 4/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\n",
"[CV 4/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-183635.089 total time= 2.0s\n",
"[CV 5/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\n",
"[CV 5/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-174366.279 total time= 1.9s\n",
"[CV 1/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\n",
"[CV 1/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-198572.375 total time= 2.1s\n",
"[CV 2/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\n",
"[CV 2/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-168368.889 total time= 2.0s\n",
"[CV 3/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\n",
"[CV 3/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-180544.446 total time= 2.0s\n",
"[CV 4/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\n",
"[CV 4/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-183649.566 total time= 1.9s\n",
"[CV 5/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\n",
"[CV 5/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-174712.347 total time= 1.9s\n",
"Best Model's Params: {'batch_size': 256, 'learning_decay': 0.7, 'n_components': 5}\n",
"Best Log Likelihood Score: -180481.7700481068\n",
"Model Perplexity: 876.0922389848871\n"
]
}
],
"source": [
"#lda_model_identification(data_vectorized)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "aa83d20f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic: 0\n",
" test, new, function, example, style, file, make, build, command, version\n",
"Topic: 1\n",
" test, issue, request, pull, bug, http, feature, git, make, install\n",
"Topic: 2\n",
" git, test, branch, change, commit, make, request, pull, release, master\n",
"Topic: 3\n",
" contribution, license, project, open, submit, developer, right, contributor, sign, patch\n",
"Topic: 4\n",
" issue, request, pull, bug, project, change, contributing, contribution, feature, open\n"
]
}
],
"source": [
"#topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f4345bd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: [0.999495078557156, '/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt'], 1: [0.9980153669818502, '/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/tantale_deprecated.git_hullabaloo_CONTRIBUTING.rst'], 2: [0.9989886873615608, '/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-firewall_hullabaloo_CONTRIBUTING.md'], 3: [0.9983908776533259, '/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/webcamoid_webcamoid.git_hullabaloo_CONTRIBUTING.md'], 4: [0.9980246890436791, '/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/processone_pkix.git_hullabaloo_CONTRIBUTING.md']}\n"
]
}
],
"source": [
"topic_prevalence = get_most_prevalent(data_vectorized, file_list)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "23468e82",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"-----------------------Topic 0 --------------------------------\n",
" 0 1 2 3 4\n",
"536 0.999495 0.000126 0.000126 0.000127 0.000126\n",
"494 0.998076 0.000483 0.000480 0.000480 0.000481\n",
"403 0.997270 0.000682 0.000683 0.000677 0.000688\n",
"147 0.992964 0.001763 0.001779 0.001722 0.001773\n",
"564 0.992964 0.001763 0.001779 0.001722 0.001773\n",
"647 0.985526 0.013136 0.000446 0.000442 0.000450\n",
"106 0.985206 0.003688 0.003672 0.003728 0.003705\n",
"422 0.977476 0.000474 0.000469 0.000469 0.021112\n",
"502 0.967482 0.031760 0.000254 0.000251 0.000252\n",
"43 0.943894 0.001284 0.001282 0.052239 0.001301\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/osmcode_libosmium.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/angband_angband_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/terser_terser_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/mishoo_UglifyJS2_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/iovisor_bpftrace_hullabaloo_CONTRIBUTING-TOOLS.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/GNOME_network-manager-applet_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/iovisor_bcc.git_hullabaloo_CONTRIBUTING-SCRIPTS.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pgbackrest_pgbackrest_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/math-comp_math-comp_hullabaloo_CONTRIBUTING.md\n",
" 0 1 2 3 4\n",
"29 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"295 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"496 0.000277 0.056604 0.856781 0.000277 0.086060\n",
"643 0.000299 0.255602 0.304359 0.276117 0.163624\n",
"467 0.000350 0.471728 0.451339 0.000347 0.076235\n",
"372 0.000371 0.631292 0.000374 0.000373 0.367590\n",
"23 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"596 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"621 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"184 0.000383 0.895847 0.103008 0.000380 0.000383\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-firewall_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-apache_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/Icinga_icingaweb2.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/opencontainers_runc_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pygraphviz_pygraphviz.git_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/chaijs_chai_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-mysql_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-postgresql.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-concat_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/wtforms_flask-wtf.git_hullabaloo_CONTRIBUTING.rst\n",
"-----------------------Topic 1 --------------------------------\n",
" 0 1 2 3 4\n",
"109 0.000495 0.998015 0.000502 0.000491 0.000495\n",
"535 0.000507 0.997964 0.000514 0.000505 0.000510\n",
"173 0.000572 0.997721 0.000571 0.000569 0.000568\n",
"54 0.000598 0.997602 0.000594 0.000614 0.000592\n",
"546 0.000670 0.997320 0.000666 0.000679 0.000666\n",
"66 0.000672 0.997304 0.000674 0.000674 0.000676\n",
"220 0.000677 0.997289 0.000678 0.000677 0.000679\n",
"388 0.000701 0.997190 0.000703 0.000701 0.000704\n",
"227 0.000702 0.997190 0.000703 0.000702 0.000703\n",
"56 0.000720 0.997112 0.000723 0.000721 0.000723\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/tantale_deprecated.git_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pallets-eco_flask-sqlalchemy_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/scikit-learn_scikit-learn.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/libwww-perl_HTTP-Message.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/libwww-perl_URI.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/perl-catalyst_Catalyst-Authentication-Credential-HTTP_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/maxmind_geoip-api-perl.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/karenetheridge_B-Hooks-Parser.git_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/karenetheridge_Module-Manifest_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/karenetheridge_B-Hooks-OP-Check_hullabaloo_CONTRIBUTING\n",
" 0 1 2 3 4\n",
"536 0.999495 0.000126 0.000126 0.000127 0.000126\n",
"670 0.293267 0.000207 0.436114 0.000207 0.270205\n",
"29 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"295 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"261 0.883447 0.000317 0.115603 0.000314 0.000319\n",
"694 0.639602 0.000366 0.129429 0.000366 0.230238\n",
"23 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"596 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"621 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"477 0.155632 0.000388 0.701898 0.000386 0.141696\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/dbus_dbus_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-firewall_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-apache_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/GNOME_folks_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/google_gopacket.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-mysql_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-postgresql.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-concat_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/ddclient_ddclient.git_hullabaloo_CONTRIBUTING.md\n",
"-----------------------Topic 2 --------------------------------\n",
" 0 1 2 3 4\n",
"29 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"295 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"23 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"596 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"621 0.000374 0.000376 0.998492 0.000379 0.000379\n",
"433 0.000398 0.000400 0.998403 0.000398 0.000401\n",
"222 0.003336 0.003295 0.986750 0.003282 0.003337\n",
"378 0.003568 0.003524 0.985751 0.003589 0.003568\n",
"711 0.003559 0.003649 0.985621 0.003575 0.003596\n",
"706 0.004627 0.004655 0.981576 0.004568 0.004574\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-firewall_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-apache_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-mysql_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-postgresql.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-concat_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-xinetd_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/jbeder_yaml-cpp_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/mapbox_mapnik-vector-tile.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OSGeo_shapelib.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/faye_faye_hullabaloo_CONTRIBUTING.md\n",
" 0 1 2 3 4\n",
"536 0.999495 0.000126 0.000126 0.000127 0.000126\n",
"502 0.967482 0.031760 0.000254 0.000251 0.000252\n",
"331 0.350577 0.425615 0.000275 0.000275 0.223258\n",
"100 0.922202 0.076817 0.000326 0.000328 0.000327\n",
"548 0.166048 0.474650 0.000343 0.000339 0.358619\n",
"69 0.032624 0.858172 0.000354 0.039253 0.069597\n",
"460 0.067754 0.551877 0.000361 0.000361 0.379647\n",
"372 0.000371 0.631292 0.000374 0.000373 0.367590\n",
"393 0.000404 0.000401 0.000401 0.998391 0.000403\n",
"71 0.000404 0.677912 0.000407 0.013453 0.307824\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pgbackrest_pgbackrest_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/encode_django-rest-framework_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/rsms_inter_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/mathjax_MathJax_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pydicom_pydicom.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/ioquake_ioq3_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/chaijs_chai_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/webcamoid_webcamoid.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenTTD_OpenTTD.git_hullabaloo_CONTRIBUTING.md\n",
"-----------------------Topic 3 --------------------------------\n",
" 0 1 2 3 4\n",
"393 0.000404 0.000401 0.000401 0.998391 0.000403\n",
"259 0.001088 0.001087 0.001085 0.995648 0.001092\n",
"246 0.001119 0.001132 0.001119 0.995493 0.001138\n",
"541 0.001222 0.001235 0.001267 0.995034 0.001242\n",
"593 0.001240 0.001247 0.001254 0.995008 0.001251\n",
"25 0.001425 0.001434 0.001446 0.994258 0.001437\n",
"649 0.001425 0.001434 0.001446 0.994258 0.001437\n",
"317 0.001537 0.001544 0.001549 0.993824 0.001546\n",
"437 0.001748 0.001748 0.001765 0.992984 0.001756\n",
"134 0.001829 0.001834 0.001843 0.992659 0.001835\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/webcamoid_webcamoid.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/lxc_lxc.git_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/open-power_skiboot.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/lxc_lxcfs.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/ocaml_dune.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/janestreet_variantslib.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/janestreet_sexplib.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/lastpass_lastpass-cli_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pub_scm_linux_kernel_git_jberg_iw.git_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/sslmate_certspotter_hullabaloo_CONTRIBUTING\n",
" 0 1 2 3 4\n",
"536 0.999495 0.000126 0.000126 0.000127 0.000126\n",
"35 0.339901 0.060447 0.599367 0.000143 0.000143\n",
"652 0.432560 0.213230 0.353690 0.000146 0.000374\n",
"92 0.311952 0.101841 0.020145 0.000149 0.565914\n",
"377 0.243054 0.040215 0.353551 0.000206 0.362974\n",
"670 0.293267 0.000207 0.436114 0.000207 0.270205\n",
"74 0.257880 0.021230 0.366006 0.000219 0.354666\n",
"359 0.208595 0.562651 0.228310 0.000221 0.000223\n",
"363 0.204923 0.366357 0.258074 0.000240 0.170405\n",
"502 0.967482 0.031760 0.000254 0.000251 0.000252\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/rdiff-backup_rdiff-backup.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/scipy_scipy.git_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/zkat_ssri.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/linuxmint_cjs.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/dbus_dbus_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/GNOME_gjs.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/sphinx-doc_sphinx.git_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pyproj4_pyproj.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pgbackrest_pgbackrest_hullabaloo_CONTRIBUTING.md\n",
"-----------------------Topic 4 --------------------------------\n",
" 0 1 2 3 4\n",
"175 0.000490 0.000493 0.000497 0.000495 0.998025\n",
"607 0.000490 0.000493 0.000497 0.000495 0.998025\n",
"663 0.000490 0.000493 0.000497 0.000495 0.998025\n",
"686 0.000490 0.000493 0.000497 0.000495 0.998025\n",
"485 0.001467 0.001482 0.001464 0.001459 0.994127\n",
"369 0.001498 0.001494 0.001486 0.001527 0.993996\n",
"112 0.002279 0.002273 0.002292 0.002283 0.990873\n",
"635 0.002539 0.002564 0.002591 0.002599 0.989706\n",
"575 0.003807 0.003792 0.003766 0.003733 0.984901\n",
"151 0.004099 0.004152 0.004123 0.004177 0.983448\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/processone_pkix.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/processone_eimp.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/processone_stun.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/processone_fast_tls.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/ocaml_ocamlbuild.git_hullabaloo_CONTRIBUTING.adoc\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/eclipse_paho.mqtt.python.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/tpm2-software_tpm2-abrmd.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/Fluidsynth_fluidsynth.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/Leaflet_Leaflet_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/zopefoundation_zope.event_hullabaloo_CONTRIBUTING.md\n",
" 0 1 2 3 4\n",
"536 0.999495 0.000126 0.000126 0.000127 0.000126\n",
"35 0.339901 0.060447 0.599367 0.000143 0.000143\n",
"359 0.208595 0.562651 0.228310 0.000221 0.000223\n",
"502 0.967482 0.031760 0.000254 0.000251 0.000252\n",
"29 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"295 0.000251 0.000252 0.998989 0.000255 0.000254\n",
"344 0.825088 0.051655 0.122628 0.000315 0.000314\n",
"281 0.259153 0.235774 0.504441 0.000315 0.000317\n",
"261 0.883447 0.000317 0.115603 0.000314 0.000319\n",
"100 0.922202 0.076817 0.000326 0.000328 0.000327\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/OpenPrinting_cups_hullabaloo_CONTRIBUTING.txt\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/rdiff-backup_rdiff-backup.git_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/sphinx-doc_sphinx.git_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/pgbackrest_pgbackrest_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-firewall_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/puppetlabs_puppetlabs-apache_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/kilobyte_memkind_hullabaloo_CONTRIBUTING\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/xonsh_xonsh.git_hullabaloo_CONTRIBUTING.rst\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/GNOME_folks_hullabaloo_CONTRIBUTING.md\n",
"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/rsms_inter_hullabaloo_CONTRIBUTING.md\n",
"0 0.160978\n",
"1 0.288113\n",
"2 0.173551\n",
"3 0.101544\n",
"4 0.275814\n",
"dtype: float64\n"
]
}
],
"source": [
"prevalent_topics(data_vectorized, file_list)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "95e3bfc9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2, 3, 4]\n"
]
}
],
"source": [
"most_frequent(topic_prevalence)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.12.2"
}
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e09a84d6-cbd4-4a12-8e96-3775f734a262",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import numpy as np\n",
"import pandas as pd\n",
"import glob\n",
"import copy\n",
"import csv\n",
"from statistics import mean, median\n",
"from strip_markdown import strip_markdown\n",
"import joblib"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9483091c-ac72-415c-932d-ac7cf7970789",
"metadata": {},
"outputs": [],
"source": [
"import gensim\n",
"import gensim.corpora as corpora\n",
"from gensim.utils import simple_preprocess\n",
"from gensim.models import CoherenceModel\n",
"from gensim.models.phrases import Phrases\n",
"\n",
"from sklearn.decomposition import LatentDirichletAllocation\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"\n",
"from statistics import mode"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3da6b590-875d-478d-aaaa-de020039c519",
"metadata": {},
"outputs": [],
"source": [
"# spacy and nltk for lemmatization\n",
"import nltk \n",
"#nltk.download('stopwords')\n",
"import spacy\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem.wordnet import WordNetLemmatizer\n",
"\n",
"stopwords = stopwords.words('english')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60c137ae-6fe9-4b03-b899-6141b1645d6b",
"metadata": {},
"outputs": [],
"source": [
"def metadata_for_file(file):\n",
" word_list = file.split()\n",
" word_count = len(word_list)\n",
" #print(word_list)\n",
" if word_count == 0:\n",
" avg_word_length = 0\n",
" else: \n",
" avg_word_length = sum(map(len, word_list)) / len(word_list)\n",
" #return number of paragraphs\n",
" return word_count, avg_word_length"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e674fef-adb4-48c9-86a0-a655c41a95f3",
"metadata": {},
"outputs": [],
"source": [
"def get_data_from_dir(directory):\n",
" files = glob.glob(f\"{directory}/*\")\n",
" data_list = []\n",
" word_counts = []\n",
" avg_word_lengths = []\n",
" file_list = []\n",
" for file in files:\n",
" text = open(file, encoding='utf-8').read()\n",
" #here's some of the descriptive text analysis\n",
" word_count, avg_word_length = metadata_for_file(text)\n",
" word_counts.append(word_count)\n",
" avg_word_lengths.append(avg_word_length)\n",
" #adding the data to the list of text\n",
" data_list.append(text)\n",
" #adding filename\n",
" file_list.append(file)\n",
" return data_list, word_counts, avg_word_lengths, file_list"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b332b10-bfc8-4566-8c52-19a8a334af00",
"metadata": {},
"outputs": [],
"source": [
"#preprocessing text data\n",
"def preprocess(corpus_list):\n",
" #extending stopwords \n",
" specific_stopwords = [\"http\", \"com\", \"www\", \"org\", \"file\", \"code\", \"time\", \"software\", \"use\", \"user\", \"set\", \"line\", \"run\", \"source\", \"github\",\n",
" \"lineno\", \"python\", \"php\", \"ruby\", \"api\"]\n",
" stopwords.extend(specific_stopwords)\n",
" D = copy.copy(corpus_list)\n",
" #stripping markdown from documents\n",
" D = [strip_markdown(doc) for doc in D]\n",
" #strip html \n",
" D = [re.sub(r'<!--.*?-->', '', doc, flags=re.DOTALL) for doc in D]\n",
" #mvp right now, can certainly be expanded as iterations of text analysis are done\n",
" D = [[token for token in simple_preprocess(doc) if token not in stopwords and len(token) > 2]for doc in D]\n",
" lemmatizer = WordNetLemmatizer()\n",
" D_lemma = [\" \".join([lemmatizer.lemmatize(token) for token in doc]) for doc in D]\n",
" return D_lemma"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7",
"metadata": {},
"outputs": [],
"source": [
"#preparing processed data for model usage\n",
"def text_preparation(lemmatized_text):\n",
" #bigrams\n",
" D_bigrams = copy.copy(lemmatized_text)\n",
" bigram = Phrases(D_bigrams, min_count=2)\n",
" for i in range(len(lemmatized_text)):\n",
" for token in bigram[D_bigrams[i]]:\n",
" if '_' in token:\n",
" D_bigrams[i].append(token)\n",
" #id2word\n",
" id2word = corpora.Dictionary(D_bigrams)\n",
" id2word.filter_extremes(no_below=5, no_above=0.5)\n",
" #bow representation \n",
" bag_of_words = [id2word.doc2bow(doc) for doc in D_bigrams]\n",
" return bag_of_words, id2word"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24799e25-2c0c-4e16-b503-68296f604f52",
"metadata": {},
"outputs": [],
"source": [
"def lda_model_identification(data_vectorized):\n",
" lda = LatentDirichletAllocation()\n",
" search_params = {'n_components': [TKTK], 'learning_decay': [.5, .7, .9], 'batch_size' : [128, 256] }\n",
" model = GridSearchCV(lda, param_grid=search_params, verbose=10)\n",
" model.fit(data_vectorized)\n",
" best_lda_model = model.best_estimator_\n",
" print(\"Best Model's Params: \", model.best_params_)\n",
" print(\"Best Log Likelihood Score: \", model.best_score_)\n",
" print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5",
"metadata": {},
"outputs": [],
"source": [
"def best_lda_model(data_vectorized, vocab):\n",
" lda = LatentDirichletAllocation(n_components=TKTK, learning_decay = TKTK, batch_size = TKTK, max_iter = TKTK)\n",
" id_topic = lda.fit_transform(data_vectorized)\n",
" topic_words = {}\n",
" for topic, comp in enumerate(lda.components_):\n",
" word_idx = np.argsort(comp)[::-1][:10]\n",
" topic_words[topic] = [vocab[i] for i in word_idx]\n",
" for topic, words in topic_words.items():\n",
" print('Topic: %d' % topic)\n",
" print(' %s' % ', '.join(words))\n",
" #lda.print_topics(num_words=10)\n",
" joblib.dump(lda, '020125_DOCTYPE_lda.jl')\n",
" #lda = joblib.load('0509_lda.jl')\n",
" return id_topic"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80bcdc6c-8a3d-4738-87b5-15e6c2db3a27",
"metadata": {},
"outputs": [],
"source": [
"def get_most_prevalent(vect_documents, documents):\n",
" lda = joblib.load('TKTK_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n",
" most_prevalent = {0: [0, \"\"],1: [0, \"\"], 2: [0, \"\"], 3: [0, \"\"], 4: [0, \"\"], 5: [0, \"\"], 6: [0, \"\"], 7: [0, \"\"]}\n",
" for i, topic_distribution in enumerate(distributions):\n",
" for j in range(8):\n",
" if topic_distribution[j] > most_prevalent[j][0]:\n",
" most_prevalent[j] = [topic_distribution[j], documents[i]]\n",
" print(most_prevalent)\n",
" return most_prevalent\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3afd27af-8e8f-43c0-8610-06f7a68d5aec",
"metadata": {},
"outputs": [],
"source": [
"def prevalent_topics(vect_documents, file_list):\n",
" lda = joblib.load('TKTKTKTK_lda.jl')\n",
" #lda = joblib.load('0514_contrib_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n",
" #figuring out what the max distribution is and then figuring out the mode\n",
" top_topic = []\n",
" count_of_multiple = 0\n",
" topic_arrays = []\n",
" for i, topic_distribution in enumerate(distributions):\n",
" max_dist = max(topic_distribution)\n",
" indexes = np.where(topic_distribution == max_dist)[0]\n",
" if len(indexes) == 1:\n",
" top_topic.append(indexes[0])\n",
" else:\n",
" count_of_multiple += 1\n",
" topic_arrays.append(topic_distribution)\n",
" most_frequent(top_topic)\n",
" print(count_of_multiple)\n",
" df = pd.DataFrame(topic_arrays)\n",
" #finding the distribution values for all documents\n",
" with open('readme_file_topic_distributions.csv', 'w', newline='') as csvfile:\n",
" fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4', 't5', 't6', 't7']\n",
" writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
" writer.writeheader()\n",
" for i, row in df.iterrows():\n",
" project_dir = {}\n",
" project_dir['filename'] = file_list[i].split(\"/\")[-1]\n",
" array_row = df.iloc[i].to_numpy()\n",
" for j in range(8):\n",
" project_dir[\"t\" + str(j)] = array_row[j]\n",
" writer.writerow(project_dir)\n",
" #print(df.sort_values(by=['0']).head(5))\n",
" '''\n",
" for i in range(8):\n",
" print(\"-----------------------Topic \" + str(i) + \" --------------------------------\")\n",
" top5 = df.nlargest(10, i)\n",
" top_indices = top5.index.to_list()\n",
" print(top5)\n",
" for index in top_indices:\n",
" print(file_list[index])\n",
" bottom5 = df.nsmallest(10, i)\n",
" bottom_indices = bottom5.index.to_list()\n",
" print(bottom5)\n",
" for index in bottom_indices:\n",
" print(file_list[index])\n",
" '''\n",
" averages = df.mean()\n",
" print(averages)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5aefbafc-0c1b-409a-afbc-655e4cef91e3",
"metadata": {},
"outputs": [],
"source": [
"def most_frequent(topic_prevalence):\n",
" most_frequent_array = []\n",
" for j in range(4):\n",
" topic = mode(topic_prevalence)\n",
" most_frequent_array.append(topic)\n",
" topic_prevalence = [i for i in topic_prevalence if i != topic]\n",
" print(most_frequent_array)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f937c2e-2714-475d-b670-602164c46642",
"metadata": {},
"outputs": [],
"source": [
"listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(readme_directory)\n",
"print(\"Mean wordcount: \", mean(wordcounts))\n",
"print(\"Median wordcount: \", median(wordcounts))\n",
"print(\"Mean wordlength: \", mean(wordlengths))\n",
"print(\"Median wordlength: \", median(wordlengths))\n",
"lemmatized_corpus = preprocess(listed_corpus)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e90e236f-8db5-40cc-88a3-60e674b9d1de",
"metadata": {},
"outputs": [],
"source": [
"vectorizer = CountVectorizer(analyzer='word', \n",
" min_df=2, \n",
" stop_words='english', \n",
" lowercase=True, \n",
" token_pattern='[a-zA-Z0-9]{2,}', \n",
" )\n",
"data_vectorized = vectorizer.fit_transform(lemmatized_corpus)\n",
"joblib.dump(vectorizer, '020125_DOCTYPE_vectorizer.joblib'"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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