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updating text analysis

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Matthew Gaughan 2025-02-07 12:15:05 -06:00
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commit 8e57295a21
17 changed files with 5875 additions and 10887 deletions
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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import os \n",
"import pandas as pd \n",
"import glob\n",
"import re\n",
"import statistics"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing_subset/\"\n",
"readme_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/readme_subset/\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 41,
"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', errors=\"ignore\").read()\n",
" pattern = r'[^a-zA-Z0-9._-]'\n",
" cleaned_text = re.sub(pattern, '', text)\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",
" # README\n",
" # 0.25 19.0\n",
" if word_count < 20:\n",
" os.remove(file)\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": 43,
"metadata": {},
"outputs": [],
"source": [
"listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(contributing_directory)\n",
"wordcount_series = pd.Series(wordcounts)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.25 19.0\n",
"0.50 98.0\n",
"0.75 266.5\n",
"dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#wordcount_series.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
"wordcount_series.quantile([ 0.25, 0.5, 0.75])\n",
"#less_than_20 = wordcount_series[wordcount_series < 200]\n",
"#len(less_than_20)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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" file_list = []\n", " file_list = []\n",
" for file in files:\n", " for file in files:\n",
" text = open(file, encoding='utf-8').read()\n", " text = open(file, encoding='utf-8', errors='ignore').read()\n",
" #here's some of the descriptive text analysis\n", " #here's some of the descriptive text analysis\n",
" word_count, avg_word_length = metadata_for_file(text)\n", " word_count, avg_word_length = metadata_for_file(text)\n",
" word_counts.append(word_count)\n", " word_counts.append(word_count)\n",
@ -133,7 +133,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 7,
"id": "2b332b10-bfc8-4566-8c52-19a8a334af00", "id": "2b332b10-bfc8-4566-8c52-19a8a334af00",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -148,7 +148,7 @@
" #stripping markdown from documents\n", " #stripping markdown from documents\n",
" D = [strip_markdown(doc) for doc in D]\n", " D = [strip_markdown(doc) for doc in D]\n",
" #strip html \n", " #strip html \n",
" D = [re.sub(r'<!--.*?-->', '', doc, flags=re.DOTALL) for doc in D]\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", " #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", " 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", " lemmatizer = WordNetLemmatizer()\n",
@ -158,7 +158,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 8,
"id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7", "id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -182,7 +182,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 9,
"id": "24799e25-2c0c-4e16-b503-68296f604f52", "id": "24799e25-2c0c-4e16-b503-68296f604f52",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -200,7 +200,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 22,
"id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5", "id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -216,20 +216,20 @@
" print('Topic: %d' % topic)\n", " print('Topic: %d' % topic)\n",
" print(' %s' % ', '.join(words))\n", " print(' %s' % ', '.join(words))\n",
" #lda.print_topics(num_words=10)\n", " #lda.print_topics(num_words=10)\n",
" joblib.dump(lda, '020125_CONTRIBUTING_lda.jl')\n", " joblib.dump(lda, '020725_CONTRIBUTING_lda.jl')\n",
" #lda = joblib.load('0509_lda.jl')\n", " #lda = joblib.load('0509_lda.jl')\n",
" return id_topic" " return id_topic"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": null,
"id": "80bcdc6c-8a3d-4738-87b5-15e6c2db3a27", "id": "80bcdc6c-8a3d-4738-87b5-15e6c2db3a27",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def get_most_prevalent(vect_documents, documents):\n", "def get_most_prevalent(vect_documents, documents):\n",
" lda = joblib.load('020125_CONTRIBUTING_lda.jl')\n", " lda = joblib.load('020725_CONTRIBUTING_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n", " distributions = lda.transform(vect_documents)\n",
" most_prevalent = {0: [0, \"\"],1: [0, \"\"], 2: [0, \"\"], 3: [0, \"\"], 4: [0, \"\"]}\n", " most_prevalent = {0: [0, \"\"],1: [0, \"\"], 2: [0, \"\"], 3: [0, \"\"], 4: [0, \"\"]}\n",
" for i, topic_distribution in enumerate(distributions):\n", " for i, topic_distribution in enumerate(distributions):\n",
@ -242,13 +242,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": null,
"id": "3afd27af-8e8f-43c0-8610-06f7a68d5aec", "id": "3afd27af-8e8f-43c0-8610-06f7a68d5aec",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def prevalent_topics(vect_documents, file_list):\n", "def prevalent_topics(vect_documents, file_list):\n",
" lda = joblib.load('020125_CONTRIBUTING_lda.jl')\n", " lda = joblib.load('020725_CONTRIBUTING_lda.jl')\n",
" #lda = joblib.load('0514_contrib_lda.jl')\n", " #lda = joblib.load('0514_contrib_lda.jl')\n",
" distributions = lda.transform(vect_documents)\n", " distributions = lda.transform(vect_documents)\n",
" #figuring out what the max distribution is and then figuring out the mode\n", " #figuring out what the max distribution is and then figuring out the mode\n",
@ -267,7 +267,7 @@
" print(count_of_multiple)\n", " print(count_of_multiple)\n",
" df = pd.DataFrame(topic_arrays)\n", " df = pd.DataFrame(topic_arrays)\n",
" #finding the distribution values for all documents\n", " #finding the distribution values for all documents\n",
" with open('020125_CONTRIBUTING_file_topic_distributions.csv', 'w', newline='') as csvfile:\n", " with open('020725_CONTRIBUTING_file_topic_distributions.csv', 'w', newline='') as csvfile:\n",
" fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4']\n", " fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4']\n",
" writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n", " writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
" writer.writeheader()\n", " writer.writeheader()\n",
@ -297,7 +297,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 13,
"id": "5aefbafc-0c1b-409a-afbc-655e4cef91e3", "id": "5aefbafc-0c1b-409a-afbc-655e4cef91e3",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -313,17 +313,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 14,
"id": "69d606fd", "id": "69d606fd",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/\"" "contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/lagged_files/contributing/\""
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 18,
"id": "1f937c2e-2714-475d-b670-602164c46642", "id": "1f937c2e-2714-475d-b670-602164c46642",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -331,10 +331,10 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Mean wordcount: 357.8979020979021\n", "Mean wordcount: 384.64285714285717\n",
"Median wordcount: 225\n", "Median wordcount: 239.5\n",
"Mean wordlength: 6.345704522542385\n", "Mean wordlength: 6.307962000946226\n",
"Median wordlength: 5.778551532033426\n" "Median wordlength: 5.78616765377696\n"
] ]
} }
], ],
@ -349,17 +349,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": null,
"id": "e90e236f-8db5-40cc-88a3-60e674b9d1de", "id": "e90e236f-8db5-40cc-88a3-60e674b9d1de",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"['020125_CONTRIBUTING_vectorizer.joblib']" "['020725_CONTRIBUTING_vectorizer.joblib']"
] ]
}, },
"execution_count": 17, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -373,24 +373,24 @@
" token_pattern='[a-zA-Z0-9]{2,}', \n", " token_pattern='[a-zA-Z0-9]{2,}', \n",
" )\n", " )\n",
"data_vectorized = vectorizer.fit_transform(lemmatized_corpus)\n", "data_vectorized = vectorizer.fit_transform(lemmatized_corpus)\n",
"joblib.dump(vectorizer, '020125_CONTRIBUTING_vectorizer.joblib')\n", "joblib.dump(vectorizer, '020725_CONTRIBUTING_vectorizer.joblib')\n",
"'''" "'''\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 20,
"id": "d68aaf7b", "id": "d68aaf7b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"vectorizer = joblib.load('020125_CONTRIBUTING_vectorizer.joblib')\n", "vectorizer = joblib.load('020725_CONTRIBUTING_vectorizer.joblib')\n",
"data_vectorized = vectorizer.transform(lemmatized_corpus) " "data_vectorized = vectorizer.transform(lemmatized_corpus) "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 21,
"id": "dd1a70c2", "id": "dd1a70c2",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -400,78 +400,78 @@
"text": [ "text": [
"Fitting 5 folds for each of 6 candidates, totalling 30 fits\n", "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] 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 1/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-200697.595 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] 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 2/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-188050.624 total time= 1.9s\n",
"[CV 3/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=5..........\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 3/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-198388.926 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] 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 4/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-175647.281 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] 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 5/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=5;, score=-197357.360 total time= 1.9s\n",
"[CV 1/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\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 1/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-200062.948 total time= 1.8s\n",
"[CV 2/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\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 2/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-189020.534 total time= 2.1s\n",
"[CV 3/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=5..........\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 3/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-198536.096 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] 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 4/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-174922.177 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] 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 5/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=5;, score=-198347.478 total time= 2.0s\n",
"[CV 1/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\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 1/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-201076.179 total time= 2.0s\n",
"[CV 2/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\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 2/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-186886.696 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] 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 3/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-198604.914 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] 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 4/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-174897.611 total time= 1.9s\n",
"[CV 5/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=5..........\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 5/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=5;, score=-197182.965 total time= 1.8s\n",
"[CV 1/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\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 1/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-200563.084 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] 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 2/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-188161.145 total time= 2.0s\n",
"[CV 3/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\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 3/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-198652.481 total time= 2.0s\n",
"[CV 4/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=5..........\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 4/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-174929.908 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] 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 5/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=5;, score=-197878.194 total time= 2.0s\n",
"[CV 1/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\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 1/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-199602.928 total time= 1.9s\n",
"[CV 2/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\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 2/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-189209.363 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] 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 3/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-196801.716 total time= 1.7s\n",
"[CV 4/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=5..........\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 4/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-175165.578 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] 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 5/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=5;, score=-197366.525 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] 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 1/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-200731.419 total time= 2.0s\n",
"[CV 2/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\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 2/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-187607.034 total time= 1.9s\n",
"[CV 3/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\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 3/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-198327.802 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] 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 4/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-175918.789 total time= 2.0s\n",
"[CV 5/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=5..........\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", "[CV 5/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=5;, score=-198321.886 total time= 2.0s\n",
"Best Model's Params: {'batch_size': 256, 'learning_decay': 0.7, 'n_components': 5}\n", "Best Model's Params: {'batch_size': 256, 'learning_decay': 0.7, 'n_components': 5}\n",
"Best Log Likelihood Score: -180481.7700481068\n", "Best Log Likelihood Score: -191629.22199429374\n",
"Model Perplexity: 876.0922389848871\n" "Model Perplexity: 904.7791908675838\n"
] ]
} }
], ],
"source": [ "source": [
"#lda_model_identification(data_vectorized)" "lda_model_identification(data_vectorized)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 23,
"id": "aa83d20f", "id": "aa83d20f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -480,20 +480,20 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Topic: 0\n", "Topic: 0\n",
" test, new, function, example, style, file, make, build, command, version\n", " http, project, git, bug, scipy, contributing, request, pull, issue, contribute\n",
"Topic: 1\n", "Topic: 1\n",
" test, issue, request, pull, bug, http, feature, git, make, install\n", " build, function, style, make, file, command, test, used, option, variable\n",
"Topic: 2\n", "Topic: 2\n",
" git, test, branch, change, commit, make, request, pull, release, master\n", " issue, request, pull, change, bug, feature, branch, git, project, make\n",
"Topic: 3\n", "Topic: 3\n",
" contribution, license, project, open, submit, developer, right, contributor, sign, patch\n", " license, contribution, patch, project, submit, test, open, sign, agreement, change\n",
"Topic: 4\n", "Topic: 4\n",
" issue, request, pull, bug, project, change, contributing, contribution, feature, open\n" " test, git, make, install, version, doc, http, change, release, commit\n"
] ]
} }
], ],
"source": [ "source": [
"#topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())" "topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())"
] ]
}, },
{ {

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text_analysis/readmetopicModel.ipynb
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