From 8e57295a21117dc6cfc1848836185b9514df4a4a Mon Sep 17 00:00:00 2001 From: Matthew Gaughan Date: Fri, 7 Feb 2025 12:15:05 -0600 Subject: [PATCH] updating text analysis --- ..._CONTRIBUTING_file_topic_distributions.csv | 716 --- text_analysis/020125_CONTRIBUTING_lda.jl | Bin 318740 -> 0 bytes .../020125_CONTRIBUTING_readability.csv | 716 --- .../020125_CONTRIBUTING_vectorizer.joblib | Bin 51635 -> 0 bytes ...020325_README_file_topic_distributions.csv | 4248 ----------------- text_analysis/020325_README_lda.jl | Bin 2309484 -> 0 bytes text_analysis/020325_README_readability.csv | 4248 ----------------- text_analysis/020325_README_vectorizer.joblib | Bin 168121 -> 0 bytes text_analysis/020425_corpus_subset.ipynb | 132 + ..._CONTRIBUTING_file_topic_distributions.csv | 715 +++ text_analysis/020725_CONTRIBUTING_lda.jl | Bin 0 -> 328180 bytes .../020725_CONTRIBUTING_vectorizer.joblib | Bin 0 -> 53239 bytes ...020725_README_file_topic_distributions.csv | 4227 ++++++++++++++++ text_analysis/020725_README_lda.jl | Bin 0 -> 2043316 bytes text_analysis/020725_README_vectorizer.joblib | Bin 0 -> 182825 bytes text_analysis/contributingtopicModel.ipynb | 150 +- text_analysis/readmetopicModel.ipynb | 1610 +++---- 17 files changed, 5875 insertions(+), 10887 deletions(-) delete mode 100644 text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv delete mode 100644 text_analysis/020125_CONTRIBUTING_lda.jl delete mode 100644 text_analysis/020125_CONTRIBUTING_readability.csv delete mode 100644 text_analysis/020125_CONTRIBUTING_vectorizer.joblib delete mode 100644 text_analysis/020325_README_file_topic_distributions.csv delete mode 100644 text_analysis/020325_README_lda.jl delete mode 100644 text_analysis/020325_README_readability.csv delete mode 100644 text_analysis/020325_README_vectorizer.joblib create mode 100644 text_analysis/020425_corpus_subset.ipynb create mode 100644 text_analysis/020725_CONTRIBUTING_file_topic_distributions.csv create mode 100644 text_analysis/020725_CONTRIBUTING_lda.jl create mode 100644 text_analysis/020725_CONTRIBUTING_vectorizer.joblib create mode 100644 text_analysis/020725_README_file_topic_distributions.csv create mode 100644 text_analysis/020725_README_lda.jl create mode 100644 text_analysis/020725_README_vectorizer.joblib diff --git a/text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv b/text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv deleted file mode 100644 index 7a457fe..0000000 --- a/text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv +++ /dev/null @@ -1,716 +0,0 @@ -filename,t0,t1,t2,t3,t4 -pytest-dev_pytest-bdd.git_hullabaloo_CONTRIBUTING.md,0.0038916719081222395,0.5367766366830218,0.4516181135221061,0.0038527654391693786,0.0038608124475803924 -lxqt_qterminal.git_hullabaloo_CONTRIBUTING.md,0.003693681420487903,0.5387301523323641,0.25662738645290184,0.0036966745868316267,0.19725210520741449 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+ "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 +} diff --git a/text_analysis/020725_CONTRIBUTING_file_topic_distributions.csv b/text_analysis/020725_CONTRIBUTING_file_topic_distributions.csv new file mode 100644 index 0000000..115f1b8 --- /dev/null +++ b/text_analysis/020725_CONTRIBUTING_file_topic_distributions.csv @@ -0,0 +1,715 @@ +filename,t0,t1,t2,t3,t4 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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", @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7", "metadata": {}, "outputs": [], @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "24799e25-2c0c-4e16-b503-68296f604f52", "metadata": {}, "outputs": [], @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5", "metadata": {}, "outputs": [], @@ -216,20 +216,20 @@ " 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", + " joblib.dump(lda, '020725_CONTRIBUTING_lda.jl')\n", " #lda = joblib.load('0509_lda.jl')\n", " return id_topic" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "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", + " lda = joblib.load('020725_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", @@ -242,13 +242,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "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('020725_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", @@ -267,7 +267,7 @@ " 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", + " with open('020725_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", @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "5aefbafc-0c1b-409a-afbc-655e4cef91e3", "metadata": {}, "outputs": [], @@ -313,17 +313,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "69d606fd", "metadata": {}, "outputs": [], "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", - "execution_count": 16, + "execution_count": 18, "id": "1f937c2e-2714-475d-b670-602164c46642", "metadata": {}, "outputs": [ @@ -331,10 +331,10 @@ "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" + "Mean wordcount: 384.64285714285717\n", + "Median wordcount: 239.5\n", + "Mean wordlength: 6.307962000946226\n", + "Median wordlength: 5.78616765377696\n" ] } ], @@ -349,17 +349,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "e90e236f-8db5-40cc-88a3-60e674b9d1de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['020125_CONTRIBUTING_vectorizer.joblib']" + "['020725_CONTRIBUTING_vectorizer.joblib']" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -373,24 +373,24 @@ " 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", - "'''" + "joblib.dump(vectorizer, '020725_CONTRIBUTING_vectorizer.joblib')\n", + "'''\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "id": "d68aaf7b", "metadata": {}, "outputs": [], "source": [ - "vectorizer = joblib.load('020125_CONTRIBUTING_vectorizer.joblib')\n", + "vectorizer = joblib.load('020725_CONTRIBUTING_vectorizer.joblib')\n", "data_vectorized = vectorizer.transform(lemmatized_corpus) " ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "dd1a70c2", "metadata": {}, "outputs": [ @@ -400,78 +400,78 @@ "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 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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 Log Likelihood Score: -180481.7700481068\n", - "Model Perplexity: 876.0922389848871\n" + "Best Log Likelihood Score: -191629.22199429374\n", + "Model Perplexity: 904.7791908675838\n" ] } ], "source": [ - "#lda_model_identification(data_vectorized)" + "lda_model_identification(data_vectorized)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "aa83d20f", "metadata": {}, "outputs": [ @@ -480,20 +480,20 @@ "output_type": "stream", "text": [ "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", - " 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", - " 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", - " 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", - " issue, request, pull, bug, project, change, contributing, contribution, feature, open\n" + " test, git, make, install, version, doc, http, change, release, commit\n" ] } ], "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())" ] }, { diff --git a/text_analysis/readmetopicModel.ipynb b/text_analysis/readmetopicModel.ipynb index 4b31ce4..480151c 100644 --- a/text_analysis/readmetopicModel.ipynb +++ b/text_analysis/readmetopicModel.ipynb @@ -35,34 +35,17 @@ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", "\n", - "from statistics import mode" + "from statistics import mode\n", + "\n", + "from collections import defaultdict" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "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" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#import nltk\n", "#nltk.download('wordnet')" @@ -70,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "3da6b590-875d-478d-aaaa-de020039c519", "metadata": {}, "outputs": [], @@ -87,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "id": "60c137ae-6fe9-4b03-b899-6141b1645d6b", "metadata": {}, "outputs": [], @@ -101,12 +84,12 @@ " 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" + " return word_count, avg_word_length, word_list" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "id": "2e674fef-adb4-48c9-86a0-a655c41a95f3", "metadata": {}, "outputs": [], @@ -117,22 +100,25 @@ " word_counts = []\n", " avg_word_lengths = []\n", " file_list = []\n", + " files_word_lists = defaultdict(list)\n", " for file in files:\n", " text = open(file, encoding='utf-8', errors='ignore').read()\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, word_list = 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", + " if word_count > 0:\n", + " files_word_lists[tuple(word_list)].append(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" + " return data_list, word_counts, avg_word_lengths, file_list, " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "2b332b10-bfc8-4566-8c52-19a8a334af00", "metadata": {}, "outputs": [], @@ -147,7 +133,7 @@ " #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", + " 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", @@ -157,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "2a26b7ef-d2df-4e1d-8aeb-66706ac6cbb7", "metadata": {}, "outputs": [], @@ -181,14 +167,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "24799e25-2c0c-4e16-b503-68296f604f52", "metadata": {}, "outputs": [], "source": [ "def lda_model_identification(data_vectorized):\n", " lda = LatentDirichletAllocation()\n", - " search_params = {'n_components': [11], 'learning_decay': [.5, .7, .9], 'batch_size' : [128, 256] }\n", + " search_params = {'n_components': [9], '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", @@ -199,13 +185,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "d3b5785f-8272-44f5-9aee-e5f5e97452e5", "metadata": {}, "outputs": [], "source": [ "def best_lda_model(data_vectorized, vocab):\n", - " lda = LatentDirichletAllocation(n_components=11, learning_decay = 0.9, batch_size = 256, max_iter = 50)\n", + " lda = LatentDirichletAllocation(n_components=9, learning_decay = 0.7, batch_size = 128, max_iter = 50)\n", " id_topic = lda.fit_transform(data_vectorized)\n", " topic_words = {}\n", " for topic, comp in enumerate(lda.components_):\n", @@ -222,17 +208,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "80bcdc6c-8a3d-4738-87b5-15e6c2db3a27", "metadata": {}, "outputs": [], "source": [ "def get_most_prevalent(vect_documents, documents):\n", - " lda = joblib.load('020325_README_lda.jl')\n", + " lda = joblib.load('020725_README_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, \"\"], 8: [0, \"\"], 9: [0, \"\"], 10: [0, \"\"]}\n", + " most_prevalent = {0: [0, \"\"],1: [0, \"\"], 2: [0, \"\"], 3: [0, \"\"], 4: [0, \"\"], 5: [0, \"\"], 6: [0, \"\"], 7: [0, \"\"], 8: [0, \"\"]}\n", " for i, topic_distribution in enumerate(distributions):\n", - " for j in range(11):\n", + " for j in range(9):\n", " if topic_distribution[j] > most_prevalent[j][0]:\n", " most_prevalent[j] = [topic_distribution[j], documents[i]]\n", " print(most_prevalent)\n", @@ -241,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "id": "3afd27af-8e8f-43c0-8610-06f7a68d5aec", "metadata": {}, "outputs": [], "source": [ "def prevalent_topics(vect_documents, file_list):\n", - " lda = joblib.load('020325_README_lda.jl')\n", + " lda = joblib.load('020725_README_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", @@ -266,19 +252,19 @@ " print(count_of_multiple)\n", " df = pd.DataFrame(topic_arrays)\n", " #finding the distribution values for all documents\n", - " with open('020325_README_file_topic_distributions.csv', 'w', newline='') as csvfile:\n", - " fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 't8', 't9', 't10']\n", + " with open('020725_README_file_topic_distributions.csv', 'w', newline='') as csvfile:\n", + " fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 't8']\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(11):\n", + " for j in range(9):\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(11):\n", + " for i in range(9):\n", " print(\"-----------------------Topic \" + str(i) + \" --------------------------------\")\n", " top5 = df.nlargest(10, i)\n", " top_indices = top5.index.to_list()\n", @@ -317,12 +303,12 @@ "metadata": {}, "outputs": [], "source": [ - "readme_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/readme/\"" + "readme_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/lagged_files/readme/\"" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "id": "1f937c2e-2714-475d-b670-602164c46642", "metadata": {}, "outputs": [ @@ -330,10 +316,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean wordcount: 271.6877796091359\n", - "Median wordcount: 98\n", - "Mean wordlength: 6.063122274716372\n", - "Median wordlength: 5.841269841269841\n" + "Mean wordcount: 324.0929957406531\n", + "Median wordcount: 156.0\n", + "Mean wordlength: 6.354120246310486\n", + "Median wordlength: 5.950514528900827\n" ] }, { @@ -346,7 +332,7 @@ } ], "source": [ - "listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(readme_directory)\n", + "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", @@ -356,17 +342,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "e90e236f-8db5-40cc-88a3-60e674b9d1de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['020325_README_vectorizer.joblib']" + "['020725_README_vectorizer.joblib']" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -381,24 +367,24 @@ " token_pattern='[a-zA-Z0-9]{2,}', \n", " )\n", "data_vectorized = vectorizer.fit_transform(lemmatized_corpus)\n", - "joblib.dump(vectorizer, '020325_README_vectorizer.joblib')\n", + "joblib.dump(vectorizer, '020725_README_vectorizer.joblib')\n", "'''\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "id": "d68aaf7b", "metadata": {}, "outputs": [], "source": [ - "vectorizer = joblib.load('020325_README_vectorizer.joblib')\n", + "vectorizer = joblib.load('020725_README_vectorizer.joblib')\n", "data_vectorized = vectorizer.transform(lemmatized_corpus) " ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "id": "dd1a70c2", "metadata": {}, "outputs": [ @@ -407,69 +393,69 @@ "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=11.........\n", - "[CV 1/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=11;, score=-1005863.489 total time= 10.2s\n", - "[CV 2/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=11.........\n", - "[CV 2/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=11;, score=-1011357.156 total time= 10.0s\n", - "[CV 3/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=11.........\n", - "[CV 3/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=11;, score=-1015386.424 total time= 10.0s\n", - "[CV 4/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=11.........\n", - "[CV 4/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=11;, score=-965023.515 total time= 10.3s\n", - "[CV 5/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=11.........\n", - "[CV 5/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=11;, score=-994223.612 total time= 9.9s\n", - "[CV 1/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=11.........\n", - "[CV 1/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=11;, score=-1006613.702 total time= 9.9s\n", - "[CV 2/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=11.........\n", - "[CV 2/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=11;, score=-1013817.544 total time= 9.9s\n", - "[CV 3/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=11.........\n", - "[CV 3/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=11;, score=-1015692.660 total time= 10.0s\n", - "[CV 4/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=11.........\n", - "[CV 4/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=11;, score=-966771.244 total time= 10.4s\n", - "[CV 5/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=11.........\n", - "[CV 5/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=11;, score=-995596.978 total time= 10.3s\n", - "[CV 1/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=11.........\n", - "[CV 1/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=11;, score=-1005180.172 total time= 10.0s\n", - "[CV 2/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=11.........\n", - "[CV 2/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=11;, score=-1015590.801 total time= 10.6s\n", - "[CV 3/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=11.........\n", - "[CV 3/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=11;, score=-1018907.455 total time= 11.1s\n", - "[CV 4/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=11.........\n", - "[CV 4/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=11;, score=-964714.891 total time= 14.5s\n", - "[CV 5/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=11.........\n", - "[CV 5/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=11;, score=-996172.263 total time= 13.9s\n", - "[CV 1/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=11.........\n", - "[CV 1/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=11;, score=-1006251.961 total time= 13.9s\n", - "[CV 2/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=11.........\n", - "[CV 2/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=11;, score=-1016869.369 total time= 14.0s\n", - "[CV 3/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=11.........\n", - "[CV 3/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=11;, score=-1017346.297 total time= 14.2s\n", - "[CV 4/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=11.........\n", - "[CV 4/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=11;, score=-964440.209 total time= 13.9s\n", - "[CV 5/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=11.........\n", - "[CV 5/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=11;, score=-997104.875 total time= 14.1s\n", - "[CV 1/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=11.........\n", - "[CV 1/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=11;, score=-1005428.337 total time= 13.9s\n", - "[CV 2/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=11.........\n", - "[CV 2/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=11;, score=-1013241.313 total time= 14.0s\n", - "[CV 3/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=11.........\n", - "[CV 3/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=11;, score=-1014764.423 total time= 13.6s\n", - "[CV 4/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=11.........\n", - "[CV 4/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=11;, score=-964830.614 total time= 14.3s\n", - "[CV 5/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=11.........\n", - "[CV 5/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=11;, score=-996764.609 total time= 14.0s\n", - "[CV 1/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=11.........\n", - "[CV 1/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=11;, score=-1004883.859 total time= 13.6s\n", - "[CV 2/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=11.........\n", - "[CV 2/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=11;, score=-1007656.712 total time= 13.5s\n", - "[CV 3/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=11.........\n", - "[CV 3/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=11;, score=-1015740.335 total time= 14.0s\n", - "[CV 4/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=11.........\n", - "[CV 4/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=11;, score=-966718.005 total time= 13.8s\n", - "[CV 5/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=11.........\n", - "[CV 5/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=11;, score=-996819.504 total time= 13.8s\n", - "Best Model's Params: {'batch_size': 256, 'learning_decay': 0.9, 'n_components': 11}\n", - "Best Log Likelihood Score: -998363.6828381404\n", - "Model Perplexity: 2076.905945051809\n" + "[CV 1/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=9..........\n", + "[CV 1/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=9;, score=-1158862.039 total time= 17.6s\n", + "[CV 2/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=9..........\n", + "[CV 2/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=9;, score=-1121276.805 total time= 12.0s\n", + "[CV 3/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=9..........\n", + "[CV 3/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=9;, score=-1058330.478 total time= 12.6s\n", + "[CV 4/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=9..........\n", + "[CV 4/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=9;, score=-1169073.807 total time= 12.7s\n", + "[CV 5/5; 1/6] START batch_size=128, learning_decay=0.5, n_components=9..........\n", + "[CV 5/5; 1/6] END batch_size=128, learning_decay=0.5, n_components=9;, score=-1308701.275 total time= 11.9s\n", + "[CV 1/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=9..........\n", + "[CV 1/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=9;, score=-1157991.152 total time= 11.8s\n", + "[CV 2/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=9..........\n", + "[CV 2/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=9;, score=-1120570.803 total time= 11.7s\n", + "[CV 3/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=9..........\n", + "[CV 3/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=9;, score=-1055699.316 total time= 12.4s\n", + "[CV 4/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=9..........\n", + "[CV 4/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=9;, score=-1168297.207 total time= 11.7s\n", + "[CV 5/5; 2/6] START batch_size=128, learning_decay=0.7, n_components=9..........\n", + "[CV 5/5; 2/6] END batch_size=128, learning_decay=0.7, n_components=9;, score=-1307949.520 total time= 12.4s\n", + "[CV 1/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=9..........\n", + "[CV 1/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=9;, score=-1157830.351 total time= 11.7s\n", + "[CV 2/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=9..........\n", + "[CV 2/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=9;, score=-1124221.589 total time= 11.8s\n", + "[CV 3/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=9..........\n", + "[CV 3/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=9;, score=-1056916.516 total time= 12.0s\n", + "[CV 4/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=9..........\n", + "[CV 4/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=9;, score=-1169168.331 total time= 12.9s\n", + "[CV 5/5; 3/6] START batch_size=128, learning_decay=0.9, n_components=9..........\n", + "[CV 5/5; 3/6] END batch_size=128, learning_decay=0.9, n_components=9;, score=-1308175.234 total time= 12.5s\n", + "[CV 1/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=9..........\n", + "[CV 1/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=9;, score=-1158540.475 total time= 12.2s\n", + "[CV 2/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=9..........\n", + "[CV 2/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=9;, score=-1120071.919 total time= 11.8s\n", + "[CV 3/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=9..........\n", + "[CV 3/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=9;, score=-1061777.082 total time= 12.3s\n", + "[CV 4/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=9..........\n", + "[CV 4/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=9;, score=-1170380.631 total time= 11.6s\n", + "[CV 5/5; 4/6] START batch_size=256, learning_decay=0.5, n_components=9..........\n", + "[CV 5/5; 4/6] END batch_size=256, learning_decay=0.5, n_components=9;, score=-1307034.410 total time= 11.6s\n", + "[CV 1/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=9..........\n", + "[CV 1/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=9;, score=-1156265.357 total time= 13.2s\n", + "[CV 2/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=9..........\n", + "[CV 2/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=9;, score=-1121786.140 total time= 11.8s\n", + "[CV 3/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=9..........\n", + "[CV 3/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=9;, score=-1060299.402 total time= 12.5s\n", + "[CV 4/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=9..........\n", + "[CV 4/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=9;, score=-1174913.458 total time= 12.1s\n", + "[CV 5/5; 5/6] START batch_size=256, learning_decay=0.7, n_components=9..........\n", + "[CV 5/5; 5/6] END batch_size=256, learning_decay=0.7, n_components=9;, score=-1305421.859 total time= 11.6s\n", + "[CV 1/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=9..........\n", + "[CV 1/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=9;, score=-1158280.857 total time= 12.0s\n", + "[CV 2/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=9..........\n", + "[CV 2/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=9;, score=-1117369.387 total time= 11.5s\n", + "[CV 3/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=9..........\n", + "[CV 3/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=9;, score=-1058230.568 total time= 12.0s\n", + "[CV 4/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=9..........\n", + "[CV 4/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=9;, score=-1171980.166 total time= 11.9s\n", + "[CV 5/5; 6/6] START batch_size=256, learning_decay=0.9, n_components=9..........\n", + "[CV 5/5; 6/6] END batch_size=256, learning_decay=0.9, n_components=9;, score=-1305822.528 total time= 11.3s\n", + "Best Model's Params: {'batch_size': 128, 'learning_decay': 0.7, 'n_components': 9}\n", + "Best Log Likelihood Score: -1162101.5996668166\n", + "Model Perplexity: 2176.5064559983784\n" ] } ], @@ -479,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "aa83d20f", "metadata": {}, "outputs": [ @@ -488,27 +474,23 @@ "output_type": "stream", "text": [ "Topic: 0\n", - " test, library, object, google, include, class, interface, using, build, example\n", + " image, data, key, file, color, option, support, format, default, mode\n", "Topic: 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