{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"BERTopic Probabilities vs LDA Probabilities: https://github.com/MaartenGr/BERTopic/issues/763 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"UsageError: Line magic function `%pip3` not found.\n"
]
}
],
"source": [
"%pip3 install -Uq sentence-transformers faiss-cpu accelerate hdbscan bertopic evaluate kaleido datasets>=2.11\n",
"%pip install bertopic"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification\n",
"from transformers import pipeline\n",
"import numpy as np\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bertopic import BERTopic\n",
"from bertopic.vectorizers import ClassTfidfTransformer\n",
"import plotly\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"model_name = 'sentence-transformers/all-MiniLM-L6-v2'\n",
"\n",
"docs = pd.read_csv(\"data/cisi-csv/docs.csv\")\n",
"queries = pd.read_csv(\"data/cisi-csv/queries.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of documents: 1460\n",
"Number of queries: 112\n"
]
}
],
"source": [
"print(\"Number of documents:\", len(docs))\n",
"print(\"Number of queries:\", len(queries))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def word_len(x):\n",
" return len(x.split(\" \"))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average length of documents in characters: 833.1465753424658\n",
"Min length of documents in characters: 70\n",
"Max length of documents in characters: 3886\n",
"Average length of documents in words: 127.7904109589041\n",
"Min length of documents in words: 10\n",
"Max length of documents in words: 567\n",
"Average length of queries in characters: 580.4642857142857\n",
"Min length of queries in characters: 38\n",
"Max length of queries in characters: 2178\n",
"Average length of queries in words: 89.10714285714286\n",
"Min length of queries in words: 4\n",
"Max length of queries in words: 362\n"
]
}
],
"source": [
"docs[\"length\"] = docs[\"text\"].apply(len)\n",
"docs[\"length_words\"] = docs[\"text\"].apply(word_len)\n",
"print(\"Average length of documents in characters:\", docs.loc[:, 'length'].mean())\n",
"print(\"Min length of documents in characters:\", docs.loc[:, 'length'].min())\n",
"print(\"Max length of documents in characters:\", docs.loc[:, 'length'].max())\n",
"print(\"Average length of documents in words:\", docs.loc[:, 'length_words'].mean())\n",
"print(\"Min length of documents in words:\", docs.loc[:, 'length_words'].min())\n",
"print(\"Max length of documents in words:\", docs.loc[:, 'length_words'].max())\n",
"queries[\"length\"] = queries[\"text\"].apply(len)\n",
"queries[\"length_words\"] = queries[\"text\"].apply(word_len)\n",
"print(\"Average length of queries in characters:\", queries.loc[:, 'length'].mean())\n",
"print(\"Min length of queries in characters:\", queries.loc[:, 'length'].min())\n",
"print(\"Max length of queries in characters:\", queries.loc[:, 'length'].max())\n",
"print(\"Average length of queries in words:\", queries.loc[:, 'length_words'].mean())\n",
"print(\"Min length of queries in words:\", queries.loc[:, 'length_words'].min())\n",
"print(\"Max length of queries in words:\", queries.loc[:, 'length_words'].max())"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"docs['length_words'].plot(kind='hist', bins=30, edgecolor='black', alpha=0.7)\n",
"plt.xlabel('Length')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Length of Documents (in words)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"queries['length_words'].plot(kind='hist', bins=30, edgecolor='black', alpha=0.7)\n",
"plt.xlabel('Length')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Length of Queries (in words)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Topic</th>\n",
" <th>Count</th>\n",
" <th>Name</th>\n",
" <th>Representation</th>\n",
" <th>Representative_Docs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>389</td>\n",
" <td>-1_literature_on_with_an</td>\n",
" <td>[literature, on, with, an, be, scientific, sub...</td>\n",
" <td>[Library Optimum Sir,-In his recent article B....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>259</td>\n",
" <td>0_libraries_library_university_academic</td>\n",
" <td>[libraries, library, university, academic, pub...</td>\n",
" <td>[Cooperation Between Types of Libraries This b...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>88</td>\n",
" <td>1_chemical_compounds_notation_search</td>\n",
" <td>[chemical, compounds, notation, search, ca, ti...</td>\n",
" <td>[Experiences of IIT Research Institute in Oper...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>82</td>\n",
" <td>2_information_science_theory_needs</td>\n",
" <td>[information, science, theory, needs, flow, in...</td>\n",
" <td>[Science and Information Theory A new scientif...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>63</td>\n",
" <td>3_automatic_indexing_index_terms</td>\n",
" <td>[automatic, indexing, index, terms, document, ...</td>\n",
" <td>[What Makes An Automatic Keyword Classificatio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4</td>\n",
" <td>61</td>\n",
" <td>4_social_psychology_sociology_science</td>\n",
" <td>[social, psychology, sociology, science, behav...</td>\n",
" <td>[Is a Scientific Revolution Taking Place in Ps...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5</td>\n",
" <td>45</td>\n",
" <td>5_relevance_answer_relevant_retrieval</td>\n",
" <td>[relevance, answer, relevant, retrieval, docum...</td>\n",
" <td>[On Relevance, Probabilistic Indexing and Info...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6</td>\n",
" <td>39</td>\n",
" <td>6_bases_data_bibliographic_line</td>\n",
" <td>[bases, data, bibliographic, line, readable, s...</td>\n",
" <td>[Survey of Commercially Available Computer-Rea...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7</td>\n",
" <td>37</td>\n",
" <td>7_citation_citations_papers_citing</td>\n",
" <td>[citation, citations, papers, citing, cross, s...</td>\n",
" <td>[Improvement of the Selectivity of Citation In...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>8_catalog_catalogs_card_cataloging</td>\n",
" <td>[catalog, catalogs, card, cataloging, divided,...</td>\n",
" <td>[The Recording of Library of Congress Bibliogr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9</td>\n",
" <td>29</td>\n",
" <td>9_communication_informal_scientists_social</td>\n",
" <td>[communication, informal, scientists, social, ...</td>\n",
" <td>[Research Studies in Patterns of Scientific Co...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10</td>\n",
" <td>28</td>\n",
" <td>10_journals_coverage_journal_articles</td>\n",
" <td>[journals, coverage, journal, articles, period...</td>\n",
" <td>[Citation Patterns fo the Cardiovascular Seria...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11</td>\n",
" <td>26</td>\n",
" <td>11_language_linguistics_linguistic_text</td>\n",
" <td>[language, linguistics, linguistic, text, sema...</td>\n",
" <td>[Functional Approach The present book sums up ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12_medical_health_hospital_manpower</td>\n",
" <td>[medical, health, hospital, manpower, hospital...</td>\n",
" <td>[Library Practice in Hospitals According to a ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>13</td>\n",
" <td>24</td>\n",
" <td>13_retrieval_user_interactive_system</td>\n",
" <td>[retrieval, user, interactive, system, systems...</td>\n",
" <td>[Human Factors in the Design of an Interactive...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14</td>\n",
" <td>21</td>\n",
" <td>14_classification_decimal_dewey_schemes</td>\n",
" <td>[classification, decimal, dewey, schemes, udc,...</td>\n",
" <td>[Adopting the Library of Congress Classificati...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>15</td>\n",
" <td>21</td>\n",
" <td>15_automation_telefacsimile_processing_automated</td>\n",
" <td>[automation, telefacsimile, processing, automa...</td>\n",
" <td>[Application of Computer Technology to Library...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" <td>16_fuzzy_sets_classification_hedge</td>\n",
" <td>[fuzzy, sets, classification, hedge, membershi...</td>\n",
" <td>[Prospects for a New General Classification In...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>17</td>\n",
" <td>17</td>\n",
" <td>17_bradford_law_references_zipf</td>\n",
" <td>[bradford, law, references, zipf, distribution...</td>\n",
" <td>[Progress in Documentation Empirical Hyperboli...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>18</td>\n",
" <td>17</td>\n",
" <td>18_evaluation_costs_cost_scale</td>\n",
" <td>[evaluation, costs, cost, scale, appraisal, sy...</td>\n",
" <td>[Standard Costing for Information Systems: Bac...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>19</td>\n",
" <td>16</td>\n",
" <td>19_centers_services_annual_systems</td>\n",
" <td>[centers, services, annual, systems, informati...</td>\n",
" <td>[Annual Review of Information Science and Tech...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>20_serials_isbd_international_entry</td>\n",
" <td>[serials, isbd, international, entry, rules, s...</td>\n",
" <td>[No Special Rules for Entry of Serials One of ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>21_thesaurus_thesauri_construction_vocabulary</td>\n",
" <td>[thesaurus, thesauri, construction, vocabulary...</td>\n",
" <td>[Automatic Construction of Thesauri and of Con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>22</td>\n",
" <td>15</td>\n",
" <td>22_microfiche_microforms_microform_microfilm</td>\n",
" <td>[microfiche, microforms, microform, microfilm,...</td>\n",
" <td>[The Microform Revolution Librarians have trie...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>23</td>\n",
" <td>14</td>\n",
" <td>23_medlars_medline_twx_medicus</td>\n",
" <td>[medlars, medline, twx, medicus, medicine, nlm...</td>\n",
" <td>[MEDLARS: A Summary Review and Evaluation of T...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>24</td>\n",
" <td>14</td>\n",
" <td>24_network_networks_cable_television</td>\n",
" <td>[network, networks, cable, television, implies...</td>\n",
" <td>[State of the Nation in Networking There is li...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>25</td>\n",
" <td>14</td>\n",
" <td>25_compression_length_coding_grams</td>\n",
" <td>[compression, length, coding, grams, character...</td>\n",
" <td>[An Information-Theoretic Approach to Text Sea...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>26</td>\n",
" <td>13</td>\n",
" <td>26_organizations_organizational_business_enter...</td>\n",
" <td>[organizations, organizational, business, ente...</td>\n",
" <td>[Principles of Operations Research with Applic...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>27</td>\n",
" <td>11</td>\n",
" <td>27_physics_viniti_journal_museum</td>\n",
" <td>[physics, viniti, journal, museum, abstract, m...</td>\n",
" <td>[Tests on Abstracts Journals The amount of sci...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>28</td>\n",
" <td>11</td>\n",
" <td>28_marc_records_pilot_cobol</td>\n",
" <td>[marc, records, pilot, cobol, ii, readable, re...</td>\n",
" <td>[The Marc II Format: A ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Topic Count Name \\\n",
"0 -1 389 -1_literature_on_with_an \n",
"1 0 259 0_libraries_library_university_academic \n",
"2 1 88 1_chemical_compounds_notation_search \n",
"3 2 82 2_information_science_theory_needs \n",
"4 3 63 3_automatic_indexing_index_terms \n",
"5 4 61 4_social_psychology_sociology_science \n",
"6 5 45 5_relevance_answer_relevant_retrieval \n",
"7 6 39 6_bases_data_bibliographic_line \n",
"8 7 37 7_citation_citations_papers_citing \n",
"9 8 32 8_catalog_catalogs_card_cataloging \n",
"10 9 29 9_communication_informal_scientists_social \n",
"11 10 28 10_journals_coverage_journal_articles \n",
"12 11 26 11_language_linguistics_linguistic_text \n",
"13 12 25 12_medical_health_hospital_manpower \n",
"14 13 24 13_retrieval_user_interactive_system \n",
"15 14 21 14_classification_decimal_dewey_schemes \n",
"16 15 21 15_automation_telefacsimile_processing_automated \n",
"17 16 18 16_fuzzy_sets_classification_hedge \n",
"18 17 17 17_bradford_law_references_zipf \n",
"19 18 17 18_evaluation_costs_cost_scale \n",
"20 19 16 19_centers_services_annual_systems \n",
"21 20 16 20_serials_isbd_international_entry \n",
"22 21 15 21_thesaurus_thesauri_construction_vocabulary \n",
"23 22 15 22_microfiche_microforms_microform_microfilm \n",
"24 23 14 23_medlars_medline_twx_medicus \n",
"25 24 14 24_network_networks_cable_television \n",
"26 25 14 25_compression_length_coding_grams \n",
"27 26 13 26_organizations_organizational_business_enter... \n",
"28 27 11 27_physics_viniti_journal_museum \n",
"29 28 11 28_marc_records_pilot_cobol \n",
"\n",
" Representation \\\n",
"0 [literature, on, with, an, be, scientific, sub... \n",
"1 [libraries, library, university, academic, pub... \n",
"2 [chemical, compounds, notation, search, ca, ti... \n",
"3 [information, science, theory, needs, flow, in... \n",
"4 [automatic, indexing, index, terms, document, ... \n",
"5 [social, psychology, sociology, science, behav... \n",
"6 [relevance, answer, relevant, retrieval, docum... \n",
"7 [bases, data, bibliographic, line, readable, s... \n",
"8 [citation, citations, papers, citing, cross, s... \n",
"9 [catalog, catalogs, card, cataloging, divided,... \n",
"10 [communication, informal, scientists, social, ... \n",
"11 [journals, coverage, journal, articles, period... \n",
"12 [language, linguistics, linguistic, text, sema... \n",
"13 [medical, health, hospital, manpower, hospital... \n",
"14 [retrieval, user, interactive, system, systems... \n",
"15 [classification, decimal, dewey, schemes, udc,... \n",
"16 [automation, telefacsimile, processing, automa... \n",
"17 [fuzzy, sets, classification, hedge, membershi... \n",
"18 [bradford, law, references, zipf, distribution... \n",
"19 [evaluation, costs, cost, scale, appraisal, sy... \n",
"20 [centers, services, annual, systems, informati... \n",
"21 [serials, isbd, international, entry, rules, s... \n",
"22 [thesaurus, thesauri, construction, vocabulary... \n",
"23 [microfiche, microforms, microform, microfilm,... \n",
"24 [medlars, medline, twx, medicus, medicine, nlm... \n",
"25 [network, networks, cable, television, implies... \n",
"26 [compression, length, coding, grams, character... \n",
"27 [organizations, organizational, business, ente... \n",
"28 [physics, viniti, journal, museum, abstract, m... \n",
"29 [marc, records, pilot, cobol, ii, readable, re... \n",
"\n",
" Representative_Docs \n",
"0 [Library Optimum Sir,-In his recent article B.... \n",
"1 [Cooperation Between Types of Libraries This b... \n",
"2 [Experiences of IIT Research Institute in Oper... \n",
"3 [Science and Information Theory A new scientif... \n",
"4 [What Makes An Automatic Keyword Classificatio... \n",
"5 [Is a Scientific Revolution Taking Place in Ps... \n",
"6 [On Relevance, Probabilistic Indexing and Info... \n",
"7 [Survey of Commercially Available Computer-Rea... \n",
"8 [Improvement of the Selectivity of Citation In... \n",
"9 [The Recording of Library of Congress Bibliogr... \n",
"10 [Research Studies in Patterns of Scientific Co... \n",
"11 [Citation Patterns fo the Cardiovascular Seria... \n",
"12 [Functional Approach The present book sums up ... \n",
"13 [Library Practice in Hospitals According to a ... \n",
"14 [Human Factors in the Design of an Interactive... \n",
"15 [Adopting the Library of Congress Classificati... \n",
"16 [Application of Computer Technology to Library... \n",
"17 [Prospects for a New General Classification In... \n",
"18 [Progress in Documentation Empirical Hyperboli... \n",
"19 [Standard Costing for Information Systems: Bac... \n",
"20 [Annual Review of Information Science and Tech... \n",
"21 [No Special Rules for Entry of Serials One of ... \n",
"22 [Automatic Construction of Thesauri and of Con... \n",
"23 [The Microform Revolution Librarians have trie... \n",
"24 [MEDLARS: A Summary Review and Evaluation of T... \n",
"25 [State of the Nation in Networking There is li... \n",
"26 [An Information-Theoretic Approach to Text Sea... \n",
"27 [Principles of Operations Research with Applic... \n",
"28 [Tests on Abstracts Journals The amount of sci... \n",
"29 [The Marc II Format: A ... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"docs_for_analysis = docs[\"text\"]\n",
"\n",
"# topic_model = BERTopic(embedding_model=model_name, ctfidf_model=ClassTfidfTransformer(reduce_frequent_words=True), nr_topics=\"auto\", calculate_probabilities=True)\n",
"# topic_model = BERTopic(embedding_model=model_name, ctfidf_model=ClassTfidfTransformer(reduce_frequent_words=True), calculate_probabilities=True)\n",
"topic_model = BERTopic(embedding_model=model_name, ctfidf_model=ClassTfidfTransformer(reduce_frequent_words=True))\n",
"topic_model.fit(docs_for_analysis)\n",
"topic_info = topic_model.get_topic_info()\n",
"topic_info\n",
"\n",
"# Topic \"-1\": When using HDBSCAN, DBSCAN, or OPTICS, a number of outlier documents might be created\n",
"# that do not fall within any of the created topics. These are labeled as -1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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{
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"execution_count": 10,
"metadata": {},
"outputs": [
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"name": "stdout",
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"text": [
"What problems and concerns are there in making up descriptive titles? What difficulties are involved in automatically retrieving articles from approximate titles? What is the usual relevance of the content of articles to their titles?\n"
]
},
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"data": {
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"execution_count": 10,
"metadata": {},
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"source": [
"# returns: Topic predictions for each documents probabilities: The topic probability distribution \n",
"# which is returned by default. If calculate_probabilities in BERTopic is set to False, then the \n",
"# probabilities are not calculated to speed up computation and decrease memory usage.\n",
"print(queries.iloc[0][\"text\"])\n",
"topic_model.transform(queries.iloc[0][\"text\"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
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},
"execution_count": 11,
"metadata": {},
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],
"source": [
"all_query_topics = topic_model.transform(queries[\"text\"])\n",
"all_query_topics"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([-1, 20, 5, 3, 14],\n",
" [0.42508858, 0.40581542, 0.40525758, 0.39575997, 0.39218175])"
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},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
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"source": [
"topic_model.find_topics(queries.iloc[0][\"text\"], top_n=5)\n",
"# topics, similarity = topic_model.find_topics(queries.iloc[0][\"text\"], top_n=5)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
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"output_type": "stream",
"text": [
"Query Topics: {1: -1, 2: 6, 3: 2, 4: 25, 5: 13, 6: 11, 7: -1, 8: 3, 9: 3, 10: 5, 11: -1, 12: 27, 13: 5, 14: 23, 15: 6, 16: 13, 17: 13, 18: 1, 19: 25, 20: 18, 21: 2, 22: 23, 23: 15, 24: 2, 25: 19, 26: 18, 27: 3, 28: 1, 29: 3, 30: 27, 31: 2, 32: 3, 33: 13, 34: 25, 35: 19, 36: 11, 37: 3, 38: 3, 39: 13, 40: 11, 41: 3, 42: 13, 43: 13, 44: 27, 45: 15, 46: 15, 47: -1, 48: 13, 49: 13, 50: 14, 51: 3, 52: 23, 53: 23, 54: 15, 55: 23, 56: 8, 57: 20, 58: 24, 59: 5, 60: 5, 61: 5, 62: 5, 63: 13, 64: 2, 65: 3, 66: 24, 67: 1, 68: 3, 69: 21, 70: 18, 71: 3, 72: 5, 73: 25, 74: 24, 75: 11, 76: 24, 77: 3, 78: 11, 79: 25, 80: 17, 81: 21, 82: 5, 83: 19, 84: 5, 85: 11, 86: 25, 87: 6, 88: 13, 89: 0, 90: 7, 91: 7, 92: 6, 93: 3, 94: 13, 95: 5, 96: 5, 97: 5, 98: 13, 99: 13, 100: 13, 101: 13, 102: 3, 103: 6, 104: 5, 105: 3, 106: 3, 107: 3, 108: 6, 109: 7, 110: -1, 111: 3, 112: 3}\n"
]
}
],
"source": [
"# transform queries to topics\n",
"query_topics = {}\n",
"query_topics_with_probs = {}\n",
"\n",
"for index, row in queries.iterrows():\n",
" query_id = row[0]\n",
" topics, probs = topic_model.find_topics(row[\"text\"], top_n=5)\n",
" query_topics[query_id] = topics[0]\n",
" query_topics_with_probs[query_id] = topic_model.find_topics(row[\"text\"], top_n=5)\n",
"print(\"Query Topics:\", query_topics)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1 5\n",
"0 1\n",
"1 3\n",
"2 5\n",
"3 20\n",
"4 0\n",
"5 13\n",
"6 6\n",
"7 3\n"
]
}
],
"source": [
"print(\"-1\", list(query_topics.values()).count(-1))\n",
"print(\"0\", list(query_topics.values()).count(0))\n",
"print(\"1\", list(query_topics.values()).count(1))\n",
"print(\"2\", list(query_topics.values()).count(2))\n",
"print(\"3\", list(query_topics.values()).count(3))\n",
"print(\"4\", list(query_topics.values()).count(4))\n",
"print(\"5\", list(query_topics.values()).count(5))\n",
"print(\"6\", list(query_topics.values()).count(6))\n",
"print(\"7\", list(query_topics.values()).count(7))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Document</th>\n",
" <th>Topic</th>\n",
" <th>Name</th>\n",
" <th>Representation</th>\n",
" <th>Representative_Docs</th>\n",
" <th>Top_n_words</th>\n",
" <th>Probability</th>\n",
" <th>Representative_document</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18 Editions of the Dewey Decimal Classificatio...</td>\n",
" <td>14</td>\n",
" <td>14_classification_decimal_dewey_schemes</td>\n",
" <td>[classification, decimal, dewey, schemes, udc,...</td>\n",
" <td>[Adopting the Library of Congress Classificati...</td>\n",
" <td>classification - decimal - dewey - schemes - u...</td>\n",
" <td>0.741910</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Use Made of Technical Libraries This report is...</td>\n",
" <td>-1</td>\n",
" <td>-1_literature_on_with_an</td>\n",
" <td>[literature, on, with, an, be, scientific, sub...</td>\n",
" <td>[Library Optimum Sir,-In his recent article B....</td>\n",
" <td>literature - on - with - an - be - scientific ...</td>\n",
" <td>0.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Two Kinds of Power An Essay on Bibliographic C...</td>\n",
" <td>-1</td>\n",
" <td>-1_literature_on_with_an</td>\n",
" <td>[literature, on, with, an, be, scientific, sub...</td>\n",
" <td>[Library Optimum Sir,-In his recent article B....</td>\n",
" <td>literature - on - with - an - be - scientific ...</td>\n",
" <td>0.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Systems Analysis of a University Library; fina...</td>\n",
" <td>0</td>\n",
" <td>0_libraries_library_university_academic</td>\n",
" <td>[libraries, library, university, academic, pub...</td>\n",
" <td>[Cooperation Between Types of Libraries This b...</td>\n",
" <td>libraries - library - university - academic - ...</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A Library Management Game: a report on a resea...</td>\n",
" <td>15</td>\n",
" <td>15_automation_telefacsimile_processing_automated</td>\n",
" <td>[automation, telefacsimile, processing, automa...</td>\n",
" <td>[Application of Computer Technology to Library...</td>\n",
" <td>automation - telefacsimile - processing - auto...</td>\n",
" <td>0.487765</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>World Dynamics Over the last several decades i...</td>\n",
" <td>4</td>\n",
" <td>4_social_psychology_sociology_science</td>\n",
" <td>[social, psychology, sociology, science, behav...</td>\n",
" <td>[Is a Scientific Revolution Taking Place in Ps...</td>\n",
" <td>social - psychology - sociology - science - be...</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>World Trends in Library Education One of the m...</td>\n",
" <td>0</td>\n",
" <td>0_libraries_library_university_academic</td>\n",
" <td>[libraries, library, university, academic, pub...</td>\n",
" <td>[Cooperation Between Types of Libraries This b...</td>\n",
" <td>libraries - library - university - academic - ...</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>Legal Restrictions on Exploitation of the Pate...</td>\n",
" <td>-1</td>\n",
" <td>-1_literature_on_with_an</td>\n",
" <td>[literature, on, with, an, be, scientific, sub...</td>\n",
" <td>[Library Optimum Sir,-In his recent article B....</td>\n",
" <td>literature - on - with - an - be - scientific ...</td>\n",
" <td>0.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>Language and Thought This book considers the b...</td>\n",
" <td>11</td>\n",
" <td>11_language_linguistics_linguistic_text</td>\n",
" <td>[language, linguistics, linguistic, text, sema...</td>\n",
" <td>[Functional Approach The present book sums up ...</td>\n",
" <td>language - linguistics - linguistic - text - s...</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>Modern Integral Information Systems for Chemis...</td>\n",
" <td>1</td>\n",
" <td>1_chemical_compounds_notation_search</td>\n",
" <td>[chemical, compounds, notation, search, ca, ti...</td>\n",
" <td>[Experiences of IIT Research Institute in Oper...</td>\n",
" <td>chemical - compounds - notation - search - ca ...</td>\n",
" <td>0.271702</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Document Topic \\\n",
"0 18 Editions of the Dewey Decimal Classificatio... 14 \n",
"1 Use Made of Technical Libraries This report is... -1 \n",
"2 Two Kinds of Power An Essay on Bibliographic C... -1 \n",
"3 Systems Analysis of a University Library; fina... 0 \n",
"4 A Library Management Game: a report on a resea... 15 \n",
"... ... ... \n",
"1455 World Dynamics Over the last several decades i... 4 \n",
"1456 World Trends in Library Education One of the m... 0 \n",
"1457 Legal Restrictions on Exploitation of the Pate... -1 \n",
"1458 Language and Thought This book considers the b... 11 \n",
"1459 Modern Integral Information Systems for Chemis... 1 \n",
"\n",
" Name \\\n",
"0 14_classification_decimal_dewey_schemes \n",
"1 -1_literature_on_with_an \n",
"2 -1_literature_on_with_an \n",
"3 0_libraries_library_university_academic \n",
"4 15_automation_telefacsimile_processing_automated \n",
"... ... \n",
"1455 4_social_psychology_sociology_science \n",
"1456 0_libraries_library_university_academic \n",
"1457 -1_literature_on_with_an \n",
"1458 11_language_linguistics_linguistic_text \n",
"1459 1_chemical_compounds_notation_search \n",
"\n",
" Representation \\\n",
"0 [classification, decimal, dewey, schemes, udc,... \n",
"1 [literature, on, with, an, be, scientific, sub... \n",
"2 [literature, on, with, an, be, scientific, sub... \n",
"3 [libraries, library, university, academic, pub... \n",
"4 [automation, telefacsimile, processing, automa... \n",
"... ... \n",
"1455 [social, psychology, sociology, science, behav... \n",
"1456 [libraries, library, university, academic, pub... \n",
"1457 [literature, on, with, an, be, scientific, sub... \n",
"1458 [language, linguistics, linguistic, text, sema... \n",
"1459 [chemical, compounds, notation, search, ca, ti... \n",
"\n",
" Representative_Docs \\\n",
"0 [Adopting the Library of Congress Classificati... \n",
"1 [Library Optimum Sir,-In his recent article B.... \n",
"2 [Library Optimum Sir,-In his recent article B.... \n",
"3 [Cooperation Between Types of Libraries This b... \n",
"4 [Application of Computer Technology to Library... \n",
"... ... \n",
"1455 [Is a Scientific Revolution Taking Place in Ps... \n",
"1456 [Cooperation Between Types of Libraries This b... \n",
"1457 [Library Optimum Sir,-In his recent article B.... \n",
"1458 [Functional Approach The present book sums up ... \n",
"1459 [Experiences of IIT Research Institute in Oper... \n",
"\n",
" Top_n_words Probability \\\n",
"0 classification - decimal - dewey - schemes - u... 0.741910 \n",
"1 literature - on - with - an - be - scientific ... 0.000000 \n",
"2 literature - on - with - an - be - scientific ... 0.000000 \n",
"3 libraries - library - university - academic - ... 1.000000 \n",
"4 automation - telefacsimile - processing - auto... 0.487765 \n",
"... ... ... \n",
"1455 social - psychology - sociology - science - be... 1.000000 \n",
"1456 libraries - library - university - academic - ... 1.000000 \n",
"1457 literature - on - with - an - be - scientific ... 0.000000 \n",
"1458 language - linguistics - linguistic - text - s... 1.000000 \n",
"1459 chemical - compounds - notation - search - ca ... 0.271702 \n",
"\n",
" Representative_document \n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False \n",
"... ... \n",
"1455 False \n",
"1456 False \n",
"1457 False \n",
"1458 False \n",
"1459 False \n",
"\n",
"[1460 rows x 8 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc_info = topic_model.get_document_info(docs[\"text\"])\n",
"doc_info"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{1: ([-1, 20, 5, 3, 14], [0.42508858, 0.40581542, 0.40525758, 0.39575997, 0.39218175]), 2: ([6, 5, -1, 13, 2], [0.5578636, 0.54351616, 0.5202661, 0.47465622, 0.46867073]), 3: ([2, 19, 18, 9, -1], [0.7133019, 0.564875, 0.54921937, 0.45864588, 0.43095225]), 4: ([25, 1, 22, 15, 28], [0.39171955, 0.36059082, 0.35910475, 0.3572228, 0.34390008]), 5: ([13, 6, -1, 19, 2], [0.64703333, 0.5929646, 0.59010124, 0.58086085, 0.57909113]), 6: ([11, 9, 21, 2, 13], [0.44849384, 0.4383688, 0.34508163, 0.32840043, 0.27865595]), 7: ([-1, 6, 19, 13, 27], [0.58543944, 0.55294424, 0.5477898, 0.5403626, 0.5302058]), 8: ([3, -1, 5, 13, 11], [0.7271405, 0.67743355, 0.67520845, 0.60538036, 0.60110706]), 9: ([3, 5, -1, 11, 21], [0.64982533, 0.6252445, 0.57588446, 0.57279235, 0.55609095]), 10: ([5, -1, 16, 13, 2], [0.5014358, 0.47784606, 0.4759782, 0.45857334, 0.4403532]), 11: ([-1, 2, 5, 7, 6], [0.59934133, 0.56825256, 0.5672239, 0.5394591, 0.5232169]), 12: ([27, 10, 7, -1, 22], [0.66234237, 0.5415038, 0.51516885, 0.5111377, 0.46366423]), 13: ([5, 13, -1, 19, 18], [0.6804598, 0.66106427, 0.6306013, 0.59457284, 0.58618534]), 14: ([23, 12, -1, 10, 27], [0.394485, 0.28473017, 0.28226718, 0.27444452, 0.258744]), 15: ([6, -1, 15, 0, 19], [0.65373707, 0.6073693, 0.60587513, 0.605869, 0.55832195]), 16: ([13, 6, 19, -1, 15], [0.66722643, 0.6369458, 0.59328675, 0.5707141, 0.5344864]), 17: ([13, 5, 6, -1, 18], [0.65762126, 0.6139238, 0.60728157, 0.58866525, 0.54790837]), 18: ([1, 25, 16, 11, 28], [0.50468534, 0.3250762, 0.30392936, 0.24357203, 0.23400743]), 19: ([25, 1, 3, 13, 6], [0.55194914, 0.48832184, 0.45208088, 0.44120616, 0.4190611]), 20: ([18, 19, 15, 13, 2], [0.557827, 0.43977207, 0.43789688, 0.43548745, 0.35338974]), 21: ([2, 19, 18, -1, 6], [0.6691359, 0.60644364, 0.5490931, 0.50098073, 0.49998498]), 22: ([23, -1, 2, 3, 12], [0.57611, 0.45073602, 0.4504422, 0.41900182, 0.41770333]), 23: ([15, 0, 19, -1, 6], [0.7930567, 0.7465304, 0.679661, 0.6681056, 0.6579131]), 24: ([2, 19, 18, 13, 24], [0.60151064, 0.55189604, 0.5194111, 0.44959784, 0.42051235]), 25: ([19, 2, -1, 24, 18], [0.5265802, 0.4686581, 0.44991255, 0.43844196, 0.43669844]), 26: ([18, 13, 15, 2, 19], [0.69062257, 0.5640918, 0.50924456, 0.4998741, 0.49338037]), 27: ([3, 13, 5, 6, -1], [0.7216635, 0.7042624, 0.6044115, 0.59886986, 0.5840592]), 28: ([1, 6, -1, 19, 2], [0.6556672, 0.5127008, 0.4916587, 0.49149424, 0.48611024]), 29: ([3, 13, 6, -1, 5], [0.6038191, 0.5734424, 0.5320734, 0.5162424, 0.5033302]), 30: ([27, -1, 10, 2, 19], [0.6936959, 0.61174935, 0.601683, 0.566511, 0.5466056]), 31: ([2, 19, 18, 9, 27], [0.5819382, 0.54143655, 0.49397776, 0.48814607, 0.48383033]), 32: ([3, -1, 6, 1, 28], [0.65872884, 0.635707, 0.608444, 0.5829582, 0.5752769]), 33: ([13, 5, 6, 3, 25], [0.7292961, 0.60775256, 0.5884522, 0.5407063, 0.53832656]), 34: ([25, 3, 1, 13, -1], [0.637954, 0.5884554, 0.49361923, 0.46198586, 0.41789788]), 35: ([19, 6, 24, 2, -1], [0.57167906, 0.47881758, 0.47715357, 0.47672853, 0.46109694]), 36: ([11, 21, -1, 19, 3], [0.5342965, 0.3540604, 0.31396708, 0.30614287, 0.28725663]), 37: ([3, 21, 14, 1, -1], [0.66179967, 0.6216998, 0.5244014, 0.52273166, 0.5224961]), 38: ([3, 21, 13, -1, 5], [0.64652365, 0.64499676, 0.63917685, 0.6109181, 0.5972434]), 39: ([13, 19, 6, 15, -1], [0.6811728, 0.6171901, 0.5795578, 0.57925373, 0.5643325]), 40: ([11, 15, 28, 19, -1], [0.4830264, 0.4367027, 0.43307197, 0.42501056, 0.42436323]), 41: ([3, -1, 14, 8, 1], [0.50050914, 0.4963848, 0.49332908, 0.4827861, 0.47361302]), 42: ([13, 5, -1, 19, 6], [0.7165978, 0.6552596, 0.63915944, 0.61551917, 0.6041337]), 43: ([13, -1, 5, 3, 18], [0.7352257, 0.6764289, 0.6328855, 0.60559034, 0.58686656]), 44: ([27, 10, -1, 7, 17], [0.6888843, 0.62323713, 0.5650345, 0.5245141, 0.47283512]), 45: ([15, 0, 19, -1, 12], [0.65599155, 0.4443518, 0.34762046, 0.3418584, 0.3243569]), 46: ([15, 6, 0, 28, 19], [0.75873804, 0.53309697, 0.5304099, 0.5202215, 0.5133062]), 47: ([-1, 6, 19, 13, 5], [0.5350419, 0.50903296, 0.50136626, 0.49770844, 0.4896045]), 48: ([13, 5, 6, -1, 19], [0.57852226, 0.5445132, 0.5434073, 0.5272524, 0.50155413]), 49: ([13, -1, 5, 6, 2], [0.66542816, 0.63717955, 0.6197554, 0.6055248, 0.5629763]), 50: ([14, 13, 5, 3, 16], [0.5940044, 0.5292071, 0.52515036, 0.5050933, 0.49033603]), 51: ([3, 5, 13, 1, 25], [0.5697411, 0.4871588, 0.48252225, 0.47071904, 0.45116085]), 52: ([23, 10, 12, -1, 7], [0.79661906, 0.5908638, 0.54812264, 0.5466073, 0.48742852]), 53: ([23, 15, -1, 6, 19], [0.5235566, 0.49062318, 0.4245513, 0.423828, 0.4199304]), 54: ([15, 0, 19, -1, 6], [0.7263851, 0.577638, 0.5167371, 0.51661485, 0.47870922]), 55: ([23, -1, 10, 5, 3], [0.7851007, 0.54155165, 0.50885713, 0.48478395, 0.46920654]), 56: ([8, 14, 1, 3, -1], [0.692508, 0.6453519, 0.5852873, 0.5707865, 0.55951554]), 57: ([20, 14, 8, 3, 28], [0.5258478, 0.5169902, 0.50255233, 0.49264035, 0.47976863]), 58: ([24, 15, 19, 0, 6], [0.7765574, 0.6140851, 0.5888598, 0.55736446, 0.53679776]), 59: ([5, 3, 13, -1, 6], [0.7858389, 0.7399318, 0.6759767, 0.6443262, 0.5526908]), 60: ([5, 7, 3, -1, 6], [0.68206596, 0.6210891, 0.6093588, 0.55330384, 0.5320042]), 61: ([5, 13, 3, -1, 6], [0.71731764, 0.6623226, 0.6215801, 0.58288157, 0.57490253]), 62: ([5, 3, 16, 13, -1], [0.59848094, 0.558551, 0.5418923, 0.5328593, 0.44477707]), 63: ([13, 3, 6, 5, -1], [0.7733216, 0.6852923, 0.67714024, 0.6762121, 0.63050437]), 64: ([2, 18, -1, 19, 5], [0.6221134, 0.46013168, 0.4596297, 0.45897162, 0.43903035]), 65: ([3, 5, 13, -1, 6], [0.65923905, 0.6097661, 0.57920605, 0.5002142, 0.49116856]), 66: ([24, 19, 6, 0, 15], [0.84010744, 0.6925629, 0.6767347, 0.6234027, 0.62311643]), 67: ([1, 3, 21, -1, 5], [0.6935794, 0.68932253, 0.6536973, 0.59040225, 0.5753021]), 68: ([3, 5, 25, -1, 7], [0.7354659, 0.6835431, 0.5855718, 0.58312124, 0.5477498]), 69: ([21, 3, -1, 1, 13], [0.76007116, 0.59630394, 0.56643784, 0.5219722, 0.50971353]), 70: ([18, 2, 13, 19, -1], [0.59311163, 0.519495, 0.51878923, 0.5115966, 0.45841444]), 71: ([3, -1, 21, 5, 1], [0.71684957, 0.6713434, 0.6336597, 0.60011536, 0.5873736]), 72: ([5, 3, -1, 13, 6], [0.7219941, 0.58950275, 0.56725645, 0.5431076, 0.52340853]), 73: ([25, 3, 1, 5, 6], [0.70677423, 0.5736163, 0.51880807, 0.498132, 0.49795717]), 74: ([24, 0, 19, 6, 15], [0.7587775, 0.59496886, 0.55387163, 0.5441983, 0.5205356]), 75: ([11, 21, 16, -1, 5], [0.6377183, 0.35087854, 0.3437996, 0.31165475, 0.29002064]), 76: ([24, 0, 19, 15, 6], [0.7386719, 0.69045925, 0.5792686, 0.56098914, 0.54014987]), 77: ([3, 5, 25, 1, 21], [0.659086, 0.60165024, 0.57904446, 0.5477652, 0.53125393]), 78: ([11, 21, 16, 3, -1], [0.6086948, 0.41993862, 0.40980315, 0.2869692, 0.28065425]), 79: ([25, 3, 5, 13, 1], [0.6384877, 0.5772792, 0.52260494, 0.47974944, 0.4618316]), 80: ([17, 10, 7, 27, -1], [0.6607008, 0.46085802, 0.44260493, 0.42012513, 0.40370655]), 81: ([21, 13, 25, 11, 3], [0.5969976, 0.5061964, 0.49686044, 0.4905312, 0.4867716]), 82: ([5, 13, 6, 3, -1], [0.58330023, 0.5655685, 0.53061336, 0.49870867, 0.48886782]), 83: ([19, 15, 13, 6, 28], [0.59379804, 0.5576351, 0.50826627, 0.5056901, 0.47991803]), 84: ([5, 3, 13, -1, 6], [0.81649184, 0.7189138, 0.67844373, 0.5948365, 0.53950953]), 85: ([11, 21, 3, 16, 13], [0.60725665, 0.49742848, 0.44739795, 0.42205644, 0.40310383]), 86: ([25, 3, 1, 5, 21], [0.5579259, 0.48615286, 0.43963972, 0.40077454, 0.37448668]), 87: ([6, 22, 28, 8, -1], [0.6848248, 0.6714087, 0.5774374, 0.5734037, 0.5719103]), 88: ([13, 11, 5, 6, 3], [0.5971294, 0.524037, 0.51919204, 0.48642057, 0.4809844]), 89: ([0, 6, -1, 18, 19], [0.68423676, 0.6241167, 0.5730027, 0.5361933, 0.5286046]), 90: ([7, -1, 11, 16, 5], [0.62654305, 0.5201823, 0.45111668, 0.4399878, 0.43930742]), 91: ([7, -1, 17, 10, 6], [0.6455877, 0.5784802, 0.53981113, 0.50081, 0.4972272]), 92: ([6, 10, -1, 7, 23], [0.54935217, 0.47624493, 0.46047568, 0.4275682, 0.42179346]), 93: ([3, 5, 25, 13, 21], [0.68364143, 0.6335913, 0.5721427, 0.56449735, 0.5362524]), 94: ([13, 6, 15, 5, -1], [0.654279, 0.6488178, 0.5932056, 0.58846515, 0.5776601]), 95: ([5, 3, 13, -1, 16], [0.64904284, 0.5616013, 0.5369834, 0.5023572, 0.499998]), 96: ([5, 3, 13, -1, 16], [0.6559278, 0.57432604, 0.54575133, 0.46943372, 0.46132255]), 97: ([5, 13, 3, 25, 6], [0.6956768, 0.642449, 0.62465465, 0.49174324, 0.4749214]), 98: ([13, 6, 5, -1, 3], [0.7661036, 0.65524805, 0.6301204, 0.6186588, 0.5668917]), 99: ([13, 6, 5, 3, -1], [0.8071288, 0.71757805, 0.701682, 0.68321455, 0.66584325]), 100: ([13, 3, 5, 6, 22], [0.6334363, 0.61054826, 0.5863518, 0.5740575, 0.5719327]), 101: ([13, 3, 5, 25, 6], [0.6283176, 0.57015043, 0.56985795, 0.55417484, 0.5221219]), 102: ([3, 5, -1, 21, 13], [0.8247145, 0.7191614, 0.6067312, 0.56410027, 0.54289997]), 103: ([6, 19, 13, 15, 28], [0.55669475, 0.5566424, 0.5294384, 0.52737856, 0.51921844]), 104: ([5, 3, 13, -1, 21], [0.70998865, 0.6981659, 0.59811026, 0.48538405, 0.47510195]), 105: ([3, 5, -1, 13, 6], [0.6857774, 0.6194889, 0.5255419, 0.5245912, 0.47831774]), 106: ([3, 5, 13, -1, 25], [0.69759953, 0.67195874, 0.6058873, 0.56095135, 0.5485104]), 107: ([3, 5, 13, -1, 21], [0.7235756, 0.70292735, 0.6856854, 0.66497403, 0.644892]), 108: ([6, 13, -1, 1, 28], [0.80061454, 0.719189, 0.66364586, 0.6288589, 0.61534774]), 109: ([7, -1, 9, 4, 10], [0.65451354, 0.5201517, 0.52002954, 0.5019696, 0.46231374]), 110: ([-1, 19, 18, 21, 26], [0.52580774, 0.49323654, 0.47214228, 0.45704564, 0.43273914]), 111: ([3, 25, -1, 5, 6], [0.6579413, 0.5295661, 0.51791054, 0.49622053, 0.4748402]), 112: ([3, 25, 5, 14, 16], [0.7041523, 0.57188284, 0.5666177, 0.53845775, 0.52973])}\n"
]
}
],
"source": [
"print(query_topics_with_probs)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query 1 has topic -1\n",
"Relevant doc topics: \n",
"5,-1,-1,-1,5,1,-1,10,1,1,12,-1,10,10,12,0,-1,11,-1,-1,1,18,-1,-1,25,20,-1,1,1,-1,1,-1,1,1,1,-1,1,-1,1,1,3,-1,1,5,-1,-1,\n",
"PCTG: 0.391304347826087\n",
"Query 2 has topic 6\n",
"Relevant doc topics: \n",
"2,5,23,-1,24,-1,5,-1,-1,-1,5,1,1,1,1,1,1,1,1,1,-1,-1,1,1,1,-1,\n",
"PCTG: 0.0\n",
"Query 3 has topic 2\n",
"Relevant doc topics: \n",
"2,2,13,6,19,2,1,19,19,19,2,0,2,2,-1,5,13,13,-1,2,-1,2,2,-1,2,0,2,3,2,2,5,13,2,-1,0,0,11,-1,2,2,2,2,-1,-1,\n",
"PCTG: 0.4090909090909091\n",
"Query 4 has topic 25\n",
"Relevant doc topics: \n",
"6,-1,25,25,-1,-1,28,-1,\n",
"PCTG: 0.25\n",
"Query 5 has topic 13\n",
"Relevant doc topics: \n",
"-1,22,-1,13,13,2,12,12,23,2,0,0,-1,13,2,13,-1,6,-1,13,1,-1,0,6,\n",
"PCTG: 0.20833333333333334\n",
"Query 6 has topic 11\n",
"Relevant doc topics: \n",
"-1,\n",
"PCTG: 0.0\n",
"Query 7 has topic -1\n",
"Relevant doc topics: \n",
"6,11,-1,19,19,6,22,-1,\n",
"PCTG: 0.25\n",
"Query 8 has topic 3\n",
"Relevant doc topics: \n",
"-1,-1,-1,6,6,1,-1,21,28,-1,-1,2,11,11,-1,-1,11,21,\n",
"PCTG: 0.0\n",
"Query 9 has topic 3\n",
"Relevant doc topics: \n",
"-1,-1,-1,-1,21,11,-1,25,3,11,3,-1,2,-1,11,-1,-1,11,2,-1,1,-1,11,2,-1,11,11,-1,-1,11,-1,-1,-1,11,\n",
"PCTG: 0.058823529411764705\n",
"Query 10 has topic 5\n",
"Relevant doc topics: \n",
"16,-1,2,2,-1,0,11,13,2,2,2,-1,13,5,24,2,-1,2,16,16,-1,16,11,2,-1,-1,\n",
"PCTG: 0.038461538461538464\n",
"Query 11 has topic -1\n",
"Relevant doc topics: \n",
"-1,9,-1,-1,-1,7,-1,13,12,-1,9,-1,7,1,-1,0,2,2,-1,-1,-1,4,-1,2,12,12,0,-1,1,1,0,-1,-1,2,6,-1,4,-1,27,2,-1,19,9,-1,10,9,12,9,9,2,2,9,2,9,23,-1,9,-1,2,-1,-1,3,1,-1,-1,-1,6,-1,1,-1,1,1,-1,-1,2,-1,-1,10,-1,2,0,0,2,0,-1,-1,-1,-1,-1,1,2,2,18,-1,2,2,2,2,27,-1,2,9,-1,2,2,2,-1,-1,-1,4,0,-1,27,0,-1,0,0,9,9,-1,23,2,9,-1,0,0,-1,\n",
"PCTG: 0.3858267716535433\n",
"Query 12 has topic 27\n",
"Relevant doc topics: \n",
"3,1,19,-1,18,19,2,2,-1,-1,27,9,-1,\n",
"PCTG: 0.07692307692307693\n",
"Query 13 has topic 5\n",
"Relevant doc topics: \n",
"18,5,-1,-1,-1,1,3,-1,5,13,3,5,23,3,5,13,-1,18,18,2,2,3,0,-1,0,-1,3,10,10,0,0,0,6,-1,9,2,3,18,3,13,-1,13,5,3,6,6,3,-1,5,5,-1,-1,13,3,5,3,3,5,18,2,2,3,1,1,1,-1,6,-1,5,5,5,5,-1,5,18,5,-1,5,25,5,-1,5,3,3,5,5,-1,19,3,5,5,\n",
"PCTG: 0.24175824175824176\n",
"Query 14 has topic 23\n",
"Relevant doc topics: \n",
"3,-1,1,\n",
"PCTG: 0.0\n",
"Query 15 has topic 6\n",
"Relevant doc topics: \n",
"-1,18,-1,-1,22,-1,13,18,-1,19,18,-1,0,-1,24,0,0,0,15,0,0,8,6,8,-1,19,0,0,0,0,-1,2,19,3,13,-1,1,18,18,-1,1,18,6,1,6,13,6,-1,18,18,1,-1,-1,22,1,6,-1,13,18,-1,0,8,15,-1,8,8,2,2,-1,-1,0,-1,-1,-1,6,22,6,-1,6,6,6,15,\n",
"PCTG: 0.12195121951219512\n",
"Query 16 has topic 13\n",
"Relevant doc topics: \n",
"6,19,-1,23,19,-1,18,6,6,6,-1,6,24,24,-1,-1,24,24,21,-1,24,-1,15,24,2,-1,\n",
"PCTG: 0.0\n",
"Query 17 has topic 13\n",
"Relevant doc topics: \n",
"3,3,1,22,19,19,-1,-1,18,-1,6,6,-1,1,-1,-1,22,22,-1,22,22,22,22,22,22,-1,\n",
"PCTG: 0.0\n",
"Query 18 has topic 1\n",
"Relevant doc topics: \n",
"13,8,-1,1,1,1,1,1,1,1,1,\n",
"PCTG: 0.7272727272727273\n",
"Query 19 has topic 25\n",
"Relevant doc topics: \n",
"3,-1,7,5,-1,3,3,-1,6,-1,-1,-1,11,-1,-1,3,-1,25,3,-1,5,3,21,6,25,5,1,25,13,3,3,3,5,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,21,-1,-1,-1,25,6,25,28,25,25,1,1,-1,-1,-1,-1,1,-1,3,-1,5,-1,-1,1,25,-1,25,-1,-1,2,-1,\n",
"PCTG: 0.1111111111111111\n",
"Query 20 has topic 18\n",
"Relevant doc topics: \n",
"18,3,5,23,3,-1,18,18,1,-1,23,23,-1,-1,0,15,15,19,23,3,3,-1,3,25,13,6,-1,1,-1,7,-1,3,-1,13,-1,5,5,3,5,3,6,-1,21,-1,5,23,-1,13,23,-1,3,3,-1,7,23,5,3,3,1,-1,5,5,18,1,1,1,-1,6,1,5,-1,10,5,5,-1,1,5,1,-1,1,18,-1,-1,3,5,-1,5,25,0,25,28,3,28,-1,-1,-1,-1,28,28,-1,-1,-1,3,5,-1,23,23,5,1,1,2,-1,18,18,3,3,5,-1,5,19,-1,1,-1,-1,-1,2,-1,3,-1,-1,5,-1,5,-1,-1,-1,19,6,6,15,-1,3,3,-1,\n",
"PCTG: 0.04861111111111111\n",
"Query 21 has topic 2\n",
"Relevant doc topics: \n",
"-1,0,0,2,0,12,12,0,0,0,-1,2,0,12,0,0,0,12,0,-1,0,12,0,12,6,\n",
"PCTG: 0.08\n",
"Query 22 has topic 23\n",
"Relevant doc topics: \n",
"1,-1,-1,3,23,1,23,23,23,-1,10,23,-1,2,24,-1,24,24,0,0,-1,1,19,23,-1,10,12,12,-1,23,1,-1,9,-1,23,23,1,23,10,10,1,-1,12,3,3,5,-1,-1,23,1,23,23,23,\n",
"PCTG: 0.2641509433962264\n",
"Query 23 has topic 15\n",
"Relevant doc topics: \n",
"-1,0,15,0,0,-1,0,0,-1,-1,10,10,2,10,-1,-1,10,0,0,14,0,0,0,-1,15,0,0,0,22,0,15,0,-1,12,-1,2,0,15,0,0,0,0,0,-1,-1,0,8,0,\n",
"PCTG: 0.08333333333333333\n",
"Query 24 has topic 2\n",
"Relevant doc topics: \n",
"-1,0,0,0,19,15,0,0,12,23,0,-1,0,15,0,0,0,0,0,0,0,0,2,-1,2,2,6,0,0,28,-1,12,0,0,0,0,0,0,-1,12,-1,12,0,-1,0,-1,0,6,0,0,11,0,\n",
"PCTG: 0.057692307692307696\n",
"Query 25 has topic 19\n",
"Relevant doc topics: \n",
"0,-1,-1,-1,19,-1,14,-1,-1,19,19,-1,23,18,6,-1,-1,20,20,20,2,-1,-1,-1,0,27,23,-1,-1,-1,-1,20,0,\n",
"PCTG: 0.09090909090909091\n",
"Query 26 has topic 18\n",
"Relevant doc topics: \n",
"-1,18,-1,13,18,-1,19,18,-1,-1,24,24,6,19,0,0,2,19,3,13,-1,-1,-1,18,-1,1,6,18,-1,6,5,-1,0,13,13,-1,18,1,1,-1,1,13,1,18,8,15,0,-1,15,2,-1,-1,6,-1,6,6,\n",
"PCTG: 0.125\n",
"Query 27 has topic 3\n",
"Relevant doc topics: \n",
"18,3,3,1,3,-1,5,3,3,3,-1,3,13,-1,6,19,3,1,-1,-1,-1,3,-1,12,23,23,21,2,14,-1,-1,-1,-1,-1,5,25,-1,2,25,7,13,3,3,-1,3,3,-1,3,3,-1,-1,-1,-1,3,3,3,3,3,13,3,3,3,3,-1,-1,7,-1,3,1,1,1,-1,7,1,1,1,1,1,1,1,3,16,-1,3,-1,10,7,3,-1,7,-1,-1,3,-1,3,1,3,1,-1,3,3,5,-1,-1,-1,3,-1,3,3,7,-1,11,-1,3,3,\n",
"PCTG: 0.33043478260869563\n",
"Query 28 has topic 1\n",
"Relevant doc topics: \n",
"1,1,1,-1,-1,10,-1,1,2,19,1,-1,1,1,1,1,7,1,1,1,-1,1,1,7,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,1,-1,1,-1,1,1,6,1,1,1,1,1,1,-1,3,-1,1,1,1,1,-1,1,\n",
"PCTG: 0.7\n",
"Query 29 has topic 3\n",
"Relevant doc topics: \n",
"3,-1,5,3,3,3,-1,3,-1,-1,-1,-1,-1,3,10,10,21,14,-1,7,3,3,-1,-1,3,3,3,3,3,1,5,3,1,1,1,-1,1,1,1,1,7,3,1,3,3,3,3,3,\n",
"PCTG: 0.4166666666666667\n",
"Query 30 has topic 27\n",
"Relevant doc topics: \n",
"-1,0,9,-1,-1,10,3,12,7,-1,4,-1,-1,-1,19,-1,0,-1,12,12,10,0,10,9,-1,-1,-1,17,0,10,0,10,-1,6,10,10,7,7,10,7,7,-1,2,27,27,10,22,22,-1,1,-1,-1,17,-1,17,10,-1,17,-1,17,10,-1,27,7,-1,17,17,-1,2,17,-1,-1,17,-1,-1,7,7,-1,10,0,-1,10,10,17,17,-1,-1,27,-1,2,27,-1,2,-1,2,2,17,2,-1,-1,17,2,-1,17,2,-1,-1,-1,0,-1,27,0,7,17,7,7,9,7,7,7,9,27,7,7,7,9,-1,-1,-1,0,-1,10,7,0,\n",
"PCTG: 0.05223880597014925\n",
"Query 31 has topic 2\n",
"Relevant doc topics: \n",
"9,1,2,1,6,1,-1,19,17,1,-1,7,18,-1,-1,19,1,-1,10,7,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,1,1,6,-1,-1,-1,7,-1,-1,-1,2,-1,3,3,1,2,2,27,-1,1,27,23,-1,20,1,\n",
"PCTG: 0.06557377049180328\n",
"Query 32 has topic 3\n",
"Relevant doc topics: \n",
"15,-1,-1,-1,22,-1,15,-1,10,10,12,-1,10,21,12,-1,0,-1,24,0,14,-1,-1,14,0,0,0,15,-1,-1,0,8,6,-1,-1,-1,8,8,-1,19,0,15,19,14,15,15,15,12,0,13,6,5,13,1,22,1,6,6,6,8,15,-1,24,8,-1,-1,-1,8,-1,-1,28,28,24,8,8,15,15,-1,8,0,20,-1,24,-1,8,15,-1,-1,8,19,28,28,28,15,-1,-1,24,22,-1,-1,8,-1,3,-1,-1,-1,19,6,22,6,6,6,6,15,-1,0,8,\n",
"PCTG: 0.008547008547008548\n",
"Query 33 has topic 13\n",
"Relevant doc topics: \n",
"19,6,-1,19,19,24,24,-1,-1,-1,24,24,-1,24,-1,24,23,19,6,15,\n",
"PCTG: 0.0\n",
"Query 34 has topic 25\n",
"Relevant doc topics: \n",
"3,3,3,3,5,8,-1,6,-1,11,7,-1,3,3,3,3,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,\n",
"PCTG: 0.0\n",
"Query 35 has topic 19\n",
"Relevant doc topics: \n",
"0,-1,-1,23,-1,24,19,-1,-1,2,15,12,22,-1,19,-1,0,0,14,15,23,1,6,-1,3,2,23,1,1,1,1,1,2,0,-1,19,-1,24,2,2,-1,-1,0,\n",
"PCTG: 0.06976744186046512\n",
"Query 37 has topic 3\n",
"Relevant doc topics: \n",
"14,14,21,3,-1,3,3,1,3,-1,3,23,3,21,10,21,1,-1,1,1,-1,7,21,21,-1,-1,21,1,21,3,-1,21,3,21,-1,-1,-1,21,21,-1,\n",
"PCTG: 0.2\n",
"Query 39 has topic 13\n",
"Relevant doc topics: \n",
"-1,0,0,4,13,15,0,-1,27,0,2,0,0,0,-1,-1,2,-1,\n",
"PCTG: 0.05555555555555555\n",
"Query 41 has topic 3\n",
"Relevant doc topics: \n",
"3,1,14,3,1,1,-1,-1,-1,14,-1,\n",
"PCTG: 0.18181818181818182\n",
"Query 42 has topic 13\n",
"Relevant doc topics: \n",
"2,2,2,-1,13,6,19,2,-1,-1,-1,0,6,2,0,-1,2,-1,-1,-1,13,-1,6,1,13,-1,2,2,19,2,-1,2,2,2,22,2,-1,15,11,-1,-1,2,2,2,2,-1,-1,2,13,-1,\n",
"PCTG: 0.08\n",
"Query 43 has topic 13\n",
"Relevant doc topics: \n",
"13,-1,18,19,-1,-1,-1,-1,25,-1,1,25,25,\n",
"PCTG: 0.07692307692307693\n",
"Query 44 has topic 27\n",
"Relevant doc topics: \n",
"-1,0,-1,-1,3,-1,-1,7,-1,-1,12,-1,9,7,0,1,-1,-1,15,-1,10,-1,2,10,-1,-1,24,12,12,10,0,0,1,-1,-1,18,-1,27,19,19,19,-1,10,12,2,-1,-1,18,-1,1,0,3,1,6,19,-1,10,1,10,-1,19,1,10,10,7,1,10,-1,-1,1,-1,3,1,-1,-1,22,-1,6,-1,10,-1,-1,27,-1,-1,7,-1,17,7,10,0,0,20,-1,10,-1,15,-1,0,0,0,10,0,0,-1,0,22,1,-1,-1,-1,1,-1,2,-1,2,-1,-1,2,0,0,2,-1,0,0,27,0,6,0,0,7,9,7,27,9,1,-1,27,9,0,-1,0,10,-1,-1,-1,0,3,-1,-1,10,0,-1,0,1,\n",
"PCTG: 0.03225806451612903\n",
"Query 45 has topic 15\n",
"Relevant doc topics: \n",
"0,15,15,0,-1,0,-1,0,22,-1,18,-1,0,19,15,0,15,15,0,-1,0,0,19,5,13,22,-1,22,-1,6,-1,1,0,0,8,-1,0,8,20,0,15,0,0,8,15,0,0,0,0,20,20,0,0,-1,-1,8,0,0,19,28,0,0,0,-1,0,0,-1,0,0,6,0,19,6,-1,0,14,0,\n",
"PCTG: 0.09090909090909091\n",
"Query 46 has topic 15\n",
"Relevant doc topics: \n",
"15,0,-1,0,0,0,-1,0,-1,18,-1,0,19,15,15,23,-1,8,0,-1,15,0,8,15,-1,8,-1,19,2,15,22,15,15,15,-1,0,21,23,-1,13,6,-1,-1,0,0,22,22,6,0,-1,6,0,8,15,-1,28,24,8,-1,-1,0,-1,-1,-1,28,-1,8,-1,-1,-1,28,0,-1,28,8,8,22,8,15,15,-1,8,0,-1,8,15,-1,-1,-1,8,19,28,28,28,15,22,-1,-1,0,8,-1,3,-1,-1,-1,19,6,6,6,6,0,15,-1,0,8,8,\n",
"PCTG: 0.12931034482758622\n",
"Query 49 has topic 13\n",
"Relevant doc topics: \n",
"-1,8,9,-1,6,21,2,14,6,19,-1,9,2,6,18,2,-1,1,1,-1,-1,-1,-1,-1,-1,2,0,16,9,9,6,-1,0,19,\n",
"PCTG: 0.0\n",
"Query 50 has topic 14\n",
"Relevant doc topics: \n",
"14,14,3,8,14,8,14,-1,-1,14,14,14,14,8,8,-1,16,-1,14,14,14,14,-1,3,3,3,3,3,-1,3,3,1,1,16,3,14,1,-1,-1,-1,3,16,3,1,1,28,14,8,0,-1,-1,-1,-1,20,7,14,1,-1,-1,2,16,16,16,1,16,-1,-1,-1,14,-1,8,8,3,7,-1,-1,17,14,-1,14,8,-1,3,3,-1,-1,14,-1,-1,\n",
"PCTG: 0.21348314606741572\n",
"Query 52 has topic 23\n",
"Relevant doc topics: \n",
"-1,-1,3,23,23,23,10,23,23,23,-1,-1,23,23,23,\n",
"PCTG: 0.6\n",
"Query 54 has topic 15\n",
"Relevant doc topics: \n",
"-1,15,0,0,0,-1,13,15,12,0,15,0,0,-1,0,27,0,0,0,-1,2,19,0,0,15,28,-1,0,0,0,15,0,0,0,0,0,0,0,-1,0,-1,24,0,0,0,-1,6,19,0,0,0,\n",
"PCTG: 0.09803921568627451\n",
"Query 55 has topic 23\n",
"Relevant doc topics: \n",
"-1,3,23,23,23,10,23,23,23,-1,3,1,5,-1,-1,23,23,23,\n",
"PCTG: 0.5\n",
"Query 56 has topic 8\n",
"Relevant doc topics: \n",
"14,0,14,-1,0,-1,1,-1,14,-1,14,-1,3,3,14,-1,3,16,28,14,-1,-1,16,16,16,1,16,-1,-1,14,-1,8,8,3,-1,-1,14,-1,14,8,-1,3,14,-1,-1,\n",
"PCTG: 0.06666666666666667\n",
"Query 57 has topic 20\n",
"Relevant doc topics: \n",
"14,-1,1,15,14,14,-1,3,3,-1,3,16,28,8,8,-1,-1,-1,\n",
"PCTG: 0.0\n",
"Query 58 has topic 24\n",
"Relevant doc topics: \n",
"0,15,0,8,-1,9,9,24,19,19,19,19,15,-1,24,24,24,0,19,19,-1,6,6,24,0,-1,28,24,24,8,-1,24,-1,24,24,28,24,0,-1,24,8,6,23,-1,0,19,\n",
"PCTG: 0.2608695652173913\n",
"Query 61 has topic 5\n",
"Relevant doc topics: \n",
"2,-1,5,5,-1,-1,-1,-1,13,11,2,\n",
"PCTG: 0.18181818181818182\n",
"Query 62 has topic 5\n",
"Relevant doc topics: \n",
"-1,3,5,16,16,5,5,5,16,16,16,2,\n",
"PCTG: 0.3333333333333333\n",
"Query 65 has topic 3\n",
"Relevant doc topics: \n",
"3,3,3,3,3,-1,-1,-1,3,3,7,7,5,\n",
"PCTG: 0.5384615384615384\n",
"Query 66 has topic 24\n",
"Relevant doc topics: \n",
"0,0,8,-1,24,19,19,19,6,-1,4,24,24,24,0,19,19,-1,6,6,24,24,24,-1,-1,24,28,-1,24,-1,24,8,23,-1,19,\n",
"PCTG: 0.2857142857142857\n",
"Query 67 has topic 1\n",
"Relevant doc topics: \n",
"-1,-1,3,3,-1,1,1,6,1,-1,-1,-1,-1,25,20,-1,1,-1,3,3,3,1,1,-1,1,1,1,3,1,1,-1,1,\n",
"PCTG: 0.375\n",
"Query 69 has topic 21\n",
"Relevant doc topics: \n",
"21,-1,3,3,21,-1,21,-1,21,21,21,-1,1,1,21,\n",
"PCTG: 0.4666666666666667\n",
"Query 71 has topic 3\n",
"Relevant doc topics: \n",
"11,21,3,-1,3,1,3,-1,3,1,-1,-1,25,-1,3,-1,-1,3,3,-1,-1,-1,3,3,3,-1,-1,\n",
"PCTG: 0.37037037037037035\n",
"Query 76 has topic 24\n",
"Relevant doc topics: \n",
"0,15,0,8,-1,9,9,24,19,19,15,10,24,12,24,0,0,27,19,0,0,-1,0,0,-1,0,24,10,0,-1,24,24,8,-1,0,0,0,0,-1,24,24,-1,0,-1,24,24,24,0,-1,24,0,0,8,6,23,0,-1,19,0,0,\n",
"PCTG: 0.2\n",
"Query 79 has topic 25\n",
"Relevant doc topics: \n",
"5,1,-1,25,13,3,-1,1,1,1,25,\n",
"PCTG: 0.18181818181818182\n",
"Query 81 has topic 21\n",
"Relevant doc topics: \n",
"-1,5,3,19,-1,25,25,21,3,11,-1,\n",
"PCTG: 0.09090909090909091\n",
"Query 82 has topic 5\n",
"Relevant doc topics: \n",
"16,5,16,4,-1,21,2,2,-1,-1,\n",
"PCTG: 0.1\n",
"Query 84 has topic 5\n",
"Relevant doc topics: \n",
"5,-1,3,5,5,5,5,-1,13,2,5,\n",
"PCTG: 0.5454545454545454\n",
"Query 90 has topic 7\n",
"Relevant doc topics: \n",
"-1,7,7,-1,7,7,10,7,9,9,-1,9,0,4,-1,9,4,9,-1,-1,-1,4,4,-1,9,9,-1,9,-1,9,-1,9,10,7,7,-1,7,7,-1,-1,4,-1,4,-1,10,7,9,4,-1,4,-1,7,7,7,7,7,9,4,7,-1,7,-1,7,-1,7,9,-1,-1,17,7,\n",
"PCTG: 0.2857142857142857\n",
"Query 92 has topic 6\n",
"Relevant doc topics: \n",
"-1,-1,-1,6,6,6,6,2,1,6,6,6,19,25,-1,6,1,6,6,-1,13,1,1,18,-1,-1,-1,-1,-1,-1,18,2,6,-1,6,6,6,6,\n",
"PCTG: 0.39473684210526316\n",
"Query 95 has topic 5\n",
"Relevant doc topics: \n",
"-1,5,16,16,5,5,5,16,16,16,-1,\n",
"PCTG: 0.36363636363636365\n",
"Query 96 has topic 5\n",
"Relevant doc topics: \n",
"5,16,16,5,5,5,16,16,16,\n",
"PCTG: 0.4444444444444444\n",
"Query 97 has topic 5\n",
"Relevant doc topics: \n",
"-1,3,3,-1,3,5,\n",
"PCTG: 0.16666666666666666\n",
"Query 98 has topic 13\n",
"Relevant doc topics: \n",
"-1,13,13,4,13,2,13,1,6,1,6,6,-1,13,13,6,13,6,6,-1,-1,13,-1,-1,-1,28,-1,6,6,\n",
"PCTG: 0.27586206896551724\n",
"Query 99 has topic 13\n",
"Relevant doc topics: \n",
"18,-1,18,-1,13,18,2,2,2,13,4,13,13,6,1,6,6,-1,13,13,6,13,6,6,-1,13,2,13,-1,-1,28,-1,6,6,\n",
"PCTG: 0.2647058823529412\n",
"Query 100 has topic 13\n",
"Relevant doc topics: \n",
"22,0,19,-1,22,19,-1,1,-1,-1,28,22,22,22,22,22,22,22,\n",
"PCTG: 0.0\n",
"Query 101 has topic 13\n",
"Relevant doc topics: \n",
"-1,\n",
"PCTG: 0.0\n",
"Query 102 has topic 3\n",
"Relevant doc topics: \n",
"11,3,3,-1,3,3,3,3,-1,3,3,3,3,3,3,-1,3,3,5,1,3,-1,3,3,\n",
"PCTG: 0.7083333333333334\n",
"Query 104 has topic 5\n",
"Relevant doc topics: \n",
"11,3,5,3,3,3,-1,3,2,3,5,\n",
"PCTG: 0.18181818181818182\n",
"Query 109 has topic 7\n",
"Relevant doc topics: \n",
"-1,7,7,-1,7,7,13,10,-1,7,9,9,-1,4,-1,-1,9,4,4,9,-1,-1,7,-1,-1,4,4,-1,9,9,-1,9,-1,9,-1,9,10,7,7,-1,7,-1,-1,4,-1,4,7,9,4,-1,4,-1,7,7,7,7,7,9,4,7,-1,7,-1,7,-1,7,9,-1,-1,17,7,\n",
"PCTG: 0.28169014084507044\n",
"Query 111 has topic 3\n",
"Relevant doc topics: \n",
"3,3,3,-1,-1,3,\n",
"PCTG: 0.6666666666666666\n",
"avg_pctg: 0.14244139328771147\n",
"min_pctg: 0.0\n",
"max_pctg: 0.7272727272727273\n"
]
}
],
"source": [
"# analyse if queries and relevant documents have similar topics\n",
"rels = pd.read_csv(\"data/cisi-csv/rels.csv\")\n",
"\n",
"doc_topics = doc_info[\"Topic\"].tolist()\n",
"avg_pctg = 0\n",
"max_pctg = 0\n",
"min_pctg = 10000\n",
"\n",
"for index, row in queries.iterrows():\n",
" queryID = row[\"id\"]\n",
" truths = rels.loc[rels['queryID'] == queryID]\n",
" true_docs = truths['docID'].tolist()\n",
" if len(true_docs) == 0:\n",
" continue\n",
" q_topic = query_topics_with_probs[queryID][0][0]\n",
" print(\"Query \" + str(queryID) + \" has topic \" + str(q_topic))\n",
" print(\"Relevant doc topics: \")\n",
" same_topic = 0\n",
" for d in true_docs:\n",
" print(doc_topics[d-1], end=\",\")\n",
" if doc_topics[d-1] == q_topic:\n",
" same_topic += 1\n",
" pctg = same_topic / len(true_docs)\n",
" print(\"\")\n",
" print(\"PCTG: \", pctg)\n",
" avg_pctg += pctg\n",
" if pctg < min_pctg:\n",
" min_pctg = pctg\n",
" if pctg > max_pctg:\n",
" max_pctg = pctg\n",
"\n",
"print(\"avg_pctg: \", avg_pctg/len(queries))\n",
"print(\"min_pctg: \", min_pctg)\n",
"print(\"max_pctg: \", max_pctg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check how many relevant documents there are per query"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 3000x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"df = pd.read_csv('data/cisi-csv/rels.csv')\n",
"\n",
"# Count the number of docID's for each queryID\n",
"query_doc_count = df.groupby('queryID').size()\n",
"\n",
"# Reset index to convert the series to a dataframe for plotting\n",
"query_doc_count = query_doc_count.reset_index(name='doc_count')\n",
"\n",
"# Plot the number of docID's for each queryID\n",
"plt.figure(figsize=(30, 7))\n",
"sns.barplot(data=query_doc_count, x='queryID', y='doc_count', )\n",
"plt.title('Number of docIDs per queryID')\n",
"plt.xlabel('Query ID')\n",
"plt.ylabel('Number of docIDs')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 76.000000\n",
"mean 40.973684\n",
"std 36.171434\n",
"min 1.000000\n",
"25% 13.000000\n",
"50% 30.500000\n",
"75% 52.250000\n",
"max 155.000000\n",
"dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_doc_count = df.groupby('queryID').size()\n",
"query_doc_count.describe()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}