{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Advanced Gargantext Tutorial (Python)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0, '/srv/gargantext')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# First import the library Gargantext Notebook\n", "from gargantext_notebook import *\n", "\n", "# This enables to draw graphics later\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Philomemies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instantiate the corpus you are working on" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "My corpus id is : 302695.\n" ] } ], "source": [ "corpus_url = \"http://localhost:8000/projects/302694/corpora/302695/\"\n", "corpus_id = corpus_url.split(\"/\")[6]\n", "print(\"My corpus id is : %s.\" % corpus_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting the Map Terms " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(21, 'environment'), (42, 'development'), (184, 'examples'), (196, 'water'), (368, 'problem'), (576, 'work'), (654, 'technology'), (712, 'number'), (738, 'operation'), (817, 'experiments')]\n" ] } ], "source": [ "from gargantext.models import *\n", "import csv\n", "\n", "map_id = session.query(MaplistNode.id).filter(MaplistNode.parent_id == corpus_id).first()\n", "\n", "mapTerms = (session.query(Ngram).join( NodeNgram, NodeNgram.ngram_id == Ngram.id)\n", " .filter(NodeNgram.node_id == map_id)\n", " .all()\n", " )\n", "\n", "print([(m.id, m.terms) for m in mapTerms[:10]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save in CSV File" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "toPrint = [(m.id,m.terms) for m in mapTerms]\n", "csvfile = \"./MapTerms.csv\"\n", "\n", "#Assuming res is a flat list\n", "with open(csvfile, \"w\") as output:\n", " writer = csv.writer(output, lineterminator='\\n')\n", " for val in toPrint:\n", " writer.writerow([val])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Next:\n", "# You can have access to your CSV file in the home of you Notebook!\n", "# Click, rename, mv, delete in your Notebook\n", "\n", "#Assuming output is a list of lists\n", "#with open(csvfile, \"w\") as output:\n", "# writer = csv.writer(output, lineterminator='\\n')\n", "# writer.writerows(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Occurrences of MapTerms by Year" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from gargantext.util.toolchain.metric_tfidf import compute_occs\n", "\n", "corpus= session.query(CorpusNode).get(corpus_id)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'abstract': 'The purpose of this paper is to develop a new fuzzy dynamic programming approach for solving hybrid multiobjective multistage decision-making problems. We first present a methodology of fuzzy evaluation and fuzzy optimization for hybrid multiobjective systems, in which the qualitative and quantitative objectives are synthetically considered. The qualitative objectives are evaluated by decision-makers with linguistic variables and the quantitative objectives are converted into proper dimensionless indices. After getting the marginal evaluations for each objective, a new aggregation method based on the principle of fuzzy pattern recognition is developed to get a global evaluation for all objectives. With the global evaluation obtained, a fuzzy optimization process is performed. Then we present a dynamic optimization algorithm by incorporating the fuzzy optimization process with the conventional dynamic programming technique to solve hybrid multiobjective multistage decision-making problems. A characteristic feature of the approach proposed is that various objectives are synthetically considered by the fuzzy systematic technique instead of the frequently employed weighted average method. Finally, an illustrative example is also given to clarify the developed approach and to demonstrate its effectiveness.',\n", " 'authors': 'Lushu Li, K.K. Lai',\n", " 'authorsRAW': [{'affiliations': ['Faculty of Administration, University of New Brunswick, Fredericton, N.B., Canada',\n", " 'Corresponding author'],\n", " 'name': 'Lushu Li'},\n", " {'affiliations': ['Department of Management Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong'],\n", " 'name': 'K.K. Lai'}],\n", " 'doi': '10.1016/S0165-0114(98)00423-0',\n", " 'genre': ['research-article'],\n", " 'id': '5E6CB638271D0121DB653AB9150D2F025346816A',\n", " 'language_iso2': 'en',\n", " 'language_iso3': 'eng',\n", " 'language_name': 'English',\n", " 'publication_date': '2001-01-01 00:00:00+00:00',\n", " 'publication_day': 1,\n", " 'publication_hour': 0,\n", " 'publication_minute': 0,\n", " 'publication_month': 1,\n", " 'publication_second': 0,\n", " 'publication_year': 2001,\n", " 'source': 'Fuzzy Sets and Systems',\n", " 'statuses': [],\n", " 'title': 'Fuzzy dynamic programming approach to hybrid multiobjective multistage decision-making problems'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To get all the documents:\n", "docs = documents(corpus_id)\n", "docs[0].hyperdata" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1954, 2), (1956, 1), (1957, 1), (1958, 5), (1960, 3), (1961, 5), (1962, 2), (1963, 11), (1964, 5), (1965, 3), (1966, 1), (1967, 8), (1968, 17), (1969, 10), (1970, 8), (1971, 20), (1972, 12), (1973, 20), (1974, 16), (1975, 17), (1976, 8), (1977, 10), (1978, 14), (1979, 16), (1980, 28), (1981, 12), (1982, 14), (1983, 15), (1984, 19), (1985, 22), (1986, 27), (1987, 28), (1988, 24), (1989, 20), (1990, 26), (1991, 54), (1992, 48), (1993, 40), (1994, 40), (1995, 28), (1996, 32), (1997, 34), (1998, 30), (1999, 25), (2000, 37), (2001, 29), (2002, 13), (2003, 19), (2004, 17), (2005, 21), (2006, 17), (2007, 11), (2008, 10), (2009, 8), (2010, 9), (2011, 9), (2012, 12), (2013, 7)]\n" ] } ], "source": [ "pubsByYear = countByField(docs, \"publication_year\")\n", "\n", "print(pubsByYear)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1954, 1956, 1957, 1958, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]\n" ] } ], "source": [ "years = [y for y in map(lambda x: x[0], pubsByYear)]\n", "print(years)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# To Add the groups you need to get the Node\n", "group_id = session.query(GrouplistNode.id).filter(GrouplistNode.parent_id == corpus_id).first()\n", "\n", "occByYear = list()\n", "\n", "# Not optmized yet since sql request is launched for each year\n", "# We will use a group by if needed, depends on the size of corpus\n", "# Clarity of the computation is first done here\n", "# Optmization will be the step After\n", "for year in years:\n", " listNgramOcc = compute_occs(corpus, groupings_id=group_id, year=year, interactiv=True)\n", " listYearNgramOcc = [(year, ngram_id, occ) for (ngram_id, occ) in listNgramOcc]\n", " occByYear.append(listYearNgramOcc)\n", " " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[(1954, 5249, 1.0), (1954, 5366, 1.0), (1954, 7019, 1.0), (1954, 10524, 1.0), (1954, 121362, 1.0), (1954, 505775, 1.0)], [(1956, 7019, 1.0), (1956, 8604, 1.0), (1956, 755610, 1.0), (1956, 2361839, 1.0)]]\n" ] } ], "source": [ "\n", "# Saving the results in file\n", "toPrint = [(m.id,m.terms) for m in mapTerms]\n", "csvfile = \"./MapTerms.csv\"\n", "\n", "#Assuming res is a flat list\n", "with open(csvfile, \"w\") as output:\n", " writer = csv.writer(output, lineterminator='\\n')\n", " for val in toPrint:\n", " writer.writerow([val])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mapTermsOcc = (session.query(Occurrences).join( MapTerms, MapTerms.ngram_id == Occurrences.ngram_id)\n", " .filter(MapTerms.node_id == map_id)\n", " \n", " .join(Documents, Documents.id == Occurrences.node2_id)\n", " .filter(Documents.parent_id == corpus_id)\n", " \n", " .filter(Occurrences.node1_id == occ_id)\n", " \n", " #.group_by(Occurrences.ngram_id)\n", " .all()\n", " )" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(303698)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group_id" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapTermsOcc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cooccurrences of MapTerms by Year" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from graph.cooccurrences import countCooccurrences" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " (cooc_id, cooc_matrix) = countCooccurrences( corpus_id=corpus_id, cooc_id= \n", " , field1=field1, field2=field2 \n", " , start=start , end =end \n", " , mapList_id=mapList_id , groupList_id=groupList_id \n", " , isMonopartite=True , threshold = threshold \n", " , distance=distance , bridgeness=bridgeness \n", " , save_on_db = True , reset = reset \n", " ) " ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GRAPH #303869 Filtering the matrix with Map and Group Lists.\n", "WeightedMatrix bulk_insert start\n", "WeightedMatrix bulk_insert stop\n", "GRAPH #303869 ... Node Cooccurrence Matrix saved\n", "GRAPH #303869 ... Parameters saved in Node.\n" ] } ], "source": [ "#countCooccurrences(corpus_id, save_on_db=False, start=\"2000-01-01\", end=\"2017-12-31\")\n", "(cooc_id, cooc_matrix) = countCooccurrences( corpus_id = corpus_id\n", " , cooc_id = None\n", " , field1=\"ngrams\", field2 = \"ngrams\"\n", " \n", " , mapList_id = map_id\n", " , groupList_id = group_id\n", " \n", " , isMonopartite =True , threshold = 2 \n", " #, distance =Non , bridgeness=bridgeness\n", " \n", " , save_on_db = True\n", " , reset = True\n", " )" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "defaultdict(float, {})" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cooc_matrix.items" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Number of Documents per year" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Date DateValue\n", "Date \n", "1954 1954 2\n", "1956 1956 1\n", "1957 1957 1\n", "1958 1958 5\n", "1960 1960 3\n", "1961 1961 5\n", "1962 1962 2\n", "1963 1963 11\n", "1964 1964 5\n", "1965 1965 3\n" ] } ], "source": [ "# To get all the documents:\n", "docs = documents(corpus_id)\n", "# If I want to count:\n", "myChart = chart(docs, \"publication_year\")\n", "print(myChart[:10])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Others example" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "139\n", "LSTM 1000\n", "Downloading page 0 to 100 results\n", "Downloading page 100 to 100 results\n", "CORPUS #303703\n", "PARSING\n", "Loading available PARSERS:\n", "\t- EuropresseParser\n", "\t- RISParser\n", "\t- PubmedParser\n", "\t- RISParser\n", "\t- ISIParser\n", "\t- RISParser\n", "\t- CSVParser\n", "\t- ISTexParser\n", "\t- CernParser\n", "\t- MultivacParser\n", "\t- HalParser\n", "\t- IsidoreParser\n", "0 docs skipped\n", "139 parsed\n", "#MAIN language of the CORPUS __unknown__\n", "CORPUS #303703: parsed 139\n", "#TAGGERS LOADED: {'__unknown__': }\n", "#SUPPORTED TAGGER LANGS ['__unknown__']\n", "INTEGRATE\n", "INTEGRATE\n", "INTEGRATE\n", "CORPUS #303703: extracted ngrams\n", "CORPUS #303703: indexed hyperdata\n", "CORPUS #303703: [2017-10-10_09:34:23] new favorites node #303843\n", "CORPUS #303703: [2017-10-10_09:34:23] starting ngram lists computation\n", "CORPUS #303703: [2017-10-10_09:34:24] new stoplist node #303844\n", "# STEMMERS LOADED {'__unknown__': }\n", "#SUPPORTED STEMMERS LANGS []\n", "CORPUS #303703: [2017-10-10_09:34:25] new grouplist node #303845\n", "CORPUS #303703: [2017-10-10_09:34:25] new occs node #303846\n", "compute_ti_ranking\n", "2017-10-10_09:34:25 : Starting Query tf_nd_query\n", "2017-10-10_09:34:26 : End Query tf_nd_quer\n", "2017-10-10_09:34:26 : tfidfsum\n", "CORPUS #303703: [2017-10-10_09:34:26] new ti ranking node #303847\n", "MAINLIST: keeping 3295 ngrams out of 4393\n", "CORPUS #303703: [2017-10-10_09:34:26] new mainlist node #303848\n", "Compute TFIDF local\n", "CORPUS #303703: [2017-10-10_09:34:26] new localtfidf node #303849\n", "COOCS: NEW matrix shape [215x361]\n", "CORPUS #303703: [2017-10-10_09:34:32] computed mainlist coocs for specif rank\n", "SPECIFICITY: computing on 209 ngrams\n", "CORPUS #303703: [2017-10-10_09:34:32] new spec-clusion node #303853\n", "CORPUS #303703: [2017-10-10_09:34:32] new gen-clusion node #303854\n", "MAPLIST quotas: {'topgen': {'multigrams': 168, 'monograms': 42}, 'topspec': {'multigrams': 112, 'monograms': 28}}\n", "MAPLIST: top_spec_monograms = 28\n", "MAPLIST: top_spec_multigrams = 55\n", "MAPLIST: top_gen_monograms = 42\n", "MAPLIST: top_gen_multigrams = 0\n", "MAPLIST: kept 125 ngrams in total \n", "CORPUS #303703: [2017-10-10_09:34:32] new maplist node #303855\n", "CORPUS #303703: [2017-10-10_09:34:32] FINISHED ngram lists computation\n" ] } ], "source": [ "#project = myProject_fromUrl(\"http://imt.gargantext.org/projects/300535\")\n", "project = myProject_fromUrl(\"http://localhost:8000/projects/301096\")\n", "corpus = newCorpus(project, source=\"hal\", name=\"Machine learning\", query=\"LSTM\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# search full text (english by default) in the corpus\n", "scan_gargantext(corpus.id, \"machine | learning & deep\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# search full text (english by default) in the corpus\n", "scan_gargantext(corpus.id, \"machine | learning & deep\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# search full text (english by default) and DELETE in the corpus\n", "scan_gargantext_and_delete(corpus.id, \"machine | learning & deep\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for doc in new_docs:\n", " new_doc = (Node( user_id = project.user_id\n", " , parent_id = corpus.id\n", " , typename= 'DOCUMENT'\n", " , name=doc[\"title\"][:50]\n", " , hyperdata=doc)\n", " )\n", " session.add(new_doc)\n", "session.commit()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L'identifiant du corpus est : 254749\n" ] } ], "source": [ "# Copier/coller l'url du corpus (avec http://): sur lequel travailler\n", "corpus_url = \"http://gargantext.org/projects/251737/corpora/254749\"\n", "\n", "corpus_id = corpus_url.split(\"/\")[6]\n", "\n", "print(\"L\\'identifiant du corpus est : %s\" % corpus_id)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# To get all the documents:\n", "docs = documents(corpus_id)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference.'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To get the title of the first document \n", "# [0] indicates the index of the first document\n", "docs[0].hyperdata['title']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "\"The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To get the abstract of the first document (0)\n", "docs[0].hyperdata['abstract']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'Shoba Ranganathan, Christian Schönbach, Janet Kelso, Burkhard Rost, Sheila Nathan, Tin Wee Tan'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To get the authors of the first document (0)\n", "docs[0].hyperdata['authors']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'BMC bioinformatics'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To get the source of the first document (0)\n", "docs[0].hyperdata['source']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# If I want to count:\n", "myChart = chart(docs, \"publication_year\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "myChart.plot.bar()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Title\n", "\n", "Here I can add some comments on the cart.\n", "1. First point\n", "2. Second point" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Lang Cleaning tools" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "detect_lang(\"Ceci est une phrase en français.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "detect_lang(\"This is an english sentence.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "chart(docs, \"language_iso2\").plot.bar()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "Counter({'de': 13,\n", " 'en': 1547,\n", " 'es': 5,\n", " 'fi': 1,\n", " 'fr': 4,\n", " 'hu': 1,\n", " 'it': 1,\n", " 'ja': 5,\n", " 'ko': 1,\n", " 'ru': 3,\n", " 'zh': 23})" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter([doc.hyperdata[\"language_iso2\"] for doc in docs])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Deleting language that is not in majority\n", "def cleanCorpusWithLang(corpus_id, lang):\n", " return (session.query(Node.id).filter(Node.parent_id == corpus_id)\n", " .filter(Node.hyperdata[\"language_iso2\"].astext != lang)\n", " .count()\n", " #.delete()\n", " )" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "57" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cleanCorpusWithLang(corpus_id, 'en')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[(True, 'This is an english paragraph.\\n '),\n", " (False, '\"This is an english paragraph.\\n\\nThis is an english paragraph.\\n ')]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abstract0 = \"\"\"\"Ceci est un paragraphe en français.\n", "\n", "This is an english paragraph.\n", " \"\"\"\n", "\n", "abstract1 = \"\"\"\"This is an english paragraph.\n", "\n", "This is an english paragraph.\n", " \"\"\"\n", "\n", "def clean_lang_inText(lang, text):\n", " \n", " texts_before = nltk.tokenize.blankline_tokenize(text)\n", " texts_after = '\\n\\n'.join([sentence \n", " for sentence in texts_before\n", " if detect_lang(sentence) == lang\n", " ])\n", " \n", " return (len(texts_before) != len(nltk.tokenize.blankline_tokenize(texts_after)), texts_after)\n", "\n", "[clean_lang_inText('en', abstract) for abstract in [abstract0, abstract1]]\n", "\n", "# TODO update each document accordingly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# TODO update all the abstract with That function" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Measures IMT Tools" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "ConnectionError", "evalue": "HTTPSConnectionPool(host='api.archives-ouvertes.fr', port=443): Max retries exceeded with url: /search?wt=json&q=machine+learning+AND+deep&fl=+title_s%0A+++++++++++++++%2C+abstract_s%0A+++++++++++++++%2C+submittedDate_s%0A+++++++++++++++%2C+journalDate_s%0A+++++++++++++++%2C+authFullName_s%0A+++++++++++++++%2C+uri_s%0A+++++++++++++++%2C+isbn_s%0A+++++++++++++++%2C+issue_s%0A+++++++++++++++%2C+docType_s%0A+++++++++++++++%2C+journalPublisher_s%0A+++++++++++++&start=1&rows=10 (Caused by NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution',))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 141\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 142\u001b[0;31m (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.5/socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 732\u001b[0m \u001b[0maddrlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 733\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 734\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mgaierror\u001b[0m: [Errno -3] Temporary failure in name resolution", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, **response_kw)\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 578\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 352\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sock'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# AppEngine might not have `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0;31m# Add certificate verification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 150\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 151\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: : Failed to establish a new connection: [Errno -3] Temporary failure in name resolution", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 404\u001b[0m )\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, **response_kw)\u001b[0m\n\u001b[1;32m 622\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 623\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='api.archives-ouvertes.fr', port=443): Max retries exceeded with url: /search?wt=json&q=machine+learning+AND+deep&fl=+title_s%0A+++++++++++++++%2C+abstract_s%0A+++++++++++++++%2C+submittedDate_s%0A+++++++++++++++%2C+journalDate_s%0A+++++++++++++++%2C+authFullName_s%0A+++++++++++++++%2C+uri_s%0A+++++++++++++++%2C+isbn_s%0A+++++++++++++++%2C+issue_s%0A+++++++++++++++%2C+docType_s%0A+++++++++++++++%2C+journalPublisher_s%0A+++++++++++++&start=1&rows=10 (Caused by NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution',))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscan_hal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"machine learning AND deep\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/srv/gargantext/gargantext_notebook.py\u001b[0m in \u001b[0;36mscan_hal\u001b[0;34m(request)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscan_hal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mhal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHalCrawler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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93\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"response\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"numFound\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/gargantext/gargantext/util/crawlers/HAL.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(self, query, fromPage, count, lang)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mURL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m 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request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/alexandre/local/logiciels/python/env/3.5_20170123/lib/python3.5/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mProxyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m 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/search?wt=json&q=machine+learning+AND+deep&fl=+title_s%0A+++++++++++++++%2C+abstract_s%0A+++++++++++++++%2C+submittedDate_s%0A+++++++++++++++%2C+journalDate_s%0A+++++++++++++++%2C+authFullName_s%0A+++++++++++++++%2C+uri_s%0A+++++++++++++++%2C+isbn_s%0A+++++++++++++++%2C+issue_s%0A+++++++++++++++%2C+docType_s%0A+++++++++++++++%2C+journalPublisher_s%0A+++++++++++++&start=1&rows=10 (Caused by NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution',))" ] } ], "source": [ "scan_hal(\"machine learning AND deep\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Request syntax\n", "# \"network analysis\" = network <-> analysis\n", "# \"network OR analysis\" = network | analysis\n", "# \"network AND analysis\" = network & analysis\n", "\n", "scan_gargantext(corpus_id, 'english', \"machine | learning & deep\")\n", "\n", "# \"network NOT analysis\" = @@ to_tsquery('network') !! to_tsquery('analysis')\n", "# (need to change the function if not has to be used)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Forces / Faiblesses de l'IMT\n", "# Hal Query Gargantext Query\n", "queries = [ (\"network analysis\" , \"network <-> analysis\" )\n", " , (\"big data AND something\" , \"(big <-> data) & something\")\n", " ]\n", "[(query[0], query[1]) for query in queries]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def imt_vs_hal(corpus_id, queryHal, queryGarg):\n", " return((scan_gargantext(corpus_id, 'english', queryGarg), scan_hal(queryHal)))\n", " #return((scan_gargantext(corpus_id, 'english', queryGarg) *100 / scan_hal(queryHal)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Then chart it to see your strenght and weakness!\n", "[imt_vs_hal(corpus_id, query[0], query[1]) for query in queries]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Graph generation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# TODO Cooccurrences optimization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# TODO optimize the distributional distance" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# List Management" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Front End add a check box to merge or to overwrite previous list" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# optimize the list merge" ] } ], "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": "3.5.3rc1" } }, "nbformat": 4, "nbformat_minor": 2 }