{"id":17581401,"url":"https://github.com/kalifou/ri_tme1","last_synced_at":"2026-04-28T08:36:07.681Z","repository":{"id":74414094,"uuid":"105136561","full_name":"kalifou/ri_tme1","owner":"kalifou","description":"Information retrieval - assignments for course at UPMC - Paris 6 ","archived":false,"fork":false,"pushed_at":"2018-01-12T13:47:53.000Z","size":24259,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-29T17:14:21.509Z","etag":null,"topics":["bm25","evaluation-metrics","hits-algorithm","information-retrieval","language-model","language-modeling","pagerank-algorithm","python"],"latest_commit_sha":null,"homepage":"http://dac.lip6.fr/master/enseignement/ues/ri/","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n## TME RI TEXTUEL\n\n\n```python\nfrom Index import Index\nfrom ParserCACM import ParserCACM\nfrom TextRepresenter import PorterStemmer\nfrom Weighter import Binary, TF, TF_IDF, Log, Log_plus\nfrom EvalMeasure import EvalIRModel\nimport numpy as np\nimport os.path\nimport time\n```\n\n\n```python\n# model_type = Random | Vectoriel | Okapi | Language | PageRank | Hits | MetaModel\nfname = \"data/cacm/cacm.txt\"\nquery_file = \"data/cacm/cacm.qry\"\nrelevance_file = \"data/cacm/cacm.rel\"\n```\n\n### Random\n\n\n```python\nt1 = time.time()\ntype = \"Random\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    \u003cclass 'IRmodel.RandomModel'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :  Random\n\n\n\n![png](plots/output_4_1.png)\n\n\n    AP:  0.00489365936907\n    Exec duration(s) :  37.8984589577\n\n\n### Vectoriel\n\n\n```python\nt1 = time.time()\ntype = \"Vectoriel\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    Creating models...Done!\n    \u003cclass 'IRmodel.Vectoriel'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :   Binary\n\n\n\n![png](plots/output_6_1.png)\n\n\n    AP:  0.123849729889\n    \n    \n    Model :   Text Frequency\n\n\n\n![png](plots/output_6_3.png)\n\n\n    AP:  0.13215026906\n    \n    \n    Model :   Text Frequency - Inverse Document Frequency\n\n\n\n![png](plots/output_6_5.png)\n\n\n    AP:  0.154529641961\n    \n    \n    Model :   Log\n\n\n\n![png](plots/output_6_7.png)\n\n\n    AP:  0.120869800911\n    \n    \n    Model :   Log +\n\n\n\n![png](plots/output_6_9.png)\n\n\n    AP:  0.143917859729\n    Exec duration(s) :  171.119966984\n\n\n### Okapi : k1=2, b=0.75\n\n\n```python\nt1 = time.time()\ntype = \"Okapi\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    L moy :  4925.14700285\n    \u003cclass 'IRmodel.Okapi'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :  Okapi\n\n\n\n![png](plots/output_8_1.png)\n\n\n    AP:  0.264364808856\n    Exec duration(s) :  540.410473108\n\n\n### Language : Lambda =0.2\n\n\n```python\nt1 = time.time()\ntype = \"Language\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    Init of Language model\n    \u003cclass 'IRmodel.LanguageModel'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :  Language Model\n\n\n\n![png](plots/output_10_1.png)\n\n\n    AP:  0.307058115209\n    Exec duration(s) :  580.641823053\n\n\n### PageRank  : N=25, K=unlimited, d=0.85\n\n\n```python\nt1 = time.time()\ntype = \"PageRank\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    \u003cclass 'IRmodel.RankModel'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :  PageRank\n\n\n\n![png](plots/output_12_1.png)\n\n\n    AP:  0.133320317291\n    Exec duration(s) :  1294.32549\n\n\n### Hits : N=25, K=unlimited\n\n\n```python\ntype = \"Hits\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\n```\n\n\n\n\n![png](plots/output_14_0.png)\n\n\n\n### MetaModel\n\n\n```python\nt1 = time.time()\ntype = \"MetaModel\" \neval_platform = EvalIRModel(fname,query_file,relevance_file,model_type=type)\nmodels_recall, models_inter_prec, models_AP = eval_platform.eval_std()\nprint \"Exec duration(s) : \",time.time()-t1\n```\n\n    Indexing database...Done!\n    Grad theta : 0.2\n    Training achieved with Grad_theta \u003c  0.1  !\n    Number of queries required : 4\n    \u003cclass 'IRmodel.MetaModel'\u003e\n    Evaluation of our models ...\n    \n    \n    \n    Model :  MetaModel\n\n\n\n![png](plots/output_16_1.png)\n\n\n    AP:  0.285386850473\n    Exec duration(s) :  2393.91500187\n\n\n\n```python\nfor idx,f in enumerate(eval_platform.models[0].listFeaturers.listFeaturers):    \n    print \"featurers :\",str(f.model)[:-25],\"\u003e\"\n    print \"theta :\",eval_platform.models[0].theta[idx],\"\\n\"\n```\n\n    featurers : \u003cIRmodel.Vectoriel  \u003e\n    theta : [ 0.01994691] \n    \n    featurers : \u003cIRmodel.Vectoriel  \u003e\n    theta : [ 0.88962339] \n    \n    featurers : \u003cIRmodel.Vectoriel  \u003e\n    theta : [ 0.06195642] \n    \n    featurers : \u003cIRmodel.Vectoriel  \u003e\n    theta : [ 0.20448804] \n    \n    featurers : \u003cIRmodel.Okapi  \u003e\n    theta : [ 1.82525156] \n    \n    featurers : \u003cIRmodel.LanguageModel  \u003e\n    theta : [ 1.11311955] \n    \n    featurers : \u003cIRmodel.RankModel  \u003e\n    theta : [ 0.74651456] \n    \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkalifou%2Fri_tme1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkalifou%2Fri_tme1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkalifou%2Fri_tme1/lists"}