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Merge pull request #3482 from ygorshenin/search-quality-doc
[search] Added README for search quality.
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search/search_quality/README.txt
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search/search_quality/README.txt
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This document describes how to use the tools for search quality analysis.
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1. Prerequisites.
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* Get the latest version of omim (https://github.com/mapsme/omim) and build it.
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* Get the latest samples.lisp file with search queries. If you don't know
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how to get it, please, contact the search team.
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* Install Common Lisp. Note that there are many implementations,
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but we recommend to use SBCL (http://www.sbcl.org/).
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* Install Python 3.x and packages for data analysis (sklearn, scipy, numpy, pandas, matplotlib).
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* Download maps necessary for search quality tests.
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For example:
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./download-maps.sh -v 160524
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will download all necessary maps of version 160524 to the current directory.
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2. This section describes how to run search engine on a set of search
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queries and how to get a CSV file with search engine output.
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i) Run gen-samples.lisp script to get search queries with lists of
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vital or relevant responses in JSON format. For example:
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./gen-samples.lisp < samples.lisp > samples.json
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ii) Run features_collector_tool from the build directory.
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For example:
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features_collector_tool --mwm_path path-to-downloaded-maps \
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--json_in samples.json \
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--stats_path /tmp/stats.txt \
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2>/dev/null >samples.csv
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runs search engine on all queries from samples.json, prints
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useful info to /tmp/stats.txt and generates a CSV file with
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search engine output on each query.
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The resulting CSV file is ready for analysis, i.e. for search
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quality evaluation, ranking models learning etc. For details,
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take a look at scoring_model.py script.
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iii) To take a quick look at what the search returns without
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launching the application, consider using search_quality_tool:
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search_quality_tool --viewport=moscow \
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--queries_path=path-to-omim/search/search_quality/search_quality_tool/queries.txt
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--top 1 \
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2>/dev/null
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By default, map files in path-to-omim/data are used.
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