Metadata-Version: 1.1
Name: trec-dd
Version: 0.3.1.dev5
Summary: TREC Dynamic Domain (DD) evaluation test harness for simulating user interaction with a search engine
Home-page: http://trec-dd.org/
Author: Diffeo, Inc.
Author-email: support@diffeo.com
License: MIT/X11 license http://opensource.org/licenses/MIT
Description: # trec-dd-simulation-harness
        
        This is the official "jig" for simulating a user interacting with a
        TREC DD system during an interactive query session.
        
        # Usage
        
        To score a runfile (see "Scoring the System"):
        
            trec_dd_scorer -c config.yaml run_file_in.txt run_file_scored.json > pretty_table.txt 2> log.txt &
        
        
        This repository also provides a baseline system that randomizes
        subtopic ordering (see "Example TREC DD Systems").  In particular this
        baseline system shows how to hook an a system up to the jig in python.
        Hooking a system up to the jig via the command line is further
        documented below.
        
            trec_dd_random_system -c config.yaml &> log.txt &
        
        The scores for this baseline system using an early version of the TREC
        DD truth data are:
        
        |Score|Metric|
        |-----|------|
        |0.659|average_err_arithmetic|
        |0.302|average_err_harmonic|
        |0.002|cube_test|
        |0.559|modified_precision_at_recall|
        |0.996|precision_at_recall|
        |0.386|reciprocal_rank_at_recall|
        
        
        # Installation
        
        The recommended way to install and use the scorer is with python
        virtualenv, which is a standard tool on all widely used platforms.
        For example on Ubuntu:
        
            apt-get install python-virtualenv
            virtualenv vpy
        
        or on CentOS:
        
            yum install python-virtualenv
            virtualenv vpy
        
        or on MacOS X
        
            brew install pyenv-virtualenv
            pyenv-virtualenv vpy
        
        or [on Windows](http://www.tylerbutler.com/2012/05/how-to-install-python-pip-and-virtualenv-on-windows-with-powershell/).
        
        You will also need a database.  We recommend postgres or mysql.  You
        can install this on your system using standard tools.  The connection
        information must be written into the config.yaml file referenced in
        the commands above.  See [config.yaml](examples/config.yaml) for an
        example.
        
        Once you have a virtualenv, the following commands will install the
        trec_dd scorer.  You should choose whether you are using mysql or
        postgres and specify that as a pip extras declaration in square
        brackets as follows:
        
            . vpy/bin/activate
            pip install trec_dd_simulation_harness[mysql]
        
        or to use postgres:
        
            . vpy/bin/activate
            pip install trec_dd_simulation_harness[postgres]
        
        That will create the shell entry points for running the two commands
        illustrated at the top of this file.
        
        
        # Simulation Harness
        
        If you wish to evaluate a TREC DD system, you must run it against the
        TREC DD simulation harness. A system interacting with the simulation
        harness will produce a "runfile" that summarizes the simulation
        session.  The "runfile", for each of the system's response, encodes
        information such as (1) "was the system's response on topic?" (2)
        "what subtopics were contained within the system's response?"  and (3)
        "how relevant was the system's response?". Please see the
        specification for a "runfile" for more information.
        
        A TREC DD system interacts with the simulation harness by invoking
        commands at the command line. Systems written in python may use
        the [HarnessAmbassadorCLI](trec_dd/system/ambassador_cli.py) to
        facilitate this communication. The HarnessAmbassadorCLI is also useful
        documentation for how one should interact with the harness via the
        command line.
        
        Once you have a "runfile", you may then score your run. Please
        see the section "Gathering Scores" for more information.
        
        # Example TREC DD Systems
        
        The directory trec\_dd/system holds example TREC DD systems to
        demonstrate interaction with the simulation harness using a TREC DD
        system. Right now, the only example system is random_system.py.
        
        # Executing the Random System
        
        ## Requirements
        
        To run the example systems, you must have a truth data csv file.
        
        ## Running the System
        
        You can run the random system in the simulation harness by
        calling
        
            trec_dd_random_system -c config.yaml &> log.txt &
        
        After this command executes, you should find the resulting system
        runfile at the path you specified in the command. The runfile summarizes
        the responses the random system gave to the harness, as well as the harness's
        thoughts on those responses. This runfile captures everything one needs to
        know in order to give a system a score.
        
        ## Scoring the System
        
        To score your runfile, you may use the trec_dd/scorer/run.py script.
        
            trec_dd_scorer -c config.yaml run_file_in.txt run_file_scored.json > pretty_table.txt 2> log.txt &
        
        Please see the section titled "Gathering Scores" for more information on the scoring
        subsystem.
        
        # Gathering Scores
        
        ## Requirements
        
        You must have a runfile generated for your system if you wish to score
        it. You must also have access to the truth data used by the harness
        when generating the runfile.
        
        ## Running the Scorer
        
        The top-level scoring script trec\_dd/scorer/run.py is used to generate
        scores. To run it:
        
            trec_dd_scorer -c config.yaml run_file_in.txt run_file_scored.json > pretty_table.txt 2> log.txt &
        
        This will go through your runfile and run each TREC DD scorer. If you
        wish to run specific scorers, rather than all of them, please see the
        '--scorer' option on the trec\_dd\_scorer command. The scorers
        specified after the --scorer option must be the names of scorers known
        to the system. These are exactly the following:
        
         * reciprocal\_rank\_at\_recall
         * precision\_at\_recall
         * modified\_precision\_at\_recall
         * average\_err\_arithmetic
         * average\_err\_harmonic
         * average\_err\_arithmetic\_binary
         * average\_err\_harmonic\_binary
        
        # Description of Scorers
        
         * reciprocal\_rank\_at\_recall calculates the reciprocal of the rank by which
         every subtopic for a topic is accounted for.
        
         * precision\_at\_recall calculates the precision of all results up to the point
         where every subtopic for a topic is accounted for.
        
         * average\_err\_arithmetic calculates the expected reciprocal rank
         for each subtopic, and then average the scores accross subtopics
         using an arithmetic average. It uses a graded relevance for computing
         stopping probabilities.
        
         * average\_err\_arithmetic\_binary calculates the expected reciprocal
         rank for each subtopic, and then averages the scores accross
         subtopics using an arithmetic average. It uses binary relevance for
         computing stopping probabilities. Hence, this scorer ignores the
         'rating' field in the runfile.
        
         * average\_err\_harmonic calculates the expected reciprocal rank for
         each subtopic, and then averages the scores accross subtopics using
         an arithmetic average. It uses graded relevance for computing
         stopping probabilities.
        
         * average\_err\_harmonic\_binary average\_err\_harmonic calculates the expected reciprocal rank for
         each subtopic, and then averages the scores accross subtopics using
         an arithmetic average. It uses binary relevance for computing stopping probabilities. Hence,
         this scorer ignores the 'rating' field in the runfile.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
