hello-tfidf's Introduction

Hello TF-IDF

A simple toy implementation of TF-IDF

Prerequests

python3 - required
virtualenv - required
fabric - required
gmake - optional

Execute Code

Installing Dependencies

fab setup

Run Demo Code

fab index

If you have already installed gmake, you can simply type

make

Example Output

$ make
fab index
[localhost] local: which python3
/path/to/project/env/bin/python3
[localhost] local: python3 index.py
Building Index ..
653  records indexed ..
Your Query (e.g. Data Mining): Data Mining
Results by Sum of TF-IDFs
[ 287 ] ( 0.6107932232871334 ) Data Mining
[ 413 ] ( 0.40719548219142226 ) Mining Data Streams
[ 554 ] ( 0.40719548219142226 ) Mining multimedia data
[ 578 ] ( 0.40719548219142226 ) Data Mining Techniques
[ 188 ] ( 0.3053966116435667 ) Mining Concept-Drifting Data Streams
[ 548 ] ( 0.3053966116435667 ) Constraint-Based Multidimensional Data Mining
[ 288 ] ( 0.25694160318259573 ) Stream Mining
[ 499 ] ( 0.24431728931485341 ) Data Mining for Web Intelligence
[ 536 ] ( 0.24431728931485341 ) Data Mining: Concepts and Techniques
[ 572 ] ( 0.24431728931485341 ) Attribute-Oriented Induction in data Mining
[ 567 ] ( 0.21693370661743228 ) OLAP Mining: Integration of OLAP with Data Mining
Results by BM25
[ 287 ] ( 1.581421045562941 ) Data Mining
[ 413 ] ( 1.1688764249813042 ) Mining Data Streams
[ 554 ] ( 1.1688764249813042 ) Mining multimedia data
[ 578 ] ( 1.1688764249813042 ) Data Mining Techniques
[ 188 ] ( 0.9270399232610343 ) Mining Concept-Drifting Data Streams
[ 548 ] ( 0.9270399232610343 ) Constraint-Based Multidimensional Data Mining
[ 499 ] ( 0.7681187935591427 ) Data Mining for Web Intelligence
[ 536 ] ( 0.7681187935591427 ) Data Mining: Concepts and Techniques
[ 572 ] ( 0.7681187935591427 ) Attribute-Oriented Induction in data Mining
[ 288 ] ( 0.746688259276887 ) Stream Mining
[ 567 ] ( 0.7054582857823681 ) OLAP Mining: Integration of OLAP with Data Mining

Done.

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