This directory includes the scripts used to reproduce "Table 1."
All scripts are made available under the terms of the
MIT License. A copy of
the license is contained in this directory.
The results presented in this workshop were based on the
pip install srlearn==0.5.0
Producing the results from Table 1
⚠️Results were produced in macOS High Sierra with the following specs, small differences are likely to occur on.
- Processor 3.4 GHz Intel Core i5
- Memory 16 GB 2400 MHz DDR4
- Graphics Radeon Pro 570 4096 MB
Mean and standard deviation are calculated automatically.
$ python webkb.py $ python imdb.py $ python uwcse.py
Mean and standard deviation are not calculated automatically.
They might be calculated from an interactive Python shell with
$ bash webkb.sh $ bash imdb.sh $ bash uwcse.sh
Copies of the datasets are included in the
directory for completeness. All modes and parameters are held constant, the
method for running (
BoostSRL) are varied.
The general way that time is calculated for the
BoostSRL numbers use Unix
epoch time from the
BSD DATE(1) command.
START_TIME=$(date +%s) END_TIME=$(date +%s) echo "$(($END_TIME - $START_TIME))"
_start = time.perf_counter() _end = time.perf_counter() print(_end - _start)
Each method has specific overheads that makes results imperfect for direct comparison.