convergence_analysis module¶
convergence_analysis.py. Study of the convergence through the evolution of the Spearman and Pearson correlation + measure of the mAP value every 5 iteration.
Convergence analysis¶
Usage:
python convergence_analysis.py -N [1] -t [2] -d [3] -c [4] -ts [5] -s [6] -nmin [7] -nmax [8] -di [9] -o [10] -te [11] -in [12] -g [13] -cfg [14] -v [15] --debug --help
- where:
- Default options are found in the
configuration.ini
file. - [1]
-i, --iter
: number of classification iterations. - [2]
-t, --threads
: number of cores to use. - [3]
-d, --dataset
: dataset to use. - [4]
-c, --classifier
: classifier to use. - [5]
-ts, --trainsize
: proportion of dataset to use for training. - [6]
-s, --sim
: similarity type to use (EM not supported). - [7]
-nmin
: minimum number of synthetic labels. - [8]
-nmax
: maximum number of synthetic labels. - [9]
-di, --distrib
: synthetic annotation mode (RND, UNI, OVA). - [10]
-o, --output
: output folder. - [11]
-te, --temp
: temporary folder. - [12]
-in
: input data file. - [13]
-g, --ground
: ground-truth file. - [14]
-cfg, --config_file
: provide a custom configuration file. - [15]
-v, --verbose
: controls verbosity level (0 to 4). -db, --debug
: debug mode (save temporary files).-h, --help
- Default options are found in the
Computes N
iterations of SIC and compares the final similarity matrix to partial matrices in past iterations (see steps
in convergence_analysis.py
).