""" Click commands for ``bob.bio.base`` """
import functools
import logging
import click
from clapper.click import verbosity_option
import bob.measure.script.figure as measure_figure
from bob.measure.script import common_options
from ..score import load
from . import figure as bio_figure
logger = logging.getLogger(__name__)
SCORE_FORMAT = (
"Files must be 4- or 5- columns format, see "
":py:func:`bob.bio.base.score.load.four_column` and "
":py:func:`bob.bio.base.score.load.five_column` for details."
)
CRITERIA = ("eer", "min-hter", "far", "mindcf", "cllr", "rr")
[docs]
def rank_option(**kwargs):
"""Get option for rank parameter"""
def custom_rank_option(func):
def callback(ctx, param, value):
value = 1 if value < 0 else value
ctx.meta["rank"] = value
return value
return click.option(
"-rk",
"--rank",
type=click.INT,
default=1,
help="Provide rank for the command",
callback=callback,
show_default=True,
**kwargs,
)(func)
return custom_rank_option
@common_options.metrics_command(
common_options.METRICS_HELP.format(
names="FtA, FAR, FRR, FMR, FNMR, HTER",
criteria=CRITERIA,
score_format=SCORE_FORMAT,
hter_note="Note that FAR = FMR * (1 - FtA), FRR = FtA + FNMR * (1 - FtA) "
"and HTER = (FMR + FNMR) / 2",
command="bob bio metrics",
),
criteria=CRITERIA,
)
@common_options.cost_option()
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def metrics(ctx, scores, evaluation, score_column, **kwargs):
if "criterion" in ctx.meta and ctx.meta["criterion"] == "rr":
process = bio_figure.Metrics(
ctx,
scores,
evaluation,
functools.partial(load.cmc, csv_score_column=score_column),
)
else:
process = bio_figure.Metrics(
ctx,
scores,
evaluation,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@common_options.roc_command(
common_options.ROC_HELP.format(
score_format=SCORE_FORMAT, command="bob bio roc"
)
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def roc(ctx, scores, evaluation, score_column, **kwargs):
process = bio_figure.Roc(
ctx,
scores,
evaluation,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@common_options.det_command(
common_options.DET_HELP.format(
score_format=SCORE_FORMAT, command="bob bio det"
)
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def det(ctx, scores, evaluation, score_column, **kwargs):
process = bio_figure.Det(
ctx,
scores,
evaluation,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@common_options.epc_command(
common_options.EPC_HELP.format(
score_format=SCORE_FORMAT, command="bob bio epc"
)
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def epc(ctx, scores, score_column, **kwargs):
process = measure_figure.Epc(
ctx,
scores,
True,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@common_options.hist_command(
common_options.HIST_HELP.format(
score_format=SCORE_FORMAT, command="bob bio hist"
)
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def hist(ctx, scores, evaluation, score_column, **kwargs):
process = bio_figure.Hist(
ctx,
scores,
evaluation,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@common_options.evaluate_command(
common_options.EVALUATE_HELP.format(
score_format=SCORE_FORMAT, command="bob bio evaluate"
),
criteria=CRITERIA,
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"NOT YET IMPLEMENTED. "
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
@common_options.cost_option()
def evaluate(ctx, scores, evaluation, score_column, **kwargs):
if score_column != "score":
raise NotImplementedError(
"'evaluate' does not yet support files with scores in columns "
"other than 'score'."
)
common_options.evaluate_flow(
ctx, scores, evaluation, metrics, roc, det, epc, hist, **kwargs
)
@common_options.multi_metrics_command(
common_options.MULTI_METRICS_HELP.format(
names="FtA, FAR, FRR, FMR, FNMR, HTER",
criteria=CRITERIA,
score_format=SCORE_FORMAT,
command="bob bio multi-metrics",
),
criteria=CRITERIA,
)
@click.option(
"--score-column",
default="score",
show_default=True,
help=(
"Selects the CSV column to consider as scores. This is ignored for "
"non-CSV files. The column must contain numerical values."
),
)
def multi_metrics(
ctx, scores, evaluation, protocols_number, score_column, **kwargs
):
ctx.meta["min_arg"] = protocols_number * (2 if evaluation else 1)
process = bio_figure.MultiMetrics(
ctx,
scores,
evaluation,
functools.partial(load.split, csv_score_column=score_column),
)
process.run()
@click.command()
@common_options.scores_argument(nargs=-1)
@common_options.titles_option()
@common_options.legends_option()
@common_options.sep_dev_eval_option()
@common_options.output_plot_file_option(default_out="cmc.pdf")
@common_options.eval_option()
@common_options.semilogx_option(True)
@common_options.axes_val_option(dflt=None)
@common_options.x_rotation_option()
@common_options.const_layout_option()
@common_options.style_option()
@common_options.linestyles_option()
@common_options.figsize_option()
@verbosity_option(logger=logger)
@click.pass_context
def cmc(ctx, scores, evaluation, **kwargs):
"""Plot CMC (cumulative match characteristic curve).
graphical presentation of results of an identification task eval, plotting
rank values on the x-axis and the probability of correct identification at
or below that rank on the y-axis. The values for the axis will be computed
using :py:func:`bob.measure.cmc`.
You need to provide one or more development score file(s) for each
experiment. You can also provide eval files along with dev files. If
eval-scores are used, the flag `--eval` must be used. is required
in that case. Files must be 4- or 5- columns format, see
:py:func:`bob.bio.base.score.load.four_column` and
:py:func:`bob.bio.base.score.load.five_column` for details.
Examples:
$ bob bio cmc -v dev-scores
$ bob bio cmc -v dev-scores1 eval-scores1 dev-scores2
eval-scores2
$ bob bio cmc -v -o my_roc.pdf dev-scores1 eval-scores1
"""
process = bio_figure.Cmc(ctx, scores, evaluation, load.cmc)
process.run()
@click.command()
@common_options.scores_argument(nargs=-1)
@common_options.titles_option()
@common_options.legends_option()
@common_options.sep_dev_eval_option()
@common_options.output_plot_file_option(default_out="dir.pdf")
@common_options.eval_option()
@common_options.semilogx_option(True)
@common_options.axes_val_option(dflt=None)
@common_options.x_rotation_option()
@rank_option()
@common_options.const_layout_option()
@common_options.style_option()
@common_options.linestyles_option()
@common_options.figsize_option()
@common_options.min_far_option()
@verbosity_option(logger=logger)
@click.pass_context
def dir(ctx, scores, evaluation, **kwargs):
"""Plots the Detection & Identification Rate curve over the FAR.
This curve is designed to be used in an open set identification protocol,
and defined in Chapter 14.1 of [LiJain2005]_. It requires to have at least
one open set probe item, i.e., with no corresponding gallery, such that the
positives for that pair are ``None``.
The detection and identification curve first computes FAR thresholds based
on the out-of-set probe scores (negative scores). For each probe item, the
**maximum** negative score is used. Then, it plots the detection and
identification rates for those thresholds, which are based on the in-set
probe scores only. See [LiJain2005]_ for more details.
.. [LiJain2005] **Stan Li and Anil K. Jain**, *Handbook of Face Recognition*, Springer, 2005
You need to provide one or more development score file(s) for each
experiment. You can also provide eval files along with dev files. If
eval-scores are used, the flag `--eval` must be used. is required
in that case. Files must be 4- or 5- columns format, see
:py:func:`bob.bio.base.score.load.four_column` and
:py:func:`bob.bio.base.score.load.five_column` for details.
Examples:
$ bob bio dir -e -v dev-scores
$ bob bio dir -v dev-scores1 eval-scores1 dev-scores2
eval-scores2
$ bob bio dir -v -o my_roc.pdf dev-scores1 eval-scores1
"""
process = bio_figure.Dir(ctx, scores, evaluation, load.cmc)
process.run()