From 4de9ef8f7837c155fee423587bfc2fa91723dd95 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Wed, 15 Jul 2020 15:26:59 +0200
Subject: [PATCH] [script.significance] Rewrite of app to improve DRY
---
bob/ip/binseg/script/significance.py | 473 +++++++++++++++------------
1 file changed, 266 insertions(+), 207 deletions(-)
diff --git a/bob/ip/binseg/script/significance.py b/bob/ip/binseg/script/significance.py
index 287ce0fc..1e4d5e8e 100755
--- a/bob/ip/binseg/script/significance.py
+++ b/bob/ip/binseg/script/significance.py
@@ -4,7 +4,6 @@
import os
import sys
import click
-import typing
from bob.extension.scripts.click_helper import (
verbosity_option,
@@ -13,142 +12,232 @@ from bob.extension.scripts.click_helper import (
)
import numpy
-import scipy.stats
-
import logging
logger = logging.getLogger(__name__)
from .evaluate import _validate_threshold, run as run_evaluation
-from ..engine.significance import patch_performances, visual_performances
+from ..engine.significance import (
+ patch_performances,
+ visual_performances,
+ write_analysis_text,
+ write_analysis_figures,
+ index_of_outliers,
+)
-def _index_of_outliers(c):
- """Finds indexes of outlines (+/- 1.5*IQR) on a pandas dataframe column"""
+def _eval_patches(
+ system_name,
+ threshold,
+ evaluate,
+ preddir,
+ dataset,
+ steps,
+ size,
+ stride,
+ outdir,
+ figure,
+ nproc,
+):
+ """Calculates the patch performances on a dataset
- iqr = c.quantile(0.75) - c.quantile(0.25)
- limits = (c.quantile(0.25) - 1.5 * iqr, c.quantile(0.75) + 1.5 * iqr)
- return (c < limits[0]) | (c > limits[1])
+ Parameters
+ ==========
-def _write_analysis_text(names, da, db, f):
- """Writes a text file containing the most important statistics"""
+ system_name : str
+ The name of the current system being analyzed
- diff = da - db
- f.write("#Samples/Median/Avg/Std.Dev./Normality Conf. F1-scores:\n")
- f.write(
- f"* {names[0]}: {len(da)}" \
- f" / {numpy.median(da):.3f}" \
- f" / {numpy.mean(da):.3f}" \
- f" / {numpy.std(da, ddof=1):.3f}\n"
- )
- f.write(
- f"* {names[1]}: {len(db)}" \
- f" / {numpy.median(db):.3f}" \
- f" / {numpy.mean(db):.3f}" \
- f" / {numpy.std(db, ddof=1):.3f}\n"
- )
- f.write(
- f"* {names[0]}-{names[1]}: {len(diff)}" \
- f" / {numpy.median(diff):.3f}" \
- f" / {numpy.mean(diff):.3f}" \
- f" / {numpy.std(diff, ddof=1):.3f}" \
- f" / gaussian? p={scipy.stats.normaltest(diff)[1]:.3f}\n"
- )
+ threshold : :py:class:`float`, :py:class:`str`
+ This number is used to define positives and negatives from probability
+ maps, and report F1-scores (a priori). By default, we expect a set
+ named 'validation' to be available at the input data. If that is not
+ the case, we use 'train', if available. You may provide the name of
+ another dataset to be used for threshold tunning otherwise. If not
+ set, or a string is input, threshold tunning is done per system,
+ individually. Optionally, you may also provide a floating-point number
+ between [0.0, 1.0] as the threshold to use for both systems.
- w, p = scipy.stats.ttest_rel(da, db)
- f.write(
- f"Paired T-test (is the difference zero?): S = {w:g}, p = {p:.5f}\n"
- )
+ evaluate : str
+ Name of the dataset key to use from ``dataset`` to evaluate (typically,
+ ``test``)
- w, p = scipy.stats.ttest_ind(da, db, equal_var=False)
- f.write(f"Ind. T-test (is the difference zero?): S = {w:g}, p = {p:.5f}\n")
+ preddir : str
+ Root path to the predictions generated by system ``system_name``. The
+ final subpath inside ``preddir`` that will be used will have the value
+ of this variable suffixed with the value of ``evaluate``. We will
+ search for ``<preddir>/<evaluate>/<stems>.hdf5``.
- w, p = scipy.stats.wilcoxon(diff)
- f.write(
- f"Wilcoxon test (is the difference zero?): W = {w:g}, p = {p:.5f}\n"
- )
+ dataset : dict
+ A dictionary mapping string keys to
+ :py:class:`torch.utils.data.dataset.Dataset` instances
- w, p = scipy.stats.wilcoxon(diff, alternative="greater")
- f.write(
- f"Wilcoxon test (md({names[0]}) < md({names[1]})?): " \
- f"W = {w:g}, p = {p:.5f}\n"
- )
+ steps : int
+ The number of threshold steps to consider when evaluating the highest
+ possible F1-score on train/test data.
- w, p = scipy.stats.wilcoxon(diff, alternative="less")
- f.write(
- f"Wilcoxon test (md({names[0]}) > md({names[1]})?): " \
- f"W = {w:g}, p = {p:.5f}\n"
- )
+ size : tuple
+ Two values indicating the size of windows to be used for patch
+ analysis. The values represent height and width respectively
+ stride : tuple
+ Two values indicating the stride of windows to be used for patch
+ analysis. The values represent height and width respectively
-def _write_analysis_figures(names, da, db, folder):
- """Writes a PDF containing most important plots for analysis"""
+ outdir : str
+ Path where to store visualizations. If set to ``None``, then do not
+ store performance visualizations.
- from matplotlib.backends.backend_pdf import PdfPages
- import matplotlib.pyplot as plt
+ figure : str
+ The name of a performance figure (e.g. ``f1_score``, or ``jaccard``) to
+ use when comparing performances
- diff = da - db
- bins = 50
-
- fname = os.path.join(folder, "statistics.pdf")
- os.makedirs(os.path.dirname(fname), exist_ok=True)
- with PdfPages(fname) as pdf:
-
- plt.figure()
- plt.grid()
- plt.hist(da, bins=bins)
- plt.title(
- f"{names[0]} - scores (N={len(da)}; M={numpy.median(da):.3f}; "
- f"$\mu$={numpy.mean(da):.3f}; $\sigma$={numpy.std(da, ddof=1):.3f})"
- )
- pdf.savefig()
- plt.close()
-
- plt.figure()
- plt.grid()
- plt.hist(db, bins=bins)
- plt.title(
- f"{names[1]} - scores (N={len(db)}; M={numpy.median(db):.3f}; "
- f"$\mu$={numpy.mean(db):.3f}; $\sigma$={numpy.std(db, ddof=1):.3f})"
+ nproc : int
+ Sets the number of parallel processes to use when running using
+ multiprocessing. A value of zero uses all reported cores. A value of
+ ``1`` avoids completely the use of multiprocessing and runs all chores
+ in the current processing context.
+
+
+ Returns
+ =======
+
+ d : dict
+ A dictionary in which keys are filename stems and values are
+ dictionaries with the following contents:
+
+ ``df``: :py:class:`pandas.DataFrame`
+ A dataframe with all the patch performances aggregated, for all
+ input images.
+
+ ``n`` : :py:class:`numpy.ndarray`
+ A 2D numpy array containing the number of performance scores for
+ every pixel in the original image
+
+ ``avg`` : :py:class:`numpy.ndarray`
+ A 2D numpy array containing the average performances for every
+ pixel on the input image considering the patch sizes and strides
+ applied when windowing the image
+
+ ``std`` : :py:class:`numpy.ndarray`
+ A 2D numpy array containing the (unbiased) standard deviations for
+ the provided performance figure, for every pixel on the input image
+ considering the patch sizes and strides applied when windowing the
+ image
+
+ """
+
+ if not isinstance(threshold, float):
+
+ assert threshold in dataset, f"No dataset named '{threshold}'"
+
+ logger.info(
+ f"Evaluating threshold on '{threshold}' set for "
+ f"'{system_name}' using {steps} steps"
)
- pdf.savefig()
- plt.close()
-
- plt.figure()
- plt.boxplot([da, db])
- plt.title(f"{names[0]} and {names[1]} (N={len(da)})")
- pdf.savefig()
- plt.close()
-
- plt.figure()
- plt.boxplot(diff)
- plt.title(f"Differences ({names[0]} - {names[1]}) (N={len(da)})")
- pdf.savefig()
- plt.close()
-
- plt.figure()
- plt.grid()
- plt.hist(diff, bins=bins)
- plt.title(
- f"Systems ({names[0]} - {names[1]}) " \
- f"(N={len(diff)}; M={numpy.median(diff):.3f}; " \
- f"$\mu$={numpy.mean(diff):.3f}; " \
- f"$\sigma$={numpy.std(diff, ddof=1):.3f})"
+ threshold = run_evaluation(
+ dataset[threshold], threshold, predictions[0], steps=steps
)
- pdf.savefig()
- plt.close()
+ logger.info(f"Set --threshold={threshold:.5f} for '{system_name}'")
+
+ # for a given threshold on each system, calculate patch performances
+ logger.info(
+ f"Evaluating patch performances on '{evaluate}' set for "
+ f"'{system_name}' using windows of size {size} and stride {stride}"
+ )
+
+ return patch_performances(
+ dataset,
+ evaluate,
+ preddir,
+ threshold,
+ size,
+ stride,
+ figure,
+ nproc,
+ outdir,
+ )
+
+
+def _eval_differences(perf1, perf2, evaluate, dataset, size, stride, outdir,
+ figure, nproc):
+ """Evaluate differences in the performance patches between two systems
+
+ Parameters
+ ----------
+
+ perf1, perf2 : dict
+ A dictionary as returned by :py:func:`_eval_patches`
+
+ evaluate : str
+ Name of the dataset key to use from ``dataset`` to evaluate (typically,
+ ``test``)
- p = scipy.stats.pearsonr(da, db)
- plt.figure()
- plt.grid()
- plt.scatter(da, db, marker=".", color="black")
- plt.xlabel("{names[0]}")
- plt.ylabel("{names[1]}")
- plt.title(f"Scatter (p={p[0]:.3f})")
- pdf.savefig()
- plt.close()
+ dataset : dict
+ A dictionary mapping string keys to
+ :py:class:`torch.utils.data.dataset.Dataset` instances
+
+ size : tuple
+ Two values indicating the size of windows to be used for patch
+ analysis. The values represent height and width respectively
+
+ stride : tuple
+ Two values indicating the stride of windows to be used for patch
+ analysis. The values represent height and width respectively
+
+ outdir : str
+ If set to ``None``, then do not output performance visualizations.
+ Otherwise, in directory ``outdir``, dumps the visualizations for the
+ performance differences between both systems.
+
+ figure : str
+ The name of a performance figure (e.g. ``f1_score``, or ``jaccard``) to
+ use when comparing performances
+
+ nproc : int
+ Sets the number of parallel processes to use when running using
+ multiprocessing. A value of zero uses all reported cores. A value of
+ ``1`` avoids completely the use of multiprocessing and runs all chores
+ in the current processing context.
+
+
+ Returns
+ -------
+
+ d : dict
+ A dictionary representing patch performance differences across all
+ files and patches. The format of this is similar to the individual
+ inputs ``perf1`` and ``perf2``.
+
+ """
+
+ perf_diff = dict([(k, perf1[k]["df"].copy()) for k in perf1])
+
+ # we can subtract these
+ to_subtract = (
+ "precision",
+ "recall",
+ "specificity",
+ "accuracy",
+ "jaccard",
+ "f1_score",
+ )
+
+ for k in perf_diff:
+ for col in to_subtract:
+ perf_diff[k][col] -= perf2[k]["df"][col]
+
+ return visual_performances(
+ dataset,
+ evaluate,
+ perf_diff,
+ size,
+ stride,
+ figure,
+ nproc,
+ outdir,
+ )
@click.command(
@@ -265,8 +354,8 @@ def _write_analysis_figures(names, da, db, folder):
@click.option(
"--figure",
"-f",
- help="The name of a performance figure (e.g. f1_score) to use for "
- "for comparing performances",
+ help="The name of a performance figure (e.g. f1_score, or jaccard) to "
+ "use when comparing performances",
default="f1_score",
type=str,
show_default=True,
@@ -285,8 +374,21 @@ def _write_analysis_figures(names, da, db, folder):
@click.option(
"--remove-outliers/--no-remove-outliers",
"-R",
- help="If set, removes outliers from both score distributions before " \
- "running statistical analysis",
+ help="If set, removes outliers from both score distributions before "
+ "running statistical analysis. Outlier removal follows a 1.5 IQR range "
+ "check from the difference in figures between both systems and assumes "
+ "most of the distribution is contained within that range (like in a "
+ "normal distribution)",
+ default=False,
+ required=True,
+ show_default=True,
+ cls=ResourceOption,
+)
+@click.option(
+ "--remove-zeros/--no-remove-zeros",
+ "-R",
+ help="If set, removes instances from the statistical analysis in which "
+ "both systems had a performance equal to zero.",
default=False,
required=True,
show_default=True,
@@ -295,7 +397,7 @@ def _write_analysis_figures(names, da, db, folder):
@click.option(
"--parallel",
"-x",
- help="Set the number of parallel processes to use when running using " \
+ help="Set the number of parallel processes to use when running using "
"multiprocessing. A value of zero uses all reported cores.",
default=1,
type=int,
@@ -316,6 +418,7 @@ def significance(
figure,
output_folder,
remove_outliers,
+ remove_zeros,
parallel,
**kwargs,
):
@@ -329,125 +432,81 @@ def significance(
threshold = _validate_threshold(threshold, dataset)
assert evaluate in dataset, f"No dataset named '{evaluate}'"
- if isinstance(threshold, float):
- threshold1 = threshold2 = threshold
-
- else: # it is a string, re-calculate it for each system individually
-
- assert threshold in dataset, f"No dataset named '{threshold}'"
-
- logger.info(
- f"Evaluating threshold on '{threshold}' set for '{names[0]}' using {steps} steps"
- )
- threshold1 = run_evaluation(
- dataset[threshold], threshold, predictions[0], steps=steps
- )
- logger.info(f"Set --threshold={threshold1:.5f} for '{names[0]}'")
-
- logger.info(
- f"Evaluating threshold on '{threshold}' set for '{names[1]}' using {steps} steps"
- )
- threshold2 = run_evaluation(
- dataset[threshold], threshold, predictions[1], steps=steps
- )
- logger.info(f"Set --threshold={threshold2:.5f} for '{names[1]}'")
-
- # for a given threshold on each system, calculate patch performances
- logger.info(
- f"Evaluating patch performances on '{evaluate}' set for '{names[0]}' using windows of size {size} and stride {stride}"
- )
- dir1 = (
- os.path.join(output_folder, names[0])
- if output_folder is not None
- else None
- )
- perf1 = patch_performances(
- dataset,
+ perf1 = _eval_patches(
+ names[0],
+ threshold,
evaluate,
predictions[0],
- threshold1,
+ dataset,
+ steps,
size,
stride,
+ (output_folder
+ if output_folder is None
+ else os.path.join(output_folder, names[0])),
figure,
- nproc=parallel,
- outdir=dir1,
+ parallel,
)
- logger.info(
- f"Evaluating patch performances on '{evaluate}' set for '{names[1]}' using windows of size {size} and stride {stride}"
- )
- dir2 = (
- os.path.join(output_folder, names[1])
- if output_folder is not None
- else None
- )
- perf2 = patch_performances(
- dataset,
+ perf2 = _eval_patches(
+ names[1],
+ threshold,
evaluate,
predictions[1],
- threshold2,
- size,
- stride,
- figure,
- nproc=parallel,
- outdir=dir2,
- )
-
- perf_diff = dict([(k, perf1[k]["df"].copy()) for k in perf1])
- to_subtract = (
- "precision",
- "recall",
- "specificity",
- "accuracy",
- "jaccard",
- "f1_score",
- )
- for k in perf_diff:
- for col in to_subtract:
- perf_diff[k][col] -= perf2[k]["df"][col]
- dirdiff = (
- os.path.join(output_folder, "diff")
- if output_folder is not None
- else None
- )
- perf_diff = visual_performances(
dataset,
- evaluate,
- perf_diff,
+ steps,
size,
stride,
+ (output_folder
+ if output_folder is None
+ else os.path.join(output_folder, names[1])),
figure,
- nproc=parallel,
- outdir=dirdiff,
+ parallel,
)
- # loads all F1-scores for the given threshold
+ perf_diff = _eval_differences(
+ perf1,
+ perf2,
+ evaluate,
+ dataset,
+ size,
+ stride,
+ (output_folder
+ if output_folder is None
+ else os.path.join(output_folder, "diff")),
+ figure,
+ parallel,
+ )
+
+ # loads all figures for the given threshold
stems = list(perf1.keys())
- da = numpy.array([perf1[k]["df"].f1_score for k in stems]).flatten()
- db = numpy.array([perf2[k]["df"].f1_score for k in stems]).flatten()
+ da = numpy.array([perf1[k]["df"][figure] for k in stems]).flatten()
+ db = numpy.array([perf2[k]["df"][figure] for k in stems]).flatten()
diff = da - db
while remove_outliers:
- outliers_diff = _index_of_outliers(diff)
+ outliers_diff = index_of_outliers(diff)
if sum(outliers_diff) == 0:
break
diff = diff[~outliers_diff]
da = da[~outliers_diff]
db = db[~outliers_diff]
- # also remove cases in which both da and db are zero
- remove_zeros = (da == 0) & (db == 0)
- diff = diff[~remove_zeros]
- da = da[~remove_zeros]
- db = db[~remove_zeros]
+ if remove_zeros:
+ remove_zeros = (da == 0) & (db == 0)
+ diff = diff[~remove_zeros]
+ da = da[~remove_zeros]
+ db = db[~remove_zeros]
if output_folder is not None:
- _write_analysis_figures(names, da, db, output_folder)
+ fname = os.path.join(output_folder, "analysis.pdf")
+ os.makedirs(os.path.dirname(fname), exist_ok=True)
+ write_analysis_figures(names, da, db, fname)
if output_folder is not None:
fname = os.path.join(output_folder, "analysis.txt")
os.makedirs(os.path.dirname(fname), exist_ok=True)
with open(fname, "wt") as f:
- _write_analysis_text(names, da, db, f)
+ write_analysis_text(names, da, db, f)
else:
- _write_analysis_text(names, da, db, sys.stdout)
+ write_analysis_text(names, da, db, sys.stdout)
--
GitLab