# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Create sample PR curve summary data.
We have 3 classes: R, G, and B. We generate colors within RGB space from 3
normal distributions (1 at each corner of the color triangle: [255, 0, 0],
[0, 255, 0], and [0, 0, 255]).
The true label of each random color is associated with the normal distribution
that generated it.
Using 3 other normal distributions (over the distance each color is from a
corner of the color triangle - RGB), we then compute the probability that each
color belongs to the class. We use those probabilities to generate PR curves.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
from tempfile import gettempdir
from absl import app
from absl import flags
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorboard.plugins.pr_curve import summary
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
task = Task.init(project_name='examples', task_name='tensorboard pr_curve')
tf.compat.v1.disable_v2_behavior()
FLAGS = flags.FLAGS
flags.DEFINE_string(
"logdir",
os.path.join(gettempdir(), "pr_curve_demo"),
"Directory into which to write TensorBoard data.",
)
flags.DEFINE_integer(
"steps", 10, "Number of steps to generate for each PR curve."
)
def start_runs(
logdir, steps, run_name, thresholds, mask_every_other_prediction=False
):
"""Generate a PR curve with precision and recall evenly weighted.
Arguments:
logdir: The directory into which to store all the runs' data.
steps: The number of steps to run for.
run_name: The name of the run.
thresholds: The number of thresholds to use for PR curves.
mask_every_other_prediction: Whether to mask every other prediction by
alternating weights between 0 and 1.
"""
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(42)
# Create a normal distribution layer used to generate true color labels.
distribution = tf.compat.v1.distributions.Normal(loc=0.0, scale=142.0)
# Sample the distribution to generate colors. Lets generate different numbers
# of each color. The first dimension is the count of examples.
# The calls to sample() are given fixed random seed values that are "magic"
# in that they correspond to the default seeds for those ops when the PR
# curve test (which depends on this code) was written. We've pinned these
# instead of continuing to use the defaults since the defaults are based on
# node IDs from the sequence of nodes added to the graph, which can silently
# change when this code or any TF op implementations it uses are modified.
# TODO(nickfelt): redo the PR curve test to avoid reliance on random seeds.
# Generate reds.
number_of_reds = 100
true_reds = tf.clip_by_value(
tf.concat(
[
255 - tf.abs(distribution.sample([number_of_reds, 1], seed=11)),
tf.abs(distribution.sample([number_of_reds, 2], seed=34)),
],
axis=1,
),
0,
255,
)
# Generate greens.
number_of_greens = 200
true_greens = tf.clip_by_value(
tf.concat(
[
tf.abs(distribution.sample([number_of_greens, 1], seed=61)),
255
- tf.abs(distribution.sample([number_of_greens, 1], seed=82)),
tf.abs(distribution.sample([number_of_greens, 1], seed=105)),
],
axis=1,
),
0,
255,
)
# Generate blues.
number_of_blues = 150
true_blues = tf.clip_by_value(
tf.concat(
[
tf.abs(distribution.sample([number_of_blues, 2], seed=132)),
255
- tf.abs(distribution.sample([number_of_blues, 1], seed=153)),
],
axis=1,
),
0,
255,
)
# Assign each color a vector of 3 booleans based on its true label.
labels = tf.concat(
[
tf.tile(tf.constant([[True, False, False]]), (number_of_reds, 1)),
tf.tile(tf.constant([[False, True, False]]), (number_of_greens, 1)),
tf.tile(tf.constant([[False, False, True]]), (number_of_blues, 1)),
],
axis=0,
)
# We introduce 3 normal distributions. They are used to predict whether a
# color falls under a certain class (based on distances from corners of the
# color triangle). The distributions vary per color. We have the distributions
# narrow over time.
initial_standard_deviations = [v + FLAGS.steps for v in (158, 200, 242)]
iteration = tf.compat.v1.placeholder(tf.int32, shape=[])
red_predictor = tf.compat.v1.distributions.Normal(
loc=0.0,
scale=tf.cast(
initial_standard_deviations[0] - iteration, dtype=tf.float32
),
)
green_predictor = tf.compat.v1.distributions.Normal(
loc=0.0,
scale=tf.cast(
initial_standard_deviations[1] - iteration, dtype=tf.float32
),
)
blue_predictor = tf.compat.v1.distributions.Normal(
loc=0.0,
scale=tf.cast(
initial_standard_deviations[2] - iteration, dtype=tf.float32
),
)
# Make predictions (assign 3 probabilities to each color based on each color's
# distance to each of the 3 corners). We seek double the area in the right
# tail of the normal distribution.
examples = tf.concat([true_reds, true_greens, true_blues], axis=0)
probabilities_colors_are_red = (
1
- red_predictor.cdf(
tf.norm(tensor=examples - tf.constant([255.0, 0, 0]), axis=1)
)
) * 2
probabilities_colors_are_green = (
1
- green_predictor.cdf(
tf.norm(tensor=examples - tf.constant([0, 255.0, 0]), axis=1)
)
) * 2
probabilities_colors_are_blue = (
1
- blue_predictor.cdf(
tf.norm(tensor=examples - tf.constant([0, 0, 255.0]), axis=1)
)
) * 2
predictions = (
probabilities_colors_are_red,
probabilities_colors_are_green,
probabilities_colors_are_blue,
)
# This is the crucial piece. We write data required for generating PR curves.
# We create 1 summary per class because we create 1 PR curve per class.
for i, color in enumerate(("red", "green", "blue")):
description = (
"The probabilities used to create this PR curve are "
"generated from a normal distribution. Its standard "
"deviation is initially %0.0f and decreases over time."
% initial_standard_deviations[i]
)
weights = None
if mask_every_other_prediction:
# Assign a weight of 0 to every even-indexed prediction. Odd-indexed
# predictions are assigned a default weight of 1.
consecutive_indices = tf.reshape(
tf.range(tf.size(input=predictions[i])),
tf.shape(input=predictions[i]),
)
weights = tf.cast(consecutive_indices % 2, dtype=tf.float32)
summary.op(
name=color,
labels=labels[:, i],
predictions=predictions[i],
num_thresholds=thresholds,
weights=weights,
display_name="classifying %s" % color,
description=description,
)
merged_summary_op = tf.compat.v1.summary.merge_all()
events_directory = os.path.join(logdir, run_name)
sess = tf.compat.v1.Session()
writer = tf.compat.v1.summary.FileWriter(events_directory, sess.graph)
for step in xrange(steps):
feed_dict = {
iteration: step,
}
merged_summary = sess.run(merged_summary_op, feed_dict=feed_dict)
writer.add_summary(merged_summary, step)
writer.close()
def run_all(logdir, steps, thresholds, verbose=False):
"""Generate PR curve summaries.
Arguments:
logdir: The directory into which to store all the runs' data.
steps: The number of steps to run for.
verbose: Whether to print the names of runs into stdout during execution.
thresholds: The number of thresholds to use for PR curves.
"""
# First, we generate data for a PR curve that assigns even weights for
# predictions of all classes.
run_name = "colors"
if verbose:
print("--- Running: %s" % run_name)
start_runs(
logdir=logdir, steps=steps, run_name=run_name, thresholds=thresholds
)
# Next, we generate data for a PR curve that assigns arbitrary weights to
# predictions.
run_name = "mask_every_other_prediction"
if verbose:
print("--- Running: %s" % run_name)
start_runs(
logdir=logdir,
steps=steps,
run_name=run_name,
thresholds=thresholds,
mask_every_other_prediction=True,
)
def main(unused_argv):
print("Saving output to %s." % FLAGS.logdir)
run_all(FLAGS.logdir, FLAGS.steps, 50, verbose=True)
print("Done. Output saved to %s." % FLAGS.logdir)
if __name__ == "__main__":
app.run(main)