I am done

rl/Lib/site-packages/tensorboard/plugins/distribution/compressor.py

@@ -0,0 +1,159 @@
+# 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.
+# ==============================================================================
+"""Package for histogram compression."""
+
+
+import dataclasses
+import numpy as np
+
+from typing import Tuple
+
+# Normal CDF for std_devs: (-Inf, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, Inf)
+# naturally gives bands around median of width 1 std dev, 2 std dev, 3 std dev,
+# and then the long tail.
+NORMAL_HISTOGRAM_BPS = (0, 668, 1587, 3085, 5000, 6915, 8413, 9332, 10000)
+
+
+@dataclasses.dataclass(frozen=True)
+class CompressedHistogramValue:
+    """Represents a value in a compressed histogram.
+
+    Attributes:
+      basis_point: Compression point represented in basis point, 1/100th of a
+        percent.
+      value: Cumulative weight at the basis point.
+    """
+
+    basis_point: float
+    value: float
+
+    def as_tuple(self) -> Tuple[float, float]:
+        """Returns the basis point and the value as a tuple."""
+        return (self.basis_point, self.value)
+
+
+# TODO(@jart): Unfork these methods.
+def compress_histogram_proto(histo, bps=NORMAL_HISTOGRAM_BPS):
+    """Creates fixed size histogram by adding compression to accumulated state.
+
+    This routine transforms a histogram at a particular step by interpolating its
+    variable number of buckets to represent their cumulative weight at a constant
+    number of compression points. This significantly reduces the size of the
+    histogram and makes it suitable for a two-dimensional area plot where the
+    output of this routine constitutes the ranges for a single x coordinate.
+
+    Args:
+      histo: A HistogramProto object.
+      bps: Compression points represented in basis points, 1/100ths of a percent.
+          Defaults to normal distribution.
+
+    Returns:
+      List of values for each basis point.
+    """
+    # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
+    if not histo.num:
+        return [CompressedHistogramValue(b, 0.0).as_tuple() for b in bps]
+    bucket = np.array(histo.bucket)
+    bucket_limit = list(histo.bucket_limit)
+    weights = (bucket * bps[-1] / (bucket.sum() or 1.0)).cumsum()
+    values = []
+    j = 0
+    while j < len(bps):
+        i = np.searchsorted(weights, bps[j], side="right")
+        while i < len(weights):
+            cumsum = weights[i]
+            cumsum_prev = weights[i - 1] if i > 0 else 0.0
+            if cumsum == cumsum_prev:  # prevent lerp divide by zero
+                i += 1
+                continue
+            if not i or not cumsum_prev:
+                lhs = histo.min
+            else:
+                lhs = max(bucket_limit[i - 1], histo.min)
+            rhs = min(bucket_limit[i], histo.max)
+            weight = _lerp(bps[j], cumsum_prev, cumsum, lhs, rhs)
+            values.append(CompressedHistogramValue(bps[j], weight).as_tuple())
+            j += 1
+            break
+        else:
+            break
+    while j < len(bps):
+        values.append(CompressedHistogramValue(bps[j], histo.max).as_tuple())
+        j += 1
+    return values
+
+
+def compress_histogram(buckets, bps=NORMAL_HISTOGRAM_BPS):
+    """Creates fixed size histogram by adding compression to accumulated state.
+
+    This routine transforms a histogram at a particular step by linearly
+    interpolating its variable number of buckets to represent their cumulative
+    weight at a constant number of compression points. This significantly reduces
+    the size of the histogram and makes it suitable for a two-dimensional area
+    plot where the output of this routine constitutes the ranges for a single x
+    coordinate.
+
+    Args:
+      buckets: A list of buckets, each of which is a 3-tuple of the form
+        `(min, max, count)`.
+      bps: Compression points represented in basis points, 1/100ths of a percent.
+          Defaults to normal distribution.
+
+    Returns:
+      List of values for each basis point.
+    """
+    # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
+    buckets = np.array(buckets)
+    if not buckets.size:
+        return [CompressedHistogramValue(b, 0.0).as_tuple() for b in bps]
+    (minmin, maxmax) = (buckets[0][0], buckets[-1][1])
+    counts = buckets[:, 2]
+    right_edges = list(buckets[:, 1])
+    weights = (counts * bps[-1] / (counts.sum() or 1.0)).cumsum()
+
+    result = []
+    bp_index = 0
+    while bp_index < len(bps):
+        i = np.searchsorted(weights, bps[bp_index], side="right")
+        while i < len(weights):
+            cumsum = weights[i]
+            cumsum_prev = weights[i - 1] if i > 0 else 0.0
+            if cumsum == cumsum_prev:  # prevent division-by-zero in `_lerp`
+                i += 1
+                continue
+            if not i or not cumsum_prev:
+                lhs = minmin
+            else:
+                lhs = max(right_edges[i - 1], minmin)
+            rhs = min(right_edges[i], maxmax)
+            weight = _lerp(bps[bp_index], cumsum_prev, cumsum, lhs, rhs)
+            result.append(
+                CompressedHistogramValue(bps[bp_index], weight).as_tuple()
+            )
+            bp_index += 1
+            break
+        else:
+            break
+    while bp_index < len(bps):
+        result.append(
+            CompressedHistogramValue(bps[bp_index], maxmax).as_tuple()
+        )
+        bp_index += 1
+    return result
+
+
+def _lerp(x, x0, x1, y0, y1):
+    """Affinely map from [x0, x1] onto [y0, y1]."""
+    return y0 + (x - x0) * float(y1 - y0) / (x1 - x0)

rl/Lib/site-packages/tensorboard/plugins/distribution/distributions_plugin.py

@@ -0,0 +1,117 @@
+# 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.
+# ==============================================================================
+"""The TensorBoard Distributions (a.k.a. compressed histograms) plugin.
+
+See `http_api.md` in this directory for specifications of the routes for
+this plugin.
+"""
+
+
+from werkzeug import wrappers
+
+from tensorboard import plugin_util
+from tensorboard.backend import http_util
+from tensorboard.plugins import base_plugin
+from tensorboard.plugins.distribution import compressor
+from tensorboard.plugins.distribution import metadata
+from tensorboard.plugins.histogram import histograms_plugin
+
+
+class DistributionsPlugin(base_plugin.TBPlugin):
+    """Distributions Plugin for TensorBoard.
+
+    This supports both old-style summaries (created with TensorFlow ops
+    that output directly to the `histo` field of the proto) and new-
+    style summaries (as created by the
+    `tensorboard.plugins.histogram.summary` module).
+    """
+
+    plugin_name = metadata.PLUGIN_NAME
+
+    # Use a round number + 1 since sampling includes both start and end steps,
+    # so N+1 samples corresponds to dividing the step sequence into N intervals.
+    SAMPLE_SIZE = 501
+
+    def __init__(self, context):
+        """Instantiates DistributionsPlugin via TensorBoard core.
+
+        Args:
+          context: A base_plugin.TBContext instance.
+        """
+        self._histograms_plugin = histograms_plugin.HistogramsPlugin(context)
+
+    def get_plugin_apps(self):
+        return {
+            "/distributions": self.distributions_route,
+            "/tags": self.tags_route,
+        }
+
+    def is_active(self):
+        """This plugin is active iff any run has at least one histogram tag.
+
+        (The distributions plugin uses the same data source as the
+        histogram plugin.)
+        """
+        return self._histograms_plugin.is_active()
+
+    def data_plugin_names(self):
+        return (self._histograms_plugin.plugin_name,)
+
+    def frontend_metadata(self):
+        return base_plugin.FrontendMetadata(
+            element_name="tf-distribution-dashboard",
+        )
+
+    def distributions_impl(self, ctx, tag, run, experiment):
+        """Result of the form `(body, mime_type)`.
+
+        Raises:
+          tensorboard.errors.PublicError: On invalid request.
+        """
+        (histograms, mime_type) = self._histograms_plugin.histograms_impl(
+            ctx, tag, run, experiment=experiment, downsample_to=self.SAMPLE_SIZE
+        )
+        return (
+            [self._compress(histogram) for histogram in histograms],
+            mime_type,
+        )
+
+    def _compress(self, histogram):
+        (wall_time, step, buckets) = histogram
+        converted_buckets = compressor.compress_histogram(buckets)
+        return [wall_time, step, converted_buckets]
+
+    def index_impl(self, ctx, experiment):
+        return self._histograms_plugin.index_impl(ctx, experiment=experiment)
+
+    @wrappers.Request.application
+    def tags_route(self, request):
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        index = self.index_impl(ctx, experiment=experiment)
+        return http_util.Respond(request, index, "application/json")
+
+    @wrappers.Request.application
+    def distributions_route(self, request):
+        """Given a tag and single run, return an array of compressed
+        histograms."""
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        tag = request.args.get("tag")
+        run = request.args.get("run")
+        (body, mime_type) = self.distributions_impl(
+            ctx, tag, run, experiment=experiment
+        )
+        return http_util.Respond(request, body, mime_type)

rl/Lib/site-packages/tensorboard/plugins/distribution/metadata.py

@@ -0,0 +1,20 @@
+# Copyright 2020 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.
+# ==============================================================================
+"""Information on the distributions plugin."""
+
+
+# This name is used as the plugin prefix route and to identify this plugin
+# generally.
+PLUGIN_NAME = "distributions"