forked from googleapis/google-api-python-client
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathchangesummary.py
More file actions
538 lines (452 loc) 路 21.9 KB
/
Copy pathchangesummary.py
File metadata and controls
538 lines (452 loc) 路 21.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
# Copyright 2021 Google LLC
# 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
# https://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.
from enum import IntEnum
import json
from multiprocessing import Pool
import pathlib
import numpy as np
import pandas as pd
BRANCH_ARTIFACTS_DIR = (
pathlib.Path(__file__).parent.resolve()
/ "googleapiclient"
/ "discovery_cache"
/ "documents"
)
MAIN_ARTIFACTS_DIR = (
pathlib.Path(__file__).parent.resolve()
/ ".."
/ "main"
/ "googleapiclient"
/ "discovery_cache"
/ "documents"
)
MULTIPROCESSING_NUM_PER_BATCH = 5
MULTIPROCESSING_NUM_AGENTS = 10
class ChangeType(IntEnum):
UNKNOWN = 0
DELETED = 1
ADDED = 2
CHANGED = 3
class DirectoryDoesNotExist(ValueError):
"""Raised when the specified directory does not exist."""
pass
class ChangeSummary:
"""Represents the change summary between 2 directories containing \
artifacts.
"""
def __init__(self, new_artifacts_dir, current_artifacts_dir, temp_dir, file_list):
"""Initializes an instance of a ChangeSummary.
Args:
new_artifacts_dir (str): The relative path to the directory with the
new discovery artifacts.
current_artifacts_dir (str): The relative path to the directory with
the current discovery artifacts.
temp_dir (str): The relative path to the directory used for
temporary storage where intermediate files will be stored.
file_list (list): A list of strings containing files to analyze.
"""
self._file_list = file_list
self._new_artifacts_dir = pathlib.Path(new_artifacts_dir)
self._current_artifacts_dir = pathlib.Path(current_artifacts_dir)
self._temp_dir = pathlib.Path(temp_dir)
# Sanity checks to ensure directories exist
self._raise_if_directory_not_found(self._new_artifacts_dir)
self._raise_if_directory_not_found(self._current_artifacts_dir)
self._raise_if_directory_not_found(self._temp_dir)
def _raise_if_directory_not_found(self, directory):
"""Raises if the `directory` doesn't exist
args:
directory (str): The relative path to the `directory`
"""
if not pathlib.Path(directory).exists():
raise DirectoryDoesNotExist(
"Directory does not exist : {0}".format(directory)
)
def _load_json_to_dataframe(self, file_path):
"""Returns a pandas dataframe from the json file provided.
args:
file_path (str): The relative path to the discovery artifact to
parse.
"""
# Create an empty dataframe as we will need to return it if the file
# doesn't exist
dataframe_doc = pd.DataFrame()
if pathlib.Path(file_path).is_file():
with open(file_path, "r") as f:
# Now load the json file into a pandas dataframe as a flat table
dataframe_doc = pd.json_normalize(json.load(f))
return dataframe_doc
def _get_discovery_differences(self, filename):
"""Returns a pandas dataframe which contains the differences with the
current and new discovery artifact directories, corresponding to the
file name provided.
args:
filename (str): The name of the discovery artifact to parse.
"""
# The paths of the 2 discovery artifacts to compare
current_artifact_path = self._current_artifacts_dir / filename
new_artifact_path = self._new_artifacts_dir / filename
# Use a helper functions to load the discovery artifacts into pandas
# dataframes
current_doc = self._load_json_to_dataframe(current_artifact_path)
new_doc = self._load_json_to_dataframe(new_artifact_path)
# Concatenate the 2 dataframes, transpose them, and create
# a new dataframe called combined_docs with columns
# `Key`, `CurrentValue`, `NewValue`.
combined_docs = (
pd.concat([current_doc, new_doc], keys=["CurrentValue", "NewValue"])
# Drop the index column
.reset_index(drop=True, level=1)
# Transpose the DataFrame, Resulting Columns should be
# ["Key", "CurrentValue", "New Value"]
.rename_axis(["Key"], axis=1).transpose()
# Drop the index column
.reset_index()
)
# When discovery documents are added, the column `CurrentValue` will
# not exist. In that case, we'll just populate with `np.nan`.
if "CurrentValue" not in combined_docs.columns:
combined_docs["CurrentValue"] = np.nan
# When discovery documents are deleted, the column `NewValue` will
# not exist. In that case, we'll just populate with `np.nan`.
if "NewValue" not in combined_docs.columns:
combined_docs["NewValue"] = np.nan
# Split the Key into 2 columns for `Parent` and `Child` in order
# to group keys with the same parents together to summarize the changes
# by parent.
parent_child_df = combined_docs["Key"].str.rsplit(".", n=1, expand=True)
# Rename the columns and join them with the combined_docs dataframe.
# If we only have a `Parent` column, it means that the Key doesn't have
# any children.
if len(parent_child_df.columns) == 1:
parent_child_df.columns = ["Parent"]
else:
parent_child_df.columns = ["Parent", "Child"]
combined_docs = combined_docs.join(parent_child_df)
# Create a new column `Added` to identify rows which have new keys.
combined_docs["Added"] = np.where(
combined_docs["CurrentValue"].isnull(), True, False
)
# Create a new column `Deleted` to identify rows which have deleted keys.
combined_docs["Deleted"] = np.where(
combined_docs["NewValue"].isnull(), True, False
)
# Aggregate the keys added by grouping keys with the same parents
# together to summarize the changes by parent rather than by key.
parent_added_agg = (
combined_docs.groupby("Parent")
.Added.value_counts(normalize=True)
.reset_index(name="Proportion")
)
# Add a column NumLevels to inicate the number of levels in the tree
# which will allow us to sort the parents in hierarchical order.
parent_added_agg["NumLevels"] = (
parent_added_agg["Parent"].str.split(".").apply(lambda x: len(x))
)
# Aggregate the keys deleted by grouping keys with the same parents
# together to summarize the changes by parent rather than by key.
parent_deleted_agg = (
combined_docs.groupby("Parent")
.Deleted.value_counts(normalize=True)
.reset_index(name="Proportion")
)
# Add a column NumLevels to inicate the number of levels in the tree
# which will allow us to sort the parents in hierarchical order.
parent_deleted_agg["NumLevels"] = (
parent_added_agg["Parent"].str.split(".").apply(lambda x: len(x))
)
# Create a list of all parents that have been added in hierarchical
# order. When `Proportion` is 1, it means that the parent is new as all
# children keys have been added.
all_added = (
parent_added_agg[
(parent_added_agg["Proportion"] == 1)
& (parent_added_agg["Added"] == True)
][["Parent", "NumLevels"]]
.sort_values("NumLevels", ascending=True)
.Parent.to_list()
)
# Create a list of all parents that have been deleted in hierarchical
# order. When `Proportion` is 1, it means that the parent is new as all
# children keys have been deleted.
all_deleted = (
parent_deleted_agg[
(parent_deleted_agg["Proportion"] == 1)
& (parent_deleted_agg["Deleted"] == True)
][["Parent", "NumLevels"]]
.sort_values("NumLevels", ascending=True)
.Parent.to_list()
)
# Go through the list of parents that have been added. If we find any
# keys with parents which are a substring of the parent in this list,
# then it means that the entire parent is new. We don't need verbose
# information about the children, so we replace the parent.
for i in range(0, len(all_added)):
word = all_added[i]
combined_docs.Parent = np.where(
combined_docs["Parent"].str.startswith(word), word, combined_docs.Parent
)
# Go through the list of parents that have been deleted. If we find any
# keys with parents which are a substring of the parent in this list,
# then it means that the entire parent is deleted. We don't need verbose
# information about the children, so we replace the parent.
for i in range(0, len(all_deleted)):
word = all_deleted[i]
combined_docs.Parent = np.where(
combined_docs["Parent"].str.startswith(word), word, combined_docs.Parent
)
# Create a new dataframe with only the keys which have changed
docs_diff = combined_docs[
combined_docs["CurrentValue"] != combined_docs["NewValue"]
].copy(deep=False)
# Get the API and Version from the file name but exclude the extension.
api_version_string = filename.split(".")[:-1]
# Create columns `Name` and `Version` using the version string
docs_diff["Name"] = api_version_string[0]
docs_diff["Version"] = ".".join(api_version_string[1:])
# These conditions are used as arguments in the `np.where` function
# below.
deleted_condition = docs_diff["NewValue"].isnull()
added_condition = docs_diff["CurrentValue"].isnull()
# Create a new `ChangeType` column. The `np.where()` function is like a
# tenary operator. When the `deleted_condition` is `True`, the
# `ChangeType` will be `ChangeType.Deleted`. If the added_condition is
# `True` the `ChangeType` will be `ChangeType.Added`, otherwise the
# `ChangeType` will be `ChangeType.Changed`.
docs_diff["ChangeType"] = np.where(
deleted_condition,
ChangeType.DELETED,
np.where(added_condition, ChangeType.ADDED, ChangeType.CHANGED),
)
# Filter out keys which rarely affect functionality. For example:
# {"description", "documentation", "enum", "etag", "revision", "title",
# "url", "rootUrl"}
docs_diff = docs_diff[
~docs_diff["Key"].str.contains(
"|".join(self._get_keys_to_ignore()), case=False
)
]
# Group keys with similar parents together and create a new column
# called 'Count' which indicates the number of keys that have been
# grouped together. The reason for the count column is that when keys
# have the same parent, we group them together to improve readability.
docs_diff_with_count = (
docs_diff.groupby(
["Parent", "Added", "Deleted", "Name", "Version", "ChangeType"]
)
.size()
.reset_index(name="Count")
)
# Add counts column
docs_diff = docs_diff.merge(docs_diff_with_count)
# When the count is greater than 1, update the key with the name of the
# parent since we are consolidating keys with the same parent.
docs_diff.loc[docs_diff["Count"] > 1, "Key"] = docs_diff["Parent"]
return docs_diff[
["Key", "Added", "Deleted", "Name", "Version", "ChangeType", "Count"]
].drop_duplicates()
def _build_summary_message(self, api_name, is_feature):
"""Returns a string containing the summary for a given api. The string
returned will be in the format `fix(<api_name>): update the API`
when `is_feature=False` and `feat(<api_name>)!: update the API`
when `is_feature=True`.
args:
api_name (str): The name of the api to include in the summary.
is_feature (bool): If True, include the prefix `feat` otherwise use
`fix`
"""
# Build the conventional commit string based on the arguments provided
commit_type = "feat" if is_feature else "fix"
return "{0}({1}): update the api".format(commit_type, api_name)
def _get_keys_to_ignore(self):
"""Returns a list of strings with keys to ignore because they rarely
affect functionality.
args: None
"""
keys_to_ignore = [
"description",
"documentation",
"enum",
"etag",
"revision",
"title",
"url",
"rootUrl",
]
return keys_to_ignore
def _get_stable_versions(self, versions):
"""Returns a pandas series `pd.Series()` of boolean values,
corresponding to the given series, indicating whether the version is
considered stable or not.
args:
versions (object): a pandas series containing version
information for all discovery artifacts.
"""
# Use a regex on the version to find versions with the pattern
# <v>.<0-9>.<0-9>.<0-9> . Any api that matches this pattern will be
# labeled as stable. In other words, v1, v1.4 and v1.4.5 is stable
# but v1b1 v1aplha and v1beta1 is not stable.
return versions.str.extract(r"(v\d?\.?\d?\.?\d+$)").notnull()
def _get_summary_and_write_to_disk(self, dataframe, directory):
"""Writes summary information to file about changes made to discovery
artifacts based on the provided dataframe and returns a dataframe
with the same. The file `'allapis.dataframe'` is saved to the current
working directory.
args:
dataframe (object): a pandas dataframe containing summary change
information for all discovery artifacts
directory (str): path where the summary file should be saved
"""
dataframe["IsStable"] = self._get_stable_versions(dataframe["Version"])
# Create a filter for features, which contains only rows which have keys
# that have been deleted or added, that will be used as an argument in
# the `np.where()` call below.
filter_features = (dataframe["ChangeType"] == ChangeType.DELETED) | (
dataframe["ChangeType"] == ChangeType.ADDED
)
# Create a new column `IsFeature` to indicate which rows should be
# considered as features.
dataframe["IsFeature"] = np.where(filter_features, True, np.nan)
# Create a new column `IsFeatureAggregate` which will be used to
# summarize the api changes. We can either have feature or fix but not
# both.
dataframe["IsFeatureAggregate"] = dataframe.groupby("Name").IsFeature.transform(
lambda x: x.any()
)
# Create a new column `Summary`, which will contain a string with the
# conventional commit message.
dataframe["Summary"] = np.vectorize(self._build_summary_message)(
dataframe["Name"], dataframe["IsFeatureAggregate"]
)
# Write the final dataframe to disk as it will be used in the
# buildprbody.py script
dataframe.to_csv(directory / "allapis.dataframe")
return dataframe
def _write_verbose_changes_to_disk(self, dataframe, directory, summary_df):
"""Writes verbose information to file about changes made to discovery
artifacts based on the provided dataframe. A separate file is saved
for each api in the current working directory. The extension of the
files will be `'.verbose'`.
args:
dataframe (object): a pandas dataframe containing verbose change
information for all discovery artifacts
directory (str): path where the summary file should be saved
summary_df (object): A dataframe containing a summary of the changes
"""
# Array of strings which will contains verbose change information for
# each api
verbose_changes = []
# Sort the dataframe to minimize file operations below.
dataframe.sort_values(
by=["Name", "Version", "ChangeType"], ascending=True, inplace=True
)
# Select only the relevant columns. We need to create verbose output
# by Api Name, Version and ChangeType so we need to group by these
# columns.
change_type_groups = dataframe[
["Name", "Version", "ChangeType", "Key", "Count"]
].groupby(["Name", "Version", "ChangeType"])
lastApi = ""
lastVersion = ""
lastType = ChangeType.UNKNOWN
f = None
for name, group in change_type_groups:
currentApi = name[0]
currentVersion = name[1]
currentType = name[2]
# We need to handing file opening and closing when processing an API
# which is different from the previous one
if lastApi != currentApi:
# If we are processing a new api, close the file used for
# processing the previous API
if f is not None:
f.writelines(verbose_changes)
f.close()
f = None
# Clear the array of strings with information from the previous
# api and reset the last version
verbose_changes = []
lastVersion = ""
# Create a file which contains verbose changes for the current
# API being processed
filename = "{0}.verbose".format(currentApi)
f = open(pathlib.Path(directory / filename), "a")
lastApi = currentApi
# Create a filter with only the rows for the current API
current_api_filter = summary_df["Name"] == currentApi
# Get the string in the `Summary` column for the current api and
# append it to `verbose_changes`. The `Summary` column contains
# the conventional commit message. Use pandas.Series.iloc[0] to
# retrieve only the first elemnt, since all the values in the
# summary column are the same for a given API.
verbose_changes.append(summary_df[current_api_filter].Summary.iloc[0])
# If the version has changed, we need to create append a new heading
# in the verbose summary which contains the api and version.
if lastVersion != currentVersion:
# Append a header string with the API and version
verbose_changes.append(
"\n\n#### {0}:{1}\n\n".format(currentApi, currentVersion)
)
lastVersion = currentVersion
lastType = ChangeType.UNKNOWN
# Whenever the change type is different, we need to create a new
# heading for the group of keys with the same change type.
if currentType != lastType:
if currentType == ChangeType.DELETED:
verbose_changes.append("\nThe following keys were deleted:\n")
elif currentType == ChangeType.ADDED:
verbose_changes.append("\nThe following keys were added:\n")
else:
verbose_changes.append("\nThe following keys were changed:\n")
lastType = currentType
# Append the keys, and corresponding count, in the same change
# type group.
verbose_changes.extend(
[
"- {0} (Total Keys: {1})\n".format(row["Key"], row["Count"])
for index, row in group[["Key", "Count"]].iterrows()
]
)
# Make sure to close the last file and write the changes.
if f is not None:
f.writelines(verbose_changes)
f.close()
f = None
def detect_discovery_changes(self):
"""Writes a summary of the changes to the discovery artifacts to disk
at the path specified in `temp_dir`.
args: None
"""
result = pd.DataFrame()
# Process files in parallel to improve performance
with Pool(processes=MULTIPROCESSING_NUM_AGENTS) as pool:
if len(self._file_list):
result = pd.concat(
pool.map(
self._get_discovery_differences,
self._file_list,
MULTIPROCESSING_NUM_PER_BATCH,
)
)
if len(result):
# Sort the resulting dataframe by `Name`, `Version`, `ChangeType`
# and `Key`
sort_columns = ["Name", "Version", "ChangeType", "Key"]
result.sort_values(by=sort_columns, ascending=True, inplace=True)
# Create a folder which be used by the `createcommits.sh` and
# `buildprbody.py` scripts.
pathlib.Path(self._temp_dir).mkdir(exist_ok=True)
# Create a summary which contains a conventional commit message
# for each API and write it to disk.
summary_df = self._get_summary_and_write_to_disk(result, self._temp_dir)
# Create verbose change information for each API which contains
# a list of changes by key and write it to disk.
self._write_verbose_changes_to_disk(result, self._temp_dir, summary_df)