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# Adapted from https://gist.github.com/thomasjpfan/edef5d164d2992a2abd16e334ff59c79 | |
from collections import defaultdict | |
import os | |
import re | |
from github import Github | |
token = os.environ["GITHUB_TOKEN"] | |
gh = Github(token) | |
sk_repo = gh.get_repo("scikit-learn/scikit-learn") | |
meta_issue_number = 22406 | |
meta_issue = sk_repo.get_issue(meta_issue_number) | |
all_files = [ | |
"examples/applications/plot_prediction_latency.py", | |
"examples/applications/plot_stock_market.py", | |
"examples/applications/wikipedia_principal_eigenvector.py", | |
"examples/calibration/plot_calibration.py", | |
"examples/classification/plot_lda_qda.py", | |
"examples/cluster/plot_affinity_propagation.py", | |
"examples/cluster/plot_coin_ward_segmentation.py", | |
"examples/cluster/plot_dbscan.py", | |
"examples/cluster/plot_dict_face_patches.py", | |
"examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py", | |
"examples/cluster/plot_mean_shift.py", | |
"examples/cluster/plot_mini_batch_kmeans.py", | |
"examples/cluster/plot_segmentation_toy.py", | |
"examples/cluster/plot_ward_structured_vs_unstructured.py", | |
"examples/covariance/plot_covariance_estimation.py", | |
"examples/covariance/plot_sparse_cov.py", | |
"examples/cross_decomposition/plot_compare_cross_decomposition.py", | |
"examples/decomposition/plot_faces_decomposition.py", | |
"examples/decomposition/plot_ica_blind_source_separation.py", | |
"examples/decomposition/plot_ica_vs_pca.py", | |
"examples/decomposition/plot_image_denoising.py", | |
"examples/decomposition/plot_pca_3d.py", | |
"examples/decomposition/plot_pca_vs_fa_model_selection.py", | |
"examples/exercises/plot_cv_diabetes.py", | |
"examples/feature_selection/plot_feature_selection.py", | |
# Nothing to do it is already using a notebook style example using `# %%`" | |
# "examples/impute/plot_missing_values.py, | |
"examples/linear_model/plot_ard.py", | |
"examples/linear_model/plot_bayesian_ridge_curvefit.py", | |
"examples/linear_model/plot_bayesian_ridge.py", | |
"examples/linear_model/plot_lasso_and_elasticnet.py", | |
"examples/linear_model/plot_lasso_dense_vs_sparse_data.py", | |
"examples/linear_model/plot_logistic_path.py", | |
"examples/linear_model/plot_multi_task_lasso_support.py", | |
"examples/linear_model/plot_ols_3d.py", | |
"examples/linear_model/plot_ridge_path.py", | |
"examples/linear_model/plot_theilsen.py", | |
"examples/miscellaneous/plot_kernel_ridge_regression.py", | |
"examples/model_selection/grid_search_text_feature_extraction.py", | |
"examples/model_selection/plot_roc_crossval.py", | |
"examples/model_selection/plot_train_error_vs_test_error.py", | |
"examples/neighbors/plot_regression.py", | |
"examples/neural_networks/plot_rbm_logistic_classification.py", | |
"examples/semi_supervised/plot_label_propagation_digits.py", | |
"examples/semi_supervised/plot_label_propagation_structure.py", | |
"examples/svm/plot_rbf_parameters.py", | |
"examples/svm/plot_svm_anova.py", | |
"examples/svm/plot_svm_regression.py", | |
"examples/text/plot_document_clustering.py", | |
] | |
print("Searching for pulls") | |
# TODO limitation: the PR description needs to mention the meta-issue. In one | |
# case it was done in a further comment and it was not found by this search | |
issues = gh.search_issues( | |
f"repo:scikit-learn/scikit-learn {meta_issue_number} in:body is:pr" | |
) | |
all_pulls = [sk_repo.get_pull(issue.number) for issue in issues] | |
# Label all open PRs as quick review | |
open_pulls = [p for p in all_pulls if p.state == "open"] | |
for p in open_pulls: | |
p.add_to_labels('Quick Review') | |
pull_infos = [ | |
(pull.title, pull.number, pull.state == "open", pull.merged) for pull in all_pulls | |
] | |
# %% | |
file_with_pulls = defaultdict(list) | |
print("Connecting pulls to files") | |
for title, number, is_open, merged in pull_infos: | |
file_found = False | |
if not is_open and not merged: | |
continue | |
for afile in all_files: | |
# TODO limitation: the basename of the example (possibly without .py) | |
# needs to be in the PR title. We could look at the files changed by the | |
# PR instead | |
if os.path.basename(afile).replace('.py', '') in title: | |
file_with_pulls[afile].append((f"#{number}", merged)) | |
file_found = True | |
if not file_found: | |
print(f"https://github.com/scikit-learn/scikit-learn/pull/{number} is not associated with any files. title is: {title}") | |
updated_files = [] | |
for afile in all_files: | |
link_to_file = f"[{afile}](https://github.com/scikit-learn/scikit-learn/blob/main/{afile})" | |
if afile not in file_with_pulls: | |
updated_files.append(f"- [ ] {link_to_file}") | |
continue | |
pulls = file_with_pulls[afile] | |
list_str = "- [ ] " | |
if any(pull[1] for pull in pulls): | |
list_str = "- [x] " | |
issue_numbers = " ".join([pull[0] for pull in pulls]) | |
if issue_numbers: | |
issue_numbers = f" {issue_numbers}" | |
updated_files.append(f"{list_str}{link_to_file}{issue_numbers}") | |
# %% | |
old_body = meta_issue.body | |
pattern = re.compile(r"^begin_auto_generated\s+(.+)^end_auto_generated", flags=re.MULTILINE | re.DOTALL) | |
old_auto_generated_content = pattern.search(old_body).group(1) | |
updated_auto_generated_content = "\n".join(updated_files) + '\n\n' | |
updated_body = old_body.replace(old_auto_generated_content, updated_auto_generated_content) | |
# TODO nice to have show a diff and ask for confirmation? I did not find a way | |
# to do diff -uw with difflib quickly enough | |
meta_issue.edit(body=updated_body) |
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