| | --- |
| | license: mit |
| | dataset_info: |
| | features: |
| | - name: input_ids |
| | sequence: int64 |
| | - name: attention_mask |
| | sequence: int64 |
| | - name: labels |
| | sequence: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 257967900 |
| | num_examples: 20973 |
| | - name: val |
| | num_bytes: 45891300 |
| | num_examples: 3731 |
| | download_size: 10916827 |
| | dataset_size: 303859200 |
| | language: |
| | - en |
| | pretty_name: github-commits |
| | size_categories: |
| | - n<1K |
| | --- |
| | This dataset contains code changes in each commit of most starred python project, stored on GitHub. |
| |
|
| | ## Code to reproduce the parsing process |
| | To parse code we performed the following steps: |
| | * Get list of most starred GitHub repos via API |
| | * With **git** python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset. |
| | * Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc. |
| | * Group files changed in 1 commit into single sample of the dataset. |
| | To reproduce these steps you need to: |
| | 1) run *src/github_parsing.ipynb* to parse repos from github |
| | 2) to clean the data and group dataset samples run *src/data_cleaning.ipynb* |
| |
|
| | ## Dataset features |
| | Dataset have the following features: |
| | 1) repo_name |
| | 2) commit_message |
| | 3) commit_changes - changes in code in all python files, contained in the commit |
| | 4) files_changed - number of files, changed in the commit |
| | 5) changes_len - number of chars in the code changes |
| | For model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model. |
| | Code changes have the following structure: |
| | ``` |
| | <filename> name_of_the_file <filename> |
| | code_of_changes |
| | <commit_msg> |
| | ``` |
| | Special tokens used in the input: |
| | * <file_name> - used to separate name of the file |
| | * <code_del> and <code_add> used to separate added or deleted lines of code in the commit |
| | * <commit_msg> used to separate commit message |
| |
|
| | Example of input for the model: |
| | ``` |
| | <filename> a/tests/test_constraint.py b/tests/test_constraint.py<filename> |
| | <code_del>--- a/tests/test_constraint.py<code_del> |
| | <code_add>+++ b/tests/test_constraint.py<code_add> |
| | @@ -87,10 +87,15 @@ def test_accurate_approximation_when_known(): |
| | n_iter=10, |
| | ) |
| | |
| | <code_del>- params = optimizer.res[0]["params"]<code_del> |
| | <code_del>- x, y = params['x'], params['y']<code_del> |
| | <code_add>+ # Exclude the last sampled point, because the constraint is not fitted on that.<code_add> |
| | <code_add>+ res = np.array([[r['target'], r['constraint'], r['params']['x'], r['params']['y']] for r in optimizer.res[:-1]])<code_add> |
| | <code_add>+<code_add> |
| | <code_add>+ xy = res[:, [2, 3]]<code_add> |
| | <code_add>+ x = res[:, 2]<code_add> |
| | <code_add>+ y = res[:, 3]<code_add> |
| | |
| | <code_del>- assert constraint_function(x, y) == approx(conmod.approx(np.array([x, y])), rel=1e-5, abs=1e-5)<code_del> |
| | <code_add>+ assert constraint_function(x, y) == approx(conmod.approx(xy), rel=1e-5, abs=1e-5)<code_add> |
| | <code_add>+ assert constraint_function(x, y) == approx(optimizer.space.constraint_values[:-1], rel=1e-5, abs=1e-5)<code_add> |
| | |
| | |
| | def test_multiple_constraints(): |
| | |
| | <commit_msg>In case of commit with the several files changed, different files are separated with 3 blank lines.<eos> |
| | |
| | |
| | ``` |
| | In case of commit with the several files changed, different files are separated with 3 blank lines. |
