Formatting processor.py with ruff (#4)

- Formatting processor.py with ruff (dfa6c803fd9fb38dba5e58ed6c82f29ebae16a24)

processor.py

@@ -54,6 +54,7 @@ FULL_CONVERSATION_MSG = """Given following log snippets, their explanation, and
 
 ANALYSIS_MSG = "Issue: {issue}\nResolution: {resolution}"
 
+
 def n_snippets(logs: dict):
     """Count number of snippets in a log."""
     cnt = 0
@@ -89,13 +90,20 @@ def fix_snippets(logs: dict):
     for log in logs:
         if logs[log]:
             for snippet in logs[log]["snippets"]:
-                snippet_text = logs[log]["content"][snippet["start_index"]:snippet["end_index"]]
+                snippet_text = logs[log]["content"][
+                    snippet["start_index"] : snippet["end_index"]
+                ]
                 if "text" not in snippet or not snippet["text"]:
                     snippet["text"] = snippet_text
     return logs
 
 
-def cleanup(dataset: pd.DataFrame, min_how_to_fix_len=10, min_fail_reason_len=10, min_total_snippet_annot_len=10):
+def cleanup(
+    dataset: pd.DataFrame,
+    min_how_to_fix_len=10,
+    min_fail_reason_len=10,
+    min_total_snippet_annot_len=10,
+):
     """Cleaning the dataset
 
     For sake of simplicity we are going to assume following.
@@ -109,30 +117,36 @@ def cleanup(dataset: pd.DataFrame, min_how_to_fix_len=10, min_fail_reason_len=10
 
     # Fixing snippets with messed up text
     filled_snippets = dataset.logs.apply(fix_snippets)
-    dataset['filled_snippets'] = filled_snippets
+    dataset["filled_snippets"] = filled_snippets
     dataset.logs = dataset.filled_snippets
 
     # Setting conditional columns
-    dataset['how_to_fix_len'] = dataset.how_to_fix.apply(len)
-    dataset['fail_reason_len'] = dataset.fail_reason.apply(len)
-    dataset["tot_snippet_annot_len"] = dataset['logs'].apply(total_snippet_annotation_len)
+    dataset["how_to_fix_len"] = dataset.how_to_fix.apply(len)
+    dataset["fail_reason_len"] = dataset.fail_reason.apply(len)
+    dataset["tot_snippet_annot_len"] = dataset["logs"].apply(
+        total_snippet_annotation_len
+    )
 
     # Conditions
-    almost_empty_fix = dataset['how_to_fix_len'] < min_how_to_fix_len
-    almost_empty_reason = dataset['fail_reason_len'] < min_fail_reason_len
-    almost_empty_snippet_annotations = dataset["tot_snippet_annot_len"] < min_total_snippet_annot_len
-    none_snippets = dataset['logs'].apply(none_snippet)
+    almost_empty_fix = dataset["how_to_fix_len"] < min_how_to_fix_len
+    almost_empty_reason = dataset["fail_reason_len"] < min_fail_reason_len
+    almost_empty_snippet_annotations = (
+        dataset["tot_snippet_annot_len"] < min_total_snippet_annot_len
+    )
+    none_snippets = dataset["logs"].apply(none_snippet)
 
     sparse_annotation_criteria = (
-        almost_empty_snippet_annotations |
-        almost_empty_reason |
-        almost_empty_fix |
-        none_snippets)
+        almost_empty_snippet_annotations
+        | almost_empty_reason
+        | almost_empty_fix
+        | none_snippets
+    )
 
     sparse_count = dataset[sparse_annotation_criteria].shape[0]
     print(
         f"Total sparse annotations: {sparse_count} \
-        Relative to original dataset: {sparse_count / dataset.shape[0]}")
+        Relative to original dataset: {sparse_count / dataset.shape[0]}"
+    )
 
     # Applying filters
     final_dataset = dataset[~sparse_annotation_criteria]
@@ -153,12 +167,14 @@ def load_data(data_path: str):
         for file in files:
             if file.endswith(".json"):
                 try:
-                    with open(os.path.join(root, file), 'r', encoding='utf-8') as f:
+                    with open(os.path.join(root, file), "r", encoding="utf-8") as f:
                         parsed_data = json.load(f)
                         parsed_data["file_name"] = file
                         data.append(parsed_data)
                 except json.decoder.JSONDecodeError:
-                    print(f"Exception encountered while parsing {os.path.join(root, file)}, skipping.")
+                    print(
+                        f"Exception encountered while parsing {os.path.join(root, file)}, skipping."
+                    )
 
     return data
 
@@ -167,23 +183,21 @@ def make_snippet_qa(snippet: dict, user_message_template: str):
     """Create a QnA pair from a snippet"""
     return {
         "question": user_message_template.format(snippet["text"]),
-        "answer": snippet["user_comment"]
+        "answer": snippet["user_comment"],
     }
 
 
-def make_analysis_qa(snippets, issue, resolution, user_message_template,
-                     analysis_template):
+def make_analysis_qa(
+    snippets, issue, resolution, user_message_template, analysis_template
+):
     """Create QnA pair from entire annotation."""
     formatted_snippets = "\n=============\n".join(
-        [
-            f'[{e["text"]}]: [{e["user_comment"]}]'
-            for e in snippets
-        ]
+        [f"[{e['text']}]: [{e['user_comment']}]" for e in snippets]
     )
 
     return {
         "question": user_message_template.format(formatted_snippets),
-        "answer": analysis_template.format(issue=issue, resolution=resolution)
+        "answer": analysis_template.format(issue=issue, resolution=resolution),
     }
 
 
@@ -204,9 +218,7 @@ def to_qa_pairs(dataframe: pd.DataFrame):
             if logfiles[file] and "snippets" in logfiles[file]:
                 for snippet in logfiles[file]["snippets"]:
                     sample_snippet_messages.append(
-                        make_snippet_qa(
-                            snippet,
-                            SNIPPET_USER_MSG)
+                        make_snippet_qa(snippet, SNIPPET_USER_MSG)
                     )
                     annotated_snippets.append(snippet)
         qa_pairs.append(
@@ -215,7 +227,9 @@ def to_qa_pairs(dataframe: pd.DataFrame):
                 data.fail_reason,
                 data.how_to_fix,
                 FULL_CONVERSATION_MSG,
-                ANALYSIS_MSG))
+                ANALYSIS_MSG,
+            )
+        )
         # Adding individual snippet messages
         qa_pairs.extend(sample_snippet_messages)
     return qa_pairs
@@ -225,14 +239,18 @@ def split_dataset(dataset: pd.DataFrame, seed: int):
     """Splits dataset into training and evaluation subset"""
     split = dataset.shape[0] // 5
     dataset = dataset.sample(frac=1.0, random_state=seed)
-    train_dataset, test_dataset =  dataset[split:], dataset[:split]
+    train_dataset, test_dataset = dataset[split:], dataset[:split]
 
     return train_dataset, test_dataset
 
 
-def main(hf_token, data_path="results/results", sanitize=True,
-         seed=42, repo_id="fedora-copr/log_detective_qna",
-        ):
+def main(
+    hf_token,
+    data_path="results/results",
+    sanitize=True,
+    seed=42,
+    repo_id="fedora-copr/log_detective_qna",
+):
     """Process entire directory and turn contents into parquet files."""
 
     # For reproducibility
@@ -252,7 +270,8 @@ def main(hf_token, data_path="results/results", sanitize=True,
 
     dataset = {
         "train": datasets.Dataset.from_list(train_dataset),
-        "test": datasets.Dataset.from_list(test_dataset)}
+        "test": datasets.Dataset.from_list(test_dataset),
+    }
 
     dataset = datasets.DatasetDict(dataset)
 
@@ -265,18 +284,34 @@ def main(hf_token, data_path="results/results", sanitize=True,
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("Dataset Processor")
     parser.add_argument(
-        "data_path", type=str, default="results/results", help="Path to annotations.")
+        "data_path", type=str, default="results/results", help="Path to annotations."
+    )
     parser.add_argument(
-        "--sanitize", action='store_true', help="Run basic data cleanup procedure, dataset build may fail without it.")
+        "--sanitize",
+        action="store_true",
+        help="Run basic data cleanup procedure, dataset build may fail without it.",
+    )
     parser.add_argument(
-        "--seed", type=int, default=42,
-        help="Seed for random generator to be used when generating split.")
+        "--seed",
+        type=int,
+        default=42,
+        help="Seed for random generator to be used when generating split.",
+    )
     parser.add_argument(
-        "--repo_id", type=str, default="fedora-copr/log_detective_qna",
-        help="ID of Hugging Face repo")
-    parser.add_argument("--token", type=str, required=True, help="Token for Hugging face")
+        "--repo_id",
+        type=str,
+        default="fedora-copr/log_detective_qna",
+        help="ID of Hugging Face repo",
+    )
+    parser.add_argument(
+        "--token", type=str, required=True, help="Token for Hugging face"
+    )
     args = parser.parse_args()
 
     main(
-        data_path=args.data_path, sanitize=args.sanitize,
-        seed=args.seed, repo_id=args.repo_id, hf_token=args.token)
+        data_path=args.data_path,
+        sanitize=args.sanitize,
+        seed=args.seed,
+        repo_id=args.repo_id,
+        hf_token=args.token,
+    )