reports/ruff/ruff_report.json

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    "fix": {
      "applicability": "safe",
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        {
          "content": "from datetime import datetime\nimport json\nfrom pathlib import Path\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nfrom hopcroft_skill_classification_tool_competition.config import PROCESSED_DATA_DIR\nfrom hopcroft_skill_classification_tool_competition.features import (\n    create_feature_dataset,\n    extract_tfidf_features,\n    get_label_columns,\n    load_data_from_db,\n    prepare_labels,\n)\n\n\n",
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    "fix": {
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        {
          "content": "\"Conflicts resolved using majority voting\"",
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          "location": {
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    "location": {
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    "fix": {
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          "content": "\"No data leakage detected\"",
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    "fix": {
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      "edits": [
        {
          "content": "not stats.get(\"split\", {}).get(\"data_leakage\", True)",
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            "column": 96,
            "row": 385
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            "column": 39,
            "row": 385
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      "column": 39,
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    "message": "Avoid equality comparisons to `False`; use `not stats.get(\"split\", {}).get(\"data_leakage\", True):` for false checks",
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    "fix": {
      "applicability": "safe",
      "edits": [
        {
          "content": "        from hopcroft_skill_classification_tool_competition.features import (\n            extract_tfidf_features,\n            get_label_columns,\n            get_text_columns,\n            load_data_from_db,\n            prepare_labels,\n        )\n",
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    "fix": {
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    "fix": {
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    "fix": {
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      "edits": [
        {
          "content": "from pathlib import Path\nimport shutil\nimport zipfile\n\nfrom huggingface_hub import hf_hub_download\n\nfrom hopcroft_skill_classification_tool_competition.config import (\n    DB_PATH,\n    HF_FILENAME,\n    HF_REPO_ID,\n    RAW_DATA_DIR,\n)\n\n\n",
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    "fix": {
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      "edits": [
        {
          "content": "from hopcroft_skill_classification_tool_competition.config import (\n    HF_FILENAME,\n    HF_REPO_ID,\n    RAW_DATA_DIR,\n)",
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    "fix": {
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          "content": "",
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    "fix": {
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        {
          "content": "import random\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import make_classification\nfrom sklearn.neighbors import NearestNeighbors\n\n\n",
          "end_location": {
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          "content": "import argparse\nimport os\nfrom pathlib import Path\n\nfrom imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler\nimport joblib\nimport lightgbm as lgb\nimport mlflow\nimport mlflow.sklearn\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import f1_score, precision_score, recall_score\nfrom sklearn.model_selection import GridSearchCV, KFold, train_test_split\nfrom sklearn.multioutput import MultiOutputClassifier\n\nfrom hopcroft_skill_classification_tool_competition.config import (\n    ADASYN_CONFIG,\n    DATA_PATHS,\n    MLFLOW_CONFIG,\n    MODEL_CONFIG,\n    PCA_CONFIG,\n    TRAINING_CONFIG,\n)\n\n",
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          "content": "    import pandas as pd\n\n    from hopcroft_skill_classification_tool_competition.mlsmote import MLSMOTE as mlsmote_function\n    from hopcroft_skill_classification_tool_competition.mlsmote import get_minority_instace\n",
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