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feat: get kaggle notebooks & disscussion text for RAG (#371)
* crawl notebooks & change to DS-Agent format text * give one function in kaggle_crawler to collect kaggle knowledge texts * fix CI * add tool for merge .py files to one py file * fix CI * delete files * changes for select function * add nbformat * jump crawler import test * del test code * CI * change * change * change
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Original file line number | Diff line number | Diff line change |
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from pathlib import Path | ||
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import nbformat as nbf | ||
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def python_files_to_notebook(competition: str, py_dir: str): | ||
py_dir: Path = Path(py_dir) | ||
save_path: Path = py_dir / "merged.ipynb" | ||
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pre_file = py_dir / "fea_share_preprocess.py" | ||
pre_py = pre_file.read_text() | ||
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pre_py = pre_py.replace("/kaggle/input", f"/kaggle/input/{competition}") | ||
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fea_files = list(py_dir.glob("feature/*.py")) | ||
fea_pys = { | ||
f"{fea_file.stem}_cls": fea_file.read_text().replace("feature_engineering_cls", f"{fea_file.stem}_cls").strip() | ||
+ "()\n" | ||
for fea_file in fea_files | ||
} | ||
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model_files = list(py_dir.glob("model/model*.py")) | ||
model_pys = {f"{model_file.stem}": model_file.read_text().strip() for model_file in model_files} | ||
for k, v in model_pys.items(): | ||
model_pys[k] = v.replace("def fit(", "def fit(self, ").replace("def predict(", "def predict(self, ") | ||
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lines = model_pys[k].split("\n") | ||
indent = False | ||
first_line = -1 | ||
for i, line in enumerate(lines): | ||
if "def " in line: | ||
indent = True | ||
if first_line == -1: | ||
first_line = i | ||
if indent: | ||
lines[i] = " " + line | ||
lines.insert(first_line, f"class {k}:\n") | ||
model_pys[k] = "\n".join(lines) | ||
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select_files = list(py_dir.glob("model/select*.py")) | ||
select_pys = { | ||
f"{select_file.stem}": select_file.read_text().replace("def select(", f"def {select_file.stem}(") | ||
for select_file in select_files | ||
} | ||
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train_file = py_dir / "train.py" | ||
train_py = train_file.read_text() | ||
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train_py = train_py.replace("from fea_share_preprocess import preprocess_script", "") | ||
train_py = train_py.replace("DIRNAME = Path(__file__).absolute().resolve().parent", "") | ||
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fea_cls_list_str = "[" + ", ".join(list(fea_pys.keys())) + "]" | ||
train_py = train_py.replace( | ||
'for f in DIRNAME.glob("feature/feat*.py"):', f"for cls in {fea_cls_list_str}:" | ||
).replace("cls = import_module_from_path(f.stem, f).feature_engineering_cls()", "") | ||
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model_cls_list_str = "[" + ", ".join(list(model_pys.keys())) + "]" | ||
train_py = ( | ||
train_py.replace('for f in DIRNAME.glob("model/model*.py"):', f"for mc in {model_cls_list_str}:") | ||
.replace("m = import_module_from_path(f.stem, f)", "m = mc()") | ||
.replace('select_python_path = f.with_name(f.stem.replace("model", "select") + f.suffix)', "") | ||
.replace( | ||
"select_m = import_module_from_path(select_python_path.stem, select_python_path)", | ||
'select_m = eval(mc.__name__.replace("model", "select"))', | ||
) | ||
.replace("select_m.select", "select_m") | ||
.replace("[2].select", "[2]") | ||
) | ||
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nb = nbf.v4.new_notebook() | ||
all_py = "" | ||
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nb.cells.append(nbf.v4.new_code_cell(pre_py)) | ||
all_py += pre_py + "\n\n" | ||
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for v in fea_pys.values(): | ||
nb.cells.append(nbf.v4.new_code_cell(v)) | ||
all_py += v + "\n\n" | ||
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for v in model_pys.values(): | ||
nb.cells.append(nbf.v4.new_code_cell(v)) | ||
all_py += v + "\n\n" | ||
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for v in select_pys.values(): | ||
nb.cells.append(nbf.v4.new_code_cell(v)) | ||
all_py += v + "\n\n" | ||
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nb.cells.append(nbf.v4.new_code_cell(train_py)) | ||
all_py += train_py + "\n" | ||
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with save_path.open("w", encoding="utf-8") as f: | ||
nbf.write(nb, f) | ||
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with save_path.with_suffix(".py").open("w", encoding="utf-8") as f: | ||
f.write(all_py) |
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Original file line number | Diff line number | Diff line change |
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@@ -62,6 +62,7 @@ st-theme | |
# kaggle crawler | ||
selenium | ||
kaggle | ||
nbformat | ||
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# tool | ||
seaborn | ||
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