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Add overfitting modes, GPU detection, and overfit-generation plotting to XGBoost notebook #26

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219 changes: 168 additions & 51 deletions notebooks/XGBWW_Catalog_Random100_XGBoost_Accuracy.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -677,6 +677,14 @@
" max_dense_elements=int(2e8),\n",
")\n",
"\n",
"OVERFIT_MODES = [\n",
" \"deep_trees\",\n",
" \"high_learning_rate\",\n",
" \"no_regularization\",\n",
" \"full_sampling\",\n",
" \"long_training\",\n",
"]\n",
"\n",
"\n",
"class ModelTrainingFailed(RuntimeError):\n",
" \"\"\"Raised when XGBoost model fitting fails for a dataset.\"\"\"\n",
Expand Down Expand Up @@ -711,9 +719,34 @@
"print(f\"XGBoost compute backend detected: {XGB_COMPUTE_BACKEND} | params={XGB_COMPUTE_PARAMS}\")\n",
"\n",
"\n",
"def fit_and_score(row_data: dict):\n",
"def apply_overfit_mode(base_params: dict, overfit_mode: str) -> dict:\n",
" params = dict(base_params)\n",
"\n",
" if overfit_mode == \"deep_trees\":\n",
" params.update({\"max_depth\": 14, \"min_child_weight\": 0.5})\n",
" elif overfit_mode == \"high_learning_rate\":\n",
" params.update({\"learning_rate\": 0.35})\n",
" elif overfit_mode == \"no_regularization\":\n",
" params.update({\"reg_lambda\": 0.0, \"reg_alpha\": 0.0, \"min_child_weight\": 0.0})\n",
" elif overfit_mode == \"full_sampling\":\n",
" params.update({\"subsample\": 1.0, \"colsample_bytree\": 1.0})\n",
" elif overfit_mode == \"long_training\":\n",
" params.update({\"learning_rate\": max(params.get(\"learning_rate\", 0.05), 0.08)})\n",
" else:\n",
" raise ValueError(f\"Unknown overfit_mode={overfit_mode!r}. Expected one of {OVERFIT_MODES}\")\n",
"\n",
" return params\n",
"\n",
"\n",
"def fit_and_score(\n",
" row_data: dict,\n",
" case_type: str = \"good\",\n",
" overfit_mode: str = \"none\",\n",
" seed_offset: int = 0,\n",
"):\n",
" dataset_uid = row_data[\"dataset_uid\"]\n",
" source = row_data[\"source\"]\n",
" run_seed = int(RANDOM_SEED + seed_offset)\n",
"\n",
" X, y, meta = load_dataset(dataset_uid, filters=filters)\n",
"\n",
Expand All @@ -725,7 +758,7 @@
"\n",
" stratify = y_enc if n_classes > 1 else None\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y_enc, test_size=TEST_SIZE, random_state=RANDOM_SEED, stratify=stratify\n",
" X, y_enc, test_size=TEST_SIZE, random_state=run_seed, stratify=stratify\n",
" )\n",
"\n",
" dtrain = xgb.DMatrix(X_train, label=y_train)\n",
Expand All @@ -743,21 +776,33 @@
" \"colsample_bytree\": 0.85,\n",
" \"min_child_weight\": 2.0,\n",
" \"reg_lambda\": 2.0,\n",
" \"seed\": RANDOM_SEED,\n",
" \"seed\": run_seed,\n",
" }\n",
" cv = xgb.cv(\n",
" params=params,\n",
" dtrain=dtrain,\n",
" num_boost_round=1200,\n",
" nfold=5,\n",
" stratified=True,\n",
" early_stopping_rounds=50,\n",
" seed=RANDOM_SEED,\n",
" verbose_eval=False,\n",
" )\n",
" rounds = len(cv)\n",
" model = xgb.train(params=params, dtrain=dtrain, num_boost_round=rounds, verbose_eval=False)\n",
"\n",
" if case_type == \"overfit\":\n",
" params = apply_overfit_mode(params, overfit_mode)\n",
" round_map = {\n",
" \"deep_trees\": 1800,\n",
" \"high_learning_rate\": 900,\n",
" \"no_regularization\": 2200,\n",
" \"full_sampling\": 1800,\n",
" \"long_training\": 3200,\n",
" }\n",
" rounds = round_map[overfit_mode]\n",
" else:\n",
" cv = xgb.cv(\n",
" params=params,\n",
" dtrain=dtrain,\n",
" num_boost_round=1200,\n",
" nfold=5,\n",
" stratified=True,\n",
" early_stopping_rounds=50,\n",
" seed=run_seed,\n",
" verbose_eval=False,\n",
" )\n",
" rounds = len(cv)\n",
"\n",
" model = xgb.train(params=params, dtrain=dtrain, num_boost_round=rounds, verbose_eval=False)\n",
" yhat_tr = (model.predict(dtrain) >= 0.5).astype(int)\n",
" yhat_te = (model.predict(dtest) >= 0.5).astype(int)\n",
" else:\n",
Expand All @@ -772,21 +817,33 @@
" \"colsample_bytree\": 0.9,\n",
" \"min_child_weight\": 1.0,\n",
" \"reg_lambda\": 1.0,\n",
" \"seed\": RANDOM_SEED,\n",
" \"seed\": run_seed,\n",
" }\n",
" cv = xgb.cv(\n",
" params=params,\n",
" dtrain=dtrain,\n",
" num_boost_round=1200,\n",
" nfold=5,\n",
" stratified=True,\n",
" early_stopping_rounds=60,\n",
" seed=RANDOM_SEED,\n",
" verbose_eval=False,\n",
" )\n",
" rounds = len(cv)\n",
" model = xgb.train(params=params, dtrain=dtrain, num_boost_round=rounds, verbose_eval=False)\n",
"\n",
" if case_type == \"overfit\":\n",
" params = apply_overfit_mode(params, overfit_mode)\n",
" round_map = {\n",
" \"deep_trees\": 1900,\n",
" \"high_learning_rate\": 1000,\n",
" \"no_regularization\": 2400,\n",
" \"full_sampling\": 1900,\n",
" \"long_training\": 3400,\n",
" }\n",
" rounds = round_map[overfit_mode]\n",
" else:\n",
" cv = xgb.cv(\n",
" params=params,\n",
" dtrain=dtrain,\n",
" num_boost_round=1200,\n",
" nfold=5,\n",
" stratified=True,\n",
" early_stopping_rounds=60,\n",
" seed=run_seed,\n",
" verbose_eval=False,\n",
" )\n",
" rounds = len(cv)\n",
"\n",
" model = xgb.train(params=params, dtrain=dtrain, num_boost_round=rounds, verbose_eval=False)\n",
" yhat_tr = np.argmax(model.predict(dtrain), axis=1)\n",
" yhat_te = np.argmax(model.predict(dtest), axis=1)\n",
" except Exception as e:\n",
Expand All @@ -799,7 +856,7 @@
" W=\"W7\",\n",
" nfolds=5,\n",
" t_points=160,\n",
" random_state=RANDOM_SEED,\n",
" random_state=run_seed,\n",
" train_params=params,\n",
" num_boost_round=rounds,\n",
" multiclass=\"avg\" if n_classes > 2 else \"error\",\n",
Expand Down Expand Up @@ -827,9 +884,12 @@
" \"rounds\": int(rounds),\n",
" \"train_accuracy\": float(accuracy_score(y_train, yhat_tr)),\n",
" \"test_accuracy\": float(accuracy_score(y_test, yhat_te)),\n",
" \"accuracy_gap\": float(accuracy_score(y_train, yhat_tr) - accuracy_score(y_test, yhat_te)),\n",
" \"alpha\": alpha,\n",
" \"ERG_gap\": erg_gap,\n",
" \"num_traps\": num_traps,\n",
" \"case_type\": case_type,\n",
" \"overfit_mode\": overfit_mode,\n",
" \"xgboost_params\": json.dumps(params, sort_keys=True),\n",
" \"status\": \"completed\",\n",
" \"error_message\": \"\",\n",
Expand Down Expand Up @@ -978,30 +1038,87 @@
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"OVERFIT_REPEATS_PER_MODEL = 5\n",
"PLOT_METRICS = [\"alpha\", \"ERG_gap\", \"num_traps\"]\n",
"\n",
"if results_df.empty:\n",
" print(\"No successful trainings to plot.\")\n",
" print(\"No completed models. Cannot build overfit histograms.\")\n",
"else:\n",
" import matplotlib.pyplot as plt\n",
"\n",
" plot_df = results_df.sort_values([\"source\", \"dataset_uid\"]).copy()\n",
"\n",
" metrics = [\"alpha\", \"ERG_gap\", \"num_traps\"]\n",
" fig, axes = plt.subplots(1, len(metrics), figsize=(6 * len(metrics), 5), squeeze=False)\n",
"\n",
" for ax, metric in zip(axes[0], metrics):\n",
" ax.scatter(plot_df[metric], plot_df[\"train_accuracy\"], label=\"Train accuracy\", alpha=0.8)\n",
" ax.scatter(plot_df[metric], plot_df[\"test_accuracy\"], label=\"Test accuracy\", alpha=0.8)\n",
" ax.set_xlabel(metric)\n",
" ax.set_ylabel(\"Accuracy\")\n",
" ax.set_ylim(0.4, 1.05)\n",
" if metric=='alpha':\n",
" ax.set_xlim(1.5,6)\n",
" ax.set_title(f\"Accuracy vs {metric}\")\n",
" ax.grid(alpha=0.2)\n",
"\n",
" axes[0, 0].legend()\n",
" fig.tight_layout()\n",
" plt.show()\n"
" good_df = results_df.copy()\n",
" if \"case_type\" in good_df.columns:\n",
" good_df = good_df[good_df[\"case_type\"].eq(\"good\")].copy()\n",
"\n",
" if good_df.empty:\n",
" print(\"No base models found for overfit generation.\")\n",
" else:\n",
" overfit_rows = []\n",
" total_runs = len(good_df) * len(OVERFIT_MODES)\n",
" run_idx = 0\n",
" print(\n",
" f\"Overfitting each model {len(OVERFIT_MODES)} ways ({OVERFIT_MODES}) \"\n",
" f\"with {OVERFIT_REPEATS_PER_MODEL} total modes per model.\"\n",
" )\n",
"\n",
" for _, row in good_df.iterrows():\n",
" row_data = row.to_dict()\n",
" uid = row_data[\"dataset_uid\"]\n",
" for mode_idx, mode in enumerate(OVERFIT_MODES):\n",
" run_idx += 1\n",
" print(f\"[overfit {run_idx}/{total_runs}] dataset={uid} mode={mode}\")\n",
" try:\n",
" overfit_rows.append(\n",
" fit_and_score(\n",
" row_data,\n",
" case_type=\"overfit\",\n",
" overfit_mode=mode,\n",
" seed_offset=10000 + mode_idx,\n",
" )\n",
" )\n",
" except Exception as e:\n",
" print(f\" SKIP dataset={uid} mode={mode}: {e}\")\n",
"\n",
" overfit_df = pd.DataFrame(overfit_rows)\n",
"\n",
" if overfit_df.empty:\n",
" print(\"Overfit generation produced no rows.\")\n",
" else:\n",
" mode_order = OVERFIT_MODES[:]\n",
" fig, axes = plt.subplots(\n",
" len(PLOT_METRICS),\n",
" len(mode_order),\n",
" figsize=(4.5 * len(mode_order), 3.8 * len(PLOT_METRICS)),\n",
" squeeze=False,\n",
" )\n",
"\n",
" print(\n",
" f\"Generated {len(overfit_df)} overfit runs from {len(good_df)} models. \"\n",
" f\"Plotting {len(PLOT_METRICS)}x{len(mode_order)}={len(PLOT_METRICS) * len(mode_order)} histograms.\"\n",
" )\n",
"\n",
" for col_idx, mode in enumerate(mode_order):\n",
" mode_df = overfit_df[overfit_df[\"overfit_mode\"] == mode]\n",
" for row_idx, metric in enumerate(PLOT_METRICS):\n",
" ax = axes[row_idx, col_idx]\n",
" values = mode_df[metric].dropna()\n",
"\n",
" if values.empty:\n",
" ax.text(0.5, 0.5, \"No data\", ha=\"center\", va=\"center\")\n",
" else:\n",
" bins = min(20, max(5, int(np.sqrt(len(values)))))\n",
" ax.hist(values, bins=bins, alpha=0.85, edgecolor=\"black\")\n",
"\n",
" if row_idx == 0:\n",
" ax.set_title(f\"Mode: {mode}\")\n",
" if col_idx == 0:\n",
" ax.set_ylabel(metric)\n",
" ax.set_xlabel(metric)\n",
" ax.grid(alpha=0.2)\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n"
],
"id": "wVlB0ke9Rp1N"
},
Expand Down