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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RMMol frozen-embedding sample\n",
"\n",
"This notebook demonstrates how to load the packaged CSV files, generate RMMol embeddings, and apply the selected downstream heads. It is RMMol-only and does not include baseline model artifacts or local output tables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import os, sys, json\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.ensemble import ExtraTreesClassifier\n",
"from sklearn.linear_model import Ridge, RidgeClassifier\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import Normalizer, StandardScaler\n",
"\n",
"RELEASE_DIR = Path(os.environ.get('RMMOL_RELEASE_DIR', Path.cwd()))\n",
"if not (RELEASE_DIR / 'dataset').exists():\n",
" RELEASE_DIR = Path.cwd().parent\n",
"RMMOL_CKPT = os.environ.get('RMMOL_CKPT', '')\n",
"RMMOL_TRUE_LOADER_DIR = Path(os.environ.get('RMMOL_TRUE_LOADER_DIR', '/home/sustech_spark/project/virtual cell/FUND/experiments'))\n",
"DEVICE = os.environ.get('RMMOL_DEVICE', 'cuda')\n",
"BATCH_SIZE = int(os.environ.get('RMMOL_BATCH_SIZE', '128'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TASK_SETTINGS = {\n",
" \"biophysics\": {\n",
" \"bbbp\": \"StandardScaler + RidgeClassifier(alpha=1.0, class_weight=None, solver='lsqr'); five-fold KFold(shuffle=True, random_state=42).\",\n",
" \"hiv\": \"Normalizer + RidgeClassifier(alpha=1.6788e-05, class_weight='balanced', solver='lsqr'); train+val fit, test report.\"\n",
" },\n",
" \"physiological\": {\n",
" \"bace\": \"ExtraTreesClassifier(n_estimators=500, criterion='entropy', max_features='sqrt', class_weight='balanced').\",\n",
" \"clintox_FDA_APPROVED\": \"l2-normalized RidgeClassifier(alpha=0.0003, class_weight=None).\",\n",
" \"tox21_and_sider\": \"Task-specific RidgeClassifier presets are embedded in the Jupyter sample notebook.\"\n",
" },\n",
" \"physical_chemistry\": {\n",
" \"esol\": \"Ridge(alpha=0.003), RMSE.\",\n",
" \"freesolv\": \"Ridge(alpha=0.003), RMSE.\",\n",
" \"lipo\": \"Ridge(alpha=0.001), RMSE.\"\n",
" },\n",
" \"quantum_mechanics\": {\n",
" \"qm9\": \"StandardScaler + Ridge(alpha=0.01), MAE over 12 QM9 targets.\"\n",
" }\n",
"}\n",
"\n",
"PHYSIOLOGICAL_TASK_PRESETS = [\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-ahr\",\n",
" \"preset\": \"l2_ridge_a0.0003_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.0003,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-ar-lbd\",\n",
" \"preset\": \"std_l2_ridge_a0.003_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.003,\n",
" \"class_weight\": null,\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-ar\",\n",
" \"preset\": \"pca64_ridge_a100_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 100,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"pca+RidgeClassifier\",\n",
" \"n_components\": 64\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-aromatase\",\n",
" \"preset\": \"std_l2_ridge_a10_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 10,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-er-lbd\",\n",
" \"preset\": \"l2_ridge_a0.0003_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.0003,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-er\",\n",
" \"preset\": \"std_l2_ridge_a0.0003_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.0003,\n",
" \"class_weight\": null,\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"nr-ppar-gamma\",\n",
" \"preset\": \"std_l2_ridge_a0.003_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.003,\n",
" \"class_weight\": null,\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"sr-are\",\n",
" \"preset\": \"std_l2_ridge_a0.003_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.003,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"sr-atad5\",\n",
" \"preset\": \"std_l2_ridge_a0.001_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.001,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"sr-hse\",\n",
" \"preset\": \"l2_ridge_a3e-05_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3e-05,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"sr-mmp\",\n",
" \"preset\": \"std_l2_ridge_a0.001_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.001,\n",
" \"class_weight\": null,\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"tox21\",\n",
" \"task\": \"sr-p53\",\n",
" \"preset\": \"std_l2_ridge_a1e-05_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1e-05,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"blood and lymphatic system disorders\",\n",
" \"preset\": \"l2_ridge_a0.3_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.3,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"cardiac disorders\",\n",
" \"preset\": \"l2_ridge_a0.3_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.3,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"congenital, familial and genetic disorders\",\n",
" \"preset\": \"svd128_ridge_a0.001_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.001,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"svd+RidgeClassifier\",\n",
" \"n_components\": 128\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"ear and labyrinth disorders\",\n",
" \"preset\": \"l2_ridge_a0.3_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.3,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"endocrine disorders\",\n",
" \"preset\": \"minmax_ridge_a0.0001_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.0001,\n",
" \"class_weight\": null,\n",
" \"head\": \"minmax+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"eye disorders\",\n",
" \"preset\": \"l2_ridge_a0.03_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.03,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"gastrointestinal disorders\",\n",
" \"preset\": \"l2_ridge_a3_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"general disorders and administration site conditions\",\n",
" \"preset\": \"l2_ridge_a0.03_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.03,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"hepatobiliary disorders\",\n",
" \"preset\": \"l2_ridge_a1_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"immune system disorders\",\n",
" \"preset\": \"l2_ridge_a1_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"infections and infestations\",\n",
" \"preset\": \"l2_ridge_a3_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"injury, poisoning and procedural complications\",\n",
" \"preset\": \"l2_ridge_a3_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"investigations\",\n",
" \"preset\": \"std_l2_ridge_a0.1_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.1,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"metabolism and nutrition disorders\",\n",
" \"preset\": \"l2_ridge_a3_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"musculoskeletal and connective tissue disorders\",\n",
" \"preset\": \"l2_ridge_a0.03_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.03,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"neoplasms benign, malignant and unspecified (incl cysts and polyps)\",\n",
" \"preset\": \"l2_ridge_a1e-05_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1e-05,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"nervous system disorders\",\n",
" \"preset\": \"minmax_ridge_a30_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 30,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"minmax+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"pregnancy, puerperium and perinatal conditions\",\n",
" \"preset\": \"svd32_ridge_a0.001_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.001,\n",
" \"class_weight\": null,\n",
" \"head\": \"svd+RidgeClassifier\",\n",
" \"n_components\": 32\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"product issues\",\n",
" \"preset\": \"l2_ridge_a1000_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1000,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"psychiatric disorders\",\n",
" \"preset\": \"l2_ridge_a1_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"renal and urinary disorders\",\n",
" \"preset\": \"l2_ridge_a0.1_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.1,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"reproductive system and breast disorders\",\n",
" \"preset\": \"l2_ridge_a3e-05_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3e-05,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"respiratory, thoracic and mediastinal disorders\",\n",
" \"preset\": \"l2_ridge_a1_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"skin and subcutaneous tissue disorders\",\n",
" \"preset\": \"l2_ridge_a1000_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1000,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"social circumstances\",\n",
" \"preset\": \"l2_ridge_a3_cwbalanced\",\n",
" \"preset_params\": {\n",
" \"alpha\": 3,\n",
" \"class_weight\": \"balanced\",\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"surgical and medical procedures\",\n",
" \"preset\": \"std_l2_ridge_a1e-05_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 1e-05,\n",
" \"class_weight\": null,\n",
" \"head\": \"std_l2+RidgeClassifier\"\n",
" }\n",
" },\n",
" {\n",
" \"dataset_key\": \"sider\",\n",
" \"task\": \"vascular disorders\",\n",
" \"preset\": \"l2_ridge_a0.0003_cwnone\",\n",
" \"preset_params\": {\n",
" \"alpha\": 0.0003,\n",
" \"class_weight\": null,\n",
" \"head\": \"l2+RidgeClassifier\"\n",
" }\n",
" }\n",
"]\n",
"\n",
"print(json.dumps(TASK_SETTINGS, indent=2))\n",
"print(f'Loaded {len(PHYSIOLOGICAL_TASK_PRESETS)} Tox21/SIDER task-specific settings')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def encode_rmmol_smiles(smiles):\n",
" if not RMMOL_CKPT:\n",
" raise RuntimeError('Set RMMOL_CKPT to the downloaded RMMol checkpoint path before encoding.')\n",
" sys.path.insert(0, str(RMMOL_TRUE_LOADER_DIR))\n",
" from rmmol_true_loader import encode_smiles_true_loader\n",
" embeddings, valid_indices, cfg = encode_smiles_true_loader(\n",
" list(smiles), checkpoint=Path(RMMOL_CKPT), batch_size=BATCH_SIZE, device=DEVICE\n",
" )\n",
" return embeddings, np.asarray(valid_indices, dtype=int), cfg\n",
"\n",
"def read_splits(domain, dataset, prefix):\n",
" frames = []\n",
" for split in ['train', 'val', 'test']:\n",
" frame = pd.read_csv(RELEASE_DIR / 'dataset' / domain / dataset / f'{prefix}_{split}.csv')\n",
" frame['_split'] = split\n",
" frames.append(frame)\n",
" return pd.concat(frames, ignore_index=True)\n",
"\n",
"def fit_auc(head_factory, x, y, folds):\n",
" scores = []\n",
" for train_idx, test_idx in folds:\n",
" model = head_factory()\n",
" model.fit(x[train_idx], y[train_idx])\n",
" pred = model.decision_function(x[test_idx])\n",
" scores.append(roc_auc_score(y[test_idx], pred))\n",
" return float(np.mean(scores)), float(np.std(scores, ddof=0))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Biophysics examples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bbbp = pd.read_csv(RELEASE_DIR / 'dataset' / 'biophysics' / 'bbbp' / 'bbbp.csv')\n",
"x_bbbp, valid_bbbp, _ = encode_rmmol_smiles(bbbp['smiles'])\n",
"y_bbbp = bbbp.iloc[valid_bbbp]['p_np'].astype(int).to_numpy()\n",
"folds = list(KFold(n_splits=5, shuffle=True, random_state=42).split(x_bbbp))\n",
"bbbp_auc, bbbp_std = fit_auc(\n",
" lambda: make_pipeline(StandardScaler(), RidgeClassifier(alpha=1.0, class_weight=None, solver='lsqr')),\n",
" x_bbbp, y_bbbp, folds\n",
")\n",
"print({'dataset': 'bbbp', 'metric': 'ROC-AUC', 'mean': bbbp_auc, 'std': bbbp_std, 'n': len(y_bbbp)})\n",
"\n",
"hiv = read_splits('biophysics', 'hiv', 'hiv')\n",
"x_hiv, valid_hiv, _ = encode_rmmol_smiles(hiv['smiles'])\n",
"hiv_valid = hiv.iloc[valid_hiv].reset_index(drop=True)\n",
"y_hiv = hiv_valid['HIV_active'].astype(int).to_numpy()\n",
"trainval = hiv_valid['_split'].isin(['train', 'val']).to_numpy()\n",
"test = hiv_valid['_split'].eq('test').to_numpy()\n",
"hiv_head = make_pipeline(Normalizer(), RidgeClassifier(alpha=1.6788e-05, class_weight='balanced', solver='lsqr'))\n",
"hiv_head.fit(x_hiv[trainval], y_hiv[trainval])\n",
"hiv_auc = roc_auc_score(y_hiv[test], hiv_head.decision_function(x_hiv[test]))\n",
"print({'dataset': 'hiv', 'metric': 'ROC-AUC', 'test': float(hiv_auc), 'n_test': int(test.sum())})\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Physical chemistry example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def regression_cv(domain, dataset, prefix, label, alpha):\n",
" frame = read_splits(domain, dataset, prefix)\n",
" x, valid, _ = encode_rmmol_smiles(frame['smiles'])\n",
" y = frame.iloc[valid][label].astype(float).to_numpy()\n",
" folds = KFold(n_splits=5, shuffle=True, random_state=42).split(x)\n",
" rmses = []\n",
" for train_idx, test_idx in folds:\n",
" model = make_pipeline(StandardScaler(), Ridge(alpha=alpha))\n",
" model.fit(x[train_idx], y[train_idx])\n",
" pred = model.predict(x[test_idx])\n",
" rmses.append(mean_squared_error(y[test_idx], pred, squared=False))\n",
" return float(np.mean(rmses)), float(np.std(rmses, ddof=0)), len(y)\n",
"\n",
"esol_rmse, esol_std, esol_n = regression_cv(\n",
" 'physical_chemistry', 'esol', 'esol', 'ESOL predicted log solubility in mols per litre', 0.003\n",
")\n",
"print({'dataset': 'esol', 'metric': 'RMSE', 'mean': esol_rmse, 'std': esol_std, 'n': esol_n})\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quantum mechanics example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"qm9_tasks = ['mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'u0', 'u298', 'h298', 'g298', 'cv']\n",
"qm9 = read_splits('quantum_mechanics', 'qm9', 'qm9')\n",
"x_qm9, valid_qm9, _ = encode_rmmol_smiles(qm9['smiles'])\n",
"y_qm9 = qm9.iloc[valid_qm9][qm9_tasks].astype(float).to_numpy()\n",
"folds = KFold(n_splits=5, shuffle=True, random_state=42).split(x_qm9)\n",
"maes = []\n",
"for train_idx, test_idx in folds:\n",
" model = make_pipeline(StandardScaler(), Ridge(alpha=0.01))\n",
" model.fit(x_qm9[train_idx], y_qm9[train_idx])\n",
" pred = model.predict(x_qm9[test_idx])\n",
" maes.append(mean_absolute_error(y_qm9[test_idx], pred))\n",
"print({'dataset': 'qm9', 'metric': 'MAE', 'mean': float(np.mean(maes)), 'std': float(np.std(maes, ddof=0)), 'n': len(y_qm9)})\n"
]
}
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