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iwatobipen/falcon-demo.ipynb

Created May 22, 2023 12:45
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falcon-demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b63e70c8",
"metadata": {},
"outputs": [],
"source": [
"#!wget https://raw.githubusercontent.com/rdkit/rdkit/master/Docs/Book/data/solubility.train.sdf\n",
"#!wget https://raw.githubusercontent.com/rdkit/rdkit/master/Docs/Book/data/solubility.test.sdf\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "942ca0a6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/iwatobipen/miniconda3/envs/chemenv1/lib/python3.9/site-packages/skl2onnx/algebra/onnx_ops.py:159: UserWarning: OpSchema.FormalParameter.typeStr is deprecated and will be removed in 1.16. Use OpSchema.FormalParameter.type_str instead.\n",
" tys = obj.typeStr or ''\n",
"/home/iwatobipen/miniconda3/envs/chemenv1/lib/python3.9/site-packages/skl2onnx/algebra/automation.py:154: UserWarning: OpSchema.FormalParameter.isHomogeneous is deprecated and will be removed in 1.16. Use OpSchema.FormalParameter.is_homogeneous instead.\n",
" if getattr(obj, 'isHomogeneous', False):\n",
"/home/iwatobipen/miniconda3/envs/chemenv1/lib/python3.9/site-packages/jinja2/environment.py:485: UserWarning: OpSchema.FormalParameter.typeStr is deprecated and will be removed in 1.16. Use OpSchema.FormalParameter.type_str instead.\n",
" return getattr(obj, attribute)\n"
]
}
],
"source": [
"from falcon import AutoML\n",
"from rdkit import Chem\n",
"from rdkit.Chem import PandasTools\n",
"from rdkit.Chem import rdFingerprintGenerator\n",
"from rdkit.Chem import DataStructs\n",
"from useful_rdkit_utils import mol2numpy_fp\n",
"import pandas as pd\n",
"import numpy as np\n",
"from plotly import express as px"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "113252a6",
"metadata": {},
"outputs": [],
"source": [
"train = [m for m in Chem.SDMolSupplier('./solubility.train.sdf')]\n",
"test = [m for m in Chem.SDMolSupplier('./solubility.test.sdf')]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e46b4b30",
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.DataFrame([mol2numpy_fp(m) for m in train])\n",
"train_data.columns = [f'fp_{idx:02}' for idx in range(2048)]\n",
"trainY = [float(m.GetProp('SOL')) for m in train]\n",
"test_data = pd.DataFrame([mol2numpy_fp(m) for m in test])\n",
"test_data.columns = [f'fp_{idx:02}' for idx in range(2048)]\n",
"testY = [float(m.GetProp('SOL')) for m in test]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fb704efe",
"metadata": {},
"outputs": [],
"source": [
"train_data['sol'] = trainY\n",
"test_data['sol'] = testY"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0dfd6b67",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Initializing a new TabularTaskManager for task `tabular_regression`\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Beginning training\n",
"Beginning the main training phase\n",
"Fitting the pipeline...\n",
"Fitting stacked model... \n",
"Setting up learner config [mid dataset]\n",
"\t -> Filtering base classifiers:\n",
"\t\t-> ElasticNet base score: 0.499997007400399\n",
"\t\t-> DecisionTreeRegressor base score: 0.733596268053291\n",
"\t\t-> HistGradientBoostingRegressor_150 base score: 0.8357948560555432\n",
"\t\t-> HistGradientBoostingRegressor base score: 0.8288608503403038\n",
"\t\t-> HistGradientBoostingRegressor_50 base score: 0.8087376620109533\n",
"\t\t-> GradientBoostingRegressor_100 base score: 0.7981725022964659\n",
"\t\t-> AdaBoostRegressor_50 base score: 0.6947513173311912\n",
"\t\t-> BaggingRegressor_10 base score: 0.7934126759222989\n",
"\t\t-> RandomForestRegressor_100 base score: 0.8190979497881101\n",
"\t\t-> ExtraTreesRegressor_100 base score: 0.747809611112622\n",
"\t\t-> GradientBoostingRegressor_10 base score: 0.661199212169705\n",
"\t\t-> GradientBoostingRegressor_25 base score: 0.7299198471122101\n",
"\t\t-> GradientBoostingRegressor_50 base score: 0.7652685557397417\n",
"\t\t-> AdaBoostRegressor_25 base score: 0.6800358185731886\n",
"\t\t-> AdaBoostRegressor_100 base score: 0.6803522173108106\n",
"\t\t-> BaggingRegressor_25 base score: 0.8196437525311552\n",
"\t\t-> BaggingRegressor_50 base score: 0.8154351237878447\n",
"\t\t-> BaggingRegressor_100 base score: 0.8198316432718461\n",
"\t\t-> RandomForestRegressor_10 base score: 0.8167106972898754\n",
"\t\t-> RandomForestRegressor_25 base score: 0.8210989851976792\n",
"\t\t-> RandomForestRegressor_50 base score: 0.8180512134998708\n",
"\t\t-> ExtraTreesRegressor_10 base score: 0.7462493176569327\n",
"\t\t-> ExtraTreesRegressor_25 base score: 0.747671347332109\n",
"\t\t-> ExtraTreesRegressor_50 base score: 0.7480761670307334\n",
"\t -> Fitting the final estimator\n",
"Finished training\n",
"The evaluation report will be provided here\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"\n",
" N_SAMPLES R2 RMSE MSE MAE RMSLE SCORE \\\n",
"train 1025 0.897913 0.651276 0.424160 0.492969 -0.428822 0.897913 \n",
"test 257 0.713126 1.079622 1.165584 0.819909 0.076611 0.713126 \n",
"\n",
" SC_SCORE \n",
"train 0.948956 \n",
"test 0.856563 \n",
"\n",
"Saving the model ...\n",
"Serializing to onnx...\n",
"\t -> Merging step 1 ::: io_map [('falcon-pl-0/transformed_column', 'falcon-pl-1/model_input')] ::: outputs: ['falcon-pl-1/variable']\n",
"Serialization completed.\n",
"The model was saved as `falcon_20230522.212221.onnx`\n"
]
}
],
"source": [
"manager = AutoML(task = 'tabular_regression', train_data = train_data, test_data = test_data, features = [f'fp_{idx:02}' for idx in range(2048)], target = 'sol')\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e17fcbbe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" N_SAMPLES R2 RMSE MSE MAE RMSLE SCORE \\\n",
"train 1025 0.897913 0.651276 0.42416 0.492969 -0.428822 0.897913 \n",
"\n",
" SC_SCORE \n",
"train 0.948956 \n",
"\n",
"{'train': {'N_SAMPLES': 1025, 'R2': 0.897912796728474, 'RMSE': 0.6512759179568169, 'MSE': 0.4241603213104946, 'MAE': 0.4929689272351379, 'RMSLE': -0.42882173591515665, 'SCORE': 0.897912796728474, 'SC_SCORE': 0.9489563983642371}}\n"
]
}
],
"source": [
"print(manager.performance_summary())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ac6a5db2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"falcon_20230522.212221.onnx solubility.train.sdf\r\n",
"solubility.test.sdf\t Untitled.ipynb\r\n"
]
}
],
"source": [
"!ls"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bc85cda1",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<rdkit.Chem.rdchem.Mol at 0x7fe1e83d5fa0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mol1 = Chem.MolFromSmiles('CC1CCCCC1')\n",
"mol2 = Chem.MolFromSmiles('OCC1CC(O)C(O)CC1')\n",
"mol2 = Chem.MolFromSmiles('CC(=O)CCCC')\n",
"mol2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a0f4edf0",
"metadata": {},
"outputs": [],
"source": [
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"fp2 = mol2numpy_fp(mol2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5a282d47",
"metadata": {},
"outputs": [
{
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "49f78039",
"metadata": {},
"outputs": [],
"source": [
"train_X = pd.DataFrame([mol2numpy_fp(m) for m in train])\n",
"pred_train = manager.predict(train_X)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e2f4ef1b",
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"id": "e39be360",
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" \n",
"var gd = document.getElementById('a05ccc39-396c-46e4-bbba-075dfa3974f2');\n",
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},
"metadata": {},
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}
],
"source": [
"fig = px.scatter(x=testY, y=pred_test)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15f136bb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
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"nbformat_minor": 5
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