From 545623f8c5b799d7d098ee2aab1afa59c78bd6f1 Mon Sep 17 00:00:00 2001 From: HW DU <44920119+dhw059@users.noreply.github.com> Date: Wed, 10 Apr 2024 19:51:12 +0800 Subject: [PATCH] Add files via upload --- .../info.json | 8 + .../matbench_notebook.ipynb | 274 ++++++++++++++++++ .../results.json.gz | Bin 0 -> 3547857 bytes 3 files changed, 282 insertions(+) create mode 100644 benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/info.json create mode 100644 benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/matbench_notebook.ipynb create mode 100644 benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/results.json.gz diff --git a/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/info.json b/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/info.json new file mode 100644 index 00000000..e22c45f1 --- /dev/null +++ b/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/info.json @@ -0,0 +1,8 @@ +{ + "authors": "Hongwei Du, Hong Wang (original code by Hongwei Du)", + "algorithm": "LGDCNN", + "algorithm_long": "Rational Design of Deep Learning Networks Based on Fusion Strategy for Improved Materials Property Predictions. See github page for more information: https://github.com/dhw059/DeepModelFusion.", + "bibtex_refs": "@article{UnderReview, author = {Hongwei Du, Hong Wang}, title = {Rational Design of Deep Learning Networks Based on Fusion Strategy for Improved Materials Property Predictions}, journal = {Journal of Chemical Theory and Computation}, volume = {}, number = {}, pages = {}, year = {2024}, doi = {Under Review}, URL = {}, eprint = {}}", + "requirements": "See GitHub page for LGDCNN.", + "notes": "" +} \ No newline at end of file diff --git a/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/matbench_notebook.ipynb b/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/matbench_notebook.ipynb new file mode 100644 index 00000000..c3ed6fb5 --- /dev/null +++ b/benchmarks/matbench_v0.1_LGDCNN_v1.0_composition/matbench_notebook.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from matbench.bench import MatbenchBenchmark\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "from sklearn.metrics import roc_auc_score \n", + "from lgdcnn.fusion_lstm_dcnn import LGDCNN\n", + "from lgdcnn.train import Model\n", + "from lgdcnn.utils.get_compute_device import get_compute_device\n", + "\n", + "compute_device = get_compute_device(prefer_last=False)\n", + "RNG_SEED = 42\n", + "torch.manual_seed(RNG_SEED)\n", + "np.random.seed(RNG_SEED)\n", + "model_name = \"L-G-DCNN-matbench\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# %%\n", + "def get_model(data_dir,model_name, mat_prop, i, classification=False, batch_size=None,\n", + " transfer=None, verbose=True):\n", + " # Get the TorchedLGDCNN architecture loaded\n", + " model = Model(LGDCNN(compute_device=compute_device).to(compute_device),\n", + " model_name=f'{mat_prop}{i}', verbose=verbose)\n", + "\n", + " # Train network starting at pretrained weights\n", + " if transfer is not None:\n", + " model.load_network(f'{transfer}.pth')\n", + " model.model_name = f'{mat_prop}'\n", + "\n", + " # Apply BCEWithLogitsLoss to model output if binary classification is True\n", + " if classification:\n", + " model.classification = True\n", + "\n", + " # Get the datafiles you will learn from\n", + " train_data = f'{data_dir}/{mat_prop}/train.csv'\n", + " val_data = f'{data_dir}/{mat_prop}/val.csv'\n", + "\n", + " # Load the train and validation data before fitting the network\n", + " data_size = pd.read_csv(train_data).shape[0]\n", + " batch_size = 2**round(np.log2(data_size)-4)\n", + " if batch_size < 2**7:\n", + " batch_size = 2**7\n", + " if batch_size > 2**12:\n", + " batch_size = 2**12\n", + " \n", + " model.load_data(train_data, batch_size=batch_size//2, train=True)\n", + " print(f'training with batchsize {model.batch_size} '\n", + " f'(2**{np.log2(model.batch_size):0.3f})')\n", + " model.load_data(val_data, batch_size=batch_size//2)\n", + "\n", + " # Set the number of epochs, decide if you want a loss curve to be plotted\n", + " model.fit(epochs=300, losscurve=False)\n", + "\n", + " # Save the network (saved as f\"{model_name}.pth\")\n", + " model.save_network(model_name)\n", + " return model\n", + "\n", + "def load_model(data_dir, model_name, mat_prop, i, classification, file_name, verbose=True):\n", + " # Load up a saved network.\n", + " model = Model(LGDCNN(compute_device=compute_device).to(compute_device),\n", + " model_name=f'{mat_prop}{i}', verbose=verbose)\n", + " model.load_network(model_name, f'{mat_prop}{i}.pth')\n", + "\n", + " # Check if classifcation task\n", + " if classification:\n", + " model.classification = True\n", + " # Load the data you want to predict with\n", + " data = f'{data_dir}/{mat_prop}/{file_name}'\n", + " # data is reloaded to model.data_loader\n", + " model.load_data(data, batch_size=2**9)\n", + " return model\n", + "\n", + "def get_results(model):\n", + " output = model.predict(model.data_loader) # predict the data saved here\n", + " return model, output\n", + "\n", + "def to_csv(output, save_name):\n", + " # parse output and save to csv\n", + " act, pred, formulae, uncertainty = output\n", + " df = pd.DataFrame([formulae, act, pred, uncertainty]).T\n", + " # df.columns = ['composition', 'target', 'pred-0', 'uncertainty']\n", + " df.columns = ['formula', 'actual', 'predicted', 'uncertainty']\n", + " save_path = 'matbench_predictions/'\n", + " os.makedirs(save_path, exist_ok=True)\n", + " df.to_csv(f'{save_path}/{save_name}', index_label='Index')\n", + " \n", + "\n", + "def save_results(data_dir, model_name,mat_prop, fold, classification, file_name, ):\n", + " model = load_model(data_dir, model_name,mat_prop, fold, classification, file_name = 'test.csv' )\n", + " model, output = get_results(model)\n", + " \n", + " # Get appropriate metrics for saving to csv\n", + " if model.classification:\n", + " auc = roc_auc_score(output[0], output[1])\n", + " print(f'\\n{mat_prop} ROC AUC: {auc:0.3f}')\n", + " else:\n", + " mae = np.abs(output[0] - output[1]).mean()\n", + " print(f'\\n{mat_prop} mae: {mae:0.3g}')\n", + "\n", + " # save predictions to a csv\n", + " fname = f'{mat_prop}_{file_name.replace(\".csv\", \"\")}_output{fold}.csv'\n", + " to_csv(output, fname)\n", + " return model, output" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#condesne_formula takes a material and returns the chemical formula in the correct format for LGDCNN\n", + "def condense_formula(mat):\n", + " if isinstance(mat, str):\n", + " return mat\n", + " else:\n", + " return mat.formula.replace(' ', '')\n", + "\n", + "#change_input runs condesne_formula on all the input data used for training\n", + "def change_input(train_inputs):\n", + " inputs = []\n", + " for input in train_inputs:\n", + " inputs.append(condense_formula(input))\n", + " return inputs\n", + "\n", + "#make_df creates a data frame containing the train inputs and outputs for LGDCNN\n", + "def make_df(train_inputs, train_outputs):\n", + " input_df = pd.DataFrame({'formula': train_inputs, 'target': train_outputs})\n", + " return input_df\n", + "\n", + "#make_df_test creates a data frame containing the test inputs for LGDCNN\n", + "def make_df_test(test_inputs, test_outputs):\n", + " test_df = pd.DataFrame({'formula' : test_inputs, 'target': test_outputs})\n", + " # test_df['target'] = np.nan\n", + " return test_df\n", + "\n", + "#split_train_val splits the training data into two sets: training and validation\n", + "def split_train_val(df):\n", + " df = df.sample(frac = 1.0, random_state = 7)\n", + " val_df = df.sample(frac = 0.1, random_state = 7)\n", + " train_df = df.drop(val_df.index) \n", + " print(train_df.shape, val_df.shape) \n", + " return train_df, val_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subset = [\"matbench_jdft2d\", \"matbench_steels\", \n", + " \"matbench_perovskites\", \"matbench_expt_gap\",\n", + " \"matbench_phonons\", \"matbench_dielectric\", \n", + " \"matbench_log_gvrh\", \"matbench_log_kvrh\",\n", + " \"matbench_mp_gap\", \"matbench_mp_e_form\"]\n", + "\n", + "mb = MatbenchBenchmark(autoload=False, subset=subset)\n", + "data_dir = 'data/matbench_temp'\n", + "os.makedirs(data_dir, exist_ok= True)\n", + "\n", + "results_dict = {}\n", + "\n", + "for task in mb.tasks:\n", + " task.load()\n", + " mat_prop = task.dataset_name\n", + " os.makedirs(f'{data_dir}/{mat_prop}', exist_ok= True)\n", + " for fold in task.folds:\n", + " train_inputs, train_outputs = task.get_train_and_val_data(fold)\n", + " test_inputs , test_outputs = task.get_test_data(fold, include_target=True)\n", + "\n", + " #Preparing the inputs data for LGDCNN\n", + " inputs = change_input(train_inputs)\n", + " df = make_df(inputs, train_outputs)\n", + "\n", + " #Creating the training and validation sets\n", + " train_df, val_df = split_train_val(df)\n", + " train_df.to_csv(f'{data_dir}/{mat_prop}/train.csv')\n", + " val_df.to_csv(f'{data_dir}/{mat_prop}/val.csv')\n", + "\n", + " #Getting and preparing the testing data\n", + " test_inputs_formula = change_input(test_inputs)\n", + " test_df = make_df_test(test_inputs_formula, test_outputs)\n", + " test_df.to_csv(f'{data_dir}/{mat_prop}/test.csv')\n", + "\n", + " #Training LGDCNN\n", + " model = get_model(data_dir, model_name, mat_prop, fold, classification = False, verbose = True, )\n", + " \n", + " model_test, output = save_results(data_dir, model_name,mat_prop, fold, classification = False,\n", + " file_name='test.csv',)\n", + " \n", + " # Recording our data!\n", + " predictions = output[1]\n", + " task.record(fold, predictions)\n", + "\n", + "# Saving our results\n", + "mb.to_file(\"LGDCNN_\"+mat_prop +\".json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gzip\n", + "import shutil\n", + "\n", + "def compress_json(input_file, output_file):\n", + " with open(input_file, 'rb') as f_in:\n", + " with gzip.open(output_file, 'wb') as f_out:\n", + " shutil.copyfileobj(f_in, f_out)\n", + "\n", + "compress_json('results.json', 'results.json.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pytorch", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.17" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": 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