From 7e93bc7e06634d41e5b9d91a8e49d28ce9c59f57 Mon Sep 17 00:00:00 2001 From: sgbaird Date: Wed, 1 Mar 2023 10:03:27 -0700 Subject: [PATCH 1/9] joblib compress --- .../1.2-jp-surrogate.ipynb | 40767 +++++++++++++++- 1 file changed, 40007 insertions(+), 760 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb index 91f44a4..8eeafc3 100644 --- a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb +++ b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb @@ -14,11 +14,10 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import pickle\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.model_selection import KFold, GroupKFold\n", @@ -37,11 +36,11 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "dummy = True\n", + "dummy = False\n", "\n", "task_name = \"crabnet_hyperparameter\"\n", "\n", @@ -62,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -108,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +133,7 @@ " mae = mean_absolute_error(y_test, y_pred)\n", " mae_scores.append(mae)\n", " # save model as .pkl\n", - " joblib.dump(model, f\"{model_name_stem}_{i}.pkl\")\n", + " joblib.dump(model, f\"{model_name_stem}_{i}.pkl\", compress=3)\n", "\n", " avg_mae = np.mean(mae_scores)\n", " std_mae = np.std(mae_scores)\n", @@ -154,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -211,14 +210,14 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for mae: 0.0886 +/- 0.0158\n" + "MAE for mae: 0.0088 +/- 0.0000\n" ] } ], @@ -235,7 +234,9 @@ ")\n", "\n", "mae_model_stem = path.join(model_dir, \"sobol_reg_mae\")\n", - "mae_results = rfr_group_mae(X_array_mae, y_array_mae, sobol_reg_mae_group, mae_model_stem, \"mae\")" + "mae_results = rfr_group_mae(\n", + " X_array_mae, y_array_mae, sobol_reg_mae_group, mae_model_stem, \"mae\"\n", + ")" ] }, { @@ -248,19 +249,19 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for rmse: 0.0883 +/- 0.0257\n" + "MAE for rmse: 0.0131 +/- 0.0001\n" ] } ], "source": [ - "rmse_features = common_features + [\"rmse_rank\"] ## ToDo: rmse or rmse_rank (rmse.1)\n", + "rmse_features = common_features + [\"rmse_rank\"] ## ToDo: rmse or rmse_rank (rmse.1)\n", "\n", "X_array_rmse = sobol_reg[rmse_features].to_numpy()\n", "y_array_rmse = sobol_reg[[\"rmse\"]].to_numpy().ravel()\n", @@ -287,14 +288,14 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for model_size: 7078125.7105 +/- 791229.1921\n" + "MAE for model_size: 317796.8646 +/- 6005.9939\n" ] } ], @@ -326,19 +327,21 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for runtime: 71.1022 +/- 12.7256\n" + "MAE for runtime: 13.9175 +/- 0.7062\n" ] } ], "source": [ - "runtime_features = common_features + [\"runtime_rank\"] ## ToDo: runtime or runtime_rank (runtime.1)\n", + "runtime_features = common_features + [\n", + " \"runtime_rank\"\n", + "] ## ToDo: runtime or runtime_rank (runtime.1)\n", "\n", "X_array_runtime = sobol_reg[runtime_features].to_numpy()\n", "y_array_runtime = sobol_reg[[\"runtime\"]].to_numpy().ravel()\n", @@ -351,841 +354,40085 @@ "\n", "runtime_model_stem = path.join(model_dir, \"sobol_reg_runtime\")\n", "runtime_results = rfr_group_mae(\n", - " X_array_runtime, y_array_runtime, sobol_reg_runtime_group, runtime_model_stem, \"runtime\"\n", + " X_array_runtime,\n", + " y_array_runtime,\n", + " sobol_reg_runtime_group,\n", + " runtime_model_stem,\n", + " \"runtime\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'mae': {'mae': [0.09736450358524826,\n", - " 0.09584076684453244,\n", - " 0.0802529964475443,\n", - " 0.10721362715898333,\n", - " 0.06212542711224781],\n", - " 'y_pred': [[1.0470445123262662,\n", - " 1.1388262858078486,\n", - " 1.0937742371687706,\n", - " 1.092910442683129,\n", - " 1.0438372602378871,\n", - " 1.0979699665832756,\n", - " 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150.86030268669128,\n", + " 139.49358105659485,\n", + " 88.6134135723114,\n", + " 437.6493685245514,\n", + " 40.6716206073761,\n", + " 46.78872752189636,\n", + " 42.54828381538391,\n", + " 94.49103426933289,\n", + " 143.6020631790161,\n", + " 210.00674605369568,\n", + " 269.227144241333,\n", + " 13.344388246536257,\n", + " 250.0733835697174,\n", + " 130.54649591445923,\n", + " 192.22115778923035,\n", + " 44.90037846565247,\n", + " 41.25598740577698,\n", + " ...]]}}" ] }, - "execution_count": 89, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1205,7 +40452,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1233,7 +40480,7 @@ " RandomForestRegressor(random_state=13)]}" ] }, - "execution_count": 90, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1250,7 +40497,7 @@ " models[key] = [joblib.load(f\"{model_path}_{i}.pkl\") for i in range(5)]\n", "\n", "with open(path.join(data_dir, \"cross_validation_models.pkl\"), \"wb\") as f:\n", - " pickle.dump(models, f)\n", + " joblib.dump(models, f, compress=3)\n", "\n", "models" ] @@ -1419,7 +40666,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "5814ca482226f814bec6d6a290f5fe630a2a0cb71af6cb85828c242f7bf3b839" + "hash": "01883adffc5ff99e80740fdb2688c7d7f1b5220f2274814f600fbe3b3887f376" } } }, From 006af32a287fe9f7e8c55cf37c96a1bbc34f1a1d Mon Sep 17 00:00:00 2001 From: sgbaird Date: Wed, 1 Mar 2023 10:03:38 -0700 Subject: [PATCH 2/9] Update 1.3-sgb-zenodo-upload.ipynb --- .../1.3-sgb-zenodo-upload.ipynb | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb index 3bc1d71..835c00e 100644 --- a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb +++ b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb @@ -79,9 +79,9 @@ "\n", "# Define the metadata that will be used on initial upload\n", "data = Metadata(\n", - " title=\"Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations\",\n", + " title=\"Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters\",\n", " upload_type=\"dataset\",\n", - " description=\"Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 438371 random hard-sphere packing simulations representing 279 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets can be used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.\",\n", + " description=\"Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.\",\n", " creators=[\n", " Creator(\n", " name=\"Baird, Sterling G.\",\n", @@ -89,9 +89,9 @@ " orcid=\"0000-0002-4491-6876\",\n", " ),\n", " Creator(\n", - " name=\"Parikh, Jeet\",\n", - " affiliation=\"\",\n", - " orcid=\"\",\n", + " name=\"Parikh, Jeet N.\",\n", + " affiliation=\"Northwood High School\",\n", + " orcid=\"0000-0002-8706-2962\",\n", " ),\n", " ],\n", ")\n", @@ -103,10 +103,14 @@ " key,\n", " data=data,\n", " paths=[\n", - " f\"data/processed/{task_name}/sobol_probability_filter.csv\",\n", " f\"data/processed/{task_name}/sobol_regression.csv\",\n", " f\"data/processed/{task_name}/model_metadata.json\",\n", - " # f\"data/processed/{task_name}/cross_validation_models.pkl\",\n", + " f\"models/{task_name}/surrogate_models.pkl\",\n", + " f\"models/{task_name}/cross_validation_models_0.pkl\",\n", + " f\"models/{task_name}/cross_validation_models_1.pkl\",\n", + " f\"models/{task_name}/cross_validation_models_2.pkl\",\n", + " f\"models/{task_name}/cross_validation_models_3.pkl\",\n", + " f\"models/{task_name}/cross_validation_models_4.pkl\",\n", " ],\n", " sandbox=sandbox, # remove this when you're ready to upload to real Zenodo\n", " access_token=access_token,\n", From 5e1d782cae834fcc2bb5a4420ace3521dc135d29 Mon Sep 17 00:00:00 2001 From: Jeet Date: Wed, 1 Mar 2023 18:12:19 -0800 Subject: [PATCH 3/9] Update 1.2-jp-surrogate.ipynb --- .../1.2-jp-surrogate.ipynb | 128 ++++++++++++++---- 1 file changed, 98 insertions(+), 30 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb index 8eeafc3..5f4fce8 100644 --- a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb +++ b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb @@ -210,17 +210,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MAE for mae: 0.0088 +/- 0.0000\n" - ] - } - ], + "outputs": [], "source": [ "mae_features = common_features + [\"mae_rank\"] ## ToDo: mae or mae_rank (mae.1)\n", "\n", @@ -249,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -288,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -327,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -364,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -40452,7 +40444,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -40492,16 +40484,92 @@ " \"model_size\": model_size_model_stem,\n", " \"runtime\": runtime_model_stem,\n", "}\n", - "models = {}\n", - "for key, model_path in model_paths.items():\n", - " models[key] = [joblib.load(f\"{model_path}_{i}.pkl\") for i in range(5)]\n", + "for i in range(5):\n", + " models = {}\n", + " for key, model_path in model_paths.items():\n", + " models[key] = joblib.load(f\"{model_path}_{i}.pkl\")\n", "\n", - "with open(path.join(data_dir, \"cross_validation_models.pkl\"), \"wb\") as f:\n", - " joblib.dump(models, f, compress=3)\n", + " with open(path.join(data_dir, f\"cross_validation_models_{i}.pkl\"), \"wb\") as f:\n", + " joblib.dump(models, f, compress=3)\n", "\n", "models" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Production models (full training data)\n", + "Six keys in the dictionary, each key is a value of a label, and its value pair is the trained model.\n", + "This trained model is stored in the models folder with the pickle file name \"trained_model.pkl\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_and_save(\n", + " sr_feat_array,\n", + " sr_labels_array,\n", + " sr_label_names,\n", + "):\n", + " models = {}\n", + "\n", + " for X1, y1, name1 in zip(sr_feat_array, sr_labels_array, sr_label_names):\n", + " print(f\"X1 sr shape: {X1.shape}, Y1 sr shape: {y1.shape}\")\n", + " model = RandomForestRegressor(random_state=13)\n", + " model.fit(X1, y1)\n", + " models[name1] = model\n", + "\n", + " return models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 spf shape: (41228, 9), Y1 spf shape: (41228,)\n", + "X1 spf shape: (41228, 9), Y1 spf shape: (41228,)\n", + "X2 sr shape: (408504, 10), Y2 sr shape: (408504,)\n", + "X2 sr shape: (338467, 10), Y2 sr shape: (338467,)\n", + "X2 sr shape: (438371, 10), Y2 sr shape: (438371,)\n", + "X2 sr shape: (438371, 10), Y2 sr shape: (438371,)\n" + ] + }, + { + "data": { + "text/plain": [ + "['..\\\\..\\\\models\\\\particle_packing\\\\surrogate_models.pkl']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# List of x_arrays, y_arrays, and target_names\n", + "sobol_reg_x_arrays = [X_array_mae, X_array_rmse, X_array_model_size, X_array_runtime]\n", + "sobol_reg_labels = [y_array_mae, y_array_rmse, y_array_model_size, y_array_runtime]\n", + "sobol_reg_target_names = [\"mae\", \"rmse\", \"model_size\", \"runtime\"]\n", + "\n", + "# Train and save the model on all the data\n", + "models = train_and_save(\n", + " sobol_reg_x_arrays,\n", + " sobol_reg_labels,\n", + " sobol_reg_target_names,\n", + ")\n", + "\n", + "joblib.dump(models, path.join(model_dir, \"surrogate_models.pkl\"), compress=3)" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -40512,7 +40580,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40530,7 +40598,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40550,7 +40618,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40563,7 +40631,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40577,7 +40645,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40587,7 +40655,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40612,7 +40680,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40622,7 +40690,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40634,7 +40702,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40666,7 +40734,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "01883adffc5ff99e80740fdb2688c7d7f1b5220f2274814f600fbe3b3887f376" + "hash": "5814ca482226f814bec6d6a290f5fe630a2a0cb71af6cb85828c242f7bf3b839" } } }, From 345d2136710e1ec2e3fce33c8453ab268df60b3a Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 16:19:21 -0700 Subject: [PATCH 4/9] .gitignore-s for folder structure --- models/crabnet_hyperparameter/.gitignore | 1 + models/crabnet_hyperparameter/cv/.gitignore | 0 models/crabnet_hyperparameter/dummy/.gitignore | 1 + models/crabnet_hyperparameter/dummy/cv/.gitignore | 2 ++ 4 files changed, 4 insertions(+) create mode 100644 models/crabnet_hyperparameter/cv/.gitignore create mode 100644 models/crabnet_hyperparameter/dummy/cv/.gitignore diff --git a/models/crabnet_hyperparameter/.gitignore b/models/crabnet_hyperparameter/.gitignore index 5da6c5d..4957a60 100644 --- a/models/crabnet_hyperparameter/.gitignore +++ b/models/crabnet_hyperparameter/.gitignore @@ -3,3 +3,4 @@ # Except this file !.gitignore !dummy +!cv diff --git a/models/crabnet_hyperparameter/cv/.gitignore b/models/crabnet_hyperparameter/cv/.gitignore new file mode 100644 index 0000000..e69de29 diff --git a/models/crabnet_hyperparameter/dummy/.gitignore b/models/crabnet_hyperparameter/dummy/.gitignore index 5e7d273..cf3699e 100644 --- a/models/crabnet_hyperparameter/dummy/.gitignore +++ b/models/crabnet_hyperparameter/dummy/.gitignore @@ -2,3 +2,4 @@ * # Except this file !.gitignore +!cv diff --git a/models/crabnet_hyperparameter/dummy/cv/.gitignore b/models/crabnet_hyperparameter/dummy/cv/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/models/crabnet_hyperparameter/dummy/cv/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore From 7ecc23396b17caeecfc850648a2b03acea409e8b Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 16:20:08 -0700 Subject: [PATCH 5/9] bring cluster_uri out --- .../crabnet_hyperparameter/1.0-sgb-collect-from-mongodb.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/notebooks/crabnet_hyperparameter/1.0-sgb-collect-from-mongodb.ipynb b/notebooks/crabnet_hyperparameter/1.0-sgb-collect-from-mongodb.ipynb index 8b20dc5..8054970 100644 --- a/notebooks/crabnet_hyperparameter/1.0-sgb-collect-from-mongodb.ipynb +++ b/notebooks/crabnet_hyperparameter/1.0-sgb-collect-from-mongodb.ipynb @@ -12,7 +12,8 @@ "# Requires the PyMongo package.\n", "# https://api.mongodb.com/python/current\n", "\n", - "client = MongoClient(f\"mongodb+srv://{MONGODB_USERNAME}:{MONGODB_PASSWORD}@matsci-opt-benchmarks.ehu7qrh.mongodb.net/?retryWrites=true&w=majority\")\n", + "cluster_uri = \"matsci-opt-benchmarks.ehu7qrh\"\n", + "client = MongoClient(f\"mongodb+srv://{MONGODB_USERNAME}:{MONGODB_PASSWORD}@{cluster_uri}.mongodb.net/?retryWrites=true&w=majority\")\n", "\n", "database_name = \"crabnet-hyperparameter\"\n", "collection_name = \"sobol\"" From d65eae9a1484fa2f158a294a565aca8225f1819b Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 16:21:36 -0700 Subject: [PATCH 6/9] fixup model directories, fix bug in groups, higher compression --- .../1.2-jp-surrogate.ipynb | 40368 +--------------- 1 file changed, 78 insertions(+), 40290 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb index 5f4fce8..52094e4 100644 --- a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb +++ b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -36,10 +36,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ + "from pathlib import Path\n", + "\n", "dummy = False\n", "\n", "task_name = \"crabnet_hyperparameter\"\n", @@ -48,7 +50,12 @@ "model_dir = path.join(\"..\", \"..\", \"models\", task_name)\n", "\n", "if dummy:\n", - " model_dir = path.join(model_dir, \"dummy\")" + " model_dir = path.join(model_dir, \"dummy\")\n", + " \n", + "cv_model_dir = path.join(model_dir, \"cv\")\n", + "\n", + "Path(model_dir).mkdir(exist_ok=True, parents=True) # technically redundant\n", + "Path(cv_model_dir).mkdir(exist_ok=True, parents=True)" ] }, { @@ -61,14 +68,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sobol_reg = pd.read_csv(path.join(data_dir, \"sobol_regression.csv\"))\n", "\n", "if dummy:\n", - " sobol_reg = sobol_reg.head(100)" + " data_dir = path.join(data_dir, \"dummy\")\n", + " sobol_reg = sobol_reg.head(100)\n", + " \n", + "Path(data_dir).mkdir(exist_ok=True, parents=True)" ] }, { @@ -81,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -107,12 +117,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# argument for rfr_mae, X_array, y_array, model_name to save model as .pkl\n", - "def rfr_group_mae(X_array, y_array, group_array, model_name_stem, objective_name, random_state=13):\n", + "def rfr_group_mae(\n", + " X_array, y_array, group_array, model_name_stem, objective_name, random_state=13\n", + "):\n", " kf = GroupKFold(n_splits=5)\n", " mae_scores = []\n", " y_preds = []\n", @@ -133,11 +145,11 @@ " mae = mean_absolute_error(y_test, y_pred)\n", " mae_scores.append(mae)\n", " # save model as .pkl\n", - " joblib.dump(model, f\"{model_name_stem}_{i}.pkl\", compress=3)\n", + " joblib.dump(model, f\"{model_name_stem}_{i}.pkl\", compress=7)\n", "\n", " avg_mae = np.mean(mae_scores)\n", " std_mae = np.std(mae_scores)\n", - " \n", + "\n", " print(f\"MAE for {objective_name}: {avg_mae:.4f} +/- {std_mae:.4f}\")\n", " results = {\"mae\": mae_scores, \"y_pred\": y_preds, \"y_true\": y_trues}\n", " return results" @@ -153,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -186,16 +198,7 @@ " \"elem_prop_mat2vec\",\n", " \"elem_prop_onehot\",\n", " \"hardware_2080ti\"\n", - "]\n", - "\n", - "# fba_isna_prob_features = common_features\n", - "# ls_isna_prob_features = common_features\n", - "\n", - "# fba_features = common_features + [\"fba_rank\"]\n", - "# ls_features = common_features + [\"ls_rank\"]\n", - "\n", - "# fba_time_s_features = common_features + [\"fba_time_s_rank\"]\n", - "# ls_time_s_features = common_features + [\"ls_time_s_rank\"]" + "]" ] }, { @@ -210,17 +213,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE for mae: 0.0217 +/- 0.0004\n" + ] + } + ], "source": [ - "mae_features = common_features + [\"mae_rank\"] ## ToDo: mae or mae_rank (mae.1)\n", + "mae_features = common_features + [\"mae_rank\"]\n", "\n", "X_array_mae = sobol_reg[mae_features].to_numpy()\n", "y_array_mae = sobol_reg[[\"mae\"]].to_numpy().ravel()\n", "\n", "sobol_reg_mae_group = (\n", - " sobol_reg[mae_features]\n", + " sobol_reg[common_features]\n", " .round(6)\n", " .apply(lambda row: \"_\".join(row.values.astype(str)), axis=1)\n", ")\n", @@ -241,25 +252,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for rmse: 0.0131 +/- 0.0001\n" + "MAE for rmse: 0.0265 +/- 0.0004\n" ] } ], "source": [ - "rmse_features = common_features + [\"rmse_rank\"] ## ToDo: rmse or rmse_rank (rmse.1)\n", + "rmse_features = common_features + [\"rmse_rank\"]\n", "\n", "X_array_rmse = sobol_reg[rmse_features].to_numpy()\n", "y_array_rmse = sobol_reg[[\"rmse\"]].to_numpy().ravel()\n", "\n", "sobol_reg_rmse_group = (\n", - " sobol_reg[rmse_features]\n", + " sobol_reg[common_features]\n", " .round(6)\n", " .apply(lambda row: \"_\".join(row.values.astype(str)), axis=1)\n", ")\n", @@ -280,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -292,20 +303,24 @@ } ], "source": [ - "model_size_features = common_features \n", + "model_size_features = common_features\n", "\n", "X_array_model_size = sobol_reg[model_size_features].to_numpy()\n", "y_array_model_size = sobol_reg[[\"model_size\"]].to_numpy().ravel()\n", "\n", "sobol_reg_model_size_group = (\n", - " sobol_reg[model_size_features]\n", + " sobol_reg[common_features]\n", " .round(6)\n", " .apply(lambda row: \"_\".join(row.values.astype(str)), axis=1)\n", ")\n", "\n", "model_size_model_stem = path.join(model_dir, \"sobol_reg_model_size\")\n", "model_size_results = rfr_group_mae(\n", - " X_array_model_size, y_array_model_size, sobol_reg_model_size_group, model_size_model_stem, \"model_size\"\n", + " X_array_model_size,\n", + " y_array_model_size,\n", + " sobol_reg_model_size_group,\n", + " model_size_model_stem,\n", + " \"model_size\",\n", ")" ] }, @@ -319,27 +334,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MAE for runtime: 13.9175 +/- 0.7062\n" + "MAE for runtime: 20.5904 +/- 1.0632\n" ] } ], "source": [ - "runtime_features = common_features + [\n", - " \"runtime_rank\"\n", - "] ## ToDo: runtime or runtime_rank (runtime.1)\n", + "runtime_features = common_features + [\"runtime_rank\"]\n", "\n", "X_array_runtime = sobol_reg[runtime_features].to_numpy()\n", "y_array_runtime = sobol_reg[[\"runtime\"]].to_numpy().ravel()\n", "\n", "sobol_reg_runtime_group = (\n", - " sobol_reg[runtime_features]\n", + " sobol_reg[common_features]\n", " .round(6)\n", " .apply(lambda row: \"_\".join(row.values.astype(str)), axis=1)\n", ")\n", @@ -356,40079 +369,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mae': {'mae': [0.008843673155521613,\n", - " 0.00876743657668446,\n", - " 0.008776292228330054,\n", - " 0.008869791280505015,\n", - " 0.00888197709113839],\n", - " 'y_pred': [[1.143685956497153,\n", - " 1.049450676093647,\n", - " 1.0132582492980349,\n", - " 1.006013309125075,\n", - " 0.37358981739413893,\n", - " 0.39343932512986624,\n", - " 0.7384827751272646,\n", - " 0.45743197004532715,\n", - " 0.9390963918693199,\n", - " 0.275529778364774,\n", - " 0.5295399840993557,\n", - " 0.7153759215229314,\n", - " 0.8367099148292506,\n", - " 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269.227144241333,\n", - " 13.344388246536257,\n", - " 250.0733835697174,\n", - " 130.54649591445923,\n", - " 192.22115778923035,\n", - " 44.90037846565247,\n", - " 41.25598740577698,\n", - " ...]]}}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "main_results = {\n", " \"mae\": mae_results,\n", @@ -40437,44 +380,25 @@ " \"runtime\": runtime_results,\n", "}\n", "with open(path.join(data_dir, \"model_metadata.json\"), \"w\") as f:\n", - " json.dump(main_results, f)\n", - " \n", - "main_results" + " json.dump(main_results, f)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'mae': [RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13)],\n", - " 'rmse': [RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13)],\n", - " 'model_size': [RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13)],\n", - " 'runtime': [RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13),\n", - " RandomForestRegressor(random_state=13)]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\sobol_reg_mae_0.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[22], line 10\u001b[0m\n\u001b[0;32m 8\u001b[0m models \u001b[39m=\u001b[39m {}\n\u001b[0;32m 9\u001b[0m \u001b[39mfor\u001b[39;00m key, model_path \u001b[39min\u001b[39;00m model_paths\u001b[39m.\u001b[39mitems():\n\u001b[1;32m---> 10\u001b[0m models[key] \u001b[39m=\u001b[39m joblib\u001b[39m.\u001b[39;49mload(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m{\u001b[39;49;00mmodel_path\u001b[39m}\u001b[39;49;00m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mi\u001b[39m}\u001b[39;49;00m\u001b[39m.pkl\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 12\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(path\u001b[39m.\u001b[39mjoin(model_dir, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcross_validation_models_\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m.pkl\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 13\u001b[0m joblib\u001b[39m.\u001b[39mdump(models, f, compress\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\sterg\\Miniconda3\\envs\\matsci-opt-benchmarks\\lib\\site-packages\\joblib\\numpy_pickle.py:650\u001b[0m, in \u001b[0;36mload\u001b[1;34m(filename, mmap_mode)\u001b[0m\n\u001b[0;32m 648\u001b[0m obj \u001b[39m=\u001b[39m _unpickle(fobj)\n\u001b[0;32m 649\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 650\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(filename, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 651\u001b[0m \u001b[39mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[39mas\u001b[39;00m fobj:\n\u001b[0;32m 652\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(fobj, \u001b[39mstr\u001b[39m):\n\u001b[0;32m 653\u001b[0m \u001b[39m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[0;32m 654\u001b[0m \u001b[39m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[0;32m 655\u001b[0m \u001b[39m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\sobol_reg_mae_0.pkl'" + ] } ], "source": [ @@ -40489,8 +413,8 @@ " for key, model_path in model_paths.items():\n", " models[key] = joblib.load(f\"{model_path}_{i}.pkl\")\n", "\n", - " with open(path.join(data_dir, f\"cross_validation_models_{i}.pkl\"), \"wb\") as f:\n", - " joblib.dump(models, f, compress=3)\n", + " with open(path.join(model_dir, f\"cross_validation_models_{i}.pkl\"), \"wb\") as f:\n", + " joblib.dump(models, f, compress=7)\n", "\n", "models" ] @@ -40536,22 +460,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "X1 spf shape: (41228, 9), Y1 spf shape: (41228,)\n", - "X1 spf shape: (41228, 9), Y1 spf shape: (41228,)\n", - "X2 sr shape: (408504, 10), Y2 sr shape: (408504,)\n", - "X2 sr shape: (338467, 10), Y2 sr shape: (338467,)\n", - "X2 sr shape: (438371, 10), Y2 sr shape: (438371,)\n", - "X2 sr shape: (438371, 10), Y2 sr shape: (438371,)\n" + "X1 sr shape: (173219, 29), Y1 sr shape: (173219,)\n", + "X1 sr shape: (173219, 29), Y1 sr shape: (173219,)\n", + "X1 sr shape: (173219, 28), Y1 sr shape: (173219,)\n", + "X1 sr shape: (173219, 29), Y1 sr shape: (173219,)\n" ] }, { "data": { "text/plain": [ - "['..\\\\..\\\\models\\\\particle_packing\\\\surrogate_models.pkl']" + "['..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\surrogate_models.pkl']" ] }, + "execution_count": 28, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -40567,80 +490,7 @@ " sobol_reg_target_names,\n", ")\n", "\n", - "joblib.dump(models, path.join(model_dir, \"surrogate_models.pkl\"), compress=3)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### code graveyard " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Ordinal Encoding\n", - "\n", - "# \"bias\" [False, True]},\n", - "# \"criterion\" [\"RobustL1\", \"RobustL2\"]},\n", - "# \"elem_prop\"[\"mat2vec\", \"magpie\", \"onehot\"],\n", - "\n", - "# sobol_reg[\"bias\"].replace([\"False\", \"True\"], [0,1], inplace=True)\n", - "# sobol_reg[\"criterion\"].replace([\"RobustL1\", \"RobustL2\"], [0,1], inplace=True)\n", - "# sobol_reg[\"elem_prop\"].replace([\"mat2vec\", \"magpie\", \"onehot\"], [0,1,2], inplace=True)\n", - "# sobol_reg[\"hardware\"].replace([\"2080ti\"], [0], inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(\"Average MAE for fba_isna_prob\",rfr_mae(X_array_fba_isna_prob, y_array_fba_isna_prob,'fba_isna_prob.pkl'))\n", - "\n", - "# load trained model\n", - "# loaded_model = joblib.load('fba_isna_prob_model.pkl')\n", - "\n", - "# Save the model\n", - "# with open('../models/fba_isna_prob.pkl', 'wb') as f:\n", - "# pickle.dump(model, f)\n", - "\n", - "# # Load the model\n", - "# with open('path/to/save/model.pkl', 'rb') as f:\n", - "# loaded_model = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# url_sobol_filter = \"https://zenodo.org/record/7513019/files/sobol_probability_filter.csv\"\n", - "# sobol_filter = pd.read_csv(url_sobol_filter)\n", - "\n", - "# url_sobol_reg = \"https://zenodo.org/record/7513019/files/sobol_regression.csv\"\n", - "# sobol_reg = pd.read_csv(url_sobol_reg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# os.getcwd()\n", - "# os.chdir(\"../data/raw\")\n", - "\n", - "# sobol_filter.to_csv('sobol_filter.csv', index=False)\n", - "\n", - "# sobol_reg.to_csv('sobol_reg.csv', index=False)" + "joblib.dump(models, path.join(model_dir, \"surrogate_models.pkl\"), compress=7)" ] }, { @@ -40648,69 +498,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# read in sobol_regression.csv\n", - "# url_sobol_reg = \"https://zenodo.org/record/7513019/files/sobol_regression.csv\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# sobol_reg_x = sobol_reg[\n", - "# [\n", - "# \"mu1_div_mu3\",\n", - "# \"mu2_div_mu3\",\n", - "# \"std1\",\n", - "# \"std2\",\n", - "# \"std3\",\n", - "# \"comp1\",\n", - "# \"comp2\",\n", - "# \"num_particles\",\n", - "# \"safety_factor\",\n", - "# \"fba_rank\",\n", - "# \"ls_rank\",\n", - "# \"fba_time_s_rank\",\n", - "# \"ls_time_s_rank\",\n", - "# ]\n", - "# ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(len(sobol_reg_x))\n", - "# print(len(fba))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(\n", - "# \"Average MAE for ls_time_s\",\n", - "# rfr_mae(X_array_fba_time_s, y_array_ls_time_s, \"sobol_reg_ls_time_s.pkl\"),\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # parse data for target \"fba_isna_prob\"\n", - "# fba_isna_prob = sobol_filter[\"fba_isna_prob\"]\n", - "# sobolPF_fba_isna_prob = sobol_filter.drop([\"ls_isna_prob\", \"fba_isna_prob\"], axis=1)\n", - "# fba_isna_prob = fba_isna_prob.to_frame()" - ] + "source": [] } ], "metadata": { @@ -40734,7 +522,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "5814ca482226f814bec6d6a290f5fe630a2a0cb71af6cb85828c242f7bf3b839" + "hash": "01883adffc5ff99e80740fdb2688c7d7f1b5220f2274814f600fbe3b3887f376" } } }, From 9660cd69ae4d46c2b6bee735f03da2596f495778 Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 16:22:34 -0700 Subject: [PATCH 7/9] fix paths, comment out `touch` --- .../1.3-sgb-zenodo-upload.ipynb | 197 ++++++++++++++++-- 1 file changed, 181 insertions(+), 16 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb index 835c00e..f3c8dfb 100644 --- a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb +++ b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb @@ -41,11 +41,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'touch' is not recognized as an internal or external command,\n", + "operable program or batch file.\n" + ] + } + ], "source": [ - "!touch $HOME/.config/zenodo.ini" + "# !touch $HOME/.config/zenodo.ini" ] }, { @@ -59,23 +68,172 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "sandbox = True" + "sandbox = False" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'conceptdoi': '10.5281/zenodo.7693715',\n", + " 'conceptrecid': '7693715',\n", + " 'created': '2023-03-02T22:14:57.764034+00:00',\n", + " 'doi': '10.5281/zenodo.7693716',\n", + " 'doi_url': 'https://doi.org/10.5281/zenodo.7693716',\n", + " 'files': [{'checksum': '7a0ab85596dd275f6261d73513c7ffe2',\n", + " 'filename': 'cross_validation_models_0.pkl',\n", + " 'filesize': 999010756,\n", + " 'id': '3ad52962-8a3a-4f2f-b4e0-976777c5d596',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_0.pkl',\n", + " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/3ad52962-8a3a-4f2f-b4e0-976777c5d596'}},\n", + " {'checksum': '305cedbd299c87f24fa75d33412bc44c',\n", + " 'filename': 'cross_validation_models_1.pkl',\n", + " 'filesize': 999216113,\n", + " 'id': '68b1bbc0-0b6d-413f-8809-13cee6e750e1',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_1.pkl',\n", + " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/68b1bbc0-0b6d-413f-8809-13cee6e750e1'}},\n", + " {'checksum': '3280fdb7cce0bf52b3094fca6930a6f2',\n", + " 'filename': 'cross_validation_models_2.pkl',\n", + " 'filesize': 999134956,\n", + " 'id': '25c1c388-f9fb-4570-a81c-2c9036e4c006',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_2.pkl',\n", + " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/25c1c388-f9fb-4570-a81c-2c9036e4c006'}},\n", + " {'checksum': '0f29be67a7e9a8a9e39a9a24fe93fe98',\n", + " 'filename': 'cross_validation_models_3.pkl',\n", + " 'filesize': 999143684,\n", + " 'id': '6ff4c3bb-3b14-439c-8824-63e93cf0f7d2',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_3.pkl',\n", + " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/6ff4c3bb-3b14-439c-8824-63e93cf0f7d2'}},\n", + " {'checksum': '5ceccbb3d9d54cead0e5a51925533ef0',\n", + " 'filename': 'cross_validation_models_4.pkl',\n", + " 'filesize': 999215796,\n", + " 'id': '8ddda854-6178-4f9d-a795-d341b565e819',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_4.pkl',\n", + " 'self': 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" 'filename': 'surrogate_models.pkl',\n", + " 'filesize': 1241641582,\n", + " 'id': 'd3a77c86-768f-4f71-a87e-527701e7293b',\n", + " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/surrogate_models.pkl',\n", + " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/d3a77c86-768f-4f71-a87e-527701e7293b'}}],\n", + " 'id': 7693716,\n", + " 'links': {'badge': 'https://zenodo.org/badge/doi/10.5281/zenodo.7693716.svg',\n", + " 'bucket': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6',\n", + " 'conceptbadge': 'https://zenodo.org/badge/doi/10.5281/zenodo.7693715.svg',\n", + " 'conceptdoi': 'https://doi.org/10.5281/zenodo.7693715',\n", + " 'doi': 'https://doi.org/10.5281/zenodo.7693716',\n", + " 'latest': 'https://zenodo.org/api/records/7693716',\n", + " 'latest_html': 'https://zenodo.org/record/7693716',\n", + " 'record': 'https://zenodo.org/api/records/7693716',\n", + " 'record_html': 'https://zenodo.org/record/7693716'},\n", + " 'metadata': {'access_right': 'open',\n", + " 'communities': [{'identifier': 'zenodo'}],\n", + " 'creators': [{'affiliation': 'University of Utah',\n", + " 'name': 'Baird, Sterling G.',\n", + " 'orcid': '0000-0002-4491-6876'},\n", + " {'affiliation': 'Northwood High School',\n", + " 'name': 'Parikh, Jeet N.',\n", + " 'orcid': '0000-0002-8706-2962'}],\n", + " 'description': 'Benchmarks are an essential driver of progress '\n", + " 'in scientific disciplines. Ideal benchmarks '\n", + " 'mimic real-world tasks as closely as possible, '\n", + " 'where insufficient difficulty or applicability '\n", + " 'can stunt growth in the field. Benchmarks should '\n", + " 'also have sufficiently low computational '\n", + " 'overhead to promote accessibility and '\n", + " 'repeatability. The goal is then to win a \"Turing '\n", + " 'test\" of sorts by creating a surrogate model '\n", + " 'that is indistinguishable from the ground truth '\n", + " 'observation (at least within the dataset bounds '\n", + " 'that were explored), necessitating a large '\n", + " 'amount of data. In materials science and '\n", + " 'chemistry, industry-relevant optimization tasks '\n", + " 'are often hierarchical, noisy, multi-fidelity, '\n", + " 'multi-objective, high-dimensional, and '\n", + " 'non-linearly correlated while exhibiting mixed '\n", + " 'numerical and categorical variables subject to '\n", + " 'linear and non-linear constraints. To complicate '\n", + " 'matters, unexpected, failed simulation or '\n", + " 'experimental regions may be present in the '\n", + " 'search space. In this study, 173219 quasi-random '\n", + " 'hyperparameter combinations were generated '\n", + " 'across 23 hyperparameters and used to train '\n", + " 'CrabNet on the Matbench experimental band gap '\n", + " 'dataset. The results were logged to a free-tier '\n", + " 'shared MongoDB Atlas dataset. This study '\n", + " 'resulted in a regression dataset mapping '\n", + " 'hyperparameter combinations (including repeats) '\n", + " 'to MAE, RMSE, computational runtime, and model '\n", + " 'size for CrabNet model trained on the Matbench '\n", + " 'experimental band gap benchmark task1. This '\n", + " 'dataset is used to create a surrogate model as '\n", + " 'close as possible to running the actual '\n", + " 'simulations by incorporating heteroskedastic '\n", + " 'noise. Failure cases for bad hyperparameter '\n", + " 'combinations were excluded via careful '\n", + " 'construction of the hyperparameter search space, '\n", + " 'and so were not considered as was done in prior '\n", + " 'work. For the regression dataset, percentile '\n", + " 'ranks were computed within each of the groups of '\n", + " 'identical parameter sets to enable capturing '\n", + " 'heteroskedastic noise. This contrasts with a '\n", + " 'more traditional approach that imposes a-priori '\n", + " 'assumptions such as Gaussian noise, e.g., by '\n", + " 'providing a mean and standard deviation. A '\n", + " 'similar approach can be applied to other '\n", + " 'benchmark datasets to bridge the gap between '\n", + " 'optimization benchmarks with low computational '\n", + " 'overhead and realistically complex, real-world '\n", + " 'optimization scenarios.',\n", + " 'doi': '10.5281/zenodo.7693716',\n", + " 'language': 'eng',\n", + " 'license': 'CC0-1.0',\n", + " 'prereserve_doi': {'doi': '10.5281/zenodo.7693716',\n", + " 'recid': 7693716},\n", + " 'publication_date': '2023-03-02',\n", + " 'title': 'Materials Science Optimization Benchmark Dataset for '\n", + " 'High-dimensional, Multi-objective, Multi-fidelity '\n", + " 'Optimization of CrabNet Hyperparameters',\n", + " 'upload_type': 'dataset',\n", + " 'version': '2023-03-02'},\n", + " 'modified': '2023-03-02T23:01:58.332431+00:00',\n", + " 'owner': 259510,\n", + " 'record_id': 7693716,\n", + " 'state': 'done',\n", + " 'submitted': True,\n", + " 'title': 'Materials Science Optimization Benchmark Dataset for '\n", + " 'High-dimensional, Multi-objective, Multi-fidelity Optimization of '\n", + " 'CrabNet Hyperparameters'}\n" + ] + } + ], "source": [ "from zenodo_client import Creator, Metadata, ensure_zenodo\n", "from my_secrets import ZENODO_API_KEY, ZENODO_SANDBOX_API_KEY\n", "\n", "task_name = \"crabnet-hyperparameter\"\n", + "task_name_underscore = task_name.replace(\"-\", \"_\")\n", "\n", "# Define the metadata that will be used on initial upload\n", "data = Metadata(\n", @@ -96,21 +254,21 @@ " ],\n", ")\n", "\n", - "# unique keys generate a new deposition ID\n", + "# unique keys generate a new deposition ID in $HOME/.config/zenodo.ini\n", "key = f\"matsciopt-{task_name}-benchmark-dataset\"\n", "access_token = ZENODO_SANDBOX_API_KEY if sandbox else ZENODO_API_KEY\n", "res = ensure_zenodo(\n", " key,\n", " data=data,\n", " paths=[\n", - " f\"data/processed/{task_name}/sobol_regression.csv\",\n", - " f\"data/processed/{task_name}/model_metadata.json\",\n", - " f\"models/{task_name}/surrogate_models.pkl\",\n", - " f\"models/{task_name}/cross_validation_models_0.pkl\",\n", - " f\"models/{task_name}/cross_validation_models_1.pkl\",\n", - " f\"models/{task_name}/cross_validation_models_2.pkl\",\n", - " f\"models/{task_name}/cross_validation_models_3.pkl\",\n", - " f\"models/{task_name}/cross_validation_models_4.pkl\",\n", + " f\"../../data/processed/{task_name_underscore}/sobol_regression.csv\",\n", + " f\"../../data/processed/{task_name_underscore}/model_metadata.json\",\n", + " f\"../../models/{task_name_underscore}/surrogate_models.pkl\",\n", + " f\"../../models/{task_name_underscore}/cv/cross_validation_models_0.pkl\",\n", + " f\"../../models/{task_name_underscore}/cv/cross_validation_models_1.pkl\",\n", + " f\"../../models/{task_name_underscore}/cv/cross_validation_models_2.pkl\",\n", + " f\"../../models/{task_name_underscore}/cv/cross_validation_models_3.pkl\",\n", + " f\"../../models/{task_name_underscore}/cv/cross_validation_models_4.pkl\",\n", " ],\n", " sandbox=sandbox, # remove this when you're ready to upload to real Zenodo\n", " access_token=access_token,\n", @@ -119,6 +277,13 @@ "\n", "pprint(res.json())\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 5884b37ab7e4c81534d37131dbdaf2639d96f338 Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 21:39:08 -0700 Subject: [PATCH 8/9] Update 1.2-jp-surrogate.ipynb --- .../1.2-jp-surrogate.ipynb | 53 ++++++++++--------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb index 52094e4..60aebd2 100644 --- a/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb +++ b/notebooks/crabnet_hyperparameter/1.2-jp-surrogate.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -252,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -385,20 +385,21 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\sobol_reg_mae_0.pkl'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[22], line 10\u001b[0m\n\u001b[0;32m 8\u001b[0m models \u001b[39m=\u001b[39m {}\n\u001b[0;32m 9\u001b[0m \u001b[39mfor\u001b[39;00m key, model_path \u001b[39min\u001b[39;00m model_paths\u001b[39m.\u001b[39mitems():\n\u001b[1;32m---> 10\u001b[0m models[key] \u001b[39m=\u001b[39m joblib\u001b[39m.\u001b[39;49mload(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m{\u001b[39;49;00mmodel_path\u001b[39m}\u001b[39;49;00m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mi\u001b[39m}\u001b[39;49;00m\u001b[39m.pkl\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 12\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(path\u001b[39m.\u001b[39mjoin(model_dir, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcross_validation_models_\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m.pkl\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 13\u001b[0m joblib\u001b[39m.\u001b[39mdump(models, f, compress\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\sterg\\Miniconda3\\envs\\matsci-opt-benchmarks\\lib\\site-packages\\joblib\\numpy_pickle.py:650\u001b[0m, in \u001b[0;36mload\u001b[1;34m(filename, mmap_mode)\u001b[0m\n\u001b[0;32m 648\u001b[0m obj \u001b[39m=\u001b[39m _unpickle(fobj)\n\u001b[0;32m 649\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 650\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(filename, \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m 651\u001b[0m \u001b[39mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[39mas\u001b[39;00m fobj:\n\u001b[0;32m 652\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(fobj, \u001b[39mstr\u001b[39m):\n\u001b[0;32m 653\u001b[0m \u001b[39m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[0;32m 654\u001b[0m \u001b[39m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[0;32m 655\u001b[0m \u001b[39m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\sobol_reg_mae_0.pkl'" - ] + "data": { + "text/plain": [ + "{'mae': RandomForestRegressor(random_state=13),\n", + " 'rmse': RandomForestRegressor(random_state=13),\n", + " 'model_size': RandomForestRegressor(random_state=13),\n", + " 'runtime': RandomForestRegressor(random_state=13)}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -413,7 +414,7 @@ " for key, model_path in model_paths.items():\n", " models[key] = joblib.load(f\"{model_path}_{i}.pkl\")\n", "\n", - " with open(path.join(model_dir, f\"cross_validation_models_{i}.pkl\"), \"wb\") as f:\n", + " with open(path.join(cv_model_dir, f\"cross_validation_models_{i}.pkl\"), \"wb\") as f:\n", " joblib.dump(models, f, compress=7)\n", "\n", "models" @@ -431,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -453,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -472,7 +473,7 @@ "['..\\\\..\\\\models\\\\crabnet_hyperparameter\\\\surrogate_models.pkl']" ] }, - "execution_count": 28, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } From 8f9716aa9e8387e4ded7651bb8da4cf0ee333d7e Mon Sep 17 00:00:00 2001 From: sgbaird Date: Thu, 2 Mar 2023 21:39:23 -0700 Subject: [PATCH 9/9] Update 1.3-sgb-zenodo-upload.ipynb --- .../1.3-sgb-zenodo-upload.ipynb | 154 +----------------- 1 file changed, 3 insertions(+), 151 deletions(-) diff --git a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb index f3c8dfb..a645c17 100644 --- a/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb +++ b/notebooks/crabnet_hyperparameter/1.3-sgb-zenodo-upload.ipynb @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -77,157 +77,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'conceptdoi': '10.5281/zenodo.7693715',\n", - " 'conceptrecid': '7693715',\n", - " 'created': '2023-03-02T22:14:57.764034+00:00',\n", - " 'doi': '10.5281/zenodo.7693716',\n", - " 'doi_url': 'https://doi.org/10.5281/zenodo.7693716',\n", - " 'files': [{'checksum': '7a0ab85596dd275f6261d73513c7ffe2',\n", - " 'filename': 'cross_validation_models_0.pkl',\n", - " 'filesize': 999010756,\n", - " 'id': '3ad52962-8a3a-4f2f-b4e0-976777c5d596',\n", - " 'links': {'download': 'https://zenodo.org/api/files/93ce3c3d-5cf6-4116-b760-809d712fb8c6/cross_validation_models_0.pkl',\n", - " 'self': 'https://zenodo.org/api/deposit/depositions/7693716/files/3ad52962-8a3a-4f2f-b4e0-976777c5d596'}},\n", - " {'checksum': '305cedbd299c87f24fa75d33412bc44c',\n", - " 'filename': 'cross_validation_models_1.pkl',\n", - " 'filesize': 999216113,\n", - " 'id': '68b1bbc0-0b6d-413f-8809-13cee6e750e1',\n", - 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" 'conceptdoi': 'https://doi.org/10.5281/zenodo.7693715',\n", - " 'doi': 'https://doi.org/10.5281/zenodo.7693716',\n", - " 'latest': 'https://zenodo.org/api/records/7693716',\n", - " 'latest_html': 'https://zenodo.org/record/7693716',\n", - " 'record': 'https://zenodo.org/api/records/7693716',\n", - " 'record_html': 'https://zenodo.org/record/7693716'},\n", - " 'metadata': {'access_right': 'open',\n", - " 'communities': [{'identifier': 'zenodo'}],\n", - " 'creators': [{'affiliation': 'University of Utah',\n", - " 'name': 'Baird, Sterling G.',\n", - " 'orcid': '0000-0002-4491-6876'},\n", - " {'affiliation': 'Northwood High School',\n", - " 'name': 'Parikh, Jeet N.',\n", - " 'orcid': '0000-0002-8706-2962'}],\n", - " 'description': 'Benchmarks are an essential driver of progress '\n", - " 'in scientific disciplines. Ideal benchmarks '\n", - " 'mimic real-world tasks as closely as possible, '\n", - " 'where insufficient difficulty or applicability '\n", - " 'can stunt growth in the field. Benchmarks should '\n", - " 'also have sufficiently low computational '\n", - " 'overhead to promote accessibility and '\n", - " 'repeatability. The goal is then to win a \"Turing '\n", - " 'test\" of sorts by creating a surrogate model '\n", - " 'that is indistinguishable from the ground truth '\n", - " 'observation (at least within the dataset bounds '\n", - " 'that were explored), necessitating a large '\n", - " 'amount of data. In materials science and '\n", - " 'chemistry, industry-relevant optimization tasks '\n", - " 'are often hierarchical, noisy, multi-fidelity, '\n", - " 'multi-objective, high-dimensional, and '\n", - " 'non-linearly correlated while exhibiting mixed '\n", - " 'numerical and categorical variables subject to '\n", - " 'linear and non-linear constraints. To complicate '\n", - " 'matters, unexpected, failed simulation or '\n", - " 'experimental regions may be present in the '\n", - " 'search space. In this study, 173219 quasi-random '\n", - " 'hyperparameter combinations were generated '\n", - " 'across 23 hyperparameters and used to train '\n", - " 'CrabNet on the Matbench experimental band gap '\n", - " 'dataset. The results were logged to a free-tier '\n", - " 'shared MongoDB Atlas dataset. This study '\n", - " 'resulted in a regression dataset mapping '\n", - " 'hyperparameter combinations (including repeats) '\n", - " 'to MAE, RMSE, computational runtime, and model '\n", - " 'size for CrabNet model trained on the Matbench '\n", - " 'experimental band gap benchmark task1. This '\n", - " 'dataset is used to create a surrogate model as '\n", - " 'close as possible to running the actual '\n", - " 'simulations by incorporating heteroskedastic '\n", - " 'noise. Failure cases for bad hyperparameter '\n", - " 'combinations were excluded via careful '\n", - " 'construction of the hyperparameter search space, '\n", - " 'and so were not considered as was done in prior '\n", - " 'work. For the regression dataset, percentile '\n", - " 'ranks were computed within each of the groups of '\n", - " 'identical parameter sets to enable capturing '\n", - " 'heteroskedastic noise. This contrasts with a '\n", - " 'more traditional approach that imposes a-priori '\n", - " 'assumptions such as Gaussian noise, e.g., by '\n", - " 'providing a mean and standard deviation. A '\n", - " 'similar approach can be applied to other '\n", - " 'benchmark datasets to bridge the gap between '\n", - " 'optimization benchmarks with low computational '\n", - " 'overhead and realistically complex, real-world '\n", - " 'optimization scenarios.',\n", - " 'doi': '10.5281/zenodo.7693716',\n", - " 'language': 'eng',\n", - " 'license': 'CC0-1.0',\n", - " 'prereserve_doi': {'doi': '10.5281/zenodo.7693716',\n", - " 'recid': 7693716},\n", - " 'publication_date': '2023-03-02',\n", - " 'title': 'Materials Science Optimization Benchmark Dataset for '\n", - " 'High-dimensional, Multi-objective, Multi-fidelity '\n", - " 'Optimization of CrabNet Hyperparameters',\n", - " 'upload_type': 'dataset',\n", - " 'version': '2023-03-02'},\n", - " 'modified': '2023-03-02T23:01:58.332431+00:00',\n", - " 'owner': 259510,\n", - " 'record_id': 7693716,\n", - " 'state': 'done',\n", - " 'submitted': True,\n", - " 'title': 'Materials Science Optimization Benchmark Dataset for '\n", - " 'High-dimensional, Multi-objective, Multi-fidelity Optimization of '\n", - " 'CrabNet Hyperparameters'}\n" - ] - } - ], + "outputs": [], "source": [ "from zenodo_client import Creator, Metadata, ensure_zenodo\n", "from my_secrets import ZENODO_API_KEY, ZENODO_SANDBOX_API_KEY\n",