From 8ed63c5c93aecf6c9681a9d704a0d91f1cff3cd3 Mon Sep 17 00:00:00 2001 From: Osman Mamun Date: Wed, 27 Mar 2024 18:22:14 -0500 Subject: [PATCH] working version --- experiments/test_1/moboqm9_results.csv | 953 +++++++++++++++++++++++++ experiments/test_1/moboqm9_results.png | Bin 0 -> 285736 bytes experiments/test_1/script.py | 18 +- src/acquisition_functions.py | 4 +- src/data/cm_featurizer.py | 27 +- src/mobo_qm9.py | 41 +- src/utils.py | 49 +- 7 files changed, 1029 insertions(+), 63 deletions(-) create mode 100644 experiments/test_1/moboqm9_results.csv create mode 100644 experiments/test_1/moboqm9_results.png diff --git a/experiments/test_1/moboqm9_results.csv b/experiments/test_1/moboqm9_results.csv new file mode 100644 index 0000000..7eacac0 --- /dev/null +++ b/experiments/test_1/moboqm9_results.csv @@ -0,0 +1,953 @@ +,iteration_qEHVI,iteration_qNEHVI,iteration_random,gap,mu +0,41.0,,,0.3175,1.4629 +1,0.0,0.0,0.0,0.256,3.2802 +2,,,,0.2505,2.4907 +3,,,,0.2583,2.2947 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znCMF(Ez~lJ4XybQ*WOfjHv(~jnVH!H_IAFlAqEL)>5vpsS<1}pp1)&i7n;8)GF?OKesYM=U+Iml; z!fu2+2{%}cRa5=@+baIK9vF9K^mI iteration vs hv [qEHVI qNEHVI random] -panel2 => pareto front for qEHVI -panel3 => pareto front for qNEHVI -panel4 => pareto front for random -""" +moboqm9.dataframe.to_csv("moboqm9_results.csv") diff --git a/src/acquisition_functions.py b/src/acquisition_functions.py index 5ac56bd..466ef1a 100644 --- a/src/acquisition_functions.py +++ b/src/acquisition_functions.py @@ -39,7 +39,7 @@ def optimize_qEHVI(model, reference, y_train, x_test, n_candidates): q=n_candidates, unique=True ) - return torch.tensor(candidates) + return candidates def optimize_qNEHVI(model, reference, x_train, x_test, n_candidates): """ @@ -69,4 +69,4 @@ def optimize_qNEHVI(model, reference, x_train, x_test, n_candidates): q=n_candidates, unique=True ) - return torch.tensor(candidates) \ No newline at end of file + return candidates \ No newline at end of file diff --git a/src/data/cm_featurizer.py b/src/data/cm_featurizer.py index d45d0e0..16ff4e4 100644 --- a/src/data/cm_featurizer.py +++ b/src/data/cm_featurizer.py @@ -1,6 +1,7 @@ from dscribe.descriptors import CoulombMatrix from ase import db - +from pathlib import Path +import numpy as np def get_max_number_of_atoms(indices): """ @@ -13,7 +14,7 @@ def get_max_number_of_atoms(indices): int: Maximum number of atoms for the MOBOQM9 model. """ max_number_of_atoms = 0 - with db.connect("QM9_data.db") as qm9: + with db.connect(str(Path(__file__).parent / "QM9_data.db")) as qm9: for i, row in enumerate(qm9.select()): if i in indices: atoms = row.toatoms() @@ -36,15 +37,23 @@ def get_coulomb_matrix(indices, targets): max_number_of_atoms = get_max_number_of_atoms(indices) cm = CoulombMatrix(n_atoms_max=max_number_of_atoms) - atoms_list, targets = [], [] - with db.connect("QM9_data.db") as qm9: + atoms_list, computed_targets = [], [] + with db.connect(str(Path(__file__).parent / "QM9_data.db")) as qm9: for i, row in enumerate(qm9.select()): if i in indices: - atoms_list.append(row.toatoms()) - target_list = [] + is_OK = True for target in targets: - target_list.append(row[target]) - targets.append(target_list) + try: + row[target] + except AttributeError: + is_OK = False + break + if is_OK: + atoms_list.append(row.toatoms()) + target_list = [] + for target in targets: + target_list.append(row[target]) + computed_targets.append(target_list) features = cm.create(atoms_list, n_jobs=4) - return features, targets \ No newline at end of file + return features, np.array(computed_targets) \ No newline at end of file diff --git a/src/mobo_qm9.py b/src/mobo_qm9.py index 7711229..7b05ad6 100644 --- a/src/mobo_qm9.py +++ b/src/mobo_qm9.py @@ -62,7 +62,7 @@ def __init__(self, params: MOBOQM9Parameters): self.dataframe = pd.DataFrame.from_dict(self.from_target_dict()) self.acq_met = {"qEHVI": False, "qNEHVI": False, "random": False} - def form_target_dict(self): + def from_target_dict(self): """ Forms the target dictionary for the MOBOQM9 model. @@ -70,16 +70,17 @@ def form_target_dict(self): target_dict: Target dictionary for the MOBOQM9 model. """ target_dict = {} + target_dict["iteration_qEHVI"] = [None] * len(self.targets) + target_dict["iteration_qNEHVI"] = [None] * len(self.targets) + target_dict["iteration_random"] = [None] * len(self.targets) for itarg, target in enumerate(self.params.targets): target_dict[target] = self.targets[:, itarg] - target_dict["iteration_qEHVI"] = [None] * len(self.params.targets) - target_dict["iteration_qNEHVI"] = [None] * len(self.params.targets) - target_dict["iteration_random"] = [None] * len(self.params.targets) - for itrain, train_mask in enumerate(self.train_indices["qEHVI"]): - if train_mask: - target_dict["iteration_qeHVI"] = 0 - target_dict["iteration_qNEHVI"] = 0 - target_dict["iteration_random"] = 0 + + for itrain, train_mask in enumerate(self.train_indices["qEHVI"]): + if train_mask: + target_dict["iteration_qEHVI"][itrain] = 0 + target_dict["iteration_qNEHVI"][itrain] = 0 + target_dict["iteration_random"][itrain] = 0 return target_dict def get_features_and_targets(self): @@ -107,9 +108,9 @@ def get_surrogate_model(self, acq): returns: model: Surrogate model for the MOBOQM9 model. """ - features = torch.tensor(self.features[self.train_indices["acq"]], + features = torch.tensor(self.features[self.train_indices[acq]], dtype=torch.double) - targets = torch.tensor(self.correct_sign(self.targets[self.train_indices["acq"]]), + targets = torch.tensor(self.correct_sign(self.targets[self.train_indices[acq]]), dtype=torch.double) var = torch.full_like(targets, 1e-6) @@ -122,7 +123,7 @@ def get_surrogate_model(self, acq): models = [SingleTaskGP(features, targets[:, i].unsqueeze(-1), - noise=var[:, i].unsqueeze(-1), + var[:, i].unsqueeze(-1), input_transform=Normalize(d=features.shape[-1]), outcome_transform=Standardize(m=1), likelihood=gpytorch.likelihoods.GaussianLikelihood(), @@ -154,11 +155,11 @@ def optimize_acquisition_function(self, model, acq): returns: candidates: Candidates for the MOBOQM9 model. """ - y_train = self.correct_sign(self.targets[self.train_indices["acq"]]) + y_train = self.correct_sign(self.targets[self.train_indices[acq]]) y_train = torch.tensor(y_train, dtype=torch.double) - x_train = torch.tensor(self.features[self.train_indices["acq"]], dtype=torch.double) - x_test = torch.tensor(self.features[~self.train_indices["acq"]], dtype=torch.double) - reference = y_train.mean(0)[0] + x_train = torch.tensor(self.features[self.train_indices[acq]], dtype=torch.double) + x_test = torch.tensor(self.features[~self.train_indices[acq]], dtype=torch.double) + reference = y_train.min(0)[0] if acq == "qEHVI": return optimize_qEHVI(model=model, reference=reference, @@ -186,11 +187,11 @@ def run_optimization(self): model = self.get_surrogate_model(acq) if acq == "random": for _ in range(self.params.num_candidates): - idx = np.random.choice(np.where(~self.train_indices)[0]) + idx = np.random.choice(np.where(~self.train_indices[acq])[0]) self.train_indices[acq][idx] = True self.dataframe.at[idx, "iteration_random"] = iter + 1 else: - candidates = self.optimize_acquisition_function(model) + candidates = self.optimize_acquisition_function(model, acq) self.update_train_indices(candidates, acq, iter) self.stopping_criteria_met(acq) @@ -204,9 +205,9 @@ def get_train_indices(self): train_indices: Train indices for the MOBOQM9 model. """ # add latin hypercube sampling if time permits - temp_indices = np.random.randint(0, self.params.num_total_points, + temp_indices = np.random.randint(0, len(self.targets), self.params.num_seed_points) - mask = np.zeros(len(self.total_indices), dtype=bool) + mask = np.zeros(len(self.targets), dtype=bool) mask[temp_indices] = True return {"qEHVI": mask, "qNEHVI": mask, "random": mask} diff --git a/src/utils.py b/src/utils.py index 00ffccb..52bea09 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,4 +1,3 @@ -import pandas as pd from botorch.utils.multi_objective.box_decompositions import DominatedPartitioning import matplotlib.pyplot as plt import torch @@ -32,9 +31,10 @@ def plot_results(df, target_bool): global_hv = hv.compute_hypervolume().item() hv_iterations = {"qEHVI": [], "qNEHVI": [], "random": []} - max_iterations = df['iteration'].max() - for iter_no in range(max_iterations + 1): - for acq in ["qEHVI", "qNEHVI", "random"]: + + for acq in ["qEHVI", "qNEHVI", "random"]: + max_iterations = int(df[f'iteration_{acq}'].max()) + for iter_no in range(max_iterations + 1): current_data = df[df[f'iteration_{acq}'] <= iter_no] local_values = torch.tensor(current_data[targets].values) for mask in target_bool: @@ -42,51 +42,60 @@ def plot_results(df, target_bool): local_values[:, mask] *= -1 hv = DominatedPartitioning(ref_point=ref_point, Y=local_values) hv_iterations[acq].append(hv.compute_hypervolume().item()) - ax[0].plot(np.array(hv_iterations["qEHVI"])/global_hv * 100, marker='o', label="qEHVI") - ax[0].plot(np.array(hv_iterations["qNEHVI"])/ global_hv * 100, marker='*', label="qNEHVI") - ax[0].plot(np.array(hv_iterations["random"]) / global_hv, marker='s', label="random") + ax[0].plot(np.array(hv_iterations["qEHVI"]) / global_hv * 100, marker='o', label="qEHVI") + ax[0].plot(np.array(hv_iterations["qNEHVI"]) / global_hv * 100, marker='*', label="qNEHVI") + ax[0].plot(np.array(hv_iterations["random"]) / global_hv * 100, marker='s', label="random") ax[0].set_xlabel('Iteration') ax[0].set_ylabel('Hypervolume') ax[0].set_title('Hypervolume per Iteration') ax[0].legend(loc="center right") # Plot qEHVI + max_iterations = int(df['iteration_qEHVI'].max()) current_data = df[df['iteration_qEHVI'] <= max_iterations] qEHVI_values = torch.tensor(current_data[targets].values) qEHVI_pareto_idx = is_non_dominated(qEHVI_values) qEHVI_pareto_front = qEHVI_values[qEHVI_pareto_idx] - ax[1].scatter(df[targets[0]], df[targets[1]], alpha=0.5) - ax[1].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], - color='blue', label="Global Pareto Front", marker="*") ax[1].scatter(qEHVI_pareto_front[:, 0], qEHVI_pareto_front[:, 1], - color='red', label="qEHVI Pareto Front", marker="s") + color='red', label="qEHVI Pareto Front", marker="*", s=100) + ax[1].scatter(df[targets[0]], df[targets[1]], alpha=0.3) + ax[1].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], + color='green', label="Global Pareto Front", marker="s") ax[1].set_xlabel(targets[0]) ax[1].set_ylabel(targets[1]) + ax[1].set_title('Pareto Front for qEHVI') + ax[1].legend(loc="upper right") # Plot qNEHVI + max_iterations = int(df['iteration_qNEHVI'].max()) current_data = df[df['iteration_qNEHVI'] <= max_iterations] qNEHVI_values = torch.tensor(current_data[targets].values) qNEHVI_pareto_idx = is_non_dominated(qNEHVI_values) qNEHVI_pareto_front = qNEHVI_values[qNEHVI_pareto_idx] - ax[2].scatter(df[targets[0]], df[targets[1]], alpha=0.5) - ax[2].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], - color='blue', label="Global Pareto Front", marker="*") ax[2].scatter(qNEHVI_pareto_front[:, 0], qNEHVI_pareto_front[:, 1], - color='red', label="qNEHVI Pareto Front", marker="s") + color='red', label="qNEHVI Pareto Front", marker="*", s=100) + ax[2].scatter(df[targets[0]], df[targets[1]], alpha=0.3) + ax[2].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], + color='green', label="Global Pareto Front", marker="s") ax[2].set_xlabel(targets[0]) ax[2].set_ylabel(targets[1]) + ax[2].set_title('Pareto Front for qNEHVI') + ax[2].legend(loc="upper right") - # Plot qEHVI + # Plot random + max_iterations = int(df['iteration_random'].max()) current_data = df[df['iteration_random'] <= max_iterations] random_values = torch.tensor(current_data[targets].values) random_pareto_idx = is_non_dominated(random_values) random_pareto_front = random_values[random_pareto_idx] - ax[3].scatter(df[targets[0]], df[targets[1]], alpha=0.5) - ax[3].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], - color='blue', label="Global Pareto Front", marker="*") ax[3].scatter(random_pareto_front[:, 0], random_pareto_front[:, 1], - color='red', label="Random Pareto Front", marker="s") + color='red', label="Random Pareto Front", marker="*", s=100) + ax[3].scatter(df[targets[0]], df[targets[1]], alpha=0.3) + ax[3].scatter(global_pareto_front[:, 0], global_pareto_front[:, 1], + color='green', label="Global Pareto Front", marker="s") ax[3].set_xlabel(targets[0]) ax[3].set_ylabel(targets[1]) + ax[3].set_title('Pareto Front for random') + ax[3].legend(loc="upper right") return fig \ No newline at end of file