diff --git a/.gitignore b/.gitignore index fa380622..657feb24 100644 --- a/.gitignore +++ b/.gitignore @@ -36,3 +36,6 @@ examples/**/job-logs/ examples/**/artifacts/ examples/**/*.csv wandb/ + +# profiling +*.prof diff --git a/README.md b/README.md index 52e5db97..0106cf72 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ [![GitHub last commit](https://img.shields.io/github/last-commit/comprhys/aviary?label=Last+Commit)](https://github.com/comprhys/aviary/commits) [![Tests](https://github.com/CompRhys/aviary/actions/workflows/test.yml/badge.svg)](https://github.com/CompRhys/aviary/actions/workflows/test.yml) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/CompRhys/aviary/main.svg)](https://results.pre-commit.ci/latest/github/CompRhys/aviary/main) -[![This project supports Python 3.9+](https://img.shields.io/badge/Python-3.9+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads) +[![This project supports Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads) @@ -50,6 +50,10 @@ python examples/roost-example.py --train --evaluate --data-path examples/inputs/ python examples/wren-example.py --train --evaluate --data-path examples/inputs/examples.csv --targets E_f --tasks regression --losses L1 --robust --epoch 10 ``` +```sh +python examples/wrenformer-example.py --train --evaluate --data-path examples/inputs/examples.csv --targets E_f --tasks regression --losses L1 --robust --epoch 10 +``` + ```sh python examples/cgcnn-example.py --train --evaluate --data-path examples/inputs/examples.json --targets E_f --tasks regression --losses L1 --robust --epoch 10 ``` diff --git a/aviary/cgcnn/data.py b/aviary/cgcnn/data.py index 993a1f12..d426e260 100644 --- a/aviary/cgcnn/data.py +++ b/aviary/cgcnn/data.py @@ -1,24 +1,19 @@ -from __future__ import annotations - import itertools import json +from collections.abc import Sequence from functools import cache -from typing import TYPE_CHECKING, Any +from typing import Any import numpy as np +import pandas as pd import torch +from pymatgen.core import Structure from torch import LongTensor, Tensor from torch.utils.data import Dataset from tqdm import tqdm from aviary import PKG_DIR -if TYPE_CHECKING: - from collections.abc import Sequence - - import pandas as pd - from pymatgen.core import Structure - class CrystalGraphData(Dataset): """Dataset class for the CGCNN structure model.""" @@ -253,9 +248,10 @@ def collate_batch( return ( (atom_fea, nbr_dist, self_idx, nbr_idx, cry_idx), tuple( - torch.stack(b_target, dim=0).to(device) for b_target in zip(*batch_targets) + torch.stack(b_target, dim=0).to(device) + for b_target in zip(*batch_targets, strict=False) ), - *zip(*batch_identifiers), + *zip(*batch_identifiers, strict=False), ) @@ -332,10 +328,12 @@ def get_structure_neighbor_info( _neighbor_dists: list[float] = [] for _, idx_group in itertools.groupby( # group by site index - zip(site_indices, neighbor_indices, neighbor_dists), key=lambda x: x[0] + zip(site_indices, neighbor_indices, neighbor_dists, strict=False), + key=lambda x: x[0], ): site_indices, neighbor_idx, neighbor_dist = zip( - *sorted(idx_group, key=lambda x: x[2]) # sort by distance + *sorted(idx_group, key=lambda x: x[2]), + strict=False, # sort by distance ) _center_indices.extend(site_indices[:max_num_nbr]) _neighbor_indices.extend(neighbor_idx[:max_num_nbr]) diff --git a/aviary/cgcnn/model.py b/aviary/cgcnn/model.py index 196cabe8..e96389cb 100644 --- a/aviary/cgcnn/model.py +++ b/aviary/cgcnn/model.py @@ -1,6 +1,4 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING +from collections.abc import Sequence import torch import torch.nn.functional as F @@ -11,9 +9,6 @@ from aviary.networks import SimpleNetwork from aviary.scatter import scatter_reduce -if TYPE_CHECKING: - from collections.abc import Sequence - @due.dcite(Doi("10.1103/PhysRevLett.120.145301"), description="CGCNN model") class CrystalGraphConvNet(BaseModelClass): @@ -215,7 +210,7 @@ def forward( Args: atom_in_fea (Tensor): Atom hidden features before convolution nbr_fea (Tensor): Bond features of each atom's neighbors - self_idx (LongTensor): _description_ + self_idx (LongTensor): Indices of the atom's self nbr_idx (LongTensor): Indices of M neighbors of each atom Returns: diff --git a/aviary/core.py b/aviary/core.py index 7425c98c..983d41a9 100644 --- a/aviary/core.py +++ b/aviary/core.py @@ -1,11 +1,10 @@ -from __future__ import annotations - import gc import os import shutil from abc import ABC from collections import defaultdict -from typing import TYPE_CHECKING, Any, Callable, Literal +from collections.abc import Callable, Mapping +from typing import Any, Literal import numpy as np import torch @@ -13,17 +12,12 @@ from sklearn.metrics import f1_score from torch import BoolTensor, Tensor, nn from torch.nn.functional import softmax +from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm from aviary import ROOT - -if TYPE_CHECKING: - from collections.abc import Mapping - - from torch.utils.data import DataLoader - - from aviary.data import InMemoryDataLoader +from aviary.data import InMemoryDataLoader, Normalizer TaskType = Literal["regression", "classification"] @@ -129,6 +123,14 @@ def fit( for metric, val in metrics.items(): writer.add_scalar(f"{task}/train/{metric}", val, epoch) + if writer == "wandb": + flat_train_metrics = {} + for task, metrics in train_metrics.items(): + for metric, val in metrics.items(): + flat_train_metrics[f"train_{task}_{metric.lower()}"] = val + flat_train_metrics["epoch"] = epoch + wandb.log(flat_train_metrics) + # Validation if val_loader is not None: with torch.no_grad(): @@ -149,6 +151,14 @@ def fit( f"{task}/validation/{metric}", val, epoch ) + if writer == "wandb": + flat_val_metrics = {} + for task, metrics in val_metrics.items(): + for metric, val in metrics.items(): + flat_val_metrics[f"val_{task}_{metric.lower()}"] = val + flat_val_metrics["epoch"] = epoch + wandb.log(flat_val_metrics) + # TODO test all tasks to see if they are best, # save a best model if any is best. # TODO what are the costs of this approach. @@ -207,9 +217,6 @@ def fit( # catch memory leak gc.collect() - if writer == "wandb": - wandb.log({"train": train_metrics, "validation": val_metrics}) - except KeyboardInterrupt: pass @@ -271,7 +278,11 @@ def evaluate( mixed_loss: Tensor = 0 # type: ignore[assignment] for target_name, targets, output, normalizer in zip( - self.target_names, targets_list, outputs, normalizer_dict.values() + self.target_names, + targets_list, + outputs, + normalizer_dict.values(), + strict=False, ): task, loss_func = loss_dict[target_name] target_metrics = epoch_metrics[target_name] @@ -318,7 +329,7 @@ def evaluate( else: raise ValueError(f"invalid task: {task}") - epoch_metrics[target_name]["Loss"].append(loss.cpu().item()) + target_metrics["Loss"].append(loss.cpu().item()) # NOTE multitasking currently just uses a direct sum of individual # target losses this should be okay but is perhaps sub-optimal @@ -396,11 +407,13 @@ def predict( # for multitask learning targets = tuple( torch.cat(targets, dim=0).view(-1).cpu().numpy() - for targets in zip(*test_targets) + for targets in zip(*test_targets, strict=False) + ) + predictions = tuple( + torch.cat(preds, dim=0) for preds in zip(*test_preds, strict=False) ) - predictions = tuple(torch.cat(preds, dim=0) for preds in zip(*test_preds)) # identifier columns - ids = tuple(np.concatenate(x) for x in zip(*test_ids)) + ids = tuple(np.concatenate(x) for x in zip(*test_ids, strict=False)) return targets, predictions, ids @torch.no_grad() @@ -445,83 +458,6 @@ def __repr__(self) -> str: return f"{cls_name} with {n_params:,} trainable params at {n_epochs:,} epochs" -class Normalizer: - """Normalize a Tensor and restore it later.""" - - def __init__(self) -> None: - """Initialize Normalizer with mean 0 and std 1.""" - self.mean = torch.tensor(0) - self.std = torch.tensor(1) - - def fit(self, tensor: Tensor, dim: int = 0, keepdim: bool = False) -> None: - """Compute the mean and standard deviation of the given tensor. - - Args: - tensor (Tensor): Tensor to determine the mean and standard deviation over. - dim (int, optional): Which dimension to take mean and standard deviation - over. Defaults to 0. - keepdim (bool, optional): Whether to keep the reduced dimension in Tensor. - Defaults to False. - """ - self.mean = torch.mean(tensor, dim, keepdim) - self.std = torch.std(tensor, dim, keepdim) - - def norm(self, tensor: Tensor) -> Tensor: - """Normalize a Tensor. - - Args: - tensor (Tensor): Tensor to be normalized - - Returns: - Tensor: Normalized Tensor - """ - return (tensor - self.mean) / self.std - - def denorm(self, normed_tensor: Tensor) -> Tensor: - """Restore normalized Tensor to original. - - Args: - normed_tensor (Tensor): Tensor to be restored - - Returns: - Tensor: Restored Tensor - """ - return normed_tensor * self.std + self.mean - - def state_dict(self) -> dict[str, Tensor]: - """Get Normalizer parameters mean and std. - - Returns: - dict[str, Tensor]: Dictionary storing Normalizer parameters. - """ - return {"mean": self.mean, "std": self.std} - - def load_state_dict(self, state_dict: dict[str, Tensor]) -> None: - """Overwrite Normalizer parameters given a new state_dict. - - Args: - state_dict (dict[str, Tensor]): Dictionary storing Normalizer parameters. - """ - self.mean = state_dict["mean"].cpu() - self.std = state_dict["std"].cpu() - - @classmethod - def from_state_dict(cls, state_dict: dict[str, Tensor]) -> Normalizer: - """Create a new Normalizer given a state_dict. - - Args: - state_dict (dict[str, Tensor]): Dictionary storing Normalizer parameters. - - Returns: - Normalizer - """ - instance = cls() - instance.mean = state_dict["mean"].cpu() - instance.std = state_dict["std"].cpu() - - return instance - - def save_checkpoint( state: dict[str, Any], is_best: bool, model_name: str, run_id: int ) -> None: @@ -662,3 +598,12 @@ def masked_min(x: Tensor, mask: BoolTensor, dim: int = 0) -> Tensor: x_inf = x.float().masked_fill(~mask, float("inf")) x_min, _ = x_inf.min(dim=dim) return x_min + + +AGGREGATORS: dict[str, Callable[[Tensor, BoolTensor, int], Tensor]] = { + "mean": masked_mean, + "std": masked_std, + "max": masked_max, + "min": masked_min, + "sum": lambda x, mask, dim: (x * mask).sum(dim=dim), +} diff --git a/aviary/data.py b/aviary/data.py index e7c16035..fe077d33 100644 --- a/aviary/data.py +++ b/aviary/data.py @@ -1,14 +1,87 @@ -from __future__ import annotations - +from collections.abc import Callable, Iterator from dataclasses import dataclass -from typing import TYPE_CHECKING, Callable import numpy as np - -if TYPE_CHECKING: - from collections.abc import Iterator - - from torch import Tensor +import torch +from torch import Tensor +from typing_extensions import Self + + +class Normalizer: + """Normalize a Tensor and restore it later.""" + + def __init__(self) -> None: + """Initialize Normalizer with mean 0 and std 1.""" + self.mean = torch.tensor(0) + self.std = torch.tensor(1) + + def fit(self, tensor: Tensor, dim: int = 0, keepdim: bool = False) -> None: + """Compute the mean and standard deviation of the given tensor. + + Args: + tensor (Tensor): Tensor to determine the mean and standard deviation over. + dim (int, optional): Which dimension to take mean and standard deviation + over. Defaults to 0. + keepdim (bool, optional): Whether to keep the reduced dimension in Tensor. + Defaults to False. + """ + self.mean = torch.mean(tensor, dim, keepdim) + self.std = torch.std(tensor, dim, keepdim) + + def norm(self, tensor: Tensor) -> Tensor: + """Normalize a Tensor. + + Args: + tensor (Tensor): Tensor to be normalized + + Returns: + Tensor: Normalized Tensor + """ + return (tensor - self.mean) / self.std + + def denorm(self, normed_tensor: Tensor) -> Tensor: + """Restore normalized Tensor to original. + + Args: + normed_tensor (Tensor): Tensor to be restored + + Returns: + Tensor: Restored Tensor + """ + return normed_tensor * self.std + self.mean + + def state_dict(self) -> dict[str, Tensor]: + """Get Normalizer parameters mean and std. + + Returns: + dict[str, Tensor]: Dictionary storing Normalizer parameters. + """ + return {"mean": self.mean, "std": self.std} + + def load_state_dict(self, state_dict: dict[str, Tensor]) -> None: + """Overwrite Normalizer parameters given a new state_dict. + + Args: + state_dict (dict[str, Tensor]): Dictionary storing Normalizer parameters. + """ + self.mean = state_dict["mean"].cpu() + self.std = state_dict["std"].cpu() + + @classmethod + def from_state_dict(cls, state_dict: dict[str, Tensor]) -> Self: + """Create a new Normalizer given a state_dict. + + Args: + state_dict (dict[str, Tensor]): Dictionary storing Normalizer parameters. + + Returns: + Normalizer + """ + instance = cls() + instance.mean = state_dict["mean"].cpu() + instance.std = state_dict["std"].cpu() + + return instance @dataclass diff --git a/aviary/networks.py b/aviary/networks.py index 32dbcb62..a688aab2 100644 --- a/aviary/networks.py +++ b/aviary/networks.py @@ -1,12 +1,7 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING +from collections.abc import Sequence from torch import Tensor, nn -if TYPE_CHECKING: - from collections.abc import Sequence - class SimpleNetwork(nn.Module): """Simple Feed Forward Neural Network.""" @@ -50,7 +45,7 @@ def __init__( def forward(self, x: Tensor) -> Tensor: """Forward pass through network.""" - for fc, bn, act in zip(self.fcs, self.bns, self.acts): + for fc, bn, act in zip(self.fcs, self.bns, self.acts, strict=False): x = act(bn(fc(x))) return self.fc_out(x) @@ -117,7 +112,9 @@ def __init__( def forward(self, x: Tensor) -> Tensor: """Forward pass through network.""" - for fc, bn, res_fc, act in zip(self.fcs, self.bns, self.res_fcs, self.acts): + for fc, bn, res_fc, act in zip( + self.fcs, self.bns, self.res_fcs, self.acts, strict=False + ): x = act(bn(fc(x))) + res_fc(x) return self.fc_out(x) diff --git a/aviary/predict.py b/aviary/predict.py index 7c0f1547..97f9c0e2 100644 --- a/aviary/predict.py +++ b/aviary/predict.py @@ -1,27 +1,13 @@ -# ruff: noqa: E501 -from __future__ import annotations - -import os -from typing import TYPE_CHECKING, Any - import numpy as np import pandas as pd import torch from torch.nn.functional import softmax +from torch.utils.data import DataLoader from tqdm import tqdm -from aviary.core import Normalizer, sampled_softmax -from aviary.utils import get_metrics, print_walltime - -if TYPE_CHECKING: - import wandb.apis.public - from torch.utils.data import DataLoader - - from aviary.core import BaseModelClass - from aviary.data import InMemoryDataLoader - -__author__ = "Janosh Riebesell" -__date__ = "2022-08-25" +from aviary.core import BaseModelClass, Normalizer, sampled_softmax +from aviary.data import InMemoryDataLoader +from aviary.utils import get_metrics def make_ensemble_predictions( @@ -38,30 +24,32 @@ def make_ensemble_predictions( """Make predictions using an ensemble of models. Args: - checkpoint_paths (list[str]): File paths to model checkpoints created with torch.save(). + checkpoint_paths (list[str]): File paths to model checkpoints created with + torch.save(). data_loader (DataLoader | InMemoryDataLoader): Data loader to use for predictions. model_cls (type[BaseModelClass]): Model class to use for predictions. - df (pd.DataFrame): Dataframe to make predictions on. Will be returned with additional - columns holding model predictions (and uncertainties for robust models) for each - model checkpoint. - target_col (str): Column holding target values. Defaults to None. If None, will not print - performance metrics. - input_col (str, optional): Column holding input values. Defaults to 'wyckoff'. - device (str, optional): torch.device. Defaults to "cuda" if torch.cuda.is_available() - else "cpu". - print_metrics (bool, optional): Whether to print performance metrics. Defaults to True - if target_col is not None. - warn_target_mismatch (bool, optional): Whether to warn if target_col != target_name from - model checkpoint. Defaults to False. + df (pd.DataFrame): Dataframe to make predictions on. Will be returned with + additional columns holding model predictions (and uncertainties for robust + columns holding model predictions (and uncertainties for robust models) + for each model checkpoint. + target_col (str): Column holding target values. Defaults to None. If None, will + not print performance metrics. + device (str, optional): torch.device. Defaults to "cuda" if + torch.cuda.is_available() else "cpu". + print_metrics (bool, optional): Whether to print performance metrics. Defaults + to True if target_col is not None. + warn_target_mismatch (bool, optional): Whether to warn if target_col != + target_name from model checkpoint. Defaults to False. pbar (bool, optional): Whether to show progress bar running over checkpoints. Defaults to True. Returns: - pd.DataFrame: Input dataframe with added columns for model and ensemble predictions. If - target_col is not None, returns a 2nd dataframe containing model and ensemble metrics. + pd.DataFrame: Input dataframe with added columns for model and ensemble + predictions. If target_col is not None, returns a 2nd dataframe + containing model and ensemble metrics. """ - # TODO: Add support for predicting all tasks a multi-task models was trained on. Currently only - # handles single targets. Low priority as multi-tasking is rarely used. + # TODO: Add support for predicting all tasks a multi-task models was trained on. + # Currently only handles single targets. Low priority as multi-tasking is rarely used. if not checkpoint_paths: raise ValueError(f"{checkpoint_paths=} must not be empty") @@ -88,8 +76,9 @@ def make_ensemble_predictions( assert task_type in ("regression", "classification"), f"invalid {task_type = }" if warn_target_mismatch and target_name != target_col: print( - f"Warning: {target_col = } does not match {target_name = } in checkpoint. If this " - "is not by accident, disable this warning by passing warn_target_mismatch=False." + f"Warning: {target_col = } does not match {target_name = } in " + f"{checkpoint_path=}. If this is not by accident, disable this warning " + "by passing warn_target_mismatch=False." ) model = model_cls(**model_params) model.to(device) @@ -184,65 +173,3 @@ def make_ensemble_predictions( return df, df_metrics return df - - -@print_walltime(end_desc="predict_from_wandb_checkpoints") -def predict_from_wandb_checkpoints( - runs: list[wandb.apis.public.Run], - checkpoint_filename: str = "checkpoint.pth", - cache_dir: str = "./checkpoint_cache", - **kwargs: Any, -) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: - """Download and cache checkpoints for an ensemble of models, then make - predictions on some dataset. Finally print ensemble metrics and store - predictions to CSV. - - Args: - runs (list[wandb.apis.public.Run]): List of WandB runs to download model - checkpoints from which are then loaded into memory to generate - predictions for the input_col in df. - checkpoint_filename (str): Name of the checkpoint file to download. - cache_dir (str): Directory to cache downloaded checkpoints in. - **kwargs: Additional keyword arguments to pass to make_ensemble_predictions(). - - Returns: - pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: Original input dataframe - with added columns for model predictions and uncertainties. The optional - 2nd dataframe holds ensemble performance metrics like mean and standard - deviation of MAE/RMSE. - """ - print(f"Using checkpoints from {len(runs)} run(s):") - - run_target = runs[0].config["target"] - assert all(run_target == run.config["target"] for run in runs), ( - f"Runs have differing targets, first {run_target=}" - ) - - target_col = kwargs.get("target_col") - if target_col and target_col != run_target: - print(f"\nWarning: {target_col=} does not match {run_target=}") - - checkpoint_paths: list[str] = [] - - for idx, run in enumerate(runs, start=1): - run_path = "/".join(run.path) - out_dir = f"{cache_dir}/{run_path}" - os.makedirs(out_dir, exist_ok=True) - - checkpoint_path = f"{out_dir}/{checkpoint_filename}" - checkpoint_paths.append(checkpoint_path) - print(f"{idx:>3}/{len(runs)}: {run.url}\n\t{checkpoint_path}\n") - - with open(f"{out_dir}/run.md", "w") as md_file: - md_file.write(f"[{run.name}]({run.url})\n") - - if not os.path.isfile(checkpoint_path): - run.file(f"{checkpoint_filename}").download(root=out_dir) - - if target_col is not None: - df, ensemble_metrics = make_ensemble_predictions(checkpoint_paths, **kwargs) - # round to save disk space and speed up cloud storage uploads - return df.round(6), ensemble_metrics - - df = make_ensemble_predictions(checkpoint_paths, **kwargs) - return df.round(6) diff --git a/aviary/roost/data.py b/aviary/roost/data.py index 00821668..3f84abb0 100644 --- a/aviary/roost/data.py +++ b/aviary/roost/data.py @@ -1,10 +1,10 @@ -from __future__ import annotations - import json +from collections.abc import Sequence from functools import cache -from typing import TYPE_CHECKING, Any +from typing import Any import numpy as np +import pandas as pd import torch from pymatgen.core import Composition from torch import LongTensor, Tensor @@ -12,11 +12,6 @@ from aviary import PKG_DIR -if TYPE_CHECKING: - from collections.abc import Sequence - - import pandas as pd - class CompositionData(Dataset): """Dataset class for the Roost composition model.""" @@ -149,10 +144,9 @@ def collate_batch( Args: samples (list): list of tuples for each data point where each tuple contains: (elem_fea, nbr_fea, nbr_idx, target) - - elem_fea (Tensor): _description_ - - nbr_fea (Tensor): - - self_idx (LongTensor): - - nbr_idx (LongTensor): + - elem_fea (Tensor): Atom hidden features before convolution + - self_idx (LongTensor): Indices of the atom's self + - nbr_idx (LongTensor): Indices of M neighbors of each atom - target (Tensor | LongTensor): target values containing floats for regression or integers as class labels for classification - cif_id: str or int @@ -207,6 +201,8 @@ def collate_batch( torch.cat(batch_nbr_idx, dim=0), torch.cat(crystal_elem_idx), ), - tuple(torch.stack(b_target, dim=0) for b_target in zip(*batch_targets)), - *zip(*batch_cry_ids), + tuple( + torch.stack(b_target, dim=0) for b_target in zip(*batch_targets, strict=False) + ), + *zip(*batch_cry_ids, strict=False), ) diff --git a/aviary/roost/model.py b/aviary/roost/model.py index ac9ea36e..fec22196 100644 --- a/aviary/roost/model.py +++ b/aviary/roost/model.py @@ -1,6 +1,4 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING +from collections.abc import Sequence import torch import torch.nn.functional as F @@ -11,9 +9,6 @@ from aviary.networks import ResidualNetwork, SimpleNetwork from aviary.segments import MessageLayer, WeightedAttentionPooling -if TYPE_CHECKING: - from collections.abc import Sequence - @due.dcite(Doi("10.1038/s41467-020-19964-7"), description="Roost model") class Roost(BaseModelClass): @@ -43,30 +38,7 @@ def __init__( out_hidden: Sequence[int] = (256, 128, 64), **kwargs, ) -> None: - """Composition-only model. - - Args: - robust (bool): If True, the number of model outputs is doubled. 2nd output - for each target will be an estimate for the aleatoric uncertainty - (uncertainty inherent to the sample) which can be used with a robust - loss function to attenuate the weighting of uncertain samples. - n_targets (list[int]): Number of targets to train on - elem_emb_len (int): Number of features in initial element embedding - elem_fea_len (int, optional): Number of hidden features to use to encode - elements. Defaults to 64. - n_graph (int, optional): Number of message passing operations to carry out. - Defaults to 3. - elem_heads (int, optional): Number of parallel attention heads per message - passing operation. Defaults to 3. - elem_gate (list[int], optional): _description_. Defaults to (256,). - elem_msg (list[int], optional): _description_. Defaults to (256,). - cry_heads (int, optional): _description_. Defaults to 3. - cry_gate (list[int], optional): _description_. Defaults to (256,). - cry_msg (list[int], optional): _description_. Defaults to (256,). - trunk_hidden (list[int], optional): _description_. Defaults to (1024, 512). - out_hidden (list[int], optional): _description_. Defaults to (256, 128, 64). - **kwargs: Additional keyword arguments to pass to BaseModelClass. - """ + """Composition-only model.""" super().__init__(robust=robust, **kwargs) desc_dict = { @@ -110,18 +82,7 @@ def forward( nbr_idx: LongTensor, cry_elem_idx: LongTensor, ) -> tuple[Tensor, ...]: - """Forward pass through the material_nn and output_nn. - - Args: - elem_weights (Tensor): _description_ - elem_fea (Tensor): _description_ - self_idx (LongTensor): _description_ - nbr_idx (LongTensor): _description_ - cry_elem_idx (LongTensor): _description_ - - Returns: - tuple[Tensor, ...]: _description_ - """ + """Forward pass through the material_nn and output_nn.""" crys_fea = self.material_nn( elem_weights, elem_fea, self_idx, nbr_idx, cry_elem_idx ) @@ -149,19 +110,6 @@ def __init__( ) -> None: """Bundles n_graph message passing layers followed by cry_heads weighted attention pooling layers. - - Args: - elem_emb_len (int): Element embedding length. - elem_fea_len (int, optional): Element feature length. Defaults to 64. - n_graph (int, optional): Number of message-passing layers. Defaults to 3. - elem_heads (int, optional): Message-passing heads in each MP layer. - Defaults to 3. - elem_gate (list[int], optional): Message gate layers in each MP layer. - Defaults to (256,). - elem_msg (list[int], optional): _description_. Defaults to (256,). - cry_heads (int, optional): _description_. Defaults to 3. - cry_gate (list[int], optional): _description_. Defaults to (256,). - cry_msg (list[int], optional): _description_. Defaults to (256,). """ super().__init__() diff --git a/aviary/segments.py b/aviary/segments.py index b101b3ef..6a9d0983 100644 --- a/aviary/segments.py +++ b/aviary/segments.py @@ -1,6 +1,4 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING +from collections.abc import Sequence import torch from torch import LongTensor, Tensor, nn @@ -8,9 +6,6 @@ from aviary.networks import SimpleNetwork from aviary.scatter import scatter_reduce -if TYPE_CHECKING: - from collections.abc import Sequence - class AttentionPooling(nn.Module): """Softmax attention layer. Currently unused.""" diff --git a/aviary/utils.py b/aviary/utils.py index ff78311e..1b924092 100644 --- a/aviary/utils.py +++ b/aviary/utils.py @@ -1,17 +1,18 @@ -from __future__ import annotations - import os import sys import time from collections import defaultdict +from collections.abc import Callable, Generator, Iterable from contextlib import contextmanager from datetime import datetime from pickle import PickleError -from typing import TYPE_CHECKING, Any, Callable, Literal +from types import ModuleType +from typing import Any, Literal import numpy as np import pandas as pd import torch +import wandb from sklearn.metrics import ( accuracy_score, balanced_accuracy_score, @@ -24,17 +25,14 @@ from torch.nn import CrossEntropyLoss, L1Loss, MSELoss, NLLLoss from torch.optim import SGD, Adam, AdamW, Optimizer from torch.optim.lr_scheduler import MultiStepLR, _LRScheduler -from torch.utils.data import DataLoader, Dataset, Subset +from torch.utils.data import DataLoader, Subset from torch.utils.tensorboard import SummaryWriter from aviary import ROOT from aviary.core import BaseModelClass, Normalizer, TaskType, sampled_softmax +from aviary.data import InMemoryDataLoader from aviary.losses import robust_l1_loss, robust_l2_loss -if TYPE_CHECKING: - from collections.abc import Generator, Iterable - from types import ModuleType - def initialize_model( model_class: BaseModelClass, @@ -283,10 +281,9 @@ def train_ensemble( run_id: int, ensemble_folds: int, epochs: int, - train_set: Dataset | Subset, - val_set: Dataset | Subset, - log: bool, - data_params: dict[str, Any], + train_loader: DataLoader, + val_loader: DataLoader | None, + log: Literal["tensorboard", "wandb"], setup_params: dict[str, Any], restart_params: dict[str, Any], model_params: dict[str, Any], @@ -303,10 +300,9 @@ def train_ensemble( run_id (int): Unique identifier of the model run. ensemble_folds (int): Number of members in ensemble. epochs (int): Number of epochs to train for. - train_set (Subset): Dataloader containing training data. - val_set (Subset): Dataloader containing validation data. + train_loader (DataLoader): Dataloader containing training data. + val_loader (DataLoader | None): Dataloader containing validation data. log (bool): Whether to log intermediate metrics to TensorBoard. - data_params (dict[str, Any]): Dictionary of data loader parameters setup_params (dict[str, Any]): Dictionary of setup parameters restart_params (dict[str, Any]): Dictionary of restart parameters model_params (dict[str, Any]): Dictionary of model parameters @@ -316,16 +312,6 @@ def train_ensemble( when early stopping. Defaults to None. verbose (bool, optional): Whether to show progress bars for each epoch. """ - train_loader = DataLoader(train_set, **data_params) - print(f"Training on {len(train_set):,} samples") - - if val_set is not None: - data_params.update({"batch_size": 16 * data_params["batch_size"]}) - val_loader = DataLoader(val_set, **data_params) - print(f"Validating on {len(val_set):,} samples") - else: - val_loader = None - # this allows us to run ensembles in parallel rather than in series # by specifying the run-id arg. for r_id in [run_id] if ensemble_folds == 1 else range(ensemble_folds): @@ -350,25 +336,44 @@ def train_ensemble( for target, normalizer in normalizer_dict.items(): if normalizer is not None: - if isinstance(train_set, Subset): - sample_target = Tensor( - train_set.dataset.df[target].iloc[train_set.indices].values - ) + if isinstance(train_loader, InMemoryDataLoader): + # FIXME: this is really brittle but it works for now. + sample_target = train_loader.tensors[1] else: - sample_target = Tensor(train_set.df[target].values) + data = train_loader.dataset + if isinstance(train_loader.dataset, Subset): + sample_target = Tensor( + data.dataset.df[target].iloc[data.indices].to_numpy() + ) + else: + sample_target = Tensor(data.df[target].to_numpy()) if not restart_params["resume"]: normalizer.fit(sample_target) print(f"Dummy MAE: {(sample_target - normalizer.mean).abs().mean():.4f}") - if log: + if log == "tensorboard": writer = SummaryWriter( f"{ROOT}/runs/{model_name}/{model_name}-r{r_id}_{datetime.now():%d-%m-%Y_%H-%M-%S}" ) + elif log == "wandb": + wandb.init( + project="lightning_logs", + # https://docs.wandb.ai/guides/track/launch#init-start-error + settings=wandb.Settings(start_method="fork"), + name=f"{model_name}-r{r_id}", + config={ + "model_params": model_params, + "setup_params": setup_params, + "restart_params": restart_params, + "loss_dict": loss_dict, + }, + ) + writer = "wandb" else: writer = None - if (val_set is not None) and (model.best_val_scores is None): + if (val_loader is not None) and (model.best_val_scores is None): print("Getting Validation Baseline") with torch.no_grad(): v_metrics = model.evaluate( @@ -406,15 +411,13 @@ def train_ensemble( ) -# TODO find a better name for this function @janosh @torch.no_grad() def results_multitask( model_class: BaseModelClass, model_name: str, run_id: int, ensemble_folds: int, - test_set: Dataset | Subset, - data_params: dict[str, Any], + test_loader: DataLoader | InMemoryDataLoader, robust: bool, task_dict: dict[str, TaskType], device: type[torch.device] | Literal["cuda", "cpu"], @@ -429,8 +432,7 @@ def results_multitask( model_name (str): String describing the model. run_id (int): Unique identifier of the model run. ensemble_folds (int): Number of members in ensemble. - test_set (Subset): Dataloader containing testing data. - data_params (dict[str, Any]): Dictionary of data loader parameters + test_loader (DataLoader): Dataloader containing testing data. robust (bool): Whether to estimate standard deviation for use in a robust loss function. task_dict (dict[str, TaskType]): Map of target names to "regression" or @@ -457,15 +459,17 @@ def results_multitask( "------------Evaluate model on Test Set------------\n" "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" ) - test_loader = DataLoader(test_set, **data_params) - print(f"Testing on {len(test_set):,} samples") - results_dict: dict[str, dict[str, list | np.ndarray]] = {} + n_test = ( + len(test_loader.tensors[0]) + if isinstance(test_loader, InMemoryDataLoader) + else len(test_loader.dataset) + ) for target_name, task_type in task_dict.items(): results_dict[target_name] = defaultdict( list if task_type == "classification" - else lambda: np.zeros((ensemble_folds, len(test_set))) # type: ignore[call-overload] + else lambda: np.zeros((ensemble_folds, n_test)) # type: ignore[call-overload] ) for ens_idx in range(ensemble_folds): @@ -488,7 +492,7 @@ def results_multitask( f"provided {task_dict=}" ) - model = model_class(**checkpoint["model_params"]) + model: BaseModelClass = model_class(**checkpoint["model_params"]) model.to(device) model.load_state_dict(checkpoint["state_dict"]) @@ -502,7 +506,7 @@ def results_multitask( y_test, outputs, *ids = model.predict(test_loader) for output, targets, (target_name, task_type), res_dict in zip( - outputs, y_test, model.task_dict.items(), results_dict.values() + outputs, y_test, model.task_dict.items(), results_dict.values(), strict=False ): if task_type == "regression": normalizer = normalizer_dict[target_name] @@ -533,10 +537,14 @@ def results_multitask( res_dict["targets"] = targets - # TODO cleaner way to get identifier names if save_results: + identifier_names = ( + [f"idx_{i}" for i in range(len(ids))] + if isinstance(test_loader, InMemoryDataLoader) + else test_loader.dataset.dataset.identifiers + ) save_results_dict( - dict(zip(test_loader.dataset.dataset.identifiers, *ids)), + dict(zip(identifier_names, *ids, strict=False)), results_dict, model_name, f"-r{run_id}", diff --git a/aviary/wren/data.py b/aviary/wren/data.py index 70e17f00..fe16937c 100644 --- a/aviary/wren/data.py +++ b/aviary/wren/data.py @@ -1,11 +1,11 @@ -from __future__ import annotations - import json +from collections.abc import Sequence from functools import cache from itertools import groupby -from typing import TYPE_CHECKING, Any +from typing import Any import numpy as np +import pandas as pd import torch from pymatgen.analysis.prototypes import ( RE_SUBST_ONE_PREFIX, @@ -18,11 +18,6 @@ from aviary import PKG_DIR -if TYPE_CHECKING: - from collections.abc import Sequence - - import pandas as pd - class WyckoffData(Dataset): """Wyckoff dataset class for the Wren model.""" @@ -33,8 +28,8 @@ def __init__( task_dict: dict[str, str], elem_embedding: str = "matscholar200", sym_emb: str = "bra-alg-off", - inputs: str = "wyckoff", - identifiers: Sequence[str] = ("material_id", "composition", "wyckoff"), + inputs: str = "protostructure", + identifiers: Sequence[str] = ("material_id", "composition", "protostructure"), ): """Data class for Wren models. @@ -48,10 +43,10 @@ def __init__( sym_emb (str): Symmetry embedding. One of "bra-alg-off" (default) or "spg-alg-off" or path to a file with custom symmetry embeddings. inputs (str, optional): df columns to be used for featurization. - Defaults to "wyckoff". + Defaults to "protostructure". identifiers (list, optional): df columns for distinguishing data points. Will be copied over into the model's output CSV. Defaults to - ["material_id", "composition", "wyckoff"]. + ["material_id", "composition", "protostructure"]. """ if len(identifiers) < 2: raise AssertionError("Two identifiers are required") @@ -180,23 +175,6 @@ def collate_batch( ) -> tuple[Any, ...]: """Collate a list of data and return a batch for predicting crystal properties. - - Args: - samples ([tuple]): list of tuples for each data point. - (elem_fea, nbr_fea, nbr_idx, target) - - elem_fea (Tensor): Node features from atom type and Wyckoff letter - nbr_fea (Tensor): _description_ - nbr_idx (LongTensor): - target (Tensor): - cif_id: str or int - - Returns: - tuple[ - tuple[Tensor * 3, LongTensor * 4]: batched Wren model inputs, - tuple[Tensor | LongTensor]: Target values for different tasks, - *tuple[str | int]]: Identifiers like material_id, composition - ] """ # define the lists batch_mult_weights = [] @@ -251,8 +229,10 @@ def collate_batch( torch.cat(crystal_wyk_idx), torch.cat(aug_cry_idx), ), - tuple(torch.stack(b_target, dim=0) for b_target in zip(*batch_targets)), - *zip(*batch_cry_ids), + tuple( + torch.stack(b_target, dim=0) for b_target in zip(*batch_targets, strict=False) + ), + *zip(*batch_cry_ids, strict=False), ) @@ -283,7 +263,7 @@ def parse_protostructure_label( elements = [] wyckoff_set = [] - for el, wyk_letters_per_elem in zip(elems, wyckoff_letters): + for el, wyk_letters_per_elem in zip(elems, wyckoff_letters, strict=False): # Normalize Wyckoff letters to start with 1 if missing digit wyk_letters_normalized = RE_WYCKOFF_NO_PREFIX.sub( RE_SUBST_ONE_PREFIX, wyk_letters_per_elem @@ -296,7 +276,7 @@ def parse_protostructure_label( mults = map(int, sep_n_wyks[0::2]) letters = sep_n_wyks[1::2] - for mult, letter in zip(mults, letters): + for mult, letter in zip(mults, letters, strict=False): elements.extend([el] * mult) wyckoff_set.extend([letter] * mult) wyckoff_site_multiplicities.extend( diff --git a/aviary/wren/model.py b/aviary/wren/model.py index f17764b1..6e6da581 100644 --- a/aviary/wren/model.py +++ b/aviary/wren/model.py @@ -1,6 +1,4 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING +from collections.abc import Sequence import torch import torch.nn.functional as F @@ -12,9 +10,6 @@ from aviary.scatter import scatter_reduce from aviary.segments import MessageLayer, WeightedAttentionPooling -if TYPE_CHECKING: - from collections.abc import Sequence - @due.dcite(Doi("10.1126/sciadv.abn4117"), description="Wren model") class Wren(BaseModelClass): @@ -46,34 +41,7 @@ def __init__( out_hidden: Sequence[int] = (256, 128, 64), **kwargs, ) -> None: - """Composition plus symmetry model. - - Args: - robust (bool): If True, the number of model outputs is doubled. 2nd output - for each target will be an estimate for the aleatoric uncertainty ( - uncertainty inherent to the sample) which can be used with a robust loss - function to attenuate the weighting of uncertain samples. - n_targets (list[int]): Number of targets to train on. 1 for regression and - number of different class labels for classification. - elem_emb_len (int): Number of features in initial element embedding - sym_emb_len (int): Number of features in initial Wyckoff letter embedding - elem_fea_len (int, optional): Number of hidden features to use to encode - elements. Defaults to 32. - sym_fea_len (int, optional): Number of hidden features to use to encode - Wyckoff letters. Defaults to 32. - n_graph (int, optional): Number of message passing operations to carry out. - Defaults to 3. - elem_heads (int, optional): Number of parallel attention heads per message - passing operation. Defaults to 1. - elem_gate (list[int], optional): _description_. Defaults to [256]. - elem_msg (list[int], optional): _description_. Defaults to [256]. - cry_heads (int, optional): _description_. Defaults to 1. - cry_gate (list[int], optional): _description_. Defaults to [256]. - cry_msg (list[int], optional): _description_. Defaults to [256]. - trunk_hidden (list[int], optional): _description_. Defaults to [1024, 512]. - out_hidden (list[int], optional): _description_. Defaults to [256, 128, 64]. - **kwargs: Additional keyword arguments to pass to BaseModelClass. - """ + """Protostructure based model.""" super().__init__(robust=robust, **kwargs) desc_dict = { @@ -123,20 +91,7 @@ def forward( cry_elem_idx: LongTensor, aug_cry_idx: LongTensor, ) -> tuple[Tensor, ...]: - """Forward pass through the material_nn and output_nn. - - Args: - elem_weights (Tensor): _description_ - elem_fea (Tensor): _description_ - sym_fea (Tensor): _description_ - self_idx (LongTensor): _description_ - nbr_idx (LongTensor): _description_ - cry_elem_idx (LongTensor): _description_ - aug_cry_idx (LongTensor): _description_ - - Returns: - tuple[Tensor, ...]: Predicted values for each target - """ + """Forward pass through the material_nn and output_nn.""" crys_fea = self.material_nn( elem_weights, elem_fea, @@ -170,25 +125,7 @@ def __init__( cry_gate: Sequence[int] = (256,), cry_msg: Sequence[int] = (256,), ): - """Message passing section of the Roost model. - - Args: - elem_emb_len (int): Number of features in initial element embedding - sym_emb_len (int): Number of features in initial Wyckoff letter embedding - elem_fea_len (int, optional): Number of hidden features to use to encode - elements. Defaults to 32. - sym_fea_len (int, optional): Number of hidden features to use to encode - Wyckoff letters. Defaults to 32. - n_graph (int, optional): Number of message passing operations to carry out. - Defaults to 3. - elem_heads (int, optional): Number of parallel attention heads per message - passing operation. Defaults to 1. - elem_gate (list[int], optional): _description_. Defaults to [256]. - elem_msg (list[int], optional): _description_. Defaults to [256]. - cry_heads (int, optional): Number of attention heads. Defaults to 1. - cry_gate (list[int], optional): _description_. Defaults to [256]. - cry_msg (list[int], optional): _description_. Defaults to [256]. - """ + """Message passing section of the Roost model.""" super().__init__() # apply linear transform to the input to get a trainable embedding diff --git a/aviary/wrenformer/data.py b/aviary/wrenformer/data.py index 75a694c9..20fa2387 100644 --- a/aviary/wrenformer/data.py +++ b/aviary/wrenformer/data.py @@ -1,10 +1,10 @@ -from __future__ import annotations - import json +from collections.abc import Callable from functools import cache -from typing import TYPE_CHECKING, Any, Literal +from typing import Any, Literal import numpy as np +import pandas as pd import torch from pymatgen.core import Composition from torch import LongTensor, Tensor, nn @@ -13,9 +13,6 @@ from aviary.data import InMemoryDataLoader from aviary.wren.data import parse_protostructure_label -if TYPE_CHECKING: - import pandas as pd - def collate_batch( features: tuple[Tensor], targets: Tensor | LongTensor, ids: list[str | int] @@ -131,7 +128,7 @@ def get_composition_embedding(formula: str) -> Tensor: Tensor: Shape (n_elements, n_features). Usually (2-6, 200). """ composition_dict = Composition(formula).get_el_amt_dict() - elements, elem_weights = zip(*composition_dict.items()) + elements, elem_weights = zip(*composition_dict.items(), strict=False) elem_weights = np.atleast_2d(elem_weights).T / sum(elem_weights) @@ -147,11 +144,12 @@ def get_composition_embedding(formula: str) -> Tensor: def df_to_in_mem_dataloader( df: pd.DataFrame, - input_col: str = "wyckoff", + input_col: str = "protostructure", target_col: str | None = None, id_col: str | None = None, - embedding_type: Literal["wyckoff", "composition"] = "wyckoff", + embedding_type: Literal["protostructure", "composition"] = "protostructure", device: str | None = None, + collate_fn: Callable = collate_batch, **kwargs: Any, ) -> InMemoryDataLoader: """Construct an InMemoryDataLoader with Wrenformer batch collation from a dataframe. @@ -162,15 +160,17 @@ def df_to_in_mem_dataloader( df (pd.DataFrame): Expected to have columns input_col, target_col, id_col. input_col (str): Column name holding the input values (Aflow Wyckoff labels or composition strings) from which initial embeddings will be constructed. - Defaults to "wyckoff". + Defaults to "protostructure". target_col (str): Column name holding the target values. Defaults to None. Only leave this empty if making predictions since target tensor will be set to list of Nones. id_col (str): Column name holding sample IDs. Defaults to None. If None, IDs will be the dataframe index. - embedding_type ('wyckoff' | 'composition'): Defaults to "wyckoff". + embedding_type ('protostructure' | 'composition'): Defaults to "protostructure". device (str): torch.device to load tensors onto. Defaults to "cuda" if torch.cuda.is_available() else "cpu". + collate_fn (Callable): Function to collate data into a batch. Defaults to + collate_batch. kwargs (dict): Keyword arguments like batch_size: int and shuffle: bool to pass to InMemoryDataLoader. Defaults to None. @@ -181,12 +181,12 @@ def df_to_in_mem_dataloader( if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" - if embedding_type not in ("wyckoff", "composition"): - raise ValueError(f"{embedding_type = } must be 'wyckoff' or 'composition'") + if embedding_type not in ("protostructure", "composition"): + raise ValueError(f"{embedding_type = } must be 'protostructure' or 'composition'") initial_embeddings = df[input_col].map( wyckoff_embedding_from_protostructure_label - if embedding_type == "wyckoff" + if embedding_type == "protostructure" else get_composition_embedding ) targets = ( @@ -202,4 +202,4 @@ def df_to_in_mem_dataloader( inputs[idx] = tensor.to(device) ids = df.get(id_col, df.index).to_numpy() - return InMemoryDataLoader([inputs, targets, ids], collate_fn=collate_batch, **kwargs) + return InMemoryDataLoader([inputs, targets, ids], collate_fn=collate_fn, **kwargs) diff --git a/aviary/wrenformer/model.py b/aviary/wrenformer/model.py index 5dc1405b..2c08eeb9 100644 --- a/aviary/wrenformer/model.py +++ b/aviary/wrenformer/model.py @@ -1,18 +1,13 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Callable +from collections.abc import Sequence import torch import torch.nn.functional as F from pymatgen.util.due import Doi, due from torch import BoolTensor, Tensor, nn -from aviary.core import BaseModelClass, masked_max, masked_mean, masked_min, masked_std +from aviary.core import AGGREGATORS, BaseModelClass from aviary.networks import ResidualNetwork -if TYPE_CHECKING: - from collections.abc import Sequence - @due.dcite(Doi("10.48550/arXiv.2308.14920"), description="Wrenformer model") @due.dcite(Doi("10.1038/s41524-021-00545-1"), description="Crabnet model") @@ -36,14 +31,15 @@ class Wrenformer(BaseModelClass): def __init__( self, + robust: bool, n_targets: Sequence[int], n_features: int, d_model: int = 128, n_attn_layers: int = 6, n_attn_heads: int = 4, + dropout: float = 0.0, trunk_hidden: Sequence[int] = (1024, 512), out_hidden: Sequence[int] = (256, 128, 64), - robust: bool = False, embedding_aggregations: Sequence[str] = ("mean",), **kwargs, ) -> None: @@ -60,6 +56,8 @@ def __init__( to 3. n_attn_heads (int): Number of attention heads to use in the transformer. d_model needs to be divisible by this number. Defaults to 4. + dropout (float, optional): Dropout rate for the transformer encoder. Defaults + to 0. trunk_hidden (list[int], optional): Number of hidden units in the trunk network which is shared across tasks when multitasking. Defaults to [1024, 512]. @@ -77,14 +75,32 @@ def __init__( """ super().__init__(robust=robust, **kwargs) + model_params = { + "robust": robust, + "n_targets": n_targets, + "n_features": n_features, + "d_model": d_model, + "n_attn_layers": n_attn_layers, + "n_attn_heads": n_attn_heads, + "dropout": dropout, + "trunk_hidden": trunk_hidden, + "out_hidden": out_hidden, + "embedding_aggregations": embedding_aggregations, + } + self.model_params.update(model_params) + # up- or down-size embedding dimension (n_features) to model dimension (d_model) self.resize_embedding = nn.Linear(n_features, d_model) transformer_layer = nn.TransformerEncoderLayer( - d_model=d_model, nhead=n_attn_heads, batch_first=True + d_model=d_model, + nhead=n_attn_heads, + batch_first=True, + norm_first=True, + dropout=dropout, ) self.transformer_encoder = nn.TransformerEncoder( - transformer_layer, num_layers=n_attn_layers + transformer_layer, num_layers=n_attn_layers, enable_nested_tensor=False ) if self.robust: @@ -103,9 +119,7 @@ def __init__( ResidualNetwork(out_hidden[0], n, out_hidden[1:]) for n in n_targets ) - def forward( # type: ignore[override] - self, features: Tensor, mask: BoolTensor, *args - ) -> tuple[Tensor, ...]: + def forward(self, features: Tensor, mask: BoolTensor, *args) -> tuple[Tensor, ...]: """Forward pass through the Wrenformer. Args: @@ -146,7 +160,7 @@ def forward( # type: ignore[override] # careful to ignore padded values when taking the mean inv_mask: torch.BoolTensor = ~mask[..., None] - aggregation_funcs = [aggregators[key] for key in self.embedding_aggregations] + aggregation_funcs = [AGGREGATORS[key] for key in self.embedding_aggregations] aggregated_embeddings = torch.cat( [func(embeddings, inv_mask, 1) for func in aggregation_funcs], dim=1 ) @@ -155,13 +169,3 @@ def forward( # type: ignore[override] predictions = F.relu(self.trunk_nn(aggregated_embeddings)) return tuple(output_nn(predictions) for output_nn in self.output_nns) - - -# map aggregation types to functions -aggregators: dict[str, Callable[[Tensor, BoolTensor, int], Tensor]] = { - "mean": masked_mean, - "std": masked_std, - "max": masked_max, - "min": masked_min, - "sum": lambda x, mask, dim: (x * mask).sum(dim=dim), -} diff --git a/examples/cgcnn-example.py b/examples/cgcnn-example.py index 7d398c33..aeb4cf57 100644 --- a/examples/cgcnn-example.py +++ b/examples/cgcnn-example.py @@ -4,6 +4,7 @@ import torch from pymatgen.core import Structure from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader from aviary import ROOT from aviary.cgcnn.data import CrystalGraphData, collate_batch @@ -85,8 +86,8 @@ def main( "Cannot fine-tune and transfer checkpoint(s) at the same time." ) - task_dict = dict(zip(targets, tasks)) - loss_dict = dict(zip(targets, losses)) + task_dict = dict(zip(targets, tasks, strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) dist_dict = { "radius": radius, @@ -200,6 +201,20 @@ def main( } if train: + train_loader = DataLoader(train_set, **data_params) + + if val_set is not None: + val_loader = DataLoader( + val_set, + **{ + **data_params, + "batch_size": 16 * data_params["batch_size"], + "shuffle": False, + }, + ) + else: + val_loader = None + train_ensemble( model_class=CrystalGraphConvNet, model_name=model_name, @@ -207,10 +222,9 @@ def main( ensemble_folds=ensemble, epochs=epochs, patience=patience, - train_set=train_set, - val_set=val_set, + train_loader=train_loader, + val_loader=val_loader, log=log, - data_params=data_params, setup_params=setup_params, restart_params=restart_params, model_params=model_params, @@ -218,19 +232,21 @@ def main( ) if evaluate: - data_reset = { - "batch_size": 16 * batch_size, # faster model inference - "shuffle": False, # need fixed data order due to ensembling - } - data_params.update(data_reset) + test_loader = DataLoader( + test_set, + **{ + **data_params, + "batch_size": 64 * data_params["batch_size"], + "shuffle": False, + }, + ) - results_multitask( + _results_dict = results_multitask( model_class=CrystalGraphConvNet, model_name=model_name, run_id=run_id, ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, + test_loader=test_loader, robust=robust, task_dict=task_dict, device=device, diff --git a/examples/inputs/examples.csv b/examples/inputs/examples.csv index 6b5ade05..9b95f0b9 100644 --- a/examples/inputs/examples.csv +++ b/examples/inputs/examples.csv @@ -1,4 +1,4 @@ -material_id,composition,E_f,wyckoff +material_id,composition,E_f,protostructure c07b988125b3a0adc329b2449eeaac24c9e7def8,Hf,0.0,A_hP2_194_c:Hf 0060794138df3ba93c68007949b0a72d19cb4c62,Hf(ZnN2)3,0.6322286350000006,AB6C3_hR30_160_a_2b_b:Hf-N-Zn 004c70f544dca7f8e1c74c6c4ff87a1e355e7cd5,Hf(ZnN2)3,0.8708339350000012,AB6C3_hR30_148_a_f_bc:Hf-N-Zn diff --git a/examples/inputs/examples.json b/examples/inputs/examples.json index 71748d80..48918362 100644 --- a/examples/inputs/examples.json +++ b/examples/inputs/examples.json @@ -1 +1 @@ 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diff --git a/examples/inputs/poscar_to_df.py b/examples/inputs/poscar_to_df.py index 012d700d..52295b86 100644 --- a/examples/inputs/poscar_to_df.py +++ b/examples/inputs/poscar_to_df.py @@ -1,6 +1,4 @@ # %% -from __future__ import annotations - import glob import os @@ -64,14 +62,16 @@ print(f"Number of points in dataset: {len(df)}") # takes ~ 15mins -df["wyckoff"] = df.final_structure.progress_map(get_protostructure_label_from_spglib) +df["protostructure"] = df.final_structure.progress_map( + get_protostructure_label_from_spglib +) # lattice, sites = zip(*df.final_structure.progress_map(get_cgcnn_input)) df["composition"] = df.final_structure.map(lambda x: x.composition.reduced_formula) df["nelements"] = df.final_structure.map(lambda x: len(x.composition.elements)) df["volume"] = df.final_structure.map(lambda x: x.volume) -df["nsites"] = df.final_structure.map(lambda x: x.num_sites) +df["n_sites"] = df.final_structure.map(lambda x: x.num_sites) # df["lattice"] = lattice # df["sites"] = sites @@ -83,7 +83,7 @@ df_el = df_el.sort_values(by=["composition", "E_vasp_per_atom"], ascending=True) el_refs = { c.composition.elements[0]: e - for c, e in zip(df_el.final_structure, df_el.E_vasp_per_atom) + for c, e in zip(df_el.final_structure, df_el.E_vasp_per_atom, strict=False) } @@ -113,7 +113,7 @@ def get_formation_energy(comp: str, energy: float, el_refs: dict[str, float]) -> # %% # Remove invalid Wyckoff Sequences -df["nwyckoff"] = df["wyckoff"].map(count_wyckoff_positions) +df["n_wyckoff"] = df["protostructure"].map(count_wyckoff_positions) df = df.query("'Invalid' not in wyckoff") print(f"Valid Wyckoff representation {len(df)}") @@ -121,8 +121,8 @@ def get_formation_energy(comp: str, energy: float, el_refs: dict[str, float]) -> # %% # Drop duplicated wyckoff representations -df = df.sort_values(by=["wyckoff", "E_vasp_per_atom"], ascending=True) -df_wyk = df.drop_duplicates(["wyckoff"], keep="first") +df = df.sort_values(by=["protostructure", "E_vasp_per_atom"], ascending=True) +df_wyk = df.drop_duplicates(["protostructure"], keep="first") print(f"Lowest energy unique wyckoff sequences: {len(df_wyk)}") @@ -141,25 +141,25 @@ def get_formation_energy(comp: str, energy: float, el_refs: dict[str, float]) -> print(f"Total systems: {len(df_wyk)}") wyk_lim = 16 -df_wyk = df_wyk[df_wyk["nwyckoff"] <= wyk_lim] +df_wyk = df_wyk[df_wyk["n_wyckoff"] <= wyk_lim] print(f"Less than {wyk_lim} Wyckoff species in cell: {len(df_wyk)}") cell_lim = 64 -df_wyk = df_wyk[df_wyk["nsites"] <= cell_lim] +df_wyk = df_wyk[df_wyk["n_sites"] <= cell_lim] print(f"Less than {cell_lim} atoms in cell: {len(df_wyk)}") vol_lim = 500 -df_wyk = df_wyk[df_wyk["volume"] / df_wyk["nsites"] < vol_lim] +df_wyk = df_wyk[df_wyk["volume"] / df_wyk["n_sites"] < vol_lim] print(f"Less than {vol_lim} A^3 per site: {len(df_wyk)}") -fields = ["material_id", "composition", "E_f", "wyckoff"] # , "lattice", "sites"] +fields = ["material_id", "composition", "E_f", "protostructure"] # , "lattice", "sites"] -df_wyk[["material_id", "composition", "E_f", "wyckoff"]].to_csv( +df_wyk[["material_id", "composition", "E_f", "protostructure"]].to_csv( final_dir + "/examples.csv", index=False ) df_wyk["structure"] = df_wyk["final_structure"].map(lambda x: x.as_dict()) -df_wyk[["material_id", "composition", "E_f", "wyckoff", "structure"]].to_json( +df_wyk[["material_id", "composition", "E_f", "protostructure", "structure"]].to_json( final_dir + "/examples.json" ) diff --git a/examples/wrenformer/matbench/make_plots.py b/examples/matbench_example/make_plots.py similarity index 94% rename from examples/wrenformer/matbench/make_plots.py rename to examples/matbench_example/make_plots.py index dac82e3a..2ea4d274 100644 --- a/examples/wrenformer/matbench/make_plots.py +++ b/examples/matbench_example/make_plots.py @@ -1,5 +1,9 @@ # %% -from __future__ import annotations +import sys +from pathlib import Path + +# Add the parent directory to system path +sys.path.append(str(Path(__file__).parent.parent)) import json import logging @@ -11,22 +15,18 @@ import pandas as pd import plotly.express as px import pymatviz as pmv -from sklearn.metrics import r2_score, roc_auc_score - -from examples.wrenformer.matbench import DATA_PATHS -from examples.wrenformer.matbench.plotting_functions import ( +from matbench import MatbenchBenchmark +from matbench.constants import CLF_KEY, REG_KEY +from matbench.metadata import mbv01_metadata as matbench_metadata +from matbench_example.plotting_functions import ( dataset_labels_html, error_heatmap, plot_leaderboard, scale_errors, ) -from examples.wrenformer.matbench.utils import recursive_dict_merge -from matbench import MatbenchBenchmark -from matbench.constants import CLF_KEY, REG_KEY -from matbench.metadata import mbv01_metadata as matbench_metadata - -__author__ = "Janosh Riebesell" -__date__ = "2022-04-25" +from matbench_example.prepare_matbench_datasets import DATA_PATHS +from matbench_example.utils import recursive_dict_merge +from sklearn.metrics import r2_score, roc_auc_score logging.getLogger("matbench").setLevel("ERROR") @@ -187,7 +187,7 @@ target = df.columns[0] -y_cols = [c for c in df if c not in [target, "composition", "wyckoff"]] +y_cols = [c for c in df if c not in [target, "composition", "protostructure"]] labels = {} for y_col in y_cols: diff --git a/examples/wrenformer/matbench/plotting_functions.py b/examples/matbench_example/plotting_functions.py similarity index 95% rename from examples/wrenformer/matbench/plotting_functions.py rename to examples/matbench_example/plotting_functions.py index 33a1eb05..856e0cb9 100644 --- a/examples/wrenformer/matbench/plotting_functions.py +++ b/examples/matbench_example/plotting_functions.py @@ -1,22 +1,13 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Any +from typing import Any import numpy as np +import pandas as pd import plotly.express as px import plotly.io as pio -from sklearn.metrics import accuracy_score, auc, roc_curve - from matbench.constants import CLF_KEY, REG_KEY from matbench.metadata import mbv01_metadata -from matbench.metadata import mbv01_metadata as matbench_metadata - -if TYPE_CHECKING: - import pandas as pd - from plotly.graph_objs._figure import Figure - -__author__ = "Janosh Riebesell" -__date__ = "2022-04-25" +from plotly.graph_objs._figure import Figure +from sklearn.metrics import accuracy_score, auc, roc_curve pio.templates.default = "plotly_white" @@ -76,7 +67,7 @@ def scale_clf_task(series: pd.Series) -> pd.Series: "matbench_mp_is_metal": "Metallicity DFT", "matbench_mp_e_form": "Eᶠ DFT", } -dataset_sizes = {k: v["n_samples"] for k, v in matbench_metadata.items()} +dataset_sizes = {k: v["n_samples"] for k, v in mbv01_metadata.items()} dataset_labels_html = { k: f"{v} {dataset_sizes[k]:,}" for k, v in dataset_labels.items() } diff --git a/examples/matbench_example/prepare_matbench_datasets.py b/examples/matbench_example/prepare_matbench_datasets.py new file mode 100644 index 00000000..2ca4f588 --- /dev/null +++ b/examples/matbench_example/prepare_matbench_datasets.py @@ -0,0 +1,67 @@ +import os +from glob import glob +from typing import Literal + +from matbench.data_ops import load +from pymatgen.analysis.prototypes import get_protostructure_label_from_spglib +from tqdm import tqdm + +tqdm.pandas() + +current_dir = os.path.dirname(os.path.abspath(__file__)) + +SMOKE_TEST = True + +matbench_datasets = [ + "matbench_steels", + "matbench_jdft2d", + "matbench_phonons", + "matbench_expt_gap", + "matbench_dielectric", + "matbench_expt_is_metal", + "matbench_glass", + "matbench_log_gvrh", + "matbench_log_kvrh", + "matbench_perovskites", + "matbench_mp_gap", + "matbench_mp_is_metal", + "matbench_mp_e_form", +] + +if SMOKE_TEST: + matbench_datasets = matbench_datasets[2:3] + +MatbenchDatasets = Literal[*matbench_datasets] + +os.makedirs(f"{current_dir}/datasets", exist_ok=True) +for dataset in matbench_datasets: + dataset_path = f"{current_dir}/datasets/{dataset}.json.bz2" + + if os.path.exists(dataset_path): + print(f"Dataset {dataset} already exists, skipping") + continue + + df = load(dataset) + + if "structure" in df: + df["composition"] = [struct.formula for struct in df.structure] + df["protostructure"] = df["structure"].progress_apply( + get_protostructure_label_from_spglib + ) + else: + raise ValueError("No structure or composition column found") + + df.to_json( + dataset_path, + default_handler=lambda x: x.as_dict(), + ) + + +DATA_PATHS = { + path.split("/")[-1].split(".")[0]: path + for path in glob(f"{current_dir}/datasets/matbench_*.json.bz2") +} + +assert len(DATA_PATHS) == len(matbench_datasets), ( + f"glob found {len(DATA_PATHS)} data sets, expected {len(matbench_datasets)}" +) diff --git a/examples/wrenformer/matbench/readme.md b/examples/matbench_example/readme.md similarity index 64% rename from examples/wrenformer/matbench/readme.md rename to examples/matbench_example/readme.md index c447f4fc..5ee6fc2c 100644 --- a/examples/wrenformer/matbench/readme.md +++ b/examples/matbench_example/readme.md @@ -1,31 +1,24 @@ # Matbench -This directory contains the files needed to create Matbench submissions for Roostformer and Wrenformer (structure tasks only for Wren) which are rewrites of Roost and Wren using PyTorch's builtin `TransformerEncoder` in favor of custom self-attention modules used by Roost and Wren. +This directory contains the files needed to create Matbench submissions for Roostformer and Wrenformer (structure tasks only for Wren) which are rewrites of Roost and Wren using PyTorch's builtin `TransformerEncoder` instead of the custom self-attention modules used by Roost and Wren. Added in [aviary#44](https://github.com/CompRhys/aviary/pull/44). -Directory is named `matbench` to avoid shadowing the `matbench` package. - -The important files are: - -- `run_matbench.py`: The function that trains and tests Roost- and Wrenformer models on a given Matbench task. -- `slurm_submit.py`: Launch a slurm array to train and evaluate models with a given set of hyperparameters on all Matbench tasks. Calls `run_matbench.py`. -- `featurize_matbench.py`: Generate Spglib Wyckoff labels for all 13 Matbench tasks. - -Less important files: - -- `make_plots.py`: Imports `plotting_functions.py` to visualize and compare ours against other models and different sets of hyperparams. -- `compare_spglib_vs_aflow_wyckoff_labels.py`: See module doc string. -- `wandb_api.py`: Change run metadata recorded on [Weights and Biases](https://wandb.ai/aviary/matbench) after the fact. - ## Speed difference between Wren and Wrenformer According to Rhys, Wren could run 500 epochs in 5.5 h on a P100 training on 120k samples of MP data (similar to the `matbench_mp_e_form` dataset with 132k samples). Wrenformer only managed 207 epochs in 4h on the more powerful A100 training on `matbench_mp_e_form`. However, to avoid out-of-memory issues, Rhys constrained Wren to only run on systems with <= 16 Wyckoff positions. The code below shows that this lightens the workload by a factor of about 7.5, likely explaining the apparent slowdown in Wrenformer. ```py +import os +import sys +from pathlib import Path + +# Add the parent directory to system path +sys.path.append(str(Path(__file__).parent.parent)) + import pandas as pd from pymatgen.analysis.prototypes import count_wyckoff_positions -from examples.wrenformer.matbench import DATA_PATHS +from matbench_example import DATA_PATHS df = pd.read_json(DATA_PATHS["matbench_mp_e_form"]) diff --git a/examples/wrenformer/matbench/train_wrenformer.py b/examples/matbench_example/train_wrenformer.py similarity index 71% rename from examples/wrenformer/matbench/train_wrenformer.py rename to examples/matbench_example/train_wrenformer.py index 103b5922..712e7992 100644 --- a/examples/wrenformer/matbench/train_wrenformer.py +++ b/examples/matbench_example/train_wrenformer.py @@ -1,30 +1,31 @@ +"""Train a Wrenformer ensemble of size n_folds on collection of Matbench datasets.""" + # %% import os +import sys +from pathlib import Path + +# Add the parent directory to system path +sys.path.append(str(Path(__file__).parent.parent)) + from datetime import datetime from itertools import product -import pandas as pd import wandb -from matbench_discovery.slurm import slurm_submit - -from aviary.core import TaskType -from aviary.train import train_wrenformer -from examples.wrenformer.matbench import DATA_PATHS -from examples.wrenformer.matbench.utils import merge_json_on_disk from matbench.metadata import mbv01_metadata from matbench.task import MatbenchTask +from matbench_example.prepare_matbench_datasets import DATA_PATHS +from matbench_example.trainer import train_wrenformer +from matbench_example.utils import merge_json_on_disk, slurm_submit +from matminer.utils.io import load_dataframe_from_json -__author__ = "Janosh Riebesell" -__date__ = "2022-04-11" +from aviary.core import TaskType MODULE_DIR = os.path.dirname(__file__) -""" -Train a Wrenformer ensemble of size n_folds on collection of Matbench datasets. -""" # %% -epochs = 300 +epochs = 10 folds = list(range(5)) timestamp = f"{datetime.now():%Y-%m-%d@%H-%M-%S}" today = timestamp.split("@")[0] @@ -38,8 +39,13 @@ datasets = list(DATA_PATHS) else: # deploy Wren on structure tasks only - datasets = [k for k, v in mbv01_metadata.items() if v.input_type == "structure"] + datasets = [ + k + for k, v in mbv01_metadata.items() + if v.input_type == "structure" and k in DATA_PATHS + ] +# NOTE: this script will run as is if you want to run it locally without slurm. slurm_submit( job_name=job_name, partition="ampere", @@ -65,7 +71,8 @@ data_path = DATA_PATHS[dataset_name] id_col = "mbid" -df = pd.read_json(data_path).set_index(id_col, drop=False) +df = load_dataframe_from_json(data_path) +df.index.name = id_col matbench_task = MatbenchTask(dataset_name, autoload=False) matbench_task.df = df @@ -78,6 +85,8 @@ train_df = matbench_task.get_train_and_val_data(fold, as_type="df") test_df = matbench_task.get_test_data(fold, as_type="df", include_target=True) +wandb_path = None + test_metrics, run_params, test_df = train_wrenformer( checkpoint=None, # None | 'local' | 'wandb' run_name=run_name, @@ -87,24 +96,31 @@ task_type=task_type, id_col=id_col, # set to None to disable logging - wandb_path="aviary/matbench", + wandb_path=wandb_path, run_params=dict(dataset=dataset_name, fold=fold), timestamp=timestamp, epochs=epochs, ) + +# %% # save model predictions to JSON preds_path = f"{MODULE_DIR}/model_preds/{timestamp}-{run_name}.json" +os.makedirs(os.path.dirname(preds_path), exist_ok=True) # record model predictions -preds_dict = test_df[[id_col, target_col, f"{target_col}_pred"]].to_dict(orient="list") +test_df[id_col] = test_df.index +preds_dict = test_df[[id_col, target_col, f"{target_col}_pred_0"]].to_dict(orient="list") merge_json_on_disk({dataset_name: {f"fold_{fold}": preds_dict}}, preds_path) # save model scores to JSON scores_path = f"{MODULE_DIR}/model_scores/{timestamp}-{run_name}.json" +os.makedirs(os.path.dirname(scores_path), exist_ok=True) + scores_dict = {dataset_name: {f"fold_{fold}": test_metrics}} scores_dict["params"] = run_params -scores_dict["wandb_run"] = wandb.run.get_url() +if wandb_path is not None: + scores_dict["wandb_run"] = wandb.run.get_url() merge_json_on_disk(scores_dict, scores_path) print(f"scores for {fold = } of task {dataset_name} written to {scores_path}") diff --git a/aviary/train.py b/examples/matbench_example/trainer.py similarity index 74% rename from aviary/train.py rename to examples/matbench_example/trainer.py index 8a1e6da3..86ebc77d 100644 --- a/aviary/train.py +++ b/examples/matbench_example/trainer.py @@ -1,37 +1,24 @@ -# ruff: noqa: E501 -from __future__ import annotations - import os from copy import deepcopy -from typing import TYPE_CHECKING, Any, Literal +from typing import Any, Literal import numpy as np import pandas as pd import torch +import wandb +from torch import nn from torch.utils.data import DataLoader from tqdm import tqdm from aviary import ROOT from aviary.core import BaseModelClass, Normalizer, TaskType, np_softmax +from aviary.data import InMemoryDataLoader from aviary.losses import robust_l1_loss +from aviary.predict import make_ensemble_predictions from aviary.utils import get_metrics, print_walltime from aviary.wrenformer.data import df_to_in_mem_dataloader from aviary.wrenformer.model import Wrenformer -try: - import wandb -except ImportError: - wandb = None # type: ignore[assignment] - -if TYPE_CHECKING: - from torch import nn - - from aviary.data import InMemoryDataLoader - -__author__ = "Janosh Riebesell" -__date__ = "2022-10-29" - - torch.manual_seed(0) # ensure reproducible results reg_key, clf_key = "regression", "classification" @@ -55,6 +42,7 @@ def train_model( task_type: TaskType, train_loader: DataLoader | InMemoryDataLoader, test_loader: DataLoader | InMemoryDataLoader, + *, # force keyword-only arguments checkpoint: Literal["local", "wandb"] | None = None, checkpoint_frequency: int = 10, learning_rate: float = 1e-4, @@ -74,18 +62,20 @@ def train_model( Wrapped by other functions like train_wrenformer() for specific datasets. Args: - run_name (str): A string to describe the training run. Should usually contain model type - (Roost/Wren) and important params. Include 'robust' to use a robust loss function and - have the model learn to predict an aleatoric uncertainty. + run_name (str): A string to describe the training run. Should usually contain + model type (Roost/Wren) and important params. Include 'robust' to use a + robust loss function and have the model learn to predict an aleatoric + uncertainty. model (BaseModelClass): A model instance subclassing aviary.core.BaseModelClass. epochs (int): How many epochs to train for. Defaults to 100. target_col (str): Name of df column containing the target values. - task_type ('regression' | 'classification'): What type of task to train the model for. - test_loader (DataLoader | InMemoryDataLoader): Test data. - train_loader (DataLoader | InMemoryDataLoader): Train data. - checkpoint (None | 'local' | 'wandb'): Whether to save the model+optimizer+scheduler state - dicts to disk (local) or upload to WandB. Defaults to None. - To later copy a wandb checkpoint file to cwd and use it: + task_type ('regression' | 'classification'): What type of task to train the + model for. + train_loader (DataLoader | InMemoryDataLoader): Training data loader. + test_loader (DataLoader | InMemoryDataLoader): Test data loader. + checkpoint (None | 'local' | 'wandb'): Whether to save the model, optimizer, + and scheduler state dicts to disk (local) or upload to WandB. + Defaults to None. To later copy a wandb checkpoint file to cwd and use it: ```py run_path = "//" # e.g. aviary/matbench/31qh7b5q checkpoint = wandb.restore("checkpoint.pth", run_path) @@ -97,39 +87,42 @@ def train_model( embedding_aggregation=("mean", "std")) for Wrenformer. run_params (dict[str, Any]): Additional parameters to merge into the run's dict of model_params. Will be logged to wandb. Can be anything really. Defaults to {}. - optimizer (str | tuple[str, dict]): Name of a torch.optim.Optimizer class like 'Adam', - 'AdamW', 'SGD', etc. Can be a string or a string and dict with params to pass to the - class. Defaults to 'AdamW'. + optimizer (str | tuple[str, dict]): Name of a torch.optim.Optimizer class like + 'Adam', 'AdamW', 'SGD', etc. Can be a string or a string and dict with params + to pass to the class. Defaults to 'AdamW'. scheduler (str | tuple[str, dict]): Name of a torch.optim.lr_scheduler class like - 'LambdaLR', 'StepLR', 'CosineAnnealingLR', etc. Defaults to 'LambdaLR'. Can be a string - to create a scheduler with all its default values or tuple[str, dict] with custom params - to pass to the class. E.g. ('CosineAnnealingLR', {'T_max': n_epochs}). - See https://stackoverflow.com/a/2121918 about pickle errors when trying to load a - LambdaLR scheduler from a torch.save() checkpoint created prior to this file having - been renamed. - swa_start (float | None): When to start using stochastic weight averaging during training. - Should be a float between 0 and 1. 0.7 means start SWA after 70% of epochs. Set to - None to disable SWA. Defaults to None. Proposed in https://arxiv.org/abs/1803.05407. - swa_lr (float): Learning rate for SWA scheduler. Defaults to learning_rate. - by the transformer encoder before passing into the ResidualNetwork. One or more of - ['mean', 'std', 'sum', 'min', 'max']. Defaults to ['mean']. - test_df (pd.DataFrame): Test data as a DataFrame. Model preds will be inserted as new - column and df returned. - timestamp (str): Will prefix the names of model checkpoint files and other output files. - Will also be included in run_params. Defaults to None. - verbose (bool): Whether to print progress and metrics to stdout. Defaults to False. - wandb_path (str | None): Path to Weights and Biases project where to log this run formatted - as '/'. Defaults to None which means logging is disabled. - wandb_kwargs (dict[str, Any]): Kwargs to pass to wandb.init() like - dict(tags=['ensemble-id-1']). Should not include keys config, project, entity as - they're already set by this function. + 'LambdaLR', 'StepLR', 'CosineAnnealingLR', etc. Can be a string to create a + scheduler with default values or tuple[str, dict] with custom params. + E.g. ('CosineAnnealingLR', {'T_max': n_epochs}). Defaults to 'LambdaLR'. + See https://stackoverflow.com/a/2121918 about pickle errors when trying to + load a LambdaLR scheduler from a torch.save() checkpoint created prior to this + file having been renamed. + swa_start (float | None): When to start using stochastic weight averaging during + training. Should be a float between 0 and 1. 0.7 means start SWA after 70% + of epochs. Set to None to disable SWA. Defaults to None. Proposed in + https://arxiv.org/abs/1803.05407. + swa_lr (float | None): Learning rate for SWA scheduler. Defaults to learning_rate. + test_df (pd.DataFrame): Test data as a DataFrame. Model preds will be inserted + as new columns and df returned. + timestamp (str | None): Will prefix the names of model checkpoint files and other + output files. Will also be included in run_params. Defaults to None. + verbose (bool): Whether to print progress and metrics to stdout. Defaults to + False. + wandb_path (str | None): Path to Weights and Biases project where to log this run + formatted as '/'. Defaults to None which means logging is + disabled. + wandb_kwargs (dict[str, Any] | None): Kwargs to pass to wandb.init() like + dict(tags=['ensemble-id-1']). Should not include keys config, project, entity + as they're already set by this function. Defaults to None. Raises: ValueError: On unknown dataset_name or invalid checkpoint. Returns: - tuple[dict[str, float], dict[str, Any]]: 1st dict are the model's test set metrics. - 2nd dict are the run's hyperparameters. 3rd is a dataframe with test set predictions. + tuple[dict[str, float], dict[str, Any], pd.DataFrame]: A tuple containing: + - Test set metrics dictionary + - Run hyperparameters dictionary + - Test dataframe with predictions """ if checkpoint not in (None, "local", "wandb"): raise ValueError(f"Unknown {checkpoint=}") @@ -161,10 +154,11 @@ def train_model( model.to(device) if isinstance(optimizer, str): optimizer_name, optimizer_params = optimizer, None - elif isinstance(optimizer, (tuple, list)): + elif isinstance(optimizer, tuple | list): optimizer_name, optimizer_params = optimizer else: - raise ValueError(f"Unknown {optimizer=}") + raise TypeError(f"Unknown {optimizer=}") + optimizer_cls = getattr(torch.optim, optimizer_name) optimizer_instance = optimizer_cls( params=model.parameters(), lr=learning_rate, **(optimizer_params or {}) @@ -174,10 +168,11 @@ def train_model( scheduler_name, scheduler_params = "LambdaLR", {"lr_lambda": lr_lambda} elif isinstance(scheduler, str): scheduler_name, scheduler_params = scheduler, None - elif isinstance(scheduler, (tuple, list)): + elif isinstance(scheduler, tuple | list): scheduler_name, scheduler_params = scheduler else: raise ValueError(f"Unknown {scheduler=}") + scheduler_cls = getattr(torch.optim.lr_scheduler, scheduler_name) lr_scheduler = scheduler_cls(optimizer_instance, **(scheduler_params or {})) @@ -252,8 +247,8 @@ def train_model( if swa_start and epoch >= int(swa_start * epochs): if epoch == int(swa_start * epochs): print("Starting stochastic weight averaging...") - swa_model.update_parameters(model) - swa_scheduler.step() + swa_model.update_parameters(model) # type: ignore[reportPossiblyUnboundVariable] + swa_scheduler.step() # type: ignore[reportPossiblyUnboundVariable] elif scheduler_name == "ReduceLROnPlateau": val_metric = val_metrics[target_col][ "MAE" if task_type == reg_key else "Accuracy" @@ -268,7 +263,7 @@ def train_model( wandb.log({"training": train_metrics, "validation": val_metrics}) if epoch % checkpoint_frequency == 0 and epoch < epochs: - inference_model = swa_model if swa_start else model + inference_model = swa_model if swa_start else model # type: ignore[reportPossiblyUnboundVariable] inference_model.eval() checkpoint_model( checkpoint_endpoint=checkpoint, @@ -294,7 +289,7 @@ def train_model( f"({swa_start=})" ) - inference_model = swa_model if swa_start else model + inference_model = swa_model if swa_start else model # type: ignore[reportPossiblyUnboundVariable] inference_model.eval() with torch.no_grad(): @@ -315,7 +310,8 @@ def train_model( ).squeeze() if test_df is None: - assert isinstance(test_loader, DataLoader) + if not isinstance(test_loader, DataLoader): + raise TypeError(f"Unknown {test_loader=}") test_df = test_loader.dataset.df if robust: @@ -399,7 +395,7 @@ def checkpoint_model( normalizer_dict: dict, run_params: dict, scheduler_name: str, -): +) -> None: """Save model checkpoint to different endpoints.""" if checkpoint_endpoint is None: return @@ -433,12 +429,13 @@ def checkpoint_model( torch.save(checkpoint_dict, checkpoint_path) if checkpoint_endpoint == "wandb": - assert wandb.run is not None, ( - "can't save model checkpoint to Weights and Biases, wandb.run is None" - ) + if wandb.run is None: + raise ValueError( + "can't save model checkpoint to Weights and Biases, wandb.run is None" + ) torch.save( checkpoint_dict, - f"{wandb.run.dir}/{timestamp + '-' if timestamp else ''}{run_name}-{epochs}.pth", + f"{wandb.run.dir}/{timestamp + '-' if timestamp else ''}{run_name}-{epochs}.pth", # noqa: E501 ) @@ -455,7 +452,7 @@ def train_wrenformer( input_col: str | None = None, model_params: dict[str, Any] | None = None, data_loader_device: str = "cpu", - **kwargs, + **kwargs: Any, ) -> tuple[dict[str, float], dict[str, Any], pd.DataFrame]: """Train a Wrenformer model on a dataframe. This function handles the DataLoader creation, then delegates to train_model(). @@ -473,13 +470,14 @@ def train_wrenformer( batch_size (int, optional): Batch size for training. Defaults to 128. inference_multiplier (int, optional): Multiplier for the test set data loader batch size. Defaults to 1. - embedding_type ('wyckoff' | 'composition', optional): Type of embedding to use. - Defaults to None meaning auto-detect based on 'wren'/'roost' in run_name. + embedding_type ('protostructure' | 'composition', optional): Type of + embedding to use. Defaults to None meaning auto-detect based on 'wren'/'roost' + in run_name. id_col (str, optional): Column name in train_df and test_df containing unique IDs for each sample. Defaults to "material_id". input_col (str, optional): Column name in train_df and test_df containing input values. Defaults to None meaning auto-detect based on 'wren'/'roost' in - run_name which default to 'wyckoff' and 'composition' respectively. + run_name which default to 'protostructure' and 'composition' respectively. model_params (dict): Passed to Wrenformer class. E.g. dict(n_attn_layers=6, embedding_aggregation=("mean", "std")). data_loader_device(str): device to store the InMemoryDataLoader's tensors on. @@ -493,8 +491,8 @@ def train_wrenformer( robust = "robust" in run_name.lower() if "wren" in run_name.lower(): - input_col = input_col or "wyckoff" - embedding_type = embedding_type or "wyckoff" + input_col = input_col or "protostructure" + embedding_type = embedding_type or "protostructure" elif "roost" in run_name.lower(): input_col = input_col or "composition" embedding_type = embedding_type or "composition" @@ -528,7 +526,8 @@ def train_wrenformer( # element) in the material embedding_len = train_loader.tensors[0][0].shape[-1] # Roost and Wren embedding size resp. - assert embedding_len in (200 + 1, 200 + 1 + 444), f"{embedding_len=}" + if embedding_len not in (200 + 1, 200 + 1 + 444): + raise ValueError(f"{embedding_len=}, expected 201 or 645") model_params = dict( # 1 for regression, n_classes for classification @@ -576,29 +575,90 @@ def df_train_test_split( Or if not 0 < test_size < 1 or not 1 < n_folds <= 10. Returns: - tuple[pd.DataFrame, pd.DataFrame]: _description_ + tuple[pd.DataFrame, pd.DataFrame]: Train and test sets. """ # shuffle samples for random train/test split - df = df.sample(frac=1, random_state=0) + df_all = df.sample(frac=1, random_state=0) if folds: n_folds, test_fold_idx = folds - assert 1 < n_folds <= 10, f"{n_folds = } must be between 2 and 10" - assert 0 <= test_fold_idx < n_folds, ( - f"{test_fold_idx = } must be between 0 and {n_folds - 1}" - ) + if not 1 < n_folds <= 10: + raise ValueError(f"{n_folds = } must be between 2 and 10") + if not 0 <= test_fold_idx < n_folds: + raise ValueError(f"{test_fold_idx = } must be between 0 and {n_folds - 1}") - df_splits: list[pd.DataFrame] = np.array_split(df, n_folds) + df_splits: list[pd.DataFrame] = np.array_split(df_all, n_folds) test_df = df_splits.pop(test_fold_idx) train_df = pd.concat(df_splits) elif test_size: - assert 0 < test_size < 1, f"{test_size = } must be between 0 and 1" + if not 0 < test_size < 1: + raise ValueError(f"{test_size = } must be between 0 and 1") - train_df = df.sample(frac=1 - test_size, random_state=0) - test_df = df.drop(train_df.index) + train_df = df_all.sample(frac=1 - test_size, random_state=0) + test_df = df_all.drop(train_df.index) else: raise ValueError(f"Specify either {folds=} or {test_size=}") if folds and test_size: raise ValueError(f"Specify either {folds=} or {test_size=}, not both") return train_df, test_df + + +@print_walltime(end_desc="predict_from_wandb_checkpoints") +def predict_from_wandb_checkpoints( + runs: list[wandb.apis.public.Run], + checkpoint_filename: str = "checkpoint.pth", + cache_dir: str = "./checkpoint_cache", + **kwargs: Any, +) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: + """Download and cache checkpoints for an ensemble of models, then make + predictions on some dataset. Finally print ensemble metrics and store + predictions to CSV. + + Args: + runs (list[wandb.apis.public.Run]): List of WandB runs to download model + checkpoints from which are then loaded into memory to generate + predictions for the input_col in df. + checkpoint_filename (str): Name of the checkpoint file to download. + cache_dir (str): Directory to cache downloaded checkpoints in. + **kwargs: Additional keyword arguments to pass to make_ensemble_predictions(). + + Returns: + pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: Original input dataframe + with added columns for model predictions and uncertainties. The optional + 2nd dataframe holds ensemble performance metrics like mean and standard + deviation of MAE/RMSE. + """ + print(f"Using checkpoints from {len(runs)} run(s):") + + run_target = runs[0].config["target"] + if not all(run_target == run.config["target"] for run in runs): + raise ValueError(f"Runs have differing targets, first {run_target=}") + + target_col = kwargs.get("target_col") + if target_col and target_col != run_target: + print(f"\nWarning: {target_col=} does not match {run_target=}") + + checkpoint_paths: list[str] = [] + + for idx, run in enumerate(runs, start=1): + run_path = "/".join(run.path) + out_dir = f"{cache_dir}/{run_path}" + os.makedirs(out_dir, exist_ok=True) + + checkpoint_path = f"{out_dir}/{checkpoint_filename}" + checkpoint_paths.append(checkpoint_path) + print(f"{idx:>3}/{len(runs)}: {run.url}\n\t{checkpoint_path}\n") + + with open(f"{out_dir}/run.md", "w") as md_file: + md_file.write(f"[{run.name}]({run.url})\n") + + if not os.path.isfile(checkpoint_path): + run.file(f"{checkpoint_filename}").download(root=out_dir) + + if target_col is not None: + df_ens, ensemble_metrics = make_ensemble_predictions(checkpoint_paths, **kwargs) + # round to save disk space and speed up cloud storage uploads + return df_ens.round(6), ensemble_metrics + + return make_ensemble_predictions(checkpoint_paths, **kwargs) diff --git a/examples/matbench_example/utils.py b/examples/matbench_example/utils.py new file mode 100644 index 00000000..a0b698f6 --- /dev/null +++ b/examples/matbench_example/utils.py @@ -0,0 +1,195 @@ +import json +import os +import shutil +import subprocess +import sys +import tempfile +from collections.abc import Sequence, Sized +from typing import Final, Literal, TypeVar + +# taken from https://slurm.schedmd.com/job_array.html#env_vars, lower-cased and +# and removed the SLURM_ prefix +SLURM_KEYS: Final[tuple[str, ...]] = ( + "job_id", + "array_job_id", + "array_task_id", + "array_task_count", + "mem_per_node", + "nodelistsubmit_host", + "job_partition", + "job_user", + "job_account", + "tasks_per_node", + "job_qos", +) +SLURM_SUBMIT_KEY: Final[str] = "slurm-submit" +HasLen = TypeVar("HasLen", bound=Sized) + + +def _int_keys(dct: dict) -> dict: + # JSON stringifies all dict keys during serialization and does not revert + # back to floats and ints during parsing. This json.load() hook converts keys + # containing only digits to ints. + return {int(k) if k.lstrip("-").isdigit() else k: v for k, v in dct.items()} + + +def recursive_dict_merge(dict1: dict, dict2: dict) -> dict: + """Merge two dicts recursively.""" + for key, val2 in dict2.items(): + if key in dict1 and isinstance(dict1[key], dict) and isinstance(val2, dict): + recursive_dict_merge(dict1[key], val2) + else: + dict1[key] = val2 + return dict1 + + +def merge_json_on_disk( + dct: dict, + file_path: str, + on_non_serializable: Literal["annotate", "error"] = "annotate", +) -> None: + """Merge a dict into a (possibly) existing JSON file. + + Args: + file_path (str): Path to JSON file. File will be created if not exist. + dct (dict): Dictionary to merge into JSON file. + on_non_serializable ('annotate' | 'error'): What to do with non-serializable + values encountered in dct. 'annotate' will replace the offending object with + a string indicating the type, e.g. ''. 'error' + will raise 'TypeError: Object of type function is not JSON serializable'. + Defaults to 'annotate'. + """ + try: + with open(file_path) as json_file: + data = json.load(json_file, object_hook=_int_keys) + + dct = recursive_dict_merge(data, dct) + except (FileNotFoundError, json.decoder.JSONDecodeError): # file missing or empty + pass + + def non_serializable_handler(obj: object) -> str: + # replace functions and classes in dct with string indicating it's a + # non-serializable type + return f"" + + with open(file_path, "w") as file: + default = non_serializable_handler if on_non_serializable == "annotate" else None + json.dump(dct, file, default=default, indent=2) + + +def _get_calling_file_path(frame: int = 1) -> str: + """Return calling file's path. + + Args: + frame (int, optional): How many function call's up? Defaults to 1. + + Returns: + str: Calling function's file path n frames up the stack. + """ + caller_path = sys._getframe(frame).f_code.co_filename + return os.path.abspath(caller_path) + + +def slurm_submit( + job_name: str, + out_dir: str, + *, + time: str | None = None, + account: str | None = None, + partition: str | None = None, + py_file_path: str | None = None, + slurm_flags: str | Sequence[str] = (), + array: str | None = None, + pre_cmd: str = "", + submit_as_temp_file: bool = True, +) -> dict[str, str]: + """Slurm submits a python script using `sbatch --wrap 'python path/to/file.py'`. + + Usage: Call this function at the top of the script (before doing any real work) and + then submit a job with `python path/to/that/script.py slurm-submit`. The slurm job + will run the whole script. + + Args: + job_name (str): Slurm job name. + out_dir (str): Directory to write slurm logs. Log file will include slurm job + ID and array task ID. + time (str): 'HH:MM:SS' time limit for the job. + Defaults to the path of the file calling slurm_submit(). + account (str): Account to charge for this job. + partition (str, optional): Slurm partition. + py_file_path (str, optional): Path to the python script to be submitted. + slurm_flags (str | list[str], optional): Extra slurm CLI flags. Defaults to (). + Examples: ('--nodes 1', '--gpus-per-node 1') or ('--mem', '16G'). + array (str, optional): Slurm array specifier. Defaults to None. Example: + '9' (for SLURM_ARRAY_TASK_ID from 0-9 inclusive), '1-10' or '1-10%2', etc. + pre_cmd (str, optional): Things like `module load` commands and environment + variables to set before running the python script go here. Example: + pre_cmd='ENV_VAR=42' or 'module load pytorch;'. Defaults to "". If running + on CPU, pre_cmd="unset OMP_NUM_THREADS" allows PyTorch to use all cores. + submit_as_temp_file (bool, optional): If True, copy the Python file to a + temporary directory before submitting. This allows the user to modify + the original file without affecting queued jobs. Defaults to True. + + Raises: + SystemExit: Exit code will be subprocess.run(['sbatch', ...]).returncode. + + Returns: + dict[str, str]: Slurm variables like job ID, array task ID, compute nodes IDs, + submission node ID and total job memory. + """ + py_file_path = py_file_path or _get_calling_file_path(frame=2) + + os.makedirs(out_dir, exist_ok=True) # slurm fails if out_dir is missing + + # Copy the file to a temporary directory if submit_as_temp_file is True + if submit_as_temp_file and SLURM_SUBMIT_KEY in sys.argv: + temp_dir = tempfile.mkdtemp(prefix="slurm_job_") + temp_file_path = f"{temp_dir}/{os.path.basename(py_file_path)}" + shutil.copy2(py_file_path, temp_file_path) + py_file_path = temp_file_path + + # ensure pre_cmd ends with a semicolon + if pre_cmd and not pre_cmd.strip().endswith(";"): + pre_cmd += ";" + + cmd = [ + *("sbatch", "--job-name", job_name), + *("--output", f"{out_dir}/slurm-%A{'-%a' if array else ''}.log"), + *(slurm_flags.split() if isinstance(slurm_flags, str) else slurm_flags), + *("--wrap", f"{pre_cmd or ''} python {py_file_path}".strip()), + ] + for flag in (f"{time=!s}", f"{account=!s}", f"{partition=!s}", f"{array=!s}"): + key, val = flag.split("=") + if val != "None": + cmd += (f"--{key}", val) + + is_log_file = not sys.stdout.isatty() + is_slurm_job = "SLURM_JOB_ID" in os.environ + + slurm_vars = { + f"slurm_{key}": os.environ[f"SLURM_{key}".upper()] + for key in SLURM_KEYS + if f"SLURM_{key}".upper() in os.environ + } + if time is not None: + slurm_vars["slurm_timelimit"] = time + if slurm_flags != (): + slurm_vars["slurm_flags"] = str(slurm_flags) + if pre_cmd not in ("", None): + slurm_vars["pre_cmd"] = pre_cmd + + # print sbatch command into slurm log file and at job submission time + # but not into terminal or Jupyter + if (is_slurm_job and is_log_file) or SLURM_SUBMIT_KEY in sys.argv: + print(f"\n{' '.join(cmd)}\n".replace(" --", "\n --")) + if is_slurm_job and is_log_file: + for key, val in slurm_vars.items(): + print(f"{key}={val}") + + if SLURM_SUBMIT_KEY not in sys.argv: + return slurm_vars # if not submitting slurm job, resume outside code as normal + + result = subprocess.run(cmd, check=True) + + # after sbatch submission, exit with slurm exit code + raise SystemExit(result.returncode) diff --git a/examples/notebooks/Roost.ipynb b/examples/notebooks/Roost.ipynb index 74379cde..ec802b15 100644 --- a/examples/notebooks/Roost.ipynb +++ b/examples/notebooks/Roost.ipynb @@ -8,12 +8,21 @@ "outputs": [], "source": [ "%%capture\n", - "from torch import __version__ as TORCH_VERSION\n", + "try:\n", + " import google.colab # noqa: F401\n", "\n", - "print(f\"{TORCH_VERSION=}\")\n", + " IN_COLAB = True\n", + "except ImportError:\n", + " IN_COLAB = False\n", "\n", - "!pip install -U git+https://github.com/CompRhys/aviary.git # install aviary\n", - "!wget -O taata.json.gz https://figshare.com/ndownloader/files/34423997" + "if IN_COLAB:\n", + " %%capture\n", + " from torch import __version__ as TORCH_VERSION\n", + "\n", + " print(f\"{TORCH_VERSION=}\")\n", + "\n", + " !pip install -U git+https://github.com/CompRhys/aviary.git # install aviary\n", + " !wget -O taata.json.gz https://figshare.com/ndownloader/files/34423997" ] }, { @@ -34,6 +43,7 @@ ")\n", "from pymatgen.core import Structure\n", "from sklearn.model_selection import train_test_split as split\n", + "from torch.utils.data import DataLoader\n", "\n", "from aviary.roost.data import CompositionData\n", "from aviary.roost.data import collate_batch as roost_cb\n", @@ -60,9 +70,9 @@ "\n", "df[\"composition\"] = [x.composition.reduced_formula for x in df.final_structure]\n", "df[\"volume_per_atom\"] = [x.volume / len(x) for x in df.final_structure]\n", - "df[\"wyckoff\"] = df[\"final_structure\"].map(get_protostructure_label_from_spglib)\n", + "df[\"protostructure\"] = df[\"final_structure\"].map(get_protostructure_label_from_spglib)\n", "\n", - "df = df[df.wyckoff.map(count_wyckoff_positions) < 16]\n", + "df = df[df.protostructure.map(count_wyckoff_positions) < 16]\n", "df[\"n_sites\"] = df.final_structure.map(len)\n", "df = df[df.n_sites < 64]\n", "df = df[df.volume_per_atom < 500]\n", @@ -130,8 +140,8 @@ " \"transfer\": transfer,\n", "}\n", "\n", - "task_dict = dict(zip(targets, tasks))\n", - "loss_dict = dict(zip(targets, losses))" + "task_dict = dict(zip(targets, tasks, strict=False))\n", + "loss_dict = dict(zip(targets, losses, strict=False))" ] }, { @@ -172,6 +182,12 @@ "\n", "train_set = torch.utils.data.Subset(dataset, train_idx[0::sample])\n", "\n", + "train_loader = DataLoader(train_set, **data_params)\n", + "val_loader = DataLoader(\n", + " val_set,\n", + " **{**data_params, \"batch_size\": 16 * data_params[\"batch_size\"], \"shuffle\": False},\n", + ")\n", + "\n", "model_params = {\n", " \"task_dict\": task_dict,\n", " \"robust\": robust,\n", @@ -205,7 +221,10 @@ " loss_dict=loss_dict,\n", ")\n", "\n", - "data_params[\"shuffle\"] = False # need fixed data order due to ensembling\n", + "test_loader = DataLoader(\n", + " test_set,\n", + " **{**data_params, \"batch_size\": 64 * data_params[\"batch_size\"], \"shuffle\": False},\n", + ")\n", "\n", "roost_results_dict = results_multitask(\n", " model_class=Roost,\n", diff --git a/examples/notebooks/Wren.ipynb b/examples/notebooks/Wren.ipynb index 413d4981..3a41df2c 100644 --- a/examples/notebooks/Wren.ipynb +++ b/examples/notebooks/Wren.ipynb @@ -8,12 +8,21 @@ "outputs": [], "source": [ "%%capture\n", - "from torch import __version__ as TORCH_VERSION\n", + "try:\n", + " import google.colab # noqa: F401\n", "\n", - "print(f\"{TORCH_VERSION=}\")\n", + " IN_COLAB = True\n", + "except ImportError:\n", + " IN_COLAB = False\n", "\n", - "!pip install -U git+https://github.com/CompRhys/aviary.git # install aviary\n", - "!wget -O taata.json.gz https://figshare.com/ndownloader/files/34423997" + "if IN_COLAB:\n", + " %%capture\n", + " from torch import __version__ as TORCH_VERSION\n", + "\n", + " print(f\"{TORCH_VERSION=}\")\n", + "\n", + " !pip install -U git+https://github.com/CompRhys/aviary.git # install aviary\n", + " !wget -O taata.json.gz https://figshare.com/ndownloader/files/34423997" ] }, { @@ -34,6 +43,7 @@ ")\n", "from pymatgen.core import Structure\n", "from sklearn.model_selection import train_test_split as split\n", + "from torch.utils.data import DataLoader\n", "\n", "from aviary.utils import results_multitask, train_ensemble\n", "from aviary.wren.data import WyckoffData\n", @@ -60,9 +70,9 @@ "\n", "df[\"composition\"] = [x.composition.reduced_formula for x in df.final_structure]\n", "df[\"volume_per_atom\"] = [x.volume / len(x) for x in df.final_structure]\n", - "df[\"wyckoff\"] = df[\"final_structure\"].map(get_protostructure_label_from_spglib)\n", + "df[\"protostructure\"] = df[\"final_structure\"].map(get_protostructure_label_from_spglib)\n", "\n", - "df = df[df.wyckoff.map(count_wyckoff_positions) < 16]\n", + "df = df[df.protostructure.map(count_wyckoff_positions) < 16]\n", "df[\"n_sites\"] = df.final_structure.map(len)\n", "df = df[df.n_sites < 64]\n", "df = df[df.volume_per_atom < 500]" @@ -125,8 +135,8 @@ " \"transfer\": transfer,\n", "}\n", "\n", - "task_dict = dict(zip(targets, tasks))\n", - "loss_dict = dict(zip(targets, losses))" + "task_dict = dict(zip(targets, tasks, strict=False))\n", + "loss_dict = dict(zip(targets, losses, strict=False))" ] }, { @@ -167,6 +177,12 @@ "\n", "train_set = torch.utils.data.Subset(dataset, train_idx[0::sample])\n", "\n", + "train_loader = DataLoader(train_set, **data_params)\n", + "val_loader = DataLoader(\n", + " val_set,\n", + " **{**data_params, \"batch_size\": 16 * data_params[\"batch_size\"], \"shuffle\": False},\n", + ")\n", + "\n", "model_params = {\n", " \"task_dict\": task_dict,\n", " \"robust\": robust,\n", @@ -192,25 +208,26 @@ " run_id=run_id,\n", " ensemble_folds=ensemble,\n", " epochs=epochs,\n", - " train_set=train_set,\n", - " val_set=val_set,\n", + " train_loader=train_loader,\n", + " val_loader=val_loader,\n", " log=log,\n", - " data_params=data_params,\n", " setup_params=setup_params,\n", " restart_params=restart_params,\n", " model_params=model_params,\n", " loss_dict=loss_dict,\n", ")\n", "\n", - "data_params[\"shuffle\"] = False # need fixed data order due to ensembling\n", + "test_loader = DataLoader(\n", + " test_set,\n", + " **{**data_params, \"batch_size\": 64 * data_params[\"batch_size\"], \"shuffle\": False},\n", + ")\n", "\n", "roost_results_dict = results_multitask(\n", " model_class=Wren,\n", " model_name=model_name,\n", " run_id=run_id,\n", " ensemble_folds=ensemble,\n", - " test_set=test_set,\n", - " data_params=data_params,\n", + " test_loader=test_loader,\n", " robust=robust,\n", " task_dict=task_dict,\n", " device=device,\n", diff --git a/examples/notebooks/Wrenformer.ipynb b/examples/notebooks/Wrenformer.ipynb new file mode 100644 index 00000000..86780193 --- /dev/null +++ b/examples/notebooks/Wrenformer.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "try:\n", + " import google.colab # noqa: F401\n", + "\n", + " IN_COLAB = True\n", + "except ImportError:\n", + " IN_COLAB = False\n", + "\n", + "if IN_COLAB:\n", + " %%capture\n", + " from torch import __version__ as TORCH_VERSION\n", + "\n", + " print(f\"{TORCH_VERSION=}\")\n", + "\n", + " !pip install -U git+https://github.com/CompRhys/aviary.git # install aviary\n", + " !wget -O taata.json.gz https://figshare.com/ndownloader/files/34423997" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import gzip\n", + "import json\n", + "\n", + "import pandas as pd\n", + "import torch\n", + "from pymatgen.analysis.prototypes import (\n", + " count_wyckoff_positions,\n", + " get_protostructure_label_from_spglib,\n", + ")\n", + "from pymatgen.core import Structure\n", + "from sklearn.model_selection import train_test_split as split\n", + "\n", + "from aviary.utils import results_multitask, train_ensemble\n", + "from aviary.wrenformer.data import collate_batch as wrenformer_cb\n", + "from aviary.wrenformer.data import df_to_in_mem_dataloader\n", + "from aviary.wrenformer.model import Wrenformer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n", + "spglib: ssm_get_exact_positions failed.\n", + "spglib: get_bravais_exact_positions_and_lattice failed.\n" + ] + } + ], + "source": [ + "with gzip.open(\"taata.json.gz\", \"r\") as fin:\n", + " json_bytes = fin.read()\n", + "\n", + "json_str = json_bytes.decode(\"utf-8\")\n", + "data = json.loads(json_str)\n", + "\n", + "df = pd.DataFrame(data[\"data\"], columns=data[\"columns\"])\n", + "\n", + "df[\"final_structure\"] = [Structure.from_dict(x) for x in df.final_structure]\n", + "\n", + "df[\"composition\"] = [x.composition.reduced_formula for x in df.final_structure]\n", + "df[\"volume_per_atom\"] = [x.volume / len(x) for x in df.final_structure]\n", + "df[\"protostructure\"] = df[\"final_structure\"].map(get_protostructure_label_from_spglib)\n", + "\n", + "df = df[df.protostructure.map(count_wyckoff_positions) < 16]\n", + "df[\"n_sites\"] = df.final_structure.map(len)\n", + "df = df[df.n_sites < 64]\n", + "df = df[df.volume_per_atom < 500]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "resume = False\n", + "fine_tune = None\n", + "transfer = None\n", + "\n", + "optim = \"AdamW\"\n", + "learning_rate = 3e-4\n", + "momentum = 0.9\n", + "weight_decay = 1e-6\n", + "batch_size = 128\n", + "workers = 0\n", + "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", + "\n", + "targets = [\"E_vasp_per_atom\"]\n", + "tasks = [\"regression\"]\n", + "losses = [\"L1\"]\n", + "robust = True\n", + "\n", + "data_seed = 42\n", + "test_size = 0.2\n", + "sample = 1\n", + "\n", + "ensemble = 1\n", + "run_id = 1\n", + "epochs = 3\n", + "log = False\n", + "\n", + "# NOTE setting workers to zero means that the data is loaded in the main\n", + "# process and enables caching\n", + "\n", + "data_params = {\n", + " \"batch_size\": batch_size,\n", + " \"num_workers\": workers,\n", + " \"pin_memory\": False,\n", + " \"shuffle\": True,\n", + "}\n", + "\n", + "setup_params = {\n", + " \"optim\": optim,\n", + " \"learning_rate\": learning_rate,\n", + " \"weight_decay\": weight_decay,\n", + " \"momentum\": momentum,\n", + " \"device\": device,\n", + "}\n", + "\n", + "restart_params = {\n", + " \"resume\": resume,\n", + " \"fine_tune\": fine_tune,\n", + " \"transfer\": transfer,\n", + "}\n", + "\n", + "task_dict = dict(zip(targets, tasks, strict=False))\n", + "loss_dict = dict(zip(targets, losses, strict=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "using 0.2 of training set as test set\n", + "No validation set used, using test set for evaluation purposes\n", + "Total Number of Trainable Parameters: 5,166,658\n", + "Dummy MAE: 0.9223\n", + "Epoch: [0/2]\n", + " train: E_vasp_per_atom N 76 MAE 0.89 Loss 1.09 RMSE 1.13 \n", + " evaluate: E_vasp_per_atom N 2 MAE 0.71 Loss 0.83 RMSE 0.95 \n", + "Epoch: [1/2]\n", + " train: E_vasp_per_atom N 76 MAE 0.57 Loss 0.60 RMSE 0.78 \n", + " evaluate: E_vasp_per_atom N 2 MAE 0.53 Loss 0.51 RMSE 0.71 \n", + "Epoch: [2/2]\n", + " train: E_vasp_per_atom N 76 MAE 0.45 Loss 0.36 RMSE 0.62 \n", + " evaluate: E_vasp_per_atom N 2 MAE 0.37 Loss 0.19 RMSE 0.52 \n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "------------Evaluate model on Test Set------------\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "\n", + "Evaluating Model\n", + "\n", + "Task: target_name='E_vasp_per_atom' on test set\n", + "Model Performance Metrics:\n", + "R2 Score: 0.7842 \n", + "MAE: 0.3913\n", + "RMSE: 0.5500\n" + ] + } + ], + "source": [ + "torch.manual_seed(0) # ensure reproducible results\n", + "\n", + "input_col = \"protostructure\"\n", + "embedding_type = \"protostructure\"\n", + "model_name = \"wrenformer-reg-test\"\n", + "\n", + "data_params[\"collate_fn\"] = wrenformer_cb\n", + "data_params[\"shuffle\"] = True\n", + "\n", + "print(f\"using {test_size} of training set as test set\")\n", + "train_df, test_df = split(df, random_state=data_seed, test_size=test_size)\n", + "\n", + "print(\"No validation set used, using test set for evaluation purposes\")\n", + "# NOTE that when using this option care must be taken not to\n", + "# peak at the test-set. The only valid model to use is the one\n", + "# obtained after the final epoch where the epoch count is\n", + "# decided in advance of the experiment.\n", + "val_df = test_df\n", + "\n", + "data_loader_kwargs = dict(\n", + " id_col=\"material_id\", # TODO this should take a list of columns\n", + " input_col=input_col,\n", + " target_col=targets[0], # TODO this should take a list of columns\n", + " embedding_type=embedding_type,\n", + " device=device,\n", + ")\n", + "\n", + "train_loader = df_to_in_mem_dataloader(\n", + " train_df,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " **data_loader_kwargs,\n", + ")\n", + "\n", + "val_loader = df_to_in_mem_dataloader(\n", + " test_df,\n", + " batch_size=batch_size * 16,\n", + " shuffle=False,\n", + " **data_loader_kwargs,\n", + ")\n", + "\n", + "n_targets = [\n", + " 1 if task_type == \"regression\" else train_df[target_col].max() + 1\n", + " for target_col, task_type in task_dict.items()\n", + "]\n", + "\n", + "model_params = {\n", + " \"task_dict\": task_dict,\n", + " \"robust\": robust,\n", + " \"n_targets\": n_targets,\n", + " \"n_features\": train_loader.tensors[0][0].shape[-1],\n", + " \"d_model\": 128,\n", + " \"n_attn_layers\": 6,\n", + " \"n_attn_heads\": 4,\n", + " \"trunk_hidden\": (1024, 512),\n", + " \"out_hidden\": (256, 128, 64),\n", + " \"embedding_aggregations\": (\"mean\",),\n", + "}\n", + "\n", + "train_ensemble(\n", + " model_class=Wrenformer,\n", + " model_name=model_name,\n", + " run_id=run_id,\n", + " ensemble_folds=ensemble,\n", + " epochs=epochs,\n", + " train_loader=train_loader,\n", + " val_loader=val_loader,\n", + " log=log,\n", + " setup_params=setup_params,\n", + " restart_params=restart_params,\n", + " model_params=model_params,\n", + " loss_dict=loss_dict,\n", + ")\n", + "\n", + "test_loader = df_to_in_mem_dataloader(\n", + " test_df,\n", + " batch_size=batch_size * 64,\n", + " shuffle=False,\n", + " **data_loader_kwargs,\n", + ")\n", + "\n", + "roost_results_dict = results_multitask(\n", + " model_class=Wrenformer,\n", + " model_name=model_name,\n", + " run_id=run_id,\n", + " ensemble_folds=ensemble,\n", + " test_loader=test_loader,\n", + " robust=robust,\n", + " task_dict=task_dict,\n", + " device=device,\n", + " eval_type=\"checkpoint\",\n", + " save_results=False,\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 ('py310')", + "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": "3.12.9" + }, + "vscode": { + "interpreter": { + "hash": "8022b3e932e045c760cb4633b91dd1cb8bc60d104ca9808334cbd1645adbe837" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/roost-example.py b/examples/roost-example.py index b69347b5..effd0716 100644 --- a/examples/roost-example.py +++ b/examples/roost-example.py @@ -3,6 +3,7 @@ import pandas as pd import torch from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader from aviary import ROOT from aviary.roost.data import CompositionData, collate_batch @@ -78,8 +79,8 @@ def main( "Cannot fine-tune and transfer checkpoint(s) at the same time." ) - task_dict = dict(zip(targets, tasks)) - loss_dict = dict(zip(targets, losses)) + task_dict = dict(zip(targets, tasks, strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) # NOTE make sure to use dense datasets, # NOTE do not use default_na as "NaN" is a valid material @@ -187,6 +188,20 @@ def main( # TODO dump all args/kwargs to a file for reproducibility. if train: + train_loader = DataLoader(train_set, **data_params) + + if val_set is not None: + val_loader = DataLoader( + val_set, + **{ + **data_params, + "batch_size": 16 * data_params["batch_size"], + "shuffle": False, + }, + ) + else: + val_loader = None + train_ensemble( model_class=Roost, model_name=model_name, @@ -194,10 +209,9 @@ def main( ensemble_folds=ensemble, epochs=epochs, patience=patience, - train_set=train_set, - val_set=val_set, + train_loader=train_loader, + val_loader=val_loader, log=log, - data_params=data_params, setup_params=setup_params, restart_params=restart_params, model_params=model_params, @@ -205,19 +219,21 @@ def main( ) if evaluate: - data_reset = { - "batch_size": 16 * batch_size, # faster model inference - "shuffle": False, # need fixed data order due to ensembling - } - data_params.update(data_reset) + test_loader = DataLoader( + test_set, + **{ + **data_params, + "batch_size": 64 * data_params["batch_size"], + "shuffle": False, + }, + ) results_multitask( model_class=Roost, model_name=model_name, run_id=run_id, ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, + test_loader=test_loader, robust=robust, task_dict=task_dict, device=device, diff --git a/examples/wren-example.py b/examples/wren-example.py index 666fe3b1..73031596 100644 --- a/examples/wren-example.py +++ b/examples/wren-example.py @@ -3,6 +3,7 @@ import pandas as pd import torch from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader from aviary import ROOT from aviary.utils import results_multitask, train_ensemble @@ -82,8 +83,8 @@ def main( # TODO CLI controls for loss dict. - task_dict = dict(zip(targets, tasks)) - loss_dict = dict(zip(targets, losses)) + task_dict = dict(zip(targets, tasks, strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) # NOTE make sure to use dense datasets, # NOTE do not use default_na as "NaN" is a valid material composition @@ -199,6 +200,20 @@ def main( } if train: + train_loader = DataLoader(train_set, **data_params) + + if val_set is not None: + val_loader = DataLoader( + val_set, + **{ + **data_params, + "batch_size": 16 * data_params["batch_size"], + "shuffle": False, + }, + ) + else: + val_loader = None + train_ensemble( model_class=Wren, model_name=model_name, @@ -206,10 +221,9 @@ def main( ensemble_folds=ensemble, epochs=epochs, patience=patience, - train_set=train_set, - val_set=val_set, + train_loader=train_loader, + val_loader=val_loader, log=log, - data_params=data_params, setup_params=setup_params, restart_params=restart_params, model_params=model_params, @@ -217,19 +231,21 @@ def main( ) if evaluate: - data_reset = { - "batch_size": 16 * batch_size, # faster model inference - "shuffle": False, # need fixed data order due to ensembling - } - data_params.update(data_reset) + test_loader = DataLoader( + test_set, + **{ + **data_params, + "batch_size": 64 * data_params["batch_size"], + "shuffle": False, + }, + ) results_multitask( model_class=Wren, model_name=model_name, run_id=run_id, ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, + test_loader=test_loader, robust=robust, task_dict=task_dict, device=device, diff --git a/examples/wrenformer-example.py b/examples/wrenformer-example.py new file mode 100644 index 00000000..dfa20d4c --- /dev/null +++ b/examples/wrenformer-example.py @@ -0,0 +1,419 @@ +import argparse + +import pandas as pd +import torch +from sklearn.model_selection import train_test_split as split + +from aviary.utils import results_multitask, train_ensemble +from aviary.wrenformer.data import df_to_in_mem_dataloader +from aviary.wrenformer.model import Wrenformer + + +def main( + data_path, + targets, + tasks, + losses, + robust, + model_name="wrenformer", + embedding_type="protostructure", + n_attn_layers=6, + n_attn_heads=4, + d_model=128, + ensemble=1, + run_id=1, + data_seed=42, + epochs=100, + log=False, + sample=1, + test_size=0.2, + test_path=None, + val_size=0, + val_path=None, + resume=False, + fine_tune=None, + transfer=None, + train=True, + evaluate=True, + optim="AdamW", + learning_rate=3e-4, + momentum=0.9, + weight_decay=1e-6, + batch_size=128, + workers=0, + device=None, + **kwargs, +): + """Train and evaluate a Wrenformer model.""" + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + print(f"The model will run on the {device} device") + + if not len(targets) == len(tasks) == len(losses): + raise AssertionError + + if not (evaluate or train): + raise AssertionError( + "No action given - At least one of 'train' or 'evaluate' cli flags required" + ) + + if test_size + val_size >= 1: + raise AssertionError( + f"'test_size'({test_size}) plus 'val_size'({val_size}) must be less than 1" + ) + + task_dict = dict(zip(targets, tasks, strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) + + # Load and preprocess data + df = pd.read_csv(data_path, keep_default_na=False, na_values=[]) + + # Split datasets + if evaluate: + if test_path: + print(f"using independent test set: {test_path}") + test_df = pd.read_csv(test_path, keep_default_na=False, na_values=[]) + else: + print(f"using {test_size} of training set as test set") + train_df, test_df = split(df, random_state=data_seed, test_size=test_size) + + if train: + if val_path: + print(f"using independent validation set: {val_path}") + val_df = pd.read_csv(val_path, keep_default_na=False, na_values=[]) + elif val_size == 0 and evaluate: + print("No validation set used, using test set for evaluation purposes") + val_df = test_df + elif val_size == 0: + val_df = None + else: + print(f"using {val_size} of training set as validation set") + test_size = val_size / (1 - test_size) + train_df, val_df = split( + train_df, random_state=data_seed, test_size=test_size + ) + + # Setup data loaders + data_loader_kwargs = dict( + id_col="material_id", + input_col="protostructure", + target_col=targets[0], + embedding_type=embedding_type, + device=device, + ) + + if train: + if sample > 1: + train_df = train_df.iloc[::sample].copy() + + train_loader = df_to_in_mem_dataloader( + train_df, + batch_size=batch_size, + shuffle=True, + **data_loader_kwargs, + ) + + val_loader = df_to_in_mem_dataloader( + val_df, + batch_size=batch_size * 16, + shuffle=False, + **data_loader_kwargs, + ) + + # Model parameters + n_targets = [ + 1 if task_type == "regression" else train_df[target_col].max() + 1 + for target_col, task_type in task_dict.items() + ] + + model_params = { + "task_dict": task_dict, + "robust": robust, + "n_targets": n_targets, + "n_features": train_loader.tensors[0][0].shape[-1], + "d_model": d_model, + "n_attn_layers": n_attn_layers, + "n_attn_heads": n_attn_heads, + "trunk_hidden": (1024, 512), + "out_hidden": (256, 128, 64), + "embedding_aggregations": ("mean",), + } + + setup_params = { + "optim": optim, + "learning_rate": learning_rate, + "weight_decay": weight_decay, + "momentum": momentum, + "device": device, + } + + restart_params = { + "resume": resume, + "fine_tune": fine_tune, + "transfer": transfer, + } + + train_ensemble( + model_class=Wrenformer, + model_name=model_name, + run_id=run_id, + ensemble_folds=ensemble, + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=log, + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + if evaluate: + test_loader = df_to_in_mem_dataloader( + test_df, + batch_size=batch_size * 64, + shuffle=False, + **data_loader_kwargs, + ) + + results_multitask( + model_class=Wrenformer, + model_name=model_name, + run_id=run_id, + ensemble_folds=ensemble, + test_loader=test_loader, + robust=robust, + task_dict=task_dict, + device=device, + eval_type="checkpoint", + save_results=False, + ) + + +def input_parser(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description=("Wrenformer")) + + # data inputs + parser.add_argument( + "--data-path", + metavar="PATH", + help="Path to main data set/training set", + ) + valid_group = parser.add_mutually_exclusive_group() + valid_group.add_argument( + "--val-path", metavar="PATH", help="Path to independent validation set" + ) + valid_group.add_argument( + "--val-size", + default=0, + type=float, + metavar="FLOAT", + help="Proportion of data used for validation", + ) + test_group = parser.add_mutually_exclusive_group() + test_group.add_argument( + "--test-path", metavar="PATH", help="Path to independent test set" + ) + test_group.add_argument( + "--test-size", + default=0.2, + type=float, + metavar="FLOAT", + help="Proportion of data set for testing", + ) + + # data loader inputs + parser.add_argument( + "--workers", + default=0, + type=int, + metavar="INT", + help="Number of data loading workers (default: 0)", + ) + parser.add_argument( + "--batch-size", + "--bsize", + default=128, + type=int, + metavar="INT", + help="Mini-batch size (default: 128)", + ) + parser.add_argument( + "--data-seed", + default=0, + type=int, + metavar="INT", + help="Seed used when splitting data sets (default: 0)", + ) + parser.add_argument( + "--sample", + default=1, + type=int, + metavar="INT", + help="Sub-sample the training set for learning curves", + ) + + # task inputs + parser.add_argument( + "--targets", nargs="+", metavar="STR", help="Task types for targets" + ) + parser.add_argument( + "--tasks", + nargs="*", + choices=("regression", "classification"), + default=["regression"], + metavar="STR", + help="Task types for targets", + ) + parser.add_argument( + "--losses", + nargs="*", + choices=("L1", "L2", "CSE"), + default=["L1"], + metavar="STR", + help="Loss function if regression (default: 'L1')", + ) + + # optimizer inputs + parser.add_argument( + "--epochs", + default=100, + type=int, + metavar="INT", + help="Number of training epochs to run (default: 100)", + ) + parser.add_argument( + "--robust", + action="store_true", + help="Specifies whether to use heteroscedastic loss variants", + ) + parser.add_argument( + "--optim", + default="AdamW", + metavar="STR", + help="Optimizer used for training (default: 'AdamW')", + ) + parser.add_argument( + "--learning-rate", + "--lr", + default=3e-4, + type=float, + metavar="FLOAT", + help="Initial learning rate (default: 3e-4)", + ) + parser.add_argument( + "--momentum", + default=0.9, + type=float, + metavar="FLOAT [0,1]", + help="Optimizer momentum (default: 0.9)", + ) + parser.add_argument( + "--weight-decay", + default=1e-6, + type=float, + metavar="FLOAT [0,1]", + help="Optimizer weight decay (default: 1e-6)", + ) + + # ensemble inputs + parser.add_argument( + "--ensemble", + default=1, + type=int, + metavar="INT", + help="Number models to ensemble", + ) + name_group = parser.add_mutually_exclusive_group() + name_group.add_argument( + "--model-name", + default=None, + metavar="STR", + help="Name for sub-directory where models will be stored", + ) + name_group.add_argument( + "--data-id", + default="wren", + metavar="STR", + help="Partial identifier for sub-directory where models will be stored", + ) + parser.add_argument( + "--run-id", + default=0, + type=int, + metavar="INT", + help="Index for model in an ensemble of models", + ) + + # restart inputs + use_group = parser.add_mutually_exclusive_group() + use_group.add_argument( + "--fine-tune", metavar="PATH", help="Checkpoint path for fine tuning" + ) + use_group.add_argument( + "--transfer", metavar="PATH", help="Checkpoint path for transfer learning" + ) + use_group.add_argument( + "--resume", action="store_true", help="Resume from previous checkpoint" + ) + + # task type + parser.add_argument( + "--evaluate", action="store_true", help="Evaluate the model/ensemble" + ) + parser.add_argument("--train", action="store_true", help="Train the model/ensemble") + + # misc + parser.add_argument("--disable-cuda", action="store_true", help="Disable CUDA") + parser.add_argument( + "--log", action="store_true", help="Log training metrics to TensorBoard" + ) + + # model architecture inputs + parser.add_argument( + "--embedding-type", + default="protostructure", + type=str, + metavar="STR", + help="Type of embedding to use (default: 'protostructure')", + ) + parser.add_argument( + "--n-attn-layers", + default=6, + type=int, + metavar="INT", + help="Number of attention layers (default: 6)", + ) + parser.add_argument( + "--n-attn-heads", + default=4, + type=int, + metavar="INT", + help="Number of attention heads per layer (default: 4)", + ) + parser.add_argument( + "--d-model", + default=128, + type=int, + metavar="INT", + help="Dimension of model embeddings (default: 128)", + ) + + args = parser.parse_args() + + if args.model_name is None: + args.model_name = f"{args.data_id}_s-{args.data_seed}_t-{args.sample}" + + args.device = ( + torch.device("cuda") + if (not args.disable_cuda) and torch.cuda.is_available() + else torch.device("cpu") + ) + + return args + + +if __name__ == "__main__": + args = input_parser() + raise SystemExit(main(**vars(args))) diff --git a/examples/wrenformer/matbench/__init__.py b/examples/wrenformer/matbench/__init__.py deleted file mode 100644 index 37dcee7d..00000000 --- a/examples/wrenformer/matbench/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -from glob import glob -from typing import Literal - -from aviary import ROOT - -__author__ = "Janosh Riebesell" -__date__ = "2022-04-11" - - -DATA_PATHS = { - path.split("/")[-1].split(".")[0]: path - for path in glob(f"{ROOT}/datasets/matbench_*.json.bz2") -} - -assert len(DATA_PATHS) == 13, f"glob found {len(DATA_PATHS)} data sets, expected 13" - -MatbenchDatasets = Literal[ - "matbench_steels", - "matbench_jdft2d", - "matbench_phonons", - "matbench_expt_gap", - "matbench_dielectric", - "matbench_expt_is_metal", - "matbench_glass", - "matbench_log_gvrh", - "matbench_log_kvrh", - "matbench_perovskites", - "matbench_mp_gap", - "matbench_mp_is_metal", - "matbench_mp_e_form", -] diff --git a/examples/wrenformer/matbench/compare_spglib_vs_aflow_wyckoff_labels.py b/examples/wrenformer/matbench/compare_spglib_vs_aflow_wyckoff_labels.py deleted file mode 100644 index 471c94df..00000000 --- a/examples/wrenformer/matbench/compare_spglib_vs_aflow_wyckoff_labels.py +++ /dev/null @@ -1,119 +0,0 @@ -# %% -import os - -import pandas as pd -import pymatviz as pmv -from matminer.datasets import load_dataset -from pymatgen.analysis.prototypes import ( - get_protostructure_label_from_aflow, - get_protostructure_label_from_spglib, -) -from pymatgen.core import Structure -from pymatgen.symmetry.analyzer import SpacegroupAnalyzer -from tqdm import tqdm - -from aviary import ROOT -from examples.wrenformer.matbench import DATA_PATHS - -__author__ = "Janosh Riebesell" -__date__ = "2022-05-17" - -""" -This notebook compares the output of Aflow and Spglib algorithms for assigning crystal -symmetries and Wyckoff position to crystal structures. Aflow is much slower but believed -to be more accurate. Materials Project uses Spglib so our spacegroups should be -identical to theirs. CCSD has their own algorithm and we found both Aflow and Spglib -occasionally disagree with their results. -""" - -MODULE_DIR = os.path.dirname(__file__) - - -# %% -df_perov = pd.read_json(DATA_PATHS["matbench_perovskites"]).set_index("mbid") -df_perov = df_perov.rename(columns={"wyckoff": "spglib_wyckoff"}) -df_perov["structure"] = df_perov.structure.map(Structure.from_dict) - - -# %% -# takes ~6h (when running uninterrupted) -for idx, struct in tqdm(df_perov.structure.items(), total=len(df_perov)): - if pd.isna(df_perov.aflow_wyckoff[idx]): - df_perov.loc[idx, "aflow_wyckoff"] = get_protostructure_label_from_aflow( - struct, "/Users/janosh/bin/aflow" - ) - - -# %% -# takes ~30 sec -for struct in tqdm(df_perov.structure, total=len(df_perov)): - get_protostructure_label_from_spglib(struct) - - -# %% -df_perov.dropna().query("wyckoff != aflow_wyckoff") - - -# %% -print( - "Percentage of materials with spglib label != aflow label: " - f"{len(df_perov.query('wyckoff != aflow_wyckoff')) / len(df_perov):.0%}" -) - - -# %% -df_perov.drop("structure", axis=1).to_csv( - f"{ROOT}/datasets/matbench_perovskites_protostructure_labels.csv" -) - - -# %% -df_perov = pd.read_csv( - f"{ROOT}/datasets/matbench_perovskites_protostructure_labels.csv" -).set_index("mbid") - - -# %% -for src in ("aflow", "spglib"): - df_perov[f"{src}_spg_num"] = ( - df_perov[f"{src}_wyckoff"].str.split("_").str[2].astype(int) - ) - - -# %% -fig = pmv.spacegroup_sunburst(df_perov.spglib_spg) -fig.update_layout(title=dict(text="Spglib Spacegroups", x=0.5, y=0.93)) -# fig.write_image(f"{MODULE_DIR}/plots/matbench_perovskites_aflow_sunburst.pdf") - - -# %% -fig = pmv.spacegroup_sunburst(df_perov.aflow_spg, title="Aflow") -fig.update_layout(title=dict(text="Aflow Spacegroups", x=0.5, y=0.85)) -# fig.write_image(f"{MODULE_DIR}/plots/matbench_perovskites_spglib_sunburst.pdf") - - -# %% -df_perov = load_dataset("matbench_perovskites") - -df_perov["spglib_spg_num"] = df_perov.structure.map( - lambda struct: SpacegroupAnalyzer(struct).get_space_group_number() -) - - -# %% -for src in ("aflow", "spglib"): - df_perov[f"{src}_crys_sys"] = df_perov[f"{src}_spg_num"].map( - pmv.utils.crystal_sys_from_spg_num - ) - - -# %% -fig = pmv.sankey_from_2_df_cols(df_perov, ["aflow_spg_num", "spglib_spg_num"]) - -fig.update_layout(title="Matbench Perovskites Aflow vs Spglib Spacegroups") - - -# %% -fig = pmv.sankey_from_2_df_cols(df_perov, ["aflow_crys_sys", "spglib_crys_sys"]) - -fig.update_layout(title="Aflow vs Spglib Crystal Systems") diff --git a/examples/wrenformer/matbench/save_matbench_aflow_labels.py b/examples/wrenformer/matbench/save_matbench_aflow_labels.py deleted file mode 100644 index a0b4c128..00000000 --- a/examples/wrenformer/matbench/save_matbench_aflow_labels.py +++ /dev/null @@ -1,39 +0,0 @@ -import pandas as pd -from pymatgen.analysis.prototypes import get_protostructure_label_from_spglib -from tqdm import tqdm - -from aviary import ROOT -from matbench import MatbenchBenchmark - -__author__ = "Janosh Riebesell" -__date__ = "2022-04-11" - - -""" -This file uses Spglib to generate Aflow Wyckoff labels for all Matbench datasets and -stores them to disk in the datasets/ folder as Bzip2-compressed JSON files. -""" - - -benchmark = MatbenchBenchmark() - -for idx, task in enumerate(benchmark.tasks, start=1): - print(f"\n\n{idx}/{len(benchmark.tasks)}") - task.load() - df: pd.DataFrame = task.df - - if "structure" in df: - df["composition"] = [struct.formula for struct in df.structure] - df["wyckoff"] = [ - get_protostructure_label_from_spglib(struct) - for struct in tqdm(df.structure, desc="Getting Aflow Wyckoff labels") - ] - elif "composition" in df: - df["composition"] = [comp.formula for comp in df.composition] - else: - raise ValueError("No structure or composition column found") - - df.to_json( - f"{ROOT}/datasets/{task.dataset_name}.json.bz2", - default_handler=lambda x: x.as_dict(), - ) diff --git a/examples/wrenformer/matbench/utils.py b/examples/wrenformer/matbench/utils.py deleted file mode 100644 index 85d37420..00000000 --- a/examples/wrenformer/matbench/utils.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import annotations - -import json -from typing import Literal - -__author__ = "Janosh Riebesell" -__date__ = "2022-05-10" - - -def _int_keys(dct: dict) -> dict: - # JSON stringifies all dict keys during serialization and does not revert - # back to floats and ints during parsing. This json.load() hook converts keys - # containing only digits to ints. - return {int(k) if k.lstrip("-").isdigit() else k: v for k, v in dct.items()} - - -def recursive_dict_merge(dict1: dict, dict2: dict) -> dict: - """Merge two dicts recursively.""" - for key, val2 in dict2.items(): - if key in dict1 and isinstance(dict1[key], dict) and isinstance(val2, dict): - recursive_dict_merge(dict1[key], val2) - else: - dict1[key] = val2 - return dict1 - - -def merge_json_on_disk( - dct: dict, - file_path: str, - on_non_serializable: Literal["annotate", "error"] = "annotate", -) -> None: - """Merge a dict into a (possibly) existing JSON file. - - Args: - file_path (str): Path to JSON file. File will be created if not exist. - dct (dict): Dictionary to merge into JSON file. - on_non_serializable ('annotate' | 'error'): What to do with non-serializable - values encountered in dct. 'annotate' will replace the offending object with - a string indicating the type, e.g. ''. 'error' - will raise 'TypeError: Object of type function is not JSON serializable'. - Defaults to 'annotate'. - """ - try: - with open(file_path) as json_file: - data = json.load(json_file, object_hook=_int_keys) - - dct = recursive_dict_merge(data, dct) - except (FileNotFoundError, json.decoder.JSONDecodeError): # file missing or empty - pass - - def non_serializable_handler(obj: object) -> str: - # replace functions and classes in dct with string indicating it's a - # non-serializable type - return f"" - - with open(file_path, "w") as file: - default = non_serializable_handler if on_non_serializable == "annotate" else None - json.dump(dct, file, default=default, indent=2) diff --git a/examples/wrenformer/mp_wbm/__init__.py b/examples/wrenformer/mp_wbm/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/wrenformer/mp_wbm/test_wrenformer.py b/examples/wrenformer/mp_wbm/test_wrenformer.py deleted file mode 100644 index eeb2d247..00000000 --- a/examples/wrenformer/mp_wbm/test_wrenformer.py +++ /dev/null @@ -1,70 +0,0 @@ -from __future__ import annotations - -import os -from datetime import datetime - -import pandas as pd -import wandb - -from aviary import ROOT -from aviary.predict import predict_from_wandb_checkpoints -from aviary.wrenformer.data import df_to_in_mem_dataloader -from aviary.wrenformer.model import Wrenformer - -__author__ = "Janosh Riebesell" -__date__ = "2022-06-23" - -""" -Script that downloads checkpoints for an ensemble of Wrenformer models trained on -the MP+WBM formation energies and makes predictions on the test set, then prints -ensemble metrics and stores predictions to CSV. -""" - -module_dir = os.path.dirname(__file__) -today = f"{datetime.now():%Y-%m-%d}" -data_path = f"{ROOT}/datasets/2022-06-09-mp+wbm.json.gz" -# data_path = f"{ROOT}/datasets/2022-06-09-mp+wbm-1k-samples.json.gz" -test_size = 0.05 -df = pd.read_json(data_path) -# shuffle with same random seed as in train_wrenformer() to get same train/test split -df = df.sample(frac=1, random_state=0) -train_df = df.sample(frac=1 - test_size, random_state=0) # unused -test_df = df.drop(train_df.index) -target_col = "e_form" - -ensemble_id = "ensemble-id-2" -runs = wandb.Api().runs("aviary/mp-wbm", filters={"tags": {"$in": [ensemble_id]}}) - -assert len(runs) == 10, f"Expected 10 runs, got {len(runs)} for {ensemble_id=}" - -data_loader = df_to_in_mem_dataloader( - df=df, - target_col=target_col, - batch_size=1024, - input_col="wyckoff", - embedding_type="wyckoff", - shuffle=False, # False is default but best be explicit -) - -test_df, ensemble_metrics = predict_from_wandb_checkpoints( - runs, - data_loader=data_loader, - df=test_df, - target_col=target_col, - model_cls=Wrenformer, -) - -test_df.round(6).to_csv(f"{module_dir}/{today}-{ensemble_id}-preds-{target_col}.csv") - -# print output: -# Predicting with 10 model checkpoint(s) -# -# Single model performance: -# MAE RMSE R2 -# mean 0.0369 0.1218 0.9864 -# std 0.0005 0.0014 0.0003 -# -# Ensemble performance: -# MAE 0.0308 -# RMSE 0.118 -# R2 0.987 diff --git a/examples/wrenformer/mp_wbm/train_wrenformer.py b/examples/wrenformer/mp_wbm/train_wrenformer.py deleted file mode 100644 index 1a7abe72..00000000 --- a/examples/wrenformer/mp_wbm/train_wrenformer.py +++ /dev/null @@ -1,79 +0,0 @@ -# %% -import os -from datetime import datetime - -import pandas as pd -from matbench_discovery.slurm import slurm_submit - -from aviary import ROOT -from aviary.train import df_train_test_split, train_wrenformer - -""" -Train a Wrenformer ensemble of size n_folds on target_col of data_path. -""" - -__author__ = "Janosh Riebesell" -__date__ = "2022-06-13" - - -# %% -epochs = 30 -target_col = "e_form" -input_col = "wyckoff" -run_name = f"wrenformer-robust-mp+wbm-{epochs=}-{target_col}" -n_folds = 10 -timestamp = f"{datetime.now():%Y-%m-%d@%H-%M-%S}" -today = timestamp.split("@")[0] -log_dir = f"{os.path.dirname(__file__)}/{today}-{run_name}" - -slurm_submit( - job_name=run_name, - partition="ampere", - account="LEE-SL3-GPU", - time="1:0:0", - array=f"1-{n_folds}", - log_dir=log_dir, - slurm_flags=("--nodes", "1", "--gpus-per-node", "1"), - # prepend into sbatch script to source module command and load default env - # for Ampere GPU partition before actual job command - pre_cmd=". /etc/profile.d/modules.sh; module load rhel8/default-amp;", -) - - -# %% -learning_rate = 3e-4 -# data_path = f"{ROOT}/datasets/2022-06-09-mp+wbm.json.gz" -# for faster testing/debugging -data_path = f"{ROOT}/datasets/2022-06-09-mp+wbm-1k-samples.json.gz" -batch_size = 128 -slurm_array_task_id = int(os.getenv("SLURM_ARRAY_TASK_ID", "0")) - -print(f"Job started running {timestamp}") -print(f"{run_name=}") -print(f"{data_path=}") - -df = pd.read_json(data_path).set_index("material_id", drop=False) -assert target_col in df, f"{target_col=} not in {list(df)}" -assert input_col in df, f"{input_col=} not in {list(df)}" -train_df, test_df = df_train_test_split(df, test_size=0.3) - -run_params = dict( - batch_size=batch_size, - train_df=dict(shape=str(train_df.shape), columns=", ".join(train_df)), - test_df=dict(shape=str(test_df.shape), columns=", ".join(test_df)), -) - -train_wrenformer( - run_name=run_name, - train_df=train_df, - test_df=test_df, - target_col=target_col, - task_type="regression", - timestamp=timestamp, - epochs=epochs, - checkpoint="wandb", # None | 'local' | 'wandb', - learning_rate=learning_rate, - batch_size=batch_size, - wandb_path="aviary/mp-wbm", - run_params=run_params, -) diff --git a/pyproject.toml b/pyproject.toml index 1ca75735..96a75b8c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,13 +27,12 @@ classifiers = [ "Operating System :: OS Independent", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.9", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Physics", ] -requires-python = ">=3.9" +requires-python = ">=3.10" dependencies = [ "numpy>=2,<3", "pandas", @@ -42,6 +41,7 @@ dependencies = [ "tensorboard", "torch>=2.3.0", "tqdm", + "typing-extensions", "wandb", ] @@ -70,7 +70,7 @@ no_implicit_optional = false [tool.ruff] line-length = 90 -target-version = "py39" +target-version = "py310" output-format = "concise" [tool.ruff.lint] diff --git a/tests/conftest.py b/tests/conftest.py index ffeac5af..539ef454 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,9 +5,6 @@ from matminer.datasets import load_dataset from pymatgen.analysis.prototypes import get_protostructure_label_from_spglib -__author__ = "Janosh Riebesell" -__date__ = "2022-04-09" - torch.manual_seed(0) # ensure reproducible results (applies to all tests) TEST_DIR = os.path.dirname(os.path.abspath(__file__)) @@ -36,7 +33,7 @@ def df_matbench_jdft2d(): df = df.set_index("material_id", drop=False) df["composition"] = [x.composition.formula.replace(" ", "") for x in df.structure] - df["wyckoff"] = df.structure.map(get_protostructure_label_from_spglib) + df["protostructure"] = df.structure.map(get_protostructure_label_from_spglib) return df @@ -46,7 +43,7 @@ def df_matbench_phonons_wyckoff(df_matbench_phonons): """Getting Aflow labels is expensive so we split into a separate fixture to avoid paying for it unless requested. """ - df_matbench_phonons["wyckoff"] = df_matbench_phonons.structure.map( + df_matbench_phonons["protostructure"] = df_matbench_phonons.structure.map( get_protostructure_label_from_spglib ) diff --git a/tests/test_cgcnn.py b/tests/test_cgcnn.py new file mode 100644 index 00000000..e79d2fb6 --- /dev/null +++ b/tests/test_cgcnn.py @@ -0,0 +1,287 @@ +import numpy as np +import pytest +import torch +from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader + +from aviary.cgcnn.data import CrystalGraphData, collate_batch +from aviary.cgcnn.model import CrystalGraphConvNet +from aviary.utils import get_metrics, results_multitask, train_ensemble + + +@pytest.fixture +def base_config(): + return { + "elem_embedding": "cgcnn92", + "robust": True, + "ensemble": 2, + "run_id": 1, + "data_seed": 42, + "log": False, + "sample": 1, + "test_size": 0.2, + } + + +@pytest.fixture +def model_architecture(): + return { + "elem_fea_len": 32, + "h_fea_len": 128, + "n_graph": 3, + "n_hidden": 1, + } + + +@pytest.fixture +def training_config(): + return { + "resume": False, + "fine_tune": None, + "transfer": None, + "optim": "AdamW", + "learning_rate": 3e-4, + "momentum": 0.9, + "weight_decay": 1e-6, + "batch_size": 128, + "workers": 0, + "device": "cuda" if torch.cuda.is_available() else "cpu", + } + + +def test_cgcnn_regression( + df_matbench_phonons, base_config, model_architecture, training_config +): + target_name = "last phdos peak" + task = "regression" + losses = ["L1"] + epochs = 25 + model_name = "cgcnn-reg-test" + + task_dict = dict(zip([target_name], [task], strict=False)) + loss_dict = dict(zip([target_name], losses, strict=False)) + + dataset = CrystalGraphData( + df=df_matbench_phonons, + elem_embedding=base_config["elem_embedding"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + nbr_fea_len = dataset.nbr_fea_dim + + train_idx = list(range(len(dataset))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + val_set = test_set + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + train_loader = DataLoader(train_set, **data_params) + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + "nbr_fea_len": nbr_fea_len, + **model_architecture, + } + + train_ensemble( + model_class=CrystalGraphConvNet, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=CrystalGraphConvNet, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + preds = results_dict[target_name]["preds"] + targets = results_dict[target_name]["targets"] + + y_ens = np.mean(preds, axis=0) + mae, rmse, r2 = get_metrics(targets, y_ens, task).values() + + assert len(targets) == len(test_set) == len(test_idx) + assert r2 > 0.7 + assert mae < 150 + assert rmse < 300 + + +def test_cgcnn_clf(df_matbench_phonons, base_config, model_architecture, training_config): + targets = ["phdos_clf"] + task = "classification" + losses = ["CSE"] + epochs = 10 + model_name = "cgcnn-clf-test" + + task_dict = dict(zip(targets, [task], strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) + + dataset = CrystalGraphData( + df=df_matbench_phonons, + elem_embedding=base_config["elem_embedding"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + nbr_fea_len = dataset.nbr_fea_dim + + train_idx = list(range(len(dataset))) + + print(f"using {base_config['test_size']} of training set as test set") + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + + print("No validation set used, using test set for evaluation purposes") + # NOTE that when using this option care must be taken not to + # peak at the test-set. The only valid model to use is the one + # obtained after the final epoch where the epoch count is + # decided in advance of the experiment. + val_set = test_set + + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + train_loader = DataLoader(train_set, **data_params) + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + "nbr_fea_len": nbr_fea_len, + **model_architecture, + } + + train_ensemble( + model_class=CrystalGraphConvNet, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=CrystalGraphConvNet, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + logits = results_dict["phdos_clf"]["logits"] + targets = results_dict["phdos_clf"]["targets"] + + # calculate metrics and errors with associated errors for ensembles + ens_logits = np.mean(logits, axis=0) + + ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() + + assert len(targets) == len(test_set) == len(test_idx) + assert ens_acc > 0.85 + assert ens_roc_auc > 0.9 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/test_cgcnn_classification.py b/tests/test_cgcnn_classification.py deleted file mode 100644 index 0c8d10cb..00000000 --- a/tests/test_cgcnn_classification.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.cgcnn.data import CrystalGraphData, collate_batch -from aviary.cgcnn.model import CrystalGraphConvNet -from aviary.utils import get_metrics, results_multitask, train_ensemble - - -def test_cgcnn_clf(df_matbench_phonons): - elem_embedding = "cgcnn92" - targets = ["phdos_clf"] - task = "classification" - losses = ["CSE"] - robust = True - model_name = "cgcnn-clf-test" - elem_fea_len = 32 - h_fea_len = 128 - n_graph = 3 - n_hidden = 1 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 10 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip(targets, [task])) - loss_dict = dict(zip(targets, losses)) - - dataset = CrystalGraphData( - df=df_matbench_phonons, elem_embedding=elem_embedding, task_dict=task_dict - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - nbr_fea_len = dataset.nbr_fea_dim - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "nbr_fea_len": nbr_fea_len, - "elem_fea_len": elem_fea_len, - "n_graph": n_graph, - "h_fea_len": h_fea_len, - "n_hidden": n_hidden, - } - - train_ensemble( - model_class=CrystalGraphConvNet, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=CrystalGraphConvNet, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - logits = results_dict["phdos_clf"]["logits"] - targets = results_dict["phdos_clf"]["targets"] - - # calculate metrics and errors with associated errors for ensembles - ens_logits = np.mean(logits, axis=0) - - ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() - - assert len(targets) == len(test_set) == len(test_idx) - assert ens_acc > 0.85 - assert ens_roc_auc > 0.9 diff --git a/tests/test_cgcnn_regression.py b/tests/test_cgcnn_regression.py deleted file mode 100644 index f6c908c6..00000000 --- a/tests/test_cgcnn_regression.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.cgcnn.data import CrystalGraphData, collate_batch -from aviary.cgcnn.model import CrystalGraphConvNet -from aviary.utils import get_metrics, results_multitask, train_ensemble - - -def test_cgcnn_regression(df_matbench_phonons): - elem_embedding = "cgcnn92" - target_name = "last phdos peak" - task = "regression" - losses = ["L1"] - robust = True - model_name = "cgcnn-reg-test" - elem_fea_len = 32 - h_fea_len = 128 - n_graph = 3 - n_hidden = 1 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 25 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip([target_name], [task])) - loss_dict = dict(zip([target_name], losses)) - - dataset = CrystalGraphData( - df=df_matbench_phonons, elem_embedding=elem_embedding, task_dict=task_dict - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - nbr_fea_len = dataset.nbr_fea_dim - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "nbr_fea_len": nbr_fea_len, - "elem_fea_len": elem_fea_len, - "n_graph": n_graph, - "h_fea_len": h_fea_len, - "n_hidden": n_hidden, - } - - train_ensemble( - model_class=CrystalGraphConvNet, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=CrystalGraphConvNet, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - preds = results_dict[target_name]["preds"] - targets = results_dict[target_name]["targets"] - - y_ens = np.mean(preds, axis=0) - - mae, rmse, r2 = get_metrics(targets, y_ens, task).values() - - assert len(targets) == len(test_set) == len(test_idx) - assert r2 > 0.7 - assert mae < 150 - assert rmse < 300 diff --git a/tests/test_core.py b/tests/test_core.py index 5ca57eb0..3e77fc0e 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -57,7 +57,7 @@ def test_masked_std(): # test against explicit calculation rand_floats = torch.rand(3, 4, 5) rand_masks = torch.randint(0, 2, (3, 4, 5)).bool() - for xi, mask in zip(rand_floats, rand_masks): + for xi, mask in zip(rand_floats, rand_masks, strict=False): for dim in (0, 1): out = masked_std(xi, mask, dim=dim) xi_nan = torch.where(mask, xi, torch.tensor(float("nan"))) diff --git a/tests/test_package.py b/tests/test_package.py index 83e9f1b7..c1fce2ca 100644 --- a/tests/test_package.py +++ b/tests/test_package.py @@ -8,10 +8,6 @@ package_sources_path = f"{ROOT}/aviary.egg-info/SOURCES.txt" -__author__ = "Janosh Riebesell" -__date__ = "2022-05-25" - - @pytest.mark.skipif( not os.path.isfile(package_sources_path), reason="No aviary.egg-info/SOURCES.txt file, run pip install . to create it", diff --git a/tests/test_roost.py b/tests/test_roost.py new file mode 100644 index 00000000..7ff74ec4 --- /dev/null +++ b/tests/test_roost.py @@ -0,0 +1,283 @@ +import numpy as np +import pytest +import torch +from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader + +from aviary.roost.data import CompositionData, collate_batch +from aviary.roost.model import Roost +from aviary.utils import get_metrics, results_multitask, train_ensemble + + +@pytest.fixture +def base_config(): + return { + "elem_embedding": "matscholar200", + "robust": True, + "ensemble": 2, + "run_id": 1, + "data_seed": 42, + "log": False, + "sample": 1, + "test_size": 0.2, + } + + +@pytest.fixture +def model_architecture(): + return { + "elem_fea_len": 64, + "n_graph": 3, + "elem_heads": 2, + "elem_gate": [256], + "elem_msg": [256], + "cry_heads": 2, + "cry_gate": [256], + "cry_msg": [256], + "trunk_hidden": [256, 256], + "out_hidden": [128, 64], + } + + +@pytest.fixture +def training_config(): + return { + "resume": False, + "fine_tune": None, + "transfer": None, + "optim": "AdamW", + "learning_rate": 3e-4, + "momentum": 0.9, + "weight_decay": 1e-6, + "batch_size": 128, + "workers": 0, + "device": "cuda" if torch.cuda.is_available() else "cpu", + } + + +def test_roost_regression( + df_matbench_phonons, base_config, model_architecture, training_config +): + target_name = "last phdos peak" + task = "regression" + losses = ["L1"] + epochs = 25 + model_name = "roost-reg-test" + + task_dict = dict(zip([target_name], [task], strict=False)) + loss_dict = dict(zip([target_name], losses, strict=False)) + + dataset = CompositionData( + df=df_matbench_phonons, + elem_embedding=base_config["elem_embedding"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + + train_idx = list(range(len(dataset))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + val_set = test_set + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + **model_architecture, # unpack all model architecture parameters + } + + train_loader = DataLoader(train_set, **data_params) + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + train_ensemble( + model_class=Roost, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=Roost, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + preds = results_dict[target_name]["preds"] + targets = results_dict[target_name]["targets"] + + y_ens = np.mean(preds, axis=0) + + mae, rmse, r2 = get_metrics(targets, y_ens, task).values() + + assert len(targets) == len(test_set) == len(test_idx) + assert r2 > 0.7 + assert mae < 150 + assert rmse < 300 + + +def test_roost_clf(df_matbench_phonons, base_config, model_architecture, training_config): + targets = ["phdos_clf"] + task = "classification" + losses = ["CSE"] + epochs = 15 + model_name = "roost-clf-test" + + task_dict = dict(zip(targets, [task], strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) + + dataset = CompositionData( + df=df_matbench_phonons, + elem_embedding=base_config["elem_embedding"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + + train_idx = list(range(len(dataset))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + val_set = test_set + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + **model_architecture, # unpack all model architecture parameters + } + + train_loader = DataLoader(train_set, **data_params) + + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + train_ensemble( + model_class=Roost, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=Roost, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + logits = results_dict["phdos_clf"]["logits"] + targets = results_dict["phdos_clf"]["targets"] + + # calculate metrics and errors with associated errors for ensembles + ens_logits = np.mean(logits, axis=0) + + ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() + + assert len(logits) == base_config["ensemble"] + assert len(targets) == len(test_set) == len(test_idx) + assert ens_acc > 0.9 + assert ens_roc_auc > 0.9 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/test_roost_classification.py b/tests/test_roost_classification.py deleted file mode 100644 index 86322bf1..00000000 --- a/tests/test_roost_classification.py +++ /dev/null @@ -1,144 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.roost.data import CompositionData, collate_batch -from aviary.roost.model import Roost -from aviary.utils import get_metrics, results_multitask, train_ensemble - - -def test_roost_clf(df_matbench_phonons): - elem_embedding = "matscholar200" - targets = ["phdos_clf"] - task = "classification" - losses = ["CSE"] - robust = True - model_name = "roost-clf-test" - elem_fea_len = 64 - n_graph = 3 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 15 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip(targets, [task])) - loss_dict = dict(zip(targets, losses)) - - dataset = CompositionData( - df=df_matbench_phonons, elem_embedding=elem_embedding, task_dict=task_dict - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "elem_fea_len": elem_fea_len, - "n_graph": n_graph, - "elem_heads": 2, - "elem_gate": [256], - "elem_msg": [256], - "cry_heads": 2, - "cry_gate": [256], - "cry_msg": [256], - "trunk_hidden": [256, 256], - "out_hidden": [128, 64], - } - - train_ensemble( - model_class=Roost, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=Roost, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - logits = results_dict["phdos_clf"]["logits"] - targets = results_dict["phdos_clf"]["targets"] - - # calculate metrics and errors with associated errors for ensembles - ens_logits = np.mean(logits, axis=0) - - ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() - - assert len(logits) == ensemble - assert len(targets) == len(test_set) == len(test_idx) - assert ens_acc > 0.9 - assert ens_roc_auc > 0.9 diff --git a/tests/test_roost_regression.py b/tests/test_roost_regression.py deleted file mode 100644 index 125b0031..00000000 --- a/tests/test_roost_regression.py +++ /dev/null @@ -1,143 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.roost.data import CompositionData, collate_batch -from aviary.roost.model import Roost -from aviary.utils import get_metrics, results_multitask, train_ensemble - - -def test_roost_regression(df_matbench_phonons): - elem_embedding = "matscholar200" - target_name = "last phdos peak" - task = "regression" - losses = ["L1"] - robust = True - model_name = "roost-reg-test" - elem_fea_len = 64 - n_graph = 3 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 25 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip([target_name], [task])) - loss_dict = dict(zip([target_name], losses)) - - dataset = CompositionData( - df=df_matbench_phonons, elem_embedding=elem_embedding, task_dict=task_dict - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "elem_fea_len": elem_fea_len, - "n_graph": n_graph, - "elem_heads": 2, - "elem_gate": [256], - "elem_msg": [256], - "cry_heads": 2, - "cry_gate": [256], - "cry_msg": [256], - "trunk_hidden": [256, 256], - "out_hidden": [128, 64], - } - - train_ensemble( - model_class=Roost, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=Roost, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - preds = results_dict[target_name]["preds"] - targets = results_dict[target_name]["targets"] - - y_ens = np.mean(preds, axis=0) - - mae, rmse, r2 = get_metrics(targets, y_ens, task).values() - - assert len(targets) == len(test_set) == len(test_idx) - assert r2 > 0.7 - assert mae < 150 - assert rmse < 300 diff --git a/tests/test_wren.py b/tests/test_wren.py new file mode 100644 index 00000000..bd391391 --- /dev/null +++ b/tests/test_wren.py @@ -0,0 +1,292 @@ +import numpy as np +import pytest +import torch +from sklearn.model_selection import train_test_split as split +from torch.utils.data import DataLoader + +from aviary.utils import get_metrics, results_multitask, train_ensemble +from aviary.wren.data import WyckoffData, collate_batch +from aviary.wren.model import Wren + + +@pytest.fixture +def base_config(): + return { + "elem_embedding": "matscholar200", + "sym_emb": "bra-alg-off", + "robust": True, + "ensemble": 2, + "run_id": 1, + "data_seed": 42, + "log": False, + "sample": 1, + "test_size": 0.2, + } + + +@pytest.fixture +def model_architecture(): + return { + "elem_fea_len": 32, + "sym_fea_len": 32, + "n_graph": 3, + "elem_heads": 2, + "elem_gate": [256], + "elem_msg": [256], + "cry_heads": 2, + "cry_gate": [256], + "cry_msg": [256], + "out_hidden": [256], + "trunk_hidden": [64], + } + + +@pytest.fixture +def training_config(): + return { + "resume": False, + "fine_tune": None, + "transfer": None, + "optim": "AdamW", + "learning_rate": 3e-4, + "momentum": 0.9, + "weight_decay": 1e-6, + "batch_size": 128, + "workers": 0, + "device": "cuda" if torch.cuda.is_available() else "cpu", + } + + +def test_wren_regression( + df_matbench_phonons_wyckoff, base_config, model_architecture, training_config +): + target_name = "last phdos peak" + task = "regression" + losses = ["L1"] + epochs = 25 + model_name = "wren-reg-test" + + task_dict = dict(zip([target_name], [task], strict=False)) + loss_dict = dict(zip([target_name], losses, strict=False)) + + dataset = WyckoffData( + df=df_matbench_phonons_wyckoff, + elem_embedding=base_config["elem_embedding"], + sym_emb=base_config["sym_emb"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + sym_emb_len = dataset.sym_emb_len + + train_idx = list(range(len(dataset))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + val_set = test_set + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + train_loader = DataLoader(train_set, **data_params) + + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + "sym_emb_len": sym_emb_len, + **model_architecture, + } + + train_ensemble( + model_class=Wren, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=Wren, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + preds = results_dict[target_name]["preds"] + targets = results_dict[target_name]["targets"] + + y_ens = np.mean(preds, axis=0) + mae, rmse, r2 = get_metrics(targets, y_ens, task).values() + + assert len(targets) == len(test_set) == len(test_idx) + assert r2 > 0.7 + assert mae < 150 + assert rmse < 300 + + +def test_wren_clf( + df_matbench_phonons_wyckoff, base_config, model_architecture, training_config +): + targets = ["phdos_clf"] + task = "classification" + losses = ["CSE"] + epochs = 15 + model_name = "wren-clf-test" + + task_dict = dict(zip(targets, [task], strict=False)) + loss_dict = dict(zip(targets, losses, strict=False)) + + dataset = WyckoffData( + df=df_matbench_phonons_wyckoff, + elem_embedding=base_config["elem_embedding"], + sym_emb=base_config["sym_emb"], + task_dict=task_dict, + ) + n_targets = dataset.n_targets + elem_emb_len = dataset.elem_emb_len + sym_emb_len = dataset.sym_emb_len + + train_idx = list(range(len(dataset))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + test_set = torch.utils.data.Subset(dataset, test_idx) + val_set = test_set + train_set = torch.utils.data.Subset(dataset, train_idx[0 :: base_config["sample"]]) + + data_params = { + "batch_size": training_config["batch_size"], + "num_workers": training_config["workers"], + "pin_memory": False, + "shuffle": True, + "collate_fn": collate_batch, + } + + train_loader = DataLoader(train_set, **data_params) + + val_loader = DataLoader( + val_set, + **{**data_params, "batch_size": 16 * data_params["batch_size"], "shuffle": False}, + ) + + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "elem_emb_len": elem_emb_len, + "sym_emb_len": sym_emb_len, + **model_architecture, + } + + train_ensemble( + model_class=Wren, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) + + test_loader = DataLoader( + test_set, + **{**data_params, "batch_size": 64 * data_params["batch_size"], "shuffle": False}, + ) + + results_dict = results_multitask( + model_class=Wren, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, + ) + + logits = results_dict["phdos_clf"]["logits"] + targets = results_dict["phdos_clf"]["targets"] + + # calculate metrics and errors with associated errors for ensembles + ens_logits = np.mean(logits, axis=0) + + ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() + + assert len(targets) == len(test_set) == len(test_idx) + assert ens_acc > 0.85 + assert ens_roc_auc > 0.9 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/test_wren_classification.py b/tests/test_wren_classification.py deleted file mode 100644 index c7db96e0..00000000 --- a/tests/test_wren_classification.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.utils import get_metrics, results_multitask, train_ensemble -from aviary.wren.data import WyckoffData, collate_batch -from aviary.wren.model import Wren - - -def test_wren_clf(df_matbench_phonons_wyckoff): - elem_embedding = "matscholar200" - sym_emb = "bra-alg-off" - targets = ["phdos_clf"] - task = "classification" - losses = ["CSE"] - robust = True - model_name = "wren-clf-test" - elem_fea_len = 32 - sym_fea_len = 32 - n_graph = 3 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 15 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip(targets, [task])) - loss_dict = dict(zip(targets, losses)) - - dataset = WyckoffData( - df=df_matbench_phonons_wyckoff, - elem_embedding=elem_embedding, - sym_emb=sym_emb, - task_dict=task_dict, - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - sym_emb_len = dataset.sym_emb_len - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "sym_emb_len": sym_emb_len, - "elem_fea_len": elem_fea_len, - "sym_fea_len": sym_fea_len, - "n_graph": n_graph, - "elem_heads": 2, - "elem_gate": [256], - "elem_msg": [256], - "cry_heads": 2, - "cry_gate": [256], - "cry_msg": [256], - "out_hidden": [256], - "trunk_hidden": [64], - } - - train_ensemble( - model_class=Wren, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=Wren, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - logits = results_dict["phdos_clf"]["logits"] - targets = results_dict["phdos_clf"]["targets"] - - # calculate metrics and errors with associated errors for ensembles - ens_logits = np.mean(logits, axis=0) - - ens_acc, *_, ens_roc_auc = get_metrics(targets, ens_logits, task).values() - - assert len(targets) == len(test_set) == len(test_idx) - assert ens_acc > 0.85 - assert ens_roc_auc > 0.9 diff --git a/tests/test_wren_regression.py b/tests/test_wren_regression.py deleted file mode 100644 index a8ad73db..00000000 --- a/tests/test_wren_regression.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -import torch -from sklearn.model_selection import train_test_split as split - -from aviary.utils import get_metrics, results_multitask, train_ensemble -from aviary.wren.data import WyckoffData, collate_batch -from aviary.wren.model import Wren - - -def test_wren_regression(df_matbench_phonons_wyckoff): - elem_embedding = "matscholar200" - sym_emb = "bra-alg-off" - target_name = "last phdos peak" - task = "regression" - losses = ["L1"] - robust = True - model_name = "wren-reg-test" - elem_fea_len = 32 - sym_fea_len = 32 - n_graph = 3 - ensemble = 2 - run_id = 1 - data_seed = 42 - epochs = 25 - log = False - sample = 1 - test_size = 0.2 - resume = False - fine_tune = None - transfer = None - optim = "AdamW" - learning_rate = 3e-4 - momentum = 0.9 - weight_decay = 1e-6 - batch_size = 128 - workers = 0 - device = "cuda" if torch.cuda.is_available() else "cpu" - - task_dict = dict(zip([target_name], [task])) - loss_dict = dict(zip([target_name], losses)) - - dataset = WyckoffData( - df=df_matbench_phonons_wyckoff, - elem_embedding=elem_embedding, - sym_emb=sym_emb, - task_dict=task_dict, - ) - n_targets = dataset.n_targets - elem_emb_len = dataset.elem_emb_len - sym_emb_len = dataset.sym_emb_len - - train_idx = list(range(len(dataset))) - - print(f"using {test_size} of training set as test set") - train_idx, test_idx = split(train_idx, random_state=data_seed, test_size=test_size) - test_set = torch.utils.data.Subset(dataset, test_idx) - - print("No validation set used, using test set for evaluation purposes") - # NOTE that when using this option care must be taken not to - # peak at the test-set. The only valid model to use is the one - # obtained after the final epoch where the epoch count is - # decided in advance of the experiment. - val_set = test_set - - train_set = torch.utils.data.Subset(dataset, train_idx[0::sample]) - - data_params = { - "batch_size": batch_size, - "num_workers": workers, - "pin_memory": False, - "shuffle": True, - "collate_fn": collate_batch, - } - - setup_params = { - "optim": optim, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "momentum": momentum, - "device": device, - } - - restart_params = { - "resume": resume, - "fine_tune": fine_tune, - "transfer": transfer, - } - - model_params = { - "task_dict": task_dict, - "robust": robust, - "n_targets": n_targets, - "elem_emb_len": elem_emb_len, - "sym_emb_len": sym_emb_len, - "elem_fea_len": elem_fea_len, - "sym_fea_len": sym_fea_len, - "n_graph": n_graph, - "elem_heads": 2, - "elem_gate": [256], - "elem_msg": [256], - "cry_heads": 2, - "cry_gate": [256], - "cry_msg": [256], - "out_hidden": [256], - "trunk_hidden": [64], - } - - train_ensemble( - model_class=Wren, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - epochs=epochs, - train_set=train_set, - val_set=val_set, - log=log, - data_params=data_params, - setup_params=setup_params, - restart_params=restart_params, - model_params=model_params, - loss_dict=loss_dict, - ) - - data_params["batch_size"] = 64 * batch_size # faster model inference - data_params["shuffle"] = False # need fixed data order due to ensembling - - results_dict = results_multitask( - model_class=Wren, - model_name=model_name, - run_id=run_id, - ensemble_folds=ensemble, - test_set=test_set, - data_params=data_params, - robust=robust, - task_dict=task_dict, - device=device, - eval_type="checkpoint", - save_results=False, - ) - - preds = results_dict[target_name]["preds"] - targets = results_dict[target_name]["targets"] - - y_ens = np.mean(preds, axis=0) - - mae, rmse, r2 = get_metrics(targets, y_ens, task).values() - - assert len(targets) == len(test_set) == len(test_idx) - assert r2 > 0.7 - assert mae < 150 - assert rmse < 300 diff --git a/tests/test_wrenformer.py b/tests/test_wrenformer.py index 6adc349a..8149d464 100644 --- a/tests/test_wrenformer.py +++ b/tests/test_wrenformer.py @@ -1,37 +1,171 @@ -from aviary.train import train_wrenformer +import numpy as np +import pytest +import torch +from sklearn.model_selection import train_test_split as split +from aviary.utils import get_metrics, results_multitask, train_ensemble +from aviary.wrenformer.data import df_to_in_mem_dataloader +from aviary.wrenformer.model import Wrenformer -def test_wrenformer_regression(df_matbench_phonons_wyckoff): - train_df = df_matbench_phonons_wyckoff.sample(frac=0.8, random_state=0) - test_df = df_matbench_phonons_wyckoff.drop(train_df.index) - test_metrics, *_ = train_wrenformer( - run_name="wrenformer", - train_df=train_df, - test_df=test_df, - target_col="last phdos peak", - task_type="regression", - model_params=dict(n_attn_layers=2), - epochs=30, +@pytest.fixture +def base_config(): + return { + "robust": True, + "ensemble": 2, + "run_id": 1, + "data_seed": 42, + "log": False, + "sample": 1, + "test_size": 0.2, + } + + +@pytest.fixture +def model_architecture(): + return { + "d_model": 128, + "n_attn_layers": 2, + "n_attn_heads": 4, + "trunk_hidden": (1024, 512), + "out_hidden": (256, 128, 64), + "embedding_aggregations": ("mean",), + } + + +@pytest.fixture +def training_config(): + return { + "resume": False, + "fine_tune": None, + "transfer": None, + "optim": "AdamW", + "learning_rate": 3e-4, + "momentum": 0.9, + "weight_decay": 1e-6, + "batch_size": 128, + "workers": 0, + "device": "cuda" if torch.cuda.is_available() else "cpu", + } + + +def test_wrenformer_regression( + df_matbench_phonons_wyckoff, base_config, model_architecture, training_config +): + target_name = "last phdos peak" + task = "regression" + losses = ["L1"] + epochs = 25 + model_name = "wrenformer-reg-test" + input_col = "protostructure" + embedding_type = "protostructure" + + task_dict = dict(zip([target_name], [task], strict=False)) + loss_dict = dict(zip([target_name], losses, strict=False)) + + train_idx = list(range(len(df_matbench_phonons_wyckoff))) + train_idx, test_idx = split( + train_idx, + random_state=base_config["data_seed"], + test_size=base_config["test_size"], + ) + + train_df = df_matbench_phonons_wyckoff.iloc[train_idx[0 :: base_config["sample"]]] + test_df = df_matbench_phonons_wyckoff.iloc[test_idx] + val_df = test_df # Using test set for validation + + data_loader_kwargs = dict( + id_col="material_id", + input_col=input_col, + target_col=target_name, + embedding_type=embedding_type, + device=training_config["device"], + ) + + train_loader = df_to_in_mem_dataloader( + train_df, + batch_size=training_config["batch_size"], + shuffle=True, + **data_loader_kwargs, + ) + + val_loader = df_to_in_mem_dataloader( + val_df, + batch_size=training_config["batch_size"] * 16, + shuffle=False, + **data_loader_kwargs, ) - assert test_metrics["MAE"] < 260, test_metrics - assert test_metrics["RMSE"] < 420, test_metrics - assert test_metrics["R2"] > 0.1, test_metrics + setup_params = { + "optim": training_config["optim"], + "learning_rate": training_config["learning_rate"], + "weight_decay": training_config["weight_decay"], + "momentum": training_config["momentum"], + "device": training_config["device"], + } + + restart_params = { + "resume": training_config["resume"], + "fine_tune": training_config["fine_tune"], + "transfer": training_config["transfer"], + } + + n_targets = [1] # Regression task has 1 target + + model_params = { + "task_dict": task_dict, + "robust": base_config["robust"], + "n_targets": n_targets, + "n_features": train_loader.tensors[0][0].shape[-1], + **model_architecture, + } + train_ensemble( + model_class=Wrenformer, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + epochs=epochs, + train_loader=train_loader, + val_loader=val_loader, + log=base_config["log"], + setup_params=setup_params, + restart_params=restart_params, + model_params=model_params, + loss_dict=loss_dict, + ) -def test_wrenformer_classification(df_matbench_phonons_wyckoff): - train_df = df_matbench_phonons_wyckoff.sample(frac=0.8, random_state=0) - test_df = df_matbench_phonons_wyckoff.drop(train_df.index) + test_loader = df_to_in_mem_dataloader( + test_df, + batch_size=training_config["batch_size"] * 64, + shuffle=False, + **data_loader_kwargs, + ) - test_metrics, _, _ = train_wrenformer( - run_name="wrenformer-robust", - train_df=train_df, - test_df=test_df, - target_col="phdos_clf", - task_type="classification", - epochs=10, + results_dict = results_multitask( + model_class=Wrenformer, + model_name=model_name, + run_id=base_config["run_id"], + ensemble_folds=base_config["ensemble"], + test_loader=test_loader, + robust=base_config["robust"], + task_dict=task_dict, + device=training_config["device"], + eval_type="checkpoint", + save_results=False, ) - assert test_metrics["accuracy"] > 0.7, test_metrics - assert test_metrics["ROCAUC"] > 0.8, test_metrics + preds = results_dict[target_name]["preds"] + targets = results_dict[target_name]["targets"] + + y_ens = np.mean(preds, axis=0) + mae, rmse, r2 = get_metrics(targets, y_ens, task).values() + + assert len(targets) == len(test_df) + assert r2 > 0.7 + assert mae < 150 + assert rmse < 300 + + +if __name__ == "__main__": + pytest.main(["-v", __file__])