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clean: make pip installable, remove unused files, ruff, add license

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Simon Mathis
2025-08-14 14:34:42 -07:00
parent da0a3b45e7
commit cd36068cd7
7 changed files with 91 additions and 453 deletions
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28
LICENSE.md Normal file
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@@ -0,0 +1,28 @@
BSD 3-Clause License
Copyright (c) 2025, Institute for Protein Design, University of Washington
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

13
environment.yaml
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@@ -1,13 +0,0 @@
name: modelhub
channels:
- nvidia/label/cuda-12.6.0
- conda-forge
- defaults
dependencies:
- pip
- python=3.12
- cuda
- pytorch=2.7
- openbabel=3.1.1
- pip:
- -r file:requirements.txt

2
lib/atomworks-dev

Submodule lib/atomworks-dev updated: 4f020cf7f4...cd9e8b76d1

49
pyproject.toml
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@@ -1,11 +1,51 @@
[project]
name = "modelhub"
name = "rf3"
dynamic = ["version"]
description = "Base repository for models at the University of Washington's Institute for Protein Design"
description = "Open-source biomolecular structure prediction for all molecules of life."
readme = "README.md"
requires-python = ">= 3.10"
requires-python = ">= 3.12"
authors = [
{ name = "Bakerlab", email = "" },
{ name = "Institute for Protein Design", email = "contact@ipd.uw.edu" },
]
license = { file = "LICENSE.md" }
dependencies = [
# ...ml tools
"torch>=2.2.0,<3",
"lightning>=2.4.0,<2.5",
"einops>=0.8.0,<1",
"einx>=0.1.0,<1",
"opt_einsum>=3.4.0,<4",
"dm-tree>=0.1.6,<1",
# ... kernels
"cuequivariance_ops_cu12>=0.5.0",
"cuequivariance_ops_torch_cu12>=0.5.0",
"cuequivariance_torch>=0.5.0",
# ... configuration & CLI
"hydra-core>=1.3.0,<1.4",
"environs>=11.0.0,<12",
# ... logging
"wandb>=0.15.10,<1",
"rich>=13.9.4,<14",
# ... typing & documentation
"jaxtyping>=0.2.17,<1",
"beartype>=0.18.0,<1",
]
[project.optional-dependencies]
dev = [
# Linters & formatters
"ruff==0.8.3",
# Debugger/interactive
"debugpy>=1.8.5,<2",
"ipykernel>=6.29.4,<7",
# Testing tools
"pytest>=8.2.0,<9", # testing framework
"pytest-testmon>=2.1.1,<3", # run only tests related to changed code
"pytest-xdist>=3.6.1,<4", # run tests in parallel
"pytest-dotenv>=0.5.2,<1", # load environment variables from .env file
"pytest-cov>=4.1.0,<5", # generate coverage report
"pytest-benchmark>=5.0.0,<6", # benchmark tests for speed
]
# Build settings ----------------------------------------------------------------------
@@ -65,7 +105,6 @@ exclude = [
"*.ipynb",
"dev.py",
"archive",
"src/modelhub/utils/predicted_error.py", # TEMPORARY
]
[tool.ruff.format]

77
requirements.txt
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@@ -1,77 +0,0 @@
# Core ML dependencies
# NOTE: torch, torchvision, torchaudio are already included in NGC container
lightning>=2.4.0,<2.5
# Small molecule libraries
rdkit>=2024.3.5
# TODO: Remove OpenBabel dependency
# openbabel will be installed via apt-get (pip installation fails due to C++ build dependencies)
# Project-related dependencies
# ... generic tools
GitPython>=3.0.0,<4
cython>=3.0.0,<4
cytoolz>=0.12.3,<1
assertpy>=1.1.0,<2 # TODO: remove this dependency
tqdm>=4.65.0,<5
rootutils>=1.0.7,<1.1
dm-tree>=0.1.6,<1 # TODO: remove this dependency
deepdiff>=8.0.0,<9 # TODO: remove this dependency
# ... configuration & CLI
fire>=0.6.0,<1
hydra-core>=1.3.0,<1.4
environs>=11.0.0,<12
# ... linear algebra, maths & ml
numpy>=1.25.0,<2
scipy>=1.13.1,<2
einops>=0.8.0,<1
einx>=0.1.0,<1
opt_einsum>=3.4.0,<4
scikit-learn>=1.6.1,<2
# ... kernels
cuequivariance_ops_cu12>=0.5.0
cuequivariance_ops_torch_cu12>=0.5.0
cuequivariance_torch>=0.5.0
# ... data tools
pandas>=2.2,<2.3
pyarrow>=17.0.0
fastparquet>=2024.5.0
seaborn>=0.13.0,<1
# ... bioinformatics
biopython>=1.83,<2
py3Dmol>=2.2.1,<3
pymol-remote>=0.0.5
biotite==1.3.0 # Fixed version - updating may involve breaking changes
hydride==1.2.3 # Fixed version - updating may involve breaking changes
# ... logging
wandb>=0.15.10,<1
rich>=13.9.4,<14
# Formatting & linting (only needed for development)
ruff==0.8.3
pre-commit==3.7.1
# Debugger & interactive tools (only needed for development)
debugpy>=1.8.5,<2
ipykernel>=6.29.4,<7
icecream>=2.0.0,<3
pymol-remote>=0.1.0
ipdb>=0.13.9
# Pytest plugins (only needed for development)
pytest>=8.2.0,<9
pytest-testmon>=2.1.1,<3
pytest-xdist>=3.6.1,<4
pytest-dotenv>=0.5.2,<1
pytest-cov>=4.1.0,<5
pytest-benchmark>=5.0.0,<6
# Typing & documentation (only needed for development)
jaxtyping>=0.2.17,<1
beartype>=0.18.0,<1

345
src/modelhub/callbacks/experimental_validation.py
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@@ -1,345 +0,0 @@
import warnings
import numpy as np
import pandas as pd
from beartype.typing import Any
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (
accuracy_score,
confusion_matrix,
roc_auc_score,
)
from sklearn.preprocessing import LabelEncoder
from modelhub.callbacks.base import BaseCallback
from modelhub.utils.ddp import RankedLogger
from modelhub.utils.logging import print_df_as_table
# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")
ranked_logger = RankedLogger(__name__, rank_zero_only=True)
def clean_and_encode(
X: pd.DataFrame, y: pd.Series
) -> tuple[pd.DataFrame, np.ndarray, LabelEncoder]:
"""Preprocess experimental data and encode categorical variables
Args:
X: Feature matrix
y: Target vector
Returns:
Processed features, encoded target, and fitted label encoder
"""
# Convert all columns to numeric where possible
for col in X.columns:
X[col] = pd.to_numeric(X[col], errors="coerce")
# Check for missing values
missing_count = X.isnull().sum().sum()
if missing_count > 0:
ranked_logger.warning(
f"Found {missing_count} missing values in feature matrix. Dropping rows."
)
X = X.dropna()
y = y.loc[X.index]
# Encode target variable
label_encoder = LabelEncoder()
y_encoded = label_encoder.fit_transform(y)
return X, y_encoded, label_encoder
def train_model(
X_train: pd.DataFrame, y_train: np.ndarray, model_params: dict | None = None
) -> RandomForestClassifier:
"""Train Random Forest classifier
Args:
X_train: Training features
y_train: Training target
model_params: Parameters for RandomForestClassifier
Returns:
Trained Random Forest classifier
"""
# Default parameters
default_params = {
"n_estimators": 20,
"max_depth": 2,
"random_state": 42,
"class_weight": "balanced",
}
# Use provided parameters or defaults
params = {**default_params, **(model_params or {})}
# Initialize and train model
model = RandomForestClassifier(**params)
model.fit(X_train, y_train)
return model
def evaluate(
model: RandomForestClassifier,
X: pd.DataFrame,
y: np.ndarray,
label_encoder: LabelEncoder,
split_name: str,
) -> dict[str, Any]:
"""Evaluate model on a dataset split and generate metrics from predictions
Args:
model: Trained classifier
X: Feature matrix
y: Target vector
label_encoder: Fitted label encoder
split_name: Dataset split (train, validation, test)
Returns:
Dictionary of evaluation metrics
"""
# Generate predictions and probabilities
y_pred = model.predict(X)
y_proba = model.predict_proba(X)
# Get class index for "ACTIVE" (assuming binary classification)
if "ACTIVE" not in label_encoder.classes_:
ranked_logger.warning(
"Class 'ACTIVE' not found in label encoder. Cannot compute experimental metrics."
)
return {}
active_class_index = list(label_encoder.classes_).index("ACTIVE")
# Calculate metrics (binary classification)
accuracy = accuracy_score(y, y_pred)
auc = roc_auc_score(y == active_class_index, y_proba[:, active_class_index])
conf_matrix = confusion_matrix(y, y_pred)
# Create metrics summary DataFrame
metrics_summary = pd.DataFrame(
{"Metric": ["Accuracy", "ROC AUC"], "Value": [accuracy, auc]}
)
# Print metrics summary
print_df_as_table(metrics_summary, title=f"{split_name.upper()} SET METRICS")
# Print confusion matrix
conf_df = pd.DataFrame(
conf_matrix, index=label_encoder.classes_, columns=label_encoder.classes_
)
conf_df.index.name = "True"
conf_df.columns.name = "Predicted"
print_df_as_table(conf_df, title=f"{split_name.upper()} CONFUSION MATRIX")
# Return metrics
return {
"accuracy": accuracy,
"auc": auc,
"confusion_matrix": conf_matrix,
}
def evaluate_model_on_all_splits(
model: RandomForestClassifier,
X_train: pd.DataFrame,
y_train: np.ndarray,
X_valid: pd.DataFrame,
y_valid: np.ndarray,
label_encoder: LabelEncoder,
X_test: pd.DataFrame | None = None,
y_test: np.ndarray | None = None,
) -> dict[str, dict[str, Any]]:
"""Evaluate model on train, validation, and (possibly) test splits
Args:
model: Trained classifier
X_train: Training features
y_train: Training target
X_valid: Validation features
y_valid: Validation target
label_encoder: Original label encoder
X_test: Test features (optional)
y_test: Test target (optional)
Returns:
Dictionary of evaluation metrics for all datasets
"""
# Define datasets to evaluate (train, validation are required)
datasets = [("train", X_train, y_train), ("validation", X_valid, y_valid)]
if X_test is not None and y_test is not None:
datasets.append(("test", X_test, y_test))
# Evaluate and store metrics for all splits (train, validation, test)
all_metrics = {}
for name, X, y in datasets:
all_metrics[name] = evaluate(model, X, y, label_encoder, name)
return all_metrics
def fit_and_evaluate(
df: pd.DataFrame,
feature_metrics: list[str],
model_params: dict | None = None,
labels_to_include: list[str] | None = ["ACTIVE", "INACTIVE"],
datasets: list[str] | None = None,
) -> dict:
"""Train models for each dataset and evaluate classification performance across train, validation, and test splits
Args:
df: DataFrame containing features and targets, grouped by dataset (distinct from "split")
feature_metrics: List of metric prefixes to use as features
model_params: Parameters for RandomForestClassifier
labels_to_include: List of labels to include in the target variable
datasets: List of datasets to process (if None, process all datasets in df)
Returns:
Dictionary of evaluation metrics for all datasets and all models
"""
# Fit a model for each dataset
results_by_dataset = {}
datasets_to_fit = datasets or df["dataset"].unique()
for dataset in datasets_to_fit:
ranked_logger.info(f"Processing dataset: {dataset}")
# ... subset data for the current dataset
dataset_df = df[df["dataset"] == dataset].copy()
assert len(dataset_df) > 0, f"No data found for dataset {dataset}!"
# ... filter out labels not in "labels_to_include"
if labels_to_include:
before_count = len(dataset_df)
dataset_df = dataset_df[
dataset_df["extra_info.activity_bin"].isin(labels_to_include)
]
after_count = len(dataset_df)
if before_count > after_count:
ranked_logger.info(
f"Filtered out {before_count - after_count} samples not in {labels_to_include}. Remaining: {after_count}"
)
# Extract target
if "extra_info.activity_bin" not in dataset_df.columns:
ranked_logger.warning(
f"Target column 'extra_info.activity_bin' not found in dataset {dataset}. Skipping."
)
continue
y = dataset_df["extra_info.activity_bin"]
# Extract features
feature_cols = dataset_df.columns[
dataset_df.columns.str.startswith(tuple(feature_metrics))
]
if len(feature_cols) == 0:
ranked_logger.warning(
f"No feature columns found for dataset {dataset}. Skipping."
)
continue
X = dataset_df[feature_cols]
# Preprocess data
X, y, label_encoder = clean_and_encode(X, y)
train_mask = dataset_df["extra_info.set"] == "train"
valid_mask = dataset_df["extra_info.set"] == "valid"
test_mask = dataset_df["extra_info.set"] == "test"
X_train, y_train = X[train_mask.values], y[train_mask.values]
X_valid, y_valid = X[valid_mask.values], y[valid_mask.values]
X_test, y_test = (
(X[test_mask.values], y[test_mask.values])
if test_mask.any()
else (None, None)
)
# Train model
model = train_model(X_train, y_train, model_params)
# Evaluate model
metrics = evaluate_model_on_all_splits(
model=model,
X_train=X_train,
y_train=y_train,
X_valid=X_valid,
y_valid=y_valid,
X_test=X_test,
y_test=y_test,
label_encoder=label_encoder,
)
# Store results
results_by_dataset[dataset] = {
"model": model,
"label_encoder": label_encoder,
"metrics": metrics,
}
return results_by_dataset
class FitAndEvaluateOnExperimentalDataCallback(BaseCallback):
def __init__(
self,
feature_metrics: list[str],
model_params: dict | None = None,
datasets: list[str] | None = None,
):
"""Callback to fit and evaluate models on experimental data
Args:
feature_metrics: List of metric prefixes to use as input features
model_params: Parameters for RandomForestClassifier
datasets: List of datasets to process (if None, process all datasets in df)
"""
super().__init__()
self.feature_metrics = feature_metrics
self.model_params = model_params
self.datasets = datasets
def on_validation_epoch_end(self, trainer: Any):
# Only fit and evaluate on experimental data for the global zero rank
if not trainer.fabric.is_global_zero:
return
# Check if validation results are available
assert hasattr(
trainer, "validation_results_path"
), "Results path not found! Ensure that StoreValidationMetricsInDFCallback is called first."
# Load validation results
df = pd.read_csv(trainer.validation_results_path)
# Subset to current epoch
current_epoch = trainer.state["current_epoch"]
df = df[df["epoch"] == current_epoch]
# Fit and evaluate models
try:
results = fit_and_evaluate(
df=df,
feature_metrics=self.feature_metrics,
model_params=self.model_params,
datasets=self.datasets,
)
# Log to Fabric
for dataset, result in results.items():
for split, metrics in result["metrics"].items():
for metric in ["accuracy", "auc"]:
trainer.fabric.log_dict(
{f"val/exp/{dataset}/{split}/{metric}": metrics[metric]},
step=trainer.state["current_epoch"],
)
except ValueError as e:
ranked_logger.error(
f"Error during experimental model fitting/evaluation: {e}"
)

24
src/modelhub/utils/predicted_error.py
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@@ -1,6 +1,7 @@
import itertools
from typing import List
import einops
import numpy as np
import pandas as pd
import torch
@@ -18,7 +19,6 @@ from modelhub.metrics.metric_utils import (
spread_batch_into_dictionary,
unbin_logits,
)
import einops
def get_mean_atomwise_plddt(
@@ -36,21 +36,25 @@ def get_mean_atomwise_plddt(
Returns:
plddt: Tensor of shape [B,] with the mean atom-wise pLDDT for each batch
"""
assert plddt_logits.ndim == 3, "plddt_logits must be a 3D tensor (B, n_token, max_atoms_in_a_token * n_bins)"
assert (
plddt_logits.ndim == 3
), "plddt_logits must be a 3D tensor (B, n_token, max_atoms_in_a_token * n_bins)"
# TODO: Replace with the last dimension of is_real_atom; right now that number is too large (36) because it includes hydrogens
max_atoms_in_a_token = NHEAVY
# Since the pLDDT logits have the last dimension (max_atoms_in_a_token * n_bins), we can calculate n_bins directly
assert plddt_logits.shape[-1] % max_atoms_in_a_token == 0, "The last dimension of plddt_logits must be divisible by max_atoms_in_a_token!"
assert (
plddt_logits.shape[-1] % max_atoms_in_a_token == 0
), "The last dimension of plddt_logits must be divisible by max_atoms_in_a_token!"
n_bins = plddt_logits.shape[-1] // max_atoms_in_a_token
# ... reshape to match what unbin_logits expects
reshaped_plddt_logits = einops.rearrange(
plddt_logits,
'... n_token (max_atoms_in_a_token n_bins) -> ... n_bins n_token max_atoms_in_a_token',
"... n_token (max_atoms_in_a_token n_bins) -> ... n_bins n_token max_atoms_in_a_token",
max_atoms_in_a_token=max_atoms_in_a_token,
n_bins=n_bins
n_bins=n_bins,
).float() # [..., n_token, n_bins * max_atoms_in_a_token] -> [ ..., n_bins, n_token, max_atoms_in_a_token]
plddt = unbin_logits(
@@ -153,7 +157,9 @@ def compile_af3_confidence_outputs(
# Aggregate confidence data
confidence_data = {
"example_id": example_id,
"mean_plddt": spread_batch_into_dictionary(compute_mean_over_subsampled_pairs(plddt, is_real_atom[..., : NHEAVY])),
"mean_plddt": spread_batch_into_dictionary(
compute_mean_over_subsampled_pairs(plddt, is_real_atom[..., :NHEAVY])
),
"mean_pae": spread_batch_into_dictionary(pae.mean(dim=(-1, -2))),
"mean_pde": spread_batch_into_dictionary(pde.mean(dim=(-1, -2))),
"chain_wise_mean_plddt": plddt_chainwise,
@@ -300,9 +306,9 @@ def compute_batch_indices_with_lowest_predicted_error(
complex_pae = pae_logits_unbinned.mean(dim=(1, 2))
complex_pde = pde_logits_unbinned.mean(dim=(1, 2))
complex_plddt = (
plddt_logits_unbinned * is_real_atom[..., : NHEAVY]
).sum(dim=(1, 2)) / is_real_atom[..., : NHEAVY].sum()
complex_plddt = (plddt_logits_unbinned * is_real_atom[..., :NHEAVY]).sum(
dim=(1, 2)
) / is_real_atom[..., :NHEAVY].sum()
return_dict["pae_idx"] = torch.argmin(complex_pae)
return_dict["pde_idx"] = torch.argmin(complex_pde)