Add support for GPytorch

CHANGELOG.md

@@ -4,9 +4,11 @@
 ## New
 * Add interface to scikit regressors (#85)
 * Add interface to HDF5 to store the training results (#88)
+* Add interface to GPyTorch (#90)
 
 ## Changed
 * Fix prediction functionality (#81)
+* Return predicted and expected values scaled back (#90)
 
 # 0.5.0 [04/05/2021]
 ## Changed

README.rst

@@ -1,14 +1,12 @@
 
-.. image:: https://api.codacy.com/project/badge/Grade/e410d9da7b654d2caf67481f33ae2de7
-    :target: https://www.codacy.com/app/nlesc-jcer/swan?utm_source=github.com&utm_medium=referral&utm_content=nlesc-nano/swan&utm_campaign=Badge_Grade
-.. image:: https://readthedocs.org/projects/swan/badge/?version=latest
-   :target: https://swan.readthedocs.io/en/latest/?badge=latest
 .. image:: https://github.com/nlesc-nano/swan/workflows/build%20with%20conda/badge.svg
    :target: https://github.com/nlesc-nano/swan/actions
 .. image:: https://codecov.io/gh/nlesc-nano/swan/branch/main/graph/badge.svg?token=1527ficjjx
    :target: https://codecov.io/gh/nlesc-nano/swan
 .. image:: https://zenodo.org/badge/191957101.svg
    :target: https://zenodo.org/badge/latestdoi/191957101
+.. image:: https://readthedocs.org/projects/swan/badge/?version=latest
+   :target: https://swan.readthedocs.io/en/latest/?badge=latest
 
 #####################################
 Screening Workflows And Nanomaterials

scripts/predict.py → scripts/predict_torch.py

@@ -16,7 +16,7 @@
 torch.set_default_dtype(torch.float32)
 
 path_files = Path("data/Carboxylic_acids/CDFT")
-PATH_DATA = "smiles.csv"  # path_files / "cdft_random_500.csv"
+PATH_DATA = path_files / "cdft_random_500.csv"
 
 # Datasets
 NUMLABELS = 1

scripts/run_gp_models.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+
+import logging
+from pathlib import Path
+
+import torch
+
+from swan.dataset import FingerprintsData, split_dataset
+from swan.modeller import GPModeller
+from swan.modeller.models import GaussianProcess
+from swan.utils.log_config import configure_logger
+from swan.utils.plot import create_confidence_plot, create_scatter_plot
+
+# Starting logger
+configure_logger(Path("."))
+LOGGER = logging.getLogger(__name__)
+
+# Set float size default
+torch.set_default_dtype(torch.float32)
+
+# Path to the DATASET
+path_data = Path("tests/files/thousand.csv")
+
+# Training variables
+nepoch = 100
+properties = [
+    # "Dissocation energy (nucleofuge)",
+    # "Dissociation energy (electrofuge)",
+    # "Electroaccepting power(w+)",
+    # "Electrodonating power (w-)",
+    # "Electronegativity (chi=-mu)",
+    # "Electronic chemical potential (mu)",
+    # "Electronic chemical potential (mu+)",
+    # "Electronic chemical potential (mu-)",
+    # "Electrophilicity index (w=omega)",
+    # "Global Dual Descriptor Deltaf+",
+    # "Global Dual Descriptor Deltaf-",
+    "Hardness (eta)",
+    # "Hyperhardness (gamma)",
+    # "Net Electrophilicity",
+    # "Softness (S)"
+]
+num_labels = len(properties)
+
+# Datasets
+data = FingerprintsData(path_data, properties=properties, sanitize=False)
+
+# Split the data into training and validation set
+partition = split_dataset(data.fingerprints, data.labels, frac=(0.8, 0.2))
+
+# Model
+model = GaussianProcess(partition.features_trainset, partition.labels_trainset.flatten())
+
+# training and validation
+researcher = GPModeller(model, data, use_cuda=False, replace_state=True)
+researcher.set_optimizer("Adam", lr=0.5)
+researcher.set_scheduler(None)
+trained_multivariate, expected_train = researcher.train_model(nepoch, partition)
+
+# # Print validation scatterplot
+print("validation regression")
+multi, label_validset = researcher.validate_model()
+
+create_confidence_plot(
+    multi, label_validset.flatten(), properties[0], "validation_scatterplot")
+
+create_scatter_plot(
+    multi.mean.reshape(-1, 1), label_validset, properties, "simple_scatterplot")

scripts/run_scikit_models.py

@@ -11,6 +11,7 @@
 from swan.dataset import FingerprintsData
 from swan.modeller import SKModeller
 from swan.utils.log_config import configure_logger
+from swan.utils.plot import create_scatter_plot
 
 configure_logger(Path("."))
 
@@ -27,26 +28,26 @@
 
 # Training variables
 properties = [
-    "Dissocation energy (nucleofuge)",
-    "Dissociation energy (electrofuge)",
-    "Electroaccepting power(w+)",
-    "Electrodonating power (w-)",
-    "Electronegativity (chi=-mu)",
-    "Electronic chemical potential (mu)",
-    "Electronic chemical potential (mu+)",
-    "Electronic chemical potential (mu-)",
-    "Electrophilicity index (w=omega)",
-    "Global Dual Descriptor Deltaf+",
-    "Global Dual Descriptor Deltaf-",
+    # "Dissocation energy (nucleofuge)",
+    # "Dissociation energy (electrofuge)",
+    # "Electroaccepting power(w+)",
+    # "Electrodonating power (w-)",
+    # "Electronegativity (chi=-mu)",
+    # "Electronic chemical potential (mu)",
+    # "Electronic chemical potential (mu+)",
+    # "Electronic chemical potential (mu-)",
+    # "Electrophilicity index (w=omega)",
+    # "Global Dual Descriptor Deltaf+",
+    # "Global Dual Descriptor Deltaf-",
     "Hardness (eta)",
-    "Hyperhardness (gamma)",
-    "Net Electrophilicity",
-    "Softness (S)"
+    # "Hyperhardness (gamma)",
+    # "Net Electrophilicity",
+    # "Softness (S)"
 ]
 
 
 def run_all(path_data: str, output_file: str):
-    nruns = 3
+    nruns = 1
     models = ["gaussian_process"]  # ["decision_tree", "svm"]
     rvalues = {}
     for p in properties:
@@ -65,10 +66,11 @@ def run_all(path_data: str, output_file: str):
 
 def run_scikit_model(name_model: str, data: FingerprintsData):
     parameters = dict_parameters[name_model]
-    modeller = SKModeller(data, name_model, **parameters)
+    modeller = SKModeller(name_model, data, **parameters)
     modeller.train_model()
     predicted, expected = modeller.validate_model()
-    reg = stats.linregress(predicted, expected.flatten())
+    create_scatter_plot(predicted, expected, ["Hardness (eta)"], "scikit_validation")
+    reg = stats.linregress(predicted.flatten(), expected.flatten())
     return reg.rvalue
 
 

scripts/run_model.py → scripts/run_torch_models.py

@@ -4,7 +4,7 @@
 from pathlib import Path
 import torch
 from swan.dataset import TorchGeometricGraphData, FingerprintsData, DGLGraphData
-from swan.modeller import Modeller
+from swan.modeller import TorchModeller
 from swan.modeller.models import FingerprintFullyConnected, MPNN, SE3Transformer
 from swan.utils.log_config import configure_logger
 from swan.utils.plot import create_scatter_plot
@@ -70,7 +70,7 @@
 
 # training and validation
 torch.set_default_dtype(torch.float32)
-researcher = Modeller(net, data, use_cuda=False)
+researcher = TorchModeller(net, data, use_cuda=False)
 researcher.set_optimizer("Adam", lr=0.0005)
 researcher.set_scheduler("StepLR", 0.1)
 researcher.data.scale_labels()

setup.py

@@ -37,7 +37,7 @@
     install_requires=[
         'e3nn@git+https://github.com/e3nn/e3nn@main',
         'equivariant_attention@git+https://github.com/nlesc-nano/se3-transformer-public@dev',
-        'h5py', 'mendeleev', 'numpy', 'pandas', 'pyyaml', 'scikit-learn',
+        'gpytorch', 'h5py', 'mendeleev', 'numpy', 'pandas', 'pyyaml', 'scikit-learn',
         'scipy', 'seaborn', 'schema',
         'torch-geometric'],
 

swan/__init__.py

@@ -1,11 +1,11 @@
 """Swan API."""
 from .__version__ import __version__
-
-from .modeller import Modeller, SKModeller
-from swan.dataset import TorchGeometricGraphData, FingerprintsData, DGLGraphData
-from .modeller.models import FingerprintFullyConnected, MPNN, SE3Transformer
+from .dataset import DGLGraphData, FingerprintsData, TorchGeometricGraphData
+from .modeller import SKModeller, TorchModeller
+from .modeller.models import (MPNN, FingerprintFullyConnected, GaussianProcess,
+                              SE3Transformer)
 
 __all__ = [
-    "__version__", "Modeller", "SKModeller",
+    "__version__", "TorchModeller", "SKModeller",
     "TorchGeometricGraphData", "FingerprintsData", "DGLGraphData",
-    "FingerprintFullyConnected", "MPNN", "SE3Transformer"]
+    "FingerprintFullyConnected", "MPNN", "SE3Transformer", "GaussianProcess"]

swan/dataset/__init__.py

@@ -1,5 +1,6 @@
+from .dgl_graph_data import DGLGraphData
 from .fingerprints_data import FingerprintsData
+from .splitter import split_dataset, load_split_dataset
 from .torch_geometric_graph_data import TorchGeometricGraphData
-from .dgl_graph_data import DGLGraphData
 
-__all__ = ["DGLGraphData", "FingerprintsData", "TorchGeometricGraphData"]
+__all__ = ["DGLGraphData", "FingerprintsData", "TorchGeometricGraphData", "load_split_dataset", "split_dataset"]

swan/dataset/splitter.py

@@ -0,0 +1,52 @@
+from typing import Generic, NamedTuple, Tuple, TypeVar, Union
+
+import numpy as np
+import torch
+
+from ..state import StateH5
+from ..type_hints import PathLike
+
+T_co = TypeVar('T_co', bound=Union[np.ndarray, torch.Tensor], covariant=True)
+
+
+class SplitDataset(NamedTuple, Generic[T_co]):
+    indices: np.ndarray       # Shuffled indices to split the data
+    ntrain: int               # Number of points used for training
+    features_trainset: T_co   # Features for training
+    features_validset: T_co   # Features for validation
+    labels_trainset: T_co     # Labels for training
+    labels_validset: T_co     # Labels for validation
+
+
+def split_dataset(features: T_co, labels: T_co, frac: Tuple[float, float] = (0.8, 0.2)) -> SplitDataset:
+    """Split the fingerprint dataset into a training and validation set.
+
+    Parameters
+    ----------
+    features
+        Dataset features
+    labels
+        Dataset labels
+    frac
+        fraction to divide the dataset, by default [0.8, 0.2]
+    """
+    # Generate random indices to train and validate the model
+    size = len(features)
+    indices = np.arange(size)
+    np.random.shuffle(indices)
+
+    ntrain = int(size * frac[0])
+    features_trainset = features[indices[:ntrain]]
+    features_validset = features[indices[ntrain:]]
+    labels_trainset = labels[indices[:ntrain]]
+    labels_validset = labels[indices[ntrain:]]
+
+    return SplitDataset(indices, ntrain, features_trainset, features_validset, labels_trainset, labels_validset)
+
+
+def load_split_dataset(state_file: PathLike = "swan_state.h5"):
+    """Load the split data used for training from the state file."""
+    state = StateH5(state_file)
+    return SplitDataset(*[
+        state.retrieve_data(x) for x in (
+            'indices', 'ntrain', 'features_trainset', 'features_validset', 'labels_trainset', 'labels_validset')])

swan/modeller/__init__.py

@@ -1,4 +1,5 @@
-from .modeller import Modeller
+from .gp_modeller import GPModeller
 from .scikit_modeller import SKModeller
+from .torch_modeller import TorchModeller
 
-_all__ = ["Modeller", "SKModeller"]
+__all__ = ["GPModeller", "SKModeller", "TorchModeller"]

swan/modeller/base_modeller.py

@@ -17,11 +17,12 @@ class BaseModeller(Generic[T_co]):
     """Base class for the modellers."""
 
     def __init__(self, data: SwanDataBase, replace_state: bool) -> None:
+        self.data = data
         self.state = StateH5(replace_state=replace_state)
         self.smiles = data.dataframe.smiles.to_numpy()
 
     @abc.abstractmethod
-    def train_model(self, frac: Tuple[float, float] = (0.8, 0.2), **kwargs):
+    def train_model(self, nepoch: int, frac: Tuple[float, float] = (0.8, 0.2), **kwargs):
         """Train the model using the given data.
 
         Parameters