From 55043df7de8e2122351e854022056aaac4840512 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Tue, 26 Mar 2024 21:40:59 -0400
Subject: [PATCH 01/50] (modelAdjusters) set up scaffold for the submodule

---
 pysipfenn/core/modelAdjusters.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 pysipfenn/core/modelAdjusters.py

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
new file mode 100644
index 0000000..bb9d59a
--- /dev/null
+++ b/pysipfenn/core/modelAdjusters.py
@@ -0,0 +1,18 @@
+
+
+class LocalAdjuster:
+    """
+    Adjuster class operating on local data provided to model as a pair of (a) CSV file plus directory of POSCAR files
+    or (b) NumPy array of feature vectors and NumPy array of target values. It can then adjust the model with some predefined
+    hyperparameters or run a fairly typical grid search, which can be interpreted manually or uploaded to the ClearML
+    platform.
+    """
+
+class OPTIMADEAdjuster(LocalAdjuster):
+    """
+    Adjuster class operating on data provided by the OPTIMADE API. Primarily geared towards tuning or retraining of the
+    models based on other atomistic databases, or their subsets, accessed through OPTIMADE, to adjust the model to a
+    different domain, which in the context of DFT datasets could mean adjusting the model to predict properties with DFT
+    settings used by that database or focusing its attention to specific chemistry like, for instance, all compounds of
+    Sn and all perovskites. It accepts OPTIMADE query as an input and then operates based on the ``LocalAdjuster`` class.
+    """

From bf83909c5bd1bc71d3c54e86eca512c2d207c21b Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Tue, 26 Mar 2024 22:30:41 -0400
Subject: [PATCH 02/50] (MA) documentation and initialization of
 `LocalAdjuster`

---
 pysipfenn/__init__.py            |  1 +
 pysipfenn/core/modelAdjusters.py | 86 ++++++++++++++++++++++++++++++--
 2 files changed, 84 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/__init__.py b/pysipfenn/__init__.py
index c87ba6a..4721383 100644
--- a/pysipfenn/__init__.py
+++ b/pysipfenn/__init__.py
@@ -1,3 +1,4 @@
 print('Importing from top pySIPFENN namespace...')
 from pysipfenn.core.pysipfenn import *
 from pysipfenn.core.modelExporters import *
+from pysipfenn.core.modelAdjusters import *
diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index bb9d59a..585b56f 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,13 +1,93 @@
+import os
+from typing import Union, Literal
+
+from pysipfenn.core.pysipfenn import Calculator
+import numpy as np
 
 
 class LocalAdjuster:
     """
-    Adjuster class operating on local data provided to model as a pair of (a) CSV file plus directory of POSCAR files
-    or (b) NumPy array of feature vectors and NumPy array of target values. It can then adjust the model with some predefined
+    Adjuster class taking a ``Calculator`` and operating on local data provided to model as a pair of descriptor data
+    (provided in several ways) and target values (provided in several ways). It can then adjust the model with some predefined
     hyperparameters or run a fairly typical grid search, which can be interpreted manually or uploaded to the ClearML
-    platform.
+    platform. Can use CPU, CUDA, or MPS (Mac M1) devices for training.
+
+    Args:
+        calculator: Instance of the ``Calculator`` class with the model to be adjusted, defined and loaded. It can
+            contain the descriptor data already in it, so that it does not have to be provided separately.
+        model: Name of the model to be adjusted in the ``Calculator``. E.g., ``SIPFENN_Krajewski2022_NN30``.
+        targetData: Target data to be used for training the model. It can be provided as a path to a NumPy ``.npy``/
+            ``.NPY`` or CSV ``.csv``/``.CSV`` file, or directly as a NumPy array. It has to be the same length as the
+            descriptor data.
+        descriptorData: Descriptor data to be used for training the model. It can be left unspecified (``None``) to
+            use the data in the ``Calculator``, or provided as a path to a NumPy ``.npy``/``.NPY`` or CSV ``.csv``/
+            ``.CSV`` file, or directly as a NumPy array. It has to be the same length as the target data. Default is
+            ``None``.
+        device: Device to be used for training the model. It Has to be one of the following: ``"cpu"``, ``"cuda"``, or
+            ``"mps"``. Default is ``"cpu"``.
+
+    Attributes:
+        calculator: Instance of the ``Calculator`` class being operated on.
+        model: The original model to be adjusted.
+        adjustedModel: A PyTorch model after the adjustment. Initially set to ``None``.
+        descriptorData: NumPy array with descriptor data to use as input for the model.
+        targetData: NumPy array with target data to use as output for the model.
     """
 
+    def __init__(
+            self,
+            calculator: Calculator,
+            model: str,
+            targetData: Union[str, np.ndarray],
+            descriptorData: Union[None, str, np.ndarray] = None,
+            device: Literal["cpu", "cuda", "mps"] = "cpu",
+    ) -> None:
+        self.adjustedModel = None
+
+        assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
+        self.calculator = calculator
+
+        assert isinstance(model, str), "The model must be a string pointing to the model to be adjusted in the Calculator."
+        assert model in self.calculator.models, "The model must be one of the models in the Calculator."
+        assert model in self.calculator.loadedModels, "The model must be loaded in the Calculator."
+        self.model = self.calculator.loadedModels[model]
+        self.model = self.model.to(device=device)
+
+        if descriptorData is None:
+            assert self.calculator.descriptorData is not None, "The descriptor data can be inferred from the data in the Calculator, but no data is present."
+            self.descriptorData = self.calculator.descriptorData
+        elif isinstance(descriptorData, np.ndarray):
+            self.descriptorData = descriptorData
+        elif isinstance(descriptorData, str):
+            # Path to NPY file with data
+            if (descriptorData.endswith(".npy") or descriptorData.endswith(".NPY")) and os.path.exists(descriptorData):
+                self.descriptorData = np.load(descriptorData)
+            # Path to CSV file with data
+            elif (descriptorData.endswith(".csv") or descriptorData.endswith(".CSV")) and os.path.exists(descriptorData):
+                self.descriptorData = np.loadtxt(descriptorData, delimiter=",")
+            else:
+                raise ValueError("If a string is provided as descriptor data parameter, it must be a path to a npy/NPY or csv/CSV file.")
+        else:
+            raise ValueError("The descriptor data must be either (a) None to use the data in the Calculator,"
+                             "(b) a path to a npy/NPY file, or (c) a path to a csv/CSV file.")
+
+        if isinstance(targetData, np.ndarray):
+            self.targetData = targetData
+        elif isinstance(targetData, str):
+            # Path to NPY file with data
+            if (targetData.endswith(".npy") or targetData.endswith(".NPY")) and os.path.exists(targetData):
+                self.targetData = np.load(targetData)
+            # Path to CSV file with data
+            elif (targetData.endswith(".csv") or targetData.endswith(".CSV")) and os.path.exists(targetData):
+                self.targetData = np.loadtxt(targetData, delimiter=",")
+            else:
+                raise ValueError("If a string is provided as target data parameter, it must be a path to a npy/NPY or csv/CSV file.")
+        else:
+            raise ValueError("The target data must be either a path to a npy/NPY file or a path to a csv/CSV file.")
+
+        assert len(self.descriptorData) == len(self.targetData), "The descriptor and target data must have the same length."
+
+
 class OPTIMADEAdjuster(LocalAdjuster):
     """
     Adjuster class operating on data provided by the OPTIMADE API. Primarily geared towards tuning or retraining of the

From 7408aed233f2d446265c4b793c367a02b1780984 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Tue, 26 Mar 2024 23:12:03 -0400
Subject: [PATCH 03/50] (MA) minor initialization additions and 2 plotting
 helper functions

---
 pysipfenn/core/modelAdjusters.py | 53 ++++++++++++++++++++++++++++++--
 1 file changed, 50 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 585b56f..26e7d60 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,9 +1,10 @@
 import os
 from typing import Union, Literal
 
-from pysipfenn.core.pysipfenn import Calculator
 import numpy as np
-
+import torch
+import plotly.express as px
+from pysipfenn.core.pysipfenn import Calculator
 
 class LocalAdjuster:
     """
@@ -25,6 +26,8 @@ class LocalAdjuster:
             ``None``.
         device: Device to be used for training the model. It Has to be one of the following: ``"cpu"``, ``"cuda"``, or
             ``"mps"``. Default is ``"cpu"``.
+        descriptor: Name of the feature vector provided in the descriptorData. It can be optionally provided to
+            check if the descriptor data is compatible.
 
     Attributes:
         calculator: Instance of the ``Calculator`` class being operated on.
@@ -41,17 +44,20 @@ def __init__(
             targetData: Union[str, np.ndarray],
             descriptorData: Union[None, str, np.ndarray] = None,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
+            descriptor: Literal["Ward2017", "KS2022"] = None
     ) -> None:
         self.adjustedModel = None
 
         assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
         self.calculator = calculator
 
+        self.device = torch.device(device)
+
         assert isinstance(model, str), "The model must be a string pointing to the model to be adjusted in the Calculator."
         assert model in self.calculator.models, "The model must be one of the models in the Calculator."
         assert model in self.calculator.loadedModels, "The model must be loaded in the Calculator."
         self.model = self.calculator.loadedModels[model]
-        self.model = self.model.to(device=device)
+        self.model = self.model.to(device=self.device)
 
         if descriptorData is None:
             assert self.calculator.descriptorData is not None, "The descriptor data can be inferred from the data in the Calculator, but no data is present."
@@ -87,6 +93,47 @@ def __init__(
 
         assert len(self.descriptorData) == len(self.targetData), "The descriptor and target data must have the same length."
 
+        if descriptor is not None:
+            if descriptor == "Ward2017":
+                assert self.descriptorData.shape[1] == 271, "The descriptor must have 271 features for the Ward2017 descriptor."
+            elif descriptor == "KS2022":
+                assert self.descriptorData.shape[1] == 256, "The descriptor must have 256 features for the KS2022 descriptor."
+            else:
+                raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
+
+    def plotStarting(self) -> None:
+        """
+        Plot the starting model (before adjustment) on the target data.
+        """
+        self.model.eval()
+        with torch.no_grad():
+            dataIn = torch.from_numpy(np.array(self.descriptorData)).to(device=self.device).float()
+            predictions = self.model(dataIn, None).detach().cpu().numpy().flatten()
+        fig = px.scatter(
+            x=self.targetData.flatten(),
+            y=predictions,
+            labels={
+                "x": "Target Data", "y": "Predictions"},
+            title="Starting (Unadjusted) Model Predictions")
+        fig.show()
+
+    def plotAdjusted(self) -> None:
+        """
+        Plot the adjusted model on the target data.
+        """
+        assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
+        self.adjustedModel.eval()
+        with torch.no_grad():
+            dataIn = torch.from_numpy(np.array(self.descriptorData)).to(device=self.device).float()
+            predictions = self.adjustedModel(dataIn, None).detach().cpu().numpy().flatten()
+        fig = px.scatter(
+            x=self.targetData.flatten(),
+            y=predictions,
+            labels={
+                "x": "Target Data", "y": "Predictions"},
+            title="Adjusted Model Predictions")
+        fig.show()
+
 
 class OPTIMADEAdjuster(LocalAdjuster):
     """

From bd93bde667bac62a6af2981a96f281f16beb06b9 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Tue, 26 Mar 2024 23:16:25 -0400
Subject: [PATCH 04/50] (core) added `plotly` to the requirements

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 119dbd7..064b998 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,7 +48,8 @@ dev = [
     "coremltools>=7.0",
     "onnxconverter_common>=1.14.0",
     "onnxsim==0.4.33",
-    "onnxruntime>=1.16.0"
+    "onnxruntime>=1.16.0",
+    "plotly>=5.15.0"
 ]
 
 [project.urls]

From f1af82873992403ef31c1f7c81a6e6a87d08f661 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 00:22:12 -0400
Subject: [PATCH 05/50] (MA) finalized the key `adjust` routine

---
 pysipfenn/core/modelAdjusters.py | 167 ++++++++++++++++++++++++++++++-
 1 file changed, 164 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 26e7d60..3c1e387 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,8 +1,10 @@
 import os
 from typing import Union, Literal
+from copy import deepcopy
 
 import numpy as np
 import torch
+from torch.utils.data import DataLoader, TensorDataset
 import plotly.express as px
 from pysipfenn.core.pysipfenn import Calculator
 
@@ -28,6 +30,8 @@ class LocalAdjuster:
             ``"mps"``. Default is ``"cpu"``.
         descriptor: Name of the feature vector provided in the descriptorData. It can be optionally provided to
             check if the descriptor data is compatible.
+        useClearML: Whether to use the ClearML platform for logging the training process. Default is ``False``.
+        taskName: Name of the task to be used. Default is ``"LocalFineTuning"``.
 
     Attributes:
         calculator: Instance of the ``Calculator`` class being operated on.
@@ -44,9 +48,13 @@ def __init__(
             targetData: Union[str, np.ndarray],
             descriptorData: Union[None, str, np.ndarray] = None,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
-            descriptor: Literal["Ward2017", "KS2022"] = None
+            descriptor: Literal["Ward2017", "KS2022"] = None,
+            useClearML: bool = False,
+            taskName: str = "LocalFineTuning"
     ) -> None:
         self.adjustedModel = None
+        self.useClearML = useClearML
+        self.taskName = taskName
 
         assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
         self.calculator = calculator
@@ -56,6 +64,7 @@ def __init__(
         assert isinstance(model, str), "The model must be a string pointing to the model to be adjusted in the Calculator."
         assert model in self.calculator.models, "The model must be one of the models in the Calculator."
         assert model in self.calculator.loadedModels, "The model must be loaded in the Calculator."
+        self.modelName = model
         self.model = self.calculator.loadedModels[model]
         self.model = self.model.to(device=self.device)
 
@@ -103,7 +112,7 @@ def __init__(
 
     def plotStarting(self) -> None:
         """
-        Plot the starting model (before adjustment) on the target data.
+        Plot the starting model (before adjustment) on the target data. By default, it will plot in your browser.
         """
         self.model.eval()
         with torch.no_grad():
@@ -119,7 +128,7 @@ def plotStarting(self) -> None:
 
     def plotAdjusted(self) -> None:
         """
-        Plot the adjusted model on the target data.
+        Plot the adjusted model on the target data. By default, it will plot in your browser.
         """
         assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
         self.adjustedModel.eval()
@@ -134,6 +143,158 @@ def plotAdjusted(self) -> None:
             title="Adjusted Model Predictions")
         fig.show()
 
+    def adjust(
+            self,
+            validation: float = 0.2,
+            learningRate: float = 1e-5,
+            epochs: int = 50,
+            batchSize: int = 32,
+            optimizer: Literal["Adam", "AdamW", "Adamax", "RMSprop"] = "Adam",
+            weightDecay: float = 1e-5,
+            lossFunction: Literal["MSE", "MAE"] = "MAE",
+            verbose: bool = True
+    ) -> None:
+        """
+        Takes the original model, copies it, and adjusts the model on the provided data. The adjusted model is stored in
+        the ``adjustedModel`` attribute of the class and can be then persisted to the original ``Calculator`` or used
+        for plotting. The default hyperparameters are selected for fine-tuning the model rather than retraining it, as
+        to slowly adjust it (1% of the typical learning rate) and not overfit it (50 epochs).
+
+        Args:
+            learningRate: The learning rate to be used for the adjustment. Default is ``1e-5`` that is 1% of a typical
+                learning rate of ``Adam`` optimizer.
+            epochs: The number of times to iterate over the data, i.e. how many times the model will see the data.
+                Default is ``50``, which is on the higher side for fine-tuning. If the model does not retrain fast enough
+                but already converged, consider lowering this number to reduce the time and possibly overfitting to the
+                training data.
+            batchSize: The number of points passed to the model at once. Default is ``32``, which is a typical batch size for
+                smaller datasets. If the dataset is large, consider increasing this number to speed up the training.
+            optimizer: Algorithm to be used for optimization. Default is ``Adam``, which is a good choice for most models
+                and one of the most popular optimizers. Other options are
+            lossFunction: Loss function to be used for optimization. Default is ``MAE`` (Mean Absolute Error / L1) that is
+                more robust to outliers than ``MSE`` (Mean Squared Error).
+            validation: Fraction of the data to be used for validation. Default is the common ``0.2`` (20% of the data).
+                If set to ``0``, the model will be trained on the whole dataset without validation and you will not be able
+                to check for overfitting or gauge the model's performance on unseen data.
+            weightDecay: Weight decay to be used for optimization. Default is ``1e-5`` that should work well if data is
+                plaintiful enough relative to the model complexity. If the model is overfitting, consider increasing this
+                number to regularize the model more.
+            verbose: Whether to print information, such as loss, during the training. Default is ``True``.
+
+        Returns:
+            None. The adjusted model is stored in the ``adjustedModel`` attribute of the class.
+        """
+
+        if verbose:
+            print("Loading the data...")
+        ddTensor = torch.from_numpy(self.descriptorData).float().to(device=self.device)
+        tdTensor = torch.from_numpy(self.targetData).float().to(device=self.device)
+        if validation > 0:
+            split = int(len(ddTensor) * (1 - validation))
+            ddTrain, ddVal = ddTensor[:split], ddTensor[split:]
+            tdTrain, tdVal = tdTensor[:split], tdTensor[split:]
+        else:
+            ddTrain, ddVal = ddTensor, None
+            tdTrain, tdVal = tdTensor, None
+
+        datasetTrain = TensorDataset(ddTrain, tdTrain)
+        dataloaderTrain = DataLoader(datasetTrain, batch_size=batchSize, shuffle=True)
+
+        if verbose:
+            print(f'LR: {learningRate} |  Optimizer: {optimizer}  |  Weight Decay: {weightDecay} |  Loss: {lossFunction}')
+        # Training a logging platform. Completely optional and does not affect the training.
+        if self.useClearML:
+            if verbose:
+                print("Using ClearML for logging. Make sure to have (1) their Python package installed and (2) the API key"
+                      " set up according to their documentation. Otherwise you will get an error.")
+            from clearml import Task
+            task = Task.create(project_name=self.taskName,
+                               task_name=f'LR:{learningRate} OPT:{optimizer} WD:{weightDecay} LS:{lossFunction}')
+            task.set_parameters({'lr': learningRate,
+                                 'epochs': epochs,
+                                 'batch_size': batchSize,
+                                 'weight_decay': weightDecay,
+                                 'loss': lossFunction,
+                                 'optimizer': optimizer,
+                                 'model': self.modelName})
+        if verbose:
+            print("Copying and initializing the model...")
+        model = deepcopy(self.model)
+        model.train()
+        if verbose:
+            print("Setting up the training...")
+        if optimizer == "Adam":
+            optimizerClass = torch.optim.Adam
+        elif optimizer == "AdamW":
+            optimizerClass = torch.optim.AdamW
+        elif optimizer == "Adamax":
+            optimizerClass = torch.optim.Adamax
+        elif optimizer == "RMSprop":
+            optimizerClass = torch.optim.RMSprop
+        else:
+            raise NotImplementedError("The optimizer must be one of the following: 'Adam', 'AdamW', 'Adamax', 'RMSprop'.")
+        optimizerInstance = optimizerClass(model.parameters(), lr=learningRate, weight_decay=weightDecay)
+
+        if lossFunction == "MSE":
+            loss = torch.nn.MSELoss()
+        elif lossFunction == "MAE":
+            loss = torch.nn.L1Loss()
+        else:
+            raise NotImplementedError("The loss function must be one of the following: 'MSE', 'MAE'.")
+
+        transferLosses = [float(loss(model(ddTrain, None), tdTrain))]
+        if validation > 0:
+            validationLosses = [float(loss(model(ddVal, None), tdVal))]
+            if verbose:
+                print(
+                    f'Train: {transferLosses[-1]:.4f} | Validation: {validationLosses[-1]:.4f} | Epoch: 0/{epochs}')
+        else:
+            validationLosses = []
+            if verbose:
+                print(f'Train: {transferLosses[-1]:.4f} | Epoch: 0/{epochs}')
+
+
+        for epoch in range(epochs):
+            model.train()
+            for data, target in dataloaderTrain:
+                optimizerInstance.zero_grad()
+                output = model(data, None)
+                lossValue = loss(output, target)
+                lossValue.backward()
+                optimizerInstance.step()
+            transferLosses.append(float(loss(model(ddTrain, None), tdTrain)))
+
+            if validation > 0:
+                model.eval()
+                validationLosses.append(float(loss(model(ddVal, None), tdVal)))
+                model.train()
+                if self.useClearML:
+                    task.get_logger().report_scalar(
+                        title='Loss',
+                        series='Validation',
+                        value=validationLosses[-1],
+                        iteration=epoch+1)
+                if verbose:
+                    print(
+                        f'Train: {transferLosses[-1]:.4f} | Validation: {validationLosses[-1]:.4f} | Epoch: {epoch + 1}/{epochs}')
+            else:
+                if verbose:
+                    print(f'Train: {transferLosses[-1]:.4f} | Epoch: {epoch + 1}/{epochs}')
+
+            if self.useClearML:
+                task.get_logger().report_scalar(
+                    title='Loss',
+                    series='Training',
+                    value=transferLosses[-1],
+                    iteration=epoch+1)
+
+        print("Training finished!")
+        if self.useClearML:
+            task.close()
+        model.eval()
+        self.adjustedModel = model
+        print("All done!")
+
 
 class OPTIMADEAdjuster(LocalAdjuster):
     """

From 6825060b2de938968e894da025867173c76121b5 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 14:43:11 -0400
Subject: [PATCH 06/50] (MA) aded returns to the `adjust` routine

---
 pysipfenn/core/modelAdjusters.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 3c1e387..0287577 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,5 +1,5 @@
 import os
-from typing import Union, Literal
+from typing import Union, Literal, Tuple, List
 from copy import deepcopy
 
 import numpy as np
@@ -153,7 +153,7 @@ def adjust(
             weightDecay: float = 1e-5,
             lossFunction: Literal["MSE", "MAE"] = "MAE",
             verbose: bool = True
-    ) -> None:
+    ) -> Tuple[torch.nn.Module, List[float], List[float]]:
         """
         Takes the original model, copies it, and adjusts the model on the provided data. The adjusted model is stored in
         the ``adjustedModel`` attribute of the class and can be then persisted to the original ``Calculator`` or used
@@ -182,7 +182,8 @@ def adjust(
             verbose: Whether to print information, such as loss, during the training. Default is ``True``.
 
         Returns:
-            None. The adjusted model is stored in the ``adjustedModel`` attribute of the class.
+            A tuple with 3 elements: (1) the adjusted model, (2) training loss list of floats, and (3) validation loss
+            list of floats. The adjusted model is also stored in the ``adjustedModel`` attribute of the class.
         """
 
         if verbose:
@@ -295,6 +296,11 @@ def adjust(
         self.adjustedModel = model
         print("All done!")
 
+        return model, transferLosses, validationLosses
+
+
+
+
 
 class OPTIMADEAdjuster(LocalAdjuster):
     """

From d69151bd76547692868a045b15e129b35b517b95 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 18:01:43 -0400
Subject: [PATCH 07/50] (MA) add a message if ClearML is to be used in case
 someone does it on accident and gets an error

---
 pysipfenn/core/modelAdjusters.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 0287577..2d84a6d 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -54,6 +54,7 @@ def __init__(
     ) -> None:
         self.adjustedModel = None
         self.useClearML = useClearML
+        self.useClearMLMessageDisplayed = False
         self.taskName = taskName
 
         assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
@@ -205,9 +206,10 @@ def adjust(
             print(f'LR: {learningRate} |  Optimizer: {optimizer}  |  Weight Decay: {weightDecay} |  Loss: {lossFunction}')
         # Training a logging platform. Completely optional and does not affect the training.
         if self.useClearML:
-            if verbose:
+            if verbose and not self.useClearMLMessageDisplayed:
                 print("Using ClearML for logging. Make sure to have (1) their Python package installed and (2) the API key"
                       " set up according to their documentation. Otherwise you will get an error.")
+                self.useClearMLMessageDisplayed = True
             from clearml import Task
             task = Task.create(project_name=self.taskName,
                                task_name=f'LR:{learningRate} OPT:{optimizer} WD:{weightDecay} LS:{lossFunction}')

From 3a4b7a0578f241cb0a90a48ce3239d0dbf17be85 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 20:57:48 -0400
Subject: [PATCH 08/50] (MA) improved memory handling during adjustments

---
 pysipfenn/core/modelAdjusters.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 2d84a6d..f81fe9b 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,6 +1,7 @@
 import os
 from typing import Union, Literal, Tuple, List
 from copy import deepcopy
+import gc
 
 import numpy as np
 import torch
@@ -117,7 +118,7 @@ def plotStarting(self) -> None:
         """
         self.model.eval()
         with torch.no_grad():
-            dataIn = torch.from_numpy(np.array(self.descriptorData)).to(device=self.device).float()
+            dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.model(dataIn, None).detach().cpu().numpy().flatten()
         fig = px.scatter(
             x=self.targetData.flatten(),
@@ -134,7 +135,7 @@ def plotAdjusted(self) -> None:
         assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
         self.adjustedModel.eval()
         with torch.no_grad():
-            dataIn = torch.from_numpy(np.array(self.descriptorData)).to(device=self.device).float()
+            dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.adjustedModel(dataIn, None).detach().cpu().numpy().flatten()
         fig = px.scatter(
             x=self.targetData.flatten(),
@@ -296,9 +297,14 @@ def adjust(
             task.close()
         model.eval()
         self.adjustedModel = model
+        del model
+        del optimizerInstance
+        del loss
+        gc.collect()
         print("All done!")
 
         return model, transferLosses, validationLosses
+        return self.adjustedModel, transferLosses, validationLosses
 
 
 

From 2faea84fb4427b139a21a2e167cb9e7769ee4219 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 21:13:25 -0400
Subject: [PATCH 09/50] (MA) implemented a hyperparameter search matrix for 27
 common options; meant mostly for tuning to smaller datasets

---
 pysipfenn/core/modelAdjusters.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f81fe9b..0198f26 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -303,7 +303,6 @@ def adjust(
         gc.collect()
         print("All done!")
 
-        return model, transferLosses, validationLosses
         return self.adjustedModel, transferLosses, validationLosses
 
 

From 47e3fbfcf1706185d6a1e7432123ba5085de65c6 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 21:13:38 -0400
Subject: [PATCH 10/50] (MA) implemented a hyperparameter search matrix for 27
 common options; meant mostly for tuning to smaller datasets

---
 pysipfenn/core/modelAdjusters.py | 161 ++++++++++++++++++++++++++++++-
 1 file changed, 159 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 0198f26..c51c2e8 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,5 +1,5 @@
 import os
-from typing import Union, Literal, Tuple, List
+from typing import Union, Literal, Tuple, List, Dict
 from copy import deepcopy
 import gc
 
@@ -7,6 +7,7 @@
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 import plotly.express as px
+import plotly.graph_objects as go
 from pysipfenn.core.pysipfenn import Calculator
 
 class LocalAdjuster:
@@ -257,7 +258,6 @@ def adjust(
             if verbose:
                 print(f'Train: {transferLosses[-1]:.4f} | Epoch: 0/{epochs}')
 
-
         for epoch in range(epochs):
             model.train()
             for data, target in dataloaderTrain:
@@ -305,7 +305,160 @@ def adjust(
 
         return self.adjustedModel, transferLosses, validationLosses
 
+    def matrixHyperParameterSearch(
+            self,
+            validation: float = 0.2,
+            epochs: int = 100,
+            batchSize: int = 32,
+            lossFunction: Literal["MSE", "MAE"] = "MAE",
+            learningRates: Tuple[float] = (1e-6, 1e-5, 1e-4),
+            optimizers: Tuple[Literal["Adam", "AdamW", "Adamax", "RMSprop"]] = ("Adam", "AdamW", "Adamax"),
+            weightDecays: Tuple[float] = (1e-5, 1e-4, 1e-3),
+            verbose: bool = True,
+            plot: bool = True
+    ) -> Tuple[torch.nn.Module, Dict[str, Union[float, str]]]:
+        """
+        Performs a grid search over the hyperparameters provided to find the best combination. By default, it will
+        plot the training history with plotly in your browser, and (b) print the best hyperparameters found. If the
+        ClearML platform was set to be used for logging (at the class initialization), the results will be uploaded
+        there as well. If the default values are used, it will test 27 combinations of learning rates, optimizers, and
+        weight decays. The method will then adjust the model to the best hyperparameters found, corresponding to the
+        lowest validation loss if validation is used, or the lowest training loss if validation is not used
+        (``validation=0``). Note that the validation is used by default.
+
+        Args:
+            validation: Same as in the ``adjust`` method. Default is ``0.2``.
+            epochs: Same as in the ``adjust`` method. Default is ``100``.
+            batchSize: Same as in the ``adjust`` method. Default is ``32``.
+            lossFunction: Same as in the ``adjust`` method. Default is ``MAE``, i.e. Mean Absolute Error or L1 loss.
+            learningRates: Tuple of floats with the learning rates to be tested. Default is ``(1e-6, 1e-5, 1e-4)``. See
+                the ``adjust`` method for more information.
+            optimizers: Tuple of strings with the optimizers to be tested. Default is ``("Adam", "AdamW", "Adamax")``. See
+                the ``adjust`` method for more information.
+            weightDecays: Tuple of floats with the weight decays to be tested. Default is ``(1e-5, 1e-4, 1e-3)``. See
+                the ``adjust`` method for more information.
+            verbose: Same as in the ``adjust`` method. Default is ``True``.
+            plot: Whether to plot the training history after all the combinations are tested. Default is ``True``.
+        """
+        if verbose:
+            print("Starting the hyperparameter search...")
+
+        bestModel: torch.nn.Module = None
+        bestTrainingLoss: float = np.inf
+        bestValidationLoss: float = np.inf
+        bestHyperparameters: Dict[str, Union[float, str, None]] = {
+            "learningRate": None,
+            "optimizer": None,
+            "weightDecay": None,
+            "epochs": None
+        }
+
+        trainLossHistory: List[List[float]] = []
+        validationLossHistory: List[List[float]] = []
+        labels: List[str] = []
+
+        for learningRate in learningRates:
+            for optimizer in optimizers:
+                for weightDecay in weightDecays:
+                    labels.append(f"LR: {learningRate} | OPT: {optimizer} | WD: {weightDecay}")
+                    model, trainingLoss, validationLoss = self.adjust(
+                        validation=validation,
+                        learningRate=learningRate,
+                        epochs=epochs,
+                        batchSize=batchSize,
+                        optimizer=optimizer,
+                        weightDecay=weightDecay,
+                        lossFunction=lossFunction,
+                        verbose=True
+                    )
+                    trainLossHistory.append(trainingLoss)
+                    validationLossHistory.append(validationLoss)
+                    if validation > 0:
+                        localBestValidationLoss, bestEpoch = min((val, idx) for idx, val in enumerate(validationLoss))
+                        if localBestValidationLoss < bestValidationLoss:
+                            print(f"New best model found with LR: {learningRate}, OPT: {optimizer}, WD: {weightDecay}, "
+                                  f"Epoch: {bestEpoch + 1}/{epochs} | Train: {trainingLoss[bestEpoch]:.4f} | "
+                                  f"Validation: {localBestValidationLoss:.4f}")
+                            del bestModel
+                            gc.collect()
+                            bestModel = model
+                            bestTrainingLoss = trainingLoss[bestEpoch]
+                            bestValidationLoss = localBestValidationLoss
+                            bestHyperparameters["learningRate"] = learningRate
+                            bestHyperparameters["optimizer"] = optimizer
+                            bestHyperparameters["weightDecay"] = weightDecay
+                            bestHyperparameters["epochs"] = bestEpoch + 1
+                        else:
+                            print(f"Model with LR: {learningRate}, OPT: {optimizer}, WD: {weightDecay} did not improve.")
+                    else:
+                        localBestTrainingLoss, bestEpoch = min((val, idx) for idx, val in enumerate(trainingLoss))
+                        if localBestTrainingLoss < bestTrainingLoss:
+                            print(f"New best model found with LR: {learningRate}, OPT: {optimizer}, WD: {weightDecay}, "
+                                  f"Epoch: {bestEpoch + 1}/{epochs} | Train: {localBestTrainingLoss:.4f}")
+                            del bestModel
+                            gc.collect()
+                            bestModel = model
+                            bestTrainingLoss = localBestTrainingLoss
+                            bestHyperparameters["learningRate"] = learningRate
+                            bestHyperparameters["optimizer"] = optimizer
+                            bestHyperparameters["weightDecay"] = weightDecay
+                            bestHyperparameters["epochs"] = bestEpoch + 1
+                        else:
+                            print(f"Model with LR: {learningRate}, OPT: {optimizer}, WD: {weightDecay} did not improve.")
+
+        if verbose:
+            print(f"\n\nBest model found with LR: {bestHyperparameters['learningRate']}, OPT: {bestHyperparameters['optimizer']}, "
+                  f"WD: {bestHyperparameters['weightDecay']}, Epoch: {bestHyperparameters['epochs']}")
+            if validation > 0:
+                print(f"Train: {bestTrainingLoss:.4f} | Validation: {bestValidationLoss:.4f}")
+            else:
+                print(f"Train: {bestTrainingLoss:.4f}")
+        assert bestModel is not None, "The best model was not found. Something went wrong during the hyperparameter search."
+        self.adjustedModel = bestModel
+        del bestModel
+        gc.collect()
 
+        if plot:
+            fig1 = go.Figure()
+            for idx, label in enumerate(labels):
+                fig1.add_trace(
+                    go.Scatter(
+                        x=np.arange(epochs+1),
+                        y=trainLossHistory[idx],
+                        mode='lines+markers',
+                        name=label)
+
+            )
+            fig1.update_layout(
+                title="Training Loss History",
+                xaxis_title="Epoch",
+                yaxis_title="Loss",
+                legend_title="Hyperparameters",
+                showlegend=True,
+                template="plotly_white"
+            )
+            fig1.show()
+            if validation > 0:
+                fig2 = go.Figure()
+                for idx, label in enumerate(labels):
+                    fig2.add_trace(
+                        go.Scatter(
+                            x=np.arange(epochs+1),
+                            y=validationLossHistory[idx],
+                            mode='lines+markers',
+                            name=label)
+                    )
+                fig2.update_layout(
+                    title="Validation Loss History",
+                    xaxis_title="Epoch",
+                    yaxis_title="Loss",
+                    legend_title="Hyperparameters",
+                    showlegend=True,
+                    template="plotly_white"
+                )
+                fig2.show()
+
+        return self.adjustedModel, bestHyperparameters
 
 
 
@@ -317,3 +470,7 @@ class OPTIMADEAdjuster(LocalAdjuster):
     settings used by that database or focusing its attention to specific chemistry like, for instance, all compounds of
     Sn and all perovskites. It accepts OPTIMADE query as an input and then operates based on the ``LocalAdjuster`` class.
     """
+
+
+
+

From a3a6c4c65de6277b06673c9a5c801df6432ffc6f Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 21:42:56 -0400
Subject: [PATCH 11/50] (MA) added `optimade` to the `dev` requirements

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 064b998..cb60a58 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,7 +49,8 @@ dev = [
     "onnxconverter_common>=1.14.0",
     "onnxsim==0.4.33",
     "onnxruntime>=1.16.0",
-    "plotly>=5.15.0"
+    "plotly>=5.15.0",
+    "optimade>=1.0.0"
 ]
 
 [project.urls]

From 96c32206324521d45721643a7d5f281701841df5 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 27 Mar 2024 23:36:03 -0400
Subject: [PATCH 12/50] (MA) implemented initialization of `OPTIMADEAdjuster`
 and its docstrings

---
 pysipfenn/core/modelAdjusters.py | 101 +++++++++++++++++++++++++++++--
 1 file changed, 96 insertions(+), 5 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index c51c2e8..093fa0c 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -1,14 +1,22 @@
+# Standard library imports
 import os
 from typing import Union, Literal, Tuple, List, Dict
 from copy import deepcopy
 import gc
 
+# Default 3rd party imports
 import numpy as np
 import torch
 from torch.utils.data import DataLoader, TensorDataset
+from pysipfenn.core.pysipfenn import Calculator
+
+# DEV requirements. Not installed by default.
 import plotly.express as px
 import plotly.graph_objects as go
-from pysipfenn.core.pysipfenn import Calculator
+from optimade.client import OptimadeClient
+from optimade.adapters.structures import pymatgen as pymatgen_adapter
+from optimade.models import StructureResource
+
 
 class LocalAdjuster:
     """
@@ -166,7 +174,7 @@ def adjust(
         Args:
             learningRate: The learning rate to be used for the adjustment. Default is ``1e-5`` that is 1% of a typical
                 learning rate of ``Adam`` optimizer.
-            epochs: The number of times to iterate over the data, i.e. how many times the model will see the data.
+            epochs: The number of times to iterate over the data, i.e., how many times the model will see the data.
                 Default is ``50``, which is on the higher side for fine-tuning. If the model does not retrain fast enough
                 but already converged, consider lowering this number to reduce the time and possibly overfitting to the
                 training data.
@@ -177,10 +185,10 @@ def adjust(
             lossFunction: Loss function to be used for optimization. Default is ``MAE`` (Mean Absolute Error / L1) that is
                 more robust to outliers than ``MSE`` (Mean Squared Error).
             validation: Fraction of the data to be used for validation. Default is the common ``0.2`` (20% of the data).
-                If set to ``0``, the model will be trained on the whole dataset without validation and you will not be able
+                If set to ``0``, the model will be trained on the whole dataset without validation, and you will not be able
                 to check for overfitting or gauge the model's performance on unseen data.
             weightDecay: Weight decay to be used for optimization. Default is ``1e-5`` that should work well if data is
-                plaintiful enough relative to the model complexity. If the model is overfitting, consider increasing this
+                abundant enough relative to the model complexity. If the model is overfitting, consider increasing this
                 number to regularize the model more.
             verbose: Whether to print information, such as loss, during the training. Default is ``True``.
 
@@ -469,8 +477,91 @@ class OPTIMADEAdjuster(LocalAdjuster):
     different domain, which in the context of DFT datasets could mean adjusting the model to predict properties with DFT
     settings used by that database or focusing its attention to specific chemistry like, for instance, all compounds of
     Sn and all perovskites. It accepts OPTIMADE query as an input and then operates based on the ``LocalAdjuster`` class.
-    """
 
+    It will set up the environment for the adjustment, letting you progressively build up the training dataset by
+    OPTIMADE queries which get featurized and their results will be concatenated, i.e., you can make one big query or
+    several smaller ones and then adjust the model on the whole dataset when you are ready.
 
+    For details on more advanced uses of the OPTIMADE API client, please refer to [the documentation](https://www.optimade.org/optimade-python-tools/latest/getting_started/client/).
+
+    Args:
+        calculator: Instance of the ``Calculator`` class with the model to be adjusted, defined and loaded. Unlike in the
+            ``LocalAdjuster``, the descriptor data will not be passed, since it will be fetched from the OPTIMADE API.
+        model: Name of the model to be adjusted in the ``Calculator``. E.g., ``SIPFENN_Krajewski2022_NN30``.
+        provider: Tuple of strings with the names of the providers to be used for the OPTIMADE queries. The type-hinting
+            gives a list of providers available at the time of writing this code, but it is by no means limited to them.
+            For the up-to-date list, along with their current status, please refer to the
+            [OPTIMADE Providers Dashboard](https://optimade.org/providers-dashboard). The default is ``"mp"`` which
+            stands for the Materials Project, but we do not recommend any particular provider over any other. One has to
+            be picked to work out of the box. Your choice should be based on the data you are interested in.
+        targetPath: Tuple of strings with the path to the target data in the OPTIMADE response. This will be dependent
+            on the provider you choose, and you will need to identify it by looking at the response. The easiest way to
+            do this is by going to their endpoint, like
+            [this, very neat one, for JARVIS](https://jarvis.nist.gov/optimade/jarvisdft/v1/structures/),
+            [this one for MP](https://optimade.materialsproject.org/v1/structures),
+             or [this one for our in-house MPDD](https://optimade.mpdd.org/v1/structures).
+        device: Same as in the ``LocalAdjuster``. Default is ``"cpu"``.
+        descriptor: *Not* the same as in the ``LocalAdjuster``. Since the descriptor data will be calculated for each
+            structure fetched from the OPTIMADE API, this parameter is needed to specify which descriptor to use. At the
+            time of writing this code, it can be either ``"Ward2017"`` or ``"KS2022"``. Special versions of ``KS2022``
+            cannot be used since assumptions cannot be made about the data fetched from the OPTIMADE API and only general
+            symmetry-based optimizations can be applied. Default is ``"KS2022"``.
+        useClearML: Same as in the ``LocalAdjuster``. Default is ``False``.
+        taskName: Same as in the ``LocalAdjuster``. Default is ``"OPTIMADEFineTuning"``, and you are encouraged to change
+            it, especially if you are using the ClearML platform.
+    """
+
+    def __init__(
+            self,
+            calculator: Calculator,
+            model: str,
+            provider:
+                Literal[
+                    "aiida",
+                    "aflow",
+                    "alexandria",
+                    "cod",
+                    "ccpnc",
+                    "cmr",
+                    "httk",
+                    "matcloud",
+                    "mcloud",
+                    "mcloudarchive",
+                    "mp",
+                    "mpdd",
+                    "mpds",
+                    "mpod",
+                    "nmd",
+                    "odbx",
+                    "omdb",
+                    "oqmd",
+                    "jarvis",
+                    "pcod",
+                    "tcod",
+                    "twodmatpedia"
+                ] = "mp",
+            targetPath: Tuple[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
+            device: Literal["cpu", "cuda", "mps"] = "cpu",
+            descriptor: Literal["Ward2017", "KS2022"] = "KS2022",
+            useClearML: bool = False,
+            taskName: str = "OPTIMADEFineTuning"
+    ) -> None:
+        super().__init__(
+            calculator=calculator,
+            model=model,
+            targetData=np.array([]),
+            descriptorData=np.array([]),
+            device=device,
+            descriptor=None,
+            useClearML=useClearML,
+            taskName=taskName,
+        )
+
+        self.targetPath = targetPath
+        self.provider = provider
+        self.client = OptimadeClient(
+            use_async=False,
+            include_providers=[provider]
+        )
 
 

From e07e7f6acb943b1e9c47188a3b5f51ac3d8b6efb Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 00:10:11 -0400
Subject: [PATCH 13/50] (MA) implemented querying OPTIMADE and passing
 structures to featurization in`OPTIMADEAdjuster`

---
 pysipfenn/core/modelAdjusters.py | 52 ++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 093fa0c..f9aa5e3 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -500,6 +500,8 @@ class OPTIMADEAdjuster(LocalAdjuster):
             [this, very neat one, for JARVIS](https://jarvis.nist.gov/optimade/jarvisdft/v1/structures/),
             [this one for MP](https://optimade.materialsproject.org/v1/structures),
              or [this one for our in-house MPDD](https://optimade.mpdd.org/v1/structures).
+        targetSize: The length of the target data to be fetched from the OPTIMADE API. This is typically ``1`` for a single
+        scalar property, but it can be more. Default is ``1``.
         device: Same as in the ``LocalAdjuster``. Default is ``"cpu"``.
         descriptor: *Not* the same as in the ``LocalAdjuster``. Since the descriptor data will be calculated for each
             structure fetched from the OPTIMADE API, this parameter is needed to specify which descriptor to use. At the
@@ -541,6 +543,7 @@ def __init__(
                     "twodmatpedia"
                 ] = "mp",
             targetPath: Tuple[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
+            targetSize: int = 1,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",
             useClearML: bool = False,
@@ -557,6 +560,7 @@ def __init__(
             taskName=taskName,
         )
 
+        self.descriptor = descriptor
         self.targetPath = targetPath
         self.provider = provider
         self.client = OptimadeClient(
@@ -564,4 +568,52 @@ def __init__(
             include_providers=[provider]
         )
 
+        if self.descriptor == "Ward2017":
+            self.descriptorData: np.ndarray = np.empty((0, 271))
+        elif self.descriptor == "KS2022":
+            self.descriptorData: np.ndarray = np.empty((0, 256))
+        else:
+            raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
+
+        self.targetData: np.ndarray = np.empty((0, targetSize))
+
+
+        print("Initialized Adjuster instance!\n")
+
+    def fetchAndFeturize(
+            self,
+            query: str,
+            parallelWorkers: int = 1,
+            verbose: bool = True
+    ) -> None:
+        response = self.client.get(query)
+        providerResponse = response['structures'][query]
+        respondingProviderURL = list(providerResponse.keys())[0]
+        data = providerResponse[respondingProviderURL]['data']
+
+        if verbose:
+            print(f"Obtained {len(data)} structures from the OPTIMADE API.")
+            print("Converting to pymatgen structures...")
+
+        structs = [pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)) for datapoint in data]
+
+        if verbose:
+            print("Featurizing the structures...")
+
+        if self.descriptor == "Ward2017":
+            self.calculator.calculate_Ward2017(structs, mode="parallel", max_workers=parallelWorkers)
+            self.descriptorData = np.concatenate((self.descriptorData, self.calculator.descriptorData), axis=0)
+
+        elif self.descriptor == "KS2022":
+            self.calculator.calculate_KS2022(structs, mode="parallel", max_workers=parallelWorkers)
+            self.descriptorData = np.concatenate((self.descriptorData, self.calculator.descriptorData), axis=0)
+
+        else:
+            raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
+
+        if verbose:
+            print("Featurization complete!")
+
 
+if __name__ == '__main__':
+    pass

From a823eca18793d420b5d5df5b85105acca1079217 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 00:11:14 -0400
Subject: [PATCH 14/50] (core) added `[http_client]` to the `optimade`
 requirement needed for querying

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index cb60a58..6dea6ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,7 +50,7 @@ dev = [
     "onnxsim==0.4.33",
     "onnxruntime>=1.16.0",
     "plotly>=5.15.0",
-    "optimade>=1.0.0"
+    "optimade[http_client]>=1.0.0"
 ]
 
 [project.urls]

From 637f1bbb96f4c9b31da3fe71256902e9b3a35295 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:09:25 -0400
Subject: [PATCH 15/50] (MA) added entry name collection to the
 `OPTIMADEAdjuster`

---
 pysipfenn/core/modelAdjusters.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f9aa5e3..f2e13eb 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -577,6 +577,8 @@ def __init__(
 
         self.targetData: np.ndarray = np.empty((0, targetSize))
 
+        self.names: List[str] = []
+
 
         print("Initialized Adjuster instance!\n")
 
@@ -595,6 +597,14 @@ def fetchAndFeturize(
             print(f"Obtained {len(data)} structures from the OPTIMADE API.")
             print("Converting to pymatgen structures...")
 
+        for datapoint in data:
+            # OPTIMADE Standard Data
+            self.names.append(datapoint['attributes']['chemical_formula_reduced'] + '-' + datapoint['id'])
+
+            # Database-specific payload existing at a specific target path (e.g., formation energy per atom in MP)
+
+
+        # Featurization of the received data
         structs = [pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)) for datapoint in data]
 
         if verbose:

From 889cad5b1ed42b215dcd0982246c6d9aada15fa9 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:28:36 -0400
Subject: [PATCH 16/50] (MA) upped the default result number limit to `10000`
 and added it as a parameter with documentation in `OPTIMADEAdjuster`

---
 pysipfenn/core/modelAdjusters.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f2e13eb..cd027dd 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -511,6 +511,9 @@ class OPTIMADEAdjuster(LocalAdjuster):
         useClearML: Same as in the ``LocalAdjuster``. Default is ``False``.
         taskName: Same as in the ``LocalAdjuster``. Default is ``"OPTIMADEFineTuning"``, and you are encouraged to change
             it, especially if you are using the ClearML platform.
+        maxResults: The maximum number of results to be fetched from the OPTIMADE API for a given query. Default is
+            ``10000`` which is a very high number for most re-training tasks. If you are fetching a lot of data, it's
+            possible the query is too broad, and you should consider narrowing it down.
     """
 
     def __init__(
@@ -542,12 +545,18 @@ def __init__(
                     "tcod",
                     "twodmatpedia"
                 ] = "mp",
-            targetPath: Tuple[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
+            targetPath: Tuple[str] = (
+                    'attributes',
+                    '_mp_stability',
+                    'gga_gga+u',
+                    'formation_energy_per_atom'
+            ),
             targetSize: int = 1,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",
             useClearML: bool = False,
-            taskName: str = "OPTIMADEFineTuning"
+            taskName: str = "OPTIMADEFineTuning",
+            maxResults: int = 10000
     ) -> None:
         super().__init__(
             calculator=calculator,
@@ -565,7 +574,8 @@ def __init__(
         self.provider = provider
         self.client = OptimadeClient(
             use_async=False,
-            include_providers=[provider]
+            include_providers=[provider],
+            max_results_per_provider=maxResults
         )
 
         if self.descriptor == "Ward2017":

From 2525b2e63d017ca9fb94d7548e90cd8164b8f28d Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:35:40 -0400
Subject: [PATCH 17/50] (core) removed info about importing from the top
 `pysipfenn` namespace in the top `__init__`

---
 pysipfenn/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pysipfenn/__init__.py b/pysipfenn/__init__.py
index 4721383..56ede84 100644
--- a/pysipfenn/__init__.py
+++ b/pysipfenn/__init__.py
@@ -1,4 +1,4 @@
-print('Importing from top pySIPFENN namespace...')
+# Importing from top pySIPFENN namespace...
 from pysipfenn.core.pysipfenn import *
 from pysipfenn.core.modelExporters import *
 from pysipfenn.core.modelAdjusters import *

From 7889c3073d17ed26a66086308ab6717f7cb23b69 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:37:26 -0400
Subject: [PATCH 18/50]  (MA) finalized the `fetchAndFeturize` routine of
 `OPTIMADEAdjuster`, now collecting names, target data, and featurizing the
 obtained structures

---
 pysipfenn/core/modelAdjusters.py | 30 ++++++++++++++++++++++++++----
 1 file changed, 26 insertions(+), 4 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index cd027dd..f52cec3 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -3,12 +3,15 @@
 from typing import Union, Literal, Tuple, List, Dict
 from copy import deepcopy
 import gc
+from functools import reduce
+import operator
 
 # Default 3rd party imports
 import numpy as np
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from pysipfenn.core.pysipfenn import Calculator
+from pymatgen.core import Structure
 
 # DEV requirements. Not installed by default.
 import plotly.express as px
@@ -603,19 +606,36 @@ def fetchAndFeturize(
         respondingProviderURL = list(providerResponse.keys())[0]
         data = providerResponse[respondingProviderURL]['data']
 
+        targetDataStage: List[List[float]] = []
+        structs: List[Structure] = []
+        missing: List[str] = []
+
         if verbose:
             print(f"Obtained {len(data)} structures from the OPTIMADE API.")
-            print("Converting to pymatgen structures...")
+            print("Extracting the data...")
 
         for datapoint in data:
             # OPTIMADE Standard Data
-            self.names.append(datapoint['attributes']['chemical_formula_reduced'] + '-' + datapoint['id'])
+            name = datapoint['attributes']['chemical_formula_reduced'] + '-' + datapoint['id']
 
             # Database-specific payload existing at a specific target path (e.g., formation energy per atom in MP)
+            try:
+                targetDataStage.append([reduce(operator.getitem, self.targetPath, datapoint)])
+            except KeyError:
+                missing.append(name)
+                continue
+
+            self.names.append(name)
+            # Stage for featurization of the received data
+            structs.append(pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)))
 
+        if missing:
+            print(f"\nCould not find the target data at the provided path: {self.targetPath}\nfor {len(missing)} "
+                  f"structures:\n{missing}\n")
 
-        # Featurization of the received data
-        structs = [pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)) for datapoint in data]
+        print(f"Extracted {len(targetDataStage)} datapoints (composition+structure+target) from the OPTIMADE API.")
+        targetDataStage = np.array(targetDataStage)
+        self.targetData = np.concatenate((self.targetData, targetDataStage), axis=0)
 
         if verbose:
             print("Featurizing the structures...")
@@ -633,6 +653,8 @@ def fetchAndFeturize(
 
         if verbose:
             print("Featurization complete!")
+            print(f"Current dataset size: {len(self.names)} with {len(set(self.names))} unique IDs.\n")
+
 
 
 if __name__ == '__main__':

From 71c8557b0b9a432ee8d7c390f71956657f043722 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:58:02 -0400
Subject: [PATCH 19/50]  (MA) add a message on possible degeneracy of datasets
 from overlapping queries

---
 pysipfenn/core/modelAdjusters.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f52cec3..6539bbd 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -654,7 +654,11 @@ def fetchAndFeturize(
         if verbose:
             print("Featurization complete!")
             print(f"Current dataset size: {len(self.names)} with {len(set(self.names))} unique IDs.\n")
-
+            if len(self.names) > len(set(self.names)):
+                print("Please note that there are duplicate IDs in the dataset. Such degenerate dataset can be used "
+                      "without issues for training (in some occasions may be even desirable to bias the model to areas "
+                      "matching multiple criteria), but the validation error may be underestimated since some data"
+                      "may be in both training and validation set.")
 
 
 if __name__ == '__main__':

From 3c5c290bec906eb6f56915ec321c09bd01576535 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 12:14:49 -0400
Subject: [PATCH 20/50]  (MA) added class parameter of `validationLabels`,
 assigned at every adjustment, to display if a datapoint has been shown to the
 model at adjustment. Displayed when plotting unadjusted and adjusted models.

---
 pysipfenn/core/modelAdjusters.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 6539bbd..1ceb851 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -124,6 +124,10 @@ def __init__(
             else:
                 raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
 
+        self.validationLabels: List[str] = []
+
+        print("Initialized Adjuster instance!\n")
+
     def plotStarting(self) -> None:
         """
         Plot the starting model (before adjustment) on the target data. By default, it will plot in your browser.
@@ -138,6 +142,17 @@ def plotStarting(self) -> None:
             labels={
                 "x": "Target Data", "y": "Predictions"},
             title="Starting (Unadjusted) Model Predictions")
+        # If the validation labels are set, color the points as blue for training, green for validation, and red for
+        # any other label, just in case advanced users want to use this method for other purposes.
+        if self.validationLabels:
+            fig.update_traces(
+                marker=dict(
+                    color=[(
+                            "blue" if label == "Training" else
+                            "green" if label == "Validation" else
+                            "red"
+                    ) for label in self.validationLabels])
+            )
         fig.show()
 
     def plotAdjusted(self) -> None:
@@ -155,6 +170,17 @@ def plotAdjusted(self) -> None:
             labels={
                 "x": "Target Data", "y": "Predictions"},
             title="Adjusted Model Predictions")
+        # If the validation labels are set, color the points as blue for training, green for validation, and red for
+        # any other label, just in case advanced users want to use this method for other purposes.
+        if self.validationLabels:
+            fig.update_traces(
+                marker=dict(
+                    color=[(
+                        "blue" if label == "Training" else
+                        "green" if label == "Validation" else
+                        "red"
+                    ) for label in self.validationLabels])
+            )
         fig.show()
 
     def adjust(
@@ -206,6 +232,7 @@ def adjust(
         tdTensor = torch.from_numpy(self.targetData).float().to(device=self.device)
         if validation > 0:
             split = int(len(ddTensor) * (1 - validation))
+            self.validationLabels = ["Training"]*split + ["Validation"]*(len(ddTensor)-split)
             ddTrain, ddVal = ddTensor[:split], ddTensor[split:]
             tdTrain, tdVal = tdTensor[:split], tdTensor[split:]
         else:

From a8fd4cdd45c59610cbd644651826861f57cb4ae5 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 12:57:34 -0400
Subject: [PATCH 21/50]  (MA) improved several printouts

---
 pysipfenn/core/modelAdjusters.py | 24 +++++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 1ceb851..f830ebb 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -124,6 +124,7 @@ def __init__(
             else:
                 raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
 
+        self.names: List[str] = []
         self.validationLabels: List[str] = []
 
         print("Initialized Adjuster instance!\n")
@@ -133,6 +134,7 @@ def plotStarting(self) -> None:
         Plot the starting model (before adjustment) on the target data. By default, it will plot in your browser.
         """
         self.model.eval()
+        print("Running the STARTING model on the data and plotting the results...")
         with torch.no_grad():
             dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.model(dataIn, None).detach().cpu().numpy().flatten()
@@ -145,6 +147,7 @@ def plotStarting(self) -> None:
         # If the validation labels are set, color the points as blue for training, green for validation, and red for
         # any other label, just in case advanced users want to use this method for other purposes.
         if self.validationLabels:
+            print("Overlaying the training and validation labels on the plot.")
             fig.update_traces(
                 marker=dict(
                     color=[(
@@ -153,6 +156,12 @@ def plotStarting(self) -> None:
                             "red"
                     ) for label in self.validationLabels])
             )
+        # If the names are set, add them to the plot.
+        if self.names:
+            print("Adding the names as hover data to the plot.")
+            fig.update_traces(
+                hovertext=self.names
+            )
         fig.show()
 
     def plotAdjusted(self) -> None:
@@ -161,6 +170,7 @@ def plotAdjusted(self) -> None:
         """
         assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
         self.adjustedModel.eval()
+        print("Running the ADJUSTED model on the data and plotting the results...")
         with torch.no_grad():
             dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.adjustedModel(dataIn, None).detach().cpu().numpy().flatten()
@@ -173,6 +183,7 @@ def plotAdjusted(self) -> None:
         # If the validation labels are set, color the points as blue for training, green for validation, and red for
         # any other label, just in case advanced users want to use this method for other purposes.
         if self.validationLabels:
+            print("Overlaying the training and validation labels on the plot.")
             fig.update_traces(
                 marker=dict(
                     color=[(
@@ -181,6 +192,12 @@ def plotAdjusted(self) -> None:
                         "red"
                     ) for label in self.validationLabels])
             )
+        # If the names are set, add them to the plot.
+        if self.names:
+            print("Adding the names as hover data to the plot.")
+            fig.update_traces(
+                   hovertext=self.names
+                )
         fig.show()
 
     def adjust(
@@ -448,9 +465,9 @@ def matrixHyperParameterSearch(
             print(f"\n\nBest model found with LR: {bestHyperparameters['learningRate']}, OPT: {bestHyperparameters['optimizer']}, "
                   f"WD: {bestHyperparameters['weightDecay']}, Epoch: {bestHyperparameters['epochs']}")
             if validation > 0:
-                print(f"Train: {bestTrainingLoss:.4f} | Validation: {bestValidationLoss:.4f}")
+                print(f"Train: {bestTrainingLoss:.4f} | Validation: {bestValidationLoss:.4f}\n")
             else:
-                print(f"Train: {bestTrainingLoss:.4f}")
+                print(f"Train: {bestTrainingLoss:.4f}\n")
         assert bestModel is not None, "The best model was not found. Something went wrong during the hyperparameter search."
         self.adjustedModel = bestModel
         del bestModel
@@ -617,9 +634,6 @@ def __init__(
 
         self.targetData: np.ndarray = np.empty((0, targetSize))
 
-        self.names: List[str] = []
-
-
         print("Initialized Adjuster instance!\n")
 
     def fetchAndFeturize(

From df8c77c642b83e396160ef67044968d5267065ce Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 12:58:03 -0400
Subject: [PATCH 22/50]  (MA) added assertions that data to operate on is
 available before tuning

---
 pysipfenn/core/modelAdjusters.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f830ebb..9bc868c 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -245,6 +245,10 @@ def adjust(
 
         if verbose:
             print("Loading the data...")
+        assert len(self.descriptorData) != 0, "The descriptor data must not be empty for the adjustment process."
+        assert len(self.targetData) != 0, "The target data must not be empty for the adjustment process."
+        assert len(self.descriptorData) == len(self.targetData), "The descriptor and target data must have the same length."
+
         ddTensor = torch.from_numpy(self.descriptorData).float().to(device=self.device)
         tdTensor = torch.from_numpy(self.targetData).float().to(device=self.device)
         if validation > 0:

From 5209b22a061ddafb42c5bca333f133e516723e30 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Thu, 28 Mar 2024 23:30:13 -0400
Subject: [PATCH 23/50]  (MA) added automatic shuffling of the
 OPTIMADE-obtained data

---
 pysipfenn/core/modelAdjusters.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 9bc868c..a3dd2a8 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -5,6 +5,7 @@
 import gc
 from functools import reduce
 import operator
+from random import shuffle
 
 # Default 3rd party imports
 import numpy as np
@@ -653,6 +654,7 @@ def fetchAndFeturize(
 
         targetDataStage: List[List[float]] = []
         structs: List[Structure] = []
+        names: List[str] = []
         missing: List[str] = []
 
         if verbose:
@@ -670,7 +672,7 @@ def fetchAndFeturize(
                 missing.append(name)
                 continue
 
-            self.names.append(name)
+            names.append(name)
             # Stage for featurization of the received data
             structs.append(pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)))
 
@@ -678,9 +680,14 @@ def fetchAndFeturize(
             print(f"\nCould not find the target data at the provided path: {self.targetPath}\nfor {len(missing)} "
                   f"structures:\n{missing}\n")
 
+        dataIn = list(zip(names, structs, targetDataStage))
+        shuffle(dataIn)
+        names, structs, targetDataStage = zip(*dataIn)
+
+        self.names.extend(names)
+
         print(f"Extracted {len(targetDataStage)} datapoints (composition+structure+target) from the OPTIMADE API.")
-        targetDataStage = np.array(targetDataStage)
-        self.targetData = np.concatenate((self.targetData, targetDataStage), axis=0)
+        self.targetData = np.concatenate((self.targetData, np.array(targetDataStage)), axis=0)
 
         if verbose:
             print("Featurizing the structures...")

From 2cbcbc150ec771415264fe8f07959eed090c44d9 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 13:04:39 -0400
Subject: [PATCH 24/50]  (core) limit the convenience import scope of the top
 level `__init__.py`

---
 pysipfenn/__init__.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/__init__.py b/pysipfenn/__init__.py
index 56ede84..7c14154 100644
--- a/pysipfenn/__init__.py
+++ b/pysipfenn/__init__.py
@@ -1,4 +1,4 @@
 # Importing from top pySIPFENN namespace...
-from pysipfenn.core.pysipfenn import *
-from pysipfenn.core.modelExporters import *
-from pysipfenn.core.modelAdjusters import *
+from pysipfenn.core.pysipfenn import Calculator, ward2ks2022
+from pysipfenn.core.modelExporters import ONNXExporter, TorchExporter, CoreMLExporter
+from pysipfenn.core.modelAdjusters import LocalAdjuster, OPTIMADEAdjuster

From 4919926e5accc45cd900bc7dfbc2332079517b49 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 13:17:04 -0400
Subject: [PATCH 25/50]  (MA) distributed optional dependency imports as needed

---
 pysipfenn/core/modelAdjusters.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index a3dd2a8..a5c89a2 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -17,9 +17,6 @@
 # DEV requirements. Not installed by default.
 import plotly.express as px
 import plotly.graph_objects as go
-from optimade.client import OptimadeClient
-from optimade.adapters.structures import pymatgen as pymatgen_adapter
-from optimade.models import StructureResource
 
 
 class LocalAdjuster:
@@ -610,6 +607,8 @@ def __init__(
             taskName: str = "OPTIMADEFineTuning",
             maxResults: int = 10000
     ) -> None:
+        from optimade.client import OptimadeClient
+
         super().__init__(
             calculator=calculator,
             model=model,
@@ -647,6 +646,9 @@ def fetchAndFeturize(
             parallelWorkers: int = 1,
             verbose: bool = True
     ) -> None:
+        from optimade.adapters.structures import pymatgen as pymatgen_adapter
+        from optimade.models import StructureResource
+
         response = self.client.get(query)
         providerResponse = response['structures'][query]
         respondingProviderURL = list(providerResponse.keys())[0]

From 6e69976fe58ddb30116ebb5218abb88bbfb90af3 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 13:17:58 -0400
Subject: [PATCH 26/50]  (deps) moved `plotly` from optional to required
 dependencies

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6dea6ff..3948c48 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
     "natsort>=8.3.0",
     "pymongo>=4.4",
     "pysmartdl2>=2.0.0",
+    "plotly>=5.15.0",
     "dnspython",
     "ruamel.yaml",
     "colorama"
@@ -49,7 +50,6 @@ dev = [
     "onnxconverter_common>=1.14.0",
     "onnxsim==0.4.33",
     "onnxruntime>=1.16.0",
-    "plotly>=5.15.0",
     "optimade[http_client]>=1.0.0"
 ]
 

From 1f6ec51ead66bcadcd7b225cb0cf7914faa44b2e Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 13:29:23 -0400
Subject: [PATCH 27/50]  (core) small addition to the top level imports

---
 pysipfenn/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pysipfenn/__init__.py b/pysipfenn/__init__.py
index 7c14154..2ab005e 100644
--- a/pysipfenn/__init__.py
+++ b/pysipfenn/__init__.py
@@ -1,4 +1,4 @@
 # Importing from top pySIPFENN namespace...
-from pysipfenn.core.pysipfenn import Calculator, ward2ks2022
+from pysipfenn.core.pysipfenn import Calculator, ward2ks2022, overwritePrototypeLibrary, string2prototype
 from pysipfenn.core.modelExporters import ONNXExporter, TorchExporter, CoreMLExporter
 from pysipfenn.core.modelAdjusters import LocalAdjuster, OPTIMADEAdjuster

From b7bb2c43e30a33e0575e8a629e906c608e74ef28 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 14:04:52 -0400
Subject: [PATCH 28/50]  (core) reverted the `__init__.py` scope narrowing for
 the top level import

---
 pysipfenn/__init__.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/__init__.py b/pysipfenn/__init__.py
index 2ab005e..56ede84 100644
--- a/pysipfenn/__init__.py
+++ b/pysipfenn/__init__.py
@@ -1,4 +1,4 @@
 # Importing from top pySIPFENN namespace...
-from pysipfenn.core.pysipfenn import Calculator, ward2ks2022, overwritePrototypeLibrary, string2prototype
-from pysipfenn.core.modelExporters import ONNXExporter, TorchExporter, CoreMLExporter
-from pysipfenn.core.modelAdjusters import LocalAdjuster, OPTIMADEAdjuster
+from pysipfenn.core.pysipfenn import *
+from pysipfenn.core.modelExporters import *
+from pysipfenn.core.modelAdjusters import *

From 231bd5f31a210246dec5b9db1a4565b2a1f80910 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:13:16 -0400
Subject: [PATCH 29/50] (MA) aded tracking of the compositions coming in from
 OPTIMADE; not used right now but important for future methods including
 crystALL

---
 pysipfenn/core/modelAdjusters.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index a5c89a2..1a0f7a5 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -14,7 +14,6 @@
 from pysipfenn.core.pysipfenn import Calculator
 from pymatgen.core import Structure
 
-# DEV requirements. Not installed by default.
 import plotly.express as px
 import plotly.graph_objects as go
 
@@ -122,6 +121,7 @@ def __init__(
             else:
                 raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
 
+        self.comps: List[str] = []
         self.names: List[str] = []
         self.validationLabels: List[str] = []
 
@@ -656,6 +656,7 @@ def fetchAndFeturize(
 
         targetDataStage: List[List[float]] = []
         structs: List[Structure] = []
+        comps: List[str] = []
         names: List[str] = []
         missing: List[str] = []
 
@@ -665,7 +666,8 @@ def fetchAndFeturize(
 
         for datapoint in data:
             # OPTIMADE Standard Data
-            name = datapoint['attributes']['chemical_formula_reduced'] + '-' + datapoint['id']
+            comp = datapoint['attributes']['chemical_formula_reduced']
+            name = comp + '-' + datapoint['id']
 
             # Database-specific payload existing at a specific target path (e.g., formation energy per atom in MP)
             try:
@@ -674,6 +676,7 @@ def fetchAndFeturize(
                 missing.append(name)
                 continue
 
+            comps.append(comp)
             names.append(name)
             # Stage for featurization of the received data
             structs.append(pymatgen_adapter.get_pymatgen(StructureResource(**datapoint)))
@@ -682,10 +685,11 @@ def fetchAndFeturize(
             print(f"\nCould not find the target data at the provided path: {self.targetPath}\nfor {len(missing)} "
                   f"structures:\n{missing}\n")
 
-        dataIn = list(zip(names, structs, targetDataStage))
+        dataIn = list(zip(comps, names, structs, targetDataStage))
         shuffle(dataIn)
-        names, structs, targetDataStage = zip(*dataIn)
+        comps, names, structs, targetDataStage = zip(*dataIn)
 
+        self.comps.extend(comps)
         self.names.extend(names)
 
         print(f"Extracted {len(targetDataStage)} datapoints (composition+structure+target) from the OPTIMADE API.")

From cb02fb8e140d61e9052f2f7f96fa2b4b9275e13a Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:13:47 -0400
Subject: [PATCH 30/50] (MA) plotting style improvements

---
 pysipfenn/core/modelAdjusters.py | 106 +++++++++++++++++++++++--------
 1 file changed, 80 insertions(+), 26 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 1a0f7a5..c3c6d56 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -131,17 +131,30 @@ def plotStarting(self) -> None:
         """
         Plot the starting model (before adjustment) on the target data. By default, it will plot in your browser.
         """
+        reference = self.targetData.flatten()
         self.model.eval()
         print("Running the STARTING model on the data and plotting the results...")
         with torch.no_grad():
             dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.model(dataIn, None).detach().cpu().numpy().flatten()
-        fig = px.scatter(
-            x=self.targetData.flatten(),
-            y=predictions,
-            labels={
-                "x": "Target Data", "y": "Predictions"},
-            title="Starting (Unadjusted) Model Predictions")
+        minVal = min(min(reference), min(predictions))
+        maxVal = max(max(reference), max(predictions))
+
+        if self.names:
+            fig = px.scatter(
+                x=reference,
+                y=predictions,
+                hover_name=self.names,
+                labels={"x": "Target Data", "y": "Predictions"},
+                title="Starting (Unadjusted) Model Predictions"
+                )
+        else:
+            fig = px.scatter(
+                x=reference,
+                y=predictions,
+                labels = {"x": "Target Data", "y": "Predictions"},
+                title = "Starting (Unadjusted) Model Predictions"
+            )
         # If the validation labels are set, color the points as blue for training, green for validation, and red for
         # any other label, just in case advanced users want to use this method for other purposes.
         if self.validationLabels:
@@ -149,17 +162,31 @@ def plotStarting(self) -> None:
             fig.update_traces(
                 marker=dict(
                     color=[(
-                            "blue" if label == "Training" else
-                            "green" if label == "Validation" else
-                            "red"
-                    ) for label in self.validationLabels])
+                        "blue" if label == "Training" else
+                        "green" if label == "Validation" else
+                        "red") for label in self.validationLabels],
+                    symbol='circle-dot',
+                    opacity=0.5,
+                    size=12
+                )
             )
-        # If the names are set, add them to the plot.
-        if self.names:
-            print("Adding the names as hover data to the plot.")
+        else:
             fig.update_traces(
-                hovertext=self.names
+                marker=dict(
+                    symbol='circle-dot',
+                    opacity=0.5,
+                    size=12
+                )
+            )
+        fig.add_trace(
+            go.Scatter(
+                x=[minVal, maxVal],
+                y=[minVal, maxVal],
+                mode='lines',
+                line=dict(color='gray'),
+                name='x=y'
             )
+        )
         fig.show()
 
     def plotAdjusted(self) -> None:
@@ -168,16 +195,29 @@ def plotAdjusted(self) -> None:
         """
         assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
         self.adjustedModel.eval()
+        reference = self.targetData.flatten()
         print("Running the ADJUSTED model on the data and plotting the results...")
         with torch.no_grad():
             dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)
             predictions = self.adjustedModel(dataIn, None).detach().cpu().numpy().flatten()
-        fig = px.scatter(
-            x=self.targetData.flatten(),
-            y=predictions,
-            labels={
-                "x": "Target Data", "y": "Predictions"},
-            title="Adjusted Model Predictions")
+        minVal = min(min(reference), min(predictions))
+        maxVal = max(max(reference), max(predictions))
+
+        if self.names:
+            fig = px.scatter(
+                x=reference,
+                y=predictions,
+                hover_name=self.names,
+                labels={"x": "Target Data", "y": "Predictions"},
+                title="Starting (Unadjusted) Model Predictions"
+                )
+        else:
+            fig = px.scatter(
+                x=reference,
+                y=predictions,
+                labels = {"x": "Target Data", "y": "Predictions"},
+                title = "Starting (Unadjusted) Model Predictions"
+            )
         # If the validation labels are set, color the points as blue for training, green for validation, and red for
         # any other label, just in case advanced users want to use this method for other purposes.
         if self.validationLabels:
@@ -187,15 +227,29 @@ def plotAdjusted(self) -> None:
                     color=[(
                         "blue" if label == "Training" else
                         "green" if label == "Validation" else
-                        "red"
-                    ) for label in self.validationLabels])
+                        "red") for label in self.validationLabels],
+                    symbol='circle-dot',
+                    opacity=0.5,
+                    size=12
+                )
             )
-        # If the names are set, add them to the plot.
-        if self.names:
-            print("Adding the names as hover data to the plot.")
+        else:
             fig.update_traces(
-                   hovertext=self.names
+                marker=dict(
+                    symbol='circle-dot',
+                    opacity=0.5,
+                    size=12
                 )
+            )
+        fig.add_trace(
+            go.Scatter(
+                x=[minVal, maxVal],
+                y=[minVal, maxVal],
+                mode='lines',
+                line=dict(color='gray'),
+                name='x=y'
+            )
+        )
         fig.show()
 
     def adjust(

From 31cad70f178714fb9613a8f04d6da3a8808488c3 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:14:35 -0400
Subject: [PATCH 31/50] (MA) added a convenience function for highlighting
 points at given indices

---
 pysipfenn/core/modelAdjusters.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index c3c6d56..8a046a6 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -571,6 +571,16 @@ def matrixHyperParameterSearch(
 
         return self.adjustedModel, bestHyperparameters
 
+    def highlightPoints(
+            self,
+            pointsIndices: List[int]
+    ) -> None:
+        if not self.validationLabels:
+            print("No validation labels set yet. Please note highlights will be overwriten by the next adjustemnt call.")
+        for p in pointsIndices:
+            assert p < len(self.validationLabels), "The index of the point to be highlighted is out of bounds."
+            self.validationLabels[p] = "Highlight"
+
 
 
 class OPTIMADEAdjuster(LocalAdjuster):

From 01d646f878bd75227bd5b459a82339c4d8bd3749 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:18:57 -0400
Subject: [PATCH 32/50] (MA) added reduced formula homogenization just in case

---
 pysipfenn/core/modelAdjusters.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 8a046a6..9edf1ab 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -12,7 +12,7 @@
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from pysipfenn.core.pysipfenn import Calculator
-from pymatgen.core import Structure
+from pymatgen.core import Structure, Composition
 
 import plotly.express as px
 import plotly.graph_objects as go
@@ -730,7 +730,7 @@ def fetchAndFeturize(
 
         for datapoint in data:
             # OPTIMADE Standard Data
-            comp = datapoint['attributes']['chemical_formula_reduced']
+            comp = Composition(datapoint['attributes']['chemical_formula_reduced']).reduced_formula
             name = comp + '-' + datapoint['id']
 
             # Database-specific payload existing at a specific target path (e.g., formation energy per atom in MP)

From 81b41de1f8c340df2b1b67cbe7d24c7f57ec6127 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:20:17 -0400
Subject: [PATCH 33/50] (MA) added printout on the number of unique
 compositions at the end of OPTIMADE fetching

---
 pysipfenn/core/modelAdjusters.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 9edf1ab..9e5c21a 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -775,7 +775,10 @@ def fetchAndFeturize(
 
         if verbose:
             print("Featurization complete!")
-            print(f"Current dataset size: {len(self.names)} with {len(set(self.names))} unique IDs.\n")
+            print(f"Current dataset size: "
+                  f"{len(self.names)} with "
+                  f"{len(set(self.names))} unique IDs belonging to "
+                  f"{len(set(self.comps))} unique compositions.\n")
             if len(self.names) > len(set(self.names)):
                 print("Please note that there are duplicate IDs in the dataset. Such degenerate dataset can be used "
                       "without issues for training (in some occasions may be even desirable to bias the model to areas "

From 6858fd4d0bbe853d196c4785323a1ef49bb1ebfd Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 16:26:16 -0400
Subject: [PATCH 34/50] (MA) added population of validation labels with
 "Training" after data is fetched from OPTIMADE but no training was done

---
 pysipfenn/core/modelAdjusters.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 9e5c21a..fd1dcb3 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -581,6 +581,17 @@ def highlightPoints(
             assert p < len(self.validationLabels), "The index of the point to be highlighted is out of bounds."
             self.validationLabels[p] = "Highlight"
 
+    def highlightCompositions(
+            self,
+            compositions: List[str]
+    ) -> None:
+        if not self.validationLabels:
+            print("No validation labels set yet. Please note highlights will be overwriten by the next adjustemnt call.")
+        assert self.comps, "The compositions must be set before highlighting them. If you use ``OPTIMADEAdjuster``, this is done automatically, but with ``LocalAdjuster``, you have to set them manually."
+        reducedFormulas = set([Composition(c).reduced_formula for c in compositions])
+        for idx, comp in enumerate(self.comps):
+            if comp in reducedFormulas:
+                self.validationLabels[idx] = "Highlight"
 
 
 class OPTIMADEAdjuster(LocalAdjuster):
@@ -773,6 +784,8 @@ def fetchAndFeturize(
         else:
             raise NotImplementedError("The descriptor must be either 'Ward2017' or 'KS2022'. Others will be added in the future.")
 
+        self.validationLabels = ["Training"]*len(self.descriptorData)
+
         if verbose:
             print("Featurization complete!")
             print(f"Current dataset size: "

From 36fce5abeed29807f41bfdca37af6ce962d39ca4 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 18:53:34 -0400
Subject: [PATCH 35/50] (MA) improved target path documentation

---
 pysipfenn/core/modelAdjusters.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index fd1dcb3..343241e 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -146,7 +146,7 @@ def plotStarting(self) -> None:
                 y=predictions,
                 hover_name=self.names,
                 labels={"x": "Target Data", "y": "Predictions"},
-                title="Starting (Unadjusted) Model Predictions"
+                title="Starting (Unadjusted) Model Predictions (Hover for Name)"
                 )
         else:
             fig = px.scatter(
@@ -209,14 +209,14 @@ def plotAdjusted(self) -> None:
                 y=predictions,
                 hover_name=self.names,
                 labels={"x": "Target Data", "y": "Predictions"},
-                title="Starting (Unadjusted) Model Predictions"
+                title="Adjusted Model Predictions (Hover for Name)"
                 )
         else:
             fig = px.scatter(
                 x=reference,
                 y=predictions,
                 labels = {"x": "Target Data", "y": "Predictions"},
-                title = "Starting (Unadjusted) Model Predictions"
+                title = "Adjusted Model Predictions"
             )
         # If the validation labels are set, color the points as blue for training, green for validation, and red for
         # any other label, just in case advanced users want to use this method for other purposes.
@@ -622,8 +622,16 @@ class OPTIMADEAdjuster(LocalAdjuster):
             on the provider you choose, and you will need to identify it by looking at the response. The easiest way to
             do this is by going to their endpoint, like
             [this, very neat one, for JARVIS](https://jarvis.nist.gov/optimade/jarvisdft/v1/structures/),
+            [this one for Alexandria PBEsol](https://alexandria.icams.rub.de/pbesol/v1/structures),
             [this one for MP](https://optimade.materialsproject.org/v1/structures),
-             or [this one for our in-house MPDD](https://optimade.mpdd.org/v1/structures).
+            or [this one for our in-house MPDD](https://optimade.mpdd.org/v1/structures). Examples include
+            ``('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom')`` for GGA+U formation energy
+            per atom in MP, or ``('attributes', '_alexandria_scan_formation_energy_per_atom')`` for the `SCAN` formation
+            energy per atom in Alexandria, or ``('attributes', '_alexandria_formation_energy_per_atom')`` for the
+            ``GGAsol`` formation energy per atom in Alexandria, or ``('attributes', '_jarvis_formation_energy_peratom')``
+            for the `optb88vdw` formation energy per atom in JARVIS, or ``('attributes',
+            '_mpdd_formationenergy_sipfenn_krajewski2020_novelmaterialsmodel')`` for the formation energy predicted
+            by the SIPFENN_Krajewski2020_NovelMaterialsModel for every structure in MPDD. Default is the MP example.
         targetSize: The length of the target data to be fetched from the OPTIMADE API. This is typically ``1`` for a single
         scalar property, but it can be more. Default is ``1``.
         device: Same as in the ``LocalAdjuster``. Default is ``"cpu"``.
@@ -669,12 +677,7 @@ def __init__(
                     "tcod",
                     "twodmatpedia"
                 ] = "mp",
-            targetPath: Tuple[str] = (
-                    'attributes',
-                    '_mp_stability',
-                    'gga_gga+u',
-                    'formation_energy_per_atom'
-            ),
+            targetPath: Tuple[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
             targetSize: int = 1,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",

From ff1621882341108b00cedafff6087f67d74f2764 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 19:02:12 -0400
Subject: [PATCH 36/50] (MA) added ETA printouts to the hyperparameter search

---
 pysipfenn/core/modelAdjusters.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 343241e..d6f5ef8 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -6,6 +6,7 @@
 from functools import reduce
 import operator
 from random import shuffle
+import time
 
 # Default 3rd party imports
 import numpy as np
@@ -451,8 +452,10 @@ def matrixHyperParameterSearch(
             verbose: Same as in the ``adjust`` method. Default is ``True``.
             plot: Whether to plot the training history after all the combinations are tested. Default is ``True``.
         """
+        nTasks = len(learningRates) * len(optimizers) * len(weightDecays)
         if verbose:
             print("Starting the hyperparameter search...")
+            print(f"{nTasks} combinations will be tested.\n")
 
         bestModel: torch.nn.Module = None
         bestTrainingLoss: float = np.inf
@@ -467,6 +470,8 @@ def matrixHyperParameterSearch(
         trainLossHistory: List[List[float]] = []
         validationLossHistory: List[List[float]] = []
         labels: List[str] = []
+        tasksDone = 0
+        t0 = time.perf_counter()
 
         for learningRate in learningRates:
             for optimizer in optimizers:
@@ -517,6 +522,11 @@ def matrixHyperParameterSearch(
                         else:
                             print(f"Model with LR: {learningRate}, OPT: {optimizer}, WD: {weightDecay} did not improve.")
 
+                    tasksDone += 1
+                    pastTimePerTask = ((time.perf_counter() - t0)/60) / tasksDone
+                    print(f"Task {tasksDone}/{nTasks} done. Estimated time left: {pastTimePerTask * (nTasks - tasksDone):.2f} minutes.\n")
+
+
         if verbose:
             print(f"\n\nBest model found with LR: {bestHyperparameters['learningRate']}, OPT: {bestHyperparameters['optimizer']}, "
                   f"WD: {bestHyperparameters['weightDecay']}, Epoch: {bestHyperparameters['epochs']}")

From bedfc09f5af11c8422b8972a30d7a5991324c214 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:32:56 -0400
Subject: [PATCH 37/50] (MA) bugfix at CSV data ingestion which did not expect
 default pySIPFENN labeling

---
 pysipfenn/core/modelAdjusters.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index d6f5ef8..f4e5615 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -91,7 +91,7 @@ def __init__(
                 self.descriptorData = np.load(descriptorData)
             # Path to CSV file with data
             elif (descriptorData.endswith(".csv") or descriptorData.endswith(".CSV")) and os.path.exists(descriptorData):
-                self.descriptorData = np.loadtxt(descriptorData, delimiter=",")
+                self.descriptorData = np.loadtxt(descriptorData, delimiter=",", skiprows=1)[:, 1:]
             else:
                 raise ValueError("If a string is provided as descriptor data parameter, it must be a path to a npy/NPY or csv/CSV file.")
         else:
@@ -106,7 +106,8 @@ def __init__(
                 self.targetData = np.load(targetData)
             # Path to CSV file with data
             elif (targetData.endswith(".csv") or targetData.endswith(".CSV")) and os.path.exists(targetData):
-                self.targetData = np.loadtxt(targetData, delimiter=",")
+                # Skip the first row if it is a header
+                self.targetData = np.loadtxt(targetData, delimiter=",", skiprows=1)[:, 1:]
             else:
                 raise ValueError("If a string is provided as target data parameter, it must be a path to a npy/NPY or csv/CSV file.")
         else:

From a079d36c843fa00417850bce88450108997db0cb Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:33:25 -0400
Subject: [PATCH 38/50] (MA) additional assertiona at the plotting step

---
 pysipfenn/core/modelAdjusters.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f4e5615..2704ed1 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -134,6 +134,8 @@ def plotStarting(self) -> None:
         Plot the starting model (before adjustment) on the target data. By default, it will plot in your browser.
         """
         reference = self.targetData.flatten()
+        assert len(reference) == len(self.descriptorData), "The target data and descriptor data must have the same length."
+        assert len(reference) != 0, "The target data must not be empty for plotting."
         self.model.eval()
         print("Running the STARTING model on the data and plotting the results...")
         with torch.no_grad():
@@ -198,6 +200,8 @@ def plotAdjusted(self) -> None:
         assert self.adjustedModel is not None, "The model must be adjusted before plotting. It is currently None."
         self.adjustedModel.eval()
         reference = self.targetData.flatten()
+        assert len(reference) == len(self.descriptorData), "The target data and descriptor data must have the same length."
+        assert len(reference) != 0, "The target data must not be empty for plotting."
         print("Running the ADJUSTED model on the data and plotting the results...")
         with torch.no_grad():
             dataIn = torch.from_numpy(np.array(self.descriptorData)).float().to(device=self.device)

From e024222bf396b13e7cca70a4a335eeae64f051dd Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:34:32 -0400
Subject: [PATCH 39/50] (MA) general minor improvements including populating
 `self.validationLabels` if no-validation adjustments were ran

---
 pysipfenn/core/modelAdjusters.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 2704ed1..80d8f7f 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -95,6 +95,7 @@ def __init__(
             else:
                 raise ValueError("If a string is provided as descriptor data parameter, it must be a path to a npy/NPY or csv/CSV file.")
         else:
+            print(descriptorData)
             raise ValueError("The descriptor data must be either (a) None to use the data in the Calculator,"
                              "(b) a path to a npy/NPY file, or (c) a path to a csv/CSV file.")
 
@@ -315,6 +316,7 @@ def adjust(
             ddTrain, ddVal = ddTensor[:split], ddTensor[split:]
             tdTrain, tdVal = tdTensor[:split], tdTensor[split:]
         else:
+            self.validationLabels = ["Training"]*len(ddTensor)
             ddTrain, ddVal = ddTensor, None
             tdTrain, tdVal = tdTensor, None
 
@@ -428,9 +430,9 @@ def matrixHyperParameterSearch(
             epochs: int = 100,
             batchSize: int = 32,
             lossFunction: Literal["MSE", "MAE"] = "MAE",
-            learningRates: Tuple[float] = (1e-6, 1e-5, 1e-4),
-            optimizers: Tuple[Literal["Adam", "AdamW", "Adamax", "RMSprop"]] = ("Adam", "AdamW", "Adamax"),
-            weightDecays: Tuple[float] = (1e-5, 1e-4, 1e-3),
+            learningRates: List[float] = (1e-6, 1e-5, 1e-4),
+            optimizers: List[Literal["Adam", "AdamW", "Adamax", "RMSprop"]] = ("Adam", "AdamW", "Adamax"),
+            weightDecays: List[float] = (1e-5, 1e-4, 1e-3),
             verbose: bool = True,
             plot: bool = True
     ) -> Tuple[torch.nn.Module, Dict[str, Union[float, str]]]:
@@ -448,11 +450,11 @@ def matrixHyperParameterSearch(
             epochs: Same as in the ``adjust`` method. Default is ``100``.
             batchSize: Same as in the ``adjust`` method. Default is ``32``.
             lossFunction: Same as in the ``adjust`` method. Default is ``MAE``, i.e. Mean Absolute Error or L1 loss.
-            learningRates: Tuple of floats with the learning rates to be tested. Default is ``(1e-6, 1e-5, 1e-4)``. See
+            learningRates: List of floats with the learning rates to be tested. Default is ``(1e-6, 1e-5, 1e-4)``. See
                 the ``adjust`` method for more information.
-            optimizers: Tuple of strings with the optimizers to be tested. Default is ``("Adam", "AdamW", "Adamax")``. See
+            optimizers: List of strings with the optimizers to be tested. Default is ``("Adam", "AdamW", "Adamax")``. See
                 the ``adjust`` method for more information.
-            weightDecays: Tuple of floats with the weight decays to be tested. Default is ``(1e-5, 1e-4, 1e-3)``. See
+            weightDecays: List of floats with the weight decays to be tested. Default is ``(1e-5, 1e-4, 1e-3)``. See
                 the ``adjust`` method for more information.
             verbose: Same as in the ``adjust`` method. Default is ``True``.
             plot: Whether to plot the training history after all the combinations are tested. Default is ``True``.
@@ -627,13 +629,13 @@ class OPTIMADEAdjuster(LocalAdjuster):
         calculator: Instance of the ``Calculator`` class with the model to be adjusted, defined and loaded. Unlike in the
             ``LocalAdjuster``, the descriptor data will not be passed, since it will be fetched from the OPTIMADE API.
         model: Name of the model to be adjusted in the ``Calculator``. E.g., ``SIPFENN_Krajewski2022_NN30``.
-        provider: Tuple of strings with the names of the providers to be used for the OPTIMADE queries. The type-hinting
+        provider: Strings with the name of the provider to be used for the OPTIMADE queries. The type-hinting
             gives a list of providers available at the time of writing this code, but it is by no means limited to them.
             For the up-to-date list, along with their current status, please refer to the
             [OPTIMADE Providers Dashboard](https://optimade.org/providers-dashboard). The default is ``"mp"`` which
             stands for the Materials Project, but we do not recommend any particular provider over any other. One has to
             be picked to work out of the box. Your choice should be based on the data you are interested in.
-        targetPath: Tuple of strings with the path to the target data in the OPTIMADE response. This will be dependent
+        targetPath: List of strings with the path to the target data in the OPTIMADE response. This will be dependent
             on the provider you choose, and you will need to identify it by looking at the response. The easiest way to
             do this is by going to their endpoint, like
             [this, very neat one, for JARVIS](https://jarvis.nist.gov/optimade/jarvisdft/v1/structures/),
@@ -692,7 +694,7 @@ def __init__(
                     "tcod",
                     "twodmatpedia"
                 ] = "mp",
-            targetPath: Tuple[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
+            targetPath: List[str] = ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'),
             targetSize: int = 1,
             device: Literal["cpu", "cuda", "mps"] = "cpu",
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",

From 8d94133216cc200542fd7de34a602654d1908c56 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:35:27 -0400
Subject: [PATCH 40/50] (test) added an example NumPy file with stored
 descriptor data

---
 .../testCaseFiles/AdjusterTestDescriptors.npy   | Bin 0 -> 53376 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.npy

diff --git a/pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.npy b/pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.npy
new file mode 100644
index 0000000000000000000000000000000000000000..12fd89a981fe3959f0462447d7acd8a4a5c0ea91
GIT binary patch
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From 8aa6dda9139c75662bebe341d75e7720508a0474 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:35:35 -0400
Subject: [PATCH 41/50] (test) added an example CSV file with stored descriptor
 data

---
 .../testCaseFiles/AdjusterTestDescriptors.csv | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.csv

diff --git a/pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.csv b/pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.csv
new file mode 100644
index 0000000..461fce6
--- /dev/null
+++ b/pysipfenn/tests/testCaseFiles/AdjusterTestDescriptors.csv
@@ -0,0 +1,53 @@
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From 6316916d27f581ca1c78ac2b6a6b24a38ea85477 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:36:06 -0400
Subject: [PATCH 42/50] (test) added an example NumPy file with a stored target
 data array

---
 .../tests/testCaseFiles/AdjusterTestTargets.npy   | Bin 0 -> 544 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 pysipfenn/tests/testCaseFiles/AdjusterTestTargets.npy

diff --git a/pysipfenn/tests/testCaseFiles/AdjusterTestTargets.npy b/pysipfenn/tests/testCaseFiles/AdjusterTestTargets.npy
new file mode 100644
index 0000000000000000000000000000000000000000..ef2a932ca320d4678c7e0e93970982bcae1deab6
GIT binary patch
literal 544
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literal 0
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From 87fb74e65442a1a38371afa4d872a68eff9dd3dc Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:36:23 -0400
Subject: [PATCH 43/50] (test) added an example CSV file with a stored target
 data table

---
 .../testCaseFiles/AdjusterTestTargets.csv     | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 pysipfenn/tests/testCaseFiles/AdjusterTestTargets.csv

diff --git a/pysipfenn/tests/testCaseFiles/AdjusterTestTargets.csv b/pysipfenn/tests/testCaseFiles/AdjusterTestTargets.csv
new file mode 100644
index 0000000..e7fedf9
--- /dev/null
+++ b/pysipfenn/tests/testCaseFiles/AdjusterTestTargets.csv
@@ -0,0 +1,53 @@
+Name, E_f [eV/atom]
+1,0.11204700625000008
+2,4.0003046025
+3,-0.2837863701674114
+4,-0.3390229200000003
+5,-0.17997961958333317
+6,-0.0791259005555555
+7,0.004546606250000214
+8,-0.3278120625000002
+9,4.4154639174999994
+10,-0.9682736355555556
+11,-0.16495270666666784
+12,3.4404967875000008
+13,0.989534965
+14,-0.17180871000000053
+15,0.24268204250000025
+16,3.2851498774999985
+17,0.4826709725520833
+18,0.8575663433333327
+19,-0.15532792000000128
+20,3.5336190875
+21,3.4123461724999995
+22,-0.08306756583333315
+23,3.6542583874999996
+24,-0.26425716500000157
+25,0.0352760779999997
+26,-0.19337707321428713
+27,-0.6388277630555544
+28,-0.5461846583333335
+29,-0.7075618358333339
+30,-0.5300890800000015
+31,0.17000970687500017
+32,0.035313649999999086
+33,3.5496084325000012
+34,-0.11673426059523706
+35,-0.21550866821428574
+36,-0.14759064909598255
+37,-0.12148937464285682
+38,-0.22380490500000047
+39,-0.1422538353571444
+40,0.07023631500000072
+41,0.18032264750000015
+42,-0.36177632333333304
+43,-0.131107012857143
+44,-0.2944636875000004
+45,-0.17348738892857188
+46,-0.3059225313095238
+47,-0.6188369750000007
+48,1.1871788166666672
+49,-0.1811493189285715
+50,3.1963209299999997
+51,0.005689999999998936
+52,-0.6340091025000003

From 8d635af4237b88b0986b19cdca6920ea0bb4d371 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:37:22 -0400
Subject: [PATCH 44/50] (test) added a pretty complete testing suite for model
 adjusters; tests all functions except for the optional, non-default ClearML
 connectivity

---
 pysipfenn/tests/test_ModelAdjusters.py | 161 +++++++++++++++++++++++++
 1 file changed, 161 insertions(+)
 create mode 100644 pysipfenn/tests/test_ModelAdjusters.py

diff --git a/pysipfenn/tests/test_ModelAdjusters.py b/pysipfenn/tests/test_ModelAdjusters.py
new file mode 100644
index 0000000..8e6ec89
--- /dev/null
+++ b/pysipfenn/tests/test_ModelAdjusters.py
@@ -0,0 +1,161 @@
+import unittest
+import pytest
+import os
+import pysipfenn
+import torch
+from importlib import resources
+
+# Skip the tests if we're in GitHub Actions and the models haven't been fetched yet
+IN_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true" and os.getenv("MODELS_FETCHED") != "true"
+
+@pytest.mark.skipif(IN_GITHUB_ACTIONS, reason="Test depends on the ONNX network files")
+class TestModelAdjusters(unittest.TestCase):
+    """
+    Test all model adjusting features that can operate on the Calculator object. Note that this will require
+    the models to be downloaded and the environment variable MODELS_FETCHED to be set to true if running in GitHub
+    Actions.
+    """
+
+    def setUp(self):
+        """
+        Initialises the Calculator and ModelAdjuster objects for testing.
+        """
+        self.c = pysipfenn.Calculator(autoLoad=False)
+        self.assertIsNotNone(self.c)
+        self.c.loadModels("SIPFENN_Krajewski2022_NN30")
+        self.assertIn('SIPFENN_Krajewski2022_NN30', self.c.loadedModels)
+
+        self.ma = pysipfenn.OPTIMADEAdjuster(self.c, "SIPFENN_Krajewski2022_NN30")
+
+    def testInit(self):
+        """
+        Test that the OPTIMADEAdjuster object has been initialized correctly.
+        """
+        self.assertEqual(self.ma.modelName, "SIPFENN_Krajewski2022_NN30")
+        self.assertIsInstance(self.ma.model, torch.nn.Module)
+        self.assertIsInstance(self.ma.calculator, pysipfenn.Calculator)
+
+        self.assertEqual(len(self.ma.comps), 0)
+        self.assertEqual(len(self.ma.names), 0)
+        self.assertEqual(len(self.ma.validationLabels), 0)
+
+    def testPlotExceptions(self):
+        """
+        Test that the plot does not plot anything when no data is present.
+        """
+        self.assertRaises(AssertionError, self.ma.plotStarting)
+        self.assertRaises(AssertionError, self.ma.plotAdjusted)
+
+    def testFullRoutine(self):
+        """
+        Test the full routine of the adjuster based on the default values pointing to Materials Project. Get the data
+        using OPTIMADE to adjust the model to Hf-Mo metallic system. Matrix search is reduced to 4 cases to speed up
+        the test.
+        """
+        self.ma.fetchAndFeturize(
+            'elements HAS "Hf" AND elements HAS "Mo" AND NOT elements HAS ANY "O","C","F","Cl","S"',
+            parallelWorkers=4)
+
+        self.ma.calculator.writeDescriptorsToCSV("KS2022", "AdjusterTestDescriptors.csv")
+        self.ma.calculator.writeDescriptorsToNPY("KS2022", "AdjusterTestDescriptors.npy")
+
+        # Check highlighting and no-last-validation plotting
+        self.ma.highlightPoints([32, 23, 21, 22])
+        self.ma.plotStarting()
+
+        # Hyperparameter search. The 1e-8 is on purpose, so that the model does not converge and always improves after
+        # the first epoch.
+        self.ma.matrixHyperParameterSearch(
+            learningRates=[1e-8, 1e-3],
+            optimizers= ["Adam"],
+            weightDecays=[1e-4, 1e-5],
+            epochs=10
+        )
+
+        self.ma.highlightPoints([0, 1, 2, 3])
+        self.ma.highlightCompositions(["Hf", "Mo", "HfMo", "Hf50 Mo50", "Hf3Mo"])
+
+        self.ma.plotStarting()
+        self.ma.plotAdjusted()
+
+        # Induce duplicates to test if they are handled
+        self.ma.fetchAndFeturize(
+            'elements HAS "Hf" AND elements HAS "Mo" AND NOT elements HAS ANY "O","C","F","Cl","S"',
+            parallelWorkers=4)
+
+        self.ma.adjust(
+            validation=0,
+            learningRate=1e-4,
+            epochs=10,
+            optimizer="Adamax",
+            weightDecay=1e-4,
+            lossFunction="MSE"
+        )
+
+        self.ma.names = []
+        self.ma.plotStarting()
+        self.ma.plotAdjusted()
+
+    def testDataLoading(self):
+        """
+        Test the data loading functionality of the adjuster.
+        """
+
+        with resources.files('pysipfenn').joinpath('tests/testCaseFiles/') as testFileDir:
+
+            # From CSV
+            self.lma1 = pysipfenn.LocalAdjuster(
+                self.c,
+                model="SIPFENN_Krajewski2022_NN30",
+                descriptorData=str(testFileDir.joinpath("AdjusterTestDescriptors.csv")),
+                targetData=str(testFileDir.joinpath("AdjusterTestTargets.csv")),
+                descriptor="KS2022"
+            )
+            assert self.lma1 is not None
+            assert len(self.lma1.descriptorData) > 0
+            assert len(self.lma1.targetData) > 0
+            del self.lma1
+
+            # From NPY
+            self.lma2 = pysipfenn.LocalAdjuster(
+                self.c,
+                model="SIPFENN_Krajewski2022_NN30",
+                descriptorData=str(testFileDir.joinpath("AdjusterTestDescriptors.npy")),
+                targetData=str(testFileDir.joinpath("AdjusterTestTargets.npy")),
+                descriptor="KS2022"
+            )
+            assert self.lma2 is not None
+            assert len(self.lma2.descriptorData) > 0
+            assert len(self.lma2.targetData) > 0
+
+            self.c.descriptorData = self.lma2.descriptorData
+
+            del self.lma2
+
+            # Implicit, from the Calculator
+            self.lma3 = pysipfenn.LocalAdjuster(
+                self.c,
+                targetData=str(testFileDir.joinpath("AdjusterTestTargets.csv")),
+                model="SIPFENN_Krajewski2022_NN30",
+                descriptor="KS2022",
+            )
+
+            # Error raising
+            with self.assertRaises(AssertionError):
+                self.lma4 = pysipfenn.LocalAdjuster(
+                    self.c,
+                    targetData=str(testFileDir.joinpath("AdjusterTestTargets.csv")),
+                    model="SIPFENN_Krajewski2022_NN30",
+                    descriptor="Ward2017",
+                )
+
+            with self.assertRaises(NotImplementedError):
+                self.lma5 = pysipfenn.LocalAdjuster(
+                    self.c,
+                    targetData=str(testFileDir.joinpath("AdjusterTestTargets.csv")),
+                    model="SIPFENN_Krajewski2022_NN30",
+                    descriptor="SomeCrazyDescriptor",
+                )
+
+
+

From f6e92e07b3e3d62d7a62edbcf385abc2823f93da Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Fri, 29 Mar 2024 22:44:41 -0400
Subject: [PATCH 45/50] (test) improved `ModelAdjusters` test documentation

---
 pysipfenn/tests/test_ModelAdjusters.py | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/tests/test_ModelAdjusters.py b/pysipfenn/tests/test_ModelAdjusters.py
index 8e6ec89..d73585c 100644
--- a/pysipfenn/tests/test_ModelAdjusters.py
+++ b/pysipfenn/tests/test_ModelAdjusters.py
@@ -14,6 +14,12 @@ class TestModelAdjusters(unittest.TestCase):
     Test all model adjusting features that can operate on the Calculator object. Note that this will require
     the models to be downloaded and the environment variable MODELS_FETCHED to be set to true if running in GitHub
     Actions.
+
+    The setup will load the Krajewski2022_NN30 model and create an ``OPTIMADEAdjuster`` object for testing that is by
+    default connected to the Materials Project ``OPTIMADE`` server and looks for their GGA+U formation energies. In the
+    ``testFullRoutine`` test, the adjuster will be used to adjust the model to the Hf-Mo metallic system. The test will
+    cover almost all adjuster functionalities in different ways to hit all anticipated code paths. It also tests the
+    ``LocalAdjuster`` class for loading data from CSV and NPY files, which is a parent class of the ``OPTIMADEAdjuster``.
     """
 
     def setUp(self):
@@ -50,7 +56,8 @@ def testFullRoutine(self):
         """
         Test the full routine of the adjuster based on the default values pointing to Materials Project. Get the data
         using OPTIMADE to adjust the model to Hf-Mo metallic system. Matrix search is reduced to 4 cases to speed up
-        the test.
+        the test and it is designed to explore all code paths in the search. The test will also check the highlighting
+        and plotting functionalities of the adjuster.
         """
         self.ma.fetchAndFeturize(
             'elements HAS "Hf" AND elements HAS "Mo" AND NOT elements HAS ANY "O","C","F","Cl","S"',
@@ -98,7 +105,11 @@ def testFullRoutine(self):
 
     def testDataLoading(self):
         """
-        Test the data loading functionality of the adjuster.
+        Test the data loading functionality of the ``LocalAdjuster`` class (note, ``OPTIMADEAdjuster`` extends it). It
+        will test loading from both CSV and NPY files exported from the Calculator object. Note that CSV files have
+        names in the first column and headers in the first row, while NPY files are just the data arrays. It tests
+        implicit loading from the ``Calculator`` object as well. Lastly, it tests the error raising for unsupported
+        descriptors and data not matching the descriptor dimensions selected (an optional feature).
         """
 
         with resources.files('pysipfenn').joinpath('tests/testCaseFiles/') as testFileDir:

From 57eb3e57ecf2ad3b9cea18c0b50a987394d855e7 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:01:21 -0400
Subject: [PATCH 46/50] (MA) reduced hyperparameter search epoch *default*
 number to 20 and improved description of the parameter to make it clear
 higher number is desired if GPU is present

---
 pysipfenn/core/modelAdjusters.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 80d8f7f..304cd30 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -427,8 +427,8 @@ def adjust(
     def matrixHyperParameterSearch(
             self,
             validation: float = 0.2,
-            epochs: int = 100,
-            batchSize: int = 32,
+            epochs: int = 20,
+            batchSize: int = 64,
             lossFunction: Literal["MSE", "MAE"] = "MAE",
             learningRates: List[float] = (1e-6, 1e-5, 1e-4),
             optimizers: List[Literal["Adam", "AdamW", "Adamax", "RMSprop"]] = ("Adam", "AdamW", "Adamax"),
@@ -447,7 +447,9 @@ def matrixHyperParameterSearch(
 
         Args:
             validation: Same as in the ``adjust`` method. Default is ``0.2``.
-            epochs: Same as in the ``adjust`` method. Default is ``100``.
+            epochs: Same as in the ``adjust`` method. Default is ``20`` to keep the search time reasonable on most
+                CPU-only machines (around 1 hour). For most cases, a good starting number of epochs is 100-200, which
+                should complete in 10-30 minutes on most modern GPUs or Mac M1-series machines (w. device set to MPS).
             batchSize: Same as in the ``adjust`` method. Default is ``32``.
             lossFunction: Same as in the ``adjust`` method. Default is ``MAE``, i.e. Mean Absolute Error or L1 loss.
             learningRates: List of floats with the learning rates to be tested. Default is ``(1e-6, 1e-5, 1e-4)``. See

From bd354c9bc48a1a3208804d04d20bcae564a8100d Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:12:10 -0400
Subject: [PATCH 47/50] (MA) added a set of assertions to the
 `OPTIMADEAdjuster` initialization

---
 pysipfenn/core/modelAdjusters.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 304cd30..f8b4630 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -652,8 +652,10 @@ class OPTIMADEAdjuster(LocalAdjuster):
             '_mpdd_formationenergy_sipfenn_krajewski2020_novelmaterialsmodel')`` for the formation energy predicted
             by the SIPFENN_Krajewski2020_NovelMaterialsModel for every structure in MPDD. Default is the MP example.
         targetSize: The length of the target data to be fetched from the OPTIMADE API. This is typically ``1`` for a single
-        scalar property, but it can be more. Default is ``1``.
-        device: Same as in the ``LocalAdjuster``. Default is ``"cpu"``.
+            scalar property, but it can be more. Default is ``1``.
+        device: Same as in the ``LocalAdjuster``. Default is ``"cpu"`` which is available on all systems. If you have a
+            GPU, you can set it to ``"cuda"``, or to ``"mps"`` if you are using a Mac M1-series machine, in order to
+            speed up the training process by orders of magnitude.
         descriptor: *Not* the same as in the ``LocalAdjuster``. Since the descriptor data will be calculated for each
             structure fetched from the OPTIMADE API, this parameter is needed to specify which descriptor to use. At the
             time of writing this code, it can be either ``"Ward2017"`` or ``"KS2022"``. Special versions of ``KS2022``
@@ -706,6 +708,18 @@ def __init__(
     ) -> None:
         from optimade.client import OptimadeClient
 
+        assert isinstance(calculator, Calculator), "The calculator must be an instance of the Calculator class."
+        assert isinstance(model, str), "The model must be a string with the name of the model to be adjusted."
+        assert isinstance(provider, str), "The provider must be a string with the name of the provider to be used."
+        assert len(provider) != 0, "The provider must not be an empty string."
+        assert targetPath and isinstance(targetPath, list), "The target path must be a list of strings pointing to the target data in the OPTIMADE response."
+        assert len(targetPath) > 0, "The target path must not be empty, i.e., it cannot point to no data."
+        if provider != "mp" and targetPath == ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'):
+            raise ValueError("You are utilizing the default (example) property target path specific to the Materials "
+                             "Project but you are connecting to a different provider. You must adjust the target path "
+                             "to receive data from the provider you are connecting to based on what they serve through "
+                             "their provider-specific OPTIMADE endpoint fields. See targetPath docstring for more info.")
+
         super().__init__(
             calculator=calculator,
             model=model,

From 785dabb64e70288250a0ec2af3f46d3fe1c2de83 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:25:39 -0400
Subject: [PATCH 48/50] (MA) added an assertion more than no data was fetched,
 so that the error message is more clear to the user

---
 pysipfenn/core/modelAdjusters.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index f8b4630..0fd17e7 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -797,6 +797,7 @@ def fetchAndFeturize(
                   f"structures:\n{missing}\n")
 
         dataIn = list(zip(comps, names, structs, targetDataStage))
+        assert len(dataIn) != 0, "No data was fetched from the OPTIMADE API. Please check both the query and the provider."
         shuffle(dataIn)
         comps, names, structs, targetDataStage = zip(*dataIn)
 

From ac819daee10df39b0544d1bbd3d6f08267543e71 Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:33:15 -0400
Subject: [PATCH 49/50] (MA) implemented `endpointOverride` parameter to allow
 user to specify the base URL to use and point to custom provider or a
 specific sub-database of a provider

---
 pysipfenn/core/modelAdjusters.py | 32 +++++++++++++++++++++++++-------
 1 file changed, 25 insertions(+), 7 deletions(-)

diff --git a/pysipfenn/core/modelAdjusters.py b/pysipfenn/core/modelAdjusters.py
index 0fd17e7..2eef925 100644
--- a/pysipfenn/core/modelAdjusters.py
+++ b/pysipfenn/core/modelAdjusters.py
@@ -667,6 +667,14 @@ class OPTIMADEAdjuster(LocalAdjuster):
         maxResults: The maximum number of results to be fetched from the OPTIMADE API for a given query. Default is
             ``10000`` which is a very high number for most re-training tasks. If you are fetching a lot of data, it's
             possible the query is too broad, and you should consider narrowing it down.
+        endpointOverride: List of URL strings with the endpoint to be used for the OPTIMADE queries. This is an advanced
+            option allowing you to ignore the ``provider`` parameter and directly specify the endpoint to be used. It is
+            useful if you want to use a specific version of the provider's endpoint or narrow down the query to a
+            sub-database (Alexandria has two different endpoints for PBEsol and SCAN, for instance). You can also use it
+            to query unofficial endpoints. Make sure to (a) include protocol (``http://`` or ``https://``) and (b) not
+            include version (``/v1/``), nor the specific endpoint (``/structures``) as the client will add them. I.e.,
+            you want ``https://alexandria.icams.rub.de/pbesol`` rather than
+            ``alexandria.icams.rub.de/pbesol/v1/structures``. Default is ``None`` which has no effect.
     """
 
     def __init__(
@@ -704,7 +712,8 @@ def __init__(
             descriptor: Literal["Ward2017", "KS2022"] = "KS2022",
             useClearML: bool = False,
             taskName: str = "OPTIMADEFineTuning",
-            maxResults: int = 10000
+            maxResults: int = 10000,
+            endpointOverride: List[str] = None
     ) -> None:
         from optimade.client import OptimadeClient
 
@@ -712,7 +721,7 @@ def __init__(
         assert isinstance(model, str), "The model must be a string with the name of the model to be adjusted."
         assert isinstance(provider, str), "The provider must be a string with the name of the provider to be used."
         assert len(provider) != 0, "The provider must not be an empty string."
-        assert targetPath and isinstance(targetPath, list), "The target path must be a list of strings pointing to the target data in the OPTIMADE response."
+        assert targetPath and isinstance(targetPath, list) or isinstance(targetPath, tuple), "The target path must be a list of strings pointing to the target data in the OPTIMADE response."
         assert len(targetPath) > 0, "The target path must not be empty, i.e., it cannot point to no data."
         if provider != "mp" and targetPath == ('attributes', '_mp_stability', 'gga_gga+u', 'formation_energy_per_atom'):
             raise ValueError("You are utilizing the default (example) property target path specific to the Materials "
@@ -734,11 +743,20 @@ def __init__(
         self.descriptor = descriptor
         self.targetPath = targetPath
         self.provider = provider
-        self.client = OptimadeClient(
-            use_async=False,
-            include_providers=[provider],
-            max_results_per_provider=maxResults
-        )
+        if endpointOverride is None:
+            self.client = OptimadeClient(
+                use_async=False,
+                include_providers=[provider],
+                max_results_per_provider=maxResults
+            )
+        else:
+            assert isinstance(endpointOverride, list) or isinstance(endpointOverride, tuple), "The endpoint override must be a list of strings."
+            assert len(endpointOverride) != 0, "The endpoint override must not be an empty list."
+            self.client = OptimadeClient(
+                use_async=False,
+                base_urls=endpointOverride,
+                max_results_per_provider=maxResults
+            )
 
         if self.descriptor == "Ward2017":
             self.descriptorData: np.ndarray = np.empty((0, 271))

From 4c12c2180d7e5147b5de2f7128207ec0f673663b Mon Sep 17 00:00:00 2001
From: "Adam M. Krajewski" <54290107+amkrajewski@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:44:20 -0400
Subject: [PATCH 50/50] (tests) added testing procedure for endpoint overriding

---
 pysipfenn/tests/test_ModelAdjusters.py | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/pysipfenn/tests/test_ModelAdjusters.py b/pysipfenn/tests/test_ModelAdjusters.py
index d73585c..3d7f4ed 100644
--- a/pysipfenn/tests/test_ModelAdjusters.py
+++ b/pysipfenn/tests/test_ModelAdjusters.py
@@ -168,5 +168,27 @@ def testDataLoading(self):
                     descriptor="SomeCrazyDescriptor",
                 )
 
-
-
+    def testEndpointOverride(self):
+        """
+        Test the endpoint override functionality of the ``OPTIMADEAdjuster`` class. It will test the override of the
+        endpoint and the data fetching from the new endpoint.
+        """
+        endpoint = ["https://alexandria.icams.rub.de/pbesol"]
+        targetPath = ['attributes', '_alexandria_formation_energy_per_atom']
+
+        self.ma2 = pysipfenn.OPTIMADEAdjuster(
+            self.c,
+            model="SIPFENN_Krajewski2022_NN30",
+            endpointOverride=endpoint,
+            targetPath=targetPath)
+
+        self.ma2.fetchAndFeturize(
+            'elements HAS "Hf" AND elements HAS "Mo" AND elements HAS "Zr"',
+            parallelWorkers=2)
+
+        self.assertGreaterEqual(len(self.ma2.comps), 0, "No compositions were found, thus no data was fetched.")
+        self.assertGreaterEqual(len(self.ma2.names), 0, "No names were found, thus no data was fetched.")
+        self.assertGreaterEqual(
+            len(self.ma2.descriptorData), 0,
+            "No descriptor data was found. If the other asserts passed, this is likely a bug in the featurization "
+            "or structural data has been made incompatible or otherwise corrupted.")
\ No newline at end of file