Updated formatting, removed ptype dependency, updated docs.

.github/workflows/python-package-conda.yml

@@ -15,7 +15,6 @@ jobs:
     - uses: mamba-org/setup-micromamba@v1
       with:
         environment-file: environment_torch.yml
-        activate-environment: test
     - shell: bash -l {0}
       run: |
         conda info
@@ -28,12 +27,14 @@ jobs:
       run: |
         micromamba install ruff
         # stop the build if there are Python syntax errors or undefined names
-        ruff . --count --select=E9,F63,F7,F82 --show-source --statistics
+        ruff check --select=E9,F63,F7,F82
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        ruff . --count --exit-zero --max-complexity=100 --max-line-length=127 --statistics
+        ruff check --output-format concise
         # Checking documentation errors
-        ruff . --count --select=D --exit-zero --max-complexity=100 --max-line-length=127 --statistics
+        ruff check  --select=D
+        ruff check  --select=D  --statistics
     - name: Test with pytest
       shell: bash -l {0}
       run: |
+        export KERAS_BACKEND="torch"
         pytest

applications/evaluate_ptype.py

@@ -10,12 +10,12 @@
 import pandas as pd
 from sklearn.metrics import precision_recall_fscore_support
 
-from ptype.reliability import (
+from mlguess.reliability import (
     compute_calibration,
     reliability_diagram,
     reliability_diagrams,
 )
-from ptype.plotting import (
+from mlguess.plotting import (
     plot_confusion_matrix,
     coverage_figures,
 )

docs/source/conf.py

@@ -17,6 +17,7 @@
 extensions = ['myst_parser',
               'sphinx.ext.napoleon',
               'sphinx.ext.intersphinx',
+              'sphinx.ext.mathjax',
               'nbsphinx']
 
 templates_path = ['_templates']

docs/source/index.rst

@@ -11,6 +11,8 @@ Welcome to miles-guess's documentation!
    :caption: Contents:
 
    install.rst
+   keras.md
+   torch.md
    mlguess.rst
    notebooks/classifier_example.ipynb
    notebooks/regression_example.ipynb

docs/source/keras.md

@@ -1,7 +1,10 @@
+# Keras in MILES-GUESS
+
 Welcome to the Keras users page. The instructions below outline how to compute various UQ quantities like aleatoric and epistemic using different modeling approaches.
 
+## Regression Models
 
-### 1a. Train/evaluate a deterministic multi-layer perceptrion (MLP) on the SL dataset:
+### 1a. Train/evaluate a deterministic multi-layer perceptron (MLP) on the SL dataset:
 ```bash
 python3 applications/train_mlp_SL.py -c config/model_mlp_SL.yml
 ```
@@ -16,6 +19,8 @@ python applications/train_gaussian_SL.py -c config/model_gaussian_SL.yml
 python applications/train_evidential_SL.py -c config/model_evidential_SL.yml
 ```
 
+## Classification Models
+
 ### 2a. Train a categorical MLP classifier on the p-type dataset:
 ```bash
 python applications/train_classifier_ptype.py -c config/model_classifier_ptype.yml
@@ -213,4 +218,4 @@ Depending on the problem, a data field is customized and also present in the con
 
 ## ECHO hyperparameter optimization 
 
-Configuration files are also supplied for use with the Earth Computing Hyperparameter Optimization (ECHO) package. See the echo package https://github.com/NCAR/echo-opt/tree/main/echo for more details on the configuration fields.
+Configuration files are also supplied for use with the Earth Computing Hyperparameter Optimization (ECHO) package. See the echo package https://github.com/NCAR/echo-opt/tree/main/echo for more details on the configuration fields.

docs/source/mlguess.keras.rst

@@ -36,10 +36,10 @@ mlguess.keras.models module
    :undoc-members:
    :show-inheritance:
 
-mlguess.keras.models\_deprecated module
+mlguess.keras.deprecated module
 ---------------------------------------
 
-.. automodule:: mlguess.keras.models_deprecated
+.. automodule:: mlguess.keras.deprecated
    :members:
    :undoc-members:
    :show-inheritance:

docs/source/torch.md

@@ -1,3 +1,5 @@
+# Pytorch in MILES-GUESS
+
 Welcome to the pyTorch users page. The instructions below outline how to compute various UQ quantities like aleatoric and epistemic using different modeling approaches. Email [email protected] for questions/concerns/fixes/etc
 
 ## Regression usage
@@ -158,4 +160,4 @@ scheduler:
 
 ### Classifier Models
 
-* `output_activation`: Set to `softmax` for standard classification. If not set, the model will use evidential classification.
+* `output_activation`: Set to `softmax` for standard classification. If not set, the model will use evidential classification.

environment_gpu.yml → environment_gpu_tf.yml

@@ -1,10 +1,10 @@
-name: guess
+name: guesstf
 channels:
   - conda-forge
   - nvidia
 dependencies:
-  - python=3.10
-  - numpy<1.24
+  - python=3.11
+  - numpy<2
   - scipy
   - matplotlib
   - xarray

environment_gpu_torch.yml

@@ -0,0 +1,36 @@
+name: guesstorch
+channels:
+  - conda-forge
+  - pytorch
+dependencies:
+  - python=3.11
+  - numpy<2
+  - scipy
+  - matplotlib
+  - xarray
+  - metpy
+  - netcdf4
+  - pandas
+  - pyyaml
+  - pytest
+  - pip
+  - pyproj
+  - jupyter
+  - jupyterlab
+  - cartopy
+  - tqdm
+  - seaborn
+  - sphinx
+  - numba
+  - properscoring
+  - pyarrow
+  - imbalanced-learn
+  - pytorch
+  - pytorch-cuda=12.1
+  - pip:
+    - keras
+    - echo-opt
+    - hagelslag
+    - bridgescaler
+    - safetensors
+    - -e .

environment_tensorflow.yml

@@ -1,9 +1,9 @@
-name: guess
+name: guesstf
 channels:
   - conda-forge
 dependencies:
   - python=3.10
-  - numpy<1.24
+  - numpy<2
   - scipy
   - matplotlib
   - xarray
@@ -31,5 +31,4 @@ dependencies:
     - echo-opt
     - hagelslag
     - bridgescaler
-    - -e git+https://github.com/ai2es/ptype-physical.git#egg=ptype
     - -e .

environment_torch.yml

@@ -31,5 +31,5 @@ dependencies:
     - echo-opt
     - hagelslag
     - bridgescaler
-    - -e git+https://github.com/ai2es/ptype-physical.git#egg=ptype
+    - safetensors
     - -e .

mlguess/VERSION

@@ -1 +1 @@
-2024.1.0b2
+2024.2.0

mlguess/keras/deprecated/models.py

@@ -97,7 +97,8 @@ def __init__(
         self.history = None
 
     def build_neural_network(self, inputs, outputs, last_layer="Dense"):
-        """Create Keras neural network model and compile it.
+        """
+        Create Keras neural network model and compile it.
 
         Args:
             inputs (int): Number of input predictor variables.
@@ -108,7 +109,6 @@ def build_neural_network(self, inputs, outputs, last_layer="Dense"):
 
         if self.activation == "leaky":
             self.activation = LeakyReLU()
-
         if self.kernel_reg == "l1":
             self.kernel_reg = L1(self.l1_weight)
         elif self.kernel_reg == "l2":
@@ -522,7 +522,8 @@ def __init__(
 
 
 class GaussianRegressorDNN(BaseRegressor):
-    """A Dense Neural Network Model that can support arbitrary numbers of hidden layers
+    """
+    A Dense Neural Network Model that can support arbitrary numbers of hidden layers
     and provides evidential uncertainty estimation.
     Inherits from BaseRegressor.
 
@@ -542,6 +543,7 @@ class GaussianRegressorDNN(BaseRegressor):
         model: Keras Model object.
         evidential_coef: Evidential regularization coefficient.
         metrics: Optional list of metrics to monitor during training.
+
     """
 
     def __init__(
@@ -571,11 +573,13 @@ def __init__(
         metrics=None,
         eps=1e-7
     ):
-        """Initialize the EvidentialRegressorDNN.
+        """
+        Initialize the EvidentialRegressorDNN.
 
         Args:
             coupling_coef: Coupling coeffient for loss fix
             evidential_coef: Evidential regularization coefficient.
+
         """
         super().__init__(  # Call the constructor of the base class
             hidden_layers,
@@ -606,7 +610,8 @@ def __init__(
         self.loss = gaussian_nll
 
     def build_neural_network(self, inputs, outputs, last_layer="DenseNormal"):
-        """Create Keras neural network model and compile it.
+        """
+        Create Keras neural network model and compile it.
 
         Args:
             inputs (int): Number of input predictor variables.
@@ -702,7 +707,8 @@ def predict_monte_carlo(self, x_test, forward_passes, scaler=None, batch_size=No
 
 
 class EvidentialRegressorDNN(BaseRegressor):
-    """A Dense Neural Network Model that can support arbitrary numbers of hidden layers
+    """
+    A Dense Neural Network Model that can support arbitrary numbers of hidden layers
     and provides evidential uncertainty estimation.
     Inherits from BaseRegressor.
 
@@ -722,6 +728,7 @@ class EvidentialRegressorDNN(BaseRegressor):
         model: Keras Model object.
         evidential_coef: Evidential regularization coefficient.
         metrics: Optional list of metrics to monitor during training.
+
     """
     def __init__(
         self,
@@ -803,7 +810,8 @@ def __init__(
         logging.info(f"Using loss: {loss}")
 
     def build_neural_network(self, inputs, outputs):
-        """Create Keras neural network model and compile it.
+        """
+        Create Keras neural network model and compile it.
 
         Args:
             inputs (int): Number of input predictor variables.
@@ -905,19 +913,17 @@ def predict_dist_params(self, x, y_scaler=None, batch_size=None):
 
         return mu, v, alpha, beta
 
-    def predict_ensemble(
-        self, x_test, scaler=None, batch_size=None
-    ):
+    def predict_ensemble(self, x_test, scaler=None, batch_size=None):
         return super().predict_ensemble(x_test, scaler=scaler, batch_size=batch_size, num_outputs=3)
 
-    def predict_monte_carlo(
-        self, x_test, forward_passes, scaler=None, batch_size=None
-    ):
-        return super().predict_monte_carlo(x_test, forward_passes, scaler=scaler, batch_size=batch_size, num_outputs=3)
+    def predict_monte_carlo(self, x_test, forward_passes, scaler=None, batch_size=None):
+        return super().predict_monte_carlo(x_test, forward_passes,
+                                           scaler=scaler, batch_size=batch_size, num_outputs=3)
 
 
 class CategoricalDNN(object):
-    """A Dense Neural Network Model that can support arbitrary numbers of hidden layers.
+    """
+    A Dense Neural Network Model that can support arbitrary numbers of hidden layers.
 
     Attributes:
         hidden_layers: Number of hidden layers
@@ -941,6 +947,7 @@ class CategoricalDNN(object):
         decay: Level of decay to apply to learning rate
         verbose: Level of detail to provide during training (0 = None, 1 = Minimal, 2 = All)
         classifier: (boolean) If training on classes
+
     """
     def __init__(
         self,
@@ -969,7 +976,7 @@ def __init__(
         decay=0,
         verbose=0,
         random_state=1000,
-        callbacks=[],
+        callbacks=None,
         balanced_classes=0,
         steps_per_epoch=0,
     ):
@@ -997,6 +1004,8 @@ def __init__(
         self.use_dropout = use_dropout
         self.dropout_alpha = dropout_alpha
         self.epochs = epochs
+        if callbacks is None:
+            self.callbacks = []
         self.callbacks = callbacks
         self.decay = decay
         self.verbose = verbose
@@ -1007,7 +1016,8 @@ def __init__(
         self.steps_per_epoch = steps_per_epoch
 
     def build_neural_network(self, inputs, outputs):
-        """Create Keras neural network model and compile it.
+        """
+        Create Keras neural network model and compile it.
 
         Args:
             inputs (int): Number of input predictor variables
@@ -1066,13 +1076,15 @@ def build_neural_network(self, inputs, outputs):
         self.model.compile(optimizer=self.optimizer_obj, loss=self.loss)
 
     def build_from_sequential(self, model, optimizer="adam", loss="mse", metrics=None):
-        """Build the neural network model using a Keras Sequential model.
+        """
+        Build the neural network model using a Keras Sequential model.
 
         Args:
             model (tf.keras.Sequential): Keras Sequential model to use.
             optimizer (str or tf.keras.optimizers.Optimizer): Optimizer for the model.
             loss (str or tf.keras.losses.Loss): Loss function for the model.
             metrics (list of str or tf.keras.metrics.Metric): Metrics for the model.
+
         """
         self.model = model
 

mlguess/keras/layers.py

@@ -1,8 +1,6 @@
 import keras
 import keras.layers as layers
 import keras.ops as ops
-from keras.src import activations
-from keras.src.layers.layer import Layer
 
 
 @keras.saving.register_keras_serializable()