Merge pull request #25 from ai2es/torch

First commit of updated torch code

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

@@ -14,25 +14,26 @@ jobs:
     - uses: actions/checkout@v2
     - uses: mamba-org/setup-micromamba@v1
       with:
-        environment-file: environment.yml
-        activate-environment: test
+        environment-file: environment_torch.yml
     - shell: bash -l {0}
       run: |
-        pip install --upgrade keras
         conda info
         conda list
         conda config --show-sources
         conda config --show
         printenv | sort
-    - name: Lint with flake8
+    - name: Lint with ruff
       shell: bash -l {0}
       run: |
-        micromamba install flake8
+        micromamba install ruff
         # stop the build if there are Python syntax errors or undefined names
-        flake8 . --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
-        flake8 . --count --exit-zero --max-complexity=100 --max-line-length=127 --statistics
+        ruff check --output-format concise --exit-zero
+        # Checking documentation errors
+        ruff check  --select=D  --exit-zero --statistics
     - name: Test with pytest
       shell: bash -l {0}
       run: |
+        export KERAS_BACKEND="torch"
         pytest

README.md

@@ -49,23 +49,26 @@ conda activate guess
 
 ## Using miles-guess
 
-The package contains three scripts for training three regression models, and one for training categorical models. 
-The regression examples are trained on our surface layer ("SL") dataset for predicting latent heat and other quantities, 
-and the categorical example is trained on a precipitation dataset ("p-type").
-
-The law of total variance for each model prediction target may be computed as 
+The law of total variance for each model prediction target may be computed as
 
 $$LoTV = E[\sigma^2] + Var[\mu]$$ 
 
-which is the sum of aleatoric and epistemic contributions, respectively. 
+which is the sum of aleatoric and epistemic contributions, respectively. The MILES-GUESS package contains options for using either Keras or PyTorch for computing quantites according to the LoTV as well as utilizing Dempster-Shafer theory uncertainty in the classifier case. 
+
+For detailed information about training with Keras, refer to [the Keras training details README](docs/source/keras.md). There three scripts for training three regression models, and one for training categorical models. The regression examples are trained on our surface layer ("SL") dataset for predicting latent heat and other quantities, 
+and the categorical example is trained on a precipitation dataset ("p-type").
+
+For pyTorch, please visit the [the pyTorch training details README](docs/source/torch.md) where details on training scripts for both evidential standard classification tasks are detailed. Torch examples use the same datasets as the Keras models. The torch training code will also scale on GPUs, and is compatitible with DDP and FSDP.
 
+<!--
 ### 1a. Train/evaluate a deterministic multi-layer perceptrion (MLP) on the SL dataset:
 ```bash
 python3 applications/train_mlp_SL.py -c config/model_mlp_SL.yml
 ```
 
 ### 1b. Train/evaluate a parametric "Gaussian" MLP on the SL dataset:
 ```bash
+
 python applications/train_gaussian_SL.py -c config/model_gaussian_SL.yml
 ```
 
@@ -271,4 +274,4 @@ 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. -->

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,
 )