From 3008979fa9003a814711eb827e721c2902675667 Mon Sep 17 00:00:00 2001
From: beckynevin <beckynevin@gmail.com>
Date: Mon, 22 Apr 2024 11:42:15 -0600
Subject: [PATCH] removing wd from defaults

---
 README.md             | 23 +++++++++++++++++------
 src/utils/defaults.py |  2 --
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 51128e7..93b3b3e 100644
--- a/README.md
+++ b/README.md
@@ -12,22 +12,33 @@ DeepUQ is a package for injecting and measuring different types of uncertainty i
 ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/owner/repo/test-repo?label=test)
 
 ## Workflow
-![Folder structure overview](images/folders_deepUQ.png)
+![Folder structure overview](images/DeepUQWorkflow_Maggie.png)
 
-Getting a little more specific:
+The scripts can be accessed via the ipython example notebooks or via the model modules (ie `DeepEnsemble.py`). For example, to ingest data and train a Deep Ensemble: 
+> cd src/scripts/
 
-![python module overview](images/workflow_deepUQ.png)
+> python DeepEnsemble.py
 
-These modules can be accessed via the ipython example notebooks or via the model modules (ie `DeepEnsemble.py`). For example, to ingest data and train a Deep Ensemble: 
-> cd src/scripts/
+With no config file specified, this command will pull settings from the `default.py` file within `utils`. For the `DeepEnsemble.py` script, it will automatically select the `DefaultsDE` dictionary.
+
+Another option is to specify your own config file:
+
+> python DeepEnsemble.py --config "path/to/config/myconfig.yaml"
 
-> python DeepEnsemble.py low 10 /Users/rnevin/Documents/DeepUQ/ --save_final_checkpoint --savefig --n_epochs=10
+Where you would modify the "path/to/config/myconfig.yaml" to specify where your own yaml lives.
+
+The third option is to input settings on the command line. These choices are then combined with the default settings and output in a temporary yaml.
+
+> python DeepEnsemble.py --noise_level "low" --n_models 10 --out_dir ./DeepUQResources/results/--save_final_checkpoint True --savefig True --n_epochs 10
 
 This command will train a 10 network, 10 epoch ensemble on the low noise data and will save figures and final checkpoints to the specified directory. Required arguments are the noise setting (low/medium/high), the number of ensembles, and the working directory.
 
 For more information on the arguments:
 > python DeepEnsemble.py --help
 
+The other available script is the `DeepEvidentialRegression.py` script:
+> python DeepEvidentialRegression.py --help
+
 ## Installation 
 
 ### Clone this repo
diff --git a/src/utils/defaults.py b/src/utils/defaults.py
index c601b29..e190c24 100644
--- a/src/utils/defaults.py
+++ b/src/utils/defaults.py
@@ -20,7 +20,6 @@
         "loss_type": "bnll_loss",
         "n_models": 100,
         "init_lr": 0.001,
-        "wd": "./",
         "BETA": 0.5,
         "n_epochs": 100,
         "save_all_checkpoints": False,
@@ -77,7 +76,6 @@
         "model_type": "DER",
         "loss_type": "SDER",
         "init_lr": 0.001,
-        "wd": "./",
         "COEFF": 0.5,
         "n_epochs": 100,
         "save_all_checkpoints": False,