Merge pull request #25 from MJ10/fixinstall

SMO Ablation + Correct version of DUE

Author: MJ10

.gitignore

@@ -9,6 +9,10 @@ runs/
 notebooks/figs
 notebooks/old notebooks
 notebooks/pickles
+notebooks/exploring-bias.ipynb
 
 results/
-figures/
+figures/
+new_results/
+
+*.DS_Store

README.md

@@ -19,7 +19,7 @@ The notebook `notebooks/fixed_training_set.ipynb` illustrates how DEUP is used t
 ## Rejecting Difficult Examples
 Install DUE:
 ```bash
-pip install git+https://github.com/y0ast/DUE.git
+pip install git+https://github.com/y0ast/DUE.git@482f9f05788ca62d539e8e5e8684cfc63d39d6f0
 ```
 
 We first train the main predictor, variance source and density source on the entire dataset and the spilts for training. The procedure is described in Appendix D.1. This script should take about a day to run on a V100 GPU.

examples/SMO/main.py

@@ -17,6 +17,7 @@
 parser.add_argument("--function", default='multi_optima', help='one of the keys of SMO.test_functions.functions')
 parser.add_argument("--noise", type=float, default=0, help='additive aleatoric noise')
 parser.add_argument("--method", default='deup', help='one of deup, gp, mcdropout, ensemble')
+parser.add_argument("--features", default='xv', help='one of xv, x, v, xd, d, xvd')
 parser.add_argument("--save_base_path", default='.', help='path to save results')
 
 args = parser.parse_args()
@@ -38,7 +39,7 @@
 
 results = np.zeros((n_seeds, 1 + n_steps))
 use_log_unc = True
-features = 'xv'
+features = args.features
 
 for seed in range(n_seeds):
     torch.manual_seed(10 + seed)
@@ -80,10 +81,13 @@
                         plot_stuff=False,
                         n_steps=n_steps, epochs=200, domain=X, domain_image=Y, print_each=100, use_log_unc=True,
                         estimator='gp')
+    else:
+        raise NotImplementedError(f"Method {args.method} not implemented !")
     results[seed] = outs[0]
+    print(results[seed])
 
-string = f"{args.method}_{args.function}_{args.n_init}"
+string = f"rebuttal_{args.method}_{features}_{args.function}_{args.n_init}"
 filename = os.path.join(args.save_base_path, string)
 pickle.dump({'results': results, 'string': string}, open(filename, 'wb'))
 
-print('Results saved !')
+print(f'Results saved in {filename}!')

notebooks/SMO.ipynb

@@ -38,7 +38,6 @@
     "import warnings\n",
     "warnings.filterwarnings('ignore')\n",
     "\n",
-    "from test_functions import functions\n",
     "from examples.SMO.test_functions import functions, bounds as boundsx\n",
     "from uncertaintylearning.features.feature_generator import FeatureGenerator\n",
     "from examples.SMO.buffer import Buffer\n",
@@ -144,6 +143,15 @@
     "    plt.show()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_gpdeup"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -169,7 +177,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.10.6 ('deup')",
    "language": "python",
    "name": "python3"
   },
@@ -183,7 +191,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "57c0ade180fa71cb589f7bf6b9e050bb8252cc64cdf7f2fc57c1f989acce9d4b"
+   }
   }
  },
  "nbformat": 4,