Updated plot_diagonal_uncertainties

Experiments/create_diagonal_uncertainties_plots.sh

+#!/bin/bash
+for data in {yacht,naval,linear,protein,concrete,kin8nm,wine,quadratic,power,sine,energy,california,cubic}; do
+echo Plotting diagonal uncertainties for $data
+python plot_diagonal_uncertainties.py --data $data
+echo done!
+done

Experiments/plot_diagonal_uncertainties.py

@@ -12,6 +12,7 @@ import torch
 import torch.backends.cudnn
 from torch.utils.data import DataLoader
 from tqdm import tqdm
+import matplotlib
 import matplotlib.pyplot as plt
 
 from EIVArchitectures import Networks
@@ -21,6 +22,12 @@ from EIVGeneral.coverage_metrics import epistemic_coverage, normalized_std,\
 from EIVData.repeated_sampling import repeated_sampling
 from EIVGeneral.linear_evaluation import linear_pred_unc, linear_coverage, compute_par_est_var
 
+font = {'family' : 'DejaVu Sans',
+        'weight' : 'normal',
+        'size'   : 20}
+
+matplotlib.rc('font', **font)
+
 # read in data via --data option
 parser = argparse.ArgumentParser()
 parser.add_argument("--data", help="Loads data", default='yacht')
@@ -36,31 +43,30 @@ with open(os.path.join('configurations',f'noneiv_{data}.json'),'r') as conf_file
     noneiv_conf_dict = json.load(conf_file)
 
 
-# assuming normalized data was used
-try:
-    assert eiv_conf_dict['normalize']
-    assert noneiv_conf_dict['normalize']
-except KeyError:
-    pass
-
-normalize = True
 
 long_dataname = eiv_conf_dict["long_dataname"]
 short_dataname = eiv_conf_dict["short_dataname"]
 
 print(f"Evaluating {long_dataname}")
 
+
 scale_outputs = False 
 load_data = importlib.import_module(f'EIVData.{long_dataname}').load_data
+train_data, _ = load_data()
+input_dim = train_data[0][0].numel()
+output_dim = train_data[0][1].numel()
+try:
+    min_x, max_x = importlib.import_module(f'EIVData.{long_dataname}').input_range
+except AttributeError:
+    max_x = torch.ones((1, input_dim))
+    min_x = -max_x
+
 try:
     sigma_y = importlib.import_module(f'EIVData.{long_dataname}').y_noise_strength
     design_matrix = importlib.import_module(f'EIVData.{long_dataname}').design_matrix
 except AttributeError:
     sigma_y = None
 
-train_data, test_data = load_data(normalize=normalize)
-input_dim = train_data[0][0].numel()
-output_dim = train_data[0][1].numel()
 
 # do computations on cpu
 device = torch.device('cpu')
@@ -136,13 +142,12 @@ def collect_predictions(x, seed=0,
 
 
 
-def create_diagonal(train, number_of_steps=100):
+def create_diagonal(train, min_x, max_x, number_of_steps=100):
     input_shape = train[0][0].shape
     assert len(input_shape) == 1
     input_dim = input_shape[0]
-    ones = 0.75 * torch.ones((1, input_dim))
     t = torch.linspace(start=0, end=1, steps=number_of_steps)[...,None]
-    return (1-t) * ones - t *  ones  
+    return (1-t) * min_x + t *  max_x  
 
 
 
@@ -154,7 +159,8 @@ eiv_uncertainties = 0
 number_of_seeds = len(seed_list)
 number_of_steps = 100
 for seed in tqdm(seed_list):
-    x_diagonal = create_diagonal(train=train_data, number_of_steps=number_of_steps)
+    x_diagonal = create_diagonal(train=train_data, min_x=min_x, max_x=max_x, 
+            number_of_steps=number_of_steps)
     results = collect_predictions(x_diagonal, seed=seed)
     noneiv_uncertainties += 1/number_of_seeds * results['noneiv']['uncertainties'].mean(dim=-1)
     eiv_uncertainties += 1/number_of_seeds * results['eiv']['uncertainties'].mean(dim=-1)
@@ -167,4 +173,5 @@ plt.fill_between(plot_x, noneiv_uncertainties, color='b', alpha=0.5)
 plt.fill_between(plot_x,  eiv_uncertainties, color='r', alpha=0.5)
 plt.xlabel(r'$\lambda$')
 plt.ylabel(r'$u$')
+plt.tight_layout()
 plt.savefig(f'results/figures/diagonal_uncertainties_{data}.pdf')