Bring back commented test _map_consensus_guarantees

Author: ales-otf

pallets/subtensor/src/tests/epoch.rs

@@ -2737,46 +2737,48 @@ fn test_blocks_since_last_step() {
     });
 }
 
-// // Map the retention graph for consensus guarantees with an single epoch on a graph with 512 nodes, of which the first 64 are validators, the graph is split into a major and minor set, each setting specific weight on itself and the complement on the other.
-// //
-// // ```import torch
-// // import matplotlib.pyplot as plt
-// // from matplotlib.pyplot import cm
-// // %matplotlib inline
-// //
-// // with open('finney_consensus_0.4.txt') as f:  # test output saved to finney_consensus.txt
-// //     retention_map = eval(f.read())
-// //
-// // major_ratios = {}
-// // avg_weight_devs = {}
-// // for major_stake, major_weight, minor_weight, avg_weight_dev, major_ratio in retention_map:
-// //     major_stake = f'{major_stake:.2f}'
-// //     maj, min = int(round(50 * major_weight)), int(round(50 * minor_weight))
-// //     avg_weight_devs.setdefault(major_stake, torch.zeros((51, 51)))
-// //     avg_weight_devs[major_stake][maj][min] = avg_weight_dev
-// //     major_ratios.setdefault(major_stake, torch.zeros((51, 51)))
-// //     major_ratios[major_stake][maj][min] = major_ratio
-// //
-// // _x = torch.linspace(0, 1, 51); _y = torch.linspace(0, 1, 51)
-// // x, y = torch.meshgrid(_x, _y, indexing='ij')
-// //
-// // fig = plt.figure(figsize=(6, 6), dpi=70); ax = fig.gca()
-// // ax.set_xticks(torch.arange(0, 1, 0.05)); ax.set_yticks(torch.arange(0, 1., 0.05))
-// // ax.set_xticklabels([f'{_:.2f}'[1:] for _ in torch.arange(0, 1., 0.05)])
-// // plt.grid(); plt.rc('grid', linestyle="dotted", color=[0.85, 0.85, 0.85])
-// //
-// // isolate = ['0.60']; stakes = [0.51, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99]
-// // colors = cm.viridis(torch.linspace(0, 1, len(stakes) + 1))
-// // for i, stake in enumerate(stakes):
-// //     contours = plt.contour(x, y, major_ratios[f'{stake:.2f}'], levels=[0., stake], colors=[colors[i + 1]])
-// //     if f'{stake:.2f}' in isolate:
-// //         contours.collections[1].set_linewidth(3)
-// //     plt.clabel(contours, inline=True, fontsize=10)
-// //
-// // plt.title(f'Major emission [$stake_{{maj}}=emission_{{maj}}$ retention lines]')
-// // plt.ylabel('Minor self-weight'); plt.xlabel('Major self-weight'); plt.show()
-// // ```
-// // #[test]
+// Map the retention graph for consensus guarantees with an single epoch on a graph with 512 nodes,
+// of which the first 64 are validators, the graph is split into a major and minor set, each setting
+// specific weight on itself and the complement on the other.
+//
+// ```import torch
+// import matplotlib.pyplot as plt
+// from matplotlib.pyplot import cm
+// %matplotlib inline
+//
+// with open('finney_consensus_0.4.txt') as f:  # test output saved to finney_consensus.txt
+//     retention_map = eval(f.read())
+//
+// major_ratios = {}
+// avg_weight_devs = {}
+// for major_stake, major_weight, minor_weight, avg_weight_dev, major_ratio in retention_map:
+//     major_stake = f'{major_stake:.2f}'
+//     maj, min = int(round(50 * major_weight)), int(round(50 * minor_weight))
+//     avg_weight_devs.setdefault(major_stake, torch.zeros((51, 51)))
+//     avg_weight_devs[major_stake][maj][min] = avg_weight_dev
+//     major_ratios.setdefault(major_stake, torch.zeros((51, 51)))
+//     major_ratios[major_stake][maj][min] = major_ratio
+//
+// _x = torch.linspace(0, 1, 51); _y = torch.linspace(0, 1, 51)
+// x, y = torch.meshgrid(_x, _y, indexing='ij')
+//
+// fig = plt.figure(figsize=(6, 6), dpi=70); ax = fig.gca()
+// ax.set_xticks(torch.arange(0, 1, 0.05)); ax.set_yticks(torch.arange(0, 1., 0.05))
+// ax.set_xticklabels([f'{_:.2f}'[1:] for _ in torch.arange(0, 1., 0.05)])
+// plt.grid(); plt.rc('grid', linestyle="dotted", color=[0.85, 0.85, 0.85])
+//
+// isolate = ['0.60']; stakes = [0.51, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99]
+// colors = cm.viridis(torch.linspace(0, 1, len(stakes) + 1))
+// for i, stake in enumerate(stakes):
+//     contours = plt.contour(x, y, major_ratios[f'{stake:.2f}'], levels=[0., stake], colors=[colors[i + 1]])
+//     if f'{stake:.2f}' in isolate:
+//         contours.collections[1].set_linewidth(3)
+//     plt.clabel(contours, inline=True, fontsize=10)
+//
+// plt.title(f'Major emission [$stake_{{maj}}=emission_{{maj}}$ retention lines]')
+// plt.ylabel('Minor self-weight'); plt.xlabel('Major self-weight'); plt.show()
+// ```
+// #[test]
 // fn _map_consensus_guarantees() {
 //     let netuid: u16 = 1;
 //     let network_n: u16 = 512;
@@ -2811,10 +2813,10 @@ fn test_blocks_since_last_step() {
 //                     network_n as usize,
 //                     interleave as usize,
 //                 );
-
+//
 //                 new_test_ext(1).execute_with(|| {
 // 					init_run_epochs(netuid, network_n, &validators, &servers, epochs, 1, true, &stake, true, &weights, true, false, 0, true);
-
+//
 // 					let mut major_emission: I64F64 = I64F64::from_num(0);
 // 					let mut minor_emission: I64F64 = I64F64::from_num(0);
 // 					for set in vec![major_validators, major_servers] {