Added plot variations for basin exp.

functionalities_test.py

@@ -78,3 +78,18 @@ def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
 
 def changing_rate(x_new, x_old):
     return x_new - x_old
+
+def test_status(net: Net) -> Net:
+
+    if is_divergent(net):
+        net.is_fixpoint = "divergent"
+    elif is_identity_function(net):  # is default value
+        net.is_fixpoint = "identity_func"
+    elif is_zero_fixpoint(net):
+        net.is_fixpoint = "fix_zero"
+    elif is_secondary_fixpoint(net):
+        net.is_fixpoint = "fix_sec"
+    else:
+        net.is_fixpoint = "other_func"
+
+    return net

journal_basins.py

@@ -1,18 +1,21 @@
 import os
 from pathlib import Path
 import pickle
+from torch import mean
 
 from tqdm import tqdm
 import random
 import copy
-from functionalities_test import is_identity_function
+from functionalities_test import is_identity_function, test_status
 from network import Net
 from visualization import plot_3d_self_train, plot_loss
 import numpy as np
 from tabulate import tabulate
 from sklearn.metrics import mean_absolute_error as MAE
 from sklearn.metrics import mean_squared_error as MSE
-
+import pandas as pd
+import seaborn as sns
+from matplotlib import pyplot as plt
 
 def prng():
     return random.random()
@@ -120,8 +123,8 @@ class SpawnExperiment:
         self.spawn_and_continue()
         self.weights_evolution_3d_experiment()
         # self.visualize_loss()
-        self.distance_matrix = distance_matrix(self.nets)
+        self.distance_matrix = distance_matrix(self.nets, print_it=False)
-        self.parent_clone_distances = distance_from_parent(self.nets)
+        self.parent_clone_distances = distance_from_parent(self.nets, print_it=False)
 
         self.save()
 
@@ -136,13 +139,13 @@ class SpawnExperiment:
             for _ in range(self.ST_steps):
                 net.self_train(1, self.log_step_size, self.net_learning_rate)
 
-            # print(f"\nLast weight matrix (epoch: {self.epochs}):\n
-            # {net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
             self.nets.append(net)
 
     def spawn_and_continue(self, number_clones: int = None):
         number_clones = number_clones or self.nr_clones
 
+        df = pd.DataFrame(columns=['parent', 'MAE_pre','MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise', 'status_post'])
+
         # For every initial net {i} after populating (that is fixpoint after first epoch);
         for i in range(self.population_size):
             net = self.nets[i]
@@ -168,26 +171,46 @@ class SpawnExperiment:
                     clone = self.apply_noise(clone, rand_noise)
                     clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
                     clone.number_trained = copy.deepcopy(net.number_trained)
+                    
+                    # Pre Training distances (after noise application of course)
+                    clone_pre_weights = clone.create_target_weights(clone.input_weight_matrix())
+                    MAE_pre = MAE(net_target_data, clone_pre_weights)
+                    MSE_pre = MSE(net_target_data, clone_pre_weights)
+                    MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights)
 
-                    # Then finish training each clone {j} (for remaining epoch-1 * ST_steps)
+                    # Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
-                    # and add to nets for plotting if they are fixpoints themselves;
                     for _ in range(self.epochs - 1):
                         for _ in range(self.ST_steps):
                             clone.self_train(1, self.log_step_size, self.net_learning_rate)
+                    
+                    # Post Training distances for comparison
+                    clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
+                    MAE_post = MAE(net_target_data, clone_post_weights)
+                    MSE_post = MSE(net_target_data, clone_post_weights)
+                    MIM_post = mean_invariate_manhattan_distance(net_target_data, clone_post_weights)
+
+                    # .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
+                    test_status(clone)
                     if is_identity_function(clone):
-                        input_data = clone.input_weight_matrix()
+                        print(f"Clone {j} (of net_{i}) is fixpoint." 
-                        target_data = clone.create_target_weights(input_data)
+                              f"\nMSE({i},{j}): {MSE_post}"
-                        print(f"Clone {j} (of net_{i}) is fixpoint. \nMSE(j,i): "
+                              f"\nMAE({i},{j}): {MAE_post}"
-                              f"{MSE(net_target_data, target_data)}, \nMAE(j,i): {MAE(net_target_data, target_data)}\n")
+                              f"\nMIM({i},{j}): {MIM_post}\n")
-                    self.nets.append(clone)
+                        self.nets.append(clone)
+
+                    df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise, clone.is_fixpoint]
 
                 # Finally take parent net {i} and finish it's training for comparison to clone development.
                 for _ in range(self.epochs - 1):
                     for _ in range(self.ST_steps):
                         net.self_train(1, self.log_step_size, self.net_learning_rate)
+                net_weights_after = net.create_target_weights(net.input_weight_matrix())
+                print(f"Parent net's distance to original position." 
+                              f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
+                              f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
+                              f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
 
-        else:
+        self.df = df
-            print("No fixpoints found.")
 
     def weights_evolution_3d_experiment(self):
         exp_name = f"ST_{str(len(self.nets))}_nets_3d_weights_PCA"
@@ -217,15 +240,16 @@ if __name__ == "__main__":
     ST_log_step_size = 10
 
     # Define number of networks & their architecture
-    nr_clones = 10
+    nr_clones = 5
-    ST_population_size = 3
+    ST_population_size = 1
     ST_net_hidden_size = 2
     ST_net_learning_rate = 0.04
     ST_name_hash = random.getrandbits(32)
 
     print(f"Running the Spawn experiment:")
-    for noise_factor in [1]:
+    exp_list = []
-        SpawnExperiment(
+    for noise_factor in range(2,5):
+        exp = SpawnExperiment(
             population_size=ST_population_size,
             log_step_size=ST_log_step_size,
             net_input_size=NET_INPUT_SIZE,
@@ -236,5 +260,16 @@ if __name__ == "__main__":
             st_steps=ST_steps,
             nr_clones=nr_clones,
             noise=pow(10, -noise_factor),
-            directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}_10e-{noise_factor}'
+            directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'10e-{noise_factor}'
         )
+        exp_list.append(exp)
+
+    # Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
+    df = pd.concat([exp.df for exp in exp_list])
+    sns.countplot(data=df, x="noise", hue="status_post")
+    plt.savefig(f"output/spawn_basin/{ST_name_hash}/fixpoint_status_countplot.png")
+
+    # Catplot (either kind="point" or "box") that shows before-after training distances to parent
+    mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance')
+    sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False)
+    plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")