simplify test- and training-set for nn

neural_network.py

@@ -1,6 +1,7 @@
 import json
 import random
 from pathlib import Path
+from typing import List
 
 import numpy as np
 import torch
@@ -13,40 +14,49 @@ from torch.utils.data import DataLoader, TensorDataset
 
 from CustomScaler import CustomScaler
 from network import Network
+from simulation import Simulation
 from simulation_list import SimulationList
 
 
+def x_array(s: Simulation) -> List[float]:
+    return [s.alpha, s.v, s.projectile_mass, s.gamma,
+            s.target_water_fraction, s.projectile_water_fraction]
+
+
+def y_array(s: Simulation) -> List[float]:
+    return [
+        s.water_retention_both, s.mantle_retention_both,
+        s.core_retention_both, s.output_mass_fraction
+    ]
+
+
 def train():
     filename = "rsmc_dataset"
 
     simulations = SimulationList.jsonlines_load(Path(f"{filename}.jsonl"))
 
-    # random.seed(1)
-    test_data = random.sample(simulations.simlist, int(len(simulations.simlist) * 0.2))
-    test_set = set(test_data)  # use a set for faster *in* computation
-    train_data = [s for s in simulations.simlist if s not in test_set]
+    random.seed(1)
+    random.shuffle(simulations.simlist)
+    num_test = int(len(simulations.simlist) * 0.2)
+    test_data = simulations.simlist[:num_test]
+    train_data = simulations.simlist[num_test:]
     print(len(train_data), len(test_data))
+    a = set(s.runid for s in train_data)
+    b = set(s.runid for s in test_data)
+    assert len(a & b) == 0, "no overlap between test data and training data"
 
-    X = np.array(
-        [[s.alpha, s.v, s.projectile_mass, s.gamma, s.target_water_fraction, s.projectile_water_fraction] for s in
-         train_data])
+    X = np.array([x_array(s) for s in train_data])
     scaler = CustomScaler()
     scaler.fit(X)
     x = scaler.transform_data(X)
+    del X
     print(x.shape)
-    Y = np.array([[
-        s.water_retention_both, s.mantle_retention_both, s.core_retention_both,
-        s.output_mass_fraction
-    ] for s in train_data])
+    Y = np.array([y_array(s) for s in train_data])
 
-    X_test = np.array(
-        [[s.alpha, s.v, s.projectile_mass, s.gamma, s.target_water_fraction, s.projectile_water_fraction] for s in
-         test_data])
-    Y_test = np.array([[
-        s.water_retention_both, s.mantle_retention_both, s.core_retention_both,
-        s.output_mass_fraction
-    ] for s in test_data])
+    X_test = np.array([x_array(s) for s in test_data])
+    Y_test = np.array([y_array(s) for s in test_data])
     x_test = scaler.transform_data(X_test)
+    del X_test
     random.seed()
 
     dataset = TensorDataset(from_numpy(x).to(torch.float), from_numpy(Y).to(torch.float))
@@ -63,7 +73,7 @@ def train():
     loss_train = []
     loss_vali = []
 
-    max_epochs = 120
+    max_epochs = 500
     epochs = 0
 
     fig: Figure = plt.figure()
@@ -119,6 +129,19 @@ def train():
         #         print("early stopping")
         #         break
     plt.ioff()
+    model_test_y = []
+    for x in x_test:
+        result = network(from_numpy(np.array(x)).to(torch.float))
+        y = result.detach().numpy()
+        model_test_y.append(y)
+    model_test_y = np.asarray(model_test_y)
+    plt.figure()
+    plt.xlabel("model output")
+    plt.ylabel("real data")
+    for i, name in enumerate(["shell","mantle","core","mass fraction"]):
+        plt.scatter(model_test_y[::, i], Y_test[::, i], s=0.2,label=name)
+    plt.legend()
+    plt.show()
     torch.save(network.state_dict(), "pytorch_model.zip")
     with open("pytorch_model.json", "w") as f:
         export_dict = {}
@@ -150,6 +173,7 @@ def train():
     print("minmax")
     print(np.nanmin(outgrid), np.nanmax(outgrid))
     cmap = "Blues"
+    plt.figure()
     plt.title(
         "m={:3.0e}, gamma={:3.1f}, wt={:2.0f}%, wp={:2.0f}%\n".format(mcode, gammacode, wtcode * 100, wpcode * 100))
     plt.imshow(outgrid, interpolation='none', cmap=cmap, aspect="auto", origin="lower", vmin=0, vmax=1,