output plots and csv data for e(x) over the trained range.

pairwise_compare.py

@@ -2,6 +2,7 @@
 # pairwise_compare.py
 import logging
 import random
+
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -21,6 +22,40 @@ BATCH_UPPER = 512.0
 DO_VERBOSE_EARLY_TRAIN = False
 MODEL_PATH = "./files/pwcomp.model"
 LOGGING_PATH = "./files/output.log"
+EMBED_CHART_PATH = "./files/embedding_chart.png"
+EMBEDDINGS_DATA = "./files/embedding_data.csv"
+
+def plt_embeddings(model: comp_nn.PairwiseComparator):
+    import matplotlib.pyplot as plt
+    import csv
+
+    log.info("Starting embeddings sweep...")
+    # samples for embedding mapping
+    with torch.no_grad():
+        xs = torch.arange(
+            BATCH_LOWER,
+            BATCH_UPPER + 1.0,
+            1.0,
+        ).unsqueeze(1).to(DEVICE)   # shape: (N, 1)
+
+        embeddings = model.embed(xs)  # shape: (N, d)
+    
+    # move data back to CPU for plotting
+    embeddings = embeddings.cpu()
+    xs = xs.cpu()
+
+    for i in range(embeddings.shape[1]):
+        plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
+        plt.legend()
+        plt.savefig(EMBED_CHART_PATH)
+        plt.show()
+
+    csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
+    with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f:
+        csv_file = csv.writer(f)
+        csv_file.writerows(csv_data)
+    
+
 
 def get_torch_info():
     log.info("PyTorch Version: %s", torch.__version__)
@@ -115,9 +150,12 @@ def infer_entry():
     with torch.no_grad():
         probs = torch.sigmoid(model(a, b))
 
+    log.info(f"Output probabilities for {pairs.count} pairs")
     for (x, y), p in zip(pairs, probs):
         log.info(f"P({x} > {y}) = {p.item():.3f}")
 
+    plt_embeddings(model)
+
 if __name__ == '__main__':
     import sys
     import os