man python stinks...

added more valves for options.
will update the console every 5 seconds when training now

infer.py

@@ -1 +0,0 @@
-pairwise_compare.py

output_graphs.py

@@ -1,37 +0,0 @@
-import re
-import pandas as pd
-import matplotlib.pyplot as plt
-
-text = r"""
-"""
-
-pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
-rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
-df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
-
-# Avoid log(0) issues for loss plot by clamping at a tiny positive value
-eps = 1e-10
-df["loss_clamped"] = df["loss"].clip(lower=eps)
-
-# Plot 1: Loss
-plt.figure(figsize=(9, 4.8))
-plt.plot(df["step"], df["loss_clamped"])
-plt.yscale("log")
-plt.xlabel("Step")
-plt.ylabel("Loss (log scale)")
-plt.title("Training Loss vs Step")
-plt.tight_layout()
-plt.savefig('./files/training_loss_v_step.png')
-plt.show()
-
-# Plot 2: Accuracy
-df["err"] = (1.0 - df["acc"]).clip(lower=eps)
-plt.figure(figsize=(9, 4.8))
-plt.plot(df["step"], df["err"])
-plt.yscale("log")
-plt.xlabel("Step")
-plt.ylabel("Error rate (1 - accuracy) (log scale)")
-plt.title("Training Error Rate vs Step")
-plt.tight_layout()
-plt.savefig('./files/training_error_v_step.png')
-plt.show()

pairwise_comp_nn.py

@@ -3,7 +3,7 @@ from torch import nn
 
 # 2) Number "embedding" network: R -> R^d
 class NumberEmbedder(nn.Module):
-    def __init__(self, d=4, hidden=16):
+    def __init__(self, d=2, hidden=4):
         super().__init__()
         self.net = nn.Sequential(
             nn.Linear(1, hidden),
@@ -14,9 +14,9 @@ class NumberEmbedder(nn.Module):
     def forward(self, x):
         return self.net(x)
 
-# 3) Comparator head: takes (ea, eb, e) -> logit for "a > b"
+# MLP Comparator head: takes (ea, eb, e) -> logit for "a > b"
 class PairwiseComparator(nn.Module):
-    def __init__(self, d=4, hidden=16, k=1.0):
+    def __init__(self, d=2, hidden=4, k=0.5):
         super().__init__()
         self.log_k = nn.Parameter(torch.tensor([k]))
         self.embed = NumberEmbedder(d, hidden)
@@ -29,7 +29,7 @@ class PairwiseComparator(nn.Module):
         )
 
     def forward(self, a, b):
-        # trying to force antisym here: h(a,b)=−h(b,a)
+        # trying to force antisym here: h(a,b)=-h(b,a)
         phi = self.head(self.embed(a-b))
         phi_neg = self.head(self.embed(b-a))
         logit = phi - phi_neg

pairwise_compare.py

@@ -2,11 +2,15 @@
 # pairwise_compare.py
 import logging
 import random
+import lzma
 
 import torch
-from torch import nn
 from torch.nn import functional as F
 
+import re
+import pandas as pd
+import matplotlib.pyplot as plt
+
 import pairwise_comp_nn as comp_nn
 
 # early pytorch device setup
@@ -14,19 +18,73 @@ DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_availab
 
 # Valves
 DIMENSIONS = 2
-TRAIN_STEPS = 5000
+HIDDEN_NEURONS = 4
+ADAMW_LR = 5e-3
+ADAMW_DECAY = 5e-4
+TRAIN_STEPS = 2000
 TRAIN_BATCHSZ = 8192
 TRAIN_PROGRESS = 10
 BATCH_LOWER = -100.0
 BATCH_UPPER = 100.0
-DO_VERBOSE_EARLY_TRAIN = False
+
+# Files
 MODEL_PATH = "./files/pwcomp.model"
 LOGGING_PATH = "./files/output.log"
 EMBED_CHART_PATH = "./files/embedding_chart.png"
-EMBEDDINGS_DATA = "./files/embedding_data.csv"
+EMBEDDINGS_DATA_PATH = "./files/embedding_data.csv"
+TRAINING_LOG_PATH = "./files/training.log.xz"
+LOSS_CHART_PATH = "./files/training_loss_v_step.png"
+ACC_CHART_PATH = "./files/training_error_v_step.png"
 
-def plt_embeddings(model: comp_nn.PairwiseComparator):
+# TODO: Move plotting into its own file
-    import matplotlib.pyplot as plt
+def parse_training_log(file_path: str) -> pd.DataFrame:
+    text: str = ""
+    with lzma.open(file_path, mode='rt') as f:
+        text = f.read()
+        
+    pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
+    rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
+    df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
+
+    # Avoid log(0) issues for loss plot by clamping at a tiny positive value
+    eps = 1e-10
+    df["loss_clamped"] = df["loss"].clip(lower=eps)
+
+    return df
+
+# TODO: Move plotting into its own file
+def plt_loss_tstep(df: pd.DataFrame) -> None:
+    # Plot 1: Loss
+    plt.figure(figsize=(10, 6))
+    plt.plot(df["step"], df["loss_clamped"])
+    plt.yscale("log")
+    plt.xlabel("Step")
+    plt.ylabel("Loss (log scale)")
+    plt.title("Training Loss vs Step")
+    plt.tight_layout()
+    plt.savefig(LOSS_CHART_PATH)
+    plt.close()
+
+    return None
+
+# TODO: Move plotting into its own file
+def plt_acc_tstep(df: pd.DataFrame, eps=1e-10) -> None:
+    # Plot 2: Accuracy
+    df["err"] = (1.0 - df["acc"]).clip(lower=eps)
+    plt.figure(figsize=(10, 6))
+    plt.plot(df["step"], df["err"])
+    plt.yscale("log")
+    plt.xlabel("Step")
+    plt.ylabel("Error rate (1 - accuracy) (log scale)")
+    plt.title("Training Error Rate vs Step")
+    plt.tight_layout()
+    plt.savefig(ACC_CHART_PATH)
+    plt.close()
+
+    return None
+
+# TODO: Move plotting into its own file
+def plt_embeddings(model: comp_nn.PairwiseComparator) -> None:
     import csv
 
     log.info("Starting embeddings sweep...")
@@ -35,7 +93,7 @@ def plt_embeddings(model: comp_nn.PairwiseComparator):
         xs = torch.arange(
             BATCH_LOWER,
             BATCH_UPPER + 1.0,
-            1.0,
+            0.1,
         ).unsqueeze(1).to(DEVICE)   # shape: (N, 1)
 
         embeddings = model.embed(xs)  # shape: (N, d)
@@ -43,19 +101,27 @@ def plt_embeddings(model: comp_nn.PairwiseComparator):
     # move data back to CPU for plotting
     embeddings = embeddings.cpu()
     xs = xs.cpu()
-
+    
+    # Plot 3: x vs h(x)
+    plt.figure(figsize=(10, 6))
     for i in range(embeddings.shape[1]):
         plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
-        
+    plt.title("x vs h(x)")
+    plt.xlabel("x [input]")
+    plt.ylabel("h(x) [embedding]")
     plt.legend()
     plt.savefig(EMBED_CHART_PATH)
-    #plt.show()
+    plt.close()
+
+    # save all our embeddings data to csv
     csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
-    with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f:
+    with open(file=EMBEDDINGS_DATA_PATH, mode="w", newline='') as f:
         csv_file = csv.writer(f)
         csv_file.writerows(csv_data)
+
+    return None
     
-def get_torch_info():
+def get_torch_info() -> None:
     log.info("PyTorch Version: %s", torch.__version__)
     log.info("HIP Version: %s", torch.version.hip)
     log.info("CUDA support: %s", torch.cuda.is_available())
@@ -65,14 +131,15 @@ def get_torch_info():
 
     log.info("Using %s compute mode", DEVICE)
 
-def set_seed(seed: int):
+def set_seed(seed: int) -> None:
     random.seed(seed)
     torch.manual_seed(seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
 
-# 1) Data: pairs (a, b) with label y = 1 if a > b else 0
+# pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
-def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
+# uses epsi to select the window in which a == b for equality training
+def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER, epsi=1e-4) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     a = (high - low) * torch.rand(batch_size, 1) + low
     b = (high - low) * torch.rand(batch_size, 1) + low
 
@@ -83,39 +150,50 @@ def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
     return a, b, y
     
 def training_entry():
+    get_torch_info()
+
     # all prng seeds to 0 for deterministic outputs durring testing
     # the seed should initialized normally otherwise
     set_seed(0)
 
-    model = comp_nn.PairwiseComparator(d=DIMENSIONS).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=DIMENSIONS, hidden=HIDDEN_NEURONS).to(DEVICE)
-    opt = torch.optim.AdamW(model.parameters(), lr=9e-4, weight_decay=1e-3)
+    opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_DECAY)
 
-    # 4) Train
+    log.info(f"Using {TRAINING_LOG_PATH} as the logging destination for training...")
-    for step in range(TRAIN_STEPS):
+    with lzma.open(TRAINING_LOG_PATH, mode='wt') as tlog:
-        a, b, y = sample_batch(TRAIN_BATCHSZ)
+        # training loop
-        a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
+        training_start_time = datetime.datetime.now()
+        last_ack = datetime.datetime.now()
 
-        logits = model(a, b)
+        for step in range(TRAIN_STEPS):
-        loss_fn = F.binary_cross_entropy_with_logits(logits, y)
+            a, b, y = sample_batch(TRAIN_BATCHSZ)
+            a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
 
-        opt.zero_grad()
+            logits = model(a, b)
-        loss_fn.backward()
+            loss_fn = F.binary_cross_entropy_with_logits(logits, y)
-        opt.step()
+
+            opt.zero_grad()
+            loss_fn.backward()
+            opt.step()
 
-        if step <= TRAIN_PROGRESS and DO_VERBOSE_EARLY_TRAIN is True:
             with torch.no_grad():
                 pred = (torch.sigmoid(logits) > 0.5).float()
                 acc = (pred == y).float().mean().item()
-            log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
+            tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
-        elif step % TRAIN_PROGRESS == 0:
-            with torch.no_grad():
-                pred = (torch.sigmoid(logits) > 0.5).float()
-                acc = (pred == y).float().mean().item()
-            log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
 
-    # 5) Quick test: evaluate final model accuracy on fresh pairs
+            # also print to normal text log occasionally to show some activity.
+            # every 10 steps check if its been longer than 5 seconds since we've updated the user
+            if step % 10 == 0:
+                if (datetime.datetime.now() - last_ack).total_seconds() > 5:
+                    log.info(f"still training... step={step} of {TRAIN_STEPS}")
+                    last_ack = datetime.datetime.now()
+
+    training_end_time = datetime.datetime.now()
+    log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")
+
+    # evaluate final model accuracy on fresh pairs
     with torch.no_grad():
-        a, b, y = sample_batch(TRAIN_BATCHSZ)
+        a, b, y = sample_batch(TRAIN_BATCHSZ*4)
         a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
         logits = model(a, b)
         pred = (torch.sigmoid(logits) > 0.5).float()
@@ -124,12 +202,14 @@ def training_entry():
     log.info(f"Final test acc: {acc} errors: {errors}")
 
     # embed model dimensions into the model serialization
-    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS}, MODEL_PATH)
+    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS, "h": HIDDEN_NEURONS}, MODEL_PATH)
     log.info(f"Saved PyTorch Model State to {MODEL_PATH}")
 
 def infer_entry():
+    get_torch_info()
+
     model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
-    model = comp_nn.PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
     model.load_state_dict(model_ckpt["state_dict"])
     model.eval()
 
@@ -149,41 +229,95 @@ def infer_entry():
     for (x, y), p in zip(pairs, probs):
         log.info(f"P({x} > {y}) = {p.item():.3f}")
 
+    
+
+def graphs_entry():
+    get_torch_info()
+
+    model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
+    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
+    model.load_state_dict(model_ckpt["state_dict"])
+    model.eval()
+
     plt_embeddings(model)
 
+    data = parse_training_log(TRAINING_LOG_PATH)
+    plt_loss_tstep(data)
+    plt_acc_tstep(data)
+
+help_text = r"""
+pairwise_compare.py — tiny pairwise "a > b?" neural comparator
+
+USAGE
+  python3 pairwise_compare.py train
+    Train a PairwiseComparator on synthetic (a,b) pairs sampled uniformly from
+    [BATCH_LOWER, BATCH_UPPER]. Labels are:
+      1.0 if a > b + epsi
+      0.0 if a < b - epsi
+      0.5 otherwise (near-equality window)
+    Writes training metrics to:
+      ./files/training.log.xz
+    Saves the trained model checkpoint to:
+      ./files/pwcomp.model
+
+  python3 pairwise_compare.py infer
+    Load ./files/pwcomp.model and run inference on a built-in list of test pairs.
+    Prints probabilities as:
+      P(a > b) = sigmoid(model(a,b))
+
+  python3 pairwise_compare.py graphs
+    Load ./files/pwcomp.model and generate plots + exports:
+      ./files/embedding_chart.png     (embed(x) vs x for each embedding dimension)
+      ./files/embedding_data.csv      (x and embedding vectors)
+      ./files/training_loss_v_step.png
+      ./files/training_error_v_step.png  (1 - acc, log scale)
+    Requires that ./files/training.log.xz exists (i.e., you ran "train" first).
+
+FILES
+  ./files/output.log          General runtime log (info/errors)
+  ./files/pwcomp.model        Torch checkpoint: {"state_dict": ..., "d": DIMENSIONS}
+  ./files/training.log.xz     step/loss/acc trace used for plots
+
+NOTES
+  - DEVICE is chosen via torch.accelerator if available, else CPU.
+  - Hyperparameters are controlled by the "Valves" constants near the top.
+"""
+
 if __name__ == '__main__':
     import sys
     import os
     import datetime
 
+    # TODO: tidy up the paths to files and checking if the directory exists
     if not os.path.exists("./files/"):
         os.mkdir("./files")
         
-    log = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO,
-    logging.basicConfig(filename=LOGGING_PATH, level=logging.INFO)
+                        format='%(asctime)s - %(levelname)s - %(message)s',
+                        handlers=[
+                            logging.FileHandler(LOGGING_PATH),
+                            logging.StreamHandler(stream=sys.stdout)
+                        ])
     
-    log.info(f"Log opened {datetime.datetime.now()}")
+    log = logging.getLogger(__name__)
-    get_torch_info()
+    log.info(f"Log file {LOGGING_PATH} opened {datetime.datetime.now()}")
 
-    name = os.path.basename(sys.argv[0])
+    # python3 pairwise_compare.py train
-    if name == 'train.py':
+    # python3 pairwise_compare.py infer
-        training_entry()
+    # python3 pairwise_compare.py graphs
-    elif name == 'infer.py':
+    if len(sys.argv) > 1:
-        infer_entry()
+        match sys.argv[1].strip().lower():
-    else:
+            case "train":
-        # alt call patern
-        # python3 pairwise_compare.py train
-        # python3 pairwise_compare.py infer
-        if len(sys.argv) > 1:
-            mode = sys.argv[1].strip().lower()
-            if mode == "train":
                 training_entry()
-            elif mode == "infer":
+            case "infer":
                 infer_entry()
-            else:
+            case "graphs":
+                graphs_entry()
+            case "help":
+                log.info(help_text)
+            case mode:
                 log.error(f"Unknown operation: {mode}")
-                log.error("Invalid call syntax, call script as \"train.py\" or \"infer.py\" or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
+                log.error("valid options are one of [\"train\", \"infer\", \"graphs\", \"help\"]")
-        else:
+                log.info(help_text)
-            log.error("Not enough arguments passed to script; call as train.py or infer.py or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
 
     log.info(f"Log closed {datetime.datetime.now()}")

train.py

@@ -1 +0,0 @@
-pairwise_compare.py