general clean up & added help text

removed symbolic link calling path

pairwise_compare.py

@@ -5,7 +5,6 @@ import random
 import lzma
 
 import torch
-from torch import nn
 from torch.nn import functional as F
 
 import re
@@ -32,7 +31,10 @@ LOGGING_PATH = "./files/output.log"
 EMBED_CHART_PATH = "./files/embedding_chart.png"
 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"
 
+# TODO: Move plotting into its own file
 def parse_training_log(file_path: str) -> pd.DataFrame:
     text: str = ""
     with lzma.open(file_path, mode='rt') as f:
@@ -49,6 +51,7 @@ def parse_training_log(file_path: str) -> pd.DataFrame:
 
     return df
 
+# TODO: Move plotting into its own file
 def plt_loss_tstep(df: pd.DataFrame) -> None:
     # Plot 1: Loss
     plt.figure(figsize=(8, 4))
@@ -58,11 +61,12 @@ def plt_loss_tstep(df: pd.DataFrame) -> None:
     plt.ylabel("Loss (log scale)")
     plt.title("Training Loss vs Step")
     plt.tight_layout()
-    plt.savefig('./files/training_loss_v_step.png')
+    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)
@@ -73,11 +77,12 @@ def plt_acc_tstep(df: pd.DataFrame, eps=1e-10) -> None:
     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.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
 
@@ -127,7 +132,8 @@ def set_seed(seed: int) -> None:
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
 
-# Data: pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
+# pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
+# 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
@@ -139,6 +145,8 @@ def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER, epsi=1e-4)
     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)
@@ -189,6 +197,8 @@ def training_entry():
     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.load_state_dict(model_ckpt["state_dict"])
@@ -210,46 +220,95 @@ 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"]).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")
         
+    logging.basicConfig(level=logging.INFO,
+                        format='%(asctime)s - %(levelname)s - %(message)s',
+                        handlers=[
+                            logging.FileHandler(LOGGING_PATH),
+                            logging.StreamHandler(stream=sys.stdout)
+                        ])
+    
     log = logging.getLogger(__name__)
-    logging.basicConfig(filename=LOGGING_PATH, level=logging.INFO)
+    log.info(f"Log file {LOGGING_PATH} opened {datetime.datetime.now()}")
 
-    log.info(f"Log opened {datetime.datetime.now()}")
+    # python3 pairwise_compare.py train
-    get_torch_info()
+    # python3 pairwise_compare.py infer
-
+    # python3 pairwise_compare.py graphs
-    name = os.path.basename(sys.argv[0])
+    if len(sys.argv) > 1:
-    if name == 'train.py':
+        match sys.argv[1].strip().lower():
-        training_entry()
+            case "train":
-    elif name == 'infer.py':
-        infer_entry()
-    else:
-        # alt call patern
-        # python3 pairwise_compare.py train
-        # python3 pairwise_compare.py infer
-        # python3 pairwise_compare.py graphs
-        if len(sys.argv) > 1:
-            mode = sys.argv[1].strip().lower()
-            if mode == "train":
                 training_entry()
-            elif mode == "infer":
+            case "infer":
                 infer_entry()
-            elif mode == "graphs":
+            case "graphs":
-                data = parse_training_log(TRAINING_LOG_PATH)
+                graphs_entry()
-                plt_loss_tstep(data)
+            case "help":
-                plt_acc_tstep(data)
+                log.info(help_text)
-            else:
+            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()}")