wip

tools/load_results.py

@@ -3,10 +3,13 @@ from __future__ import annotations
 import json
 import pickle
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import polars as pl
+from polars.testing import assert_frame_equal
+
+from diff_df import recursive_diff_frames
 
 # ------------------------------------------------------------
 # Config you can tweak
@@ -247,6 +250,14 @@ def read_pickle(p: Path) -> Any:
 # ------------------------------------------------------------
 # Extractors for each model
 # ------------------------------------------------------------
+
+counting = {
+    (label_method, eval_method): []
+    for label_method in ["exp_based", "manual_based"]
+    for eval_method in ["roc", "prc"]
+}
+
+
 def rows_from_deepsad(data: dict, evals: List[str]) -> Dict[str, dict]:
     """
     deepsad under data['test'][eval], with extra per-eval arrays and AP present.
@@ -257,6 +268,8 @@ def rows_from_deepsad(data: dict, evals: List[str]) -> Dict[str, dict]:
         evd = test.get(ev)
         if not isinstance(evd, dict):
             continue
+        counting[(ev, "roc")].append(len(evd["roc"][0]))
+        counting[(ev, "prc")].append(len(evd["prc"][0]))
         out[ev] = {
             "auc": float(evd["auc"])
             if "auc" in evd and evd["auc"] is not None
@@ -585,12 +598,53 @@ def load_pretraining_results_dataframe(
 
 
 def main():
-    root = Path("/home/fedex/mt/results/done")
-    df = load_results_dataframe(root, allow_cache=True)
-    print(df.shape, df.head())
+    root = Path("/home/fedex/mt/results/copy")
+    df1 = load_results_dataframe(root, allow_cache=True)
+    exit(0)
 
-    df_pre = load_pretraining_results_dataframe(root, allow_cache=True)
-    print("pretraining:", df_pre.shape, df_pre.head())
+    retest_root = Path("/home/fedex/mt/results/copy/retest_nodrop")
+    df2 = load_results_dataframe(retest_root, allow_cache=False).drop("folder")
+
+    # exact schema & shape first (optional but helpful messages)
+    assert df1.shape == df2.shape, f"Shape differs: {df1.shape} vs {df2.shape}"
+    assert set(df1.columns) == set(df2.columns), (
+        f"Column sets differ: {df1.columns} vs {df2.columns}"
+    )
+
+    # allow small float diffs, ignore column order differences if you want
+    df1_sorted = df1.select(sorted(df1.columns))
+    df2_sorted = df2.select(sorted(df2.columns))
+
+    # Optionally pre-align/sort both frames by a stable key before diffing.
+    summary, leaves = recursive_diff_frames(
+        df1,
+        df2,
+        ignore=["timestamp"],  # columns to ignore
+        float_atol=0.1,  # absolute tolerance for floats
+        float_rtol=0.0,  # relative tolerance for floats
+        max_rows_per_column=20,  # limit expansion per column
+        max_leafs_per_row=200,  # cap leaves per row
+    )
+
+    pl.Config.set_fmt_table_cell_list_len(100)
+    pl.Config.set_tbl_rows(100)
+
+    print(summary)  # which columns differ & how many rows
+    print(leaves)  # exact nested paths + scalar diffs
+
+    # check_exact=False lets us use atol/rtol for floats
+    assert_frame_equal(
+        df1_sorted,
+        df2_sorted,
+        check_exact=False,
+        atol=0.1,  # absolute tolerance for floats
+        rtol=0.0,  # relative tolerance (set if you want % based)
+        check_dtypes=True,  # set False if you only care about values
+    )
+    print("DataFrames match within tolerance ✅")
+
+    # df_pre = load_pretraining_results_dataframe(root, allow_cache=True)
+    # print("pretraining:", df_pre.shape, df_pre.head())
 
 
 if __name__ == "__main__":