From af0f1c7085d91c0070cf8c9a292e1e76ef7f97cd Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Fri, 5 Apr 2024 17:22:59 +0200
Subject: [PATCH] generators updated, cleaning

---
 quacc/experiments/generators.py |  6 +++---
 test_imdb_max_shift.py          | 16 ----------------
 test_postprocess.py             |  9 ---------
 3 files changed, 3 insertions(+), 28 deletions(-)
 delete mode 100644 test_imdb_max_shift.py
 delete mode 100644 test_postprocess.py

diff --git a/quacc/experiments/generators.py b/quacc/experiments/generators.py
index 50e5cc9..d29762b 100644
--- a/quacc/experiments/generators.py
+++ b/quacc/experiments/generators.py
@@ -67,9 +67,9 @@ def gen_bin_datasets(
         "imdb",
     ]
     _RCV1 = [
-        # "CCAT",
-        # "GCAT",
-        # "MCAT",
+        "CCAT",
+        "GCAT",
+        "MCAT",
     ]
     for dn in _IMDB:
         dval = None if only_names else DP.imdb()
diff --git a/test_imdb_max_shift.py b/test_imdb_max_shift.py
deleted file mode 100644
index 469ca91..0000000
--- a/test_imdb_max_shift.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pandas as pd
-
-from quacc.legacy.evaluation.report import DatasetReport
-
-dr = DatasetReport.unpickle("output/main/imdb/imdb.pickle")
-
-_data = dr.data(
-    metric="acc", estimators=["bin_sld_lr_mc", "bin_sld_lr_ne", "bin_sld_lr_c"]
-)
-d1 = _data.loc[((0.9, 0.1), (1.0, 0.0), slice(None)), :]
-d2 = _data.loc[((0.1, 0.9), (0.0, 1.0), slice(None)), :]
-dd = pd.concat([d1, d2], axis=0)
-
-print(d1.to_numpy(), "\n", d1.mean(), "\n")
-print(d2.to_numpy(), "\n", d2.mean(), "\n")
-print(dd.to_numpy(), "\n", dd.mean(), "\n")
diff --git a/test_postprocess.py b/test_postprocess.py
deleted file mode 100644
index e54f037..0000000
--- a/test_postprocess.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from quacc.legacy.evaluation.report import DatasetReport
-
-dr = DatasetReport.unpickle("output/main/imdb/imdb.pickle")
-_estimators = ["sld_lr_gs", "bin_sld_lr_gs", "mul_sld_lr_gs", "m3w_sld_lr_gs"]
-_data = dr.data(metric="acc", estimators=_estimators)
-for idx, cr in zip(_data.index.unique(0), dr.crs[::-1]):
-    print(cr.train_prev)
-    print({k: v for k, v in cr.fit_scores.items() if k in _estimators})
-    print(_data.loc[(idx, slice(None), slice(None)), :])