NgIoh variants (#1551)

* po * newplots * Dactoid with many new methods (#1546) * bobyqa_coming * black_and_static * Update recastlib.py * Add_ax * smac * scipyplus * scipyplus * fix * fix * fix * fix * fix * fix * black * Update recastlib.py * Update mypy.ini * Update recastlib.py * Update mypy.ini * fix * fix * fix * fix * wtf * fix * fix * flute * fix * fix * wtf * black * smac * fix * fix * fix * fix * so_dirty * smac2 * Add a bound option to ArtificialFunction * Use normalizer for BO optimizers * changelo * fix * Polishing * Changes in experiments.py for xp in bounded domains * black * Add normalizer to AX * Add normalizer to SMAC * smac / no-transform: fix bounds thx Jeremy * fix bounds ax * risky * fixadcas * fix * fix * ouie * fix * fix * fix * fix * fix * fix * fix * fix * po * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * po * fix * fix * fix * fix * fix * fix * fix * okarc * fix * fix * fix * fix * fix * fix * bfgs (#1532) * fix * fix * fix * fix * fix * fix * fix * fix * pou * fix * fix * fix * fix * fix * oldstuff * qopso * fix * fix * fix * fix * po * po * fix * fix * fix * noslowcircleci * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * po * po * fo --------- Co-authored-by: Elena Raponi <[email protected]> Co-authored-by: Jeremy Rapin <[email protected]> * fix * ZZpo * rmrfvenv * fixbudgetnone * black * po * fix * fix * po * fix * fix * fix --------- Co-authored-by: Elena Raponi <[email protected]> Co-authored-by: Jeremy Rapin <[email protected]>
facebookresearch · Sep 19, 2023 · 144c999 · 144c999
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commit 144c999
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Showing 10 changed files with 390 additions and 50 deletions.
diff --git a/nevergrad/benchmark/core.py b/nevergrad/benchmark/core.py
@@ -38,7 +38,12 @@ def save_or_append_to_csv(df: pd.DataFrame, path: Path) -> None:
     """Saves a dataframe to a file in append mode"""
     if path.exists():
         print("Appending to existing file")
-        predf = pd.read_csv(str(path))
+        try:
+            predf = pd.read_csv(
+                str(path), on_bad_lines="warn"
+            )  # , error_bad_lines=False)#, error_bad_lines=False)
+        except:  # for some versions of Panda.
+            predf = pd.read_csv(str(path))  # , error_bad_lines=False)#, error_bad_lines=False)
         df = pd.concat([predf, df], sort=False)
     df.to_csv(path, index=False)
 

diff --git a/nevergrad/benchmark/experiments.py b/nevergrad/benchmark/experiments.py
@@ -51,7 +51,7 @@
 
 def refactor_optims(x: tp.List[tp.Any]) -> tp.List[tp.Any]:
     # return ["RandomSearch", "OnePlusOne", "DE", "PSO"]
-    return ["NgIoh", "Shiwa", "NGOpt"]
+
     algos = {}
     algos["aquacrop_fao"] = [
         "CMA",
@@ -409,7 +409,7 @@ def refactor_optims(x: tp.List[tp.Any]) -> tp.List[tp.Any]:
 
     # Below, we use the best in the records above.
     benchmark = str(inspect.stack()[1].function)
-    if benchmark in algos and "tunin" in benchmark and np.random.randint(2) > 0 and False:
+    if benchmark in algos and "tunin" in benchmark and np.random.randint(2) > 0:
         return algos[benchmark][:5]
 
     # Here, we pseudo-randomly draw one optim in the provided list,
@@ -462,34 +462,11 @@ def doint(s):  # Converting a string into an int.
     import socket
 
     host = socket.gethostname()
-    list_optims = ["Carola1", "Carola2", "Carola3", "NgIoh", "NgIoh", "NgIoh", "MetaModel", "Cobyla"]
-    list_optims = ["NgIoh", "NgOpt"]
-    if "tuning" in benchmark:
-        list_optims = [
-            "NgIoh",
-            "NgIoh",
-            "NgIoh",
-            "NgIoh",
-            "NGOpt",
-            "NGOpt",
-            "NGOpt",
-            "NGOpt",
-            "HyperOpt",
-            "RandomSearch",
-            "PSO",
-            "DE",
-            "SQOPSO",
-            "Cobyla",
-            # "AX",
-            "LHSSearch",
-            "QODE",
-            "SODE",
-        ]
-    if "iscre" in benchmark:
-        list_optims = ["RecombiningPortfolioDiscreteOnePlusOne"]
 
     return [list_optims[doint(host) % len(list_optims)]]
-    return x  # ["Zero"] #return x
+
+
+#    return x  # ["Zero"] #return x
 
 
 #    return ["MultiSQP", "MultiCobyla", "MultiBFGS"]
@@ -3373,8 +3350,8 @@ def pbo_suite(seed: tp.Optional[int] = None, reduced: bool = False) -> tp.Iterat
             for x in ng.optimizers.registry.keys()
             if "iscre" in x and "ois" not in x and "ptim" not in x and "oerr" not in x
         ]
-    optims = ["NGOpt", "NGOptRW"]
-    optims = refactor_optims(optims)
+    list_optims = ["NGOpt", "NGOptRW"]
+    list_optims = refactor_optims(list_optims)
     for dim in [16, 64, 100]:
         for fid in range(1, 24):
             for iid in range(1, 5):

diff --git a/nevergrad/benchmark/plotting.py b/nevergrad/benchmark/plotting.py
@@ -367,7 +367,7 @@ def create_plots(
             print("\n# new case #", fixed, case)
             casedf = df.select(**dict(zip(fixed, case)))
             data_df = FightPlotter.winrates_from_selection(
-                casedf, fight_descriptors, num_rows=num_rows, num_cols=50
+                casedf, fight_descriptors, num_rows=num_rows, num_cols=350
             )
             fplotter = FightPlotter(data_df)
             # Competence maps: we find out the best algorithm for each attribute1=valuei/attribute2=valuej.
@@ -403,7 +403,7 @@ def create_plots(
                 fplotter.save(str(output_folder / "fight_all_pure.png"), dpi=_DPI)
             else:
                 fplotter.save(str(output_folder / name) + "_pure.png", dpi=_DPI)
-                print(f"# {len(data_df.columns[:])}")
+                print(f"# {len(data_df.columns[:])}  {data_df.columns[:]}")
             if order == 2 and competencemaps and best_algo:  # With order 2 we can create a competence map.
                 print("\n# Competence map")
                 name = "competencemap_" + ",".join("{}".format(x) for x in fixed) + ".tex"
@@ -779,10 +779,15 @@ def __init__(self, winrates_df: pd.DataFrame) -> None:
         self.winrates = winrates_df
         self._fig = plt.figure()
         self._ax = self._fig.add_subplot(111)
+        max_cols = 25
         self._cax = self._ax.imshow(
-            100 * np.array(self.winrates), cmap=cm.seismic, interpolation="none", vmin=0, vmax=100
+            100 * np.array(self.winrates)[:, :max_cols],
+            cmap=cm.seismic,
+            interpolation="none",
+            vmin=0,
+            vmax=100,
         )
-        x_names = self.winrates.columns
+        x_names = self.winrates.columns[:max_cols]  # we plot only the 50 best
         self._ax.set_xticks(list(range(len(x_names))))
         self._ax.set_xticklabels(x_names, rotation=45, ha="right", fontsize=7)
         y_names = self.winrates.index
@@ -799,7 +804,7 @@ def winrates_from_selection(
         df: utils.Selector,
         categories: tp.List[str],
         num_rows: int = 5,
-        num_cols: int = 50,
+        num_cols: int = 350,
         complete_runs_only: bool = False,
     ) -> pd.DataFrame:
         """Creates a fight plot win rate data out of the given run dataframe,

diff --git a/nevergrad/functions/ml/mlfunctionlib.py b/nevergrad/functions/ml/mlfunctionlib.py
@@ -244,9 +244,15 @@ def make_dataset(self, data_dimension: tp.Optional[int], dataset: str) -> None:
             assert dataset in ["diabetes", "kerasBoston", "auto-mpg", "red-wine", "white-wine"]
             assert data_dimension is None
             sets_url = {
-                "auto-mpg": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/auto-mpg.data",
-                "red-wine": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/winequality-red.csv",
-                "white-wine": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/winequality-white.csv",
+                # "auto-mpg": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/auto-mpg.data",
+                # "red-wine": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/winequality-red.csv",
+                # "white-wine": "http://www-lisic.univ-littoral.fr/~teytaud/files/Cours/Apprentissage/data/winequality-white.csv",
+                # "auto-mpg": "https://github.com/plotly/datasets/blob/master/auto-mpg.csv",
+                # "red-wine": "https://github.com/plotly/datasets/blob/master/winequality-red.csv",
+                # "white-wine": "https://github.com/stedy/Machine-Learning-with-R-datasets/blob/master/winequality-white.csv",
+                "auto-mpg": "https://raw.githubusercontent.com/plotly/datasets/master/auto-mpg.csv",
+                "red-wine": "https://raw.githubusercontent.com/plotly/datasets/master/winequality-red.csv",
+                "white-wine": "https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/winequality-white.csv",
             }
             sets_tag = {"auto-mpg": "mpg", "red-wine": "quality", "white-wine": "quality"}
             if dataset == "kerasBoston":
@@ -259,7 +265,10 @@ def make_dataset(self, data_dimension: tp.Optional[int], dataset: str) -> None:
 
                 data = keras.datasets.boston_housing
             elif dataset in sets_tag:
-                data = pd.read_csv(sets_url[dataset])
+                try:
+                    data = pd.read_csv(sets_url[dataset])
+                except Exception as e:
+                    assert False, f"failing with error {e} for dataset {dataset}"
             else:
                 data = {"diabetes": sklearn.datasets.load_diabetes,}[
                     # data = {"boston": sklearn.datasets.load_boston, "diabetes": sklearn.datasets.load_diabetes,}[

diff --git a/nevergrad/functions/rocket/rocket.py b/nevergrad/functions/rocket/rocket.py
@@ -228,7 +228,7 @@ def net_force(Ex, Ey, Ez, Evx, Evy, Evz, m):
         percentage = row[0] / np.sum(
             thrust_list
         )  # percentage of total thrust to find percentage of mass lost
-        assert percentage >= 0.0
+        assert percentage >= -1e-5, f"percentage is {percentage}"
         assert percentage <= 1.0
         mass_loss = mass_reman * percentage
         mass_reman -= mass_loss