Add csv export

src/everest/api/everest_data_api.py

@@ -1,3 +1,4 @@
+import os
 from pathlib import Path
 
 import polars
@@ -268,7 +269,176 @@ def summary_values(self, batches=None, keys=None):
     def output_folder(self):
         return self._config.output_dir
 
+    def export_dataframes(
+        self,
+    ) -> tuple[polars.DataFrame, polars.DataFrame, polars.DataFrame]:
+        batch_dfs_to_join = {}
+        realization_dfs_to_join = {}
+        perturbation_dfs_to_join = {}
+
+        batch_ids = [b.batch_id for b in self._ever_storage.data.batches]
+        all_controls = self._ever_storage.data.controls["control_name"].to_list()
+
+        def _try_append_df(
+            batch_id: int,
+            df: polars.DataFrame | None,
+            target: dict[str, list[polars.DataFrame]],
+        ):
+            if df is not None:
+                if batch_id not in target:
+                    target[batch.batch_id] = []
+
+                target[batch_id].append(df)
+
+        def try_append_batch_dfs(batch_id: int, *dfs: polars.DataFrame):
+            for df_ in dfs:
+                _try_append_df(batch_id, df_, batch_dfs_to_join)
+
+        def try_append_realization_dfs(batch_id: int, *dfs: polars.DataFrame):
+            for df_ in dfs:
+                _try_append_df(batch_id, df_, realization_dfs_to_join)
+
+        def try_append_perturbation_dfs(batch_id: int, *dfs: polars.DataFrame):
+            for df_ in dfs:
+                _try_append_df(batch_id, df_, perturbation_dfs_to_join)
+
+        def pivot_gradient(df: polars.DataFrame) -> polars.DataFrame:
+            pivoted_ = df.pivot(on="control_name", index="batch_id", separator=" wrt ")
+            return pivoted_.rename(
+                {
+                    col: f"grad({col})"
+                    for col in pivoted_.columns
+                    if col != "batch_id" and col not in all_controls
+                }
+            )
+
+        for batch in self._ever_storage.data.batches:
+            try_append_perturbation_dfs(
+                batch.batch_id,
+                batch.perturbation_objectives,
+                batch.perturbation_constraints,
+            )
+
+            try_append_realization_dfs(
+                batch.batch_id,
+                batch.realization_objectives,
+                batch.realization_controls,
+                batch.realization_constraints,
+            )
+
+            if batch.batch_objective_gradient is not None:
+                try_append_batch_dfs(
+                    batch.batch_id, pivot_gradient(batch.batch_objective_gradient)
+                )
+
+            if batch.batch_constraint_gradient is not None:
+                try_append_batch_dfs(
+                    batch.batch_id,
+                    pivot_gradient(batch.batch_constraint_gradient),
+                )
+
+            try_append_batch_dfs(
+                batch.batch_id, batch.batch_objectives, batch.batch_constraints
+            )
+
+        def _join_by_batch(
+            dfs: dict[int, list[polars.DataFrame]], on: list[str]
+        ) -> list[polars.DataFrame]:
+            """
+            Creates one dataframe per batch, with one column per input/output,
+            including control, objective, constraint, gradient value.
+            """
+            dfs_to_concat_ = []
+            for batch_id in batch_ids:
+                if batch_id not in dfs:
+                    continue
+
+                batch_df_ = dfs[batch_id][0]
+                for bdf_ in dfs[batch_id][1:]:
+                    if set(all_controls).issubset(set(bdf_.columns)) and set(
+                        all_controls
+                    ).issubset(set(batch_df_.columns)):
+                        bdf_ = bdf_.drop(all_controls)
+
+                    batch_df_ = batch_df_.join(
+                        bdf_,
+                        on=on,
+                    )
+
+                dfs_to_concat_.append(batch_df_)
+
+            return dfs_to_concat_
+
+        batch_dfs_to_concat = _join_by_batch(batch_dfs_to_join, on=["batch_id"])
+        batch_df = polars.concat(batch_dfs_to_concat, how="diagonal")
+
+        realization_dfs_to_concat = _join_by_batch(
+            realization_dfs_to_join, on=["batch_id", "realization", "simulation_id"]
+        )
+        realization_df = polars.concat(realization_dfs_to_concat, how="diagonal")
+
+        perturbation_dfs_to_concat = _join_by_batch(
+            perturbation_dfs_to_join, on=["batch_id", "realization", "perturbation"]
+        )
+        perturbation_df = polars.concat(perturbation_dfs_to_concat, how="diagonal")
+
+        pert_real_df = polars.concat([realization_df, perturbation_df], how="diagonal")
+
+        pert_real_df = pert_real_df.select(
+            "batch_id",
+            "realization",
+            "perturbation",
+            *list(
+                set(pert_real_df.columns) - {"batch_id", "realization", "perturbation"}
+            ),
+        )
+
+        # Avoid name collisions when joining with simulations
+        batch_df_renamed = batch_df.rename(
+            {
+                col: f"batch_{col}"
+                for col in batch_df.columns
+                if col != "batch_id" and not col.startswith("grad")
+            }
+        )
+        combined_df = pert_real_df.join(
+            batch_df_renamed, on="batch_id", how="full", coalesce=True
+        )
+
+        def _sort_df(df: polars.DataFrame, index: list[str]):
+            sorted_cols = index + sorted(set(df.columns) - set(index))
+            df_ = df.select(sorted_cols).sort(by=index)
+            return df_
+
+        return (
+            _sort_df(
+                combined_df,
+                ["batch_id", "realization", "simulation_id", "perturbation"],
+            ),
+            _sort_df(
+                pert_real_df,
+                [
+                    "batch_id",
+                    "realization",
+                    "perturbation",
+                    "simulation_id",
+                ],
+            ),
+            _sort_df(batch_df, ["batch_id", "total_objective_value"]),
+        )
+
     @property
-    def csv_export(self):
-        print(self._ever_storage.data)
-        return {}
+    def everest_csv(self):
+        export_filename = (
+            self._config.export.csv_output_filepath
+            if self._config.export is not None
+            else f"{self._config.config_file}.csv"
+        )
+
+        full_path = os.path.join(self.output_folder, export_filename)
+
+        if not os.path.exists(full_path):
+            combined_df, _, _ = self.export_dataframes()
+            combined_df.write_csv(full_path)
+
+        return os.path.join(self.output_folder, export_filename)

tests/everest/snapshots/test_api_snapshots/test_csv_export/config_advanced.yml/batch_df.csv

@@ -0,0 +1,7 @@
+batch_id,distance,grad(distance wrt point_x-0),grad(distance wrt point_x-1),grad(distance wrt point_x-2),grad(distance.total wrt point_x-0),grad(distance.total wrt point_x-1),grad(distance.total wrt point_x-2),merit_value,point_x-0,point_x-1,point_x-2,total_objective_value,x-0_coord,x-0_coord.violation
+0,-1.6875,-0.4907662687030493,-0.5019101422144723,0.5042454858798938,-0.4907662687030493,-0.5019101422144723,0.5042454858798938,,0.9999923242971123,-0.00002866170271420768,-0.00003482388228645279,-1.6875,0.15,0.0
+1,-1.6928177326917648,-0.5220932558524333,-0.2857933053410886,0.6486969102170087,-0.5220932558524333,-0.2857933053410886,0.6486969102170087,389.85565356542713,1.0000197666303703,7.318438233973908e-6,0.000054286921875937026,-1.6928177326917648,0.15904500484466552,0.0
+2,-1.735619194805622,-0.6428129143568377,-0.005219520850941156,0.7315266224689232,-0.6428129143568377,-0.005219520850941156,0.7315266224689232,381.4612304677212,0.9999868957126514,2.561012506432357e-6,0.000025109042352951607,-1.735619194805622,0.21863600015640258,0.0
+3,-1.6236748024821281,-0.4727620137163322,-0.07854769964884697,0.5153976529252893,-0.4727620137163322,-0.07854769964884697,0.5153976529252893,29.58547833176726,1.0000243522797598,-0.000045940653105887436,7.74341493223104e-6,-1.6236748024821281,0.1411780059337616,0.0
+4,-1.539792999625206,-0.3036942605860327,0.023143745090389523,0.25834193323625815,-0.3036942605860327,0.023143745090389523,0.25834193323625815,13.246954160900696,0.9999717043148817,8.450350216384595e-6,-5.568694767103104e-6,-1.539792999625206,0.050914996862411493,0.0
+5,-1.5256189703941345,-0.21656121993381236,-0.005788275557673861,0.21722039756315908,-0.21656121993381236,-0.005788275557673861,0.21722039756315908,11.750325264919065,0.9999855616128173,-0.000012794222306632448,3.542883847480084e-7,-1.5256189703941345,0.014527001976966852,0.0