fix: lp-polars api

Author: CharlieFModo

linopy/io.py

@@ -40,6 +40,7 @@
 concat_kwargs = dict(dim=CONCAT_DIM, coords="minimal")
 
 TQDM_COLOR = "#80bfff"
+COEFF_THRESHOLD = 1e-12
 
 
 def handle_batch(batch: list[str], f: TextIOWrapper, batch_size: int) -> list[str]:
@@ -550,8 +551,10 @@ def objective_to_file_polars(
     f.write(f"{sense}\n\nobj:\n\n".encode())
     df = m.objective.to_polars()
 
+    # Filter out zero coefficients like the regular LP version does
     if m.is_linear:
-        objective_write_linear_terms_polars(f, df, print_variable)
+        df_filtered = df.filter(pl.col("coeffs").abs() > COEFF_THRESHOLD)
+        objective_write_linear_terms_polars(f, df_filtered, print_variable)
 
     elif m.is_quadratic:
         linear_terms = df.filter(pl.col("vars1").eq(-1) | pl.col("vars2").eq(-1))
@@ -561,9 +564,13 @@ def objective_to_file_polars(
             .otherwise(pl.col("vars1"))
             .alias("vars")
         )
+        # Filter out zero coefficients
+        linear_terms = linear_terms.filter(pl.col("coeffs").abs() > COEFF_THRESHOLD)
         objective_write_linear_terms_polars(f, linear_terms, print_variable)
 
         quads = df.filter(pl.col("vars1").ne(-1) & pl.col("vars2").ne(-1))
+        # Filter out zero coefficients
+        quads = quads.filter(pl.col("coeffs").abs() > COEFF_THRESHOLD)
         objective_write_quadratic_terms_polars(f, quads, print_variable)
 
 
@@ -731,28 +738,69 @@ def constraints_to_file_polars(
         for con_slice in con.iterate_slices(slice_size):
             df = con_slice.to_polars()
 
-            # df = df.lazy()
-            # filter out repeated label values
-            df = df.with_columns(
-                pl.when(pl.col("labels").is_first_distinct())
-                .then(pl.col("labels"))
-                .otherwise(pl.lit(None))
-                .alias("labels")
+            # Filter out rows with zero coefficients or invalid variables - but KEEP RHS rows
+            # RHS rows have null coeffs/vars but contain the constraint sign/rhs
+            df = df.filter(
+                # Keep RHS rows (have sign/rhs but null coeffs/vars)
+                (pl.col("sign").is_not_null() & pl.col("rhs").is_not_null())
+                |
+                # OR keep valid coefficient rows
+                (
+                    (pl.col("coeffs").abs() > COEFF_THRESHOLD)
+                    & (pl.col("vars").is_not_null())
+                    & (pl.col("vars") >= 0)
+                )
+            )
+
+            if df.height == 0:
+                continue
+
+            # Ensure each constraint has both coefficient and RHS terms
+            analysis = df.group_by("labels").agg(
+                [
+                    pl.col("coeffs").is_not_null().sum().alias("coeff_rows"),
+                    pl.col("sign").is_not_null().sum().alias("rhs_rows"),
+                ]
+            )
+
+            valid = analysis.filter(
+                (pl.col("coeff_rows") > 0) & (pl.col("rhs_rows") > 0)
+            )
+
+            if valid.height == 0:
+                continue
+
+            df = df.join(valid.select("labels"), on="labels", how="inner")
+
+            # Sort by labels for proper grouping and mark first/last occurrences
+            df = df.sort("labels").with_columns(
+                [
+                    pl.when(pl.col("labels").is_first_distinct())
+                    .then(pl.col("labels"))
+                    .otherwise(pl.lit(None))
+                    .alias("labels_first"),
+                    (pl.col("labels") != pl.col("labels").shift(-1))
+                    .fill_null(True)
+                    .alias("is_last_in_group"),
+                ]
             )
 
-            row_labels = print_constraint(pl.col("labels"))
+            # Build output columns
+            row_labels = print_constraint(pl.col("labels_first"))
             col_labels = print_variable(pl.col("vars"))
             columns = [
-                pl.when(pl.col("labels").is_not_null()).then(row_labels[0]),
-                pl.when(pl.col("labels").is_not_null()).then(row_labels[1]),
-                pl.when(pl.col("labels").is_not_null()).then(pl.lit(":\n")).alias(":"),
+                pl.when(pl.col("labels_first").is_not_null()).then(row_labels[0]),
+                pl.when(pl.col("labels_first").is_not_null()).then(row_labels[1]),
+                pl.when(pl.col("labels_first").is_not_null())
+                .then(pl.lit(":\n"))
+                .alias(":"),
                 pl.when(pl.col("coeffs") >= 0).then(pl.lit("+")),
                 pl.col("coeffs").cast(pl.String),
                 pl.when(pl.col("vars").is_not_null()).then(col_labels[0]),
                 pl.when(pl.col("vars").is_not_null()).then(col_labels[1]),
-                "sign",
-                pl.lit(" "),
-                pl.col("rhs").cast(pl.String),
+                pl.when(pl.col("is_last_in_group")).then(pl.col("sign")),
+                pl.when(pl.col("is_last_in_group")).then(pl.lit(" ")),
+                pl.when(pl.col("is_last_in_group")).then(pl.col("rhs").cast(pl.String)),
             ]
 
             kwargs: Any = dict(