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199 | class InvalidValueReplacementApplier(BaseApplier):
@apply_method
def apply(self, X: Any, _y: Any, params: dict[str, Any]) -> Any: # pylint: disable=arguments-differ
return apply_dual_engine(X, params, self._apply_polars, self._apply_pandas)
@staticmethod
def _apply_polars(X: Any, _y: Any, params: dict[str, Any]) -> tuple[Any, Any]:
import polars as pl
valid = resolve_valid_columns(X, params.get("columns", []))
if not valid:
return X, _y
replace_inf = params.get("replace_inf", False)
replace_neg_inf = params.get("replace_neg_inf", False)
rule = params.get("rule")
final_replacement = _resolve_invalid_replacement(params)
min_value = params.get("min_value")
max_value = params.get("max_value")
exprs = []
for col in valid:
expr = pl.col(col)
expr = _invalid_inf_replacement_polars(
expr, replace_inf, replace_neg_inf, final_replacement
)
expr = _invalid_rule_polars(expr, rule, final_replacement, min_value, max_value)
exprs.append(expr.alias(col))
return X.with_columns(exprs), _y
@staticmethod
def _apply_pandas_column(
df_out: Any,
col: str,
replace_inf: bool,
replace_neg_inf: bool,
rule: Any,
final_replacement: Any,
min_value: Any,
max_value: Any,
) -> None:
"""Coerce, replace inf/-inf, and apply the invalid-value rule for a single column in-place."""
to_replace = []
if replace_inf:
to_replace.append(np.inf)
if replace_neg_inf:
to_replace.append(-np.inf)
# Skip entirely when no rule/inf-replacement is configured for this
# column -- a true no-op, matching the polars path. Previously this
# unconditionally ran pd.to_numeric(..., errors="coerce"), which
# silently NaN'd out non-numeric columns even when nothing was
# actually configured to change.
if not to_replace and rule is None:
return
df_out[col] = pd.to_numeric(df_out[col], errors="coerce")
if to_replace:
df_out[col] = df_out[col].replace(to_replace, final_replacement)
mask = _invalid_rule_pandas_mask(df_out[col], rule, min_value, max_value)
if mask is not None:
df_out.loc[mask, col] = final_replacement
@staticmethod
def _apply_pandas(X: Any, _y: Any, params: dict[str, Any]) -> tuple[Any, Any]:
valid = resolve_valid_columns(X, params.get("columns", []))
if not valid:
return X, _y
replace_inf = params.get("replace_inf", False)
replace_neg_inf = params.get("replace_neg_inf", False)
rule = params.get("rule")
final_replacement = _resolve_invalid_replacement(params)
min_value = params.get("min_value")
max_value = params.get("max_value")
df_out = X.copy()
for col in valid:
InvalidValueReplacementApplier._apply_pandas_column(
df_out,
col,
replace_inf,
replace_neg_inf,
rule,
final_replacement,
min_value,
max_value,
)
return df_out, _y
|