Mise à jour de Monitor.py et autres scripts

myenv/lib/python3.11/site-packages/narwhals/_ibis/expr.py

@@ -0,0 +1,648 @@
+from __future__ import annotations
+
+import operator
+from functools import partial
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, cast
+
+import ibis
+
+from narwhals._compliant import LazyExpr, WindowInputs
+from narwhals._expression_parsing import (
+    combine_alias_output_names,
+    combine_evaluate_output_names,
+)
+from narwhals._ibis.expr_dt import IbisExprDateTimeNamespace
+from narwhals._ibis.expr_list import IbisExprListNamespace
+from narwhals._ibis.expr_str import IbisExprStringNamespace
+from narwhals._ibis.expr_struct import IbisExprStructNamespace
+from narwhals._ibis.utils import is_floating, lit, narwhals_to_native_dtype
+from narwhals._utils import Implementation, not_implemented
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Sequence
+
+    import ibis.expr.types as ir
+    from typing_extensions import Self
+
+    from narwhals._compliant.typing import (
+        AliasNames,
+        EvalNames,
+        EvalSeries,
+        WindowFunction,
+    )
+    from narwhals._expression_parsing import ExprKind, ExprMetadata
+    from narwhals._ibis.dataframe import IbisLazyFrame
+    from narwhals._ibis.namespace import IbisNamespace
+    from narwhals._utils import Version, _LimitedContext
+    from narwhals.typing import IntoDType, RankMethod, RollingInterpolationMethod
+
+    ExprT = TypeVar("ExprT", bound=ir.Value)
+    IbisWindowFunction = WindowFunction[IbisLazyFrame, ir.Value]
+    IbisWindowInputs = WindowInputs[ir.Value]
+
+
+class IbisExpr(LazyExpr["IbisLazyFrame", "ir.Column"]):
+    _implementation = Implementation.IBIS
+
+    def __init__(
+        self,
+        call: EvalSeries[IbisLazyFrame, ir.Value],
+        window_function: IbisWindowFunction | None = None,
+        *,
+        evaluate_output_names: EvalNames[IbisLazyFrame],
+        alias_output_names: AliasNames | None,
+        version: Version,
+    ) -> None:
+        self._call = call
+        self._evaluate_output_names = evaluate_output_names
+        self._alias_output_names = alias_output_names
+        self._version = version
+        self._metadata: ExprMetadata | None = None
+        self._window_function: IbisWindowFunction | None = window_function
+
+    @property
+    def window_function(self) -> IbisWindowFunction:
+        def default_window_func(
+            df: IbisLazyFrame, window_inputs: IbisWindowInputs
+        ) -> list[ir.Value]:
+            return [
+                expr.over(
+                    ibis.window(
+                        group_by=window_inputs.partition_by,
+                        order_by=self._sort(*window_inputs.order_by),
+                    )
+                )
+                for expr in self(df)
+            ]
+
+        return self._window_function or default_window_func
+
+    def __call__(self, df: IbisLazyFrame) -> Sequence[ir.Value]:
+        return self._call(df)
+
+    def __narwhals_expr__(self) -> None: ...
+
+    def __narwhals_namespace__(self) -> IbisNamespace:  # pragma: no cover
+        from narwhals._ibis.namespace import IbisNamespace
+
+        return IbisNamespace(version=self._version)
+
+    def _cum_window_func(
+        self, *, reverse: bool, func_name: Literal["sum", "max", "min", "count"]
+    ) -> IbisWindowFunction:
+        def func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            window = ibis.window(
+                group_by=list(inputs.partition_by),
+                order_by=self._sort(
+                    *inputs.order_by, descending=reverse, nulls_last=reverse
+                ),
+                preceding=None,  # unbounded
+                following=0,
+            )
+
+            return [getattr(expr, func_name)().over(window) for expr in self(df)]
+
+        return func
+
+    def _rolling_window_func(
+        self,
+        *,
+        func_name: Literal["sum", "mean", "std", "var"],
+        center: bool,
+        window_size: int,
+        min_samples: int,
+        ddof: int | None = None,
+    ) -> IbisWindowFunction:
+        supported_funcs = ["sum", "mean", "std", "var"]
+
+        if center:
+            preceding = window_size // 2
+            following = window_size - preceding - 1
+        else:
+            preceding = window_size - 1
+            following = 0
+
+        def func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            window = ibis.window(
+                group_by=list(inputs.partition_by),
+                order_by=self._sort(*inputs.order_by),
+                preceding=preceding,
+                following=following,
+            )
+
+            def inner_f(expr: ir.NumericColumn) -> ir.Value:
+                if func_name in {"sum", "mean"}:
+                    func_ = getattr(expr, func_name)()
+                elif func_name == "var" and ddof == 0:
+                    func_ = expr.var(how="pop")
+                elif func_name in "var" and ddof == 1:
+                    func_ = expr.var(how="sample")
+                elif func_name == "std" and ddof == 0:
+                    func_ = expr.std(how="pop")
+                elif func_name == "std" and ddof == 1:
+                    func_ = expr.std(how="sample")
+                elif func_name in {"var", "std"}:  # pragma: no cover
+                    msg = f"Only ddof=0 and ddof=1 are currently supported for rolling_{func_name}."
+                    raise ValueError(msg)
+                else:  # pragma: no cover
+                    msg = f"Only the following functions are supported: {supported_funcs}.\nGot: {func_name}."
+                    raise ValueError(msg)
+
+                rolling_calc = func_.over(window)
+                valid_count = expr.count().over(window)
+                return ibis.cases(
+                    (valid_count >= ibis.literal(min_samples), rolling_calc),
+                    else_=ibis.null(),
+                )
+
+            return [inner_f(cast("ir.NumericColumn", expr)) for expr in self(df)]
+
+        return func
+
+    def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self:
+        # Ibis does its own broadcasting.
+        return self
+
+    def _sort(
+        self, *cols: ir.Column | str, descending: bool = False, nulls_last: bool = False
+    ) -> Iterator[ir.Column]:
+        mapping = {
+            (False, False): partial(ibis.asc, nulls_first=True),
+            (False, True): partial(ibis.asc, nulls_first=False),
+            (True, False): partial(ibis.desc, nulls_first=True),
+            (True, True): partial(ibis.desc, nulls_first=False),
+        }
+        sort = mapping[(descending, nulls_last)]
+        yield from (cast("ir.Column", sort(col)) for col in cols)
+
+    @classmethod
+    def from_column_names(
+        cls: type[Self],
+        evaluate_column_names: EvalNames[IbisLazyFrame],
+        /,
+        *,
+        context: _LimitedContext,
+    ) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.Column]:
+            return [df.native[name] for name in evaluate_column_names(df)]
+
+        return cls(
+            func,
+            evaluate_output_names=evaluate_column_names,
+            alias_output_names=None,
+            version=context._version,
+        )
+
+    @classmethod
+    def from_column_indices(cls, *column_indices: int, context: _LimitedContext) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.Column]:
+            return [df.native[i] for i in column_indices]
+
+        return cls(
+            func,
+            evaluate_output_names=cls._eval_names_indices(column_indices),
+            alias_output_names=None,
+            version=context._version,
+        )
+
+    @classmethod
+    def _from_elementwise_horizontal_op(
+        cls, func: Callable[[Iterable[ir.Value]], ir.Value], *exprs: Self
+    ) -> Self:
+        def call(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = (col for _expr in exprs for col in _expr(df))
+            return [func(cols)]
+
+        context = exprs[0]
+        return cls(
+            call=call,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            version=context._version,
+        )
+
+    def _with_callable(
+        self, call: Callable[..., ir.Value], /, **expressifiable_args: Self | Any
+    ) -> Self:
+        """Create expression from callable.
+
+        Arguments:
+            call: Callable from compliant DataFrame to native Expression
+            expr_name: Expression name
+            expressifiable_args: arguments pass to expression which should be parsed
+                as expressions (e.g. in `nw.col('a').is_between('b', 'c')`)
+        """
+
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            native_series_list = self(df)
+            other_native_series = {
+                key: df._evaluate_expr(value) if self._is_expr(value) else lit(value)
+                for key, value in expressifiable_args.items()
+            }
+            return [
+                call(native_series, **other_native_series)
+                for native_series in native_series_list
+            ]
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            version=self._version,
+        )
+
+    def _with_binary(self, op: Callable[..., ir.Value], other: Self | Any) -> Self:
+        return self._with_callable(op, other=other)
+
+    def _with_alias_output_names(self, func: AliasNames | None, /) -> Self:
+        return type(self)(
+            self._call,
+            self._window_function,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=func,
+            version=self._version,
+        )
+
+    def _with_window_function(self, window_function: IbisWindowFunction) -> Self:
+        return self.__class__(
+            self._call,
+            window_function,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            version=self._version,
+        )
+
+    @classmethod
+    def _alias_native(cls, expr: ExprT, name: str, /) -> ExprT:
+        return cast("ExprT", expr.name(name))
+
+    def __invert__(self) -> Self:
+        invert = cast("Callable[..., ir.Value]", operator.invert)
+        return self._with_callable(invert)
+
+    def abs(self) -> Self:
+        return self._with_callable(lambda expr: expr.abs())
+
+    def mean(self) -> Self:
+        return self._with_callable(lambda expr: expr.mean())
+
+    def median(self) -> Self:
+        return self._with_callable(lambda expr: expr.median())
+
+    def all(self) -> Self:
+        return self._with_callable(lambda expr: expr.all().fill_null(lit(True)))  # noqa: FBT003
+
+    def any(self) -> Self:
+        return self._with_callable(lambda expr: expr.any().fill_null(lit(False)))  # noqa: FBT003
+
+    def quantile(
+        self, quantile: float, interpolation: RollingInterpolationMethod
+    ) -> Self:
+        if interpolation != "linear":
+            msg = "Only linear interpolation methods are supported for Ibis quantile."
+            raise NotImplementedError(msg)
+        return self._with_callable(lambda expr: expr.quantile(quantile))
+
+    def clip(self, lower_bound: Any, upper_bound: Any) -> Self:
+        def _clip(
+            expr: ir.NumericValue, lower: Any | None = None, upper: Any | None = None
+        ) -> ir.NumericValue:
+            return expr.clip(lower=lower, upper=upper)
+
+        if lower_bound is None:
+            return self._with_callable(_clip, upper=upper_bound)
+        if upper_bound is None:
+            return self._with_callable(_clip, lower=lower_bound)
+        return self._with_callable(_clip, lower=lower_bound, upper=upper_bound)
+
+    def sum(self) -> Self:
+        return self._with_callable(lambda expr: expr.sum().fill_null(lit(0)))
+
+    def n_unique(self) -> Self:
+        return self._with_callable(
+            lambda expr: expr.nunique() + expr.isnull().any().cast("int8")
+        )
+
+    def count(self) -> Self:
+        return self._with_callable(lambda expr: expr.count())
+
+    def len(self) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.IntegerScalar]:
+            return [df.native.count()]
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            version=self._version,
+        )
+
+    def std(self, ddof: int) -> Self:
+        def _std(expr: ir.NumericColumn, ddof: int) -> ir.Value:
+            if ddof == 0:
+                return expr.std(how="pop")
+            elif ddof == 1:
+                return expr.std(how="sample")
+            else:
+                n_samples = expr.count()
+                std_pop = expr.std(how="pop")
+                ddof_lit = cast("ir.IntegerScalar", ibis.literal(ddof))
+                return std_pop * n_samples.sqrt() / (n_samples - ddof_lit).sqrt()
+
+        return self._with_callable(lambda expr: _std(expr, ddof))
+
+    def var(self, ddof: int) -> Self:
+        def _var(expr: ir.NumericColumn, ddof: int) -> ir.Value:
+            if ddof == 0:
+                return expr.var(how="pop")
+            elif ddof == 1:
+                return expr.var(how="sample")
+            else:
+                n_samples = expr.count()
+                var_pop = expr.var(how="pop")
+                ddof_lit = cast("ir.IntegerScalar", ibis.literal(ddof))
+                return var_pop * n_samples / (n_samples - ddof_lit)
+
+        return self._with_callable(lambda expr: _var(expr, ddof))
+
+    def max(self) -> Self:
+        return self._with_callable(lambda expr: expr.max())
+
+    def min(self) -> Self:
+        return self._with_callable(lambda expr: expr.min())
+
+    def null_count(self) -> Self:
+        return self._with_callable(lambda expr: expr.isnull().sum())
+
+    def over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self:
+        def func(df: IbisLazyFrame) -> Sequence[ir.Value]:
+            return self.window_function(df, WindowInputs(partition_by, order_by))
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            version=self._version,
+        )
+
+    def is_null(self) -> Self:
+        return self._with_callable(lambda expr: expr.isnull())
+
+    def is_nan(self) -> Self:
+        def func(expr: ir.FloatingValue | Any) -> ir.Value:
+            otherwise = expr.isnan() if is_floating(expr.type()) else False
+            return ibis.ifelse(expr.isnull(), None, otherwise)
+
+        return self._with_callable(func)
+
+    def is_finite(self) -> Self:
+        return self._with_callable(lambda expr: ~(expr.isinf() | expr.isnan()))
+
+    def is_in(self, other: Sequence[Any]) -> Self:
+        return self._with_callable(lambda expr: expr.isin(other))
+
+    def round(self, decimals: int) -> Self:
+        return self._with_callable(lambda expr: expr.round(decimals))
+
+    def shift(self, n: int) -> Self:
+        def _func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            return [
+                expr.lag(n).over(  # type: ignore[attr-defined, unused-ignore]
+                    ibis.window(
+                        group_by=inputs.partition_by,
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(_func)
+
+    def is_first_distinct(self) -> Self:
+        def func(
+            df: IbisLazyFrame, inputs: IbisWindowInputs
+        ) -> Sequence[ir.BooleanValue]:
+            # ibis row_number starts at 0, so need to compare with 0 instead of the usual `1`
+            return [
+                ibis.row_number().over(
+                    ibis.window(
+                        group_by=[*inputs.partition_by, expr],
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                == lit(0)
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(func)
+
+    def is_last_distinct(self) -> Self:
+        def func(
+            df: IbisLazyFrame, inputs: IbisWindowInputs
+        ) -> Sequence[ir.BooleanValue]:
+            # ibis row_number starts at 0, so need to compare with 0 instead of the usual `1`
+            return [
+                ibis.row_number().over(
+                    ibis.window(
+                        group_by=[*inputs.partition_by, expr],
+                        order_by=self._sort(
+                            *inputs.order_by, descending=True, nulls_last=True
+                        ),
+                    )
+                )
+                == lit(0)
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(func)
+
+    def diff(self) -> Self:
+        def _func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            return [
+                expr
+                - expr.lag().over(  # type: ignore[attr-defined, unused-ignore]
+                    ibis.window(
+                        following=0,
+                        group_by=inputs.partition_by,
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(_func)
+
+    def cum_sum(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="sum")
+        )
+
+    def cum_max(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="max")
+        )
+
+    def cum_min(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="min")
+        )
+
+    def cum_count(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="count")
+        )
+
+    def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="sum",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+            )
+        )
+
+    def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="mean",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+            )
+        )
+
+    def rolling_var(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="var",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+                ddof=ddof,
+            )
+        )
+
+    def rolling_std(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="std",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+                ddof=ddof,
+            )
+        )
+
+    def fill_null(self, value: Self | Any, strategy: Any, limit: int | None) -> Self:
+        # Ibis doesn't yet allow ignoring nulls in first/last with window functions, which makes forward/backward
+        # strategies inconsistent when there are nulls present: https://github.com/ibis-project/ibis/issues/9539
+        if strategy is not None:
+            msg = "`strategy` is not supported for the Ibis backend"
+            raise NotImplementedError(msg)
+        if limit is not None:
+            msg = "`limit` is not supported for the Ibis backend"  # pragma: no cover
+            raise NotImplementedError(msg)
+
+        def _fill_null(expr: ir.Value, value: ir.Scalar) -> ir.Value:
+            return expr.fill_null(value)
+
+        return self._with_callable(_fill_null, value=value)
+
+    def cast(self, dtype: IntoDType) -> Self:
+        def _func(expr: ir.Column) -> ir.Value:
+            native_dtype = narwhals_to_native_dtype(dtype, self._version)
+            # ibis `cast` overloads do not include DataType, only literals
+            return expr.cast(native_dtype)  # type: ignore[unused-ignore]
+
+        return self._with_callable(_func)
+
+    def is_unique(self) -> Self:
+        return self._with_callable(
+            lambda expr: expr.isnull().count().over(ibis.window(group_by=(expr))) == 1
+        )
+
+    def rank(self, method: RankMethod, *, descending: bool) -> Self:
+        def _rank(expr: ir.Column) -> ir.Column:
+            order_by = next(self._sort(expr, descending=descending, nulls_last=True))
+            window = ibis.window(order_by=order_by)
+
+            if method == "dense":
+                rank_ = order_by.dense_rank()
+            elif method == "ordinal":
+                rank_ = cast("ir.IntegerColumn", ibis.row_number().over(window))
+            else:
+                rank_ = order_by.rank()
+
+            # Ibis uses 0-based ranking. Add 1 to match polars 1-based rank.
+            rank_ = rank_ + cast("ir.IntegerValue", lit(1))
+
+            # For "max" and "average", adjust using the count of rows in the partition.
+            if method == "max":
+                # Define a window partitioned by expr (i.e. each distinct value)
+                partition = ibis.window(group_by=[expr])
+                cnt = cast("ir.IntegerValue", expr.count().over(partition))
+                rank_ = rank_ + cnt - cast("ir.IntegerValue", lit(1))
+            elif method == "average":
+                partition = ibis.window(group_by=[expr])
+                cnt = cast("ir.IntegerValue", expr.count().over(partition))
+                avg = cast(
+                    "ir.NumericValue", (cnt - cast("ir.IntegerScalar", lit(1))) / lit(2.0)
+                )
+                rank_ = rank_ + avg
+
+            return cast("ir.Column", ibis.cases((expr.notnull(), rank_)))
+
+        return self._with_callable(_rank)
+
+    def log(self, base: float) -> Self:
+        def _log(expr: ir.NumericColumn) -> ir.Value:
+            otherwise = expr.log(cast("ir.NumericValue", lit(base)))
+            return ibis.cases(
+                (expr < lit(0), lit(float("nan"))),
+                (expr == lit(0), lit(float("-inf"))),
+                else_=otherwise,
+            )
+
+        return self._with_callable(_log)
+
+    def exp(self) -> Self:
+        def _exp(expr: ir.NumericColumn) -> ir.Value:
+            return expr.exp()
+
+        return self._with_callable(_exp)
+
+    def sqrt(self) -> Self:
+        def _sqrt(expr: ir.NumericColumn) -> ir.Value:
+            return ibis.cases((expr < lit(0), lit(float("nan"))), else_=expr.sqrt())
+
+        return self._with_callable(_sqrt)
+
+    @property
+    def str(self) -> IbisExprStringNamespace:
+        return IbisExprStringNamespace(self)
+
+    @property
+    def dt(self) -> IbisExprDateTimeNamespace:
+        return IbisExprDateTimeNamespace(self)
+
+    @property
+    def list(self) -> IbisExprListNamespace:
+        return IbisExprListNamespace(self)
+
+    @property
+    def struct(self) -> IbisExprStructNamespace:
+        return IbisExprStructNamespace(self)
+
+    # NOTE: https://github.com/ibis-project/ibis/issues/10542
+    cum_prod = not_implemented()
+    drop_nulls = not_implemented()
+
+    # NOTE: https://github.com/ibis-project/ibis/issues/11176
+    skew = not_implemented()
+    kurtosis = not_implemented()
+    unique = not_implemented()