Library to evaluate ML model performances. 

SANDWORM_MODE: Shai-Hulud-Style npm Worm Hijacks CI Workflows and Poisons AI Toolchains

arkas

Library to evaluate ML model performances

tdurand

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		Metadata-Version: 2.1
		Name: arkas
		Version: 0.0.1a13
		Version: 0.0.1a14
		Summary: Library to evaluate ML model performances
		@@ -5,0 +5,0 @@ Home-page: https://github.com/durandtibo/arkas

		[tool.poetry]
		name = "arkas"
		version = "0.0.1a13"
		version = "0.0.1a14"
		description = "Library to evaluate ML model performances"
		@@ -5,0 +5,0 @@ readme = "README.md"

		@@ -45,3 +45,3 @@ r"""Implement an analyzer that analyzes the correlation between numeric
		no warning message appears.
		sort_key: The key used to sort the correlation table.
		sort_metric: The key used to sort the correlation table.

		@@ -56,3 +56,3 @@ Example usage:
		>>> analyzer
		ColumnCorrelationAnalyzer(target_column='col3', sort_key='spearman_coeff', columns=None, exclude_columns=(), missing_policy='raise')
		ColumnCorrelationAnalyzer(target_column='col3', sort_metric='spearman_coeff', columns=None, exclude_columns=(), missing_policy='raise')
		>>> frame = pl.DataFrame(
		@@ -68,3 +68,3 @@ ... {
		ColumnCorrelationOutput(
		(state): TargetDataFrameState(dataframe=(7, 3), target_column='col3', nan_policy='propagate', figure_config=MatplotlibFigureConfig(), sort_key='spearman_coeff')
		(state): TargetDataFrameState(dataframe=(7, 3), target_column='col3', nan_policy='propagate', figure_config=MatplotlibFigureConfig(), sort_metric='spearman_coeff')
		)
		@@ -81,3 +81,3 @@
		missing_policy: str = "raise",
		sort_key: str = "spearman_coeff",
		sort_metric: str = "spearman_coeff",
		) -> None:
		@@ -88,3 +88,3 @@ super().__init__(
		self._target_column = target_column
		self._sort_key = sort_key
		self._sort_metric = sort_metric

		@@ -100,3 +100,3 @@ def find_columns(self, frame: pl.DataFrame) -> tuple[str, ...]:
		"target_column": self._target_column,
		"sort_key": self._sort_key,
		"sort_metric": self._sort_metric,
		} \| super().get_args()
		@@ -114,3 +114,3 @@
		f"Analyzing the correlation between {self._target_column} and {self._columns} \| "
		f"sort_key={self._sort_key!r} ..."
		f"sort_metric={self._sort_metric!r} ..."
		)
		@@ -122,4 +122,4 @@ columns = list(self.find_common_columns(frame))
		state=TargetDataFrameState(
		dataframe=out, target_column=self._target_column, sort_key=self._sort_key
		dataframe=out, target_column=self._target_column, sort_metric=self._sort_metric
		)
		)

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