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Misc documentation and name tweaks before release
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@ -138,7 +138,7 @@ class ArithmeticSeries(ArithmeticObject):
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for task in self._tasks:
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if task.op_name == "__add__":
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value = f"({value} + {task.object.resolve()})"
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elif task.op_name in ("__truediv__", "__div__"):
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elif task.op_name in {"__truediv__", "__div__"}:
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value = f"({value} / {task.object.resolve()})"
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elif task.op_name == "__floordiv__":
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value = f"Math.floor({value} / {task.object.resolve()})"
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@ -152,7 +152,7 @@ class ArithmeticSeries(ArithmeticObject):
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value = f"({value} - {task.object.resolve()})"
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elif task.op_name == "__radd__":
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value = f"({task.object.resolve()} + {value})"
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elif task.op_name == "__rtruediv__":
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elif task.op_name in {"__rtruediv__", "__rdiv__"}:
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value = f"({task.object.resolve()} / {value})"
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elif task.op_name == "__rfloordiv__":
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value = f"Math.floor({task.object.resolve()} / {value})"
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@ -198,7 +198,7 @@ class ImportedMLModel(MLModel):
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if es_if_exists not in ("fail", "replace"):
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raise ValueError("'es_if_exists' must be either 'fail' or 'replace'")
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elif es_if_exists == "fail":
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if self.check_existing_model():
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if self.exists_model():
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raise ValueError(
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f"Trained machine learning model {model_id} already exists"
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)
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@ -58,9 +58,9 @@ class MLModel:
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except elasticsearch.NotFoundError:
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pass
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def check_existing_model(self) -> bool:
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def exists_model(self) -> bool:
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"""
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Check If model exists in Elastic
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Check if the model already exists in Elasticsearch
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"""
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try:
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self._client.ml.get_trained_models(
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@ -426,7 +426,13 @@ class Series(NDFrame):
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@property
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def dtype(self) -> np.dtype:
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# DO NOT MAKE PUBLIC (i.e. def dtype) as this breaks query eval implementation
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"""
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Return the dtype object of the underlying data.
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See Also
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--------
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:pandas_api_docs:`pandas.Series.dtype`
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"""
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return self._query_compiler.dtypes[0]
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def __gt__(self, other: Union[int, float, "Series"]) -> BooleanFilter:
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