From 6eb4e9cfa4136aad4f658670da99db89fe48ccdf Mon Sep 17 00:00:00 2001 From: Amrit Krishnan Date: Mon, 27 Nov 2023 15:03:55 -0500 Subject: [PATCH] Update pyarrow version, use pyarrow backend for pandas (#513) * Update pyarrow version, los prediction notebook * Small fix to unique (cast as np array) * Update dependencies in pyproject.toml * Fix data type check in `filter_range` function --------- Co-authored-by: Franklin <41602287+fcogidi@users.noreply.github.com> --- cyclops/data/slicer.py | 7 +- cyclops/process/feature/handle_types.py | 2 +- poetry.lock | 305 +++++++++++------------- pyproject.toml | 10 +- tests/cyclops/data/test_slicer.py | 24 +- 5 files changed, 164 insertions(+), 184 deletions(-) diff --git a/cyclops/data/slicer.py b/cyclops/data/slicer.py index 0c4bc4d7a..fe0ebbce9 100644 --- a/cyclops/data/slicer.py +++ b/cyclops/data/slicer.py @@ -8,6 +8,7 @@ import numpy as np import pandas as pd from dateutil.parser import parse +from pandas.api.types import is_datetime64_any_dtype, is_numeric_dtype @dataclass @@ -552,11 +553,11 @@ def filter_range( example_values = pd.Series( examples[column_name], dtype="datetime64[ns]" if value_is_datetime else None, - ).to_numpy() + ) if not ( # column does not contain number or datetime values - np.issubdtype(example_values.dtype, np.number) - or np.issubdtype(example_values.dtype, np.datetime64) + is_numeric_dtype(example_values.dtype) + or is_datetime64_any_dtype(example_values.dtype) ): raise TypeError( "Expected feature to be numeric or datetime, but got " diff --git a/cyclops/process/feature/handle_types.py b/cyclops/process/feature/handle_types.py index 97e6a571c..816779f5e 100644 --- a/cyclops/process/feature/handle_types.py +++ b/cyclops/process/feature/handle_types.py @@ -50,7 +50,7 @@ def get_unique( """ if unique is None: - return values.unique() # type: ignore + return np.array(values.unique()) # type: ignore return unique diff --git a/poetry.lock b/poetry.lock index a370b2892..e6b1baa56 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"^0.11.0", extras = ["torch"] } llvmlite = "^0.40.0" array-api-compat = {version = "^1.4"} -monai = { version = "^1.1.0", extras = ["itk"] } +monai = { version = "^1.3.0", extras = ["itk"] } [tool.poetry.group.test] optional = true @@ -63,7 +63,7 @@ nbstripout = "^0.6.1" mypy = "^1.0.0" ruff = "^0.1.0" nbqa = { version = "^1.7.0", extras = ["toolchain"] } -cycquery = "^0.1.0" # used for integration test +cycquery = "^0.1.2" # used for integration test torchmetrics = {version = "^1.2.0", extras = ["classification"]} [tool.poetry.group.docs] diff --git a/tests/cyclops/data/test_slicer.py b/tests/cyclops/data/test_slicer.py index d40898829..a1f864a3e 100644 --- a/tests/cyclops/data/test_slicer.py +++ b/tests/cyclops/data/test_slicer.py @@ -10,6 +10,7 @@ from cycquery import OMOPQuerier from datasets import Dataset from datasets.splits import Split +from pandas.api.types import is_datetime64_any_dtype, is_numeric_dtype from cyclops.data.slicer import ( SliceSpec, @@ -184,19 +185,9 @@ def test_filter_range( max_value, ) - # If the column is not numeric or datetime, we expect a ValueError. - col_dtype = ( - pd.Series( - table[column_name], - dtype="datetime64[ns]" if value_is_datetime else None, - ) - .to_numpy() - .dtype - ) - - if not ( - np.issubdtype(col_dtype, np.number) or np.issubdtype(col_dtype, np.datetime64) - ): + # If the column is not numeric or datetime, we expect a TypeError. + col_dtype = table[column_name].dtype + if not (is_numeric_dtype(col_dtype) or is_datetime64_any_dtype(col_dtype)): with pytest.raises(TypeError): filtered_ds = get_filtered_dataset(table=table, filter_func=filter_func) return @@ -344,8 +335,11 @@ def test_filter_string_contains( else: result &= pd.notnull(col) - expected = table[result][column_name] - assert np.array_equal(examples, expected) + expected = table[result][column_name].reset_index(drop=True) + pd.testing.assert_series_equal( + pd.Series(examples, dtype=expected.dtype, name=column_name), + expected, + ) @pytest.mark.integration_test()