
from scipy.stats import pearsonr

import datasets


UpperCamelCase__      :		str       							=  "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


UpperCamelCase__      :		str       							=  "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


UpperCamelCase__      :		Dict       							=  "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,					_KWARGS_DESCRIPTION)
class    _a				(datasets.Metric):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""float"""      ),
                      """references""": datasets.Value("""float"""      ),
                  }      )       ,							reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=False      )    ->			Tuple:
          if return_pvalue:
             _SCREAMING_SNAKE_CASE      =				pearsonr(A__       ,							A__      )
             return {"pearsonr": results[0], "p-value": results[1]}
          else:
             return {"pearsonr": float(pearsonr(A__       ,							A__      )[0]      )}








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






