





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	np.ndarray:
    return input_array.reshape((input_array.size, 1)					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	np.ndarray:
    lowerCamelCase__       : Optional[Any]   					=						np.nan
    for i in range(_UpperCAmelCase					):
        lowerCamelCase__       : List[str]   					=						features[:, labels == i]
        lowerCamelCase__       : Optional[Any]   					=						data.mean(1					)
        # Centralize the data of class i
        lowerCamelCase__       : List[str]   					=						data - column_reshape(_UpperCAmelCase					)
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += np.dot(_UpperCAmelCase		,      centered_data.T					)
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            lowerCamelCase__       : Optional[Any]   					=						np.dot(_UpperCAmelCase		,      centered_data.T					)

    return covariance_sum / features.shape[1]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	np.ndarray:
    lowerCamelCase__       : Any   					=						features.mean(1					)
    lowerCamelCase__       : int   					=						np.nan
    for i in range(_UpperCAmelCase					):
        lowerCamelCase__       : Tuple   					=						features[:, labels == i]
        lowerCamelCase__       : List[str]   					=						data.shape[1]
        lowerCamelCase__       : Union[str, Any]   					=						data.mean(1					)
        if i > 0:
            # If covariance_sum is not None
            covariance_sum += device_data * np.dot(
                column_reshape(_UpperCAmelCase					) - column_reshape(_UpperCAmelCase					)		,      (column_reshape(_UpperCAmelCase					) - column_reshape(_UpperCAmelCase					)).T		,      )
        else:
            # If covariance_sum is np.nan (i.e. first loop)
            lowerCamelCase__       : List[str]   					=						device_data * np.dot(
                column_reshape(_UpperCAmelCase					) - column_reshape(_UpperCAmelCase					)		,      (column_reshape(_UpperCAmelCase					) - column_reshape(_UpperCAmelCase					)).T		,      )

    return covariance_sum / features.shape[1]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	np.ndarray:

    # Check if the features have been loaded
    if features.any():
        lowerCamelCase__       : Optional[Any]   					=						features.mean(1					)
        # Center the dataset
        lowerCamelCase__       : Optional[int]   					=						features - np.reshape(_UpperCAmelCase		,      (data_mean.size, 1)					)
        lowerCamelCase__       : Optional[int]   					=						np.dot(_UpperCAmelCase		,      centered_data.T					) / features.shape[1]
        lowerCamelCase__					,				lowerCamelCase__       : Union[str, Any]   					=						np.linalg.eigh(_UpperCAmelCase					)
        # Take all the columns in the reverse order (-1), and then takes only the first
        lowerCamelCase__       : Any   					=						eigenvectors[:, ::-1][:, 0:dimensions]
        # Project the database on the new space
        lowerCamelCase__       : str   					=						np.dot(filtered_eigenvectors.T		,      _UpperCAmelCase					)
        logging.info('Principal Component Analysis computed'					)

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR		,      format='%(message)s'		,      force=_UpperCAmelCase					)
        logging.error('Dataset empty'					)
        raise AssertionError


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	np.ndarray:
    assert classes > dimensions

    # Check if features have been already loaded
    if features.any:
        lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						eigh(
            covariance_between_classes(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)		,      covariance_within_classes(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)		,      )
        lowerCamelCase__       : List[str]   					=						eigenvectors[:, ::-1][:, :dimensions]
        lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Any   					=						np.linalg.svd(_UpperCAmelCase					)
        lowerCamelCase__       : str   					=						svd_matrix[:, 0:dimensions]
        lowerCamelCase__       : Optional[Any]   					=						np.dot(filtered_svd_matrix.T		,      _UpperCAmelCase					)
        logging.info('Linear Discriminant Analysis computed'					)

        return projected_data
    else:
        logging.basicConfig(level=logging.ERROR		,      format='%(message)s'		,      force=_UpperCAmelCase					)
        logging.error('Dataset empty'					)
        raise AssertionError


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    # Create dummy dataset with 2 classes and 3 features
    lowerCamelCase__       : Optional[int]   					=						np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]					)
    lowerCamelCase__       : int   					=						np.array([0, 0, 0, 1, 1]					)
    lowerCamelCase__       : Optional[Any]   					=						2
    lowerCamelCase__       : str   					=						2

    # Assert that the function raises an AssertionError if dimensions > classes
    with pytest.raises(_UpperCAmelCase					) as error_info:
        lowerCamelCase__       : List[str]   					=						linear_discriminant_analysis(
            _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)
        if isinstance(_UpperCAmelCase		,      np.ndarray					):
            raise AssertionError(
                'Did not raise AssertionError for dimensions > classes'					)
        assert error_info.type is AssertionError


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : Any   					=						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]					)
    lowerCamelCase__       : List[Any]   					=						2
    lowerCamelCase__       : List[str]   					=						np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]					)

    with pytest.raises(_UpperCAmelCase					) as error_info:
        lowerCamelCase__       : Union[str, Any]   					=						principal_component_analysis(_UpperCAmelCase		,      _UpperCAmelCase					)
        if not np.allclose(_UpperCAmelCase		,      _UpperCAmelCase					):
            raise AssertionError
        assert error_info.type is AssertionError


if __name__ == "__main__":
    import doctest

    doctest.testmod()












import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_UpperCAmelCase							:			Optional[Any]        =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
             for file in itertools.chain.from_iterable(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      pa.Table:
     if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[Any]   					=						self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
             raise ValueError(
                 F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""      )
     for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						pa.Table.from_batches([record_batch]      )
                     # Uncomment for debugging (will print the Arrow table size and elements)
                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                     yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





