




import numpy as np
import requests




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     requests.get(
			    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
			    "master/Week1/ADRvsRating.csv"  )
			a       =     response.text.splitlines()
			a       =     []
			for item in lines:
						a       =     item.split(","  )
						data.append(A  )
			data.pop(0  )  # This is for removing the labels from the list
			a       =     np.matrix(A  )
			return dataset




def      __magic_name__							(					A						:   Tuple,    A						:   Any,    A						:   Tuple,    A						:   int,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			a       =     len_data

			a       =     np.dot(A,    data_x.transpose()  )
			prod -= data_y.transpose()
			a       =     np.dot(A,    A  )
			a       =     theta - (alpha / n) * sum_grad
			return theta




def      __magic_name__							(					A						:   str,    A						:   Union[str, Any],    A						:   str,    A						:   Any  ):






			'''simple docstring'''







			a       =     np.dot(A,    data_x.transpose()  )
			prod -= data_y.transpose()
			a       =     np.sum(np.square(A  )  )
			a       =     sum_elem / (2 * len_data)
			return error




def      __magic_name__							(					A						:   Dict,    A						:   str  ):






			'''simple docstring'''







			a       =     100000
			a       =     0.0_00_15_50

			a       =     data_x.shape[1]
			a       =     data_x.shape[0] - 1

			a       =     np.zeros((1, no_features)  )

			for i in range(0,    A  ):
						a       =     run_steep_gradient_descent(A,    A,    A,    A,    A  )
						a       =     sum_of_square_error(A,    A,    A,    A  )
						print(F"""At Iteration {i + 1} - Error is {error:.5f}"""  )

			return theta




def      __magic_name__							(					A						:   Optional[Any],    A						:   Optional[Any]  ):






			'''simple docstring'''







			a       =     sum(abs(y - predicted_y[i]  ) for i, y in enumerate(A  )  )
			return total / len(A  )




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     collect_dataset()

			a       =     data.shape[0]
			a       =     np.c_[np.ones(A  ), data[:, :-1]].astype(A  )
			a       =     data[:, -1].astype(A  )

			a       =     run_linear_regression(A,    A  )
			a       =     theta.shape[1]
			print("Resultant Feature vector : "  )
			for i in range(0,    A  ):
						print(F"""{theta[0, i]:.5f}"""  )


if __name__ == "__main__":
		main()














"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_						:					Optional[int]      				=			"""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_						:					Any      				=			"""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_						:					int      				=			"""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


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


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return datasets.MetricInfo(
                description=_DESCRIPTION				, citation=_CITATION				, homepage='''https://github.com/chakki-works/seqeval'''				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                        '''references''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                    }    )				, codebase_urls=['''https://github.com/chakki-works/seqeval''']				, reference_urls=['''https://github.com/chakki-works/seqeval''']				, )







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =classification_report(
                y_true=a				, y_pred=a				, suffix=a				, output_dict=a				, scheme=a				, mode=a				, sample_weight=a				, zero_division=a				, )
            report.pop('''macro avg'''    )
            report.pop('''weighted avg'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

            __snake_case   :     str	      ={
                type_name: {
                    '''precision''': score['''precision'''],
                    '''recall''': score['''recall'''],
                    '''f1''': score['''f1-score'''],
                    '''number''': score['''support'''],
                }
                for type_name, score in report.items()
            }
            __snake_case   :     str	      =overall_score['''precision''']
            __snake_case   :     int	      =overall_score['''recall''']
            __snake_case   :     str	      =overall_score['''f1-score''']
            __snake_case   :     Union[str, Any]	      =accuracy_score(y_true=a				, y_pred=a    )

            return scores


