


"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list  ):
				SCREAMING_SNAKE_CASE__     							=				np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase_  )]  )
				SCREAMING_SNAKE_CASE__     							=				np.array(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()						,					lowerCamelCase_  )  )						,					x.transpose()  )						,					lowerCamelCase_  )
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list  ):
				SCREAMING_SNAKE_CASE__     							=				(1, 2, 1)
				SCREAMING_SNAKE_CASE__     							=				(1, 1, 0, 7)
				SCREAMING_SNAKE_CASE__     							=				SARIMAX(
				    lowerCamelCase_						,					exog=lowerCamelCase_						,					order=lowerCamelCase_						,					seasonal_order=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				model.fit(disp=lowerCamelCase_						,					maxiter=600						,					method="nm"  )
				SCREAMING_SNAKE_CASE__     							=				model_fit.predict(1						,					len(lowerCamelCase_  )						,					exog=[test_match]  )
				return result[0]


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list  ):
				SCREAMING_SNAKE_CASE__     							=				SVR(kernel="rbf"						,					C=1						,					gamma=0.1						,					epsilon=0.1  )
				regressor.fit(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				regressor.predict(lowerCamelCase_  )
				return y_pred[0]


def __lowercase     (      lowerCamelCase_  :      list  ):
				train_user.sort()
				SCREAMING_SNAKE_CASE__     							=				np.percentile(lowerCamelCase_						,					25  )
				SCREAMING_SNAKE_CASE__     							=				np.percentile(lowerCamelCase_						,					75  )
				SCREAMING_SNAKE_CASE__     							=				qa - qa
				SCREAMING_SNAKE_CASE__     							=				qa - (iqr * 0.1)
				return low_lim


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      float  ):
				SCREAMING_SNAKE_CASE__     							=				0
				SCREAMING_SNAKE_CASE__     							=				0
				for i in list_vote:
								if i > actual_result:
												SCREAMING_SNAKE_CASE__     							=				not_safe + 1
								else:
												if abs(abs(lowerCamelCase_  ) - abs(lowerCamelCase_  )  ) <= 0.1:
																safe += 1
												else:
																not_safe += 1
				return safe > not_safe


if __name__ == "__main__":
	# data_input_df = pd.read_csv("ex_data.csv", header=None)
	_lowerCamelCase     =  [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	_lowerCamelCase     =  pd.DataFrame(
	    data_input, columns=['total_user', 'total_even', 'days']
	)
	_lowerCamelCase     =  Normalizer().fit_transform(data_input_df.values)
	# split data
	_lowerCamelCase     =  normalize_df[:, 2].tolist()
	_lowerCamelCase     =  normalize_df[:, 0].tolist()
	_lowerCamelCase     =  normalize_df[:, 1].tolist()

	# for svr (input variable = total date and total match)
	_lowerCamelCase     =  normalize_df[:, [1, 2]].tolist()
	_lowerCamelCase     =  x[: len(x) - 1]
	_lowerCamelCase     =  x[len(x) - 1 :]

	# for linear regression & sarimax
	_lowerCamelCase     =  total_date[: len(total_date) - 1]
	_lowerCamelCase     =  total_user[: len(total_user) - 1]
	_lowerCamelCase     =  total_match[: len(total_match) - 1]

	_lowerCamelCase     =  total_date[len(total_date) - 1 :]
	_lowerCamelCase     =  total_user[len(total_user) - 1 :]
	_lowerCamelCase     =  total_match[len(total_match) - 1 :]

	# voting system with forecasting
	_lowerCamelCase     =  [
	    linear_regression_prediction(
	        trn_date, trn_user, trn_match, tst_date, tst_match
	    ),
	    sarimax_predictor(trn_user, trn_match, tst_match),
	    support_vector_regressor(x_train, x_test, trn_user),
	]

	# check the safety of today's data
	_lowerCamelCase     =  '' if data_safety_checker(res_vote, tst_user) else 'not '
	print('Today\'s data is {not_str}safe.')




"""simple docstring"""






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCamelCase     =  datasets.logging.get_logger(__name__)


_lowerCamelCase     =  '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_lowerCamelCase     =  '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_lowerCamelCase     =  '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_lowerCamelCase     =  {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


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





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,		citation=_CITATION       ,		homepage="https://github.com/google-research/bleurt"       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"       ,		id="sequence"     ),
								            "references": datasets.Value("string"       ,		id="sequence"     ),
								        }     )       ,		codebase_urls=["https://github.com/google-research/bleurt"]       ,		reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# check that config name specifies a valid BLEURT model
								if self.config_name == "default":
												logger.warning(
												    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
												    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.upper()

								else:
												raise KeyError(
												    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''     )

								# download the model checkpoint specified by self.config_name and set up the scorer
								SCREAMING_SNAKE_CASE__     							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
								SCREAMING_SNAKE_CASE__     							=				score.BleurtScorer(os.path.join(UpperCAmelCase__       ,		UpperCAmelCase__     )     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
