


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    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				float:
       lowercase__:   Dict  			=   np.array([[1, item, train_mtch[i]] for i, item in enumerate(snake_case     )]     )
       lowercase__:   Optional[int]  			=   np.array(snake_case     )
       lowercase__:   Optional[Any]  			=   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,							snake_case     )     )			,							x.transpose()     )			,							snake_case     )
       return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				float:
       lowercase__:   int  			=   (1, 2, 1)
       lowercase__:   int  			=   (1, 1, 0, 7)
       lowercase__:   str  			=   SARIMAX(
           snake_case			,							exog=snake_case			,							order=snake_case			,							seasonal_order=snake_case     )
       lowercase__:   Optional[int]  			=   model.fit(disp=snake_case			,							maxiter=6_00			,							method='nm'     )
       lowercase__:   Dict  			=   model_fit.predict(1			,							len(snake_case     )			,							exog=[test_match]     )
       return result[0]




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				float:
       lowercase__:   List[Any]  			=   SVR(kernel='rbf'			,							C=1			,							gamma=0.1			,							epsilon=0.1     )
       regressor.fit(snake_case			,							snake_case     )
       lowercase__:   int  			=   regressor.predict(snake_case     )
       return y_pred[0]




def    snake_case_     (  snake_case     )  ->				float:
       train_user.sort()
       lowercase__:   List[Any]  			=   np.percentile(snake_case			,							25     )
       lowercase__:   Union[str, Any]  			=   np.percentile(snake_case			,							75     )
       lowercase__:   Optional[int]  			=   qa - qa
       lowercase__:   Dict  			=   qa - (iqr * 0.1)
       return low_lim




def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       lowercase__:   int  			=   0
       lowercase__:   List[Any]  			=   0
       for i in list_vote:
              if i > actual_result:
                     lowercase__:   Any  			=   not_safe + 1
              else:
                     if abs(abs(snake_case     ) - abs(snake_case     )     ) <= 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		       =							[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 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.''')






import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   Any  			=   params

        lowercase__:   List[Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   np.array([len(lowerCAmelCase__ ) for t in data] )

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()
 def __getitem__(   self				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        return (self.token_ids[index], self.lengths[index])
 def __len__(   self )					->				List[Any]:




        '''simple docstring'''
        return len(self.lengths )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        assert len(self.token_ids ) == len(self.lengths )
        assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   self.params.max_model_input_size
        lowercase__:   Dict  			=   self.lengths > max_len
        logger.info(F'Splitting {sum(lowerCAmelCase__ )} too long sequences.' )

        def divide_chunks(lowerCAmelCase__				, lowerCAmelCase__ ):
               return [l[i : i + n] for i in range(0				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )]

        lowercase__:   str  			=   []
        lowercase__:   List[str]  			=   []
        if self.params.mlm:
               lowercase__			,						lowercase__:   str  			=   self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
               lowercase__			,						lowercase__:   int  			=   self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

        for seq_, len_ in zip(self.token_ids				, self.lengths ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                      new_tok_ids.append(seq_ )
                      new_lengths.append(len_ )
               else:
                      lowercase__:   Optional[Any]  			=   []
                      for sub_s in divide_chunks(seq_				, max_len - 2 ):
                             if sub_s[0] != cls_id:
                                    lowercase__:   int  			=   np.insert(lowerCAmelCase__				, 0				, lowerCAmelCase__ )
                             if sub_s[-1] != sep_id:
                                    lowercase__:   Union[str, Any]  			=   np.insert(lowerCAmelCase__				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )
                             assert len(lowerCAmelCase__ ) <= max_len
                             assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                             sub_seqs.append(lowerCAmelCase__ )

                      new_tok_ids.extend(lowerCAmelCase__ )
                      new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )

        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Dict  			=   len(self )
        lowercase__:   Union[str, Any]  			=   self.lengths > 11
        lowercase__:   List[Any]  			=   self.token_ids[indices]
        lowercase__:   Optional[Any]  			=   self.lengths[indices]
        lowercase__:   List[str]  			=   len(self )
        logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        if "unk_token" not in self.params.special_tok_ids:
               return
        else:
               lowercase__:   List[str]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   str  			=   len(self )
        lowercase__:   Tuple  			=   np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
        lowercase__:   Any  			=   (unk_occs / self.lengths) < 0.5
        lowercase__:   Tuple  			=   self.token_ids[indices]
        lowercase__:   str  			=   self.lengths[indices]
        lowercase__:   Dict  			=   len(self )
        logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        if not self.params.is_master:
               return
        logger.info(F'{len(self )} sequences' )
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   [t[0] for t in batch]
        lowercase__:   Dict  			=   [t[1] for t in batch]
        assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )

        # Max for paddings
        lowercase__:   List[Any]  			=   max(lowerCAmelCase__ )

        # Pad token ids
        if self.params.mlm:
               lowercase__:   Dict  			=   self.params.special_tok_ids['pad_token']
        else:
               lowercase__:   Optional[Any]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   int  			=   [list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
        assert len(tk_ ) == len(lowerCAmelCase__ )
        assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )

        lowercase__:   Tuple  			=   torch.tensor(tk_ )  # (bs, max_seq_len_)
        lowercase__:   Optional[Any]  			=   torch.tensor(lowerCAmelCase__ )  # (bs)
        return tk_t, lg_t


