






'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor







def 	__magic_name__( _A					):
						'''simple docstring'''





						return (data["data"], data["target"])







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = XGBRegressor(verbosity=0						,      random_state=42					)
						xgb.fit(_A						,      _A					)
						# Predict target for test data
						UpperCamelCase__         = xgb.predict(_A					)
						UpperCamelCase__         = predictions.reshape(len(_A					)						,      1					)
						return predictions







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = fetch_california_housing()
						UpperCamelCase__    ,   UpperCamelCase__         = data_handling(_A					)
						UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = train_test_split(
						    _A						,      _A						,      test_size=0.2_5						,      random_state=1					)
						UpperCamelCase__         = xgboost(_A						,      _A						,      _A					)
						# Error printing
						print(f"Mean Absolute Error : {mean_absolute_error(_A						,      _A					)}"					)
						print(f"Mean Square Error  : {mean_squared_error(_A						,      _A					)}"					)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()








'''simple docstring'''


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowerCamelCase_				: Union[str, Any]    	=							re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

lowerCamelCase_				: int    	=							None







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""					)
						parser.add_argument("""data_file"""						,      metavar="""data.json"""						,      help="""Input data JSON file."""					)
						parser.add_argument("""pred_file"""						,      metavar="""pred.json"""						,      help="""Model predictions."""					)
						parser.add_argument(
						    """--out-file"""						,      """-o"""						,      metavar="""eval.json"""						,      help="""Write accuracy metrics to file (default is stdout)."""					)
						parser.add_argument(
						    """--na-prob-file"""						,      """-n"""						,      metavar="""na_prob.json"""						,      help="""Model estimates of probability of no answer."""					)
						parser.add_argument(
						    """--na-prob-thresh"""						,      """-t"""						,      type=_A						,      default=1.0						,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""						,      )
						parser.add_argument(
						    """--out-image-dir"""						,      """-p"""						,      metavar="""out_images"""						,      default=_A						,      help="""Save precision-recall curves to directory."""					)
						parser.add_argument("""--verbose"""						,      """-v"""						,      action="""store_true"""					)
						if len(sys.argv					) == 1:
												parser.print_help()
												sys.exit(1					)
						return parser.parse_args()







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = bool(qa["""answers"""]["""text"""]					)
						return qid_to_has_ans







def 	__magic_name__( _A					):
						'''simple docstring'''





						def remove_articles(_A					):
												return ARTICLES_REGEX.sub(""" """						,      _A					)

						def white_space_fix(_A					):
												return " ".join(text.split()					)

						def remove_punc(_A					):
												UpperCamelCase__         = set(string.punctuation					)
												return "".join(ch for ch in text if ch not in exclude					)

						def lower(_A					):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if not s:
												return []
						return normalize_answer(_A					).split()







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return int(normalize_answer(_A					) == normalize_answer(_A					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = collections.Counter(_A					) & collections.Counter(_A					)
						UpperCamelCase__         = sum(common.values()					)
						if len(_A					) == 0 or len(_A					) == 0:
												# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
												return int(gold_toks == pred_toks					)
						if num_same == 0:
												return 0
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = (2 * precision * recall) / (precision + recall)
						return fa







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = qa["""id"""]
																								UpperCamelCase__         = [t for t in qa["""answers"""]["""text"""] if normalize_answer(_A					)]
																								if not gold_answers:
																														# For unanswerable questions, only correct answer is empty string
																														UpperCamelCase__         = [""""""]
																								if qid not in preds:
																														print(f"Missing prediction for {qid}"					)
																														continue
																								UpperCamelCase__         = preds[qid]
																								# Take max over all gold answers
																								UpperCamelCase__         = max(compute_exact(_A						,      _A					) for a in gold_answers					)
																								UpperCamelCase__         = max(compute_fa(_A						,      _A					) for a in gold_answers					)
						return exact_scores, fa_scores







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for qid, s in scores.items():
												UpperCamelCase__         = na_probs[qid] > na_prob_thresh
												if pred_na:
																		UpperCamelCase__         = float(not qid_to_has_ans[qid]					)
												else:
																		UpperCamelCase__         = s
						return new_scores







def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if not qid_list:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores.values()					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores.values()					) / total),
												        ("""total""", total),
												    ]					)
						else:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores[k] for k in qid_list					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores[k] for k in qid_list					) / total),
												        ("""total""", total),
												    ]					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						for k in new_eval:
												UpperCamelCase__         = new_eval[k]







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						plt.step(_A						,      _A						,      color="""b"""						,      alpha=0.2						,      where="""post"""					)
						plt.fill_between(_A						,      _A						,      step="""post"""						,      alpha=0.2						,      color="""b"""					)
						plt.xlabel("""Recall"""					)
						plt.ylabel("""Precision"""					)
						plt.xlim([0.0, 1.0_5]					)
						plt.ylim([0.0, 1.0_5]					)
						plt.title(_A					)
						plt.savefig(_A					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A=None						,      _A=None					):
						'''simple docstring'''





						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = 1.0
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = [1.0]
						UpperCamelCase__         = [0.0]
						UpperCamelCase__         = 0.0
						for i, qid in enumerate(_A					):
												if qid_to_has_ans[qid]:
																		true_pos += scores[qid]
												UpperCamelCase__         = true_pos / float(i + 1					)
												UpperCamelCase__         = true_pos / float(_A					)
												if i == len(_A					) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																		# i.e., if we can put a threshold after this point
																		avg_prec += cur_p * (cur_r - recalls[-1])
																		precisions.append(_A					)
																		recalls.append(_A					)
						if out_image:
												plot_pr_curve(_A						,      _A						,      _A						,      _A					)
						return {"ap": 1_0_0.0 * avg_prec}







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if out_image_dir and not os.path.exists(_A					):
												os.makedirs(_A					)
						UpperCamelCase__         = sum(1 for v in qid_to_has_ans.values() if v					)
						if num_true_pos == 0:
												return
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_exact.png"""					)						,      title="""Precision-Recall curve for Exact Match score"""						,      )
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_f1.png"""					)						,      title="""Precision-Recall curve for F1 score"""						,      )
						UpperCamelCase__         = {k: float(_A					) for k, v in qid_to_has_ans.items()}
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_oracle.png"""					)						,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""						,      )
						merge_eval(_A						,      _A						,      """pr_exact"""					)
						merge_eval(_A						,      _A						,      """pr_f1"""					)
						merge_eval(_A						,      _A						,      """pr_oracle"""					)







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if not qid_list:
												return
						UpperCamelCase__         = [na_probs[k] for k in qid_list]
						UpperCamelCase__         = np.ones_like(_A					) / float(len(_A					)					)
						plt.hist(_A						,      weights=_A						,      bins=20						,      range=(0.0, 1.0)					)
						plt.xlabel("""Model probability of no-answer"""					)
						plt.ylabel("""Proportion of dataset"""					)
						plt.title(f"Histogram of no-answer probability: {name}"					)
						plt.savefig(os.path.join(_A						,      f"na_prob_hist_{name}.png"					)					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]					)
						UpperCamelCase__         = num_no_ans
						UpperCamelCase__         = cur_score
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						for i, qid in enumerate(_A					):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		UpperCamelCase__         = scores[qid]
												else:
																		if preds[qid]:
																								UpperCamelCase__         = -1
																		else:
																								UpperCamelCase__         = 0
												cur_score += diff
												if cur_score > best_score:
																		UpperCamelCase__         = cur_score
																		UpperCamelCase__         = na_probs[qid]
						return 1_0_0.0 * best_score / len(_A					), best_thresh







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__         = best_exact
						UpperCamelCase__         = exact_thresh
						UpperCamelCase__         = best_fa
						UpperCamelCase__         = fa_thresh







def 	__magic_name__( ):
						'''simple docstring'''





						with open(OPTS.data_file					) as f:
												UpperCamelCase__         = json.load(_A					)
												UpperCamelCase__         = dataset_json["""data"""]
						with open(OPTS.pred_file					) as f:
												UpperCamelCase__         = json.load(_A					)
						if OPTS.na_prob_file:
												with open(OPTS.na_prob_file					) as f:
																		UpperCamelCase__         = json.load(_A					)
						else:
												UpperCamelCase__         = {k: 0.0 for k in preds}
						UpperCamelCase__         = make_qid_to_has_ans(_A					)  # maps qid to True/False
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if v]
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if not v]
						UpperCamelCase__    ,   UpperCamelCase__         = get_raw_scores(_A						,      _A					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = make_eval_dict(_A						,      _A					)
						if has_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """HasAns"""					)
						if no_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """NoAns"""					)
						if OPTS.na_prob_file:
												find_all_best_thresh(_A						,      _A						,      _A						,      _A						,      _A						,      _A					)
						if OPTS.na_prob_file and OPTS.out_image_dir:
												run_precision_recall_analysis(_A						,      _A						,      _A						,      _A						,      _A						,      OPTS.out_image_dir					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """hasAns"""					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """noAns"""					)
						if OPTS.out_file:
												with open(OPTS.out_file						,      """w"""					) as f:
																		json.dump(_A						,      _A					)
						else:
												print(json.dumps(_A						,      indent=2					)					)


if __name__ == "__main__":
					lowerCamelCase_				: str    	=							parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use('''Agg''')
										import matplotlib.pyplot as plt
					main()
