

from sklearn.metrics import recall_score

import datasets


UpperCAmelCase_							:					Dict		 =   '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''


UpperCAmelCase_							:					List[Any]		 =   '''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {\'recall\': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {\'recall\': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {\'recall\': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'recall\': array([1., 0., 0.])}
'''


UpperCAmelCase_							:					Tuple		 =   '''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''






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




	def        lowerCAmelCase_       (     self						:     int							):
		return datasets.MetricInfo(
		    description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
		        {
		            """predictions""": datasets.Sequence(datasets.Value("""int32"""							)							),
		            """references""": datasets.Sequence(datasets.Value("""int32"""							)							),
		        }
		        if self.config_name == """multilabel"""
		        else {
		            """predictions""": datasets.Value("""int32"""							),
		            """references""": datasets.Value("""int32"""							),
		        }							)					,      reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]					,      )




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Union[str, Any]					,      __A						:     Any					,      __A						:     Any=None					,      __A						:     str=1					,      __A						:     int="binary"					,      __A						:     Any=None					,      __A						:     List[str]="warn"					,      ):
		__A :			Optional[Any]		    =			recall_score(
		    __A					,      __A					,      labels=__A					,      pos_label=__A					,      average=__A					,      sample_weight=__A					,      zero_division=__A					,      )
		return {"recall": float(__A							) if score.size == 1 else score}








class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     int					,      __A						:     Tuple					,      __A						:     List[Any]							):
		__A :			Optional[int]		    =			None
		__A :			Any		    =			None
		__A :			int		    =			graph

		self._normalize_graph(__A					,      __A							)
		__A :			str		    =			len(__A							)
		__A :			Optional[int]		    =			None




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     Any					,      __A						:     Optional[Any]							):
		if sources is int:
			__A :			Dict		    =			[sources]
		if sinks is int:
			__A :			Optional[int]		    =			[sinks]

		if len(__A							) == 0 or len(__A							) == 0:
			return

		__A :			str		    =			sources[0]
		__A :			Dict		    =			sinks[0]

		# make fake vertex if there are more
		# than one source or sink
		if len(__A							) > 1 or len(__A							) > 1:
			__A :			Optional[Any]		    =			0
			for i in sources:
				max_input_flow += sum(self.graph[i]							)

			__A :			List[Any]		    =			len(self.graph							) + 1
			for room in self.graph:
				room.insert(0					,      0							)
			self.graph.insert(0					,      [0] * size							)
			for i in sources:
				__A :			str		    =			max_input_flow
			__A :			Union[str, Any]		    =			0

			__A :			Any		    =			len(self.graph							) + 1
			for room in self.graph:
				room.append(0							)
			self.graph.append([0] * size							)
			for i in sinks:
				__A :			int		    =			max_input_flow
			__A :			Optional[Any]		    =			size - 1




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		if self.maximum_flow_algorithm is None:
			raise Exception("""You need to set maximum flow algorithm before."""							)
		if self.source_index is None or self.sink_index is None:
			return 0

		self.maximum_flow_algorithm.execute()
		return self.maximum_flow_algorithm.getMaximumFlow()




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Dict							):
		__A :			Dict		    =			algorithm(self							)






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     str							):
		__A :			Any		    =			flow_network
		__A :			int		    =			flow_network.verticesCount
		__A :			List[Any]		    =			flow_network.sourceIndex
		__A :			Union[str, Any]		    =			flow_network.sinkIndex
		# it's just a reference, so you shouldn't change
		# it in your algorithms, use deep copy before doing that
		__A :			Optional[int]		    =			flow_network.graph
		__A :			str		    =			False




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		if not self.executed:
			self._algorithm()
			__A :			Any		    =			True




	def        lowerCAmelCase_       (     self						:     List[str]							):
		pass






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     Any					,      __A						:     List[str]							):
		super().__init__(__A							)
		# use this to save your result
		__A :			str		    =			-1




	def        lowerCAmelCase_       (     self						:     Any							):
		if not self.executed:
			raise Exception("""You should execute algorithm before using its result!"""							)

		return self.maximum_flow






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     List[Any]					,      __A						:     Dict							):
		super().__init__(__A							)

		__A :			Tuple		    =			[[0] * self.verticies_count for i in range(self.verticies_count							)]

		__A :			Optional[Any]		    =			[0] * self.verticies_count
		__A :			Union[str, Any]		    =			[0] * self.verticies_count




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Optional[int]		    =			self.verticies_count

		# push some substance to graph
		for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]							):
			self.preflow[self.source_index][nextvertex_index] += bandwidth
			self.preflow[nextvertex_index][self.source_index] -= bandwidth
			self.excesses[nextvertex_index] += bandwidth

		# Relabel-to-front selection rule
		__A :			List[str]		    =			[
		    i
		    for i in range(self.verticies_count							)
		    if i != self.source_index and i != self.sink_index
		]

		# move through list
		__A :			Dict		    =			0
		while i < len(__A							):
			__A :			List[Any]		    =			vertices_list[i]
			__A :			Optional[Any]		    =			self.heights[vertex_index]
			self.process_vertex(__A							)
			if self.heights[vertex_index] > previous_height:
				# if it was relabeled, swap elements
				# and start from 0 index
				vertices_list.insert(0					,      vertices_list.pop(__A							)							)
				__A :			Any		    =			0
			else:
				i += 1

		__A :			Optional[int]		    =			sum(self.preflow[self.source_index]							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     str							):
		while self.excesses[vertex_index] > 0:
			for neighbour_index in range(self.verticies_count							):
				# if it's neighbour and current vertex is higher
				if (
				    self.graph[vertex_index][neighbour_index]
				    - self.preflow[vertex_index][neighbour_index]
				    > 0
				    and self.heights[vertex_index] > self.heights[neighbour_index]
				):
					self.push(__A					,      __A							)

			self.relabel(__A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     List[str]					,      __A						:     Optional[Any]							):
		__A :			Union[str, Any]		    =			min(
		    self.excesses[from_index]					,      self.graph[from_index][to_index] - self.preflow[from_index][to_index]					,      )
		self.preflow[from_index][to_index] += preflow_delta
		self.preflow[to_index][from_index] -= preflow_delta
		self.excesses[from_index] -= preflow_delta
		self.excesses[to_index] += preflow_delta




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Tuple		    =			None
		for to_index in range(self.verticies_count							):
			if (
			    self.graph[vertex_index][to_index]
			    - self.preflow[vertex_index][to_index]
			    > 0
			) and (min_height is None or self.heights[to_index] < min_height):
				__A :			Dict		    =			self.heights[to_index]

		if min_height is not None:
			__A :			Optional[int]		    =			min_height + 1


if __name__ == "__main__":
			UpperCAmelCase_							:					Union[str, Any]		 =   [0]
			UpperCAmelCase_							:					Dict		 =   [3]
			# graph = [
			#     [0, 0, 4, 6, 0, 0],
			#     [0, 0, 5, 2, 0, 0],
			#     [0, 0, 0, 0, 4, 4],
			#     [0, 0, 0, 0, 6, 6],
			#     [0, 0, 0, 0, 0, 0],
			#     [0, 0, 0, 0, 0, 0],
			# ]
			UpperCAmelCase_							:					int		 =   [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

			# prepare our network
			UpperCAmelCase_							:					str		 =   FlowNetwork(graph, entrances, exits)
			# set algorithm
			flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
			# and calculate
			UpperCAmelCase_							:					int		 =   flow_network.find_maximum_flow()

			print(f"""maximum flow is {maximum_flow}""")





