import numpy as np

from skmultiflow.core import MultiOutputMixin
from skmultiflow.trees import iSOUPTreeRegressor

from ._nodes import SSTActiveLearningNode
from ._nodes import SSTActiveLearningNodeAdaptive
from ._nodes import SSTInactiveLearningNode
from ._nodes import SSTInactiveLearningNodeAdaptive


class StackedSingleTargetHoeffdingTreeRegressor(iSOUPTreeRegressor, MultiOutputMixin):
    """Stacked Single-target Hoeffding Tree regressor.

    Implementation of the Stacked Single-target Hoeffding Tree (SST-HT) method
    for multi-target regression as proposed by <NAME>, <NAME>.,
    and <NAME> [1]_.

    Parameters
    ----------
    max_byte_size: int (default=33554432)
        Maximum memory consumed by the tree.
    memory_estimate_period: int (default=1000000)
        Number of instances between memory consumption checks.
    grace_period: int (default=200)
        Number of instances a leaf should observe between split attempts.
    split_confidence: float (default=0.0000001)
        Allowed error in split decision, a value closer to 0 takes longer to
        decide.
    tie_threshold: float (default=0.05)
        Threshold below which a split will be forced to break ties.
    binary_split: boolean (default=False)
        If True, only allow binary splits.
    stop_mem_management: boolean (default=False)
        If True, stop growing as soon as memory limit is hit.
    remove_poor_atts: boolean (default=False)
        If True, disable poor attributes.
    no_preprune: boolean (default=False)
        If True, disable pre-pruning.
    leaf_prediction: string (default='perceptron')
        | Prediction mechanism used at leafs.
        | 'perceptron' - Stacked perceptron
        | 'adaptive' - Adaptively chooses between the best predictor (mean,
          perceptron or stacked perceptron)
    nb_threshold: int (default=0)
        Number of instances a leaf should observe before allowing Naive Bayes.
    nominal_attributes: list, optional
        List of Nominal attributes. If emtpy, then assume that all attributes
        are numerical.
    learning_ratio_perceptron: float
        The learning rate of the perceptron.
    learning_ratio_decay: float
        Decay multiplier for the learning rate of the perceptron
    learning_ratio_const: Bool
        If False the learning ratio will decay with the number of examples seen
    random_state: int, RandomState instance or None, optional (default=None)
       If int, random_state is the seed used by the random number generator;
       If RandomState instance, random_state is the random number generator;
       If None, the random number generator is the RandomState instance used
       by `np.random`. Used when leaf_prediction is 'perceptron'.

    References
    ----------
    .. [1] <NAME>., <NAME>., <NAME>, <NAME>. (2019).
       "Online Multi-target regression trees with stacked leaf models". arXiv
       preprint arXiv:1903.12483.

    Examples
    --------
    >>> # Imports
    >>> from skmultiflow.data import RegressionGenerator
    >>> from skmultiflow.trees import StackedSingleTargetHoeffdingTreeRegressor
    >>> import numpy as np
    >>>
    >>> # Setup a data stream
    >>> n_targets = 3
    >>> stream = RegressionGenerator(n_targets=n_targets, random_state=1, n_samples=200)
    >>>
    >>> # Setup the Stacked Single-target Hoeffding Tree Regressor
    >>> sst_ht = StackedSingleTargetHoeffdingTreeRegressor()
    >>>
    >>> # Auxiliary variables to control loop and track performance
    >>> n_samples = 0
    >>> max_samples = 200
    >>> y_pred = np.zeros((max_samples, n_targets))
    >>> y_true = np.zeros((max_samples, n_targets))
    >>>
    >>> # Run test-then-train loop for max_samples and while there is data
    >>> while n_samples < max_samples and stream.has_more_samples():
    >>>     X, y = stream.next_sample()
    >>>     y_true[n_samples] = y[0]
    >>>     y_pred[n_samples] = sst_ht.predict(X)[0]
    >>>     sst_ht.partial_fit(X, y)
    >>>     n_samples += 1
    >>>
    >>> # Display results
    >>> print('Stacked Single-target Hoeffding Tree regressor example')
    >>> print('{} samples analyzed.'.format(n_samples))
    >>> print('Mean absolute error: {}'.format(np.mean(np.abs(y_true - y_pred))))
    """

    # =====================================================================
    # == Stacked Single-target Hoeffding Regression Tree implementation ===
    # =====================================================================

    def __init__(self,
                 max_byte_size=33554432,
                 memory_estimate_period=1000000,
                 grace_period=200,
                 split_confidence=0.0000001,
                 tie_threshold=0.05,
                 binary_split=False,
                 stop_mem_management=False,
                 remove_poor_atts=False,
                 leaf_prediction='perceptron',
                 no_preprune=False,
                 nb_threshold=0,
                 nominal_attributes=None,
                 learning_ratio_perceptron=0.02,
                 learning_ratio_decay=0.001,
                 learning_ratio_const=True,
                 random_state=None):
        super().__init__(max_byte_size=max_byte_size,
                         memory_estimate_period=memory_estimate_period,
                         grace_period=grace_period,
                         split_confidence=split_confidence,
                         tie_threshold=tie_threshold,
                         binary_split=binary_split,
                         stop_mem_management=stop_mem_management,
                         remove_poor_atts=remove_poor_atts,
                         no_preprune=no_preprune,
                         leaf_prediction=leaf_prediction,
                         nb_threshold=nb_threshold,
                         nominal_attributes=nominal_attributes)
        self.split_criterion = 'icvr'   # intra cluster variance reduction
        self.learning_ratio_perceptron = learning_ratio_perceptron
        self.learning_ratio_decay = learning_ratio_decay
        self.learning_ratio_const = learning_ratio_const
        self.random_state = random_state

        self._tree_root = None
        self._decision_node_cnt = 0
        self._active_leaf_node_cnt = 0
        self._inactive_leaf_node_cnt = 0
        self._inactive_leaf_byte_size_estimate = 0.0
        self._active_leaf_byte_size_estimate = 0.0
        self._byte_size_estimate_overhead_fraction = 1.0
        self._growth_allowed = True
        self._train_weight_seen_by_model = 0.0

        self.examples_seen = 0
        self.sum_of_values = 0.0
        self.sum_of_squares = 0.0
        self.sum_of_attribute_values = 0.0
        self.sum_of_attribute_squares = 0.0

        # To add the n_targets property once
        self._n_targets_set = False

    @property
    def leaf_prediction(self):
        return self._leaf_prediction

    @leaf_prediction.setter
    def leaf_prediction(self, leaf_prediction):
        if leaf_prediction not in {self._PERCEPTRON, self._ADAPTIVE}:
            print("Invalid leaf_prediction option {}', will use default '{}'".
                  format(leaf_prediction, self._PERCEPTRON))
            self._leaf_prediction = self._PERCEPTRON
        else:
            self._leaf_prediction = leaf_prediction

    def _new_learning_node(self, initial_stats=None, parent_node=None,
                           is_active=True):
        """Create a new learning node. The type of learning node depends on
        the tree configuration.
        """
        if initial_stats is None:
            initial_stats = {}

        if is_active:
            if self.leaf_prediction == self._PERCEPTRON:
                return SSTActiveLearningNode(initial_stats, parent_node,
                                             random_state=self.random_state)
            elif self.leaf_prediction == self._ADAPTIVE:
                new_node = SSTActiveLearningNodeAdaptive(initial_stats, parent_node,
                                                         random_state=self.random_state)
                # Resets faded errors
                new_node.fMAE_M = np.zeros(self._n_targets, dtype=np.float64)
                new_node.fMAE_P = np.zeros(self._n_targets, dtype=np.float64)
                new_node.fMAE_SP = np.zeros(self._n_targets, dtype=np.float64)
                return new_node
        else:
            if self.leaf_prediction == self._PERCEPTRON:
                return SSTInactiveLearningNode(initial_stats, parent_node,
                                               random_state=parent_node.random_state)
            elif self.leaf_prediction == self._ADAPTIVE:
                new_node = SSTInactiveLearningNodeAdaptive(initial_stats, parent_node,
                                                           random_state=parent_node.random_state)
                new_node.fMAE_M = parent_node.fMAE_M
                new_node.fMAE_P = parent_node.fMAE_P
                new_node.fMAE_SP = parent_node.fMAE_SP
                return new_node

    def algorithm_type(self):
        return "REGRESSION"

import numpy as np

from skmultiflow.core import MultiOutputMixin
from skmultiflow.trees import iSOUPTreeRegressor

from ._nodes import SSTActiveLearningNode
from ._nodes import SSTActiveLearningNodeAdaptive
from ._nodes import SSTInactiveLearningNode
from ._nodes import SSTInactiveLearningNodeAdaptive


class StackedSingleTargetHoeffdingTreeRegressor(iSOUPTreeRegressor, MultiOutputMixin):
    """Stacked Single-target Hoeffding Tree regressor.

    Implementation of the Stacked Single-target Hoeffding Tree (SST-HT) method
    for multi-target regression as proposed by <NAME>, <NAME>.,
    and <NAME> [1]_.

    Parameters
    ----------
    max_byte_size: int (default=33554432)
        Maximum memory consumed by the tree.
    memory_estimate_period: int (default=1000000)
        Number of instances between memory consumption checks.
    grace_period: int (default=200)
        Number of instances a leaf should observe between split attempts.
    split_confidence: float (default=0.0000001)
        Allowed error in split decision, a value closer to 0 takes longer to
        decide.
    tie_threshold: float (default=0.05)
        Threshold below which a split will be forced to break ties.
    binary_split: boolean (default=False)
        If True, only allow binary splits.
    stop_mem_management: boolean (default=False)
        If True, stop growing as soon as memory limit is hit.
    remove_poor_atts: boolean (default=False)
        If True, disable poor attributes.
    no_preprune: boolean (default=False)
        If True, disable pre-pruning.
    leaf_prediction: string (default='perceptron')
        | Prediction mechanism used at leafs.
        | 'perceptron' - Stacked perceptron
        | 'adaptive' - Adaptively chooses between the best predictor (mean,
          perceptron or stacked perceptron)
    nb_threshold: int (default=0)
        Number of instances a leaf should observe before allowing Naive Bayes.
    nominal_attributes: list, optional
        List of Nominal attributes. If emtpy, then assume that all attributes
        are numerical.
    learning_ratio_perceptron: float
        The learning rate of the perceptron.
    learning_ratio_decay: float
        Decay multiplier for the learning rate of the perceptron
    learning_ratio_const: Bool
        If False the learning ratio will decay with the number of examples seen
    random_state: int, RandomState instance or None, optional (default=None)
       If int, random_state is the seed used by the random number generator;
       If RandomState instance, random_state is the random number generator;
       If None, the random number generator is the RandomState instance used
       by `np.random`. Used when leaf_prediction is 'perceptron'.

    References
    ----------
    .. [1] <NAME>., <NAME>., <NAME>, <NAME>. (2019).
       "Online Multi-target regression trees with stacked leaf models". arXiv
       preprint arXiv:1903.12483.

    Examples
    --------
    >>> # Imports
    >>> from skmultiflow.data import RegressionGenerator
    >>> from skmultiflow.trees import StackedSingleTargetHoeffdingTreeRegressor
    >>> import numpy as np
    >>>
    >>> # Setup a data stream
    >>> n_targets = 3
    >>> stream = RegressionGenerator(n_targets=n_targets, random_state=1, n_samples=200)
    >>>
    >>> # Setup the Stacked Single-target Hoeffding Tree Regressor
    >>> sst_ht = StackedSingleTargetHoeffdingTreeRegressor()
    >>>
    >>> # Auxiliary variables to control loop and track performance
    >>> n_samples = 0
    >>> max_samples = 200
    >>> y_pred = np.zeros((max_samples, n_targets))
    >>> y_true = np.zeros((max_samples, n_targets))
    >>>
    >>> # Run test-then-train loop for max_samples and while there is data
    >>> while n_samples < max_samples and stream.has_more_samples():
    >>>     X, y = stream.next_sample()
    >>>     y_true[n_samples] = y[0]
    >>>     y_pred[n_samples] = sst_ht.predict(X)[0]
    >>>     sst_ht.partial_fit(X, y)
    >>>     n_samples += 1
    >>>
    >>> # Display results
    >>> print('Stacked Single-target Hoeffding Tree regressor example')
    >>> print('{} samples analyzed.'.format(n_samples))
    >>> print('Mean absolute error: {}'.format(np.mean(np.abs(y_true - y_pred))))
    """

    # =====================================================================
    # == Stacked Single-target Hoeffding Regression Tree implementation ===
    # =====================================================================

    def __init__(self,
                 max_byte_size=33554432,
                 memory_estimate_period=1000000,
                 grace_period=200,
                 split_confidence=0.0000001,
                 tie_threshold=0.05,
                 binary_split=False,
                 stop_mem_management=False,
                 remove_poor_atts=False,
                 leaf_prediction='perceptron',
                 no_preprune=False,
                 nb_threshold=0,
                 nominal_attributes=None,
                 learning_ratio_perceptron=0.02,
                 learning_ratio_decay=0.001,
                 learning_ratio_const=True,
                 random_state=None):
        super().__init__(max_byte_size=max_byte_size,
                         memory_estimate_period=memory_estimate_period,
                         grace_period=grace_period,
                         split_confidence=split_confidence,
                         tie_threshold=tie_threshold,
                         binary_split=binary_split,
                         stop_mem_management=stop_mem_management,
                         remove_poor_atts=remove_poor_atts,
                         no_preprune=no_preprune,
                         leaf_prediction=leaf_prediction,
                         nb_threshold=nb_threshold,
                         nominal_attributes=nominal_attributes)
        self.split_criterion = 'icvr'   # intra cluster variance reduction
        self.learning_ratio_perceptron = learning_ratio_perceptron
        self.learning_ratio_decay = learning_ratio_decay
        self.learning_ratio_const = learning_ratio_const
        self.random_state = random_state

        self._tree_root = None
        self._decision_node_cnt = 0
        self._active_leaf_node_cnt = 0
        self._inactive_leaf_node_cnt = 0
        self._inactive_leaf_byte_size_estimate = 0.0
        self._active_leaf_byte_size_estimate = 0.0
        self._byte_size_estimate_overhead_fraction = 1.0
        self._growth_allowed = True
        self._train_weight_seen_by_model = 0.0

        self.examples_seen = 0
        self.sum_of_values = 0.0
        self.sum_of_squares = 0.0
        self.sum_of_attribute_values = 0.0
        self.sum_of_attribute_squares = 0.0

        # To add the n_targets property once
        self._n_targets_set = False

    @property
    def leaf_prediction(self):
        return self._leaf_prediction

    @leaf_prediction.setter
    def leaf_prediction(self, leaf_prediction):
        if leaf_prediction not in {self._PERCEPTRON, self._ADAPTIVE}:
            print("Invalid leaf_prediction option {}', will use default '{}'".
                  format(leaf_prediction, self._PERCEPTRON))
            self._leaf_prediction = self._PERCEPTRON
        else:
            self._leaf_prediction = leaf_prediction

    def _new_learning_node(self, initial_stats=None, parent_node=None,
                           is_active=True):
        """Create a new learning node. The type of learning node depends on
        the tree configuration.
        """
        if initial_stats is None:
            initial_stats = {}

        if is_active:
            if self.leaf_prediction == self._PERCEPTRON:
                return SSTActiveLearningNode(initial_stats, parent_node,
                                             random_state=self.random_state)
            elif self.leaf_prediction == self._ADAPTIVE:
                new_node = SSTActiveLearningNodeAdaptive(initial_stats, parent_node,
                                                         random_state=self.random_state)
                # Resets faded errors
                new_node.fMAE_M = np.zeros(self._n_targets, dtype=np.float64)
                new_node.fMAE_P = np.zeros(self._n_targets, dtype=np.float64)
                new_node.fMAE_SP = np.zeros(self._n_targets, dtype=np.float64)
                return new_node
        else:
            if self.leaf_prediction == self._PERCEPTRON:
                return SSTInactiveLearningNode(initial_stats, parent_node,
                                               random_state=parent_node.random_state)
            elif self.leaf_prediction == self._ADAPTIVE:
                new_node = SSTInactiveLearningNodeAdaptive(initial_stats, parent_node,
                                                           random_state=parent_node.random_state)
                new_node.fMAE_M = parent_node.fMAE_M
                new_node.fMAE_P = parent_node.fMAE_P
                new_node.fMAE_SP = parent_node.fMAE_SP
                return new_node

    def algorithm_type(self):
        return "REGRESSION"

Stacked Single-target Hoeffding Tree regressor.      Implementation of the Stacked Single-target Hoeffding Tree (SST-HT) method     for multi-target regression as proposed by <NAME>, <NAME>.,     and <NAME> [1]_.      Parameters     ----------     max_byte_size: int (default=33554432)         Maximum memory consumed by the tree.     memory_estimate_period: int (default=1000000)         Number of instances between memory consumption checks.     grace_period: int (default=200)         Number of instances a leaf should observe between split attempts.     split_confidence: float (default=0.0000001)         Allowed error in split decision, a value closer to 0 takes longer to         decide.     tie_threshold: float (default=0.05)         Threshold below which a split will be forced to break ties.     binary_split: boolean (default=False)         If True, only allow binary splits.     stop_mem_management: boolean (default=False)         If True, stop growing as soon as memory limit is hit.     remove_poor_atts: boolean (default=False)         If True, disable poor attributes.     no_preprune: boolean (default=False)         If True, disable pre-pruning.     leaf_prediction: string (default='perceptron')         | Prediction mechanism used at leafs.         | 'perceptron' - Stacked perceptron         | 'adaptive' - Adaptively chooses between the best predictor (mean,           perceptron or stacked perceptron)     nb_threshold: int (default=0)         Number of instances a leaf should observe before allowing Naive Bayes.     nominal_attributes: list, optional         List of Nominal attributes. If emtpy, then assume that all attributes         are numerical.     learning_ratio_perceptron: float         The learning rate of the perceptron.     learning_ratio_decay: float         Decay multiplier for the learning rate of the perceptron     learning_ratio_const: Bool         If False the learning ratio will decay with the number of examples seen     random_state: int, RandomState instance or None, optional (default=None)        If int, random_state is the seed used by the random number generator;        If RandomState instance, random_state is the random number generator;        If None, the random number generator is the RandomState instance used        by `np.random`. Used when leaf_prediction is 'perceptron'.      References     ----------     .. [1] <NAME>., <NAME>., <NAME>, <NAME>. (2019).        "Online Multi-target regression trees with stacked leaf models". arXiv        preprint arXiv:1903.12483.      Examples     --------     >>> # Imports     >>> from skmultiflow.data import RegressionGenerator     >>> from skmultiflow.trees import StackedSingleTargetHoeffdingTreeRegressor     >>> import numpy as np     >>>     >>> # Setup a data stream     >>> n_targets = 3     >>> stream = RegressionGenerator(n_targets=n_targets, random_state=1, n_samples=200)     >>>     >>> # Setup the Stacked Single-target Hoeffding Tree Regressor     >>> sst_ht = StackedSingleTargetHoeffdingTreeRegressor()     >>>     >>> # Auxiliary variables to control loop and track performance     >>> n_samples = 0     >>> max_samples = 200     >>> y_pred = np.zeros((max_samples, n_targets))     >>> y_true = np.zeros((max_samples, n_targets))     >>>     >>> # Run test-then-train loop for max_samples and while there is data     >>> while n_samples < max_samples and stream.has_more_samples():     >>>     X, y = stream.next_sample()     >>>     y_true[n_samples] = y[0]     >>>     y_pred[n_samples] = sst_ht.predict(X)[0]     >>>     sst_ht.partial_fit(X, y)     >>>     n_samples += 1     >>>     >>> # Display results     >>> print('Stacked Single-target Hoeffding Tree regressor example')     >>> print('{} samples analyzed.'.format(n_samples))     >>> print('Mean absolute error: {}'.format(np.mean(np.abs(y_true - y_pred)))) # ===================================================================== # == Stacked Single-target Hoeffding Regression Tree implementation === # ===================================================================== # intra cluster variance reduction # To add the n_targets property once Create a new learning node. The type of learning node depends on         the tree configuration. # Resets faded errors