import warnings

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree._tree import TREE_LEAF

# from sklearn.exceptions import UndefinedMetricWarning


def learn_local_decision_tree(Z, Yb, weights, class_values, multi_label=False, one_vs_rest=False, cv=5,
                              prune_tree=False):
    dt = DecisionTreeClassifier()
    if prune_tree:
        param_list = {'min_samples_split': [0.002, 0.01, 0.05, 0.1, 0.2],
                      'min_samples_leaf': [0.001, 0.01, 0.05, 0.1, 0.2],
                      'max_depth': [None, 2, 4, 6, 8, 10, 12, 16]
                      }

        if not multi_label or (multi_label and one_vs_rest):
            if len(class_values) == 2 or (multi_label and one_vs_rest):
                scoring = 'f1'
            else:
                scoring = 'f1_macro'
                # scoring = 'f1_micro'
        else:
            scoring = 'f1_samples'

        # print(datetime.datetime.now())
        dt_search = GridSearchCV(dt, param_grid=param_list, scoring=scoring, cv=cv, n_jobs=-1, iid=False)
        dt_search.fit(Z, Yb, sample_weight=weights)

        # print(datetime.datetime.now())
        dt = dt_search.best_estimator_
        prune_duplicate_leaves(dt)
    else:
        dt.fit(Z, Yb, sample_weight=weights)

    return dt


def is_leaf(inner_tree, index):
    # Check whether node is leaf node
    return (inner_tree.children_left[index] == TREE_LEAF and
            inner_tree.children_right[index] == TREE_LEAF)


def prune_index(inner_tree, decisions, index=0):
    # Start pruning from the bottom - if we start from the top, we might miss
    # nodes that become leaves during pruning.
    # Do not use this directly - use prune_duplicate_leaves instead.
    if not is_leaf(inner_tree, inner_tree.children_left[index]):
        prune_index(inner_tree, decisions, inner_tree.children_left[index])
    if not is_leaf(inner_tree, inner_tree.children_right[index]):
        prune_index(inner_tree, decisions, inner_tree.children_right[index])

    # Prune children if both children are leaves now and make the same decision:
    if (is_leaf(inner_tree, inner_tree.children_left[index]) and
        is_leaf(inner_tree, inner_tree.children_right[index]) and
        (decisions[index] == decisions[inner_tree.children_left[index]]) and
        (decisions[index] == decisions[inner_tree.children_right[index]])):
        # turn node into a leaf by "unlinking" its children
        inner_tree.children_left[index] = TREE_LEAF
        inner_tree.children_right[index] = TREE_LEAF
        # print("Pruned {}".format(index))


def prune_duplicate_leaves(dt):
    # Remove leaves if both
    decisions = dt.tree_.value.argmax(axis=2).flatten().tolist()  # Decision for each node
    prune_index(dt.tree_, decisions)
