import numpy as np
import sklearn

from .. import (
    DeepExplainer,
    GradientExplainer,
    KernelExplainer,
    LinearExplainer,
    SamplingExplainer,
    TreeExplainer,
    kmeans,
)
from ..explainers import other
from .models import KerasWrap


def linear_shap_corr(model, data):
    """Linear SHAP (corr 1000)"""
    return LinearExplainer(model, data, feature_perturbation="correlation_dependent", nsamples=1000).shap_values


def linear_shap_ind(model, data):
    """Linear SHAP (ind)"""
    return LinearExplainer(model, data, feature_perturbation="interventional").shap_values


def coef(model, data):
    """Coefficients"""
    return other.CoefficentExplainer(model).attributions


def random(model, data):
    """Random
    color = #777777
    linestyle = solid
    """
    return other.RandomExplainer().attributions


def kernel_shap_1000_meanref(model, data):
    """Kernel SHAP 1000 mean ref.
    color = red_blue_circle(0.5)
    linestyle = solid
    """
    return lambda X: KernelExplainer(model.predict, kmeans(data, 1)).shap_values(X, nsamples=1000, l1_reg=0)


def sampling_shap_1000(model, data):
    """IME 1000
    color = red_blue_circle(0.5)
    linestyle = dashed
    """
    return lambda X: SamplingExplainer(model.predict, data).shap_values(X, nsamples=1000)


def tree_shap_tree_path_dependent(model, data):
    """TreeExplainer
    color = red_blue_circle(0)
    linestyle = solid
    """
    return TreeExplainer(model, feature_perturbation="tree_path_dependent").shap_values


def tree_shap_independent_200(model, data):
    """TreeExplainer (independent)
    color = red_blue_circle(0)
    linestyle = dashed
    """
    data_subsample = sklearn.utils.resample(data, replace=False, n_samples=min(200, data.shape[0]), random_state=0)
    return TreeExplainer(model, data_subsample, feature_perturbation="interventional").shap_values


def mean_abs_tree_shap(model, data):
    """mean(|TreeExplainer|)
    color = red_blue_circle(0.25)
    linestyle = solid
    """

    def f(X):
        v = TreeExplainer(model).shap_values(X)
        if isinstance(v, list):
            return [np.tile(np.abs(sv).mean(0), (X.shape[0], 1)) for sv in v]
        else:
            return np.tile(np.abs(v).mean(0), (X.shape[0], 1))

    return f


def saabas(model, data):
    """Saabas
    color = red_blue_circle(0)
    linestyle = dotted
    """
    return lambda X: TreeExplainer(model).shap_values(X, approximate=True)


def tree_gain(model, data):
    """Gain/Gini Importance
    color = red_blue_circle(0.25)
    linestyle = dotted
    """
    return other.TreeGainExplainer(model).attributions


def lime_tabular_regression_1000(model, data):
    """LIME Tabular 1000
    color = red_blue_circle(0.75)
    """
    return lambda X: other.LimeTabularExplainer(model.predict, data, mode="regression").attributions(X, nsamples=1000)


def lime_tabular_classification_1000(model, data):
    """LIME Tabular 1000
    color = red_blue_circle(0.75)
    """
    return lambda X: other.LimeTabularExplainer(model.predict_proba, data, mode="classification").attributions(
        X, nsamples=1000
    )[1]


def maple(model, data):
    """MAPLE
    color = red_blue_circle(0.6)
    """
    return lambda X: other.MapleExplainer(model.predict, data).attributions(X, multiply_by_input=False)


def tree_maple(model, data):
    """Tree MAPLE
    color = red_blue_circle(0.6)
    linestyle = dashed
    """
    return lambda X: other.TreeMapleExplainer(model, data).attributions(X, multiply_by_input=False)


def deep_shap(model, data):
    """Deep SHAP (DeepLIFT)"""
    if isinstance(model, KerasWrap):
        model = model.model
    explainer = DeepExplainer(model, kmeans(data, 1).data)

    def f(X):
        phi = explainer.shap_values(X)
        if isinstance(phi, list) and len(phi) == 1:
            return phi[0]
        else:
            return phi

    return f


def expected_gradients(model, data):
    """Expected Gradients"""
    if isinstance(model, KerasWrap):
        model = model.model
    explainer = GradientExplainer(model, data)

    def f(X):
        phi = explainer.shap_values(X)
        if isinstance(phi, list) and len(phi) == 1:
            return phi[0]
        else:
            return phi

    return f