train_and_plot.py

# SPDX-FileCopyrightText: 2024 Nico Hambauer, Sven Kruschel
#
# SPDX-License-Identifier: MIT

import os
import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from sklearn.linear_model import LogisticRegression, ElasticNet
from interpret.glassbox import ExplainableBoostingClassifier, ExplainableBoostingRegressor
from interpret import show
from pygam import LogisticGAM, LinearGAM
from pygam import terms, s, f
from gaminet import GAMINet
from baseline.exnn.exnn.exnn import ExNN
from matplotlib import pyplot as plt
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer, StandardScaler, RobustScaler, OneHotEncoder
from sklearn.utils import shuffle
from sklearn.preprocessing import MinMaxScaler
from load_datasets import Dataset
import tensorflow as tf
import torch
from scipy.stats.mstats import winsorize

import pytorch_lightning as pl
from baseline.nam.config import defaults
from baseline.nam.data import NAMDataset
from baseline.nam.models import NAM, get_num_units
from baseline.nam.trainer import LitNAM
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks import ModelCheckpoint

from EBMLocalExplainer import EBMLocalExplainer

from typing import Dict
from dataclasses import dataclass, field
from igann import IGANN

random_state = 1

dataset = Dataset("crimes")
task = dataset.problem

X = dataset.X
y = dataset.y
X, y = shuffle(X, y, random_state=random_state)

transformers = [
    ('ohe', OneHotEncoder(sparse=False, handle_unknown='ignore', drop='if_binary'), dataset.categorical_cols),
    ('num', FunctionTransformer(), dataset.numerical_cols)
]
ct = ColumnTransformer(transformers=transformers, remainder='drop')
ct.fit(X)
X_original = X
X = ct.transform(X)

cat_cols = ct.named_transformers_['ohe'].get_feature_names_out(dataset.categorical_cols) if len(
    dataset.categorical_cols) > 0 else []
X = pd.DataFrame(X, columns=np.concatenate((cat_cols, dataset.numerical_cols)))

scaler_dict = {}
scaler_dict_gaminet = {}
X_gaminet = X.copy()
for c in dataset.numerical_cols:
    # sx = MinMaxScaler((0, 1))
    # sx.fit([[0], [1]])
    # X[c] = sx.transform(X[c].values.reshape(-1, 1))

    mms = MinMaxScaler()
    X_gaminet[c] = mms.fit_transform(X[c].values.reshape(-1, 1))
    scaler_dict_gaminet[c] = mms

    sx = StandardScaler()
    X[c] = sx.fit_transform(X[c].values.reshape(-1, 1))
    # scaler = RobustScaler()
    # X[c] = scaler.fit_transform(X[c].values.reshape(-1, 1))
    scaler_dict[c] = sx

if task == "regression":
    y_scaler = StandardScaler()
    y = pd.Series(y_scaler.fit_transform(y.values.reshape(-1, 1)).flatten())

# test if directory "plots" exists, if not create it
if not os.path.isdir('plots'):
    os.mkdir('plots')
if not os.path.isdir(f'plots/{dataset.name}'):
    os.mkdir(f'plots/{dataset.name}')


@dataclass
class PlotData:
    _plot_dict: Dict[str, Dict[str, float]] = field(default_factory=dict)

    def add_datapoint(self, column: str, label: str, not_given_category_value: float, given_category_value: float):
        if column not in self._plot_dict:
            self._add_column(column)
        self._plot_dict[column][label] = {0: not_given_category_value, 1: given_category_value}
    def _add_column(self, name: str):
        self._plot_dict[name] = {}

    @property
    def entries(self):
        return self._plot_dict

def winsorize_col(col, upper_limit, lower_limit):
    return winsorize(col, limits=[upper_limit,lower_limit])

def map_y_range(a, b, Y):
    #TODO: could be smart to extract a as max and min from Y
    """
    Maps values from interval a[a1, a2] to interval b[b1, b2] for the given values of Y.
    Args:
    a (tuple): A tuple of two values (a1, a2) representing the lower and upper bounds of the input interval a.
    b (tuple): A tuple of two values (b1, b2) representing the lower and upper bounds of the output interval b.
    Y (list): A list of values to be mapped from interval a to interval b.
    Returns:
    A list of mapped values corresponding to the values in Y, with values in interval a[a1, a2] mapped to interval b[b1, b2].
    Example:
    >>> map_y_range((0, 10), (0, 1), [2, 5, 8])
    [0.2, 0.5, 0.8]
    """
    (a1, a2), (b1, b2) = a, b
    return [b1 + ((y - a1) * (b2 - b1) / (a2 - a1)) for y in Y]

def feature_importance_visualize(data_dict_global, folder="./results/", name="demo", save_png=False, save_eps=False,
                                 n_features=None):
    feature_importances = []
    feature_names = []
    for key, item in data_dict_global.items():
        if item["importance"] > 0:
            feature_importances.append(item["importance"])
            feature_names.append(key)

    feature_importances, feature_names = zip(
        *sorted(zip(feature_importances, feature_names), reverse=True)[:n_features])

    if feature_importances:
        # fig = plt.figure()
        fig, ax = plt.subplots()
        fig.set_size_inches(5, 5)
        fig.set_dpi(100)

        ax = plt.axes()
        ax.bar(np.arange(len(feature_importances)), feature_importances, color="grey")
        ax.set_xticks(np.arange(len(feature_importances)))
        ax.set_xticklabels(feature_names, rotation=90)
        plt.xlabel("Feature Name", fontsize=12)
        plt.ylim(0, np.max(feature_importances) + 0.05)
        plt.xlim(-1, len(feature_names))
        plt.title("Feature Importance")

        fig.tight_layout()
        save_path = folder + name
        if save_eps:
            if not os.path.exists(folder):
                os.makedirs(folder)
            fig.savefig("%s.eps" % save_path, bbox_inches="tight", dpi=100)
        if save_png:
            if not os.path.exists(folder):
                os.makedirs(folder)
            fig.savefig("%s.png" % save_path, bbox_inches="tight", dpi=100)
    plt.show()


# %%

def make_plot(X, Y, feature_name, model_name, dataset_name, scale_back=True, scale_y=False, distribution_plot = True):

    X = np.array(X)
    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        Y = y_scaler.inverse_transform(Y.reshape(-1, 1)).squeeze()
    Y = map_y_range((min(Y),max(Y)),(0,100),Y) if scale_y else Y

    # rescale numerical features
    if feature_name in dataset.numerical_cols and scale_back:
        X = scaler_dict[feature_name].inverse_transform(X.reshape(-1, 1)).squeeze()

    # create distribution plot
    if distribution_plot:
        fig, (ax1, ax2) = plt.subplots(nrows=2, gridspec_kw={'height_ratios': [0.8, 0.2]})
        if feature_name in dataset.numerical_cols:
            bins_values, _, _ = ax2.hist(X_original[feature_name], bins=10, rwidth=0.9, color='grey')
        else:
            bins_values, _, _ = ax2.hist(X[feature_name], bins=10, rwidth=0.9, color='grey')
        ax2.set_xlabel("Distribution")
        ax2.set_xticks([])
        ax2.set_yticks([0, max(bins_values)])

    else:
        fig, ax1 = plt.subplots(nrows=1)

    fig.set_size_inches(5, 5)
    fig.set_dpi(100)

    if model_name != "EBM":
        ax1.plot(X, Y, color='black',alpha = 1)
    else:
        ax1.step(X, Y, where="post", color='black')
    ax1.set_title(f'Feature:{feature_name}')
    ax1.set_xlabel(f'Feature value')
    ax1.set_ylabel('Feature effect on model output')
    fig.tight_layout()
    plt.savefig(f'plots/{dataset_name}/{model_name}_shape_{feature_name}.pdf')
    plt.show()
    plt.close(fig)


# def plot_continuous_bar(
#     data_dict, feature_name, model_name, dataset_name, multiclass=False, show_error=True, title=None, xtitle="", ytitle=""
# ):
#     if feature_name == "capital.gain":
#         print("BUG")
#
#     if data_dict.get("scores", None) is None:  # pragma: no cover
#         return None
#
#     x_vals = data_dict["names"].copy()
#     y_vals = data_dict["scores"].copy()
#     y_hi = data_dict.get("upper_bounds", None)
#     y_lo = data_dict.get("lower_bounds", None)
#
#     # x_min = min(x_vals)
#     # x_max = max(x_vals)
#
#     if y_hi is None or multiclass:
#         show_error = False
#
#     def extend_x_range(x):
#         return x
#
#     def extend_y_range(y):
#         return np.r_[y, y[np.newaxis, -1]]
#
#     new_x_vals = extend_x_range(x_vals)
#     new_y_vals = extend_y_range(y_vals)
#     if show_error:
#         new_y_hi = extend_y_range(y_hi)
#         new_y_lo = extend_y_range(y_lo)
#
#     data = []
#     fill = "none"
#     if show_error:
#         fill = "tonexty"
#
#     if multiclass:
#         for i in range(y_vals.shape[1]):
#             class_name = (
#                 "Class {}".format(i)
#                 if "meta" not in data_dict
#                 else data_dict["meta"]["label_names"][i]
#             )
#             class_line = go.Scatter(
#                 x=new_x_vals,
#                 y=new_y_vals[:, i],
#                 line=dict(shape="hv"),
#                 name=class_name,
#                 mode="lines",
#             )
#             data.append(class_line)
#     else:
#         main_line = go.Scatter(
#             x=new_x_vals,
#             y=new_y_vals,
#             name="Main",
#             mode="lines",
#             line=dict(color="rgb(31, 119, 180)", shape="hv"),
#             fillcolor="rgba(68, 68, 68, 0.15)",
#             fill=fill,
#         )
#         data.append(main_line)
#
#     if show_error:
#         upper_bound = go.Scatter(
#             name="Upper Bound",
#             x=new_x_vals,
#             y=new_y_hi,
#             mode="lines",
#             marker=dict(color="#444"),
#             line=dict(width=0, shape="hv"),
#             fillcolor="rgba(68, 68, 68, 0.15)",
#             fill="tonexty",
#         )
#         lower_bound = go.Scatter(
#             name="Lower Bound",
#             x=new_x_vals,
#             y=new_y_lo,
#             marker=dict(color="#444"),
#             line=dict(width=0, shape="hv"),
#             mode="lines",
#         )
#         data = [lower_bound, main_line, upper_bound]
#
#     show_legend = True if multiclass or not show_error else False
#     layout = go.Layout(
#         title=title,
#         showlegend=show_legend,
#         xaxis=dict(title=xtitle),
#         yaxis=dict(title=ytitle),
#     )
#     yrange = None
#     if data_dict.get("scores_range", None) is not None:
#         scores_range = data_dict["scores_range"]
#         yrange = scores_range
#
#     main_fig = go.Figure(data=data, layout=layout)
#     main_fig.show()
#     main_fig.write_image(f'plots/{model_name}_{dataset_name}_shape_{feature_name}.pdf')


# def make_plot_ebm(data_dict, feature_name, model_name, dataset_name, num_epochs='', debug=False):
#     x_vals = data_dict["names"].copy()
#     y_vals = data_dict["scores"].copy()
#
#     # This is important since you do not plot plt.stairs with len(edges) == len(vals) + 1, which will have a drop to zero at the end
#     y_vals = np.r_[y_vals, y_vals[np.newaxis, -1]]
#
#     # This is the code interpretml also uses: https://github.com/interpretml/interpret/blob/2327384678bd365b2c22e014f8591e6ea656263a/python/interpret-core/interpret/visual/plot.py#L115
#
#     # main_line = go.Scatter(
#     #     x=x_vals,
#     #     y=y_vals,
#     #     name="Main",
#     #     mode="lines",
#     #     line=dict(color="rgb(31, 119, 180)", shape="hv"),
#     #     fillcolor="rgba(68, 68, 68, 0.15)",
#     #     fill="none",
#     # )
#     #
#     # main_fig = go.Figure(data=[main_line])
#     # main_fig.show()
#     # main_fig.write_image(f'plots/{model_name}_{dataset_name}_shape_{feature_name}_{num_epochs}epochs.pdf')
#
#     # This is my custom code used for plotting
#
#     x = np.array(x_vals)
#     if debug:
#         print("Num cols:", dataset.numerical_cols)
#     if feature_name in dataset.numerical_cols:
#         if debug:
#             print("Feature to scale back:", feature_name)
#         x = scaler_dict[feature_name].inverse_transform(x.reshape(-1, 1)).squeeze()
#     else:
#         if debug:
#             print("Feature not to scale back:", feature_name)
#
#     fig, (ax1, ax2) = plt.subplots(nrows=2, gridspec_kw={'height_ratios': [0.8, 0.2]})
#     fig.set_size_inches(5, 5)
#     fig.set_dpi(100)
#
#     ax1.step(x, y_vals, where="post", color='black')
#     bins_values, _, _ = ax2.hist(X_original[feature_name], bins=10, rwidth=0.9, color='grey')
#
#     # ax1.legend(loc='best')
#     ax1.set_title(f'Feature:{feature_name}')
#     ax1.set_xlabel(f'Feature value')
#     ax1.set_ylabel('Feature effect on model output')
#     ax2.set_xlabel("Distribution")
#     ax2.set_xticks([])
#     ax2.set_yticks([0, max(bins_values)])
#
#     # plt.xlabel(f'Feature value')
#     # plt.ylabel('Feature effect on model output')
#     # plt.title(f'Feature:{feature_name}')
#     fig.tight_layout()
#     plt.savefig(f'plots/{model_name}_{dataset_name}_shape_{feature_name}_{num_epochs}epochs.pdf')
#     plt.show()


def make_plot_interaction(left_names, right_names, scores, feature_name_left, feature_name_right, model_name,
                          dataset_name, scale_back=True):
    """
    This function is used to plot the interaction plot in a heatmap style.
    It is used by EBM and Gaminet.

    :param left_names:
    :param right_names:
    :param scores:
    :param feature_name_left:
    :param feature_name_right:
    :param model_name:
    :param dataset_name:
    :param scale_back:
    :return:
    """

    left_names = np.array(left_names)
    right_names = np.array(right_names)


    if feature_name_left in dataset.numerical_cols and scale_back:
        left_names = scaler_dict[feature_name_left].inverse_transform(left_names.reshape(-1, 1)).squeeze()
    if feature_name_right in dataset.numerical_cols and scale_back:
        right_names = scaler_dict[feature_name_right].inverse_transform(right_names.reshape(-1, 1)).squeeze()

    if "_" in feature_name_right:
        right_names = right_names.astype('str')

    if "_" in feature_name_left:
        left_names = left_names.astype('str')

    fig, ax = plt.subplots()
    fig.set_size_inches(5, 5)
    fig.set_dpi(100)


    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        scores = y_scaler.inverse_transform(scores.reshape(-1, 1))
    im = ax.pcolormesh(left_names, right_names, scores, shading='auto')
    fig.colorbar(im, ax=ax)
    plt.xlabel(feature_name_left)
    plt.ylabel(feature_name_right)
    plt.title(f'Feature: {feature_name_left.replace("?", "missing")} x {feature_name_right.replace("?", "missing")}')
    fig.tight_layout()
    plt.savefig(
        f'plots/{dataset_name}/{model_name}_interact_{feature_name_left.replace("?", "missing")} x {feature_name_right.replace("?", "missing")}.pdf')
    plt.show()

def make_plot_interaction_onehot_line_gaminet(categorical_input, continuous_input, output,
                                              categorical_name,
                                              continuous_name,
                                              model_name,
                                              dataset_name):
    """

    :param categorical_input:
    :param continuous_input:
    :param output:
    :param categorical_name:
    :param continuous_name:
    :param model_name:
    :param dataset_name:
    :return:
    """
    categorical_input = np.array(categorical_input)
    continuous_input = np.array(continuous_input)

    if "_" in continuous_name:
        continuous_input = continuous_input.astype('str')  # since we want categorical values and not a range
    bin_vals = output.transpose(-1, 0)
    bin_vals = np.ascontiguousarray(bin_vals)

    bin_vals_y_0 = bin_vals[0]
    bin_vals_y_1 = bin_vals[1]
    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        bin_vals_y_0 = y_scaler.inverse_transform(bin_vals_y_0.reshape(-1, 1))
        bin_vals_y_1 = y_scaler.inverse_transform(bin_vals_y_1.reshape(-1, 1))

    fig, ax = plt.subplots()
    fig.set_size_inches(5, 5)
    fig.set_dpi(100)

    plt.plot(continuous_input, bin_vals_y_0, color='black',
             label=f"{categorical_name} = {int(categorical_input[0])}")  # y_vals[0]
    plt.plot(continuous_input, bin_vals_y_1, color='grey',
             label=f"{categorical_name} = {int(categorical_input[1])}")  # y_vals[1]
    plt.legend(loc='best')
    # plt.fill_between(x, lower_bounds, mean, color='gray')
    # plt.fill_between(x, mean, upper_bounds, color='gray')
    plt.xlabel(f'Feature value')
    plt.ylabel('Feature effect on model output')
    plt.title(f'Feature:{categorical_name} x {continuous_name}')
    fig.tight_layout()
    plt.savefig(f'plots/{dataset_name}/{model_name}_interact_{categorical_name} x {continuous_name}.pdf')
    plt.show()


def make_plot_interaction_onehot_line_ebm(data_dict,
                                          categorical_name,
                                          continuous_name,
                                          model_name,
                                          dataset_name):
    x_positions = np.array(data_dict["right_names"])
    categories = np.array(data_dict["left_names"])
    bin_vals = data_dict["scores"]
    bin_vals = np.ascontiguousarray(bin_vals)

    bin_vals_y_0 = bin_vals[0]
    bin_vals_y_1 = bin_vals[1]
    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        bin_vals_y_0 = y_scaler.inverse_transform(bin_vals_y_0.reshape(-1, 1)).squeeze()
        bin_vals_y_1 = y_scaler.inverse_transform(bin_vals_y_1.reshape(-1, 1)).squeeze()

    bin_vals_y_0 = np.r_[bin_vals_y_0, bin_vals_y_0[np.newaxis, -1]]
    bin_vals_y_1 = np.r_[bin_vals_y_1, bin_vals_y_1[np.newaxis, -1]]

    if continuous_name in dataset.numerical_cols:
        x_positions = scaler_dict[continuous_name].inverse_transform(x_positions.reshape(-1, 1)).squeeze()
        # inverse_transform x_positions with the one hot encoder

    fig, (ax1, ax2) = plt.subplots(nrows=2, gridspec_kw={'height_ratios': [0.8, 0.2]})
    fig.set_size_inches(5, 5)
    fig.set_dpi(100)

    ax1.step(x_positions, bin_vals_y_0, where="post", color='grey',
             label=f"{categorical_name} = {int(float(categories[0]))}")  # y_vals[0]
    ax1.step(x_positions, bin_vals_y_1, where="post", color='black',
             label=f"{categorical_name} = {int(float(categories[1]))}")  # y_vals[1]
    bins_values, _, _ = ax2.hist(X_original[continuous_name], align="mid", bins=10, rwidth=0.9, color='grey')

    ax1.legend(loc='best')
    ax1.set_title(f'Feature:{categorical_name} x {continuous_name}')
    ax1.set_xlabel(f'Feature value: {continuous_name}')
    ax1.set_ylabel('Feature effect on model output')
    ax2.set_xlabel("Distribution")
    ax2.set_xticks([])
    ax2.set_yticks([0, max(bins_values)])
    fig.tight_layout()

    # fig.title(f'Feature:{categorical_name} x {continuous_name}')
    plt.savefig(f'plots/{dataset_name}/{model_name}_interact_{categorical_name} x {continuous_name}.pdf')
    plt.show()


def plot_pairwise_heatmap(data_dict,
                          x_name,
                          y_name,
                          model_name,
                          dataset_name,
                          title="",
                          xtitle="",
                          ytitle=""):

    if len(data_dict.get("scores", [])) != 2:
        warnings.warn("Only 2 classes supported for now. The data_dict in plot_pairwise_heatmap should have exactly 2 classes. No plot will be generated.")
        return

    if x_name in dataset.numerical_cols or y_name in dataset.numerical_cols:
        warnings.warn(
            "plot_pairwise_heatmap() is ment to be used only for cat x cat interactions. One of the plot features is actually numerical. No plot will be generated.")
        return

    bin_vals = data_dict["scores"]
    # Has to be transposed so the heatmap will be shown correctly using pcolormesh
    bin_vals = np.ascontiguousarray(np.transpose(bin_vals, (1, 0)))
    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        bin_vals = y_scaler.inverse_transform(bin_vals.reshape(-1, 1))

    fig, ax = plt.subplots()
    fig.set_size_inches(5, 5)
    fig.set_dpi(100)
    ax.tick_params(axis='both', which='major', labelsize=8)

    im = ax.pcolormesh(bin_vals, shading='auto')
    fig.colorbar(im, ax=ax)
    bin_labels_x = [f'{x_name.split("_")[1]} not given', f'{x_name.split("_")[1]} given']
    bin_labels_y = [f'{y_name.split("_")[1]} not given', f'{y_name.split("_")[1]} given']
    plt.xticks([0.5, 1.5], bin_labels_x)
    plt.yticks([0.5, 1.5], bin_labels_y, rotation=90, va="center")

    plt.title(f'Feature: {x_name.replace("?", "")} x {y_name.replace("?", "")}')
    plt.xlabel(x_name)
    plt.ylabel(y_name)
    fig.tight_layout()
    plt.savefig(f'plots/{dataset_name}/{model_name}_interact_{x_name.replace("?", "")} x {y_name.replace("?", "")}.pdf')
    plt.show()

def make_one_hot_plot(class_zero, class_one, feature_name, model_name, dataset_name):
    # This snippet is for binary plots
    original_feature_name = feature_name.split('_')[0]
    if task == "regression":
        # inverse transform y (series) to original scale by using y_scaler
        class_zero = y_scaler.inverse_transform(class_zero.reshape(1, -1)).item()
        class_one = y_scaler.inverse_transform(class_one.reshape(1, -1)).item()


    fig, (ax1, ax2) = plt.subplots(nrows=2, gridspec_kw={'height_ratios': [0.8, 0.2]})
    fig.set_size_inches(5, 5)
    fig.set_dpi(100)

    if X_original[original_feature_name].value_counts().size == 2:
        category_0 = X_original[original_feature_name].values.categories[0]
        category_1 = X_original[original_feature_name].values.categories[1]
        categories = [category_0, category_1]
        ax1.bar([0, 1], [class_zero, class_one], color='gray', tick_label=[f'{categories[0]}', f'{categories[1]} '])

    # This snippet is for all plots but binary categories
    # TODO: Binary-Class One Hot Plot für Multi-Class Plots rausnehmen
    else:
        ax1.bar([0, 1], [class_zero, class_one], color='gray',
                tick_label=[f'{feature_name} = 0', f'{feature_name} = 1'])

    bins_values, _, _ = ax2.hist(X_original[original_feature_name], bins=2, rwidth=0.9, color='grey')
    plt.title(f'Feature:{feature_name.split("_")[0]}')
    ax1.set_ylabel("Feature effect on model output")
    ax2.set_xlabel("Distribution")
    ax2.set_xticks([])
    ax2.set_yticks([0, max(bins_values)])

    fig.tight_layout()
    plt.savefig(f'plots/{dataset_name}/{model_name}_onehot_{str(feature_name).replace("?", "")}.pdf')
    plt.show()


def make_one_hot_multi_plot(plot_data: PlotData, model_name, dataset_name, distibution_plot=True):
    for feature_name in plot_data.entries:
        position_list = np.arange(len(plot_data.entries[feature_name]))
        y_values = list(plot_data.entries[feature_name].values())
        y_list_not_given_class = [list(dict_element.values())[0] for dict_element in y_values]
        y_list_given_class = [list(dict_element.values())[1] for dict_element in y_values]
        if task == "regression":
            # inverse transform y (series) to original scale by using y_scaler
            y_list_not_given_class = y_scaler.inverse_transform(np.array(y_list_not_given_class).reshape((-1, 1))).squeeze()
            y_list_given_class = y_scaler.inverse_transform(np.array(y_list_given_class).reshape((-1, 1))).squeeze()

        x_list = list(plot_data.entries[feature_name].keys())

        if distibution_plot:
            fig, (ax1, ax2) = plt.subplots(nrows=2, gridspec_kw={'height_ratios': [0.8, 0.2]})
            bins_values, _, _ = ax2.hist(X_original[feature_name], bins=len(x_list), rwidth=0.8, color='grey')
            ax2.set_xlabel("Distribution")
            ax2.set_xticks([])
            ax2.set_yticks([0, max(bins_values)])
        else:
            fig, ax1 = plt.subplots()
        fig.set_size_inches(5, 5)
        fig.set_dpi(100)

        # The for loop is used to calculate the y values for the plot (add up the given value and all not given values of the other classes)
        # e.g. for class 1: add given value of class 1 + not_given_value class 0 + not_given_value class 2 + not_given_value class 3
        y_plot_value = []
        for i in range(len(y_values)):
            y_not_given_values = sum([value for index, value in enumerate(y_list_not_given_class) if index != i])
            y_plot_value.append((y_list_given_class[i] + y_not_given_values).item())


        ax1.bar(position_list, y_plot_value, color='gray', width=0.8)

        # Relict of showing y_list_not_given_class and y_list_given_class seperately
        #ax1.bar(position_list-0.2, y_list_not_given_class, color='silver', width=0.4)
        #ax1.bar(position_list+0.2, y_list_given_class, color='gray', width=0.4)
        #ax1.legend(['Not given', 'Given'])
        ax1.set_ylabel('Feature effect on model output')
        ax1.set_title(f'Feature:{feature_name}')
        ax1.set_xticks(position_list)
        ax1.set_xticklabels(x_list, rotation=90)
        fig.tight_layout()
        plt.savefig(f'plots/{dataset_name}/{model_name}_multi_onehot_{str(feature_name).replace("?", "")}.pdf',
                    bbox_inches="tight")
        plt.show()


# %%

def EBM_show(X, y):
    m4 = ExplainableBoostingRegressor(interactions=10, max_bins=256)
    m4.fit(X, y)
    ebm_global = m4.explain_global()
    show(ebm_global)


def EBM(X, y, dataset_name, model_name='EBM'):
    if task == "classification":
         ebm = ExplainableBoostingClassifier(interactions=2, max_bins=512, outer_bags=16, inner_bags=4)
    else:
        ebm = ExplainableBoostingRegressor(interactions=2, max_bins=512, outer_bags=16, inner_bags=4)
        # epsilon, delta
    ebm.fit(X, y)
    ebm_global = ebm.explain_global()

    plot_data = PlotData()
    for i, _ in enumerate(ebm_global.data()['names']):  # shape_name besserer Name?
        data_names = ebm_global.data()
        print(data_names['names'][i])
        feature_name = data_names['names'][i]
        shape_data = ebm_global.data(i)
        print(feature_name)

        if shape_data['type'] == 'interaction':
            x_name, y_name = feature_name.split(' x ')

            if len(shape_data['left_names']) == 2 and len(shape_data['right_names']) > 2:
                # plot_pairwise_heatmap(shape_data, x_name, y_name, model_name, dataset_name)
                make_plot_interaction_onehot_line_ebm(shape_data, x_name, y_name, model_name, dataset_name)
            elif len(shape_data['left_names']) > 2 and len(shape_data['right_names']) == 2:
                raise ValueError('Left Names contains continue values. We thought that should not happen. Fix it.')
            elif len(shape_data['left_names']) == 2 and len(shape_data['right_names']) == 2:
                # TODO: cat x cat verify
                plot_pairwise_heatmap(shape_data, x_name, y_name, model_name, dataset_name)
            else:
                make_plot_interaction(shape_data['left_names'], shape_data['right_names'],
                                      np.transpose(shape_data['scores']),
                                      x_name, y_name, model_name, dataset_name)

            continue

        elif (shape_data['type'] == 'univariate'):

            # if feature only has two categories, make single one hot plot
            original_feature_name = feature_name.split('_')[0]
            if X_original[original_feature_name].value_counts().size == 2:
                make_one_hot_plot(shape_data['scores'][0], shape_data['scores'][1], feature_name, model_name,
                                  dataset_name)

            # else, if feature has more than two categories, make multi one hot plot
            elif feature_name.split('_')[0] not in dataset.numerical_cols:
                column_name = feature_name.split('_')[0]
                class_name = feature_name.split('_')[1]
                not_given_class_score = shape_data['scores'][0]
                given_class_score = shape_data['scores'][1]

                plot_data.add_datapoint(column_name, class_name, not_given_class_score, given_class_score)

            else:
                X_values = shape_data["names"].copy()
                Y_values = shape_data["scores"].copy()
                Y_values = np.r_[Y_values, Y_values[np.newaxis, -1]]

                make_plot(X_values,Y_values, feature_name, model_name, dataset_name)

    # this function call uses the collected plot_data to plot all multi one hot plots
    make_one_hot_multi_plot(plot_data, model_name, dataset_name)

    feat_for_vis = dict()
    for i, n in enumerate(ebm_global.data()['names']):
        feat_for_vis[n] = {'importance': ebm_global.data()['scores'][i]}
    feature_importance_visualize(feat_for_vis, save_png=True, folder='.', name=f'ebm_feat_imp_{dataset_name}', n_features=10)

    ebm_local_explainer = EBMLocalExplainer(ebm, X[:50], y[:50])
    #ebm_local_explainer.add_number_word_filter(150, "hr", exact_match=True)
    ebm_local_explainer.explain()
    # ebm_local_explainer.add_word_filter("race_Caucasian", exact_match=False)
    # ebm_local_explainer.add_number_word_filter(0, "race_African-American", exact_match=True)

    # ebm_local_explainer.add_number_word_filter(1, "hours.per.week", exact_match=True)
    # ebm_local_explainer.add_number_word_filter(0.5, "race", exact_match=True)
    # ebm_local_explainer.add_number_filter(1)

    # data = ebm_local.data(0)
    # plotly_fig = ebm_local.visualize(0)
    # plotly_fig.write_image(f"plots/local_explain_fig_0.png")
    # import time
    # time.sleep(1000)


# def modify_interaction_ranges(ebm_global, min_heatmap_val, max_heatmap_val):
#     for data_dict in ebm_global._internal_obj['specific']:
#         if data_dict['type'] == 'interaction':
#             data_dict['scores_range'] = (min_heatmap_val, max_heatmap_val)

def PYGAM(X, y, dataset_name, model_name='PYGAM'):
    # TODO: Integrate terms as parameters on model initialization
    tms = terms.TermList(
        *[f(i) if X.columns[i] in dataset.categorical_cols else s(i, n_splines=20,
                                                             lam=0.6) for i in
                  range(X.shape[1])])

    if task == "classification":
        PYGAM = LogisticGAM(tms).fit(X, y)
    elif task == "regression":
        PYGAM = LinearGAM(tms).fit(X, y)

    plot_data = PlotData()
    for i, term in enumerate(PYGAM.terms):
        if term.isintercept:
            continue
        XX = PYGAM.generate_X_grid(term=i)
        pdep, confi = PYGAM.partial_dependence(term=i, X=XX, width=0.95)
        # make_plot(XX[:,i].squeeze(), pdep, confi[:,0], confi[:,1], X.columns[i])

        original_feature_name = X[X.columns[i]].name.split("_")[0]
        if (X_original[original_feature_name].value_counts().size > 2) and (
                original_feature_name in dataset.categorical_cols):
            column_name = original_feature_name
            class_name = X[X.columns[i]].name.split("_")[1]
            not_given_class_score = pdep[0]
            given_class_score = pdep[-1]

            plot_data.add_datapoint(column_name, class_name, not_given_class_score, given_class_score)

        if len(X[X.columns[i]].unique()) == 2:
            make_one_hot_plot(pdep[0], pdep[-1], X.columns[i], model_name, dataset_name)
        else:
            make_plot(XX[:, i].squeeze(), pdep, X.columns[i], model_name, dataset_name)

    make_one_hot_multi_plot(plot_data, model_name, dataset_name)

    print(PYGAM.summary())


def Gaminet(X, y, dataset_name, model_name='Gaminet'):
    x_types = {}
    for i in range(len(X.columns)):
        if "_" in X.columns[i]:
            x_types[X.columns[i]] = {'type': 'categorical', "values": [0, 1]}
        else:
            x_types[X.columns[i]] = {'type': 'continuous'}

    # meta_info = {X.columns[i]: {'type': 'continuous'} for i in range(len(X.columns))}
    meta_info = x_types
    meta_info.update({'Y': {'type': 'target'}})

    # from sklearn.preprocessing import FunctionTransformer
    identity = FunctionTransformer()

    for i, (key, item) in enumerate(meta_info.items()):
        if item['type'] == 'target':
            continue
        # sx = MinMaxScaler((0, 1))
        # sx.fit([[0], [1]])
        # print(scaler_dict.keys())
        # print(X.columns)
        if key in scaler_dict_gaminet and item['type'] != 'categorical':
            meta_info[key]['scaler'] = scaler_dict_gaminet[key]
        else:
            meta_info[key]['scaler'] = identity

    if task == "classification":  # 190 it/ s Maineffect, 65 it/s Interaction
        model_to_run = GAMINet(meta_info=meta_info, interact_num=20,
                               # include_interaction_list=[(6, 1)], # == ('age', 'hours.per.week')
                               interact_arch=[40] * 5, subnet_arch=[40] * 5,
                               batch_size=1024, task_type="Classification", activation_func=tf.nn.sigmoid,
                               main_effect_epochs=5000, interaction_epochs=5000, tuning_epochs=500,
                               lr_bp=[0.0001, 0.0001, 0.0001], early_stop_thres=[50, 50, 50],
                               heredity=False, loss_threshold=0.01, reg_clarity=0.1,  # clarity 1.0
                               mono_increasing_list=[], mono_decreasing_list=[],  # the indices list of features
                               verbose=True, val_ratio=0.2, random_state=random_state)
        print(np.array(y).shape)
        model_to_run.fit(np.array(X), np.array(y).reshape(-1, 1))

    elif task == "regression":
        model_to_run = GAMINet(meta_info=meta_info, interact_num=20,
                               interact_arch=[40] * 5, subnet_arch=[40] * 5,
                               batch_size=1024, task_type="Regression", activation_func=tf.nn.sigmoid,
                               main_effect_epochs=5000, interaction_epochs=5000, tuning_epochs=500,
                               lr_bp=[0.0001, 0.0001, 0.0001], early_stop_thres=[50, 50, 50],
                               heredity=True, loss_threshold=0.01, reg_clarity=0.1,
                               mono_increasing_list=[], mono_decreasing_list=[],  # the indices list of features
                               verbose=True, val_ratio=0.2, random_state=random_state)
        model_to_run.fit(np.array(X), np.array(y).reshape(-1, 1))

    data_dict = model_to_run.global_explain(save_dict=False, main_grid_size=256)

    Xnames2Featurenames = dict(zip([X.columns[i] for i in range(X.shape[1])], X.columns))
    print(Xnames2Featurenames)

    plot_data = PlotData()
    for effect in data_dict.keys():
        if data_dict[effect]['type'] == 'pairwise':
            x_name, y_name = effect.split(' vs. ')

            if len(data_dict[effect]['input1']) == 2 and len(
                    data_dict[effect]['input2']) > 2:  # if there is continous x categirical
                make_plot_interaction_onehot_line_gaminet(data_dict[effect]['input1'], data_dict[effect]['input2'],
                                                          data_dict[effect]['outputs'], x_name, y_name, model_name,
                                                          dataset_name)
            else:
                make_plot_interaction(data_dict[effect]['input1'], data_dict[effect]['input2'],
                                      data_dict[effect]['outputs'],
                                      x_name,
                                      y_name,
                                      model_name, dataset_name, scale_back=False)

        elif data_dict[effect]['type'] == 'continuous':
            make_plot(data_dict[effect]['inputs'], data_dict[effect]['outputs'], Xnames2Featurenames[effect], model_name, dataset_name,
                      scale_back=False)

        # Classical one hot plot
        elif data_dict[effect]['type'] == 'categorical' and X_original[effect.split('_')[0]].value_counts().size == 2:
            make_one_hot_plot(data_dict[effect]['outputs'][0], data_dict[effect]['outputs'][-1],
                              Xnames2Featurenames[effect], model_name, dataset_name)

        # Multi-Class One Hot Plot
        elif data_dict[effect]['type'] == 'categorical' and X_original[effect.split('_')[0]].value_counts().size > 2:
            column_name = Xnames2Featurenames[effect].split("_")[0]
            class_name = Xnames2Featurenames[effect].split("_")[1]
            not_given_class_score = data_dict[effect]['outputs'][0]
            given_class_score = data_dict[effect]['outputs'][-1]

            plot_data.add_datapoint(column_name, class_name, not_given_class_score, given_class_score)

        else:
            continue

    make_one_hot_multi_plot(plot_data, model_name, dataset_name)

    feat_for_vis = dict()
    for i, effect in enumerate(data_dict.keys()):
        if 'vs.' in effect:
            feature_name_left, feature_name_right = effect.split(' vs. ')

            feat_for_vis[f'{feature_name_left}\nx\n{feature_name_right}'] = {
                'importance': data_dict[effect]['importance']}
        else:
            feat_for_vis[Xnames2Featurenames[effect]] = {'importance': data_dict[effect]['importance']}

    feature_importance_visualize(feat_for_vis, save_png=True, folder='.', name='gaminet_feat_imp', n_features=10)


def EXNN(X, y, dataset_name, model_name='ExNN'):
    meta_info = {"X" + str(i + 1): {'type': 'continuous'} for i in range(len(X.columns))}
    meta_info.update({'Y': {'type': 'target'}})

    for i, (key, item) in enumerate(meta_info.items()):
        if item['type'] == 'target':
            continue
        sx = MinMaxScaler((0, 1))
        sx.fit([[0], [1]])
        meta_info[key]['scaler'] = sx

    X_arr = np.array(X)
    y_arr = np.array(y)

    if task == "classification":
        model_to_run = ExNN(meta_info=meta_info, subnet_num=10, subnet_arch=[10, 6], task_type="Classification",
                            activation_func=tf.tanh, batch_size=min(1000, int(X.shape[0] * 0.2)),
                            training_epochs=10000,  # default 10000
                            lr_bp=0.001, lr_cl=0.1, beta_threshold=0.05, tuning_epochs=100, l1_proj=0.0001,

                            l1_subnet=0.00316,
                            l2_smooth=10 ** (-6), verbose=True, val_ratio=0.2, early_stop_thres=500)
        model_to_run.fit(X_arr, y_arr)

        model_to_run.visualize(save_png=True, folder='plots/', name=f'{model_name}_{dataset_name}_shape')

    elif task == "regression":
        model_to_run = ExNN(meta_info=meta_info, subnet_num=10, subnet_arch=[10, 6], task_type="Regression",
                            activation_func=tf.tanh, batch_size=min(1000, int(X.shape[0] * 0.2)),
                            training_epochs=10000,  # default
                            lr_bp=0.001, lr_cl=0.1, beta_threshold=0.05, tuning_epochs=100, l1_proj=0.0001,
                            l1_subnet=0.00316,
                            l2_smooth=10 ** (-6), verbose=True, val_ratio=0.2, early_stop_thres=500)
        model_to_run.fit(X_arr, y_arr)

        model_to_run.visualize(save_png=True, folder='plots/', name=f'{model_name}_{dataset_name}_shape')


def LR(X, y, dataset_name, model_name='LR'):
    if task == 'regression':
        m = ElasticNet(alpha=1, l1_ratio=0)
    else:
        m = LogisticRegression(C=0.1, penalty='l2',
                                              class_weight=None,
                                              solver='lbfgs', l1_ratio= None, max_iter=100, n_jobs=-1)
    m.fit(X, y)
    plot_data = PlotData()
    word_to_coef = dict(zip(m.feature_names_in_, m.coef_.squeeze()))
    dict(sorted(word_to_coef.items(), key=lambda item: item[1]))

    for i, feature_name in enumerate(X.columns):
        original_feature_name = feature_name.split('_')[0]
        if original_feature_name in dataset.categorical_cols:
            if X_original[original_feature_name].value_counts().size > 2:
                column_name = original_feature_name
                class_name = feature_name.split("_")[1]
                class_score = word_to_coef[feature_name]
                plot_data.add_datapoint(column_name, class_name, 0, class_score)
            else:
                make_one_hot_plot(0, word_to_coef[feature_name], feature_name, model_name, dataset_name) #zero as value for class one correct?
        else:
            inp = torch.linspace(X[feature_name].min(), X[feature_name].max(), 1000)
            outp = word_to_coef[feature_name] * inp
            make_plot(inp, outp, feature_name, model_name = model_name, dataset_name = dataset_name)
    make_one_hot_multi_plot(plot_data, model_name = model_name, dataset_name = dataset_name)

def I_GANN(X, y, dataset_name, model_name='IGANN'):
    m = IGANN(task=dataset.problem)
    m.fit(X,y)

    plot_data = PlotData()
    shape_data = m.get_shape_functions_as_dict()
    for feature in shape_data:
        original_feature_name = feature["name"].split("_")[0]
        if original_feature_name in dataset.categorical_cols:
            if (X_original[original_feature_name].value_counts().size > 2):
                # print(feature)
                column_name = original_feature_name
                class_name = feature["name"].split("_")[1]
                not_given_category_value = feature["y"].numpy()[0]
                if len(feature["y"].numpy()) == 2:
                    given_category_value = feature["y"].numpy()[1]
                elif len(feature["y"].numpy()) == 1:
                    given_category_value = 0
                else:
                    raise ValueError("Feature has neither than 2 nor 1 value. This should not be possible.")
                plot_data.add_datapoint(column_name, class_name, not_given_category_value, given_category_value)
            else:
                make_one_hot_plot(feature["y"].numpy()[0], feature["y"].numpy()[1] , feature["name"], model_name, dataset_name)
        else:
            make_plot(feature["x"].numpy(), feature["y"].numpy(), feature["name"], model_name, dataset_name)

    make_one_hot_multi_plot(plot_data, model_name, dataset_name)

print(X.head())
# EBM_show(X, y) # for EBM_Show copy paste this script into a jupyter notebook and only run the EBM_Show dashboard
#EBM(X, y, dataset.name)
PYGAM(X, y, dataset.name)
#Gaminet(X_gaminet, y, dataset.name)
#EXNN(X, y, dataset.name)
#LR(X, y, dataset.name)
#I_GANN(X, y, dataset.name)