from graphviz import Digraph

# List of nodes
l_nodes = ['1', 'x0', 'x1', 'y']

# Dictionary mapping from label name to the edge between two nodes
d_edges = {'b': ('1', 'y'), 
           'w0': ('x0', 'y'), 
           'w1': ('x1', 'y')}

# Create Digraph object
dot = Digraph()
dot.attr(rankdir='LR')

# Add nodes
for n in l_nodes:
    dot.node(n)        

# Add edges
for label, edges in d_edges.items(): 
    dot.edge(edges[0], edges[1], label=label)

# Fill node 1 by gray
dot.node('1', style='filled')
    
# Visualize the graph
dot

def AND(x0, x1, w0=0.5, w1=0.5, b=0.6):
    return ((x0 * w0 + x1 * w1) > b) * 1.0

for x0, x1 in [(0, 0), (0, 1), (1, 0), (1, 1)]:
    print(f"AND(x0={x0}, x1={x1}) = {AND(x0, x1)}")

def NAND(x0, x1, w0=-0.5, w1=-0.5, b=-0.6):
    return ((x0 * w0 + x1 * w1) > b) * 1.0

for x0, x1 in [(0, 0), (0, 1), (1, 0), (1, 1)]:
    print(f"NAND(x0={x0}, x1={x1}) = {NAND(x0, x1)}")

def OR(x0, x1, w0=0.5, w1=0.5, b=0.2):
    return ((x0 * w0 + x1 * w1) > b) * 1.0

for x0, x1 in [(0, 0), (0, 1), (1, 0), (1, 1)]:
    print(f"OR(x0={x0}, x1={x1}) = {OR(x0, x1)}")

def XOR(x0, x1):
    n0 = NAND(x0, x1)
    n1 = OR(x0, x1)
    return AND(n0, n1)

for x0, x1 in [(0, 0), (0, 1), (1, 0), (1, 1)]:
    print(f"XOR(x0={x0}, x1={x1}) = {XOR(x0, x1)}")
    

import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "png"

df = px.data.election()
df.head()

fig = px.parallel_coordinates(df,color='total', color_continuous_scale=px.colors.sequential.Inferno)
fig

df = px.data.election()
df.head()

fig = px.parallel_categories(df, color="total", color_continuous_scale=px.colors.sequential.Inferno)
fig

from pathlib import Path
from graphviz.backend import run, view
import matplotlib.pyplot as plt
from dtreeviz.shadow import *
from numbers import Number
import matplotlib.patches as patches
import tempfile
import os
from sys import platform as PLATFORM
from colour import Color, rgb2hex
from typing import Mapping, List
from dtreeviz.utils import inline_svg_images, myround
from dtreeviz.shadow import ShadowDecTree, ShadowDecTreeNode
from dtreeviz.colors import adjust_colors
from sklearn import tree
import graphviz

from dtreeviz.trees import *

# How many bins should we have based upon number of classes
NUM_BINS = [0, 0, 10, 9, 8, 6, 6, 6, 5, 5, 5]
          # 0, 1, 2,  3, 4, 5, 6, 7, 8, 9, 10

def ctreeviz_univar(ax, x_train, y_train, max_depth, feature_name, class_names,
                    target_name,
                    fontsize=14, fontname="Arial", nbins=25, gtype='strip',
                    show={'title','legend','splits'},
                    colors=None):
    if isinstance(x_train, pd.Series):
        x_train = x_train.values
    if isinstance(y_train, pd.Series):
        y_train = y_train.values

    colors = adjust_colors(colors)

    #    ax.set_facecolor('#F9F9F9')
    ct = tree.DecisionTreeClassifier(max_depth=max_depth)
    ct.fit(x_train.reshape(-1, 1), y_train)

    shadow_tree = ShadowDecTree(ct, x_train.reshape(-1, 1), y_train,
                                feature_names=[feature_name], class_names=class_names)

    n_classes = shadow_tree.nclasses()
    overall_feature_range = (np.min(x_train), np.max(x_train))
    class_values = shadow_tree.unique_target_values
    color_values = colors['classes'][n_classes]
    color_map = {v: color_values[i] for i, v in enumerate(class_values)}
    X_colors = [color_map[cl] for cl in class_values]

    ax.set_xlabel(f"{feature_name}", fontsize=fontsize, fontname=fontname,
                  color=colors['axis_label'])
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.yaxis.set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_linewidth(.3)

    X_hist = [x_train[y_train == cl] for cl in class_values]

    if gtype == 'barstacked':
        bins = np.linspace(start=overall_feature_range[0], stop=overall_feature_range[1], num=nbins, endpoint=True)
        hist, bins, barcontainers = ax.hist(X_hist,
                                            color=X_colors,
                                            align='mid',
                                            histtype='barstacked',
                                            bins=bins,
                                            label=class_names)

        for patch in barcontainers:
            for rect in patch.patches:
                rect.set_linewidth(.5)
                rect.set_edgecolor(colors['edge'])
        ax.set_xlim(*overall_feature_range)
        ax.set_xticks(overall_feature_range)
        ax.set_yticks([0, max([max(h) for h in hist])])
    elif gtype == 'strip':
        # user should pass in short and wide fig
        sigma = .013
        mu = .08
        class_step = .08
        dot_w = 20
        ax.set_ylim(0, mu + n_classes*class_step)
        print('X_hist', X_hist)
        for i, bucket in enumerate(X_hist):
            y_noise = np.random.normal(mu+i*class_step, sigma, size=len(bucket))
            ax.scatter(bucket, y_noise, alpha=.7, marker='o', s=dot_w, c=color_map[i],
                       edgecolors=colors['scatter_edge'], lw=.3)

    ax.tick_params(axis='both', which='major', width=.3, labelcolor=colors['tick_label'],
                   labelsize=fontsize)

    splits = [node.split() for node in shadow_tree.internal]
    splits = sorted(splits)
    bins = [ax.get_xlim()[0]] + splits + [ax.get_xlim()[1]]

    pred_box_height = .07 * ax.get_ylim()[1]
    preds = []
    for i in range(len(bins) - 1):
        left = bins[i]
        right = bins[i + 1]
        inrange = y_train[(x_train >= left) & (x_train <= right)]
        values, counts = np.unique(inrange, return_counts=True)
        pred = values[np.argmax(counts)]
        rect = patches.Rectangle((left, 0), (right - left), pred_box_height, linewidth=.3,
                                 edgecolor=colors['edge'], facecolor=color_map[pred])
        ax.add_patch(rect)
        preds.append(pred)

    if 'legend' in show:
        add_classifier_legend(ax, class_names, class_values, color_map, target_name, colors)

    if 'title' in show:
        accur = ct.score(x_train.reshape(-1, 1), y_train)
        title = f"Classifier tree depth {max_depth}, training accuracy={accur*100:.2f}%"
        plt.title(title, fontsize=fontsize, color=colors['title'])

    if 'splits' in show:
        for split in splits:
            plt.plot([split, split], [*ax.get_ylim()], '--', color=colors['split_line'], linewidth=1)

import numpy as np
import graphviz 
from sklearn import tree

X = np.array([0, 1, 0.5, 10, 11, 12, 20, 21, 22, 30, 30, 32]).reshape(-1, 1)
Y = np.array(['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd']).reshape(-1, 1)
clf = tree.DecisionTreeClassifier(max_depth=3)
clf = clf.fit(X, Y)

df = pd.DataFrame(data={'X':X.ravel(), 'Y': Y.ravel()}, index=range(len(X)))
df.plot(kind='bar');
plt.title('Sample Data for Univariate Regression');

fig, ax = plt.subplots(1)
ctreeviz_univar(ax, pd.Series(X.ravel()), pd.Series(Y.ravel()), 
                feature_name='X', 
                target_name='Y',
                max_depth=4, 
                class_names=['a', 'b', 'c', 'd'], 
                gtype = 'barstacked',
                show={'title', 'splits'}
               )

from pathlib import Path
from graphviz.backend import run, view
import matplotlib.pyplot as plt
from dtreeviz.shadow import *
from numbers import Number
import matplotlib.patches as patches
import tempfile
import os
from sys import platform as PLATFORM
from colour import Color, rgb2hex
from typing import Mapping, List
from dtreeviz.utils import inline_svg_images, myround
from dtreeviz.shadow import ShadowDecTree, ShadowDecTreeNode
from dtreeviz.colors import adjust_colors
from sklearn import tree
import graphviz

# How many bins should we have based upon number of classes
NUM_BINS = [0, 0, 10, 9, 8, 6, 6, 6, 5, 5, 5]
          # 0, 1, 2,  3, 4, 5, 6, 7, 8, 9, 10

def rtreeviz_univar(ax,
                    x_train: (pd.Series, np.ndarray),  # 1 vector of X data
                    y_train: (pd.Series, np.ndarray),
                    max_depth = 10,
                    feature_name: str = None,
                    target_name: str = None,
                    min_samples_leaf = 1,
                    fontsize: int = 14,
                    show={'title','splits'},
                    split_linewidth=.5,
                    mean_linewidth = 2,
                    markersize=None,
                    colors=None):
    if isinstance(x_train, pd.Series):
        x_train = x_train.values
    if isinstance(y_train, pd.Series):
        y_train = y_train.values

    colors = adjust_colors(colors)

    y_range = (min(y_train), max(y_train))  # same y axis for all
    overall_feature_range = (np.min(x_train), np.max(x_train))

    t = tree.DecisionTreeRegressor(max_depth=max_depth, min_samples_leaf=min_samples_leaf)
    t.fit(x_train.reshape(-1,1), y_train)

    shadow_tree = ShadowDecTree(t, x_train.reshape(-1,1), y_train, feature_names=[feature_name])
    splits = []
    for node in shadow_tree.internal:
        splits.append(node.split())
    splits = sorted(splits)
    bins = [overall_feature_range[0]] + splits + [overall_feature_range[1]]

    means = []
    for i in range(len(bins) - 1):
        left = bins[i]
        right = bins[i + 1]
        inrange = y_train[(x_train >= left) & (x_train <= right)]
        means.append(np.mean(inrange))

    ax.scatter(x_train, y_train, marker='o', alpha=.4, c=colors['scatter_marker'], s=markersize,
               edgecolor=colors['scatter_edge'], lw=.3)

    if 'splits' in show:
        for split in splits:
            ax.plot([split, split], [*y_range], '--', color=colors['split_line'], linewidth=split_linewidth)

        prevX = overall_feature_range[0]
        for i, m in enumerate(means):
            split = overall_feature_range[1]
            if i < len(splits):
                split = splits[i]
            ax.plot([prevX, split], [m, m], '-', color=colors['mean_line'], linewidth=mean_linewidth)
            prevX = split

    ax.tick_params(axis='both', which='major', width=.3, labelcolor=colors['tick_label'], labelsize=fontsize)

    if 'title' in show:
        title = f"Regression tree depth {max_depth}, samples per leaf {min_samples_leaf},\nTraining $R^2$={t.score(x_train.reshape(-1,1),y_train):.3f}"
        plt.title(title, fontsize=fontsize, color=colors['title'])

    plt.xlabel(feature_name, fontsize=fontsize, color=colors['axis_label'])
    plt.ylabel(target_name, fontsize=fontsize, color=colors['axis_label'])

import numpy as np
import graphviz 
from sklearn import tree

X = np.array([0, 1, 0.5, 10, 11, 12, 20, 21, 22, 30, 30, 32]).reshape(-1, 1)
Y = np.array([0., 0, 0, 50, 49, 50, 20, 21, 19, 90, 89, 91]).reshape(-1, 1)
clf = tree.DecisionTreeRegressor(max_depth=3)
clf = clf.fit(X, Y)

plt.scatter(x=X, y=Y, s=5);
plt.title('Sample Data for Univariate Regression');

fig, ax = plt.subplots(1)
rtreeviz_univar(ax, pd.Series(X.ravel()), pd.Series(Y.ravel()), 
                feature_name='X', 
                target_name='Y',
                markersize=15)