import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
x = np.linspace(-np.pi, np.pi, 100)
df_data = pd.DataFrame(data={'sin x': np.sin(x), 'cos x': np.cos(x)}, index=x)
df_data.head()
df_data.plot(title='Normal Matplotlib Style');
with plt.xkcd():
df_data.plot(title='XKCD Style');
This post aims to go through each function in dtreeviz module to fully understand what is implemented. After fully understanding this, I would like to contribute to this module and submit a pull request.
I really like this module and would like to see this works for other tree-based modules like XGBoost or Lightgbm. I found the exact same issue (issues 15) in github so I hope I could contribute to this issue.
You would just have to get ShadowDecisionTree wrappers for those trees.
Based on this comment, I need first understand the class object ShadowDecisionTree
In this post, we will deep dive into the core module dtreeviz
This module comprises of 4 python files.
__init__.py is empty so we can skip it.
Let's see one by one.
Lantern is a python module for a toolkit collection for data exploration from a variety of dataset to visualization.
In this post, I will walk through the followings:
lantern
lantern can doThe goal in this post is to introduce graphviz to draw the graph when we explain graph-related algorithm e.g., tree, binary search etc. It would be nicer to have such a visualization to quickly digest problems and solutions.
Since we work with TreeNode and trees in a list-expresion e.g., [1, 2, null, 3] in LeetCode, the goal of this post is to easily convert the given tree in a list-expression into the visualization like below.
from IPython.display import Image
Image('digraph.png')
