#!/usr/bin/env python
# coding: utf-8

# # UMAP & MNIST
# 
# This notebook walks you through the use of a really amazing TSNE-like dimensionality reduction technique called [UMAP](https://github.com/lmcinnes/umap) on the MNIST dataset to terraform a Planetoid. Follow the link to learn more about it and get it installed.

# In[3]:


import planetoids as pt
import pandas as pd


# Technically, you can use any labelled data in two dimensions to generate a new planetoid. However, this package was specifically designed with algorithms like UMAP in mind for dealing with larger datasets.
# 
# You will have to play around a bit with the hyperparameters of your chosen algorithm to produce some nice separated clusters.
# 
# I like the pairing of UMAP & Planetoids as it produces globular clusters with a convenient API to control the density of the produced clusters.

# In[4]:


from sklearn.datasets import load_digits
from umap import UMAP
import matplotlib.pyplot as plt

#load the mnist dataset
data = load_digits()

#reduce the data down to two dimensions using 
#here we are specifically using UMAP in a supervised
#manner leveraging the target labels
reducer = UMAP(n_components=2,
                    min_dist=1.7,
                    spread=2,
                    target_weight=0.5,
                    random_state=42,
                    n_epochs=50
                   )

embedding = reducer.fit_transform(data.data, y=data.target)

reduced = pd.DataFrame(embedding, columns=['Component1', 'Component2'])
reduced['Cluster'] = data.target

reduced.plot(kind='scatter', x='Component2', y='Component1', c='Cluster', cmap='tab10', s=3, alpha=0.5)
plt.show()


# As you can see in the scatter plot above, the labelled handwritten digits have been grouped into their respective clusters ready to seed the creation of a Planetoid!

# In[5]:


mnist = pt.Planetoid(reduced,
                     'Component1',
                     'Component2',
                     'Cluster')


# In[7]:


mnist.fit_terraform(planet_name='MNIST Demo')