#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 10 18:23:14 2017

@author: valerie
from https://medium.com/@luckylwk/visualising-high-dimensional-datasets-using-pca-and-t-sne-in-python-8ef87e7915b
"""


import numpy as np
from sklearn.datasets import fetch_mldata

mnist = fetch_mldata("MNIST original")
X = mnist.data / 255.0
y = mnist.target

print X.shape, y.shape

import pandas as pd

feat_cols = [ 'pixel'+str(i) for i in range(X.shape[1]) ]

df = pd.DataFrame(X,columns=feat_cols)
df['label'] = y
df['label'] = df['label'].apply(lambda i: str(i))

X, y = None, None

print 'Size of the dataframe: {}'.format(df.shape)

%matplotlib inline
import matplotlib.pyplot as plt

# Plot the graph
plt.gray()
fig = plt.figure( figsize=(16,7) )
for i in range(0,30):
    ax = fig.add_subplot(3,10,i+1, title='Digit: ' + str(df.loc[rndperm[i],'label']) )
     ax.matshow(df.loc[rndperm[i],feat_cols].values.reshape((28,28)).astype(float))
plt.show()


## PCA
from sklearn.decomposition import PCA

pca = PCA(n_components=3)
pca_result = pca.fit_transform(df[feat_cols].values)

df['pca-one'] = pca_result[:,0]
df['pca-two'] = pca_result[:,1] 
df['pca-three'] = pca_result[:,2]

print 'Explained variation per principal component: {}'.format(pca.explained_variance_ratio_)

from ggplot import *

chart = ggplot( df.loc[rndperm[:3000],:], aes(x='pca-one', y='pca-two', color='label') ) \
        + geom_point(size=75,alpha=0.8) \
        + ggtitle("First and Second Principal Components colored by digit")
chart

## t-SNE
import time

from sklearn.manifold import TSNE

n_sne = 7000

time_start = time.time()
tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)
tsne_results = tsne.fit_transform(df.loc[rndperm[:n_sne],feat_cols].values)

print 't-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start)

df_tsne = df.loc[rndperm[:n_sne],:].copy()
df_tsne['x-tsne'] = tsne_results[:,0]
df_tsne['y-tsne'] = tsne_results[:,1]

chart = ggplot( df_tsne, aes(x='x-tsne', y='y-tsne', color='label') ) \
        + geom_point(size=70,alpha=0.1) \
        + ggtitle("tSNE dimensions colored by digit")
chart