calstad

Keybase proof

I hereby claim:

• I am calstad on github.
• I am calstad (https://keybase.io/calstad) on keybase.
• I have a public key ASALfeGf_mtOEUAF5VyKkgwcaKXhUuXafbGt-1mtsV99Jwo

To claim this, I am signing this object:

calstad

run_plot_tsne(dict(n_iter=10000, init='pca', learning_rate=500, perplexity=5))

calstad

# Assign a color to each ICD code for use in plots
icd_codes = icd_df.icd_code.unique()
icd_codes.sort()
colors = cm.rainbow(np.linspace(0,1,len(icd_codes)))
icd_colors = dict(zip(icd_codes, colors))
row_colors = list()
for idx, row in icd_df.iterrows():
    row_icd = row['icd_code']
    row_color = icd_colors[row_icd]
    row_colors.append(row_color)

calstad

scaler = preprocessing.StandardScaler().fit(encoded_df)
scaled_data = scaler.transform(encoded_df)

calstad

df = pd.pd.read_pickle('claims.pickle')
encoded_df = pd.get_dummies(icd_df, columns=['npi', 'patient_id', 'icd_code'])

calstad

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from sklearn import preprocessing, manifold

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.