Colin Alstad 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.

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	%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

	# 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)

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

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