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Answering this question on Cross Validated.

kwinkunks

Regressor comparison

I wrote a blog post about this here.

I tweeted about it here and again

Inspired, of course, by the various wonderful comparisons in the sklearn docs, like this one for classifiers.

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# We want to get unique items in a sequence, but to keep the order in which they appear.
# There are quite a few solutions here > http://www.peterbe.com/plog/uniqifiers-benchmark
# Good, up to date summary of methods > https://stackoverflow.com/a/17016257/3381305

# Some test data: text...
tdat = 100 * ['c', 'a', 'c', 'b', 'e', 'd', 'f', 'g', 'h', 'i', 'j', 'j']
tarr = np.array(tdat)
tser = pd.Series(tdat)
# ... and numbers.
narr = np.random.randint(0, 10, size=1200)

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def read_zmapplusgrid(inputfile,dtype=np.float64):
    """Read ZmapPlus grids
    Format is explained here http://lists.osgeo.org/pipermail/gdal-dev/2011-June/029173.html"""
    
    # read header, read until second '@', record header lines and content
    infile = open(inputfile,'r')
    comments=[]
    head=[]
    a=0 # count '@'s
    headers=0 # cound header+comment lines

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# DTW, Dynamic Thing Warping
# Where Thing might be time, or depth, or some other linear basis.
# Apache 2.0 licence.
import numpy as np

def cost(s1, s2):
    """
    Very basic algorithm, no windowing.
    
    This cost matrix algorithm was adapted from this blog post by Abhishek Mishra:

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import numpy as np

def get_data(day, dataset):
    with open(f'../js/day{day:02d}/{dataset}.txt', 'r') as f:
        return np.array(list(map(int, f.read().split(','))))

def part1(data):
    return np.abs(data - np.median(data)).sum()

def part2(data):

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