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%matplotlib inline

Using the missing_inds function to label interpolated values

If you have data with missing values, Hypertools will try to interpolate them using PPCA. To visualize how well its doing, you can use the missing_inds function and then highlight the values that were interpolated. Here, we generated some synthetic data, removed some values, and then plotted the original data, data with missing values and highlighted the missing datapoints with stars.



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# Code source: Andrew Heusser
# License: MIT

# import
from scipy.linalg import toeplitz
import numpy as np
from copy import copy
import hypertools as hyp

# simulate data
K = 10 - toeplitz(np.arange(10))
data1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)
data2 = copy(data1)

# randomly remove 5% of the data
missing = .01
inds = [(i,j) for i in range(data1.shape[0]) for j in range(data1.shape[1])]
missing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]
for i,j in missing_data:
    data2[i,j]=np.nan

# reduce the data
data1_r,data2_r = hyp.reduce([data1, data2], ndims=3)

# pull out missing inds
missing_inds = hyp.tools.missing_inds(data2)
missing_data = data2_r[missing_inds, :]

# plot
hyp.plot([data1_r, data2_r, missing_data], ['-', '--', '*'],
         legend=['Full', 'Missing', 'Missing Points'])