Customizing Optimization Progress Display#

See also

This can also be viewed as a Jupyter Notebook Download H2MM_DisplayProgress.ipynb

The data file can be downloaded here: sample_data_3det.txt

As always, lets get the imports and loading data out of the way:

import os
import numpy as np
from matplotlib import pyplot as plt

import H2MM_C as hm

# load the data
color3 = list()
times3 = list()

i = 0
with open('sample_data_3det.txt','r') as f:
    for line in f:
        if i % 2 == 0:
            times3.append(np.array([int(x) for x in line.split()],dtype='Q'))
        else:
            color3.append(np.array([int(x) for x in line.split()],dtype='L'))
        i += 1

Basic print_func Options#

Note

These are best demonstrated in Jupyer notebooks

If you’ve been following along in a Jupyter notebook, you will probably have noticed that whenever EM_H2MM_C() or h2mm_model.optimize() is run, you see a little display saying how many iterations the optimization has conducted so far. This behavior can be modified in various ways.

The print_func keyword argument let’s us choose the format of the display.

The basic options are:

  • 'iter' Prints only the iteration number- a compact way to track optimization progress (the default)

  • 'all': Prints the representation of the entire current model. This option is very verbose

  • 'diff' Prints teh difference between the previous model and current model loglikelihoods, and the current loglikelihood

  • 'diff_time' Same as 'diff', but with additional information about the durration of the current iteration, adn the total time of the optimization. This is using the inaccurate C clock, so the times are not very reliable, and often fast

  • 'comp' Print the old and current loglikelihoods

  • 'comp_time' Similar to 'diff_time', prints 'comp' with time information added

  • None Supress all printing (this is NOT a string, it is simply the python varaible None

Passing one of these strings or None to the print_func keyword argument of either EM_H2MM_C() or h2mm_model.optimize() will cause the respective display option while optimizing the model.

For instance, if we run the following in a jupyter notebooks, in the middle of the optimization, you will see this displayed:

model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff')
Iteration: 73, loglik:-4.093793e+05, improvement:3.552414e-02

Recording Each Iteration with print_args#

Usually it is most useful to just see the current state of the optimization. But if you want to keep a record of each output, there’s an option for that.

This is the print_args keyword argument. There are several options/methods for specifing this argument.

If you simply pass print_args = True then the result of every iteration will be printed:

model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_args=True)
Iteration: 0, loglik:-4.387899e+05, improvement: inf
Iteration: 1, loglik:-4.203991e+05, improvement:1.839076e+04
Iteration: 2, loglik:-4.172495e+05, improvement:3.149621e+03
Iteration: 3, loglik:-4.168160e+05, improvement:4.334909e+02
Iteration: 4, loglik:-4.166697e+05, improvement:1.463433e+02
Iteration: 5, loglik:-4.165714e+05, improvement:9.831467e+01
Iteration: 6, loglik:-4.164868e+05, improvement:8.460410e+01
Iteration: 7, loglik:-4.164030e+05, improvement:8.370726e+01
Iteration: 8, loglik:-4.163116e+05, improvement:9.142352e+01
Iteration: 9, loglik:-4.162055e+05, improvement:1.061152e+02
Iteration: 10, loglik:-4.160785e+05, improvement:1.269588e+02
Iteration: 11, loglik:-4.159256e+05, improvement:1.529380e+02
Iteration: 12, loglik:-4.157447e+05, improvement:1.808611e+02
Iteration: 13, loglik:-4.155401e+05, improvement:2.046643e+02
Iteration: 14, loglik:-4.153189e+05, improvement:2.211944e+02
Iteration: 15, loglik:-4.150866e+05, improvement:2.322782e+02
Iteration: 16, loglik:-4.148451e+05, improvement:2.415008e+02
Iteration: 17, loglik:-4.145964e+05, improvement:2.486755e+02
Iteration: 18, loglik:-4.143435e+05, improvement:2.529542e+02
Iteration: 19, loglik:-4.140881e+05, improvement:2.554187e+02
Iteration: 20, loglik:-4.138319e+05, improvement:2.561196e+02
Iteration: 21, loglik:-4.135787e+05, improvement:2.532579e+02
Iteration: 22, loglik:-4.133311e+05, improvement:2.475722e+02
Iteration: 23, loglik:-4.130892e+05, improvement:2.418696e+02
Iteration: 24, loglik:-4.128514e+05, improvement:2.378508e+02
Iteration: 25, loglik:-4.126157e+05, improvement:2.356415e+02
Iteration: 26, loglik:-4.123811e+05, improvement:2.346992e+02
Iteration: 27, loglik:-4.121466e+05, improvement:2.344752e+02
Iteration: 28, loglik:-4.119119e+05, improvement:2.346358e+02
Iteration: 29, loglik:-4.116771e+05, improvement:2.348715e+02
Iteration: 30, loglik:-4.114426e+05, improvement:2.344301e+02
Iteration: 31, loglik:-4.112106e+05, improvement:2.320622e+02
Iteration: 32, loglik:-4.109840e+05, improvement:2.266024e+02
Iteration: 33, loglik:-4.107665e+05, improvement:2.175059e+02
Iteration: 34, loglik:-4.105615e+05, improvement:2.049792e+02
Iteration: 35, loglik:-4.103717e+05, improvement:1.897585e+02
Iteration: 36, loglik:-4.101993e+05, improvement:1.724524e+02
Iteration: 37, loglik:-4.100461e+05, improvement:1.531493e+02
Iteration: 38, loglik:-4.099140e+05, improvement:1.321656e+02
Iteration: 39, loglik:-4.098032e+05, improvement:1.107497e+02
Iteration: 40, loglik:-4.097127e+05, improvement:9.049932e+01
Iteration: 41, loglik:-4.096402e+05, improvement:7.254715e+01
Iteration: 42, loglik:-4.095828e+05, improvement:5.736204e+01
Iteration: 43, loglik:-4.095379e+05, improvement:4.492736e+01
Iteration: 44, loglik:-4.095029e+05, improvement:3.497448e+01
Iteration: 45, loglik:-4.094758e+05, improvement:2.713812e+01
Iteration: 46, loglik:-4.094547e+05, improvement:2.103855e+01
Iteration: 47, loglik:-4.094384e+05, improvement:1.632438e+01
Iteration: 48, loglik:-4.094257e+05, improvement:1.269319e+01
Iteration: 49, loglik:-4.094158e+05, improvement:9.897353e+00
Iteration: 50, loglik:-4.094081e+05, improvement:7.740962e+00
Iteration: 51, loglik:-4.094020e+05, improvement:6.072558e+00
Iteration: 52, loglik:-4.093972e+05, improvement:4.776702e+00
Iteration: 53, loglik:-4.093935e+05, improvement:3.766078e+00
Iteration: 54, loglik:-4.093905e+05, improvement:2.974800e+00
Iteration: 55, loglik:-4.093881e+05, improvement:2.353085e+00
Iteration: 56, loglik:-4.093863e+05, improvement:1.863156e+00
Iteration: 57, loglik:-4.093848e+05, improvement:1.476176e+00
Iteration: 58, loglik:-4.093836e+05, improvement:1.169979e+00
Iteration: 59, loglik:-4.093827e+05, improvement:9.274102e-01
Iteration: 60, loglik:-4.093820e+05, improvement:7.351031e-01
Iteration: 61, loglik:-4.093814e+05, improvement:5.825859e-01
Iteration: 62, loglik:-4.093809e+05, improvement:4.616147e-01
Iteration: 63, loglik:-4.093805e+05, improvement:3.656763e-01
Iteration: 64, loglik:-4.093803e+05, improvement:2.896107e-01
Iteration: 65, loglik:-4.093800e+05, improvement:2.293229e-01
Iteration: 66, loglik:-4.093798e+05, improvement:1.815593e-01
Iteration: 67, loglik:-4.093797e+05, improvement:1.437336e-01
Iteration: 68, loglik:-4.093796e+05, improvement:1.137899e-01
Iteration: 69, loglik:-4.093795e+05, improvement:9.009406e-02
Iteration: 70, loglik:-4.093794e+05, improvement:7.134807e-02
Iteration: 71, loglik:-4.093794e+05, improvement:5.652128e-02
Iteration: 72, loglik:-4.093793e+05, improvement:4.479603e-02
Iteration: 73, loglik:-4.093793e+05, improvement:3.552414e-02
Iteration: 74, loglik:-4.093793e+05, improvement:2.819196e-02
Iteration: 75, loglik:-4.093792e+05, improvement:2.239292e-02
Iteration: 76, loglik:-4.093792e+05, improvement:1.780534e-02
Iteration: 77, loglik:-4.093792e+05, improvement:1.417483e-02
Iteration: 78, loglik:-4.093792e+05, improvement:1.130034e-02
Iteration: 79, loglik:-4.093792e+05, improvement:9.023055e-03
Iteration: 80, loglik:-4.093792e+05, improvement:7.217588e-03
Iteration: 81, loglik:-4.093792e+05, improvement:5.784895e-03
Iteration: 82, loglik:-4.093792e+05, improvement:4.646821e-03
Iteration: 83, loglik:-4.093792e+05, improvement:3.741739e-03
Iteration: 84, loglik:-4.093792e+05, improvement:3.020924e-03
Iteration: 85, loglik:-4.093792e+05, improvement:2.445975e-03
Iteration: 86, loglik:-4.093792e+05, improvement:1.986614e-03
Iteration: 87, loglik:-4.093792e+05, improvement:1.618845e-03
Iteration: 88, loglik:-4.093792e+05, improvement:1.323799e-03
Iteration: 89, loglik:-4.093792e+05, improvement:1.086528e-03
Iteration: 90, loglik:-4.093792e+05, improvement:8.952366e-04
Iteration: 91, loglik:-4.093792e+05, improvement:7.405641e-04
Iteration: 92, loglik:-4.093792e+05, improvement:6.151425e-04
Iteration: 93, loglik:-4.093792e+05, improvement:5.131122e-04
Iteration: 94, loglik:-4.093791e+05, improvement:4.298041e-04
Iteration: 95, loglik:-4.093791e+05, improvement:3.615554e-04
Iteration: 96, loglik:-4.093791e+05, improvement:3.054184e-04
Iteration: 97, loglik:-4.093791e+05, improvement:2.590739e-04
Iteration: 98, loglik:-4.093791e+05, improvement:2.206618e-04
Iteration: 99, loglik:-4.093791e+05, improvement:1.886659e-04
Iteration: 100, loglik:-4.093791e+05, improvement:1.619285e-04
Iteration: 101, loglik:-4.093791e+05, improvement:1.394759e-04
Iteration: 102, loglik:-4.093791e+05, improvement:1.205574e-04
Iteration: 103, loglik:-4.093791e+05, improvement:1.045387e-04
Iteration: 104, loglik:-4.093791e+05, improvement:9.091827e-05
Iteration: 105, loglik:-4.093791e+05, improvement:7.927959e-05
Iteration: 106, loglik:-4.093791e+05, improvement:6.932020e-05
Iteration: 107, loglik:-4.093791e+05, improvement:6.076449e-05
Iteration: 108, loglik:-4.093791e+05, improvement:5.337631e-05
Iteration: 109, loglik:-4.093791e+05, improvement:4.697498e-05
Iteration: 110, loglik:-4.093791e+05, improvement:4.142558e-05
Iteration: 111, loglik:-4.093791e+05, improvement:3.657222e-05
Iteration: 112, loglik:-4.093791e+05, improvement:3.234588e-05
Iteration: 113, loglik:-4.093791e+05, improvement:2.865586e-05
Iteration: 114, loglik:-4.093791e+05, improvement:2.540683e-05
Iteration: 115, loglik:-4.093791e+05, improvement:2.255174e-05
Iteration: 116, loglik:-4.093791e+05, improvement:2.003415e-05
Iteration: 117, loglik:-4.093791e+05, improvement:1.780386e-05
Iteration: 118, loglik:-4.093791e+05, improvement:1.586386e-05
Iteration: 119, loglik:-4.093791e+05, improvement:1.410692e-05
Iteration: 120, loglik:-4.093791e+05, improvement:1.258217e-05
Iteration: 121, loglik:-4.093791e+05, improvement:1.121568e-05
Iteration: 122, loglik:-4.093791e+05, improvement:1.000246e-05
Iteration: 123, loglik:-4.093791e+05, improvement:8.932839e-06
Iteration: 124, loglik:-4.093791e+05, improvement:7.965660e-06
Iteration: 125, loglik:-4.093791e+05, improvement:7.115363e-06
Iteration: 126, loglik:-4.093791e+05, improvement:6.358838e-06
Iteration: 127, loglik:-4.093791e+05, improvement:5.671347e-06
Iteration: 128, loglik:-4.093791e+05, improvement:5.068083e-06
Iteration: 129, loglik:-4.093791e+05, improvement:4.545087e-06
Iteration: 130, loglik:-4.093791e+05, improvement:4.037225e-06
Iteration: 131, loglik:-4.093791e+05, improvement:3.644091e-06
Iteration: 132, loglik:-4.093791e+05, improvement:3.237044e-06
Iteration: 133, loglik:-4.093791e+05, improvement:2.882327e-06
Iteration: 134, loglik:-4.093791e+05, improvement:2.597284e-06
Iteration: 135, loglik:-4.093791e+05, improvement:2.326095e-06
Iteration: 136, loglik:-4.093791e+05, improvement:2.074637e-06
Iteration: 137, loglik:-4.093791e+05, improvement:1.864275e-06
Iteration: 138, loglik:-4.093791e+05, improvement:1.654669e-06
Iteration: 139, loglik:-4.093791e+05, improvement:1.485634e-06
Iteration: 140, loglik:-4.093791e+05, improvement:1.323759e-06
Iteration: 141, loglik:-4.093791e+05, improvement:1.185108e-06
Iteration: 142, loglik:-4.093791e+05, improvement:1.082022e-06
Iteration: 143, loglik:-4.093791e+05, improvement:9.350479e-07
Iteration: 144, loglik:-4.093791e+05, improvement:8.596107e-07
Iteration: 145, loglik:-4.093791e+05, improvement:7.714843e-07
Iteration: 146, loglik:-4.093791e+05, improvement:6.818445e-07
Iteration: 147, loglik:-4.093791e+05, improvement:6.159535e-07
Iteration: 148, loglik:-4.093791e+05, improvement:5.329493e-07
Iteration: 149, loglik:-4.093791e+05, improvement:4.933681e-07
Iteration: 150, loglik:-4.093791e+05, improvement:4.384201e-07
Iteration: 151, loglik:-4.093791e+05, improvement:3.998866e-07
Iteration: 152, loglik:-4.093791e+05, improvement:3.416790e-07
Iteration: 153, loglik:-4.093791e+05, improvement:3.172318e-07
Iteration: 154, loglik:-4.093791e+05, improvement:2.878951e-07
Iteration: 155, loglik:-4.093791e+05, improvement:2.462184e-07
Iteration: 156, loglik:-4.093791e+05, improvement:2.315501e-07
Iteration: 157, loglik:-4.093791e+05, improvement:2.052402e-07
Iteration: 158, loglik:-4.093791e+05, improvement:1.821318e-07
Iteration: 159, loglik:-4.093791e+05, improvement:1.493609e-07
Iteration: 160, loglik:-4.093791e+05, improvement:1.412700e-07
Iteration: 161, loglik:-4.093791e+05, improvement:1.399894e-07
Iteration: 162, loglik:-4.093791e+05, improvement:1.111766e-07
Iteration: 163, loglik:-4.093791e+05, improvement:1.127482e-07
Iteration: 164, loglik:-4.093791e+05, improvement:9.097857e-08
Iteration: 165, loglik:-4.093791e+05, improvement:7.415656e-08
Iteration: 166, loglik:-4.093791e+05, improvement:7.945346e-08
Iteration: 167, loglik:-4.093791e+05, improvement:5.890615e-08
Iteration: 168, loglik:-4.093791e+05, improvement:7.031485e-08
Iteration: 169, loglik:-4.093791e+05, improvement:4.714821e-08
Iteration: 170, loglik:-4.093791e+05, improvement:5.762558e-08
Iteration: 171, loglik:-4.093791e+05, improvement:3.405148e-08
Iteration: 172, loglik:-4.093791e+05, improvement:3.521563e-08
Iteration: 173, loglik:-4.093791e+05, improvement:4.225876e-08
Iteration: 174, loglik:-4.093791e+05, improvement:2.479646e-08
Iteration: 175, loglik:-4.093791e+05, improvement:2.601882e-08
Iteration: 176, loglik:-4.093791e+05, improvement:2.625166e-08
Iteration: 177, loglik:-4.093791e+05, improvement:2.695015e-08
Iteration: 178, loglik:-4.093791e+05, improvement:1.856824e-08
Iteration: 179, loglik:-4.093791e+05, improvement:1.600711e-08
Iteration: 180, loglik:-4.093791e+05, improvement:1.257285e-08
Iteration: 181, loglik:-4.093791e+05, improvement:1.612352e-08
Iteration: 182, loglik:-4.093791e+05, improvement:1.705484e-08
Iteration: 183, loglik:-4.093791e+05, improvement:9.720679e-09
Iteration: 184, loglik:-4.093791e+05, improvement:6.577466e-09
Iteration: 185, loglik:-4.093791e+05, improvement:1.059379e-08
Iteration: 186, loglik:-4.093791e+05, improvement:1.006993e-08
Iteration: 187, loglik:-4.093791e+05, improvement:1.292210e-08
Iteration: 188, loglik:-4.093791e+05, improvement:5.529728e-09
Iteration: 189, loglik:-4.093791e+05, improvement:6.519258e-09
Iteration: 190, loglik:-4.093791e+05, improvement:4.773028e-09
Iteration: 191, loglik:-4.093791e+05, improvement:-1.746230e-10
The model converged after 191 iterations

As you can see, this is very verbose.

Changing the Frequency of Display Updates#

If you would rather have periodic updates, then you can hand print_args an integer instead. Then the display will update only every x number of iterations.

model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_args=10)

Will show:

Iteration: 20, loglik:-4.138319e+05, improvement:2.561196e+02

and then (errasing the previous):

Iteration: 30, loglik:-4.114426e+05, improvement:2.344301e+02

and so on.

You can even combine the integer argument and True/False argument into a tuple like print_args = (10, True) to keep the previous display, but only show certain numbers of iterations.

model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_args=(10, True))
Iteration: 0, loglik:-4.387899e+05, improvement: inf
Iteration: 10, loglik:-4.160785e+05, improvement:1.269588e+02
Iteration: 20, loglik:-4.138319e+05, improvement:2.561196e+02
Iteration: 30, loglik:-4.114426e+05, improvement:2.344301e+02
Iteration: 40, loglik:-4.097127e+05, improvement:9.049932e+01
Iteration: 50, loglik:-4.094081e+05, improvement:7.740962e+00
Iteration: 60, loglik:-4.093820e+05, improvement:7.351031e-01
Iteration: 70, loglik:-4.093794e+05, improvement:7.134807e-02
Iteration: 80, loglik:-4.093792e+05, improvement:7.217589e-03
Iteration: 90, loglik:-4.093792e+05, improvement:8.952374e-04
Iteration: 100, loglik:-4.093791e+05, improvement:1.619274e-04
Iteration: 110, loglik:-4.093791e+05, improvement:4.142273e-05
Iteration: 120, loglik:-4.093791e+05, improvement:1.258438e-05
Iteration: 130, loglik:-4.093791e+05, improvement:4.036992e-06
Iteration: 140, loglik:-4.093791e+05, improvement:1.325563e-06
Iteration: 150, loglik:-4.093791e+05, improvement:4.360336e-07
Iteration: 160, loglik:-4.093791e+05, improvement:1.417357e-07
Iteration: 170, loglik:-4.093791e+05, improvement:5.646143e-08
Iteration: 180, loglik:-4.093791e+05, improvement:1.420267e-08
Iteration: 190, loglik:-4.093791e+05, improvement:4.365575e-09
The model converged after 191 iterations

Custom Printing Functions#

If you want to customize the display environment further, you can define your own display function.

It must have at least 5 arguments, as EM_H2MM_C() or h2mm_model.optimize() will call it like so:

print_func(niter, new_model, current_model, olde_model, t_iter, t_total)`

Where

  • niter is the number of iterations

  • new_model is the h2mm_model object that whose loglikelihood will be evaluate next (the product of the latest round in the optimization)

  • current_model is the h2mm_model whose loglikelihood was just calculated, the “current” round of the optimization

  • old_model is the h2mm_model that was calculated the iteration just before.

  • t_iter is the time (using the inaccurate C clock) it took to calculate the current iteration

  • t_total

Your function should return a string of what you want to be displayed.

You can still specify print_args as before, controlling how often the display is updated and if the previous display is kept or overwritten.

Bellow is an example function:

def silly_print(niter, new, current, old, titer, time):
    return f"""We haven't finished after {niter} iterations
    with {new.loglik - current.loglik} improvement in loglik
    after {time} (inaccurate) seconds"""

And an example of it’s use in action:

model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3, times3, print_func=silly_print, print_args=(10, True))
We haven’t finished after 0 iterations
with 438789.96130430285 improvement in loglik
after 0.121331 (inaccurate) secondsWe haven’t finished after 10 iterations
with 416078.5463625849 improvement in loglik
after 1.258256 (inaccurate) secondsWe haven’t finished after 20 iterations
with 413831.9416083104 improvement in loglik
after 2.373244 (inaccurate) secondsWe haven’t finished after 30 iterations
with 411442.6378319904 improvement in loglik
after 3.452714 (inaccurate) secondsWe haven’t finished after 40 iterations
with 409712.71341160056 improvement in loglik
after 4.559021 (inaccurate) secondsWe haven’t finished after 50 iterations
with 409408.0698347561 improvement in loglik
after 5.632227 (inaccurate) secondsWe haven’t finished after 60 iterations
with 409381.95478790614 improvement in loglik
after 6.736231 (inaccurate) secondsWe haven’t finished after 70 iterations
with 409379.4254526214 improvement in loglik
after 7.767289 (inaccurate) secondsWe haven’t finished after 80 iterations
with 409379.1785051314 improvement in loglik
after 8.860826 (inaccurate) secondsWe haven’t finished after 90 iterations
with 409379.1519537501 improvement in loglik
after 9.932235 (inaccurate) secondsWe haven’t finished after 100 iterations
with 409379.14815781976 improvement in loglik
after 11.066373 (inaccurate) secondsWe haven’t finished after 110 iterations
with 409379.1473511942 improvement in loglik
after 12.210938 (inaccurate) secondsWe haven’t finished after 120 iterations
with 409379.14712526737 improvement in loglik
after 13.3879 (inaccurate) secondsWe haven’t finished after 130 iterations
with 409379.1470543588 improvement in loglik
after 14.505477 (inaccurate) secondsWe haven’t finished after 140 iterations
with 409379.14703126845 improvement in loglik
after 15.61771 (inaccurate) secondsWe haven’t finished after 150 iterations
with 409379.14702367247 improvement in loglik
after 16.747291 (inaccurate) secondsWe haven’t finished after 160 iterations
with 409379.1470211701 improvement in loglik
after 17.880479 (inaccurate) secondsWe haven’t finished after 170 iterations
with 409379.14702032757 improvement in loglik
after 18.939075 (inaccurate) secondsWe haven’t finished after 180 iterations
with 409379.14702006435 improvement in loglik
after 20.013219 (inaccurate) secondsWe haven’t finished after 190 iterations
with 409379.1470199641 improvement in loglik
after 21.129933 (inaccurate) secondsThe model converged after 192 iterations

Extra Arguments for print_func#

Sometimes you might want even more flexibility in the display, maybe for instance you want to change display for several different optimizations, but would still like to use the same printing function.

For that, H2MM_C still has you covered.

For this, you can add additional arguments to your printer function (probably best as *args), and supply those additional arguments by adding more elements to the print_args tuple- just make sure to specify the frequency and whether or not you want to keep the previous display:

def silly_print(niter, new, current, old, titer, time, *args):
    return f"""
    We haven't finished after {niter} iterations
    with {new.loglik - current.loglik} improvement in loglik
    after {time} (inaccurate) seconds, {args}"""

model_3s3d = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3, times3,
                          print_func=silly_print, print_args=(75, True, "I'm very silly"))
We haven’t finished after 0 iterations
with 438789.88126171724 improvement in loglik
after 0.118648 (inaccurate) seconds, (“I’m very silly”,)
We haven’t finished after 75 iterations
with 409379.2380262778 improvement in loglik
after 8.351019 (inaccurate) seconds, (“I’m very silly”,)
We haven’t finished after 150 iterations
with 409379.14702367294 improvement in loglik
after 16.491106 (inaccurate) seconds, (“I’m very silly”,)The model converged after 191 iterations

Non-string Returning print_func Functions#

While print_func always accepts the same arguments, it does not necessarily have to return a string. It can also return nothing, in which case the user can call print from within the function, or other form of display.

In this case H2MM_C will not clear previous print statements, you are stuck recording everything.

Let’s see an example:

def silly_print(niter, new, current, old, titer, time, *args):
    return f"""
    We haven't finished after {niter} iterations
    with {new.loglik - current.loglik} improvement in loglik
    after {time} (inaccurate) seconds, {args}"""

model_3s3d = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3, times3,
                          print_func=silly_fix_print, print_args=(75, True, "eggs"))
The model converged after 191 iterations

We haven’t finished after 1 iterations
with 0.0002 improvement in loglik
after 0.2 (inaccurate) seconds, (‘eggs’,)
We haven’t finished after 0 iterations
with 438789.88126171665 improvement in loglik
after 0.112852 (inaccurate) seconds, (‘eggs’,)
We haven’t finished after 75 iterations
with 409379.2380262789 improvement in loglik
after 8.097362 (inaccurate) seconds, (‘eggs’,)
We haven’t finished after 150 iterations
with 409379.1470236713 improvement in loglik
after 16.135376 (inaccurate) seconds, (‘eggs’,)