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
def load_txtdata(filename):
    color = list()
    times = list()
    with open(filename,'r') as f:
        for i, line in enumerate(f):
            if i % 2 == 0:
                times.append(np.array([int(x) for x in line.split()],dtype=np.int64))
            else:
                color.append(np.array([int(x) for x in line.split()],dtype=np.uint8))
    return color, times

color3, times3 = load_txtdata('sample_data_3det.txt')

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

Changing the Frequency of Display Updates#

print_freq is used to specify how frequently the display updates, by passing an integer value into print_freq, then the display will only update after that many iterations.

Update the display every 10 iterations:

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

Custom Printing Functions#

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

The function should have the following general signature:

print_func(niter:int, new_model:h2mm_model, current_model:h2mm_model, old_model:h2mm_model, iter_time:int, total_time:float)->str

Note

It is not necessary, but recommended to keep these variable names in the function declaration.

where: - niter is the number of iterations - new_model is a h2mm_model object that represents the next model to be optimized (before checking for out of bounds values) note that its .loglik will be irrelevant because it has not been calculated yet. - current_model is a h2mm_model object that represents the model whose .loglik was just calculated - old_model is a h2mm_model object that represents the model from the previous iteration. - iter_time is a float which is the time in seconds based on the inaccurate C clock that it took to calculate the latest iteration - total_time is a float which is the time in seconds based on the inaccurate C clock that the full optimization has taken

The output of print_func is converted to a string (str()) and displayed, unless print_func returns None, in which case nothing is printed (unless print_func internally calls its own print method)

So below is an example of a custom print function:

def silly_print(niter, new, current, old, titer, time):
    return (f"We haven't finished after {niter} iterations "
           f"with {new.loglik - current.loglik} improvement "
           f"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_freq=10)
We 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

Passing additional args to print_func with print_args#

The true signature of print_args is

print_func(niter:int, new_model:h2mm_model, current_model:h2mm_model, old_model:h2mm_model, t_iter:int, t_total:float, *print_args, **print_kwargs)->str

EM_H2MM_C() has two keyword arguments, print_args and print_kwargs, only used when print_func is a callable, which are passed to print_func as its args and kwargs. Note that in both cases, a value of None is converted into an empty tuple/dict respectively. Further, if a non-tuple is passed to print_args it is treated as a single argument.)

def silly_print_arg_kwarg(niter, new, current, old, titer, time, *args, modulus=None):
    return f"niter={niter} args={args}, modulus={modulus}"

hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3, times3,
             print_func=silly_print_arg_kwarg, print_args="I'm very silly", print_kwargs=dict(modulus=1))
niter=10, args=(“I’m very silly”, ), modulus=1

Advanced options: Formatter#

By default, optimization progress is printed to sys.stdout, and each update over-writes the previous update. This is done using the print_formatter.

Keeping display of each iteration#

The default print_formatter has a single keyword argument: keep. We can set this using the print_fmt_kwargs keyword argument in EM_H2MM_C(), if it is set to True, then the text of each new iteration will be displayed on a new line.

>>> model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_fmt_kwargs=dict(keep=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
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Iteration:   40, loglik:-4.097127e+05, improvement:9.049932e+01
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Iteration:  190, loglik:-4.093791e+05, improvement:7.974450e-09
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Iteration:  192, loglik:-4.093791e+05, improvement:3.201421e-09
Iteration:  193, loglik:-4.093791e+05, improvement:2.619345e-09
Iteration:  194, loglik:-4.093791e+05, improvement:1.629815e-09
Iteration:  195, loglik:-4.093791e+05, improvement:3.550667e-09
Iteration:  196, loglik:-4.093791e+05, improvement:4.598405e-09
Iteration:  197, loglik:-4.093791e+05, improvement:1.979060e-09
Iteration:  198, loglik:-4.093791e+05, improvement:3.783498e-09
The model converged after 198 iterations

Changing output stream with print_stream#

The print_formatter can be instructed to direct the output to another stream, such as sys.stderr by specifying the print_stream keyword argument

import sys
model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_stream=sys.stderr)

Whats actually happeing#

Now that simple options have been described, let’s go through what actually is happeing.

Before an optimization begins, EM_H2MM_C() creates a formatter object. These are typically a subclass of Printer, the default being StdPrinter. It does so by calling print_formatter(print_stream, *print_fmt_args, **print_fmt_kwargs)

Note

If print_formatter is None, then print_formatter will be the default formater specified by optimization_limits.formatter.

If print_stream is None, it will be the default specified by calling hm.optmiziation_limits.outstream(), this means that hm.optmiziation_limits.outstream should be a callable that takes no arguments. This allows for a “factory” function to be creates so that the stream can be dynamically assigned.

Then optimization begins.

Upon the completion of an iteration of optimization, EM_H2MM_C() first checks if the iteration should be updated based on print_freq.

If it is time to update the display, then EM_H2MM_C() calls print_func with the cooresponding arguments, and hands the output as the single argument to formatter.update. The total call look like: formatter.update(print_func(niter, new, current, old, iter_time, total_time, *print_args, **print_kwargs))

At the end of the optimization formatter.close() is called, to ensure any finalization should be conducted.

The default StdPrinter has __init__ signature of (buffer, keep=False). From this you can see how specifying print_stream=sys.stderr and print_fmt_kwargs=dict(keep=False) changes where the output is printed and how it is printed respectively.

The Printer.update() method takes the one argument, formats it according to keep and then sends the formatted output to print_stream.write(text), and the calls print_stream.flush() to ensure the output is actually displayed.

IPyPrinter#

Out of the box, H2MM_C comes with one other Printer class: IPyPrinter. This class requires IPython(https://ipython.org/) to be installed, if it is not, then this class will not exist.

IPyPrinter is initiallized with the following signature: (handle, keep=False) Where handle is a IPython.display.DisplayHandle So the default sys.stdout will not work for the print_stream argument. Instead, you will have to generate a DisplayHandle to use IPyPrinter

Below see a simple call using IPyPrinter.

from IPython.display import DisplayHandle
model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='all')
…
Iteration time:0.1345, Total:0.682079
nstate: 3, ndet: 3, nphot: 436084, niter: 6, loglik: -416486.75115250226 converged state: 0x3
prior:
0.11801555761578152, 0.17674035701948754, 0.7052440853647308
trans:
0.9999204691004234, 3.30832791970862e-05, 4.644762037947176e-05
4.215187645885762e-06, 0.9999758910815926, 1.989373076155992e-05
2.0649794030150514e-06, 7.668846607352397e-06, 0.9999902661739896
obs:
0.1554361303878464, 0.4480538780317254, 0.3965099915804283
0.1569004844542735, 0.24341579368194147, 0.599683721863785
0.5223655621688783, 0.08363832319456292, 0.3939961146365587
…

What is the advantage of IPyPrinter?

Because it uses a DisplayHandle, it can fully clear the output each iteration, while StdPrinter

relies on \r characters to overwrite the previous output, which can only overwrite the last line.

So if you plan to use print_func='all', then IPyPrinter is recomended.

On the other hand, using DisplayHandle means that the IPyPrinter is slightly slower.

Custom Printers#

H2MM_C does not strictly enforce any typing on print_formatter objects, operating on the principle of Duck Typing

All that is required is that calling print_formatter(print_stream, *print_args, **print_kwargs) will create an object that has .update and .close methods that take 1 and 0 arguments respectively, with the .update method accepting whatever the output of print_func is.

However, H2MM_C provides Printer as an abstract base class, that is recomended as the parent class of any print_formatter.

Indeed, both StdPrinter and IPyPrinter are subclasses of Printer

Below is the code for StdPrinter

class StdPrinter(hm.Printer):
    __slots__ = ('buffer', 'width', 'keep',)
    def __init__(self, buffer, keep=False):
        self.buffer = buffer
        self.width = 0
        self.keep = bool(keep)

    def update(self, text):
        text = str(text)
        if self.keep:
            text = text + '\n'
        else:
            ln = len(text)
            text = '\r' + text + ' '*(self.width - ln)
            if ln > self.width:
                self.width = ln
        self.buffer.write(text)
        self.buffer.flush()

    def close(self):
        self.buffer.write("\n")

>>> model_3d3c = hm.EM_H2MM_C(hm.factory_h2mm_model(3,3), color3,  times3, print_func='diff', print_formatter=StdPrinter, print_stream=sys.stdout)
The model converged after 198 iterations