soops 2020.1rc2

Run parametric studies and scoop output files.

soops = scoop output of parametric studies

Utilities to run parametric studies in parallel using dask, and to scoop the output files produced by the studies into a pandas dataframe.

Installation

The latest release:

pip install soops

The source code of the development version in git:

git clone https://github.com/rc/soops.git
cd soops
pip install .

or the development version via pip:

pip install git+https://github.com/rc/soops.git

Testing

Install pytest:

pip install pytest

Install soops from sources (in the current directory):

pip install .

Run the tests:

pytest .

Example

Before we begin - TL;DR:

• Run a script in parallel with many combinations of parameters.

• Scoop all the results in many output directories into a big DataFrame.

• Work with the DataFrame.

A Script

Suppose we have a script that takes a number of command line arguments. The actual arguments are not so important, neither what the script does. Nevertheless, to have something to work with, let us simulate the Monty Hall problem in Python.

For the first reading of the example below, it is advisable not to delve in details of the script outputs and code listings and just read the text to get an overall idea. After understanding the idea, return to the details, or just have a look at the complete example script.

This is our script and its arguments:

$ python ./examples/monty_hall.py -h
usage: monty_hall.py [-h] [--switch] [--host {random,first}] [--num int]
                     [--repeat int] [--seed int] [--plot-opts dict-like] [-n]
                     [--silent]
                     output_dir

The Monty Hall problem simulator parameterizable with soops.

https://en.wikipedia.org/wiki/Monty_Hall_problem

<snip>

positional arguments:
  output_dir            output directory

optional arguments:
  -h, --help            show this help message and exit
  --switch              if given, the contestant always switches the door,
                        otherwise never switches
  --host {random,first}
                        the host strategy for opening doors
  --num int             the number of rounds in a single simulation [default:
                        100]
  --repeat int          the number of simulations [default: 5]
  --seed int            if given, the random seed is fixed to the given value
  --plot-opts dict-like
                        matplotlib plot() options [default:
                        "linewidth=3,alpha=0.5"]
  -n, --no-show         do not call matplotlib show()
  --silent              do not print messages to screen

Basic Run

A run with the default parameters:

$ python examples/monty_hall.py output
monty_hall: num: 100
monty_hall: repeat: 5
monty_hall: switch: False
monty_hall: host strategy: random
monty_hall: elapsed: 0.004662119084969163
monty_hall: win rate: 0.25
monty_hall: elapsed: 0.0042096920078620315
monty_hall: win rate: 0.3
monty_hall: elapsed: 0.003894180990755558
monty_hall: win rate: 0.31
monty_hall: elapsed: 0.003928505931980908
monty_hall: win rate: 0.35
monty_hall: elapsed: 0.0035342529881745577
monty_hall: win rate: 0.31

produces some results:

Parameterization

Now we would like to run it for various combinations of arguments and their values, for example:

• –num=[100,1000,10000]

• –repeat=[10,20]

• –switch either given or not

• –seed either given or not, changing together with –seed

• –host=[‘random’, ‘first’]

and then collect and analyze the all results. Doing this manually is quite tedious, but soops can help.

In order to run a parametric study, first we have to define a function describing the arguments of our script:

def get_run_info():
    run_cmd = """
    {python} {script_dir}/monty_hall.py
    --num={--num} --repeat={--repeat}
    {output_dir}
    """
    run_cmd = ' '.join(run_cmd.split())

    # Arguments allowed to be missing in soops-run calls.
    opt_args = {
        '--switch' : ' --switch',
        '--host' : ' --host={--host}',
        '--seed' : ' --seed={--seed}',
        '--plot-opts' : ' --plot-opts={--plot-opts}',
        '--no-show' : ' --no-show',
        '--silent' : ' --silent',
    }

    is_finished_basename = 'wins.png'
    output_dir_key = 'output_dir'

    return run_cmd, opt_args, output_dir_key, is_finished_basename

The get_run_info() functions should provide four items:

1. A command to run given as a string, with the non-optional arguments and their values (if any) given as str.format() keys.

2. A dictionary of optional arguments and their values (if any) given as str.format() keys.

3. A special format key, that denotes the output directory argument of the command. Note that the script must have an argument allowing an output directory specification.

4. A function is_finished() taking the output directory argument that returns True, if the results are already present in that directory. Instead of a function, a file name can be given, as in get_run_info() above. Then the existence of a file with the specified name means that the results are present in the directory.

Run Parametric Study

Putting get_run_info() into our script allows running a parametric study using soops-run:

$ soops-run -h
usage: soops-run [-h] [-r {0,1,2}] [-c key1+key2+..., ...] [-n int] [--silent]
                 [--shell] [-o path]
                 conf script

Run parametric studies.

positional arguments:
  conf                  a dict-like parametric study configuration
  script                the script to run

optional arguments:
  -h, --help            show this help message and exit
  -r {0,1,2}, --recompute {0,1,2}
                        recomputation strategy: 0: do not recompute, 1:
                        recompute only if is_finished() returns False, 2:
                        always recompute [default: 1]
  -c key1+key2+..., ..., --contract key1+key2+..., ...
                        list of option keys that should be contracted to vary
                        in lockstep
  -n int, --n-workers int
                        the number of dask workers [default: 2]
  --silent              do not print messages to screen
  --shell               run ipython shell after all computations
  -o path, --output-dir path
                        output directory [default: output]

In our case (the arguments with no value (flags) can be specified either as '@defined' or '@undefined'):

soops-run -r 1 -n 3 -c='--switch + --seed' -o output "python='python3', output_dir='output/study/%s', --num=[100,1000,10000], --repeat=[10,20], --switch=['@undefined', '@defined', '@undefined', '@defined'], --seed=['@undefined', '@undefined', 12345, 12345], --host=['random', 'first'], --silent=@defined, --no-show=@defined" examples/monty_hall.py

This command runs our script using three dask workers (-n 3 option) and produces a directory for each parameter set:

$ ls output/study/
0_0_0_0_0_0_0_0_0/  0_0_1_1_0_0_0_0_0/  1_0_0_0_0_0_0_0_0/  1_0_1_1_0_0_0_0_0/
0_0_0_0_1_0_1_0_0/  0_0_1_1_1_0_1_0_0/  1_0_0_0_1_0_1_0_0/  1_0_1_1_1_0_1_0_0/
0_0_0_0_2_0_2_0_0/  0_0_1_1_2_0_2_0_0/  1_0_0_0_2_0_2_0_0/  1_0_1_1_2_0_2_0_0/
0_0_0_0_3_0_3_0_0/  0_0_1_1_3_0_3_0_0/  1_0_0_0_3_0_3_0_0/  1_0_1_1_3_0_3_0_0/
0_0_0_1_0_0_0_0_0/  0_0_2_0_0_0_0_0_0/  1_0_0_1_0_0_0_0_0/  1_0_2_0_0_0_0_0_0/
0_0_0_1_1_0_1_0_0/  0_0_2_0_1_0_1_0_0/  1_0_0_1_1_0_1_0_0/  1_0_2_0_1_0_1_0_0/
0_0_0_1_2_0_2_0_0/  0_0_2_0_2_0_2_0_0/  1_0_0_1_2_0_2_0_0/  1_0_2_0_2_0_2_0_0/
0_0_0_1_3_0_3_0_0/  0_0_2_0_3_0_3_0_0/  1_0_0_1_3_0_3_0_0/  1_0_2_0_3_0_3_0_0/
0_0_1_0_0_0_0_0_0/  0_0_2_1_0_0_0_0_0/  1_0_1_0_0_0_0_0_0/  1_0_2_1_0_0_0_0_0/
0_0_1_0_1_0_1_0_0/  0_0_2_1_1_0_1_0_0/  1_0_1_0_1_0_1_0_0/  1_0_2_1_1_0_1_0_0/
0_0_1_0_2_0_2_0_0/  0_0_2_1_2_0_2_0_0/  1_0_1_0_2_0_2_0_0/  1_0_2_1_2_0_2_0_0/
0_0_1_0_3_0_3_0_0/  0_0_2_1_3_0_3_0_0/  1_0_1_0_3_0_3_0_0/  1_0_2_1_3_0_3_0_0/

In each directory, there are three files:

$ ls output/study/0_0_0_0_0_0_0_0_0/
options.txt  output_log.txt  wins.png

just like in the basic run above. Our example script stores the values of command line arguments in options.txt for possible re-runs and inspection:

$ cat output/study/0_0_0_0_0_0_0_0_0/options.txt

command line
------------

"examples/monty_hall.py" "--num=100" "--repeat=10" "output/study/0_0_0_0_0_0_0_0_0" "--host=random" "--no-show" "--silent"

options
-------

host: random
num: 100
output_dir: output/study/0_0_0_0_0_0_0_0_0
plot_opts: {'linewidth': 3, 'alpha': 0.5}
repeat: 10
seed: None
show: False
silent: True
switch: False

Scoop Outputs of the Parametric Study

In order to use soops-scoop to scoop/collect outputs of our parametric study, a new function needs to be defined:

import soops.scoop_outputs as sc

def get_scoop_info():
    info = [
        ('options.txt', partial(
            sc.load_split_options,
            split_keys=None,
        )),
        ('output_log.txt', scrape_output),
    ]

    return info

The function for loading the 'options.txt' files is already in soops, the function to get useful information from 'output_log.txt' needs to be provided:

def scrape_output(filename, rdata=None):
    out = {}
    with open(filename, 'r') as fd:
        repeat = rdata['repeat']
        for ii in range(4):
            next(fd)

        elapsed = []
        win_rate = []
        for ii in range(repeat):
            line = next(fd).split()
            elapsed.append(float(line[-1]))
            line = next(fd).split()
            win_rate.append(float(line[-1]))

        out['elapsed'] = np.array(elapsed)
        out['win_rate'] = np.array(win_rate)

    return out

Then we are ready to run soops-scoop:

usage: soops-scoop [-h] [-s column[,columns,...]] [-r filename] [--no-plugins]
                   [--use-plugins name[,name,...] | --omit-plugins
                   name[,name,...]] [--shell] [-o path]
                   script directories [directories ...]

Scoop output files.

positional arguments:
  script                the script that was run to generate the results
  directories           results directories

optional arguments:
  -h, --help            show this help message and exit
  -s column[,columns,...], --sort column[,columns,...]
                        column keys for sorting of DataFrame rows
  -r filename, --results filename
                        reuse previously scooped results file
  --no-plugins          do not call post-processing plugins
  --use-plugins name[,name,...]
                        use only the named plugins (no effect with --no-
                        plugins)
  --omit-plugins name[,name,...]
                        omit the named plugins (no effect with --no-plugins)
  --shell               run ipython shell after all computations
  -o path, --output-dir path
                        output directory [default: .]

as follows:

$ soops-scoop examples/monty_hall.py output/study/ -s rdir -o output/study --no-plugins --shell

<snip>

Python 3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.13.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: df.keys()
Out[1]:
Index(['rdir', 'host', 'num', 'output_dir', 'plot_opts', 'repeat', 'seed',
       'show', 'silent', 'switch', 'elapsed', 'win_rate', 'time'],
      dtype='object')

In [2]: df.win_rate.head()
Out[2]:
0    [0.35, 0.28, 0.26, 0.41, 0.32, 0.37, 0.29, 0.3...
1    [0.59, 0.65, 0.67, 0.73, 0.72, 0.74, 0.69, 0.6...
2    [0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.3...
3    [0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.6...
4    [0.34, 0.35, 0.31, 0.32, 0.38, 0.31, 0.42, 0.3...
Name: win_rate, dtype: object

In [3]: df.iloc[0]
Out[3]:
rdir            ~/projects/soops/output/study/0_0_0_0_0_0_0_0_0
host                                                     random
num                                                         100
output_dir                       output/study/0_0_0_0_0_0_0_0_0
plot_opts                        {'linewidth': 3, 'alpha': 0.5}
repeat                                                       10
seed                                                        NaN
show                                                      False
silent                                                     True
switch                                                    False
elapsed       [0.004276808933354914, 0.003945986973121762, 0...
win_rate      [0.35, 0.28, 0.26, 0.41, 0.32, 0.37, 0.29, 0.3...
time                                 2020-04-01 19:04:34.712128
Name: 0, dtype: object

The DataFrame with the all results is saved in output/study/results.h5 for reuse.

Post-processing Plugins

It is also possible to define simple plugins that act on the resulting DataFrame. First, define a function that will register the plugins:

def get_plugin_info():
    from soops.plugins import show_figures

    info = [plot_win_rates, show_figures]

    return info

The show_figures() plugin is defined in soops. The plot_win_rates() plugin allows plotting the all results combined:

def plot_win_rates(df, data=None):
    import soops.plot_selected as sps

    df = df.copy()
    df['seed'] = df['seed'].where(df['seed'].notnull(), -1)

    omit = {'win_rate', 'output_dir', 'elapsed'}
    uniques = sc.get_parametric_uniques(df, omit=omit)
    for key, val in uniques.items():
        output(key, val)

    selected = sps.normalize_selected(uniques)

    styles = {key : {} for key in selected.keys()}
    styles['seed'] = {'alpha' : [0.9, 0.1]}
    styles['num'] = {'color' : 'viridis'}
    styles['repeat'] = {'lw' : np.linspace(3, 2,
                                           len(selected.get('repeat', [1])))}
    styles['host'] = {'ls' : ['-', ':']}
    styles['switch'] = {'marker' : ['x', 'o'], 'mfc' : 'None', 'ms' : 10}

    styles = sps.setup_plot_styles(selected, styles)

    fig, ax = plt.subplots()
    sps.plot_selected(ax, df, 'win_rate', selected, {}, styles)
    fig.tight_layout()
    fig.savefig(os.path.join(data.output_dir, 'win_rates.png'))

    return data

Then, running:

soops-scoop examples/monty_hall.py output/study/ -s rdir -o output/study -r output/study/results.h5

reuses the results.h5 file and plots the combined results: