Metadata-Version: 1.2
Name: soops
Version: 2020.1
Summary: Run parametric studies and scoop output files.
Home-page: https://github.com/rc/soops
Author: Robert Cimrman
Author-email: cimrman3@ntc.zcu.cz
License: BSD
Description: soops
        =====
        
        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 <https://en.wikipedia.org/wiki/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 <examples/monty_hall.py>`_.
        
        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:
        
        .. image:: doc/readme/wins.png
           :alt: wins.png
        
        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:
        
        .. code:: python
        
           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',
               }
        
               output_dir_key = 'output_dir'
               is_finished_basename = 'wins.png'
        
               return run_cmd, opt_args, output_dir_key, is_finished_basename
        
        The `get_run_info()` functions should provide four items:
        
        #. A command to run given as a string, with the non-optional arguments and
           their values (if any) given as ``str.format()`` keys.
        
        #. A dictionary of optional arguments and their values (if any) given as
           ``str.format()`` keys.
        
        #. 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.
        
        #. 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 run_mod
        
          Run parametric studies.
        
          positional arguments:
            conf                  a dict-like parametric study configuration
            run_mod               the importable script/module with get_run_info()
        
          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:
        
        .. code:: python
        
           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:
        
        .. code:: python
        
           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``::
        
          $ soops-scoop -h
          usage: soops-scoop [-h] [-s column[,columns,...]] [-r filename] [--no-plugins]
                             [--use-plugins name[,name,...] | --omit-plugins
                             name[,name,...]] [-p module] [--shell] [-o path]
                             scoop_mod directories [directories ...]
        
          Scoop output files.
        
          positional arguments:
            scoop_mod             the importable script/module with get_scoop_info()
            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)
            -p module, --plugin-mod module
                                  if given, the module that has get_plugin_info()
                                  instead of scoop_mod
            --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:
        
        .. code:: python
        
           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:
        
        .. code:: python
        
           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:
        
        .. image:: doc/readme/win_rates.png
           :alt: win_rates.png
        
        Notes
        '''''
        
        - The `get_run_info()`, `get_scoop_info()` and `get_plugin_info()` info
          function can be in different modules.
        - The script that is being parameterized need not be a Python module - any
          executable which can be run from a command line can be used.
        
Keywords: run parametric studies,scoop output
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.5
