scientific-experiment-manager 0.1.0

SEM: streamline your scientific experiment management

SEM: Scientific Experiment Manager

Streamline IO operations, storage, and retrieval of your scientific results

SEM helps streamline IO operations and organization of scientific results in Python.
At its core, SEM is based on regular expressions and simply creates, parses and manages intricate folder structures containing experimental results.

Minimal example

Consider the results organized in the example/example_results folder.
These are different directories containing the results of the same experiment, where two parameters are varied: the random seed and a threshold value eps. Every one of the folders contains some output files from

example_results
│
└───seed=111
│   └───eps_1.3
│   │   └───...
│   └───eps_7.4
│       └───...
│   
└───seed=222
│   └───...
│
└───seed=333
│   └───...
│   
└───useless_files

SEM does not take care of loading and/or saving files.
Rather, it takes care of the folder structure, leaving the user the freedom to manage the result's format.
To retrieve the parameters relative to these results, ResultManager parses the folders' names and only returns the path relative to those that match.

import re
from pathlib import Path

from sem.manager import ResultManager

example_res = Path("./example_results")

parsers = [re.compile(r"seed=(?P<seed_value>\d+)"), re.compile(r"eps_(?P<eps>\d+.\d+)")]
manager = ResultManager(root_dir=example_res, parsers=parsers)
manager.parse_paths()

In the case above, the parser for seed_value expects a positive integer, specified by the regular expression "\d+", and eps a float format.
The results are stored in manager.df, a pandas DataFrame, which contains the parsed parameter values, as well as the path to the deepest sub-directories

                           __PATH__ seed_value  eps
0  example_results/seed=333/eps_1.1        333  1.1
1  example_results/seed=333/eps_0.3        333  0.3
2  example_results/seed=222/eps_7.4        222  7.4
3  example_results/seed=222/eps_2.7        222  2.7
4  example_results/seed=111/eps_1.3        111  1.3
5  example_results/seed=111/eps_7.4        111  7.4
...

Directories whose names don't match the patterns are ignored, e.g. example_results/useless_files.
Notice that, since they are the results of parsing, all the values in the data frame are strings.
The conversion to a different data type can be performed after parsing:

manager.df["seed_value"] = manager.df["seed_value"].astype(int)
manager.df["eps"] = manager.df["eps"].astype(float)

Utilizing the parsed directories

Once the directory names have been parsed, the main utility of the manager is to have a coupling between the parameters and the results.
For example, one can read and insert the computational time of every experiment in the data frame:

def read_comp_time(res_dir):
    with open(res_dir / "computational_time.txt", "r") as file:
        time = float(file.read())
    return time


manager.df["time"] = manager.df["__PATH__"].map(read_comp_time)

From there, conventional pandas operations can be used. For example, the average computational time for seed 111 is given by

df = manager.df
times = df["time"].loc[df["seed_value"] == 111]
times.mean()

Loading more complex objects

Pandas data frames can contain arbitrary objects. For example, one can create a column of numpy arrays from a model:

import numpy as np


def load_mat(path):
    return np.load(path / "result_params.npy")


df["mat"] = df["__PATH__"].map(load_mat)

Creating default paths

Standardizing result structure reduces the amount of code needed for simple IO operations, and eases compatibility across machines, e.g. local vs cloud or cluster results.
To this end, SEM offers a way to create saving paths which only depend on the parameters specified by the user.
For example, the paths of a repository with three levels and different parameters, can be created as:

root_dir = Path(".") / "tmp"
for param1 in [True, False]:
    for param2 in ["a", "b"]:
        for param3 in [1, 2, 3]:
            values = [
                {"param1": param1, "param2": param2},
                "results_of_my_experiments",
                {"param3": param3},
            ]
            new_path = ResultManager.create_default_path(
                root_dir, values, auto_sort=True
            )
            new_path.mkdir(parents=True)
            print(new_path)

which produces

tmp/param1=True_param2=a/results_of_my_experiments/param3=1
tmp/param1=True_param2=a/results_of_my_experiments/param3=2
tmp/param1=True_param2=a/results_of_my_experiments/param3=3
tmp/param1=True_param2=b/results_of_my_experiments/param3=1
...
tmp/param1=False_param2=a/results_of_my_experiments/param3=1
...

If desired, the argument auto_sort imposes a uniform order at every directory level.
For example, using {"param2": param2, "param1": param1} would produce the same paths a above if auto_sort=True.
Parsing directories with this structure is similarly easy:

manager = ResultManager.from_arguments(
    root_dir,
    arguments=[
        {"param1": "True|False", "param2": "a|b"},
        "results_of_my_experiments",
        {"param3": r"\d+"},
    ],
    auto_sort=True
)
manager.parse_paths()

which yields

                                             __PATH__ param1 param2 param3
0   tmp/param1=False_param2=b/results_of_my_experi...  False      b      1
1   tmp/param1=False_param2=b/results_of_my_experi...  False      b      3
2   tmp/param1=False_param2=b/results_of_my_experi...  False      b      2
3   tmp/param1=True_param2=b/results_of_my_experim...   True      b      1
...

Initialization

Notice that the advantage of using the default directory naming, as opposed to a custom one, is that the ResultManager can be initialized as above, by only specifying the arguments in ResultManager.from_arguments.
A more flexible initialization for custom paths, can be performed by giving as input regular expression patterns. For example, an equivalent initialization to that above is given by:

parsers = [
    re.compile("param1=(?P<param1>True|False)_param2=(?P<param2>a|b)"),
    re.compile("results_of_my_experiments"),
    re.compile("param3=(?P<param3>\d+)"),
]
manager = ResultManager(root_dir, parsers)
manager.parse_paths()

Other utilities and tricks

Filtering results

Another useful ResultManager method is ResultManager.filter. This method filters the rows of the results' data frame. Results can be selected by specifying exact column values or a list of possible values. For example, for a manager whose data frame has columns

                                             __PATH__ param1 param2 param3
0   tmp/param1=False_param2=b/results_of_my_experi...  False      b      1
1   tmp/param1=False_param2=b/results_of_my_experi...  False      b      3
2   tmp/param1=False_param2=b/results_of_my_experi...  False      b      2
3   tmp/param1=True_param2=b/results_of_my_experim...   True      b      1
...

the query

manager.filter_results(
    equal={"param1": True},
    contained={"param3": [1, 3]}
)

yields a filtered data frame

                                             __PATH__ param1 param2 param3
3   tmp/param1=True_param2=b/results_of_my_experim...   True      b      1
4   tmp/param1=True_param2=b/results_of_my_experim...   True      b      3
9   tmp/param1=True_param2=a/results_of_my_experim...   True      a      1
10  tmp/param1=True_param2=a/results_of_my_experim...   True      a      3

Loading fewer results

While results can be filtered a posteriori as just explained, one can also load fewer results in the first place.
This is done by specifying an appropriate regular expression parser in the first place.
For example, to select only configurations where param1 is equal to True, one can write

parsers = [
    re.compile("param1=(?P<param1>True)_param2=(?P<param2>a|b)"),
    re.compile("results_of_my_experiments"),
    re.compile("param3=(?P<param3>\d+)"),
]
manager = ResultManager(root_dir, parsers)

In general, any regular expression with named groups is considered valid, check the docs for further details.

Common parsing patterns

Some common regular expression patterns are available at sem.re_patterns.
These are strings that can be utilized for initializing parsers

from sem.re_patterns import INT_PATTERN

parsers = [
    re.compile("param1=(?P<param1>True|False)_param2=(?P<param2>a|b)"),
    re.compile("results_of_my_experiments"),
    re.compile(f"param3=(?P<param3>{INT_PATTERN})"),
]
manager = ResultManager(root_dir, parsers)

or ResultManager arguments

manager = ResultManager.from_arguments(
    root_dir,
    arguments=[
        {"param1": "True|False", "param2": "a|b"},
        "results_of_my_experiments",
        {"param3": INT_PATTERN},
    ],
)

Common type conversion from string

Some common type conversion functions from string are available at sem.str_to_type.
These are useful in combination with the argparse package, for command line inputs

from argparse import ArgumentParser
from sem.str_to_type import bool_type, unit_float_or_positive_integer, none_or_type

parser = ArgumentParser()    
parser.add_argument("--flag", type=bool_type)
parser.add_argument("--train_size", type=unit_float_or_positive_integer)
parser.add_argument("--K", type=none_or_type(int))

Importantly, bool_type correctly converts both string inputs "0" or "1", as well as the case-insensitive strings "true", "True", "False", etc.

Alternatively, these functions can also be used for type conversion inside pandas data frames

manager = ResultManager(root_dir, parsers)
manager.parse_paths()

manager.df["flag"] = manager.df["flag"].map(bool_type)

Installation

You can install this package by downloading the GitHub repository and, from inside the downloaded folder, running

pip install .