module `benchhelper.grid_benchmark`¶

Short summary¶

module pyquickhelper.benchhelper.grid_benchmark

Grid benchmark.

Classes¶

class	truncated documentation
`GridBenchMark`	Compares a couple of machine learning models.

Properties¶

property	truncated documentation
`Appendix`	Returns the metrics.
`Graphs`	Returns images of graphs.
`Metadata`	Returns the metrics.
`Metrics`	Returns the metrics.
`Name`	Returns the name of the benchmark.

Methods¶

method	truncated documentation
`__init__`
`bench`	run an experiment multiple times, parameter di is the dataset to use
`bench_experiment`	function to overload
`init`	Skips it.
`init_main`	initialisation
`predict_score_experiment`	function to overload
`preprocess_dataset`	split the dataset into train and test
`run`	Runs the benchmark.

Documentation¶

Grid benchmark.

source on GitHub

class pyquickhelper.benchhelper.grid_benchmark.GridBenchMark(name, datasets, clog=None, fLOG=<function noLOG>, path_to_images='.', cache_file=None, repetition=1, progressbar=None, **params)[source][source]¶

Bases: pyquickhelper.benchhelper.benchmark.BenchMark

Compares a couple of machine learning models.

source on GitHub

Parameters

name – name of the test
datasets – list of dictionary of dataframes
clog – see CustomLog or string
fLOG – logging function
params – extra parameters
path_to_images – path to images
cache_file – cache file
repetition – repetition of the experiment (to get confidence interval)
progressbar – relies on tqdm, example tnrange

If cache_file is specified, the class will store the results of the method bench. On a second run, the function load the cache and run modified or new run (in param_list).

datasets should be a dictionary with dataframes a values with the following keys:

'X': features
'Y': labels (optional)

source on GitHub

__init__(name, datasets, clog=None, fLOG=<function noLOG>, path_to_images='.', cache_file=None, repetition=1, progressbar=None, **params)[source][source]¶

Parameters

name – name of the test
datasets – list of dictionary of dataframes
clog – see CustomLog or string
fLOG – logging function
params – extra parameters
path_to_images – path to images
cache_file – cache file
repetition – repetition of the experiment (to get confidence interval)
progressbar – relies on tqdm, example tnrange

If cache_file is specified, the class will store the results of the method bench. On a second run, the function load the cache and run modified or new run (in param_list).

datasets should be a dictionary with dataframes a values with the following keys: