module ml.ml_grid_benchmark

Inheritance diagram of mlstatpy.ml.ml_grid_benchmark

Short summary

module mlstatpy.ml.ml_grid_benchmark

About Machine Learning Benchmark

source on GitHub

Classes

class

truncated documentation

MlGridBenchMark

The class tests a list of model over a list of datasets.

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

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__init__

bench_experiment

Calls meth fit.

end

nothing to do

fit

Trains a model.

graphs

Plots multiples graphs.

plot_graphs

Plots all graphs in the same graphs.

predict_score_experiment

Calls method score.

preprocess_dataset

Splits the dataset into train and test.

score

Scores a model.

Documentation

About Machine Learning Benchmark

source on GitHub

class mlstatpy.ml.ml_grid_benchmark.MlGridBenchMark(name, datasets, clog=None, fLOG=<function noLOG>, path_to_images='.', cache_file=None, progressbar=None, graphx=None, graphy=None, **params)[source]

Bases : pyquickhelper.benchhelper.grid_benchmark.GridBenchMark

The class tests a list of model over a list of datasets.

source on GitHub

Paramètres
  • 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 and intermediate results

  • cache_file – cache file

  • progressbar – relies on tqdm, example tnrange

  • graphx – list of variables to use as X axis

  • graphy – list of variables to use as Y axis

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, progressbar=None, graphx=None, graphy=None, **params)[source]
Paramètres
  • 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 and intermediate results

  • cache_file – cache file

  • progressbar – relies on tqdm, example tnrange

  • graphx – list of variables to use as X axis

  • graphy – list of variables to use as Y axis

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

bench_experiment(ds, **params)[source]

Calls meth fit.

source on GitHub

end()[source]

nothing to do

source on GitHub

fit(ds, model, **params)[source]

Trains a model.

Paramètres
  • ds – dictionary with the data to use for training

  • model – model to train

source on GitHub

graphs(path_to_images)[source]

Plots multiples graphs.

Paramètres

path_to_images – where to store images

Renvoie

list of tuple (image_name, function to create the graph)

source on GitHub

plot_graphs(grid=None, text=True, **kwargs)[source]

Plots all graphs in the same graphs.

Paramètres
  • grid – grid of axes

  • text – add legend title on the graph

Renvoie

grid

source on GitHub

predict_score_experiment(ds, model, **params)[source]

Calls method score.

source on GitHub

preprocess_dataset(dsi, **params)[source]

Splits the dataset into train and test.

Paramètres

params – additional parameters

Renvoie

dataset (like info), dictionary for metrics

source on GitHub

score(ds, model, **params)[source]

Scores a model.

source on GitHub