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  • module __init__
  • module helpers
  • module helpers.parameters
  • module helpers.pipeline
  • module metrics
  • module metrics.correlations
  • module metrics.scoring_metrics
  • module mlbatch
  • module mlbatch.cache_model
  • module mlbatch.pipeline_cache
  • module mlmodel
  • module mlmodel._extended_features_polynomial
  • module mlmodel._kmeans_022
  • module mlmodel._kmeans_constraint_
  • module mlmodel._piecewise_tree_regression_common
  • module mlmodel.anmf_predictor
  • module mlmodel.categories_to_integers
  • module mlmodel.classification_kmeans
  • module mlmodel.decision_tree_logreg
  • module mlmodel.direct_blas_lapack
  • module mlmodel.extended_features
  • module mlmodel.interval_regressor
  • module mlmodel.kmeans_constraint
  • module mlmodel.kmeans_l1
  • module mlmodel.ml_featurizer
  • module mlmodel.piecewise_estimator
  • module mlmodel.piecewise_tree_regression
  • module mlmodel.piecewise_tree_regression_criterion
  • module mlmodel.piecewise_tree_regression_criterion_fast
  • module mlmodel.piecewise_tree_regression_criterion_linear
  • module mlmodel.predictable_tsne
  • module mlmodel.quantile_mlpregressor
  • module mlmodel.quantile_regression
  • module mlmodel.sklearn_testing
  • module mlmodel.sklearn_text
  • module mlmodel.sklearn_transform_inv
  • module mlmodel.sklearn_transform_inv_fct
  • module mlmodel.target_predictors
  • module mlmodel.transfer_transformer
  • module mltree
  • module mltree._tree_digitize
  • module mltree.tree_digitize
  • module mltree.tree_structure
  • module plotting
  • module plotting.gallery
  • module plotting.visualize
  • module search_rank
  • module search_rank.search_engine_predictions
  • module search_rank.search_engine_predictions_images
  • module search_rank.search_engine_vectors
  • module sklapi
  • module sklapi.sklearn_base
  • module sklapi.sklearn_base_classifier
  • module sklapi.sklearn_base_learner
  • module sklapi.sklearn_base_regressor
  • module sklapi.sklearn_base_transform
  • module sklapi.sklearn_base_transform_learner
  • module sklapi.sklearn_base_transform_stacking
  • module sklapi.sklearn_parameters
  • module timeseries
  • module timeseries.agg
  • module timeseries.ar
  • module timeseries.base
  • module timeseries.datasets
  • module timeseries.dummies
  • module timeseries.metrics
  • module timeseries.patterns
  • module timeseries.plotting
  • module timeseries.preprocessing
  • module timeseries.utils

module plotting.gallery#

Short summary#

module mlinsights.plotting.gallery

Featurizers for machine learned models.

source on GitHub

Functions#

function

truncated documentation

plot_gallery_images

Plots a gallery of images using matplotlib.

Documentation#

Featurizers for machine learned models.

source on GitHub

mlinsights.plotting.gallery.plot_gallery_images(imgs, texts=None, width=4, return_figure=False, ax=None, folder_image=None, **figure)#

Plots a gallery of images using matplotlib.

Parameters:
  • imgs – list of images (filename, urls or Pillow objects),

  • texts – text to display (if None, print 'img % i')

  • width – number of images on the same line (unused if imgs is a matrix)

  • figure – additional parameters when the figure is created

  • return_figure – return the figure as well as the axes

  • ax – None or existing axes, it should have the same shape of imgs

  • folder_image – image paths may be relative to some folder, in that case, they should be relative to this folder

Returns:

axes or (figure, axes) if return_figure is True

../../_images/gal.jpg

See also notebook Search images with deep learning (keras) or Search images with deep learning (torch) for an example. imgs can also be a matrix to force the function to use the same coordinates.

source on GitHub

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  • Short summary
  • Functions
  • Documentation
    • plot_gallery_images()
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