minerva.analysis.model_analysis

Classes

TSNEAnalysis	Perform t-SNE analysis on the embeddings generated by a model.
_ModelAnalysis	Main interface for model analysis. A model analysis is a post-training

Module Contents

class minerva.analysis.model_analysis.TSNEAnalysis(label_names=None, height=800, width=800, text_size=12, title=None, x_axis_title='x', y_axis_title='y', legend_title='Label', output_filename='tsne.png', seed=42, n_components=2)

Bases: _ModelAnalysis

Perform t-SNE analysis on the embeddings generated by a model. A t-SNE plot is generated using the embeddings and saved in the path directory. The plot is saved as a PNG image file.

Plot a t-SNE plot of the embeddings generated by a model.

Parameters

label_namesOptional[Dict[Union[int, str], str]], optional

Labels to use for the plot, instead of the original labels in the data (y). The keys are the original labels and the values are the new labels to use in the plot. If None, the original labels are used as they are. By default None

heightint, optional

Height of the figure, by default 800

widthint, optional

Width of the figure, by default 800

text_sizeint, optional

Size of font used in plot, by default 12

titlestr, optional

Title of graph, by default None

x_axis_titlestr, optional

Name of x-axis, by default “x”

y_axis_titlestr, optional

Name of y-axis, by default “y”

legend_titlestr, optional

Name for legend title, by default “Label”

output_filenamePathLike, optional

Name of the output file to save the plot as a PNG image file. The file will be saved in the path directory with this name. By default “tsne.png”

seedint, optional

Random seed for t-SNE, by default 42

n_componentsint, optional

Number of components to use in t-SNE, by default 2

compute(model, data)

Parameters:

• model (lightning.LightningModule)

• data (lightning.LightningDataModule)

height = 800

label_names = None

legend_title = 'Label'

n_components = 2

output_filename

seed = 42

text_size = 12

title = None

width = 800

x_axis_title = 'x'

y_axis_title = 'y'

Parameters:

• label_names (Optional[Dict[Union[int, str], str]])

• height (int)

• width (int)

• text_size (int)

• title (Optional[str])

• x_axis_title (str)

• y_axis_title (str)

• legend_title (str)

• output_filename (minerva.utils.typing.PathLike)

• seed (int)

• n_components (int)

class minerva.analysis.model_analysis._ModelAnalysis(path=None)

Main interface for model analysis. A model analysis is a post-training analysis that can be run on a trained model to generate insights about the model’s performance. It has a path attribute that specifies the directory where the analysis results will be saved. The compute method should be implemented by subclasses to perform the actual analysis. All insights generated by the analysis should be saved in the path directory. Note that, differently from Metric, _ModelAnalysis does not return any value. Instead, the results of the analysis should be saved in the path directory. All subclasses of _ModelAnalysis should implement the compute method. Inside a pipeline the path will be automatically set to the pipeline.log_dir attribute.

Parameters:

path (Optional[minerva.utils.typing.PathLike])

_path = None

abstract compute(model, data)

Parameters:

• model (lightning.LightningModule)

• data (lightning.LightningDataModule)

property path