slideflow.SlideMap

:class:`slideflow.SlideMap` assists with visualizing tiles and slides in two-dimensional space.

Once a model has been trained, tile-level predictions and intermediate layer activations can be calculated across an entire dataset with :class:`slideflow.DatasetFeatures`. The :class:`slideflow.SlideMap` class can then perform dimensionality reduction on these dataset-wide activations, plotting tiles and slides in two-dimensional space. Visualizing the distribution and clustering of tile-level and slide-level layer activations can help reveal underlying structures in the dataset and shared visual features among classes.

The primary method of use is first generating an :class:`slideflow.DatasetFeatures` from a trained model, then using :meth:`slideflow.DatasetFeatures.map_activations`, which returns an instance of :class:`slideflow.SlideMap`.

ftrs = sf.DatasetFeatures(model='/path/', ...)
slide_map = ftrs.map_activations()

Alternatively, if you would like to map slides from a dataset in two-dimensional space using pre-calculated x and y coordinates, you can use the :meth:`sldieflow.SlideMap.from_xy` class method. In addition to X and Y, this method requires supplying tile-level metadata in the form of a list of dicts. Each dict must contain the name of the origin slide and the tile index in the slide TFRecord.

x = np.array(...)
y = np.array(...)
slides = ['slide1', 'slide1', 'slide5', ...]
slide_map = sf.SlideMap.from_xy(x=x, y=y, slides=slides)