This package provides a python implementation of alok-ai-lab/DeepInsight as originally described in DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture [1]. This is not guaranteed to give the same results as the published MatLab code and should be considered experimental.
python3 -m pip -q install git+https://github.com/alok-ai-lab/pyDeepInsight.git#egg=pyDeepInsight
The pyDeepInsight package provides classes that aid in the transformation of non-image data into image matrices that can be used to train a CNN.
Transforms features to an image matrix using dimensionality reduction and discritization.
class pyDeepInsight.ImageTransformer(feature_extractor='tsne',
discretization='bin', pixels=(224, 224))- feature_extractor: string of value ('tsne', 'pca', 'kpca') or a class instance with method 'fit_transform' that returns a 2-dimensional array of extracted features. The string values use the same parameters as those described in the original paper. Providing a class instance is preferred and allows for greater customization of the feature extractor, such as modifying perplexity in t-SNE, or use of alternative feature extractors, such as UMAP.
- discretization: string of values ('bin', 'assignment'). Defines the
method for discretizing dimensionally reduced data to pixel coordinates.
The default 'bin' is the method implemented in the original paper and maps features to pixels based on a direct scaling of the extracted features to the pixel space.
The 'assignment' values applies SciPy's solution to the linear sum assignment problem to the exponent of the euclidean distance between the extracted features and pixels to assign a features to pixels with no overlap. In cases where the number of features exceeds the number of pixels, the features are clustered using K-means clustering, with k equal to the number of pixels, and those clusters are assigned to pixels. - pixels: int (square matrix) or tuple of ints (height, width) that defines the size of the image matrix. A default of 224 × 224 is used as that is the minimum size expected by torchvision models.
Extracts important features from a trained PyTorch model using class activation mapping (CAM) as proposed in DeepFeature: feature selection in nonimage data using convolutional neural network [2].
class DeepInsight.CAMFeatureSelector(model, it, target_layer, cam_method="GradCAM")- model: a pytorch.nn.Module CNN model trained on the output from an ImageTransformer instance. The torchvision.models subpackage provides many common CNN architechtures.
- it: the ImageTransformer instance used to generate the images used to train the model.
- target_layer: the target layer of the model on which the CAM is computed. Can be specified using the name provided by nn.Module.named_modules or a by providing a pointer to the layer directly. If no layer is specified, the last non-reduced convolutional layer is selected as determined by the 'locate_candidate_layer' method of the TorchCAM [3] package by François-Guillaume Fernandez.
- cam_method: the name of a CAM method class provided by the pytorch_grad_cam [4] package by Jacob Gildenblat. Default is "GradCAM".
Performs log normalization and scaling procedure as described as norm-2 in the DeepInsight paper supplementary information. This method attempts keep the topology of the features by using a global maximum in the logarithmic scale.
>>> from pyDeepInsight import LogScaler
>>> import numpy as np
>>> data = np.ndarray([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
>>> scaler = LogScaler()
>>> _ = scalar.fit(data)
>>> print(scaler._min0, scaler._max)
[-1. 2.] 3.044522437723423
>>> print(scaler.transform(data))
[[0. 0.52863395]
[0.13317856 0.72169761]
[0.22767025 0.84248003]
[0.36084881 1. ]]
>>> print(scaler.transform([[2, 2]]))
[0.4553405 0.52863395][1] Sharma A, Vans E, Shigemizu D, Boroevich KA, & Tsunoda T. DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep 9, 11399 (2019). https://doi.org/10.1038/s41598-019-47765-6
[2] Sharma A, Lysenko A, Boroevich KA, Vans E, & Tsunoda T. DeepFeature: feature selection in nonimage data using convolutional neural network, Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab297. https://doi.org/10.1093/bib/bbab297
[3] François-Guillaume Fernandez. (2020). TorchCAM: class activation explorer. https://github.com/frgfm/torch-cam
[4] Jacob Gildenblat, & contributors. (2021). PyTorch library for CAM methods. https://github.com/jacobgil/pytorch-grad-cam