metadata
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: local_compartment_classifier_bd_boxes.skops
widget:
- structuredData:
area_nm2:
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area_nm2_neighbor_mean:
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area_nm2_neighbor_std:
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max_dt_nm:
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max_dt_nm_neighbor_mean:
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max_dt_nm_neighbor_std:
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mean_dt_nm:
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mean_dt_nm_neighbor_mean:
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mean_dt_nm_neighbor_std:
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pca_ratio_01:
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pca_ratio_01_neighbor_mean:
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pca_ratio_01_neighbor_std:
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pca_unwrapped_0:
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pca_unwrapped_0_neighbor_mean:
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pca_unwrapped_0_neighbor_std:
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pca_unwrapped_1:
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pca_unwrapped_1_neighbor_mean:
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pca_unwrapped_1_neighbor_std:
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pca_unwrapped_2:
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pca_unwrapped_2_neighbor_mean:
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pca_unwrapped_2_neighbor_std:
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pca_unwrapped_3:
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pca_unwrapped_3_neighbor_mean:
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pca_unwrapped_3_neighbor_std:
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pca_unwrapped_4:
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pca_unwrapped_4_neighbor_mean:
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pca_unwrapped_4_neighbor_std:
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pca_unwrapped_5:
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pca_unwrapped_5_neighbor_mean:
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pca_unwrapped_5_neighbor_std:
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pca_unwrapped_6_neighbor_mean:
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pca_unwrapped_6_neighbor_std:
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pca_unwrapped_7:
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pca_unwrapped_7_neighbor_mean:
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pca_unwrapped_7_neighbor_std:
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pca_unwrapped_8:
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pca_unwrapped_8_neighbor_mean:
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pca_unwrapped_8_neighbor_std:
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pca_val_unwrapped_0:
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pca_val_unwrapped_0_neighbor_mean:
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pca_val_unwrapped_0_neighbor_std:
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pca_val_unwrapped_1:
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pca_val_unwrapped_1_neighbor_mean:
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pca_val_unwrapped_1_neighbor_std:
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pca_val_unwrapped_2:
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pca_val_unwrapped_2_neighbor_mean:
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pca_val_unwrapped_2_neighbor_std:
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post_synapse_count:
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post_synapse_count_neighbor_mean:
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post_synapse_count_neighbor_std:
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pre_synapse_count:
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pre_synapse_count_neighbor_mean:
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pre_synapse_count_neighbor_std:
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size_nm3:
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size_nm3_neighbor_mean:
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size_nm3_neighbor_std:
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Model description
This is a model trained to classify pieces of neuron as axon, dendrite, soma, or glia, based only on their local shape and synapse features.The model is a linear discriminant classifier which was trained on compartment labels generated by Bethanny Danskin for 3 6x6x6 um boxes in the Minnie65 Phase3 dataset.
Intended uses & limitations
This model could be used to predict some compartment labels in mouse cortical connectomes, but it is unclear to what extent this model will generalize.
Training Procedure
The model was trained on local (level 2 cache) and synapse count features from 3 6x6x6 um boxes in the Minnie65 Phase3 dataset. These features were also locally aggregated in 5-hop neighborhood windows and concatenated to each level 2 node's features. The labels were generated by Bethanny Danskin and include axon, dendrite, soma, and glia compartments. The classification model was trained using a linear discriminant classifier.
Hyperparameters
Click to expand
| Hyperparameter | Value |
|---|---|
| memory | |
| steps | [('transformer', QuantileTransformer(output_distribution='normal')), ('lda', LinearDiscriminantAnalysis(n_components=3))] |
| verbose | False |
| transformer | QuantileTransformer(output_distribution='normal') |
| lda | LinearDiscriminantAnalysis(n_components=3) |
| transformer__copy | True |
| transformer__ignore_implicit_zeros | False |
| transformer__n_quantiles | 1000 |
| transformer__output_distribution | normal |
| transformer__random_state | |
| transformer__subsample | 10000 |
| lda__covariance_estimator | |
| lda__n_components | 3 |
| lda__priors | |
| lda__shrinkage | |
| lda__solver | svd |
| lda__store_covariance | False |
| lda__tol | 0.0001 |
Model Plot
Pipeline(steps=[('transformer',QuantileTransformer(output_distribution='normal')),('lda', LinearDiscriminantAnalysis(n_components=3))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('transformer',QuantileTransformer(output_distribution='normal')),('lda', LinearDiscriminantAnalysis(n_components=3))])QuantileTransformer(output_distribution='normal')
LinearDiscriminantAnalysis(n_components=3)
Evaluation Results
Classification Report (overall)
| type | precision | recall | f1-score | support |
|---|---|---|---|---|
| accuracy | 0.944357 | 0.944357 | 0.944357 | 0.944357 |
| macro avg | 0.854825 | 0.917289 | 0.878753 | 31307 |
| weighted avg | 0.946879 | 0.944357 | 0.945155 | 31307 |
Classification Report (by class)
| class | precision | recall | f1-score | support |
|---|---|---|---|---|
| axon | 0.956309 | 0.964704 | 0.960488 | 16404 |
| dendrite | 0.928038 | 0.911341 | 0.919614 | 6948 |
| glia | 0.964442 | 0.935279 | 0.949636 | 7540 |
| soma | 0.570513 | 0.857831 | 0.685274 | 415 |
How to Get Started with the Model
[More Information Needed]
Model Card Authors
Ben Pedigo Bethanny Danskin