felixriese / susi Goto Github PK

If I need to plot AUC-ROC curve as in other ML techniques in SKLEARN, is there any function like predict_proba() ? or manually has to calculate? Because it returns 'SOMClassifier' object has no attribute 'predict_proba'... Is there any function for the same?

Been bashing my head against this one. I have a hyperspectral cube from drill core scanning:

box.shape
(3590, 30, 249)

som = Susi.SOMClustering()
test = som.fit(box)

ValueError: Found array with dim 3. None expected <= 2.

Hi Felix,

I noticed that the fit function for SOMRegressor only allows y to be a matrix of shape = [n_samples, 1]. Is there any way to allow for multi-output regression using SOMRegressor?

Thank you!

In this notebook, why we need to save som to SOMRegressor.p in the local disk when the som has been trained?
Does anybody can explain this ?
Thank you.

Hi there!

Does SUSI has a save/open dict method? If not, any ideas to do it quicky?

Here it says that y is Optional which makes sense since for an unsupervised SOM it isn't necessary, however the check_estimation_input function does not allow y to be None.
Am I missing something?

Hi Felix, thank you for what seems to be a great SOM implementation. Seems to be because I cannot import susi. It seems that sklearn has completely removed sklearn.utils.fixes.parallel_helper.

I think that Line 501 should become: delayed(self.get_bmu)(som_array, dp)

Greetings,

Bram

Hello,

I think there might be an issue with the Quantization calculation in Susi. The error seems to be much large then when I attempt the calculation myself. I believe the line below is missing the axis being specified, (axis=1):

    quantization_errors = np.linalg.norm(
        np.subtract(weights_per_datapoint, X)
    ) 

Thanks,

ImportError Traceback (most recent call last) in () ----> 1 import susi ~\Anaconda3\lib\site-packages\susi_init_.py in () 6 import numpy as np 7 import scipy.spatial.distance as dist ----> 8 from scipy.special import softmax 9 from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin 10 from sklearn.decomposition import PCA ImportError: cannot import name 'softmax'

Hi, thank you for the great package.
I just wanted to ask whether the support for sparse matrices is still on the roadmap?
Some comments in the code suggest that it was planned.