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When I run the code:

nmf_cm = StandardNmf(pd.DataFrame(data_tfidf), k = 20)
nmf_cm.run(iterations = 1000, trials = 20)

I get the following error:

ValueError
30 nmf_cm = StandardNmf(pd.DataFrame(data_tfidf), k = k)
---> 31 nmf_cm.run(iterations = 100, trials = 20, verbose = 0)
32 nmf_cm.calc_consensus_matrices()
33 nmf_cm.calc_cophenetic_correlation()

File ...\bignmf\models\snmf\snmf_base.py:52, in SnmfBase.run(self, trials, iterations, verbose)
50 print("\tTrial: %i completed with Error: %f " % (i, self.error))
51 # Normalization
---> 52 self.consensus_matrix_w = self.reorder_consensus_matrix(self.consensus_matrix_w / trials)
53 self.consensus_matrix_h = self.reorder_consensus_matrix(self.consensus_matrix_h / trials)
54 # Converting values to DataFrames

File ...\bignmf\models\nmf.py:69, in NmfBase.reorder_consensus_matrix(M)
67 M = pd.DataFrame(M)
68 Y = 1 - M
---> 69 Z = linkage(squareform(Y), method='average')
70 ivl = leaves_list(Z)
71 ivl = ivl[::-1]

File ...\scipy\spatial\distance.py:2354, in squareform(X, force, checks)
2352 raise ValueError('The matrix argument must be square.')
...
2436 'be zero.') % name)
2437 else:
2438 raise ValueError('Distance matrix diagonal must be zero.')

ValueError: Distance matrix 'X' diagonal must be zero.

My input is TFIDF data and there are no negative elements in it. Is there something wrong in the implementation of the function or am I making a mistake?

Running the first example in the README finished with this error:

ValueError: Operands are not aligned. Do left, right = left.align(right, axis=1, copy=False) before operating.