def _sample_from_zeros(n: int, sparse: sp.csr_matrix) -> List[List[int]]: """ Sample n zeros from spacse matrix. Parameters ---------- n : int Number of samples to get from matrix. sparse : sp.csr_matrix Sparse matrix. Returns ------- List[List[int]] List of 2-D indices of zeros. """ zeros = np.argwhere(np.logical_not(sparse.todense())) ids = np.random.choice(range(len(zeros)), size=(n, )) return zeros[ids].tolist()
def predict_proba(self, X: csr_matrix): return self.clf.predict_proba(X.todense())
def predict(self, X: csr_matrix): X = X.todense() # TensorFlow/Skflow doesn't support sparse matrices return self.clf.predict(X)
def sparse_to_tensor(in_sparse: sparse.csr_matrix) -> torch.Tensor: return torch.Tensor(in_sparse.todense())
def to_tensor(inp: csr_matrix) -> tf.Tensor: return tf.convert_to_tensor(inp.todense(), dtype=TENSOR_OUTPUT_TYPE)
def compute_jaccard_distance_matrix(feature_vectors: csr_matrix): return squareform(pdist(feature_vectors.todense(), metric='jaccard'))