Laplacians initiation in glmm #10

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1. iricchi

   changed the title [-]Laplacian initiation in glmm[/-] [+]Laplacians initiation in glmm[/+] on Sep 20, 2024
2. iricchi commented on Sep 20, 2024

   

   iricchi

   on Sep 20, 2024

   Last edited by iricchi

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   Error if I give laplacians init as input, after adding the laplacian in the _estimate_gauss_laplacian_parameters as inputs and editing lines 131, 158 + if conditional if laplacians_init is a np array.

   Dimensionality error : not sure whether the dimension should be Nnodes x Nnodes or Nnodes x Nnodes x ncomponent.

   `---------------------------------------------------------------------------
   ValueError Traceback (most recent call last)
   Cell In[31], line 3
   1 # delta doesn't change much ... amplitute but to check
   2 glmm = GLMM(n_components=K,avg_degree=ave_deg,laplacian_init=laplacians,init_params="k-means++",max_iter=10000)
   ----> 3 glmm.fit(X) # -> no labels but means, laplacian glmm.means_ , glmm.laplacians_
   4 # # and then run probability to get gamma hats
   5 gamma_hats = glmm.predict_proba(X)

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/sklearn/mixture/_base.py:181, in BaseMixture.fit(self, X, y)
   155 """Estimate model parameters with the EM algorithm.
   156
   157 The method fits the model n_init times and sets the parameters with
   (...)
   178 The fitted mixture.
   179 """
   180 # parameters are validated in fit_predict
   --> 181 self.fit_predict(X, y)
   182 return self

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/sklearn/base.py:1473, in _fit_context..decorator..wrapper(estimator, *args, **kwargs)
   1466 estimator._validate_params()
   1468 with config_context(
   1469 skip_parameter_validation=(
   1470 prefer_skip_nested_validation or global_skip_validation
   1471 )
   1472 ):
   -> 1473 return fit_method(estimator, *args, **kwargs)

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/sklearn/mixture/_base.py:235, in BaseMixture.fit_predict(self, X, y)
   232 self._print_verbose_msg_init_beg(init)
   234 if do_init:
   --> 235 self.initialize_parameters(X, random_state)
   237 lower_bound = -np.inf if do_init else self.lower_bound
   239 if self.max_iter == 0:

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/sklearn/mixture/_base.py:140, in BaseMixture._initialize_parameters(self, X, random_state)
   133 _, indices = kmeans_plusplus(
   134 X,
   135 self.n_components,
   136 random_state=random_state,
   137 )
   138 resp[indices, np.arange(self.n_components)] = 1
   --> 140 self._initialize(X, resp)

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/graph_learn/clustering/glmm.py:133, in GLMM.initialize(self, x, resp)
   130 def initialize(self, x, resp):
   131 n_samples, self.n_nodes = x.shape
   --> 133 self.weights, self.means, laplacians = estimate_gauss_laplacian_parameters(
   134 x, resp, self.avg_degree, self.delta, self.laplacian_init
   135 )
   137 if self.laplacian_init is None:
   138 self.laplacians = laplacians

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/graph_learn/clustering/glmm.py:35, in _estimate_gauss_laplacian_parameters(x, resp, avg_degree, delta, laplacian)
   32 sq_dist = np.sum((y[..., np.newaxis] - y[..., np.newaxis, :]) ** 2, axis=0)
   34 # theta = np.mean(sq_dist) / norm_par
   ---> 35 edge_weights = delta * gsp_learn_graph_log_degrees(
   36 square_to_vec(sq_dist) * [[get_theta(sqd, avg_degree)] for sqd in sq_dist],
   37 alpha=1,
   38 beta=1,
   39 edge_init=square_to_vec(-laplacian)
   40 )
   42 laplacians = laplacian_squareform_vec(edge_weights)
   44 return weights, means, laplacians

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/graph_learn/smooth_learning.py:154, in gsp_learn_graph_log_degrees(distances, alpha, beta, edge_init, maxit, tol, step_size, edge_tol)
   152 else:
   153 edge_w = np.zeros_like(distances)
   --> 154 d_n = sum_op @ edge_w
   156 for i in range(maxit):
   157 y_n = (1 - step_size * 2 * beta) * edge_w - step_size * (sum_op_t @ d_n)

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/scipy/sparse/_base.py:695, in _spbase.matmul(self, other)
   692 if isscalarlike(other):
   693 raise ValueError("Scalar operands are not allowed, "
   694 "use '*' instead")
   --> 695 return self._matmul_dispatch(other)

   File ~/anaconda3/envs/python310/lib/python3.10/site-packages/scipy/sparse/_base.py:642, in _spbase._matmul_dispatch(self, other)
   637 elif other.ndim == 2:
   638 ##
   639 # dense 2D array or matrix ("multivector")
   641 if other.shape[0] != N:
   --> 642 raise ValueError('dimension mismatch')
   644 result = self._matmul_multivector(np.asarray(other))
   646 if isinstance(other, np.matrix):

   ValueError: dimension mismatch`