U mdL@sLdZddlZddgZejdd ddZejddd dZd d ZdS)a This module provides the functions for node classification problem. The functions in this module are not imported into the top level `networkx` namespace. You can access these functions by importing the `networkx.algorithms.node_classification` modules, then accessing the functions as attributes of `node_classification`. For example: >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> node_classification.harmonic_function(G) ['A', 'A', 'B', 'B'] References ---------- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919). Nharmonic_functionlocal_and_global_consistencyZdirectedlabelcCs*ddl}ddl}ddl}t|}t||\}}|jddkrPtd|d|jd} |jd} || | f} |j dd} d| | dk<|j |j j d| dd} | | }d||dddf<|| | f}d||dddf|dddff<t|D]}|| |} q||j| ddS) aNode classification by Harmonic function Function for computing Harmonic function algorithm by Zhu et al. Parameters ---------- G : NetworkX Graph max_iter : int maximum number of iterations allowed label_name : string name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.harmonic_function(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August). Semi-supervised learning using gaussian fields and harmonic functions. In ICML (Vol. 3, pp. 912-919). rN*No node on the input graph is labeled by ''.Zaxis?offsets)numpyscipy scipy.sparsenxto_scipy_sparse_array_get_label_infoshape NetworkXErrorzerossumsparse csr_arraydiagsZtolilrangeargmaxtolist)Gmax_iter label_namenpsprXlabels label_dict n_samples n_classesFdegreesDPB_r-`/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/networkx/algorithms/node_classification.pyrs,,       $ Gz?cCs"ddl}ddl}ddl}t|}t||\}} |jddkrPtd|d|jd} | jd} || | f} |j dd} d| | dk<| |j |j j d| dd}||||}|| | f}d|||dddf|dddff<t|D]}|| |} q| |j| ddS) uNode classification by Local and Global Consistency Function for computing Local and global consistency algorithm by Zhou et al. Parameters ---------- G : NetworkX Graph alpha : float Clamping factor max_iter : int Maximum number of iterations allowed label_name : string Name of target labels to predict Returns ------- predicted : list List of length ``len(G)`` with the predicted labels for each node. Raises ------ NetworkXError If no nodes in `G` have attribute `label_name`. Examples -------- >>> from networkx.algorithms import node_classification >>> G = nx.path_graph(4) >>> G.nodes[0]["label"] = "A" >>> G.nodes[3]["label"] = "B" >>> G.nodes(data=True) NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) >>> G.edges() EdgeView([(0, 1), (1, 2), (2, 3)]) >>> predicted = node_classification.local_and_global_consistency(G) >>> predicted ['A', 'A', 'B', 'B'] References ---------- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in neural information processing systems, 16(16), 321-328. rNrrrr r r )r rrrrrrrrrsqrtrrrrrr)ralpharrr r!rr"r#r$r%r&r'r(ZD2r*r+r,r-r-r.rks*.      "( c Csddl}g}i}d}t|jddD]J\}}||dkr$|d|}||kr\|||<|d7}||||gq$||}|ddt|dd d D} || fS) aGet and return information of labels from the input graph Parameters ---------- G : Network X graph label_name : string Name of the target label Returns ---------- labels : numpy array, shape = [n_labeled_samples, 2] Array of pairs of labeled node ID and label ID label_dict : numpy array, shape = [n_classes] Array of labels i-th element contains the label corresponding label ID `i` rNT)datar cSsg|] \}}|qSr-r-).0rr,r-r-r. sz#_get_label_info..cSs|dS)Nr r-)xr-r-r.z!_get_label_info..)key)r enumerateZnodesappendarraysorteditems) rrr r#Z label_to_idlidinrr$r-r-r.rs    r)rr)r/rr) __doc__Znetworkxr__all__utilsZnot_implemented_forrrrr-r-r-r.s  L  M