<s> import numpy as np <EOL> from scipy import sparse <EOL> from sklearn . cluster import KMeans <EOL> from . import GraphCRF <EOL> from . . inference import inference_dispatch <EOL> from . . utils import expand_sym , compress_sym <EOL> def kmeans_init ( X , Y , n_labels , n_hidden_states , latent_node_features = False ) : <EOL> all_feats = [ ] <EOL> for x , y in zip ( X , Y ) : <EOL> if len ( x ) == <NUM_LIT:3> : <EOL> features , edges , n_hidden = x <EOL> elif len ( x ) == <NUM_LIT:4> : <EOL> features , edges , _ , n_hidden = x <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> n_visible = features . shape [ <NUM_LIT:0> ] <EOL> if latent_node_features : <EOL> n_visible -= n_hidden <EOL> if np . max ( edges ) != n_hidden + n_visible - <NUM_LIT:1> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> labels_one_hot = np . zeros ( ( n_visible , n_labels ) , dtype = np . int ) <EOL> y = y . ravel ( ) <EOL> gx = np . ogrid [ : n_visible ] <EOL> labels_one_hot [ gx , y ] = <NUM_LIT:1> <EOL> graph = sparse . coo_matrix ( ( np . ones ( edges . shape [ <NUM_LIT:0> ] ) , edges . T ) , <EOL> ( n_visible + n_hidden , n_visible + n_hidden ) ) <EOL> graph = ( graph + graph . T ) [ - n_hidden : , : n_visible ] <EOL> neighbors = graph * labels_one_hot . reshape ( n_visible , - <NUM_LIT:1> ) <EOL> neighbors /= np . maximum ( neighbors . sum ( axis = <NUM_LIT:1> ) [ : , np . newaxis ] , <NUM_LIT:1> ) <EOL> all_feats . append ( neighbors ) <EOL> all_feats_stacked = np . vstack ( all_feats ) <EOL> try : <EOL> km = KMeans ( n_clusters = n_hidden_states ) <EOL> except TypeError : <EOL> km = KMeans ( k = n_hidden_states ) <EOL> km . fit ( all_feats_stacked ) <EOL> H = [ ] <EOL> for y , feats in zip ( Y , all_feats ) : <EOL> H . append ( np . hstack ( [ y , km . predict ( feats ) + n_labels ] ) ) <EOL> return H <EOL> class LatentNodeCRF ( GraphCRF ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , n_labels = None , n_features = None , n_hidden_states = <NUM_LIT:2> , <EOL> inference_method = None , class_weight = None , <EOL> latent_node_features = False ) : <EOL> self . n_labels = n_labels <EOL> self . n_hidden_states = n_hidden_states <EOL> if n_labels is not None : <EOL> n_states = n_hidden_states + n_labels <EOL> else : <EOL> n_states = None <EOL> self . latent_node_features = latent_node_features <EOL> GraphCRF . __init__ ( self , n_states , n_features , <EOL> inference_method = inference_method , <EOL> class_weight = class_weight ) <EOL> def _set_size_joint_feature ( self ) : <EOL> if None in [ self . n_states , self . n_features ] : <EOL> return <EOL> if self . latent_node_features : <EOL> n_input_states = self . n_states <EOL> else : <EOL> n_input_states = self . n_labels <EOL> self . n_input_states = n_input_states <EOL> self . size_joint_feature = ( n_input_states * self . n_features + <EOL> self . n_states * ( self . n_states + <NUM_LIT:1> ) / <NUM_LIT:2> ) <EOL> def initialize ( self , X , Y ) : <EOL> n_features = X [ <NUM_LIT:0> ] [ <NUM_LIT:0> ] . shape [ <NUM_LIT:1> ] <EOL> if self . n_features is None : <EOL> self . n_features = n_features <EOL> elif self . n_features != n_features : <EOL> raise ValueError ( "<STR_LIT>" <EOL> % ( self . n_features , n_features ) ) <EOL> n_labels = len ( np . unique ( np . hstack ( [ y . ravel ( ) for y in Y ] ) ) ) <EOL> if self . n_labels is None : <EOL> self . n_labels = n_labels <EOL> elif self . n_labels != n_labels : <EOL> raise ValueError ( "<STR_LIT>" <EOL> % ( self . n_labels , n_labels ) ) <EOL> self . n_states = self . n_hidden_states + n_labels <EOL> self . _set_size_joint_feature ( ) <EOL> self . _set_class_weight ( ) <EOL> def _get_pairwise_potentials ( self , x , w ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_w ( w ) <EOL> self . _check_size_x ( x ) <EOL> return expand_sym ( w [ self . n_input_states * self . n_features : ] ) <EOL> def _get_unary_potentials ( self , x , w ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_w ( w ) <EOL> self . _check_size_x ( x ) <EOL> features = self . _get_features ( x ) <EOL> unary_params = w [ : self . n_input_states * self . n_features ] . reshape ( <EOL> self . n_input_states , self . n_features ) <EOL> if self . latent_node_features : <EOL> unaries = np . dot ( features , unary_params . T ) <EOL> n_hidden = x [ <NUM_LIT:2> ] <EOL> n_visible = features . shape [ <NUM_LIT:0> ] - n_hidden <EOL> else : <EOL> n_visible , n_hidden = features . shape [ <NUM_LIT:0> ] , x [ <NUM_LIT:2> ] <EOL> unaries = np . zeros ( ( n_visible + n_hidden , self . n_states ) ) <EOL> unaries_observed = np . dot ( features , unary_params . T ) <EOL> unaries [ : n_visible , : self . n_labels ] = unaries_observed <EOL> max_entry = np . maximum ( np . max ( unaries ) , <NUM_LIT:1> ) <EOL> unaries [ : n_visible , self . n_labels : ] = - <NUM_LIT> * max_entry <EOL> unaries [ n_visible : , : self . n_labels ] = - <NUM_LIT> * max_entry <EOL> return unaries <EOL> def loss_augmented_inference ( self , x , h , w , relaxed = False , <EOL> return_energy = False ) : <EOL> self . inference_calls += <NUM_LIT:1> <EOL> self . _check_size_w ( w ) <EOL> unary_potentials = self . _get_unary_potentials ( x , w ) <EOL> pairwise_potentials = self . _get_pairwise_potentials ( x , w ) <EOL> edges = self . _get_edges ( x ) <EOL> for l in np . arange ( self . n_states ) : <EOL> inds = np . where ( self . label_from_latent ( h ) != l ) [ <NUM_LIT:0> ] <EOL> unary_potentials [ inds , l ] += self . class_weight [ <EOL> self . label_from_latent ( h ) ] [ inds ] <EOL> return inference_dispatch ( unary_potentials , pairwise_potentials , edges , <EOL> self . inference_method , relaxed = relaxed , <EOL> return_energy = return_energy ) <EOL> def latent ( self , x , y , w ) : <EOL> unary_potentials = self . _get_unary_potentials ( x , w ) <EOL> max_entry = np . maximum ( np . max ( unary_potentials ) , <NUM_LIT:1> ) <EOL> unary_potentials [ np . arange ( len ( y ) ) , y ] = <NUM_LIT> * max_entry <EOL> pairwise_potentials = self . _get_pairwise_potentials ( x , w ) <EOL> edges = self . _get_edges ( x ) <EOL> h = inference_dispatch ( unary_potentials , pairwise_potentials , edges , <EOL> self . inference_method , relaxed = False ) <EOL> if ( h [ : len ( y ) ] != y ) . any ( ) : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> h [ : len ( y ) ] = y <EOL> return h <EOL> def label_from_latent ( self , h ) : <EOL> return h [ h < self . n_labels ] <EOL> def loss ( self , h , h_hat ) : <EOL> if isinstance ( h_hat , tuple ) : <EOL> return self . continuous_loss ( h , h_hat [ <NUM_LIT:0> ] ) <EOL> return GraphCRF . loss ( self , self . label_from_latent ( h ) , <EOL> self . label_from_latent ( h_hat ) ) <EOL> def base_loss ( self , y , y_hat ) : <EOL> if isinstance ( y_hat , tuple ) : <EOL> return GraphCRF . continuous_loss ( self , y , y_hat ) <EOL> return GraphCRF . loss ( self , y , y_hat ) <EOL> def continuous_loss ( self , y , y_hat ) : <EOL> y_org = self . label_from_latent ( y ) <EOL> y_hat_org = y_hat [ : y_org . size , : self . n_labels ] <EOL> return GraphCRF . continuous_loss ( self , y_org , y_hat_org ) <EOL> def joint_feature ( self , x , y ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_x ( x ) <EOL> features , edges = self . _get_features ( x ) , self . _get_edges ( x ) <EOL> if isinstance ( y , tuple ) : <EOL> unary_marginals , pw = y <EOL> pw = pw . reshape ( - <NUM_LIT:1> , self . n_states , self . n_states ) . sum ( axis = <NUM_LIT:0> ) <EOL> else : <EOL> n_nodes = y . size <EOL> gx = np . ogrid [ : n_nodes ] <EOL> unary_marginals = np . zeros ( ( n_nodes , self . n_states ) , dtype = np . int ) <EOL> gx = np . ogrid [ : n_nodes ] <EOL> unary_marginals [ gx , y ] = <NUM_LIT:1> <EOL> pw = np . dot ( unary_marginals [ edges [ : , <NUM_LIT:0> ] ] . T , <EOL> unary_marginals [ edges [ : , <NUM_LIT:1> ] ] ) <EOL> n_visible = features . shape [ <NUM_LIT:0> ] <EOL> unaries_acc = np . dot ( unary_marginals [ : n_visible , <EOL> : self . n_input_states ] . T , features ) <EOL> joint_feature_vector = np . hstack ( [ unaries_acc . ravel ( ) , <EOL> compress_sym ( pw ) ] ) <EOL> return joint_feature_vector <EOL> def init_latent ( self , X , Y ) : <EOL> return kmeans_init ( X , Y , n_labels = self . n_labels , <EOL> n_hidden_states = self . n_hidden_states , <EOL> latent_node_features = self . latent_node_features ) <EOL> def max_loss ( self , h ) : <EOL> y = self . label_from_latent ( h ) <EOL> if hasattr ( self , '<STR_LIT>' ) : <EOL> return np . sum ( self . class_weight [ y ] ) <EOL> class EdgeFeatureLatentNodeCRF ( LatentNodeCRF ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , n_labels = <NUM_LIT:2> , n_features = None , n_edge_features = <NUM_LIT:1> , <EOL> n_hidden_states = <NUM_LIT:2> , inference_method = None , class_weight = None , <EOL> latent_node_features = False , symmetric_edge_features = None , <EOL> antisymmetric_edge_features = None ) : <EOL> self . n_labels = n_labels <EOL> if n_features is None : <EOL> n_features = n_labels <EOL> self . n_hidden_states = n_hidden_states <EOL> n_states = n_hidden_states + n_labels <EOL> self . n_edge_features = n_edge_features <EOL> if latent_node_features : <EOL> n_input_states = n_states <EOL> else : <EOL> n_input_states = n_labels <EOL> self . n_input_states = n_input_states <EOL> self . size_joint_feature = ( <EOL> n_input_states * n_features + n_edge_features * n_states ** <NUM_LIT:2> ) <EOL> self . latent_node_features = latent_node_features <EOL> if symmetric_edge_features is None : <EOL> symmetric_edge_features = [ ] <EOL> if antisymmetric_edge_features is None : <EOL> antisymmetric_edge_features = [ ] <EOL> if not set ( symmetric_edge_features ) . isdisjoint ( <EOL> antisymmetric_edge_features ) : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> self . symmetric_edge_features = symmetric_edge_features <EOL> self . antisymmetric_edge_features = antisymmetric_edge_features <EOL> GraphCRF . __init__ ( self , n_states , n_features , <EOL> inference_method = inference_method , <EOL> class_weight = class_weight ) <EOL> def _set_size_joint_feature ( self ) : <EOL> if None in [ self . n_states , self . n_features ] : <EOL> return <EOL> if self . latent_node_features : <EOL> n_input_states = self . n_states <EOL> else : <EOL> n_input_states = self . n_labels <EOL> self . n_input_states = n_input_states <EOL> self . size_joint_feature = ( <EOL> n_input_states * self . n_features + self . n_edge_features * <EOL> self . n_states ** <NUM_LIT:2> ) <EOL> def _check_size_x ( self , x ) : <EOL> GraphCRF . _check_size_x ( self , x ) <EOL> _ , edges , edge_features , n_hidden = x <EOL> if edges . shape [ <NUM_LIT:0> ] != edge_features . shape [ <NUM_LIT:0> ] : <EOL> raise ValueError ( "<STR_LIT>" <EOL> % ( edges . shape [ <NUM_LIT:0> ] , edge_features . shape [ <NUM_LIT:0> ] ) ) <EOL> if edge_features . shape [ <NUM_LIT:1> ] != self . n_edge_features : <EOL> raise ValueError ( "<STR_LIT>" <EOL> % ( edge_features . shape [ <NUM_LIT:1> ] , self . n_edge_features ) ) <EOL> def _get_pairwise_potentials ( self , x , w ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_w ( w ) <EOL> self . _check_size_x ( x ) <EOL> edge_features = x [ <NUM_LIT:2> ] <EOL> pairwise = np . asarray ( w [ self . n_input_states * self . n_features : ] ) <EOL> pairwise = pairwise . reshape ( self . n_edge_features , - <NUM_LIT:1> ) <EOL> return np . dot ( edge_features , pairwise ) . reshape ( <EOL> edge_features . shape [ <NUM_LIT:0> ] , self . n_states , self . n_states ) <EOL> def _get_unary_potentials ( self , x , w ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_w ( w ) <EOL> self . _check_size_x ( x ) <EOL> features = self . _get_features ( x ) <EOL> unary_params = w [ : self . n_input_states * self . n_features ] . reshape ( <EOL> self . n_input_states , self . n_features ) <EOL> if self . latent_node_features : <EOL> unaries = np . dot ( features , unary_params . T ) <EOL> n_hidden = x [ <NUM_LIT:2> ] <EOL> n_visible = features . shape [ <NUM_LIT:0> ] - n_hidden <EOL> else : <EOL> n_visible , n_hidden = features . shape [ <NUM_LIT:0> ] , x [ <NUM_LIT:3> ] <EOL> unaries = np . zeros ( ( n_visible + n_hidden , self . n_states ) ) <EOL> unaries_observed = np . dot ( features , unary_params . T ) <EOL> unaries [ : n_visible , : self . n_labels ] = unaries_observed <EOL> max_entry = np . maximum ( np . max ( unaries ) , <NUM_LIT:1> ) <EOL> unaries [ : n_visible , self . n_labels : ] = - <NUM_LIT> * max_entry <EOL> unaries [ n_visible : , : self . n_labels ] = - <NUM_LIT> * max_entry <EOL> return unaries <EOL> def loss_augmented_inference ( self , x , h , w , relaxed = False , <EOL> return_energy = False ) : <EOL> self . inference_calls += <NUM_LIT:1> <EOL> self . _check_size_w ( w ) <EOL> unary_potentials = self . _get_unary_potentials ( x , w ) <EOL> pairwise_potentials = self . _get_pairwise_potentials ( x , w ) <EOL> edges = self . _get_edges ( x ) <EOL> for l in np . arange ( self . n_states ) : <EOL> inds = np . where ( self . label_from_latent ( h ) != l ) [ <NUM_LIT:0> ] <EOL> unary_potentials [ inds , l ] += self . class_weight [ <EOL> self . label_from_latent ( h ) ] [ inds ] <EOL> return inference_dispatch ( unary_potentials , pairwise_potentials , edges , <EOL> self . inference_method , relaxed = relaxed , <EOL> return_energy = return_energy ) <EOL> def latent ( self , x , y , w ) : <EOL> unary_potentials = self . _get_unary_potentials ( x , w ) <EOL> max_entry = np . maximum ( np . max ( unary_potentials ) , <NUM_LIT:1> ) <EOL> unary_potentials [ np . arange ( len ( y ) ) , y ] = <NUM_LIT> * max_entry <EOL> pairwise_potentials = self . _get_pairwise_potentials ( x , w ) <EOL> edges = self . _get_edges ( x ) <EOL> h = inference_dispatch ( unary_potentials , pairwise_potentials , edges , <EOL> self . inference_method , relaxed = False ) <EOL> if ( h [ : len ( y ) ] != y ) . any ( ) : <EOL> print ( "<STR_LIT>" ) <EOL> h [ : len ( y ) ] = y <EOL> return h <EOL> def label_from_latent ( self , h ) : <EOL> return h [ h < self . n_labels ] <EOL> def loss ( self , h , h_hat ) : <EOL> if isinstance ( h_hat , tuple ) : <EOL> return self . continuous_loss ( h , h_hat [ <NUM_LIT:0> ] ) <EOL> return GraphCRF . loss ( self , self . label_from_latent ( h ) , <EOL> self . label_from_latent ( h_hat ) ) <EOL> def base_loss ( self , y , y_hat ) : <EOL> if isinstance ( y_hat , tuple ) : <EOL> return GraphCRF . continuous_loss ( self , y , y_hat ) <EOL> return GraphCRF . loss ( self , y , y_hat ) <EOL> def continuous_loss ( self , y , y_hat ) : <EOL> y_org = self . label_from_latent ( y ) <EOL> y_hat_org = y_hat [ : y_org . size , : self . n_labels ] <EOL> return GraphCRF . continuous_loss ( self , y_org , y_hat_org ) <EOL> def joint_feature ( self , x , y ) : <EOL> """<STR_LIT>""" <EOL> self . _check_size_x ( x ) <EOL> features , edges = self . _get_features ( x ) , self . _get_edges ( x ) <EOL> n_nodes = features . shape [ <NUM_LIT:0> ] <EOL> edge_features = x [ <NUM_LIT:2> ] <EOL> if isinstance ( y , tuple ) : <EOL> unary_marginals , pw = y <EOL> else : <EOL> n_nodes = y . size <EOL> gx = np . ogrid [ : n_nodes ] <EOL> unary_marginals = np . zeros ( ( n_nodes , self . n_states ) , dtype = np . int ) <EOL> gx = np . ogrid [ : n_nodes ] <EOL> unary_marginals [ gx , y ] = <NUM_LIT:1> <EOL> pw = [ np . outer ( unary_marginals [ edge [ <NUM_LIT:0> ] ] . T , <EOL> unary_marginals [ edge [ <NUM_LIT:1> ] ] ) . ravel ( ) <EOL> for edge in edges ] <EOL> pw = np . vstack ( pw ) <EOL> pw = np . dot ( edge_features . T , pw ) <EOL> for i in self . symmetric_edge_features : <EOL> pw_ = pw [ i ] . reshape ( self . n_states , self . n_states ) <EOL> pw [ i ] = ( pw_ + pw_ . T ) . ravel ( ) / <NUM_LIT> <EOL> for i in self . antisymmetric_edge_features : <EOL> pw_ = pw [ i ] . reshape ( self . n_states , self . n_states ) <EOL> pw [ i ] = ( pw_ - pw_ . T ) . ravel ( ) / <NUM_LIT> <EOL> n_visible = features . shape [ <NUM_LIT:0> ] <EOL> unaries_acc = np . dot ( unary_marginals [ : n_visible , <EOL> : self . n_input_states ] . T , features ) <EOL> joint_feature_vector = np . hstack ( [ unaries_acc . ravel ( ) , pw . ravel ( ) ] ) <EOL> return joint_feature_vector <EOL> def init_latent ( self , X , Y ) : <EOL> return kmeans_init ( X , Y , n_labels = self . n_labels , <EOL> n_hidden_states = self . n_hidden_states , <EOL> latent_node_features = self . latent_node_features ) <EOL> def max_loss ( self , h ) : <EOL> y = self . label_from_latent ( h ) <EOL> if hasattr ( self , '<STR_LIT>' ) : <EOL> return np . sum ( self . class_weight [ y ] ) <EOL> return y . size <EOL> return y . size </s>
