treetime/treeanc.py
def _ml_anc_joint(self, store_compressed=True, final=True, sample_from_profile=False,
                            debug=False, **kwargs):

        """
        Perform joint ML reconstruction of the ancestral states. In contrast to
        marginal reconstructions, this only needs to compare and multiply LH and
        can hence operate in log space.

        Parameters
        ----------

         store_compressed : bool, default True
            attach a reduced representation of sequence changed to each branch

         final : bool, default True
            stop full length by expanding sites with identical alignment patterns

         sample_from_profile : str
            This parameter can take the value 'root' in which case probabilistic
            sampling will happen at the root. otherwise sequences at ALL nodes are
            set to the value that jointly optimized the likelihood.

        """
        N_diff = 0 # number of sites differ from perv reconstruction
        L = self.multiplicity.shape[0]
        n_states = self.gtr.alphabet.shape[0]

        self.logger("TreeAnc._ml_anc_joint: type of reconstruction: Joint", 2)

        self.logger("TreeAnc._ml_anc_joint: Walking up the tree, computing likelihoods... ", 3)
        # for the internal nodes, scan over all states j of this node, maximize the likelihood
        for node in self.tree.find_clades(order='postorder'):
            if node.up is None:
                node.joint_Cx=None # not needed for root
                continue

            # preallocate storage
            node.joint_Lx = np.zeros((L, n_states))             # likelihood array
            node.joint_Cx = np.zeros((L, n_states), dtype=int)  # max LH indices
            branch_len = self._branch_length_to_gtr(node)
            # transition matrix from parent states to the current node states.
            # denoted as Pij(i), where j - parent state, i - node state
            log_transitions = np.log(np.maximum(ttconf.TINY_NUMBER, self.gtr.expQt(branch_len)))
            if node.is_terminal():
                try:
                    msg_from_children = np.log(np.maximum(seq2prof(node.original_cseq, self.gtr.profile_map), ttconf.TINY_NUMBER))
                except:
                    raise ValueError("sequence assignment to node "+node.name+" failed")
                msg_from_children[np.isnan(msg_from_children) | np.isinf(msg_from_children)] = -ttconf.BIG_NUMBER
            else:
                # Product (sum-Log) over all child subtree likelihoods.
                # this is prod_ch L_x(i)
                msg_from_children = np.sum(np.stack([c.joint_Lx for c in node.clades], axis=0), axis=0)

            # for every possible state of the parent node,
            # get the best state of the current node
            # and compute the likelihood of this state
            for char_i, char in enumerate(self.gtr.alphabet):
                # Pij(i) * L_ch(i) for given parent state j
                msg_to_parent = (log_transitions[:,char_i].T + msg_from_children)
                # For this parent state, choose the best state of the current node:
                node.joint_Cx[:, char_i] = msg_to_parent.argmax(axis=1)
                # compute the likelihood of the best state of the current node
                # given the state of the parent (char_i)
                node.joint_Lx[:, char_i] = msg_to_parent.max(axis=1)

        # root node profile = likelihood of the total tree
        msg_from_children = np.sum(np.stack([c.joint_Lx for c in self.tree.root], axis = 0), axis=0)
        # Pi(i) * Prod_ch Lch(i)
        self.tree.root.joint_Lx = msg_from_children + np.log(self.gtr.Pi).T
        normalized_profile = (self.tree.root.joint_Lx.T - self.tree.root.joint_Lx.max(axis=1)).T

        # choose sequence characters from this profile.
        # treat root node differently to avoid piling up mutations on the longer branch
        if sample_from_profile=='root':
            root_sample_from_profile = True
        elif isinstance(sample_from_profile, bool):
            root_sample_from_profile = sample_from_profile

        seq, anc_lh_vals, idxs = prof2seq(np.exp(normalized_profile),
                                    self.gtr, sample_from_prof = root_sample_from_profile)

        # compute the likelihood of the most probable root sequence
        self.tree.sequence_LH = np.choose(idxs, self.tree.root.joint_Lx.T)
        self.tree.sequence_joint_LH = (self.tree.sequence_LH*self.multiplicity).sum()
        self.tree.root.cseq = seq
        self.tree.root.seq_idx = idxs
        if final:
            if self.is_vcf:
                self.tree.root.sequence = self.dict_sequence(self.tree.root)
            else:
                self.tree.root.sequence = self.expanded_sequence(self.tree.root)

        self.logger("TreeAnc._ml_anc_joint: Walking down the tree, computing maximum likelihood sequences...",3)
        # for each node, resolve the conditioning on the parent node
        for node in self.tree.find_clades(order='preorder'):

            # root node has no mutations, everything else has been alread y set
            if node.up is None:
                node.mutations = []
                continue

            # choose the value of the Cx(i), corresponding to the state of the
            # parent node i. This is the state of the current node
            node.seq_idx = np.choose(node.up.seq_idx, node.joint_Cx.T)
            # reconstruct seq, etc
            tmp_sequence = np.choose(node.seq_idx, self.gtr.alphabet)
            if hasattr(node, 'sequence') and node.cseq is not None:
                N_diff += (tmp_sequence!=node.cseq).sum()
            else:
                N_diff += L

            node.cseq = tmp_sequence
            if final:
                node.mutations = self.get_mutations(node)
                if self.is_vcf:
                    node.sequence = self.dict_sequence(node)
                else:
                    node.sequence = self.expanded_sequence(node)


        self.logger("TreeAnc._ml_anc_joint: ...done", 3)
        if store_compressed:
            self._store_compressed_sequence_pairs()

        # do clean-up
        if not debug:
            for node in self.tree.find_clades(order='preorder'):
                del node.joint_Lx
                del node.joint_Cx
                del node.seq_idx

        return N_diff
def _ml_anc_joint(self, store_compressed=True, final=True, sample_from_profile=False,
                            debug=False, **kwargs):

        """
        Perform joint ML reconstruction of the ancestral states. In contrast to
        marginal reconstructions, this only needs to compare and multiply LH and
        can hence operate in log space.

        Parameters
        ----------

         store_compressed : bool, default True
            attach a reduced representation of sequence changed to each branch

         final : bool, default True
            stop full length by expanding sites with identical alignment patterns

         sample_from_profile : str
            This parameter can take the value 'root' in which case probabilistic
            sampling will happen at the root. otherwise sequences at ALL nodes are
            set to the value that jointly optimized the likelihood.

        """
        N_diff = 0 # number of sites differ from perv reconstruction
        L = self.multiplicity.shape[0]
        n_states = self.gtr.alphabet.shape[0]

        self.logger("TreeAnc._ml_anc_joint: type of reconstruction: Joint", 2)

        self.logger("TreeAnc._ml_anc_joint: Walking up the tree, computing likelihoods... ", 3)
        # for the internal nodes, scan over all states j of this node, maximize the likelihood
        for node in self.tree.find_clades(order='postorder'):
            if node.up is None:
                node.joint_Cx=None # not needed for root
                continue

            # preallocate storage
            node.joint_Lx = np.zeros((L, n_states))             # likelihood array
            node.joint_Cx = np.zeros((L, n_states), dtype=int)  # max LH indices
            branch_len = self._branch_length_to_gtr(node)
            # transition matrix from parent states to the current node states.
            # denoted as Pij(i), where j - parent state, i - node state
            log_transitions = np.log(np.maximum(ttconf.TINY_NUMBER, self.gtr.expQt(branch_len)))
            if node.is_terminal():
                try:
                    msg_from_children = np.log(np.maximum(seq2prof(node.original_cseq, self.gtr.profile_map), ttconf.TINY_NUMBER))
                except:
                    raise ValueError("sequence assignment to node "+node.name+" failed")
                msg_from_children[np.isnan(msg_from_children) | np.isinf(msg_from_children)] = -ttconf.BIG_NUMBER
            else:
                # Product (sum-Log) over all child subtree likelihoods.
                # this is prod_ch L_x(i)
                msg_from_children = np.sum(np.stack([c.joint_Lx for c in node.clades], axis=0), axis=0)

            # for every possible state of the parent node,
            # get the best state of the current node
            # and compute the likelihood of this state
            for char_i, char in enumerate(self.gtr.alphabet):
                # Pij(i) * L_ch(i) for given parent state j
                msg_to_parent = (log_transitions[:,char_i].T + msg_from_children)
                # For this parent state, choose the best state of the current node:
                node.joint_Cx[:, char_i] = msg_to_parent.argmax(axis=1)
                # compute the likelihood of the best state of the current node
                # given the state of the parent (char_i)
                node.joint_Lx[:, char_i] = msg_to_parent.max(axis=1)

        # root node profile = likelihood of the total tree
        msg_from_children = np.sum(np.stack([c.joint_Lx for c in self.tree.root], axis = 0), axis=0)
        # Pi(i) * Prod_ch Lch(i)
        self.tree.root.joint_Lx = msg_from_children + np.log(self.gtr.Pi).T
        normalized_profile = (self.tree.root.joint_Lx.T - self.tree.root.joint_Lx.max(axis=1)).T

        # choose sequence characters from this profile.
        # treat root node differently to avoid piling up mutations on the longer branch
        if sample_from_profile=='root':
            root_sample_from_profile = True
        elif isinstance(sample_from_profile, bool):
            root_sample_from_profile = sample_from_profile

        seq, anc_lh_vals, idxs = prof2seq(np.exp(normalized_profile),
                                    self.gtr, sample_from_prof = root_sample_from_profile)

        # compute the likelihood of the most probable root sequence
        self.tree.sequence_LH = np.choose(idxs, self.tree.root.joint_Lx.T)
        self.tree.sequence_joint_LH = (self.tree.sequence_LH*self.multiplicity).sum()
        self.tree.root.cseq = seq
        self.tree.root.seq_idx = idxs
        if final:
            if self.is_vcf:
                self.tree.root.sequence = self.dict_sequence(self.tree.root)
            else:
                self.tree.root.sequence = self.expanded_sequence(self.tree.root)

        self.logger("TreeAnc._ml_anc_joint: Walking down the tree, computing maximum likelihood sequences...",3)
        # for each node, resolve the conditioning on the parent node
        for node in self.tree.find_clades(order='preorder'):

            # root node has no mutations, everything else has been alread y set
            if node.up is None:
                node.mutations = []
                continue

            # choose the value of the Cx(i), corresponding to the state of the
            # parent node i. This is the state of the current node
            node.seq_idx = np.choose(node.up.seq_idx, node.joint_Cx.T)
            # reconstruct seq, etc
            tmp_sequence = np.choose(node.seq_idx, self.gtr.alphabet)
            if hasattr(node, 'sequence') and node.cseq is not None:
                N_diff += (tmp_sequence!=node.cseq).sum()
            else:
                N_diff += L

            node.cseq = tmp_sequence
            if final:
                node.mutations = self.get_mutations(node)
                if self.is_vcf:
                    node.sequence = self.dict_sequence(node)
                else:
                    node.sequence = self.expanded_sequence(node)


        self.logger("TreeAnc._ml_anc_joint: ...done", 3)
        if store_compressed:
            self._store_compressed_sequence_pairs()

        # do clean-up
        if not debug:
            for node in self.tree.find_clades(order='preorder'):
                del node.joint_Lx
                del node.joint_Cx
                del node.seq_idx

        return N_diff
def _ml_anc_joint ( self , store_compressed = True , final = True , sample_from_profile = False , debug = False , * * kwargs ) : N_diff = 0 # number of sites differ from perv reconstruction L = self . multiplicity . shape [ 0 ] n_states = self . gtr . alphabet . shape [ 0 ] self . logger ( "TreeAnc._ml_anc_joint: type of reconstruction: Joint" , 2 ) self . logger ( "TreeAnc._ml_anc_joint: Walking up the tree, computing likelihoods... " , 3 ) # for the internal nodes, scan over all states j of this node, maximize the likelihood for node in self . tree . find_clades ( order = 'postorder' ) : if node . up is None : node . joint_Cx = None # not needed for root continue # preallocate storage node . joint_Lx = np . zeros ( ( L , n_states ) ) # likelihood array node . joint_Cx = np . zeros ( ( L , n_states ) , dtype = int ) # max LH indices branch_len = self . _branch_length_to_gtr ( node ) # transition matrix from parent states to the current node states. # denoted as Pij(i), where j - parent state, i - node state log_transitions = np . log ( np . maximum ( ttconf . TINY_NUMBER , self . gtr . expQt ( branch_len ) ) ) if node . is_terminal ( ) : try : msg_from_children = np . log ( np . maximum ( seq2prof ( node . original_cseq , self . gtr . profile_map ) , ttconf . TINY_NUMBER ) ) except : raise ValueError ( "sequence assignment to node " + node . name + " failed" ) msg_from_children [ np . isnan ( msg_from_children ) | np . isinf ( msg_from_children ) ] = - ttconf . BIG_NUMBER else : # Product (sum-Log) over all child subtree likelihoods. # this is prod_ch L_x(i) msg_from_children = np . sum ( np . stack ( [ c . joint_Lx for c in node . clades ] , axis = 0 ) , axis = 0 ) # for every possible state of the parent node, # get the best state of the current node # and compute the likelihood of this state for char_i , char in enumerate ( self . gtr . alphabet ) : # Pij(i) * L_ch(i) for given parent state j msg_to_parent = ( log_transitions [ : , char_i ] . T + msg_from_children ) # For this parent state, choose the best state of the current node: node . joint_Cx [ : , char_i ] = msg_to_parent . argmax ( axis = 1 ) # compute the likelihood of the best state of the current node # given the state of the parent (char_i) node . joint_Lx [ : , char_i ] = msg_to_parent . max ( axis = 1 ) # root node profile = likelihood of the total tree msg_from_children = np . sum ( np . stack ( [ c . joint_Lx for c in self . tree . root ] , axis = 0 ) , axis = 0 ) # Pi(i) * Prod_ch Lch(i) self . tree . root . joint_Lx = msg_from_children + np . log ( self . gtr . Pi ) . T normalized_profile = ( self . tree . root . joint_Lx . T - self . tree . root . joint_Lx . max ( axis = 1 ) ) . T # choose sequence characters from this profile. # treat root node differently to avoid piling up mutations on the longer branch if sample_from_profile == 'root' : root_sample_from_profile = True elif isinstance ( sample_from_profile , bool ) : root_sample_from_profile = sample_from_profile seq , anc_lh_vals , idxs = prof2seq ( np . exp ( normalized_profile ) , self . gtr , sample_from_prof = root_sample_from_profile ) # compute the likelihood of the most probable root sequence self . tree . sequence_LH = np . choose ( idxs , self . tree . root . joint_Lx . T ) self . tree . sequence_joint_LH = ( self . tree . sequence_LH * self . multiplicity ) . sum ( ) self . tree . root . cseq = seq self . tree . root . seq_idx = idxs if final : if self . is_vcf : self . tree . root . sequence = self . dict_sequence ( self . tree . root ) else : self . tree . root . sequence = self . expanded_sequence ( self . tree . root ) self . logger ( "TreeAnc._ml_anc_joint: Walking down the tree, computing maximum likelihood sequences..." , 3 ) # for each node, resolve the conditioning on the parent node for node in self . tree . find_clades ( order = 'preorder' ) : # root node has no mutations, everything else has been alread y set if node . up is None : node . mutations = [ ] continue # choose the value of the Cx(i), corresponding to the state of the # parent node i. This is the state of the current node node . seq_idx = np . choose ( node . up . seq_idx , node . joint_Cx . T ) # reconstruct seq, etc tmp_sequence = np . choose ( node . seq_idx , self . gtr . alphabet ) if hasattr ( node , 'sequence' ) and node . cseq is not None : N_diff += ( tmp_sequence != node . cseq ) . sum ( ) else : N_diff += L node . cseq = tmp_sequence if final : node . mutations = self . get_mutations ( node ) if self . is_vcf : node . sequence = self . dict_sequence ( node ) else : node . sequence = self . expanded_sequence ( node ) self . logger ( "TreeAnc._ml_anc_joint: ...done" , 3 ) if store_compressed : self . _store_compressed_sequence_pairs ( ) # do clean-up if not debug : for node in self . tree . find_clades ( order = 'preorder' ) : del node . joint_Lx del node . joint_Cx del node . seq_idx return N_diff
Perform joint ML reconstruction of the ancestral states. In contrast to
        marginal reconstructions, this only needs to compare and multiply LH and
        can hence operate in log space.

        Parameters
        ----------

         store_compressed : bool, default True
            attach a reduced representation of sequence changed to each branch

         final : bool, default True
            stop full length by expanding sites with identical alignment patterns

         sample_from_profile : str
            This parameter can take the value 'root' in which case probabilistic
            sampling will happen at the root. otherwise sequences at ALL nodes are
            set to the value that jointly optimized the likelihood.
Perform joint ML reconstruction of the ancestral states . In contrast to marginal reconstructions this only needs to compare and multiply LH and can hence operate in log space .