def _score(self, alignment):
if self.use_seq_weights:
seq_weights = alignment.get_seq_weights()
else:
seq_weights = [1.0] * len(alignment.msa)
if self.bg_distribution is None:
# Estimate bg distribution from this alignment
q = weighted_freq_count_pseudocount((aa for seq in alignment.msa for aa in seq), seq_weights, PSEUDOCOUNT)
else:
q = self.bg_distribution
scores = []
for i in xrange(len(alignment.msa[0])):
col = get_column(i, alignment.msa)
n_gaps = col.count("-")
assert n_gaps < len(col)
if self.gap_cutoff != 1 and n_gaps / len(col) > self.gap_cutoff:
score = self.SCORE_OVER_GAP_CUTOFF
else:
score = self._score_col(col, seq_weights, q)
if self.use_gap_penalty:
# vn_entropy has this commented out for some reason
score *= weighted_gap_penalty(col, seq_weights)
scores.append(score)
return scores
def _score_col(self, col, seq_weights):
"""
Calculate the relative entropy of a column col relative to a
partition of the amino acids. Similar to Williamson '95. See shannon_entropy()
for more general info.
"""
if len(self.bg_distribution) == len(self.property_partition):
prop_bg_freq = self.bg_distribution
else:
# XXX: shouldn't we sum the bg distribution frequencies instead of using
# some fixed prop bg freq?
prop_bg_freq = self.prop_bg_freq
fc = weighted_freq_count_pseudocount(col, seq_weights, PSEUDOCOUNT)
# sum the aa frequencies to get the property frequencies
prop_fc = [0.] * len(self.property_partition)
for p in xrange(len(self.property_partition)):
for aa in self.property_partition[p]:
prop_fc[p] += fc[aa_to_index[aa]]
d = 0.
for i in xrange(len(prop_fc)):
if prop_fc[i] and prop_bg_freq[i]:
d += prop_fc[i] * math.log(prop_fc[i] / prop_bg_freq[i], 2)
# Convert score so that it's between 0 and 1.
# XXX: why is relative entropy assumed to be bounded?
d /= math.log(len(prop_fc))
return d
def _score_col(self, col, seq_weights):
"""
Calculate the entropy of a column col relative to a partition of the
amino acids. Similar to Mirny '99.
"""
fc = weighted_freq_count_pseudocount(col, seq_weights, PSEUDOCOUNT)
# sum the aa frequencies to get the property frequencies
prop_fc = [0.] * len(self.property_partition)
for p in range(len(self.property_partition)):
for aa in self.property_partition[p]:
prop_fc[p] += fc[aa_to_index[aa]]
h = 0.
for pfc_i in prop_fc:
if pfc_i:
h -= pfc_i * math.log(pfc_i)
# Convert score so that it's between 0 and 1.
# Recall that shannon entropy is between 0 and log(number of values with nonzero freq)
# XXX: Why involve len(col) if we have a pseudocount?
h /= math.log(min(len(self.property_partition), len(col)))
# Convert score so that 1 is conserved, and 0 is not.
return 1 - h
def _score_col(self, col, seq_weights):
"""
Calculate the relative entropy of the column distribution with a
background distribution specified in bg_distr. This is similar to the
approach proposed in Wang and Samudrala 06.
"""
q = self.bg_distribution
with_gap = (len(q) == 21)
fc = weighted_freq_count_pseudocount(col, seq_weights, PSEUDOCOUNT, with_gap)
assert len(fc) == len(q)
d = np.sum(fc * np.log(fc/q))
# Convert score so that it's between 0 and 1.
# XXX: why is relative entropy assumed to be bounded?
d /= np.log(len(fc))
return d
def _score_col(self, col, seq_weights, q):
"""
Return the Jensen-Shannon Divergence for the column with the background
distribution q.
"""
lamb1 = self.lambda_prior
lamb2 = 1 - self.lambda_prior
# get frequency distribution
with_gap = len(q) == 21
pc = weighted_freq_count_pseudocount(col, seq_weights, PSEUDOCOUNT, with_gap)
assert len(pc) == len(q)
# make r distriubtion
r = lamb1 * pc + lamb2 * q
# sum relative entropies
d1 = lamb1 * sum(pc[i] * math.log(pc[i] / r[i], 2) for i in xrange(len(pc)) if pc[i])
d2 = lamb2 * sum(q[i] * math.log(q[i] / r[i], 2) for i in xrange(len(pc)) if pc[i])
return d1 + d2
def _score_col(self, col, seq_weights):
"""
Calculates the Shannon entropy of the column col.
The entropy will be between zero and one because of its base. See p.13 of
Valdar 02 for details. The information score 1 - h is returned for the sake
of consistency with other scores.
"""
fc = weighted_freq_count_pseudocount(col, seq_weights, PSEUDOCOUNT)
h = 0.
for fc_i in fc:
if fc_i:
h -= fc_i * math.log(fc_i)
# Convert score so that it's between 0 and 1.
# Recall that shannon entropy is between 0 and log(number of values with nonzero freq)
# XXX: Why involve len(col) if we have a pseudocount?
h /= math.log(len(fc))#math.log(min(len(fc), len(col)))
# Convert score so that 1 is conserved, and 0 is not.
return 1 - h
