/usr/lib/python3/dist-packages/csb/bio/hmm/pseudocounts.py is in python3-csb 1.2.2+dfsg-2ubuntu1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 | import sys
import csb.core
import csb.bio.sequence as sequence
from csb.bio.hmm import States, ScoreUnits, Transition, State
GONNET = [10227, 3430, 2875, 3869, 1625, 2393, 4590, 6500, 2352, 3225, 5819, 4172, 1435,
1579, 3728, 4610, 6264, 418, 1824, 5709, 3430, 7780, 2209, 2589, 584, 2369,
3368, 3080, 2173, 1493, 3093, 5701, 763, 859, 1893, 2287, 3487, 444, 1338, 2356,
2875, 2209, 3868, 3601, 501, 1541, 2956, 3325, 1951, 1065, 2012, 2879, 532, 688,
1480, 2304, 3204, 219, 1148, 1759, 3869, 2589, 3601, 8618, 488, 2172, 6021, 4176,
2184, 1139, 2151, 3616, 595, 670, 2086, 2828, 3843, 204, 1119, 2015, 1625, 584,
501, 488, 5034, 355, 566, 900, 516, 741, 1336, 591, 337, 549, 419, 901,
1197, 187, 664, 1373, 2393, 2369, 1541, 2172, 355, 1987, 2891, 1959, 1587, 1066,
2260, 2751, 570, 628, 1415, 1595, 2323, 219, 871, 1682, 4590, 3368, 2956, 6021,
566, 2891, 8201, 3758, 2418, 1624, 3140, 4704, 830, 852, 2418, 2923, 4159, 278, 1268, 2809,
6500, 3080, 3325, 4176, 900, 1959, 3758, 26066, 2016, 1354, 2741, 3496, 741, 797, 2369,
3863, 4169, 375, 1186, 2569, 2352, 2173, 1951, 2184, 516, 1587, 2418, 2016, 5409,
1123, 2380, 2524, 600, 1259, 1298, 1642, 2446, 383, 876, 1691, 3225, 1493, 1065,
1139, 741, 1066, 1624, 1354, 1123, 6417, 9630, 1858, 1975, 2225, 1260, 1558, 3131,
417, 1697, 7504, 5819, 3093, 2012, 2151, 1336, 2260, 3140, 2741, 2380, 9630, 25113,
3677, 4187, 5540, 2670, 2876, 5272, 1063, 3945, 11005, 4172, 5701, 2879, 3616, 591,
2751, 4704, 3496, 2524, 1858, 3677, 7430, 949, 975, 2355, 2847, 4340, 333, 1451, 2932,
1435, 763, 532, 595, 337, 570, 830, 741, 600, 1975, 4187, 949, 1300, 1111, 573,
743, 1361, 218, 828, 2310, 1579, 859, 688, 670, 549, 628, 852, 797, 1259, 2225,
5540, 975, 1111, 6126, 661, 856, 1498, 1000, 4464, 2602, 3728, 1893, 1480, 2086, 419,
1415, 2418, 2369, 1298, 1260, 2670, 2355, 573, 661, 11834, 2320, 3300, 179, 876, 2179,
4610, 2287, 2304, 2828, 901, 1595, 2923, 3863, 1642, 1558, 2876, 2847, 743, 856, 2320,
3611, 4686, 272, 1188, 2695, 6264, 3487, 3204, 3843, 1197, 2323, 4159, 4169, 2446, 3131,
5272, 4340, 1361, 1498, 3300, 4686, 8995, 397, 1812, 5172, 418, 444, 219, 204, 187,
219, 278, 375, 383, 417, 1063, 333, 218, 1000, 179, 272, 397, 4101, 1266, 499,
1824, 1338, 1148, 1119, 664, 871, 1268, 1186, 876, 1697, 3945, 1451, 828, 4464, 876,
1188, 1812, 1266, 9380, 2227, 5709, 2356, 1759, 2015, 1373, 1682, 2809, 2569, 1691, 7504,
11005, 2932, 2310, 2602, 2179, 2695, 5172, 499, 2227.0, 11569.0]
"""
Gonnet matrix frequencies taken from HHpred
"""
class PseudocountBuilder(object):
"""
Constructs profile HMMs with pseudocounts.
"""
def __init__(self, hmm):
self._hmm = hmm
@property
def hmm(self):
return self._hmm
def add_emission_pseudocounts(self, tau=0.1, pca=2.5, pcb=0.5, pcc=1.0):
"""
Port from HHpred, it uses the conditional background probabilities,
inferred from the Gonnet matrix.
@param tau: admission weight, i.e how much of the final score is
determined by the background probabilities.
0.0=no pseudocounts.
@type tau: float
"""
from numpy import array, dot, transpose, clip
if self.hmm.pseudocounts or self.hmm.emission_pseudocounts:
return
if abs(tau) < 1e-6:
return
# Assume probabilities
if not self.hmm.score_units == ScoreUnits.Probability:
self.hmm.convert_scores(units=ScoreUnits.Probability)
alphabet = csb.core.Enum.values(sequence.StdProteinAlphabet)
## S = SubstitutionMatrix(substitution_matrix)
s_mat = array(GONNET)
#Normalize
s_mat /= s_mat.sum()
s_mat = s_mat.reshape((len(alphabet), len(alphabet)))
# Marginalize matrix
s_marginal = s_mat.sum(-1)
s_conditional = s_mat / s_marginal
# Get data and info from hmm
em = array([ [layer[States.Match].emission[aa] or 0.0 for aa in alphabet]
for layer in self.hmm.layers])
em = clip(em, sys.float_info.min, 1.)
neff_m = array([l.effective_matches for l in self.hmm.layers])
g = dot(em, transpose(s_conditional))
if neff_m is not None:
tau = clip(pca / (1. + (neff_m / pcb) ** pcc), 0.0, pcc)
e = transpose((1. - tau) * transpose(em) + tau * transpose(g))
else:
e = (1. - tau) * em + tau * g
# Renormalize e
e = transpose(transpose(e) / e.sum(-1))
for i, layer in enumerate(self.hmm.layers):
layer[States.Match].emission.set(dict(zip(alphabet, e[i])))
self.hmm.emission_pseudocounts = True
return
def add_transition_pseudocounts(self, gapb=1., gapd=0.15, gape=1.0, gapf=0.6, gapg=0.6, gapi=0.6):
"""
Add pseudocounts to the transitions. A port from hhsearch
-gapb 1.0 -gapd 0.15 -gape 1.0 -gapf 0.6 -gapg 0.6 -gapi 0.6
"""
from numpy import array
if not self.hmm._score_units == ScoreUnits.Probability:
self.hmm.convert_scores(units=ScoreUnits.Probability)
if self.hmm.pseudocounts or self.hmm.transition_pseudocounts:
return
# We need a fully populated HMM so first add all missing states
states = [States.Match, States.Insertion, States.Deletion]
background = self.hmm.layers[1][States.Match].background
for layer in self.hmm.layers:
rank = layer.rank
for state in states:
if state not in layer:
if state is States.Deletion:
# Add a new Deletion state
deletion = State(States.Deletion)
deletion.rank = rank
layer.append(deletion)
elif state is States.Insertion:
# Add a new Deletion state
insertion = State(States.Insertion,
emit=csb.core.Enum.members(
sequence.SequenceAlphabets.Protein))
insertion.background.set(background)
insertion.emission.set(background)
insertion.rank = rank
layer.append(insertion)
if not self.hmm.start_insertion:
insertion = State(States.Insertion,
emit=csb.core.Enum.members(
sequence.SequenceAlphabets.Protein))
insertion.background.set(background)
insertion.emission.set(background)
insertion.rank = 0
self.hmm.start_insertion = insertion
# make hmm completly connected
for i in range(1, self.hmm.layers.length):
layer = self.hmm.layers[i]
#Start with match state
state = layer[States.Match]
if not States.Insertion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i][States.Insertion],
0.0))
if not States.Deletion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i + 1][States.Deletion],
0.0))
state = layer[States.Insertion]
if not States.Insertion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i][States.Insertion],
0.0))
if not States.Match in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i + 1][States.Match],
0.0))
state = layer[States.Deletion]
if not States.Deletion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i + 1][States.Deletion],
0.0))
if not States.Match in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[i + 1][States.Match],
0.0))
# start layer
state = self.hmm.start
if not States.Insertion in self.hmm.start.transitions:
state.transitions.append(Transition(self.hmm.start,
self.hmm.start_insertion,
0.0))
if not States.Deletion in self.hmm.start.transitions:
state.transitions.append(Transition(self.hmm.start,
self.hmm.layers[1][States.Deletion],
0.0))
state = self.hmm.start_insertion
if not States.Insertion in self.hmm.start_insertion.transitions:
state.transitions.append(Transition(self.hmm.start_insertion,
self.hmm.start_insertion,
0.0))
if not States.Match in self.hmm.start_insertion.transitions:
state.transitions.append(Transition(self.hmm.start_insertion,
self.hmm.layers[1][States.Match],
0.0))
# last layer
state = self.hmm.layers[-1][States.Match]
if not States.Insertion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[-1][States.Insertion],
0.0))
state = self.hmm.layers[-1][States.Insertion]
if not States.Insertion in state.transitions:
state.transitions.append(Transition(state,
self.hmm.layers[-1][States.Insertion],
0.0))
if not States.End in state.transitions:
state.transitions.append(Transition(state,
self.hmm.end,
0.0))
state = self.hmm.layers[-1][States.Deletion]
if not States.End in state.transitions:
state.transitions.append(Transition(state,
self.hmm.end,
0.0))
# Now we have created a fully connected HMM
# Lates add pseuod counts
# Calculate pseudo counts
# to be honest I really do not know how they came up with this
pc_MD = pc_MI = 0.0286 * gapd
pc_MM = 1. - 2 * pc_MD
pc_DD = pc_II = gape / (gape - 1 + 1 / 0.75)
pc_DM = pc_IM = 1. - pc_II
# Get current transtion probabilities
t_mm = self.hmm.start.transitions[States.Match].probability
t_mi = self.hmm.start.transitions[States.Insertion].probability
t_md = self.hmm.start.transitions[States.Deletion].probability
# Transitions from Match state
n_eff = self.hmm.effective_matches
t = array([(n_eff - 1) * t_mm + gapb * pc_MM,
(n_eff - 1) * t_mi + gapb * pc_MI,
(n_eff - 1) * t_md + gapb * pc_MD])
# normalize to one
t /= t.sum()
# Set
self.hmm.start.transitions[States.Match].probability = t[0]
self.hmm.start.transitions[States.Insertion].probability = t[1]
self.hmm.start.transitions[States.Deletion].probability = t[2]
# Rinse and repeat
t_im = self.hmm.start_insertion.transitions[States.Match].probability
t_ii = self.hmm.start_insertion.transitions[States.Insertion].probability
t = array([t_im + gapb * pc_IM, t_ii + gapb * pc_II])
t /= t.sum()
self.hmm.start_insertion.transitions[States.Match].probability = t[0]
t_ii = self.hmm.start_insertion.transitions[States.Insertion].probability = t[1]
# And now for all layers
for layer in self.hmm.layers[:-1]:
# Get current transtion probabilities
t_mm = layer[States.Match].transitions[States.Match].probability
t_mi = layer[States.Match].transitions[States.Insertion].probability
t_md = layer[States.Match].transitions[States.Deletion].probability
n_eff = layer.effective_matches
t = array([(n_eff - 1) * t_mm + gapb * pc_MM,
(n_eff - 1) * t_mi + gapb * pc_MI,
(n_eff - 1) * t_md + gapb * pc_MD])
# normalize to one
t /= t.sum()
layer[States.Match].transitions[States.Match].probability = t[0]
layer[States.Match].transitions[States.Insertion].probability = t[1]
layer[States.Match].transitions[States.Deletion].probability = t[2]
# Transitions from insert state
t_im = layer[States.Insertion].transitions[States.Match].probability
t_ii = layer[States.Insertion].transitions[States.Insertion].probability
n_eff = layer.effective_insertions
t = array([t_im * n_eff + gapb * pc_IM,
t_im * n_eff + gapb * pc_II])
# normalize to one
t /= t.sum()
layer[States.Insertion].transitions[States.Match].probability = t[0]
layer[States.Insertion].transitions[States.Insertion].probability = t[1]
# Transitions form deletion state
t_dm = layer[States.Deletion].transitions[States.Match].probability
t_dd = layer[States.Deletion].transitions[States.Deletion].probability
n_eff = layer.effective_deletions
t = array([t_dm * n_eff + gapb * pc_DM,
t_dd * n_eff + gapb * pc_DD])
# normalize to one
t /= t.sum()
layer[States.Deletion].transitions[States.Match].probability = t[0]
layer[States.Deletion].transitions[States.Deletion].probability = t[1]
#Last layer
layer = self.hmm.layers[-1]
t_mm = layer[States.Match].transitions[States.End].probability
t_mi = layer[States.Match].transitions[States.Insertion].probability
n_eff = layer.effective_matches
# No deletion
t = array([(n_eff - 1) * t_mm + gapb * pc_MM,
(n_eff - 1) * t_mi + gapb * pc_MI])
# normalize to one
t /= t.sum()
layer[States.Match].transitions[States.End].probability = t[0]
layer[States.Match].transitions[States.Insertion].probability = t[1]
# Transitions from insert state
t_im = layer[States.Insertion].transitions[States.End].probability
t_ii = layer[States.Insertion].transitions[States.Insertion].probability
n_eff = layer.effective_insertions
t = array([t_im * n_eff + gapb * pc_IM,
t_im * n_eff + gapb * pc_II])
# normalize to one
t /= t.sum()
layer[States.Insertion].transitions[States.End].probability = t[0]
layer[States.Insertion].transitions[States.Insertion].probability = t[1]
layer[States.Deletion].transitions[States.End].probability = 1.
self.hmm.transition_pseudocounts = True
return
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