/usr/lib/python2.7/dist-packages/cogent/app/rdp_classifier.py is in python-cogent 1.9-9.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 | #!/usr/bin/env python
"""Application controller for rdp_classifier-2.0
"""
__author__ = "Kyle Bittinger"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Kyle Bittinger","Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Kyle Bittinger"
__email__ = "kylebittinger@gmail.com"
__status__ = "Prototype"
import os.path
import re
from os import remove, environ, getenv, path
from optparse import OptionParser
from shutil import rmtree
import tempfile
import warnings
from cogent.app.parameters import Parameter, ValuedParameter, Parameters
from cogent.parse.fasta import MinimalFastaParser
from cogent.app.util import CommandLineApplication, CommandLineAppResult, \
FilePath, ResultPath, guess_input_handler, system,\
ApplicationNotFoundError, ApplicationError
from cogent.util.misc import app_path
class RdpClassifier(CommandLineApplication):
"""RDP Classifier application controller
The RDP Classifier program is distributed as a java archive (.jar)
file. If the file 'rdp_classifier-2.2.jar' is not found in the
current directory, the app controller uses the JAR file specified
by the environment variable RDP_JAR_PATH. If this variable is not
set, and 'rdp_classifier-2.2.jar' is not found in the current
directory, the application controller raises an
ApplicationNotFoundError.
The RDP Classifier often requires memory in excess of Java's
default 64M. To correct this situation, the authors recommend
increasing the maximum heap size for the java virtual machine. An
option '-Xmx' (default 1000M) is provided for this purpose.
Details on this option may be found at
http://java.sun.com/j2se/1.5.0/docs/tooldocs/solaris/java.html
The classifier may optionally use a custom training set. The full
path to the training set may be provided in the option
'-training-data'.
"""
_input_handler = '_input_as_lines'
_command = "/usr/share/java/rdp_classifier.jar"
_options = {
# output file name for classification assignment
'-o': ValuedParameter('-', Name='o', Delimiter=' ', IsPath=True),
# a property file contains the mapping of the training
# files. Note: the training files and the property file should
# be in the same directory. The default property file is set
# to data/classifier/rRNAClassifier.properties.
'-t': ValuedParameter('-', Name='t', Delimiter=' ', IsPath=True),
# all tab delimited output format: [allrank|fixrank|db].
# Default is allrank.
#
# allrank: outputs the results for all ranks applied for
# each sequence: seqname, orientation, taxon name, rank,
# conf, ...
#
# fixrank: only outputs the results for fixed ranks in
# order: no rank, domain, phylum, class, order, family,
# genus
#
# db: outputs the seqname, trainset_no, tax_id, conf. This
# is good for storing in a database
'-f': ValuedParameter('-', Name='f', Delimiter=' '),
}
# The following are available in the attributes JvmParameters,
# JarParameters, and PositionalParameters
_jvm_synonyms = {}
_jvm_parameters = {
# Maximum heap size for JVM.
'-Xmx': ValuedParameter('-', Name='Xmx', Delimiter='', Value='1000m'),
}
_parameters = {}
_parameters.update(_options)
_parameters.update(_jvm_parameters)
def getHelp(self):
"""Returns documentation string"""
# Summary paragraph copied from rdp_classifier-2.0, which is
# licensed under the GPL 2.0 and Copyright 2008 Michigan State
# University Board of Trustees
help_str = """\
usage: ClassifierCmd [-f <arg>] [-o <arg>] [-q <arg>] [-t <arg>]
-f,--format <arg> all tab delimited output format:
[allrank|fixrank|db]. Default is allrank.
allrank: outputs the results for all ranks applied for each
sequence: seqname, orientation, taxon name, rank, conf, ...
fixrank: only outputs the results for fixed ranks in order:
no rank, domain, phylum, class, order, family, genus
db: outputs the seqname, trainset_no, tax_id, conf. This is
good for storing in a database
-o,--outputFile <arg> output file name for classification
assignment
-q,--queryFile <arg> query file contains sequences in one of
the following formats: Fasta, Genbank and EMBL
-t,--train_propfile <arg> a property file contains the mapping
of the training files.
Note: the training files and the property file should be in
the same directory. The default property file is set to
data/classifier/rRNAClassifier.properties."""
return help_str
def _accept_exit_status(self, status):
"""Returns false if an error occurred in execution
"""
return (status == 0)
def _error_on_missing_application(self,params):
"""Raise an ApplicationNotFoundError if the app is not accessible
In this case, checks for the java runtime and the RDP jar file.
"""
if not (os.path.exists('java') or app_path('java')):
raise ApplicationNotFoundError(
"Cannot find java runtime. Is it installed? Is it in your "
"path?")
jar_fp = self._get_jar_fp()
if jar_fp is None:
raise ApplicationNotFoundError(
"JAR file not found in current directory and the RDP_JAR_PATH "
"environment variable is not set. Please set RDP_JAR_PATH to "
"the full pathname of the JAR file.")
if not os.path.exists(jar_fp):
raise ApplicationNotFoundError(
"JAR file %s does not exist." % jar_fp)
def _get_jar_fp(self):
"""Returns the full path to the JAR file.
If the JAR file cannot be found in the current directory and
the environment variable RDP_JAR_PATH is not set, returns
None.
"""
# handles case where the jar file is in the current working directory
if os.path.exists(self._command):
return self._command
# handles the case where the user has specified the location via
# an environment variable
elif 'RDP_JAR_PATH' in environ:
return getenv('RDP_JAR_PATH')
else:
return None
# Overridden to pull out JVM-specific command-line arguments.
def _get_base_command(self):
"""Returns the base command plus command-line options.
Does not include input file, output file, and training set.
"""
cd_command = ''.join(['cd ', str(self.WorkingDir), ';'])
jvm_command = "java"
jvm_arguments = self._commandline_join(
[self.Parameters[k] for k in self._jvm_parameters])
jar_arguments = '-jar "%s"' % self._get_jar_fp()
rdp_arguments = self._commandline_join(
[self.Parameters[k] for k in self._options])
command_parts = [
cd_command, jvm_command, jvm_arguments, jar_arguments,
rdp_arguments, '-q']
return self._commandline_join(command_parts).strip()
BaseCommand = property(_get_base_command)
def _commandline_join(self, tokens):
"""Formats a list of tokens as a shell command
This seems to be a repeated pattern; may be useful in
superclass.
"""
commands = filter(None, map(str, tokens))
return self._command_delimiter.join(commands).strip()
def _get_result_paths(self,data):
""" Return a dict of ResultPath objects representing all possible output
"""
assignment_fp = str(self.Parameters['-o'].Value).strip('"')
if not os.path.isabs(assignment_fp):
assignment_fp = os.path.relpath(assignment_fp, self.WorkingDir)
return {'Assignments': ResultPath(assignment_fp, IsWritten=True)}
class RdpTrainer(RdpClassifier):
_input_handler = '_input_as_lines'
TrainingClass = 'edu.msu.cme.rdp.classifier.train.ClassifierTraineeMaker'
PropertiesFile = 'RdpClassifier.properties'
_parameters = {
'taxonomy_file': ValuedParameter(None, None, IsPath=True),
'model_output_dir': ValuedParameter(None, None, IsPath=True),
'training_set_id': ValuedParameter(None, None, Value='1'),
'taxonomy_version': ValuedParameter(None, None, Value='version1'),
'modification_info': ValuedParameter(None, None, Value='cogent'),
}
_jvm_parameters = {
# Maximum heap size for JVM.
'-Xmx': ValuedParameter('-', Name='Xmx', Delimiter='', Value='1000m'),
}
_parameters.update(_jvm_parameters)
def _get_base_command(self):
"""Returns the base command plus command-line options.
Handles everything up to and including the classpath. The
positional training parameters are added by the
_input_handler_decorator method.
"""
cd_command = ''.join(['cd ', str(self.WorkingDir), ';'])
jvm_command = "java"
jvm_args = self._commandline_join(
[self.Parameters[k] for k in self._jvm_parameters])
cp_args = '-cp "%s" %s' % (self._get_jar_fp(), self.TrainingClass)
command_parts = [cd_command, jvm_command, jvm_args, cp_args]
return self._commandline_join(command_parts).strip()
BaseCommand = property(_get_base_command)
def _set_input_handler(self, method_name):
"""Stores the selected input handler in a private attribute.
"""
self.__InputHandler = method_name
def _get_input_handler(self):
"""Returns decorator that wraps the requested input handler.
"""
return '_input_handler_decorator'
InputHandler = property(_get_input_handler, _set_input_handler)
@property
def ModelDir(self):
"""Absolute FilePath to the training output directory.
"""
model_dir = self.Parameters['model_output_dir'].Value
absolute_model_dir = os.path.abspath(model_dir)
return FilePath(absolute_model_dir)
def _input_handler_decorator(self, data):
"""Adds positional parameters to selected input_handler's results.
"""
input_handler = getattr(self, self.__InputHandler)
input_parts = [
self.Parameters['taxonomy_file'],
input_handler(data),
self.Parameters['training_set_id'],
self.Parameters['taxonomy_version'],
self.Parameters['modification_info'],
self.ModelDir,
]
return self._commandline_join(input_parts)
def _get_result_paths(self, output_dir):
"""Return a dict of output files.
"""
# Only include the properties file here. Add the other result
# paths in the __call__ method, so we can catch errors if an
# output file is not written.
self._write_properties_file()
properties_fp = os.path.join(self.ModelDir, self.PropertiesFile)
result_paths = {
'properties': ResultPath(properties_fp, IsWritten=True,)
}
return result_paths
def _write_properties_file(self):
"""Write an RDP training properties file manually.
"""
# The properties file specifies the names of the files in the
# training directory. We use the example properties file
# directly from the rdp_classifier distribution, which lists
# the default set of files created by the application. We
# must write this file manually after generating the
# training data.
properties_fp = os.path.join(self.ModelDir, self.PropertiesFile)
properties_file = open(properties_fp, 'w')
properties_file.write(
"# Sample ResourceBundle properties file\n"
"bergeyTree=bergeyTrainingTree.xml\n"
"probabilityList=genus_wordConditionalProbList.txt\n"
"probabilityIndex=wordConditionalProbIndexArr.txt\n"
"wordPrior=logWordPrior.txt\n"
"classifierVersion=Naive Bayesian rRNA Classifier Version 1.0, "
"November 2003\n"
)
properties_file.close()
def __call__(self, data=None, remove_tmp=True):
"""Run the application with the specified kwargs on data
data: anything that can be cast into a string or written out
to a file. Usually either a list of things or a single
string or number. input_handler will be called on this data
before it is passed as part of the command-line argument, so
by creating your own input handlers you can customize what
kind of data you want your application to accept
remove_tmp: if True, removes tmp files
"""
result = super(RdpClassifier, self).__call__(data=data, remove_tmp=remove_tmp)
training_files = {
'bergeyTree': 'bergeyTrainingTree.xml',
'probabilityList': 'genus_wordConditionalProbList.txt',
'probabilityIndex': 'wordConditionalProbIndexArr.txt',
'wordPrior': 'logWordPrior.txt',
}
for key, training_fn in sorted(training_files.items()):
training_fp = os.path.join(self.ModelDir, training_fn)
if not os.path.exists(training_fp):
exception_msg = (
"Training output file %s not found. This may "
"happen if an error occurred during the RDP training "
"process. More details may be available in the "
"standard error, printed below.\n\n" % training_fp
)
stderr_msg = result["StdErr"].read()
result["StdErr"].seek(0)
raise ApplicationError(exception_msg + stderr_msg)
# Not in try/except clause because we already know the
# file exists. Failure would be truly exceptional, and we
# want to maintain the original exception in that case.
result[key] = open(training_fp)
return result
def parse_command_line_parameters(argv=None):
""" Parses command line arguments """
usage =\
'usage: %prog [options] input_sequences_filepath'
version = 'Version: %prog ' + __version__
parser = OptionParser(usage=usage, version=version)
parser.add_option('-o','--output_fp',action='store',\
type='string',dest='output_fp',help='Path to store '+\
'output file [default: generated from input_sequences_filepath]')
parser.add_option('-c','--min_confidence',action='store',\
type='float',dest='min_confidence',help='minimum confidence '+\
'level to return a classification [default: %default]')
parser.set_defaults(verbose=False, min_confidence=0.80)
opts, args = parser.parse_args(argv)
if len(args) != 1:
parser.error('Exactly one argument is required.')
return opts, args
def assign_taxonomy(
data, min_confidence=0.80, output_fp=None, training_data_fp=None,
fixrank=True, max_memory=None, tmp_dir=None):
"""Assign taxonomy to each sequence in data with the RDP classifier
data: open fasta file object or list of fasta lines
confidence: minimum support threshold to assign taxonomy to a sequence
output_fp: path to write output; if not provided, result will be
returned in a dict of {seq_id:(taxonomy_assignment,confidence)}
"""
# Going to iterate through this twice in succession, best to force
# evaluation now
data = list(data)
# RDP classifier doesn't preserve identifiers with spaces
# Use lookup table
seq_id_lookup = {}
for seq_id, seq in MinimalFastaParser(data):
seq_id_lookup[seq_id.split()[0]] = seq_id
app_kwargs = {}
if tmp_dir is not None:
app_kwargs['TmpDir'] = tmp_dir
app = RdpClassifier(**app_kwargs)
if max_memory is not None:
app.Parameters['-Xmx'].on(max_memory)
temp_output_file = tempfile.NamedTemporaryFile(
prefix='RdpAssignments_', suffix='.txt', dir=tmp_dir)
app.Parameters['-o'].on(temp_output_file.name)
if training_data_fp is not None:
app.Parameters['-t'].on(training_data_fp)
if fixrank:
app.Parameters['-f'].on('fixrank')
else:
app.Parameters['-f'].on('allrank')
app_result = app(data)
assignments = {}
# ShortSequenceException messages are written to stdout
# Tag these ID's as unassignable
for line in app_result['StdOut']:
excep = parse_rdp_exception(line)
if excep is not None:
_, rdp_id = excep
orig_id = seq_id_lookup[rdp_id]
assignments[orig_id] = ('Unassignable', 1.0)
for line in app_result['Assignments']:
rdp_id, direction, taxa = parse_rdp_assignment(line)
if taxa[0][0] == "Root":
taxa = taxa[1:]
orig_id = seq_id_lookup[rdp_id]
lineage, confidence = get_rdp_lineage(taxa, min_confidence)
if lineage:
assignments[orig_id] = (';'.join(lineage), confidence)
else:
assignments[orig_id] = ('Unclassified', 1.0)
if output_fp:
try:
output_file = open(output_fp, 'w')
except OSError:
raise OSError("Can't open output file for writing: %s" % output_fp)
for seq_id, assignment in assignments.items():
lineage, confidence = assignment
output_file.write(
'%s\t%s\t%1.3f\n' % (seq_id, lineage, confidence))
output_file.close()
return None
else:
return assignments
def train_rdp_classifier(
training_seqs_file, taxonomy_file, model_output_dir, max_memory=None,
tmp_dir=None):
""" Train RDP Classifier, saving to model_output_dir
training_seqs_file, taxonomy_file: file-like objects used to
train the RDP Classifier (see RdpTrainer documentation for
format of training data)
model_output_dir: directory in which to save the files
necessary to classify sequences according to the training
data
Once the model data has been generated, the RDP Classifier may
"""
app_kwargs = {}
if tmp_dir is not None:
app_kwargs['TmpDir'] = tmp_dir
app = RdpTrainer(**app_kwargs)
if max_memory is not None:
app.Parameters['-Xmx'].on(max_memory)
temp_taxonomy_file = tempfile.NamedTemporaryFile(
prefix='RdpTaxonomy_', suffix='.txt', dir=tmp_dir)
temp_taxonomy_file.write(taxonomy_file.read())
temp_taxonomy_file.seek(0)
app.Parameters['taxonomy_file'].on(temp_taxonomy_file.name)
app.Parameters['model_output_dir'].on(model_output_dir)
return app(training_seqs_file)
def train_rdp_classifier_and_assign_taxonomy(
training_seqs_file, taxonomy_file, seqs_to_classify, min_confidence=0.80,
model_output_dir=None, classification_output_fp=None, max_memory=None,
tmp_dir=None):
""" Train RDP Classifier and assign taxonomy in one fell swoop
The file objects training_seqs_file and taxonomy_file are used to
train the RDP Classifier (see RdpTrainer documentation for
details). Model data is stored in model_output_dir. If
model_output_dir is not provided, a temporary directory is created
and removed after classification.
The sequences in seqs_to_classify are classified according to the
model and filtered at the desired confidence level (default:
0.80).
The results are saved to classification_output_fp if provided,
otherwise a dict of {seq_id:(taxonomy_assignment,confidence)} is
returned.
"""
if model_output_dir is None:
training_dir = tempfile.mkdtemp(prefix='RdpTrainer_', dir=tmp_dir)
else:
training_dir = model_output_dir
training_results = train_rdp_classifier(
training_seqs_file, taxonomy_file, training_dir, max_memory=max_memory,
tmp_dir=tmp_dir)
training_data_fp = training_results['properties'].name
assignment_results = assign_taxonomy(
seqs_to_classify, min_confidence=min_confidence,
output_fp=classification_output_fp, training_data_fp=training_data_fp,
max_memory=max_memory, fixrank=False, tmp_dir=tmp_dir)
if model_output_dir is None:
# Forum user reported an error on the call to os.rmtree:
# https://groups.google.com/d/topic/qiime-forum/MkNe7-JtSBw/discussion
# We were not able to replicate the problem and fix it
# properly. However, even if an error occurs, we would like
# to return results, along with a warning.
try:
rmtree(training_dir)
except OSError:
msg = (
"Temporary training directory %s not removed" % training_dir)
if os.path.isdir(training_dir):
training_dir_files = os.listdir(training_dir)
msg += "\nDetected files %s" % training_dir_files
warnings.warn(msg, RuntimeWarning)
return assignment_results
def get_rdp_lineage(rdp_taxa, min_confidence):
lineage = []
obs_confidence = 1.0
for taxon, rank, confidence in rdp_taxa:
if confidence >= min_confidence:
obs_confidence = confidence
lineage.append(taxon)
else:
break
return lineage, obs_confidence
def parse_rdp_exception(line):
if line.startswith('ShortSequenceException'):
matchobj = re.search('recordID=(\S+)', line)
if matchobj:
rdp_id = matchobj.group(1)
return ('ShortSequenceException', rdp_id)
return None
def parse_rdp_assignment(line):
"""Returns a list of assigned taxa from an RDP classification line
"""
toks = line.strip().split('\t')
seq_id = toks.pop(0)
direction = toks.pop(0)
if ((len(toks) % 3) != 0):
raise ValueError(
"Expected assignments in a repeating series of (rank, name, "
"confidence), received %s" % toks)
assignments = []
# Fancy way to create list of triples using consecutive items from
# input. See grouper function in documentation for itertools for
# more general example.
itoks = iter(toks)
for taxon, rank, confidence_str in zip(itoks, itoks, itoks):
if not taxon:
continue
assignments.append((taxon.strip('"'), rank, float(confidence_str)))
return seq_id, direction, assignments
if __name__ == "__main__":
opts, args = parse_command_line_parameters()
assign_taxonomy(
open(args[0]), min_confidence=opts.min_confidence,
output_fp=opts.output_fp)
|