def test_parallel_first_and_second(self):
pi = 3.141
e = 2.718
value = {'pi' : pi, 'e' : e}
pipeline = cons_split_wire() >> \
(cons_dictionary_wire({'pi' : 'PI'}) >> cons_function_component(lambda a, s: {'PI' : a['PI']})).first() >> \
(cons_dictionary_wire({'e' : 'E'}) >> cons_function_component(lambda a, s: {'E' : a['E']})).second()
result = ParallelPypelineHelperUnitTest.test(1, pipeline, value, None, eval_pipeline)
self.assertEquals(({'PI' : pi}, {'E' : e}), result)
def test_parallel_split(self):
pi = 3.141
value = {'pi' : pi}
pipeline = cons_function_component(lambda a, s: a) >> \
cons_split_wire() >> \
cons_function_component(lambda a, s: {'PI' : a['pi']}).first() >> \
(cons_function_component(lambda a, s: {'PI' : a['PI']}) ** \
cons_function_component(lambda a, s: a)) >> \
cons_unsplit_wire(lambda t, b: {'PI' : t['PI'], 'pi' : b['pi']})
result = ParallelPypelineHelperUnitTest.test(1, pipeline, value, None, eval_pipeline)
self.assertEquals({'PI' : pi, 'pi' : pi}, result)
def main(src_lang, trg_lang, src_filename, trg_filename):
# Global configuration
# One day, this configuration shall be constructed from
# command line options, or a properties file.
configuration = {
'moses_installation_dir': os.environ['MOSES_HOME'],
'irstlm_installation_dir': os.environ['IRSTLM'],
'giza_installation_dir': os.environ['GIZA_HOME'],
'src_lang': src_lang,
'src_tokenisation_dir': './tokenisation',
'trg_lang': trg_lang,
'trg_tokenisation_dir': './tokenisation',
'segment_length_limit': 60,
'irstlm_smoothing_method': 'improved-kneser-ney',
'language_model_directory': './language-model',
'translation_model_directory': './translation-model',
'mert_working_directory': './mert',
'evaluation_data_size': 100,
'development_data_size': 100
}
# The modules to load
# In the future, the components shall be specified in some kind
# pipeline description file.
component_modules = {
'src_tokenizer': 'training.components.tokenizer.src_tokenizer',
'trg_tokenizer': 'training.components.tokenizer.trg_tokenizer',
'cleanup': 'training.components.cleanup.cleanup',
'data_split': 'training.components.data_split.data_split',
'irstlm_build': 'training.components.irstlm_build.irstlm_build',
'model_training': 'training.components.model_training.model_training',
'mert': 'training.components.mert.mert'
}
# The thread pool
executor = ThreadPoolExecutor(max_workers=3)
# Phew, build the required components
components, component_config = build_components(component_modules,
configuration, executor)
#
# Wire up components
# Description of wiring should be, in the future, alongside the component
# specification in some kind of confuguration file. Components shall be
# declared then used, i.e., bind a component instance to a unique component
# identifier, then wire component instances together by identifier.
#
#
# Tokenisation of source and target...
#
# IRSTLM Build components
irstlm_build_component = cons_split_wire() >> \
(cons_wire(lambda a, s: {'input_filename': a['tokenised_trg_filename']}) >> \
components['irstlm_build']).second() >> \
cons_unsplit_wire(lambda t, b: {'tokenised_trg_filename': t['tokenised_trg_filename'],
'trg_language_model_filename': b['compiled_lm_filename']})
# The complete tokenisation component
tokenisation_component = (components['src_tokenizer'] & components['trg_tokenizer']) >> \
irstlm_build_component.second() >> \
cons_unsplit_wire(lambda t, b: {'src_filename': t['tokenised_src_filename'],
'trg_filename': b['tokenised_trg_filename'],
'trg_language_model_filename': b['trg_language_model_filename']})
#
# Cleanup and Data Spliting...
#
#
# A function that clips off the last '.' delimited string
#
def clip_last_bit(filename):
bn = os.path.basename(filename)
directory = os.path.dirname(filename)
bits = bn.split(".")
bits.pop()
return os.path.join(directory, ".".join(bits))
cleanup_datasplit_component = components['cleanup'] >> \
cons_wire(lambda a, s: {'src_filename': a['cleaned_src_filename'],
'trg_filename': a['cleaned_trg_filename']}) >> \
components['data_split'] >> \
cons_wire(lambda a, s: {'training_data_filename': clip_last_bit(a['train_src_filename']),
'eval_src_filename': a['eval_src_filename'],
'eval_trg_filename': a['eval_trg_filename']})
#
# Translation model training
#
translation_model_component = cons_split_wire() >> \
components['model_training'].first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': b['eval_src_filename']})
#
# The whole pipeline
#
pipeline = tokenisation_component >> \
cons_split_wire() >> \
(cleanup_datasplit_component >> translation_model_component).first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': clip_last_bit(t['development_data_filename']),
'trg_language_model_filename': b['trg_language_model_filename'],
'trg_language_model_order': 3,
'trg_language_model_type': 9}) >> \
components['mert']
#
# The input to the pipeline
#
value = {'src_filename': src_filename, 'trg_filename': trg_filename}
#
# Evaluate the pipeline
#
logger.info("Evaluating pipeline with input [%s]..." % value)
new_value = eval_pipeline(executor, pipeline, value, component_config)
#
# Wait for all components to finish
#
executor.shutdown(True)
logger.info("Pipeline evaluated to %s" % new_value)
def main(src_lang, trg_lang, src_filename, trg_filename):
# Global configuration
# One day, this configuration shall be constructed from
# command line options, or a properties file.
configuration = {
'moses_installation_dir': os.environ['MOSES_HOME'],
'irstlm_installation_dir': os.environ['IRSTLM'],
'giza_installation_dir': os.environ['GIZA_HOME'],
'src_lang': src_lang,
'src_tokenisation_dir': './tokenisation',
'trg_lang': trg_lang,
'trg_tokenisation_dir': './tokenisation',
'segment_length_limit': 60,
'irstlm_smoothing_method': 'improved-kneser-ney',
'language_model_directory': './language-model',
'translation_model_directory': './translation-model',
'mert_working_directory': './mert',
'evaluation_data_size': 100,
'development_data_size': 100
}
# The modules to load
# In the future, the components shall be specified in some kind
# pipeline description file.
component_modules = {
'src_tokenizer': 'training.components.tokenizer.src_tokenizer',
'trg_tokenizer': 'training.components.tokenizer.trg_tokenizer',
'cleanup': 'training.components.cleanup.cleanup',
'data_split': 'training.components.data_split.data_split',
'irstlm_build': 'training.components.irstlm_build.irstlm_build',
'model_training': 'training.components.model_training.model_training',
'mert': 'training.components.mert.mert'
}
# The thread pool
executor = ThreadPoolExecutor(max_workers = 3)
# Phew, build the required components
components, component_config = build_components(component_modules, configuration, executor)
#
# Wire up components
# Description of wiring should be, in the future, alongside the component
# specification in some kind of confuguration file. Components shall be
# declared then used, i.e., bind a component instance to a unique component
# identifier, then wire component instances together by identifier.
#
#
# Tokenisation of source and target...
#
# IRSTLM Build components
irstlm_build_component = cons_split_wire() >> \
(cons_wire(lambda a, s: {'input_filename': a['tokenised_trg_filename']}) >> \
components['irstlm_build']).second() >> \
cons_unsplit_wire(lambda t, b: {'tokenised_trg_filename': t['tokenised_trg_filename'],
'trg_language_model_filename': b['compiled_lm_filename']})
# The complete tokenisation component
tokenisation_component = (components['src_tokenizer'] & components['trg_tokenizer']) >> \
irstlm_build_component.second() >> \
cons_unsplit_wire(lambda t, b: {'src_filename': t['tokenised_src_filename'],
'trg_filename': b['tokenised_trg_filename'],
'trg_language_model_filename': b['trg_language_model_filename']})
#
# Cleanup and Data Spliting...
#
#
# A function that clips off the last '.' delimited string
#
def clip_last_bit(filename):
bn = os.path.basename(filename)
directory = os.path.dirname(filename)
bits = bn.split(".")
bits.pop()
return os.path.join(directory, ".".join(bits))
cleanup_datasplit_component = components['cleanup'] >> \
cons_wire(lambda a, s: {'src_filename': a['cleaned_src_filename'],
'trg_filename': a['cleaned_trg_filename']}) >> \
components['data_split'] >> \
cons_wire(lambda a, s: {'training_data_filename': clip_last_bit(a['train_src_filename']),
'eval_src_filename': a['eval_src_filename'],
'eval_trg_filename': a['eval_trg_filename']})
#
# Translation model training
#
translation_model_component = cons_split_wire() >> \
components['model_training'].first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': b['eval_src_filename']})
#
# The whole pipeline
#
pipeline = tokenisation_component >> \
cons_split_wire() >> \
(cleanup_datasplit_component >> translation_model_component).first() >> \
cons_unsplit_wire(lambda t, b: {'moses_ini_file': t['moses_ini_file'],
'development_data_filename': clip_last_bit(t['development_data_filename']),
'trg_language_model_filename': b['trg_language_model_filename'],
'trg_language_model_order': 3,
'trg_language_model_type': 9}) >> \
components['mert']
#
# The input to the pipeline
#
value = {'src_filename': src_filename,
'trg_filename': trg_filename}
#
# Evaluate the pipeline
#
logger.info("Evaluating pipeline with input [%s]..." % value)
new_value = eval_pipeline(executor, pipeline, value, component_config)
#
# Wait for all components to finish
#
executor.shutdown(True)
logger.info("Pipeline evaluated to %s" % new_value)