def importCorpus(self, root_input_dir, file_mask="*.xml", import_options={}, maxfiles=10000000000):
"""
Does all that is necessary for the initial import of the corpus
"""
inputdir=ensureTrailingBackslash(root_input_dir)
print("Starting ingestion of corpus...")
print("Creating database...")
cp.Corpus.createAndInitializeDatabase()
cp.Corpus.connectToDB()
cp.Corpus.metadata_index=self.metadata_index
cp.Corpus.FILES_TO_IGNORE=self.files_to_ignore
self.start_time=datetime.datetime.now()
ALL_INPUT_FILES=self.loadListOrListAllFiles(inputdir,file_mask)
self.num_files_to_process=min(len(ALL_INPUT_FILES),FILES_TO_PROCESS_TO-FILES_TO_PROCESS_FROM)
if import_options.get("convert_and_import_docs",True):
print("Converting input files to SciDoc format and loading metadata...")
self.convertAllFilesAndAddToDB(ALL_INPUT_FILES, inputdir, import_options)
if import_options.get("update_doc_references",True):
print("Updating in-collection links...")
## ALL_GUIDS=cp.Corpus.SQLQuery("SELECT guid FROM papers where metadata.original_citation_style = \"AFI\" or metadata.original_citation_style = null or metadata.pmc_id <> null limit 20000000")
## ALL_GUIDS=cp.Corpus.SQLQuery("SELECT guid FROM papers where metadata.original_citation_style = \"AFI\" or metadata.original_citation_style = null or metadata.pmc_id <> null limit 200")
ALL_GUIDS=cp.Corpus.listPapers("metadata.collection_id:\"%s\"" % self.collection_id)
## assert False
self.updateInCollectionReferences(ALL_GUIDS, import_options)
self.end_time=datetime.datetime.now()
print("All done. Processed %d files. Took %s" % (self.num_files_to_process, str(self.end_time-self.start_time)))
def saveGraphForResults(filename,metric):
"""
"""
dir=ensureTrailingBackslash(getFileDir(filename))
drawSimilaritiesGraph(filename,metric,True)
name=getFileName(filename)
plt.savefig(dir+name+'.png', bbox_inches='tight')
plt.close()
def makeAllGraphsForExperiment(exp_dir):
"""
Iterates through all weight*.csv files in the experiment's directory and
saves a graph for each
"""
## metric="avg_mrr"
## metric="precision_total"
metric="avg_precision"
exp_dir=ensureTrailingBackslash(exp_dir)
## working_dir=Corpus.dir_experiments+exp_name+os.sep
for path in glob.glob(exp_dir+"weight*.csv"):
saveGraphForResults(path,metric)
def connectCorpus(self, base_directory, initializing_corpus=False,suppress_error=False):
"""
If DB has been created, connect to it. If not, initialize it first.
Args:
base_directory: root dir of this corpus
initializing_corpus: if True, create DB and directories
suppress_error: if true, db doesn't complain if it's connected already
"""
self.setPaths(ensureTrailingBackslash(base_directory))
if initializing_corpus:
self.createAndInitializeDatabase()
self.connectToDB(suppress_error)