def startLogging(self):
"""
Creates the results logger and starts counting and logging.
"""
output_filename = os.path.join(self.exp["exp_dir"], self.exp.get("output_filename", "results.csv"))
self.logger = ResultsLogger(
False, dump_filename=output_filename, message_text="Running precomputed queries", dot_every_xitems=1
) # init all the logging/counting
self.logger.startCounting() # for timing the process, start now
class BaseTestingPipeline(object):
"""
Base class for testing pipelines
"""
def __init__(self, retrieval_class=BaseRetrieval, use_celery=False):
# This points to the the class of retrieval we are using
self.retrieval_class = retrieval_class
self.use_celery = use_celery
self.tasks = []
self.exp = None
self.options = None
self.precomputed_queries = []
self.tfidfmodels = {}
self.files_dict = {}
self.main_all_doc_methods = {}
self.current_all_doc_methods = {}
self.save_terms = False
self.max_per_class_results = 1000
def loadModel(self, guid):
"""
Loads all the retrieval models for a single file
"""
for model in self.files_dict[guid]["tfidf_models"]:
# create a search instance for each method
self.tfidfmodels[model["method"]] = self.retrieval_class(
model["actual_dir"],
model["method"],
logger=None,
use_default_similarity=self.exp["use_default_similarity"],
)
def generateRetrievalModels(self, all_doc_methods, all_files):
"""
Generates the files_dict with the paths to the retrieval models
"""
for guid in all_files:
self.files_dict[guid]["tfidf_models"] = []
for method in all_doc_methods:
actual_dir = cp.Corpus.getRetrievalIndexPath(
guid, all_doc_methods[method]["index_filename"], self.exp["full_corpus"]
)
self.files_dict[guid]["tfidf_models"].append({"method": method, "actual_dir": actual_dir})
def addRandomControlResult(self, guid, precomputed_query):
"""
Adds a result that is purely based on analytical chance, for
comparison.
"""
result_dict = {
"file_guid": guid,
"citation_id": precomputed_query["citation_id"],
"doc_position": precomputed_query["doc_position"],
"query_method": precomputed_query["query_method"],
"match_guid": precomputed_query["match_guid"],
"doc_method": "RANDOM",
"mrr_score": analyticalRandomChanceMRR(self.files_dict[guid]["in_collection_references"]),
"precision_score": 1 / float(self.files_dict[guid]["in_collection_references"]),
"ndcg_score": 0,
"rank": 0,
"first_result": "",
}
# Deal here with CoreSC/AZ/CFC annotation
for annotation in self.exp.get("rhetorical_annotations", []):
result_dict[annotation] = precomputed_query.get(annotation)
self.logger.addResolutionResultDict(result_dict)
def initializePipeline(self):
"""
Whatever needs to happen before we start the pipeline: inializing
connections, VMs, whatever.
This function should be overriden by descendant classes if anything
is to be done.
"""
if self.retrieval_class.__name__.startswith("Lucene"):
import lucene
try:
lucene.initVM(maxheap="640m") # init Lucene VM
except ValueError:
# VM already up
print(sys.exc_info()[1])
def startLogging(self):
"""
Creates the results logger and starts counting and logging.
"""
output_filename = os.path.join(self.exp["exp_dir"], self.exp.get("output_filename", "results.csv"))
self.logger = ResultsLogger(
False, dump_filename=output_filename, message_text="Running precomputed queries", dot_every_xitems=1
) # init all the logging/counting
self.logger.startCounting() # for timing the process, start now
def loadQueriesAndFileList(self):
"""
Loads the precomputed queries and the list of test files to process.
"""
precomputed_queries_file_path = self.exp.get("precomputed_queries_file_path", None)
if not precomputed_queries_file_path:
precomputed_queries_file_path = os.path.join(
self.exp["exp_dir"], self.exp.get("precomputed_queries_filename", "precomputed_queries.json")
)
self.precomputed_queries = json.load(open(precomputed_queries_file_path, "r"))
files_dict_filename = os.path.join(self.exp["exp_dir"], self.exp.get("files_dict_filename", "files_dict.json"))
self.files_dict = json.load(open(files_dict_filename, "r"))
self.files_dict["ALL_FILES"] = {}
def populateMethods(self):
"""
Fills dict with all the test methods, parameters and options, including
the retrieval instances
"""
self.tfidfmodels = {}
all_doc_methods = None
if self.exp.get("doc_methods", None):
all_doc_methods = getDictOfTestingMethods(self.exp["doc_methods"])
# essentially this overrides whatever is in files_dict, if testing_methods was passed as parameter
if self.exp["full_corpus"]:
all_files = ["ALL_FILES"]
else:
all_files = self.files_dict.keys()
self.generateRetrievalModels(all_doc_methods, all_files)
else:
all_doc_methods = self.files_dict["ALL_FILES"]["doc_methods"] # load from files_dict
if self.exp["full_corpus"]:
for model in self.files_dict["ALL_FILES"]["tfidf_models"]:
# create a search instance for each method
self.tfidfmodels[model["method"]] = self.retrieval_class(
model["actual_dir"],
model["method"],
logger=None,
use_default_similarity=self.exp["use_default_similarity"],
max_results=self.exp["max_results_recall"],
save_terms=self.save_terms,
)
self.main_all_doc_methods = all_doc_methods
def newResultDict(self, guid, precomputed_query, doc_method):
"""
Creates and populates a new result dict.
"""
result_dict = {
"file_guid": guid,
"citation_id": precomputed_query["citation_id"],
"doc_position": precomputed_query["doc_position"],
"query_method": precomputed_query["query_method"],
"doc_method": doc_method,
"match_guid": precomputed_query["match_guid"],
}
# Deal here with CoreSC/AZ/CFC annotation
for annotation in self.exp.get("rhetorical_annotations", []):
result_dict[annotation] = precomputed_query.get(annotation)
return result_dict
def addEmptyResult(self, guid, precomputed_query, doc_method):
"""
Adds an empty result, that is, a result with 0 score due to some error.
"""
result_dict = self.newResultDict(guid, precomputed_query, doc_method)
result_dict["mrr_score"] = 0
result_dict["precision_score"] = 0
result_dict["ndcg_score"] = 0
result_dict["rank"] = 0
result_dict["first_result"] = ""
self.logger.addResolutionResultDict(result_dict)
def addResult(self, guid, precomputed_query, doc_method, retrieved_results):
"""
Adds a normal (successful) result to the result log.
"""
result_dict = self.newResultDict(guid, precomputed_query, doc_method)
self.logger.measureScoreAndLog(retrieved_results, precomputed_query["citation_multi"], result_dict)
## rank_per_method[result["doc_method"]].append(result["rank"])
## precision_per_method[result["doc_method"]].append(result["precision_score"])
## def logTextAndReferences(self, doctext, queries, qmethod):
## """
## Extra logging, not used right now
## """
## pre_selection_text=doctext[queries[qmethod]["left_start"]-300:queries[qmethod]["left_start"]]
## draft_text=doctext[queries[qmethod]["left_start"]:queries[qmethod]["right_end"]]
## post_selection_text=doctext[queries[qmethod]["right_end"]:queries[qmethod]["left_start"]+300]
## draft_text=u"<span class=document_text>{}</span> <span class=selected_text>{}</span> <span class=document_text>{}</span>".format(pre_selection_text, draft_text, post_selection_text)
## print(draft_text)
def saveResultsAndCleanUp(self):
"""
This executes after the whole pipeline is done. This is where we
save all data that needs to be saved, report statistics, etc.
"""
self.logger.writeDataToCSV()
self.logger.showFinalSummary()
def processOneQuery(self, precomputed_query):
"""
Runs the retrieval and evaluation for a single query
"""
if self.exp.get("queries_classification", "") not in ["", None]:
q_type = precomputed_query[self.exp.get("queries_classification")]
if self.per_class_count[q_type] < self.max_per_class_results:
self.per_class_count[q_type] += 1
else:
print("Too many queries of type %s already" % q_type)
return
guid = precomputed_query["file_guid"]
self.logger.total_citations += self.files_dict[guid]["resolvable_citations"]
all_doc_methods = deepcopy(self.main_all_doc_methods)
# If we're running per-file resolution and we are now on a different file, load its model
if not self.exp["full_corpus"] and guid != self.previous_guid:
self.previous_guid = guid
self.loadModel(guid)
# create a dict where every field gets a weight of 1
for method in self.main_all_doc_methods:
all_doc_methods[method]["runtime_parameters"] = {
x: 1 for x in self.main_all_doc_methods[method]["runtime_parameters"]
}
self.current_all_doc_methods = all_doc_methods
# for every method used for extracting BOWs
for doc_method in all_doc_methods:
# Log everything if the logger is enabled
## self.logger.logReport("Citation: "+precomputed_query["citation_id"]+"\n Query method:"+precomputed_query["query_method"]+" \nDoc method: "+doc_method +"\n")
## self.logger.logReport(precomputed_query["query_text"]+"\n")
# ACTUAL RETRIEVAL HAPPENING - run query
retrieved = self.tfidfmodels[doc_method].runQuery(
precomputed_query,
addExtraWeights(all_doc_methods[doc_method]["runtime_parameters"], self.exp),
guid,
max_results=exp.get("max_results_recall", MAX_RESULTS_RECALL),
)
if not retrieved: # the query was empty or something
self.addEmptyResult(guid, precomputed_query, doc_method)
else:
self.addResult(guid, precomputed_query, doc_method, retrieved)
if self.exp.get("add_random_control_result", False):
self.addRandomControlResult(guid, precomputed_query)
self.logger.showProgressReport(guid) # prints out info on how it's going
def processAllQueries(self):
"""
MAIN LOOP over all precomputed queries
"""
for precomputed_query in self.precomputed_queries:
self.processOneQuery(precomputed_query)
def runPipeline(self, exp, options):
"""
Run the whole experiment pipeline, loading everything from
precomputed json
:param exp: experiment dict
"""
self.exp = exp
self.options = options
self.max_per_class_results = self.exp.get("max_per_class_results", self.max_per_class_results)
self.per_class_count = defaultdict(lambda: 0)
if self.exp.get("similiarity_tie_breaker", 0):
for model in self.tfidfmodels.items():
model.tie_breaker = self.exp["similiarity_tie_breaker"]
self.startLogging()
self.initializePipeline()
self.loadQueriesAndFileList()
self.logger.setNumItems(len(self.precomputed_queries))
self.populateMethods()
self.previous_guid = ""
# MAIN LOOP over all precomputed queries
self.processAllQueries()
self.saveResultsAndCleanUp()
def measurePrecomputedResolution(self, retrieval_results, method, parameters, citation_az="*"):
"""
This is kind of like measureCitationResolution:
it takes a list of precomputed retrieval_results, then applies the new
parameters to them. This is how we recompute what Lucene gives us,
avoiding having to call Lucene again and so speeding it up a lot.
All we need to do is adjust the weights on the already available
explanation formulas.
"""
logger = ResultsLogger(False, dump_straight_to_disk=False) # init all the logging/counting
logger.startCounting() # for timing the process, start now
logger.setNumItems(len(retrieval_results), print_out=False)
# for each query-result: (results are packed inside each query for each method)
for result in retrieval_results:
# select only the method we're testing for
if "formulas" not in result:
# there was an error reading this result
continue
formulas = result["formulas"]
retrieved = runPrecomputedQuery(formulas, parameters)
result_dict = {
"file_guid": result["file_guid"],
"citation_id": result["citation_id"],
"doc_position": result["doc_position"],
"query_method": result["query_method"],
"doc_method": method,
"az": result["az"],
"cfc": result["cfc"],
"match_guid": result["match_guid"],
}
if not retrieved or len(retrieved) == 0: # the query was empty or something
## print "Error: ", doc_method , qmethod,tfidfmodels[method].indexDir
## logger.addResolutionResult(guid,m,doc_position,qmethod,doc_method ,0,0,0)
result_dict["mrr_score"] = 0
result_dict["precision_score"] = 0
result_dict["ndcg_score"] = 0
result_dict["rank"] = 0
result_dict["first_result"] = ""
logger.addResolutionResultDict(result_dict)
else:
result = logger.measureScoreAndLog(retrieved, result["citation_multi"], result_dict)
logger.computeAverageScores()
results = []
for query_method in logger.averages:
for doc_method in logger.averages[query_method]:
weights = parameters
data_line = {"query_method": query_method, "doc_method": doc_method, "citation_az": citation_az}
for metric in logger.averages[query_method][doc_method]:
data_line["avg_" + metric] = logger.averages[query_method][doc_method][metric]
data_line["precision_total"] = logger.scores["precision"][query_method][doc_method]
results.append(data_line)
return results