def run(places=None):
try:
start = datetime.now()
if places is None:
# will probably have to do a yield thing at some point
#places = Place.objects.exclude(featureplace=None, featureplace__correct=True, featureplace__feature__verified=True)
places = Place.objects.exclude(featureplace=None)
place_ids = Feature.objects.filter(
featureplace__correct=True,
featureplace__feature__verified=True).values_list(
"featureplace__place_id", flat=True)
print("place_ids:", len(place_ids))
for place_id in place_ids:
counter = Counter()
for feature in Feature.objects.filter(
verified=True,
featureplace__place_id=place_id,
featureplace__correct=True).exclude(text=None).exclude(
text=""):
if feature.text:
topic_id = get_topic(feature.text)
if topic_id:
counter[topic_id] += 1
else:
print(feature.text)
print("counter:", counter)
most_common_topics = counter.most_common(1)
if most_common_topics:
most_common_topic_tuple = most_common_topics[0]
if most_common_topic_tuple:
most_common_topic_id = most_common_topic_tuple[0]
print("\tmost_common_topic for",
Place.objects.get(id=place_id), "is",
Topic.objects.get(id=most_common_topic_id).name)
print("took", (datetime.now() - start).total_seconds(), "seconds")
except Exception as e:
print(e)
def run(places=None):
try:
start = datetime.now()
if places is None:
# will probably have to do a yield thing at some point
#places = Place.objects.exclude(featureplace=None, featureplace__correct=True, featureplace__feature__verified=True)
places = Place.objects.exclude(featureplace=None)
place_ids = Feature.objects.filter(featureplace__correct=True, featureplace__feature__verified=True).values_list("featureplace__place_id", flat=True)
print "place_ids:", len(place_ids)
for place_id in place_ids:
counter = Counter()
for feature in Feature.objects.filter(verified=True, featureplace__place_id=place_id, featureplace__correct=True).exclude(text=None).exclude(text=""):
if feature.text:
topic_id = get_topic(feature.text)
if topic_id:
counter[topic_id] += 1
else:
print feature.text
print "counter:", counter
most_common_topics = counter.most_common(1)
if most_common_topics:
most_common_topic_tuple = most_common_topics[0]
if most_common_topic_tuple:
most_common_topic_id = most_common_topic_tuple[0]
print "\tmost_common_topic for", Place.objects.get(id=place_id), "is", Topic.objects.get(id=most_common_topic_id).name
print "took", (datetime.now() - start).total_seconds(), "seconds"
except Exception as e:
print e
def resolve_locations(locations,
order_id,
max_seconds=10,
countries=[],
admin1codes=[],
debug=True,
end_user_timezone=None,
case_insensitive=None):
try:
print("starting resolve_locations with", type(locations))
print("" * 4, "locations = ", len(locations), locations[:5])
print("" * 4, "countries:", countries)
print("" * 4, "admin1codes:", admin1codes)
print("" * 4, "end_user_timezone:", end_user_timezone)
print("" * 4, "case_insensitive:", case_insensitive)
start = datetime.now()
order = Order.objects.get(id=order_id)
# make more resilient to unidecode
name_country = {}
name_country_code = {}
name_location = {}
name_topic = {}
names = []
normalized_names = set()
for location in locations:
#cleaning name a little just to play it safe; sometimes have blank depending on extract method
name = location['name'] = location['name'].strip()
try:
print("name:", name)
except Exception as e:
pass
#skipping over places with commas and parentheses in them.. because won't find in db anyway... probably need more longterm solution like escape quoting in psql
if name and not any(
char in name
for char in [',', ')', '(', '?', "'", '"', "}", "{"]):
names.append(name)
normalized = normalize(name)
normalized_names.add(normalized)
name_location[normalized] = location
if location.get('country', None):
name_country[normalized] = location['country']
if location.get('country_code', None):
name_country_code[normalized] = location['country_code']
if "context" in location:
topic_id = get_topic(location['context'])
location['topic_id'] = topic_id
name_topic[normalized] = topic_id
else:
name_topic[normalized] = None
number_of_locations = len(locations)
print("number_of_locations:", number_of_locations)
#print "names", len(names), names[:5]
# randomize order in order to minimize statistical bias
shuffle(names)
names = list(set(names))
try:
print("names:", names)
except UnicodeEncodeError:
print("couldn't print statement because non-ascii")
places = Place.objects.filter(
name_normalized__in=normalized_names).values()
number_of_places = len(places)
if number_of_places == 0:
return False
print("places", type(places), len(places))
copy_prop(places, "name_normalized", "feature_id")
print("set places", len(places))
places = marge_utils.to_dicts(marge_resolver.resolve(places))
print("MARGE resolved:", len(places))
maxes = marge_utils.max_by_group(places, "score", "feature_id")
print("maxes:", maxes)
for option in places:
fid = option["feature_id"]
prob = option["score"]
option["correct"] = prob == maxes[fid]
print("resolver SET CORRECT")
# recomposing target places
target_places = group_into_dict(places, "name_normalized")
print("recomposed:", list(target_places.keys()))
#Feature, FeaturePlace
featureplaces = []
for target, options in list(target_places.items()):
print("target:", target)
print("\toptions:", options)
l = name_location[target]
topic_id = name_topic.get(target, None)
count = l['count'] if 'count' in l else 1
correct_option = next(option for option in options
if option["correct"])
geometry_used = "Shape" if correct_option["mpoly"] else "Point"
feature = Feature.objects.create(count=count,
name=l["name"],
geometry_used=geometry_used,
order_id=order_id,
topic_id=topic_id,
verified=False)
need_to_save = False
if "context" in l:
feature.text = l['context']
need_to_save = True
if "date" in l:
feature.end = l['date']
feature.start = l['date']
need_to_save = True
if need_to_save:
feature.save()
for option in options:
featureplaces.append(
FeaturePlace(confidence=option["score"],
correct=bool(option["correct"]),
feature=feature,
place_id=option["id"],
sort_order=-1))
FeaturePlace.objects.bulk_create(featureplaces)
print("resolved locations for order " + str(order_id))
print("took:", (datetime.now() - start).total_seconds())
return len(featureplaces) > 0
except Exception as e:
print(e)
def resolve_locations(locations, order_id, max_seconds=10, countries=[], admin1codes=[]):
try:
print "starting resolve_locations with", type(locations)
print "locations = ", len(locations), locations[:5]
print "countries:", countries
print "admin1codes:", admin1codes
start = datetime.now()
order = Order.objects.get(id=order_id)
name_location = {}
name_topic = {}
names = []
for location in locations:
#cleaning name a little just to play it safe; sometimes have blank depending on extract method
name = location['name'] = location['name'].strip()
#skipping over places with commas and parentheses in them.. because won't find in db anyway... probably need more longterm solution like escape quoting in psql
if name and not any(char in name for char in [',', ')', '(', '?', "'", '"']):
names.append(name)
name_location[name] = location
if "context" in location:
topic_id = get_topic(location['context'])
location['topic_id'] = topic_id
name_topic[name] = topic_id
else:
name_topic[name] = None
number_of_locations = len(locations)
#print "names", len(names), names[:5]
# randomize order in order to minimize statistic bias
shuffle(names)
names = set(names)
print "names:", names
cursor = connection.cursor()
seconds_left = max_seconds - (datetime.now().replace(tzinfo=UTC) - order.start).total_seconds()
print "seconds_left:", seconds_left
if seconds_left > 60:
if countries:
statement = "SELECT * FROM fdgis_resolve_with_countries('{" + ", ".join(names) + "}'::TEXT[], '{" + ", ".join(countries) + "}'::TEXT[], true);"
else:
statement = "SELECT * FROM fdgis_resolve('{" + ", ".join(names) + "}'::TEXT[], true);"
else:
if countries:
statement = "SELECT * FROM fdgis_resolve_with_countries('{" + ", ".join(names) + "}'::TEXT[], '{" + ", ".join(countries) + "}'::TEXT[], false);"
else:
statement = "SELECT * FROM fdgis_resolve('{" + ", ".join(names) + "}'::TEXT[], false);"
print "statement:\n", statement
cursor.execute(statement)
#print "executed"
geoentities = [GeoEntity(row) for row in cursor.fetchall()]
if admin1codes:
geoentities = [g for g in geoentities if g.admin1code in admin1codes]
print "filtering out geoentities that don't match admin1 code if there is an admin1 code match"
for location in locations:
if 'admin1code' in location:
name = location['name']
admin1code = location['admin1code']
if admin1code:
print "name:", name
print "admin1code:", admin1code
# are there any in geoentities that match
matches = []
not_matches = []
for geoentity in geoentities:
if geoentity.place_name == name or geoentity.alias == name:
if geoentity.admin1code == admin1code:
matches.append(geoentity)
else:
not_matches.append(geoentity)
#print "matches:", matches
#print "not_matches:", not_matches
if matches:
for geoentity in not_matches:
geoentities.remove(geoentity)
number_of_geoentities = len(geoentities)
#print "geoentities", type(geoentities), len(geoentities)
# calculate median distance from every other point
#all_cords = [geoentity.point.coords for geoentity in geoentities]
target_geoentities = defaultdict(list)
target_coords = defaultdict(list)
all_coords = []
for geoentity in geoentities:
all_coords.append(geoentity.point.coords)
target_geoentities[geoentity.target].append(geoentity)
target_coords[geoentity.target].append(geoentity.point.coords)
#number_of_clusters = max(3, number_of_locations/20)
number_of_clusters = 3
print "number_of_clusters:", number_of_clusters
#centroids = kmeans(all_coords, number_of_clusters)[0]
#print "centroids:", centroids
estimator = KMeans(n_clusters=number_of_clusters)
estimator.fit(all_coords)
labels = estimator.labels_
cluster_count = Counter()
for cluster in labels:
cluster_count[cluster] += 1
cluster_frequency = {cluster: float(count) / number_of_geoentities for cluster, count in cluster_count.iteritems() }
for i in range(number_of_geoentities):
geoentities[i].cluster_frequency = cluster_frequency[labels[i]]
#print "target_geoentities:", len(target_geoentities)
for target, options in target_geoentities.items():
#print "target:", target
for i, v in enumerate(median(cdist(target_coords[target], all_coords), axis=1)):
target_geoentities[target][i].median_distance_from_all_other_points = int(v)
#print "name_topic names are", name_topic.keys()
topic_id = name_topic[target]
#print "topic:", topic_id
for option in options:
#print "\toption.topic_id:", option.topic_id
option.matches_topic = option.topic_id == topic_id
print "add probability to each geoentity"
predict.run(geoentities)
# need to choose one for each target based on highest probability
for target, options in target_geoentities.items():
max_probability = max([o.probability for o in options])
found_correct = False
for option in options:
if not found_correct and option.probability == max_probability:
option.correct = True
found_correct = True
else:
option.correct = False
#Feature, FeaturePlace
featureplaces = []
for target, options in target_geoentities.items():
l = name_location[target]
topic_id = name_topic[target] if target in name_topic else None
count = l['count'] if 'count' in l else 1
feature = Feature.objects.create(count=count, name=target, geometry_used="Point", order_id=order_id, topic_id=topic_id, verified=False)
need_to_save = False
if "context" in l:
feature.text = l['context']
need_to_save = True
if "date" in l:
feature.end = l['date']
feature.start = l['date']
need_to_save = True
if need_to_save:
feature.save()
for option in options:
featureplaces.append(FeaturePlace(confidence=float(option.probability), correct=option.correct, country_rank=option.country_rank, feature=feature, median_distance=option.median_distance_from_all_other_points, place_id=option.place_id, popularity=option.popularity))
FeaturePlace.objects.bulk_create(featureplaces)
print "resolved locations for order " + str(order_id)
print "took:", (datetime.now() - start).total_seconds()
return len(featureplaces) > 0
except Exception as e:
print e
