def train(
rdd: RDD,
torch_obj: str,
server: Server,
iters: int = 10,
partition_shuffles: int = 1,
verbose: int = 1,
early_stop_patience: int = -1,
mini_batch: int = -1,
validation_pct: float = 0.0
) -> Dict:
try:
torch_obj, _ = load_base_torch(torch_obj)
master_url = str(server.master_url)
for i in range(partition_shuffles):
rdd.mapPartitions(
lambda x: handle_model(
x,
torch_obj=torch_obj,
master_url=master_url,
iters=iters,
verbose=verbose,
early_stop_patience=early_stop_patience,
mini_batch=mini_batch,
validation_pct=validation_pct
)
).foreach(lambda x: x)
if partition_shuffles - i > 1:
num_partitions = rdd.getNumPartitions()
rdd = rdd.repartition(num_partitions)
state_dict = get_state_dict(master_url)
server.stop_server()
return state_dict
except Exception as e:
server.stop_server()
raise e
def __init__(self, ctx, resource_read=None, query=None, **kwargs): kwargs = make_es_config(kwargs, resource_read=resource_read, query=query) kwargs = as_java_object(ctx._gateway, kwargs) jrdd = helper(ctx).esJsonRDD(ctx._jsc, kwargs) rdd = RDD(jrdd, ctx, NoOpSerializer()) # read the rdd in batches of two (first key then value / doc) def pairwise(iterable): iterator = iter(iterable) return izip(iterator, iterator) kvRdd = rdd.mapPartitions(pairwise, True) super(EsRDD, self).__init__(kvRdd._jrdd, ctx)
def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema
def applySchema(it):
cls = _create_cls(schema)
return map(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema
def applySchema(it):
cls = _create_cls(schema)
return map(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def __init__(self, ctx, resource_read=None, query=None, **kwargs):
kwargs = make_es_config(kwargs,
resource_read=resource_read,
query=query)
kwargs = as_java_object(ctx._gateway, kwargs)
jrdd = helper(ctx).esJsonRDD(ctx._jsc, kwargs)
rdd = RDD(jrdd, ctx, NoOpSerializer())
# read the rdd in batches of two (first key then value / doc)
def pairwise(iterable):
iterator = iter(iterable)
return izip(iterator, iterator)
kvRdd = rdd.mapPartitions(pairwise, True)
super(EsRDD, self).__init__(kvRdd._jrdd, ctx)
def rdd(self):
"""
Return the content of the :class:`DataFrame` as an :class:`RDD`
of :class:`Row` s.
"""
if not hasattr(self, '_lazy_rdd'):
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema
def applySchema(it):
cls = _create_cls(schema)
return itertools.imap(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def rdd(self):
"""
Return the content of the :class:`DataFrame` as an :class:`RDD`
of :class:`Row` s.
"""
if not hasattr(self, '_lazy_rdd'):
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema
def applySchema(it):
cls = _create_cls(schema)
return itertools.imap(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def son(baskets: RDD, support_threshold_total=support_threshold_total) -> list:
def pcy_for_list(partition: list, support_threshold_total=support_threshold_total) -> dict:
# partition = baskets
num_baskets_chunk = len(partition)
support_threshold = math.ceil(support_threshold_total * num_baskets_chunk / num_baskets)
# first pass
singleton_counts = {}
bucket_counts = {}
for basket in partition:
for item in basket:
singleton_counts[item] = singleton_counts.get(item, 0) + 1
pairs = generate_combination(basket, size=2)
for pair in pairs:
key = hash_pair(pair)
bucket_counts[key] = bucket_counts.get(key, 0) + 1
for key, value in bucket_counts.items():
if value >= support_threshold:
bucket_counts[key] = 1
else:
bucket_counts[key] = 0
frequent_itemsets = {}
for key, value in singleton_counts.items():
if value >= support_threshold:
frequent_itemsets[key] = None # store all frequent singletons
# print("singleton_counts", singleton_counts)
# print("frequent singletons", frequent_itemsets)
del singleton_counts
gc.collect()
# second pass
itemset_counts = {}
for basket in partition:
pairs = generate_combination(basket, size=2)
for pair in pairs:
if qualified_as_candidate_pair(pair, frequent_itemsets, bitmap=bucket_counts):
key = tuple(pair)
itemset_counts[key] = itemset_counts.get(key, 0) + 1
for key, value in itemset_counts.items():
if value >= support_threshold:
frequent_itemsets[key] = None # store all frequent pairs
# print("pair counts", itemset_counts)
del itemset_counts
gc.collect()
# more passes for larger-size itemsets
size = 3
num_frequent_itemsets = len(frequent_itemsets)
while True:
itemset_counts = {}
for basket in partition:
itemsets = generate_combination_with_filter(basket, frequent_itemsets, size)
for itemset in itemsets:
key = tuple(itemset)
itemset_counts[key] = itemset_counts.get(key, 0) + 1
for key, value in itemset_counts.items():
if value >= support_threshold:
frequent_itemsets[key] = None # store all frequent pairs
del itemset_counts
gc.collect()
current_num_frequent_itemsets = len(frequent_itemsets)
# print("frequent_itemsets", frequent_itemsets)
if current_num_frequent_itemsets == num_frequent_itemsets: # no more new frequent itemsets
# print("break")
break
num_frequent_itemsets = current_num_frequent_itemsets
size += 1
# print("frequent_itemsets", frequent_itemsets)
return frequent_itemsets
# First stage
num_baskets = baskets.count()
candidate_itemsets = dict.fromkeys(baskets.mapPartitions(lambda _: pcy_for_list(list(_), support_threshold_total)).distinct().collect(), 0)
# print("candidate_itemsets", candidate_itemsets)
# Second stage
def qualified_as_candidate_itemset(itemset):
try:
_ = candidate_itemsets[itemset]
return True
except:
return False
singleton_counts = baskets.\
flatMap(lambda basket: basket).\
filter(lambda item: qualified_as_candidate_itemset(item)).\
map(lambda _: (_, 1)).\
reduceByKey(lambda x,y: x+y).\
filter(lambda pair: pair[1] >= support_threshold_total).keys().collect()
frequent_itemsets = [sorted(singleton_counts)]
del singleton_counts
gc.collect()
size = 2
while True:
frequent_itemsets_for_particular_size = baskets.\
flatMap(lambda _: generate_combination_with_filter(_, candidate_itemsets, size)).\
filter(lambda _: qualified_as_candidate_itemset(tuple(_))).\
map(lambda _: (tuple(_), 1)).\
reduceByKey(lambda x,y: x+y).\
filter(lambda pair: pair[1] >= support_threshold_total).keys().collect()
if frequent_itemsets_for_particular_size == []:
break
else:
frequent_itemsets.append(sorted(frequent_itemsets_for_particular_size))
size += 1
del frequent_itemsets_for_particular_size
gc.collect()
return frequent_itemsets
