def naive_multiplication_rdd(mat_a: pyspark.RDD, mat_b: pyspark.RDD, is_triangle=False):
"""
mat_a is the left matrix
mat_b is the right matix
:param mat_a:
:param mat_b:
:param is_triangle:
:return:
"""
if is_triangle:
left_rdd = (
mat_a.flatMap(lambda x: [((x.j, x.i), x.value), ((x.i, x.j), x.value)])
.aggregateByKey(zeroValue=(0.0, 0.0),
seqFunc=lambda x, y: (x[0]+y, x[1]+1),
combFunc=lambda x, y: (x[0] + y[0], x[1]+y[1]))
.mapValues(lambda x: x[0] / x[1])
.map(lambda x: (x[0][0], (x[0][1], x[1])))
)
else:
left_rdd = mat_a.map(lambda x: (x.j, (x.i, x.value)))
right_rdd = mat_b.map(lambda x: (x.i, (x.j, x.value)))
combined_rdd = (left_rdd.join(right_rdd).map(lambda x: x[1])
.map(lambda x: ((x[0][0], x[1][0]), x[0][1]*x[1][1]))
.reduceByKey(lambda x, y: x+y)
.map(lambda x: distributed.MatrixEntry(i=x[0][0], j=x[0][1], value=x[1]))
)
return combined_rdd
def __init_parameters(self, train: RDD):
"""
_n_buckets/_n_items:
The number of distinct buckets/items in the train RDD.
_bucket_block_size/_cross_block_size/_item_block_size:
The size of blocks when dividing buckets/cross buckets/items into blocks.
_n_bucket_block/_n_cross_block/_n_item_block:
The number of blocks when dividing buckets/cross buckets/items into blocks.
"""
self._n_buckets = train.map(lambda u: u[0]).distinct().count()
if self._n_buckets <= self._k:
self._k = float("inf")
# For the bucket dimension.
if self._bucket_block_size is None:
# Interpret bucket_block_size from n_bucket_block
self._bucket_block_size = self._n_buckets // self._n_bucket_block + 1
else:
self._n_bucket_block = self._n_buckets // self._bucket_block_size + 1
# For the cross dimension.
if self._cross_block_size is None:
self._cross_block_size = self._n_buckets // self._n_cross_block + 1
else:
self._n_cross_block = self._n_buckets // self._cross_block_size + 1
# For the item dimension
self._n_items = train.map(lambda u: u[1]).distinct().count()
if self._item_block_size is None:
self._item_block_size = self._n_items // self._n_item_block + 1
else:
self._n_item_block = self._n_item // self._item_block_size + 1
return self
def join_multiple_keys(left: RDD, right: RDD, n: int) -> RDD:
"""
Join RDDs with multiple keys.
((key1, key2, ...), value_left) x (key_i, value_right_i) ->
((key1, key2, ...), (value_left, value_right_1, value_right_2, ...))
:param left: RDD<tuple<int>, value>
:param right: RDD<int, value>
:param n: int, the length of the key in left-RDD
:return: joint RDD.
"""
left = left.map(
lambda u: (-1, (u[0], (u[1],)))
) # (_, (tuple<key>, tuple<value>))
right = right.map(
lambda u: (u[0], (u[1],))
).cache() # (_, tuple<value>)
for key_order in range(n):
left = left.map(
lambda u: (u[1][0][key_order], u[1]) # (_, (tuple<key>, tuple<value>))
).join(
right # (_, ((tuple<key>, tuple<value>), tuple<value>))
).map(
lambda u: (-1, (u[1][0][0], u[1][0][1] + u[1][1]))
) # (_, (tuple<key>, tuple<value>))
left = left.map(
lambda u: u[1]
) # (tuple<key>, tuple<value>)
return left
def cStress(rdd: RDD) -> RDD:
# TODO: TWH Temporary
ecg_sampling_frequency = 64.0
rip_sampling_frequency = 64.0
accel_sampling_frequency = 64.0 / 6.0
# Timestamp correct datastreams
ecg_corrected = rdd.map(lambda ds: (
ds['participant'],
timestamp_correct(datastream=ds['ecg'],
sampling_frequency=ecg_sampling_frequency)))
rip_corrected = rdd.map(lambda ds: (
ds['participant'],
timestamp_correct(datastream=ds['rip'],
sampling_frequency=rip_sampling_frequency)))
accelx_corrected = rdd.map(lambda ds: (
ds['participant'],
timestamp_correct(datastream=ds['accelx'],
sampling_frequency=accel_sampling_frequency)))
accely_corrected = rdd.map(lambda ds: (
ds['participant'],
timestamp_correct(datastream=ds['accely'],
sampling_frequency=accel_sampling_frequency)))
accelz_corrected = rdd.map(lambda ds: (
ds['participant'],
timestamp_correct(datastream=ds['accelz'],
sampling_frequency=accel_sampling_frequency)))
accel_group = accelx_corrected.join(accely_corrected).join(
accelz_corrected).map(fix_two_joins)
accel = accel_group.map(lambda ds: (
ds[0],
autosense_sequence_align(datastreams=[ds[1][0], ds[1][1], ds[1][2]],
sampling_frequency=accel_sampling_frequency)))
# Accelerometer Feature Computation
accel_features = accel.map(
lambda ds: (ds[0], accelerometer_features(ds[1], window_length=10.0)))
# rip features
peak_valley = rip_corrected.map(
lambda ds: (ds[0], rip.compute_peak_valley(rip=ds[1])))
rip_features = peak_valley.map(
lambda ds: (ds[0], rip_feature_computation(ds[1][0], ds[1][1])))
# r-peak datastream computation
ecg_rr_rdd = ecg_corrected.map(lambda ds: (ds[
0], compute_rr_intervals(ds[1], ecg_sampling_frequency)))
ecg_features = ecg_rr_rdd.map(lambda ds: (ds[
0], ecg_feature_computation(ds[1], window_size=60, window_offset=60)))
# return rip_features.join(ecg_features).join(accel_features).map(fix_two_joins)
return ecg_features
def evaluate(truth: RDD, prediction: RDD) -> float:
"""
Calculate RMSE between truth and predictions.
:param truth: RDD<Hashable, Hashable, float> = RDD<(bucket, item, rating)>
:param prediction: RDD<Hashable, Hashable, float> = RDD<(bucket, item, rating)>
:return: float = RMSE
"""
truth = truth.map(lambda u: ((u[0], u[1]), u[2]))
prediction = prediction.map(lambda u: ((u[0], u[1]), u[2]))
return truth.join(prediction).map(lambda u:
(u[1][0] - u[1][1])**2).mean()**0.5
def __preprocessRdd(self, rdd: RDD):
rddc = rddCorrector()
rdd = rdd.map(lambda l: rddc.correct(l))
if rdd != None:
if (rdd.isEmpty() == False):
rdd = rdd.map(lambda l: l.replace("<tweet>", ""))
rdd = rdd.map(lambda l: l.replace("</tweet>", ""))
df = DataFrameWorks().convertDataFrame(rdd, self.__spark)
df = CleanText().clean(df, self.__spark)
return df
return None
def convertDataFrame(self, rdd: RDD, SqlObject) -> DataFrame:
"""RDD to DataFrame"""
#rdd = rdd.map(lambda l: l.replace("½",""))
rdd = rdd.map(lambda l: (l[:19], l[19:]))
schema = [StructField("id", StringType(), False),
StructField("rawData", StringType(), False),
StructField("preprocessedData", ArrayType(elementType=StringType(), containsNull=True), True),
StructField("sentiment", FloatType(), True)]
final_struct = StructType(fields=schema)
rdd =rdd.map(lambda l: (l[0],l[1], [None], None))
return SqlObject.createDataFrame(rdd, schema=final_struct)
def java_to_python_rdd(sc, rdd, is_pair, is_json):
jrdd = sc._jvm.SerDe.javaToPython(rdd)
output = RDD(jrdd, sc)
if is_pair:
if is_json:
return output.map(lambda x: (x.split("\t")[0], json.loads(x.split("\t")[1])))
else:
return output.map(lambda x: (x.split("\t")[0], x.split("\t")[1]))
if is_json:
return output.map(lambda x: json.loads(x))
return output
def extract_items(wikidata_items: RDD, b_property_map: Broadcast,
b_item_page_map: Broadcast):
def parse_item(item):
property_map = b_property_map.value
item_page_map = b_item_page_map.value
if "enwiki" in item["sitelinks"]:
page_title = item["sitelinks"]["enwiki"]["title"]
else:
return None, None
claims = {}
for prop_id, property_claims in item["claims"].items():
if prop_id in property_map:
prop_name = property_map[prop_id]
parsed_claims = []
for c in property_claims:
if "datavalue" in c["mainsnak"]:
c = c["mainsnak"]["datavalue"]["value"]
if type(c) == dict and "entity-type" in c:
claim_item_id = c["id"]
if claim_item_id in item_page_map:
c = item_page_map[c["id"]]
else:
continue
parsed_claims.append(c)
claims[prop_name] = parsed_claims
return page_title, claims
return (wikidata_items.map(parse_item).filter(lambda pc: pc[
0] is not None).reduceByKey(lambda x, y: x).collectAsMap())
def __call__(self, rdd: RDD) -> RDD:
def select_fields(row):
return Row(**{f: getattr(row, f) for f in self.fields})
res = rdd.map(select_fields)
if self.explained:
self._log.info("toDebugString():\n%s", res.toDebugString().decode())
return res
def transform_online_retail(
sc: SparkSession,
raw_rdd: RDD,
schema: str,
max_month: Optional[int] = None,
) -> DataFrame:
"""Method to transform online retail dataset to its correct dataformats, specific
for online retail
:return:
"""
# initial transformation of the raw RDD
raw_rdd = raw_rdd.map(lambda retail: (
retail[0], # InvoiceNo
retail[1], # StockCode
retail[2] if retail[2] != '' else None, # Description
int(retail[3]), # Quantity
datetime.strptime(retail[4], '%d/%m/%Y %H:%M') if
int(retail[4].split('/')[1]) < max_month
else datetime.strptime(retail[4], '%m/%d/%Y %H:%M'), # InvoiceDate
float(retail[5]), # UnitPrice
int(retail[6]) if retail[6] != '' else None, # CustomerID
retail[7] if retail[7] != '' else None) # Country
)
return sc.createDataFrame(
raw_rdd,
schema=schema
)
def __compute_signature(self, data: RDD) -> RDD:
"""
Compute signature for items.
:param data: RDD<(Hashable, Iterator<Hashable>)>
= RDD<(item, content)>
:return: RDD<(Hashable, tuple<int>)>
= RDD<(item, signature)>
"""
hashing_range = self.__hashing_range
signature_length = self.__signature_length
random_seed = self.__seed
min_hash_func = self.__min_hash
def _signature(key_values: (Hashable, Iterator)) -> (Hashable, tuple):
"""
Compute signature for each item
:return (Hashable, tuple<int>)
= (item, signature)
"""
item, content = key_values
signature = [hashing_range for _ in range(signature_length)]
for element in content:
for index_i, hashed_value in enumerate(
min_hash_func(element, signature_length, hashing_range,
random_seed)):
signature[index_i] = min(hashed_value, signature[index_i])
return item, tuple(signature)
return data.map(_signature)
def _compute_tfid(texts: RDD) -> IDFModel:
tf = HashingTF().transform(texts.map(lambda t: t.words))
tf.cache()
idf = IDF().fit(tf)
tfidfs = idf.transform(tf)
text_tfs = texts.zip(tfidfs)
return text_tfs.map(lambda t: t[0].set_tfidf(t[1]))
def run(self, data_rdd: RDD, query_rdd: RDD, n_dim: int) -> RDD: # type: ignore
empty_result_rdd = query_rdd.map(lambda idx_coords: (idx_coords[0], 0))
data_rdd = data_rdd.map(
lambda idx_coords: ((), idx_coords[1], (DATA, idx_coords[0]))
)
query_rdd = query_rdd.map(
lambda idx_coords: ((), idx_coords[1], (QUERY, idx_coords[0]))
)
rdd = data_rdd.union(query_rdd)
for _ in range(n_dim):
rdd = self.assign_next_label(rdd=rdd) # type: ignore
rdd = empty_result_rdd.union(self.get_results_by_label(rdd)) # type: ignore
rdd = self.aggregate_results_by_query(rdd).sortByKey() # type: ignore
return rdd
def parseDNSInfo(pcap_packets: RDD) -> RDD:
timer = Timer()
rddDns = pcap_packets.map(lambda bytes_packet: DNSInfo(bytes_packet[0], bytes_packet[1])).filter(
lambda dns: not dns.notDns and dns.sip not in Global.TRUSTED_DNS and dns.dip not in Global.TRUSTED_DNS)
log.info(f'Time spent on parsing chunk = {timer.elapsed()}')
return rddDns
def extract_item_map(wikidata_items: RDD):
def parse_item(item):
if 'en' in item['labels']:
label = item['labels']['en']['value']
return item['id'], label
else:
return None
return wikidata_items.map(parse_item).filter(lambda i: i is not None).collectAsMap()
def __blocking_matrix(self,
train: RDD = None,
test: RDD = None,
similarity=None) -> RDD:
"""
Divide matrix into blocks for the purpose of reduce key number.
:param train: RDD<(Hashable, Hashable, float)>
= RDD<bucket, item, rating>
:param test: RDD<(Hashable, Hashable)>
= RDD<bucket, item>
:param similarity: RDD<(Hashable, Hashable, float)>
RDD<bucket, bucket, similarity>
:return: RDD<(int, int)(Hashable, Hashable, float)>
= RDD<(bucket_block, item_block), (bucket, item, rating)> or
RDD<(bucket_block, bucket_block), (bucket, bucket, similarity)>
"""
seed = self._seed
n_bucket_block = self._n_bucket_block
n_item_block = self._n_item_block
n_cross_block = self._n_cross_block
if train is not None:
train = train.map(lambda u: ((hash2int(
u[0], max_value=n_cross_block, seed=seed
), hash2int(u[1], max_value=n_item_block, seed=seed)), u)).cache()
train.count()
return train
if test is not None:
test = test.map(lambda u: ((hash2int(
u[0], max_value=n_bucket_block, seed=seed
), hash2int(u[1], max_value=n_item_block, seed=seed)), u)).cache()
test.count()
return test
if similarity is not None:
similarity = similarity.flatMap(lambda u: [(u[0], u[1], u[
2]), (u[1], u[0], u[2])]).map(lambda u: (
(hash2int(u[0], max_value=n_bucket_block, seed=seed),
hash2int(u[1], max_value=n_cross_block, seed=seed)), u)
).cache()
similarity.count()
return similarity
def extract_item_map(wikidata_items: RDD):
def parse_item(item):
if "en" in item["labels"]:
label = item["labels"]["en"]["value"]
return item["id"], label
else:
return None
return wikidata_items.map(parse_item).filter(
lambda i: i is not None).collectAsMap()
def calc_llr(ss: SparkSession, from_rdd: RDD, to_rdd: RDD) -> RDD:
"""
Расчет матрицы co-/cross- occurence LLR для списка списков объектов для рекомендаций A -> B
Вначале вычисляется произведение матриц left_rdd.Transpose * right_rdd.
Затем LogLikelihoodRatio для каждой ячейки.
:param ss: Spark Session объект
:param from_rdd: rdd с разреженной матрицей со статистикой по объектам A. Формат значений - (ID списка, ID объекта A)
:param to_rdd: rdd с разреженной матрицей со статистикой по объектам B. Формат значений - (ID списка, ID объекта B)
:return: Разреженная матрица co-/cross- occurence LLR между треками
"""
logger.info("Calculating co-/cross- occurence LLR matrix...")
sc = ss.sparkContext
lists_count = from_rdd.map(itemgetter(0)).distinct().count()
bc_lists_count = sc.broadcast(lists_count)
from_items_counts = from_rdd.map(itemgetter(1)).countByValue()
bc_from_items_counts = sc.broadcast(from_items_counts)
to_items_counts = to_rdd.map(itemgetter(1)).countByValue()
bc_to_items_counts = sc.broadcast(to_items_counts)
def llr_cell(x):
i, j, cooc_count = x
i_count = bc_to_items_counts.value[i]
j_count = bc_from_items_counts.value[j]
res_llr = llr_sqrt(cooc_count,
j_count - cooc_count,
i_count - cooc_count,
bc_lists_count.value - i_count - j_count + cooc_count)
return i, j, res_llr
llr_rdd = to_rdd \
.join(from_rdd) \
.map(lambda x: (x[1], 1)) \
.reduceByKey(add) \
.map(lambda x: (x[0][0], x[0][1], x[1])) \
.map(llr_cell)
logger.info("Co-/cross- occurence LLR matrix calculated")
return llr_rdd
def extract_item_map(wikidata_items: RDD):
def parse_item(item):
if 'en' in item['labels']:
label = item['labels']['en']['value']
return item['id'], label
else:
return None
return wikidata_items.map(parse_item).filter(
lambda i: i is not None).collectAsMap()
def evaluate(self, lables_and_predictions: RDD):
TP = lables_and_predictions.map(lambda x:
(set(x[0]), set([p for p,w in x[1][:self._pred_n]]))). \
filter(lambda x:
len(x[0].intersection(x[1])) > self._intersect_n)
accuracy = 100.0 * TP.count() / lables_and_predictions.count()
if self._verbose:
print('accuracy: ', accuracy)
self._results.append(accuracy)
return accuracy
def evaluate(self, lables_and_predictions: RDD):
TP = lables_and_predictions.map(lambda x:
(set(x[0]), set([p for p,w in x[1][:self._pred_n]]))). \
filter(lambda x:
len(x[0].intersection(x[1])) > self._intersect_n)
accuracy = 100.0 * TP.count() / lables_and_predictions.count()
if self._verbose:
print('accuracy: ', accuracy)
self._results.append(accuracy)
return accuracy
def to_pandas_df(rdd: RDD, string_conversion=False, init_condition: dict = None):
if init_condition is not None and string_conversion is False:
# Typefull
return to_spark(rdd=rdd, init_condition=init_condition).toPandas()
elif init_condition is None and string_conversion is True:
# String
return rdd.map(lambda d: Row(**dict([(k, str(v)) for k, v in d.items()]))).toDF()
else:
# Typeless
return to_pandas(rdd)
def lp_to_simple_rdd(lp_rdd: RDD,
categorical: bool = False,
nb_classes: int = None):
"""Convert a LabeledPoint RDD into an RDD of feature-label pairs
:param lp_rdd: LabeledPoint RDD of features and labels
:param categorical: boolean, if labels should be one-hot encode when returned
:param nb_classes: int, number of total classes
:return: Spark RDD with feature-label pairs
"""
if categorical:
if not nb_classes:
labels = np.asarray(lp_rdd.map(lambda lp: lp.label).collect(),
dtype='int32')
nb_classes = np.max(labels) + 1
rdd = lp_rdd.map(lambda lp: (from_vector(lp.features),
encode_label(lp.label, nb_classes)))
else:
rdd = lp_rdd.map(lambda lp: (from_vector(lp.features), lp.label))
return rdd
def from_labeled_point(rdd: RDD,
categorical: bool = False,
nb_classes: int = None):
"""Convert a LabeledPoint RDD back to a pair of numpy arrays
:param rdd: LabeledPoint RDD
:param categorical: boolean, if labels should be one-hot encode when returned
:param nb_classes: optional int, indicating the number of class labels
:return: pair of numpy arrays, features and labels
"""
features = np.asarray(
rdd.map(lambda lp: from_vector(lp.features)).collect())
labels = np.asarray(rdd.map(lambda lp: lp.label).collect(), dtype='int32')
if categorical:
if not nb_classes:
nb_classes = np.max(labels) + 1
temp = np.zeros((len(labels), nb_classes))
for i, label in enumerate(labels):
temp[i, label] = 1.
labels = temp
return features, labels
def toDf(cls, spatialPairRDD: RDD, sparkSession: SparkSession):
"""
:param spatialPairRDD:
:param sparkSession:
:return:
"""
spatialPairRDD_mapped = spatialPairRDD.map(
lambda x: [x[0].geom, *x[0].getUserData().split("\t"), x[1].geom, *x[1].getUserData().split("\t")]
)
df = sparkSession.createDataFrame(spatialPairRDD_mapped)
return df
def calculate_node_tree(
config: dict,
record_rdd: RDD,
spark: SparkSession,
repo_type: str = "ecflow",
) -> dict:
"""Calculate node tree for each date in record RDDs.
Parameters
----------
config: dict
processor's config
record_rdd: RDD
records RDD
spark: SparkSession
spark session
repo_type: ["ecflow", "sms"]
repo type
Returns
-------
dict
bunch map dict, key: date, value: bunch map
"""
# **STEP**: map to (date, node_path)
# record object => (date, node_path) distinct
def node_path_map(record):
return record.date, record.node_path
date_node_path_rdd = record_rdd.map(node_path_map).distinct()
# **STEP**: group by date
# (date, node_path) => (record_date, list of record_fullname)
date_node_path_list_rdd = date_node_path_rdd.groupByKey()
# **STEP**: collect
date_with_node_path_list = date_node_path_list_rdd.collect()
# **STEP**: generate bunch
logger.info("Generating bunch...")
bunch_class = get_bunch_class(repo_type)
bunch_map = {}
for (day, node_path_list) in date_with_node_path_list:
bunch = bunch_class()
for node_path in node_path_list:
if node_path is not None:
bunch.add_node(node_path)
logger.info(f"Generating bunch...done for {day}")
bunch_map[day] = bunch
return bunch_map
def __call__(self, head: RDD):
if self.keymap is None:
return head.coalesce(self.partitions, self.shuffle)
# partitionBy the key extracted using self.keymap
try:
# this checks if keymap is an identity
probe = self.keymap("probe")
except: # noqa: E722
probe = None
if probe != "probe":
head = head.map(lambda x: (self.keymap(x), x))
return head \
.partitionBy(self.partitions) \
.map(lambda x: x[1])
def _check_data(train: RDD = None, test: RDD = None) -> (RDD, int):
# Data-type check
if isinstance(train, RDD):
is_legal_train = train.map(
lambda u: len(u) >= 3 and u[0] is not None and u[1] is not None
and isinstance(u[2], Number)).reduce(lambda u1, u2: u1 and u2)
if not is_legal_train:
raise ValueError(
"Parameter train should be an RDD<(user, item, rating)>")
num_partitions_of_train = train.getNumPartitions()
return train
if isinstance(test, RDD):
is_legal_test = test.map(lambda u: len(u) >= 2 and u[0] is not None
and u[1] is not None).reduce(
lambda u1, u2: u1 and u2)
if not is_legal_test:
raise ValueError(
"Parameter train should be an RDD<(user, item, rating)>")
num_partitions_of_test = test.getNumPartitions()
return test
raise ValueError("RDD train/test need to be input.")
def to_spark_df(rdd: RDD, spark: SparkSession = None, init_condition: dict = None):
if init_condition is not None and spark is not None:
# Typefull
return to_spark(rdd, init_condition)
elif spark is None and init_condition is None:
# String
return rdd.map(lambda d: Row(**dict([(k, str(v)) for k, v in d.items()]))).toDF()
else:
# Typeless
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "true")
warnings.simplefilter(action='ignore', category=UserWarning)
pdf_from_rdd: DataFrame = to_pandas(rdd)
result = spark.createDataFrame(pdf_from_rdd)
del pdf_from_rdd
return result
def make_dataframe_from_alerts(rdd: RDD, colnames: list) -> DataFrame:
""" Make a Dataframe from a RDD of alerts and columns names.
Parameters
----------
rdd: Apache Spark RDD of dictionaries
RDD whose elements are dictionaries (decoded alerts)
colnames: list of str
List containing the keys to include. For nested levels, just chain
it using double dot: firstdic:seconddic:key
Returns
----------
out: DataFrame
Dataframe from the input RDD and columns names.
"""
return rdd.map(lambda x: tuple(ret(x, k) for k in colnames)).toDF(colnames)
def __calculate_similarity(train: RDD, lsh_params: dict,
maximum_num_partitions: int) -> RDD:
"""
Calculate Jaccard Similarity from train-RDD.
:param train: RDD<(Hashable, Hashable, float)>
:return: RDD<Hashable, Hashable, float>
= RDD<bucket, bucket, similarity>
"""
train = train.map(lambda u: (u[0], u[1]))\
.groupByKey().map(lambda u: (u[0], list(u[1]))).cache()
similarity_among_buckets = JaccardSimilarity(
**lsh_params).predict(train).cache()
if similarity_among_buckets.getNumPartitions(
) > maximum_num_partitions:
similarity_among_buckets = similarity_among_buckets.coalesce(
maximum_num_partitions).cache()
return similarity_among_buckets
def normal_order(self, terms: RDD, **kwargs):
"""Normal order the terms in the RDD."""
if len(kwargs) > 0:
raise ValueError('Invalid keyword arguments', kwargs)
symms = self.symms
swapper = self.swapper
resolvers = self.resolvers
init = terms.map(lambda term: _NOState(
pivot=1, front=2, term=term.canon4normal(symms.value)))
res = nest_bind(
init,
lambda x: _sort_vec(x, swapper=swapper, resolvers=resolvers.value),
full_balance=self.full_balance)
return res.map(lambda x: x.term)
def predict(self, x):
"""
Predict the label of one or more examples.
:param x: Data point (feature vector),
or an RDD of data points (feature vectors).
"""
pythonAPI = self._sc._jvm.PythonMLLibAPI()
if isinstance(x, RDD):
# Bulk prediction
if x.count() == 0:
return self._sc.parallelize([])
dataBytes = _get_unmangled_double_vector_rdd(x, cache=False)
jSerializedPreds = \
pythonAPI.predictDecisionTreeModel(self._java_model,
dataBytes._jrdd)
serializedPreds = RDD(jSerializedPreds, self._sc, NoOpSerializer())
return serializedPreds.map(lambda bytes: _deserialize_double(bytearray(bytes)))
else:
# Assume x is a single data point.
x_ = _serialize_double_vector(x)
return pythonAPI.predictDecisionTreeModel(self._java_model, x_)
def evaluate(self, lables_and_predictions: RDD):
result = lables_and_predictions.map(lambda p: _hamming_loss(p[0], p[1])). \
mean()
self._results.append(result)
return result
