def recordstream(df, epoch_id):
# First splitting the value from Spark DF to get the timestamp from data and later applying window on the datetime
split_col = F.split(df.value, ',')
df = df.withColumn(
'TimeStamp',
F.to_timestamp(F.regexp_replace(split_col.getItem(0), '"', ''),
'yyyy-mm-dd HH:mm:ssss'))
df = df.withColumn('RT_Temp', split_col.getItem(1).cast(tp.DoubleType()))
df = df.withColumn(
'Nu_Temp',
F.regexp_replace(split_col.getItem(2), '"', '').cast(tp.DoubleType()))
df = df.drop('value')
# Saving input stream to master data set
dfw = df.selectExpr('TimeStamp as ts', 'RT_Temp', 'Nu_Temp')
dfw.write.saveAsTable(name='tsa.turbine_master',
format='hive',
mode='append')
dfp = df.select('TimeStamp', 'RT_Temp', 'Nu_Temp')
if len(dfp.take(1)) != 0:
# print('Calling Predictions & Model path is',g_model)
df_final = fitVar(2, dfp, g_model)
df_final.show(5)
df_final.write.saveAsTable(name='tsa.batch_predictions',
format='hive',
mode='append')
def preprocessing(spark: SparkSession, pppath: Path, datadir: str):
print("--- preprocessing -----------------------")
schema = T.StructType([
T.StructField('year', T.IntegerType(), True),
T.StructField('month', T.IntegerType(), True),
T.StructField('dn', T.IntegerType(), True),
T.StructField('wday', T.IntegerType(), True),
T.StructField('snap', T.IntegerType(), True),
T.StructField('dept_id', T.StringType(), True),
T.StructField('item_id', T.StringType(), True),
T.StructField('store_id', T.StringType(), True),
T.StructField('sales', T.DoubleType(), True),
T.StructField('flag_ram', T.IntegerType(), True),
T.StructField('Sales_Pred', T.DoubleType(), True)
])
csv_path = str(Path(datadir, "Sales5_Ab2011_InklPred.csv"))
print(f"--- Reading: '{csv_path}'")
sales5: DataFrame = spark.read.csv(csv_path, header='true', schema=schema) \
.withColumn("label", F.col('sales'))
ppdf = prepro(sales5)
print(f"--- Writing: '{pppath}'")
ppdf.write \
.format("parquet") \
.mode("overwrite") \
.save(str(pppath))
def process_song_data(spark, input_bucket, output_data):
""" Reads the songs dataset, transforms it, creating artists and
songs table in parquet files"""
song_data = get_files_paths_s3(input_bucket, "song_data")
# song_data = get_local_song_data()
# specify schema to improve read speed
song_schema = T.StructType()\
.add("num_songs", T.IntegerType())\
.add("artist_id", T.StringType())\
.add("artist_latitude", T.DoubleType())\
.add("artist_longitude", T.DoubleType())\
.add("artist_location", T.StringType())\
.add("artist_name", T.StringType())\
.add("song_id", T.StringType())\
.add("title", T.StringType())\
.add("duration", T.DoubleType())\
.add("year", T.IntegerType())
df = spark.read.schema(song_schema).json(song_data)
songs_table = df.select(
["song_id", "title", "artist_id", "year", "duration"])
songs_table.write.partitionBy("year", "artist_id")\
.parquet("{}/songs_table.parquet".format(output_data),
mode="overwrite")
# Since the data is song based there can be duplicated artists
artists_table = df.selectExpr(["artist_id", "artist_name as name",
"artist_location as location",
"artist_latitude as latitude",
"artist_longitude as longitude"])\
.dropDuplicates(["artist_id"])
artists_table.write.parquet("{}artists_table.parquet".format(output_data),
mode="overwrite")
def import_twitter_data(spark_session, tweets_file_path):
"""Imports the twitter data and returns resulting DataFrame.
Args:
spark_session -- An active SparkSession.
tweets_file_path -- A file path.
"""
tweets_schema = types.StructType([
types.StructField('id', types.LongType()),
types.StructField('timestamp', types.LongType(), nullable=False),
types.StructField('postalCode', types.StringType()),
types.StructField('lon', types.DoubleType(), nullable=False),
types.StructField('lat', types.DoubleType(), nullable=False),
types.StructField('tweet', types.StringType(), nullable=False),
types.StructField('user_id', types.LongType()),
types.StructField('application', types.StringType()),
types.StructField('source', types.StringType())
])
tweets_df = spark_session.read.csv(tweets_file_path,
escape='"',
header='true',
schema=tweets_schema,
mode='DROPMALFORMED')
tweets_df = tweets_df.select(['timestamp', 'lon', 'lat', 'tweet'])
return tweets_df
def predict(df, epoch_id):
split_col = F.split(df.value, ',')
# df = df.withColumn('TimeStamp', F.to_timestamp(F.regexp_replace(split_col.getItem(0), '"', ''),
# 'yyyy-mm-dd HH:mm:ss.SSS'))
df = df.withColumn(
'TimeStamp',
F.regexp_replace(split_col.getItem(0), '"',
'').cast(tp.TimestampType()))
df = df.withColumn('RT_Temp', split_col.getItem(1).cast(tp.DoubleType()))
df = df.withColumn(
'Nu_Temp',
F.regexp_replace(split_col.getItem(2), '"', '').cast(tp.DoubleType()))
df = df.drop('value')
dfw = df.select('TimeStamp', 'RT_Temp', 'Nu_Temp')
if len(dfw.take(1)) != 0:
df_final = fitVar(2, dfw)
# Converting selective columns from prediction dataframe to single column dataframe value
df_final = df_final.withColumn('value', (F.concat(
col("TS"), lit(","), col("RT_Temp"), lit(","),
col("RT_Temp_Predict"), lit(","), col("Nu_Temp"), lit(","),
col("Nu_Temp_Predict"), lit(","),
col("RMSE_Score"))).cast(tp.StringType()))
ds = df_final.select('value')
# ds.show(5)
# Sending each row of dataframe on Kafka message
print('Now sending Message on Kafka topic', sink_topic)
ds.selectExpr("CAST(value AS STRING)")\
.write\
.format("kafka")\
.option("kafka.bootstrap.servers", broker)\
.option("topic", sink_topic)\
.save()
def spark():
try:
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
from pyspark.sql import types
conf = SparkConf()
conf.set("spark.jars.ivy", "/home/jovyan/.ivy2/")
conf.set("spark.driver.extraClassPath",
"jars/scala-udf-similarity-0.0.7.jar")
conf.set("spark.jars", "jars/scala-udf-similarity-0.0.7.jar")
conf.set("spark.driver.memory", "4g")
conf.set("spark.sql.shuffle.partitions", "12")
sc = SparkContext.getOrCreate(conf=conf)
spark = SparkSession(sc)
udfs = [
("jaro_winkler_sim", "JaroWinklerSimilarity", types.DoubleType()),
("jaccard_sim", "JaccardSimilarity", types.DoubleType()),
("cosine_distance", "CosineDistance", types.DoubleType()),
("Dmetaphone", "DoubleMetaphone", types.StringType()),
("QgramTokeniser", "QgramTokeniser", types.StringType()),
("Q3gramTokeniser", "Q3gramTokeniser", types.StringType()),
("Q4gramTokeniser", "Q4gramTokeniser", types.StringType()),
("Q5gramTokeniser", "Q5gramTokeniser", types.StringType()),
("DmetaphoneAlt", "DoubleMetaphoneAlt", types.StringType()),
]
for a, b, c in udfs:
spark.udf.registerJavaFunction(a, "uk.gov.moj.dash.linkage." + b,
c)
rt = types.ArrayType(
types.StructType([
types.StructField("_1", types.StringType()),
types.StructField("_2", types.StringType()),
]))
spark.udf.registerJavaFunction(
name="DualArrayExplode",
javaClassName="uk.gov.moj.dash.linkage.DualArrayExplode",
returnType=rt,
)
SPARK_EXISTS = True
except:
SPARK_EXISTS = False
if SPARK_EXISTS:
print("Spark exists, running spark tests")
yield spark
else:
spark = None
logger.error("Spark not available")
print("Spark not available")
yield spark
def main(link, key):
crime_schema = schema_def()
# API request is made to retrieve data from Vancouver Open Data
urllib.request.urlretrieve(link, "Vancouver.zip")
compressed_file = zipfile.ZipFile('Vancouver.zip')
csv_file = compressed_file.open('crimedata_csv_all_years.csv')
pd_crimes = pd.read_csv(csv_file)
# Creation of Spark DataFrame
df_crime_init = spark.createDataFrame(pd_crimes,
schema=crime_schema).cache()
# Tagging City to Vancouver
df_crime_init = df_crime_init.withColumn("city",
functions.lit("Vancouver"))
# UDF to apply UUID for entire dataframe
genuuid = functions.udf(lambda: str(uuid.uuid4()))
df_crime_init = df_crime_init.withColumn("uuid", genuuid()).cache()
# Changing NaN values to 0 for numeric columns & Filtering those rows that has not latitude and longitude
df_crime_init = df_crime_init.na.fill(0)
df_crime = df_crime_init.where((df_crime_init["X"] > 0)
| (df_crime_init["Y"] > 0))
# Conversion of UTM co-orindates to Latitude and Longitude
utm_udf_x = functions.udf(
lambda x, y: utm.to_latlon(x, y, 10, 'U')[0].item(),
types.DoubleType())
utm_udf_y = functions.udf(
lambda x, y: utm.to_latlon(x, y, 10, 'U')[1].item(),
types.DoubleType())
df_crime = df_crime.withColumn(
'lat', utm_udf_x(functions.col('X'), functions.col('Y')))
df_crime = df_crime.withColumn(
'long', utm_udf_y(functions.col('X'), functions.col('Y')))
# Creating a new dataframe to store those records that does not have co-ordinates. We would need this for further study
df_crime_nan = df_crime_init.where(df_crime_init["X"] == 0.0)
df_crime_nan = df_crime_nan.withColumn("lat", functions.lit(0.0))
df_crime_nan = df_crime_nan.withColumn("long", functions.lit(0.0))
# Union of both dataframes
df_crime_full = df_crime.union(df_crime_nan)
# Calling Cassandra DB crime_type to tag subtypes to one common type
crimepred = spark.read.format("org.apache.spark.sql.cassandra").options(
table='crime_type', keyspace=key).load()
crimepred.registerTempTable("crimetype")
df_crime_full.registerTempTable("crime")
df_table = spark.sql(
"select c.city,t.type as crime_type,c.uuid,c.subtype as crimesub_type,c.hour,c.lat,c.long,c.month,c.neighbourhood,c.year from crime c left join crimetype t on c.subtype = t.sub_type"
)
df_table = df_table.withColumn('count', functions.lit(1))
df_na = df_table.where(df_table["crime_type"].isNull()).show()
#df_crime_nan.show()
print(df_table.count())
# Loading of data into base_table
df_table.write.format("org.apache.spark.sql.cassandra").options(
table='base_table', keyspace=key).mode('overwrite').save()
def setUp(self):
sc = SparkContext.getOrCreate()
sql_context = SQLContext(sc)
struct_feat = [T.StructField('f1', T.FloatType())]
struct_lab = [T.StructField('l1', T.StringType())]
self.default_param_dict = {
'algorithm': 'LogisticRegression',
'elasticNetParam': (0.0, 0.5),
'fitIntercept': True,
'labelCol': 'label',
'maxIter': (100, 150),
'predictionCol': 'prediction',
'probabilityCol': 'probability',
'rawPredictionCol': 'rawPrediction',
'regParam': (0.0, 0.5),
'threshold': (0.0, 0.5),
'tol': (1e-06, 0.01)
}
self.default_features = [
T.StructField('AarsVaerk_1', T.DoubleType(), True),
T.StructField('AarsVaerk_2', T.DoubleType(), True),
T.StructField('AarsVaerk_3', T.DoubleType(), True)
]
self.default_standard = True
self.workflow = ExecuteWorkflowClassification(self.default_param_dict,
self.default_standard,
self.default_features)
def main():
schema = types.StructType([
types.StructField('station', types.StringType(), True),
types.StructField('date', types.StringType(), True),
types.StructField('element', types.StringType(), True),
types.StructField('value1', types.DoubleType(), True),
types.StructField('mflag', types.StringType(), True),
types.StructField('qflag', types.StringType(), True),
])
df = spark.read.csv(inputs, schema)
getRange = functions.udf(get_dif, types.DoubleType())
df_by_date = df.select('station', 'date', 'value1')\
.where((df.element == 'TMAX') | (df.element == 'TMIN'))\
.groupBy('station', 'date') \
.agg(functions.collect_list('value1').alias('range'))\
.withColumn('range', getRange('range'))
df_by_date = df_by_date.where(df_by_date.range > 1).sort('date',
ascending=True)
df_max = df_by_date.groupBy('date').max('range').select(
'date',
functions.col('max(range)').alias('range'))
joined_df = df_max.join(df_by_date, ["date", "range"], 'inner')
joined_df = joined_df.select('date', 'station', 'range')
joined_df.show()
if not os.path.exists(output):
os.makedirs(output)
joined_df.write.csv(output, sep=' ', mode='overwrite')
def sensor_schema():
sen_schema = types.StructType([
types.StructField('timestamp', types.StringType()),
types.StructField('X', types.DoubleType()),
types.StructField('Y', types.DoubleType()),
types.StructField('Z', types.DoubleType()),
])
return sen_schema
def clean_input(dataframe, start, end):
input_columns = [
"client_id",
"timestamp",
"is_default_browser",
"search_counts",
"country",
"profile_creation_date",
"channel",
"os",
"hours",
]
columns = {col: F.col(col) for col in input_columns}
# normalize countries against a whitelist
columns["country"] = (
F.when(F.col("country").isin(countries), F.col("country"))
.otherwise("Other")
.alias("country")
)
# clean operating system based on CEP naming scheme
pattern = {
"Windows": ["Windows%", "WINNT%"],
"Mac": ["Darwin%"],
"Linux": ["%Linux%", "%BSD%", "%SunOS%"],
}
columns["os"] = column_like("os", pattern, "Other")
# rename normalized channel to channel
columns["channel"] = F.col("normalized_channel")
# convert profile creation date into seconds (day -> seconds)
columns["profile_creation_date"] = (
F.when(F.col("profile_creation_date") >= 0,
F.col("profile_creation_date") * seconds_per_day)
.otherwise(0.0)
.cast(types.DoubleType())
)
# generate hours of usage from subsession length (seconds -> hours)
columns["hours"] = (
F.when((F.col("subsession_length") >= 0) &
(F.col("subsession_length") < 180 * seconds_per_day),
F.col("subsession_length") / seconds_per_hour)
.otherwise(0.0)
.cast(types.DoubleType())
)
# clean the dataset
clean = (
dataframe
.where(F.col("submission_date_s3") >= start)
.where(F.col("submission_date_s3") < end)
.select([expr.alias(name) for name, expr in columns.iteritems()])
)
return clean
def get_schema():
return t.StructType([
t.StructField("ReportAsOfEOD", t.StringType(), True),
t.StructField("LoanID", t.StringType(), True),
t.StructField("Date", t.StringType(), True),
t.StructField("PrincipalRepayment", t.DoubleType(), True),
t.StructField("InterestRepayment", t.DoubleType(), True),
t.StructField("LateFeesRepayment", t.DoubleType(), True),
])
def get_schema():
return TableSchema(
[
t.StructField("UserName", t.StringType(), True),
t.StructField("Loans", t.LongType(), False),
t.StructField("TotalInterestRepayment", t.DoubleType(), True),
t.StructField("TotalLateFeesRepayment", t.DoubleType(), True),
],
primary_key="UserName",
)
def process_columns(self, data_frame: DataFrame) -> DataFrame:
return (data_frame.withColumn(
"conversion_rate_multiplier",
F.col("conversion_rate_multiplier").substr(1, 8).cast(
T.IntegerType()) + F.col("conversion_rate_multiplier").substr(
9, 7).cast(T.DoubleType()) * .0000001).withColumn(
"conversion_rate_divisor",
F.col("conversion_rate_divisor").substr(1, 8).cast(
T.IntegerType()) +
F.col("conversion_rate_divisor").substr(9, 7).cast(
T.DoubleType()) * .0000001).drop("value"))
def getSchema(): # noqa: N802
return t.StructType(
[
t.StructField("COUNTYFP", t.IntegerType(), True),
t.StructField("NEVER", t.DoubleType(), True),
t.StructField("RARELY", t.DoubleType(), True),
t.StructField("SOMETIMES", t.DoubleType(), True),
t.StructField("FREQUENTLY", t.DoubleType(), True),
t.StructField("ALWAYS", t.DoubleType(), True),
t.StructField("INSERT_TS", t.TimestampType(), True),
]
)
def pivot(trades, prices):
"""
Pivot and fill the columns on the event id so that each row contains a
column for each id + column combination where the value is the most recent
non-null value for that id. For example, given the above input tables the
expected output is:
+---+-------------+-----+-----+-----+--------+------+------+--------+-----------+------+------+--------+-----------+
| id| timestamp| bid| ask|price|quantity|10_bid|10_ask|10_price|10_quantity|20_bid|20_ask|20_price|20_quantity|
+---+-------------+-----+-----+-----+--------+------+------+--------+-----------+------+------+--------+-----------+
| 10|1546300799000| 37.5|37.51| null| null| 37.5| 37.51| null| null| null| null| null| null|
| 10|1546300800000| null| null| 37.5| 100.0| 37.5| 37.51| 37.5| 100.0| null| null| null| null|
| 10|1546300801000| null| null|37.51| 100.0| 37.5| 37.51| 37.51| 100.0| null| null| null| null|
| 10|1546300802000|37.51|37.52| null| null| 37.51| 37.52| 37.51| 100.0| null| null| null| null|
| 20|1546300804000| null| null|12.67| 300.0| 37.51| 37.52| 37.51| 100.0| null| null| 12.67| 300.0|
| 10|1546300806000| 37.5|37.51| null| null| 37.5| 37.51| 37.51| 100.0| null| null| 12.67| 300.0|
| 10|1546300807000| null| null| 37.5| 200.0| 37.5| 37.51| 37.5| 200.0| null| null| 12.67| 300.0|
+---+-------------+-----+-----+-----+--------+------+------+--------+-----------+------+------+--------+-----------+
:param trades: DataFrame of trade events
:param prices: DataFrame of price events
:return: A DataFrame of the combined events and pivoted columns.
"""
trades_prices = trades. \
join(prices, ['id', 'timestamp'], 'outer'). \
select('id', 'timestamp', 'bid', 'ask', 'price', 'quantity'). \
orderBy(asc("timestamp"))
unique_ids = trades_prices.select('id').distinct().collect()
result = None
for row in unique_ids:
id = str(row.id)
dyn_columns = trades_prices. \
withColumn("bid", when(col("id") != row.id, lit(None).cast(T.DoubleType())).otherwise(lit(col('bid')).cast(T.DoubleType()))).\
withColumn("ask", when(col("id") != row.id, lit(None).cast(T.DoubleType())).otherwise(lit(col('ask')).cast(T.DoubleType()))).\
withColumn("price", when(col("id") != row.id, lit(None).cast(T.DoubleType())).otherwise(lit(col('price')).cast(T.DoubleType()))).\
withColumn("quantity", when(col("id") != row.id, lit(None).cast(T.DoubleType())).otherwise(lit(col('quantity')).cast(T.DoubleType()))).\
withColumn(id+"_id", when(col("id") == row.id, lit(id).cast(T.IntegerType())).otherwise(lit(id).cast(T.IntegerType()))).\
withColumn(id + "_bid", func.last('bid', True).over(
Window.partitionBy(id+"_id").orderBy('timestamp').rowsBetween(-sys.maxsize, 0))). \
withColumn(id + "_ask", func.last('ask', True).over(
Window.partitionBy(id+"_id").orderBy('timestamp').rowsBetween(-sys.maxsize, 0))). \
withColumn(id + "_price", func.last('price', True).over(
Window.partitionBy(id+"_id").orderBy('timestamp').rowsBetween(-sys.maxsize, 0))). \
withColumn(id + "_quantity", func.last('quantity', True).over(
Window.partitionBy(id+"_id").orderBy('timestamp').rowsBetween(-sys.maxsize, 0))).\
drop('bid', 'ask', 'price', 'quantity', id + "_id")
if result is None:
result = trades_prices.join(dyn_columns, ['id', 'timestamp'], how='outer')
else:
result = result.join(dyn_columns, ['id', 'timestamp'], how='outer')
return result.orderBy('timestamp')
def get_schema():
return TableSchema(
[
t.StructField("ReportAsOfEOD", t.DateType(), True),
t.StructField("LoanID", t.StringType(), True),
t.StructField("Date", t.DateType(), True),
t.StructField("PrincipalRepayment", t.DoubleType(), True),
t.StructField("InterestRepayment", t.DoubleType(), True),
t.StructField("LateFeesRepayment", t.DoubleType(), True),
],
primary_key=["LoanID", "Date"],
# partition_by = "Date" #---takes a very long time
)
def prepro(spark: SparkSession, datadir: Path, nam: str):
def pp(s5: DataFrame) -> DataFrame:
stages = []
catvars = ['dept_id', 'item_id', 'store_id', 'wday']
for v in catvars:
stages += [StringIndexer(inputCol=v, outputCol=f"i{v}")]
stages += [
OneHotEncoderEstimator(inputCols=[f"i{v}" for v in catvars],
outputCols=[f"v{v}" for v in catvars])
]
stages += [
VectorAssembler(inputCols=[
'vwday', 'vitem_id', 'vdept_id', 'vstore_id', 'flag_ram',
'snap', 'dn', 'month', 'year'
],
outputCol='features')
]
pip: Pipeline = Pipeline(stages=stages)
pipm = pip.fit(s5)
df: DataFrame = pipm.transform(s5)
return df.drop('idept_id', 'iitem_id', 'istore_id', 'iwday',
'vdept_id', 'vtem_id', 'vstore_id', 'vwday')
print("--- preprocessing -----------------------")
schema = stype.StructType([
stype.StructField('year', stype.IntegerType(), True),
stype.StructField('month', stype.IntegerType(), True),
stype.StructField('dn', stype.IntegerType(), True),
stype.StructField('wday', stype.IntegerType(), True),
stype.StructField('snap', stype.IntegerType(), True),
stype.StructField('dept_id', stype.StringType(), True),
stype.StructField('item_id', stype.StringType(), True),
stype.StructField('store_id', stype.StringType(), True),
stype.StructField('sales', stype.DoubleType(), True),
stype.StructField('flag_ram', stype.IntegerType(), True),
stype.StructField('Sales_Pred', stype.DoubleType(), True),
])
csv_path = datadir / "Sales5_Ab2011_InklPred.csv"
print(f"--- Reading: '{csv_path}'")
sales5: DataFrame = spark.read.csv(str(csv_path), header='true', schema=schema) \
.withColumn("label", sfunc.col('sales'))
ppdf = pp(sales5)
print(f"--- Writing: '{nam}'")
hlp.writeToDatadirParquet(ppdf, nam)
def load_prices(spark):
data = [
(10, 1546300799000, 37.50, 37.51),
(10, 1546300802000, 37.51, 37.52),
(10, 1546300806000, 37.50, 37.51),
]
schema = T.StructType([
T.StructField("id", T.LongType()),
T.StructField("timestamp", T.LongType()),
T.StructField("bid", T.DoubleType()),
T.StructField("ask", T.DoubleType()),
])
return spark.createDataFrame(data, schema)
def load_trades(spark):
data = [
(10, 1546300800000, 37.50, 100.000),
(10, 1546300801000, 37.51, 100.000),
(20, 1546300804000, 12.67, 300.000),
(10, 1546300807000, 37.50, 200.000),
]
schema = T.StructType([
T.StructField("id", T.LongType()),
T.StructField("timestamp", T.LongType()),
T.StructField("price", T.DoubleType()),
T.StructField("quantity", T.DoubleType()),
])
return spark.createDataFrame(data, schema)
def weighted_predict(df, epoch_id):
split_col = F.split(df.value, ',')
# df = df.withColumn('TimeStamp', F.to_timestamp(F.regexp_replace(split_col.getItem(0), '"', ''),
# 'yyyy-mm-dd HH:mm:ss.SSS'))
df = df.withColumn(
'TimeStamp',
F.regexp_replace(split_col.getItem(0), '"',
'').cast(tp.TimestampType()))
df = df.withColumn('RT_Temp', split_col.getItem(1).cast(tp.DoubleType()))
df = df.withColumn('RT_Temp_Predict',
split_col.getItem(2).cast(tp.DoubleType()))
df = df.withColumn('Nu_Temp', split_col.getItem(3).cast(tp.DoubleType()))
df = df.withColumn('Nu_Temp_Predict',
split_col.getItem(4).cast(tp.DoubleType()))
df = df.withColumn(
'RMSE_Score',
F.regexp_replace(split_col.getItem(4), '"', '').cast(tp.DoubleType()))
df = df.drop('value')
# df.show()
sp_df = df.select('TimeStamp','RT_Temp','RT_Temp_Predict','Nu_Temp','Nu_Temp_Predict','RMSE_Score')\
.where("topic='{}'".format(str(sp_topic)))
bt_df = df.select('TimeStamp','RT_Temp','RT_Temp_Predict','Nu_Temp','Nu_Temp_Predict','RMSE_Score')\
.where("topic='{}'".format(str(bl_topic)))
# print("Speed Layer Predictions....")
# sp_df.show(5)
# print("Batch Layer Predictions....")
# bt_df.show(5)
df_final = (sp_df.alias('sp').join(
bt_df.alias('bt'),
on=sp_df['TimeStamp'] == bt_df['TimeStamp'],
how='inner'
).selectExpr(
'sp.TimeStamp as TS', 'round(sp.RT_Temp,3) as RT_Temp',
'round(sp.RT_Temp_Predict,3) as Speed_RT_Temp',
'round(bt.RT_Temp_Predict,3) as Batch_RT_Temp',
'round(({}*sp.RT_Temp_Predict + {}*bt.RT_Temp_Predict),3) as Wt_RT_Temp'
.format(str(s_wt), str(b_wt)), 'round(sp.Nu_Temp,3) as Nu_Temp',
'round(sp.Nu_Temp_Predict,3) as Speed_Nu_Temp',
'round(bt.Nu_Temp_Predict,3) as Batch_Nu_Temp',
'round(({}*sp.Nu_Temp_Predict + {}*bt.Nu_Temp_Predict),3) as Wt_Nu_Temp'
.format(str(s_wt), str(b_wt)), 'round(sp.RMSE_Score,3) as Speed_RMSE',
'round(bt.RMSE_Score,3) as Batch_RMSE'))
df_final.show(5)
# df = spark.sql("select * FROM default.turbine")
df_final.write.saveAsTable(name='tsa.serving_predictions',
format='hive',
mode='append')
def verification(self, candDF, threshold):
"""
Input: $candDF is the output DataFrame from the 'filtering' function.
$threshold is a float value between (0, 1]
Output: Return a new DataFrame $resultDF that represents the ER result.
It has five columns: id1, joinKey1, id2, joinKey2, jaccard
Comments: There are two differences between $candDF and $resultDF
(1) $resultDF adds a new column, called jaccard, which stores the jaccard similarity
between $joinKey1 and $joinKey2
(2) $resultDF removes the rows whose jaccard similarity is smaller than $threshold
"""
def get_jaccard_similarity(set_1, set_2):
set_1 = set(set_1)
set_2 = set(set_2)
return len(set_1 & set_2) * 1.00 / len(set_1 | set_2) * 1.00
calculate_jaccard = functions.udf(get_jaccard_similarity,
types.DoubleType())
candDF = candDF.withColumn(
'jaccard', calculate_jaccard(candDF['joinKey1'],
candDF['joinKey2']))
candDF = candDF.filter(candDF.jaccard >= threshold)
return candDF
def _generate_select_expression_for_extended_string_to_double(
source_column, name):
"""
More robust conversion from StringType to DoubleType.
Is able to additionally handle (compared to implicit Spark conversion):
* Preceding whitespace
* Trailing whitespace
* Preceeding and trailing whitespace
* underscores as thousand separators
Hint
----
Please have a look at the tests to get a better feeling how it behaves under
tests/unit/transformer/test_mapper_custom_data_types.py::TestExtendedStringConversions and
tests/data/test_fixtures/mapper_custom_data_types_fixtures.py
Example
-------
>>> from spooq.transformer import Mapper
>>>
>>> input_df.head(3)
[Row(input_string=" 21474838464.70 "),
Row(input_string="Hello"),
Row(input_string="21_474_838_464.70")]
>>> mapping = [("output_value", "input_string", "extended_string_to_double")]
>>> output_df = Mapper(mapping).transform(input_df)
>>> output_df.head(3)
[Row(input_string=21474838464.7),
Row(input_string=None),
Row(input_string=21474838464.70)]
"""
return F.regexp_replace(F.trim(source_column), "_",
"").cast(T.DoubleType()).alias(name)
def test_fit_model_multiclass(self):
model = create_mnist_model()
optimizer = tf.keras.optimizers.Adadelta(1.0)
loss = tf.keras.losses.categorical_crossentropy
for num_cores in [2, constants.TOTAL_BUFFER_MEMORY_CAP_GIB + 1]:
with spark_session('test_fit_model_multiclass', cores=num_cores) as spark:
df = create_mnist_data(spark)
with local_store() as store:
keras_estimator = hvd.KerasEstimator(
num_proc=num_cores,
store=store,
model=model,
optimizer=optimizer,
loss=loss,
metrics=['accuracy'],
feature_cols=['features'],
label_cols=['label_vec'],
batch_size=2,
epochs=2,
verbose=2)
keras_model = keras_estimator.fit(df).setOutputCols(['label_prob'])
pred_df = keras_model.transform(df)
argmax = udf(lambda v: float(np.argmax(v)), returnType=T.DoubleType())
pred_df = pred_df.withColumn('label_pred', argmax(pred_df.label_prob))
preds = pred_df.collect()
assert len(preds) == df.count()
row = preds[0]
label_prob = row.label_prob.toArray().tolist()
assert label_prob[int(row.label_pred)] == max(label_prob)
def extract_embedding(spark, glove_model_path, output_folder):
glove = Glove.load(glove_model_path)
dictionary_schema = T.StructType([
T.StructField('index', T.IntegerType(), True),
T.StructField('standard_concept_id', T.IntegerType(), True)
])
dictionary_df = spark.createDataFrame([
Row(index=k, standard_concept_id=int(v))
for k, v in glove.inverse_dictionary.items()
], dictionary_schema)
vector_schema = T.StructType([
T.StructField('index', T.IntegerType(), True),
T.StructField('vector', T.ArrayType(T.DoubleType()), True)
])
vector_df = spark.createDataFrame([
Row(index=idx, vector=vector.tolist())
for idx, vector in enumerate(glove.word_vectors)
], vector_schema)
dictionary_df.join(vector_df, 'index').select(
'standard_concept_id',
'vector').write.mode('overwrite').parquet(output_folder)
def get_coefficients(
split_urls_and_word_frequency_orders: DataFrame,
s: float,
additional_weight_function: Callable[[int], float] = lambda e: 1
) -> DataFrame:
"""
:param split_urls_and_word_frequency_orders: A DataFrame of split URLs and word frequency orders with columns:
id, url, split_url, word_frequency_orders.
:param s: s parameter of Zipf distribution.
:param additional_weight_function: additional weight function to be applied additional weight beside Zipf to
word vector.
:return: A DataFrame of split URLs and coefficient of each term with columns: id, url, split_url, coefficients
"""
def calculate_coefficients(word_frequency_orders):
coefficients = []
for i in range(len(word_frequency_orders)):
coefficients.append(
additional_weight_function(i) *
URLVectorCalculator.get_zipf_coefficient(
word_frequency_orders[i], s))
return coefficients
get_coefficients_udf = F.udf(calculate_coefficients,
T.ArrayType(T.DoubleType()))
split_urls_and_coefficients = split_urls_and_word_frequency_orders \
.select("id",
"url",
"split_url",
get_coefficients_udf("word_frequency_orders").alias("coefficients"))
return split_urls_and_coefficients
def sum_word_vectors(
urls_and_weighted_word_vectors: DataFrame) -> DataFrame:
"""
Sums weighted word vectors and their corresponding coefficients for each URL.
:param urls_and_weighted_word_vectors: A DataFrame of URLs and weighted word vectors with columns: id, url, pos,
word, weighted_word_vector, coefficient.
:return: A DataFrame of URLs and their corresponding sum of word vectors and sum of coefficients with columns:
id, url, split_url, coefficients, summed_vectors, summed_coefficients.
"""
word_array_sorter_udf = F.udf(
URLVectorCalculator.sort_list_of_2_tuples_by_0th_item,
T.ArrayType(T.StringType()))
coefficient_array_sorter_udf = F.udf(
URLVectorCalculator.sort_list_of_2_tuples_by_0th_item,
T.ArrayType(T.DoubleType()))
vector_size = len(
urls_and_weighted_word_vectors.select(
'weighted_word_vector').first()[0])
return urls_and_weighted_word_vectors \
.groupBy("id", "url") \
.agg(F.collect_list(F.struct("pos", "word")).alias("positions_and_words"),
F.collect_list(F.struct("pos", "coefficient")).alias("positions_and_coefficients"),
F.sum("coefficient").alias("summed_coefficients"),
F.array(*[F.sum(F.col("weighted_word_vector")[i])
for i in range(vector_size)]).alias("summed_vectors")) \
.select("id", "url", "summed_coefficients", "summed_vectors",
word_array_sorter_udf("positions_and_words").alias("split_url"),
coefficient_array_sorter_udf("positions_and_coefficients").alias("coefficients"))
def cond_pandas(pyData):
groupby_columns = ['grp', 'subgrp']
agg_columns = ['mean_of_C', 'max_of_D', 'cond_var_of_E', 'cond_var_of_E2']
df = spark.createDataFrame(pyData)
postAggSchema = DataTypes.StructType(
[x for x in DataPointSchema.fields if x.name in groupby_columns] + [
DataTypes.StructField(name, DataTypes.DoubleType(), False)
for name in agg_columns
])
#
@pandas_udf(postAggSchema, PandasUDFType.GROUPED_MAP)
def inner_agg_method(dfPartition):
group_key = dfPartition['grp'].iloc[0]
subgroup_key = dfPartition['subgrp'].iloc[0]
C = dfPartition['C']
D = dfPartition['D']
posE = dfPartition[dfPartition.E < 0]['E']
return pd.DataFrame([[
group_key,
subgroup_key,
C.mean(),
D.max(),
posE.var(),
posE \
.agg(lambda E: \
((E * E).sum() -
E.sum()**2/E.count())/(E.count()-1)) \
.mean(),
]], columns=groupby_columns + agg_columns)
#
aggregates = df \
.groupby(df.grp, df.subgrp).apply(inner_agg_method) \
.orderBy('grp', 'subgrp')
return aggregates, None
def calc(df):
## function to calculate the appoximating function and its derivative
def foo(x,y):
y_arr = np.array(y)
gy = g(y_arr)
gp = gprime(y_arr)
x_arr = np.array(x)
res = np.outer(gy,x_arr)
return([res.flatten().tolist(), gp.tolist()])
udf_foo = f.udf(foo, t.ArrayType(t.ArrayType(t.DoubleType())))
df2 = df.withColumn("vals", udf_foo("features","Y"))
df2 = df2.select("id", f.col("vals").getItem(0).alias("gy"), f.col("vals").getItem(1).alias("gy_"))
GY_ = np.array(df2.agg(f.array([f.sum(f.col("gy")[i])
for i in range(n_comp**2)])).collect()[0][0]).reshape(n_comp,n_comp)/num_rows
GY_AVG_V = np.array(df2.agg(f.array([f.avg(f.col("gy_")[i])
for i in range(n_comp)])).collect()[0][0]).reshape(n_comp,1)*V
return(GY_, GY_AVG_V)
def get_spark():
conf = SparkConf()
# Load in a jar that provides extended string comparison functions such as Jaro Winkler.
# Splink
# No longer needed in spark 3.0?
# conf.set("spark.driver.extraClassPath", "jars/scala-udf-similarity-0.0.7.jar")
conf.set("spark.jars", "jars/scala-udf-similarity-0.0.7.jar")
conf.set("spark.jars.packages",
"graphframes:graphframes:0.8.0-spark3.0-s_2.12")
# WARNING:
# These config options are appropriate only if you're running Spark locally!!!
conf.set("spark.driver.memory", "4g")
conf.set("spark.sql.shuffle.partitions", "8")
sc = SparkContext.getOrCreate(conf=conf)
sc.setCheckpointDir("temp_graphframes/")
spark = SparkSession(sc)
# Register UDFs
from pyspark.sql import types
spark.udf.registerJavaFunction(
"jaro_winkler_sim",
"uk.gov.moj.dash.linkage.JaroWinklerSimilarity",
types.DoubleType(),
)
spark.udf.registerJavaFunction("Dmetaphone",
"uk.gov.moj.dash.linkage.DoubleMetaphone",
types.StringType())
return spark
