def get_top_ratings(self, user_id, count):
"""Retrun top <count> bussiness
Calls
"""
start_time = time.time()
#bid = self.reviewDF.select('business_id_num','business_id').distinct().cache()
businessDF_vegas_food_save = os.path.join(
'dataset', 'businessDF_vegas_food.parquet')
businessDF_vegas_food = self.ss.read.parquet(
businessDF_vegas_food_save)
#bid.show(20)
logger.error('{} seconds has elapsed. {} entries remained'.format(
time.time() - start_time, businessDF_vegas_food.count()))
#predDF = bid.filter(bid['user_id'] == user_id)
#build user request using input id
logger.error(
'{} seconds has elapsed before loading building predDF'.format(
time.time() - start_time))
bid = businessDF_vegas_food.select('business_id', 'latitude',
'longitude')
indexer_business_save = os.path.join('model', 'bus_ind_model')
indexer_business_model = StringIndexerModel.load(indexer_business_save)
bid = indexer_business_model.transform(bid)
predDF = bid.withColumn("user_id", lit(user_id)).cache()
logger.error('{} seconds has elapsed before loading indexer'.format(
time.time() - start_time))
indexer_user_model = StringIndexerModel.load(self.indexer_user_save)
predDF = indexer_user_model.transform(predDF)
'''user_id_converter = IndexToString(inputCol= 'user_id',outputCol='user_id')
convert_df = '''
#predDF.show(10)
logger.error('{} seconds has elapsed before model'.format(time.time() -
start_time))
model = ALSModel.load(self.model_save)
prediction_user = model.transform(predDF)
#prediction_user.show(20)
ratings = prediction_user.sort(desc('prediction')).limit(count).select(
'business_id', 'prediction', 'latitude', 'longitude')
#ratings.show(20)
#ratings.printSchema()
logger.error('{} seconds has elapsed'.format(time.time() - start_time))
return ratings.toPandas().to_json(orient='records')
def main(spark, user_indexer_model, item_indexer_model, test_file, save_test):
'''
Parameters
----------
spark : SparkSession object
data_file : string, path to the parquet file to load
model_file : string, path to store the serialized model file
'''
# Load the parquet file
test = spark.read.parquet(test_file)
user_index = StringIndexerModel.load(user_indexer_model)
item_index = StringIndexerModel.load(item_indexer_model)
test = user_index.transform(test)
test = item_index.transform(test)
test = test.sort('count', ascending = False)
relevant_docs = test.groupBy('user').agg(F.collect_list('item').alias('item'))
relevant_docs.write.parquet(save_test)
def modelPredicting(testSetWoeDF, fn):
# 数据预转换,满足ML-linearRegression输入格式要求
strInd = StringIndexerModel.load(savePath +
'{}/{}/strInd'.format(curDate, fn))
lrModel = LogisticRegressionModel.load(savePath +
'{}/{}/lrModel'.format(curDate, fn))
testSetVecAse = vecAseembler.transform(testSetWoeDF)
testSetVecAseStrInd = strInd.transform(testSetVecAse)
testSetWithProba = lrModel.transform(testSetVecAseStrInd)
return (testSetWithProba)
def indexToString(infoData):
stringIndexerPath = infoData.get(mc.INDEXERPATH)
inverterColm = infoData.get(mc.COLMTOINVERT)
dataset = infoData.get(mc.DATASET)
stringIndexer = StringIndexerModel.load(stringIndexerPath)
inverter = IndexToString(inputCol=inverterColm, outputCol=mc.DMXINVERTEDCOLM,
labels=stringIndexer.labels)
dataset = inverter.transform(dataset)
#drop the indexed colm and rename the new unindexed colm with the actual one
dataset = dataset.drop(inverterColm)
dataset = dataset.withColumnRenamed(mc.DMXINVERTEDCOLM, inverterColm)
return dataset
def load(self, path, spark_session=None):
self.iforest_model = IForestModel.load(self._get_iforest_path(path))
self.scaler_model = StandardScalerModel.load(
self._get_scaler_path(path))
file_manager = FileManager(path, spark_session)
params = file_manager.load_from_file(self._get_params_path(path),
format='json')
self.set_params(**params)
self.indexes = []
for feature in self.categorical_features:
self.indexes.append(
StringIndexerModel.load(self._get_index_path(path, feature)))
def main(stations_indexer_path, onehot_path, weather_indexer_path,
model_data_folder, station_id, hour, month, dayofyear, visibility,
air_temp, wind_speed, weather_class):
#Starting session
spark = SparkSession.builder.appName('BigDataML').getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
#Loading data
columns = [
"hour", 'dayofyear', 'month', 'air_temp', 'wind_speed', 'visibility',
'weather_class', 'station'
]
vals = [(hour, dayofyear, month, air_temp, wind_speed, visibility,
weather_class, station_id)]
data = spark.createDataFrame(vals, columns)
model = PipelineModel.load(model_data_folder)
stringIndexer = StringIndexerModel.load(stations_indexer_path)
data = stringIndexer.transform(data)
stringIndexer_weather = StringIndexerModel.load(weather_indexer_path)
data = stringIndexer_weather.transform(data)
encoder = OneHotEncoderModel.load(onehot_path)
data = encoder.transform(data)
features =\
VectorAssembler(inputCols=["hour", 'dayofyear', 'month', 'air_temp', 'wind_speed', 'visibility', 'weather_index', 'station_vector'],
outputCol="features")
test_data = features.transform(data)
model = PipelineModel.load(model_data_folder)
# Make predictions.
predictions = model.transform(test_data).collect()
print("Predicted count is: {}".format(int(predictions[0]['prediction'])))
def main():
feature_model = VectorIndexerModel.load(featureIndexer_path)
vectorAssembler = vectorAssembler.load(vectorAssembler_path)
ohe_model = OneHotEncoderModel.load(ohe_model_path)
stringIndexer_model = StringIndexerModel.load(stringIndexerPath)
lr_model = LinearRegressionModel.load(model_path)
spark = SparkSession.builder.master("local").appName("Connection").getOrCreate()
json_data = request.get_json()
availability = json_data.availability
minimum_nights = json_data.minimum_nights
latitude = json_data.latitude
longitude = json_data.longitude
name = json_data.name
neighbourhood_group = json_data.neighbourhood_group
neighbourhood = json_data.neighbourhood
room_type = json_data.room_type
dept = [(name,neighbourhood_group,neighbourhood,room_type,latitude,longitude,0.0,minimum_nights,0.0,1.0,availability,0.0)]
df = spark.createDataFrame(data=dept, schema = deptColumns)
df = stringIndexer_model.transform(df)
df = df.drop(*["neighbourhood_group", 'neighbourhood', 'room_type'])
df = ohe_model.transform(df)
df = df.drop(*["neighbourhood_group_int", 'neighbourhood_int', 'room_type_int'])
df = df.withColumn("minimum_nights", when(df["minimum_nights_int"] > 30, 30).otherwise(df["minimum_nights_int"])).drop('minimum_nights_int')
df = df.withColumn('name_length', length('name')).drop('name')
df = vectorAssembler.transform(df)
df = df.select(['features'])
df = feature_model.transform(df)
df = df.select(['features_vec'])
lr_predictions = lr_model.transform(df)
return jsonify(data=lr_predictions.collect()[-1].prediction)
def main(base_path):
APP_NAME = "make_predictions_streaming.py"
# 10초마다 데이터 처리
PERIOD = 10
BROKERS = 'localhost:9092'
PREDICTION_TOPIC = 'flight_delay_classification_request'
try:
sc and ssc
except NameError as e:
import findspark
# 스트리밍 패키지 추가 및 초기화
findspark.add_packages(
["org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0"])
findspark.init()
import pyspark
import pyspark.sql
import pyspark.streaming
conf = SparkConf().set("spark.default.parallelism", 1)
sc = SparkContext(
appName="Agile Data Science: PySpark Streaming 'Hello, World!'",
conf=conf)
ssc = StreamingContext(sc, PERIOD)
spark = pyspark.sql.SparkSession(sc).builder.appName(
APP_NAME).getOrCreate()
#
# 예측 생성에 사용된 모든 모델 적재
#
# 도착 지연 구간화 모델 적재
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(
base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# 모든 문자열 필드 벡터화 파이프라인을 dict에 적재
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path, column)
string_indexer_model = StringIndexerModel.load(
string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# 숫자 벡터 어셈블러 적재
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(
base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# 분류 모델 적재
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path)
rfc = RandomForestClassificationModel.load(random_forest_model_path)
#
# 스트리밍에서 예측 요청 처리
#
stream = KafkaUtils.createDirectStream(ssc, [PREDICTION_TOPIC], {
"metadata.broker.list": BROKERS,
"group.id": "0",
})
object_stream = stream.map(lambda x: json.loads(x[1]))
object_stream.pprint()
row_stream = object_stream.map(
lambda x: Row(FlightDate=iso8601.parse_date(x['FlightDate']),
Origin=x['Origin'],
Distance=x['Distance'],
DayOfMonth=x['DayOfMonth'],
DayOfYear=x['DayOfYear'],
UUID=x['UUID'],
DepDelay=x['DepDelay'],
DayOfWeek=x['DayOfWeek'],
FlightNum=x['FlightNum'],
Dest=x['Dest'],
Timestamp=iso8601.parse_date(x['Timestamp']),
Carrier=x['Carrier']))
row_stream.pprint()
#
# RDD 기반 객체 스트림에서 dataframe 생성
#
def classify_prediction_requests(rdd):
from pyspark.sql.types import StringType, IntegerType, DoubleType, DateType, TimestampType
from pyspark.sql.types import StructType, StructField
prediction_request_schema = StructType([
StructField("Carrier", StringType(), True),
StructField("DayOfMonth", IntegerType(), True),
StructField("DayOfWeek", IntegerType(), True),
StructField("DayOfYear", IntegerType(), True),
StructField("DepDelay", DoubleType(), True),
StructField("Dest", StringType(), True),
StructField("Distance", DoubleType(), True),
StructField("FlightDate", DateType(), True),
StructField("FlightNum", StringType(), True),
StructField("Origin", StringType(), True),
StructField("Timestamp", TimestampType(), True),
StructField("UUID", StringType(), True),
])
prediction_requests_df = spark.createDataFrame(
rdd, schema=prediction_request_schema)
prediction_requests_df.show()
#
# FlightNum을 대체할 Route 변수 추가
#
from pyspark.sql.functions import lit, concat
prediction_requests_with_route = prediction_requests_df.withColumn(
'Route',
concat(prediction_requests_df.Origin, lit('-'),
prediction_requests_df.Dest))
prediction_requests_with_route.show(6)
# 문자열 필드를 해당 열에 대응하는 파이프라인으로 벡터화
# 범주 필드를 범주형 특징 벡터로 변환한 다음 중간 결과 필드 삭제
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(
prediction_requests_with_route)
# 숫사 열 벡터화: DepDelay, Distance, 인덱스 열
final_vectorized_features = vector_assembler.transform(
prediction_requests_with_route)
# 벡터 검사
final_vectorized_features.show()
# 개별 인덱스 열 제거
index_columns = [
"Carrier_index", "Origin_index", "Dest_index", "Route_index"
]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# 확정된 특징 검사
final_vectorized_features.show()
# 예측 생성
predictions = rfc.transform(final_vectorized_features)
# 원 필드에 제공하기 위해 특징 벡터와 예측 메타데이터 제거
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop(
"rawPrediction").drop("probability")
# 결과 검사
final_predictions.show()
# 몽고DB에 저장
if final_predictions.count() > 0:
final_predictions.rdd.map(lambda x: x.asDict()).saveToMongoDB(
"mongodb://localhost:27017/agile_data_science.flight_delay_classification_response"
)
# 분류를 수행하고 몽고 DB에 저장
row_stream.foreachRDD(classify_prediction_requests)
ssc.start()
ssc.awaitTermination()
def main(base_path):
APP_NAME = "make_predictions_streaming.py"
# Process data every 10 seconds
PERIOD = 10
BROKERS = 'localhost:9092'
PREDICTION_TOPIC = 'flight_delay_classification_request'
try:
sc and ssc
except NameError as e:
import findspark
# Add the streaming package and initialize
findspark.add_packages(["org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0"])
findspark.init()
import pyspark
import pyspark.sql
import pyspark.streaming
conf = SparkConf().set("spark.default.parallelism", 1)
sc = SparkContext(appName="Agile Data Science: PySpark Streaming 'Hello, World!'", conf=conf)
ssc = StreamingContext(sc, PERIOD)
spark = pyspark.sql.SparkSession(sc).builder.appName(APP_NAME).getOrCreate()
#
# Load all models to be used in making predictions
#
# Load the arrival delay bucketizer
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# Load all the string field vectorizer pipelines into a dict
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path,
column
)
string_indexer_model = StringIndexerModel.load(string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# Load the numeric vector assembler
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# Load the classifier model
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path
)
rfc = RandomForestClassificationModel.load(
random_forest_model_path
)
#
# Process Prediction Requests in Streaming
#
stream = KafkaUtils.createDirectStream(
ssc,
[PREDICTION_TOPIC],
{
"metadata.broker.list": BROKERS,
"group.id": "0",
}
)
object_stream = stream.map(lambda x: json.loads(x[1]))
object_stream.pprint()
row_stream = object_stream.map(
lambda x: Row(
FlightDate=iso8601.parse_date(x['FlightDate']),
Origin=x['Origin'],
Distance=x['Distance'],
DayOfMonth=x['DayOfMonth'],
DayOfYear=x['DayOfYear'],
UUID=x['UUID'],
DepDelay=x['DepDelay'],
DayOfWeek=x['DayOfWeek'],
FlightNum=x['FlightNum'],
Dest=x['Dest'],
Timestamp=iso8601.parse_date(x['Timestamp']),
Carrier=x['Carrier']
)
)
row_stream.pprint()
#
# Create a dataframe from the RDD-based object stream
#
def classify_prediction_requests(rdd):
from pyspark.sql.types import StringType, IntegerType, DoubleType, DateType, TimestampType
from pyspark.sql.types import StructType, StructField
prediction_request_schema = StructType([
StructField("Carrier", StringType(), True),
StructField("DayOfMonth", IntegerType(), True),
StructField("DayOfWeek", IntegerType(), True),
StructField("DayOfYear", IntegerType(), True),
StructField("DepDelay", DoubleType(), True),
StructField("Dest", StringType(), True),
StructField("Distance", DoubleType(), True),
StructField("FlightDate", DateType(), True),
StructField("FlightNum", StringType(), True),
StructField("Origin", StringType(), True),
StructField("Timestamp", TimestampType(), True),
StructField("UUID", StringType(), True),
])
prediction_requests_df = spark.createDataFrame(rdd, schema=prediction_request_schema)
prediction_requests_df.show()
#
# Add a Route variable to replace FlightNum
#
from pyspark.sql.functions import lit, concat
prediction_requests_with_route = prediction_requests_df.withColumn(
'Route',
concat(
prediction_requests_df.Origin,
lit('-'),
prediction_requests_df.Dest
)
)
prediction_requests_with_route.show(6)
# Vectorize string fields with the corresponding pipeline for that column
# Turn category fields into categoric feature vectors, then drop intermediate fields
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(prediction_requests_with_route)
# Vectorize numeric columns: DepDelay, Distance and index columns
final_vectorized_features = vector_assembler.transform(prediction_requests_with_route)
# Inspect the vectors
final_vectorized_features.show()
# Drop the individual index columns
index_columns = ["Carrier_index", "Origin_index", "Dest_index", "Route_index"]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# Inspect the finalized features
final_vectorized_features.show()
# Make the prediction
predictions = rfc.transform(final_vectorized_features)
# Drop the features vector and prediction metadata to give the original fields
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop("rawPrediction").drop("probability")
# Inspect the output
final_predictions.show()
# Store to Mongo
if final_predictions.count() > 0:
final_predictions.rdd.map(lambda x: x.asDict()).saveToMongoDB(
"mongodb://localhost:27017/agile_data_science.flight_delay_classification_response"
)
# Do the classification and store to Mongo
row_stream.foreachRDD(classify_prediction_requests)
ssc.start()
ssc.awaitTermination()
def main(iso_date, base_path):
APP_NAME = "make_predictions.py"
# SparkSession이 없으면 환경 생성
try:
sc and spark
except NameError as e:
import findspark
findspark.init()
import pyspark
import pyspark.sql
sc = pyspark.SparkContext()
spark = pyspark.sql.SparkSession(sc).builder.appName(APP_NAME).getOrCreate()
#
# 파이프라인에 모든 모델을 적재
#
# 도착 지연 구간 설정 모델을 적재
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# 모든 문자열 인덱서를 dict에 적재
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in ["Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear",
"Origin", "Dest", "Route"]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path,
column
)
string_indexer_model = StringIndexerModel.load(string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# 수치 벡터 어셈블러 적재
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# 분류 모델 적재
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path
)
rfc = RandomForestClassificationModel.load(
random_forest_model_path
)
#
# 요청을 훈련 데이터로부터 변환을 통해 실행
#
# 쿼리 범위를 지정하기 위해 ISO 문자열로 오늘과 내일 날짜 가져오기
today_dt = iso8601.parse_date(iso_date)
rounded_today = today_dt.date()쿼리 범위를 지정하기 위해 ISO 문자열로 오늘과 내일 날짜 가져오기
iso_today = rounded_today.isoformat()
# 해당 날짜의 입력 경로 생성: 날짜 기반의 프라이머리 키 디렉터리 구조
today_input_path = "{}/data/prediction_tasks_daily.json/{}".format(
base_path,
iso_today
)
from pyspark.sql.types import StringType, IntegerType, DoubleType, DateType, TimestampType
from pyspark.sql.types import StructType, StructField
schema = StructType([
StructField("Carrier", StringType(), True),
StructField("DayOfMonth", IntegerType(), True),
StructField("DayOfWeek", IntegerType(), True),
StructField("DayOfYear", IntegerType(), True),
StructField("DepDelay", DoubleType(), True),
StructField("Dest", StringType(), True),
StructField("Distance", DoubleType(), True),
StructField("FlightDate", DateType(), True),
StructField("FlightNum", StringType(), True),
StructField("Origin", StringType(), True),
StructField("Timestamp", TimestampType(), True),
])
prediction_requests = spark.read.json(today_input_path, schema=schema)
prediction_requests.show()
#
# FlightNum을 대체할 Route 변수 추가
#
from pyspark.sql.functions import lit, concat
prediction_requests_with_route = prediction_requests.withColumn(
'Route',
concat(
prediction_requests.Origin,
lit('-'),
prediction_requests.Dest
)
)
prediction_requests_with_route.show(6)
# 해당 열에 대응하는 인덱서로 문자열 필드를 인덱싱
for column in ["Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear",
"Origin", "Dest", "Route"]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(prediction_requests_with_route)
# 수치열 벡터화: DepDelay, Distance
final_vectorized_features = vector_assembler.transform(prediction_requests_with_route)
# 명목형 필드를 위한 인덱스 제거
index_columns = ["Carrier_index", "DayOfMonth_index","DayOfWeek_index",
"DayOfYear_index", "Origin_index", "Origin_index",
"Dest_index", "Route_index"]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# 확정된 특징 검사
final_vectorized_features.show()
# 예측 생성
predictions = rfc.transform(final_vectorized_features)
# 원래 필드를 제공하기 위해 특징 벡터와 예측 메타데이터를 제거
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop("rawPrediction").drop("probability")
# 결과 검사
final_predictions.show()
# 해당 날짜의 경로 생성: 날짜 기반의 프라이머리 키 디렉터리 구조
today_output_path = "{}/data/prediction_results_daily.json/{}".format(
base_path,
iso_today
)
# 일별 구간에 결과 저장
final_predictions.repartition(1).write.mode("overwrite").json(today_output_path)
def read_parquet(parquet_path):
parquet_df = spark.read.parquet(parquet_path)
parquet_df = parquet_df.drop('id')
parquet_df = parquet_df.drop('one_area_price')
parquet_df = parquet_df.drop('agency_nameVec')
parquet_df = parquet_df.drop('districtVec')
parquet_df = parquet_df.drop('room_type')
parquet_df.show(truncate=False)
print('parquet_df.count()==========11', parquet_df.count(),
parquet_df.columns)
for i in parquet_df.columns:
if ('Vec' not in i) & ('facilities_vectors' not in i):
if parquet_df.filter(parquet_df[i].isNull()).count() > 0:
parquet_df = parquet_df.na.fill(0, i)
elif parquet_df.filter(parquet_df[i] == 'NULL').count() > 0:
parquet_df = parquet_df.filter(parquet_df[i] != 'NULL')
parquet_df = parquet_df.select(
'*', parquet_df[i].cast('float').alias('tmp_name')).drop(i)
parquet_df = parquet_df.withColumnRenamed('tmp_name', i)
parquet_df = parquet_df.filter(parquet_df[i].isNotNull())
print('parquet_df.count()==========22', i, parquet_df.count())
columns = parquet_df.columns
columns.remove('price')
from pyspark.ml.feature import OneHotEncoder, StringIndexer, StringIndexerModel
from pyspark.ml.feature import CountVectorizer, CountVectorizerModel
model_path = "/user/limeng/ganji_daxing_save_models/"
columns_list = []
for i in columns:
if i == 'facilities_vectors':
loadedCountVectorizerModel = CountVectorizerModel.load(
model_path + 'count-vectorizer-model')
temp = loadedCountVectorizerModel.vocabulary
columns_list.extend(temp)
elif i == 'rent_typeVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modelrent_type')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
elif i == 'agency_nameVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modelagency_name')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
elif i == 'directionVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modeldirection')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
elif i == 'zoneVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modelzone')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
elif i == 'pay_typeVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modelpay_type')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
elif i == 'districtVec':
loadedStringIndexerModel = StringIndexerModel.load(
model_path + 'stringIndexer_modeldistrict')
temp = loadedStringIndexerModel.labels
columns_list.extend(temp)
else:
columns_list.append(i)
vecAssembler = VectorAssembler(inputCols=columns, outputCol="features")
parquet_df = vecAssembler.transform(parquet_df).select('features', 'price')
parquet_df = parquet_df.withColumnRenamed('price', 'label')
return parquet_df, columns_list
'./cf_train_subset_idx_full.parquet', './cf_train_subset_idx.parquet',
'./cf_train_idx.parquet', './cf_train_subset.parquet',
'./cf_train_extra.parquet'
]
model_file = './sc2_final1'
u_idx_model_file = './sc2_final_u_indexer'
t_idx_model_file = './sc2_final_t_indexer'
print(datetime.now())
data_file = path + files[1]
data = spark.read.parquet(data_file)
data = data.sample(False, 0.001)
u_model = StringIndexerModel.load(u_idx_model_file)
t_model = StringIndexerModel.load(t_idx_model_file)
transformed_data = u_model.transform(data)
transformed_data = t_model.transform(transformed_data).select(
'u_id', 't_id', 'count')
ratings = transformed_data.rdd.map(
lambda l: Rating(l.u_id, l.t_id, l['count']))
rank = 10
model = ALS.trainImplicit(ratings, rank)
val_data_file = path + files[1]
val_data = spark.read.parquet(val_data_file)
val_data = val_data.sample(False, 0.001)
dataset3 = dataset2.select("conn.srcip", "conn.sport", "conn.dstip",
"conn.dsport", "conn.proto", "conn.dur",
"conn.sbytes", "conn.dbytes", "conn.service",
"conn.Spkts", "conn.Dpkts")
dataset3.printSchema()
print(type(dataset3))
#Rellena los valores nulos con un 0 (prevencion de errores)
dataset3 = dataset3.fillna(0)
# Procesamiento de caracteristicas
string_indexer_models = {}
for column in ['proto', 'service', 'attack_cat']:
string_indexer_model_path = "{}/data/str_indexer_extended/str_indexer_model_extended_{}.bin".format(
base_path, column)
string_indexer = StringIndexerModel.load(string_indexer_model_path)
string_indexer_models[column] = string_indexer
for column in ['proto', 'service', 'attack_cat']:
string_indexer_model = string_indexer_models[column]
dataset3 = string_indexer_model.transform(dataset3)
vector_assembler_path = "{}/data/numeric_vector_assembler_RFE.bin".format(
base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
finalDataset = vector_assembler.transform(dataset3)
# Carga el modelo de Machine Learning y lo aplica a los datos recibidos
model_path = "{}/data/RandomForest_extended.bin".format(base_path)
model = RandomForestClassificationModel.load(model_path)
predictions = model.transform(finalDataset)
def prepare_data_ml3(spark, jenkins_builds, sonar_issues, sonar_analyses,
spark_artefacts_dir, run_mode):
# Change build result to only SUCCESS/FAIL for binary classification
modify_result = udf(lambda x: "SUCCESS" if x == "SUCCESS" else "FAIL",
StringType())
spark.udf.register("modify_result", modify_result)
if jenkins_builds is not None:
jenkins_builds = jenkins_builds.withColumn("result",
modify_result("result"))
pipeline_path = Path(spark_artefacts_dir).joinpath("pipeline_3")
label_idx_model_path = Path(spark_artefacts_dir).joinpath(
"label_indexer_3")
# Getting pipeline and label indexer models
if run_mode == "first":
pipeline_model = get_ml3_pipeline().fit(sonar_issues)
pipeline_model.write().overwrite().save(str(pipeline_path.absolute()))
label_idx_model = StringIndexer(
inputCol="result", outputCol="label",
handleInvalid="skip").fit(jenkins_builds)
label_idx_model.write().overwrite().save(
str(label_idx_model_path.absolute()))
elif run_mode == "incremental":
pipeline_model = PipelineModel.load(str(pipeline_path.absolute()))
label_idx_model = StringIndexerModel.load(
str(label_idx_model_path.absolute()))
# Columns to return
rules = pipeline_model.stages[0].labels
columns = list(map(lambda x: "removed_" + x, rules)) + list(
map(lambda x: "introduced_" + x, rules))
# Preparing
removed_rules_df = sonar_issues.filter(
"status IN ('RESOLVED', 'CLOSED', 'REVIEWED')").select(
"current_analysis_key", "rule")
df1 = pipeline_model.transform(removed_rules_df)
rdd1 = df1.rdd.map(lambda x : (x[0],x[3])).reduceByKey(lambda v1,v2: sum_sparse_vectors(v1,v2)) \
.map(lambda x: Row(current_analysis_key = x[0], removed_rule_vec = x[1]))
if rdd1.count() == 0:
return None, columns
removed_issues_rule_vec_df = spark.createDataFrame(rdd1)
introduced_rules_df = sonar_issues.filter(
"status IN ('OPEN', 'REOPENED', 'CONFIRMED', 'TO_REVIEW')").select(
"creation_analysis_key", "rule")
df2 = pipeline_model.transform(introduced_rules_df)
rdd2 = df2.rdd.map(lambda x : (x[0],x[3])).reduceByKey(lambda v1,v2: sum_sparse_vectors(v1,v2)) \
.map(lambda x: Row(creation_analysis_key = x[0], introduced_rule_vec = x[1]))
if rdd2.count() == 0:
return None, columns
introduced_issues_rule_vec_df = spark.createDataFrame(rdd2)
joined_sonar_rules_df = removed_issues_rule_vec_df.join(
introduced_issues_rule_vec_df,
removed_issues_rule_vec_df.current_analysis_key ==
introduced_issues_rule_vec_df.creation_analysis_key,
how="outer")
joined_sonar_rules_df.createOrReplaceTempView("sonar_rules")
joined_sonar_rules_df = spark.sql("""SELECT
coalesce(current_analysis_key, creation_analysis_key) AS analysis_key,
introduced_rule_vec,
removed_rule_vec
FROM sonar_rules
""")
num_rules = len(pipeline_model.stages[0].labels)
imputed_sonar_rules_rdd = joined_sonar_rules_df.rdd.map(
lambda row: Row(analysis_key=row[0],
introduced_rule_vec=SparseVector(num_rules, {})
if row[1] is None else row[1],
removed_rule_vec=SparseVector(num_rules, {})
if row[2] is None else row[2]))
imputed_sonar_rules_df = spark.createDataFrame(imputed_sonar_rules_rdd)
v_assembler = VectorAssembler(
inputCols=["removed_rule_vec", "introduced_rule_vec"],
outputCol="features")
sonar_issues_df = v_assembler.transform(imputed_sonar_rules_df).select(
"analysis_key", "features")
sonar_df = sonar_issues_df.join(
sonar_analyses,
sonar_issues_df.analysis_key == sonar_analyses.analysis_key,
how="inner")
df = sonar_df.join(jenkins_builds,
sonar_df.revision == jenkins_builds.revision_number,
how="inner").select("result", "features")
ml_df = label_idx_model.transform(df).select("label", "features")
return ml_df, columns
def main(spark, indexer_user, indexer_item, train_data_file, val_data_file):
'''
Parameters
----------
spark : SparkSession object
data_file : string, path to the parquet file to load
model_file : string, path to store the serialized model file
'''
# Load the parquet file
train = spark.read.parquet(train_data_file)
val = spark.read.parquet(val_data_file)
user_index = StringIndexerModel.load(indexer_user)
item_index = StringIndexerModel.load(indexer_item)
train = user_index.transform(train)
train = item_index.transform(train)
# train = train.sample(withReplacement = False, fraction = 0.8)
rank = [10, 20, 30] #default is 10
regularization = [.01, .1, 1] #default is 1
alpha = [.5, 1, 10] #default is 1
rank_list = []
reg_list = []
alpha_list = []
precisions = []
for i in rank:
for j in regularization:
for k in alpha:
als = ALS(userCol='user',
itemCol='item',
implicitPrefs=True,
ratingCol='count',
rank=i,
regParam=j,
alpha=k)
model = als.fit(train)
subset = val.select('user').distinct()
predictions = model.recommendForUserSubset(subset, 50)
predictions = predictions.select(
"user",
col("recommendations.item").alias("item")).sort('user')
val = val.sort('user')
predictionAndLabels = predictions.join(
val, ["user"],
"inner").rdd.map(lambda tup: (tup[1], tup[2]))
metrics = RankingMetrics(predictionAndLabels)
precision = metrics.meanAveragePrecision
rank_list.append(i)
reg_list.append(j)
alpha_list.append(k)
precisions.append(precision)
print('rank: %f, reg: %f, alpha: %f' % (i, j, k))
print(precision)
print(rank_list)
print(reg_list)
print(alpha_list)
print(rmses)
print('Max MAP value: %f' % max(precisions))
ind = np.argmax(rmses)
print('Rank: %f' % rank_list[ind])
print('Reg: %f' % reg_list[ind])
print('Alpha: %f' % alpha_list[ind])
def main(base_path):
APP_NAME = "make_predictions_streaming.py"
# Process data every 10 seconds
PERIOD = 10
BROKERS = 'localhost:9092'
PREDICTION_TOPIC = 'flight_delay_classification_request'
try:
sc and ssc
except NameError as e:
import findspark
# Add the streaming package and initialize
findspark.add_packages(
["org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0"])
findspark.init()
import pyspark
import pyspark.sql
import pyspark.streaming
conf = SparkConf().set("spark.default.parallelism", 1)
sc = SparkContext(
appName="Agile Data Science: PySpark Streaming 'Hello, World!'",
conf=conf)
ssc = StreamingContext(sc, PERIOD)
spark = pyspark.sql.SparkSession(sc).builder.appName(
APP_NAME).getOrCreate()
#
# Load all models to be used in making predictions
#
# Load the arrival delay bucketizer
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(
base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# Load all the string field vectorizer pipelines into a dict
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in [
"Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear", "Origin",
"Dest", "Route"
]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path, column)
string_indexer_model = StringIndexerModel.load(
string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# Load the numeric vector assembler
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(
base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# Load the classifier model
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path)
rfc = RandomForestClassificationModel.load(random_forest_model_path)
#
# Process Prediction Requests in Streaming
#
stream = KafkaUtils.createDirectStream(ssc, [PREDICTION_TOPIC], {
"metadata.broker.list": BROKERS,
"group.id": "0",
})
object_stream = stream.map(lambda x: json.loads(x[1]))
object_stream.pprint()
row_stream = object_stream.map(
lambda x: Row(FlightDate=iso8601.parse_date(x['FlightDate']),
Origin=x['Origin'],
Distance=x['Distance'],
DayOfMonth=x['DayOfMonth'],
DayOfYear=x['DayOfYear'],
UUID=x['UUID'],
DepDelay=x['DepDelay'],
DayOfWeek=x['DayOfWeek'],
FlightNum=x['FlightNum'],
Dest=x['Dest'],
Timestamp=iso8601.parse_date(x['Timestamp']),
Carrier=x['Carrier']))
row_stream.pprint()
#
# Create a dataframe from the RDD-based object stream
#
def classify_prediction_requests(rdd):
from pyspark.sql.types import StringType, IntegerType, DoubleType, DateType, TimestampType
from pyspark.sql.types import StructType, StructField
prediction_request_schema = StructType([
StructField("Carrier", StringType(), True),
StructField("DayOfMonth", IntegerType(), True),
StructField("DayOfWeek", IntegerType(), True),
StructField("DayOfYear", IntegerType(), True),
StructField("DepDelay", DoubleType(), True),
StructField("Dest", StringType(), True),
StructField("Distance", DoubleType(), True),
StructField("FlightDate", DateType(), True),
StructField("FlightNum", StringType(), True),
StructField("Origin", StringType(), True),
StructField("Timestamp", TimestampType(), True),
StructField("UUID", StringType(), True),
])
prediction_requests_df = spark.createDataFrame(
rdd, schema=prediction_request_schema)
prediction_requests_df.show()
#
# Add a Route variable to replace FlightNum
#
from pyspark.sql.functions import lit, concat
prediction_requests_with_route = prediction_requests_df.withColumn(
'Route',
concat(prediction_requests_df.Origin, lit('-'),
prediction_requests_df.Dest))
prediction_requests_with_route.show(6)
# Vectorize string fields with the corresponding pipeline for that column
# Turn category fields into categoric feature vectors, then drop intermediate fields
for column in [
"Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear", "Origin",
"Dest", "Route"
]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(
prediction_requests_with_route)
# Vectorize numeric columns: DepDelay, Distance and index columns
final_vectorized_features = vector_assembler.transform(
prediction_requests_with_route)
# Inspect the vectors
final_vectorized_features.show()
# Drop the individual index columns
index_columns = [
"Carrier_index", "DayOfMonth_index", "DayOfWeek_index",
"DayOfYear_index", "Origin_index", "Dest_index", "Route_index"
]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# Inspect the finalized features
final_vectorized_features.show()
# Make the prediction
predictions = rfc.transform(final_vectorized_features)
# Drop the features vector and prediction metadata to give the original fields
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop(
"rawPrediction").drop("probability")
# Inspect the output
final_predictions.show()
# Store to Mongo
if final_predictions.count() > 0:
final_predictions.rdd.map(lambda x: x.asDict()).saveToMongoDB(
"mongodb://localhost:27017/agile_data_science.flight_delay_classification_response"
)
# Do the classification and store to Mongo
row_stream.foreachRDD(classify_prediction_requests)
ssc.start()
ssc.awaitTermination()
def main(base_path):
spark = SparkSession.builder.config("spark.default.parallelism",
1).appName(APP_NAME).getOrCreate()
#
# Load all models to be used in making predictions
#
# Load the arrival delay bucketizer
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(
base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# Load all the string field vectorizer pipelines into a dict
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path, column)
string_indexer_model = StringIndexerModel.load(
string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# Load the numeric vector assembler
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(
base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# Load the classifier model
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path)
rfc = RandomForestClassificationModel.load(random_forest_model_path)
#
# Messages look like:
#
# {
# "Carrier": "DL",
# "DayOfMonth": 25,
# "DayOfWeek": 4,
# "DayOfYear": 359,
# "DepDelay": 10.0,
# "Dest": "LAX",
# "Distance": 2475.0,
# "FlightDate": "2015-12-25",
# "FlightNum": null,
# "Origin": "JFK",
# "Timestamp": "2019-10-31T00:19:47.633280",
# "UUID": "af74b096-ecc7-4493-a79a-ebcdff699385"
# }
#
# Process Prediction Requests from Kafka
#
message_df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", BROKERS) \
.option("subscribe", PREDICTION_TOPIC) \
.load()
# Create a DataFrame out of the one-hot encoded RDD
schema = T.StructType([
T.StructField("Carrier", T.StringType()),
T.StructField("DayOfMonth", T.IntegerType()),
T.StructField("DayOfWeek", T.IntegerType()),
T.StructField("DayOfYear", T.IntegerType()),
T.StructField("DepDelay", T.FloatType()),
T.StructField("Dest", T.StringType()),
T.StructField("Distance", T.FloatType()),
T.StructField("FlightDate", T.StringType()),
T.StructField("FlightNum", T.StringType()),
T.StructField("Origin", T.StringType()),
T.StructField("Timestamp", T.TimestampType()),
T.StructField("UUID", T.StringType()),
])
prediction_requests_df = message_df.select(
F.from_json(F.col("value").cast("string"),
schema).alias("data")).select("data.*")
#
# Add a Route variable to replace FlightNum
#
prediction_requests_with_route = prediction_requests_df.withColumn(
'Route',
F.concat(prediction_requests_df.Origin, F.lit('-'),
prediction_requests_df.Dest))
# Vectorize string fields with the corresponding pipeline for that column
# Turn category fields into categoric feature vectors, then drop intermediate fields
for column in ["Carrier", "Origin", "Dest", "Route"]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(
prediction_requests_with_route)
# Vectorize numeric columns: DepDelay, Distance and index columns
final_vectorized_features = vector_assembler.transform(
prediction_requests_with_route)
# Drop the individual index columns
index_columns = [
"Carrier_index", "Origin_index", "Dest_index", "Route_index"
]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# Make the prediction
predictions = rfc.transform(final_vectorized_features)
# Drop the features vector and prediction metadata to give the original fields
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop(
"rawPrediction").drop("probability")
# Store the results to MongoDB
class MongoWriter:
def open(self, partition_id, epoch_id):
print(f"Opened partition id: {partition_id}, epoch: {epoch_id}")
self.mongo_client = pymongo.MongoClient()
print(f"Opened MongoClient: {self.mongo_client}")
return True
def process(self, row):
print(f"Processing row: {row}")
as_dict = row.asDict()
print(f"Inserting row.asDict(): {as_dict}")
id = self.mongo_client.agile_data_science.flight_delay_classification_response.insert_one(
as_dict)
print(f"Inserted row, got ID: {id.inserted_id}")
self.mongo_client.close()
return True
def close(self, error):
print("Closed with error: %s" % str(error))
return True
query = final_predictions.writeStream.foreach(MongoWriter()).start()
query.awaitTermination()
print('====> Start computation')
dataset = spark.read.csv('/user/ronghui_safe/hgy/nid/datasets/{}_{}'.format(args.query_month, args.mode), header=True, inferSchema=True)
dataset = dataset.withColumn('source', F.when(F.col('source') == '__HIVE_DEFAULT_PARTITION__', 'null').otherwise(F.col('source')))
dataset = dataset.withColumn('source', F.when(F.col('source') == 'cm_mail', 'null').otherwise(F.col('source')))
if args.mode != 'test':
dataset = dataset.withColumn('duration', F.when(F.col('duration') == 0, 1e-6).otherwise(F.col('duration')))
dataset = dataset.withColumn('duration', F.log(F.lit(1e-6))/F.col('duration'))
dataset = dataset.withColumn('duration', F.exp(F.col('duration')))
stringIndex_model = None
if args.mode == 'train':
stringIndexer = StringIndexer(inputCol='source', outputCol='source_index')
stringIndex_model = stringIndexer.fit(dataset)
stringIndex_model.save('/user/ronghui_safe/hgy/nid/edw/stringIndex_model_v2')
else:
stringIndex_model = StringIndexerModel.load('/user/ronghui_safe/hgy/nid/edw/stringIndex_model_v2')
dataset = stringIndex_model.transform(dataset)
encoder_model = None
if args.mode == 'train':
encoder = OneHotEncoder(inputCol='source_index', outputCol='source_vec')
encoder_model = encoder.fit(dataset)
encoder_model.save('/user/ronghui_safe/hgy/nid/edw/oneHotEncoder_model_v2')
else:
encoder_model = OneHotEncoderModel.load('/user/ronghui_safe/hgy/nid/edw/oneHotEncoder_model_v2')
dataset = encoder_model.transform(dataset)
feature_cols = ['source_vec', 'aging', 'PC1', 'PC2', 'PC3', 'PC4']
assembler = VectorAssembler(inputCols=feature_cols, outputCol='feature_vec')
dataset = assembler.transform(dataset)
scaler_model = None
if args.mode == 'train':
scaler = StandardScaler(inputCol='feature_vec', outputCol='scaled_feature_vec', withStd=True, withMean=True)
def preprocess_renewal_model_scoring_data(pargs, params):
"""
Function to pre-process raw scoring data for renewal model
"""
# Load parameters
score_filter_flag = configs['score_filter_flag']
score_filter_condition = configs['score_filter_condition']
score_sampling_flag = configs['score_sampling_flag']
score_sampling_fraction = configs['score_sampling_fraction']
primary_key_columns = configs['primary_key_columns']
fillna_non_categorical_value = configs['fillna_non_categorical_value']
fillna_categorical_value = configs['fillna_categorical_value']
target_variable = configs['target_variable']
seed = configs['seed']
s3_bucket = configs['s3_bucket']
# Load raw scoring data
score_raw = spark.read.parquet(
data_paths['yr1_renewal_scoring_raw'].format(run_mode=run['run_mode'],
run_id=run['run_id']))
# Load feature config saved in the pre-processing step
feature_config = util.load_yml_file_from_s3(
s3_bucket,
data_paths['renewal_feature_config'].format(run_mode=run['run_mode'],
run_id=run['run_id'])[12:])
feature_list = feature_config['feature_list']
feature_list_indexed = feature_config['feature_list_indexed']
categorical_columns = feature_config['categorical_columns']
non_categorical_columns = feature_config['non_categorical_columns']
# Load string indexers
string_indexers = []
for i in range(len(categorical_columns)):
string_indexers.append(
StringIndexerModel.load(
data_paths['renewal_string_indexer'].format(
run_mode=run['run_mode'], run_id=run['run_id'], i=i)))
# Select only the features used in model training
score_raw = score_raw.select(primary_key_columns + feature_list)
# Filter scoring data
if score_filter_flag:
score_raw = score_raw.filter(score_filter_condition)
# Sample scoring data
if score_sampling_flag:
score_raw = model.sampling(score_raw, score_sampling_fraction, seed)
# Ensure that all "n_" cols are indeed numeric
score_raw = model.ensureColsAreNumeric(score_raw, non_categorical_columns)
# Ensure that all "i_" cols are indeed string
score_raw = model.ensureColsAreString(score_raw, categorical_columns)
# Impute missing values
score_raw = model.imputeMissing(score_raw, non_categorical_columns,
categorical_columns,
fillna_non_categorical_value,
fillna_categorical_value)
# Apply string indexer on string columns
score_raw, string_indexers, categorical_columns_indexed = model.applyStringIndexer(
score_raw, categorical_columns, string_indexers)
# Assemble the final feature list
score_raw = model.assembleFeaturesIntoVector(score_raw,
feature_list_indexed)
# Save score data
score_raw.write.parquet(data_paths['renewal_score'].format(
run_mode=run['run_mode'], run_id=run['run_id']),
mode='overwrite')
def main(iso_date, base_path):
APP_NAME = "make_predictions.py"
# If there is no SparkSession, create the environment
try:
sc and spark
except NameError as e:
import findspark
findspark.init()
import pyspark
import pyspark.sql
sc = pyspark.SparkContext()
spark = pyspark.sql.SparkSession(sc).builder.appName(
APP_NAME).getOrCreate()
#
# Load each and every model in the pipeline
#
# Load the arrival delay bucketizer
from pyspark.ml.feature import Bucketizer
arrival_bucketizer_path = "{}/models/arrival_bucketizer_2.0.bin".format(
base_path)
arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)
# Load all the string indexers into a dict
from pyspark.ml.feature import StringIndexerModel
string_indexer_models = {}
for column in [
"Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear", "Origin",
"Dest", "Route"
]:
string_indexer_model_path = "{}/models/string_indexer_model_{}.bin".format(
base_path, column)
string_indexer_model = StringIndexerModel.load(
string_indexer_model_path)
string_indexer_models[column] = string_indexer_model
# Load the numeric vector assembler
from pyspark.ml.feature import VectorAssembler
vector_assembler_path = "{}/models/numeric_vector_assembler.bin".format(
base_path)
vector_assembler = VectorAssembler.load(vector_assembler_path)
# Load the classifier model
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
random_forest_model_path = "{}/models/spark_random_forest_classifier.flight_delays.5.0.bin".format(
base_path)
rfc = RandomForestClassificationModel.load(random_forest_model_path)
#
# Run the requests through the transformations from training
#
# Get today and tomorrow's dates as iso strings to scope query
today_dt = iso8601.parse_date(iso_date)
rounded_today = today_dt.date()
iso_today = rounded_today.isoformat()
# Build the day's input path: a date based primary key directory structure
today_input_path = "{}/data/prediction_tasks_daily.json/{}".format(
base_path, iso_today)
from pyspark.sql.types import StringType, IntegerType, DoubleType, DateType, TimestampType
from pyspark.sql.types import StructType, StructField
schema = StructType([
StructField("Carrier", StringType(), True),
StructField("DayOfMonth", IntegerType(), True),
StructField("DayOfWeek", IntegerType(), True),
StructField("DayOfYear", IntegerType(), True),
StructField("DepDelay", DoubleType(), True),
StructField("Dest", StringType(), True),
StructField("Distance", DoubleType(), True),
StructField("FlightDate", DateType(), True),
StructField("FlightNum", StringType(), True),
StructField("Origin", StringType(), True),
StructField("Timestamp", TimestampType(), True),
])
prediction_requests = spark.read.json(today_input_path, schema=schema)
prediction_requests.show()
#
# Add a Route variable to replace FlightNum
#
from pyspark.sql.functions import lit, concat
prediction_requests_with_route = prediction_requests.withColumn(
'Route',
concat(prediction_requests.Origin, lit('-'), prediction_requests.Dest))
prediction_requests_with_route.show(6)
# Index string fields with the corresponding indexer for that column
for column in [
"Carrier", "DayOfMonth", "DayOfWeek", "DayOfYear", "Origin",
"Dest", "Route"
]:
string_indexer_model = string_indexer_models[column]
prediction_requests_with_route = string_indexer_model.transform(
prediction_requests_with_route)
# Vectorize numeric columns: DepDelay and Distance
final_vectorized_features = vector_assembler.transform(
prediction_requests_with_route)
# Drop the indexes for the nominal fields
index_columns = [
"Carrier_index", "DayOfMonth_index", "DayOfWeek_index",
"DayOfYear_index", "Origin_index", "Origin_index", "Dest_index",
"Route_index"
]
for column in index_columns:
final_vectorized_features = final_vectorized_features.drop(column)
# Inspect the finalized features
final_vectorized_features.show()
# Make the prediction
predictions = rfc.transform(final_vectorized_features)
# Drop the features vector and prediction metadata to give the original fields
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop(
"rawPrediction").drop("probability")
# Inspect the output
final_predictions.show()
# Build the day's output path: a date based primary key directory structure
today_output_path = "{}/data/prediction_results_daily.json/{}".format(
base_path, iso_today)
# Save the output to its daily bucket
final_predictions.repartition(1).write.mode("overwrite").json(
today_output_path)
return ss
if __name__ == '__main__':
start_time = time.time()
ss = init_spark_session() #initial spark session
final_indexed_save = os.path.join('dataset', 'review_vegas_als.parquet')
reviewDF = ss.read.parquet(final_indexed_save).cache()
model_save = os.path.join('model', 'als_model_vegas')
indexer_user_save = os.path.join('model', 'user_ind_model')
model = ALSModel.load(model_save)
uid = reviewDF.select('user_id').rdd.takeSample(False, 1)
logger.error('{} seconds has elapsed'.format(str(uid)))
bid = reviewDF.select('business_id_int', 'business_id').distinct()
bid.show(20)
logger.error('{} seconds has elapsed. {} rows remain'.format(
time.time() - start_time, bid.count()))
#predDF = bid.filter(bid['user_id'] == user_id)
#build user request using input id
predDF = bid.withColumn("user_id", lit(uid))
indexer_model = StringIndexerModel.load(indexer_user_save)
predDF = indexer_model.transform(predDF)
'''user_id_converter = IndexToString(inputCol= 'user_id',outputCol='user_id')
convert_df = '''
predDF.show(20)
prediction_user = model.transform(predDF)
ratings = prediction_user.sort(
desc('prediction')).limit(count).select('business_id')
logger.error('{} seconds has elapsed'.format(time.time() - start_time))