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query.py
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import pandas as pd
import numpy as np
import os
import argparse
import subprocess
import time
import concurrent.futures
import traceback
import datetime
try:
import cudf
import rmm
import cuspatial
import cuml
from numba import cuda
except ModuleNotFoundError:
pass
class Location:
def __init__(self, minX, minY, width, height):
self.minX = minX
self.minY = minY
self.width = width
self.height = height
self.maxX = minX + width
self.maxY = minY + height
class RideshareData:
def __init__(
self,
driverCount,
riderCount,
rideCount,
locationCount,
rideReqCount,
driverStatusCount,
):
self.driverCount = driverCount
self.riderCount = riderCount
self.rideCount = rideCount
self.locationCount = locationCount
self.rideReqCount = rideReqCount
self.driverStatusCount = driverStatusCount
def _genLocations(self, outputPath):
self.locations = []
grids = int(np.floor(np.sqrt(self.locationCount)))
width = 1.0 / grids
height = 1.0 / grids
for i in range(self.locationCount):
x = int(i % grids)
y = int(i / grids)
l = Location(x * width, y * height, width, height)
self.locations.append(l)
locationId = np.arange(self.locationCount)
minX = np.zeros(self.locationCount)
minY = np.zeros(self.locationCount)
maxX = np.zeros(self.locationCount)
maxY = np.zeros(self.locationCount)
for i in range(self.locationCount):
minX[i] = self.locations[i].minX
minY[i] = self.locations[i].minY
maxX[i] = self.locations[i].maxX
maxY[i] = self.locations[i].maxY
locationDict = {
'locationId': locationId,
'boundsMinX': minX,
'boundsMinY': minY,
'boundsMaxX': maxX,
'boundsMaxY': maxY,
}
self.locationTable = pd.DataFrame(data=locationDict)
featureCount = 10
for i in range(0, featureCount):
self.locationTable['c{}'.format(i)] = np.random.rand(self.locationCount)
header = ['locationId', 'boundsMinX', 'boundsMinY', 'boundsMaxX', 'boundsMaxY']
for i in range(0, featureCount):
header.append('c{}'.format(i))
self.locationTable.to_csv(
os.path.join(outputPath, 'location.csv'),
index = False,
columns = header
)
def _genRideReqTable(self, outputPath):
locationIndex = np.random.randint(len(self.locations), size=self.rideReqCount)
rideId = np.arange(self.rideReqCount)
startXLocation = np.random.rand(self.rideReqCount)
startYLocation = np.random.rand(self.rideReqCount)
startX = np.zeros(self.rideReqCount)
startY = np.zeros(self.rideReqCount)
for i in range(self.rideReqCount):
loc = self.locations[locationIndex[i]]
startX[i] = loc.minX + loc.width * startXLocation[i]
startY[i] = loc.minY + loc.height * startYLocation[i]
seatCount = np.random.randint(4, size=self.rideReqCount)
riderId = np.random.randint(self.riderCount, size=self.rideReqCount)
time = np.arange(self.rideReqCount)
bins = np.arange(0, self.rideReqCount, 10000)
timeBins = np.digitize(time, bins)
rideReqDict = {
'time': time,
'timeBins': timeBins,
'rideId': rideId,
'riderId': riderId,
'seatCount': seatCount,
'startX': startX,
'startY': startY,
}
self.rideReqTable = pd.DataFrame(data=rideReqDict)
self.rideReqTable['timeDate'] = pd.to_datetime(self.rideReqTable['time'], unit='s')
self.rideReqTable = self.rideReqTable.sort_values(by=['time'])
featureCount = 12
for i in range(0, featureCount):
self.rideReqTable['c{}'.format(i)] = np.random.rand(self.rideReqCount)
header = ['time', 'timeBins', 'timeDate', 'rideId', 'riderId', 'seatCount', 'startX', 'startY']
for i in range(0, featureCount):
header.append('c{}'.format(i))
self.rideReqTable.to_csv(
os.path.join(outputPath, 'ride_req.csv'),
index = False,
columns = header
)
def _genDriverStatusTable(self, outputPath):
posX = np.random.rand(self.driverStatusCount)
posY = np.random.rand(self.driverStatusCount)
time = np.arange(self.driverStatusCount)
driverId = np.random.randint(self.driverCount, size=self.driverStatusCount)
driverDict = {
'time': time,
'driverId': driverId,
'posX': posX,
'posY': posY,
}
self.driverStatusTable = pd.DataFrame(data=driverDict)
featureCount = 12
for i in range(0, featureCount):
self.driverStatusTable['c{}'.format(i)] = np.random.rand(self.driverStatusCount)
self.driverStatusTable['timeDate'] = pd.to_datetime(self.driverStatusTable['time'], unit='s')
self.driverStatusTable = self.driverStatusTable.sort_values(by=['time'])
header = ['time', 'timeDate', 'driverId', 'posX', 'posY']
for i in range(0, featureCount):
header.append('c{}'.format(i))
self.driverStatusTable.to_csv(
os.path.join(outputPath, 'driver_status.csv'),
index = False,
columns = header
)
def _genDriverTable(self, outputPath):
driverId = np.arange(self.driverCount)
seatCount = np.random.randint(4, size=self.driverCount)
rating = np.random.rand(self.driverCount)
driverDict = {
'driverId': driverId,
'seatCount': seatCount,
'rating': rating,
}
self.driverTable = pd.DataFrame(data=driverDict)
header = ['driverId', 'seatCount', 'rating']
self.driverTable.to_csv(
os.path.join(outputPath, 'driver.csv'),
index = False,
columns = header
)
def _genRiderTable(self, outputPath):
riderId = np.arange(self.riderCount)
rating = np.random.rand(self.riderCount)
time = np.arange(self.riderCount)
riderDict = {
'riderId': riderId,
'rating': rating,
'signupTime': time,
}
self.riderTable = pd.DataFrame(data=riderDict)
self.riderTable['signupTimeDate'] = pd.to_datetime(self.riderTable['signupTime'], unit='s')
header = ['riderId', 'rating', 'signupTime', 'signupTimeDate']
self.riderTable.to_csv(
os.path.join(outputPath, 'rider.csv'),
index = False,
columns = header
)
def _genRideTable(self, outputPath):
rideId = np.arange(self.rideCount)
riderId = np.random.randint(self.riderCount, size=self.rideCount)
driverId = np.random.randint(self.driverCount, size=self.rideCount)
startX = np.random.rand(self.rideCount)
startY = np.random.rand(self.rideCount)
endX = np.random.rand(self.rideCount)
endY = np.random.rand(self.rideCount)
startTime = np.random.randint(self.riderCount, size=self.rideCount)
rideDict = {
'rideId': rideId,
'riderId': riderId,
'driverId': driverId,
'startX': startX,
'startY': startY,
'endX': endX,
'endY': endY,
'startTime': startTime,
}
self.rideTable = pd.DataFrame(data=rideDict)
self.rideTable['startTimeDate'] = pd.to_datetime(self.rideTable['startTime'], unit='s')
featureCount = 12
for i in range(0, featureCount):
self.rideTable['c{}'.format(i)] = np.random.rand(self.rideCount)
predCount = 5
for i in range(0, predCount):
self.rideTable['pred{}'.format(i)] = np.random.rand(self.rideCount)
header = ['rideId', 'riderId', 'driverId', 'startX', 'startY', 'endX', 'endY', 'startTime', 'startTimeDate']
for i in range(0, featureCount):
header.append('c{}'.format(i))
for i in range(0, predCount):
header.append('pred{}'.format(i))
self.rideTable.to_csv(
os.path.join(outputPath, 'ride.csv'),
index = False,
columns = header
)
def gen(self, outputPath):
print('Generating {}'.format(outputPath))
try:
os.makedirs(outputPath)
except:
pass
self._genLocations(outputPath)
self._genDriverTable(outputPath)
self._genDriverStatusTable(outputPath)
self._genRideReqTable(outputPath)
self._genRiderTable(outputPath)
self._genRideTable(outputPath)
print('Done {}'.format(outputPath))
class QueryGen:
def __init__(self, args):
np.random.seed(0)
self.args = args
def genData(self):
s = self.args.scale
genAll = not self.args.queries
rideshareQ1 = RideshareData(
driverCount=s * 1000,
riderCount=s * 10000,
rideCount=0,
locationCount=s * 1000,
rideReqCount=s * 1000000,
driverStatusCount=s * 10000,
)
rideshareQ2 = RideshareData(
driverCount=s * 10000,
riderCount=s * 10000,
rideCount=0,
locationCount=s * 1000,
rideReqCount=s * 1000000,
driverStatusCount=0,
)
rideshareQ3 = RideshareData(
driverCount=s * 10000,
riderCount=s * 1000,
rideCount=0,
locationCount=s * 100,
rideReqCount=s * 1000000,
driverStatusCount=s * 100000,
)
rideshareQ4 = RideshareData(
driverCount=s * 100000,
riderCount=s * 100000,
rideCount=s * 100000,
locationCount=s * 100,
rideReqCount=s * 1000,
driverStatusCount=0,
)
rideshareQ5 = RideshareData(
driverCount=s * 1000,
riderCount=0,
rideCount=0,
locationCount=s * 1000,
rideReqCount=0,
driverStatusCount=s * 100000,
)
rideshareQ6 = RideshareData(
driverCount=s * 10000,
riderCount=s * 10000,
rideCount=0,
locationCount=s * 100,
rideReqCount=s * 100000,
driverStatusCount=s * 10000,
)
rideshareQ7 = RideshareData(
driverCount=s * 100000,
riderCount=s * 100000,
rideCount=s * 1000000,
locationCount=s * 1000,
rideReqCount=0,
driverStatusCount=0,
)
rideshareQ8 = RideshareData(
driverCount=s * 10000,
riderCount=s * 10000,
rideCount=s * 100000,
locationCount=s * 100,
rideReqCount=0,
driverStatusCount=0,
)
rideshareQ9 = RideshareData(
driverCount=s * 10000,
riderCount=s * 10000,
rideCount=0,
locationCount=s * 1000,
rideReqCount=s * 100000,
driverStatusCount=s * 10000,
)
queries = {
'query1': rideshareQ1,
'query2': rideshareQ2,
'query3': rideshareQ3,
'query4': rideshareQ4,
'query5': rideshareQ5,
'query6': rideshareQ6,
'query7': rideshareQ7,
'query8': rideshareQ8,
'query9': rideshareQ9,
}
queryFilter = dict(filter(lambda x: genAll or x[0] in self.args.queries, queries.items()))
with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
futures = { executor.submit(v.gen, 'data/s{}/{}'.format(s, k)): v for k, v in queryFilter.items()}
for f in concurrent.futures.as_completed(futures):
q = futures[f]
try:
f.result()
except Exception as e:
print(e)
class QueryRunGpu:
def __init__(self, args):
self.args = args
if (self.args.debug):
rmm.reinitialize(
logging=True,
log_file_name="memlog"
)
def _loadTables(
self,
queryPath
):
scalePath = 'data/s{}'.format(self.args.scale)
driverCsv = os.path.join(scalePath, queryPath, 'driver.csv')
driverStatusCsv = os.path.join(scalePath, queryPath, 'driver_status.csv')
rideReqCsv = os.path.join(scalePath, queryPath, 'ride_req.csv')
rideCsv = os.path.join(scalePath, queryPath, 'ride.csv')
riderCsv = os.path.join(scalePath, queryPath, 'rider.csv')
locationCsv = os.path.join(scalePath, queryPath, 'location.csv')
self.driverTable = cudf.read_csv(driverCsv)
if self.driverTable.shape[0] > 0:
self.driverTable.columns = ['drv.' + str(col) for col in self.driverTable]
self.driverStatusTable = cudf.read_csv(driverStatusCsv)
if self.driverStatusTable.shape[0] > 0:
self.driverStatusTable.columns = ['drvStat.' + str(col) for col in self.driverStatusTable]
self.rideReqTable = cudf.read_csv(rideReqCsv)
if self.rideReqTable.shape[0] > 0:
self.rideReqTable.columns = ['rideReq.' + str(col) for col in self.rideReqTable]
self.rideTable = cudf.read_csv(rideCsv)
if self.rideTable.shape[0] > 0:
self.rideTable.columns = ['ride.' + str(col) for col in self.rideTable]
self.riderTable = cudf.read_csv(riderCsv)
if self.riderTable.shape[0] > 0:
self.riderTable.columns = ['rider.' + str(col) for col in self.riderTable]
self.locationTable = cudf.read_csv(locationCsv)
if self.locationTable.shape[0] > 0:
self.locationTable.columns = ['loc.' + str(col) for col in self.locationTable]
def _createIndex(self, df, colName):
out = cuspatial.quadtree_on_points(
df[colName + 'X'],
df[colName + 'Y'],
0,
1,
0,
1,
1,
15,
20
)
return out
def _polygonFromBox(
self,
box
):
pointCount = box.shape[0] * 4
polyOffset = cudf.Series(np.arange(box.shape[0]))
ringOffset = cudf.Series(np.arange(start=0, step=4, stop=pointCount))
xPoints = np.zeros(pointCount)
yPoints = np.zeros(pointCount)
xMin = box['xMin']
xMax = box['xMax']
yMin = box['yMin']
yMax = box['yMax']
xPoints[0::4] = xMin
xPoints[1::4] = xMax
xPoints[2::4] = xMax
xPoints[3::4] = xMin
yPoints[0::4] = yMax
yPoints[1::4] = yMax
yPoints[2::4] = yMin
yPoints[3::4] = yMin
return (polyOffset, ringOffset, xPoints, yPoints)
def _createPoint(
self,
inDf,
colName,
radius,
):
pandas = inDf.to_pandas()
box = pd.DataFrame({
'xMin': pandas[colName + 'X'] - radius,
'xMax': pandas[colName + 'X'] + radius,
'yMin': pandas[colName + 'Y'] - radius,
'yMax': pandas[colName + 'Y'] + radius,
})
out = self._polygonFromBox(box)
return out
def _createBox(
self,
inDf,
colName
):
pandas = inDf.to_pandas()
box = pd.DataFrame({
'xMin': pandas[colName + 'MinX'],
'xMax': pandas[colName + 'MaxX'],
'yMin': pandas[colName + 'MinY'],
'yMax': pandas[colName + 'MaxY'],
})
out = self._polygonFromBox(box)
return out
def _spatialJoinDist(
self,
ldf,
rdf,
lName,
rName,
lTree,
polygon,
dist
):
(polyOffset, ringOffset, xPoints, yPoints) = polygon
(points, tree) = lTree
boundingBox = cuspatial.polygon_bounding_boxes(
polyOffset,
ringOffset,
xPoints,
yPoints
)
joinFilter = cuspatial.join_quadtree_and_bounding_boxes(
tree,
boundingBox,
0.0,
1.0,
0.0,
1.0,
1.0,
15
)
joinPolygon = cuspatial.quadtree_point_in_polygon(
joinFilter,
tree,
points,
ldf[lName + 'X'],
ldf[lName + 'Y'],
polyOffset,
ringOffset,
xPoints,
yPoints,
)
# https://github.com/rapidsai/cuspatial/issues/284
lGather = ldf.take(points.take(joinPolygon['point_index'])).reset_index(drop=True)
rGather = rdf.take(joinPolygon['polygon_index']).reset_index(drop=True)
dfConcat = cudf.concat([lGather, rGather], axis=1)
dfConcat['distPred'] = False
@cuda.jit
def distPredFunc(lX, lY, rX, rY, out, dist):
i = cuda.grid(1)
if i < lX.shape[0]:
dX = lX[i] - rX[i]
dY = lY[i] - rY[i]
dSquare = (dX * dX) + (dY * dY)
out[i] = dSquare < (dist * dist)
numbaTime = 0.0
if dist > 0.0:
startTime = time.time()
distPredFunc.forall(dfConcat.shape[0])(
dfConcat[lName + 'X'],
dfConcat[lName + 'Y'],
dfConcat[rName + 'X'],
dfConcat[rName + 'Y'],
dfConcat['distPred'],
dist
)
endTime = time.time()
numbaTime = endTime - startTime
dfConcat = dfConcat[dfConcat['distPred']]
return (dfConcat, numbaTime)
def _spatialJoinKnn(
self,
ldf,
rdf,
lName,
rName,
k,
):
assert(ldf.shape[0] == 1)
@cuda.jit
def distFunc(lX, lY, rX, rY, out):
i = cuda.grid(1)
if i < lX.shape[0]:
dX = lX[i] - rX[i]
dY = lY[i] - rY[i]
dSquare = (dX * dX) + (dY * dY)
out[i] = dSquare
out = rdf
out['dist'] = 0.0
startTime = time.time()
distFunc.forall(rdf.shape[0])(
ldf[lName + 'X'],
ldf[lName + 'Y'],
rdf[rName + 'X'],
rdf[rName + 'Y'],
out['dist'],
)
endTime = time.time()
numbaTime = endTime - startTime
out = out.sort_values('dist')
out = out.head(k)
dummyR = ldf
dummyR['dummy'] = 0
out['dummy'] = 0
out = out.merge(dummyR, on='dummy', how='outer')
return (out, numbaTime)
def _query1(self):
self._loadTables('query1')
self.driverStatusTable = self.driverStatusTable[self.driverStatusTable['drvStat.time'] < self.driverStatusTable.shape[0] / 500]
self.rideReqTable = self.rideReqTable[self.rideReqTable['rideReq.time'] < self.rideReqTable.shape[0] / 10]
radius = 0.025 / np.sqrt(self.args.scale)
driverPolygon = self._createPoint(
self.driverStatusTable,
'drvStat.pos',
radius
)
startTime = time.time()
rideReqIndex = self._createIndex(
self.rideReqTable,
'rideReq.start',
)
(joinStatus, numbaTime) = self._spatialJoinDist(
self.rideReqTable,
self.driverStatusTable,
'rideReq.start',
'drvStat.pos',
rideReqIndex,
driverPolygon,
radius
)
joinDriver = joinStatus.merge(self.driverTable, left_on='drvStat.driverId', right_on='drv.driverId')
cond = joinDriver[joinDriver['rideReq.seatCount'] < joinDriver['drv.seatCount']]
cond['count'] = 0
group = cond[['drv.driverId', 'count']].groupby(
['drv.driverId'],
).agg({
'count': 'count',
}).reset_index()
endTime = time.time()
group.to_csv(
'query1_gpu.csv',
index=False
)
return endTime - startTime - numbaTime
def _query2(self):
self._loadTables('query2')
rideReqIndex = self._createIndex(
self.rideReqTable,
'rideReq.start',
)
filterLocation = self.locationTable[self.locationTable['loc.locationId'] == 0]
locationPolygon = self._createBox(
filterLocation,
'loc.bounds',
)
startTime = time.time()
(joinLocation, numbaTime) = self._spatialJoinDist(
self.rideReqTable,
filterLocation,
'rideReq.start',
'loc.bounds',
rideReqIndex,
locationPolygon,
0.0
)
joinLocation['count'] = 0
group = joinLocation.groupby(
['rideReq.timeBins'],
).agg({
'count': 'count',
}).reset_index()
out = group.sort_values('count')
endTime = time.time()
out.to_csv(
'query2_gpu.csv',
index=False,
)
return endTime - startTime - numbaTime
def _query3(self):
self._loadTables('query3')
self.rideReqTable = self.rideReqTable[self.rideReqTable['rideReq.time'] < self.rideReqTable.shape[0] / 100]
locationPolygon = self._createBox(
self.locationTable,
'loc.bounds',
)
startTime = time.time()
rideReqIndex = self._createIndex(
self.rideReqTable,
'rideReq.start',
)
(joinLocation, numbaTime) = self._spatialJoinDist(
self.rideReqTable,
self.locationTable,
'rideReq.start',
'loc.bounds',
rideReqIndex,
locationPolygon,
0.0
)
joinLocation['count'] = 0
group = joinLocation.groupby(
['loc.locationId'],
).agg({
'count': 'count',
}).reset_index()
out = group.sort_values('count')
endTime = time.time()
out.to_csv(
'query3_gpu.csv',
index=False,
)
return endTime - startTime - numbaTime
def _query4(self):
self._loadTables('query4')
self.rideTable = self.rideTable[self.rideTable['ride.startTime'] < self.rideTable.shape[0] / 10]
knn = cuml.neighbors.KNeighborsRegressor(n_neighbors=5)
rideReqIndex = self._createIndex(
self.rideReqTable,
'rideReq.start',
)
locationFilter = self.locationTable[self.locationTable['loc.locationId'] == 0]
locationPolygon = self._createBox(
locationFilter,
'loc.bounds',
)
startTime = time.time()
rideIndex = self._createIndex(
self.rideTable,
'ride.start',
)
(joinRide, numbaTime0) = self._spatialJoinDist(
self.rideTable,
locationFilter,
'ride.start',
'loc.bounds',
rideIndex,
locationPolygon,
0.0
)
featureNameRide = [
'ride.c0',
'ride.c1',
'ride.c2',
'ride.c3',
'ride.c4',
'ride.c5',
'ride.c6',
'ride.c7',
'ride.c8',
'ride.c9',
'ride.c10',
'ride.c11'
]
featureNameRideReq = [
'rideReq.c0',
'rideReq.c1',
'rideReq.c2',
'rideReq.c3',
'rideReq.c4',
'rideReq.c5',
'rideReq.c6',
'rideReq.c7',
'rideReq.c8',
'rideReq.c9',
'rideReq.c10',
'rideReq.c11'
]
trainX = joinRide[featureNameRide]
trainY = joinRide['ride.pred0']
knn.fit(trainX, trainY)
(joinReq, numbaTime1) = self._spatialJoinDist(
self.rideReqTable,
locationFilter,
'rideReq.start',
'loc.bounds',
rideReqIndex,
locationPolygon,
0.0
)
joinReq = joinReq.head(1)
joinReq['predict'] = knn.predict(joinReq[featureNameRideReq])
endTime = time.time()
joinReq.to_csv(
'query4_gpu.csv',
index=False,
)
return endTime - startTime - numbaTime0 - numbaTime1
def _query5(self):
self._loadTables('query5')
supportVec = 8
xVal = np.random.rand(supportVec, 12)
yVal = np.random.choice([-1.0, 1.0], size=supportVec)
svm = cuml.SVC(kernel='poly', degree=2)
svm.fit(xVal, yVal)
featureName = [
'drvStat.c0',
'drvStat.c1',
'drvStat.c2',
'drvStat.c3',
'drvStat.c4',
'drvStat.c5',
'drvStat.c6',
'drvStat.c7',
'drvStat.c8',
'drvStat.c9',
'drvStat.c10',
'drvStat.c11'
]
startTime = time.time()
join = self.driverStatusTable.merge(self.driverTable, left_on='drvStat.driverId', right_on='drv.driverId')
groupby = join.groupby(['drv.driverId'])[featureName].rolling(3, min_periods=1).mean()
predict = svm.predict(groupby[featureName])
endTime = time.time()
groupby.to_csv(
'query5_gpu.csv',
index=False,
)
return endTime - startTime
def _query6(self):
self._loadTables('query6')
self.rideReqTable = self.rideReqTable[self.rideReqTable['rideReq.time'] < self.rideReqTable.shape[0] / 10]
rideReqIndex = self._createIndex(
self.rideReqTable,
'rideReq.start',
)
driverStatusIndex = self._createIndex(
self.driverStatusTable,
'drvStat.pos',
)
locationPolygon = self._createBox(
self.locationTable,
'loc.bounds',
)
trainX = {}
for i in range(10):
trainX['c{}'.format(i)] = np.random.rand(1000)
trainX = cudf.DataFrame(trainX)
trainY = np.random.choice([0.0, 1.0], size=1000)
trainY = cudf.Series(trainY)
linReg = cuml.LinearRegression()
linReg.fit(trainX, trainY)
startTime = time.time()
(joinRideReq, numbaTime0) = self._spatialJoinDist(
self.rideReqTable,
self.locationTable,
'rideReq.start',
'loc.bounds',
rideReqIndex,
locationPolygon,
0.0
)
joinRideReq['count'] = 0
reqGroup = joinRideReq.groupby(
['loc.locationId'],
).agg({
'count': 'count',
}).reset_index()
(joinDriver, numbaTime1) = self._spatialJoinDist(
self.driverStatusTable,
self.locationTable,
'drvStat.pos',
'loc.bounds',
driverStatusIndex,
locationPolygon,
0.0
)
joinDriver['count'] = 0
driverGroup = joinDriver.groupby(
['loc.locationId'],
).agg({
'count': 'count',
}).reset_index()
join0 = reqGroup.merge(driverGroup, on='loc.locationId')
join1 = join0.merge(self.locationTable, on='loc.locationId')
featureName = [
'loc.c0',
'loc.c1',
'loc.c2',
'loc.c3',
'loc.c4',
'loc.c5',
'loc.c6',
'loc.c7',
'loc.c8',
'loc.c9',
]
join1['infer'] = linReg.predict(join1[featureName])
endTime = time.time()
join1.to_csv(
'query6_gpu.csv',
index=False,
)
return endTime - startTime - numbaTime0 - numbaTime1
def _query7(self):
self._loadTables('query7')
trainX = {}
for i in range(12):
trainX['c{}'.format(i)] = np.random.rand(1000)
trainX = cudf.DataFrame(trainX)
trainY = np.random.choice([0.0, 1.0], size=1000)
trainY = cudf.Series(trainY)
logReg = cuml.LogisticRegression()
logReg.fit(trainX, trainY)
self.riderTable = self.riderTable[self.riderTable['rider.signupTime'] < self.riderTable.shape[0] / 2]
startTime = time.time()