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#######################################################
# Script:
# trainPerf.py
# Usage:
# python trainPerf.py <input_file> <output_file>
# Description:
# Build the prediction model based on training data
# Pass 1: prediction based on hours in a week
# Authors:
# Jasmin Nakic, jnakic@salesforce.com
# Samir Pilipovic, spilipovic@salesforce.com
#######################################################
import sys
import numpy as np
from sklearn import linear_model
from sklearn.externals import joblib
# Imports required for visualization (plotly)
import plotly.graph_objs as go
from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
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# Script debugging flag
debugFlag = False
# Feature lists for different models
simpleCols = ["dateFrac"]
trigCols = ["dateFrac", "weekdaySin", "weekdayCos", "hourSin", "hourCos"]
hourDayCols = ["dateFrac", "isMonday", "isTuesday", "isWednesday", "isThursday", "isFriday", "isSaturday", "isSunday",
"isHour0", "isHour1", "isHour2", "isHour3", "isHour4", "isHour5", "isHour6", "isHour7",
"isHour8", "isHour9", "isHour10", "isHour11", "isHour12", "isHour13", "isHour14", "isHour15",
"isHour16", "isHour17", "isHour18", "isHour19", "isHour20", "isHour21", "isHour22", "isHour23"]
hourWeekCols = ["dateFrac"]
for d in range(0,7):
for h in range(0,24):
hourWeekCols.append("H_" + str(d) + "_" + str(h))
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# Add columns to the existing array and populate with data
def addColumns(dest, src, colNames):
# Initialize temporary array
tmpArr = np.empty(src.shape[0])
cols = 0
# Copy column content
for name in colNames:
if cols == 0: # first column
tmpArr = np.copy(src[name])
tmpArr = np.reshape(tmpArr,(-1,1))
else:
tmpCol = np.copy(src[name])
tmpCol = np.reshape(tmpCol,(-1,1))
tmpArr = np.append(tmpArr,tmpCol,1)
cols = cols + 1
return np.append(dest,tmpArr,1)
#end addColumns
# Generate linear regression model
def genModel(data,colList,modelName):
# Initialize array
X = np.zeros(data.shape[0])
X = np.reshape(X,(-1,1))
# Add columns
X = addColumns(X,data,colList)
if debugFlag:
print("X 0: ", X[0:5])
Y = np.copy(data["cnt"])
if debugFlag:
print("Y 0: ", Y[0:5])
model = linear_model.LinearRegression()
print(model.fit(X, Y))
print("INTERCEPT: ", model.intercept_)
print("COEFFICIENT shape: ", model.coef_.shape)
print("COEFFICIENT values: ", model.coef_)
print("SCORE values: ", model.score(X,Y))
P = model.predict(X)
if debugFlag:
print("P 0-5: ", P[0:5])
joblib.dump(model,modelName)
return P
#end genModel
# Generate linear regression model
def genRidgeModel(data,colList,modelName,ridgeAlpha):
# Initialize array
X = np.zeros(data.shape[0])
X = np.reshape(X,(-1,1))
# Add columns
X = addColumns(X,data,colList)
if debugFlag:
print("X 0: ", X[0:5])
Y = np.copy(data["cnt"])
if debugFlag:
print("Y 0: ", Y[0:5])
model = linear_model.Ridge(alpha=ridgeAlpha)
print(model.fit(X, Y))
print("INTERCEPT: ", model.intercept_)
print("COEFFICIENT shape: ", model.coef_.shape)
print("COEFFICIENT values: ", model.coef_)
print("SCORE values: ", model.score(X,Y))
P = model.predict(X)
if debugFlag:
print("P 0-5: ", P[0:5])
joblib.dump(model,modelName)
return P
#end genModel
# Generate linear regression model
def genLassoModel(data,colList,modelName,lassoAlpha):
# Initialize array
X = np.zeros(data.shape[0])
X = np.reshape(X,(-1,1))
# Add columns
X = addColumns(X,data,colList)
if debugFlag:
print("X 0: ", X[0:5])
Y = np.copy(data["cnt"])
if debugFlag:
print("Y 0: ", Y[0:5])
model = linear_model.Lasso(alpha=lassoAlpha,max_iter=5000)
print(model.fit(X, Y))
print("INTERCEPT: ", model.intercept_)
print("COEFFICIENT shape: ", model.coef_.shape)
print("COEFFICIENT values: ", model.coef_)
print("SCORE values: ", model.score(X,Y))
P = model.predict(X)
if debugFlag:
print("P 0-5: ", P[0:5])
joblib.dump(model,modelName)
return P
#end genModel
# Write predictions to the output file
def writeResult(output,data,p1,p2,p3,p4):
# generate result file
result = np.array(
np.empty(data.shape[0]),
dtype=[
("timeStamp","|U19"),
("dateFrac",float),
("isHoliday",int),
("isSunday",int),
("cnt",int),
("predSimple",int),
("predTrig",int),
("predHourDay",int),
("predHourWeek",int)
]
)
result["timeStamp"] = data["timeStamp"]
result["dateFrac"] = data["dateFrac"]
result["isHoliday"] = data["isHoliday"]
result["isSunday"] = data["isSunday"]
result["cnt"] = data["cnt"]
result["predSimple"] = p1
result["predTrig"] = p2
result["predHourDay"] = p3
result["predHourWeek"] = p4
if debugFlag:
print("R 0-5: ", result[0:5])
hdr = "timeStamp\tdateFrac\tisHoliday\tisSunday\tcnt\tpredSimple\tpredTrig\tpredHourDay\tpredHourWeek"
np.savetxt(output,result,fmt="%s",delimiter="\t",header=hdr,comments="")
#end writeResult
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# Start
inputFileName = "train_data.txt"
outputFileName = "train_hourly.txt"
# All input columns - data types are strings, float and int
inputData = np.genfromtxt(
inputFileName,
delimiter='\t',
names=True,
dtype=("|U19","|S10",int,float,int,float,float,int,float,float,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int,
int,int,int,int,int,int,int,int,int,int
)
)
print(inputData[1:5])
# P1 = genRidgeModel(inputData,simpleCols,"modelSimple",0.1)
# P2 = genRidgeModel(inputData,trigCols,"modelTrig",0.1)
# P3 = genRidgeModel(inputData,hourDayCols,"modelHourDay",0.1)
# P4 = genRidgeModel(inputData,hourWeekCols,"modelHourWeek",0.1)
# P1 = genLassoModel(inputData,simpleCols,"modelSimple",0.4)
# P2 = genLassoModel(inputData,trigCols,"modelTrig",0.4)
# P3 = genLassoModel(inputData,hourDayCols,"modelHourDay",0.4)
# P4 = genLassoModel(inputData,hourWeekCols,"modelHourWeek",0.4)
P1 = genModel(inputData,simpleCols,"modelSimple")
P2 = genModel(inputData,trigCols,"modelTrig")
P3 = genModel(inputData,hourDayCols,"modelHourDay")
P4 = genModel(inputData,hourWeekCols,"modelHourWeek")
writeResult(outputFileName,inputData,P1,P2,P3,P4)
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# Load the training data from file generated above using correct data types
results = np.genfromtxt(
outputFileName,
dtype=("|U19",float,int,int,int,int,int,int,int),
delimiter='\t',
names=True
)
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# Examine training data
print("Shape:", results.shape)
print("Columns:", len(results.dtype.names))
print(results[1:5])
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# Generate chart with predicitons based on training data (using plotly)
print("Plotly version", __version__) # requires plotly version >= 1.9.0
init_notebook_mode(connected=True)
set1 = go.Bar(
x=results["dateFrac"],
y=results["cnt"],
# marker=dict(color='blue'),
name='Actual'
)
set2 = go.Bar(
x=results["dateFrac"],
y=results["predHourWeek"],
# marker=dict(color='crimson'),
opacity=0.6,
name='Prediction'
)
barData = [set1, set2]
barLayout = go.Layout(barmode='group', title="Prediction vs. Actual")
fig = go.Figure(data=barData, layout=barLayout)
iplot(fig)
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