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predict_certification.py
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predict_certification.py
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import util
import pandas
import cPickle
import pandas
import math
import numpy as np
import sklearn.metrics
import sklearn.linear_model
from common import loadCourseDates, loadData, getCourseStartAndEndDates, NUM_WEEKS_HEURISTIC, getDummiesFixedSet
BATCH_SIZE = 100
WEEK = np.timedelta64(7, 'D')
MIN_EXAMPLES = 10
START_DATES, PREDICTION_DATES_0_5, PREDICTION_DATES_1_0 = loadCourseDates()
#START_DATES = {
# 'HarvardX/SW25x/1T2014': np.datetime64('2014-02-25'),
# 'HarvardX/SW12x/2013_SOND': np.datetime64('2013-10-31'),
# 'HarvardX/SW12.2x/1T2014': np.datetime64('2014-01-02'),
# 'HarvardX/SW12.3x/1T2014': np.datetime64('2014-02-13'),
# 'HarvardX/SW12.4x/1T2014': np.datetime64('2014-03-20'),
# 'HarvardX/SW12.5x/2T2014': np.datetime64('2014-04-24'),
# 'HarvardX/SW12.6x/2T2014': np.datetime64('2014-05-22'),
# 'HarvardX/SW12.7x/3T2014': np.datetime64('2014-09-04'),
# 'HarvardX/SW12.8x/3T2014': np.datetime64('2014-10-09'),
# 'HarvardX/SW12.9x/3T2014': np.datetime64('2014-11-20'),
# 'HarvardX/SW12.10x/1T2015': np.datetime64('2015-01-08'),
# 'HarvardX/PH231x/1T2016': np.datetime64('2016-01-25'),
# 'HarvardX/PH557/3T2015': np.datetime64('2015-12-03'),
# 'HarvardX/PH525.4x/3T2015': np.datetime64('2016-01-15'),
# 'HarvardX/MUS24.3x/1T2016': np.datetime64('2016-01-21'),
# #'HarvardX/PH556/2015T3': np.datetime64('2016-01-20'),
# 'HarvardX/SW12.1x/2015': np.datetime64('2015-10-27'),
# 'HarvardX/SW12.2x/2015': np.datetime64('2015-10-27'),
# 'HarvardX/SW12.3x/2015': np.datetime64('2015-10-27'),
# 'HarvardX/SW12.4x/2015': np.datetime64('2015-10-27'),
# 'HarvardX/SW12.5x/2015': np.datetime64('2015-10-27'),
# 'HarvardX/SW12.6x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.7x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.8x/2015': np.datetime64('2015-11-20'),
# #'HarvardX/SW12.9x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.10x/2015': np.datetime64('2015-11-20'),
#}
# Dates corresponding to when students can earn 0.5 * number of points necessary for certification
#PREDICTION_DATES_0_5 = {
# 'HarvardX/SW25x/1T2014': np.datetime64('2014-03-18 17:00:00'),
# 'HarvardX/SW12x/2013_SOND': np.datetime64('2013-11-14 17:00:00'),
# 'HarvardX/SW12.2x/1T2014': np.datetime64('2014-01-08 05:00:00'),
# 'HarvardX/SW12.3x/1T2014': np.datetime64('2014-02-20 22:00:00'),
# 'HarvardX/SW12.4x/1T2014': np.datetime64('2014-03-27 22:00:00'),
# 'HarvardX/SW12.5x/2T2014': np.datetime64('2014-04-24 22:00:00'),
# 'HarvardX/SW12.6x/2T2014': np.datetime64('2014-06-05 22:30:00'),
# 'HarvardX/SW12.7x/3T2014': np.datetime64('2014-09-11 19:00:00'),
# 'HarvardX/SW12.8x/3T2014': np.datetime64('2014-10-24 04:00:00'),
# 'HarvardX/SW12.9x/3T2014': np.datetime64('2014-12-05 05:00:00'),
# 'HarvardX/SW12.10x/1T2015': np.datetime64('2015-01-29 20:00:00'),
# 'HarvardX/PH231x/1T2016': np.datetime64('2016-02-29'),
# 'HarvardX/PH557/3T2015': np.datetime64('2016-02-29'),
# 'HarvardX/PH525.4x/3T2015': np.datetime64('2016-02-29'),
# 'HarvardX/MUS24.3x/1T2016': np.datetime64('2016-02-29'),
# #'HarvardX/PH556/2015T3': np.datetime64('2016-02-29'),
# 'HarvardX/SW12.1x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.2x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.3x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.4x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.5x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.6x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.7x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.8x/2015': np.datetime64('2015-11-20'),
# #'HarvardX/SW12.9x/2015': np.datetime64('2030-01-01'),
# 'HarvardX/SW12.10x/2015': np.datetime64('2015-11-20')
#}
# Dates corresponding to when students can earn 1.0 * number of points necessary for certification
#PREDICTION_DATES_1_0 = {
# 'HarvardX/SW25x/1T2014': np.datetime64('2014-04-02 00:00:00'),
# 'HarvardX/SW12x/2013_SOND': np.datetime64('2013-12-05 21:00:00'),
# 'HarvardX/SW12.2x/1T2014': np.datetime64('2014-01-23 17:30:00'),
# 'HarvardX/SW12.3x/1T2014': np.datetime64('2014-02-27 22:00:00'),
# 'HarvardX/SW12.4x/1T2014': np.datetime64('2014-04-10 18:00:00'),
# 'HarvardX/SW12.5x/2T2014': np.datetime64('2014-05-08 19:00:00'),
# 'HarvardX/SW12.6x/2T2014': np.datetime64('2014-06-19 20:30:00'),
# 'HarvardX/SW12.7x/3T2014': np.datetime64('2014-09-25 20:00:00'),
# 'HarvardX/SW12.8x/3T2014': np.datetime64('2014-11-06 20:00:00'),
# 'HarvardX/SW12.9x/3T2014': np.datetime64('2014-12-19 02:30:00'),
# 'HarvardX/SW12.10x/1T2015': np.datetime64('2015-02-27 02:00:00'),
# 'HarvardX/PH231x/1T2016': np.datetime64('2016-02-29'),
# 'HarvardX/PH557/3T2015': np.datetime64('2016-02-29'),
# 'HarvardX/PH525.4x/3T2015': np.datetime64('2016-02-29'),
# 'HarvardX/MUS24.3x/1T2016': np.datetime64('2016-02-29'),
# #'HarvardX/PH556/2015T3': np.datetime64('2016-02-29'),
# 'HarvardX/SW12.1x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.2x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.3x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.4x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.5x/2015': np.datetime64('2015-10-27 14:00:00'),
# 'HarvardX/SW12.6x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.7x/2015': np.datetime64('2015-11-20'),
# 'HarvardX/SW12.8x/2015': np.datetime64('2015-11-20'),
# #'HarvardX/SW12.9x/2015': np.datetime64('2030-01-01'),
# 'HarvardX/SW12.10x/2015': np.datetime64('2015-11-20')
#}
# For each course:
# Get demographic information from person-course dataset
# Get list of rows from person-course-day dataset
# for all days between T0 and Tc; fill in any missing entries with 0s.
# Predict certification using fixed number of observations (# days x # fields/day)
# Compare to baseline predictors: P(certification | timeSinceLastAction) = logistic(- timeSinceLastAction)
def loadPersonCourseData ():
#d = pandas.io.parsers.read_csv('/nfs/home/J/jwhitehill/shared_space/ci3_jwaldo/BigQuery/person_course_HarvardX_2015-11-11-051632.csv')
d = pandas.io.parsers.read_csv('/nfs/home/J/jwhitehill/shared_space/ci3_charlesriverx/HarvardX/CoursesAll/person_course.csv')
d = convertTimes(d, 'start_time')
return d
def loadPrecourseSurveyData ():
d = pandas.io.parsers.read_csv('/nfs/home/J/jwhitehill/shared_space/ci3_charlesriverx/HarvardX/CoursesAll/person_course_survey_latest.csv')
return d
def loadPersonCourseDayData ():
# Combine datasets
d = pandas.io.parsers.read_csv('/nfs/home/J/jwhitehill/shared_space/ci3_charlesriverx/HarvardX/CoursesAll/person_course_day.csv')
d = convertTimes(d, 'date')
courseIds = np.unique(d.course_id)
e = {}
for courseId in courseIds:
idxs = np.nonzero(d.course_id == courseId)[0]
e[courseId] = d.iloc[idxs]
return e
def convertTimes (d, colName):
goodIdxs = []
idx = 0
dates = []
for dateStr in d[colName]:
try:
date = np.datetime64(dateStr[0:10]) # "0:10" -- only extract the date
dates.append(date)
goodIdxs.append(idx)
except:
pass
idx += 1
d = d.iloc[goodIdxs]
dates = np.array(dates, dtype=np.datetime64)
d[colName] = dates
return d
def computeCourseDates (courseId, startDates):
T0 = startDates[courseId]
Tc = PREDICTION_DATES_1_0[courseId]
return T0, Tc
def convertYoB (YoB):
REF_YEAR = 2012
ages = REF_YEAR - YoB
ageRanges = [ -float('inf'), 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, +float('inf') ]
newYoB = np.zeros_like(ages)
for i in range(len(ageRanges) - 1):
minAge = ageRanges[i]
maxAge = ageRanges[i+1]
idxs = np.nonzero((ages >= minAge) & (ages < maxAge))
# In code below, we add 1 ("+ 1") so that the minimum index corresponding to
# any valid age range is 1, not 0. It follows that any invalid age range (i.e., NaN)
# will retain value 0.
newYoB[idxs] = i + 1
return newYoB
def computeDaysSinceLastEvent (nevents, pcdDates, T0, Tc, idxsOfUser):
if len(idxsOfUser) > 0:
nonzeroEventIdxs = idxsOfUser[np.nonzero(nevents.iloc[idxsOfUser] > 0)[0]]
else:
nonzeroEventIdxs = []
if len(nonzeroEventIdxs) > 0:
maxNonzeroEventDate = np.datetime64(np.max(pcdDates.iloc[nonzeroEventIdxs]))
else:
maxNonzeroEventDate = T0
return (Tc - maxNonzeroEventDate) / np.timedelta64(1, 'D')
def getXandY (pc, pcd, survey, usernames, T0, Tc, demographicsOnly):
# Restrict analysis to days between T0 and Tc
idxs = np.nonzero((pcd.date >= T0) & (pcd.date < Tc))[0]
pcd = pcd.iloc[idxs]
# Create dummy variables
pcUsernames = pc.username
usernamesToCertifiedMap = { pcUsernames.iloc[i]:pc.certified.iloc[i] for i in range(len(pcUsernames)) }
usernamesToLastEventMap = { pcUsernames.iloc[i]:pc.last_event.iloc[i] for i in range(len(pcUsernames)) }
DEMOGRAPHIC_FIELDS = [ 'continent', 'YoB', 'LoE', 'gender' ]
pc = pc[DEMOGRAPHIC_FIELDS]
pc.YoB = convertYoB(pc.YoB)
pc = getDummiesFixedSet(pc)
#pc = pandas.get_dummies(pc, columns = [ 'continent', 'LoE', 'gender', 'YoB' ], dummy_na = True)
# For efficiency, figure out which rows of the person-course and person-course-day
# datasets belong to which users
usernamesToPcIdxsMap = dict(zip(pcUsernames, range(len(pc))))
usernamesToCompletedSurveyMap = dict(zip(survey.username, survey.prs_ResponseID.notnull()))
usernamesToPcdIdxsMap = {}
for i in range(pcd.shape[0]):
username = pcd.username.iloc[i]
usernamesToPcdIdxsMap.setdefault(username, [])
usernamesToPcdIdxsMap[username].append(i)
### Only analyze users who appear in the person-course-day dataset
##usernames = list(set(usernames).intersection(usernamesToPcdIdxsMap.keys()))
# Extract features for all users and put them into the design matrix X
pcdDates = pcd.date
pcd = pcd.drop([ 'username', 'course_id', 'date', 'last_event' ], axis=1)
# Convert NaNs in person-course-day dataset to 0
pcd = pcd.fillna(value=0)
NUM_DAYS = 1
NUM_FEATURES = NUM_DAYS * len(pcd.columns) + len(pc.columns) + 2 # "+ 2" -- completion of precourse survey; and numDaysSinceLastEvent
X = np.zeros((len(usernames), NUM_FEATURES))
Xheur = np.zeros(len(usernames))
y = np.zeros(len(usernames))
sumDts = np.zeros((len(usernames), NUM_DAYS)) # Keep track of sum_dt as a special feature
goodIdxs = []
for i, username in enumerate(usernames):
if username in usernamesToPcdIdxsMap.keys():
idxs = np.array(usernamesToPcdIdxsMap[username])
# For each row in the person-course-day dataset for this user, put the
# features into the correct column range for that user in the design matrix X.
X[i,0:len(pcd.columns)] = np.sum(pcd.iloc[idxs].as_matrix(), axis=0) # Call as_matrix() so nan is treated as nan in sum!
sumDts[i] = np.sum(pcd.sum_dt.iloc[idxs])
else:
idxs = []
X[i,0:len(pcd.columns)] = np.zeros(len(pcd.columns))
sumDts[i] = 0
# Now append the demographic features
demographics = pc.iloc[usernamesToPcIdxsMap[username]]
X[i,NUM_DAYS * len(pcd.columns):NUM_FEATURES-2] = demographics
# "Heuristic" predictor -- whether the student's last event time is before/after the first week of the course
lastEvent = usernamesToLastEventMap[username]
# Last 2 features
usernamesToCompletedSurveyMap.setdefault(username, False)
completedSurvey = usernamesToCompletedSurveyMap[username]
X[i,NUM_FEATURES-2] = completedSurvey
numDaysSinceLastEvent = computeDaysSinceLastEvent(pcd.nevents, pcdDates, T0, Tc, idxs)
X[i,NUM_FEATURES-1] = numDaysSinceLastEvent
Xheur[i] = numDaysSinceLastEvent * -1 # "*-1" -- so that fewer days since last action means higher prob. of certification
y[i] = usernamesToCertifiedMap[username]
if np.isfinite(np.sum(X[i,:])):
goodIdxs.append(i)
if demographicsOnly:
# Zero out the non-demographics information
X[:,0:NUM_DAYS*len(pcd.columns)] = 0
X[:,NUM_FEATURES-2:] = 0
return X[goodIdxs,:], Xheur[goodIdxs], y[goodIdxs], np.sum(sumDts[goodIdxs,:], axis=1)
def normalize (trainX, testX):
mx = np.mean(trainX, axis=0)
sx = np.std(trainX, axis=0)
sx[sx == 0] = 1
trainX -= np.tile(np.atleast_2d(mx), (trainX.shape[0], 1))
trainX /= np.tile(np.atleast_2d(sx), (trainX.shape[0], 1))
# Scale testing data using parameters estimated on training set
testX -= np.tile(np.atleast_2d(mx), (testX.shape[0], 1))
testX /= np.tile(np.atleast_2d(sx), (testX.shape[0], 1))
return trainX, testX
def split (X, Xheur, y, sumDts, trainIdxs = None, testIdxs = None):
if trainIdxs == None:
idxs = np.random.permutation(X.shape[0])
numTraining = int(len(idxs) * 0.5)
trainIdxs = idxs[0:numTraining]
testIdxs = idxs[numTraining:]
return X[trainIdxs,:], Xheur[trainIdxs], y[trainIdxs], sumDts[trainIdxs], X[testIdxs,:], Xheur[testIdxs], y[testIdxs], sumDts[testIdxs], trainIdxs, testIdxs
def sampleWithReplacement (x, n):
if len(x) == 0:
return []
idxs = (np.random.random(n) * len(x)).astype(np.int32)
return x[idxs]
# Evaluate using bootstrapping to estimate estimate performance with a *uniform* distribution
# of sumDt for *both* classes.
def evaluateWithUniformSumDts (y, yhat, sumDts):
sortedSumDts = np.sort(sumDts)
pct1 = sortedSumDts[int(len(sumDts)*0.01)]
pct99 = sortedSumDts[int(len(sumDts)*0.99)]
NUM_CHUNKS = 20
NUM_EXAMPLES_PER_CHUNK_PER_CLASS = 100
chunkSize = (pct99 - pct1) / NUM_CHUNKS
allY = []
allYhat = []
for sumDt in np.arange(pct1, pct99, chunkSize):
sumDt1 = sumDt
sumDt2 = sumDt + chunkSize
posIdxs = np.nonzero((sumDts >= sumDt1) & (sumDts < sumDt2) & (y == 1))[0]
negIdxs = np.nonzero((sumDts >= sumDt1) & (sumDts < sumDt2) & (y == 0))[0]
posIdxs = sampleWithReplacement(posIdxs, NUM_EXAMPLES_PER_CHUNK_PER_CLASS)
negIdxs = sampleWithReplacement(negIdxs, NUM_EXAMPLES_PER_CHUNK_PER_CLASS)
allY += list(y[posIdxs]) + list(y[negIdxs])
allYhat += list(yhat[posIdxs]) + list(yhat[negIdxs])
return sklearn.metrics.roc_auc_score(allY, allYhat)
def splitAndGetNormalizedFeatures (somePc, somePcd, someSurvey, usernames, T0, Tc, demographicsOnly):
# Get features and target values
X, Xheur, y, sumDts = getXandY(somePc, somePcd, someSurvey, usernames, T0, Tc, demographicsOnly)
if len(np.nonzero(y == 0)[0]) < MIN_EXAMPLES or len(np.nonzero(y == 1)[0]) < MIN_EXAMPLES:
raise ValueError("Too few examples or all one class")
# Split into training and testing folds
trainX, trainXheur, trainY, trainSumDts, testX, testXheur, testY, testSumDts, trainIdxs, testIdxs = split(X, Xheur, y, sumDts)
trainX, testX = normalize(trainX, testX)
return trainX, trainXheur, trainY, testX, testXheur, testY
def trainMLR (trainX, trainY, testX, testY, mlrReg):
baselineModel = sklearn.linear_model.LogisticRegression(C=mlrReg)
baselineModel.fit(trainX, trainY)
yhat = baselineModel.predict_proba(testX)[:,1]
aucMLR = sklearn.metrics.roc_auc_score(testY, yhat)
return baselineModel, aucMLR, (testY, yhat)
def prepareAllData (startDates, endDates, demographicsOnly):
print "Preparing data..."
allCourseData = {}
#for courseId in set(pcd.keys()).intersection(START_DATES.keys()): # For each course
for courseId in set(startDates.keys()).intersection(START_DATES.keys()): # For each course
# Load data for this course
print "Loading {}...".format(courseId)
try:
somePc, someSurvey, somePcd = loadData(courseId)
except (IOError, pandas.io.parsers.EmptyDataError):
print "Skipping"
continue
# If no certifiers, then skip
if (np.sum(somePc.certified) < MIN_EXAMPLES) or (np.sum(somePc.certified) >= len(somePc) - MIN_EXAMPLES):
print "Skipping"
continue
T0, Tc = computeCourseDates(courseId, startDates)
allCourseData[courseId] = []
print "...done"
Tcutoffs = np.arange(T0 + 1*WEEK, Tc+np.timedelta64(1, 'D'), WEEK)
for Tcutoff in Tcutoffs:
usernames = getRelevantUsers(somePc, Tcutoff)
allData = splitAndGetNormalizedFeatures(somePc, somePcd, someSurvey, usernames, T0, Tcutoff, demographicsOnly)
allCourseData[courseId].append(allData)
print "...done"
return allCourseData
def runExperimentsHeuristic ():
allAucs = {}
for courseId in set(allCourseData.keys()).intersection(START_DATES.keys()): # For each course
allAucs[courseId] = []
for i, weekData in enumerate(allCourseData[courseId]):
if i >= (NUM_WEEKS_HEURISTIC - 1):
(trainX, trainXheur, trainY, testX, testXheur, testY) = weekData
auc = sklearn.metrics.roc_auc_score(testY, testXheur)
allAucs[courseId].append(auc)
return allAucs
def compareToCrossTrain (courseId, testX, testY, weekIdxWRTTc):
discipline = COURSE_TO_DISCIPLINE_MAP[courseId]
(xtrainCourseId, modelList) = PRETRAINED_MODELS[discipline]
if xtrainCourseId == courseId:
return (float('nan'), float('nan')) # Not allowed to "cross-train" on same course!
# Select model that corresponds most closely in time (relative to T_c) to specified weekIdx
if weekIdxWRTTc < len(modelList):
idx = len(modelList) - weekIdxWRTTc - 1
else:
idx = 0 # As far back in time as we can go
model = modelList[idx]
acc = sklearn.metrics.roc_auc_score(testY, model.predict_proba(testX)[:,1])
# Compute mean model over all courses *except* this course
almostAllModels = []
meanModel = sklearn.linear_model.LogisticRegression(C=1.0)
for xcourseId in ALL_MODELS:
if xcourseId != courseId:
for model in ALL_MODELS[xcourseId]:
almostAllModels.append(model)
meanModel.coef_ = np.mean([ model.coef_ for model in almostAllModels ], axis=0)
meanModel.intercept_ = np.mean([ model.intercept_ for model in almostAllModels ], axis=0)
accMean = sklearn.metrics.roc_auc_score(testY, meanModel.predict_proba(testX)[:,1])
#print "Crosstrain ", courseId, acc, accMean
return (acc, accMean)
def runExperiments (allCourseData):
allAucs = {}
allCrosstrainAucs = {}
allDists = {}
models = {}
for courseId in set(allCourseData.keys()).intersection(START_DATES.keys()): # For each course
print courseId
allAucs[courseId] = []
allCrosstrainAucs[courseId] = []
allDists[courseId] = []
models[courseId] = []
for i, weekData in enumerate(allCourseData[courseId]):
(trainX, trainXheur, trainY, testX, testXheur, testY) = weekData
global MLR_REG
print MLR_REG
model, auc, dist = trainMLR(trainX, trainY, testX, testY, MLR_REG)
weekIdxWRTTc = len(allCourseData[courseId]) - i - 1
aucCrosstrain = compareToCrossTrain(courseId, testX, testY, weekIdxWRTTc)
allCrosstrainAucs[courseId].append(aucCrosstrain)
allAucs[courseId].append(auc)
allDists[courseId].append(dist)
models[courseId].append(model)
print "comp {} {}".format(auc, aucCrosstrain[1])
return allAucs, allCrosstrainAucs, allDists, models
def trainAllHeuristic ():
allAucs = runExperimentsHeuristic()
cPickle.dump(allAucs, open("results_heuristic.pkl", "wb"))
def trainAll (allCourseData, demographicsOnly, save=True):
allAucs, allCrosstrainAucs, allDists, allModels = runExperiments(allCourseData)
if save:
cPickle.dump(allAucs, open("results_prong1{}.pkl".format("_demog" if demographicsOnly else ""), "wb"))
cPickle.dump(allCrosstrainAucs, open("results_xtrain_prong1{}.pkl".format("_demog" if demographicsOnly else ""), "wb"))
cPickle.dump(allDists, open("results_prong1_dists.pkl", "wb"))
cPickle.dump(allModels, open("results_prong1_models.pkl", "wb"))
return allAucs
def optimize (allCourseData):
MLR_REG_SET = 10. ** np.arange(-5, +6).astype(np.float32)
bestAuc = -1
for paramValue in MLR_REG_SET:
global MLR_REG
MLR_REG = float(paramValue)
allAucs, = runExperiments(allCourseData)
avgAuc = np.mean(aucs.values())
print avgAuc
if avgAuc > bestAuc:
bestAuc = avgAuc
bestParamValue = paramValue
print "Accuracy: {} for {}".format(bestAuc, bestParamValue)
def loadCourseToDisciplineMap ():
d = pandas.read_csv('course_to_discipline.csv')
return { d.course_id.iloc[i]:d.discipline_grouping.iloc[i] for i in range(len(d)) }
def loadPretrainedModels ():
models = cPickle.load(open('results_prong1_models.pkl', 'rb'))
# These are courses from different disciplines that were among top 20 HX courses with most certifying participants
courseIds = [ 'HarvardX/GSE2x/2T2014', 'HarvardX/PH525.1x/1T2015', 'HarvardX/SPU30x/2T2014', 'HarvardX/AmPoX.4/1T2015' ]
theMap = { COURSE_TO_DISCIPLINE_MAP[courseId]:(courseId, models[courseId]) for courseId in courseIds }
return models, theMap
if __name__ == "__main__":
COURSE_TO_DISCIPLINE_MAP = loadCourseToDisciplineMap()
ALL_MODELS, PRETRAINED_MODELS = loadPretrainedModels()
DEMOGRAPHICS_ONLY = False
if 'startDates' not in globals():
startDates, endDates = getCourseStartAndEndDates()
if 'allCourseData' not in globals():
allCourseData = prepareAllData(startDates, endDates, DEMOGRAPHICS_ONLY)
#optimize(allCourseData)
MLR_REG = 1.
allAucs = trainAll(allCourseData, DEMOGRAPHICS_ONLY, save=True)
trainAllHeuristic()