def evaluation_on_day_prediction_one_user(user_id):
print "loading matrix user "+str(user_id)+"..."
data_matrix = MDataExtractor.load_matrix(user_id)
rows_labels = MDataExtractor.load_labels_vector(user_id)
columns_labels = MDataExtractor.load_time_vector(user_id)
importance_scores = MDataExtractor.load_importance_scores(user_id)
print "user "+str(user_id)+" has "+str(len(rows_labels))+" features (rows) and "+str(len(columns_labels))+" realization (columns)"
print "loading rfv user "+str(user_id)+"..."
[rfvdata, featuresnames, valuesnames, recordsdates] = RVFDataExtractor.load_rvf(user_id)
vocab_size = [len(valuesnames[f]) for f in range(len(valuesnames.keys()))]
print "user "+str(user_id)+" has "+str(len(rfvdata))+" records"
print "loading rv user "+str(user_id)+"..."
[rv_data, realizationsnames, recordsdates] = RVDataExtractor.load_rv(user_id)
realization_size = len(realizationsnames)
classifiers = {"Random ": RandomClassifier()}#add the random classifier
classifiers = {"Most Frequent ": SVDClassifier("idf", 1)}#add the most frequent classifier
'''
for k in range(1,21):
classifiers["GHCM_MDT k="+str(k)] = GHCM_MDT_Value_Classifier(k, vocab_size) #add a ghcmdt classifier for each k
classifiers["LCBMF k="+str(k)] = LCBMFClassifier(None, None, None, k) #add a lcbmf classifier for each k
#add a svd classifier for each k'''
'''for k in [1,10,20,30,40,50,60]:
c =GHCM_MDT_Value_Classifier(k, vocab_size)
classifiers["GHCM_MDT k="+str(k)] = c'''
for k in [1,10,20,30,50,70]:
classifiers["SVD k="+str(k)] = SVDClassifier("idf", k)
classifiers["LDA k="+str(k)] = LDA_Value_Classifier(k, realization_size)
classifiers["HCM_MDT k="+str(k)] = HCM_MDT_Value_Classifier(k, vocab_size)
classifiers["GHCM_MDT k="+str(k)] = GHCM_MDT_Value_Classifier(k, vocab_size)
classifiers["LCBMF k="+str(k)] = LCBMFClassifier(None, None, None, k)
eval_handler = FpEvaluatorHandler()
eval_handler.instanciate_m (MDayPredictionEvaluator, data_matrix, rows_labels, columns_labels)
eval_handler.instanciate_rvf (RfvDayPredictionEvaluator, rfvdata, featuresnames, valuesnames)
eval_handler.instantiate_rv(RvDayPredictionEvaluator,rv_data, realizationsnames)
evaluations = {}
nb=1
for name, classifier in classifiers.iteritems():
print "evaluating "+name+" on user "+str(user_id)+" nb "+str(nb)+"/"+str(len(classifiers))
#results = [good_predictions, total_predictions, accuracy, macro_average_acc_by_class, accuracy_class1,...,accuracy_classn]
results = eval_handler.evaluate(classifier)
evaluations[name] = results
nb+=1
classes = eval_handler.classes
return [evaluations, classes]
def evaluation_on_applaunch_prediction_one_user(user_id):
print "loading rfv user " + str(user_id) + "..."
[rfvdata, featuresnames, valuesnames,
recordsdates] = RVFDataExtractor.load_rvf(user_id)
vocab_size = [len(valuesnames[f]) for f in range(len(valuesnames.keys()))]
print "user " + str(user_id) + " has " + str(len(rfvdata)) + " records"
print "loading rv user " + str(user_id) + "..."
[rv_data, realizationsnames,
recordsdates] = RVDataExtractor.load_rv(user_id)
realization_size = len(realizationsnames)
classifiers = {"Random ": RandomClassifier()} #add the random classifier
classifiers = {
"Most Frequent ": HCM_MDT_Value_Classifier(1, vocab_size)
} #add the most frequent classifier
'''
for k in range(1,21):
classifiers["GHCM_MDT k="+str(k)] = GHCM_MDT_Value_Classifier(k, vocab_size) #add a ghcmdt classifier for each k
classifiers["LCBMF k="+str(k)] = LCBMFClassifier(None, None, None, k) #add a lcbmf classifier for each k
#add a svd classifier for each k'''
'''for k in [1,10,20,30,40,50,60]:
c =GHCM_MDT_Value_Classifier(k, vocab_size)
classifiers["GHCM_MDT k="+str(k)] = c'''
for k in [1, 10, 20, 30, 50, 70]:
classifiers["LDA k=" + str(k)] = LDA_Value_Classifier(
k, realization_size)
classifiers["GHCM_MDT k=" + str(k)] = GHCM_MDT_Value_Classifier(
k, vocab_size)
classifiers["HCM_MDT k=" + str(k)] = HCM_MDT_Value_Classifier(
k, vocab_size)
eval_handler = FpEvaluatorHandler()
eval_handler.instanciate_rvf(RfvApplaunchPredictionEvaluator, rfvdata,
featuresnames, valuesnames)
eval_handler.instantiate_rv(RvApplaunchPredictionEvaluator, rv_data,
realizationsnames)
evaluations = {}
nb = 1
for name, classifier in classifiers.iteritems():
print "evaluating " + name + " on user " + str(user_id) + " nb " + str(
nb) + "/" + str(len(classifiers))
#results = [good_predictions, total_predictions, accuracy, macro_average_acc_by_class, accuracy_class1,...,accuracy_classn]
results = eval_handler.evaluate(classifier)
evaluations[name] = results
nb += 1
classes = eval_handler.classes
return [evaluations, classes]
def evaluation_on_location_prediction_one_user(user_id):
print "loading matrix user " + str(user_id) + "..."
data_matrix = MDataExtractor.load_matrix(user_id)
rows_labels = MDataExtractor.load_labels_vector(user_id)
columns_labels = MDataExtractor.load_time_vector(user_id)
importance_scores = MDataExtractor.load_importance_scores(user_id)
print "user " + str(user_id) + " has " + str(
len(rows_labels)) + " features (rows) and " + str(
len(columns_labels)) + " realization (columns)"
print "loading rfv user " + str(user_id) + "..."
[rfvdata, featuresnames, valuesnames,
recordsdates] = RVFDataExtractor.load_rvf(user_id)
vocab_size = [len(valuesnames[f]) for f in range(len(valuesnames.keys()))]
print "user " + str(user_id) + " has " + str(len(rfvdata)) + " records"
print "loading rv user " + str(user_id) + "..."
[rv_data, realizationsnames,
recordsdates] = RVDataExtractor.load_rv(user_id)
realization_size = len(realizationsnames)
classifiers = {"Random ": RandomClassifier()} #add the random classifier
classifiers = {
"Most Frequent ": SVDClassifier("idf", 1)
} #add the most frequent classifier
'''
for k in range(1,21):
classifiers["GHCM_MDT k="+str(k)] = GHCM_MDT_Value_Classifier(k, vocab_size) #add a ghcmdt classifier for each k
classifiers["LCBMF k="+str(k)] = LCBMFClassifier(None, None, None, k) #add a lcbmf classifier for each k
classifiers["SVD k="+str(k)] = SVDClassifier("idf", k) #add a svd classifier for each k'''
'''for k in [1,10,20,30,40,50,60]:
c =GHCM_MDT_Value_Classifier(k, vocab_size)
classifiers["GHCM_MDT k="+str(k)] = c'''
'''for k in [10,15,20,25,30,35]:
c =GHCM_MDT_Value_Classifier(k, vocab_size)
classifiers["GHCM_MDT k="+str(k)] = c'''
for k in [1, 10, 20, 30, 50, 70]:
classifiers["SVD k=" + str(k)] = SVDClassifier("idf", k)
classifiers["LDA k=" + str(k)] = LDA_Value_Classifier(
k, realization_size)
classifiers["HCM_MDT k=" + str(k)] = HCM_MDT_Value_Classifier(
k, vocab_size)
classifiers["GHCM_MDT k=" + str(k)] = GHCM_MDT_Value_Classifier(
k, vocab_size)
classifiers["LCBMF k=" + str(k)] = LCBMFClassifier(None, None, None, k)
eval_handler = FpEvaluatorHandler()
eval_handler.instanciate_m(MLocationPredictionEvaluator, data_matrix,
rows_labels, columns_labels)
eval_handler.instanciate_rvf(RfvLocationPredictionEvaluator, rfvdata,
featuresnames, valuesnames)
eval_handler.instantiate_rv(RvLocationPredictionEvaluator, rv_data,
realizationsnames)
evaluations = {}
for name, classifier in classifiers.iteritems():
print "evaluating " + name + " on user " + str(user_id)
#results = [good_predictions, total_predictions, accuracy, macro_average_acc_by_class, accuracy_class1,...,accuracy_classn]
results = eval_handler.evaluate(classifier)
evaluations[name] = results
classes = eval_handler.classes
return [evaluations, classes]