# Que 4 part B
lambda_values = [0.01]
r, w = utility.get_R()
k = 10
truePos = 0
falsePos = 0
falseNeg = 0
precisionArray = []
recallArray = []
rows, cols = r.shape
threshold_ranges = [x / 10.0 for x in range(0, 50, 5)]
# Run Regularized ALS for different values of lambda
for lambda_val in lambda_values:
uv = utility.weightedRegALS(r, lambda_val, k, w, 20)
for threshold in threshold_ranges:
for i in range(len(r)):
for j in range(len(r[i])):
if (uv[i, j] >= threshold):
if (r[i, j] >= threshold):
truePos += 1
else:
falsePos += 1
else:
if (r[i, j] >= threshold):
#if(uv[i, j] < threshold):
falseNeg += 1
print 'Precision for threshold: %f' % threshold
pre = float(truePos) / float(truePos + falsePos)
# Que 5 part B
# hit-rate is assumed to be precision
# failure rate is total - precision
r, w = utility.get_R()
L = 5
lambda_val = 0.1
k = 100
n_iter = 20
hit_rate_list = []
false_alarm_list = []
recall_list = []
# replace this with RegALS( )
#u, v = utility.nmf(w, K_VALUE, r) # Swap R & W
#uv = numpy.dot(u, v)
uv = utility.weightedRegALS(w, lambda_val, k, r, n_iter)
for i in range(1, L + 1):
top_l_orig_matrix = gettopN(r, i, w)
top_l_predict_matrix = gettopN(uv, i, w)
hit_rate, false_alarm = get_mean_precision(top_l_orig_matrix,
top_l_predict_matrix)
print "iteration"
print i
print "hit rate"
print hit_rate
hit_rate_list.append(hit_rate)
false_alarm_list.append(false_alarm)
# Plot false alarm rate vs hit-rate
# Que 4 part B
lambda_values = [0.01]
r, w = utility.get_R()
k = 10
truePos = 0;
falsePos = 0;
falseNeg = 0;
precisionArray = []
recallArray = []
rows,cols = r.shape
threshold_ranges = [x/10.0 for x in range(0, 50, 5)]
# Run Regularized ALS for different values of lambda
for lambda_val in lambda_values:
uv = utility.weightedRegALS(r, lambda_val, k, w, 20)
for threshold in threshold_ranges:
for i in range(len(r)):
for j in range(len(r[i])):
if(uv[i, j] >= threshold):
if(r[i, j] >= threshold):
truePos += 1
else: falsePos += 1
else:
if(r[i, j] >= threshold):
#if(uv[i, j] < threshold):
falseNeg += 1
print 'Precision for threshold: %f' %threshold
pre = float (truePos) / float (truePos + falsePos)
print pre
# Que 5 part B
# hit-rate is assumed to be precision
# failure rate is total - precision
r, w = utility.get_R()
L = 5
lambda_val = 0.1
k = 100
n_iter = 20
hit_rate_list = []
false_alarm_list = []
recall_list = []
# replace this with RegALS( )
#u, v = utility.nmf(w, K_VALUE, r) # Swap R & W
#uv = numpy.dot(u, v)
uv = utility.weightedRegALS(w, lambda_val, k, r, n_iter)
for i in range(1, L+1):
top_l_orig_matrix = gettopN(r, i, w)
top_l_predict_matrix = gettopN(uv, i, w)
hit_rate, false_alarm = get_mean_precision(top_l_orig_matrix, top_l_predict_matrix)
print "iteration"
print i
print "hit rate"
print hit_rate
hit_rate_list.append(hit_rate)
false_alarm_list.append(false_alarm)
# Plot false alarm rate vs hit-rate
