import utils
import predict
import time as tm
import numpy as np
# This file is intended to demonstrate how we would evaluate your code
# The data loader needs to know how many feature dimensions are there
dictSize = 225
(X, y) = utils.loadData("train", dictSize=dictSize)
# Get error class predictions from predict.py and time the thing
tic = tm.perf_counter()
yPred = predict.findErrorClass(X, 5)
toc = tm.perf_counter()
print("Total time taken is %.6f seconds " % (toc - tic))
preck = utils.getPrecAtK(y, yPred, 5)
# The macro precision code takes a bit longer to execute due to the for loop over labels
mpreck = utils.getMPrecAtK(y, yPred, 5)
# According to our definitions, both prec@k and mprec@k should go up as k goes up i.e. for your
# method, prec@i > prec@j if i > j and mprec@i > mprec@j if i > j. See the assignment description
# to convince yourself why this must be the case.
print("prec@1: %0.3f" % preck[0], "prec@3: %0.3f" % preck[2],
"prec@5: %0.3f" % preck[4])
# Dont be surprised if mprec is small -- it is hard to do well on rare error classes
print("mprec@1: %0.3e" % mpreck[0], "mprec@3: %0.3e" % mpreck[2],
"mprec@5: %0.3e" % mpreck[4])
import time as tm
import numpy as np
# This file is intended to demonstrate how we would evaluate your code
# The data loader needs to know how many feature dimensions and labels are there
d = 16385
L = 3400
(X, y) = utils.load_data("data", d, L)
# Get recommendations from predict.py and time the thing
tic = tm.perf_counter()
yPred = predict.getReco(X, 5)
toc = tm.perf_counter()
print("Total time taken is %0.6f seconds " % (toc - tic))
# Need to do a bit reshaping since what we get is technically a sparse matrix
preck = np.asarray(utils.getPrecAtK(y, yPred, 5)).reshape(-1)
# The macro precision code takes a bit longer to execute due to the for loop over labels
mpreck = np.asarray(utils.getMPrecAtK(y, yPred, 5)).reshape(-1)
# Warning: although it is always true that mprek@i > mprec@j if i > j
# the same is not true for prec@k -- prec@k may go up or down as k increases
# See the definition of mprec@k and prec@k carefully to convince yourself why this is so
print("prec@1: %0.3f" % preck[0], "prec@3: %0.3f" % preck[2],
"prec@5: %0.3f" % preck[4])
# Dont be surprised if mprec is very small -- it is really hard to do well on rare labels
print("mprec@1: %0.3e" % mpreck[0], "mprec@3: %0.3e" % mpreck[2],
"mprec@5: %0.3e" % mpreck[4])
sparse_matrix
# In[4]:
# arr_ll=sparse_matrix.tolil()
arr_ll = sparse_matrix
arr_ll.indices
# In[11]:
k = 1
tic = tm.perf_counter()
yPred = predict.getReco(arr_ll, k)
toc = tm.perf_counter()
print("time for prediction: ", toc - tic)
prec_k = utils.getPrecAtK(arr_ll, yPred, k)
print("prec@", k, ": ", prec_k)
mprec_k = utils.getMPrecAtK(arr_ll, yPred, k)
print("mprec@", k, ": ", mprec_k)
# In[12]:
k = 3
tic = tm.perf_counter()
yPred = predict.getReco(arr_ll, k)
toc = tm.perf_counter()
print("time for prediction: ", toc - tic)
prec_k = utils.getPrecAtK(arr_ll, yPred, k)
print("prec@", k, ": ", prec_k)
mprec_k = utils.getMPrecAtK(arr_ll, yPred, k)
print("mprec@", k, ": ", mprec_k)