def screens_budget_summer_lglglg():
mat,key,regy,_ = rs.extract_feats([rs.metadata_feats])
screen_ind = key['number_of_screens']
budget_ind = key['production_budget']
summer_ind = key['summer_release']
screens = mat.getcol(screen_ind).todense()
budget = mat.getcol(budget_ind).todense()
summer = mat.getcol(summer_ind).todense()
def safelog(x):
if x <= 0.:
return 0.
else:
return math.log(x)
fns = [safelog, safelog, safelog, safelog]
bs_check = lambda x:x[1] > 0. and x[2] > 0.
bns_check = lambda x:x[1] > 0. and x[2] == 0.
nbs_check = lambda x:x[1] == 0. and x[2] > 0.
nbns_check = lambda x:x[1] == 0. and x[2] == 0.
bs_arr = format_arr([screens,budget,summer], regy, fns, bs_check)
bns_arr = format_arr([screens,budget,summer], regy, fns, bns_check)
nbs_arr = format_arr([screens,budget,summer], regy, fns, nbs_check)
nbns_arr = format_arr([screens,budget,summer], regy, fns, nbns_check)
budget_basis_fns = [lambda x:1, lambda x:x[0], lambda x:x[0]**2,
lambda x:x[1], lambda x:x[1]**2]
no_budget_basis_fns = [lambda x:1, lambda x:x[0], lambda x:x[0]**2]
bs_coeffs = freg.coeffs(budget_basis_fns, bs_arr)
bns_coeffs = freg.coeffs(budget_basis_fns, bns_arr)
nbs_coeffs = freg.coeffs(no_budget_basis_fns, nbs_arr)
nbns_coeffs = freg.coeffs(no_budget_basis_fns, nbns_arr)
test,_,_,ids = rs.extract_feats([rs.metadata_feats],'testcases.xml',
global_feat_dict = key)
test_len = test.shape[0]
preds = []
for i in range(test_len):
prod = 0
x = [test[i,screen_ind], test[i,budget_ind], test[i,summer_ind]]
logx = tuple([safelog(feat) for feat in x])
if bs_check(x):
prod = freg.product(logx, bs_coeffs, budget_basis_fns)
elif bns_check(x):
prod = freg.product(logx, bns_coeffs, budget_basis_fns)
elif nbs_check(x):
prod = freg.product(logx, nbs_coeffs, no_budget_basis_fns)
elif nbns_check(x):
prod = freg.product(logx, nbns_coeffs, no_budget_basis_fns)
if prod < 0:
prod = 0
preds.append(math.e**prod)
util.write_predictions(preds, ids, 'screens_budget_summer_lglglg-2.csv')
def screens_budget_lglglg():
mat,key,regy,_ = rs.extract_feats([rs.metadata_feats])
screen_ind = key['number_of_screens']
budget_ind = key['production_budget']
screens = mat.getcol(screen_ind).todense()
budget = mat.getcol(budget_ind).todense()
budget_fns = [lambda x:math.log(x) for i in range(3)]
budget_check = lambda x:x[1] > 0.
budget_arr = format_arr([screens,budget], regy, budget_fns, budget_check)
no_budget_arr = format_arr([screens],regy,[lambda x:math.log(x),lambda x:math.log(x)])
budget_basis_fns = [lambda x:1, lambda x:x[0], lambda x:x[0]**2,
lambda x:x[1], lambda x:x[1]**2]
no_budget_basis_fns = [lambda x:1, lambda x:x[0], lambda x:x[0]**2]
budget_coeffs = freg.coeffs(budget_basis_fns, budget_arr)
no_budget_coeffs = freg.coeffs(no_budget_basis_fns, no_budget_arr)
test,_,_,ids = rs.extract_feats([rs.metadata_feats],'testcases.xml',
global_feat_dict = key)
test_len = test.shape[0]
preds = []
for i in range(test_len):
prod = 0
if test[i,budget_ind] > 0.:
x = (budget_fns[0](test[i,screen_ind]),
budget_fns[1](test[i,budget_ind]))
prod = freg.product(x, budget_coeffs, budget_basis_fns)
else:
x = (math.log(test[i,screen_ind]),)
prod = freg.product(x, no_budget_coeffs, no_budget_basis_fns)
if prod < 0:
prod = 0
preds.append(math.e**prod)
util.write_predictions(preds, ids, 'screens_budget_lglglg-2.csv')
def screens(basis_fns, fns, inv_fn, outfile):
mat,key,regy,_ = rs.extract_feats([rs.metadata_feats])
screen_ind = key['number_of_screens']
screens = mat.getcol(screen_ind).todense()
train_arr = format_arr([screens], regy, fns)
coeffs = freg.coeffs(basis_fns, train_arr)
test,_,_,ids = rs.extract_feats([rs.metadata_feats],'testcases.xml',
global_feat_dict = key)
test_len = test.shape[0]
preds = []
for i in range(test_len):
prod = freg.product((fns[0](test[i,screen_ind]),), coeffs, basis_fns)
if prod < 0:
prod = 0
preds.append(inv_fn(prod))
util.write_predictions(preds, ids, outfile)
