def boot_sim(results, preds, nboot, acc):
loss = []
for nb in range(nboot):
n_shuffle = np.round(len(results) * (1 - acc)).astype("int")
np.random.shuffle(results)
results = results.astype("bool")
# results[:n_shuffle] = ~results[:n_shuffle]
results[
(len(results) - np.ceil(n_shuffle / 2).astype("int")) : (
len(results) + np.ceil(n_shuffle / 2).astype("int")
)
] = ~results[
(len(results) - np.ceil(n_shuffle / 2).astype("int")) : (
len(results) + np.ceil(n_shuffle / 2).astype("int")
)
]
results = results.astype("int")
loss.append(loss_score(results, preds))
return np.array(loss)
from tkFileDialog import askopenfilename as uiopen
import sklearn.preprocessing as pp
import cPickle as p
from scipy.stats import logistic
import scikits.bootstrap as boot
load_filename = "C:\Users\pnlawlor\Dropbox\Data\comb_pracPredsResults.csv"
temp = pandas.io.parsers.read_csv(load_filename, header=None, index_col=0)
completed_games = ~temp[3].isnull()
results = temp.ix[completed_games, 3]
conservative_preds = temp.ix[completed_games, 1]
aggressive_preds = temp.ix[completed_games, 2]
conservative_score = loss_score(results, conservative_preds)
# conservative_score_CI = boot.ci([results, conservative_preds],loss_score)
aggressive_score = loss_score(results, aggressive_preds)
print "Conservative score: %s" % conservative_score
print "Aggressive score: %s" % aggressive_score
simulate = False
def boot_sim(results, preds, nboot, acc):
loss = []
for nb in range(nboot):
n_shuffle = np.round(len(results) * (1 - acc)).astype("int")
np.random.shuffle(results)