def predictresult(models, traindatestart, traindateend, testdateend):
data = readdata()
don, need_train_data = usercluseter(data)
train_data, don_not_have_before = xandy(
need_train_data, traindatestart, traindateend, testdateend)
pymid_and_ys = testp(models, train_data)
ty = onetimeintveraldata(data, traindateend, testdateend)
a = pd.merge(pymid_and_ys[0], pymid_and_ys[1],
left_index=True, right_index=True, how="outer")
b = pd.merge(
a, pymid_and_ys[2], left_index=True, right_index=True, how="outer")
py = b.fillna(0)
pyy = ty.copy(deep=True)
print id(pyy), id(ty)
# print py.ix[:, :]
# sys, exit()
mindex = pyy["mid"].isin(py.index)
pyy.loc[
mindex, ["forward_count", "comment_count", "like_count"]] = py.values[3:6]
# print pyy.loc[~mindex, ["forward_count", "comment_count", "like_count"]]
pyy.loc[~mindex, ["forward_count", "comment_count", "like_count"]] = 0
f = pyy.values[:, 3:6]
t = ty.values[:, 3:6]
print f, t
print scores(f, t)
ty = onetimeintveraldata(data, traindateend, testdateend)
a = pd.merge(pymid_and_ys[0], pymid_and_ys[1],
left_index=True, right_index=True, how="outer")
b = pd.merge(
a, pymid_and_ys[2], left_index=True, right_index=True, how="outer")
py = b.fillna(0)
pyy = ty.copy(deep=True)
print id(pyy), id(ty)
# print py.ix[:, :]
# sys, exit()
mindex = pyy["mid"].isin(py.index)
pyy.loc[
mindex, ["forward_count", "comment_count", "like_count"]] = py.values[3:6]
# print pyy.loc[~mindex, ["forward_count", "comment_count", "like_count"]]
pyy.loc[~mindex, ["forward_count", "comment_count", "like_count"]] = 0
f = pyy.values[:, 3:6]
t = ty.values[:, 3:6]
print f, t
print scores(f, t)
# return pyy, ty
if __name__ == '__main__':
data = readdata()
# print type(data)
traindatestart, traindateend, testdateend = "2014-08-01", "2014-09-01", "2014-10-01"