def _generate(self, force_calc):
pro_file_name = PRO_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._pro = read_input_file(pro_file_name)
if not len(self._pro):
for k in self._k:
if k[TYPE_COL] == TYPE_1_COL:
cl_data = self._b1
else:
cl_data = self._a2
calc_lo_data, lo_pos = Pro.get_data_for_pro(
k[NAME_LO_COL], LO_P_RANGE, cl_data)
calc_vi_data, vi_pos = Pro.get_data_for_pro(
k[NAME_VI_COL], VI_P_RANGE, cl_data)
self._pro.append(combine_lo_vi(calc_lo_data, calc_vi_data))
if self.generated:
save_data_to_csv(pro_file_name, self._pro)
def _generate(self, force_calc):
pro_file_name = PRO_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._pro = read_input_file(pro_file_name)
if not len(self._pro):
for k in self._k:
if k[TYPE_COL] == TYPE_1_COL:
cl_data = self._b1
else:
cl_data = self._a2
calc_lo_data, lo_pos = Pro.get_data_for_pro(k[NAME_LO_COL],
LO_P_RANGE,
cl_data)
calc_vi_data, vi_pos = Pro.get_data_for_pro(k[NAME_VI_COL],
VI_P_RANGE,
cl_data)
self._pro.append(combine_lo_vi(calc_lo_data, calc_vi_data))
if self.generated:
save_data_to_csv(pro_file_name, self._pro)
def calculate(self):
avpos = []
# Compile files with cl.
self._compile_cl()
# Process the files sorted by name.
files_sorted = sorted(self._cl_files_order)
for fs in files_sorted:
idx = self._cl_files_order.index(fs)
clda = ClDat(NO_READ_INDEX)
full_file_name = os.path.join(self._dir, self._cl_files[idx])
clda.read_cldata(full_file_name)
avpos = self._add_cl(avpos, clda.b1)
avpos = self._add_cl(avpos, clda.a2)
save_data_to_csv(AVPOS_FILE, avpos)
for a in avpos:
self._avpos.update({a[0]: a[1:]})
def generate_final_hist(index, b1_hist, a2_hist, k, pro_data, pre_data, p_data, cldat):
hist = b1_hist + a2_hist
for i, k_elt in enumerate(k):
if k_elt[K_TYPE_COL] == TYPE_1_COL:
cl = cldat.b1
else:
cl = cldat.a2
new_row = [int(k_elt[TYPE_COL]), '']
if HIST_TYPE:
new_row.extend(pro_data[i])
new_row.extend(pre_data[i])
new_row.extend(p_data[i])
else:
cl_lo = get_cl_data_for_name(k_elt[NAME_LO_COL], cl)
new_row.extend([cl_lo[i] for i in LO_D_RANGE])
cl_vi = get_cl_data_for_name(k_elt[NAME_VI_COL], cl)
new_row.extend([cl_vi[i] for i in VI_D_RANGE])
new_row.extend([k_elt[NAME_LO_COL], k_elt[NAME_VI_COL]])
hist.append(new_row)
save_data_to_csv(AP_HIST_FILE, hist)
def main():
clda = ClDat()
if clda.loaded:
b1_res = read_res_file(B1_RES_FILE)
a2_res = read_res_file(A2_RES_FILE)
if len(b1_res) and len(a2_res):
mdls_b1 = evaluate_all_models(clda.b1, b1_res)
mdls_a2 = evaluate_all_models(clda.a2, a2_res)
save_data_to_csv(MODELS_FILENAME, mdls_b1 + mdls_a2)
else:
print "Res data couldn't be read."
else:
print "Cl data couldn't be loaded."
def _calculate(self, force_calc):
p_file_name = P_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._p = read_input_file(p_file_name)
if not len(self._p) or force_calc:
for i in range(NUM_ROWS):
p_value = PDat.calc_final_p(self._pro[i], self._pre[i],
self._k[i][TYPE_COL])
self._p.append(p_value)
if self.generated:
save_data_to_csv(p_file_name, self._p)
def _generate(self, force_calc):
pre_file_name = PRE_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._pre = read_input_file(pre_file_name)
if not len(self._pre):
b1_res = read_res_file(B1_RES_FILE)
a2_res = read_res_file(A2_RES_FILE)
for k in self._k:
if k[TYPE_COL] == TYPE_1_COL:
cl_data = self._b1
res_data = b1_res
else:
cl_data = self._a2
res_data = a2_res
lo_data, lo_target_data = Pre.get_data_for_pre(k[NAME_LO_COL],
cl_data,
res_data, True)
lo_mdl = Pre.get_mdl(k[NAME_LO_COL])
vi_data, vi_target_data = Pre.get_data_for_pre(k[NAME_VI_COL],
cl_data,
res_data, False)
vi_mdl = Pre.get_mdl(k[NAME_VI_COL])
print "Predicting: %s - %s" % (k[NAME_LO_COL], k[NAME_VI_COL])
lo_pre = self.get_pre_values(lo_data, lo_target_data,
lo_mdl.lo_mdls)
vi_pre = self.get_pre_values(vi_data, vi_target_data,
vi_mdl.vi_mdls)
self._pre.append(combine_lo_vi(lo_pre, vi_pre))
if self.generated:
save_data_to_csv(pre_file_name, self._pre)
def _calculate(self, force_calc):
p_file_name = P_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._p = read_input_file(p_file_name)
if not len(self._p) or force_calc:
for i in range(NUM_ROWS):
p_value = PDat.calc_final_p(self._pro[i],
self._pre[i],
self._k[i][TYPE_COL])
self._p.append(p_value)
if self.generated:
save_data_to_csv(p_file_name, self._p)
def gen_hist(res, cl, file_name, p_type):
print "Generating historical data for: %s" % file_name
hist = read_input_file(file_name)
j_res_max = max([ int(r[R_J_COL]) for r in res
if int(r[R_J_COL]) >= FIRST_ENTRY_HIST])
for j in range(FIRST_ENTRY_HIST, j_res_max + 1):
res_j = [ r for r in res if int(r[R_J_COL]) == j ]
res_data = [ r for r in res if int(r[R_J_COL]) < j ]
for r in res_j:
new_row = [int(p_type), r[R_M_COL]]
if HIST_TYPE:
pro_data = calc_pro_data(r, cl)
pre_data = calc_pre_data(r, cl, res_data)
p_data = PDat.calc_final_p(pro_data, pre_data, p_type)
new_row = [int(p_type), r[R_M_COL]]
new_row.extend(pro_data)
new_row.extend(pre_data)
new_row.extend(p_data)
new_row.extend([r[R_NAME_1_COL], r[R_NAME_2_COL]])
else:
cl_lo = get_cl_data_for_name(r[R_NAME_1_COL], cl)
new_row.extend([cl_lo[i] for i in LO_D_RANGE])
cl_vi = get_cl_data_for_name(r[R_NAME_2_COL], cl)
new_row.extend([cl_vi[i] for i in VI_D_RANGE])
new_row.extend([r[R_NAME_1_COL], r[R_NAME_2_COL]])
hist.append(new_row)
save_data_to_csv(file_name, hist)
return hist
def _generate(self, force_calc):
pre_file_name = PRE_FILE_NAME_PREFIX + self._index + INPUT_FILE_NAME_EXT
if not force_calc:
self._pre = read_input_file(pre_file_name)
if not len(self._pre):
b1_res = read_res_file(B1_RES_FILE)
a2_res = read_res_file(A2_RES_FILE)
for k in self._k:
if k[TYPE_COL] == TYPE_1_COL:
cl_data = self._b1
res_data = b1_res
else:
cl_data = self._a2
res_data = a2_res
lo_data, lo_target_data = Pre.get_data_for_pre(
k[NAME_LO_COL], cl_data, res_data, True)
lo_mdl = Pre.get_mdl(k[NAME_LO_COL])
vi_data, vi_target_data = Pre.get_data_for_pre(
k[NAME_VI_COL], cl_data, res_data, False)
vi_mdl = Pre.get_mdl(k[NAME_VI_COL])
print "Predicting: %s - %s" % (k[NAME_LO_COL], k[NAME_VI_COL])
lo_pre = self.get_pre_values(lo_data, lo_target_data,
lo_mdl.lo_mdls)
vi_pre = self.get_pre_values(vi_data, vi_target_data,
vi_mdl.vi_mdls)
self._pre.append(combine_lo_vi(lo_pre, vi_pre))
if self.generated:
save_data_to_csv(pre_file_name, self._pre)
def _save_mean(self):
output_file = MEAN_FILE_NAME_PREFIX + self._index + OUTPUT_FILE_NAME_EXT
save_data_to_csv(output_file, self._mean)
def _save_mean(self):
output_file = MEAN_FILE_NAME_PREFIX + self._index + OUTPUT_FILE_NAME_EXT
save_data_to_csv(output_file, self._mean)
