def L1_L2_const_obj_func (x):
n_calls[0] += 1
t1 = time.time();
score = SC.CV_score(X = X_control,
Y = Y_pre_control,
X_treat = X_treated,
Y_treat = Y_pre_treated,
# if v_pen is a single value, we get a single score, If it's an array of values, we get an array of scores.
v_pen = best_L1_penalty_ct * np.exp(x[0]),
w_pen = w_pen_start_ct / np.exp(x[0]),
# suppress the analysis type message
quiet = True)
t2 = time.time();
temp_results.append((n_calls[0],x,score))
print("calls: %s, time: %0.4f, x0: %0.4f, Cross Validation Error: %s, out-of-sample R-Squared: %s" % (n_calls[0], t2 - t1, x[0], score, 1 - score / SS ))
#print("calls: %s, time: %0.4f, x0: %0.4f, x1: %0.4f, Cross Validation Error: %s, R-Squared: %s" % (n_calls[0], t2 - t1, x[0], x[1], score, 1 - score / SS ))
return score
# ------------------------------------------------------------
if False:
print("starting grid scoring for treat / control scenario", grid*L1_max_ct)
grid_scores_ct = SC.CV_score(
X = X_control,
Y = Y_pre_control,
X_treat = X_treated,
Y_treat = Y_pre_treated,
# if v_pen is a single value, we get a single score, If it's an array of values, we get an array of scores.
v_pen = grid * L1_max_ct,
w_pen = w_pen_start_ct,
# CACHE THE V MATRIX BETWEEN v_pen PARAMETERS (generally faster, but path dependent)
cache = False, # False by Default
# Run each of the Cross-validation folds in parallel? Often slower
# for large sample sizes because numpy.linalg.solve() already runs
# in parallel for large matrices
parallel=False,
# ANNOUNCE COMPLETION OF EACH ITERATION
progress = True)
best_L1_penalty_ct = (grid * L1_max_ct)[np.argmin(grid_scores_ct)]
if False: