def find_best_label_regression(df_old, col, y):
least_sq = {}
sarray = []
df = df_old.copy()
sorted = df.sort([col]).reset_index(drop=True)
# sorted.reset_index(drop=True)
# for row in enumerate(df.sort([col])):
# sarray.append(row)
# sorted = pd.DataFrame(sarray)
# sorted.columns = df.columns
print "Print reset index"
print df[y][0:10]
print sorted[y][0:10]
# sys.exit()
i = 0
print "Finding label for " + col
for _, row in sorted.iterrows():
i += 1
if i == 1 or i > len(sorted) - 1:
continue
# print 'i:' + str(i)
# print list(sorted[y])[i:len(sorted[y])]
# print 'ls {} + {}'.format(len(list(sorted[y])[0:i]), len(list(sorted[y])[i:len(sorted[y])]))
lsq = mystats.least_squares(list(sorted[y])[0:i]) + mystats.least_squares(list(sorted[y])[i : len(sorted[y])])
least_sq[row[col]] = lsq
# print 'ls {} + {}'.format(str(least_squares(list(sorted[y])[0:i])), least_squares(list(sorted[y])[i:len(sorted[y])]))
return min(least_sq, key=lambda k: least_sq[k])
def compute_info_gain_regression(df, feature, split, y):
A = df[[feature, y]]
# series = [split for x in range(0, len(A[feature]))]
# print series
mask = A[feature] <= split
B = A[mask]
C = A[~mask]
info_gain = mystats.least_squares(A, y) - mystats.least_squares(B, y) - mystats.least_squares(C, y)
print "Information Gain: %s" % info_gain
return info_gain
