def main():
im = Importer()
train = im.get_training_set()
test = im.get_test_set()
logis = linear_model.LogisticRegression()
for i in range(100):
mu1 = train['mu1'][:, i]
mu0 = train['mu0'][:, i]
yf = train['yf'][:, i]
ycf = train['ycf'][:, i]
t = train['t'][:, i]
x = train['x'][:, :, i]
#x = x[:, 0][:, np.newaxis]
logis.fit(x, t)
mu1_test = test['mu1'][:, i]
mu0_test = test['mu0'][:, i]
yf_test = test['yf'][:, i]
ycf_test = test['ycf'][:, i]
t_test = test['t'][:, i]
x_test = test['x'][:, :, i]
mask = []
for num in t_test:
if num == 0:
mask.append(False)
else:
mask.append(True)
#x_test = x_test[:, 0][:, np.newaxis]
ptx = logis.predict_proba(x_test)
pt1 = np.sum(t_test)/len(t_test)
pt0 = 1 - pt1
ptx1 = ptx[mask]
ptx0 = ptx[np.invert(mask)]
#print('pt', pt1, pt0)
#print('ptx', ptx1, ptx0)
w1 = pt1/ptx1[:, 1]
w0 = pt0/ptx0[:, 0]
#print('w', w1, w0)
yw1 = yf_test[mask]*w1
yw0 = yf_test[np.invert(mask)]*w0
#print('yf', yf_test)
#print('yw', yw1, yw0)
avg_yw1 = np.sum(yw1)/len(yw1)
avg_yw0 = np.sum(yw0)/len(yw0)
#print(avg_yw1, avg_yw0)
print(avg_yw1 - avg_yw0)
from Importer import Importer
im = Importer()
for t in im.get_test_set()['t']:
print(t)
from Importer import Importer
from sklearn import linear_model
import numpy as np
im = Importer()
train = im.get_training_set()
test = im.get_test_set()
for i in range(1):
mu1 = train['mu1'][:, i]
mu0 = train['mu0'][:, i]
yf = train['yf'][:, i]
ycf = train['ycf'][:, i]
t = train['t'][:, i]
x = train['x'][:, :, i]
mask = []
for num in t:
if num == 0:
mask.append(False)
else:
mask.append(True)
lm_t = linear_model.LinearRegression()
model_t = lm_t.fit(x[mask], yf[mask])
lm_nt = linear_model.LinearRegression()
model_nt = lm_nt.fit(x[np.invert(mask)], yf[np.invert(mask)])
te = sum(lm_t.predict(test['x'][:, :, i])) / len(test['x'][:, :, i])
nte = sum(lm_nt.predict(test['x'][:, :, i])) / len(test['x'][:, :, i])
