Semiparametrics Notebook AGMM
import os
import numpy as np
import pandas as pd
import mliv.dgps_nested as dgps
import mliv.dgps_mediated as dgps
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from sklearn.cluster import KMeans
#from mliv.neuralnet.deepiv_fit import deep_iv_fit
from mliv.neuralnet.rbflayer import gaussian, inverse_multiquadric
from mliv.neuralnet import AGMM, AGMM2, AGMM2L2
from mliv.tsls import tsls, regtsls
# Now you can import the module
from dml_mediated import DML_mediated
p = 0.1 # dropout prob of dropout layers throughout notebook
n_hidden = 100 # width of hidden layers throughout notebook
device = torch.cuda.current_device() if torch.cuda.is_available() else None
fn_number = 0
tau_fn = dgps.get_tau_fn(fn_number)
tauinv_fn = dgps.get_tauinv_fn(fn_number)
W, Z, X, M, D, Y, tau_fn = dgps.get_data(2000, tau_fn)
V = np.random.rand(Y.shape[0])
V = V.reshape(-1, 1)
print(np.column_stack((W,X,Z)).shape)
ind = np.where(D==0)[0]
W0 = W[ind]
X0 = X[ind,:]
W0_test = np.zeros((1000, 1+X.shape[1]))
W0_test += np.median(np.column_stack((X0,W0)), axis=0, keepdims=True)
W0_test[:, 2] = np.linspace(np.percentile(
W0[:, 0], 5), np.percentile(W0[:, 0], 95), 1000)
# True parameters
b_yd = 2.0; b_ym = 1.0; b_yx = np.array([[-1.0],[-1.0]]); b_yu = -1.0; b_yw = 2.0; b_y0 = 2.0
b_wx = np.array([[0.2],[0.2]]); b_wu = -0.6; b_w0 = 0.3
b_md = -0.3; b_mx = np.array([[-0.5],[-0.5]]); b_mu = 0.4; b_m0 = 0.0
gamma_1w = (b_yw*b_wu + b_yu)/b_wu
gamma_1x = b_yw*b_wx + b_yx - gamma_1w*b_wx
gamma_1m = b_ym
gamma_10 = b_y0 + b_yd + b_yw*b_w0 - gamma_1w*b_w0
gamma_0w = (gamma_1m*b_mu + gamma_1w*b_wu)/b_wu
gamma_0x = gamma_1m*b_mx + gamma_1w*b_wx + gamma_1x - gamma_0w*b_wx
gamma_00 = gamma_10 + gamma_1m*b_m0 + gamma_1w*b_w0 - gamma_0w*b_w0
# True nuisance function
expected_te = gamma_00 + tauinv_fn(W0_test)@np.row_stack((gamma_0x, gamma_0w))
D_ = D.copy()
(2000, 4)
def _get_learner(n_t):
return nn.Sequential(nn.Dropout(p=p), nn.Linear(n_t, n_hidden), nn.LeakyReLU(),
nn.Dropout(p=p), nn.Linear(n_hidden, 1))
def _get_adversary(n_z):
return nn.Sequential(nn.Dropout(p=p), nn.Linear(n_z, n_hidden), nn.LeakyReLU(),
nn.Dropout(p=p), nn.Linear(n_hidden, 1))
agmm_1 = AGMM(_get_learner(4),_get_adversary(4))
agmm_2 = AGMM(_get_learner(3),_get_adversary(3))
dml_agmm = DML_mediated(Y, D, M, W, Z, X,
estimator='OR',
estimand='E[Y(1,M(0))]',
model1 = agmm_1,
model2 = agmm_2,
modelq1 = agmm_2,
modelq2 = agmm_1,
n_folds=5, n_rep=1,
CHIM = False,
nn_1 = True,
nn_2 = True,
nn_q1 = True,
nn_q2 = True,
fitargs1 = {'n_epochs': 300, 'bs': 100, 'learner_lr': 1e-4, 'adversary_lr': 1e-4, 'learner_l2': 1e-3, 'adversary_l2': 1e-4, 'adversary_norm_reg' : 1e-3},
fitargs2 = {'n_epochs': 300, 'bs': 100, 'learner_lr': 1e-4, 'adversary_lr': 1e-4, 'learner_l2': 1e-3, 'adversary_l2': 1e-4},
fitargsq1 = {'n_epochs': 300, 'bs': 100, 'learner_lr': 1e-4, 'adversary_lr': 1e-4, 'learner_l2': 1e-3, 'adversary_l2': 1e-4},
fitargsq2 = {'n_epochs': 300, 'bs': 100, 'learner_lr': 1e-4, 'adversary_lr': 1e-4, 'learner_l2': 1e-3, 'adversary_l2': 1e-4},
opts = {'lin_degree': 1, 'burnin': 200})
print(dml_agmm.dml())
Rep: 1
80%|████████ | 4/5 [05:21<00:47, 47.79s/it] Exception ignored in: <finalize object at 0x227ec129d00; dead>
Traceback (most recent call last):
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libweakref.py", line 591, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libtempfile.py", line 820, in _cleanup
cls._rmtree(name)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libtempfile.py", line 816, in _rmtree
_shutil.rmtree(name, onerror=onerror)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libshutil.py", line 759, in rmtree
return _rmtree_unsafe(path, onerror)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libshutil.py", line 629, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libtempfile.py", line 808, in onerror
cls._rmtree(path)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libtempfile.py", line 816, in _rmtree
_shutil.rmtree(name, onerror=onerror)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libshutil.py", line 759, in rmtree
return _rmtree_unsafe(path, onerror)
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libshutil.py", line 610, in _rmtree_unsafe
onerror(os.scandir, path, sys.exc_info())
File "C:Program FilesWindowsAppsPythonSoftwareFoundation.Python.3.9_3.9.3568.0_x64__qbz5n2kfra8p0libshutil.py", line 607, in _rmtree_unsafe
with os.scandir(path) as scandir_it:
NotADirectoryError: [WinError 267] The directory name is invalid: '.\tmp3q0ij066\epoch292'
100%|██████████| 5/5 [05:23<00:00, 64.69s/it]
(4.1756363, 2.2139876, array([4.1104255, 4.240847 ], dtype=float32))
fitargs1 = {
'n_epochs': 300,
'bs': 100,
'learner_lr': 1e-4,
'adversary_lr': 1e-4,
'learner_l2': 1e-3,
'adversary_l2': 1e-4,
'adversary_norm_reg': 1e-3
}
fitargs2 = {
'n_epochs': 300,
'bs': 100,
'learner_lr': 1e-4,
'adversary_lr': 1e-4,
'learner_l2': 1e-3,
'adversary_l2': 1e-4
}
Y, D, M, W, X, Z = map(lambda x: torch.Tensor(x), [Y, D, M, W, X, Z])
ind = np.where(D == 1)[0]
M1 = M[ind]
W1 = W[ind]
X1 = X[ind, :]
Z1 = Z[ind]
Y1 = Y[ind]
A2 = torch.cat((M1, X1, Z1), 1)
A1 = torch.cat((M1, X1, W1), 1)
bridge_1 = agmm_1.fit(A2, A1, Y1, **fitargs1)
A1 = torch.cat((M, X, W), 1)
bridge_1_hat = torch.Tensor(bridge_1.predict(A1.to(device), model='avg', burn_in=200))
D, W, X, Z, bridge_1_hat = map(lambda x: torch.Tensor(x), [D, W, X, Z, bridge_1_hat])
ind = np.where(D == 0)[0]
W0 = W[ind]
X0 = X[ind, :]
Z0 = Z[ind]
bridge_1_hat = bridge_1_hat[ind]
B2 = torch.cat((X0, Z0), 1)
B1 = torch.cat((X0, W0), 1)
bridge_2 = agmm_2.fit(B2, B1, bridge_1_hat, **fitargs2)
gamma_0_hat = bridge_2.predict(torch.cat((X, W), 1).to(device), model='avg', burn_in=200)
print(np.mean(gamma_0_hat))
print(np.var(gamma_0_hat))
4.175998
5.155775
A = torch.tensor(np.column_stack((M,X,W)), dtype=torch.float32)
D = torch.tensor(D_, dtype=torch.float32)
E = torch.tensor(np.column_stack((M,X,Z)), dtype=torch.float32)
B = torch.tensor(np.column_stack((X,W)), dtype=torch.float32)
C = torch.tensor(np.column_stack((X,Z)), dtype=torch.float32)
fitargs = {
'n_epochs': 100,
'bs': 100,
'learner_lr': 0.001,
'adversary_lr': 0.001,
'learner_l2': 1e-3,
'adversary_l2': 1e-4
}
agmm2_model = AGMM2(learnerh = _get_learner(B.shape[1]), learnerg = _get_learner(A.shape[1]),
adversary1 = _get_adversary(E.shape[1]), adversary2 = _get_adversary(C.shape[1]))
agmm2_pred_b, agmm2_pred_a = agmm2_model.fit(A, B, C, E, Y, subsetted=True, subset_ind1=D, **fitargs).predict(B.to(device), A.to(device), model='avg', burn_in=10)
print(np.mean(agmm2_pred_b))
print(np.var(agmm2_pred_b))
3.7640064
10.023633