def deep_iv_fit(x, z, t, y, epochs=100, hidden=[128, 64, 32]):
from deepiv.models import Treatment, Response
import deepiv.architectures as architectures
import deepiv.densities as densities
from keras.layers import Input, Dense
from keras.models import Model
from keras.layers.merge import Concatenate
n = z.shape[0]
dropout_rate = min(1000. / (1000. + n), 0.5)
batch_size = 100
images = False
act = "relu"
n_components = 10
instruments = Input(shape=(z.shape[1], ), name="instruments")
features = Input(shape=(x.shape[1], ), name="features")
treatment_input = Concatenate(axis=1)([instruments, features])
est_treat = architectures.feed_forward_net(
treatment_input,
lambda x: densities.mixture_of_gaussian_output(x, n_components),
hidden_layers=hidden,
dropout_rate=dropout_rate,
l2=0.0001,
activations=act)
treatment_model = Treatment(inputs=[instruments, features],
outputs=est_treat)
treatment_model.compile('adam',
loss="mixture_of_gaussians",
n_components=n_components)
treatment_model.fit([z, x], t, epochs=epochs, batch_size=batch_size)
# Build and fit response model
treatment = Input(shape=(t.shape[1], ), name="treatment")
response_input = Concatenate(axis=1)([features, treatment])
est_response = architectures.feed_forward_net(response_input,
Dense(1),
activations=act,
hidden_layers=hidden,
l2=0.001,
dropout_rate=dropout_rate)
response_model = Response(treatment=treatment_model,
inputs=[features, treatment],
outputs=est_response)
response_model.compile('adam', loss='mse')
response_model.fit([z, x],
y,
epochs=epochs,
verbose=1,
batch_size=batch_size,
samples_per_batch=2)
return response_model
't': t.shape,
'y': y.shape
}))
# Build and fit treatment model
if K.image_data_format() == "channels_first":
image_shape = (1, 28, 28)
else:
image_shape = (28, 28, 1)
images = Input(shape=(28 * 28, ), name='treat_images')
image_reshaped = Reshape(image_shape)(images) # reshape
time = Input(shape=(1, ), name='treat_time')
instruments = Input(shape=(z.shape[1], ), name='treat_instruments')
mix_gaussian_output = lambda x: densities.mixture_of_gaussian_output(x, 10)
treatment_output = conv_embedding(image_reshaped,
mix_gaussian_output, [time, instruments],
dropout_rate=dropout_rate,
embedding_dropout=embedding_dropout,
embedding_l2=embedding_l2)
treatment_model = Treatment(inputs=[instruments, time, images],
outputs=treatment_output)
treatment_model.compile('adam', loss="mixture_of_gaussians", n_components=10)
treatment_model.fit([z, x[:, 0:1], x[:, 1:]],
t,
epochs=epochs,
batch_size=batch_size)
Treament:{t},\n\
Response:{y}".format(**{'x':x.shape, 'z':z.shape,
't':t.shape, 'y':y.shape}))
# Build and fit treatment model
instruments = Input(shape=(z.shape[1],), name="instruments")
features = Input(shape=(x.shape[1],), name="features")
treatment_input = Concatenate(axis=1)([instruments, features])
hidden = [128, 64, 32]
act = "relu"
n_components = 10
est_treat = architectures.feed_forward_net(treatment_input, lambda x: densities.mixture_of_gaussian_output(x, n_components),
hidden_layers=hidden,
dropout_rate=dropout_rate, l2=0.0001,
activations=act)
treatment_model = Treatment(inputs=[instruments, features], outputs=est_treat)
treatment_model.compile('adam',
loss="mixture_of_gaussians",
n_components=n_components)
treatment_model.fit([z, x], t, epochs=epochs, batch_size=batch_size)
# Build and fit response model
treatment = Input(shape=(t.shape[1],), name="treatment")
response_input = Concatenate(axis=1)([features, treatment])
for j in range(n_trials):
x, z, t, y, g_true = datafunction(n, 1)
print('N={}, DeepIV Trial: %{}\n'.format(n, j / n_trials * 100))
# print("Data shapes:\n\
# Features:{x},\n\
# Instruments:{z},\n\
# Treament:{t},\n\
# Response:{y}".format(**{'x':x.shape, 'z':z.shape,
# 't':t.shape, 'y':y.shape}))
# Build and fit treatment model
instruments = Input(shape=(z.shape[1], ), name="instruments")
features = Input(shape=(x.shape[1], ), name="features")
treatment_input = Concatenate(axis=1)([instruments, features])
est_treat = architectures.feed_forward_net(
treatment_input,
lambda x: densities.mixture_of_gaussian_output(x, n_components),
hidden_layers=hidden,
dropout_rate=dropout_rate,
l2=0.0001,
activations=act)
treatment_model = Treatment(inputs=[instruments, features],
outputs=est_treat)
treatment_model.compile('adam',
loss="mixture_of_gaussians",
n_components=n_components)
treatment_model.fit([z, x],
t,
epochs=epochs,
batch_size=batch_size,
verbose=False)