def test_deepiv_shape(self):
"""Make sure that arbitrary sizes for t, z, x, and y don't break the basic operations."""
for _ in range(5):
d_t = np.random.choice(range(1, 4)) # number of treatments
d_z = np.random.choice(range(1, 4)) # number of instruments
d_x = np.random.choice(range(1, 4)) # number of features
d_y = np.random.choice(range(1, 4)) # number of responses
n = 500
# simple DGP only for illustration
x = np.random.uniform(size=(n, d_x))
z = np.random.uniform(size=(n, d_z))
p_x_t = np.random.uniform(size=(d_x, d_t))
p_z_t = np.random.uniform(size=(d_z, d_t))
t = x @ p_x_t + z @ p_z_t
p_xt_y = np.random.uniform(size=(d_x * d_t, d_y))
y = (x.reshape(n, -1, 1) * t.reshape(n, 1, -1)).reshape(n, -1) @ p_xt_y
# Define the treatment model neural network architecture
# This will take the concatenation of one-dimensional values z and x as input,
# so the input shape is (d_z + d_x,)
# The exact shape of the final layer is not critical because the Deep IV framework will
# add extra layers on top for the mixture density network
treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(d_z + d_x,)),
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17)])
# Define the response model neural network architecture
# This will take the concatenation of one-dimensional values t and x as input,
# so the input shape is (d_t + d_x,)
# The output should match the shape of y, so it must have shape (d_y,) in this case
# NOTE: For the response model, it is important to define the model *outside*
# of the lambda passed to the DeepIvEstimator, as we do here,
# so that the same weights will be reused in each instantiation
response_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(d_t + d_x,)),
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(d_y)])
deepIv = DeepIVEstimator(n_components=10, # number of gaussians in our mixture density network
m=lambda z, x: treatment_model(
keras.layers.concatenate([z, x])), # treatment model
h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # response model
n_samples=1, # number of samples to use to estimate the response
use_upper_bound_loss=False, # whether to use an approximation to the true loss
# number of samples to use in second estimate of the response
# (to make loss estimate unbiased)
n_gradient_samples=1,
# Keras optimizer to use for training - see https://keras.io/optimizers/
optimizer='adam')
deepIv.fit(Y=y, T=t, X=x, Z=z)
# do something with predictions...
deepIv.predict(T=t, X=x)
deepIv.effect(x, np.zeros_like(t), t)
def test_deepiv(self):
X = TestPandasIntegration.df[TestPandasIntegration.features]
Y = TestPandasIntegration.df[TestPandasIntegration.outcome]
T = TestPandasIntegration.df[TestPandasIntegration.cont_treat]
Z = TestPandasIntegration.df[TestPandasIntegration.instrument]
# Test DeepIV
treatment_model = keras.Sequential([
keras.layers.Dense(128, activation='relu', input_shape=(3, )),
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17)
])
response_model = keras.Sequential([
keras.layers.Dense(128, activation='relu', input_shape=(3, )),
keras.layers.Dropout(0.17),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.17),
keras.layers.Dense(1)
])
est = DeepIVEstimator(
n_components=
10, # Number of gaussians in the mixture density networks)
m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])
), # Treatment model
h=lambda t, x: response_model(keras.layers.concatenate([t, x])
), # Response model
n_samples=1 # Number of samples used to estimate the response
)
est.fit(Y, T, X=X, Z=Z)
treatment_effects = est.effect(X)