def observation_space():
"""Return observation space.
The state is (susceptible, exposed, infected, recovered).
"""
state_dim = State.num_variables()
state_space_low = np.zeros(state_dim)
state_space_high = np.inf * np.ones(state_dim)
return spaces.Box(state_space_low, state_space_high, dtype=np.float64)
def credit_action_space(initial_state):
"""Return action space for credit simulator.
The action space is the vector of possible logistic regression
parameters, which depends on the dimensionality of the features.
"""
num_features = initial_state.features.shape[1]
return spaces.Box(low=-np.inf,
high=np.inf,
shape=(num_features, ),
dtype=np.float64)
def action_space():
"""Return action space.
There are four control variables in the model:
- Treated bednet use
- Condom use
- Direct treatment of infected humans
- Indoor residual spray use.
"""
return spaces.Box(np.zeros(4), np.ones(4), dtype=np.float64)
def observation_space():
"""Return observation space.
The state is (uninfected_T1, infected_T1, uninfected_T2, infected_T2,
free_virus, immune_response) in units (cells/ml, cells/ml, cells/ml,
cells/ml, copies/ml, cells/ml).
"""
state_dim = State.num_variables()
state_space_low = np.zeros(state_dim)
state_space_high = np.inf * np.ones(state_dim)
return spaces.Box(state_space_low, state_space_high, dtype=np.float64)
def credit_observation_space(initial_state):
"""Return observation space for credit simulator.
The observation space is the vector of possible datasets, which
must have the same dimensions as the initial state.
"""
return spaces.Dict({
"features":
spaces.Box(
low=-np.inf,
high=np.inf,
shape=initial_state.features.shape,
dtype=np.float64,
),
"labels":
spaces.Box(
low=-np.inf,
high=np.inf,
shape=initial_state.labels.shape,
dtype=np.float64,
),
})
def observation_space():
"""Return the model observation space."""
num_states = State.num_variables()
state_space_low = np.zeros(num_states)
state_space_high = np.inf * np.ones(num_states)
return spaces.Box(state_space_low, state_space_high, dtype=np.float64)
def compute_intervention(action, time):
"""Return intervention that changes the classifier parameters to action."""
return Intervention(time=time, theta=action)
CreditEnv = ODEEnvBuilder(
simulate_fn=simulate,
config=Config(),
# The initial state is the baseline features and labels in the credit dataset
initial_state=State(features=CreditData.features,
labels=CreditData.labels),
# Action space is classifiers with the same number of parameters are
# features.
action_space=spaces.Box(low=-np.inf,
high=np.inf,
shape=(CreditData.num_features, ),
dtype=np.float64),
# Observation space is the strategically adapted features and labels
observation_space=spaces.Dict({
"features":
spaces.Box(
low=-np.inf,
high=np.inf,
shape=CreditData.features.shape,
dtype=np.float64,
),
"labels":
spaces.Box(
low=-np.inf,
high=np.inf,
shape=CreditData.labels.shape,
def observation_space():
"""Return the observation space. The state is (nonmedical_users, oud_useres, illicit_users)."""
state_dim = State.num_variables()
state_space_low = np.zeros(state_dim)
state_space_high = np.inf * np.ones(state_dim)
return spaces.Box(state_space_low, state_space_high, dtype=np.float64)
def observation_space():
"""Return observation space, the positive orthant."""
state_dim = State.num_variables()
state_space_low = np.zeros(state_dim)
state_space_high = np.inf * np.ones(state_dim)
return spaces.Box(state_space_low, state_space_high, dtype=np.float64)
