# Copyright (c) 2020 Graphcore Ltd. All rights reserved.

# switch between IPU and CPU execution
use_ipu: true

# IPU replica
replication_factor: 2

# IPU convolution optimization
# Default is 0.6.
# Must be passed as a string.
# Try reducing this value if compilation results in memory error.
availableMemoryProportion: "0.5"

## debug flags
# profiling setup in case the IPU is used
gen_report : false
# test code without data transfer
synthetic: false
# observe XLA files
xla_dump: false
# dry run without IPU (sometimes helpful when execution report causes OOM error)
dry_run: false

# Data generation process parameters
n_samples: 1000  # dataset size
n_instruments: 1  # x-dimension
n_treatments: 1  # w-dimension
n_outcomes: 1  # y-dimension
# intervention strength
iv_strength: 0.5

# algorithm parameters
batch_size: 200  # for gradients of modeler and critic
batch_size_hedge: 1000  # for critic weight update
n_steps: 6000  # training iterations
iterations_per_loop: 1000  # number of batches per training loop

# modeler parameters
hidden_layers: [1000, 1000, 1000]
learning_rate_modeler: 0.01
l1_regularization: 0.1
l2_regularization: 0.1
# critics parameters
n_critics: 50  # number of Gaussian functions
learning_rate_hedge: 0.16   # weight hedge update learning rate
learning_rate_critic_gradient: 0.01
cluster_type: "kmeans"  # choose: kmeans, random_points, forest

# function choices
rho: "regression"
target_function: "step"