lan_network_inspectors.rst

Network Inspectors

The network_inspectors() module allows you to inspect the LANs directly. We will be grateful if you report any strange behavior you might find.

# MODULE IMPORTS ----
import numpy as np
import hddm

Direct access to batch predictions

You can use the hddm.network_inspectors.get_torch_mlp() function to access network predictions.

# Specify model
model = 'angle'
lan_angle = hddm.network_inspectors.get_torch_mlp(model = model)

# Make some random parameter set
parameter_df = hddm.simulators.make_parameter_vectors_nn(model = model,
                                                         param_dict = None,
                                                         n_parameter_vectors = 1)

parameter_matrix = np.tile(np.squeeze(parameter_df.values), (200, 1))

# Initialize network input
network_input = np.zeros((parameter_matrix.shape[0], parameter_matrix.shape[1] + 2)) # Note the + 2 on the right --> we append the parameter vectors with reaction times (+1 columns) and choices (+1 columns)

# Add reaction times
network_input[:, -2] = np.linspace(0, 3, parameter_matrix.shape[0])

# Add choices
network_input[:, -1] = np.repeat(np.random.choice([-1, 1]), parameter_matrix.shape[0])

# Note: The networks expects float32 inputs
network_input = network_input.astype(np.float32)

# Show example output
print('Some network outputs')
print(lan_angle(network_input)[:10]) # printing the first 10 outputs
print('Shape')
print(lan_angle(network_input).shape) # original shape of output

Some network outputs [[-6.5302606 ] [ 0.5264375 ] [ 0.410895 ] [-0.52280986] [-1.0521754 ] [-1.552991 ] [-2.0735168 ] [-2.6183672 ] [-3.2071779 ] [-3.878473 ]] Shape (200, 1)

Plotting Utilities

HDDM provides two plotting function to investigate the network outputs directly. The kde_vs_lan_likelihoods() plot and the lan_manifold() plot.

NOTE: These utilities are designed for 2-choice models at the moment.

kde_vs_lan_likelihoods()

# Make some parameters
parameter_df = hddm.simulators.make_parameter_vectors_nn(model = model,
                                                         param_dict = None,
                                                         n_parameter_vectors = 10)

parameter_df

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	v	a	z	t	theta
0	2.729321	1.184634	0.798893	0.186882	0.225510
1	0.550569	1.473085	0.389967	0.583149	0.317908
2	0.297733	1.241166	0.630780	1.617812	0.455768
3	-2.918573	0.972126	0.307551	1.496773	0.898875
4	0.666445	1.882498	0.530820	0.259892	1.028040
5	1.629646	0.507156	0.692670	0.529111	0.845566
6	-1.255036	1.741760	0.678846	1.849304	1.233386
7	0.891731	0.795798	0.721093	1.705487	0.704186
8	-2.954760	1.518239	0.419388	0.623301	0.607989
9	0.360199	1.369782	0.629768	0.098295	0.497529

hddm.network_inspectors.kde_vs_lan_likelihoods(parameter_df = parameter_df, 
                                               model = model,
                                               cols = 3,
                                               n_samples = 2000,
                                               n_reps = 10,
                                               show = True)

1 of 10 2 of 10 3 of 10 4 of 10 5 of 10 6 of 10 7 of 10 8 of 10 9 of 10 10 of 10

lan_manifold()

Lastly, you can use the lan_manifold() plot to investigate the LAN likelihoods over a range of parameters.

The idea is to use a base parameter vector and vary one of the parameters in a prespecificed range.

This plot can be informative if you would like to understand better how a parameter affects model behavior.

# Now plotting
hddm.network_inspectors.lan_manifold(parameter_df = parameter_df,
                                     vary_dict = {'v': np.linspace(-2, 2, 20)},
                                     model = model,
                                     n_rt_steps = 300,
                                     fig_scale = 1.0,
                                     max_rt = 5,
                                     save = True,
                                     show = True)

Using only the first row of the supplied parameter array !