def quick_statespace(model_dir):
"""Quick state space analysis using simply PCA."""
rules = ['contextdm1', 'contextdm2']
h_lastts = dict()
model = Model(model_dir)
hp = model.hp
with tf.Session() as sess:
model.restore()
for rule in rules:
# Generate a batch of trial from the test mode
trial = generate_trials(rule, hp, mode='test')
feed_dict = tools.gen_feed_dict(model, trial, hp)
h = sess.run(model.h, feed_dict=feed_dict)
lastt = trial.epochs['stim1'][-1]
h_lastts[rule] = h[lastt,:,:]
from sklearn.decomposition import PCA
model = PCA(n_components=5)
model.fit(np.concatenate(h_lastts.values(), axis=0))
fig = plt.figure(figsize=(2,2))
ax = fig.add_axes([.3, .3, .6, .6])
for rule, color in zip(rules, ['red', 'blue']):
data_trans = model.transform(h_lastts[rule])
ax.scatter(data_trans[:, 0], data_trans[:, 1], s=1,
label=rule_name[rule], color=color)
plt.tick_params(axis='both', which='major', labelsize=7)
ax.set_xlabel('PC 1', fontsize=7)
ax.set_ylabel('PC 2', fontsize=7)
lg = ax.legend(fontsize=7, ncol=1, bbox_to_anchor=(1,0.3),
loc=1, frameon=False)
if save:
plt.savefig('figure/choiceatt_quickstatespace.pdf',transparent=True)
def run_experiments():
experiments_hyper_params = json.load(open(hyper_params_path))
for params in experiments_hyper_params:
name = params.pop('name')
layers_dims = params.pop('layers')
layers_dims[-2]['num_nodes'] = NUM_CATEGORIES
model = Model(X_train.shape[1], layers_dims, model_name=name)
model.fit(X_train, X_val, y_train, y_val, **params)
_predict_test(model, name)
