def test_match_sklearn(self):
clf_sklearn = linear_model.LogisticRegression(penalty='none')
clf_sklearn.fit(self.data_x, self.data_y)
self.assertAlmostEqual(clf_sklearn.intercept_[0], -0.0271, places=3)
self.assertAlmostEqual(clf_sklearn.coef_[0][0], 1.0987, places=3)
self.assertAlmostEqual(clf_sklearn.coef_[0][1], -1.0216, places=3)
model = train_proxy.LogisticReg()
erm_loss = train_proxy.make_loss_func(model,
self.data,
erm_weight=1.,
bias_lamb=0.)
init_params = train_proxy.initialize_params(model,
mdim=self.data_x.shape[1],
seed=0)
erm_params, _ = train_proxy.train(erm_loss,
init_params,
lr=1.,
nsteps=1000)
b, w = jax.tree_leaves(erm_params)
self.assertAlmostEqual(w[0].item(), 1.0988, places=3)
self.assertAlmostEqual(w[1].item(), -1.0216, places=3)
self.assertAlmostEqual(b.item(), -0.0274, places=3)
def test_policy_bias_regularization(self):
model = train_proxy.LogisticReg()
mix_loss = train_proxy.make_loss_func(model,
self.data,
erm_weight=1.,
bias_lamb=10.)
init_params = train_proxy.initialize_params(model,
mdim=self.data_x.shape[1],
seed=0)
mix_params, _ = train_proxy.train(mix_loss,
init_params,
lr=1.,
nsteps=1000)
b, w = jax.tree_leaves(mix_params)
self.assertAlmostEqual(w[0].item(), 1.4478, places=3)
self.assertAlmostEqual(w[1].item(), -1.2915, places=3)
self.assertAlmostEqual(b.item(), 0.1693, places=3)
def test_l2_regularization(self):
model = train_proxy.LogisticReg()
erm_loss_reg = train_proxy.make_loss_func(model,
self.data,
erm_weight=1.,
l2_lamb=10.,
bias_lamb=0.)
init_params = train_proxy.initialize_params(model,
mdim=self.data_x.shape[1],
seed=0)
erm_params_reg, _ = train_proxy.train(erm_loss_reg,
init_params,
lr=1.,
nsteps=1000)
b, w = jax.tree_leaves(erm_params_reg)
self.assertAlmostEqual(w[0].item(), 0.0030, places=3)
self.assertAlmostEqual(w[1].item(), -0.0028, places=3)
self.assertAlmostEqual(b.item(), 0.0158, places=3)
def load_and_train():
"""Load data from file and return checkpoints from training."""
simulation_path = f'{FLAGS.data_path}/{FLAGS.simulation_dir}'
with gfile.GFile(f'{simulation_path}/{FLAGS.data_file}', 'r') as f:
df = pd.read_csv(f)
# Split this into train and validate
rng = np.random.default_rng(FLAGS.data_seed)
users = np.unique(df['user'])
users = rng.permutation(users)
n_users = users.shape[0]
n_train_users = int(n_users / 2)
users_train = users[:n_train_users]
users_val = users[n_train_users:]
assert users_val.shape[0] + users_train.shape[0] == n_users
df_tr = df.query('user in @users_train').copy()
df_val = df.query('user in @users_val').copy()
a_tr = df_tr['rec'].to_numpy()
m_tr = df_tr[['diversity', 'rating']].to_numpy()
y_tr = df_tr['ltr'].to_numpy()
t_tr = np.ones_like(a_tr)
a_val = df_val['rec'].to_numpy()
m_val = df_val[['diversity', 'rating']].to_numpy()
y_val = df_val['ltr'].to_numpy()
t_val = np.ones_like(a_val)
model = train_proxy.LogisticReg()
data_tr = {
'a': a_tr,
'm': m_tr,
'y': y_tr,
't': t_tr,
}
data_val = {
'a': a_val,
'm': m_val,
'y': y_val,
't': t_val,
}
init_params = train_proxy.initialize_params(model, mdim=2, seed=FLAGS.seed)
loss_tr = train_proxy.make_loss_func(model,
data_tr,
erm_weight=FLAGS.erm_weight,
bias_lamb=FLAGS.bias_lamb,
bias_norm=FLAGS.bias_norm)
loss_val = train_proxy.make_loss_func(model,
data_val,
erm_weight=FLAGS.erm_weight,
bias_lamb=FLAGS.bias_lamb,
bias_norm=FLAGS.bias_norm)
_, checkpoints = train_proxy.train(loss_tr,
init_params,
validation_loss=loss_val,
lr=FLAGS.learning_rate,
nsteps=FLAGS.nsteps,
tol=FLAGS.tol,
verbose=True,
log=True)
return checkpoints
