model_gamma = Sequential()
# First compute the kernel
model_gamma.add(
Dense(var_dic['gamma'].shape[1],
use_bias=False,
activation=alt_softmax,
kernel_initializer=gamma_initializer,
kernel_constraint=LowerDiagWeight()))
model_gamma.compile(loss=GCM.pos_log_loss, optimizer=RMSprop())
model_tau = Sequential()
model_tau.add(
Dense(var_dic['tau'].shape[1],
input_dim=var_dic['tau'].shape[1],
activation=None,
use_bias=False,
kernel_initializer=Identity(),
trainable=False))
model_tau.compile('rmsprop', 'binary_crossentropy'
) # No trainable weights, so doesn't really matter
var_models = {'phi_A': model_phi_A, 'gamma': model_gamma, 'tau': model_tau}
res = GCM.runEM(click_mat.to_numpy(),
var_dic,
var_models,
item_pos_mat.to_numpy(),
model_def,
verbose=True)
print(res[2])
.pivot(index='session', columns='item_order', values='click') \
.to_numpy()
item_pos_mat = click_data.loc[:, ['session', 'item_order', 'item']] \
.pivot(index='session', columns='item_order', values='item') \
.to_numpy()
# Ensure that the order is correct
item_feature_mat_A = pd.get_dummies(
click_data['item'].sort_values().unique())
model_phi_A = Sequential()
model_phi_A.add(
Dense(1,
input_dim=item_feature_mat_A.shape[1],
activation='sigmoid',
use_bias=False))
model_phi_A.compile(loss=GCM.pos_log_loss, optimizer=RMSprop())
var_dic = {'phi_A': item_feature_mat_A}
var_models = {'phi_A': model_phi_A}
res = GCM.runEM(click_mat,
var_dic,
var_models,
item_pos_mat,
model_def,
verbose=True,
n_jobs=1)
print(res[2])
use_bias=False))
model_phi_A.compile(loss=GCM.pos_log_loss, optimizer=Adagrad())
model_gamma = Sequential()
# First compute the kernel
model_gamma.add(
Dense(1,
input_dim=item_feature_mat_gamma.shape[1],
activation='sigmoid',
use_bias=False))
model_gamma.add(RepeatVector(no_states**2))
model_gamma.compile(loss=GCM.pos_log_loss, optimizer=Adagrad())
var_dic = {'phi_A': item_feature_mat_A, 'gamma': item_feature_mat_gamma}
var_models = {'phi_A': model_phi_A, 'gamma': model_gamma}
res = GCM.runEM(click_mat,
var_dic,
var_models,
item_pos_mat,
model_def,
verbose=True,
earlystop_patience=10,
n_jobs=1)
pl.dump(res[1], open("./data/small_example/state_prob.pl", "wb"))
pl.dump(res[2], open("./data/small_example/convergence.pl", "wb"))
pl.dump(res[3], open("./data/small_example/click_probs.pl", "wb"))
print(res[2])