error_metric_f, ml_train_loss_f, ml_val_loss_f, ml_test_loss_f = \
ml_model_obj.evaluate(ml_x_t,train_y_t, ml_val_x_t, val_y_t, ml_test_x,
test_y_t, scale = NN_scaling)
title = 'metric learning'
print_stat(title, error_metric, ml_train_loss, ml_val_loss, ml_test_loss)
if fine_tuning:
title = 'metric learning with fine tuning'
print_stat(title, error_metric_f, ml_train_loss_f, ml_val_loss_f,
ml_test_loss_f)
# =============================================================================
from plotters import error_dist
error_dist(ml_x_s,
train_y_s,
ml_x_t,
train_y_t,
error_metric,
test_y_t,
title=title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_metric, 100)
plt.show()
# del ml_model
# del ml_model_obj
emb_val_x_s = embedder.fit_transform(val_x_s)
emb_val_x_t = embedder.fit_transform(val_x_t)
emb_test_x = embedder.fit_transform(test_x_t)
# =============================================================================
num_inputs = emb_x_s.shape[1]# input layer size
# =============================================================================
model_obj = my_models(num_inputs, dropout = dropout_pr)
model = model_obj.build_model()
model = model_obj.fit(emb_x_s, train_y_s, emb_val_x_s, val_y_s,
scale = NN_scaling)
model = model_obj.fit(emb_x_t, train_y_t, emb_val_x_t, val_y_t,
scale = NN_scaling)
error_fine_tuning, train_loss, val_loss, test_loss = \
model_obj.evaluate(emb_x_t,train_y_t, emb_val_x_t, val_y_t, emb_test_x,
test_y_t, scale = NN_scaling)
title = 'Naive learning + fine tuning'
print_stat(title, error_fine_tuning, train_loss, val_loss, test_loss)
# =============================================================================
from plotters import error_dist
error_dist(emb_x_s, train_y_s, emb_x_t, train_y_t, error_fine_tuning,
test_y_t, title = title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_fine_tuning, 100)
plt.show()
train_y_t,
title='measure difference',
ax=ax1)
plot_scatter_colored(error_test,
test_y_t,
title='error distribution',
ax=ax2)
else:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 5))
plot_scatter_colored(rssi_diff,
train_y_t,
title='measure difference',
ax=ax1)
plot_scatter_colored(error_test,
test_y_t,
title='error distribution',
ax=ax2)
plot_scatter_colored(weights,
train_y_s,
title='weights based on kernel',
ax=ax3)
if title is not None:
fig.suptitle(title)
error_dist(emb_x_s, emb_x_t, error_sample_bias, weights=coef_s, title=title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_sample_bias, 100)
plt.show()
error_metric_plus_sample_f, train_loss_f, val_loss_f, test_loss_f =\
w_model_obj.evaluate(emb_u_x_t, u_train_y_t, emb_u_val_x_t, u_val_y_t,
emb_test_x, test_y_t, scale = NN_scaling)
title = 'metric training plus sample selection bias'
print_stat(title, error_metric_plus_sample, train_loss, val_loss, test_loss)
if fine_tuning:
title = 'metric training plus sample selection bias'
print_stat(title, error_metric_plus_sample_f, train_loss_f, val_loss_f,
test_loss_f)
# =============================================================================
from plotters import error_dist
error_dist(emb_x_s,
train_y_s,
emb_u_x_t,
u_train_y_t,
error_metric_plus_sample,
test_y_t,
weights=coef_s,
title=title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_metric_plus_sample, 100)
plt.show()
#
#del model
#del w_model_obj
del model_obj
model_obj = my_models(num_inputs, dropout=dropout_pr)
model = model_obj.build_model()
model = model_obj.fit(emb_x_t,
train_y_t,
emb_val_x_t,
val_y_t,
scale=NN_scaling)
error_normal, train_loss, val_loss, test_loss = \
model_obj.evaluate(emb_x_t,train_y_t, emb_val_x_t, val_y_t, emb_test_x,
test_y_t, scale = NN_scaling)
title = 'normal learning'
print_stat(title, error_normal, train_loss, val_loss, test_loss)
# =============================================================================
from plotters import error_dist
#error_dist(emb_x_s, train_y_s, emb_x_t, train_y_t, error_naive,
# test_y_t, title = title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_normal, 100)
plt.show()
#
#del model
#del model_obj
error_naive, nl_train_loss, nl_val_loss, nl_test_loss = \
model_obj.evaluate(emb_x_t,train_y_t, emb_val_x_t, val_y_t, emb_test_x,
test_y_t, scale = NN_scaling)
if fine_tuning:
model = model_obj.fit(emb_x_t, train_y_t, emb_val_x_t, val_y_t,
scale = NN_scaling)
error_naive_f, nl_train_loss_f, nl_val_loss_f, nl_test_loss_f = \
model_obj.evaluate(emb_x_t,train_y_t, emb_val_x_t, val_y_t, emb_test_x,
test_y_t, scale = NN_scaling)
title = 'Naive learning'
print_stat(title, error_naive, nl_train_loss, nl_val_loss, nl_test_loss)
if fine_tuning:
title = 'Naive learning with fine-tuning'
print_stat(title, error_naive_f, nl_train_loss_f, nl_val_loss_f, nl_test_loss_f)
# =============================================================================
from plotters import error_dist
error_dist(emb_x_s, train_y_s, emb_x_t, train_y_t, error_naive,
test_y_t, title = title)
plt.show()
from plotters import plot_cdf
plot_cdf(error_naive, 100)
plt.show()
del model
del model_obj
