import matplotlib.pyplot as plt
import numpy as np
from denspp.offline.plot_helper import (
save_figure,
get_plot_color
)
[docs]
def plot_common_loss(metric: dict, path2save: str='', show_plots: bool=False) -> None:
"""Function for plotting the loss function of both models with sweeping the feature space size
:param metric: Dictionary with Metrics to extract feature size from
:param path2save: Path for saving the figure
:param show_plots: If True, show the plot
:return: None
"""
feat_size = metric['feat']
feat_size_ticks = feat_size if len(feat_size) < 6 else np.linspace(feat_size[0], feat_size[-1], 11,
endpoint=True, dtype=np.uint16)
keys_ae = ['loss_train', 'loss_valid']
keys_cl = ['train_acc', 'valid_acc']
## --- Subplot #1: Loss / Acc.
_, ax1 = plt.subplots(nrows=1, ncols=1, sharex=True)
ln0 = ax1.plot(feat_size, metric['ae'][keys_ae[0]], 'k.-', label='Loss_AE (Train)')
ln1 = ax1.plot(feat_size, metric['ae'][keys_ae[1]], 'k.--', label='Loss_AE (Valid)')
ax1.set_ylabel("Loss, Autoencoder", fontsize=14)
ax2 = ax1.twinx()
ln2 = ax2.plot(feat_size, metric['cl'][keys_cl[0]], 'r.-', label='Acc._CL (Train)')
ln3 = ax2.plot(feat_size, metric['cl'][keys_cl[1]], 'r.--', label='Acc._CL (Valid)')
ax2.set_ylabel("Accuracy, Classifier", fontsize=14)
ax1.set_yticks(np.linspace(ax1.get_ybound()[0], ax1.get_ybound()[1], 7))
ax2.set_yticks(np.linspace(ax2.get_ybound()[0], ax2.get_ybound()[1], 7))
lns = ln0 + ln1 + ln2 + ln3
labs = [l.get_label() for l in lns]
ax2.legend(lns, labs, loc='best')
## --- End processing
ax1.grid()
ax1.set_xlabel('Feature Size', fontsize=14)
ax1.set_xticks(feat_size_ticks)
ax1.set_xlim([feat_size[0], feat_size[-1]])
plt.tight_layout()
if path2save:
save_figure(plt, path2save, 'sweep_dnn_common_loss', ['svg'])
if show_plots:
plt.show(block=True)
[docs]
def plot_common_params(metric: dict, path2save: str= '', show_plots: bool=False) -> None:
"""Function for plotting the parameter numbers of both models with sweeping the feature space size
:param metric: Dictionary with Metrics to extract feature size from
:param path2save: Path for saving the figure
:param show_plots: If True, show the plot
:return: None
"""
feat_size = metric['feat']
feat_size_ticks = feat_size if len(feat_size) < 6 else np.linspace(feat_size[0], feat_size[-1], 11,
endpoint=True, dtype=np.uint16)
keys_ae = ['model_params']
keys_cl = ['model_params']
_, ax1 = plt.subplots(nrows=1, ncols=1, sharex=True)
ln4 = ax1.plot(feat_size, metric['ae'][keys_ae[0]], 'k.-', label='Autoencoder (AE)')
ax2 = ax1.twinx()
ln5 = ax2.plot(feat_size, metric['cl'][keys_cl[0]], 'r.-', label='Classifier (CL)')
ax1.set_ylabel('Model parameters', fontsize=14)
ax1.set_yticks(np.linspace(ax1.get_ybound()[0], ax1.get_ybound()[1], 7))
ax2.set_yticks(np.linspace(ax2.get_ybound()[0], ax2.get_ybound()[1], 7))
lns = ln4 + ln5
labs = [l.get_label() for l in lns]
ax2.legend(lns, labs, loc='best')
## --- End processing
ax1.grid()
ax1.set_xlabel('Feature Size', fontsize=14)
ax1.set_xticks(feat_size_ticks)
ax1.set_xlim([feat_size[0], feat_size[-1]])
plt.tight_layout()
if path2save:
save_figure(plt, path2save, 'sweep_dnn_common_params', ['svg'])
if show_plots:
plt.show(block=True)
[docs]
def plot_architecture_violin(metric: dict, path2save: str = '', show_plots: bool=False, label_dict=None) -> None:
"""Function for plotting the architecture violin plot
:param metric: Dictionary with Metrics to extract feature size from
:param path2save: Path for saving the figure
:param show_plots: If True, show the plot
:param label_dict: Dictionary with Labels to extract feature size from
:return: None
"""
feat_size = metric['feat']
feat_size_ticks = feat_size if len(feat_size) < 6 else np.linspace(feat_size[0], feat_size[-1], 11,
endpoint=True, dtype=np.uint16)
keys_ae = ['dsnr_cl']
keys_cl = ['precision']
num_cluster = metric['cl']['output_size'][-1]
if label_dict is None:
label_dict = [f'Neuron #{idx}' for idx in range(num_cluster)]
for key0, key1 in zip(keys_ae, keys_cl):
_, axs = plt.subplots(nrows=1, ncols=2, sharex=True)
data_ae = metric['ae'][key0]
data_cl = metric['cl'][key1]
# --- Processing AE Data
transformed_data_ae_cluster = [[] for idx in range(num_cluster)]
for data_feat in data_ae:
for idx, data_cluster in enumerate(data_feat):
transformed_data_ae_cluster[idx].append(data_cluster)
for idx, data_boxplot in enumerate(transformed_data_ae_cluster):
axs[0].violinplot(data_boxplot, showmedians=True, positions=np.array(feat_size)+idx/num_cluster, widths=0.5/num_cluster)
axs[0].legend(label_dict)
axs[0].set_ylabel(key0, fontsize=14)
# --- Processing CL Data
transformed_data_cl_cluster = np.zeros((num_cluster, len(feat_size)))
for idx, data_cluster in enumerate(data_cl):
transformed_data_cl_cluster[:, idx] = data_cluster
for idx, data_plot in enumerate(transformed_data_cl_cluster):
axs[1].plot(feat_size, data_plot, f'{get_plot_color(idx)}.-', label=label_dict[idx])
axs[1].set_ylabel(key1, fontsize=14)
axs[1].legend()
## --- End processing
for ax in axs:
ax.grid()
ax.set_xlabel('Feature Size', fontsize=14)
axs[0].set_xticks(feat_size_ticks)
axs[0].set_xlim([feat_size[0]-1, feat_size[-1]+1])
plt.tight_layout()
if path2save:
save_figure(plt, path2save, 'sweep_dnn_architecture', ['svg'])
if show_plots:
plt.show(block=True)
[docs]
def plot_architecture_metrics_isolated(metric: dict, path2save: str = '', show_plots: bool=False, label_dict=None) -> None:
"""Function for plotting the metrics in isolated plots for the autoencoder and classifier model
:param metric: Dictionary with Metrics to extract feature size from
:param path2save: Path for saving the figure
:param show_plots: If True, show the plot
:param label_dict: Dictionary with Labels to extract feature size from
:return: None
"""
feat_size = metric['feat']
feat_size_ticks = feat_size if len(feat_size) < 6 else np.linspace(feat_size[0], feat_size[-1], 11,
endpoint=True, dtype=np.uint16)
keys_ae = ['dsnr_cl']
keys_cl = ['precision']
num_cluster = metric['cl']['output_size'][-1]
if label_dict is None:
label_dict = [f'Neuron #{idx}' for idx in range(num_cluster)]
num_rows = 2
num_cols = 3
# --- Figure #1: Autoencoder
for key0 in keys_ae:
_, axs = plt.subplots(nrows=num_rows, ncols=num_cols, sharex=True)
data_ae = metric['ae'][key0]
transformed_data_ae_cluster = [[] for idx in range(num_cluster)]
transformed_data_ae_median = np.zeros((num_cluster, len(feat_size)))
for idy, data_feat in enumerate(data_ae):
for idx, data_cluster in enumerate(data_feat):
transformed_data_ae_cluster[idx].append(data_cluster)
transformed_data_ae_median[idx, idy] = np.median(data_cluster)
pass
for idx, data_boxplot in enumerate(transformed_data_ae_cluster):
axs[int(idx / num_cols), idx % num_cols].plot(feat_size, transformed_data_ae_median[idx, :], 'k.--', linewidth=1.0)
axs[int(idx/num_cols), idx % num_cols].violinplot(data_boxplot, showmedians=True, positions=feat_size)
axs[int(idx/num_cols), idx % num_cols].set_ylabel(f'{key0} ({label_dict[idx]})', fontsize=14)
axs[int(idx/num_cols), idx % num_cols].grid()
## --- End processing
axs[1, 1].set_xlabel('Feature Size', fontsize=14)
axs[0, 0].set_xticks(feat_size_ticks)
axs[0, 0].set_xlim([feat_size[0] - 0.25, feat_size[-1] + 0.25])
plt.subplots_adjust(wspace=0.3, hspace=0.05)
if path2save:
save_figure(plt, path2save, 'sweep_dnn_architecture_ae', ['svg'])
# --- Figure #2: Classifier
for key1 in keys_cl:
_, axs = plt.subplots(nrows=1, ncols=len(keys_cl), sharex=True)
# --- Processing CL Data
data_cl = metric['cl'][key1]
transformed_data_cl_cluster = np.zeros((num_cluster, len(feat_size)))
for idx, data_cluster in enumerate(data_cl):
transformed_data_cl_cluster[:, idx] = data_cluster
for idx, data_plot in enumerate(transformed_data_cl_cluster):
axs.plot(feat_size, data_plot, f'{get_plot_color(idx)}.-', label=label_dict[idx])
axs.set_ylabel(key1, fontsize=14)
axs.legend()
axs.grid()
## --- End processing
axs.set_xlabel('Feature Size', fontsize=14)
axs.set_xticks(feat_size_ticks)
axs.set_xlim([feat_size[0]-0.25, feat_size[-1]+0.25])
plt.subplots_adjust(wspace=0.3, hspace=0.05)
if path2save:
save_figure(plt, path2save, 'sweep_dnn_architecture_cl', ['svg'])
if show_plots:
plt.show(block=True)