def plot_biclusters():
co_grid = ParameterGrid(
{'n_clusters': np.arange(2, 10, 1), 'n_init': [20]}
)
_y = pd.read_csv('./../../data_source/to_analysis/original_images/dfs_original_images.csv', index_col=0)
y_orig = np.squeeze(_y.values)
X_orig = pd.read_csv('./../../data_source/to_analysis/original_images/all_features_original_images.csv', index_col=0)
scaler = StandardScaler()
X_orig_std = scaler.fit_transform(X_orig.values)
#_run_experiment(co_grid, X_orig_std)
df_avg_co_scores = pd.read_csv('bic_scores.csv', index_col=0)
best_co_config = co_grid[
np.argmin(df_avg_co_scores.loc[:, 'tvr'].values) - 1
]
print(best_co_config, min(df_avg_co_scores.loc[:, 'tvr'].values))
orig_co_model = SpectralCoclustering(random_state=0, svd_method='arpack')
orig_co_model.set_params(**best_co_config)
orig_co_model.fit(X_orig_std)
#plt.figure()
#_plot_tve(df_avg_co_scores, co_grid)
plt.figure()
_plot_bicmaps(X_orig_std, best_co_config)
def _plot_bicmaps(X_orig_std, best_co_config):
# Train model with best config.
orig_co_model = SpectralCoclustering(random_state=0, svd_method='arpack')
orig_co_model.set_params(**best_co_config)
orig_co_model.fit(X_orig_std)
orig_co_row_sorted = X_orig_std[np.argsort(orig_co_model.row_labels_), :]
orig_co_fit_data = orig_co_row_sorted[:, np.argsort(orig_co_model.column_labels_)]
hmap = sns.heatmap(
orig_co_fit_data,
robust=True,
cmap=plt.cm.viridis,
fmt='f',
vmin=np.min(orig_co_fit_data),
vmax=np.max(orig_co_fit_data),
cbar=False
)
coords = bic_coords(orig_co_model, best_co_config['n_clusters'])
for num in coords.index:
plt.plot(
(coords.loc[num, ['x1', 'x2', 'x2', 'x1', 'x1']]),
(coords.loc[num, ['y1', 'y1', 'y2', 'y2', 'y1']]),
c='darkred'
)
plt.ylabel('Patients')
plt.xlabel('Features')
plt.yticks([], [])
plt.xticks([], [])
ax_divider = make_axes_locatable(hmap)
cax = ax_divider.append_axes('right', size='3%', pad='2%')
colorbar.colorbar(
hmap.get_children()[0],
cax=cax,
orientation='vertical'
)
#cax.xaxis.set_label_text('AUC', fontname='Sans')
#cax.xaxis.set_label_position('top')
cbar_ticks = np.linspace(
np.nanmin(orig_co_fit_data),
np.nanmax(orig_co_fit_data),
6
)
cax.yaxis.set_ticks(cbar_ticks)
cax.yaxis.set_ticklabels([f'{num:.01f}' for num in cbar_ticks])
plt.savefig(
'../biclustering/bic_map_original_images.pdf',
bbox_inches='tight',
transparent=True,
dpi=CONFIG.DPI,
)
