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visualize_mimic3.py
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"""
Matching communication network with email network in the MC3 dataset
"""
import dev.util as util
import matplotlib
import matplotlib.pyplot as plt
from model.GromovWassersteinLearning import GromovWassersteinLearning
import numpy as np
import pickle
from sklearn.manifold import TSNE
import torch
def heatmap(data, row_labels, col_labels, ax=None,
cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and two lists of labels.
Arguments:
data : A 2D numpy array of shape (N,M)
row_labels : A list or array of length N with the labels
for the rows
col_labels : A list or array of length M with the labels
for the columns
Optional arguments:
ax : A matplotlib.axes.Axes instance to which the heatmap
is plotted. If not provided, use current axes or
create a new one.
cbar_kw : A dictionary with arguments to
:meth:`matplotlib.Figure.colorbar`.
cbarlabel : The label for the colorbar
All other arguments are directly passed on to the imshow call.
"""
if not ax:
ax = plt.gca()
# Plot the heatmap
im = ax.imshow(data, **kwargs)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks...
ax.set_xticks(np.arange(data.shape[1]))
ax.set_yticks(np.arange(data.shape[0]))
# ... and label them with the respective list entries.
ax.set_xticklabels(col_labels, fontsize='xx-small')
ax.set_yticklabels(row_labels, fontsize='xx-small')
# Let the horizontal axes labeling appear on top.
ax.tick_params(top=True, bottom=False,
labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=-75, ha="right", rotation_mode="anchor")
# Turn spines off and create white grid.
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=2)
ax.tick_params(which="minor", bottom=False, left=False)
return im, cbar
def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
textcolors=["black", "white"],
threshold=None, **textkw):
"""
A function to annotate a heatmap.
Arguments:
im : The AxesImage to be labeled.
Optional arguments:
data : Data used to annotate. If None, the image's data is used.
valfmt : The format of the annotations inside the heatmap.
This should either use the string format method, e.g.
"$ {x:.2f}", or be a :class:`matplotlib.ticker.Formatter`.
textcolors : A list or array of two color specifications. The first is
used for values below a threshold, the second for those
above.
threshold : Value in data units according to which the colors from
textcolors are applied. If None (the default) uses the
middle of the colormap as separation.
Further arguments are passed on to the created text labels.
"""
if not isinstance(data, (list, np.ndarray)):
data = im.get_array()
# Normalize the threshold to the images color range.
if threshold is not None:
threshold = im.norm(threshold)
else:
threshold = im.norm(data.max())/2.
# Set default alignment to center, but allow it to be
# overwritten by textkw.
kw = dict(horizontalalignment="center",
verticalalignment="center",
fontsize=2)
kw.update(textkw)
# Get the formatter in case a string is supplied
if isinstance(valfmt, str):
valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
# Loop over the data and create a `Text` for each "pixel".
# Change the text's color depending on the data.
texts = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if data[i, j] > 0.15:
kw.update(color=textcolors[im.norm(data[i, j]) > threshold])
text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
texts.append(text)
return texts
def plot_results(gwl_model, index_s, index_t, epoch):
# tsne
embs_s = gwl_model.emb_model[0](index_s)
embs_t = gwl_model.emb_model[1](index_t)
embs = np.concatenate((embs_s.cpu().data.numpy(), embs_t.cpu().data.numpy()), axis=0)
embs = TSNE(n_components=2).fit_transform(embs)
plt.figure(figsize=(5, 5))
plt.scatter(embs[:embs_s.size(0), 0], embs[:embs_s.size(0), 1],
marker='x', s=10, c='b', edgecolors='b', label='Diseases')
plt.scatter(embs[-embs_t.size(0):, 0], embs[-embs_t.size(0):, 1],
marker='o', s=10, c='', edgecolors='r', label='Procedures')
leg = plt.legend(loc='upper left', ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel('T-SNE of node embeddings')
plt.savefig('mimic3_epoch{}.pdf'.format(epoch))
plt.close("all")
data_name = 'mimic3_2'
result_folder = 'match_mimic3_2'
cost_type = ['cosine']
method = ['proximal']
filename = '{}/result_{}_{}.pkl'.format(result_folder, method[0], cost_type[0])
with open(filename, 'rb') as f: # Python 3: open(..., 'wb')
result_mc3 = pickle.load(f)
filename = '{}/{}_database.pickle'.format(util.DATA_TRAIN_DIR, data_name)
with open(filename, 'rb') as f: # Python 3: open(..., 'wb')
data_mc3 = pickle.load(f)
disease = []
procedure = []
for key in data_mc3['src_index'].keys():
# idx = data_mc3['src_index'][key]
disease.append('d'+key)
for key in data_mc3['tar_index'].keys():
# idx = data_mc3['tar_index'][key]
procedure.append('p'+key)
for m in method:
for c in cost_type:
hyperpara_dict = {'src_number': len(data_mc3['src_index']),
'tar_number': len(data_mc3['tar_index']),
'dimension': 50,
'loss_type': 'L2',
'cost_type': c,
'ot_method': m}
index_s = torch.LongTensor(list(range(hyperpara_dict['src_number'])))
index_t = torch.LongTensor(list(range(hyperpara_dict['tar_number'])))
gwd_model = GromovWassersteinLearning(hyperpara_dict)
# load model
gwd_model.load_model('{}/model_{}_{}.pt'.format(result_folder, m, c))
cost_st = gwd_model.gwl_model.mutual_cost_mat(index_s, index_t).cpu().data.numpy().transpose()
harvest = result_mc3[4].transpose() # gwd_model.trans.transpose()
for i in range(harvest.shape[1]):
dis = disease[i][1:]
dis = data_mc3['src_title'][dis]
for j in range(harvest.shape[0]):
pro = procedure[j][1:]
pro = data_mc3['tar_title'][pro]
if harvest[j, i] > 0.15:
print('ot={:.2f}, {}: {} --> {}: {}'.format(harvest[j, i], disease[i], dis, procedure[j], pro))
fig, ax = plt.subplots()
im, cbar = heatmap(harvest, procedure, disease, ax=ax,
cmap="Wistia", cbarlabel="The optimal transport from diseases to procedures")
texts = annotate_heatmap(im, valfmt="{x:.1f}")
fig.tight_layout()
plt.savefig('maps.pdf')
fig, ax = plt.subplots()
im, cbar = heatmap(cost_st, procedure, disease, ax=ax,
cmap="Wistia", cbarlabel="The optimal transport from diseases to procedures")
texts = annotate_heatmap(im, valfmt="{x:.1f}")
fig.tight_layout()
plt.savefig('maps2.pdf')