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# %%
import numpy as np
import torch
from pathlib import Path
import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
HEAD = Path(os.getcwd()).parent.parent
if __name__=="__main__":
# load baseline and LEDM data
metrics = {"dice": [], "precision": [], "recall": [], "exp": [], "datasize": [], "dataset":[]}
files_needed = ["JSRT_val_predictions.pt", "JSRT_test_predictions.pt", "NIH_predictions.pt", "Montgomery_predictions.pt",]
head = HEAD / 'logs'
for exp in ['baseline', 'LEDM']:
for datasize in [1, 3, 6, 12, 24, 49, 98, 197]:
if len(set(files_needed) - set(os.listdir(head / exp / str(datasize)))) == 0:
print(f"Experiment {exp} {datasize}")
output = torch.load(head / exp / str(datasize) / "JSRT_val_predictions.pt")
print(f"{output['dice'].mean()}\t{output['dice'].std()}")
for file in files_needed[1:]:
output = torch.load(head / exp / str(datasize) / file)
metrics_datasize = 197 if datasize == "None" else int(datasize)
metrics["dice"].append(output["dice"].numpy())
metrics["precision"].append(output["precision"].numpy())
metrics["recall"].append(output["recall"].numpy())
metrics["exp"].append(np.array([exp] * len(output["dice"])))
metrics["datasize"].append(np.array([int(datasize)] * len(output["dice"])))
metrics["dataset"].append(np.array([file.split("_")[0]]*len(output["dice"])))
else:
print(f"Experiment {exp} is missing files")
for key in metrics:
metrics[key] = np.concatenate([el.squeeze() for el in metrics[key]])
df = pd.DataFrame(metrics)
df.head()
# %% Load step data
metrics2 = {"dice": [], "precision": [], "recall": [], "exp": [], "datasize": [], "dataset":[], 'timestep':[]}
for timestep in [1, 10, 25, 50, 500, 950]:
exp = f"Step_{timestep}"
for datasize in [197, 98, 49, 24, 12, 6, 3, 1]:
if os.path.isdir(head / exp / str(datasize)):
if len(set(files_needed) - set(os.listdir(head / exp / str(datasize)))) == 0:
print(f"Experiment {datasize} {timestep}")
output = torch.load(head / exp / str(datasize)/ "JSRT_val_predictions.pt")
print(f"{output['dice'].mean()}\t{output['dice'].std()}")
for file in files_needed[1:]:
output = torch.load(head / exp / str(datasize) / file)
metrics_datasize = datasize if datasize is not None else 197
metrics2["dice"].append(output["dice"].numpy())
metrics2["precision"].append(output["precision"].numpy())
metrics2["recall"].append(output["recall"].numpy())
metrics2["exp"].append(np.array([exp] * len(output["dice"])))
metrics2["datasize"].append(np.array([metrics_datasize] * len(output["dice"])))
metrics2["dataset"].append(np.array([file.split("_")[0]]*len(output["dice"])))
metrics2["timestep"].append(np.array([timestep] * len(output["dice"])))
else:
print(f"Experiment {datasize} is missing files")
for key in metrics2:
metrics2[key] = np.concatenate(metrics2[key]).squeeze()
print(key, metrics2[key].shape)
df2 = pd.DataFrame(metrics2)
# %% figure with line for baseline and datasetDM and boxplots for the rest
# separating dice from precision and recall
font = 16
x = [1, 1, 3, 3, 6, 6, 12, 12, 24, 24, 49, 49, 197, 197]
plot_x = np.concatenate([np.array([-.4, .4]) + i for i in range(len(x)//2)]).flatten()
fig, axs = plt.subplots(3, 1, figsize=[12, 10])
sns.set_style("whitegrid")
m = 'dice'
for i, dataset in enumerate(["JSRT", "NIH", "Montgomery"]):
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'baseline') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_std = np.quantile(ys, (.25, .75), axis=1, )
axs[i ].fill_between(plot_x, ys_std[0], ys_std[1], alpha=.2, zorder=0, color='C6')
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'LEDM') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_std = np.quantile(ys, (.25, .75), axis=1, )
axs[i ].fill_between(plot_x, ys_std[0], ys_std[1], alpha=.2, zorder=0, color='C8')
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'baseline') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_mean = np.quantile(ys, .5, axis=1)
axs[i ].plot(plot_x, ys_mean, label="baseline", c='C6', zorder=0)
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'LEDM') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_mean = np.quantile(ys, .5, axis=1)
axs[i ].plot(plot_x, ys_mean, label="LEDM" , c='C7', zorder=0)
for i, dataset in enumerate(["JSRT", "NIH", "Montgomery"]):
temp_df = df2[(df2.dataset == dataset) & (df2.datasize != 98)]
out = sns.boxplot(data=temp_df, x="datasize", y=m, hue="timestep", ax=axs[i ], showfliers=False, saturation=1,)
axs[i ].set_title(f"{dataset}", fontsize=font)
axs[i ].set_xlabel("" )
y_min, _ = axs[i ].get_ylim()
axs[i ].set_ylim(y_min, 1)
h, l = axs[i].get_legend_handles_labels()
axs[i].get_legend().remove()
axs[i].set_ylabel("Dice", fontsize=font)
sns.despine(ax=axs[0 ], offset=10, trim=True, bottom=True)
sns.despine(ax=axs[1 ], offset=10, trim=True, bottom=True)
sns.despine(ax=axs[2 ], offset=10, trim=True)
axs[0].set_xticks([])
axs[1].set_xticks([])
axs[-1 ].set_xlabel("Training dataset size", fontsize=font)
# Shrink current axis by 20%
for i, ax in enumerate(axs):
box = ax.get_position()
ax.tick_params(axis='both', labelsize=font)
ax.set_position([box.x0, box.y0, box.width , box.height])
# Put a legend to the right of the current axis
fig.legend(h, ['baseline', 'LEDM'] + ['step ' + _l for _l in l[2:]], title="", ncol=4,
loc='center left', bbox_to_anchor=(0.2, -0.03), fontsize=font)
plt.tight_layout()
#plt.savefig("results_per_timestep.png")
plt.savefig("results_per_timestep_dice.pdf", bbox_inches='tight')
plt.show()
# %%
x = [1, 1, 3, 3, 6, 6, 12, 12, 24, 24, 49, 49, 197, 197]
plot_x = np.concatenate([np.array([-.4, .4]) + i for i in range(len(x)//2)]).flatten()
fig, axs = plt.subplots(3, 2, figsize=[15, 15])
sns.set_style("whitegrid")
for j, m in enumerate(["precision", "recall"]):
for i, dataset in enumerate(["JSRT", "NIH", "Montgomery"]):
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'baseline') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_std = np.quantile(ys, (.25, .75), axis=1, )
axs[i, j].fill_between(plot_x, ys_std[0], ys_std[1], alpha=.2, zorder=0, color='C6')
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'LEDM') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_std = np.quantile(ys, (.25, .75), axis=1, )
axs[i, j].fill_between(plot_x, ys_std[0], ys_std[1], alpha=.2, zorder=0, color='C8')
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'baseline') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_mean = np.quantile(ys, .5, axis=1)
axs[i, j].plot(plot_x, ys_mean, label="baseline", c='C6', zorder=0)
ys = np.stack([df.loc[(df.dataset == dataset)& (df.exp == 'LEDM') & (df.datasize == _x), m].to_numpy() for _x in x])
ys_mean = np.quantile(ys, .5, axis=1)
axs[i, j].plot(plot_x, ys_mean, label="LEDM" , c='C7', zorder=0)
##
temp_df = df2[(df2.dataset == dataset) & (df2.datasize != 98)]
out = sns.boxplot(data=temp_df, x="datasize", y=m, hue="timestep", ax=axs[i,j], showfliers=False, saturation=1)
axs[i,j].set_title(f"{dataset}", fontsize=font)
y_min, _ = axs[i,j].get_ylim()
axs[i,j].set_ylim(y_min, 1)
sns.despine(ax=axs[i,j], offset=10, trim=True)
h, l = axs[i,j].get_legend_handles_labels()
axs[i,j].get_legend().remove()
axs[i, 0].set_ylabel("Precison", fontsize=font)
axs[i, 1].set_ylabel("Recall", fontsize=font)
axs[i,j].set_xlabel("")
for ax in axs.flatten():
ax.tick_params(axis='both', labelsize=font)
for ax in [axs[:, 0], axs[:, 1]]:
sns.despine(ax=ax[0 ], offset=10, trim=True, bottom=True)
sns.despine(ax=ax[1 ], offset=10, trim=True, bottom=True)
sns.despine(ax=ax[2 ], offset=10, trim=True)
ax[0].set_xticks([])
ax[1].set_xticks([])
ax[-1 ].set_xlabel("Training dataset size", fontsize=font)
# Put a legend to the right of the current axis
fig.legend(h, ['baseline', 'LEDM'] + ['step ' + _l for _l in l[2:]], title="", ncol=4,
loc='center left', bbox_to_anchor=(0.25, -0.03), fontsize=font)
plt.tight_layout()
#plt.savefig("results_per_timestep.png")
plt.savefig("results_per_timestep_prec_recall.pdf", bbox_inches='tight')
plt.show()
# %%
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