| import os |
| from typing import List |
|
|
| import fire |
| import numpy as np |
| import pandas as pd |
| import torch |
| from matplotlib import pyplot as plt |
| from tqdm import tqdm |
|
|
|
|
| def rst_weights_plot(model_name, k, forward_arcs=True, backward_arcs=True, plot_width=8, plot_height=-1, xlim=-1): |
| def real_coeffs(theta: torch.tensor, beta: torch.tensor, relations) -> pd.Series: |
| """ What are the real coefficients c_ij for specific RST relations in the final model. """ |
| weights_dict = dict(zip(relations, torch.nn.functional.relu(theta + beta) / beta)) |
| return pd.Series(weights_dict).map(lambda tensor: tensor.item()).sort_values() |
|
|
| def weights_stats(weights: List[pd.Series], backward=False) -> list: |
| if not weights: return [] |
| w_stats = pd.DataFrame(weights).agg([np.mean, np.std]) |
| w_stats.columns = [column[0].upper() + column[1:] + '⁻¹' * backward for column in w_stats.columns] |
| if 'Root⁻¹' in w_stats.columns: del w_stats['Root⁻¹'] |
| return [w_stats] |
|
|
| pos_res = [] |
| neg_res = [] |
| for fold in tqdm(range(k), desc=f'Processing folds'): |
| weights = torch.load(f'{model_name}/fold_{fold}/best.th', map_location='cpu') |
| relations = [rel.strip() for rel in |
| open(f'{model_name}/fold_{fold}/vocabulary/rst_rels_labels.txt', 'r').readlines()] |
|
|
| if forward_arcs: |
| theta, beta = weights['ff_rst_rels_arc.weight'][0], weights['ff_rst_rels_arc.bias'][0] |
| pos_res.append(real_coeffs(theta, beta, relations)) |
|
|
| if backward_arcs: |
| theta, beta = weights['ff_rst_rels_arc_back.weight'][0], weights['ff_rst_rels_arc_back.bias'][0] |
| neg_res.append(real_coeffs(theta, beta, relations)) |
|
|
| all_rels = pd.concat(weights_stats(pos_res) + weights_stats(neg_res, backward=True), axis=1) |
| std = all_rels.T.max()['std'] |
| std = 0. if np.isnan(std) else std |
| xlim = xlim if xlim != -1 else all_rels.T.max()[ |
| 'mean'] + std |
| plot_height = plot_height if plot_height != -1 else (all_rels.shape[ |
| 1] + 1) * 0.2 |
| ax = all_rels.T.sort_values('mean').plot(kind='barh', y='mean', legend=False, xerr='std', logx=False, |
| color='#81AD75', |
| xlim=(0, xlim), |
| figsize=(plot_width, plot_height)) |
| ax.axvline(x=1, color='r', linestyle='--', lw=2) |
| ax.set_xlabel('Weight') |
| ax.set_ylabel('RST relation') |
| |
| plt.savefig(f"{model_name}/rst_coeff_{os.path.basename(model_name)}.pdf", format="pdf", bbox_inches="tight") |
| return all_rels.T.sort_values('mean') |
|
|
|
|
| if __name__ == '__main__': |
| fire.Fire(rst_weights_plot) |
|
|
