Datasets:
File size: 10,267 Bytes
84a242a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 | # Copyright 2021 Zhongyang Zhang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pathlib2 import Path
import torch
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import spearmanr
from scipy.cluster.hierarchy import linkage, fcluster
def refine_portfolio(M_train, eps=0.0, cut_height=0.5, method="cluster", K=5):
"""
M_train: (n_instances, n_algorithms) training composite scores
method: "cluster" -> rank-correlation clustering + rep by coverage
"facility" -> greedy facility-location selection
Returns: list of selected algorithm indices
"""
# --- normalize per instance (ranks to remove scale) ---
ranks = np.apply_along_axis(lambda r: r.argsort().argsort(), 1, M_train)
M_norm = ranks / ranks.max(axis=1, keepdims=True)
if method == "facility":
# Greedy facility-location: pick algs that cover most instances
n, A = M_norm.shape
chosen, current = [], np.full(n, -np.inf)
for _ in range(K):
gains = [np.maximum(current, M_norm[:,a]).sum() - current.sum()
for a in range(A) if a not in chosen]
a = [a for a in range(A) if a not in chosen][int(np.argmax(gains))]
chosen.append(a)
current = np.maximum(current, M_norm[:,a])
return chosen
else:
# --- Spearman correlation between algorithms ---
corr, _ = spearmanr(M_norm, axis=0)
dist = (1 - corr) / 2
iu = np.triu_indices(dist.shape[0], k=1)
Z = linkage(dist[iu], method='average')
labels = fcluster(Z, t=cut_height, criterion='distance')
# --- pick one rep per cluster (best mean score) ---
reps = []
for c in np.unique(labels):
idx = np.where(labels == c)[0]
mean_scores = M_train[:, idx].mean(axis=0)
reps.append(idx[np.argmax(mean_scores)])
return reps
def load_model_path(root=None, version=None, v_num=None, best=False):
""" When best = True, return the best model's path in a directory
by selecting the best model with largest epoch. If not, return
the last model saved. You must provide at least one of the
first three args.
Args:
root: The root directory of checkpoints. It can also be a
model ckpt file. Then the function will return it.
version: The name of the version you are going to load.
v_num: The version's number that you are going to load.
best: Whether return the best model.
"""
def sort_by_epoch(path):
name = path.stem
epoch=int(name.split('-')[1].split('=')[1])
return epoch
def generate_root():
if root is not None:
return root
elif version is not None:
return str(Path('lightning_logs', version, 'checkpoints'))
else:
return str(Path('lightning_logs', f'version_{v_num}', 'checkpoints'))
if root==version==v_num==None:
return None
root = generate_root()
if Path(root).is_file():
return root
if best:
files=[i for i in list(Path(root).iterdir()) if i.stem.startswith('best')]
files.sort(key=sort_by_epoch, reverse=True)
res = str(files[0])
else:
res = str(Path(root) / 'last.ckpt')
return res
def load_model_path_by_args(args):
# Use getattr with default None to avoid AttributeError if missing
return load_model_path(
root=getattr(args, 'load_dir', None),
version=getattr(args, 'load_ver', None),
v_num=getattr(args, 'load_v_num', None)
)
def ndcg_score(y_true, y_score, k=3):
"""
Compute nDCG for a single sample.
y_true: true relevance scores (1D tensor or array)
y_score: predicted scores (1D tensor or array)
k: rank cutoff (if None, use all)
"""
# Convert to numpy arrays if needed
if isinstance(y_true, torch.Tensor):
y_true = y_true.detach().cpu().numpy()
if isinstance(y_score, torch.Tensor):
y_score = y_score.detach().cpu().numpy()
y_true = np.asarray(y_true).flatten()
y_score = np.asarray(y_score).flatten()
n = len(y_true)
if n == 0:
return 0.0
if k is None or k > n:
k = n
# DCG
order = np.argsort(y_score)[::-1][:k]
gains = 2 ** y_true[order] - 1
discounts = np.log2(np.arange(2, k + 2))
dcg = np.sum(gains / discounts)
# iDCG
ideal_order = np.argsort(y_true)[::-1][:k]
ideal_gains = 2 ** y_true[ideal_order] - 1
ideal_dcg = np.sum(ideal_gains / discounts)
return float(dcg / ideal_dcg) if ideal_dcg > 0 else 0.0
def plot_rmsd_metrics(eval_results, metrics_to_plot=None):
"""
Plot horizontal bar charts for RMSD metrics from eval_results dictionary.
Args:
eval_results (dict): Dictionary containing evaluation results with RMSD metrics
metrics_to_plot (list): List of metrics to plot. If None, plots all available metrics.
Options: ['rmsd_lt_1', 'rmsd_lt_2', 'rmsd_lt_5', 'rmsd_lt_2_valid', 'median_rmsd']
"""
# Filter to only include specific models
target_models = ['AS', 'uni', 'surf', 'gnina', 'smina', 'qvina', 'diffL', 'diff', 'karma', 'oracle', '2nd']
models = [model for model in target_models if model in eval_results]
# Define available metrics and their properties
metric_configs = {
'rmsd_lt_1': {
'key': 'Percent RMSD < 1',
'label': 'RMSD < 1',
'color': 'lightgreen',
'unit': '%',
'is_percentage': True
},
'rmsd_lt_2': {
'key': 'Percent RMSD < 2',
'label': 'RMSD < 2',
'color': 'skyblue',
'unit': '%',
'is_percentage': True
},
'rmsd_lt_2_valid': {
'key': 'Percent RMSD < 2 (Valid Poses)',
'label': 'RMSD < 2 (Valid poses)',
'color': 'lightcoral',
'unit': '%',
'is_percentage': True
},
'rmsd_lt_5': {
'key': 'Percent RMSD < 5',
'label': 'RMSD < 5',
'color': 'orange',
'unit': '%',
'is_percentage': True
},
'median_rmsd': {
'key': 'Median RMSD',
'label': 'Median RMSD',
'color': 'purple',
'unit': 'Å',
'is_percentage': False
}
}
# Default to plotting percentage metrics if not specified (now in the desired order)
if metrics_to_plot is None:
metrics_to_plot = ['rmsd_lt_1', 'rmsd_lt_2', 'rmsd_lt_2_valid', 'rmsd_lt_5']
# Check if we need separate subplots for different units
has_percentage = any(metric_configs[m]['is_percentage'] for m in metrics_to_plot)
has_non_percentage = any(not metric_configs[m]['is_percentage'] for m in metrics_to_plot)
if has_percentage and has_non_percentage:
# Create subplots for different units
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Plot percentage metrics
percentage_metrics = [m for m in metrics_to_plot if metric_configs[m]['is_percentage']]
_plot_metrics_group(ax1, models, eval_results, percentage_metrics, metric_configs, 'Percentage (%)')
# Plot non-percentage metrics
non_percentage_metrics = [m for m in metrics_to_plot if not metric_configs[m]['is_percentage']]
_plot_metrics_group(ax2, models, eval_results, non_percentage_metrics, metric_configs, 'RMSD (Å)')
else:
# Single subplot
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
unit_label = 'Percentage (%)' if has_percentage else 'RMSD (Å)'
_plot_metrics_group(ax, models, eval_results, metrics_to_plot, metric_configs, unit_label)
plt.tight_layout()
plt.show()
return fig
def _plot_metrics_group(ax, models, eval_results, metrics_to_plot, metric_configs, xlabel):
"""Helper function to plot a group of metrics on a single axis."""
# Extract data for all metrics
metric_data = {}
for metric in metrics_to_plot:
config = metric_configs[metric]
values = []
for model in models:
val = eval_results[model].get(config['key'], 0)
# Handle 'N/A' values
if val == 'N/A':
val = 0
values.append(val)
metric_data[metric] = values
# Set up bar positions
y_pos = np.arange(len(models))
n_metrics = len(metrics_to_plot)
width = 0.8 / n_metrics # Adjust width based on number of metrics
# Create horizontal bars
bars = []
for i, metric in enumerate(metrics_to_plot):
config = metric_configs[metric]
offset = (i - (n_metrics - 1) / 2) * width
bar = ax.barh(y_pos + offset, metric_data[metric], width,
label=config['label'], color=config['color'], alpha=0.7)
bars.append(bar)
# Customize the plot
ax.set_yticks(y_pos)
ax.set_yticklabels(models)
ax.set_xlabel(xlabel)
ax.set_title(f'RMSD Metrics Comparison ({xlabel.split("(")[0].strip()})')
ax.legend()
ax.grid(axis='x', alpha=0.3)
# Add value labels on bars
for i, bar_group in enumerate(bars):
config = metric_configs[metrics_to_plot[i]]
for bar in bar_group:
width_val = bar.get_width()
if config['is_percentage']:
label = f'{width_val:.1f}%'
else:
label = f'{width_val:.2f}Å'
ax.text(width_val + (ax.get_xlim()[1] * 0.01), bar.get_y() + bar.get_height()/2,
label, ha='left', va='center', fontsize=8) |