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714cf46 | 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 | import pandas as pd
import numpy as np
import os
import torch
import time
from typing import List, Optional
from scipy.stats import spearmanr
from .scorer import ProteinGymScorer
from .data_loader import load_proteingym_dms
from .dms_ids import ALL_SUBSTITUTION_DMS_IDS
try:
from base_models.get_base_models import get_base_model
except ImportError:
from ...base_models.get_base_models import get_base_model
def compare_scoring_methods(
model_names: List[str],
device: Optional[str] = None,
methods: Optional[List[str]] = None,
dms_ids: Optional[List[str]] = None,
progress: bool = True,
output_csv: Optional[str] = None,
batch_size: int = 32,
scoring_window: str = "optimal"
) -> pd.DataFrame:
"""
Compare scoring methods across one or more models and DMS assays.
Args:
model_names: List of model names to evaluate
device: Device string like 'cuda' or 'cpu'
methods: List of scoring methods to compare
dms_ids: List of DMS IDs to evaluate
progress: Whether to show progress bars
output_csv: Optional path to save results CSV
batch_size: Batch size for inference (default: 32)
scoring_window: Windowing strategy ('optimal' or 'sliding')
Returns:
DataFrame with model_name, scoring_method, Average_Spearman, Average_Time_Seconds, Total_Time_Seconds, and n_assays columns
"""
if methods is None:
methods = ["masked_marginal", "mutant_marginal", "wildtype_marginal", "global_log_prob"]
if dms_ids is None:
dms_ids = ALL_SUBSTITUTION_DMS_IDS
all_summary_results = []
device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
for model_name in model_names:
print(f"\n{'='*80}")
print(f"PROCESSING MODEL: {model_name}")
print(f"{'='*80}")
try:
# Store results for each assay
assay_results = []
spearman_results = []
timing_results = []
# Load model once per model and reuse across methods/assays
model, tokenizer = get_base_model(model_name, masked_lm=True)
model = model.to(device).eval()
scorer = ProteinGymScorer(
model_name=model_name,
model=model,
tokenizer=tokenizer,
device=device,
batch_size=batch_size,
)
for dms_id in dms_ids:
print(f"\nProcessing DMS ID: {dms_id}")
df = load_proteingym_dms(
dms_id=dms_id,
mode="benchmark",
repo_id="GleghornLab/ProteinGym_DMS",
)
assay_result = df.copy()
assay_result['dms_id'] = dms_id
for method in methods:
print(f"Running {method} scoring for {dms_id}...")
# Measure timing for this scoring method
start_time = time.time()
scored_df = scorer.score_substitutions(
df=df,
scoring_method=method,
scoring_window=scoring_window,
)
end_time = time.time()
method_duration = end_time - start_time
print(f" {method} scoring completed in {method_duration:.2f} seconds")
assay_result[f'{method}_score'] = scored_df['delta_log_prob']
# Calculate Spearman
x = scored_df["delta_log_prob"].to_numpy()
y = scored_df["DMS_score"].to_numpy()
if np.all(np.isnan(x)) or np.all(np.isnan(y)):
print(f"No valid scores for {method} scoring for {dms_id}")
spearman_rho = np.nan
else:
mask = ~(np.isnan(x) | np.isnan(y))
if mask.sum() < 2:
print(f"Not enough valid scores for {method} scoring for {dms_id}")
spearman_rho = np.nan
else:
rho, _ = spearmanr(x[mask], y[mask])
spearman_rho = rho
print(f"Spearman correlation for {method} on {dms_id}: {rho:.4f}")
assay_result[f'{method}_spearman_rho'] = spearman_rho
# Store for summary calculation
spearman_results.append({
'dms_id': dms_id,
'method': method,
'spearman_rho': spearman_rho
})
# Store timing results
timing_results.append({
'dms_id': dms_id,
'method': method,
'duration_seconds': method_duration
})
assay_results.append(assay_result)
# Calculate average Spearman correlations and timing for this model
spearman_df = pd.DataFrame(spearman_results)
timing_df = pd.DataFrame(timing_results)
summary_results = []
for method in methods:
method_data = spearman_df[spearman_df['method'] == method]['spearman_rho']
valid_correlations = method_data[~np.isnan(method_data)]
if len(valid_correlations) > 0:
avg_spearman = valid_correlations.mean()
n_assays = len(valid_correlations)
else:
avg_spearman = np.nan
n_assays = 0
# Calculate timing statistics for this method
method_timing_data = timing_df[timing_df['method'] == method]['duration_seconds']
if len(method_timing_data) > 0:
avg_time = method_timing_data.mean()
total_time = method_timing_data.sum()
else:
avg_time = np.nan
total_time = np.nan
summary_results.append({
'model_name': model_name,
'scoring_method': method,
'Average_Spearman': avg_spearman,
'Average_Time_Seconds': avg_time,
'Total_Time_Seconds': total_time,
'n_assays': n_assays
})
model_summary_df = pd.DataFrame(summary_results)
all_summary_results.append(model_summary_df)
# Print summary for this model
print(f"\n{'='*60}")
print(f"SUMMARY FOR MODEL: {model_name}")
print(f"{'='*60}")
print(model_summary_df.to_string(index=False))
except Exception as e:
print(f"Error processing model {model_name}: {e}")
# Create empty summary for failed model
failed_summary = pd.DataFrame([{
'model_name': model_name,
'scoring_method': method,
'Average_Spearman': np.nan,
'Average_Time_Seconds': np.nan,
'Total_Time_Seconds': np.nan,
'n_assays': 0
} for method in methods])
all_summary_results.append(failed_summary)
# Combine all summary results
if all_summary_results:
combined_summary = pd.concat(all_summary_results, ignore_index=True)
else:
combined_summary = pd.DataFrame()
# Save results if output path provided
if output_csv:
os.makedirs(os.path.dirname(output_csv), exist_ok=True)
combined_summary.to_csv(output_csv, index=False)
print(f"\nResults saved to {output_csv}")
# Print final summary
print(f"\n{'='*80}")
print("FINAL SUMMARY - AVERAGE SPEARMAN CORRELATIONS")
print(f"{'='*80}")
print(combined_summary.to_string(index=False))
return combined_summary |