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857c2e9 | 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 | #!/usr/bin/env python3
"""
Simple validation script for the Robometer dataset format.
Checks fields and data types only.
"""
import argparse
from typing import Any
import numpy as np
from datasets import Dataset, load_from_disk
def validate_dataset_fields_and_types(dataset: Dataset, sample_size: int = 10) -> dict[str, Any]:
"""Validate dataset fields and data types."""
print(f"Validating dataset fields and data types on {sample_size} sample entries...")
validation_results = {
"valid": True,
"errors": [],
"warnings": [],
"stats": {"dataset_size": len(dataset), "samples_checked": 0},
}
# Expected schema for the new format
expected_fields = [
"id",
"task",
"lang_vector",
"data_source",
"frames",
"is_robot",
"quality_label",
"preference_group_id",
"preference_rank",
]
# Check if dataset has features
if not hasattr(dataset, "features") or dataset.features is None:
validation_results["valid"] = False
validation_results["errors"].append("Dataset has no features defined")
return validation_results
print(f"Dataset size: {len(dataset)} entries")
print(f"Dataset features: {list(dataset.features.keys())}")
# Check required fields
for field_name in expected_fields:
if field_name not in dataset.features:
validation_results["valid"] = False
validation_results["errors"].append(f"Missing required field: {field_name}")
else:
print(f"✓ Field '{field_name}' present")
# Sample entries for validation
sample_indices = np.random.choice(len(dataset), min(sample_size, len(dataset)), replace=False)
validation_results["stats"]["samples_checked"] = len(sample_indices)
for idx in sample_indices:
trajectory = dataset[idx]
try:
# Validate each field
if not isinstance(trajectory["id"], str):
validation_results["errors"].append(f"Trajectory {idx}: 'id' is not a string")
if not isinstance(trajectory["task"], str):
validation_results["errors"].append(f"Trajectory {idx}: 'task' is not a string")
# lang_vector should be length-384 sequence
lv = trajectory["lang_vector"]
if isinstance(lv, np.ndarray):
if lv.shape != (384,):
validation_results["errors"].append(
f"Trajectory {idx}: 'lang_vector' shape is {lv.shape}, expected (384,)"
)
elif isinstance(lv, list):
if len(lv) != 384:
validation_results["errors"].append(
f"Trajectory {idx}: 'lang_vector' length is {len(lv)}, expected 384"
)
else:
# check element types
if not all(isinstance(x, (int, float, np.floating, np.integer)) for x in lv):
validation_results["warnings"].append(
f"Trajectory {idx}: 'lang_vector' contains non-numeric elements"
)
else:
validation_results["errors"].append(f"Trajectory {idx}: 'lang_vector' has unexpected type {type(lv)}")
if not isinstance(trajectory["data_source"], str):
validation_results["errors"].append(f"Trajectory {idx}: 'data_source' is not a string")
if not isinstance(trajectory["frames"], str):
validation_results["errors"].append(f"Trajectory {idx}: 'frames' is not a string path")
if not isinstance(trajectory["is_robot"], bool):
validation_results["errors"].append(f"Trajectory {idx}: 'is_robot' is not a boolean")
if not isinstance(trajectory["quality_label"], str):
validation_results["errors"].append(f"Trajectory {idx}: 'quality_label' is not a string")
else:
if trajectory["quality_label"] not in {"successful", "failure", "suboptimal"}:
validation_results["warnings"].append(
f"Trajectory {idx}: 'quality_label' has unexpected value '{trajectory['quality_label']}'"
)
# preference fields can be None
if trajectory.get("preference_group_id") is not None and not isinstance(
trajectory["preference_group_id"], str
):
validation_results["errors"].append(
f"Trajectory {idx}: 'preference_group_id' is neither None nor string"
)
if trajectory.get("preference_rank") is not None and not isinstance(trajectory["preference_rank"], int):
validation_results["errors"].append(f"Trajectory {idx}: 'preference_rank' is neither None nor int")
# Print sample task for first trajectory
if idx == sample_indices[0]:
print("\nSample task from first trajectory:")
print(f" Task: {trajectory['task']}")
print(f" ID: {trajectory['id']}")
except Exception as e:
validation_results["errors"].append(f"Trajectory {idx}: Error during validation: {e}")
if validation_results["errors"]:
validation_results["valid"] = False
return validation_results
def print_validation_summary(validation_results: dict[str, Any]):
"""Print validation summary."""
print("\n" + "=" * 50)
print("VALIDATION SUMMARY")
print("=" * 50)
status = "✅ PASS" if validation_results["valid"] else "❌ FAIL"
print(f"Status: {status}")
print(f"Dataset size: {validation_results['stats']['dataset_size']}")
print(f"Samples checked: {validation_results['stats']['samples_checked']}")
if validation_results.get("errors"):
print(f"\nErrors ({len(validation_results['errors'])}):")
for error in validation_results["errors"][:10]: # Show first 10 errors
print(f" - {error}")
if len(validation_results["errors"]) > 10:
print(f" ... and {len(validation_results['errors']) - 10} more errors")
if validation_results.get("warnings"):
print(f"\nWarnings ({len(validation_results['warnings'])}):")
for warning in validation_results["warnings"][:5]: # Show first 5 warnings
print(f" - {warning}")
if len(validation_results["warnings"]) > 5:
print(f" ... and {len(validation_results['warnings']) - 5} more warnings")
print("=" * 50)
def main():
"""Main validation function."""
parser = argparse.ArgumentParser(description="Validate dataset fields and data types")
parser.add_argument("dataset_path", help="Path to the HuggingFace dataset")
parser.add_argument("--sample-size", type=int, default=10, help="Number of samples to check")
args = parser.parse_args()
# Load dataset
print(f"Loading dataset from: {args.dataset_path}")
try:
dataset = load_from_disk(args.dataset_path)
except Exception as e:
print(f"Error loading dataset: {e}")
return
print("Dataset loaded successfully.")
# Run validation
validation_results = validate_dataset_fields_and_types(dataset, args.sample_size)
# Print summary
print_validation_summary(validation_results)
# Exit with error code if validation failed
if not validation_results["valid"]:
exit(1)
if __name__ == "__main__":
main()
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