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9a76208 | 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 | from __future__ import annotations
import argparse
import json
import warnings
import time
from pathlib import Path
import sys
from typing import Any
import cProfile
import io
import pstats
import numpy as np
import pandas as pd
from pandas.errors import PerformanceWarning
try:
from sklearn.exceptions import InconsistentVersionWarning
except Exception: # pragma: no cover
InconsistentVersionWarning = Warning # type: ignore[misc]
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
warnings.filterwarnings("ignore", category=PerformanceWarning)
warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
from app.main import (
DATA_PATH,
MODEL_PATH,
ARTIFACTS_PATH,
load_model,
load_preprocessor,
preprocess_input,
new_features_creation,
_apply_correlated_imputation,
_ensure_required_columns,
_validate_numeric_inputs,
_validate_numeric_ranges,
)
def preprocess_input_legacy(df_raw: pd.DataFrame, artifacts) -> pd.DataFrame:
df = df_raw.copy()
for col in artifacts.required_input_columns:
if col not in df.columns:
df[col] = np.nan
_ensure_required_columns(df, artifacts.required_input_columns)
_validate_numeric_inputs(df, artifacts.numeric_required_columns)
_validate_numeric_ranges(
df,
{k: v for k, v in artifacts.numeric_ranges.items() if k in artifacts.numeric_required_columns},
)
df["is_train"] = 0
df["is_test"] = 1
if "TARGET" not in df.columns:
df["TARGET"] = 0
df = new_features_creation(df)
df.replace([np.inf, -np.inf], np.nan, inplace=True)
for col in artifacts.columns_keep:
if col not in df.columns:
df[col] = np.nan
df = df[artifacts.columns_keep]
_apply_correlated_imputation(df, artifacts)
for col, median in artifacts.numeric_medians.items():
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors="coerce")
df[col] = df[col].fillna(median)
for col in artifacts.categorical_columns:
if col in df.columns:
df[col] = df[col].fillna("Unknown")
_ensure_required_columns(df, artifacts.required_input_columns)
if "CODE_GENDER" in df.columns and (df["CODE_GENDER"] == "XNA").any():
raise ValueError("CODE_GENDER cannot be 'XNA' based on training rules.")
for col, max_val in artifacts.outlier_maxes.items():
if col in df.columns and (df[col] >= max_val).any():
raise ValueError(f"Input contains outlier values removed during training: {col}")
df_hot = pd.get_dummies(df, columns=artifacts.categorical_columns)
for col in artifacts.features_to_scaled:
if col not in df_hot.columns:
df_hot[col] = 0
df_hot = df_hot[artifacts.features_to_scaled]
scaled = artifacts.scaler.transform(df_hot)
return pd.DataFrame(scaled, columns=artifacts.features_to_scaled, index=df.index)
def _load_input_sample(data_path: Path, columns: list[str], sample_size: int) -> pd.DataFrame:
df = pd.read_parquet(data_path, columns=columns)
if sample_size and len(df) > sample_size:
df = df.sample(sample_size, random_state=42)
return df.reset_index(drop=True)
def _fill_required_inputs(df: pd.DataFrame, artifacts) -> pd.DataFrame:
df_filled = df.copy()
for col in artifacts.required_input_columns:
if col not in df_filled.columns:
df_filled[col] = np.nan
if col in artifacts.numeric_medians:
df_filled[col] = pd.to_numeric(df_filled[col], errors="coerce").fillna(
artifacts.numeric_medians[col]
)
if col in artifacts.numeric_ranges:
min_val, max_val = artifacts.numeric_ranges[col]
df_filled[col] = df_filled[col].clip(min_val, max_val)
elif col in artifacts.categorical_columns:
df_filled[col] = df_filled[col].fillna("Unknown")
else:
df_filled[col] = df_filled[col].fillna(0)
if col in artifacts.outlier_maxes:
max_val = artifacts.outlier_maxes[col]
if pd.api.types.is_integer_dtype(df_filled[col]):
replace_val = max_val - 1
else:
replace_val = np.nextafter(max_val, -np.inf)
df_filled.loc[df_filled[col] >= max_val, col] = replace_val
return df_filled
def _benchmark(
*,
name: str,
preprocess_fn,
model,
artifacts,
df_inputs: pd.DataFrame,
batch_size: int,
runs: int,
) -> dict[str, Any]:
durations = []
for _ in range(runs):
for start in range(0, len(df_inputs), batch_size):
batch = df_inputs.iloc[start:start + batch_size]
t0 = time.perf_counter()
features = preprocess_fn(batch, artifacts)
if hasattr(model, "predict_proba"):
_ = model.predict_proba(features)[:, 1]
else:
_ = model.predict(features)
durations.append((time.perf_counter() - t0) * 1000.0)
durations = np.array(durations, dtype=float)
return {
"name": name,
"batches": int(len(durations)),
"batch_size": int(batch_size),
"mean_ms": float(durations.mean()) if durations.size else 0.0,
"p50_ms": float(np.percentile(durations, 50)) if durations.size else 0.0,
"p95_ms": float(np.percentile(durations, 95)) if durations.size else 0.0,
"throughput_rows_per_sec": float(
(batch_size / (durations.mean() / 1000.0)) if durations.size else 0.0
),
}
def _profile(preprocess_fn, model, artifacts, df_inputs: pd.DataFrame, batch_size: int) -> str:
profiler = cProfile.Profile()
batch = df_inputs.iloc[:batch_size]
profiler.enable()
features = preprocess_fn(batch, artifacts)
if hasattr(model, "predict_proba"):
_ = model.predict_proba(features)[:, 1]
else:
_ = model.predict(features)
profiler.disable()
stream = io.StringIO()
stats = pstats.Stats(profiler, stream=stream).sort_stats("cumulative")
stats.print_stats(30)
return stream.getvalue()
def main() -> None:
parser = argparse.ArgumentParser(description="Profile and benchmark inference latency.")
parser.add_argument("--data-path", type=Path, default=DATA_PATH)
parser.add_argument("--model-path", type=Path, default=MODEL_PATH)
parser.add_argument("--artifacts-path", type=Path, default=ARTIFACTS_PATH)
parser.add_argument("--sample-size", type=int, default=2000)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--runs", type=int, default=3)
parser.add_argument("--output-json", type=Path, default=Path("docs/performance/benchmark_results.json"))
parser.add_argument("--output-profile", type=Path, default=Path("docs/performance/profile_summary.txt"))
args = parser.parse_args()
preprocessor = load_preprocessor(args.data_path, args.artifacts_path)
model = load_model(args.model_path)
input_cols = list(preprocessor.required_input_columns)
df_inputs = _load_input_sample(args.data_path, input_cols, args.sample_size)
df_inputs = _fill_required_inputs(df_inputs, preprocessor)
results = []
results.append(
_benchmark(
name="optimized_preprocess",
preprocess_fn=preprocess_input,
model=model,
artifacts=preprocessor,
df_inputs=df_inputs,
batch_size=args.batch_size,
runs=args.runs,
)
)
results.append(
_benchmark(
name="legacy_preprocess_alignment",
preprocess_fn=preprocess_input_legacy,
model=model,
artifacts=preprocessor,
df_inputs=df_inputs,
batch_size=args.batch_size,
runs=args.runs,
)
)
args.output_json.parent.mkdir(parents=True, exist_ok=True)
args.output_json.write_text(json.dumps(results, indent=2), encoding="utf-8")
profile_text = _profile(preprocess_input, model, preprocessor, df_inputs, args.batch_size)
args.output_profile.parent.mkdir(parents=True, exist_ok=True)
args.output_profile.write_text(profile_text, encoding="utf-8")
print(f"Saved benchmarks to {args.output_json}")
print(f"Saved profile to {args.output_profile}")
if __name__ == "__main__":
main()
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