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1aa566a | 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 | """Drift simulation engine.
Supports four drift types:
gradual - features shift linearly over N steps
sudden - abrupt distribution change at a single point
seasonal - sinusoidal oscillation
mixed - combination of gradual and seasonal
"""
from __future__ import annotations
import numpy as np
import pandas as pd
from typing import Literal, Optional, Sequence
from src.utils.logging_config import get_logger
log = get_logger(__name__)
DriftType = Literal["gradual", "sudden", "seasonal", "mixed"]
class DriftSimulator:
"""Inject configurable drift into feature DataFrames."""
DRIFTABLE_CONTINUOUS = [
"trip_distance",
"passenger_count",
"pickup_hour",
]
DRIFTABLE_CATEGORICAL = [
"rate_code_id",
"payment_type",
"pu_location_zone",
"do_location_zone",
"vendor_id",
]
def __init__(self, random_seed: int = 42) -> None:
self.rng = np.random.default_rng(random_seed)
log.info("DriftSimulator initialised (seed=%d)", random_seed)
def apply(
self,
df: pd.DataFrame,
drift_type: DriftType = "gradual",
affected_features: Optional[Sequence[str]] = None,
severity: float = 1.0,
step: int = 0,
total_steps: int = 500,
) -> pd.DataFrame:
"""Apply drift to `df` and return a modified copy."""
df = df.copy()
if affected_features is None:
n_features = self.rng.integers(2, 4)
affected_features = list(
self.rng.choice(
self.DRIFTABLE_CONTINUOUS,
size=min(n_features, len(self.DRIFTABLE_CONTINUOUS)),
replace=False,
)
)
log.debug(
"Applying %s drift (step=%d/%d, features=%s, severity=%.2f)",
drift_type, step, total_steps, affected_features, severity,
)
if drift_type == "gradual":
df = self._gradual(df, affected_features, severity, step, total_steps)
elif drift_type == "sudden":
df = self._sudden(df, affected_features, severity)
elif drift_type == "seasonal":
df = self._seasonal(df, affected_features, severity, step)
elif drift_type == "mixed":
df = self._gradual(df, affected_features[:1], severity * 0.5, step, total_steps)
df = self._seasonal(df, affected_features[1:2], severity * 0.7, step)
else:
raise ValueError(f"Unknown drift_type: {drift_type!r}")
return df
def generate_drift_scenario(
self,
base_df: pd.DataFrame,
drift_type: DriftType = "gradual",
n_steps: int = 1000,
severity: float = 1.0,
) -> tuple[list[pd.DataFrame], dict]:
"""Generate a full drift scenario as a sequence of DataFrames."""
batch_size = max(1, len(base_df) // n_steps)
drifted_batches: list[pd.DataFrame] = []
metadata: dict = {
"drift_type": drift_type,
"n_steps": n_steps,
"severity": severity,
"affected_features": [],
}
n_features = self.rng.integers(2, 4)
affected = list(
self.rng.choice(
self.DRIFTABLE_CONTINUOUS,
size=min(n_features, len(self.DRIFTABLE_CONTINUOUS)),
replace=False,
)
)
metadata["affected_features"] = affected
for step in range(n_steps):
batch = base_df.sample(n=batch_size, replace=True, random_state=int(self.rng.integers(0, 100000)))
drifted = self.apply(
batch,
drift_type=drift_type,
affected_features=affected,
severity=severity,
step=step,
total_steps=n_steps,
)
drifted_batches.append(drifted)
log.info(
"Generated drift scenario: type=%s, steps=%d, features=%s",
drift_type, n_steps, affected,
)
return drifted_batches, metadata
def _gradual(
self,
df: pd.DataFrame,
features: Sequence[str],
severity: float,
step: int,
total_steps: int,
) -> pd.DataFrame:
progress = step / max(total_steps, 1)
for feat in features:
if feat not in df.columns:
continue
col = df[feat].to_numpy(dtype=float)
shift = severity * progress * col.std() * 2.0
df[feat] = col + shift + self.rng.normal(0, 0.1 * shift + 0.01, size=len(col))
return df
def _sudden(
self,
df: pd.DataFrame,
features: Sequence[str],
severity: float,
) -> pd.DataFrame:
for feat in features:
if feat not in df.columns:
continue
col = df[feat].to_numpy(dtype=float)
df[feat] = col + severity * col.std() * 3.0
return df
def _seasonal(
self,
df: pd.DataFrame,
features: Sequence[str],
severity: float,
step: int,
period: int = 100,
) -> pd.DataFrame:
angle = 2 * np.pi * (step % period) / period
for feat in features:
if feat not in df.columns:
continue
col = df[feat].to_numpy(dtype=float)
amplitude = severity * col.std() * 1.5
df[feat] = col + amplitude * np.sin(angle)
return df
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