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import pandas as pd
import numpy as np
from chronos import Chronos2Pipeline
from pathlib import Path
from utils.metrics import mae, bias
# ---------------------------------------------------------
# CONFIG
# ---------------------------------------------------------
HORIZON = 14
INFERENCE_INPUT_PATH = Path("data/processed/lgbm_ready/inference/inference_train.csv")
INFERENCE_TARGET_PATH = Path("data/processed/lgbm_ready/inference/inference_target.csv")
CHRONOS_OUTPUT_PATH = Path("metrics/chronos_predictions.csv")
CHRONOS_METRICS_PATH = Path("metrics/chronos_metrics.csv")
# ---------------------------------------------------------
# LOAD CHRONOS MODEL
# ---------------------------------------------------------
pipeline = Chronos2Pipeline.from_pretrained(
"amazon/chronos-2",
device_map="cpu", # you can switch to "cuda" once your env is ready
)
# ---------------------------------------------------------
# LOAD DATA (wide: id, 1..T)
# ---------------------------------------------------------
df_infer = pd.read_csv(INFERENCE_INPUT_PATH)
df_true = pd.read_csv(INFERENCE_TARGET_PATH)
# ---------------------------------------------------------
# Helper: wide -> Chronos long format
# ---------------------------------------------------------
def reshape_for_chronos(df: pd.DataFrame) -> pd.DataFrame:
"""
Convert wide format to Chronos format:
id | timestamp | target
We create a synthetic daily calendar starting at 2024-01-01.
"""
melted = df.melt(id_vars=["id"], var_name="day", value_name="target")
melted["day"] = melted["day"].astype(int)
melted["timestamp"] = pd.to_datetime("2024-01-01") + pd.to_timedelta(
melted["day"], unit="D"
)
return melted[["id", "timestamp", "target"]]
context_df = reshape_for_chronos(df_infer)
truth_df = reshape_for_chronos(df_true)
# ---------------------------------------------------------
# CHRONOS INFERENCE (univariate, per-id)
# ---------------------------------------------------------
prediction_rows = []
for id_val, df_series in context_df.groupby("id"):
df_series_sorted = df_series.sort_values("timestamp")
pred_df = pipeline.predict_df(
df_series_sorted,
future_df=None,
prediction_length=HORIZON,
quantile_levels=[0.1, 0.5, 0.9],
id_column="id",
timestamp_column="timestamp",
target="target",
)
# Chronos returns columns like: timestamp, target_name, predictions, 0.1, 0.5, 0.9
pred_df["id"] = id_val
pred_df["h"] = np.arange(1, HORIZON + 1) # horizon index
prediction_rows.append(pred_df)
chronos_preds = pd.concat(prediction_rows, ignore_index=True)
print(chronos_preds.head())
# ---------------------------------------------------------
# Save predictions (rename quantile columns)
# ---------------------------------------------------------
chronos_preds.rename(
columns={
"0.1": "q10",
"0.5": "q50",
"0.9": "q90",
},
inplace=True,
)
chronos_preds.to_csv(CHRONOS_OUTPUT_PATH, index=False)
print(f"Saved Chronos forecasts → {CHRONOS_OUTPUT_PATH}")
# ---------------------------------------------------------
# METRICS — align truth with horizon index h
# ---------------------------------------------------------
# Take last HORIZON days per id from truth_df, ordered by timestamp
true_filtered = (
truth_df.sort_values(["id", "timestamp"])
.groupby("id", group_keys=False)
.tail(HORIZON)
)
# Assign horizon index 1..H per id (aligned with Chronos h)
true_filtered["h"] = (
true_filtered.sort_values(["id", "timestamp"])
.groupby("id")
.cumcount() + 1
)
print(true_filtered.head())
# Merge on id + h, use q50 as point forecast
merged = pd.merge(
true_filtered[["id", "h", "target"]],
chronos_preds[["id", "h", "q50"]],
on=["id", "h"],
how="inner",
)
merged.rename(columns={"q50": "forecast"}, inplace=True)
# ---------------------------------------------------------
# Compute metrics per id
# ---------------------------------------------------------
metric_rows = []
for id_val, g in merged.groupby("id"):
y_true = g["target"].values
y_pred = g["forecast"].values
m = mae(y_true, y_pred)
b = bias(y_true, y_pred)
s = m + abs(b)
metric_rows.append(
{
"id": id_val,
"model": "chronos2",
"mae": float(m),
"bias": float(b),
"score": float(s),
}
)
metrics_df = pd.DataFrame(metric_rows)
metrics_df.to_csv(CHRONOS_METRICS_PATH, index=False)
print(f"Saved metrics → {CHRONOS_METRICS_PATH}")
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