#!/usr/bin/env python3
"""Plan a CatBoost model for a CSV: which columns are cat_features / text_features, the loss and
metric for the target, starting parameters, and (with --code) a runnable training script.
Column handling:
cat_features non-numeric low/medium-cardinality columns, numeric-looking codes (zip, *_code,
*_id with repeats); NaN must become a string ("__nan__") - CatBoost rejects NaN here
text_features long free-text columns (mean length >= 30 chars, many words); CatBoost tokenises them
numeric everything numeric; NaN allowed (nan_mode="Min" by default)
dropped near-unique IDs, constant columns, date columns (derive parts first), the time
column (used for ordering only)
Target: binary -> Logloss / AUC + PRAUC; multiclass -> MultiClass / TotalF1; numeric -> RMSE
(or Tweedie / Poisson / Quantile when --loss says so).
Usage:
python catboost_plan.py data.csv --target churned --drop customer_id
python catboost_plan.py tickets.csv --target queue --code -o train_catboost.py
python catboost_plan.py data.csv --target price --loss "Tweedie:variance_power=1.5" --time-col sold_at --code
Exit codes: 0 printed/written, 2 usage or read error.
"""
import argparse
import csv
import math
import os
import re
import sys
from itertools import islice
MISSING = {"", "na", "n/a", "nan", "null", "none", "?"}
def is_num(v):
try:
return math.isfinite(float(v))
except ValueError:
return False
def plan(header, rows, target, drop, time_col):
cats, texts, nums, dropped = [], [], [], []
for c in header:
if c in (target, time_col) or c in drop:
if c == time_col:
dropped.append((c, "time column: ordering / has_time, not a feature"))
continue
vals = [(r.get(c) or "").strip() for r in rows]
present = [v for v in vals if v.lower() not in MISSING]
if not present or len(set(present)) == 1:
dropped.append((c, "empty or constant"))
continue
k = len(set(present))
if sum(bool(re.match(r"^\d{4}[-/]\d{1,2}[-/]\d{1,2}", v)) for v in present) / len(present) > 0.9:
dropped.append((c, "date: derive year/month/day-of-week/days-since features first"))
continue
numeric = all(is_num(v) for v in present)
code_like = bool(re.search(r"(zip|postcode|_code$|^code$|_id$)", c, re.I))
if numeric and not code_like:
nums.append(c)
elif numeric and code_like:
if k > 0.9 * len(present):
dropped.append((c, "near-unique identifier (for codes such as zip, use a prefix like zip3 as a cat feature)"))
else:
cats.append(c)
else:
mean_len = sum(len(v) for v in present) / len(present)
words = sum(len(v.split()) for v in present) / len(present)
if mean_len >= 30 and words >= 4:
texts.append(c)
elif k > 0.9 * len(present) and len(present) > 50:
dropped.append((c, "near-unique text (ID)"))
else:
cats.append(c)
return cats, texts, nums, dropped
SCRIPT = '''"""CatBoost model for {target!r}. Generated by catboost_plan.py - review, then version.
Run: python {script} (pandas, catboost >= 1.2, scikit-learn)
"""
import numpy as np
import pandas as pd
from catboost import CatBoostClassifier, CatBoostRegressor, Pool
from sklearn.model_selection import train_test_split
DATA, TARGET = {data!r}, {target!r}
CAT = {cats!r}
TEXT = {texts!r}
NUM = {nums!r}
SEED = 0
df = pd.read_csv(DATA)
df = df[df[TARGET].notna()]
{order}for c in CAT: # CatBoost needs strings/ints for cat features, never NaN
df[c] = df[c].astype("string").fillna("__nan__").astype(str)
for c in TEXT:
df[c] = df[c].fillna("").astype(str)
X, y = df[CAT + TEXT + NUM], df[TARGET]
{split}
train_pool = Pool(X_tr, y_tr, cat_features=CAT, text_features=TEXT or None)
valid_pool = Pool(X_va, y_va, cat_features=CAT, text_features=TEXT or None)
test_pool = Pool(X_te, y_te, cat_features=CAT, text_features=TEXT or None)
model = {cls}(
loss_function={loss!r},
eval_metric={metric!r},{custom}
iterations=5000,
learning_rate={lr},
depth=6,
l2_leaf_reg=3.0,
od_type="Iter",
od_wait=200,
random_seed=SEED,
allow_writing_files=False,
verbose=500,{extra}
)
model.fit(train_pool, eval_set=valid_pool, use_best_model=True)
print("best iteration:", model.get_best_iteration(), "best valid score:", model.get_best_score()["validation"])
print("test:", model.eval_metrics(test_pool, metrics=[{metric!r}], ntree_start=model.get_best_iteration() - 1)[{metric!r}][-1])
imp = model.get_feature_importance(train_pool, prettified=True) # PredictionValuesChange
print(imp.head(15).to_string(index=False))
shap = model.get_feature_importance(test_pool, type="ShapValues") # rows x (features + 1 expected value)
print("SHAP shape:", np.shape(shap))
model.save_model("catboost_model.cbm")
'''
def main():
ap = argparse.ArgumentParser(description="Plan CatBoost features and parameters for a CSV.", epilog=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("csv")
ap.add_argument("--target", required=True)
ap.add_argument("--drop", nargs="*", default=[])
ap.add_argument("--time-col", help="split by time and set has_time=True (ordered boosting follows row order)")
ap.add_argument("--loss", help="override loss_function, e.g. 'Tweedie:variance_power=1.5', 'Quantile:alpha=0.9'")
ap.add_argument("--code", action="store_true", help="emit a runnable training script")
ap.add_argument("-o", "--output")
a = ap.parse_args()
try:
with open(a.csv, newline="", encoding="utf-8-sig") as fh:
reader = csv.DictReader(fh)
header = reader.fieldnames or []
rows = list(islice(reader, 20000))
except OSError as e:
print(f"error: {e}", file=sys.stderr)
return 2
for c in (a.target, a.time_col):
if c and c not in header:
print(f"error: column {c!r} not found", file=sys.stderr)
return 2
cats, texts, nums, dropped = plan(header, rows, a.target, set(a.drop), a.time_col)
tv = [(r.get(a.target) or "").strip() for r in rows if (r.get(a.target) or "").strip()]
k = len(set(tv))
if k == 2:
task, cls, loss, metric, custom = "binary", "CatBoostClassifier", "Logloss", "AUC", '\n custom_metric=["PRAUC", "Logloss"],'
elif k <= 50 and not all(is_num(v) for v in tv) or (k <= 20 and all(is_num(v) and float(v).is_integer() for v in tv)):
task, cls, loss, metric, custom = "multiclass", "CatBoostClassifier", "MultiClass", "TotalF1:average=Macro", '\n custom_metric=["Accuracy"],'
else:
task, cls, loss, metric, custom = "regression", "CatBoostRegressor", "RMSE", "RMSE", '\n custom_metric=["MAE", "R2"],'
if a.loss:
loss = a.loss
if task == "regression":
metric = a.loss.split(":")[0] if a.loss.split(":")[0] in ("Tweedie", "Poisson", "Quantile", "MAE", "RMSE") else metric
if metric == "Tweedie":
metric = a.loss
if metric == "Quantile":
metric = a.loss
n = len(rows)
lr = 0.05 if n < 100_000 else 0.08
extra = ""
if a.time_col:
extra += "\n has_time=True, # keep the time order for ordered boosting / statistics"
if task == "binary":
pos = min(set(tv), key=tv.count)
rate = tv.count(pos) / len(tv)
if rate < 0.05:
extra += ("\n # rare positives: keep probabilities calibrated (no weights) and watch PRAUC;"
"\n # auto_class_weights='Balanced' only if recall at the default threshold matters")
if texts:
extra += "\n # text_features are tokenised into bag-of-words + naive Bayes / BM25 estimates by default"
if a.time_col:
split = (f"df_sorted = df.sort_values({a.time_col!r})\n"
"n = len(df_sorted)\n"
"idx_tr, idx_va, idx_te = df_sorted.index[:int(.7 * n)], df_sorted.index[int(.7 * n):int(.85 * n)], df_sorted.index[int(.85 * n):]\n"
"X_tr, X_va, X_te = X.loc[idx_tr], X.loc[idx_va], X.loc[idx_te]\n"
"y_tr, y_va, y_te = y.loc[idx_tr], y.loc[idx_va], y.loc[idx_te]")
order = ""
else:
strat = "stratify=y, " if task != "regression" else ""
split = (f"X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.15, {strat}random_state=SEED)\n"
f"X_tr, X_va, y_tr, y_va = train_test_split(X_tr, y_tr, test_size=0.15, "
f"{'stratify=y_tr, ' if strat else ''}random_state=SEED)")
order = ""
if a.code:
text = SCRIPT.format(target=a.target, script=os.path.basename(a.output or "train_catboost.py"), data=a.csv,
cats=cats, texts=texts, nums=nums, order=order, split=split, cls=cls, loss=loss,
metric=metric, custom=custom, lr=lr, extra=extra)
if a.output:
with open(a.output, "w", encoding="utf-8") as f:
f.write(text)
print(f"wrote {a.output}", file=sys.stderr)
else:
sys.stdout.write(text)
return 0
print(f"CatBoost plan for {a.target!r} ({task}, {n:,} rows sampled)")
print(f" cat_features ({len(cats)}): {', '.join(cats) or '-'}")
print(f" text_features ({len(texts)}): {', '.join(texts) or '-'}")
print(f" numeric ({len(nums)}): {', '.join(nums) or '-'}")
for c, why in dropped:
print(f" dropped: {c} ({why})")
print(f"\n {cls}(loss_function={loss!r}, eval_metric={metric!r}, iterations=5000, learning_rate={lr}, depth=6,")
print(" l2_leaf_reg=3, od_type='Iter', od_wait=200, random_seed=0" + (", has_time=True" if a.time_col else "") + ")")
print(" fit(Pool(X_tr, y_tr, cat_features=..., text_features=...), eval_set=valid_pool, use_best_model=True)")
print("\n Cat columns must be str/int (fill NaN with '__nan__'); no one-hot or target encoding beforehand -")
print(" CatBoost's ordered target statistics do that without leakage. Run with --code for the full script.")
return 0
if __name__ == "__main__":
sys.exit(main())
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