def train_lightgbm(X: pd.DataFrame, y: pd.Series, config: Config):
params = {
"objective": "regression" if config.is_regression() else "binary",
"metric": "rmse" if config.is_regression() else "auc",
"verbosity": -1,
"seed": 1,
}
X_sample, y_sample = data_sample(X, y, config, nrows=20000)
hyperparams = hyperopt_lightgbm(X_sample, y_sample, params, config)
X_train, X_val, y_train, y_val = data_split(X, y, config)
config["model"] = lgb.train(
{**params, **hyperparams},
lgb.Dataset(X_train, label=y_train),
5000,
lgb.Dataset(X_val, label=y_val),
early_stopping_rounds=100,
verbose_eval=100,
)
config.save()
try:
with time_limit(config.time_left() - 10):
config["model"] = lgb.train(
{**params, **hyperparams},
lgb.Dataset(X, label=y),
int(1.2 * config["model"].best_iteration),
)
except TimeoutException:
Log.print("Timed out!")
def time_series_detect(df: pd.DataFrame, config: Config):
sample_size = 10000
model_params = {
"objective": "regression" if config["mode"] == "regression" else "binary",
"metric": "rmse" if config["mode"] == "regression" else "auc",
"learning_rate": 0.01,
"verbosity": -1,
"seed": 1,
"max_depth": -1,
}
if config.is_train():
datetime_columns = [c for c in df if c.startswith("datetime_")]
id_columns = [c for c in df if c.startswith("id_")]
sort_columns = []
for dc in datetime_columns:
sort_columns.append([dc])
for ic in id_columns:
sort_columns.append([ic, dc])
else:
for ic in id_columns:
sort_columns.append([ic])
scores = []
config.limit_time_fraction(0.1)
for sc in sort_columns:
if config.is_time_fraction_limit():
break
Log.silent(True)
df.sort_values(sc, inplace=True)
config_sample = copy.deepcopy(config)
df_sample = df.iloc[-sample_size:].copy() if len(df) > sample_size else df.copy()
df_sample = df_sample[[c for c in df_sample if c.startswith("number_") or c == "target" or c in sc]]
shift_columns(df_sample, group= sc[0] if len(sc) > 1 else None)
transform(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
X_train, X_test, y_train, y_test = ts_split(X, y, test_size=0.5)
model_sorted = lgb.train(model_params, lgb.Dataset(X_train, label=y_train), 3000, lgb.Dataset(X_test, label=y_test),
early_stopping_rounds=100, verbose_eval=False)
score_sorted = model_sorted.best_score["valid_0"][model_params["metric"]]
sampled_columns = [c for c in X if "_shift" not in c]
model_sampled = lgb.train(model_params, lgb.Dataset(X_train[sampled_columns], label=y_train), 3000, lgb.Dataset(X_test[sampled_columns], label=y_test),
early_stopping_rounds=100, verbose_eval=False)
score_sampled = model_sampled.best_score["valid_0"][model_params["metric"]]
if config.is_classification():
score_sorted = -score_sorted
score_sampled = -score_sampled
Log.silent(False)
Log.print("Sort: {}. Score sorted: {:0.4f}. Score sampled: {:0.4f}".format(sc, score_sorted, score_sampled))
score_ratio = score_sampled / score_sorted if config.is_regression() else abs(score_sorted / score_sampled)
if score_ratio >= 1.03:
Log.print(score_ratio)
scores.append((score_sorted, sc))
if len(scores) > 0:
scores = sorted(scores, key=lambda x: x[0])
Log.print("Scores: {}".format(scores))
config["sort_values"] = scores[0][1]
df.sort_values(config["sort_values"], inplace=True)
config_sample = copy.deepcopy(config)
df_sample = df.iloc[-sample_size:].copy() if len(df) > sample_size else df.copy()
shift_columns(df_sample, group=config["sort_values"][0] if len(config["sort_values"]) > 1 else None)
transform(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
model = lgb.train(model_params, lgb.Dataset(X, label=y), 1000)
fi = pd.Series(model.feature_importance(importance_type="gain"), index=X.columns)
fi = fi[fi > 0].sort_values()
selected_columns = fi[fi >= fi.quantile(0.75)].index.tolist()
selected_shift_columns = [c.replace("_shift", "") for c in selected_columns if "_shift" in c]
if len(selected_shift_columns) > 0:
Log.print("Shift columns: {}".format(selected_shift_columns))
config["shift_columns"] = selected_shift_columns
if "shift_columns" in config:
shift_columns(df, group=config["sort_values"][0] if len(config["sort_values"]) > 1 else None, number_columns=config["shift_columns"])
