def train_lightgbm(X: pd.DataFrame, y: pd.Series, stored_models_key: str,
save_to_disk: bool, config: Config):
config[stored_models_key] = []
data = lgb.Dataset(X, label=y, free_raw_data=False)
data.construct()
gc.collect()
params = {
"objective": config["objective"],
"metric": config["metric"],
"seed": config["seed"],
'num_threads': config['n_threads'],
"verbosity": -1,
}
seed = config["seed"]
space = {
"learning_rate":
hp.uniform("learning_rate", 0.01, 0.4),
"max_depth":
hp.choice("max_depth", [-1, 2, 3, 4, 5, 6, 10]),
"num_leaves":
hp.choice("num_leaves", np.linspace(4, 200, 50, dtype=int)),
"feature_fraction":
hp.quniform("feature_fraction", 0.1, 1., 0.1),
"bagging_fraction":
hp.quniform("bagging_fraction", 0.1, 1., 0.1),
"bagging_freq":
hp.choice("bagging_freq", np.linspace(0, 20, 10, dtype=int)),
"reg_alpha":
hp.uniform("reg_alpha", 0, 30),
"reg_lambda":
hp.uniform("reg_lambda", 0, 30),
"min_child_weight":
hp.uniform('min_child_weight', 1e-10, 20),
"max_bin":
hp.choice('max_bin', [50, 100, 255]),
'boosting_type':
hp.choice(
'boosting_type',
[
{
'boosting_type': 'gbdt',
},
{
'boosting_type':
'dart',
'drop_rate':
hp.uniform('drop_rate', 0.01, 0.6),
'max_drop':
hp.choice(
"max_drop",
np.linspace(5,
config["train_num_boost_round"] * .9,
10,
dtype=int)),
'skip_drop':
hp.uniform('skip_drop', 0.1, 0.7),
},
# {'boosting_type': 'rf',
# 'bagging_freq': 1,
# },
# {'boosting_type': 'goss',
# 'bagging_freq': 0,
# },
]),
#train params
'early_stopping_rounds':
hp.choice("early_stopping_rounds", [None, 50]),
'cv_splits':
hp.choice("cv_splits", np.linspace(3, 12, 10, dtype=int)), # [4,8]
'shuffle':
hp.choice("shuffle", [True, False]),
}
if config.is_classification():
space['scale_pos_weight'] = hp.uniform('scale_pos_weight', 0.5, 10)
else:
space['objective'] = hp.choice(
"objective",
[
'regression',
'huber',
# 'fair',
# 'regression_l1',
])
def objective(space_sample):
iteration_start = time.time()
hyperparams = copy.deepcopy(space_sample)
boosting_type = {}
if 'boosting_type' in hyperparams.keys():
boosting_type = hyperparams.pop('boosting_type')
hyperparams = {**params, **hyperparams, **boosting_type}
scores, models, y_oof = train_lightgbm_cv(data=data,
hyperparams=hyperparams,
config=config)
if config.is_classification(): scores['oof'] = -scores['oof']
iteration_time = time.time() - iteration_start
log('iteration time %.1f, loss %.5f' % (iteration_time, scores['oof']))
elapsed_time = (time.time() - config['start_time'])
have_time = (config["time_limit"] - elapsed_time - iteration_time) > 25
if have_time:
save_model(models, hyperparams, scores, y_oof, stored_models_key,
save_to_disk, config)
status = STATUS_OK
else:
status = STATUS_FAIL
return {
'loss': scores['oof'],
'runtime': iteration_time,
'scores': scores,
'models': models,
'y_oof': y_oof,
'status': status
}
have_time = True
eval_n = 0
trials = Trials()
while have_time:
iteration_start = time.time()
best = hyperopt.fmin(
fn=objective,
space=space,
trials=trials,
algo=tpe.suggest,
max_evals=eval_n + 1,
verbose=1,
rstate=np.random.RandomState(eval_n)
) #TODO: (bug) if seed the same - in some cases it samples same values forever
iteration_time = time.time() - iteration_start
elapsed_time = (time.time() - config['start_time'])
have_time = (config["time_limit"] - elapsed_time - iteration_time) > 25
eval_n += 1
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"])
