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learner_lightgbm.py
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learner_lightgbm.py
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import logging
import copy
import numpy as np
import pandas as pd
import os
from supervised.config import storage_path
from supervised.models.learner import Learner
from supervised.tuner.registry import ModelsRegistry
from supervised.tuner.registry import BINARY_CLASSIFICATION
import multiprocessing
import lightgbm as lgb
import operator
log = logging.getLogger(__name__)
class LightgbmLearner(Learner):
algorithm_name = "LightGBM"
algorithm_short_name = "LightGBM"
def __init__(self, params):
super(LightgbmLearner, self).__init__(params)
self.library_version = lgb.__version__
self.model_file = self.uid + ".lgbm.model"
self.model_file_path = os.path.join(storage_path, self.model_file)
self.rounds = additional.get("one_step", 50)
self.max_iters = additional.get("max_steps", 3)
self.learner_params = {
"boosting_type": "gbdt",
"objective": "binary",
"metric": self.params.get("metric", "binary_logloss"),
"num_threads": multiprocessing.cpu_count(),
"num_leaves": self.params.get("num_leaves", 16),
"learning_rate": self.params.get("learning_rate", 0.01),
"feature_fraction": self.params.get("feature_fraction", 0.7),
"bagging_fraction": self.params.get("bagging_fraction", 0.7),
"bagging_freq": self.params.get("bagging_freq", 1),
"verbose": -1,
}
log.debug("LightgbmLearner __init__")
def update(self, update_params):
pass
def fit(self, X, y):
lgb_train = lgb.Dataset(X, y)
self.model = lgb.train(
self.learner_params,
lgb_train,
num_boost_round=self.rounds,
init_model=self.model,
)
def predict(self, X):
return self.model.predict(X)
def copy(self):
return copy.deepcopy(self)
def save(self):
self.model.save_model(self.model_file_path)
json_desc = {
"library_version": self.library_version,
"algorithm_name": self.algorithm_name,
"algorithm_short_name": self.algorithm_short_name,
"uid": self.uid,
"model_file": self.model_file,
"model_file_path": self.model_file_path,
"params": self.params,
}
log.debug("LightgbmLearner save model to %s" % self.model_file_path)
return json_desc
def load(self, json_desc):
self.library_version = json_desc.get("library_version", self.library_version)
self.algorithm_name = json_desc.get("algorithm_name", self.algorithm_name)
self.algorithm_short_name = json_desc.get(
"algorithm_short_name", self.algorithm_short_name
)
self.uid = json_desc.get("uid", self.uid)
self.model_file = json_desc.get("model_file", self.model_file)
self.model_file_path = json_desc.get("model_file_path", self.model_file_path)
self.params = json_desc.get("params", self.params)
log.debug("LightgbmLearner load model from %s" % self.model_file_path)
self.model = lgb.Booster(model_file=self.model_file_path)
def importance(self, column_names, normalize=True):
return None
LightgbmLearnerBinaryClassificationParams = {
"objective": ["binary"],
"metric": ["binary_logloss", "auc"],
"num_leaves": [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
"learning_rate": [
0.0025,
0.005,
0.0075,
0.01,
0.025,
0.05,
0.075,
0.1,
0.15,
0.2,
0.25,
0.3,
],
"feature_fraction": [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
"bagging_fraction": [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
"bagging_freq": [0, 1, 2, 3, 4, 5],
}
additional = {
"one_step": 10,
"train_cant_improve_limit": 5,
"max_steps": 500,
"max_rows_limit": None,
"max_cols_limit": None,
}
required_preprocessing = [
"missing_values_inputation",
"convert_categorical",
"target_preprocessing",
]
ModelsRegistry.add(
BINARY_CLASSIFICATION,
LightgbmLearner,
LightgbmLearnerBinaryClassificationParams,
required_preprocessing,
additional,
)