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InferenceLightGBM.py
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InferenceLightGBM.py
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import pandas as pd
import numpy as np
import scipy
import json
from util import timeit
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
#
class InferenceLightGBM(object):
def __init__(self ,model_json,feature_names, cat_feature_map):
self.model_json = model_json
#print(self.model_json)
self.feature_names = self.model_json['feature_names']
self.categories = cat_feature_map
def predict(self ,X):
try:
columns = list(X.columns)
except :
print('{} should be a pandas.DataFrame'.format(X))
if self.model_json['feature_names'] == columns:
y = self._predict(X)
return y
else:
raise Exception("columns should be {}".format(self.feature_names) ,)
def _sigmoid(self ,z):
return 1.0 /( 1 + np.exp(-z))
def _predict(self ,X):
feat_names = self.feature_names
results = pd.Series(index=X.index)
trees = self.model_json['tree_info']
for idx in X.index:
X_sample = X.loc[idx:idx ,:]
leaf_values = 0.0
for tree in trees:
tree_structure = tree['tree_structure']
leaf_value = self._walkthrough_tree(tree_structure ,X_sample)
leaf_values += leaf_value
results[idx] = self._sigmoid(leaf_values)
return results
#
# def _walkthrough_tree(self ,tree_structure ,X_sample):
#
# if 'leaf_index' in tree_structure.keys():
#
# return tree_structure['leaf_value']
# else:
#
# split_feature = X_sample.iloc[0 ,tree_structure['split_feature']]
# decision_type = tree_structure['decision_type']
# threshold = tree_structure['threshold']
#
# if decision_type == '==':
# feat_name = self.feature_names[tree_structure['split_feature']]
# categories = self.categories[feat_name]
# category = categories[str(split_feature)]
# category = str(category)
# threshold = threshold.split('||')
# if category in threshold:
# tree_structure = tree_structure['left_child']
# else:
# tree_structure = tree_structure['right_child']
# return self._walkthrough_tree(tree_structure ,X_sample)
# # 数值特征
# elif decision_type == '<=':
# if split_feature <= threshold:
# tree_structure = tree_structure['left_child']
# else:
# tree_structure = tree_structure['right_child']
#
# return self._walkthrough_tree(tree_structure ,X_sample)
# else:
# print(tree_structure)
# print('decision_type: {} is not == or <='.format(decision_type))
# return None
def _walkthrough_tree(self ,tree_structure ,X_sample,tree_index):
if 'leaf_index' in tree_structure.keys():
return [tree_index + '_leaf_index_'+str(tree_structure['leaf_index'])]
else:
#print(tree_structure['split_feature'])
split_feature = '%f'%(X_sample[tree_structure['split_feature']])
decision_type = tree_structure['decision_type']
threshold = tree_structure['threshold']
split_index = tree_index + '_split_index_'+str(tree_structure['split_index'])
if decision_type == '==':
feat_name = self.feature_names[tree_structure['split_feature']]
categories = self.categories[feat_name]
category = categories[str(split_feature)]
category = str(category)
threshold = threshold.split('||')
print(category, threshold)
if category in threshold:
tree_structure = tree_structure['left_child']
else:
tree_structure = tree_structure['right_child']
tmp = self._walkthrough_tree(tree_structure, X_sample,tree_index)
return tmp #[split_index] + tmp
elif decision_type == '<=':
split_feature = float(split_feature)
if split_feature <= threshold:
tree_structure = tree_structure['left_child']
else:
tree_structure = tree_structure['right_child']
tmp = self._walkthrough_tree(tree_structure, X_sample,tree_index)
return tmp #[split_index] + tmp
else:
print('decision_type: {} is not == or <='.format(decision_type))
return [split_index]
def get_feaure(self,X_sample):
trees = self.model_json['tree_info']
features=[]
for tree in trees:
tree_structure = tree['tree_structure']
features.extend(self._walkthrough_tree(tree_structure, X_sample,"tree_index_"+str(tree["tree_index"])))
return features
def get_node_id_feature_sparse(self,X):
pool = ThreadPool(40)
#results = map(self.get_feaure, np.array(X.values))
results = pool.map(self.get_feaure, np.array(X.values))
results = list(results)
#print(results)
#results = np.array(results)
#print(results)
results = pd.DataFrame(results)
print(results.columns)
print("-------------")
results = pd.SparseDataFrame(pd.get_dummies(results)).astype("float")
print(results)
# columns = results.columns
# results = scipy.sparse.csr_matrix(results)
print(results.columns)
return results