ZSX zanshuxun
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| # -*- coding: utf-8 -*- | |
| # | |
| # Author: Taylor G Smith | |
| # | |
| # Recommender system ranking metrics derived from Spark source for use with | |
| # Python-based recommender libraries (i.e., implicit, | |
| # https://github.com/benfred/implicit/). These metrics are derived from the | |
| # original Spark Scala source code for recommender metrics. | |
| # https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala |
halegreen
/ Model_Ensemble.py
Created
November 17, 2017 02:40
用了两层的模型融合，Level 1使用了：XGBoost、LightGBM、RandomForest、ExtraTrees、DecisionTree、AdaBoost，一共6个模型，Level 2使用了LinearRegression来拟合第一层的结果
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| class Ensemble(object): | |
| def __init__(self, n_splits, stacker, base_models): | |
| self.n_splits = n_splits | |
| self.stacker = stacker | |
| self.base_models = base_models | |
| def fit_predict(self, X, y, T): | |
| X = np.array(X) | |
| y = np.array(y) | |
| T = np.array(T) |
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| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |