LambdaMART implementation with latest master rebase

csrank/experiments/constants.py

@@ -27,6 +27,7 @@
 FATELINEAR_RANKER = "fatelinear_ranker"
 FETALINEAR_RANKER = "fetalinear_ranker"
 RANDOM_RANKER = "random_ranker"
+LAMBDAMART = "lambdamart"
 
 FETA_CHOICE = 'feta_choice'
 FETALINEAR_CHOICE = "fetalinear_choice"
@@ -59,4 +60,4 @@
 DCMS = [FETA_DC, FATE_DC, RANKNET_DC, MNL, NLM, GEV, PCL, MLM, RANKSVM_DC, FATELINEAR_DC, FETALINEAR_DC, RANDOM_DC]
 CHOICE_FUNCTIONS = [FETA_CHOICE, FATE_CHOICE, RANKNET_CHOICE, RANKSVM_CHOICE, GLM_CHOICE, RANDOM_CHOICE,
                     FATELINEAR_CHOICE, FETALINEAR_CHOICE]
-OBJECT_RANKERS = [FATE_RANKER, FETA_RANKER, FATELINEAR_RANKER, FETALINEAR_RANKER, RANKSVM, ERR, RANKNET, LISTNET, RANDOM_RANKER]
+OBJECT_RANKERS = [FATE_RANKER, FETA_RANKER, FATELINEAR_RANKER, FETALINEAR_RANKER, RANKSVM, ERR, RANKNET, LISTNET, RANDOM_RANKER, LAMBDAMART]

csrank/metrics.py

@@ -47,6 +47,7 @@
 import numpy as np
 import tensorflow as tf
 from keras import backend as K
+import math
 
 from csrank.tensorflow_util import scores_to_rankings, get_instances_objects, tensorify
 
@@ -330,4 +331,4 @@ def err(y_true, y_pred, utility_function=None, probability_mapping=None):
     discounted_document_values = tf.cast(satisfied_at_rank, tf.float64) * discount_at_rank
     results = tf.reduce_sum(discounted_document_values, axis=1)
 
-    return K.mean(results)
+    return K.mean(results)

csrank/objectranking/__init__.py

@@ -8,3 +8,4 @@
 from .rank_net import RankNet
 from .rank_svm import RankSVM
 from .baseline import RandomBaselineRanker
+from .lambdamart import LambdaMART