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dmlc / xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
https://xgboost.readthedocs.io/en/stable/
Apache License 2.0
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Number of features in xgb.predict function #6106

Closed kaushikepi closed 4 years ago

kaushikepi commented 4 years ago

Trained a XGB Classifier model with 5178 features and saved it as a pickle file. But the issue is when loading the saved xgb model and if I am passing a feature vector of shape less than 5178 (let's suppose 5174) , it still gives list of probabilities for both the class.

Can anyone explain this ?

kaushikepi commented 4 years ago

Saved the model as 

import _pickle as cPickle
with open(filename,'wb') as f:
       cPickle.dump(xgb_clf, f)

with open(filename, 'rb') as fr:
    xgb_clf = cPickle.load(fr)

xgb_clf.predict_proba(feat_vec_all)

shape of feat_vect = (1,5174) But xgb_clf was trained on 5178 features

kaushikepi commented 4 years ago

Can you help ? @hcho3

hcho3 commented 4 years ago

Which XGBoost version are you using?

kaushikepi commented 4 years ago

version 1.2.0 @hcho3 

import xgboost as xgb
from xgboost.sklearn import XGBClassifier
xgb_model = XGBClassifier(eval_metric = 'auc', booster = 'gbtree', objective = 'binary:logistic', random_state = 0)
xgb_model.fit(x_train,y_train)

xgb_model.n_features_in_. This returns 5178. 1) when I am using xgb_model.predict_proba(feat_vec_all) where feat_vec_all is (1,5174) csr_matrix , it is still returning probability score for it.

output 

((1, 5174)
[[0.29071766 0.70928234]]

2) And when passing feat_vec_all of shape (1,5174) in xgb_model.predict_proba(feat_vec_all, validate_features = True) then it throws error which makes sense.

(1, 5174)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-2714583838ee> in <module>
----> 1 print(detect_summ_label('visit summary of a patient suffering from corona virus', 10))

<ipython-input-18-b0592f618a63> in detect_summ_label(encounter_text, no_of_pages)
     43     print(feat_vec_all.shape)
     44 
---> 45     return xgb_model.predict_proba(feat_vec_all, validate_features=True)

~/anaconda3/envs/py36/lib/python3.6/site-packages/xgboost/sklearn.py in predict_proba(self, data, ntree_limit, validate_features, base_margin)
    946         class_probs = self.get_booster().predict(test_dmatrix,
    947                                                  ntree_limit=ntree_limit,
--> 948                                                  validate_features=validate_features)
    949         if self.objective == "multi:softprob":
    950             return class_probs

~/anaconda3/envs/py36/lib/python3.6/site-packages/xgboost/core.py in predict(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features, training)
   1362 
   1363         if validate_features:
-> 1364             self._validate_features(data)
   1365 
   1366         length = c_bst_ulong()

~/anaconda3/envs/py36/lib/python3.6/site-packages/xgboost/core.py in _validate_features(self, data)
   1934 
   1935                 raise ValueError(msg.format(self.feature_names,
-> 1936                                             data.feature_names))
   1937 
   1938     def get_split_value_histogram(self, feature, fmap='', bins=None,

ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24', 'f25', 'f26', 'f27', 'f28', 'f29', 'f30', 'f31', 'f32', 'f33', 'f34', 'f35', 'f36', 'f37', 'f38', 'f39', 'f40', 'f41', 'f42', 'f43', 'f44', 'f45', 'f46', 'f47', 'f48', 'f49', 'f50', 'f51', 'f52', 'f53', 'f54', 'f55', 'f56', 'f57', 'f58', 'f59', 'f60', 'f61', 'f62', 'f63', 'f64', 'f65', 'f66', 'f67', 'f68', 'f69', 'f70', 'f71', 'f72', 'f73', 'f74', 'f75', 'f76', 'f77', 'f78', 'f79', 'f80', 'f81', 'f82', 'f83', 'f84', 'f85', 'f86', 'f87', 'f88', 'f89', 'f90', 'f91', 'f92', 'f93', 'f94', 'f95', 'f96', 'f97', 'f98', 'f99', 'f100', 'f101', 'f102', 'f103', 'f104', 'f105', 'f106', 'f107', 'f108', 'f109', 'f110', 'f111', 'f112', 'f113', 'f114', 'f115', 'f116', 'f117', 'f118', 'f119', 'f120', 'f121', 'f122', 'f123', 'f124', 'f125', 'f126', 'f127', 'f128', 'f129', 'f130', 'f131', 'f132', 'f133', 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'f4500', 'f4501', 'f4502', 'f4503', 'f4504', 'f4505', 'f4506', 'f4507', 'f4508', 'f4509', 'f4510', 'f4511', 'f4512', 'f4513', 'f4514', 'f4515', 'f4516', 'f4517', 'f4518', 'f4519', 'f4520', 'f4521', 'f4522', 'f4523', 'f4524', 'f4525', 'f4526', 'f4527', 'f4528', 'f4529', 'f4530', 'f4531', 'f4532', 'f4533', 'f4534', 'f4535', 'f4536', 'f4537', 'f4538', 'f4539', 'f4540', 'f4541', 'f4542', 'f4543', 'f4544', 'f4545', 'f4546', 'f4547', 'f4548', 'f4549', 'f4550', 'f4551', 'f4552', 'f4553', 'f4554', 'f4555', 'f4556', 'f4557', 'f4558', 'f4559', 'f4560', 'f4561', 'f4562', 'f4563', 'f4564', 'f4565', 'f4566', 'f4567', 'f4568', 'f4569', 'f4570', 'f4571', 'f4572', 'f4573', 'f4574', 'f4575', 'f4576', 'f4577', 'f4578', 'f4579', 'f4580', 'f4581', 'f4582', 'f4583', 'f4584', 'f4585', 'f4586', 'f4587', 'f4588', 'f4589', 'f4590', 'f4591', 'f4592', 'f4593', 'f4594', 'f4595', 'f4596', 'f4597', 'f4598', 'f4599', 'f4600', 'f4601', 'f4602', 'f4603', 'f4604', 'f4605', 'f4606', 'f4607', 'f4608', 'f4609', 'f4610', 'f4611', 'f4612', 'f4613', 'f4614', 'f4615', 'f4616', 'f4617', 'f4618', 'f4619', 'f4620', 'f4621', 'f4622', 'f4623', 'f4624', 'f4625', 'f4626', 'f4627', 'f4628', 'f4629', 'f4630', 'f4631', 'f4632', 'f4633', 'f4634', 'f4635', 'f4636', 'f4637', 'f4638', 'f4639', 'f4640', 'f4641', 'f4642', 'f4643', 'f4644', 'f4645', 'f4646', 'f4647', 'f4648', 'f4649', 'f4650', 'f4651', 'f4652', 'f4653', 'f4654', 'f4655', 'f4656', 'f4657', 'f4658', 'f4659', 'f4660', 'f4661', 'f4662', 'f4663', 'f4664', 'f4665', 'f4666', 'f4667', 'f4668', 'f4669', 'f4670', 'f4671', 'f4672', 'f4673', 'f4674', 'f4675', 'f4676', 'f4677', 'f4678', 'f4679', 'f4680', 'f4681', 'f4682', 'f4683', 'f4684', 'f4685', 'f4686', 'f4687', 'f4688', 'f4689', 'f4690', 'f4691', 'f4692', 'f4693', 'f4694', 'f4695', 'f4696', 'f4697', 'f4698', 'f4699', 'f4700', 'f4701', 'f4702', 'f4703', 'f4704', 'f4705', 'f4706', 'f4707', 'f4708', 'f4709', 'f4710', 'f4711', 'f4712', 'f4713', 'f4714', 'f4715', 'f4716', 'f4717', 'f4718', 'f4719', 'f4720', 'f4721', 'f4722', 'f4723', 'f4724', 'f4725', 'f4726', 'f4727', 'f4728', 'f4729', 'f4730', 'f4731', 'f4732', 'f4733', 'f4734', 'f4735', 'f4736', 'f4737', 'f4738', 'f4739', 'f4740', 'f4741', 'f4742', 'f4743', 'f4744', 'f4745', 'f4746', 'f4747', 'f4748', 'f4749', 'f4750', 'f4751', 'f4752', 'f4753', 'f4754', 'f4755', 'f4756', 'f4757', 'f4758', 'f4759', 'f4760', 'f4761', 'f4762', 'f4763', 'f4764', 'f4765', 'f4766', 'f4767', 'f4768', 'f4769', 'f4770', 'f4771', 'f4772', 'f4773', 'f4774', 'f4775', 'f4776', 'f4777', 'f4778', 'f4779', 'f4780', 'f4781', 'f4782', 'f4783', 'f4784', 'f4785', 'f4786', 'f4787', 'f4788', 'f4789', 'f4790', 'f4791', 'f4792', 'f4793', 'f4794', 'f4795', 'f4796', 'f4797', 'f4798', 'f4799', 'f4800', 'f4801', 'f4802', 'f4803', 'f4804', 'f4805', 'f4806', 'f4807', 'f4808', 'f4809', 'f4810', 'f4811', 'f4812', 'f4813', 'f4814', 'f4815', 'f4816', 'f4817', 'f4818', 'f4819', 'f4820', 'f4821', 'f4822', 'f4823', 'f4824', 'f4825', 'f4826', 'f4827', 'f4828', 'f4829', 'f4830', 'f4831', 'f4832', 'f4833', 'f4834', 'f4835', 'f4836', 'f4837', 'f4838', 'f4839', 'f4840', 'f4841', 'f4842', 'f4843', 'f4844', 'f4845', 'f4846', 'f4847', 'f4848', 'f4849', 'f4850', 'f4851', 'f4852', 'f4853', 'f4854', 'f4855', 'f4856', 'f4857', 'f4858', 'f4859', 'f4860', 'f4861', 'f4862', 'f4863', 'f4864', 'f4865', 'f4866', 'f4867', 'f4868', 'f4869', 'f4870', 'f4871', 'f4872', 'f4873', 'f4874', 'f4875', 'f4876', 'f4877', 'f4878', 'f4879', 'f4880', 'f4881', 'f4882', 'f4883', 'f4884', 'f4885', 'f4886', 'f4887', 'f4888', 'f4889', 'f4890', 'f4891', 'f4892', 'f4893', 'f4894', 'f4895', 'f4896', 'f4897', 'f4898', 'f4899', 'f4900', 'f4901', 'f4902', 'f4903', 'f4904', 'f4905', 'f4906', 'f4907', 'f4908', 'f4909', 'f4910', 'f4911', 'f4912', 'f4913', 'f4914', 'f4915', 'f4916', 'f4917', 'f4918', 'f4919', 'f4920', 'f4921', 'f4922', 'f4923', 'f4924', 'f4925', 'f4926', 'f4927', 'f4928', 'f4929', 'f4930', 'f4931', 'f4932', 'f4933', 'f4934', 'f4935', 'f4936', 'f4937', 'f4938', 'f4939', 'f4940', 'f4941', 'f4942', 'f4943', 'f4944', 'f4945', 'f4946', 'f4947', 'f4948', 'f4949', 'f4950', 'f4951', 'f4952', 'f4953', 'f4954', 'f4955', 'f4956', 'f4957', 'f4958', 'f4959', 'f4960', 'f4961', 'f4962', 'f4963', 'f4964', 'f4965', 'f4966', 'f4967', 'f4968', 'f4969', 'f4970', 'f4971', 'f4972', 'f4973', 'f4974', 'f4975', 'f4976', 'f4977', 'f4978', 'f4979', 'f4980', 'f4981', 'f4982', 'f4983', 'f4984', 'f4985', 'f4986', 'f4987', 'f4988', 'f4989', 'f4990', 'f4991', 'f4992', 'f4993', 'f4994', 'f4995', 'f4996', 'f4997', 'f4998', 'f4999', 'f5000', 'f5001', 'f5002', 'f5003', 'f5004', 'f5005', 'f5006', 'f5007', 'f5008', 'f5009', 'f5010', 'f5011', 'f5012', 'f5013', 'f5014', 'f5015', 'f5016', 'f5017', 'f5018', 'f5019', 'f5020', 'f5021', 'f5022', 'f5023', 'f5024', 'f5025', 'f5026', 'f5027', 'f5028', 'f5029', 'f5030', 'f5031', 'f5032', 'f5033', 'f5034', 'f5035', 'f5036', 'f5037', 'f5038', 'f5039', 'f5040', 'f5041', 'f5042', 'f5043', 'f5044', 'f5045', 'f5046', 'f5047', 'f5048', 'f5049', 'f5050', 'f5051', 'f5052', 'f5053', 'f5054', 'f5055', 'f5056', 'f5057', 'f5058', 'f5059', 'f5060', 'f5061', 'f5062', 'f5063', 'f5064', 'f5065', 'f5066', 'f5067', 'f5068', 'f5069', 'f5070', 'f5071', 'f5072', 'f5073', 'f5074', 'f5075', 'f5076', 'f5077', 'f5078', 'f5079', 'f5080', 'f5081', 'f5082', 'f5083', 'f5084', 'f5085', 'f5086', 'f5087', 'f5088', 'f5089', 'f5090', 'f5091', 'f5092', 'f5093', 'f5094', 'f5095', 'f5096', 'f5097', 'f5098', 'f5099', 'f5100', 'f5101', 'f5102', 'f5103', 'f5104', 'f5105', 'f5106', 'f5107', 'f5108', 'f5109', 'f5110', 'f5111', 'f5112', 'f5113', 'f5114', 'f5115', 'f5116', 'f5117', 'f5118', 'f5119', 'f5120', 'f5121', 'f5122', 'f5123', 'f5124', 'f5125', 'f5126', 'f5127', 'f5128', 'f5129', 'f5130', 'f5131', 'f5132', 'f5133', 'f5134', 'f5135', 'f5136', 'f5137', 'f5138', 'f5139', 'f5140', 'f5141', 'f5142', 'f5143', 'f5144', 'f5145', 'f5146', 'f5147', 'f5148', 'f5149', 'f5150', 'f5151', 'f5152', 'f5153', 'f5154', 'f5155', 'f5156', 'f5157', 'f5158', 'f5159', 'f5160', 'f5161', 'f5162', 'f5163', 'f5164', 'f5165', 'f5166', 'f5167', 'f5168', 'f5169', 'f5170', 'f5171', 'f5172', 'f5173']
expected f5177, f5176, f5175, f5174 in input data
hcho3 commented 4 years ago

The last four features are treated as missing values.

XGBoost models have provisions for missing values in the input vector. See https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf for details about how missing values are handled.

kaushikepi commented 4 years ago

@hcho3 Thanks for the help. Will read the paper