Weight benefit evaluator for training data

Abstract

Technologies are generally described for methods and systems effective to determine a weight benefit associated with application of weights to training data in a machine learning environment. In an example, a device may determine a first function based on the training data, where the training data includes training inputs and training labels. The device may determine a second function based on weighted training data, which is based on application of weights to the training data. The device may determine a third function based on target data, where the target data is generated based on a target function. The target data may include target labels different from the training labels. The device may determine a fourth function based on weighted target data, which is a result of application of weights to the target data. The device may determine the weight benefit based on the first, second, third, and fourth functions.

What is claimed is: 
     
         1 . A method to determine whether to apply a set of weights to training data in a machine learning environment, the method comprising:
 applying, by a device, test inputs to a first function to generate test data, wherein the first function is based on the training data, the training data includes training inputs and training labels, and the test data includes the test inputs and test labels;   applying, by the device, the test inputs to a second function to generate weighted test data, wherein the second function is based on weighted training data, the weighted training data is based on the set of weights, and the weighted test data includes the test inputs and weighted test labels;   determining, by the device, a third function based on target data, wherein the target data is based on a target function, the target data includes the training inputs, and the target data includes target labels different from the training labels;   applying, by the device, the test inputs to the third function to generate artificial test data, wherein the artificial test data includes the test inputs and artificial test labels;   determining, by the device, a fourth function based on the set of weights and the target data;   applying, by the device, the test inputs to the fourth function to generate artificial weighted test data, wherein the artificial weighted test data includes the test inputs and artificial weighted test labels;   determining, by the device, an evaluation value based on the test data, the weighted test data, the artificial test data, and the artificial weighted test data;   determining, by the device, the weight benefit based on the evaluation value, wherein the weight benefit is associated with a benefit to apply the set of weights to the training data; and   determining, by the device, whether to apply the set of weights to the training data based on the weight benefit.   
     
     
         2 . The method of  claim 1 , further comprising, prior to applying the test inputs to the first function:
 receiving, by the device, the first function;   receiving, by the device, the second function;   receiving, by the device, the target function; and   generating, by the device, the target data based on the target function.   
     
     
         3 . The method of  claim 1 , further comprising, prior to applying the test inputs to the first function:
 receiving, by the device, the first function;   receiving, by the device, the second function;   generating, by the device, the target function; and   generating, by the device, the target data based on the target function.   
     
     
         4 . The method of  claim 3 , wherein generating the target function comprises:
 determining, by the device, a set of parameters to generate an artificial function;   generating, by the device, artificial data based on the training inputs and the artificial function, wherein the artificial data includes the training inputs and artificial labels; and   generating, by the device, the target function based on the training data and the artificial data.   
     
     
         5 . The method of  claim 1 , wherein determining the weight benefit comprises:
 determining, by the device, an expected value between the first function and the second function;   comparing, by the device, the evaluation value with the expected value;   determining, by the device, a count based on the comparison of the evaluation value with the expected value;   comparing, by the evaluation module of the device, the count with a threshold; and   determining, by the device, the weight benefit based on the comparison of the count with the threshold.   
     
     
         6 . The method of  claim 5 , wherein the expected value is a first expected value, the method further comprises:
 determining, by the device, a second expected value between the third function and the target function;   determining, by the device, a third expected value between the fourth function and the target function;   determining, by the device, a fourth expected value between the third function and the fourth function; and   determining, by the device, the evaluation value based on the second, third, and fourth expected values.   
     
     
         7 . The method of  claim 5 , further comprising:
 determining, by the device, the count is greater than the threshold based on the comparison of the count with the threshold; and   deploying, by the device, the first function in the machine learning environment in response to the determination that the count is greater than the threshold.   
     
     
         8 . The method of  claim 5 , further comprising:
 determining, by the device, the count is less than the threshold based on the comparison of the count with the threshold; and   applying, by the device, the set of weights to the training data in response to the determination that the count is less than the threshold.   
     
     
         9 . The method of  claim 8 , further comprising deploying, by the device, the second function in the machine learning environment in response to applying the set of weights to the training data. 
     
     
         10 . A system effective to whether to apply a set of weights to training data in a machine learning environment, the system comprising:
 a memory configured to:
 store the training data, wherein the training data includes training inputs and training labels; 
 store the set of weights; and 
 store a set of test inputs; 
   a machine learning module configured to be in communication with the memory, the machine learning module being configured to:
 apply the test inputs to a first function to generate test data, wherein the first function is based on the training data, the training data includes training inputs and training labels, and the test data includes the test inputs and test labels; 
 apply the test inputs to a second function to generate weighted test data, wherein the second function is based on weighted training data, the weighted training data is based on the set of weights, and the weighted test data includes the test inputs and weighted test labels; 
 determine a third function based on the target data based on target data, wherein the target data is based on a target function, the target data includes the training inputs, and the target data includes target labels different from the training labels; 
 apply the test inputs to the third function to generate artificial test data, wherein the artificial test data includes the test inputs and artificial test labels;
 determine a fourth function based on the set of weights and the target data; 
 
 apply the test inputs to the fourth function to generate artificial weighted test data, wherein the artificial weighted test data includes the test inputs and artificial weighted test labels; 
   an evaluation module configured to be in communication with the machine learning module and the memory, the evaluation module being configured to:
 determine an evaluation value based on the test data, the weighted test data, the artificial test data, and the artificial weighted test data; and
 determine the weight benefit based on the evaluation value, wherein the weight benefit is associated with a benefit to apply the set of weights to the training data; and 
 
   a processing module configured to be in communication with the evaluation module, the machine learning module, and the memory, the processing module being configured to determine whether to apply the set of weights to the training data based on the weight benefit.   
     
     
         11 . The system of  claim 10 , further comprising a target function generation module configured to be in communication with the processing module, the evaluation module, the machine learning module, and the memory, the target function generation module being configured to:
 determine a set of parameters to generate an artificial function;   generate artificial data based on the training inputs and the artificial function, wherein the artificial data includes the training inputs and artificial labels;   generate the target function based on the training data and the artificial data;   generate the target data based on the target function; and   store the target data in the memory;   
     
     
         12 . The system of  claim 10 , wherein the evaluation device is further configured to:
 determine an expected value between the first function and the second function;   compare the evaluation value with the expected value;   determine a count based on the comparison of the evaluation value with the expected value;   compare the count with a threshold; and   determine the weight benefit based on the comparison of the count with the threshold.   
     
     
         13 . The system of  claim 12 , wherein the processing module is further configured to:
 determine the count is greater than the threshold based on the comparison of the count with the threshold; and   deploy the first function in the machine learning environment in response to the determination that the count is greater than the threshold.   
     
     
         14 . The system of  claim 12 , wherein the processing module is further configured to:
 determine the count is less than the threshold based on the comparison of the count with the threshold; and   apply the set of weights to the training data in response to the determination that the count is less than the threshold.   
     
     
         15 . The system of  claim 14 , wherein the processing module is further configured to deploy the second function in the machine learning environment in response to applying the set of weights to the training data. 
     
     
         16 . A method to determine whether to deploy a first function or a second function in a machine learning environment, the method comprising:
 applying, by a device, test inputs to a first function to generate test data, wherein the first function is based on the training data, the training data includes training inputs and training labels, and the test data includes the test inputs and test labels;   applying, by the device, the test inputs to a second function to generate weighted test data, wherein the second function is based on weighted training data, the weighted training data is based on the set of weights, and the weighted test data includes the test inputs and weighted test labels;   determining, by the device, a third function based on target data, wherein the target data is based on a target function, the target data includes the training inputs, and the target data includes target labels different from the training labels;   applying, by the device, the test inputs to the third function to generate artificial test data, wherein the artificial test data includes the test inputs and artificial test labels;   determining, by the device, a fourth function based on the set of weights and the target data;   applying, by the device, the test inputs to the fourth function to generate artificial weighted test data, wherein the artificial weighted test data includes the test inputs and artificial weighted test labels;   determining, by the device, an evaluation value based on the third and fourth functions;   comparing, by the device, the evaluation value with an expected value between the first function and the second function;   determining, by the device, a count based on the comparison of the evaluation value with the expected value;   comparing, by the evaluation module of the device, the count with a threshold; and   determining, by the device, whether to deploy the first function or the second function in the machine learning environment based on the comparison of the count with the threshold.   
     
     
         17 . The method of  claim 16 , wherein the expected value is a first expected value, the method further comprises:
 determining, by the device, a second expected value between the third function and the target function;   determining, by the device, a third expected value between the fourth function and the target function;   determining, by the device, a fourth expected value between the third function and the fourth function; and   determining, by the device, the evaluation value based on the second, third, and fourth expected values.   
     
     
         18 . The method of  claim 16 , further comprising:
 determining, by the device, the count is greater than the threshold based on the comparison of the count with the threshold; and   deploying, by the device, the first function in the machine learning environment in response to the determination that the count is greater than the threshold.   
     
     
         19 . The method of  claim 16 , further comprising:
 determining, by the device, the count is less than the threshold based on the comparison of the count with the threshold; and   applying, by the device, the set of weights to the training data in response to the determination that the count is less than the threshold.   
     
     
         20 . The method of  claim 16 , further comprising deploying, by the device, the second function in the machine learning environment in response to applying the set of weights to the training data.