Patent Publication Number: US-2020293906-A1

Title: Deep forest model development and training

Description:
BACKGROUND 
     The disclosure relates generally to systems and methods for developing and training models for analyzing data. The disclosure relates particularly to systems using deep random forests to automatically develop and train models to analyze data. 
     Deep learning is well known at least in part due to successful applications of deep neural networks, particularly in tasks involving image and speech information. Deep neural networks have deficiencies as well. Deep neural networks may require a large amount of data for training, which makes deep neural networks not quite suitable for small-scale data. Deep neural networks may be very complicated models which include too many hyper-parameters. Careful tuning of these parameters may be needed to reach a desired learning accuracy making the training of deep neural networks more like art, rather than science or engineering. 
     Zhou et. al. proposed a new deep learning method, gcForest (multi-Grained Cascade Forest) [Z.-H. Zhou and F. Ji.  Deep forest: towards an alternative to deep neural networks.  arXiv:1702.08835v2, 2017]. This method tries to realize the crucial deep learning idea, i.e., representation learning, by a cascade structure of random forests, where each layer of the cascade receives feature information created by the preceding layer, and outputs its results to the next layer. gcForest can achieve performance competitive to deep neural networks but with less of the aforementioned deficiencies. 
     Random forest was proposed by Breiman in 2001 [L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001]. It is an ensemble of decision trees. Special treatments are applied when growing these decision trees in the ensemble. First, each tree is grown on a bootstrap sample. The bootstrap sample is obtained by sampling with replacement from the training data and the sample size is equivalent with the size of training data. Second, each tree needs to grow fully on the sample until each leaf node contains only the same class of instances, and no pruning is required. More importantly, when splitting any node in the tree, it randomly selects a fraction of predictors, from which it chooses the one with the best gini value for split. 
     When scoring a data instance, random forest uses majority voting to combine the predictions from individual trees. For example, consider a target variable with 3 classes, c1, c2, and c3, and 1000 decision trees in the forest. Suppose that the numbers of votes for the 3 classes are 200, 300, and 500 respectively. Then, random forest will report a vector of class probabilities, i.e., [0.2, 0.3, 0.5], for the instance under scoring. A label prediction of c3 will also be reported since it has the maximal prediction probability. 
     If the instance is a training instance, random forest provides an option of generating Out-of-bag (OOB) prediction. Such a prediction is a result counted with the votes of trees which have not included the instance in their bootstrap samples. Continuing with the example above, if there are 400 trees which have not used the instance for training, and the numbers of votes for c1, c2, and c3 are 100, 100, and 200 respectively, the OOB prediction (probability vector) for the instance will be [0.25, 0.25, 0.50]. A label prediction will be c3 because it corresponds to the maximal probability. OOB accuracy is computed for the forest by comparing the instance labels with the OOB label predictions on the training data. 
     The method of gcForest uses cross-validation to generate new feature information. Though cross-validation is effective, it also brings challenges. First, the method of gcForest includes multiple random forest learners N in each layer, and it requires a k-fold cross-validation for each learner. Thus, a total of N*k learners is needed for each layer. When the data set is large, performance issues may arise since the number of learners increases with the size of the data set and it is expensive to build even a single learner. Very powerful computational facilities will be required. Second, training data may not be fully used due to cross-validation and the need for a validation data set derived from the training data set. The method of gcForest splits data into a training sample and a validation sample and stops training if the accuracy on the validation sample cannot be improved. The usage of the validation sample makes the small data issue even worse. 
     SUMMARY 
     Data may be analyzed by systems, methods and computer program products utilizing deep random forests having a reduced set of hyper parameters and reduced tuning requirements. In one aspect, a machine learning model is automatically constructed of layers of random forests. Decision trees for a random forest are grown from a data set. Out-of-bag (OOB) predictions and class label predictions are determined for the random forest. The OOB predictions for each instance of the data set are appended to the data set. The model is expanded by adding more layers of forests. Each new forest is grown from the appended version of the data set created by the previous layer. A combiner layer is added after the final data appending layer to produce the model output. 
     In one aspect computing resources may be reduced by using a method wherein the single user provided hyper parameter is the number of trees per forest. In this aspect, a machine learning model may be automatically constructed by receiving a training data set and a specified number of decision trees per random forest. The specified number of decision trees are then grown from the training data set. An OOB prediction and class label are determined for each instance of the training data set. The OOB prediction is appended to the training data for each instance and the appended data set is then used to create a next layer of forests. The OOB accuracy of each layer is determined using the OOB label prediction. Additional layers are added until the OOB accuracy ceases to significantly improve with additional layers. A combiner is added to consolidate the output of the last additional forest layer and provide the model output. 
     In one aspect, decision trees for a forest are grown from a training data set. The number of trees per forest and a class vector dimensionality, to be used in growing the trees are specified. One forest is provided per layer of the model having a plurality of layers, thereby reducing the computational resource needs for the categorization. An out-of-bag (OOB) prediction for the forest is determined. The OOB prediction is appended to the data set as a new feature for each data set instance. Appending the OOB prediction provides a means for adding new feature information from each layer to the succeeding layer without the use of models which may result in feature information being lost. An OOB accuracy is determined for the forest. Additional layers of a single forest are added by repeating the described steps and using the previous layer&#39;s appended data set for training until the new layer&#39;s OOB accuracy does not significantly improve. The use of OOB accuracy eliminates the need for cross-validation in the analysis, further reducing computation resources. The output of the last forest is combined, and the complete model is used to analyze data from outside the training data set. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  provides a schematic illustration of a system, according to an embodiment of the invention. 
         FIG. 2  provides a flowchart depicting an operational sequence, according to an embodiment of the invention. 
         FIG. 3  depicts data flow, according to an embodiment of the invention. 
         FIG. 4  depicts a cloud computing environment, according to an embodiment of the invention. 
         FIG. 5  depicts abstraction model layers, according to an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Deep neural network (DNN) structures have been successfully applied to machine learning applications. DNNs may include a number of hyper-parameters and therefore may be difficult to tune to the problem and data set at hand. DNNs may also be difficult to apply to applications relating to small data sets as the DNN may require a large data set to train the model to an acceptable level of accuracy. The complexity and opacity of the operation of DNN structures may result in systems where it is difficult to determine the optimal structure in terms of layer number and nodes per layer necessary to achieve the desired or necessary accuracy without undue experimentation in developing or specifying the DNN structure. What is needed is a machine learning model structure which is applicable to even small data sets, can be easily specified and tuned without the effort associated with DNNs, and which can automatically complete model development when optimal accuracy has been achieved. 
     The disclosed systems, methods and computer products provide machine learning model development and training which is applicable to small data sets due to the use of bootstrapped training data sets selected with replacement from a provided training data set. Embodiments of the invention describe only a single hyper-parameter for model development—the number of random trees per forest. Embodiments of the invention provide for a simplified structure of a single random forest per layer and automatically stops model development when further improvements in accuracy are unlikely, resulting in an accurate model of relatively low computing complexity. In an embodiment, models having more than a single forest per layer may be created. Such forests require additional computational resources and may not demonstrate any significant improvement in model accuracy. 
     In an embodiment, a computer implemented method for developing and training models for analyzing data may begin with the development and construction of a machine learning model. The model may be used for classification or regression analysis. The model development arises from a training data set associated with the machine learning task for which the model is intended. The training data may be provided over a network, obtained locally from sensors, or provided via computer readable media. 
     The data may comprise numeric, text, audio, video, or image data. The data may be associated with location, speech, music, entertainment, healthcare, financial information, vehicle data, logistics data, sales data, or other data types relegated to machine learning analysis. 
     The number of random decision trees per forest required by the model must be specified. The number may be specified as a user input or may be set to a default quantity based upon previous model development efforts. In an embodiment, the number may be specified as five hundred trees. In an embodiment, the number may be randomly selected using a random number generator or a pseudo-random number generator. The number of trees per forest may remain constant as each layer/forest is added to the model. In an embodiment, the number of trees per forest may be varied for each layer/forest. 
     Each decision tree is grown from a bootstrapped data sample selected with replacement from the training data set. The bootstrapped sample is equivalent in size to the training data set. Selecting with replacement provides a way to grow the trees from the training data set while reducing the risk of overfitting the trees to the training data. As an example, for a training data set [1, 2, 3, 4, 5, 6] a bootstrapped sample selected with replacement may comprise [1, 2, 2, 3, 5, 6]. 
     Each tree is fully grown from its respective bootstrapped sample until each node of the tree contains only the same class of instances and no pruning is required. As each tree is grown, a random selection of class predictors defines the split of tree nodes, with the fractional set having the lowest gini impurity value being used to define the node. The number of dimensions of the class vector may be specified as parameter for constructing the model or the model may be developed by considering a range of possible class vector dimension values. The gini impurity value reflects the likelihood that a data instance, chosen at random from the data set, would be incorrectly labeled. A gini impurity value of zero indicates a 0% probability of incorrect labeling of the instance indicating that all instances of the node are of the same class. 
     As an example, a training data set includes a thousand data instances each comprising four instance attributes, d, e, f and g, and a classification label c. In growing the tree, the system will evaluate the results of splitting the data at the root node using different combinations of instance attributes to divide the data set. The evaluation selects the combination of attributes having the lowest likelihood of incorrectly labeling a random data instance, i.e., the lowest class impurity. For the example, the root node is divided based upon the value of attribute d. Each possible value of attribute d, is defined as a branch from the root node, for example, attribute d has four different values, d1, d2, d3, and d4, so four branches would be defined from the root node. The evaluation process then proceeds for each node defined by the attribute d branches. Again, the attribute or combination of attributes which yield a data division least likely to incorrectly classify a random data instance is chosen to define the branches at each node. This selection may differ for each of the nodes defined by the original splitting of the data. For example, the combinations of: d1, e; d2, e; d3, f; and d4, g; define the next set of branches. This process is continued for each branch still containing more than a single class of labeled data while also having more than a minimum number of instances on the branch, the specified maximum number of node levels has not been reached, or the class impurity cannot be improved by another split. 
     In an embodiment, an Out-of-bag (OOB) prediction may be calculated for the forest of random trees. The OOB prediction constitutes a vector comprising dimensions derived from a summation of the votes for a particular training data set data instance by all random trees which did not have the particular instance as part of their bootstrapped data set. As an example, consider a forest having five hundred random decision trees analyzing data having three classes, c1, c2, and c3. In this example instance 1 of the training data was not a part of the bootstrapped data set for two hundred of the trees. Evaluation of instance 1 by those two hundred trees led to one hundred classifications as c1, and fifty classifications for each of c2 and c3. The OOB vector for the forest for instance 1 would be [0.5, 0.25, 0.25] where the three dimensions represent the probability of the classification being selected by the trees. An OOB label prediction for each instance is also determined as the highest probability class from the OOB prediction vector. For the example, the OOB label prediction would be c1. 
     In an embodiment, the OOB prediction class vector may be appended to the data instance as a new feature of the instance. In the example, instance 1 of the data set would have the vector [0.5, 0.25, 0.25] appended to the data instance as a new feature determined by the forest. The appended data set is then passed to the next layer of the model for use in growing the decision trees of that layer&#39;s single forest. 
     In an embodiment, an OOB accuracy for a forest/layer is also calculated. The OOB accuracy is calculated as a comparison of the OOB label prediction to the actual data label for labeled data. In the example, instance 1, labeled as c1 and having an OOB prediction of [0.5, 0.25, 0.25] has an OOB label prediction of c1, is labeled correctly. The OOB accuracy of the entire forest over the entire data set is calculated as the percentage of instances which are classified correctly by the label prediction. 
     In an embodiment, a second forest/layer is created using the appended data set output from the original layer as the new training data set. Each tree of the predetermined number of random trees for the second forest is grown from a bootstrapped data set, taken with replacement, from the appended training data set. OOB predictions are calculated for the new forest and further appended to the training data set. An OOB accuracy for the new forest is determined and compared to the OOB accuracy of the previous forest/layer of the model. 
     The iterative: forest creation, data appending, OOB prediction and OOB accuracy calculation, continues until the current layer OOB accuracy does not vary significantly from that of the preceding layer. In an embodiment, variation in the OOB accuracy of more than 0.005% is considered significant improvement. 
     In an embodiment, the output of the forest of each layer is appended to the data set such that each instance of the data set has a new feature added for each layer of the model used. In this embodiment, the addition of the new features to each data instance provides the new information about each instance to each succeeding layer, rather than condensing new information determined about the instances into a supplemental data model for succeeding layers. Such a supplemental model may result in the loss of feature information due to model fidelity issues as the model may smooth feature information across data instances rather than appending the specific new feature information to each instance. 
     After the addition of new forests fails to significantly improve the OOB accuracy, a combiner function is added to combine the output of the last layer. Another random forest may be used as the combiner, without the calculation of OOB predictions or OOB accuracy. In an embodiment, a gradient boosting function, such as XGBoost may be used as the function to combine the output of the final forest. The addition of the combiner exploits the additional feature appended to the data set by the final forest added to the model. Utilizing the model without the combiner would yield results without consideration of this final OOB predictor added to the data set and provide a lower accuracy of prediction. After the combiner has been added to the layer/forests, the model may be used for the intended purpose of analyzing data from beyond the training data set in a machine learning context. 
     Examples 
     In an embodiment, a machine learning model is constructed from a data set. A random forest of decision trees is grown from the data set. OOB predictions for the forest are determined and appended to the data set. An additional forest of decision trees is grown from the appended data set. A combiner is added to the model to combine the output of the additional forest and provide the model output. 
     In an embodiment, a training data set is received together with a specification for the number of decision trees per forest. A first random forest having the specified number of decision trees is grown from the data set. OOB predictions and class labels are determined using the random forest. The OOB predictions are appended to the training data set. An OOB accuracy of the forest is determined. An additional forest having the specified number of decision trees is grown from the appended data set. OOB predictions and class labels are determined for the additional forest. The OOB predictions are appended to the already-appended data set. The OOB accuracy of the additional forest is determined and compared to the original forest OOB accuracy. The process of growing a forest, determining OOB predictions, class labels and accuracy, appending the predictions to the data set and comparing the accuracy to that of the preceding forest continues until there is no significant (&lt;0.005%) improvement in OOB accuracy due to the new forest. A combining structure is added to the last forest to provide the output of the model. 
     In an embodiment, a training data set is received together with a specification for the number of decision trees per forest and a class vector specification for the decision trees. A model is then constructed having a plurality of sequential layers. Each layer includes a single random forest to reduce computational resource requirements in constructing the model. A random forest having the specified number of decision trees is grown from the data set. OOB predictions and class labels are determined using the random forest. The OOB predictions are appended to the training data set. An OOB accuracy of the forest is determined. An additional forest having the specified number of decision trees is grown from the appended data set. OOB predictions and class labels are determined for the additional forest. The OOB predictions are appended to the already-appended data set. The OOB accuracy of the additional forest is determined and compared to the original forest OOB accuracy. The process of growing a forest, determining OOB predictions, class labels and accuracy, appending the predictions to the data set and comparing the accuracy to that of the preceding forest continues until there is no significant (&lt;0.005%) improvement in OOB accuracy due to the new forest. A combining structure is added to the last forest to provide the output of the model. 
     Experimental Results 
     Embodiments of the invention were constructed and compared to standard: random forest, XGBoost, and gcforest models, using standard data sets. For the experiment, the number of trees per forest was specified as five hundred, the number of randomly selected attributes was √d (d is the number of data instance attributes) and each tree is grown to pure leaf nodes. The embodiments of the invention were constructed with each of a random forest and XGBoost combiner. For XGBoost, default setting were used. For GCForest, each layer consisted of four completely random forests and four regular random forests. A three-fold class vector was used for class vector generation. 
     Each data set was split into a training sample (50%) and a testing sample (50%). The training sample was used to train the model sand the testing sample was used to evaluate the trained models. Each model type was constructed, trained, and evaluated five times for each data set. The results are provided in Table 1 below. The embodiments of the invention are labeled Incremental Deep Forest (IDF) with random forest combiner, and IDF with XGBoost combiner. As illustrated in the table, the accuracy of the embodiments surpasses the accuracy of the known machine learning models I for most of the standard data sets evaluated. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Model Accuracy (%) 
               
            
           
           
               
               
            
               
                   
                 Data set 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Random 
                   
                   
                 IDF 
                 IDF 
               
               
                   
                 Forest 
                 XGBoost 
                 gcForest 
                 RF 
                 XGBoost 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Hinselmann 
                 85.71 
                 100.00 
                 82.04 
                 96.33 
                 100.00 
               
               
                 Car Data 
                 94.79 
                 94.10 
                 95.72 
                 96.76 
                 96.64 
               
               
                 Cardioto- 
                 88.62 
                 88.98 
                 86.85 
                 89.14 
                 89.80 
               
               
                 cography 
               
               
                 Splice 
                 96.22 
                 96.07 
                 97.02 
                 96.42 
                 96.46 
               
               
                 DNA 
                 96.31 
                 96.83 
                 96.73 
                 97.01 
                 97.10 
               
               
                 Frogs MFCCs 
                 97.67 
                 97.00 
                 98.14 
                 98.31 
                 98.38 
               
               
                 genus 
               
               
                 Frogs MFCCs 
                 97.81 
                 97.47 
                 98.07 
                 98.26 
                 98.39 
               
               
                 species 
               
               
                 Frogs MFCCs 
                 98.28 
                 97.65 
                 98.36 
                 98.51 
                 98.61 
               
               
                 family 
               
               
                 Ringnorm 
                 95.77 
                 96.77 
                 96.97 
                 97.43 
                 97.63 
               
               
                 Chess 
                 65.74 
                 48.36 
                 67.90 
                 71.07 
                 70.56 
               
               
                 Bank full 
                 90.52 
                 90.43 
                 90.27 
                 90.55 
                 90.75 
               
               
                 Connect 4 
                 82.01 
                 75.82 
                 83.43 
                 83.23 
                 83.26 
               
               
                   
               
            
           
         
       
     
       FIG. 1  provides a schematic illustration of exemplary network resources associated with practicing the disclosed inventions. The inventions may be practiced in the processors of any of the disclosed elements which process an instruction stream. As shown in the figure, a networked Client device  110  connects wirelessly to server sub-system  102 . Client device  104  connects wirelessly to server sub-system  102  via network  114 . Client devices  104  and  110  comprise machine learning program (not shown) together with sufficient computing resource (processor, memory, network communications hardware) to execute the program. As shown in  FIG. 1 , server sub-system  102  comprises a server computer  150 .  FIG. 1  depicts a block diagram of components of server computer  150  within a networked computer system  1000 , in accordance with an embodiment of the present invention. It should be appreciated that  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made. 
     Server computer  150  can include processor(s)  154 , cache  162 , memory  158 , persistent storage  170 , communications unit  152 , input/output (I/O) interface(s)  156  and communications fabric  140 . Communications fabric  140  provides communications between cache  162 , memory  158 , persistent storage  170 , communications unit  152 , and input/output (I/O) interface(s)  156 . Communications fabric  140  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  140  can be implemented with one or more buses. 
     Memory  158  and persistent storage  170  are computer readable storage media. In this embodiment, memory  158  includes random access memory  160  (RAM). In general, memory  158  can include any suitable volatile or non-volatile computer readable storage media. Cache  162  is a fast memory that enhances the performance of processor(s)  154  by holding recently accessed data, and data near recently accessed data, from memory  158 . 
     Program instructions and data used to practice embodiments of the present invention, e.g., the machine learning program  175 , are stored in persistent storage  170  for execution and/or access by one or more of the respective processor(s)  154  of server computer  150  via cache  162 . In this embodiment, persistent storage  170  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  170  can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  170  may also be removable. For example, a removable hard drive may be used for persistent storage  170 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  170 . 
     Communications unit  152 , in these examples, provides for communications with other data processing systems or devices, including resources of client computing devices  104 , and  110 . In these examples, communications unit  152  includes one or more network interface cards. Communications unit  152  may provide communications through the use of either or both physical and wireless communications links. Software distribution programs, and other programs and data used for implementation of the present invention, may be downloaded to persistent storage  170  of server computer  150  through communications unit  152 . 
     I/O interface(s)  156  allows for input and output of data with other devices that may be connected to server computer  150 . For example, I/O interface(s)  156  may provide a connection to external device(s)  190  such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)  190  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., machine learning program  175  on server computer  150 , can be stored on such portable computer readable storage media and can be loaded onto persistent storage  170  via I/O interface(s)  156 . I/O interface(s)  156  also connect to a display  180 . 
     Display  180  provides a mechanism to display data to a user and may be, for example, a computer monitor. Display  180  can also function as a touch screen, such as a display of a tablet computer. 
       FIG. 2  provides a flowchart  200 , illustrating exemplary activities associated with the practice of embodiments of the invention. After program start, a training data set is received at  210 . The training data set may comprise any form of data associated with the machine learning task for which the desired model is intended. Examples include numeric, character, audio, video and image data and combinations thereof. The number of random trees per forest is received at  220 . The number may be chosen by a user, automatically determined according to a data set and the nature of the machine learning task, or randomly chosen using a random or pseudo-random number generator. The random trees are grown at  230 . Each tree may be grown from a bootstrapped data sample equivalent in size to the training data set and selected with replacement from the training data set. The bootstrapped data may be selected without replacement and a set smaller than the training data may be used but these selections may reduce the accuracy of the completed model and lead to overfitting the model to the training data set. An Out-of-bag (OOB) prediction and label prediction for each instance of the training data are determined at  240 . The OOB prediction provides new feature information about each instance of data and is appended to the respective instance of the data set at 250. An OOB accuracy is determined for the complete forest at  260 . The OOB accuracy is calculated using the OOB label prediction for each instance and aggregating the correct predictions across the forest and across the training data set. The OOB accuracy is compared to the OOB accuracy from the previous layer of the model at  270 . If there is significant (In an embodiment, improvement of &gt;0.005% constitutes significant improvement) improvement in the OOB accuracy, the method returns to step  230  and another layer/forest is grown and added to the model. In an embodiment, after significant improvement, the method returns to step  220  and the number of trees in the new layer/forest may be determined and may differ from the previous layer/forest. If there is not significant improvement in the OOB accuracy, a combiner is added to the model at  280  to aggregate the output of the final layer/forest for use. The model of the method comprises only the number of forest/layers necessary to optimize the OOB accuracy. The model passes all new feature information (OOB predictions) to each subsequent layer such that no new feature information determined by the model is lost in the transfer. 
       FIG. 3  provides an illustration of the evolution of the training data set as the model is constructed. As shown in the figure, Data Set  300 , comprising data instance X, is provided as an input to Forest  1   310 . For each data instance of the Data Set  300 , a class vector P 1 , is determined by Forest  1   310  for instance X, and appended instance X, yielding appended data set  320 . The appended data set  320 , is then provided as an input to Forest  2   330 , which yields class vector P 2  for instance X. Class vector P 2 , is appended to the instance yielding appended data set  340 . This continues until the OOB accuracy of a new forest does not significantly improve over the previous forest. A combiner  350  is added to the model to aggregate the output of the last added forest, represented in the figure as appended data set  350  comprising all appended class vectors including the final class vector Pn. The output  370 , of the combiner  360  constitutes the class prediction for a data instance. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 4 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 4  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 5 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 4 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 5  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture-based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and machine learning program  175 . 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The invention may be beneficially practiced in any system, single or parallel, which processes an instruction stream. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.