Visible to Intel only — GUID: GUID-912B34D6-7F50-4045-9D06-3D628C49A41E
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cov_dense_batch.cpp
dbscan_brute_force_batch.cpp
df_cls_hist_batch.cpp
df_cls_traverse_model.cpp
df_reg_hist_batch.cpp
df_reg_traverse_model.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_reg_brute_force_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
pca_cor_dense_batch.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cov_dense_batch.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
jaccard_batch.cpp
jaccard_batch_app.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_cls_kd_tree_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
louvain_batch.cpp
pca_dense_batch.cpp
pca_precomputed_dense_batch.cpp
polynomial_kernel_dense_batch.cpp
rbf_kernel_dense_batch.cpp
shortest_paths_batch.cpp
sigmoid_kernel_dense_batch.cpp
subgraph_isomorphism_batch.cpp
svm_multi_class_thunder_dense_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
triangle_counting_batch.cpp
K-Means Clustering
Density-Based Spatial Clustering of Applications with Noise
Correlation and Variance-Covariance Matrices
Principal Component Analysis
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Kernel Functions
Expectation-Maximization
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Quantile
Quality Metrics
Sorting
Normalization
Optimization Solvers
Decision Forest
Decision Trees
Gradient Boosted Trees
Stump
Linear and Ridge Regressions
LASSO and Elastic Net Regressions
k-Nearest Neighbors (kNN) Classifier
Implicit Alternating Least Squares
Logistic Regression
Naïve Bayes Classifier
Support Vector Machine Classifier
Multi-class Classifier
Boosting
Visible to Intel only — GUID: GUID-912B34D6-7F50-4045-9D06-3D628C49A41E
Data Sources
Data sources define interfaces for access and management of data in raw format and out-of-memory data. A data source is closely coupled with the data dictionary that describes the structure of the data associated with the data source. To create the associated data dictionary, you can do one of the following:
While constructing a data source object, specify whether it should automatically create and initialize the associated data dictionary.
Call the createDictionaryFromContext() method.
The getDictionary() method returns the dictionary associated with the data source.
Data sources stream and transform raw out-of-memory data into numeric in-memory data accessible through numeric table interfaces. A data source is associated with the corresponding numeric table. To allocate the associated numeric table, you can do one of the following:
While constructing a data source object, specify whether it should automatically allocate the numeric table.
Call the allocateNumericTable() method.
The getNumericTable() method returns the numeric table associated with the data source.
To retrieve the number of columns (features) in a raw data set, use the getNumberOfColumns() method. To retrieve the number of rows (observations) available in a raw data set, use the getNumberOfAvailableRows() method. The getStatus() method returns the current status of the data source:
readyForLoad - the data is available for the load operation.
waitingForData - the data source is waiting for new data to arrive later; designated for data sources that deal with asynchronous data streaming, that is, the data arriving in blocks at different points in time.
endOfData- all the data is already loaded.
Because the entire out-of-memory data set may fail to fit into memory, as well as for performance reasons, oneDAL implements data loading in blocks. Use the loadDataBlock() method to load the next block of data into the numeric table. This method enables you to load a data block into an internally allocated numeric table or into the provided numeric table. In both cases, you can specify the number of rows or not. The method also recalculates basic statistics associated with this numeric table.
oneDAL maintains the list of possible values associated with categorical features to convert them into a numeric form. In this list, a new index is assigned to each new value found in the raw data set. You can get the list of possible values from the possibleValues collection associated with the corresponding feature in the data source. In the case you have several data sets with same data structure and you want to use continuous indexing, do the following:
Retrieve the data dictionary from the last data source using the getDictionary() method.
Assign this dictionary to the next data source using the setDictionary() method.
Repeat these steps for each next data source.
Reading from a Data Source
oneDAL implements classes for some popular types of data sources. Each of these classes takes a feature manager class as the class template parameter. The feature manager parses, filters, and normalizes the data and converts it into a numeric format. The following are the data sources and the corresponding feature manager classes:
Text file (FileDataSource class), to be used with the CSVFeatureManager class
ODBC (ODBCDataSource class), to be used with the MySQLFeatureManager class
In-memory text (StringDataSource class), to be used with the CSVFeatureManager class
KDB relational database (KDBDataSource class), to be used with the KDBFeatureManager class
CSVFeatureManager provides additional capabilities for features modification. Use addModifier() to enable specific modification when loading data to a numeric table:
Add the ColumnFilter object if you need to have a predefined subset of features loaded
Add the OneHotEncoder object if you need a categorical feature to be encoded using the one-hot scheme
Feature managers provide additional capabilities for the modification of the input data during its loading. Use the Feature modifier entity to define desired modification. Feature modifiers enables you to implement a wide range of feature extraction or transformation techniques, for instance, feature binarization, one-hot-encoding, or polynomial features generation. To enable specific modification, use the addModifier() method that accepts two parameters:
featureIds - a subset of feature identifiers for which you want to apply modification.
featureModifier - an implementation of the Feature modifier, an object that implements the FeatureModifierIface interface and specifies the way how features of the input data set should be modified and written to the output numeric table.
Typical feature modifiers usage scenario is the following:
Create the data source object and specify a feature manager and its parameters.
Define a subset of features for modification and proper feature modifier.
Add modifier to the feature manager of the data source object.
Call loadDataBlock(), it causes data set loading and applying specified modification procedure to the features of the data set.
The code block bellow demonstrates feature modifiers usage scenario in case of FileDataSource and CSVFeatureManager.
// Crate DataSource object (for example FileDataSource)
FileDataSource<CSVFeatureManager> ds("file.csv", options);
// Specify features subset and modifier
auto featureIds = features::list("f1", "f2");
auto featureModifier = modifiers::csv::continuous();
// Add modifier to feature manager
ds.getFeatureManager().addModifier(featureIds, modifier);
// Cause data loading
ds.loadDataBlock();
A feature subset may be defined with the functions list(…) , range(…), all(), or allReverse() located in the namespace data_management::features. For example, you can use numerical or string identifiers to refer to the particular feature in the data set. A string identifier may correspond to a feature name (for instance, name in CSV header or in SQL table column name) and numerical one to the index of a feature. The following code block shows several ways to define a feature subset. f1 , f2, and f4 are the names of the respective columns in CSV file or SQL table, and the numbers 0, 2 - 4 are the indices of columns starting from the left one.
features::list("f1", "f2") // String identifiers
features::list(0, 3); // Numerical identifiers
features::list("f1", 2); // Mixed identifiers
features::range(0, 4); // Range of features, the same as list(0,…,4)
features::range("f1", "f4"); // Range with string identifiers
features::all(); // Refer to all features in the data set
features::allReverse() // Like features::all() but in reverse order
// With STL vector
std::vector<features::IdFactory> fv;
fv.push_back("f2"); fv.push_back(3);
features::list(fv);
// With C++ 11 initializer list
features::list({ "f2", 3, "f1" });
We will use the term input features to refer to the columns of raw out-of-memory data and the term output features for the columns of numeric in-memory data. A feature modifier transforms specified input features subset to the output features. The number of output features is determined by the modifier. A feature modifier is expected to read the values corresponding to specified input features from the i-th row and write modified values to the i-th row of the output numeric table. In general case, feature modifier is able to process arbitrary number of input features to arbitrary number of output features. Let’s assume that we added m modifiers along with the features subsets 
and the j-th modifier has the 
output columns, where 
are specified input features of interest, 
, 
are all possible features, p is the number of features in the input data. The output numeric table will contain 
columns. The j-th feature modifier writes result to the columns starting with the index 
, in particular the first feature modifier writes to the first 
columns, and the last to the last 
columns of the output table. The following picture demonstrates the case of two modifiers. Feature Modifier 1 reads the features 
from an input data set, performs data transformation and writes the result to the columns 1, 2 in the output numeric table. Feature Modifier 2 behaves similarly, but processes features 
and has 3 output features.
Feature Modifiers
The oneDAL has several predefined feature modifiers available for CSV and SQL feature managers.
continuous - parses input values as real numbers, the number of output features is equal to the number of input features.
categorical - parses input values as categorical features (described above), the number of output features is equal to the number of input features.
automatic - automatically selects appropriate parsing scheme (continuous or categorical)
oneHotEncoder - apply one-hot-encoding scheme for input features, the number of output features is equal to the sum of unique values for features in the input data set.
NOTE:
The current version of the library does not provide predefined feature modifiers for handling ordinal features.
You can implement you own feature modifier by inheriting from FeatureModifierBase and overriding its methods. An example interface of user-defined feature modifier is shown in the code block bellow:
class MyFeatureModifier : public modifiers::csv::FeatureModifierBase
{
public:
virtual void initialize(modifiers::csv::Config &config);
virtual void apply(modifiers::csv::Context &context);
virtual void finalize(modifiers::csv::Config &config);
};
Use the addModifier(…) method to add the user-defined modifier to the feature manager:
ds.getFeatureManager().addModifier( features::list(0, 3), modifiers::custom<MyFeatureModifier>() );
Feature modifier’s lifetime consists of three stages:
Initialization. Feature manager performs modifier initialization by calling the initialize method. The Config class provides methods to change configuration of the modifier. For example use the Config::setNumberOfOutputFeatures(…) to adjust numbers of output features produced by the modifier. By default, the number of output feature is equal to the number of input features.
Applying loop. Feature manager calls the apply method for every row in the input data set, information about the current row is provided via context object. To implement this method, you need to get the input data from the context, carry out desired transformation and write result back to the context output buffer. You can get the output buffer by calling the Context::getOutputBuffer() method, the buffer’s size must be equal to the number of output features you specified at the initialization stage.
Finalization. Finalization happens when feature manager calls the finalize method with the same config object passed at the initialization stage. For example, you may use this method to release intermediate buffers when the data transformation is done.
Note that exact set of methods available for Config and Context depends on the data source type. Please refer to Developer Reference to get detailed information about supported methods.