# Microsoft.ML.Data Namespace

Namespace containing data loading and saving, data schema definitions, and model training metrics components.

## Classes

 Evaluation results for anomaly detection(unsupervised learning algorithm). Base class for the ISingleFeaturePredictionTransformer working on anomaly detection tasks. Evaluation results for binary classifiers, excluding probabilistic metrics. The BinaryClassificationMetricsStatistics class holds summary statistics over multiple observations of BinaryClassificationMetrics. This class represents one data point on Precision-Recall curve for binary classification. Base class for the ISingleFeaturePredictionTransformer working on binary classification tasks. The standard boolean type. This has representation type of Boolean. Note this can have only one possible value, accessible by the singleton static property Instance. Evaluation results for binary classifiers, including probabilistic metrics. The metrics generated after evaluating the clustering predictions. Base class for the ISingleFeaturePredictionTransformer working on clustering tasks. ITransformer resulting from fitting an ColumnConcatenatingEstimator. Extension methods that allow to extract values of a single column of an IDataView as an IEnumerable. Allows a member to specify IDataView column name directly, as opposed to the default behavior of using the member name as the column name. This class represents a data loader that applies a transformer chain after loading. It also has methods to save itself to a repository. An estimator class for composite data loader. It can be used to build a 'trainable smart data loader', although this pattern is not very common. Represents the confusion matrix of the classification results. Describes how an input column should be mapped to an IDataView column. The settings for DatabaseLoader Specifies the range of indices or names of input columns that should be mapped to an output column. Exposes the data required for opening a database for reading. This class represents an eager 'preview' of a IDataView. This is the abstract base class for all types in the IDataView type system. DataViewTypeAttribute should be used to decorated class properties and fields, if that class' instances will be loaded as ML.NET IDataView. The function Register() will be called to register a DataViewType for a Type with its Attributes. Whenever a value typed to the registered Type and its Attributes, that value's type (i.e., a Type) in IDataView would be the associated DataViewType. A singleton class for managing the map between ML.NET DataViewType and C# Type. To support custom column type in IDataView, the column's underlying type (e.g., a C# class's type) should be registered with a class derived from DataViewType. The standard date time type. This has representation type of DateTime. Note this can have only one possible value, accessible by the singleton static property Instance. The standard date time offset type. This has representation type of DateTimeOffset. Note this can have only one possible value, accessible by the singleton static property Instance. Represents a chain (potentially empty) of estimators that end with a TLastTransformer. If the chain is empty, TLastTransformer is always ITransformer. Wraps an IFileHandle as an IMultiStreamSource. ITransformer resulting from fitting a ImageLoadingEstimator. Defines the cardinality, or count, of valid values of a KeyDataViewType column. This needs to be strictly positive. It is used by TextLoader and TypeConvertingEstimator. Type representing categorical or enumerated values, most commonly used for the values of labels in multiclass classification models. Allow member to be marked as a KeyDataViewType. Allow member to specify mapping to field(s) in text file. To override name of IDataView column use ColumnNameAttribute. Allow member to specify mapping to field(s) in database. To override name of IDataView column use ColumnNameAttribute. The MetricsStatistics class computes summary statistics over multiple observations of a metric. Provide interfaces for imaging operations. Evaluation results for multi-class classification trainers. The MulticlassClassificationMetricsStatistics class holds summary statistics over multiple observations of MulticlassClassificationMetrics. Base class for the ISingleFeaturePredictionTransformer working on multi-class classification tasks. Wraps a potentially compound path as an IMultiStreamSource. Mark this member as not being exposed as a IDataView column in the DataViewSchema. The standard number type. This class is not directly instantiable. All allowed instances of this type are singletons, and are accessible as static properties on this class. Base class for transformer which operates on pairs input and output columns. Base class for transformers with no feature column, or more than one feature columns. The abstract base class for all primitive types. Values of these types can be freely copied without concern for ownership, mutation, or disposing. Options to control the output of the RankingEvaluator Evaluation results for rankers. The RankingMetricsStatistics class holds summary statistics over multiple observations of RankingMetrics. Base class for the ISingleFeaturePredictionTransformer working on ranking tasks. Evaluation results regression algorithms (supervised learning algorithm). The RegressionMetricsStatistics class holds summary statistics over multiple observations of RegressionMetrics. Base class for the ISingleFeaturePredictionTransformer working on regression tasks. The RowIdDataViewType type. This has representation type of DataViewRowId. Note this can have only one possible value, accessible by the singleton static property Instance. Base class for transformer which produce new columns, but doesn't affect existing ones. Extension methods to facilitate easy consumption of popular contents of Annotations. This class defines a schema of a typed data view. One column of the data view. A simple disk-based file handle. The base class for all the transformers implementing the ISingleFeaturePredictionTransformer. Those are all the transformers that work with one feature column. The abstract base class for all non-primitive types. This attempts to reads data in a format close to the SVM-light format, the goal being that the majority of SVM-light formatted data should be interpretable by this loader. The standard text type. This has representation type of ReadOnlyMemory with type parameter Char. Note this can have only one possible value, accessible by the singleton static property Instance. Loads a text file into an IDataView. Supports basic mapping from input columns to IDataView columns. Describes how an input column should be mapped to an IDataView column. The settings for TextLoader Specifies the range of indices of input columns that should be mapped to an output column. The standard timespan type. This has representation type of TimeSpan. Note this can have only one possible value, accessible by the singleton static property Instance. A chain of transformers (possibly empty) that end with a TLastTransformer. For an empty chain, TLastTransformer is always ITransformer. The trivial implementation of IEstimator that already has the transformer and returns it on every call to Fit(IDataView). Concrete implementations still have to provide the schema propagation mechanism, since there is no easy way to infer it from the transformer. Various methods for creating VBufferEditor instances. The standard vector type. The representation type of this is VBuffer, where the type parameter is in ItemType. Allows a member to be marked as a VectorDataViewType, primarily allowing one to set the dimensionality of the resulting array.

## Structs

 A structure serving as the identifier of a row of IDataView. For datasets with millions of records, those IDs need to be unique, therefore the need for such a large structure to hold the values. Those Ids are derived from other Ids of the previous components of the pipelines, and dividing the structure in two: high order and low order of bits, and reduces the changes of those collisions even further. A buffer that supports both dense and sparse representations. This is the representation type for all VectorDataViewType instances. The explicitly defined values of this vector are exposed through GetValues() and, if not dense, GetIndices(). An object capable of editing a VBuffer by filling out Values (and Indices if the buffer is not dense).

## Interfaces

 A file handle. An interface for exposing some number of items that can be opened for reading. This interface maps an input DataViewRow to an output DataViewRow. Typically, the output contains both the input columns and new columns added by the implementing class, although some implementations may return a subset of the input columns. This interface is similar to Microsoft.ML.Data.ISchemaBoundRowMapper, except it does not have any input role mappings, so to rebind, the same input column names must be used. Implementations of this interface are typically created over defined input DataViewSchema.

## Enums

 Specifies a simple data type. Specifies the format of the color data for each pixel in the image. This enum allows for 'tagging' the estimators (and subsequently transformers) in the chain to be used 'only for training', 'for training and evaluation' etc. Most notable example is, transformations over the label column should not be used for scoring, so the scope should be Training or TrainTest.