dsbox-primitives 1.6.2

DSBox data processing primitives for both cleaning and featurizer

ISI Primitives

ISI DSBox Cleaning Primitives

The git repository containing DSBox cleaning related primitives is here. The git repository for DSBox primitives related to featurization is located here.

Data cleaning primitives

d3m.primitives.dsbox.CleaningFeaturizer

This is a multi-purpose cleaning featurizer primitive. This primitive requires metadata annotations from ISI's profiling primitive, see d3m.primitives.dsbox.Profiler below. The cleaning featurization operations supported include:

• Split date column into multiple columns, e.g. year, month, date, day
• Split US phone number into multiple columns.
• Split column with consistent alpha-numeric value patterns, e.g. '2days' into multiple columns.
• Split column with consistent puntucation value patterns, e.g. 'NY_US' into multiple columns.

d3m.primitives.dsbox.FoldColumns

Fold multiple columns into one column based on common column name prefix. For example, fold columns with names 'month-jan', 'month-feb', 'month-mar' and so on, into one column named 'month'.

Encoding primitives

d3m.primitives.dsbox.Encoder

Performs one-hot encoding for categorical attributes. This encoder can handle missing values, and it allows user to specify the upper limit of columns to generate per cagtegorical attribute, n_limit.

d3m.primitives.dsbox.UnaryEncoder

Performs unary encoding, which useful for ordinal data.

Imputation primitives

d3m.primitives.dsbox.MeanImputation

Performs mean missing value imputation for numerical columns, and mode imputation for categorical columns.

d3m.primitives.dsbox.GreedyImputation

Performs missing value imputation by greedy search over simple imputation methods, i.e. mean, min, max, and zero.

d3m.primitives.dsbox.IterativeRegressionImputation

Performs missing value imputation by regression, then improve the imputation by iterating over columns with missing values.

Profiling Primitive

d3m.primitives.dsbox.Profiler

This primitive generates metadata by examining the given data. The types of metadata include:

• Column contains values tokenizable as an American phone number
• Column contains values tokenizable by puntucation
• Column contains values tokenizable into numerical tokens and alpha tokens
• Column value tokenization features (most common tokens, number of distinct tokens, ratio of distinct tokens, and so on)
• Column value features (most common values, number of distinct values, ration of distinct values, and so on)
• Column contains filename-like values
• Column contains missing values (number of missing values, ratio of missing values)
• Number of outlier values
• Correlation between columns (Pearson, Spearman)

Datamart Primitives

d3m.primitives.dsbox.QueryDataframe

Queries datamart for available datasets. The JSON query specification is defined Datamart Query API. The primitive returns a list of dataset metadata.

d3m.primitives.dsbox.Join

Joins two dataframes into one dataframe. The primtive takes two dataframes, left_dataframe and right_dataframe, and two lists specifing the join columns, left_columns and right_columns.

ISI DSBox Featurization Primitives

The git repository for DSBox primitives related to featurization is located here. The git repository containing DSBox cleaning related primitives is here.

Image Featurization Primitives

d3m.primitives.dsbox.ResNet50ImageFeature

Generate features using pre-trained ResNet50 deep neural network. Use hyperparameter layer_index to select the network layer to use for featurization.

d3m.primitives.dsbox.Vgg16ImageFeature

Generate features using pre-trained VGG16 deep neural network. Use hyperparameter layer_index to select the network layer to use for featurization.

d3m.primitives.dsbox.DataFrameToTensor

Reads in image files and generates a tensor that suitable as input to d3m.primitives.dsbox.ResNet50ImageFeature and d3m.primitives.dsbox.Vgg16ImageFeature.

Timeseries Featuration Primitives

d3m.primitives.dsbox.RNNTimeSeries

Performs forecasting of one timeseries using recursive neural network.

d3m.primitives.dsbox.AutoArima

Performs forecasting of one timeseries using AutoArima.

d3m.primitives.dsbox.GroupUpByTimeSeries

Performs forecasting of one timeseries using Group Up.

d3m.primitives.dsbox.RandomProjectionTimeSeriesFeaturization

Generate features of multiple timeseries by random projecting the timeseries matrix into lower dimendions.

d3m.primitives.dsbox.TimeseriesToList

Reads in timeseries csv files and generate output List that is suitable as input to d3m.primitives.dsbox.RandomProjectionTimeSeriesFeaturization.

Multi-table Join Primitive

d3m.primitives.dsbox.MultiTableFeaturization

Automatically detect foriegn key relationships among multiple tables, and join the tables into one table using aggregation.

Miscellaneous

d3m.primitives.dsbox.DoNothing

This an identity function primitive that returns the input dataframe as output. This useful for bypassing a step in a pipeline without having to modify the pipeline structure.