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MADLib Expressions (ML)

Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical, graph and machine learning methods for structured and unstructured data.

1 - Data Type Transformations

1.1 - Array Operations

Provides support functions enabling fast array operations

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.array_add(array1,array2);

In PlaidCloud Expressions & Filters

func.madlib.array_add(array1,array2)

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.2 - Encoding Categorical Variables

Coding categorical variables into one-hot, dummy, effects, orthogonal, and Helmert

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.encode_categorical_variables ('abalone', 'abalone_out', 'height::TEXT');

In PlaidCloud Expressions & Filters

func.madlib.encode_categorical_variables ('abalone', 'abalone_out', 'height::TEXT')

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.3 - Low-Rank Matrix Factorization

Represent an incomplete matrix using a low-rank approximation

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.lmf_igd_run('lmf_model', 'lmf_data', 'row', 'col', 'val', 999, 10000, 3, 0.1, 2, 10, 1e-9);

In PlaidCloud Expressions & Filters

func.madlib.lmf_igd_run('lmf_model', 'lmf_data', 'row', 'col', 'val', 999, 10000, 3, 0.1, 2, 10, 1e-9)

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.4 - Matrix Operations

Provides basic matrix operations for matrices that are too big to fit in memory

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.matrix_trans('"mat_B"', 'row=row_id, val=vector', 'mat_r');

In PlaidCloud Expressions & Filters

func.madlib.matrix_trans('"mat_B"', 'row=row_id, val=vector', 'mat_r')

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.5 - Norms and Distance Functions

Useful utility functions for basic linear algebra operations

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.squared_dist_norm2(a, b);

In PlaidCloud Expressions & Filters

func.madlib.squared_dist_norm2(a, b)

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.6 - Path

Performs regular pattern matching over a sequence of rows

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.path('eventlog', 'path_output', 'session_id', 'event_timestamp ASC', 'buy:=page=''CHECKOUT''', '(buy)', 'sum(revenue) as checkout_rev', TRUE);

In PlaidCloud Expressions & Filters

func.madlib.path('eventlog', 'path_output', 'session_id', 'event_timestamp ASC', "buy:=page='CHECKOUT'", '(buy)', 'sum(revenue) as checkout_rev', True)

External References

Apache MADLib Official Documentation for this method can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.7 - Pivot

Perform basic OLAP type operations on data

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.pivot('pivset_ext', 'pivout', 'id', 'piv', 'val', 'sum');

In PlaidCloud Expressions & Filters

func.madlib.pivot('pivset_ext', 'pivout', 'id', 'piv', 'val', 'sum')

External References

Apache MADLib Official Documentation for this method can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.8 - Sessionize

Performs time-oriented session reconstruction on a data set comprising a sequence of events

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.sessionize('eventlog', 'sessionize_output_view', 'user_id', 'event_timestamp', '0:30:0');

In PlaidCloud Expressions & Filters

func.madlib.sessionize('eventlog', 'sessionize_output_view', 'user_id', 'event_timestamp', '0:30:0')

External References

Apache MADLib Official Documentation for this method can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.9 - Single Value Decomposition

Factorization of a real or complex matrix, with many useful applications in signal processing and statistics

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.matrix_sparsify('mat', 'row=row_id, val=row_vec', 'mat_sparse', 'row=row_id, col=col_id, val=value');

In PlaidCloud Expressions & Filters

func.madlib.matrix_sparsify('mat', 'row=row_id, val=row_vec', 'mat_sparse', 'row=row_id, col=col_id, val=value')

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.10 - Sparse Vectors

Provides compressed storage of vectors that have many duplicate elements

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.gen_doc_svecs('svec_output', 'dictionary_table', 'id', 'term', 'documents_table', 'id', 'term', 'count');

In PlaidCloud Expressions & Filters

func.madlib.gen_doc_svecs('svec_output', 'dictionary_table', 'id', 'term', 'documents_table', 'id', 'term', 'count')

External References

Apache MADLib Official Documentation for these methods can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

1.11 - Stemming

Provides a basic stemming operation for text input using the Porter Stemming Algorithm

PlaidCloud expressions and filters provide use of most non-administrative Apache MADLib methods. Apache MADLib methods are accessed by prefixing the standard method name with func.madlib..

In SQL

madlib.stem_token(word)

In PlaidCloud Expressions & Filters

func.madlib.stem_token(word)

External References

Apache MADLib Official Documentation for this method can be found here.

Additional capabilities and usage examples can be found in the Apache MADLib documentation.

2 - Deep Learning

Content coming soon

3 - Machine Learning

Make processing your database tables easier with 'MADlib'

Analyze utilizes the expansive and powerful MADLib extension. MADlib helps you take advantage of the investments you’ve made in your database while using its computational power rather than extracting the data into an external system.

Additional documentation on how to use machine learning is coming soon.