add decision tree and random forest first draft

[j143]

decisionTree

# This script implements decision trees for recoded and binned categorical and
# numerical input features. We train a single CART (classification and
# regression tree) decision trees depending on the provided labels y, either
# classification (majority vote per leaf) or regression (average per leaf).
#
# .. code-block::
#
#   For example, give a feature matrix with features [a,b,c,d]
#   and the following trees, M would look as follows:
#
#   (L1)               |d<5|
#                     /     \
#   (L2)           P1:2    |a<7|
#                          /   \
#   (L3)                 P2:2 P3:1
#
#   --> M :=
#   [[4, 5, 0, 2, 1, 7, 0, 0, 0, 0, 0, 2, 0, 1]]
#    |(L1)| |  (L2)   | |        (L3)         |
#
#
#
# INPUT:
# ------------------------------------------------------------------------------
# X               Feature matrix in recoded/binned representation
# y               Label matrix in recoded/binned representation
# ctypes          Row-Vector of column types [1 scale/ordinal, 2 categorical]
#                 of shape 1-by-(ncol(X)+1), where the last entry is the y type
# max_depth       Maximum depth of the learned tree (stopping criterion)
# min_leaf        Minimum number of samples in leaf nodes (stopping criterion),
#                 odd number recommended to avoid 50/50 leaf label decisions
# min_split       Minimum number of samples in leaf for attempting a split
# max_features    Parameter controlling the number of features used as split
#                 candidates at tree nodes: m = ceil(num_features^max_features)
# max_values      Parameter controlling the number of values per feature used
#                 as split candidates: nb = ceil(num_values^max_values)
# max_dataratio   Parameter in [0,1] controlling when to materialize data
#                 subsets of X and y on node splits. When set to 0, we always
#                 scan the original X and y, which has the benefit of avoiding
#                 the allocation and maintenance of data for all active nodes.
#                 When set to 0.01 we rematerialize whenever the sub-tree data
#                 would be less than 1% of last the parent materialize data size.
# impurity        Impurity measure: entropy, gini (default), rss (regression)
# seed            Fixed seed for randomization of samples and split candidates
# verbose         Flag indicating verbose debug output
# ------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------
# M              Matrix M containing the learned trees, in linearized form
# ------------------------------------------------------------------------------

m_decisionTree = function(Matrix[Double] X, Matrix[Double] y, Matrix[Double] ctypes,
    Int max_depth = 10, Int min_leaf = 20, Int min_split = 50,
    Double max_features = 0.5, Double max_values = 1.0, Double max_dataratio = 0.25,
    String impurity = "gini", Int seed = -1, Boolean verbose = FALSE)
  return(Matrix[Double] M)
{

and

decisionTreePredict

# This script implements random forest prediction for recoded and binned
# categorical and numerical input features.
# Hummingbird paper (https://www.usenix.org/system/files/osdi20-nakandala.pdf).
#
# INPUT:
# ------------------------------------------------------------------------------
# X               Feature matrix in recoded/binned representation
# y               Label matrix in recoded/binned representation,
#                 optional for accuracy evaluation
# ctypes          Row-Vector of column types [1 scale/ordinal, 2 categorical]
# M               Matrix M holding the learned tree in linearized form
#                 see decisionTree() for the detailed tree representation.
# strategy        Prediction strategy, can be one of ["GEMM", "TT", "PTT"],
#                 referring to "Generic matrix multiplication",
#                 "Tree traversal", and "Perfect tree traversal", respectively
# verbose         Flag indicating verbose debug output
# ------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------
# yhat            Label vector of predictions
# ------------------------------------------------------------------------------

m_decisionTreePredict = function(Matrix[Double] X, Matrix[Double] y = matrix(0,0,0),
    Matrix[Double] ctypes, Matrix[Double] M, String strategy="TT", Boolean verbose = FALSE)
  return (Matrix[Double] yhat)
{

[j143]

[ERROR] Could not find 'None' in 'dropped' column for logistic regression accuracy. Using 0.0 as fallback.
25/05/03 05:13:05 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Traceback (most recent call last):
File "/workspaces/heart-attack-analysis/heart_attack_systemds.py", line 234, in
dt_pred = decisionTreePredict(X_test_sds, dt_model).compute()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: decisionTreePredict() missing 1 required positional argument: 'M'

[j143]

[ERROR] Could not find 'None' in 'dropped' column for logistic regression accuracy. Using 0.0 as fallback.
25/05/03 05:15:50 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Traceback (most recent call last):
  File "/workspaces/heart-attack-analysis/heart_attack_systemds.py", line 234, in <module>
    dt_pred = decisionTreePredict(X_test_sds, dt_model, X_train_sds).compute()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/systemds/operator/nodes/matrix.py", line 108, in compute
    return super().compute(verbose, lineage)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/systemds/operator/operation_node.py", line 108, in compute
    result_variables = self._script.execute()
                       ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/systemds/script_building/script.py", line 100, in execute
    self.sds_context.exception_and_close(exception_str, trace_back_limit)
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/systemds/context/systemds_context.py", line 143, in exception_and_close
    raise RuntimeError(message)
RuntimeError: 

An error occurred while calling o1.prepareScript.
: org.apache.sysds.api.DMLException: org.apache.sysds.parser.LanguageException: ERROR: [line 5:0] -> V4=decisionTree(X=V1,y=V2,ctypes=V3,icpt=1,max_depth=5); -- Named function call parameter 'icpt' does not exist in signature of function 'm_decisionTree'. Function signature: [X, y, ctypes, max_depth, min_leaf, min_split, max_features, max_values, max_dataratio, impurity, seed, verbose]
        at org.apache.sysds.api.jmlc.Connection.prepareScript(Connection.java:284)
        at org.apache.sysds.api.jmlc.Connection.prepareScript(Connection.java:223)
        at org.apache.sysds.api.jmlc.Connection.prepareScript(Connection.java:210)
        at java.base/jdk.internal.reflect.DirectMethodHandleAccessor.invoke(DirectMethodHandleAccessor.java:103)
        at java.base/java.lang.reflect.Method.invoke(Method.java:580)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
        at py4j.Gateway.invoke(Gateway.java:282)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:238)
        at java.base/java.lang.Thread.run(Thread.java:1583)
Caused by: org.apache.sysds.parser.LanguageException: ERROR: [line 5:0] -> V4=decisionTree(X=V1,y=V2,ctypes=V3,icpt=1,max_depth=5); -- Named function call parameter 'icpt' does not exist in signature of function 'm_decisionTree'. Function signature: [X, y, ctypes, max_depth, min_leaf, min_split, max_features, max_values, max_dataratio, impurity, seed, verbose]
        at org.apache.sysds.parser.Expression.raiseValidateError(Expression.java:395)
        at org.apache.sysds.parser.Expression.raiseValidateError(Expression.java:362)
        at org.apache.sysds.parser.FunctionCallIdentifier.validateExpression(FunctionCallIdentifier.java:162)
        at org.apache.sysds.parser.StatementBlock.validateAssignmentStatement(StatementBlock.java:933)
        at org.apache.sysds.parser.StatementBlock.validate(StatementBlock.java:881)
        at org.apache.sysds.parser.DMLTranslator.validateParseTree(DMLTranslator.java:135)
        at org.apache.sysds.parser.DMLTranslator.validateParseTree(DMLTranslator.java:110)
        at org.apache.sysds.api.jmlc.Connection.prepareScript(Connection.java:261)
        ... 11 more