util.convertLGBMToRootTree Namespace Reference

Classes
class	LGBMTextNode

Functions
	lgbm_rawresponse_each_tree (model, my_input)
	list2stdvector (values, dtype="float")
	dump_tree (tree_structure)
	dump2ROOT (model, output_filename, output_treename="lgbm")
	convertLGBMToRootTree (model, output_filename, tree_name="lgbm")
	test (model_file, tree_file, tree_name="lgbm", ntests=10000, test_file=None)
	get_test_data (feature_names, test_file=None, ntests=None)
	test_generic (booster, mvautils_predict, mva_utils, data_input)
	_ff (tree, node_infos)
	test_detail_event (booster, mva_utils, input_values)
	test_regression (booster, mva_utils, ntests=None, test_file=None)
	test_binary (booster, mva_utils, ntests=None, test_file=None)
	test_multiclass (booster, mva_utils, ntests=10000, test_file=None)
	check_file (fn)

Variables
str	__author__ = "Ruggero Turra"
	level
	parser = argparse.ArgumentParser(description=__doc__)
	help
	nargs
	default
	action
	type
	int
	args = parser.parse_args()
	output
	output_treename = convertLGBMToRootTree(args.input, args.output, args.tree_name)
	result = test(args.input, args.output, args.tree_name, args.ntests, args.test_file)
	booster = lgb.Booster(model_file=args.input)
	objective = booster.dump_model()["objective"]

Detailed Description

Convert LightGBM model to TTree to be used with MVAUtils. 

Function Documentation

_ff()

util.convertLGBMToRootTree._ff ( tree, node_infos )

protected

Definition at line 341 of file convertLGBMToRootTree.py.

def _ff(tree, node_infos):
    if "left_child" in tree:
        node_infos.append((tree["split_feature"], tree["threshold"]))
        _ff(tree["left_child"])
        _ff(tree["right_child"])
 
 

check_file()

util.convertLGBMToRootTree.check_file

(

fn

)

Definition at line 531 of file convertLGBMToRootTree.py.

def check_file(fn):
    f = ROOT.TFile.Open(fn)
    keys = f.GetListOfKeys()
    keys = list(keys)
    if len(keys) != 1:
        logging.info("file %s is empty", fn)
        return False
    tree = f.Get(keys[0].GetName())
    if type(tree) is not ROOT.TTree:
        logging.info("cannot find TTree in file %s", fn)
        return False
    if not tree.GetEntries():
        logging.info("tree is empty")
        return False
    return True
 
 

convertLGBMToRootTree()

util.convertLGBMToRootTree.convertLGBMToRootTree	(		model,
			output_filename,
			tree_name = "lgbm" )

Model: - a string, in this case, it is the name of
the input file containing the lgbm model you
can get this model with lgbm with
`boosted.save_model('my_model.txt')
- directly a lgbm booster object

Definition at line 192 of file convertLGBMToRootTree.py.

def convertLGBMToRootTree(model, output_filename, tree_name="lgbm"):
    """
    Model: - a string, in this case, it is the name of
    the input file containing the lgbm model you
    can get this model with lgbm with
    `boosted.save_model('my_model.txt')
    - directly a lgbm booster object
    """
    if type(model) is str:
        model = lgb.Booster(model_file=model)
        return dump2ROOT(model, output_filename, tree_name)
    else:
        return dump2ROOT(model, output_filename, tree_name)
 
 

dump2ROOT()

util.convertLGBMToRootTree.dump2ROOT	(		model,
			output_filename,
			output_treename = "lgbm" )

Definition at line 141 of file convertLGBMToRootTree.py.

def dump2ROOT(model, output_filename, output_treename="lgbm"):
    model = model.dump_model()
    fout = ROOT.TFile.Open(output_filename, "recreate")
 
    features_array = ROOT.std.vector("int")()
    values_array = ROOT.std.vector("float")()
    default_lefts_array = ROOT.std.vector("bool")()
 
    simple = True
    node_type = "node_type=lgbm_simple"
    for tree in model["tree_info"]:
        tree_structure = tree["tree_structure"]
        features, values, default_lefts, simple_tree = dump_tree(tree_structure)
        if not simple_tree:
            simple = False
            node_type = "node_type=lgbm"
 
    infos = ";".join(["%s=%s" % (k, str(v)) for k, v in model.items() if type(v) is not list])
    title = ";".join(("creator=lgbm", node_type, infos))
    root_tree = ROOT.TTree(output_treename, title)
    root_tree.Branch("vars", "vector<int>", ROOT.AddressOf(features_array))
    root_tree.Branch("values", "vector<float>", ROOT.AddressOf(values_array))
 
    if not simple:
        logging.info("tree support nan: using full implementation (LGBMNode)")
        root_tree.Branch("default_left", "vector<bool>", ROOT.AddressOf(default_lefts_array))
    if simple:
        logging.info("tree do not support nan:" "using simple implementation (LGBMNodeSimple)")
 
    for tree in model["tree_info"]:
        tree_structure = tree["tree_structure"]
        features, values, default_lefts, simple_tree = dump_tree(tree_structure)
 
        features_array.clear()
        values_array.clear()
        default_lefts_array.clear()
 
        for value in values:
            values_array.push_back(value)
        for feature in features:
            features_array.push_back(feature)
        for default_left in default_lefts:
            default_lefts_array.push_back(default_left)
 
        root_tree.Fill()
 
    root_tree.Write()
    fout.Close()
    return output_treename
 
 

dump_tree()

util.convertLGBMToRootTree.dump_tree

(

tree_structure

)

dump a single decision tree to arrays to be written into the TTree

Definition at line 101 of file convertLGBMToRootTree.py.

def dump_tree(tree_structure):
    """
    dump a single decision tree to arrays to be written into the TTree
    """
 
    split_values = []
    split_features = []
    default_left = []
    top = LGBMTextNode(tree_structure)
    simple = [True]  # python2 lack of nonlocal variables
 
    def preorder(node):
        # visit root
        split_features.append(node.get_split_feature())
        split_values.append(node.get_value())
        default_left.append(node.get_default_left())
 
        if not node.get_default_left():
            simple[0] = False
 
        if "decision_type" in node and node["decision_type"] != "<=":
            raise ValueError(
                "do not support categorical input BDT (decision_type = %s)" % node["decision_type"]
            )
 
        if "missing_type" in node:
            if node["missing_type"] not in ("NaN", "None"):
                raise ValueError("do not support missing values different from NaN or None")
 
        # visit left
        if node.get_left() is not None:
            preorder(node.get_left())
        # visit right
        if node.get_right() is not None:
            preorder(node.get_right())
 
    preorder(top)
    return split_features, split_values, default_left, simple[0]
 
 

get_test_data()

util.convertLGBMToRootTree.get_test_data	(		feature_names,
			test_file = None,
			ntests = None )

Definition at line 257 of file convertLGBMToRootTree.py.

def get_test_data(feature_names, test_file=None, ntests=None):
    nvars = len(feature_names)
    if test_file is not None:
        if ".root" in test_file:
            if ":" not in test_file:
                raise ValueError("when using ROOT file as test use the syntax filename:treename")
            fn, tn = test_file.split(":")
            f = ROOT.TFile.Open(fn)
            if not f:
                raise IOError("cannot find ROOT file %s" % fn)
            tree = f.Get(tn)
            if not tree:
                raise IOError("cannot find TTree %s in %s" % (fn, tn))
            branch_names = [br.GetName() for br in tree.GetListOfBranches()]
            for feature in feature_names:
                if feature not in branch_names:
                    raise IOError("required feature %s not in TTree")
            rdf = ROOT.RDataFrame(tree, feature_names)
            data_input = rdf.AsNumpy()
            data_input = np.stack([data_input[k] for k in feature_names]).T
            if ntests is not None:
                data_input = data_input[:ntests]
            logging.info(
                "using as input %s inputs from TTree %s from ROOT file %s", len(data_input), tn, fn
            )
        else:
            data_input = np.load(test_file)
            if ntests is not None:
                data_input = data_input[:ntests]
            logging.info("using as input %s inputs from pickle file %s", len(data_input), test_file)
    else:
        if ntests is None:
            ntests = 10000
        logging.info("using as input %s random uniform inputs (-100,100)", ntests)
        logging.warning(
            "using random uniform input as test: this is not safe" "provide an input test file"
        )
        data_input = np.random.uniform(-100, 100, size=(ntests, nvars))
 
    # to match what mvautils is doing (using c-float)
    data_input = data_input.astype(np.float32)
    return data_input
 
 

lgbm_rawresponse_each_tree()

util.convertLGBMToRootTree.lgbm_rawresponse_each_tree	(		model,
			my_input )

Definition at line 28 of file convertLGBMToRootTree.py.

def lgbm_rawresponse_each_tree(model, my_input):
    nclasses = model.num_model_per_iteration()
    output_values = np.array(
        [np.array([[0] * nclasses])]
        + [
            model.predict(np.atleast_2d(my_input), raw_score=True, num_iteration=itree)
            for itree in range(1, (model.num_trees() // nclasses + 1))
        ]
    )
    output_trees = np.diff(output_values, axis=0)
    return output_trees
 
 

list2stdvector()

util.convertLGBMToRootTree.list2stdvector	(		values,
			dtype = "float" )

Definition at line 41 of file convertLGBMToRootTree.py.

def list2stdvector(values, dtype="float"):
    result = ROOT.std.vector(dtype)()
    for v in values:
        result.push_back(v)
    return result
 
 

test()

util.convertLGBMToRootTree.test	(		model_file,
			tree_file,
			tree_name = "lgbm",
			ntests = 10000,
			test_file = None )

Definition at line 207 of file convertLGBMToRootTree.py.

def test(model_file, tree_file, tree_name="lgbm", ntests=10000, test_file=None):
    booster = lgb.Booster(model_file=model_file)
    f = ROOT.TFile.Open(tree_file)
    tree = f.Get(tree_name)
    try:
        _ = ROOT.MVAUtils.BDT
    except Exception:
        print("cannot import MVAUtils")
        return None
 
    mva_utils = ROOT.MVAUtils.BDT(tree)
 
    objective = booster.dump_model()["objective"]
 
    # sometimes options are inlined with objective
    # we don't support non-default options
    objective = objective.replace("sigmoid:1", "")
    objective = objective.strip()
    
    # binary and xentropy are not the exact same thing when training but the output value is the same
    # same for l1/l2/huber/... regression
    # (https://lightgbm.readthedocs.io/en/latest/Parameters.html)
    binary_aliases = ("binary", "cross_entropy", "xentropy")
    regression_aliases = (
        (
            "regression_l2",
            "l2",
            "mean_squared_error",
            "mse",
            "l2_root",
            "root_mean_squared_error",
            "rmse",
        )
        + ("regression_l1", "l1", "mean_absolute_error", "mae")
        + ("huber",)
    )
    multiclass_aliases = ("multiclass", "softmax")
    if objective in multiclass_aliases:
        logging.info("assuming multiclass, testing")
        return test_multiclass(booster, mva_utils, ntests, test_file)
    elif objective in binary_aliases:
        logging.info("assuming binary classification, testing")
        return test_binary(booster, mva_utils, ntests, test_file)
    elif objective in regression_aliases:
        logging.info("assuming regression, testing")
        return test_regression(booster, mva_utils, ntests, test_file)
    else:
        print("cannot understand objective '%s'" % objective)
 
 

test_binary()

util.convertLGBMToRootTree.test_binary	(		booster,
			mva_utils,
			ntests = None,
			test_file = None )

Definition at line 419 of file convertLGBMToRootTree.py.

def test_binary(booster, mva_utils, ntests=None, test_file=None):
    data_input = get_test_data(booster.feature_name(), test_file, ntests)
    return test_generic(booster, mva_utils.GetClassification, mva_utils, data_input)
 
 

test_detail_event()

util.convertLGBMToRootTree.test_detail_event	(		booster,
			mva_utils,
			input_values )

Definition at line 348 of file convertLGBMToRootTree.py.

def test_detail_event(booster, mva_utils, input_values):
    logging.info("input values")
    for ivar, input_value in enumerate(input_values):
        logging.info("var %d: %.15f", ivar, input_value)
    logging.info("=" * 50)
 
    ntrees_mva_utils = mva_utils.GetNTrees()
    if ntrees_mva_utils != booster.num_trees():
        logging.info("Number of trees are different mvautils: %s lgbm: %s", ntrees_mva_utils, booster.num_trees())
    tree_outputs_lgbm = lgbm_rawresponse_each_tree(booster, input_values)
 
    # loop over the trees
    is_problem_found = False
    for itree in range(ntrees_mva_utils):
        tree_output_mvautils = mva_utils.GetTreeResponse(list2stdvector(input_values), itree)
        tree_output_lgbm = tree_outputs_lgbm[itree][0]
        if not np.allclose(tree_output_mvautils, tree_output_lgbm):
            is_tree_ok = False
            is_problem_found = True
            logging.info("tree %d/%d are different", itree, ntrees_mva_utils)
            logging.info("lgbm: %f", tree_output_lgbm)
            logging.info("MVAUtils: %f", tree_output_mvautils)
            logging.info("Tree details from MVAUtils")
            mva_utils.PrintTree(itree)
 
            # dump the tree from lightgbm
            node_infos = []
            _ff(
                 booster.dump_model()["tree_info"][itree][
                     "tree_structure"
                 ],
                 node_infos
            )
 
            # we now which tree is failing, check if this is
            # due to input values very close to the threshold
            # the problem is that lgbm is using double,
            # while mva_utils is using float
 
            for node_info in node_infos:
                value = input_values[node_info[0]]
                threshold = node_info[1]
                if not np.isnan(value) and (value <= threshold) != (
                    np.float32(value) <= np.float32(threshold)
                ):
                    logging.info(
                        "the problem could be due to double"
                        "(lgbm) -> float (mvautil) conversion"
                        " for variable %d: %.10f and threshold %.10f",
                        node_info[0],
                        value,
                        threshold,
                    )
                    # we consider this ok
                    is_tree_ok = True
                    break
            if not is_tree_ok:
                return False
 
    if is_problem_found:
        # if we have found the problem, but we arrive here
        # it means that we found the problematic tree,
        # but it is ok
        return True
 
 

test_generic()

util.convertLGBMToRootTree.test_generic	(		booster,
			mvautils_predict,
			mva_utils,
			data_input )

Definition at line 301 of file convertLGBMToRootTree.py.

def test_generic(booster, mvautils_predict, mva_utils, data_input):
    start = time.time()
    results_lgbm = booster.predict(data_input)
    logging.info("lgbm (vectorized) timing = %d/s", len(data_input) / (time.time() - start))
 
    input_values_vector = ROOT.std.vector("float")()
    results_MVAUtils = []
    start = time.time()
    for input_values in data_input:
        input_values_vector.clear()
        for v in input_values:
            input_values_vector.push_back(v)
        output_MVAUtils = mvautils_predict(input_values_vector)
        results_MVAUtils.append(output_MVAUtils)
    logging.info(
        "mvautils (not vectorized+overhead) timing = %d/s", len(data_input) / (time.time() - start)
    )
 
    nevents_tested = 0
    nevents_different = 0
    for ievent, (input_values, output_lgbm, output_MVAUtils) in enumerate(
        zip(data_input, results_lgbm, results_MVAUtils), 1
    ):
        nevents_tested += 1
        if not np.allclose(output_lgbm, output_MVAUtils, rtol=1e-4):
            nevents_different += 1
            logging.info(
                "--> output are different on input %d/%d mvautils: %s lgbm: %s",
                ievent,
                len(data_input),
                output_MVAUtils,
                output_lgbm,
            )
            if not test_detail_event(booster, mva_utils, input_values):
                return False
    logging.info("number of different events %d/%d", nevents_different, nevents_tested)
    return True
 
 
# helper for tree traversal

test_multiclass()

util.convertLGBMToRootTree.test_multiclass	(		booster,
			mva_utils,
			ntests = 10000,
			test_file = None )

Definition at line 424 of file convertLGBMToRootTree.py.

def test_multiclass(booster, mva_utils, ntests=10000, test_file=None):
    import numpy as np
 
    nvars = booster.num_feature()
    nclasses = booster.num_model_per_iteration()
    logging.info("using %d input features with %d classes", nvars, nclasses)
 
    data_input = get_test_data(booster.feature_name(), test_file, ntests)
 
    start = time.time()
    results_lgbm = booster.predict(data_input)
    logging.info(
        "lgbm (vectorized) timing = %s ms/input", (time.time() - start) * 1000 / len(data_input)
    )
 
    input_values_vector = ROOT.std.vector("float")()
    results_MVAUtils = []
    start = time.time()
    for input_values in data_input:
        input_values_vector.clear()
        for v in input_values:
            input_values_vector.push_back(v)
        output_MVAUtils = np.asarray(mva_utils.GetMultiResponse(input_values_vector, nclasses))
        results_MVAUtils.append(output_MVAUtils)
    logging.info(
        "mvautils (not vectorized+overhead) timing = %s ms/input",
        (time.time() - start) * 1000 / len(data_input),
    )
 
    stop_event_loop = False
    for ievent, (input_values, output_lgbm, output_MVAUtils) in enumerate(
        zip(data_input, results_lgbm, results_MVAUtils), 1
    ):
        if not np.allclose(output_lgbm, output_MVAUtils):
            stop_event_loop = True
            logging.info("--> output are different on input %d/%d:\n", ievent, len(data_input))
            for ivar, input_value in enumerate(input_values):
                logging.info("var %d: %.15f", ivar, input_value)
            logging.info("=" * 50)
            logging.info("              mvautils       lgbm")
            for ioutput, (o1, o2) in enumerate(zip(output_MVAUtils, output_lgbm)):
                diff_flag = "" if np.allclose(o1, o2) else "<---"
                logging.info("output %3d    %.5e    %.5e  %s", ioutput, o1, o2, diff_flag)
            output_trees_lgbm = lgbm_rawresponse_each_tree(booster, [input_values])
 
            stop_tree_loop = False
            for itree, output_tree_lgbm in enumerate(output_trees_lgbm):
                output_tree_mva_utils = [
                    mva_utils.GetTreeResponse(list2stdvector(input_values), itree * nclasses + c)
                    for c in range(nclasses)
                ]
                if not np.allclose(output_tree_mva_utils, output_tree_lgbm[0]):
                    stop_tree_loop = True
                    logging.info("first tree/class with different answer (%d)", itree)
                    for isubtree, (ol, om) in enumerate(
                        zip(output_tree_lgbm[0], output_tree_mva_utils)
                    ):
                        if not np.allclose(ol, om):
                            logging.info("different in position %d", isubtree)
                            logging.info("lgbm:     %f", ol)
                            logging.info("mvautils: %f", om)
                            logging.info("=" * 50)
                            logging.info(
                                "tree %d (itree) * %d (nclasses)" "+ %d (isubtree) = %d",
                                itree,
                                nclasses,
                                isubtree,
                                itree * nclasses + isubtree,
                            )
                            mva_utils.PrintTree(itree * nclasses + isubtree)
 
                            node_infos = []
 
                            # we now which tree is failing, check if this is
                            # due to input values very close to the threshold
                            # the problem is that lgbm is using double,
                            # while mva_utils is using float
                            _ff(
                                 booster.dump_model()["tree_info"][itree * nclasses + isubtree][
                                     "tree_structure"
                                 ],
                                 node_infos
                            )
                            for node_info in node_infos:
                                value = input_values[node_info[0]]
                                threshold = node_info[1]
                                if not np.isnan(value) and (value <= threshold) != (
                                    np.float32(value) <= np.float32(threshold)
                                ):
                                    logging.info(
                                        "the problem could be due to double"
                                        "(lgbm) -> float (mvautil) conversion"
                                        "for variable %d: %f and threshold %f",
                                        node_info[0],
                                        value,
                                        threshold,
                                    )
                                    stop_tree_loop = False
                                    stop_event_loop = False
 
                            if stop_tree_loop:
                                break
            if stop_event_loop:
                return False
    return True
 
 

test_regression()

util.convertLGBMToRootTree.test_regression	(		booster,
			mva_utils,
			ntests = None,
			test_file = None )

Definition at line 414 of file convertLGBMToRootTree.py.

def test_regression(booster, mva_utils, ntests=None, test_file=None):
    data_input = get_test_data(booster.feature_name(), test_file, ntests)
    return test_generic(booster, mva_utils.GetResponse, mva_utils, data_input)
 
 

Variable Documentation

__author__

str util.convertLGBMToRootTree.__author__ = "Ruggero Turra"

private

Definition at line 7 of file convertLGBMToRootTree.py.

action

util.convertLGBMToRootTree.action

Definition at line 555 of file convertLGBMToRootTree.py.

args

util.convertLGBMToRootTree.args = parser.parse_args()

Definition at line 561 of file convertLGBMToRootTree.py.

booster

util.convertLGBMToRootTree.booster = lgb.Booster(model_file=args.input)

Definition at line 588 of file convertLGBMToRootTree.py.

default

util.convertLGBMToRootTree.default

Definition at line 554 of file convertLGBMToRootTree.py.

help

util.convertLGBMToRootTree.help

Definition at line 552 of file convertLGBMToRootTree.py.

int

util.convertLGBMToRootTree.int

Definition at line 556 of file convertLGBMToRootTree.py.

level

util.convertLGBMToRootTree.level

Definition at line 25 of file convertLGBMToRootTree.py.

nargs

util.convertLGBMToRootTree.nargs

Definition at line 553 of file convertLGBMToRootTree.py.

objective

util.convertLGBMToRootTree.objective = booster.dump_model()["objective"]

Definition at line 589 of file convertLGBMToRootTree.py.

output

util.convertLGBMToRootTree.output

Definition at line 566 of file convertLGBMToRootTree.py.

output_treename

util.convertLGBMToRootTree.output_treename = convertLGBMToRootTree(args.input, args.output, args.tree_name)

Definition at line 569 of file convertLGBMToRootTree.py.

parser

util.convertLGBMToRootTree.parser = argparse.ArgumentParser(description=__doc__)

Definition at line 551 of file convertLGBMToRootTree.py.

result

util.convertLGBMToRootTree.result = test(args.input, args.output, args.tree_name, args.ntests, args.test_file)

Definition at line 576 of file convertLGBMToRootTree.py.

type

util.convertLGBMToRootTree.type

Definition at line 556 of file convertLGBMToRootTree.py.