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util.convertLGBMToRootTree Namespace Reference

Classes

class  LGBMTextNode
 

Functions

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

Variables

string __author__ = "Ruggero Turra"
 
 level
 
 parser = argparse.ArgumentParser(description=__doc__)
 
 help
 
 nargs
 
 default
 
 action
 
 type
 
 int
 
 args = parser.parse_args()
 
 output
 
def output_treename = convertLGBMToRootTree(args.input, args.output, args.tree_name)
 
def 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"]
 

Function Documentation

◆ _ff()

def util.convertLGBMToRootTree._ff (   tree,
  node_infos 
)
private

Definition at line 341 of file convertLGBMToRootTree.py.

341 def _ff(tree, node_infos):
342  if "left_child" in tree:
343  node_infos.append((tree["split_feature"], tree["threshold"]))
344  _ff(tree["left_child"])
345  _ff(tree["right_child"])
346 
347 

◆ check_file()

def util.convertLGBMToRootTree.check_file (   fn)

Definition at line 531 of file convertLGBMToRootTree.py.

531 def check_file(fn):
532  f = ROOT.TFile.Open(fn)
533  keys = f.GetListOfKeys()
534  keys = list(keys)
535  if len(keys) != 1:
536  logging.info("file %s is empty", fn)
537  return False
538  tree = f.Get(keys[0].GetName())
539  if type(tree) is not ROOT.TTree:
540  logging.info("cannot find TTree in file %s", fn)
541  return False
542  if not tree.GetEntries():
543  logging.info("tree is empty")
544  return False
545  return True
546 
547 

◆ convertLGBMToRootTree()

def 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.

192 def convertLGBMToRootTree(model, output_filename, tree_name="lgbm"):
193  """
194  Model: - a string, in this case, it is the name of
195  the input file containing the lgbm model you
196  can get this model with lgbm with
197  `boosted.save_model('my_model.txt')
198  - directly a lgbm booster object
199  """
200  if type(model) is str:
201  model = lgb.Booster(model_file=model)
202  return dump2ROOT(model, output_filename, tree_name)
203  else:
204  return dump2ROOT(model, output_filename, tree_name)
205 
206 

◆ dump2ROOT()

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

Definition at line 141 of file convertLGBMToRootTree.py.

141 def dump2ROOT(model, output_filename, output_treename="lgbm"):
142  model = model.dump_model()
143  fout = ROOT.TFile.Open(output_filename, "recreate")
144 
145  features_array = ROOT.std.vector("int")()
146  values_array = ROOT.std.vector("float")()
147  default_lefts_array = ROOT.std.vector("bool")()
148 
149  simple = True
150  node_type = "node_type=lgbm_simple"
151  for tree in model["tree_info"]:
152  tree_structure = tree["tree_structure"]
153  features, values, default_lefts, simple_tree = dump_tree(tree_structure)
154  if not simple_tree:
155  simple = False
156  node_type = "node_type=lgbm"
157 
158  infos = ";".join(["%s=%s" % (k, str(v)) for k, v in model.items() if type(v) is not list])
159  title = ";".join(("creator=lgbm", node_type, infos))
160  root_tree = ROOT.TTree(output_treename, title)
161  root_tree.Branch("vars", "vector<int>", ROOT.AddressOf(features_array))
162  root_tree.Branch("values", "vector<float>", ROOT.AddressOf(values_array))
163 
164  if not simple:
165  logging.info("tree support nan: using full implementation (LGBMNode)")
166  root_tree.Branch("default_left", "vector<bool>", ROOT.AddressOf(default_lefts_array))
167  if simple:
168  logging.info("tree do not support nan:" "using simple implementation (LGBMNodeSimple)")
169 
170  for tree in model["tree_info"]:
171  tree_structure = tree["tree_structure"]
172  features, values, default_lefts, simple_tree = dump_tree(tree_structure)
173 
174  features_array.clear()
175  values_array.clear()
176  default_lefts_array.clear()
177 
178  for value in values:
179  values_array.push_back(value)
180  for feature in features:
181  features_array.push_back(feature)
182  for default_left in default_lefts:
183  default_lefts_array.push_back(default_left)
184 
185  root_tree.Fill()
186 
187  root_tree.Write()
188  fout.Close()
189  return output_treename
190 
191 

◆ dump_tree()

def 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.

101 def dump_tree(tree_structure):
102  """
103  dump a single decision tree to arrays to be written into the TTree
104  """
105 
106  split_values = []
107  split_features = []
108  default_left = []
109  top = LGBMTextNode(tree_structure)
110  simple = [True] # python2 lack of nonlocal variables
111 
112  def preorder(node):
113  # visit root
114  split_features.append(node.get_split_feature())
115  split_values.append(node.get_value())
116  default_left.append(node.get_default_left())
117 
118  if not node.get_default_left():
119  simple[0] = False
120 
121  if "decision_type" in node and node["decision_type"] != "<=":
122  raise ValueError(
123  "do not support categorical input BDT (decision_type = %s)" % node["decision_type"]
124  )
125 
126  if "missing_type" in node:
127  if node["missing_type"] not in ("NaN", "None"):
128  raise ValueError("do not support missing values different from NaN or None")
129 
130  # visit left
131  if node.get_left() is not None:
132  preorder(node.get_left())
133  # visit right
134  if node.get_right() is not None:
135  preorder(node.get_right())
136 
137  preorder(top)
138  return split_features, split_values, default_left, simple[0]
139 
140 

◆ get_test_data()

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

Definition at line 257 of file convertLGBMToRootTree.py.

257 def get_test_data(feature_names, test_file=None, ntests=None):
258  nvars = len(feature_names)
259  if test_file is not None:
260  if ".root" in test_file:
261  if ":" not in test_file:
262  raise ValueError("when using ROOT file as test use the syntax filename:treename")
263  fn, tn = test_file.split(":")
264  f = ROOT.TFile.Open(fn)
265  if not f:
266  raise IOError("cannot find ROOT file %s" % fn)
267  tree = f.Get(tn)
268  if not tree:
269  raise IOError("cannot find TTree %s in %s" % (fn, tn))
270  branch_names = [br.GetName() for br in tree.GetListOfBranches()]
271  for feature in feature_names:
272  if feature not in branch_names:
273  raise IOError("required feature %s not in TTree")
274  rdf = ROOT.RDataFrame(tree, feature_names)
275  data_input = rdf.AsNumpy()
276  data_input = np.stack([data_input[k] for k in feature_names]).T
277  if ntests is not None:
278  data_input = data_input[:ntests]
279  logging.info(
280  "using as input %s inputs from TTree %s from ROOT file %s", len(data_input), tn, fn
281  )
282  else:
283  data_input = np.load(test_file)
284  if ntests is not None:
285  data_input = data_input[:ntests]
286  logging.info("using as input %s inputs from pickle file %s", len(data_input), test_file)
287  else:
288  if ntests is None:
289  ntests = 10000
290  logging.info("using as input %s random uniform inputs (-100,100)", ntests)
291  logging.warning(
292  "using random uniform input as test: this is not safe" "provide an input test file"
293  )
294  data_input = np.random.uniform(-100, 100, size=(ntests, nvars))
295 
296  # to match what mvautils is doing (using c-float)
297  data_input = data_input.astype(np.float32)
298  return data_input
299 
300 

◆ lgbm_rawresponse_each_tree()

def util.convertLGBMToRootTree.lgbm_rawresponse_each_tree (   model,
  my_input 
)

Definition at line 28 of file convertLGBMToRootTree.py.

28 def lgbm_rawresponse_each_tree(model, my_input):
29  nclasses = model.num_model_per_iteration()
30  output_values = np.array(
31  [np.array([[0] * nclasses])]
32  + [
33  model.predict(np.atleast_2d(my_input), raw_score=True, num_iteration=itree)
34  for itree in range(1, (model.num_trees() // nclasses + 1))
35  ]
36  )
37  output_trees = np.diff(output_values, axis=0)
38  return output_trees
39 
40 

◆ list2stdvector()

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

Definition at line 41 of file convertLGBMToRootTree.py.

41 def list2stdvector(values, dtype="float"):
42  result = ROOT.std.vector(dtype)()
43  for v in values:
44  result.push_back(v)
45  return result
46 
47 

◆ test()

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

Definition at line 207 of file convertLGBMToRootTree.py.

207 def test(model_file, tree_file, tree_name="lgbm", ntests=10000, test_file=None):
208  booster = lgb.Booster(model_file=model_file)
209  f = ROOT.TFile.Open(tree_file)
210  tree = f.Get(tree_name)
211  try:
212  _ = ROOT.MVAUtils.BDT
213  except Exception:
214  print("cannot import MVAUtils")
215  return None
216 
217  mva_utils = ROOT.MVAUtils.BDT(tree)
218 
219  objective = booster.dump_model()["objective"]
220 
221  # sometimes options are inlined with objective
222  # we don't support non-default options
223  objective = objective.replace("sigmoid:1", "")
224  objective = objective.strip()
225 
226  # binary and xentropy are not the exact same thing when training but the output value is the same
227  # same for l1/l2/huber/... regression
228  # (https://lightgbm.readthedocs.io/en/latest/Parameters.html)
229  binary_aliases = ("binary", "cross_entropy", "xentropy")
230  regression_aliases = (
231  (
232  "regression_l2",
233  "l2",
234  "mean_squared_error",
235  "mse",
236  "l2_root",
237  "root_mean_squared_error",
238  "rmse",
239  )
240  + ("regression_l1", "l1", "mean_absolute_error", "mae")
241  + ("huber",)
242  )
243  multiclass_aliases = ("multiclass", "softmax")
244  if objective in multiclass_aliases:
245  logging.info("assuming multiclass, testing")
246  return test_multiclass(booster, mva_utils, ntests, test_file)
247  elif objective in binary_aliases:
248  logging.info("assuming binary classification, testing")
249  return test_binary(booster, mva_utils, ntests, test_file)
250  elif objective in regression_aliases:
251  logging.info("assuming regression, testing")
252  return test_regression(booster, mva_utils, ntests, test_file)
253  else:
254  print("cannot understand objective '%s'" % objective)
255 
256 

◆ test_binary()

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

Definition at line 419 of file convertLGBMToRootTree.py.

419 def test_binary(booster, mva_utils, ntests=None, test_file=None):
420  data_input = get_test_data(booster.feature_name(), test_file, ntests)
421  return test_generic(booster, mva_utils.GetClassification, mva_utils, data_input)
422 
423 

◆ test_detail_event()

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

Definition at line 348 of file convertLGBMToRootTree.py.

348 def test_detail_event(booster, mva_utils, input_values):
349  logging.info("input values")
350  for ivar, input_value in enumerate(input_values):
351  logging.info("var %d: %.15f", ivar, input_value)
352  logging.info("=" * 50)
353 
354  ntrees_mva_utils = mva_utils.GetNTrees()
355  if ntrees_mva_utils != booster.num_trees():
356  logging.info("Number of trees are different mvautils: %s lgbm: %s", ntrees_mva_utils, booster.num_trees())
357  tree_outputs_lgbm = lgbm_rawresponse_each_tree(booster, input_values)
358 
359  # loop over the trees
360  is_problem_found = False
361  for itree in range(ntrees_mva_utils):
362  tree_output_mvautils = mva_utils.GetTreeResponse(list2stdvector(input_values), itree)
363  tree_output_lgbm = tree_outputs_lgbm[itree][0]
364  if not np.allclose(tree_output_mvautils, tree_output_lgbm):
365  is_tree_ok = False
366  is_problem_found = True
367  logging.info("tree %d/%d are different", itree, ntrees_mva_utils)
368  logging.info("lgbm: %f", tree_output_lgbm)
369  logging.info("MVAUtils: %f", tree_output_mvautils)
370  logging.info("Tree details from MVAUtils")
371  mva_utils.PrintTree(itree)
372 
373  # dump the tree from lightgbm
374  node_infos = []
375  _ff(
376  booster.dump_model()["tree_info"][itree][
377  "tree_structure"
378  ],
379  node_infos
380  )
381 
382  # we now which tree is failing, check if this is
383  # due to input values very close to the threshold
384  # the problem is that lgbm is using double,
385  # while mva_utils is using float
386 
387  for node_info in node_infos:
388  value = input_values[node_info[0]]
389  threshold = node_info[1]
390  if not np.isnan(value) and (value <= threshold) != (
391  np.float32(value) <= np.float32(threshold)
392  ):
393  logging.info(
394  "the problem could be due to double"
395  "(lgbm) -> float (mvautil) conversion"
396  " for variable %d: %.10f and threshold %.10f",
397  node_info[0],
398  value,
399  threshold,
400  )
401  # we consider this ok
402  is_tree_ok = True
403  break
404  if not is_tree_ok:
405  return False
406 
407  if is_problem_found:
408  # if we have found the problem, but we arrive here
409  # it means that we found the problematic tree,
410  # but it is ok
411  return True
412 
413 

◆ test_generic()

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

Definition at line 301 of file convertLGBMToRootTree.py.

301 def test_generic(booster, mvautils_predict, mva_utils, data_input):
302  start = time.time()
303  results_lgbm = booster.predict(data_input)
304  logging.info("lgbm (vectorized) timing = %d/s", len(data_input) / (time.time() - start))
305 
306  input_values_vector = ROOT.std.vector("float")()
307  results_MVAUtils = []
308  start = time.time()
309  for input_values in data_input:
310  input_values_vector.clear()
311  for v in input_values:
312  input_values_vector.push_back(v)
313  output_MVAUtils = mvautils_predict(input_values_vector)
314  results_MVAUtils.append(output_MVAUtils)
315  logging.info(
316  "mvautils (not vectorized+overhead) timing = %d/s", len(data_input) / (time.time() - start)
317  )
318 
319  nevents_tested = 0
320  nevents_different = 0
321  for ievent, (input_values, output_lgbm, output_MVAUtils) in enumerate(
322  zip(data_input, results_lgbm, results_MVAUtils), 1
323  ):
324  nevents_tested += 1
325  if not np.allclose(output_lgbm, output_MVAUtils, rtol=1e-4):
326  nevents_different += 1
327  logging.info(
328  "--> output are different on input %d/%d mvautils: %s lgbm: %s",
329  ievent,
330  len(data_input),
331  output_MVAUtils,
332  output_lgbm,
333  )
334  if not test_detail_event(booster, mva_utils, input_values):
335  return False
336  logging.info("number of different events %d/%d", nevents_different, nevents_tested)
337  return True
338 
339 
340 # helper for tree traversal

◆ test_multiclass()

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

Definition at line 424 of file convertLGBMToRootTree.py.

424 def test_multiclass(booster, mva_utils, ntests=10000, test_file=None):
425  import numpy as np
426 
427  nvars = booster.num_feature()
428  nclasses = booster.num_model_per_iteration()
429  logging.info("using %d input features with %d classes", nvars, nclasses)
430 
431  data_input = get_test_data(booster.feature_name(), test_file, ntests)
432 
433  start = time.time()
434  results_lgbm = booster.predict(data_input)
435  logging.info(
436  "lgbm (vectorized) timing = %s ms/input", (time.time() - start) * 1000 / len(data_input)
437  )
438 
439  input_values_vector = ROOT.std.vector("float")()
440  results_MVAUtils = []
441  start = time.time()
442  for input_values in data_input:
443  input_values_vector.clear()
444  for v in input_values:
445  input_values_vector.push_back(v)
446  output_MVAUtils = np.asarray(mva_utils.GetMultiResponse(input_values_vector, nclasses))
447  results_MVAUtils.append(output_MVAUtils)
448  logging.info(
449  "mvautils (not vectorized+overhead) timing = %s ms/input",
450  (time.time() - start) * 1000 / len(data_input),
451  )
452 
453  stop_event_loop = False
454  for ievent, (input_values, output_lgbm, output_MVAUtils) in enumerate(
455  zip(data_input, results_lgbm, results_MVAUtils), 1
456  ):
457  if not np.allclose(output_lgbm, output_MVAUtils):
458  stop_event_loop = True
459  logging.info("--> output are different on input %d/%d:\n", ievent, len(data_input))
460  for ivar, input_value in enumerate(input_values):
461  logging.info("var %d: %.15f", ivar, input_value)
462  logging.info("=" * 50)
463  logging.info(" mvautils lgbm")
464  for ioutput, (o1, o2) in enumerate(zip(output_MVAUtils, output_lgbm)):
465  diff_flag = "" if np.allclose(o1, o2) else "<---"
466  logging.info("output %3d %.5e %.5e %s", ioutput, o1, o2, diff_flag)
467  output_trees_lgbm = lgbm_rawresponse_each_tree(booster, [input_values])
468 
469  stop_tree_loop = False
470  for itree, output_tree_lgbm in enumerate(output_trees_lgbm):
471  output_tree_mva_utils = [
472  mva_utils.GetTreeResponse(list2stdvector(input_values), itree * nclasses + c)
473  for c in range(nclasses)
474  ]
475  if not np.allclose(output_tree_mva_utils, output_tree_lgbm[0]):
476  stop_tree_loop = True
477  logging.info("first tree/class with different answer (%d)", itree)
478  for isubtree, (ol, om) in enumerate(
479  zip(output_tree_lgbm[0], output_tree_mva_utils)
480  ):
481  if not np.allclose(ol, om):
482  logging.info("different in position %d", isubtree)
483  logging.info("lgbm: %f", ol)
484  logging.info("mvautils: %f", om)
485  logging.info("=" * 50)
486  logging.info(
487  "tree %d (itree) * %d (nclasses)" "+ %d (isubtree) = %d",
488  itree,
489  nclasses,
490  isubtree,
491  itree * nclasses + isubtree,
492  )
493  mva_utils.PrintTree(itree * nclasses + isubtree)
494 
495  node_infos = []
496 
497  # we now which tree is failing, check if this is
498  # due to input values very close to the threshold
499  # the problem is that lgbm is using double,
500  # while mva_utils is using float
501  _ff(
502  booster.dump_model()["tree_info"][itree * nclasses + isubtree][
503  "tree_structure"
504  ],
505  node_infos
506  )
507  for node_info in node_infos:
508  value = input_values[node_info[0]]
509  threshold = node_info[1]
510  if not np.isnan(value) and (value <= threshold) != (
511  np.float32(value) <= np.float32(threshold)
512  ):
513  logging.info(
514  "the problem could be due to double"
515  "(lgbm) -> float (mvautil) conversion"
516  "for variable %d: %f and threshold %f",
517  node_info[0],
518  value,
519  threshold,
520  )
521  stop_tree_loop = False
522  stop_event_loop = False
523 
524  if stop_tree_loop:
525  break
526  if stop_event_loop:
527  return False
528  return True
529 
530 

◆ test_regression()

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

Definition at line 414 of file convertLGBMToRootTree.py.

414 def test_regression(booster, mva_utils, ntests=None, test_file=None):
415  data_input = get_test_data(booster.feature_name(), test_file, ntests)
416  return test_generic(booster, mva_utils.GetResponse, mva_utils, data_input)
417 
418 

Variable Documentation

◆ __author__

string 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

def 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

def 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.

util.convertLGBMToRootTree.test
def test(model_file, tree_file, tree_name="lgbm", ntests=10000, test_file=None)
Definition: convertLGBMToRootTree.py:207
util.convertLGBMToRootTree.dump2ROOT
def dump2ROOT(model, output_filename, output_treename="lgbm")
Definition: convertLGBMToRootTree.py:141
util.convertLGBMToRootTree.list2stdvector
def list2stdvector(values, dtype="float")
Definition: convertLGBMToRootTree.py:41
util.convertLGBMToRootTree._ff
def _ff(tree, node_infos)
Definition: convertLGBMToRootTree.py:341
util.convertLGBMToRootTree.type
type
Definition: convertLGBMToRootTree.py:556
util.convertLGBMToRootTree.get_test_data
def get_test_data(feature_names, test_file=None, ntests=None)
Definition: convertLGBMToRootTree.py:257
util.convertLGBMToRootTree.check_file
def check_file(fn)
Definition: convertLGBMToRootTree.py:531
plotBeamSpotVxVal.range
range
Definition: plotBeamSpotVxVal.py:194
histSizes.list
def list(name, path='/')
Definition: histSizes.py:38
util.convertLGBMToRootTree.lgbm_rawresponse_each_tree
def lgbm_rawresponse_each_tree(model, my_input)
Definition: convertLGBMToRootTree.py:28
print
void print(char *figname, TCanvas *c1)
Definition: TRTCalib_StrawStatusPlots.cxx:25
util.convertLGBMToRootTree.test_detail_event
def test_detail_event(booster, mva_utils, input_values)
Definition: convertLGBMToRootTree.py:348
TCS::join
std::string join(const std::vector< std::string > &v, const char c=',')
Definition: Trigger/TrigT1/L1Topo/L1TopoCommon/Root/StringUtils.cxx:10
util.convertLGBMToRootTree.dump_tree
def dump_tree(tree_structure)
Definition: convertLGBMToRootTree.py:101
util.convertLGBMToRootTree.test_generic
def test_generic(booster, mvautils_predict, mva_utils, data_input)
Definition: convertLGBMToRootTree.py:301
util.convertLGBMToRootTree.test_binary
def test_binary(booster, mva_utils, ntests=None, test_file=None)
Definition: convertLGBMToRootTree.py:419
util.convertLGBMToRootTree.test_multiclass
def test_multiclass(booster, mva_utils, ntests=10000, test_file=None)
Definition: convertLGBMToRootTree.py:424
util.convertLGBMToRootTree.convertLGBMToRootTree
def convertLGBMToRootTree(model, output_filename, tree_name="lgbm")
Definition: convertLGBMToRootTree.py:192
str
Definition: BTagTrackIpAccessor.cxx:11
util.convertLGBMToRootTree.test_regression
def test_regression(booster, mva_utils, ntests=None, test_file=None)
Definition: convertLGBMToRootTree.py:414