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Commit fa15332e authored by Rakib Hasan's avatar Rakib Hasan
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Removing the string to decimal conversion for float 

We dont need it we aren't doing any computation with
float values. We print out whatever values are
read from the LightGBM_model.txt as a string.
parent 1b7643ba
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Showing with 83 additions and 93 deletions
pmml/README.md
View file @ fa15332e
PMML Generator
==============
The script pmml.py can be used to translate the LightGBM models, found in LightGBM_model.txt, to predictive model markup language (PMML). These models can then be imported by other analytics applications. The models that the language can describe includes decision trees. The specification of PMML can be found here at the Data Mining Group's [website](http://dmg.org/pmml/v4-3/GeneralStructure.html).
The script pmml.py can be used to translate the LightGBM models, found in LightGBM_model.txt, to predictive model markup language (PMML). These models can then be imported by other analytics applications. The models that the language can describe includes decision trees. The specification of PMML can be found here at the Data Mining Group's [website](http://dmg.org/pmml/v4-3/GeneralStructure.html).
In order to generate pmml files do the following steps.
```
......
pmml/pmml.py
View file @ fa15332e
from __future__ import print_function
from builtins import map
from builtins import next
from decimal import Decimal
import sys
import os
import traceback
def unique_id():
global unique_node_id
nid = unique_node_id
unique_node_id += 1
return nid
import itertools
def get_value_string(line):
......@@ -22,11 +18,7 @@ def get_array_strings(line):
def get_array_ints(line):
return map(lambda x: int(x), line[line.index('=') + 1:].split())
def get_array_floats(line):
return map(lambda x: Decimal(x), line[line.index('=') + 1:].split())
return list(map(int, line[line.index('=') + 1:].split()))
def get_field_name(node_id, prev_node_idx, is_child):
......@@ -73,7 +65,7 @@ def print_nodes_pmml(**kwargs):
(
"<Node id=\"{0}\" score=\"{1}\" " +
" recordCount=\"{2}\">").format(
unique_id(),
next(unique_id),
score,
recordCount),
file=pmml_out)
......@@ -116,8 +108,8 @@ def print_pmml(pmml_out):
(feature), file=pmml_out)
print("\t\t\t\t\t</MiningSchema>", file=pmml_out)
# begin printing out the decision tree
print("\t\t\t\t\t<Node id=\"%d\" score=\"%s\" recordCount=\"%d\">" %
(unique_id(), internal_value[0], internal_count[0]), file=pmml_out)
print("\t\t\t\t\t<Node id=\"{0}\" score=\"{1}\" recordCount=\"{2}\">".format(
next(unique_id), internal_value[0], internal_count[0]), file=pmml_out)
print("\t\t\t\t\t\t<True/>", file=pmml_out)
print_nodes_pmml(
node_id=left_child[0],
......@@ -139,80 +131,78 @@ if len(sys.argv) != 2:
sys.exit(0)
# open the model file and then process it
try:
with open(sys.argv[1]) as model_in:
model_content = filter(
lambda line: line != '',
model_in.read().strip().split('\n'))
objective = get_value_string(model_content[4])
sigmoid = Decimal(get_value_string(model_content[5]))
feature_names = get_array_strings(model_content[6])
model_content = model_content[7:]
line_no = 0
segment_id = 1
with open('LightGBM_pmml.xml', 'w') as pmml_out:
print(
"<PMML version=\"4.3\" \n" +
"\t\txmlns=\"http://www.dmg.org/PMML-4_3\"\n" +
"\t\txmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n" +
"\t\txsi:schemaLocation=\"http://www.dmg.org/PMML-4_3 http://dmg.org/pmml/v4-3/pmml-4-3.xsd\"" +
">",
file=pmml_out)
print("\t<Header copyright=\"Microsoft\">", file=pmml_out)
print("\t\t<Application name=\"LightGBM\"/>", file=pmml_out)
print("\t</Header>", file=pmml_out)
# print out data dictionary entries for each column
print(
"\t<DataDictionary numberOfFields=\"%d\">" %
len(feature_names), file=pmml_out)
# not adding any interval definition, all values are currently
# valid
for feature in feature_names:
print(
"\t\t<DataField name=\"" +
feature +
"\" optype=\"continuous\" dataType=\"double\"/>",
file=pmml_out)
print("\t</DataDictionary>", file=pmml_out)
print("\t<MiningModel functionName=\"regression\">", file=pmml_out)
print("\t\t<MiningSchema>", file=pmml_out)
# list each feature name as a mining field, and treat all outliers
# as is, unless specified
for feature in feature_names:
print(
"\t\t\t<MiningField name=\"%s\"/>" %
(feature), file=pmml_out)
print("\t\t</MiningSchema>", file=pmml_out)
print(
"\t\t<Segmentation multipleModelMethod=\"sum\">",
file=pmml_out)
# read each array that contains pertinent information for the pmml
# these arrays will be used to recreate the traverse the decision
# tree
while model_content[line_no][:4] == 'Tree':
print("\t\t\t<Segment id=\"%d\">" % segment_id, file=pmml_out)
print("\t\t\t\t<True/>", file=pmml_out)
tree_no = model_content[line_no][5:]
num_leaves = int(get_value_string(model_content[line_no + 1]))
split_feature = get_array_ints(model_content[line_no + 2])
threshold = get_array_floats(model_content[line_no + 4])
decision_type = get_array_ints(model_content[line_no + 5])
left_child = get_array_ints(model_content[line_no + 6])
right_child = get_array_ints(model_content[line_no + 7])
leaf_parent = get_array_ints(model_content[line_no + 8])
leaf_value = get_array_floats(model_content[line_no + 9])
leaf_count = get_array_ints(model_content[line_no + 10])
internal_value = get_array_floats(model_content[line_no + 11])
internal_count = get_array_ints(model_content[line_no + 12])
unique_node_id = 0
print_pmml(pmml_out)
print("\t\t\t</Segment>", file=pmml_out)
line_no += 13
segment_id += 1
print("\t\t</Segmentation>", file=pmml_out)
print("\t</MiningModel>", file=pmml_out)
print("</PMML>", file=pmml_out)
except Exception as ioex:
print(ioex)
with open(sys.argv[1], 'r') as model_in:
model_content = [l for l in model_in.read().splitlines() if l]
objective = get_value_string(model_content[4])
sigmoid = Decimal(get_value_string(model_content[5]))
feature_names = get_array_strings(model_content[6])
model_content = model_content[7:]
segment_id = 1
with open('LightGBM_pmml.xml', 'w') as pmml_out:
print(
"<PMML version=\"4.3\" \n" +
"\t\txmlns=\"http://www.dmg.org/PMML-4_3\"\n" +
"\t\txmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n" +
"\t\txsi:schemaLocation=\"http://www.dmg.org/PMML-4_3 http://dmg.org/pmml/v4-3/pmml-4-3.xsd\"" +
">",
file=pmml_out)
print("\t<Header copyright=\"Microsoft\">", file=pmml_out)
print("\t\t<Application name=\"LightGBM\"/>", file=pmml_out)
print("\t</Header>", file=pmml_out)
# print out data dictionary entries for each column
print(
"\t<DataDictionary numberOfFields=\"%d\">" %
len(feature_names), file=pmml_out)
# not adding any interval definition, all values are currently
# valid
for feature in feature_names:
print(
"\t\t<DataField name=\"" +
feature +
"\" optype=\"continuous\" dataType=\"double\"/>",
file=pmml_out)
print("\t</DataDictionary>", file=pmml_out)
print("\t<MiningModel functionName=\"regression\">", file=pmml_out)
print("\t\t<MiningSchema>", file=pmml_out)
# list each feature name as a mining field, and treat all outliers
# as is, unless specified
for feature in feature_names:
print(
"\t\t\t<MiningField name=\"%s\"/>" %
(feature), file=pmml_out)
print("\t\t</MiningSchema>", file=pmml_out)
print(
"\t\t<Segmentation multipleModelMethod=\"sum\">",
file=pmml_out)
# read each array that contains pertinent information for the pmml
# these arrays will be used to recreate the traverse the decision
# tree
model_content = iter(model_content)
tree_start = next(model_content)
while tree_start[:4] == 'Tree':
print("\t\t\t<Segment id=\"%d\">" % segment_id, file=pmml_out)
print("\t\t\t\t<True/>", file=pmml_out)
tree_no = tree_start[5:]
num_leaves = int(get_value_string(next(model_content)))
split_feature = get_array_ints(next(model_content))
split_gain = next(model_content)
threshold = get_array_strings(next(model_content))
decision_type = get_array_ints(next(model_content))
left_child = get_array_ints(next(model_content))
right_child = get_array_ints(next(model_content))
leaf_parent = get_array_ints(next(model_content))
leaf_value = get_array_strings(next(model_content))
leaf_count = get_array_strings(next(model_content))
internal_value = get_array_strings(next(model_content))
internal_count = get_array_strings(next(model_content))
tree_start = next(model_content)
unique_id = itertools.count(1)
print_pmml(pmml_out)
print("\t\t\t</Segment>", file=pmml_out)
segment_id += 1
print("\t\t</Segmentation>", file=pmml_out)
print("\t</MiningModel>", file=pmml_out)
print("</PMML>", file=pmml_out)
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