Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Handle categorical split in model histogram and dataframe. (#7065)

Browse Source 
* Error on get_split_value_histogram when feature is categorical
* Add a category column to output dataframe
This commit is contained in:
Jiaming Yuan
2021-07-02 13:10:36 +08:00
committed by GitHub
parent 1cd20efe68
commit a5d222fcdb
3 changed files with 96 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

46
python-package/xgboost/core.py
View File

@@ -2225,7 +2225,7 @@ class Booster(object):
results[feat] = float(score)
return results
def trees_to_dataframe(self, fmap=''):
def trees_to_dataframe(self, fmap=''): # pylint: disable=too-many-statements
"""Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
@@ -2251,6 +2251,7 @@ class Booster(object):
node_ids = []
fids = []
splits = []
categories = []
y_directs = []
n_directs = []
missings = []
@@ -2275,6 +2276,7 @@ class Booster(object):
node_ids.append(int(re.findall(r'\b\d+\b', parse[0])[0]))
fids.append('Leaf')
splits.append(float('NAN'))
categories.append(float('NAN'))
y_directs.append(float('NAN'))
n_directs.append(float('NAN'))
missings.append(float('NAN'))
@@ -2284,14 +2286,26 @@ class Booster(object):
else:
# parse string
fid = arr[1].split(']')
parse = fid[0].split('<')
if fid[0].find("<") != -1:
# numerical
parse = fid[0].split('<')
splits.append(float(parse[1]))
categories.append(None)
elif fid[0].find(":{") != -1:
# categorical
parse = fid[0].split(":")
cats = parse[1][1:-1] # strip the {}
cats = cats.split(",")
splits.append(float("NAN"))
categories.append(cats if cats else None)
else:
raise ValueError("Failed to parse model text dump.")
stats = re.split('=|,', fid[1])
# append to lists
tree_ids.append(i)
node_ids.append(int(re.findall(r'\b\d+\b', arr[0])[0]))
fids.append(parse[0])
splits.append(float(parse[1]))
str_i = str(i)
y_directs.append(str_i + '-' + stats[1])
n_directs.append(str_i + '-' + stats[3])
@@ -2303,7 +2317,7 @@ class Booster(object):
df = DataFrame({'Tree': tree_ids, 'Node': node_ids, 'ID': ids,
'Feature': fids, 'Split': splits, 'Yes': y_directs,
'No': n_directs, 'Missing': missings, 'Gain': gains,
'Cover': covers})
'Cover': covers, "Category": categories})
if callable(getattr(df, 'sort_values', None)):
# pylint: disable=no-member
@@ -2381,9 +2395,29 @@ class Booster(object):
nph = np.column_stack((nph[1][1:], nph[0]))
nph = nph[nph[:, 1] > 0]
if nph.size == 0:
ft = self.feature_types
fn = self.feature_names
if fn is None:
# Let xgboost generate the feature names.
fn = ["f{0}".format(i) for i in range(self.num_features())]
try:
index = fn.index(feature)
feature_t = ft[index]
except (ValueError, AttributeError, TypeError):
# None.index: attr err, None[0]: type err, fn.index(-1): value err
feature_t = None
if feature_t == "categorical":
raise ValueError(
"Split value historgam doesn't support categorical split."
)
if as_pandas and PANDAS_INSTALLED:
return DataFrame(nph, columns=['SplitValue', 'Count'])
if as_pandas and not PANDAS_INSTALLED:
sys.stderr.write(
"Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
warnings.warn(
"Returning histogram as ndarray"
" (as_pandas == True, but pandas is not installed).",
UserWarning
)
return nph