Handle categorical split in model histogram and dataframe. (#7065)
* Error on get_split_value_histogram when feature is categorical * Add a category column to output dataframe
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Jiaming Yuan
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@ -2225,7 +2225,7 @@ class Booster(object):
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results[feat] = float(score)
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return results
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def trees_to_dataframe(self, fmap=''):
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def trees_to_dataframe(self, fmap=''): # pylint: disable=too-many-statements
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"""Parse a boosted tree model text dump into a pandas DataFrame structure.
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This feature is only defined when the decision tree model is chosen as base
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@ -2251,6 +2251,7 @@ class Booster(object):
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node_ids = []
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fids = []
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splits = []
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categories = []
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y_directs = []
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n_directs = []
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missings = []
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@ -2275,6 +2276,7 @@ class Booster(object):
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node_ids.append(int(re.findall(r'\b\d+\b', parse[0])[0]))
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fids.append('Leaf')
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splits.append(float('NAN'))
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categories.append(float('NAN'))
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y_directs.append(float('NAN'))
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n_directs.append(float('NAN'))
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missings.append(float('NAN'))
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@ -2284,14 +2286,26 @@ class Booster(object):
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else:
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# parse string
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fid = arr[1].split(']')
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parse = fid[0].split('<')
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if fid[0].find("<") != -1:
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# numerical
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parse = fid[0].split('<')
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splits.append(float(parse[1]))
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categories.append(None)
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elif fid[0].find(":{") != -1:
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# categorical
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parse = fid[0].split(":")
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cats = parse[1][1:-1] # strip the {}
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cats = cats.split(",")
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splits.append(float("NAN"))
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categories.append(cats if cats else None)
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else:
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raise ValueError("Failed to parse model text dump.")
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stats = re.split('=|,', fid[1])
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# append to lists
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tree_ids.append(i)
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node_ids.append(int(re.findall(r'\b\d+\b', arr[0])[0]))
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fids.append(parse[0])
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splits.append(float(parse[1]))
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str_i = str(i)
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y_directs.append(str_i + '-' + stats[1])
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n_directs.append(str_i + '-' + stats[3])
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@ -2303,7 +2317,7 @@ class Booster(object):
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df = DataFrame({'Tree': tree_ids, 'Node': node_ids, 'ID': ids,
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'Feature': fids, 'Split': splits, 'Yes': y_directs,
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'No': n_directs, 'Missing': missings, 'Gain': gains,
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'Cover': covers})
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'Cover': covers, "Category": categories})
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if callable(getattr(df, 'sort_values', None)):
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# pylint: disable=no-member
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@ -2381,9 +2395,29 @@ class Booster(object):
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nph = np.column_stack((nph[1][1:], nph[0]))
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nph = nph[nph[:, 1] > 0]
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if nph.size == 0:
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ft = self.feature_types
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fn = self.feature_names
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if fn is None:
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# Let xgboost generate the feature names.
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fn = ["f{0}".format(i) for i in range(self.num_features())]
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try:
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index = fn.index(feature)
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feature_t = ft[index]
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except (ValueError, AttributeError, TypeError):
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# None.index: attr err, None[0]: type err, fn.index(-1): value err
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feature_t = None
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if feature_t == "categorical":
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raise ValueError(
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"Split value historgam doesn't support categorical split."
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)
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if as_pandas and PANDAS_INSTALLED:
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return DataFrame(nph, columns=['SplitValue', 'Count'])
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if as_pandas and not PANDAS_INSTALLED:
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sys.stderr.write(
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"Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
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warnings.warn(
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"Returning histogram as ndarray"
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" (as_pandas == True, but pandas is not installed).",
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UserWarning
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)
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return nph
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25
tests/python-gpu/test_gpu_parse_tree.py
Normal file
25
tests/python-gpu/test_gpu_parse_tree.py
Normal file
@ -0,0 +1,25 @@
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import sys
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import pytest
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import xgboost as xgb
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sys.path.append("tests/python")
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import testing as tm
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def test_tree_to_df_categorical():
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X, y = tm.make_categorical(100, 10, 31, False)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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booster = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=10)
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df = booster.trees_to_dataframe()
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for _, x in df.iterrows():
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if x["Feature"] != "Leaf":
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assert len(x["Category"]) == 1
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def test_split_value_histograms():
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X, y = tm.make_categorical(1000, 10, 13, False)
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reg = xgb.XGBRegressor(tree_method="gpu_hist", enable_categorical=True)
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reg.fit(X, y)
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with pytest.raises(ValueError, match="doesn't"):
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reg.get_booster().get_split_value_histogram("3", bins=5)
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@ -32,15 +32,14 @@ def train_result(param, dmat, num_rounds):
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class TestGPUUpdaters:
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@given(parameter_strategy, strategies.integers(1, 20),
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tm.dataset_strategy)
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@given(parameter_strategy, strategies.integers(1, 20), tm.dataset_strategy)
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@settings(deadline=None)
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def test_gpu_hist(self, param, num_rounds, dataset):
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param['tree_method'] = 'gpu_hist'
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param["tree_method"] = "gpu_hist"
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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note(result)
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assert tm.non_increasing(result['train'][dataset.metric])
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assert tm.non_increasing(result["train"][dataset.metric])
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def run_categorical_basic(self, rows, cols, rounds, cats):
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onehot, label = tm.make_categorical(rows, cols, cats, True)
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@ -49,25 +48,40 @@ class TestGPUUpdaters:
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by_etl_results = {}
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by_builtin_results = {}
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parameters = {'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor'}
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parameters = {"tree_method": "gpu_hist", "predictor": "gpu_predictor"}
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m = xgb.DMatrix(onehot, label, enable_categorical=True)
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xgb.train(parameters, m,
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num_boost_round=rounds,
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evals=[(m, 'Train')], evals_result=by_etl_results)
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m = xgb.DMatrix(onehot, label, enable_categorical=False)
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xgb.train(
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parameters,
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m,
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num_boost_round=rounds,
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evals=[(m, "Train")],
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evals_result=by_etl_results,
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)
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m = xgb.DMatrix(cat, label, enable_categorical=True)
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xgb.train(parameters, m,
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num_boost_round=rounds,
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evals=[(m, 'Train')], evals_result=by_builtin_results)
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xgb.train(
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parameters,
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m,
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num_boost_round=rounds,
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evals=[(m, "Train")],
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evals_result=by_builtin_results,
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)
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# There are guidelines on how to specify tolerance based on considering output as
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# random variables. But in here the tree construction is extremely sensitive to
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# floating point errors. An 1e-5 error in a histogram bin can lead to an entirely
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# different tree. So even though the test is quite lenient, hypothesis can still
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# pick up falsifying examples from time to time.
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np.testing.assert_allclose(
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np.array(by_etl_results['Train']['rmse']),
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np.array(by_builtin_results['Train']['rmse']),
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rtol=1e-3)
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assert tm.non_increasing(by_builtin_results['Train']['rmse'])
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np.array(by_etl_results["Train"]["rmse"]),
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np.array(by_builtin_results["Train"]["rmse"]),
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rtol=1e-3,
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)
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assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
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@given(strategies.integers(10, 400), strategies.integers(3, 8),
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strategies.integers(1, 5), strategies.integers(4, 7))
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strategies.integers(1, 2), strategies.integers(4, 7))
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@settings(deadline=None)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical(self, rows, cols, rounds, cats):
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