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Fix metainfo from DataFrame. (#5216)

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* Fix metainfo from DataFrame.

* Unify helper functions for data and meta.
This commit is contained in:
Jiaming Yuan
2020-01-22 16:29:44 +08:00
committed by GitHub
parent 5d4c24a1fc
commit 1891cc766d
5 changed files with 225 additions and 237 deletions
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105
tests/python/test_dt.py
View File

@@ -1,51 +1,54 @@
# -*- coding: utf-8 -*-
import unittest
import pytest
import testing as tm
import xgboost as xgb
try:
import datatable as dt
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_dt()['condition'] or tm.no_pandas()['condition'],
reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
class TestDataTable(unittest.TestCase):
def test_dt(self):
df = pd.DataFrame([[1, 2., True], [2, 3., False]],
columns=['a', 'b', 'c'])
dtable = dt.Frame(df)
labels = dt.Frame([1, 2])
dm = xgb.DMatrix(dtable, label=labels)
assert dm.feature_names == ['a', 'b', 'c']
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
# overwrite feature_names
dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
feature_names=['x', 'y', 'z'])
assert dm.feature_names == ['x', 'y', 'z']
assert dm.num_row() == 2
assert dm.num_col() == 3
# incorrect dtypes
df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
dtable = dt.Frame(df)
self.assertRaises(ValueError, xgb.DMatrix, dtable)
df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
dtable = dt.Frame(df)
dm = xgb.DMatrix(dtable)
assert dm.feature_names == ['A=1', 'A=2']
assert dm.feature_types == ['int', 'int']
assert dm.num_row() == 3
assert dm.num_col() == 2
# -*- coding: utf-8 -*-
import unittest
import pytest
import numpy as np
import testing as tm
import xgboost as xgb
try:
import datatable as dt
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_dt()['condition'] or tm.no_pandas()['condition'],
reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
class TestDataTable(unittest.TestCase):
def test_dt(self):
df = pd.DataFrame([[1, 2., True], [2, 3., False]],
columns=['a', 'b', 'c'])
dtable = dt.Frame(df)
labels = dt.Frame([1, 2])
dm = xgb.DMatrix(dtable, label=labels)
assert dm.feature_names == ['a', 'b', 'c']
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
np.testing.assert_array_equal(np.array([1, 2]), dm.get_label())
# overwrite feature_names
dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
feature_names=['x', 'y', 'z'])
assert dm.feature_names == ['x', 'y', 'z']
assert dm.num_row() == 2
assert dm.num_col() == 3
# incorrect dtypes
df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
dtable = dt.Frame(df)
self.assertRaises(ValueError, xgb.DMatrix, dtable)
df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
dtable = dt.Frame(df)
dm = xgb.DMatrix(dtable)
assert dm.feature_names == ['A=1', 'A=2']
assert dm.feature_types == ['int', 'int']
assert dm.num_row() == 3
assert dm.num_col() == 2

27
tests/python/test_with_pandas.py
View File

@@ -29,6 +29,7 @@ class TestPandas(unittest.TestCase):
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
# overwrite feature_names and feature_types
dm = xgb.DMatrix(df, label=pd.Series([1, 2]),
@@ -51,6 +52,7 @@ class TestPandas(unittest.TestCase):
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
df = pd.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
@@ -110,21 +112,38 @@ class TestPandas(unittest.TestCase):
def test_pandas_label(self):
# label must be a single column
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
self.assertRaises(ValueError, xgb.core._maybe_pandas_label, df)
self.assertRaises(ValueError, xgb.core._maybe_pandas_data, df,
None, None, 'label', 'float')
# label must be supported dtype
df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
self.assertRaises(ValueError, xgb.core._maybe_pandas_label, df)
self.assertRaises(ValueError, xgb.core._maybe_pandas_data, df,
None, None, 'label', 'float')
df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
result = xgb.core._maybe_pandas_label(df)
result, _, _ = xgb.core._maybe_pandas_data(df, None, None,
'label', 'float')
np.testing.assert_array_equal(result, np.array([[1.], [2.], [3.]],
dtype=float))
dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
assert dm.num_row() == 3
assert dm.num_col() == 2
def test_pandas_weight(self):
kRows = 32
kCols = 8
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
w = np.random.randn(kRows).astype(np.float32)
w_pd = pd.DataFrame(w)
data = xgb.DMatrix(X, y, w_pd)
assert data.num_row() == kRows
assert data.num_col() == kCols
np.testing.assert_array_equal(data.get_weight(), w)
def test_cv_as_pandas(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0,

1
tests/python/test_with_sklearn.py
View File

@@ -97,6 +97,7 @@ def test_ranking():
valid_data = xgb.DMatrix(x_valid, y_valid)
test_data = xgb.DMatrix(x_test)
train_data.set_group(train_group)
assert train_data.get_label().shape[0] == x_train.shape[0]
valid_data.set_group(valid_group)
params_orig = {'tree_method': 'exact', 'objective': 'rank:pairwise',