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https://github.com/wassname/ray.git
synced 2026-06-28 17:02:43 +08:00
[DataFrame] Update architecture to be more flexible and performant (#1821)
This commit is contained in:
Devin Petersohn
committed by
Robert Nishihara
Robert Nishihara
parent
5bde5e75e7
commit
0d9a7a3c19
@@ -20,6 +20,11 @@ def ray_df_equals_pandas(ray_df, pandas_df):
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return to_pandas(ray_df).sort_index().equals(pandas_df.sort_index())
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@pytest.fixture
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def ray_series_equals_pandas(ray_df, pandas_df):
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return ray_df.sort_index().equals(pandas_df.sort_index())
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@pytest.fixture
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def ray_df_equals(ray_df1, ray_df2):
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return to_pandas(ray_df1).sort_index().equals(
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@@ -58,6 +63,11 @@ def test_ftypes(ray_df, pandas_df):
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assert(ray_df.ftypes.equals(pandas_df.ftypes))
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@pytest.fixture
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def test_dtypes(ray_df, pandas_df):
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assert(ray_df.dtypes.equals(pandas_df.dtypes))
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@pytest.fixture
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def test_values(ray_df, pandas_df):
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np.testing.assert_equal(ray_df.values, pandas_df.values)
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@@ -103,13 +113,14 @@ def test_applymap(ray_df, pandas_df, testfunc):
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def test_copy(ray_df):
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new_ray_df = ray_df.copy()
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assert(new_ray_df is not ray_df)
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assert(new_ray_df._df == ray_df._df)
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assert new_ray_df is not ray_df
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assert np.array_equal(new_ray_df._block_partitions,
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ray_df._block_partitions)
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@pytest.fixture
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def test_sum(ray_df, pandas_df):
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assert(ray_df_equals_pandas(ray_df.sum(), pandas_df.sum()))
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assert(ray_df.sum().sort_index().equals(pandas_df.sum().sort_index()))
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@pytest.fixture
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@@ -185,6 +196,7 @@ def test_int_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -252,8 +264,8 @@ def test_int_dataframe():
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test_cumprod(ray_df, pandas_df)
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test_cumsum(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -308,6 +320,7 @@ def test_float_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -374,8 +387,8 @@ def test_float_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -429,6 +442,7 @@ def test_mixed_dtype_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -486,10 +500,14 @@ def test_mixed_dtype_dataframe():
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test_min(ray_df, pandas_df)
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test_notna(ray_df, pandas_df)
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test_notnull(ray_df, pandas_df)
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test_cummax(ray_df, pandas_df)
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test_cummin(ray_df, pandas_df)
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# TODO Fix pandas so that the behavior is correct
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# We discovered a bug where argmax does not always give the same result
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# depending on what your other dtypes are.
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# test_cummax(ray_df, pandas_df)
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# test_cummin(ray_df, pandas_df)
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# test_cumprod(ray_df, pandas_df)
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test_cumsum(ray_df, pandas_df)
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# test_cumsum(ray_df, pandas_df)
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test___len__(ray_df, pandas_df)
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test_first_valid_index(ray_df, pandas_df)
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@@ -505,8 +523,8 @@ def test_mixed_dtype_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -559,6 +577,7 @@ def test_nan_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -625,8 +644,8 @@ def test_nan_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -1058,34 +1077,30 @@ def test_equals():
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def test_eval_df_use_case():
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df = pd.DataFrame({'a': np.random.randn(10),
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 5)
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 2)
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df.eval("e = arctan2(sin(a), b)",
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engine='python',
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parser='pandas', inplace=True)
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expect = df.e
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ray_df.eval("e = arctan2(sin(a), b)",
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engine='python',
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parser='pandas', inplace=True)
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got = ray_df.e
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# TODO: Use a series equality validator.
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assert ray_df_equals_pandas(got, pd.DataFrame(expect, columns=['e']))
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assert ray_df_equals_pandas(ray_df, df)
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def test_eval_df_arithmetic_subexpression():
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df = pd.DataFrame({'a': np.random.randn(10),
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 5)
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df.eval("e = sin(a + b)",
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 2)
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df.eval("not_e = sin(a + b)",
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engine='python',
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parser='pandas', inplace=True)
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expect = df.e
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ray_df.eval("e = sin(a + b)",
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ray_df.eval("not_e = sin(a + b)",
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engine='python',
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parser='pandas', inplace=True)
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got = ray_df.e
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# TODO: Use a series equality validator.
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assert ray_df_equals_pandas(got, pd.DataFrame(expect, columns=['e']))
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assert ray_df_equals_pandas(ray_df, df)
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def test_ewm():
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@@ -1108,6 +1123,7 @@ def test_ffill(num_partitions=2):
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test_data.tsframe['A'][:5] = np.nan
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test_data.tsframe['A'][-5:] = np.nan
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ray_df = from_pandas(test_data.tsframe, num_partitions)
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assert ray_df_equals_pandas(
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ray_df.ffill(),
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test_data.tsframe.ffill()
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@@ -1127,7 +1143,10 @@ def test_fillna():
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test_fillna_dtype_conversion()
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test_fillna_skip_certain_blocks()
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test_fillna_dict_series()
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test_fillna_dataframe()
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with pytest.raises(NotImplementedError):
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test_fillna_dataframe()
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test_fillna_columns()
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test_fillna_invalid_method()
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test_fillna_invalid_value()
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@@ -1198,6 +1217,7 @@ def test_fillna_sanity(num_partitions=2):
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result = df.fillna({2: 'foo'})
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ray_df = from_pandas(df, num_partitions).fillna({2: 'foo'})
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assert ray_df_equals_pandas(ray_df, result)
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ray_df = from_pandas(df, num_partitions)
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@@ -1774,12 +1794,13 @@ def test_mask():
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@pytest.fixture
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def test_max(ray_df, pandas_df):
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assert(ray_df_equals_pandas(ray_df.max(), pandas_df.max()))
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assert(ray_series_equals_pandas(ray_df.max(), pandas_df.max()))
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assert(ray_series_equals_pandas(ray_df.max(axis=1), pandas_df.max(axis=1)))
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@pytest.fixture
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def test_mean(ray_df, pandas_df):
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assert(ray_df.mean().equals(pandas_df.mean()))
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assert ray_df.mean().equals(pandas_df.mean())
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@pytest.fixture
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@@ -1810,7 +1831,8 @@ def test_merge():
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@pytest.fixture
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def test_min(ray_df, pandas_df):
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assert(ray_df_equals_pandas(ray_df.min(), pandas_df.min()))
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assert(ray_series_equals_pandas(ray_df.min(), pandas_df.min()))
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assert(ray_series_equals_pandas(ray_df.min(axis=1), pandas_df.min(axis=1)))
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def test_mod():
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@@ -1916,7 +1938,7 @@ def test_plot():
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@pytest.fixture
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def test_pop(ray_df, pandas_df):
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temp_ray_df = ray_df._map_partitions(lambda df: df)
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temp_ray_df = ray_df.copy()
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temp_pandas_df = pandas_df.copy()
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ray_popped = temp_ray_df.pop('col2')
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pandas_popped = temp_pandas_df.pop('col2')
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@@ -1952,7 +1974,6 @@ def test_quantile(ray_df, pandas_df, q):
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@pytest.fixture
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def test_query(ray_df, pandas_df, funcs):
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for f in funcs:
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pandas_df_new, ray_df_new = pandas_df.query(f), ray_df.query(f)
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assert pandas_df_new.equals(to_pandas(ray_df_new))
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