def test_compare_frame(seed, nelem):
np.random.seed(seed)
max_part_size = nelem
# Make LHS
lhs = pygdf.DataFrame()
lhs['a'] = lhs_a = np.random.random(nelem)
lhs['b'] = lhs_b = np.random.random(nelem)
# Make RHS
rhs = pygdf.DataFrame()
rhs['a'] = rhs_a = np.random.random(nelem)
rhs['b'] = rhs_b = np.random.random(nelem)
# Sort by column "a"
got_a = batcher_sortnet._compare_frame(lhs, rhs, max_part_size, by='a')
# Check
expect_a = np.hstack([lhs_a, rhs_a])
expect_a.sort()
np.testing.assert_array_equal(got_a[0].a.to_array(), expect_a[:nelem])
np.testing.assert_array_equal(got_a[1].a.to_array(), expect_a[nelem:])
# Sort by column "b"
got_b = batcher_sortnet._compare_frame(lhs, rhs, max_part_size, by='b')
# Check
expect_b = np.hstack([lhs_b, rhs_b])
expect_b.sort()
np.testing.assert_array_equal(got_b[0].b.to_array(), expect_b[:nelem])
np.testing.assert_array_equal(got_b[1].b.to_array(), expect_b[nelem:])
def test_join_left(left_nrows, right_nrows, left_nkeys, right_nkeys, how):
chunksize = 50
np.random.seed(0)
# PyGDF
left = gd.DataFrame({'x': np.random.randint(0, left_nkeys,
size=left_nrows),
'a': np.arange(left_nrows, dtype=np.float64)}.items())
right = gd.DataFrame({'x': np.random.randint(0, right_nkeys,
size=right_nrows),
'a': 1000 * np.arange(right_nrows,
dtype=np.float64)}.items())
expect = left.set_index('x').join(right.set_index('x'), how=how,
sort=True, lsuffix='l', rsuffix='r')
expect = expect.to_pandas()
# Dask GDf
left = dgd.from_pygdf(left, chunksize=chunksize)
right = dgd.from_pygdf(right, chunksize=chunksize)
joined = left.set_index('x').join(right.set_index('x'), how=how,
lsuffix='l', rsuffix='r')
got = joined.compute().to_pandas()
# Check index
np.testing.assert_array_equal(expect.index.values,
got.index.values)
# Check rows in each groups
expect_rows = {}
got_rows = {}
def gather(df, grows):
cola = np.sort(np.asarray(df.al))
colb = np.sort(np.asarray(df.ar))
grows[df['index'].values[0]] = (cola, colb)
expect.reset_index().groupby('index')\
.apply(partial(gather, grows=expect_rows))
expect.reset_index().groupby('index')\
.apply(partial(gather, grows=got_rows))
for k in expect_rows:
np.testing.assert_array_equal(expect_rows[k][0],
got_rows[k][0])
np.testing.assert_array_equal(expect_rows[k][1],
got_rows[k][1])
def pd2pygdf(df):
if isinstance(df, np.ndarray):
return np2pygdf(df)
pdf = pygdf.DataFrame()
for c, column in enumerate(df):
pdf[c] = df[column]
return pdf
def test_dataframe_to_delayed():
nelem = 100
df = gd.DataFrame()
df['x'] = np.arange(nelem)
df['y'] = np.random.randint(nelem, size=nelem)
ddf = dgd.from_pygdf(df, npartitions=5)
delays = ddf.to_delayed()
assert len(delays) == 5
# Concat the delayed partitions
got = gd.concat([d.compute() for d in delays])
assert_frame_equal(got.to_pandas(), df.to_pandas())
# Check individual partitions
divs = ddf.divisions
assert len(divs) == len(delays) + 1
for i, part in enumerate(delays):
s = divs[i]
# The last divisions in the last index
e = None if i + 1 == len(delays) else divs[i + 1]
expect = df[s:e].to_pandas()
got = part.compute().to_pandas()
assert_frame_equal(got, expect)
def test_serialize_dataframe():
df = pygdf.DataFrame()
df['a'] = np.arange(100)
df['b'] = np.arange(100, dtype=np.float32)
df['c'] = pd.Categorical(['a', 'b', 'c', '_', '_'] * 20,
categories=['a', 'b', 'c'])
outdf = deserialize(*serialize(df))
pd.util.testing.assert_frame_equal(df.to_pandas(), outdf.to_pandas())
def make_empty():
df = gd.DataFrame()
for k in on:
df[k] = np.asarray([], dtype=dtypes[k])
for k in left_val_names:
df[fix_name(k, lsuffix)] = np.asarray([], dtype=dtypes[k])
for k in right_val_names:
df[fix_name(k, rsuffix)] = np.asarray([], dtype=dtypes[k])
return df
def query(df, expr, callenv):
boolmask = gd.queryutils.query_execute(df, expr, callenv)
selected = gd.Series.from_array(boolmask)
newdf = gd.DataFrame()
for col in df.columns:
newseries = df[col][selected]
newdf[col] = newseries
return newdf
def test_sort_values(nelem, nparts, by):
df = pygdf.DataFrame()
df['a'] = np.ascontiguousarray(np.arange(nelem)[::-1])
df['b'] = np.arange(100, nelem + 100)
ddf = dgd.from_pygdf(df, npartitions=nparts)
got = ddf.sort_values(by=by).compute().to_pandas()
expect = df.sort_values(by=by).to_pandas().reset_index(drop=True)
pd.util.testing.assert_frame_equal(got, expect)
def test_serialize_dataframe_with_index():
df = pygdf.DataFrame()
df['a'] = np.arange(100)
df['b'] = np.random.random(100)
df['c'] = pd.Categorical(['a', 'b', 'c', '_', '_'] * 20,
categories=['a', 'b', 'c'])
df = df.sort_values('b')
outdf = deserialize(*serialize(df))
pd.util.testing.assert_frame_equal(df.to_pandas(), outdf.to_pandas())
def test_compare_frame_with_none():
df = pygdf.DataFrame()
max_part_size = 1
df['a'] = [0]
res = batcher_sortnet._compare_frame(df, None, max_part_size, by='a')
assert res[0] is not None, res[1] is None
res = batcher_sortnet._compare_frame(None, df, max_part_size, by='a')
assert res[0] is not None, res[1] is None
res = batcher_sortnet._compare_frame(None, None, max_part_size, by='a')
assert res == (None, None)
def test_merge_left(left_nrows, right_nrows, left_nkeys, right_nkeys,
how='left'):
print(left_nrows, right_nrows, left_nkeys, right_nkeys)
chunksize = 3
np.random.seed(0)
# PyGDF
left = gd.DataFrame({'x': np.random.randint(0, left_nkeys,
size=left_nrows),
'y': np.random.randint(0, left_nkeys,
size=left_nrows),
'a': np.arange(left_nrows, dtype=np.float64)}.items())
right = gd.DataFrame({'x': np.random.randint(0, right_nkeys,
size=right_nrows),
'y': np.random.randint(0, right_nkeys,
size=right_nrows),
'a': 1000 * np.arange(right_nrows,
dtype=np.float64)}.items())
print(left.to_pandas())
print(right.to_pandas())
expect = left.merge(right, on=('x', 'y'), how=how)
expect = expect.to_pandas().sort_values(['x', 'y', 'a_x', 'a_y'])\
.reset_index(drop=True)
print("Expect".center(80, '='))
print(expect)
# Dask GDf
left = dgd.from_pygdf(left, chunksize=chunksize)
right = dgd.from_pygdf(right, chunksize=chunksize)
joined = left.merge(right, on=('x', 'y'), how=how)
print("Got".center(80, '='))
got = joined.compute().to_pandas()
got = got.sort_values(['x', 'y', 'a_x', 'a_y']).reset_index(drop=True)
print(got)
pd.util.testing.assert_frame_equal(expect, got)
def test_serialize_groupby():
df = pygdf.DataFrame()
df['key'] = np.random.randint(0, 20, 100)
df['val'] = np.arange(100, dtype=np.float32)
gb = df.groupby('key')
outgb = deserialize(*serialize(gb))
got = gb.mean()
expect = outgb.mean()
pd.util.testing.assert_frame_equal(got.to_pandas(), expect.to_pandas())
def test_join_inner(left_nrows, right_nrows, left_nkeys, right_nkeys):
chunksize = 50
np.random.seed(0)
# PyGDF
left = gd.DataFrame({'x': np.random.randint(0, left_nkeys,
size=left_nrows),
'a': np.arange(left_nrows)}.items())
right = gd.DataFrame({'x': np.random.randint(0, right_nkeys,
size=right_nrows),
'a': 1000 * np.arange(right_nrows)}.items())
expect = left.set_index('x').join(right.set_index('x'), how='inner',
sort=True, lsuffix='l', rsuffix='r')
expect = expect.to_pandas()
# Dask GDf
left = dgd.from_pygdf(left, chunksize=chunksize)
right = dgd.from_pygdf(right, chunksize=chunksize)
joined = left.set_index('x').join(right.set_index('x'), how='inner',
lsuffix='l', rsuffix='r')
got = joined.compute().to_pandas()
# Check index
np.testing.assert_array_equal(expect.index.values,
got.index.values)
# Check rows in each groups
expect_rows = {}
got_rows = {}
def gather(df, grows):
grows[df['index'].values[0]] = (set(df.al), set(df.ar))
expect.reset_index().groupby('index')\
.apply(partial(gather, grows=expect_rows))
expect.reset_index().groupby('index')\
.apply(partial(gather, grows=got_rows))
assert got_rows == expect_rows
def fix_left(df):
newdf = gd.DataFrame()
df = df.reset_index()
for k in on:
newdf[k] = df[k]
for k in left_val_names:
newdf[fix_name(k, lsuffix)] = df[k]
for k in right_val_names:
newdf[fix_name(k, rsuffix)] = nullcolumn(len(df), dtypes[k])
return newdf
def test_from_scalar_typing(data_type):
if data_type == 'datetime64[ms]':
scalar = np.dtype('int64').type(np.random.randint(0, 5))\
.astype('datetime64[ms]')
else:
scalar = np.dtype(data_type).type(np.random.randint(0, 5))
gdf = gd.DataFrame()
gdf['a'] = [1, 2, 3, 4, 5]
gdf['b'] = scalar
assert (gdf['b'].dtype == np.dtype(data_type))
assert (len(gdf['b']) == len(gdf['a']))
def test_pca_inverse_transform(datatype):
gdf = pygdf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
cutsvd = cuTSVD(n_components=1)
Xcutsvd = cutsvd.fit_transform(gdf)
print("Calling inverse_transform")
input_gdf = cutsvd.inverse_transform(Xcutsvd)
print(input_gdf)
assert array_equal(input_gdf, gdf, 0.4, with_sign=True)
def test_pca_inverse_transform(datatype):
gdf = pygdf.DataFrame()
gdf['0']=np.asarray([-1,-2,-3,1,2,3],dtype=datatype)
gdf['1']=np.asarray([-1,-1,-2,1,1,2],dtype=datatype)
cupca = cuPCA(n_components = 2)
Xcupca = cupca.fit_transform(gdf)
print("Calling inverse_transform")
input_gdf = cupca.inverse_transform(Xcupca)
assert array_equal(input_gdf, gdf,
1e-3,with_sign=True)
def test_frame_dtype_error():
nelem = 20
df1 = gd.DataFrame()
df1['bad'] = np.arange(nelem)
df1['bad'] = np.arange(nelem, dtype=np.float64)
df2 = gd.DataFrame()
df2['bad'] = np.arange(nelem)
df2['bad'] = np.arange(nelem, dtype=np.float32)
ddf1 = dgd.from_pygdf(df1, npartitions=5)
ddf2 = dgd.from_pygdf(df2, npartitions=5)
combined = dgd.from_delayed(ddf1.to_delayed() + ddf2.to_delayed())
with pytest.raises(ValueError) as raises:
combined.compute()
print("out")
raises.match(r"^Metadata mismatch found in `from_delayed`.")
raises.match(r"\s+\|\s+".join(['bad', 'float32', 'float64']))
def test_pca_fit_transform(datatype):
gdf = pygdf.DataFrame()
gdf['0']=np.asarray([-1,-2,-3,1,2,3],dtype=datatype)
gdf['1']=np.asarray([-1,-1,-2,1,1,2],dtype=datatype)
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]], dtype = datatype)
print("Calling fit_transform")
cupca = cuPCA(n_components = 2)
Xcupca = cupca.fit_transform(gdf)
skpca = skPCA(n_components = 2)
Xskpca = skpca.fit_transform(X)
assert array_equal(Xcupca, Xskpca,
1e-3,with_sign=False)
def test_dbscan_predict(datatype):
gdf = pygdf.DataFrame()
gdf['0'] = np.asarray([1, 2, 2, 8, 8, 25], dtype=datatype)
gdf['1'] = np.asarray([2, 2, 3, 7, 8, 80], dtype=datatype)
X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]],
dtype=datatype)
print("Calling fit_predict")
cudbscan = cuDBSCAN(eps=3, min_samples=2)
cu_labels = cudbscan.fit_predict(gdf)
skdbscan = skDBSCAN(eps=3, min_samples=2)
sk_labels = skdbscan.fit_predict(X)
print(X.shape[0])
for i in range(X.shape[0]):
assert cu_labels[i] == sk_labels[i]
def fix_column(lhs):
df = gd.DataFrame()
for k in lhs.columns:
df[k + lsuffix] = lhs[k]
for k, dtype in rhs_dtypes:
data = np.zeros(len(lhs), dtype=dtype)
mask_size = gd.utils.calc_chunk_size(data.size,
gd.utils.mask_bitsize)
mask = np.zeros(mask_size, dtype=gd.utils.mask_dtype)
sr = gd.Series.from_masked_array(data=data,
mask=mask,
null_count=data.size)
df[k + rsuffix] = sr.set_index(df.index)
return df
def test_pca_fit(datatype):
gdf = pygdf.DataFrame()
gdf['0']=np.asarray([-1,-2,-3,1,2,3],dtype=datatype)
gdf['1']=np.asarray([-1,-1,-2,1,1,2],dtype=datatype)
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]], dtype = datatype)
print("Calling fit")
cupca = cuPCA(n_components = 2)
cupca.fit(gdf)
skpca = skPCA(n_components = 2)
skpca.fit(X)
for attr in ['singular_values_','components_','explained_variance_','explained_variance_ratio_','noise_variance_']:
with_sign = False if attr in ['components_'] else True
assert array_equal(getattr(cupca,attr),getattr(skpca,attr),
1e-3,with_sign=with_sign)
def test_frame_extra_columns_error():
nelem = 20
df = gd.DataFrame()
df['x'] = np.arange(nelem)
df['y'] = np.random.randint(nelem, size=nelem)
ddf1 = dgd.from_pygdf(df, npartitions=5)
df['z'] = np.arange(nelem)
ddf2 = dgd.from_pygdf(df, npartitions=5)
combined = dgd.from_delayed(ddf1.to_delayed() + ddf2.to_delayed())
with pytest.raises(ValueError) as raises:
out = combined.compute()
raises.match(r"^Metadata mismatch found in `from_delayed`.")
raises.match(r"extra columns")
def test_mixing_series_frame_error():
nelem = 20
df = gd.DataFrame()
df['x'] = np.arange(nelem)
df['y'] = np.random.randint(nelem, size=nelem)
ddf = dgd.from_pygdf(df, npartitions=5)
delay_frame = ddf.to_delayed()
delay_series = ddf.x.to_delayed()
combined = dgd.from_delayed(delay_frame + delay_series)
with pytest.raises(ValueError) as raises:
out = combined.compute()
raises.match(r"^Metadata mismatch found in `from_delayed`.")
raises.match(r"Expected partition of type `DataFrame` but got `Series`")
def test_sort_values_binned():
np.random.seed(43)
nelem = 100
nparts = 5
by = 'a'
df = pygdf.DataFrame()
df['a'] = np.random.randint(1, 5, nelem)
ddf = dgd.from_pygdf(df, npartitions=nparts)
parts = ddf.sort_values_binned(by=by).to_delayed()
part_uniques = []
for i, p in enumerate(parts):
part = dask.compute(p)[0]
part_uniques.append(set(part.a.unique()))
# Partitions do not have intersecting keys
for i in range(len(part_uniques)):
for j in range(i + 1, len(part_uniques)):
assert not (part_uniques[i] & part_uniques[j]), \
"should have empty intersection"
def test_tsvd_fit(datatype):
gdf = pygdf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
print("Calling fit")
cutsvd = cuTSVD(n_components=1)
cutsvd.fit(gdf)
sktsvd = skTSVD(n_components=1)
sktsvd.fit(X)
for attr in [
'singular_values_', 'components_', 'explained_variance_ratio_'
]:
with_sign = False if attr in ['components_'] else True
assert array_equal(getattr(cutsvd, attr),
getattr(sktsvd, attr),
0.4,
with_sign=with_sign)
def test_index_in_dataframe_constructor():
a = pd.DataFrame({'x': [1, 2, 3]}, index=[4., 5., 6.])
b = gd.DataFrame({'x': [1, 2, 3]}, index=[4., 5., 6.])
pd.testing.assert_frame_equal(a, b.to_pandas())
assert pd.testing.assert_frame_equal(a.loc[4:], b.loc[4:].to_pandas())
def np2pygdf(df):
pdf = pygdf.DataFrame()
for c in range(df.shape[1]):
pdf[c] = df[:, c]
return pdf
def load_data(nelem, ident):
df = gd.DataFrame()
df['x'] = np.arange(nelem)
df['ident'] = np.asarray([ident] * nelem)
return df
