def test_c_bind_pair(self):
f1 = Frame(self.sds, self.df_cb_1)
f2 = Frame(self.sds, self.df_cb_2)
result_df = f1.cbind(f2).compute()
self.assertTrue(isinstance(result_df, pd.DataFrame))
target_df= pd.concat([self.df_cb_1, self.df_cb_2], axis=1)
self.assertTrue(target_df.equals(result_df))
def test_r_bind_pair(self):
f1 = Frame(self.sds, self.df_rb_1)
f2 = Frame(self.sds, self.df_rb_2)
result_df = f1.rbind(f2).compute()
self.assertTrue(isinstance(result_df, pd.DataFrame))
target_df = pd.concat([self.df_rb_1, self.df_rb_2], ignore_index=True)
self.assertTrue(target_df.equals(result_df))
def test_write_read_csv(self):
frame = Frame(self.sds, self.df)
frame.write(self.temp_dir + "02", header=True, format="csv").compute()
NX = self.sds.read(self.temp_dir + "02",
data_type="frame",
format="csv")
result_df = NX.compute()
self.assertTrue(isinstance(result_df, pd.DataFrame))
self.assertTrue(self.df.equals(result_df))
def read(self, path: os.PathLike,
**kwargs: Dict[str, VALID_INPUT_TYPES]) -> 'OperationNode':
""" Read an file from disk. Supportted types include:
CSV, Matrix Market(coordinate), Text(i,j,v), SystemDS Binay
See: http://apache.github.io/systemds/site/dml-language-reference#readwrite-built-in-functions for more details
:return: an Operation Node, containing the read data.
"""
data_type = kwargs.get("data_type", None)
file_format = kwargs.get("format", None)
if data_type == "frame":
kwargs["data_type"] = f'"{data_type}"'
if isinstance(file_format, str):
kwargs["format"] = f'"{kwargs["format"]}"'
return Frame(self, None, f'"{path}"', **kwargs)
elif data_type == "scalar":
kwargs["data_type"] = f'"{data_type}"'
value_type = kwargs.get("value_type", None)
if value_type == "string":
kwargs["value_type"] = f'"{kwargs["value_type"]}"'
return OperationNode(
self,
"read",
[f'"{path}"'],
named_input_nodes=kwargs,
shape=(-1, ),
output_type=OutputType.SCALAR,
)
return OperationNode(self,
"read", [f'"{path}"'],
named_input_nodes=kwargs,
shape=(-1, ))
def test_apply_recode_bin(self):
with open(self.JSPEC_PATH) as jspec_file:
JSPEC = json.load(jspec_file)
F1 = self.sds.read(
self.HOMES_PATH,
data_type="frame",
schema=self.HOMES_SCHEMA,
format="csv",
header=True,
)
pd_F1 = F1.compute()
jspec = self.sds.read(self.JSPEC_PATH, data_type="scalar", value_type="string")
X, M = F1.transform_encode(spec=jspec).compute()
self.assertTrue(isinstance(X, np.ndarray))
self.assertTrue(isinstance(M, pd.DataFrame))
self.assertTrue(X.shape == pd_F1.shape)
self.assertTrue(np.all(np.isreal(X)))
relevant_columns = set()
for col_name in JSPEC["recode"]:
relevant_columns.add(pd_F1.columns.get_loc(col_name))
self.assertTrue(M[col_name].nunique() == pd_F1[col_name].nunique())
for binning in JSPEC["bin"]:
col_name = binning["name"]
relevant_columns.add(pd_F1.columns.get_loc(col_name))
self.assertTrue(M[col_name].nunique() == binning["numbins"])
X2 = F1.transform_apply(spec=jspec, meta=Frame(self.sds, M)).compute()
self.assertTrue(X.shape == X2.shape)
self.assertTrue(np.all(np.isreal(X2)))
def test_r_bind_triple_twostep(self):
f1 = Frame(self.sds, self.df_rb_1)
f2 = Frame(self.sds, self.df_rb_2)
f3 = Frame(self.sds, self.df_rb_3)
tmp_df = f1.rbind(f2).compute()
result_df = Frame(self.sds, tmp_df).rbind(f3).compute()
self.assertTrue(isinstance(result_df, pd.DataFrame))
target_df = pd.concat([self.df_rb_1, self.df_rb_2, self.df_rb_3], ignore_index=True)
self.assertTrue(target_df.equals(result_df))
def test_write_read_binary(self):
frame = Frame(self.sds, self.df)
frame.write(self.temp_dir + "01").compute()
NX = self.sds.read(self.temp_dir + "01", data_type="frame")
result_df = NX.compute()
self.assertTrue((self.df.values == result_df.values).all())