def create(self, df, func, suffix=None, output="df", *args, **kwargs):
"""
This is a helper function that output python tests for Spark Dataframes.
:param df: Spark Dataframe
:param suffix: The create method will try to create a test function with the func param given.
If you want to test a function with different params you can use suffix.
:param func: Spark dataframe function to be tested
:param output: can be a 'df' or a 'json'
:param args: Arguments to be used in the function
:param kwargs: Keyword arguments to be used in the functions
:return:
"""
buffer = []
def add_buffer(value):
buffer.append("\t" + value)
if suffix is None:
suffix = ""
else:
suffix = "_" + suffix
# Create func test name. If is None we just test the create.df function a not transform the data frame in
# any way
if func is None:
func_test_name = "test_" + "create_df" + suffix + "()"
else:
func_test_name = "test_" + func.replace(".", "_") + suffix + "()"
print("Creating {test} test function...".format(test=func_test_name))
logging.info(func_test_name)
add_buffer("@staticmethod\n")
add_buffer("def " + func_test_name + ":\n")
if df is not None:
source_df = "\tsource_df=op.create.df(" + df.export() + ")\n"
df_func = df
add_buffer(source_df)
else:
df_func = self.df
# Process simple arguments
_args = []
for v in args:
if is_str(v):
_args.append("'" + v + "'")
elif is_numeric(v):
_args.append(str(v))
elif is_list(v):
if is_list_of_strings(v):
lst = ["'" + x + "'" for x in v]
elif is_list_of_numeric(v):
lst = [str(x) for x in v]
elif is_list_of_tuples(v):
lst = [str(x) for x in v]
_args.append('[' + ','.join(lst) + ']')
_args = ','.join(_args)
_kwargs = []
# print(_args)
# Process keywords arguments
for k, v in kwargs.items():
if is_str(v):
v = "'" + v + "'"
_kwargs.append(k + "=" + str(v))
# Separator if we have positional and keyword arguments
separator = ""
if (not is_list_empty(args)) & (not is_list_empty(kwargs)):
separator = ","
if func is None:
add_buffer("\tactual_df = source_df\n")
else:
add_buffer("\tactual_df = source_df." + func + "(" + _args +
separator + ','.join(_kwargs) + ")\n")
# Apply function to the dataframe
if func is None:
df_result = self.op.create.df(*args, **kwargs)
else:
for f in func.split("."):
df_func = getattr(df_func, f)
df_result = df_func(*args, **kwargs)
if output == "df":
expected = "\texpected_df = op.create.df(" + df_result.export(
) + ")\n"
elif output == "json":
if is_str(df_result):
df_result = "'" + df_result + "'"
else:
df_result = str(df_result)
expected = "\texpected_value =" + df_result + "\n"
add_buffer(expected)
if output == "df":
add_buffer(
"\tassert (expected_df.collect() == actual_df.collect())\n")
elif output == "json":
add_buffer("\tassert (expected_value == actual_df)\n")
return "".join(buffer)
def create(self, df, func, suffix=None, output="df", *args, **kwargs):
"""
This is a helper function that output python tests for Spark Dataframes.
:param df: Spark Dataframe
:param suffix: The create method will try to create a test function with the func param given.
If you want to test a function with different params you can use suffix.
:param func: Spark dataframe function to be tested
:param output: can be a 'df' or a 'json'
:param args: Arguments to be used in the function
:param kwargs: Keyword arguments to be used in the functions
:return:
"""
buffer = []
def add_buffer(value):
buffer.append("\t" + value)
if suffix is None:
suffix = ""
else:
suffix = "_" + suffix
# Create func test name. If is None we just test the create.df function a not transform the data frame in
# any way
if func is None:
func_test_name = "test_" + "create_df" + suffix + "()"
else:
func_test_name = "test_" + func.replace(".", "_") + suffix + "()"
print("Creating {test} test function...".format(test=func_test_name))
logger.print(func_test_name)
add_buffer("@staticmethod\n")
add_buffer("def " + func_test_name + ":\n")
source = "source_df"
if df is None:
# Use the main df
df_func = self.df
elif isinstance(df, pyspark.sql.dataframe.DataFrame):
source_df = "\tsource_df=op.create.df(" + df.export() + ")\n"
df_func = df
add_buffer(source_df)
else:
# TODO: op is not supposed to be hardcoded
source = "op"
df_func = df
# Process simple arguments
_args = []
for v in args:
if is_str(v):
_args.append("'" + v + "'")
elif is_numeric(v):
_args.append(str(v))
elif is_list(v):
if is_list_of_strings(v):
lst = ["'" + x + "'" for x in v]
elif is_list_of_numeric(v):
lst = [str(x) for x in v]
elif is_list_of_tuples(v):
lst = [str(x) for x in v]
_args.append('[' + ','.join(lst) + ']')
elif is_function(v):
_args.append(v.__qualname__)
# else:
# import marshal
# code_string = marshal.dumps(v.__code__)
# add_buffer("\tfunction = '" + code_string + "'\n")
# import marshal, types
#
# code = marshal.loads(code_string)
# func = types.FunctionType(code, globals(), "some_func_name")
_args = ','.join(_args)
_kwargs = []
# print(_args)
# Process keywords arguments
for k, v in kwargs.items():
if is_str(v):
v = "'" + v + "'"
_kwargs.append(k + "=" + str(v))
# Separator if we have positional and keyword arguments
separator = ""
if (not is_list_empty(args)) & (not is_list_empty(kwargs)):
separator = ","
if func is None:
add_buffer("\tactual_df = source_df\n")
else:
add_buffer("\tactual_df =" + source + "." + func + "(" + _args +
separator + ','.join(_kwargs) + ")\n")
# Apply function to the dataframe
if func is None:
df_result = self.op.create.df(*args, **kwargs)
else:
# Here we construct the method to be applied to the source object
for f in func.split("."):
df_func = getattr(df_func, f)
df_result = df_func(*args, **kwargs)
if output == "df":
expected = "\texpected_df = op.create.df(" + df_result.export(
) + ")\n"
elif output == "json":
if is_str(df_result):
df_result = "'" + df_result + "'"
else:
df_result = str(df_result)
add_buffer("\tactual_df =json_enconding(actual_df)\n")
expected = "\texpected_value =json_enconding(" + df_result + ")\n"
else:
expected = "\t\n"
add_buffer(expected)
if output == "df":
add_buffer(
"\tassert (expected_df.collect() == actual_df.collect())\n")
elif output == "json":
add_buffer("\tassert (expected_value == actual_df)\n")
return "".join(buffer)