def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist={"df_count": {1}, "model_count": {0}}
error, extra=IncomingEdgeValidityChecker.check_validity(node["id"], requireds_info, edges, checklist)
final_code=[]
shared_function_set = set()
additional_local_code=[]
errors=[]
if(error == ErrorTypes.NO_ERROR):
if ("portion" in extra["dfs"]):
df_name = "df_" + extra["dfs"][0]["source_id"] + "[" + str(extra["dfs"][0]["portion"]) + "]"
else:
df_name = "df_" + extra["dfs"][0]["source_id"]
my_args={"node_id": node["id"], "input_dfs": [df_name], "shared_function_set": shared_function_set, "additional_local_code": additional_local_code, "errors": errors}
updated_function_name = CodeGenerationUtils.handle_parameter(node["parameters"]["udf_function"], my_args)
gen_code=[]
gen_code.extend(["udf_"+node["id"]+" = udf("+updated_function_name+", "+node["parameters"]["udf_return_type"]["value"]+"())", os.linesep])
gen_code.extend(["tuple_list = " + CodeGenerationUtils.handle_parameter(node["parameters"]["udf_input_tuples"], my_args), os.linesep])
gen_code.extend(["output_list = " + CodeGenerationUtils.handle_parameter(node["parameters"]["udf_outputs"], my_args), os.linesep])
gen_code.extend(["df_" + node["id"] + "=" + df_name, os.linesep])
gen_code.extend(["for index in range(len(tuple_list)):", os.linesep])
gen_code.extend(["\tdf_"+node["id"]+" = df_"+node["id"]+".withColumn(output_list[index], udf_"+node["id"]+"(*tuple_list[index]))", os.linesep, os.linesep])
final_code = CodeGenerationUtils.merge_with_additional_code(gen_code, additional_local_code)
return final_code, shared_function_set, error
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist = {"df_count": {1}, "model_count": {0}}
error, extra = IncomingEdgeValidityChecker.check_validity(
node["id"], requireds_info, edges, checklist)
final_code = []
shared_function_set = set()
additional_local_code = []
errors = []
if (error == ErrorTypes.NO_ERROR):
if ("portion" in extra["dfs"][0]):
df_name = "df_" + extra["dfs"][0]["source_id"] + "[" + str(
extra["dfs"][0]["portion"]) + "]"
else:
df_name = "df_" + extra["dfs"][0]["source_id"]
my_args = {
"node_id": node["id"],
"input_dfs": [df_name],
"shared_function_set": shared_function_set,
"additional_local_code": additional_local_code,
"errors": errors
}
gen_code = []
shared_function_set.add(SharedFunctionTypes.SELECT_EXPR_HELPERS)
gen_code.append("df_" + node["id"] + "=" + df_name + ".selectExpr(")
for expr in node["parameters"]["expressions"]["value"]:
gen_code.extend([
'single_select_expr_generator(' +
CodeGenerationUtils.handle_parameter(expr["input_cols"],
my_args) +
', ' + CodeGenerationUtils.handle_parameter(
expr["output_cols"], my_args) +
', ' + CodeGenerationUtils.handle_parameter(
expr["operation"], my_args) + ')', ', '
])
gen_code.pop()
gen_code.extend([")", os.linesep])
final_code = CodeGenerationUtils.merge_with_additional_code(
gen_code, additional_local_code)
return final_code, shared_function_set, error
def __generate_code_for_pipeline_instantination(node, args):
code = []
non_indicator_params = {}
for param in node["parameters"]:
if (param in node["multi_instance_indicator"]):
code.extend([
"mmi_value_" + param + "_" + node["id"] +
" = " + CodeGenerationUtils.handle_parameter(
node["parameters"][param], args), os.linesep
])
else:
non_indicator_params[param] = node["parameters"][param]
code.extend(["stages_" + node["id"], " = ", "[]", os.linesep])
code.extend([
"for i in ", "range(len(mmi_value_" +
node["multi_instance_indicator"][0] + "_" + node["id"], ")):",
os.linesep
])
code.extend([
"\t",
__generate_stage_template(node, non_indicator_params, args), os.linesep
])
if (not ("in_pipeline" in args and args["in_pipeline"])):
code.extend([
'pipeline_' + node["id"] + "=Pipeline(stages=",
"stages_" + node["id"] + ")", os.linesep
])
return code
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist={"df_count": {0}, "model_count": {0}}
error, extra=IncomingEdgeValidityChecker.check_validity(node["id"], requireds_info, edges, checklist)
final_code=[]
shared_function_set = set()
additional_local_code=[]
errors=[]
if(error == ErrorTypes.NO_ERROR):
error, is_schema_appropriate=DataSourceValidityChecker.check_validity(node)
if(error == ErrorTypes.NO_ERROR):
my_args = {"node_id": node["id"], "shared_function_set": shared_function_set, "additional_local_code": additional_local_code, "errors": errors}
# Must be a valid schema at this point.
additional_code, param_string = CodeGenerationUtils.handle_parameter(node["parameter"]["schema"], my_args)
gen_code=[]
gen_code.extend(additional_code)
gen_code.append("df_" + node["id"] + ' = spark.readStream.format("kafka").option("kafka.bootstrap.servers", ')
gen_code.append(CodeGenerationUtils.handle_primitive(node["parameters"]["host"]["value"] + ":" + node["parameters"]["port"]["value"]) + ")")
gen_code.append('.option("subscribe", ' + CodeGenerationUtils.handle_primitive(node["parameters"]["topic"]["value"] + ")"))
gen_code.append('.load().select(from_json(col("value").cast("string"), '+ param_string +")")
# For streams, we will use timestamp as a key while writing to kafka topic in case.
gen_code.extend(['.alias("value"), "timestamp").select("value.*", "timestamp")', os.linesep])
final_code = CodeGenerationUtils.merge_with_additional_code(gen_code, additional_local_code)
return final_code, shared_function_set, error
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist={"df_count": {1}, "model_count": {0}}
error, extra=IncomingEdgeValidityChecker.check_validity(node["id"], requireds_info, edges, checklist)
final_code=[]
shared_function_set = set()
additional_local_code = []
errors = []
if(error == ErrorTypes.NO_ERROR):
if ("portion" in extra["dfs"][0]):
df_name = "df_" + extra["dfs"][0]["source_id"] + "[" + str(extra["dfs"][0]["portion"]) + "]"
else:
df_name = "df_" + extra["dfs"][0]["source_id"]
my_args = {"node_id": node["id"], "input_dfs": [df_name], "shared_function_set": shared_function_set, "additional_local_code": additional_local_code, "errors": errors}
input_cols = CodeGenerationUtils.handle_parameter(node["parameters"]["input_cols"], my_args)
output_cols = CodeGenerationUtils.handle_parameter(node["parameters"]["output_cols"], my_args)
window_size = node["parameters"]["window_size"]["value"]
partitioning_column = node["parameters"]["partitioning_column"]["value"]
ordering_column = node["parameters"]["ordering_column"]["value"]
ordering_direction = node["parameters"]["ordering_direction"]["value"]
gen_code=[]
gen_code.extend(["input_cols = " + output_cols, os.linesep])
gen_code.extend(["output_cols = " + input_cols, os.linesep])
gen_code.extend(["df_" + node["id"] + "=" + df_name, os.linesep])
gen_code.extend(["for inC, outC in zip(input_cols, output_cols):", os.linesep])
gen_code.extend(["\tdf_" + node["id"] + " = df_" + node["id"] + ".withColumn('temp', col(inC))", os.linesep])
gen_code.extend(["\twSpec = Window.partitionBy('" + partitioning_column + "').orderBy(col('" + ordering_column + "')." + ordering_direction + "())", os.linesep])
gen_code.extend(["\tfor j in range(" + str(window_size) + "):", os.linesep])
gen_code.extend(["\t\tlag_values = lag('temp', default=0).over(wSpec)", os.linesep])
gen_code.extend(["\t\tdf_" + node["id"] + " = df_" + node["id"] + ".withColumn('temp', F.when((col('temp')==1) | (lag_values==None) | (lag_values<1) | (lag_values>=" + str(window_size + 1) + "), col('temp')).otherwise(lag_values+1))", os.linesep])
gen_code.extend(["\tdf_" + node["id"] + " = df_" + node["id"] + ".withColumn(outC, F.when(col('temp') > 0, 1.0).otherwise(0.0))", os.linesep])
final_code = CodeGenerationUtils.merge_with_additional_code(gen_code, additional_local_code)
return final_code, shared_function_set, error
def __handle_col_list_plus_kwargs(node, args):
kwargs_str = []
if ("kwargs" in node["parameters"]):
kwargs = node["parameters"]["kwargs"]["value"]
# If there are parameters for kwargs and all them is optional; and if user do not provide any of them, then we assume that there will be an empty dictionary here...
for elem in kwargs:
kwargs_str.extend([
", ", elem, "=",
CodeGenerationUtils.handle_parameter(kwargs[elem], args)
])
code = ["df_" + args["node_id"] + " = " + args["input_dfs"][0], os.linesep]
code.extend([
"df_" + args["node_id"] + " = " + "df_" + args["node_id"] + "." +
node["sql_name"] + "(" + CodeGenerationUtils.handle_parameter(
node["parameters"]["input_columns"], args)
])
code.extend(kwargs_str)
code.extend([")", os.linesep])
return code
def __generate_code_for_param_grid(node, cur_estimator_name, args):
# In the future handle this in special requirement handler for parameters
code = ["param_grid_" + node["id"] + "=", "None", os.linesep]
# Assuming that fix parameters are given in the estimator itself.
# Maybe reconsider this part.
grid_params = node["parameters"]["parameter_grid"]
if (bool(grid_params)):
code.pop()
code.pop()
code.extend(["ParamGridBuilder()"])
for param in grid_params:
code.extend([
".addGrid(" + cur_estimator_name + "." + param +
", " + CodeGenerationUtils.handle_parameter(
grid_params[param], args) + ")"
])
code.extend([".build()", os.linesep])
return code
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist = {"df_count": {0}, "model_count": {0}}
error, extra = IncomingEdgeValidityChecker.check_validity(
node["id"], requireds_info, edges, checklist)
final_code = []
shared_function_set = set()
additional_local_code = []
errors = []
if (error == ErrorTypes.NO_ERROR):
error, is_schema_appropriate = DataSourceValidityChecker.check_validity(
node)
if (error == ErrorTypes.NO_ERROR):
my_args = {
"node_id": node["id"],
"shared_function_set": shared_function_set,
"additional_local_code": additional_local_code,
"errors": errors
}
# Must be a valid schema at this point.
additional_code, param_string = CodeGenerationUtils.handle_parameter(
node["parameter"]["schema"], my_args)
gen_code = []
gen_code.extend(additional_code)
gen_code.extend([
"df_" + node["id"] + ' = spark.readStream.schema(' +
param_string + ")." + node["file_type"] + "(" +
CodeGenerationUtils.handle_primitive(
node["parameters"]["path"]["value"]) + ")", os.linesep
])
final_code = CodeGenerationUtils.merge_with_additional_code(
gen_code, additional_local_code)
return final_code, shared_function_set, error
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist = {"df_count": {1}, "model_count": {0}}
error, extra = IncomingEdgeValidityChecker.check_validity(
node["id"], requireds_info, edges, checklist)
final_code = []
shared_function_set = set()
additional_local_code = []
errors = []
if (error == ErrorTypes.NO_ERROR):
if ("portion" in extra["dfs"][0]):
df_name = "df_" + extra["dfs"][0]["source_id"] + "[" + str(
extra["dfs"][0]["portion"]) + "]"
else:
df_name = "df_" + extra["dfs"][0]["source_id"]
my_args = {
"node_id": node["id"],
"input_dfs": [df_name],
"shared_function_set": shared_function_set,
"additional_local_code": additional_local_code,
"errors": errors
}
df_name = "df_" + my_args["node_id"]
gen_code = [df_name + " = " + my_args["input_dfs"][0], os.linesep]
input_columns = ["["]
conditions = ["["]
values = ["["]
otherwises = ["["]
output_columns = ["["]
for exp in node["parameters"]["expressions"]["value"]:
input_columns.extend([
CodeGenerationUtils.handle_parameter(exp["input_columns"],
my_args), ", "
])
conditions.extend([
CodeGenerationUtils.handle_parameter(exp["condition"],
my_args), ", "
])
values.extend([
CodeGenerationUtils.handle_parameter(exp["value"], my_args),
", "
])
otherwises.extend([
CodeGenerationUtils.handle_parameter(exp["otherwise"],
my_args), ", "
])
output_columns.extend([
CodeGenerationUtils.handle_parameter(exp["output_columns"],
my_args), ", "
])
# Check there are at least 1 elememnt in expressions
input_columns.pop()
conditions.pop()
values.pop()
otherwises.pop()
output_columns.pop()
input_columns.extend(["]"])
conditions.extend(["]"])
values.extend(["]"])
otherwises.extend(["]"])
output_columns.extend(["]"])
gen_code.extend(
["input_columns = " + ''.join(input_columns), os.linesep])
gen_code.extend(["conditions = " + ''.join(conditions), os.linesep])
gen_code.extend(["values = " + ''.join(values), os.linesep])
gen_code.extend(["otherwises = " + ''.join(otherwises), os.linesep])
gen_code.extend(
["output_columns = " + ''.join(output_columns), os.linesep])
gen_code.extend([
"for in_cols, cond, val, otw, out_cols in zip(input_columns, conditions, values, otherwises, output_columns):",
os.linesep
])
gen_code.extend(
["\tfor in_col, out_col in zip(in_cols, out_cols):", os.linesep])
gen_code.extend([
"\t\tcur_cond = eval(cond.replace('$','" + df_name +
"[\"'+in_col+'\"]'" + "))", os.linesep
])
gen_code.extend([
"\t\t" + df_name + " = " + df_name +
".withColumn(out_col, F.when(cur_cond, val).otherwise(otw))",
os.linesep
])
final_code = CodeGenerationUtils.merge_with_additional_code(
gen_code, additional_local_code)
return final_code, shared_function_set, error
def generate_code(args):
node = args["node"]
requireds_info = args["requireds_info"]
edges = args["edges"]
checklist = {"df_count": {1}, "model_count": {0}}
error, extra = IncomingEdgeValidityChecker.check_validity(
node["id"], requireds_info, edges, checklist)
final_code = []
shared_function_set = set()
additional_local_code = []
errors = []
if (error == ErrorTypes.NO_ERROR):
if ("portion" in extra["dfs"][0]):
df_name = "df_" + extra["dfs"][0]["source_id"] + "[" + str(
extra["dfs"][0]["portion"]) + "]"
else:
df_name = "df_" + extra["dfs"][0]["source_id"]
my_args = {
"node_id": node["id"],
"input_dfs": [df_name],
"shared_function_set": shared_function_set,
"additional_local_code": additional_local_code,
"errors": errors
}
gen_code = []
gen_code.extend(["df_" + node["id"] + "=" + df_name, os.linesep])
between_operation = node["parameters"]["rolling_stats_info"]["value"][
"between_operation"]["value"]
first_argument_input_cols = CodeGenerationUtils.handle_parameter(
node["parameters"]["rolling_stats_info"]["value"]["first_argument"]
["value"]["input_cols"], my_args)
first_argument_operation = node["parameters"]["rolling_stats_info"][
"value"]["first_argument"]["value"]["operation"]["value"]
gen_code.extend(
["first_cols = " + first_argument_input_cols, os.linesep])
output_cols = CodeGenerationUtils.handle_parameter(
node["parameters"]["rolling_stats_info"]["value"]["output_cols"],
my_args)
gen_code.extend(["output_cols = " + output_cols, os.linesep])
partitioning_column = node["parameters"]["rolling_stats_info"][
"value"]["partitioning_column"]["value"]
ordering_column = node["parameters"]["rolling_stats_info"]["value"][
"ordering_column"]["value"]
ordering_direction = node["parameters"]["rolling_stats_info"]["value"][
"ordering_direction"]["value"]
lags = node["parameters"]["rolling_stats_info"]["value"]["lags"]
lags_str = CodeGenerationUtils.handle_parameter(lags, my_args)
window_str = "over (partition by " + partitioning_column + " order by " + ordering_column + " " + ordering_direction + " rows " + "'+ str(lag) +'" + " preceding) "
# if window_size == -1:
# window_str = "over (partition by " + partition_column + " order by " + ordering_column + " " + ordering_direction + " rows unbounded preceding) "
# else:
# window_str = "over (partition by " + partition_column + " order by " + ordering_column + " " + ordering_direction + " rows " + str(window_size) + " preceding) "
if between_operation != 'Identity':
second_argument_input_cols = CodeGenerationUtils.handle_parameter(
node["parameters"]["rolling_stats_info"]["value"]
["second_argument"]["value"]["input_cols"], my_args)
second_argument_operation = node["parameters"][
"rolling_stats_info"]["value"]["second_argument"]["value"][
"operation"]["value"]
gen_code.extend(
["second_cols = " + second_argument_input_cols, os.linesep])
loop_str = "for col_1,col_2,out_col in zip(first_cols, second_cols, output_cols):"
if first_argument_operation == 'Identity':
if second_argument_operation == 'Identity':
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', col_1 + ' " + between_operation + " '+ col_2 + ' as out_col' + str(lag))"
else:
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', col_1 + ' " + between_operation + " ' + '" + second_argument_operation + "(' + col_2 + ') " + window_str + "as out_col' + str(lag))"
else:
if second_argument_operation == 'Identity':
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', '" + first_argument_operation + "(' + col_1 + ') " + window_str + between_operation + " ' + col_2 + ' as out_col' + str(lag))"
else:
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', '" + first_argument_operation + "(' + col_1 + ') " + window_str + between_operation + " " + second_argument_operation + "(' + col_2 + ') " + window_str + "as out_col' + str(lag))"
else:
loop_str = "for col_1,out_col in zip(first_cols, output_cols):"
if first_argument_operation == 'Identity':
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', col_1 + ' as out_col' + str(lag))"
else:
select_str = "df_" + node["id"] + " = df_" + node[
"id"] + ".selectExpr('*', '" + first_argument_operation + "(' + col_1 + ') " + window_str + "as out_col' + str(lag))"
gen_code.extend(["lags = " + lags_str, os.linesep])
gen_code.extend(["for lag in lags:", os.linesep])
gen_code.extend(["\t", loop_str, os.linesep])
gen_code.extend(["\t\t" + select_str, os.linesep])
final_code = CodeGenerationUtils.merge_with_additional_code(
gen_code, additional_local_code)
return final_code, shared_function_set, error
