def get_python_ast(output_name, input_name, column_names) -> ast.AST:
col_name_str = ""
for col_name in column_names:
col_name_str += "\"%s\"," % col_name
python_string = "%s = %s[[%s]]" % \
(strip_linenos_from_var(output_name), strip_linenos_from_var(input_name), col_name_str)
python_ast = ast.parse(python_string, "exec")
return python_ast.body[0]
def get_python_ast(output_name, input_names, keyword_args) -> ast.AST:
input_string = ""
for input_name in input_names:
input_string += "%s," % strip_linenos_from_var(input_name)
python_string = "%s = pd.concat([%s])" % (strip_linenos_from_var(output_name), input_string)
python_ast = ast.parse(python_string, "exec")
python_ast.body[0].value.keywords = keyword_args
return python_ast.body[0]
def get_python_ast(self, input_names, output_name) -> ast.AST:
input_names_string = ""
for name in input_names:
input_names_string += "%s," % strip_linenos_from_var(name)
python_string = "%s = np.hstack((%s))" % (
strip_linenos_from_var(output_name), input_names_string)
python_ast = ast.parse(python_string, "exec")
return python_ast.body[0]
def get_python_ast(self, more_important_names, less_important_names,
small_model_output_name, output_name) -> ast.AST:
more_important_vecs = [
strip_linenos_from_var(name) for name in more_important_names
]
less_important_vecs = [
strip_linenos_from_var(name) for name in less_important_names
]
more_important_str, less_important_str = "", ""
for vec in more_important_vecs:
more_important_str += "%s," % vec
for vec in less_important_vecs:
less_important_str += "%s," % vec
python_string = "%s = cascade_dense_stacker([%s], [%s], %s)" % (
strip_linenos_from_var(output_name),
more_important_str, less_important_str,
strip_linenos_from_var(small_model_output_name))
python_ast = ast.parse(python_string, "exec")
return python_ast.body[0]
def __init__(self, x_name: str, y_name: str, x_node: WillumpGraphNode,
y_node: WillumpGraphNode, output_name: str,
train_x_y: tuple) -> None:
self.x_name = x_name
self.y_name = y_name
self.x_node = x_node
self.y_node = y_node
self._output_name = output_name
self._train_x_y = train_x_y
x, _ = train_x_y
self.input_width = x.shape[1]
train_statement = "%s = willump_train_function(%s, %s)" % (
strip_linenos_from_var(output_name),
strip_linenos_from_var(x_name), strip_linenos_from_var(y_name))
train_ast: ast.Module = ast.parse(train_statement, "exec").body[0]
super(WillumpTrainingNode, self).__init__(python_ast=train_ast,
input_names=[x_name, y_name],
output_names=[output_name],
in_nodes=[x_node, y_node],
output_types=[])
def get_python_ast(self, small_model_output_name) -> ast.AST:
python_string = \
"""if {0}[0] != 2:\n""" \
"""\treturn {0}[0]""".format(strip_linenos_from_var(small_model_output_name))
python_ast = ast.parse(python_string, "exec")
return python_ast.body[0]
def get_python_ast(self, input_name, output_name, reshape_args) -> ast.AST:
python_string = "%s = %s.reshape(1, -1)" % (strip_linenos_from_var(output_name),
strip_linenos_from_var(input_name))
python_ast = ast.parse(python_string, "exec")
python_ast.body[0].value.args = reshape_args
return python_ast.body[0]