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]