How to use class_method_meta method in pandera

Best Python code snippet using pandera_python

generate_graph.py

Source:generate_graph.py

1import argparse2import json3from multiprocessing import Pool4from networkx.drawing import nx_agraph5import networkx as nx6from pandas.io import parsers7import pygraphviz8import torch9import numpy as np10from torch_geometric.data import Data11import os.path as osp12import pandas as pd13import collections14import tqdm15from utils.TokenizerW2V import TokenIns16import os17import pandas as pd18Declariation=1319VariableDeclaration=320word2vec_file="tokens/jars/emb_100.txt"21tokenizer_file="tokens/jars/fun.model"22def nx_to_graph_data_obj_simple(G):23    """24    Converts nx graph to pytorch geometric Data object. Assume node indices25    are numbered from 0 to num_nodes - 1. NB: Uses simplified atom and bond26    features, and represent as indices. NB: possible issues with27    recapitulating relative stereochemistry since the edges in the nx28    object are unordered.29    :param G: nx graph obj30    :return: pytorch geometric Data object31    """32    # nodes, node feat, node type33    atom_features_list = []34    atom_node_type_list = []35    variable_declar_mask_list = []36    ins_length_list = []37    for _, node in G.nodes(data=True):38        atom_feature = node['feat']39        atom_features_list.append(atom_feature)40        atom_node_type_list.append( node['type'] )41        ins_length_list.append( node["ins_length"] )42        if node['type'] == Declariation:43           variable_declar_mask_list.append( True )44        else:45           variable_declar_mask_list.append( False )  46    x = torch.tensor(np.array(atom_features_list), dtype=torch.long )47    x_type = torch.tensor(np.array(atom_node_type_list), dtype=torch.long )48    ins_length = torch.tensor( np.array( ins_length_list), dtype=torch.long )49    variable_declar_mask = torch.tensor(np.array(variable_declar_mask_list ) , dtype=bool)50    # edges, edge type51    dv_mask_list = []52    if len(G.edges()) > 0:  53        edges_list = []54        edge_label_list = []55        edge_node_type_list = []56        for i, j, edge in G.edges(data=True):57            edge_type =  edge['label'] 58            edges_list.append((i, j))59            edge_node_type_list.append(edge['type'])60            edge_label_list.append(edge_type)61            # edges_list.append((j, i))62            # edge_type_list.append(edge_type)63            if edge_type == VariableDeclaration:64                dv_mask_list.append(True)65            else:66                dv_mask_list.append(False)67                        68        # data.edge_index: Graph connectivity in COO format with shape [2, num_edges]69        edge_index = torch.tensor(np.array(edges_list).T, dtype=torch.long )70        dv_mask = torch.tensor(np.array(dv_mask_list), dtype=bool)71        # data.edge_attr: Edge feature matrix with shape [num_edges, num_edge_features]72        # edge_type_list_np = np.array(edge_type_list)73        # print(np.array(edge_type_list))74        edge_attr = torch.tensor(np.array(edge_label_list), dtype=torch.long)75        edge_node_attr =  torch.tensor(np.array(edge_node_type_list), dtype=torch.long)76        #print(edge_attr.size())77    else:   # mol has no bonds78        edge_index = torch.empty((2, 0), dtype=torch.long)79        edge_attr = torch.empty((0), dtype=torch.long)80        edge_node_attr = torch.empty((2, 0), dtype=torch.long)81        dv_mask =  torch.empty((0), dtype=torch.long)82    assert edge_index.shape[-1] == len(edge_attr), f"{edge_index}, {edge_attr.shape}, {len(G.nodes()) }"83    data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr)84    data.node_type = x_type85    data.variable_declar_mask = variable_declar_mask86    data.dv_mask = dv_mask87    data.ins_length = ins_length88    return data89 90def preprocess(class_method_id_json, graph_json , rawins_json91    , nodetype, edgetype, tokenizer_word2vec, dataname, outputdir, mid_list):92    class_method_meta = json.load( open( class_method_id_json ) )93    inputgraph_meta = json.load( open( graph_json ) )94    rawins_meta = json.load( open( rawins_json ) )95    graph_id = [ ]96    graph_labels = []97    data_list = []98    class_file = collections.defaultdict(int)99   100    print(class_method_id_json)101    for class_name in tqdm.tqdm( class_method_meta):102        class_file[class_name.split("$")[0]+".java"]+=1103        for method_id in class_method_meta[ class_name ]:104                if method_id not in mid_list:105                    continue106                metthod_meta = class_method_meta[ class_name ][ method_id ]107                if metthod_meta["name"] == "<init>":108                    continue109            110                graph_string = inputgraph_meta[ method_id ]111                instructions = {}112                if method_id not in rawins_meta:113                    print(f"{class_method_id_json}, {method_id} ")114                instructions.update( rawins_meta[ method_id ]["Local"] )115                instructions.update( rawins_meta[ method_id ]["Unit"] )116                g_meta = nx_agraph.from_agraph(pygraphviz.AGraph(graph_string, directed=True))117                if len(g_meta.nodes) == 0:118                    continue119                simple_graph = nx.DiGraph()120                121                id_rename_mapping = {}122                for (nid, info) in list( g_meta.nodes(data=True) ):123                    ntype = int( nodetype[ info["type"] ] )124                    ins = instructions[nid]125                    subwordsOfins = tokenizer_word2vec.get_tokens_id(ins.strip())126                    feat = subwordsOfins + [ 0 ] * (100 - len(subwordsOfins) ) if len(subwordsOfins) < 100 else subwordsOfins[:100]127                    simple_graph.add_node(nid, type= ntype, feat=feat,ins_length = min(100, len(subwordsOfins) ))128                for e1, e2, a in g_meta.edges( data=True ):129                    if len( g_meta[e1][e2] ) > 1:130                        #print( g_meta[e1][e2] )131                        paralle_edges=[ edgetype[ g_meta[e1][e2][i]["label"]] for i in g_meta[e1][e2] ]132                        paralle_edges = sorted(paralle_edges)133                        if paralle_edges == [0, 1]:134                            etype =  int( edgetype[ "DataDependence|ControlDependence" ] ) 135                        if paralle_edges == [0, 2]:136                            etype = int( edgetype[ "Controlfow|DataDependence" ] )137                        if paralle_edges == [1, 2]:138                            etype = int( edgetype[ "Controlfow|ControlDependence" ] ) 139                        if paralle_edges == [0, 1, 2]:140                            etype= int( edgetype[ "Controlfow|ControlDependence|ControlDependence" ] )141                    else:142                        etype = int( edgetype[ a["label"] ] ) 143                    simple_graph.add_edge( e1, e2, label=etype, type=[int(simple_graph.nodes[e1]["type"]), int(simple_graph.nodes[e2]["type"])] )144                        145                # rename id from 0 to number of nodes - 1146                counter = 0147                for i in sorted(simple_graph):148                    id_rename_mapping[i] = counter149                    counter += 1150                simple_graph = nx.relabel_nodes( simple_graph, id_rename_mapping, copy=False )151                data_geometric = nx_to_graph_data_obj_simple( simple_graph )152                data_geometric.y = int(mid_list[method_id]["IsFlaky"])153                data_geometric.id = int(mid_list[method_id]["Counter"])154               # print(data_geometric.y )155                data_geometric.id = int(method_id) 156                data_list.append( data_geometric  )157                graph_id.append( int(method_id) )158                del g_meta159                del simple_graph160    161   # torch.save([data_list,graph_labels, graph_id ], osp.join(outputdir, f"{dataname}.pt"))162   # json.dump(class_file, open( osp.join(outputdir, "class_file.json") , "w"))163    return [data_list,graph_labels, graph_id ]164def task(outputdir, dataname):165    df = pd.read_csv("graph_info.csv", index_col=0)166    id_list = {}167    counter = 0 168    data_list,graph_labels, graph_id = [], [], []169    for inputdir, df_group in df.groupby("project"):170        print(inputdir)171        mid_list = {}172        for index, row in df_group.iterrows():173            methodid = row["Method"]174            testname = row["Test"]175            isflaky = row["IsFlaky"]176            project = row["project"]177            mid_list[str(methodid)] = {"IsFlaky":isflaky, "Test":testname, "Counter":counter, "project":project }178            id_list[testname+"_"+str(methodid)] =  {"Method":methodid, "IsFlaky":isflaky, "Test":testname, "Counter":counter, "project":project }179            counter += 1180        tokenizer_word2vec = TokenIns(181            word2vec_file=word2vec_file,182            tokenizer_file=tokenizer_file183        )184        class_method_id_json = os.path.join( inputdir, "class_method_id_mapping.json" )185        graph_json = os.path.join(inputdir, f"{dataname}.json")186        rawins_json = os.path.join(inputdir, "RawIns.json")187        nodetype = json.load( open("tokens/instruction_type.json") )188        edgetype = json.load( open("tokens/edge_type.json") )189        data = preprocess(class_method_id_json, graph_json, rawins_json, nodetype, edgetype, tokenizer_word2vec, dataname, outputdir, mid_list )190        data_list.extend(data[0])191        graph_labels.extend(data[1])192        graph_id.extend(data[2])193    json.dump( id_list, open("id_mapping.json", "w"), indent=6 )194    torch.save([data_list,graph_labels, graph_id ], osp.join(outputdir, f"{dataname}.pt"))195    return len(data_list)196from argparse import ArgumentParser197if __name__ == "__main__":198    parser = argparse.ArgumentParser()199    parser.add_argument("-o", "--output", dest="output", default="")200    args = parser.parse_args()201    202   203    outputdir = args.output204    os.makedirs( outputdir, exist_ok=True)205    for dataname in [ "DU_CFG", "DV_CFG", "DV_PDG", "DVDU_CFG", "ORG_CFG", "ORG_PDG" ]:206        task( outputdir, dataname  )207                ...

test_inspection_utils.py

Source:test_inspection_utils.py

...16    def regular_method_meta(cls):17        return cls1819    @classmethod20    def class_method_meta(mcs):21        return mcs2223    @staticmethod24    def static_method_meta():25        return 1262728class SomeClass(metaclass=SomeMeta):29    def regular_method(self):30        return self3132    @classmethod33    def class_method(cls):34        return cls
...

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