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How to use test_role_file method in molecule
Best Python code snippet using molecule_python
decompose.py
Source:decompose.py 
1from __future__ import unicode_literals, print_function, division2from io import open3import unicodedata4import string5import re6import random7from random import shuffle8import torch9import torch.nn as nn10from torch.autograd import Variable11from torch import optim12import torch.nn.functional as F13import sys14import os15import time16import math17import pickle18import argparse19from sklearn import metrics20from tasks import *21from training import *22from models import *23from evaluation import *24from role_assignment_functions import *25from rolelearner.role_learning_tensor_product_encoder import RoleLearningTensorProductEncoder26import numpy as np27# Code for performing a tensor product decomposition on an28# existing set of vectors29use_cuda = torch.cuda.is_available()30if use_cuda:31 device = torch.device('cuda')32else:33 device = torch.device('cpu')34parser = argparse.ArgumentParser()35parser.add_argument("--data_prefix", help="prefix for the vectors", type=str, default=None)36parser.add_argument("--role_prefix", help="prefix for a file of roles (if used)", type=str,37 default=None)38parser.add_argument("--role_scheme", help="pre-coded role scheme to use", type=str, default=None)39parser.add_argument("--test_decoder", help="whether to test the decoder (in addition to MSE",40 type=str, default="False")41parser.add_argument("--decoder", help="decoder type", type=str, default="ltr")42parser.add_argument("--decoder_prefix", help="prefix for the decoder to test", type=str,43 default=None)44parser.add_argument("--decoder_embedding_size", help="embedding size for decoder", type=int,45 default=20)46parser.add_argument("--decoder_task", help="task performed by the decoder", type=str,47 default="auto")48parser.add_argument("--filler_dim", help="embedding dimension for fillers", type=int, default=10)49parser.add_argument("--role_dim", help="embedding dimension for roles", type=int, default=6)50parser.add_argument("--vocab_size", help="vocab size for the training language", type=int,51 default=10)52parser.add_argument("--hidden_size", help="size of the encodings", type=int, default=60)53parser.add_argument("--save_vectors",54 help="whether to save vectors generated by the fitted TPR model", type=str,55 default="False")56parser.add_argument("--save_role_dicts",57 help="whether to save role_to_index and index_to_role or not", type=str,58 default="False")59parser.add_argument("--embedding_file", help="file containing pretrained embeddings", type=str,60 default=None)61parser.add_argument("--unseen_words",62 help="if using pretrained embeddings: whether to use all zeroes for unseen "63 "words' embeddings, or to give them random vectors",64 type=str, default="zero")65parser.add_argument("--extra_test_set", help="additional file to print predictions for", type=str,66 default=None)67parser.add_argument("--train", help="whether or not to train the model", type=str, default="True")68parser.add_argument("--neighbor_analysis", help="whether to use a neighbor analysis", type=str,69 default="True")70parser.add_argument("--digits", help="whether this is one of the digit task", type=str,71 default="True")72parser.add_argument("--final_linear", help="whether to have a final linear layer", type=str,73 default="True")74parser.add_argument("--embed_squeeze", help="original dimension to be squeezed to filler_dim",75 type=int, default=None)76parser.add_argument("--role_learning", help="A flag for whether to enable role learning or use "77 "the provided roles.", action="store_true")78parser.add_argument("--bidirectional", help="A flag for whether the role learning module is "79 "bidirectional.", action="store_true")80parser.add_argument(81 "--role_assignment_shrink_filler_dim",82 help="If specified, the filler embedding is shrunk to this size before being input to the "83 "role assignment LSTM.",84 default=None,85 type=int)86parser.add_argument(87 "--use_one_hot_temperature",88 help="A flag for whether role learning one hot regularization should have an increasing "89 "temperature.",90 action="store_true")91parser.add_argument(92 "--role_assigner_num_layers",93 help="The number of layers for the role assignment network.",94 default=1,95 type=int96)97parser.add_argument(98 "--output_dir",99 help="An optional output folder where files can be saved to.",100 type=str,101 default=None102)103parser.add_argument(104 "--patience",105 help="The number of epochs to train if validation loss isn't improving",106 type=int,107 default=10108)109parser.add_argument(110 "--pretrained_filler_embedding",111 help="A weight file containing a pretrained filler embedding",112 type=str,113 default=None114)115parser.add_argument(116 "--softmax_roles",117 help="Whether the role predictions should be run through a softmax",118 action="store_true"119)120parser.add_argument(121 "--batch_size",122 help="The batch size.",123 default=32,124 type=int125)126parser.add_argument(127 "--one_hot_regularization_weight",128 help="The weight applied to the one hot regularization term",129 type=float,130 default=1.0131)132parser.add_argument(133 "--l2_norm_regularization_weight",134 help="The weight applied to the l2 norm regularization term",135 type=float,136 default=1.0137)138parser.add_argument(139 "--unique_role_regularization_weight",140 help="The weight applied to the unique role regularization term",141 type=float,142 default=1.0143)144parser.add_argument(145 "--num_roles",146 help="The number of roles to give a role learning network. This value is only used when "147 "--role_learning is enabled.",148 type=int,149 default=None150)151parser.add_argument(152 "--data_path",153 help="The location of the data files.",154 type=str,155 default="data"156)157parser.add_argument(158 "--burn_in",159 help="The number of epochs to train without regularization",160 type=int,161 default=0162)163parser.add_argument(164 "--shuffle",165 help="Whether to shuffle the input fillers and corresponding embedding. This is to generate a"166 " value by which to normalize the MSE to compare different role schemes across "167 "different tasks.",168 action="store_true"169)170parser.add_argument(171 "--scan_checkpoint",172 help="The location of a SCAN checkpoint. Settings this argument enables the SCAN task.",173 type=str,174 default=None,175 required=False176)177args = parser.parse_args()178output_dir = None179if args.output_dir:180 output_dir = os.path.join('output/', args.output_dir)181 if not os.path.isdir(output_dir):182 os.makedirs(output_dir)183 with open(os.path.join(output_dir, 'arguments.txt'), 'w') as arguments_file:184 for argument, value in sorted(vars(args).items()):185 arguments_file.write('{}: {}\n'.format(argument, value))186# Create the logfile187if output_dir:188 results_page = open(os.path.join(output_dir, 'log.txt'), 'w')189else:190 if args.final_linear != "True":191 results_page = open(192 "logs/" + args.data_prefix.split("/")[-1] + str(args.role_prefix).split("/")[-1] + str(193 args.role_scheme) + ".filler" + str(args.filler_dim) + ".role" + str(194 args.role_dim) + ".tpr_decomp.nf", "w")195 else:196 results_page = open(197 "logs/" + args.data_prefix.split("/")[-1] + str(args.role_prefix).split("/")[-1] + str(198 args.role_scheme) + ".filler" + str(args.filler_dim) + ".role" + str(199 args.role_dim) + "." + str(args.embed_squeeze) + ".tpr_decomp", "w")200# Load the decoder for computing swapping accuracy201if args.test_decoder == "True" and not args.scan_checkpoint:202 if args.decoder == "ltr":203 decoder = DecoderRNN(args.vocab_size, args.decoder_embedding_size, args.hidden_size)204 elif args.decoder == "bi":205 decoder = DecoderBiRNN(args.vocab_size, args.decoder_embedding_size, args.hidden_size)206 elif args.decoder == "tree":207 decoder = DecoderTreeRNN(args.vocab_size, args.decoder_embedding_size, args.hidden_size)208 else:209 print("Invalid decoder type")210 input_to_output = lambda seq: transform(seq, args.decoder_task)211 if use_cuda:212 decoder.load_state_dict(torch.load("models/decoder_" + args.decoder_prefix + ".weights"))213 else:214 decoder.load_state_dict(215 torch.load("models/decoder_" + args.decoder_prefix + ".weights", map_location='cpu')216 )217 if use_cuda:218 decoder = decoder.cuda()219elif args.test_decoder == "True" and args.scan_checkpoint:220 decoder = SCANDecoderRNN(221 hidden_size=args.hidden_size,222 output_size=args.vocab_size,223 mytype='GRU',224 n_layers=1,225 dropout_p=0226 )227 decoder.eval()228 print('Loading checkpoint: ' + args.scan_checkpoint)229 if use_cuda:230 checkpoint = torch.load(args.scan_checkpoint)231 else:232 checkpoint = torch.load(args.scan_checkpoint, map_location=lambda storage, loc: storage)233 decoder.load_state_dict(checkpoint['decoder_state_dict'])234 if use_cuda:235 decoder = decoder.cuda()236 pairs_test = checkpoint['pairs_test']237 input_to_output_dict = {}238 for input_, output in pairs_test:239 input_to_output_dict[input_] = output240 input_to_output = lambda key: input_to_output_dict[key]241 input_lang = checkpoint['input_lang']242 output_lang = checkpoint['output_lang']243# Prepare the train, dev, and test data244unindexed_train = []245unindexed_dev = []246unindexed_test = []247unindexed_extra = []248filler_to_index = {}249index_to_filler = {}250role_to_index = {}251index_to_role = {}252filler_counter = 0253role_counter = 0254max_length = 0255train_file = open(os.path.join(args.data_path, args.data_prefix + ".data_from_train"), "r")256for line in train_file:257 sequence, vector = line.strip().split("\t")258 unindexed_train.append(([value for value in sequence.split()], Variable(259 torch.FloatTensor(np.array([float(value) for value in vector.split()])))))260 if len(sequence.split()) > max_length:261 max_length = len(sequence.split())262 for filler in sequence.split():263 if filler not in filler_to_index:264 filler_to_index[filler] = filler_counter265 index_to_filler[filler_counter] = filler266 filler_counter += 1267dev_file = open(os.path.join(args.data_path, args.data_prefix + ".data_from_dev"), "r")268for line in dev_file:269 sequence, vector = line.strip().split("\t")270 unindexed_dev.append(([value for value in sequence.split()], Variable(271 torch.FloatTensor(np.array([float(value) for value in vector.split()])))))272 if len(sequence.split()) > max_length:273 max_length = len(sequence.split())274 for filler in sequence.split():275 if filler not in filler_to_index:276 filler_to_index[filler] = filler_counter277 index_to_filler[filler_counter] = filler278 filler_counter += 1279if args.shuffle:280 print("Shuffling the input sequences and corresponding embeddings")281 sequences = []282 embeddings = []283 for sequence, embedding in unindexed_train:284 sequences.append(sequence)285 embeddings.append(embedding)286 shuffle(sequences)287 unindexed_train = []288 for i in range(len(sequences)):289 unindexed_train.append((sequences[i], embeddings[i]))290 sequences = []291 embeddings = []292 for sequence, embedding in unindexed_dev:293 sequences.append(sequence)294 embeddings.append(embedding)295 shuffle(sequences)296 unindexed_dev = []297 for i in range(len(sequences)):298 unindexed_dev.append((sequences[i], embeddings[i]))299test_file = open(os.path.join(args.data_path, args.data_prefix + ".data_from_test"), "r")300for line in test_file:301 sequence, vector = line.strip().split("\t")302 unindexed_test.append(([value for value in sequence.split()], Variable(303 torch.FloatTensor(np.array([float(value) for value in vector.split()])))))304 if len(sequence.split()) > max_length:305 max_length = len(sequence.split())306 for filler in sequence.split():307 if filler not in filler_to_index:308 filler_to_index[filler] = filler_counter309 index_to_filler[filler_counter] = filler310 filler_counter += 1311if args.extra_test_set is not None:312 extra_file = open(os.path.join(args.data_path, args.extra_test_set), "r")313 for line in extra_file:314 sequence, vector = line.strip().split("\t")315 unindexed_extra.append(([value for value in sequence.split()], Variable(316 torch.FloatTensor(np.array([float(value) for value in vector.split()]))).cuda()))317 if len(sequence.split()) > max_length:318 max_length = len(sequence.split())319 for filler in sequence.split():320 if filler not in filler_to_index:321 filler_to_index[filler] = filler_counter322 index_to_filler[filler_counter] = filler323 filler_counter += 1324if args.digits == "True":325 for i in range(10):326 filler_to_index[str(i)] = i327 index_to_filler[i] = str(i)328indexed_train = [([filler_to_index[filler] for filler in elt[0]], elt[1]) for elt in329 unindexed_train]330indexed_dev = [([filler_to_index[filler] for filler in elt[0]], elt[1]) for elt in unindexed_dev]331indexed_test = [([filler_to_index[filler] for filler in elt[0]], elt[1]) for elt in unindexed_test]332indexed_extra = [([filler_to_index[filler] for filler in elt[0]], elt[1]) for elt in333 unindexed_extra]334unindexed_train_roles = []335unindexed_dev_roles = []336unindexed_test_roles = []337unindexed_extra_roles = []338n_r = -1339# If there is a file of roles for the fillers, load those roles340if args.role_prefix is not None:341 train_role_file = open(342 os.path.join(args.data_path, args.role_prefix + ".data_from_train.roles"), "r")343 for line in train_role_file:344 unindexed_train_roles.append(line.strip().split())345 for role in line.strip().split():346 if role not in role_to_index:347 role_to_index[role] = role_counter348 index_to_role[role_counter] = role349 role_counter += 1350 dev_role_file = open(351 os.path.join(args.data_path, args.role_prefix + ".data_from_dev.roles"), "r")352 for line in dev_role_file:353 unindexed_dev_roles.append(line.strip().split())354 for role in line.strip().split():355 if role not in role_to_index:356 role_to_index[role] = role_counter357 index_to_role[role_counter] = role358 role_counter += 1359 test_role_file = open(360 os.path.join(args.data_path, args.role_prefix + ".data_from_test.roles"), "r")361 for line in test_role_file:362 unindexed_test_roles.append(line.strip().split())363 for role in line.strip().split():364 if role not in role_to_index:365 role_to_index[role] = role_counter366 index_to_role[role_counter] = role367 role_counter += 1368 if args.extra_test_set is not None:369 extra_role_file = open(os.path.join(args.data_path, args.extra_test_set + ".roles"), "r")370 for line in extra_role_file:371 unindexed_extra_roles.append(line.strip().split())372 for role in line.strip().split():373 if role not in role_to_index:374 role_to_index[role] = role_counter375 index_to_role[role_counter] = role376 role_counter += 1377# Or, if a predefined role scheme is being used, prepare it378elif args.role_scheme is not None:379 if args.role_scheme == "bow":380 n_r, seq_to_roles = create_bow_roles(max_length, len(filler_to_index.keys()))381 elif args.role_scheme == "ltr":382 n_r, seq_to_roles = create_ltr_roles(max_length, len(filler_to_index.keys()))383 elif args.role_scheme == "rtl":384 n_r, seq_to_roles = create_rtl_roles(max_length, len(filler_to_index.keys()))385 elif args.role_scheme == "bi":386 n_r, seq_to_roles = create_bidirectional_roles(max_length, len(filler_to_index.keys()))387 elif args.role_scheme == "wickel":388 n_r, seq_to_roles = create_wickel_roles(max_length, len(filler_to_index.keys()))389 elif args.role_scheme == "tree":390 n_r, seq_to_roles = create_tree_roles(max_length, len(filler_to_index.keys()))391 elif args.role_scheme == "interleave":392 n_r, seq_to_roles = create_interleaving_tree_roles(max_length, len(filler_to_index.keys()))393 else:394 print("Invalid role scheme")395 for pair in indexed_train:396 these_roles = seq_to_roles(pair[0])397 unindexed_train_roles.append(these_roles)398 for role in these_roles:399 if role not in role_to_index:400 role_to_index[role] = role401 index_to_role[role] = role402 role_counter += 1403 for pair in indexed_dev:404 these_roles = seq_to_roles(pair[0])405 unindexed_dev_roles.append(these_roles)406 for role in these_roles:407 if role not in role_to_index:408 role_to_index[role] = role409 index_to_role[role] = role410 role_counter += 1411 for pair in indexed_test:412 these_roles = seq_to_roles(pair[0])413 unindexed_test_roles.append(these_roles)414 for role in these_roles:415 if role not in role_to_index:416 role_to_index[role] = role417 index_to_role[role] = role418 role_counter += 1419 for pair in indexed_extra:420 these_roles = seq_to_roles(pair[0])421 unindexed_extra_roles.append(these_roles)422 for role in these_roles:423 if role not in role_to_index:424 role_to_index[role] = role425 index_to_role[role] = role426 role_counter += 1427else:428 print("No role scheme specified")429indexed_train_roles = [[role_to_index[role] for role in roles] for roles in unindexed_train_roles]430indexed_dev_roles = [[role_to_index[role] for role in roles] for roles in unindexed_dev_roles]431indexed_test_roles = [[role_to_index[role] for role in roles] for roles in unindexed_test_roles]432indexed_extra_roles = [[role_to_index[role] for role in roles] for roles in unindexed_extra_roles]433all_train_data = []434all_dev_data = []435all_test_data = []436all_extra_data = []437if not args.role_learning:438 # Make sure the number of fillers and the number of roles always matches439 for index, element in enumerate(indexed_train):440 if len(element[0]) != len(indexed_train_roles[index]):441 print(index, "ERROR!!!", element[0], indexed_train_roles[index])442 else:443 all_train_data.append((element[0], indexed_train_roles[index], element[1]))444 for index, element in enumerate(indexed_dev):445 if len(element[0]) != len(indexed_dev_roles[index]):446 print(index, "ERROR!!!", element[0], indexed_dev_roles[index])447 else:448 all_dev_data.append((element[0], indexed_dev_roles[index], element[1]))449 for index, element in enumerate(indexed_test):450 if len(element[0]) != len(indexed_test_roles[index]):451 print(index, "ERROR!!!", element[0], indexed_test_roles[index])452 else:453 all_test_data.append((element[0], indexed_test_roles[index], element[1]))454 for index, element in enumerate(indexed_extra):455 if len(element[0]) != len(indexed_extra_roles[index]):456 print(index, "ERROR!!!", element[0], indexed_extra_roles[index])457 else:458 all_extra_data.append((element[0], indexed_extra_roles[index], element[1]))459else:460 # Add dummy roles to the training data, these will be ignored since we are role learning461 for index, element in enumerate(indexed_train):462 all_train_data.append((element[0], 0, element[1]))463 for index, element in enumerate(indexed_dev):464 all_dev_data.append((element[0], 0, element[1]))465 for index, element in enumerate(indexed_test):466 all_test_data.append((element[0], 0, element[1]))467 for index, element in enumerate(indexed_extra):468 all_extra_data.append((element[0], 0, element[1]))469weights_matrix = None470# Prepare the embeddings471# If a file of embeddings was provided, use those.472embedding_dict = None473if args.embedding_file is not None:474 embedding_dict = {}475 embed_file = open(args.embedding_file, "r")476 for line in embed_file:477 parts = line.strip().split()478 if len(parts) == args.filler_dim + 1:479 embedding_dict[parts[0]] = list(map(lambda x: float(x), parts[1:]))480 matrix_len = len(filler_to_index.keys())481 if args.embed_squeeze is not None:482 weights_matrix = np.zeros((matrix_len, args.embed_squeeze))483 else:484 weights_matrix = np.zeros((matrix_len, args.filler_dim))485 for i in range(matrix_len):486 word = index_to_filler[i]487 if word in embedding_dict:488 weights_matrix[i] = embedding_dict[word]489 else:490 if args.unseen_words == "random":491 weights_matrix[i] = np.random.normal(scale=0.6, size=(args.filler_dim,))492 elif args.unseen_words == "zero":493 pass # It was initialized as zero, so don't need to do anything494 else:495 print("Invalid choice for embeddings of unseen words")496# Initialize the TPDN497if n_r != -1:498 role_counter = n_r499if args.final_linear == "True":500 final_layer_width = args.hidden_size501else:502 final_layer_width = None503if args.role_learning:504 if args.num_roles:505 role_counter = args.num_roles506 print("Using RoleLearningTensorProductEncoder with {} roles".format(role_counter))507 tpr_encoder = RoleLearningTensorProductEncoder(508 n_roles=role_counter,509 n_fillers=filler_counter,510 final_layer_width=final_layer_width,511 filler_dim=args.filler_dim,512 role_dim=args.role_dim,513 pretrained_filler_embeddings=args.pretrained_filler_embedding,514 embedder_squeeze=args.embed_squeeze,515 role_assignment_shrink_filler_dim=args.role_assignment_shrink_filler_dim,516 bidirectional=args.bidirectional,517 num_layers=args.role_assigner_num_layers,518 softmax_roles=args.softmax_roles,519 pretrained_embeddings=weights_matrix,520 one_hot_regularization_weight=args.one_hot_regularization_weight,521 l2_norm_regularization_weight=args.l2_norm_regularization_weight,522 unique_role_regularization_weight=args.unique_role_regularization_weight,523 )524else:525 print("Using TensorProductEncoder")526 tpr_encoder = TensorProductEncoder(527 n_roles=role_counter,528 n_fillers=filler_counter,529 final_layer_width=final_layer_width,530 filler_dim=args.filler_dim,531 role_dim=args.role_dim,532 pretrained_embeddings=weights_matrix,533 embedder_squeeze=args.embed_squeeze,534 pretrained_filler_embeddings=args.pretrained_filler_embedding535 )536if use_cuda:537 tpr_encoder = tpr_encoder.cuda()538#args.data_prefix = args.data_prefix.split("/")[-1] + ".filler" + str(539# args.filler_dim) + ".role" + str(args.role_dim)540#if args.final_linear != "True":541# args.data_prefix += ".no_final"542# Train the TPDN543args.role_prefix = str(args.role_prefix).split("/")[-1]544if args.train == "True":545 if output_dir:546 weight_file = os.path.join(output_dir, 'model.tpr')547 else:548 weight_file = "models/" + args.data_prefix + str(549 args.role_prefix) + str(args.role_scheme) + ".tpr"550 end_loss = trainIters_tpr(551 all_train_data,552 all_dev_data,553 tpr_encoder,554 n_epochs=1000,555 learning_rate=0.001,556 weight_file=weight_file,557 batch_size=args.batch_size,558 use_one_hot_temperature=args.use_one_hot_temperature,559 patience=args.patience,560 burn_in=args.burn_in561 )562print("Finished training")563# Load the trained TPDN564tpr_encoder.load_state_dict(torch.load(weight_file, map_location=device))565# Prepare test data566all_test_data_orig = all_test_data567all_test_data = batchify_tpr(all_test_data, 1)568test_data_sets = [(Variable(torch.LongTensor([item[0] for item in batch])),569 Variable(torch.LongTensor([item[1] for item in batch])),570 torch.cat([item[2].unsqueeze(0).unsqueeze(0) for item in batch], 1)) for571 batch in all_test_data]572neighbor_counter = 0573neighbor_total_rank = 0574neighbor_correct = 0575# Evaluate on test set576test_symbolic_mse = 0577test_continuous_mse = 0578test_one_hot_loss = 0579test_l2_loss = 0580test_unique_role_loss = 0581for i in range(len(test_data_sets)):582 input_fillers = test_data_sets[i][0]583 input_roles = test_data_sets[i][1]584 target_variable = test_data_sets[i][2]585 if use_cuda:586 input_fillers = input_fillers.cuda()587 input_roles = input_roles.cuda()588 target_variable = target_variable.cuda()589 if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):590 tpr_encoder.eval()591 encoding, role_predictions = tpr_encoder(input_fillers, input_roles)592 test_symbolic_mse += torch.mean(torch.pow(encoding.data - target_variable.data, 2))593 tpr_encoder.train()594 encoding, role_predictions = tpr_encoder(input_fillers, input_roles)595 test_continuous_mse += torch.mean(torch.pow(encoding.data - target_variable.data, 2))596 batch_one_hot_loss, batch_l2_norm_loss, batch_unique_role_loss = \597 tpr_encoder.get_regularization_loss(role_predictions)598 test_one_hot_loss += batch_one_hot_loss599 test_l2_loss += batch_l2_norm_loss600 test_unique_role_loss += batch_unique_role_loss601 else:602 encoding = tpr_encoder(input_fillers, input_roles)603 test_symbolic_mse += torch.mean(torch.pow(encoding.data - target_variable.data, 2))604test_symbolic_mse = test_symbolic_mse / len(all_test_data)605test_continuous_mse = test_continuous_mse / len(all_test_data)606test_one_hot_loss = test_one_hot_loss / len(all_test_data)607test_l2_loss = test_l2_loss / len(all_test_data)608test_unique_role_loss = test_unique_role_loss / len(all_test_data)609total_symbolic_test_loss = \610 test_symbolic_mse + test_one_hot_loss + test_l2_loss + test_unique_role_loss611total_continuous_test_loss = \612 test_continuous_mse + test_one_hot_loss + test_l2_loss + test_unique_role_loss613if args.output_dir:614 results_page.write(output_dir + "\n")615if args.test_decoder == "True" and not args.scan_checkpoint:616 if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):617 tpr_encoder.eval()618 correct, total = score2(tpr_encoder, decoder, input_to_output, batchify(all_test_data, 1),619 index_to_filler)620 results_page.write(args.data_prefix + str(args.role_prefix) + str(args.role_scheme) +621 ".tpr" + " Discrete Swapping encoder performance: " + str(correct)622 + " " + str(total) + "\n")623 if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):624 tpr_encoder.train()625 correct, total = score2(tpr_encoder, decoder, input_to_output, batchify(all_test_data, 1),626 index_to_filler)627 results_page.write(args.data_prefix + str(args.role_prefix) + str(args.role_scheme) +628 ".tpr" + " Continuous Swapping encoder performance: " + str(correct)629 + " " + str(total) + "\n")630elif args.test_decoder == "True" and args.scan_checkpoint:631 if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):632 tpr_encoder.eval()633 correct, total = scoreSCAN(tpr_encoder, decoder, input_to_output, batchify(all_test_data, 1),634 index_to_filler, output_lang)635 results_page.write('Discrete swapping performance: {} {}\n'.format(correct, total))636 if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):637 tpr_encoder.train()638 correct, total = scoreSCAN(tpr_encoder, decoder, input_to_output, batchify(all_test_data, 1),639 index_to_filler, output_lang)640 results_page.write('Continuous swapping performance: {} {}\n'.format(correct, total))641results_page.write('Test symbolic MSE loss: {}\n'.format(test_symbolic_mse))642results_page.write('Test symbolic loss: {}\n'.format(total_symbolic_test_loss.item()))643if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):644 results_page.write('Test continuous MSE loss: {}\n'.format(test_continuous_mse))645 results_page.write('Test continuous loss: {}\n'.format(total_continuous_test_loss.item()))646results_page.write('Test one hot loss: {}\n'.format(test_one_hot_loss))647results_page.write('Test unique role loss: {}\n'.format(test_unique_role_loss))648results_page.write('Test l2 norm loss: {}\n'.format(test_l2_loss))649# Run cluster performance analysis650if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):651 tpr_encoder.train()652 role_assigner = tpr_encoder.role_assigner653 roles_true = []654 roles_predicted = []655 role_indices = torch.tensor([x for x in range(role_counter)], device=device)656 role_embeddings = role_assigner.role_embedding(role_indices)657 num_elements = 0658 num_elements_role_low = 0659 for test_example in all_test_data:660 sequence = test_example[0][0]661 roles_true.extend(test_example[0][1])662 role_emb, role_weights = role_assigner(torch.tensor([sequence], device=device))663 for i in range(len(role_weights)):664 role_prediction = np.argmax(role_weights[i][0].cpu().detach().numpy())665 if role_weights[i][0][role_prediction] < .98:666 num_elements_role_low += 1667 num_elements += 1668 roles_predicted.append(role_prediction)669 # Get the predicted role using cosine distance from the role embeddings670 #for i in range(len(role_emb)):671 # similarities = []672 # for j in range(role_counter):673 # similarities.append(F.cosine_similarity(role_emb[i][0], role_embeddings[j], dim=0))674 # roles_predicted.append(np.argmax(similarities))675 results_page.write(676 'number of roles used: {}\n'.format(len(np.unique(roles_predicted)))677 )678 results_page.write(679 'percentage low role prediction: {}\n'.format(100 * num_elements_role_low / num_elements)680 )681 results_page.write(682 'adjusted rand index: {}\n'.format(metrics.adjusted_rand_score(roles_true, roles_predicted))683 )684 results_page.write(685 'normalized mutual info: {}\n'.format(metrics.normalized_mutual_info_score(roles_true, roles_predicted))686 )687 results_page.write(688 'adjusted mutual info: {}\n'.format(metrics.adjusted_mutual_info_score(roles_true, roles_predicted))689 )690 results_page.write(691 'homogenity: {}\n'.format(metrics.homogeneity_score(roles_true, roles_predicted))692 )693 results_page.write(694 'completeness: {}\n'.format(metrics.completeness_score(roles_true, roles_predicted))695 )696 results_page.write(697 'v measure score: {}\n'.format(metrics.v_measure_score(roles_true, roles_predicted))698 )699 results_page.write(700 'fowlkes mallows score: {}\n'.format(metrics.fowlkes_mallows_score(roles_true, roles_predicted))701 )702 results_page.write('\n')703# Generate role predictions704if isinstance(tpr_encoder, RoleLearningTensorProductEncoder):705 print('Generating role predictions')706 tpr_encoder.train()707 role_assigner = tpr_encoder.role_assigner708 output_dir = os.path.join('output/', args.output_dir)709 basename = os.path.basename(output_dir)710 train_data_file = open(os.path.join(args.data_path, args.data_prefix + '.data_from_train'), 'r')711 train_role_file = open(712 os.path.join(output_dir, args.data_prefix + '.' + basename + '.data_from_train.roles'), 'w')713 dev_data_file = open(os.path.join(args.data_path, args.data_prefix + '.data_from_dev'), 'r')714 dev_role_file = open(715 os.path.join(output_dir, args.data_prefix + '.' + basename + '.data_from_dev.roles'), 'w')716 test_data_file = open(os.path.join(args.data_path, args.data_prefix + '.data_from_test'), 'r')717 test_role_file = open(718 os.path.join(output_dir, args.data_prefix + '.' + basename + '.data_from_test.roles'), 'w')719 for line in train_data_file:720 sequence, embedding = line.strip().split('\t')721 sequence = sequence.split()722 sequence = list(map(lambda filler: filler_to_index[filler], sequence))723 sequence = torch.LongTensor([sequence])724 if use_cuda:725 sequence = sequence.cuda()726 role_emb, role_weights = role_assigner(sequence)727 roles = []728 for i in range(len(role_weights)):729 roles.append(str(np.argmax(role_weights[i].cpu().detach().numpy())))730 train_role_file.write(' '.join(roles) + '\n')731 train_role_file.close()732 for line in dev_data_file:733 sequence, embedding = line.strip().split("\t")734 sequence = sequence.split()735 sequence = list(map(lambda filler: filler_to_index[filler], sequence))736 sequence = torch.LongTensor([sequence])737 if use_cuda:738 sequence = sequence.cuda()739 role_emb, role_weights = role_assigner(sequence)740 roles = []741 for i in range(len(role_weights)):742 roles.append(str(np.argmax(role_weights[i].cpu().detach().numpy())))743 dev_role_file.write(' '.join(roles) + '\n')744 dev_role_file.close()745 for line in test_data_file:746 sequence, embedding = line.strip().split("\t")747 sequence = sequence.split()748 sequence = list(map(lambda filler: filler_to_index[filler], sequence))749 sequence = torch.LongTensor([sequence])750 if use_cuda:751 sequence = sequence.cuda()752 role_emb, role_weights = role_assigner(sequence)753 roles = []754 for i in range(len(role_weights)):755 roles.append(str(np.argmax(role_weights[i].cpu().detach().numpy())))756 test_role_file.write(' '.join(roles) + '\n')757 test_role_file.close()758# Save the test set predictions, if desired759if args.save_vectors == "True":760 fo_pred = open(761 args.data_prefix + str(args.role_prefix) + str(args.role_scheme) + ".tpr.test_preds", "w")762 for i in range(len(test_data_sets)):763 sequence = all_test_data[i][0]764 pred = tpr_encoder(test_data_sets[i][0], test_data_sets[i][1]).data.cpu().numpy()[0][0]765 sequence = [str(x) for x in sequence]766 pred = [str(x) for x in pred]767 fo_pred.write(" ".join(sequence) + "\t" + " ".join(pred) + "\n")768# Save the role dictionaries, if desired769if args.save_role_dicts == "True":770 with open(args.data_prefix + str(args.role_prefix) + str(771 args.role_scheme) + '.role_to_index.pickle', 'wb') as handle:772 pickle.dump(role_to_index, handle, protocol=pickle.HIGHEST_PROTOCOL)773 with open(args.data_prefix + str(args.role_prefix) + str(774 args.role_scheme) + '.index_to_role.pickle', 'wb') as handle:...
test_ansible_galaxy.py
Source:test_ansible_galaxy.py
...154 _instance.options['roles-path'])155 assert not os.path.isdir(role_directory)156 _instance._setup()157 assert os.path.isdir(role_directory)158def test_role_file(role_file, _instance):159 assert role_file == _instance._role_file()160def test_has_requirements_file(_instance):...
test_roles.py
Source:test_roles.py
...160 )161 assert not os.path.isdir(role_directory)162 _instance._setup()163 assert os.path.isdir(role_directory)164def test_role_file(role_file, _instance):165 assert role_file == _instance.requirements_file166def test_has_requirements_file(_instance):...
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