162 lines
8.7 KiB
Python
162 lines
8.7 KiB
Python
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import os
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# import and build cython
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import pyximport
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pyximport.install()
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from main.data.MIDFreebase15kReader import MIDFreebase15kReader
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from main.data.TypedRelationInstances import TypedRelationInstances
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from main.data.Vocabs import Vocabs
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from main.graphs.AdjacencyGraph import AdjacencyGraph
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from main.features.PathExtractor import PathExtractor
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from main.data.Split import Split
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from main.features.PathReader import PathReader
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from main.experiments.CVSMDriver import CVSMDriver
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from main.experiments.PRADriver import PRADriver
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from main.playground.make_data_format import process_paths
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from main.playground.model2.CompositionalVectorAlgorithm import CompositionalVectorAlgorithm
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# This script is used to run FB15k237 experiments (only our method) with 1:10 postive to negative ratio.
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if __name__ == "__main__":
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# location of Das et al.'s repo
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CVSM_RUN_DIR = "/home/weiyu/Research/Path_Baselines/CVSM/ChainsofReasoning"
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# location of Matt's PRA scala repo
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PRA_RUN_DIR = "/home/weiyu/Research/Path_Baselines/SFE/pra_scala"
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DATASET_NAME = "fb15k237"
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DATASET_FOLDER = os.path.join("data", DATASET_NAME)
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PRA_TEMPLATE_DIR = "pra_templates"
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FREEBASE_DIR = DATASET_FOLDER
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DOMAIN_FILENAME = os.path.join(DATASET_FOLDER, "domains.tsv")
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RANGE_FILENAME = os.path.join(DATASET_FOLDER, "ranges.tsv")
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EDGES_FILENAME = os.path.join(DATASET_FOLDER, "edges.txt")
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PRA_DIR = os.path.join(DATASET_FOLDER, "pra")
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SPLIT_DIR = os.path.join(DATASET_FOLDER, "split")
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CPR_PATH_DIR = os.path.join(DATASET_FOLDER, "cpr_paths")
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WORD2VEC_FILENAME = "data/word2vec/knowledge-vectors-skipgram1000.bin"
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ENTITY2VEC_FILENAME = os.path.join(DATASET_FOLDER, "synonym2vec.pkl")
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# ToDo: modify code to have cvsm_ret, cvsm_r, cvsm_rt, cvsm_re folders (r: relation, t: type, e: entity)
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CVSM_DATA_DIR = os.path.join(DATASET_FOLDER, "cvsm")
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PRA_PATH_DIR = os.path.join(DATASET_FOLDER, "pra_paths")
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RELATION_PATH_DIR = os.path.join(DATASET_FOLDER, "relation_paths")
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PATH_DIR = os.path.join(DATASET_FOLDER, "paths")
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NEW_PATH_DIR = os.path.join(DATASET_FOLDER, "new_paths")
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AUGMENT_PATH_DIR = os.path.join(DATASET_FOLDER, "paths_augment")
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CVSM_RET_DIR = os.path.join(DATASET_FOLDER, "cvsm_entity")
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ENTITY_TYPE2VEC_FILENAME = os.path.join(DATASET_FOLDER, "entity_type2vec.pkl")
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run_step = 1
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# 1. first run main/data/MIDFreebase15kReader.py to process data and generate necessary files
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if run_step == 1:
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fb = MIDFreebase15kReader(FREEBASE_DIR,
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filter=True,
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word2vec_filename=WORD2VEC_FILENAME)
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fb.read_data()
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fb.write_relation_domain_and_ranges()
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fb.write_edges()
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# get the dictionary from freebase mids to names
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# fb.get_mid_to_name()
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# 2. use PRA scala code and code here to create train/test/dev split and negative examples
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if run_step == 2:
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pra_driver = PRADriver(DATASET_FOLDER, PRA_TEMPLATE_DIR, PRA_RUN_DIR, DATASET_NAME)
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pra_driver.prepare_split()
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# 3. Extract paths with entities
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if run_step == 3:
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typed_relation_instances = TypedRelationInstances()
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typed_relation_instances.read_domains_and_ranges(DOMAIN_FILENAME, RANGE_FILENAME)
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typed_relation_instances.construct_from_labeled_edges(EDGES_FILENAME, entity_name_is_typed=False,
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is_labeled=False)
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vocabs = Vocabs()
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vocabs.build_vocabs(typed_relation_instances)
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split = Split()
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split.read_splits(SPLIT_DIR, vocabs, entity_name_is_typed=True)
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graph = AdjacencyGraph()
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graph.build_graph(typed_relation_instances, vocabs)
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path_extractor = PathExtractor(max_length=4, include_entity=True, save_dir=PATH_DIR, include_path_len1=True,
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max_paths_per_pair=200, multiple_instances_per_pair=False,
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max_instances_per_pair=None, paths_sample_method="all_lengths")
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path_extractor.extract_paths(graph, split, vocabs)
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path_extractor.write_paths(split)
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# Note: we can extract path up to 6 but eliminate nodes with large fan-out
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# 4. Extract paths statistics
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if run_step == 4:
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typed_relation_instances = TypedRelationInstances()
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typed_relation_instances.read_domains_and_ranges(DOMAIN_FILENAME, RANGE_FILENAME)
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typed_relation_instances.construct_from_labeled_edges(EDGES_FILENAME, entity_name_is_typed=False,
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is_labeled=False)
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vocabs = Vocabs()
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vocabs.build_vocabs(typed_relation_instances)
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split = Split()
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split.read_splits(SPLIT_DIR, vocabs, entity_name_is_typed=True)
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path_reader = PathReader(save_dir=PATH_DIR)
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path_reader.read_paths(split)
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# 5. Process data for running the model
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if run_step == 5:
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# first convert paths to cvsm format
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cvsm_driver = CVSMDriver(FREEBASE_DIR, CVSM_RUN_DIR, dataset="freebase", include_entity=True, has_entity=True,
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augment_data=False, include_entity_type=True)
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cvsm_driver.setup_cvsm_dir()
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# then vectorize cvsm format data
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process_paths(input_dir=os.path.join(CVSM_RET_DIR, "data/data_input"),
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output_dir=os.path.join(CVSM_RET_DIR, "data/data_output"),
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vocab_dir=os.path.join(CVSM_RET_DIR, "data/vocab"),
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isOnlyRelation=False,
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getOnlyRelation=False,
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MAX_POSSIBLE_LENGTH_PATH=8, # the max number of relations in a path + 1
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NUM_ENTITY_TYPES_SLOTS=7, # the number of types + 1 (the reason we +1 is to create a meaningless type for all entities)
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pre_padding=True)
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# 6. test run of the model
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# use $tensorboard --logdir runs to see the training progress
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if run_step == 6:
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None, attention_method="sat", early_stopping_metric="map")
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# cvsm.train_and_test()
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# Uncomment if need to train only one relation
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cvsm.train(os.path.join(CVSM_RET_DIR, "data/data_output/|food|food|nutrients.|food|nutrition_fact|nutrient"))
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# 7. test different path pooling methods
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if run_step == 7:
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None,
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pooling_method="lse", attention_method="sat",
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early_stopping_metric="map")
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cvsm.train_and_test()
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None,
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pooling_method="avg", attention_method="sat",
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early_stopping_metric="map")
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cvsm.train_and_test()
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None,
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pooling_method="max", attention_method="sat",
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early_stopping_metric="map")
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cvsm.train_and_test()
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# 8. visualize type attention
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if run_step == 8:
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None,
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pooling_method="sat", attention_method="sat", early_stopping_metric="map",
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visualize=False, calculate_path_attn_stats=False, calculate_type_attn_stats=True,
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mid2name_filename="/home/weiyu/Research/ChainsOfReasoningWithAbstractEntities/data/fb15k237/mid2name.pkl",
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best_models={'|people|person|profession': {'epoch': 10}, '|award|award_winning_work|awards_won.|award|award_honor|award': {'epoch': 16}, '|tv|tv_program|regular_cast.|tv|regular_tv_appearance|actor': {'epoch': 23}, '|music|record_label|artist': {'epoch': 27}, '|award|award_category|nominees.|award|award_nomination|nominated_for': {'epoch': 15}, '|film|film|production_companies': {'epoch': 24}, '|film|film|genre': {'epoch': 13}, '|sports|sports_position|players.|sports|sports_team_roster|team': {'epoch': 25}, '|food|food|nutrients.|food|nutrition_fact|nutrient': {'epoch': 17}, '|education|educational_institution|students_graduates.|education|education|major_field_of_study': {'epoch': 21}})
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cvsm.train_and_test()
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# 9. run ablation on type selection
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if run_step == 9:
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cvsm = CompositionalVectorAlgorithm("freebase", CVSM_RET_DIR, None,
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pooling_method="lse", attention_method="specific",
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early_stopping_metric="map")
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cvsm.train_and_test()
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