ABLkit/examples/hed/framework_hed.py

# coding: utf-8
# ================================================================#
#   Copyright (C) 2021 Freecss All rights reserved.
#
#   File Name     ：framework.py
#   Author        ：freecss
#   Email         ：karlfreecss@gmail.com
#   Created Date  ：2021/06/07
#   Description   ：
#
# ================================================================#

import torch
import torch.nn as nn
import numpy as np
import os

from abl.utils.plog import INFO
from abl.utils.utils import flatten, reform_idx
from abl.learning.basic_nn import BasicNN, BasicDataset

from utils import gen_mappings, mapping_res, remapping_res
from models.nn import SymbolNetAutoencoder
from torch.utils.data import RandomSampler
from datasets.get_hed import get_pretrain_data


def hed_pretrain(kb, cls, recorder):
    cls_autoencoder = SymbolNetAutoencoder(num_classes=len(kb.pseudo_label_list))
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    if not os.path.exists("./weights/pretrain_weights.pth"):
        INFO("Pretrain Start")
        pretrain_data_X, pretrain_data_Y = get_pretrain_data(["0", "1", "10", "11"])
        pretrain_data = BasicDataset(pretrain_data_X, pretrain_data_Y)
        pretrain_data_loader = torch.utils.data.DataLoader(
            pretrain_data, batch_size=64, shuffle=True
        )

        criterion = nn.MSELoss()
        optimizer = torch.optim.RMSprop(
            cls_autoencoder.parameters(), lr=0.001, alpha=0.9, weight_decay=1e-6
        )

        pretrain_model = BasicNN(
            cls_autoencoder,
            criterion,
            optimizer,
            device,
            save_interval=1,
            save_dir=recorder.save_dir,
            num_epochs=10,
            recorder=recorder,
        )
        pretrain_model.fit(pretrain_data_loader)
        torch.save(
            cls_autoencoder.base_model.state_dict(), "./weights/pretrain_weights.pth"
        )
        cls.load_state_dict(cls_autoencoder.base_model.state_dict())

    else:
        cls.load_state_dict(torch.load("./weights/pretrain_weights.pth"))


def _get_char_acc(model, X, consistent_pred_res, mapping):
    original_pred_res = model.predict(X)["label"]
    pred_res = flatten(mapping_res(original_pred_res, mapping))
    INFO("Current model's output: ", pred_res)
    INFO("Abduced labels:         ", flatten(consistent_pred_res))
    assert len(pred_res) == len(flatten(consistent_pred_res))
    return sum(
        [
            pred_res[idx] == flatten(consistent_pred_res)[idx]
            for idx in range(len(pred_res))
        ]
    ) / len(pred_res)


def abduce_and_train(model, abducer, mapping, train_X_true, select_num):
    select_idx = RandomSampler(train_X_true, num_samples=select_num,replacement=False)
    X = [train_X_true[idx] for idx in select_idx]

    # original_pred_res = model.predict(X)['label']
    pred_label = model.predict(X)["label"]

    if mapping == None:
        mappings = gen_mappings([0, 1, 2, 3], ["+", "=", 0, 1])
    else:
        mappings = [mapping]

    consistent_idx = []
    consistent_pred_res = []

    for m in mappings:
        pred_pseudo_label = mapping_res(pred_label, m)
        max_revision_num = 20
        solution = abducer.zoopt_get_solution(
            pred_label,
            pred_pseudo_label,
            [None] * len(pred_label),
            [None] * len(pred_label),
            max_revision_num,
        )
        all_address_flag = reform_idx(solution, pred_label)

        consistent_idx_tmp = []
        consistent_pred_res_tmp = []

        for idx in range(len(pred_label)):
            address_idx = [
                i for i, flag in enumerate(all_address_flag[idx]) if flag != 0
            ]
            candidate = abducer.revise_by_idx([pred_pseudo_label[idx]], None, address_idx)
            if len(candidate) > 0:
                consistent_idx_tmp.append(idx)
                consistent_pred_res_tmp.append(candidate[0][0])

        if len(consistent_idx_tmp) > len(consistent_idx):
            consistent_idx = consistent_idx_tmp
            consistent_pred_res = consistent_pred_res_tmp
            if len(mappings) > 1:
                mapping = m

    if len(consistent_idx) == 0:
        return 0, 0, None

    INFO("Train pool size is:", len(flatten(consistent_pred_res)))
    INFO("Start to use abduced pseudo label to train model...")
    model.train(
        [X[idx] for idx in consistent_idx], remapping_res(consistent_pred_res, mapping)
    )

    consistent_acc = len(consistent_idx) / select_num
    char_acc = _get_char_acc(
        model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping
    )
    INFO("consistent_acc is %s, char_acc is %s" % (consistent_acc, char_acc))
    return consistent_acc, char_acc, mapping


# def abduce_and_train(model, abducer, mapping, train_X_true, select_num):
#     select_idx = np.random.randint(len(train_X_true), size=select_num)
#     X = []
#     for idx in select_idx:
#         X.append(train_X_true[idx])

#     original_pred_res = model.predict(X)['label']

#     if mapping == None:
#         mappings = gen_mappings([0, 1, 2, 3],['+', '=', 0, 1])
#     else:
#         mappings = [mapping]

#     consistent_idx = []
#     consistent_pred_res = []

#     for m in mappings:
#         pred_res = mapping_res(original_pred_res, m)
#         max_abduce_num = 20
#         solution = abducer.zoopt_get_solution(pred_res, [None] * len(pred_res), [None] * len(pred_res), max_abduce_num)
#         all_address_flag = reform_idx(solution, pred_res)

#         consistent_idx_tmp = []
#         consistent_pred_res_tmp = []

#         for idx in range(len(pred_res)):
#             address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0]
#             candidate = abducer.revise_by_idx([pred_res[idx]], None, address_idx)
#             if len(candidate) > 0:
#                 consistent_idx_tmp.append(idx)
#                 consistent_pred_res_tmp.append(candidate[0][0])

#         if len(consistent_idx_tmp) > len(consistent_idx):
#             consistent_idx = consistent_idx_tmp
#             consistent_pred_res = consistent_pred_res_tmp
#             if len(mappings) > 1:
#                 mapping = m

#     if len(consistent_idx) == 0:
#         return 0, 0, None

#     INFO('Train pool size is:', len(flatten(consistent_pred_res)))
#     INFO("Start to use abduced pseudo label to train model...")
#     model.train([X[idx] for idx in consistent_idx], remapping_res(consistent_pred_res, mapping))

#     consistent_acc = len(consistent_idx) / select_num
#     char_acc = _get_char_acc(model, [X[idx] for idx in consistent_idx], consistent_pred_res, mapping)
#     INFO('consistent_acc is %s, char_acc is %s' % (consistent_acc, char_acc))
#     return consistent_acc, char_acc, mapping


def _remove_duplicate_rule(rule_dict):
    add_nums_dict = {}
    for r in list(rule_dict):
        add_nums = str(r.split("]")[0].split("[")[1]) + str(
            r.split("]")[1].split("[")[1]
        )  # r = 'my_op([1], [0], [1, 0])' then add_nums = '10'
        if add_nums in add_nums_dict:
            old_r = add_nums_dict[add_nums]
            if rule_dict[r] >= rule_dict[old_r]:
                rule_dict.pop(old_r)
                add_nums_dict[add_nums] = r
            else:
                rule_dict.pop(r)
        else:
            add_nums_dict[add_nums] = r
    return list(rule_dict)


def get_rules_from_data(
    model, abducer, mapping, train_X_true, samples_per_rule, samples_num
):
    rules = []
    for _ in range(samples_num):
        while True:
            select_idx = np.random.randint(len(train_X_true), size=samples_per_rule)
            X = []
            for idx in select_idx:
                X.append(train_X_true[idx])
            original_pred_res = model.predict(X)["label"]
            pred_res = mapping_res(original_pred_res, mapping)

            consistent_idx = []
            consistent_pred_res = []
            for idx in range(len(pred_res)):
                if abducer.kb.logic_forward([pred_res[idx]]):
                    consistent_idx.append(idx)
                    consistent_pred_res.append(pred_res[idx])

            if len(consistent_pred_res) != 0:
                rule = abducer.abduce_rules(consistent_pred_res)
                if rule != None:
                    break
        rules.append(rule)

    all_rule_dict = {}
    for rule in rules:
        for r in rule:
            all_rule_dict[r] = 1 if r not in all_rule_dict else all_rule_dict[r] + 1
    rule_dict = {rule: cnt for rule, cnt in all_rule_dict.items() if cnt >= 5}
    rules = _remove_duplicate_rule(rule_dict)

    return rules


def _get_consist_rule_acc(model, abducer, mapping, rules, X):
    cnt = 0
    for x in X:
        original_pred_res = model.predict([x])["label"]
        pred_res = flatten(mapping_res(original_pred_res, mapping))
        if abducer.kb.consist_rule(pred_res, rules):
            cnt += 1
    return cnt / len(X)


def train_with_rule(
    model, abducer, train_data, val_data, select_num=10, min_len=5, max_len=8
):
    train_X = train_data
    val_X = val_data

    samples_num = 50
    samples_per_rule = 3

    # Start training / for each length of equations
    for equation_len in range(min_len, max_len):
        INFO(
            "============== equation_len: %d-%d ================"
            % (equation_len, equation_len + 1)
        )
        train_X_true = train_X[1][equation_len]
        train_X_false = train_X[0][equation_len]
        val_X_true = val_X[1][equation_len]
        val_X_false = val_X[0][equation_len]

        train_X_true.extend(train_X[1][equation_len + 1])
        train_X_false.extend(train_X[0][equation_len + 1])
        val_X_true.extend(val_X[1][equation_len + 1])
        val_X_false.extend(val_X[0][equation_len + 1])

        condition_cnt = 0
        while True:
            if equation_len == min_len:
                mapping = None

            # Abduce and train NN
            consistent_acc, char_acc, mapping = abduce_and_train(
                model, abducer, mapping, train_X_true, select_num
            )
            if consistent_acc == 0:
                continue

            # Test if we can use mlp to evaluate
            if consistent_acc >= 0.9 and char_acc >= 0.9:
                condition_cnt += 1
            else:
                condition_cnt = 0

            # The condition has been satisfied continuously five times
            if condition_cnt >= 5:
                INFO("Now checking if we can go to next course")
                rules = get_rules_from_data(
                    model, abducer, mapping, train_X_true, samples_per_rule, samples_num
                )
                INFO("Learned rules from data:", rules)

                true_consist_rule_acc = _get_consist_rule_acc(
                    model, abducer, mapping, rules, val_X_true
                )
                false_consist_rule_acc = _get_consist_rule_acc(
                    model, abducer, mapping, rules, val_X_false
                )

                INFO(
                    "consist_rule_acc is %f, %f\n"
                    % (true_consist_rule_acc, false_consist_rule_acc)
                )
                # decide next course or restart
                if true_consist_rule_acc > 0.95 and false_consist_rule_acc < 0.1:
                    torch.save(
                        model.classifier_list[0].model.state_dict(),
                        "./weights/weights_%d.pth" % equation_len,
                    )
                    break
                else:
                    if equation_len == min_len:
                        INFO("Final mapping is: ", mapping)
                        model.classifier_list[0].model.load_state_dict(
                            torch.load("./weights/pretrain_weights.pth")
                        )
                    else:
                        model.classifier_list[0].model.load_state_dict(
                            torch.load("./weights/weights_%d.pth" % (equation_len - 1))
                        )
                    condition_cnt = 0
                    INFO("Reload Model and retrain")

    return model, mapping


def hed_test(model, abducer, mapping, train_data, test_data, min_len=5, max_len=8):
    train_X = train_data
    test_X = test_data

    # Calcualte how many equations should be selected in each length
    # for each length, there are equation_samples_num[equation_len] rules
    print("Now begin to train final mlp model")
    equation_samples_num = []
    len_cnt = max_len - min_len + 1
    samples_num = 50
    equation_samples_num += [0] * min_len
    if samples_num % len_cnt == 0:
        equation_samples_num += [samples_num // len_cnt] * len_cnt
    else:
        equation_samples_num += [samples_num // len_cnt] * len_cnt
        equation_samples_num[-1] += samples_num % len_cnt
    assert sum(equation_samples_num) == samples_num

    # Abduce rules
    rules = []
    samples_per_rule = 3
    for equation_len in range(min_len, max_len + 1):
        equation_rules = get_rules_from_data(
            model,
            abducer,
            mapping,
            train_X[1][equation_len],
            samples_per_rule,
            equation_samples_num[equation_len],
        )
        rules.extend(equation_rules)
    rules = list(set(rules))
    INFO("Learned rules from data:", rules)

    for equation_len in range(5, 27):
        true_consist_rule_acc = _get_consist_rule_acc(
            model, abducer, mapping, rules, test_X[1][equation_len]
        )
        false_consist_rule_acc = _get_consist_rule_acc(
            model, abducer, mapping, rules, test_X[0][equation_len]
        )
        INFO(
            "consist_rule_acc of testing length %d equations are %f, %f"
            % (equation_len, true_consist_rule_acc, false_consist_rule_acc)
        )


if __name__ == "__main__":
    pass