解释这段代码 def load_data_set(self, train_file, test_file): train_dict = defaultdict(list) test_dict = defaultdict(list) train_user_items_dict = defaultdict(list) train_item_users_dict = defaultdict(list) test_user_items_dict = defaultdict(list) test_item_users_dict = defaultdict(list) train_data = [] test_data = [] with open(train_file) as f_train: with open(test_file) as f_test: for l in f_train: uid, iid = l.strip().split('\t') train_dict[int(uid)].append(int(iid)-self.n_users) train_user_items_dict[int(uid)].append(int(iid)) train_item_users_dict[int(iid)].append(int(uid)) train_data.append((int(uid), int(iid))) for l in f_test.readlines(): uid, iid = l.strip().split('\t') test_dict[int(uid)].append(int(iid)-self.n_users) test_user_items_dict[int(uid)].append(int(iid)) test_item_users_dict[int(iid)].append(int(uid)) test_data.append((int(uid), int(iid))) return train_data, test_data, train_dict, test_dict, train_user_items_dict, train_item_users_dict, test_user_items_dict, test_item_users_dict

这是一段 Python 代码，load_data_set 函数的作用是从 train_file 和 test_file 中读入数据，并将数据处理成不同的字典形式，同时将数据分为训练集和测试集。具体来说，train_dict 和 test_dict 存储了用户和物品之间的映射关系，train_user_items_dict 和 test_user_items_dict 存储了每个用户对应的物品列表，train_item_users_dict 和 test_item_users_dict 存储了每个物品对应的用户列表，train_data 和 test_data 分别存储了训练集和测试集中的数据，每个数据是一个二元组，分别表示用户 ID 和物品 ID。这段代码使用了 defaultdict 数据结构来避免键不存在时的 KeyError 异常，同时也比使用普通字典更加方便。

相关问题

人工智能语音识别python代码

以下是从提供的《实验指导.docx》文档中提炼出来的关于人工智能语音识别的Python代码概要：

### 1. 解压数据集

!unzip -q data/data300576/recordings.zip -d wc_work

### 2. 切分数据集

import os
import random

# 获取所有音频文件路径
recordings = ['recordings/' + name for name in os.listdir('work/recordings')]
total = []

# 遍历每个音频文件路径，提取标签
for recording in recordings:
    label = int(recording[11])
    total.append(f'{recording}\t{label}')

# 创建训练集、验证集和测试集文件
train = open('work/train.tsv', 'w', encoding='UTF-8')
dev = open('work/dev.tsv', 'w', encoding='UTF-8')
test = open('work/test.tsv', 'w', encoding='UTF-8')

# 打乱数据顺序
random.shuffle(total)

# 确定数据集划分的索引
split_num = int((len(total) - 100) * 0.9)

# 写入训练集数据
for line in total[:split_num]:
    train.write(line)

# 写入验证集数据
for line in total[split_num:-100]:
    dev.write(line)

# 写入测试集数据
for line in total[-100:]:
    test.write(line)

# 关闭文件
train.close()
dev.close()
test.close()

### 3. 音频数据预处理

import random
import numpy as np
import scipy.io.wavfile as wav
from python_speech_features import mfcc, delta

def get_mfcc(data, fs):
    # 提取MFCC特征
    wav_feature = mfcc(data, fs)
    # 计算一阶差分
    d_mfcc_feat = delta(wav_feature, 1)
    # 计算二阶差分
    d_mfcc_feat2 = delta(wav_feature, 2)
    
    # 拼接特征
    feature = np.concatenate([
        wav_feature.reshape(1, -1, 13),
        d_mfcc_feat.reshape(1, -1, 13),
        d_mfcc_feat2.reshape(1, -1, 13)
    ], axis=0)
    
    # 统一时间维度
    if feature.shape[1] > 64:
        feature = feature[:, :64, :]
    else:
        feature = np.pad(feature, ((0, 0), (0, 64 - feature.shape[1]), (0, 0)), 'constant')
    
    # 调整数据维度
    feature = feature.transpose((2, 0, 1))
    feature = feature[np.newaxis, :]
    
    return feature

def loader(tsv):
    datas = []
    with open(tsv, 'r', encoding='UTF-8') as f:
        for line in f:
            audio, label = line.strip().split('\t')
            fs, signal = wav.read('work/' + audio)
            feature = get_mfcc(signal, fs)
            datas.append([feature, int(label)])
    return datas

def reader(datas, batch_size, is_random=True):
    features = []
    labels = []
    if is_random:
        random.shuffle(datas)
    
    for data in datas:
        feature, label = data
        features.append(feature)
        labels.append(label)
        
        if len(labels) == batch_size:
            features = np.concatenate(features, axis=0).reshape(-1, 13, 3, 64).astype('float32')
            labels = np.array(labels).reshape(-1, 1).astype('int64')
            yield features, labels
            features = []
            labels = []

### 4. 模型搭建

import paddle.fluid as fluid
from paddle.fluid.dygraph import Linear, Conv2D, BatchNorm
from paddle.fluid.layers import softmax_with_cross_entropy, accuracy, reshape

class Audio(fluid.dygraph.Layer):
    def __init__(self):
        super(Audio, self).__init__()
        self.conv1 = Conv2D(13, 16, 3, 1, 1)
        self.conv2 = Conv2D(16, 16, (3, 2), (1, 2), (1, 0))
        self.conv3 = Conv2D(16, 32, 3, 1, 1)
        self.conv4 = Conv2D(32, 32, (3, 2), (1, 2), (1, 0))
        self.conv5 = Conv2D(32, 64, 3, 1, 1)
        self.conv6 = Conv2D(64, 64, (3, 2), 2)
        self.fc1 = Linear(8 * 64, 128)
        self.fc2 = Linear(128, 10)

    def forward(self, inputs, labels=None):
        out = self.conv1(inputs)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = self.conv6(out)
        
        out = reshape(out, [-1, 8 * 64])
        out = self.fc1(out)
        out = self.fc2(out)
        
        if labels is not None:
            loss = softmax_with_cross_entropy(out, labels)
            acc = accuracy(out, labels)
            return loss, acc
        else:
            return out

### 5. 查看网络结构

import paddle

audio_network = Audio()
paddle.summary(audio_network, input_size=[(64, 13, 3, 64)], dtypes=['float32'])

### 6. 模型训练

import numpy as np
import paddle.fluid as fluid
from visualdl import LogWriter
from paddle.fluid.optimizer import Adam
from paddle.fluid.dygraph import to_variable, save_dygraph

writer = LogWriter(logdir="./log/train")

train_datas = loader('work/train.tsv')
dev_datas = loader('work/dev.tsv')

place = fluid.CPUPlace()
epochs = 10

with fluid.dygraph.guard(place):
    model = Audio()
    optimizer = Adam(learning_rate=0.001, parameter_list=model.parameters())
    global_step = 0
    max_acc = 0
    
    for epoch in range(epochs):
        model.train()
        train_reader = reader(train_datas, batch_size=64)
        
        for step, data in enumerate(train_reader):
            signal, label = [to_variable(_) for _ in data]
            loss, acc = model(signal, label)
            
            if step % 20 == 0:
                print(f'train epoch: {epoch} step: {step}, loss: {loss.numpy().mean()}, acc: {acc.numpy()}')
                writer.add_scalar(tag='train_loss', step=global_step, value=loss.numpy().mean())
                writer.add_scalar(tag='train_acc', step=global_step, value=acc.numpy())
                global_step += 1
            
            loss.backward()
            optimizer.minimize(loss)
            model.clear_gradients()
        
        model.eval()
        dev_reader = reader(dev_datas, batch_size=64, is_random=False)
        accs = []
        losses = []
        
        for data in dev_reader:
            signal, label = [to_variable(_) for _ in data]
            loss, acc = model(signal, label)
            losses.append(loss.numpy().mean())
            accs.append(acc.numpy())
        
        avg_acc = np.array(accs).mean()
        avg_loss = np.array(losses).mean()
        
        if avg_acc > max_acc:
            max_acc = avg_acc
            print(f'the best accuracy: {max_acc}')
            print('saving the best model')
            save_dygraph(optimizer.state_dict(), 'best_model')
            save_dygraph(model.state_dict(), 'best_model')
        
        print(f'dev epoch: {epoch}, loss: {avg_loss}, acc: {avg_acc}')
        writer.add_scalar(tag='dev_loss', step=epoch, value=avg_loss)
        writer.add_scalar(tag='dev_acc', step=epoch, value=avg_acc)
    
    print(f'the best accuracy: {max_acc}')
    print('saving the final model')
    save_dygraph(optimizer.state_dict(), 'final_model')
    save_dygraph(model.state_dict(), 'final_model')

### 7. 模型测试

import os
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable, load_dygraph

test_datas = loader('work/test.tsv')
print(f'{len(test_datas)} data in test set')

with fluid.dygraph.guard(fluid.CPUPlace()):
    model = Audio()
    model.eval()
    
    params_dict, _ = load_dygraph('best_model')
    model.set_dict(params_dict)
    
    test_reader = reader(test_datas, batch_size=100, is_random=False)
    accs = []
    
    for data in test_reader:
        signal, label = [to_variable(_) for _ in data]
        _, acc = model(signal, label)
        accs.append(acc.numpy())
    
    avg_acc = np.array(accs).mean()
    print(f'test acc: {avg_acc}')

### 8. 用训练好的模型识别语音

import numpy as np
import webrtcvad
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable, load_dygraph

def vad(file_path, mode=3):
    samp_rate, signal_data = wav.read(file_path)
    vad = webrtcvad.Vad(mode=mode)
    signal = np.pad(signal_data, (0, 160 - (signal_data.shape[0] % int(samp_rate * 0.02))), 'constant')
    lens = signal.shape[0]
    signals = np.split(signal, lens // int(samp_rate * 0.02))
    audio = []
    audios = []
    
    for signal_item in signals:
        if vad.is_speech(signal_item.tobytes(), samp_rate):
            audio.append(signal_item)
        elif len(audio) > 0 and not vad.is_speech(signal_item.tobytes(), samp_rate):
            audios.append(np.concatenate(audio, 0))
            audio = []
    
    return audios, samp_rate

audios, samp_rate = vad('data/audio.wav')

features = []
for audio in audios:
    feature = get_mfcc(audio, samp_rate)
    features.append(feature)

features = np.concatenate(features, 0).astype('float32')

with fluid.dygraph.guard(place=fluid.CPUPlace()):
    model = Audio()
    params_dict, _ = load_dygraph('final_model')
    model.set_dict(params_dict)
    model.eval()
    
    features = to_variable(features)
    out = model(features)
    result = ' '.join([str(num) for num in np.argmax(out.numpy(), 1).tolist()])
    
    print(f'语音数字的识别结果是：{result}')

以上代码涵盖了从数据准备、预处理、模型构建、训练、测试到实际应用的完整流程。希望这些代码对你有所帮助！

给出训练模型部分的代码

根据您提供的文档内容，训练模型的部分代码如下所示。这段代码实现了基于标签的推荐算法，并使用了三种不同的推荐策略：Simple Tag-based、Norm Tag-based 和 Tag-based TFIDF。

# -*- coding: utf-8 -*-
"""
Created on 2020/11/18 13:54
@author: Irvinfaith
@email: Irvinfaith@hotmail.com
"""
import operator
import time
import random
import pandas as pd
import numpy as np

class TagBased(object):
    def __init__(self, data_path, sep='\t'):
        self.data_path = data_path
        self.sep = sep
        self.calc_result = {}
        self.table = self.__load_data(data_path, sep)

    def __load_data(self, data_path, sep):
        table = pd.read_table(data_path, sep=sep)
        return table

    def __calc_frequency(self, table):
        # user -> item
        user_item = table.groupby(by=['userID', 'bookmarkID'])['tagID'].count()
        # user -> tag
        user_tag = table.groupby(by=['userID', 'tagID'])['bookmarkID'].count()
        # tag -> item
        tag_item = table.groupby(by=['tagID', 'bookmarkID'])['userID'].count()
        # tag -> user
        tag_user = table.groupby(by=['tagID', 'userID'])['bookmarkID'].count()
        return {"user_item": user_item, "user_tag": user_tag, "tag_item": tag_item, "tag_user": tag_user}

    def train_test_split(self, ratio, seed):
        return self.__train_test_split(self.table, ratio, seed)

    def __train_test_split(self, table, ratio, seed):
        random.seed(seed)
        t1 = time.time()
        stratify_count = table.groupby(by='userID')['userID'].count()
        stratify_df = pd.DataFrame({"count": stratify_count})
        stratify_df['test_num'] = (stratify_df['count'] * ratio).apply(int)
        test_id = []
        train_id = []

        stratify_df['ids'] = stratify_df.index.map(lambda x: table[table['userID'] == x].index.tolist())
        stratify_df['test_index'] = stratify_df.apply(lambda x: random.sample(x['ids'], x['test_num']), axis=1)
        stratify_df['train_index'] = stratify_df.apply(lambda x: list(set(x['ids']) - set(x['test_index'])), axis=1)
        stratify_df['test_index'].apply(lambda x: test_id.extend(x))
        stratify_df['train_index'].apply(lambda x: train_id.extend(x))

        train_data = table.iloc[train_id].reset_index(drop=True)
        test_data = table.iloc[test_id].reset_index(drop=True)
        print("Split train test dataset by stratification, time took: %.4f" % (time.time() - t1))
        return {"train_data": train_data, "test_data": test_data}

    def fit(self, train_data):
        self.calc_result = self.__calc_frequency(train_data)

    def predict(self, user_id, n, method='simple'):
        return self.__calc_item_recommendation(user_id,
                                               self.calc_result['user_item'],
                                               self.calc_result['user_tag'],
                                               self.calc_result['tag_item'],
                                               n,
                                               method)

    def eval(self, n, test_data):
        t1 = time.time()
        test_data_user_id = test_data['userID'].unique()
        total_tp = 0
        tpfn = 0
        tpfp = 0
        check = []
        for user_id in test_data_user_id:
            train_recommend = self.predict(user_id, n)
            user_test_data = test_data[test_data['userID'] == user_id]
            total_tp += self.__eval(train_recommend, user_test_data)
            tpfn += len(user_test_data['bookmarkID'].unique())
            tpfp += n
            check.append((user_id, total_tp, tpfn, tpfp))
        recall = total_tp / tpfn
        precision = total_tp / tpfp
        print("Recall: %10.4f" % (recall * 100))
        print("Precision: %10.4f" % (precision * 100))
        print(time.time() - t1)
        return recall, precision, check

    def __calc_item_recommendation(self, user_id, user_item, user_tag, tag_item, n, method):
        marked_item = user_item[user_id].index
        recommend = {}
        marked_tag = user_tag.loc[user_id]
        marked_tag_sum = marked_tag.values.sum()
        for tag_index, tag_count in marked_tag.iteritems():
            selected_item = tag_item.loc[tag_index]
            selected_item_sum = selected_item.values.sum()
            tag_selected_users_sum = self.calc_result['tag_user'].loc[tag_index].values.sum()
            for item_index, tag_item_count in selected_item.iteritems():
                if item_index in marked_item:
                    continue
                if item_index not in recommend:
                    if method == 'norm':
                        recommend[item_index] = (tag_count / marked_tag_sum) * (tag_item_count / selected_item_sum)
                    elif method == 'simple':
                        recommend[item_index] = tag_count * tag_item_count
                    elif method == 'tfidf':
                        recommend[item_index] = tag_count / np.log(1 + tag_selected_users_sum) * tag_item_count
                    else:
                        raise TypeError("Invalid method `{}`, `method` only support `norm`, `simple` and `tfidf`".format(method))
                else:
                    if method == 'norm':
                        recommend[item_index] += (tag_count / marked_tag_sum) * (tag_item_count / selected_item_sum)
                    elif method == 'simple':
                        recommend[item_index] += tag_count * tag_item_count
                    elif method == 'tfidf':
                        recommend[item_index] += tag_count / np.log(1 + tag_selected_users_sum) * tag_item_count
                    else:
                        raise TypeError("Invalid method `{}`, `method` only support `norm`, `simple` and `tfidf`".format(method))
        sorted_recommend = sorted(recommend.items(), key=lambda x: (x[1]), reverse=True)[:n]
        return {user_id: dict(sorted_recommend)}

    def __eval(self, train_recommend, test_data):
        user_id = [i for i in train_recommend.keys()][0]
        test_data_item = test_data['bookmarkID'].unique()
        tp = len(set(test_data_item) & set(train_recommend[user_id].keys()))
        return tp

if __name__ == '__main__':
    file_path = "user_taggedbookmarks-timestamps.dat"
    tb = TagBased(file_path, '\t')
    train_test_data = tb.train_test_split(0.2, 88)
    tb.fit(train_test_data['train_data'])
    calc_result = tb.calc_result

    # 使用3种方法，预测用户id为8 的 排序前10的item
    p1_simple = tb.predict(8, 10)
    p1_tf = tb.predict(8, 10, method='tfidf')
    p1_normal = tb.predict(8, 10, method='norm')

### 代码说明
1. **初始化**：`__init__` 方法加载数据并初始化一些变量。
2. **数据加载**：`__load_data` 方法读取数据文件。
3. **频率计算**：`__calc_frequency` 方法计算用户-物品、用户-标签、标签-物品和标签-用户的频率。
4. **数据集划分**：`train_test_split` 方法将数据集划分为训练集和测试集。
5. **模型训练**：`fit` 方法计算训练数据的频率统计结果。
6. **预测**：`predict` 方法根据用户ID和推荐方法预测推荐的物品。
7. **评估**：`eval` 方法评估模型的性能，计算召回率和精确率。
8. **推荐计算**：`__calc_item_recommendation` 方法根据不同的推荐方法计算推荐分数。
9. **评估辅助函数**：`__eval` 方法计算真实推荐和预测推荐的交集数量。

希望这段代码能满足您的需求！如果有任何问题或需要进一步的帮助，请随时告诉我。