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MSCI 641 Assignment Total

AI悦创原创Python 一对一教学Python 一对一教学大约 34 分钟...约 10141 字

Assignment 1 (code only, 5%) Due date: June 8

Write a python script to perform the following data preparation activities:

  1. Tokenize the corpus
  2. Remove the following special characters: !"#$%&()*+/:;<=>@[\]^`{|}~\t\n
  3. Create two versions of your dataset: (1) with stopwords and (2) without stopwords. Stopword lists are available online.
  4. Randomly split your data into training (80%), validation (10%) and test (10%) sets.

Details:

  1. Command-line argument: Path to the folder where pos.txt and neg.txt reside.
  2. You must implement the required techniques in core Python. For instance, you can use Python’s random module to create the train/val/test split and can read the .txt files in Python (using the in-built open function) instead of using pandas.
  3. The tokenized sentences should be stored in a file called ‘out.csv’ where each line follows the format mentioned below. Note that the quotes below are just to show that they are strings, you don’t need to explicitly add them around every token: [‘My’, ‘daughter’, ‘wanted’, ‘this’, ‘book’, ‘and’, ‘the’, ‘price’, ‘on’, ‘Amazon’, ‘was’, ‘the’, ‘best.’]
  4. Although not required, you can remove any additional punctuation marks such as ‘-’. Additionally, it doesn’t matter if you store those punctuations as a separate token or concatenate it with the previous word.
  5. Expected output files:
    1. out.csv: tokenized sentences w/ stopwords
    2. train.csv: training set w/ stopwords
    3. val.csv: validation set w/ stopwords
    4. test.csv: test set w/ stopwords
    5. out_ns.csv: tokenized sentences w/o stopwords
    6. train_ns.csv: training set w/o stopwords
    7. val_ns.csv: validation set w/o stopwords
    8. test_ns.csv: test set w/o stopwords
  6. It is recommended that you generate the labels as well in this assignment even though it is not mandatory
详情
我的题目如下：
Write a python script to perform the following data preparation activities:

1. Tokenize the corpus
2. Remove the following special characters: !"#$%&()*+/:;<=>@[\\]^`{|}~\t\n
3. Create two versions of your dataset: (1) with stopwords and (2) without stopwords. Stopword lists are available online.
4. Randomly split your data into training (80%), validation (10%) and test (10%) sets.
Details:

1. **Command-line argument:** Path to the folder where pos.txt and neg.txt reside. 
2. You must implement the required techniques in core Python. For instance, you can use Python’s `random` module to create the train/val/test split and can read the .txt files in Python (using the in-built `open` function) instead of using `pandas`. 
3. The tokenized sentences should be stored in a file called ‘out.csv’ where each line follows the format mentioned below. Note that the quotes below are just to show that they are strings, you don’t need to explicitly add them around every token: [‘My’, ‘daughter’, ‘wanted’, ‘this’, ‘book’, ‘and’, ‘the’, ‘price’, ‘on’, ‘Amazon’, ‘was’, ‘the’, ‘best.’] 
4. Although not required, you can remove any additional punctuation marks such as ‘-’.  Additionally, it doesn’t matter if you store those punctuations as a separate token or concatenate it with the previous word. 
5. **Expected output files:** 
    1. out.csv: tokenized sentences w/ stopwords 
    2. train.csv: training set w/ stopwords 
    3. val.csv: validation set w/ stopwords 
    4. test.csv: test set w/ stopwords 
    5. out_ns.csv: tokenized sentences w/o stopwords 
    6. train_ns.csv: training set w/o stopwords 
    7. val_ns.csv: validation set w/o stopwords 
    8. test_ns.csv: test set w/o stopwords
6. It is recommended that you generate the labels as well in this assignment even though it is not mandatory
我有两个文件，分别为：neg.txt、pos.txt。
neg.txt 的部分数据和格式如下：
```txt
I bought this when I bought the pop maker.
As for "pop embellishing" well, that wasn't too hard to figure out, either.
I'd save the money and spend it instead on extra pop sticks, which seem to disappear the way socks do.
didn't really care many of the cakes at all.
not up to normal standing for wilton yearbooks of the past.
Buy a Wilton magazine for less money and get more ideas and instructions for your investment.
Bag tore with almost nothing in it - Just caught the corner of a small cracker box and that was that.
```
pos.txt
```txt
My daughter wanted this book and the price on Amazon was the best.
She has already tried one recipe a day after receiving the book.
I bought this zoku quick pop for my daughterr with her zoku quick maker.
She loves it and have fun to make her own ice cream.
I was hoping there were more where those came from.
This book emphasizes very sweet dessert pops, however.
```

1. Solution

default
import os
import csv
import argparse
import string
import random
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def remove_special_chars(tokens, special_chars):
    return [token for token in tokens if token not in special_chars]

def remove_stopwords(tokens, stopword_list):
    return [token for token in tokens if token not in stopword_list]

def process_text(input_path, output_path, output_ns_path, stopword_list, label):
    with open(input_path, 'r') as input_file, \
         open(output_path, 'a', newline='') as output_file, \
         open(output_ns_path, 'a', newline='') as output_ns_file:
        csv_writer = csv.writer(output_file)
        csv_writer_ns = csv.writer(output_ns_file)
        for line in input_file:
            line = line.strip()
            tokens = word_tokenize(line)
            tokens = remove_special_chars(tokens, string.punctuation)
            csv_writer.writerow([label, ' '.join(tokens)])
            
            tokens_no_stopwords = remove_stopwords(tokens, stopword_list)
            csv_writer_ns.writerow([label, ' '.join(tokens_no_stopwords)])

def split_data(input_path, train_path, val_path, test_path, split_ratio=(0.8, 0.1, 0.1)):
    with open(input_path, 'r') as input_file:
        lines = list(csv.reader(input_file))
        random.shuffle(lines)
        
        train_size = int(len(lines) * split_ratio[0])
        val_size = int(len(lines) * split_ratio[1])
        
        train_lines = lines[:train_size]
        val_lines = lines[train_size:train_size+val_size]
        test_lines = lines[train_size+val_size:]
        
        with open(train_path, 'w', newline='') as train_file, \
             open(val_path, 'w', newline='') as val_file, \
             open(test_path, 'w', newline='') as test_file:
            train_writer = csv.writer(train_file)
            val_writer = csv.writer(val_file)
            test_writer = csv.writer(test_file)
            train_writer.writerows(train_lines)
            val_writer.writerows(val_lines)
            test_writer.writerows(test_lines)

def main():
    parser = argparse.ArgumentParser(description='Prepare data')
    parser.add_argument('--folder_path', type=str, help='Path to the folder where pos.txt and neg.txt reside.')
    args = parser.parse_args()
    
    stopword_list = stopwords.words('english')
    special_chars = string.punctuation + "\t\n"
    
    # Prepare out.csv and out_ns.csv with headers
    with open(os.path.join(args.folder_path, 'out.csv'), 'w', newline='') as file, \
         open(os.path.join(args.folder_path, 'out_ns.csv'), 'w', newline='') as file_ns:
        writer = csv.writer(file)
        writer_ns = csv.writer(file_ns)
        writer.writerow(['labels', 'text'])
        writer_ns.writerow(['labels', 'text'])
    
    for sentiment in ['pos', 'neg']:
        input_path = os.path.join(args.folder_path, f'{sentiment}.txt')
        output_path = os.path.join(args.folder_path, 'out.csv')
        output_ns_path = os.path.join(args.folder_path, 'out_ns.csv')
        process_text(input_path, output_path, output_ns_path, stopword_list, sentiment)
        
    for filename in ['out', 'out_ns']:
        input_path = os.path.join(args.folder_path, f'{filename}.csv')
        train_path = os.path.join(args.folder_path, f'{filename}_train.csv')
        val_path = os.path.join(args.folder_path, f'{filename}_val.csv')
        test_path = os.path.join(args.folder_path, f'{filename}_test.csv')
        split_data(input_path, train_path, val_path, test_path)
        
if __name__ == "__main__":
    main()

Assignment 3 (code + short report, 7%). Due date: June 22

  1. Write a python script using genism library to train a Word2Vec model on the Amazon corpus.
  2. Use genism library to get the most similar words to a given word. Find 20 most similar words to “good” and “bad”. Are the words most similar to “good” positive, and words most similar to “bad” negative? Why this is or isn’t the case? Explain your intuition briefly (in 5-6 sentences).
  3. Command-line argument: Path to the folder where pos.txt and neg.txt reside
  4. Write the report (README.md) as succinctly as possible and DO NOT overshoot the word limit (5-6 sentences).
  5. Output files (must be included in your submission):
    1. w2v.model: Word2vec model trained on the entire Amazon corpus (pos.txt + neg.txt)
  6. Code requirement:
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an inference.py script, which generates the top-20 most similar words for a given word. It should accept one command-line argument described below.
    2. arg1: Path to a .txt file, which contains some words compiled for evaluation. There will be one word per line.
详情
我的题目如下：
1. Write a python script using genism library to train a Word2Vec model on the Amazon corpus.
2. Use genism library to get the most similar words to a given word. Find 20 most similar words to “good” and “bad”. Are the words most similar to “good” positive, and words most similar to “bad” negative? Why this **is** or **isn’t** the case? Explain your intuition briefly (in 5-6 sentences).
3. **Command-line argument:** Path to the folder where pos.txt and neg.txt reside 
4. Write the report (README.md) as succinctly as possible and **DO NOT** overshoot the word limit (5-6 sentences).
5. **Output files (must be included in your submission):** 
    1. w2v.model: Word2vec model trained on the entire Amazon corpus (pos.txt + neg.txt) 
6. **Code requirement:**
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an `inference.py` script, which generates the top-20 most similar words for a given word. It should accept **one** command-line argument described below. 
    2. arg1: Path to a .txt file, which contains some words compiled for evaluation. There will be one word per line. 
    
我有两个文件，分别为：neg.txt、pos.txt。
neg.txt 的部分数据和格式如下：
```txt
I bought this when I bought the pop maker.
As for "pop embellishing" well, that wasn't too hard to figure out, either.
I'd save the money and spend it instead on extra pop sticks, which seem to disappear the way socks do.
didn't really care many of the cakes at all.
not up to normal standing for wilton yearbooks of the past.
Buy a Wilton magazine for less money and get more ideas and instructions for your investment.
Bag tore with almost nothing in it - Just caught the corner of a small cracker box and that was that.
```
pos.txt
```txt
My daughter wanted this book and the price on Amazon was the best.
She has already tried one recipe a day after receiving the book.
I bought this zoku quick pop for my daughterr with her zoku quick maker.
She loves it and have fun to make her own ice cream.
I was hoping there were more where those came from.
This book emphasizes very sweet dessert pops, however.
```

Solution

train_w2v.py
import argparse
import gensim
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string

def read_corpus(pos_path, neg_path):
    stop_words = set(stopwords.words('english'))

    def tokenize_and_remove_stopwords(file_path):
        with open(file_path, 'r', encoding='utf-8') as file:
            for line in file:
                tokens = word_tokenize(line)
                yield [token.lower() for token in tokens if token not in string.punctuation and token.lower() not in stop_words]

    return list(tokenize_and_remove_stopwords(pos_path)) + list(tokenize_and_remove_stopwords(neg_path))

def main():
    parser = argparse.ArgumentParser(description='Train Word2Vec on Amazon Corpus')
    parser.add_argument('--folder_path', type=str, help='Path to the folder where pos.txt and neg.txt reside.')
    args = parser.parse_args()

    corpus = read_corpus(args.folder_path + "/pos.txt", args.folder_path + "/neg.txt")

    model = gensim.models.Word2Vec(sentences=corpus, vector_size=100, window=5, min_count=1, workers=4)
    model.save(args.folder_path + "/w2v.model")

if __name__ == "__main__":
    main()

Assignment 4 (code + short report, 10%). Due date: July 3

Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of:

使用 PyTorch/Keras 编写一个 Python 脚本来训练一个全连接前馈神经网络分类器，将 Amazon 语料库中的文档分为积极和消极两类。您的网络必须包含以下内容：

  1. Input layer of the word2vec embeddings you prepared in Assignment 3.

在第三个任务中，您准备的word2vec嵌入层。

  1. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.

一个隐藏层。对于隐藏层，尝试以下激活函数：ReLU、sigmoid和tanh。

  1. Final layer with softmax activation function.

最后一层使用softmax激活函数。

  1. Use cross-entropy as the loss function.

使用交叉熵作为损失函数。

  1. Add L2-norm regularization.

添加L2范数正则化。

  1. Add dropout. Try a few different dropout rates.

添加丢弃层。尝试几种不同的丢弃率。

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the training set. You may only tune your models on your validation set. Once the development is complete, run your classifier on your test set.

针对这个任务，您必须使用您在作业1中划分的训练/验证/测试数据集。在训练集上训练您的模型。您只能在验证集上进行模型调整。完成开发后，使用您的分类器对测试集进行运行。

Report your classification accuracy results in a table with three different activation functions in the hidden layer (ReLU, sigmoid and tanh). What effect do activation functions have on your results? What effect does addition of L2-norm regularization have on the results? What effect does dropout have on the results? Explain your intuitions briefly (up to 10 sentences).

请将三种不同的激活函数（ReLU、sigmoid和tanh）在隐藏层中的分类准确率结果制作成表格并报告。激活函数对结果有什么影响？L2范数正则化的添加对结果有什么影响？丢弃（dropout）对结果有什么影响？请简要解释您的直觉（最多10句）。

  1. Command-line argument: Path to the folder containing the split from A1

命令行参数： 包含来自A1的拆分的文件夹路径

  1. DO NOT tune your models on the test set.

请勿在测试集上调整模型。

  1. You need to submit only one model that is trained on either with or without stopwords version (whichever gives you better performance).

您只需提交一个模型，该模型可以是在包含停用词或不包含停用词的版本上训练得到的（选择那个能给您更好性能的版本）。

  1. Write the report (README.md) as succinctly as possible and DO NOT overshoot the word limit.

请尽量简洁地编写报告（README.md），切勿超过字数限制。

  1. Output files (must be included in your submission):

    输出文件（必须包含在您的提交中）：

5.1 nn_relu.model: Classifier w/ ReLU activation「nn_relu.model: 具有ReLU激活函数的分类器」

5.2 nn_sigmoid.model: Classifier w/ Sigmoid activation 「nn_sigmoid.model：具有Sigmoid激活函数的分类器」

5.3 nn_tanh.model: Classifier w/ Tanh activation「nn_tanh.model: 具有双曲正切激活函数的分类器」

  1. Code requirement: 「代码要求：」
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an inference.py script, which classifies a given sentence into a positive/negative class. It should accept the two command-line arguments described below. 「除了文档开头发布的通用说明外，您还应提交一个名为inference.py的脚本，该脚本将给定的句子分类为积极/消极类别。它应接受下面描述的两个命令行参数。」
      1. arg1: Path to a .txt file, which contains some sentences compiled for evaluation. There will be one sentence per line. 「arg1：一个 .txt 文件的路径，其中包含用于评估的一些句子。每行将有一句话。」
      2. arg2: Type of classifier to use. Its value will be one of the following – relu, sigmoid, tanh. Example: relu indicates that the neural network with ReLU activation should be selected for classifying sentences in the aforementioned .txt file. 「arg2: 分类器的类型。它的取值将是以下之一 – relu、sigmoid、tanh。例子： relu 表示应该选择具有 ReLU 激活的神经网络来对上述 .txt 文件中的句子进行分类。」
详情
我的题目如下：
Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of: 
1. Input layer of the word2vec embeddings you prepared in Assignment 3.
2. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.
3. Final layer with softmax activation function.
4. Use cross-entropy as the loss function.
5. Add L2-norm regularization.
6. Add dropout. Try a few different dropout rates.

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the **training** set. You may only tune your models on your **validation** set. Once the development is complete, run your classifier on your **test** set.

1. **Command-line argument:** Path to the folder containing the split from A1 
2. DO NOT tune your models on the **test** set. 
3. You need to submit only **one** model that is trained on either with or without stopwords version (whichever gives you better performance). 
4. **Output files (must be included in your submission):**
    1. nn_relu.model: Classifier w/ ReLU activation
    2. nn_sigmoid.model: Classifier w/ Sigmoid activation 
    3. nn_tanh.model: Classifier w/ Tanh activation
5. **Code requirement:** 
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an `inference.py` script, which classifies a given sentence into a positive/negative class. It should accept the **two** command-line arguments described below. 
        1. arg1: Path to a .txt file, which contains some sentences compiled for evaluation. There will be one sentence per line. 
        2. arg2: Type of classifier to use. Its value will be one of the following – relu, sigmoid, tanh. **Example:** relu indicates that the neural network with ReLU activation should be selected for classifying sentences in the aforementioned .txt file. 
现在所拥有的文件以及部分数据：
训练集：out_train.csv、out_ns_train.csv
验证集：out_val.csv、out_ns_val.csv
测试集：out_test.csv、out_ns_test.csv
其中包含ns的代表内容已经移除 stopword。
所有文件的csv第一列是liable（也就是neg或者pos）第二例是text（好评或者差评内容）
我的题目如下：
Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of: 

1. Input layer of the word2vec embeddings you prepared in Assignment 3.
2. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.
3. Final layer with softmax activation function.
4. Use cross-entropy as the loss function.
5. Add L2-norm regularization.
6. Add dropout. Try a few different dropout rates.

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the **training** set. You may only tune your models on your **validation** set. Once the development is complete, run your classifier on your **test** set.

Report your classification accuracy results in a table with three different activation functions in the hidden layer (ReLU, sigmoid and tanh). What effect do activation functions have on your results? What effect does addition of L2-norm regularization have on the results? What effect does dropout have on the results? Explain your intuitions briefly (up to 10 sentences).

1. **Command-line argument:** Path to the folder containing the split from A1 
2. DO NOT tune your models on the **test** set. 
3. You need to submit only **one** model that is trained on either with or without stopwords version (whichever gives you better performance). 
4. Write the report (README.md) as succinctly as possible and **DO NOT** overshoot the word limit. 
5. **Output files (must be included in your submission):**
    1. nn_relu.model: Classifier w/ ReLU activation
    2. nn_sigmoid.model: Classifier w/ Sigmoid activation 
    3. nn_tanh.model: Classifier w/ Tanh activation
6. **Code requirement:** 
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an `inference.py` script, which classifies a given sentence into a positive/negative class. It should accept the **two** command-line arguments described below. 
        1. arg1: Path to a .txt file, which contains some sentences compiled for evaluation. There will be one sentence per line. 
        2. arg2: Type of classifier to use. Its value will be one of the following – relu, sigmoid, tanh. **Example:** relu indicates that the neural network with ReLU activation should be selected for classifying sentences in the aforementioned .txt file. 
现在所拥有的文件以及部分数据：
我作业3训练的模型名称是：w2v.model
训练集：out_train.csv、out_ns_train.csv
验证集：out_val.csv、out_ns_val.csv
测试集：out_test.csv、out_ns_test.csv
其中包含ns的代表内容已经移除 stopword。
所有文件的数格式如下：
```csv
label,text
neg,I had to take an antihistamine to calm the itching
neg,The bottle is lovely and very girlie I love the amethyst jewel tone of purple
neg,When I got my new computer I was not at all happy with the USB keyboard that came with it
neg,I will get this brand of Halvah.has too many artificial ingredients
neg,It lingers forever long after you 've finished your last sip and it 's not very pleasant
neg,This stuff was so fine it went right through my strainer when I tried to minimize the particles
neg,I bought this cable and it does n't work
```
接下来，我会发题给你，你必须阅读题目到最后一行，再开始带我解答这个题目。你必须认真读完我发给你的全部内容，然后解答题目：
Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of:

1. Input layer of the word2vec embeddings you prepared in Assignment 3.
2. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.
3. Final layer with softmax activation function.
4. Use cross-entropy as the loss function.
5. Add L2-norm regularization.
6. Add dropout. Try a few different dropout rates.

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the **training** set. You may only tune your models on your **validation** set. 

Once the development is complete, run your classifier on your **test** set.

Report your classification accuracy results in a table with three different activation functions in the hidden layer (ReLU, sigmoid and tanh). What effect do activation functions have on your results? What effect does addition of L2-norm regularization have on the results? What effect does dropout have on the results? Explain your intuitions briefly (up to 10 sentences).
我作业三训练的模型名称是：w2v.model

额外要求：
必须使用 HW1 中输出的训练、验证和测试集来训练模型，只能用训练集进行训练，验证集进行调试，并用测试集来比对准确率， 可以使用带 stopword 或不带 stopword 的集。（即带_ns和不带_ns的）哪个给出的准确率更高用哪个

输出三个模型，分别是
nn_relu.model，使用RELU作为激活函数
nn_sigmoid.model，使用sigmoid作为激活函数
nn_tanh.model，使用tanh作为激活函数

建立一个inference.py文件，需要接受两个命令行指令
1：路径到一个.txt文件，文件中包含用于测试的句子，每行一句
2：使用的分类器类型，暨输出的三个模型之一

现在所拥有的文件以及部分数据：
我作业3训练的模型名称是：w2v.model
训练集：out_train.csv、out_ns_train.csv
验证集：out_val.csv、out_ns_val.csv
测试集：out_test.csv、out_ns_test.csv
其中包含ns的代表内容已经移除 stopword。
所有数据的结构如下：第一列是标签、第二例是文本
```csv
label,text
neg,I had to take an antihistamine to calm the itching
neg,The bottle is lovely and very girlie I love the amethyst jewel tone of purple
neg,When I got my new computer I was not at all happy with the USB keyboard that came with it
neg,I will get this brand of Halvah.has too many artificial ingredients
neg,It lingers forever long after you 've finished your last sip and it 's not very pleasant
neg,This stuff was so fine it went right through my strainer when I tried to minimize the particles
neg,I bought this cable and it does n't work
```
我的题目如下：
Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of: 

1. Input layer of the word2vec embeddings you prepared in Assignment 3.
2. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.
3. Final layer with softmax activation function.
4. Use cross-entropy as the loss function.
5. Add L2-norm regularization.
6. Add dropout. Try a few different dropout rates.

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the **training** set. You may only tune your models on your **validation** set. Once the development is complete, run your classifier on your **test** set.

Report your classification accuracy results in a table with three different activation functions in the hidden layer (ReLU, sigmoid and tanh). What effect do activation functions have on your results? What effect does addition of L2-norm regularization have on the results? What effect does dropout have on the results? Explain your intuitions briefly (up to 10 sentences).

1. DO NOT tune your models on the **test** set. 
2. You need to submit only **one** model that is trained on either with or without stopwords version (whichever gives you better performance). 

3. **Output files (must be included in your submission):**
    1. nn_relu.model: Classifier w/ ReLU activation
    2. nn_sigmoid.model: Classifier w/ Sigmoid activation 
    3. nn_tanh.model: Classifier w/ Tanh activation
4. **Code requirement:** 
    1. In addition to the general instructions posted at the beginning of the document, you should also submit an `inference.py` script, which classifies a given sentence into a positive/negative class. It should accept the **two** command-line arguments described below. 
        1. arg1: Path to a .txt file, which contains some sentences compiled for evaluation. There will be one sentence per line. 
        2. arg2: Type of classifier to use. Its value will be one of the following – relu, sigmoid, tanh. **Example:** relu indicates that the neural network with ReLU activation should be selected for classifying sentences in the aforementioned .txt file. 
现在所拥有的文件以及部分数据：
训练集：out_train.csv、out_ns_train.csv
验证集：out_val.csv、out_ns_val.csv
测试集：out_test.csv、out_ns_test.csv
其中包含ns的代表内容已经移除 stopword。
所有文件的数格式如下：
```csv
label,text
neg,I had to take an antihistamine to calm the itching
neg,The bottle is lovely and very girlie I love the amethyst jewel tone of purple
neg,When I got my new computer I was not at all happy with the USB keyboard that came with it
neg,I will get this brand of Halvah.has too many artificial ingredients
neg,It lingers forever long after you 've finished your last sip and it 's not very pleasant
neg,This stuff was so fine it went right through my strainer when I tried to minimize the particles
neg,I bought this cable and it does n't work
```
作业三的代码如下：
# -*- coding: utf-8 -*-
# @Time    : 2023/7/3 17:24
# @Author  : AI悦创
# @FileName: generate.py
# @Software: PyCharm
# @Blog    ：https://bornforthis.cn/
import argparse
import gensim
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string


def read_corpus(pos_path, neg_path):
    stop_words = set(stopwords.words('english'))

    def tokenize_and_remove_stopwords(file_path):
        with open(file_path, 'r', encoding='utf-8') as file:
            for line in file:
                tokens = word_tokenize(line)
                yield [token.lower() for token in tokens if
                       token not in string.punctuation and token.lower() not in stop_words]

    return list(tokenize_and_remove_stopwords(pos_path)) + list(tokenize_and_remove_stopwords(neg_path))


def main():
    parser = argparse.ArgumentParser(description='Train Word2Vec on Amazon Corpus')
    parser.add_argument('--folder_path', type=str, help='Path to the folder where pos.txt and neg.txt reside.')
    args = parser.parse_args()

    corpus = read_corpus(args.folder_path + "/pos.txt", args.folder_path + "/neg.txt")

    model = gensim.models.Word2Vec(sentences=corpus, vector_size=100, window=5, min_count=1, workers=4)
    model.save(args.folder_path + "/w2v.model")


if __name__ == "__main__":
    main()
接下来，我会发题给你，你必须阅读题目到最后一行，再开始带我解答这个题目。你必须认真读完我发给你的全部内容，然后解答题目：
Write a python script using PyTorch/Keras to train a fully-connected feed-forward neural network classifier to classify documents in the Amazon corpus into positive and negative classes. Your network must consist of:

1. Input layer of the word2vec embeddings you prepared in Assignment 3.
2. One hidden layer. For the hidden layer, try the following activation functions: ReLU, sigmoid and tanh.
3. Final layer with softmax activation function.
4. Use cross-entropy as the loss function.
5. Add L2-norm regularization.
6. Add dropout. Try a few different dropout rates.

For this assignment, you must use your training/validation/test data splits from Assignment 1. Train your models on the **training** set. You may only tune your models on your **validation** set. 

Once the development is complete, run your classifier on your **test** set.

Report your classification accuracy results in a table with three different activation functions in the hidden layer (ReLU, sigmoid and tanh). What effect do activation functions have on your results? What effect does addition of L2-norm regularization have on the results? What effect does dropout have on the results? Explain your intuitions briefly (up to 10 sentences).
我作业三的代码如下：
```python
import argparse
import gensim
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string


def read_corpus(pos_path, neg_path):
    stop_words = set(stopwords.words('english'))

    def tokenize_and_remove_stopwords(file_path):
        with open(file_path, 'r', encoding='utf-8') as file:
            for line in file:
                tokens = word_tokenize(line)
                yield [token.lower() for token in tokens if
                       token not in string.punctuation and token.lower() not in stop_words]

    return list(tokenize_and_remove_stopwords(pos_path)) + list(tokenize_and_remove_stopwords(neg_path))


def main():
    parser = argparse.ArgumentParser(description='Train Word2Vec on Amazon Corpus')
    parser.add_argument('--folder_path', type=str, help='Path to the folder where pos.txt and neg.txt reside.')
    args = parser.parse_args()

    corpus = read_corpus(args.folder_path + "/pos.txt", args.folder_path + "/neg.txt")

    model = gensim.models.Word2Vec(sentences=corpus, vector_size=100, window=5, min_count=1, workers=4)
    model.save(args.folder_path + "/w2v.model")


if __name__ == "__main__":
    main()

```
额外要求：

必须使用 HW1 中输出的训练、验证和测试集来训练模型，只能用训练集进行训练，验证集进行调试，并用测试集来比对准确率， 可以使用带 stopword 或不带 stopword 的集。（即带_ns和不带_ns的）哪个给出的准确率更高用哪个

输出三个模型，分别是
nn_relu.model，使用RELU作为激活函数
nn_sigmoid.model，使用sigmoid作为激活函数
nn_tanh.model，使用tanh作为激活函数

建立一个inference.py文件，需要接受两个命令行指令
1：路径到一个.txt文件，文件中包含用于测试的句子，每行一句
2：使用的分类器类型，暨输出的三个模型之一

现在所拥有的文件以及部分数据：
训练集：train.csv、train_ns.csv
验证集：val_ns.csv、val.csv
测试集：test.csv、test_ns.csv
其中_ns后缀代表内容已经移除 stopword。
注意1：我所有的数据都有 label和text标签
注意2：不要假设已经将数据加载到了 pandas DataFrame 中，并且 DataFrame 的列为 'label' 和 'text'。切记我并没有提前完成，我其他的也是，全部没有提前完成。
接下来我给你提供每个文件的部分数据。
train_ns.csv 部分数据如下：
```csv
label,text
neg,This item worked first day 's
neg,You probably enjoy reading book much playing game
pos,This reason I gave 4 instead 5 stars
pos,I dont actually use bake cookies fondant cutters work great
pos,I super excited get start using
pos,My husband mechanic dropped case prevents damage phone
neg,What n't care flavor ... lack thereof
```
train.csv 部分数据如下：
```csv
label,text
pos,The con price I still think well worth money
neg,The case slack phone well making good fit impossible
neg,It took get two tubes suspiciously resemble couple little dildos box
neg,That drive three would recognize tracks therefore would recognize disc
neg,Soon spray 've got color The fumes arent harsh still kinda stinks
pos,I needed small tongs serving carrot celery sticks dip
```
val_ns.csv 部分数据如下：
```csv
label,text
pos,The inside soft material protect screen
neg,The first one I purchased year ago made differently item sold
neg,In general son enjoy much
neg,Have already said game easy First time playing ranking A
neg,In order work I put bake broil burns outside cook inside
pos,worth wait wo n't recharge
```
val.csv 部分数据如下：
```csv
negative,Very,hard,to,adjust,the,volume,when,the,static,begins,,,,,,,,,,,,,,,
positive,Why?,Because,I,have,been,cutting,fruit,for,so,long,that,I,can,do,it,practically,blindfolded,,,,,,,,
negative,But,I,want,a,boar,bristle,brush,not,a,everyday,brushI,was,expecting,great,things,from,my,first,boar,hair,ionic,bristle,combo,brush,
```
test_ns.csv 部分数据如下：
```csv
label,text
neg,I had to take an antihistamine to calm the itching
neg,The bottle is lovely and very girlie I love the amethyst jewel tone of purple
neg,When I got my new computer I was not at all happy with the USB keyboard that came with it
neg,I will get this brand of Halvah.has too many artificial ingredients
neg,It lingers forever long after you 've finished your last sip and it 's not very pleasant
neg,This stuff was so fine it went right through my strainer when I tried to minimize the particles
neg,I bought this cable and it does n't work
```

test.csv 部分数据如下：
```csv
label,text
pos,It covers all the buttons power camera volume/zoom and menu .4
neg,If you do n't know what your doing it wo n't go on right
neg,Now I have two giant cans that I probably wo n't drink
neg,You will be flipping left and right going up and down with no sence of direction
```

Solution

1. 添加列名

使用 Python 的 pandas 库来执行此操作。以下是一种可能的方法：

1
import pandas as pd

# 读取csv数据
df = pd.read_csv('your_file.csv', header=None)

# 给第一列和第二列添加标签
df.columns = ['label', 'text']

# 写回到csv文件
df.to_csv('new_file.csv', index=False)

在这段代码中，我们首先使用 pandas 的 read_csv 函数读取 CSV 文件。然后我们设置列标签为 'label' 和 'text'。最后，我们使用to_csv函数将带有新标签的 DataFrame 写回CSV文件。请将 'your_file.csv' 和 'new_file.csv' 替换为您实际的文件路径和名称。

这段代码默认您的 CSV 文件中的数据没有头部，且每一列以逗号分隔。如果您的数据格式不同，请根据实际情况调整 read_csv 和to_csv 函数的参数。

另外，请注意，这段代码将直接覆盖 'new_file.csv' 文件。如果你不希望覆盖原始文件，可以选择不同的文件名。

Q1

提示

我上面的代码能正常模型预期处理的文本长度只有94？

在你的代码中，你使用了下面这行代码来计算训练集中的序列最大长度：

max_len = max([len(x) for x in train_sequences])

这会得到训练集中最长序列的长度，然后你使用这个长度来填充所有的序列，确保它们的长度相同。因此，如果max_len的值是94，那就意味着训练集中最长的文本长度为94。

然而，需要注意的是，在处理文本数据时，输入到神经网络模型中的序列长度可以是一个需要调优的参数。虽然在这里你选择了训练集中的最大长度作为填充长度，但在其他情况下，你可能会选择一个不同的长度，例如，所有序列的平均长度或者长度的中位数，或者根据你对问题的理解选择一个固定的长度。

如果你发现选择的序列长度使得模型性能不佳（例如，如果很多文本的重要信息被截断，或者模型需要处理很多填充的0），那么你可能需要试验不同的序列长度，来看哪个长度可以得到最好的模型性能。

在这里，我将给出计算序列平均长度和中位数的代码示例，并将这两个值用于填充序列。

# 计算序列长度的列表
seq_lengths = [len(x) for x in train_sequences]

# 计算平均长度
avg_len = int(np.mean(seq_lengths))

# 计算中位数长度
median_len = int(np.median(seq_lengths))

# 根据平均长度填充序列
train_data_avg = pad_sequences(train_sequences, maxlen=avg_len)
val_data_avg = pad_sequences(val_sequences, maxlen=avg_len)
test_data_avg = pad_sequences(test_sequences, maxlen=avg_len)

# 根据中位数长度填充序列
train_data_med = pad_sequences(train_sequences, maxlen=median_len)
val_data_med = pad_sequences(val_sequences, maxlen=median_len)
test_data_med = pad_sequences(test_sequences, maxlen=median_len)

现在，你可以分别使用train_data_avg、val_data_avg、test_data_avg和train_data_med、val_data_med、test_data_med来训练模型，并对比它们的性能。

需要注意的是，选择合适的序列长度可能会对模型性能产生显著影响，所以你可能需要试验不同的长度，来找到最佳的长度。

pip install -q keras==2.13.1 numpy==1.25.0 pandas==2.0.3 gensim==4.3.1 tensorflow==2.13.0-rc2
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