ChatGPT可以自动完成哪些数据科学领域的任务？
- 1. 使用ChatGPT为机器学习模型创建一个FastAPI
- 2. 使用Python和弹性搜索创建搜索引擎
- 3. 用ChatGPT写一个Pytorch图像分类的卷积神经网络
- 4. 使用ChatGPT为NLP任务训练XGBoost模型
- 5. 使用ChatGPT让MLflow来跟踪机器学习实验
- 6. 使用ChatGPT编写GitHub操作，以自动部署FastAPI应用程序
- 小结
- 内容来源
目前最热门的领域要数AIGC了，其中ChatGPT更是引爆了人工智能新一轮的革命浪潮。这里，我们关注ChatGPT在数据科学领域中有哪些可以应用的地方。
或许，你已经听说过ChatGPT，或者已经使用过ChatGPT了。这是一款由OpenAI开发的新型人工智能聊天机器人，OpenAI是GPT-3、DALL.E2及其他产品的幕后开发公司。它仿佛是Google、StackOverflow和Readthedocs三者的组合体。自出世以来，它和以前的聊天机器人完全不同。更令人难以置信的是：它能针对各种问题生成详细而全面的答案。例如，它可以回答哲学、数学或计算机科学中的技术问题、可以和ChatGPT闲聊、写与机器学习相关的诗，甚至可以改变它的写作风格。
这里我们重点关注的是它对于编写和解释代码的能力，当然，ChatGPT可以非常出色地编写和解释代码。本文中将用一些涉及机器学习和软件工程的编程问题来挑战ChatGPT。我们将要求它编写常用的代码片段和函数，看看它是否能自动完成。
1. 使用ChatGPT为机器学习模型创建一个FastAPI
当我们需要搭建机器学习模型时，首选的web框架绝对是FastAPI。然而，当涉及编写基本例程时，往往总会忘记语法。因此，我们需要使用ChatGPT来帮助我们创建一个FastAPI:
Create a FastAPI endpoint to server a machine learning model
Sure, I can help you with that! Here's an example of how you can create a FastAPI endpoint to serve a machine learning model:
First, you need to have a trained machine learning model that you want to use. For this example, let's assume that you have a model that can classify images into different categories.
Next, you need to create a FastAPI app and define an endpoint that will accept image data and return the predicted category. Here's an example:
import uvicorn
from fastapi import FastAPI, File, UploadFile
from PIL import Image
import io
import numpy as np
import tensorflow as tf
# Load the trained model
model = tf.keras.models.load_model('my_model.h5')
# Create a FastAPI app
app = FastAPI()
# Define an endpoint that accepts image data and returns the predicted category
@app.post('/predict')
async def predict(image: UploadFile = File(...)):
# Read the image file and convert it to a format that can be used by the model
img_bytes = await image.read()
img = Image.open(io.BytesIO(img_bytes)).convert('RGB')
img = img.resize((224, 224))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Use the model to make a prediction
predictions = model.predict(img_array)
# Return the predicted category
category = np.argmax(predictions[0])
return {'category': category}
In this example, the endpoint is defined using the @app.post('/predict') decorator. It accepts an image file as input using the UploadFile type, which is provided by FastAPI.
The image file is then read and converted to a format that can be used by the model. In this case, the image is resized to 224x224 and normalized to values between 0 and 1.
The model is then used to make a prediction on the image data, and the predicted category is returned as a JSON response.
To run the app, you can use the uvicorn command:
uvicorn main:app --reload
This will start the app on http://localhost:8000. You can test the endpoint using a tool like curl or httpie:
curl -X POST -F "image=@my_image.jpg" http://localhost:8000/predict
This will upload the my_image.jpg file and return the predicted category as a JSON response.
它不仅能生成正确的API代码，而且还对代码进行注释，并通过示例进行详细解释。它甚至使用Pydantic风格来进行输入和输入验证。当然，对于代码可以根据实际用例进行调整。
2. 使用Python和弹性搜索创建搜索引擎
当我们需要利用搜索引擎来查询文本数据时，通常依赖于弹性搜索。当使用弹性搜索时，首先应创建一个索引和一个映射。要了解如何定义和创建对象，无需通读文档（再一次强调！），来看看ChatGPT说了些什么：
How to create a search engine using Python and Elasticsearch?
To create a search engine using Python and Elasticsearch, you'll need to follow these general steps:
1. Install Elasticsearch: You can download and install Elasticsearch from the official website or use a cloud-based service like Elastic Cloud or Amazon Elasticsearch Service.
2. Install the Elasticsearch Python client library: You can install it using pip by running pip install elasticsearch.
3. Create an Elasticsearch index: An index is like a database in Elasticsearch where you can store and search for data. You can create an index using the Elasticsearch Python client library like this:
from elasticsearch import Elasticsearch
es = Elasticsearch()
index_name = "my_index"
if not es.indices.exists(index=index_name):
es.indices.create(index=index_name)
4. Index your data: Before you can search your data, you need to index it in Elasticsearch. You can do this by adding documents to your index using the index method of the Elasticsearch Python client library:
doc = {
"title": "My first document",
"content": "This is the content of my first document."
}
es.index(index=index_name, body=doc)
You can add as many documents as you want to your index.
5. Search your data: You can search your data using the search method of the Elasticsearch Python client library. Here's an example of a simple search query:
query = {
"query": {
"match": {
"content": "first document"
}
}
}
results = es.search(index=index_name, body=query)
This will return all the documents in your index that contain the words "first" and "document" in their "content" field.
6. Display the search results: You can loop through the search results and display them however you want. Here's an example:
for hit in results['hits']['hits']:
print(hit['_source']['title'])
print(hit['_source']['content'])
print('-' * 10)
This will print the title and content of each document that matches the search query.
These are the basic steps to create a search engine using Python and Elasticsearch. You can customize your search engine by adding more fields to your documents, creating more complex search queries, and displaying the search results in different ways.
3. 用ChatGPT写一个Pytorch图像分类的卷积神经网络
以前，当需要训练一个神经网络时，通常是从以前的项目中复制粘贴样板代码。现在，我们可以使用ChatGPT:
Train a convolutional neural network with Pytorch that classifiers images
Sure, I can help you with that! Here's an example of how to train a convolutional neural network (CNN) with PyTorch that classifies images:
1. Import the necessary libraries:
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
2. Load the data:
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
This code loads the CIFAR-10 dataset and applies a transformation to the images that normalizes their pixel values.
3. Define the CNN:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
This code defines a simple CNN with two convolutional layers, two pooling layers, and three fully connected layers.
4. Define the loss function and optimizer:
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
This code uses the cross-entropy loss function and stochastic gradient descent (SGD) optimizer with a learning rate of 0.001 and momentum of 0.9.
5. Train the CNN:
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
This code trains the CNN on the CIFAR-10 dataset for two epochs. It loops over the training data in batches of size 4, computes the loss, backpropagates the error, and updates the weights using the optimizer.
6. Test the CNN:
correct = 0
total = 0
4. 使用ChatGPT为NLP任务训练XGBoost模型
让ChatGPT利用XGBoost构建一个文本分类器，实现文本数据的特征工程。ChatGPT对此做了下述响应，它使用了一个TFIDF矢量转换器，看上去不错!
How to I train an xgboost model for an NLP task?
Training an XGBoost model for an NLP task involves a few specific steps that are different from training a model for a tabular data. Here are the general steps:
1. Prepare the data:
The first step is to prepare your text data into a format that can be used by XGBoost. Typically, this involves tokenizing the text data, vectorizing the tokens, and converting the labels to numerical values. For example, you can use the CountVectorizer or TfidfVectorizer from the scikit-learn library to convert the text data into a numerical format.
2. Create the XGBoost model:
The next step is to create an XGBoost model using the XGBClassifier class from the XGBoost library. This model can be used to classify the text data based on the input features. You can specify the hyperparameters of the model, such as the learning rate, maximum depth, and number of trees.
3. Train the XGBoost model:
Once you have prepared the data and created the XGBoost model, the next step is to train the model on your text data. You can do this using the fit() method of the XGBClassifier class. It takes the training data and labels as inputs, along with the specified hyperparameters.
4. Evaluate the XGBoost model:
After training the model, you can evaluate its performance on a test set using the predict() method of the XGBClassifier class. This method takes the test data as input and returns the predicted labels. You can then compare the predicted labels with the true labels to calculate the accuracy, precision, recall, and F1-score of the model.
Here is some sample code that demonstrates how to train an XGBoost model for an NLP task using the IMDB movie review dataset:
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load the data
data = pd.read_csv('imdb_dataset.csv')
# Prepare the data
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(data['review'].values)
y = data['sentiment'].values
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create the XGBoost model
model = xgb.XGBClassifier(learning_rate=0.1, max_depth=5, n_estimators=100)
# Train the XGBoost model
model.fit(X_train, y_train)
# Evaluate the XGBoost model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %.2f%%' % (accuracy * 100.0))
This code loads the IMDB movie review dataset, tokenizes the text data using CountVectorizer, and trains an XGBoost model using the XGBClassifier class. Finally, it evaluates the accuracy of the model on a test set using the accuracy_score() function from the scikit-learn library.
5. 使用ChatGPT让MLflow来跟踪机器学习实验
当我们需要运行不同的机器学习实验并对它们进行比较时，通常会用MLflow在中央服务器中跟踪机器学习实验。那么ChatGPT会如何实现这一目的呢？
Write a python script that uses mlflow to track a machine learning experiment
Sure! Here is an example Python script that uses mlflow to track a machine learning experiment:
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
# Load the Boston Housing dataset
data = load_boston()
X = data['data']
y = data['target']
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Start the mlflow experiment
mlflow.set_experiment('Boston Housing Prices')
# Define the hyperparameters
n_estimators = 100
max_depth = 5
# Train the model
with mlflow.start_run():
# Create the model
model = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth)
# Fit the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Calculate the evaluation metrics
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
# Log the hyperparameters and evaluation metrics
mlflow.log_param('n_estimators', n_estimators)
mlflow.log_param('max_depth', max_depth)
mlflow.log_metric('mse', mse)
mlflow.log_metric('mae', mae)
mlflow.log_metric('r2', r2)
# Save the model
mlflow.sklearn.log_model(model, 'random_forest_model')
This script uses the mlflow library to track a machine learning experiment on the Boston Housing dataset. It trains a random forest regression model, evaluates it on a test set, and logs the hyperparameters and evaluation metrics using the mlflow API. Finally, it saves the trained model as an artifact using the mlflow.sklearn.log_model() function.
To run this script, you need to have mlflow installed and running on your system. You can start mlflow by running the following command in your terminal:
mlflow server
Then, you can run the script using the following command:
python experiment.py
This will start a new mlflow experiment and log the results in the mlflow UI. You can view the results by opening the mlflow UI in your web browser at http://localhost:5000.
6. 使用ChatGPT编写GitHub操作，以自动部署FastAPI应用程序
如果需要将应用程序投入生产，很可能依赖某些CI/CD管道来自动化部署，或在代码库上运行一套集成测试。通常使用以下Github动作:
Sure, here is an example GitHub Action that automates the development of a FastAPI app:
name: Continuous Integration
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: '3.9'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Lint code
run: |
pylint app
pylint tests
- name: Run tests
run: |
pytest
- name: Build and run Docker image
uses: docker/build-push-action@v2
with:
context: .
push: false
tags: your-image-name:latest
env:
APP_MODULE: app.main:app
- name: Deploy to Heroku
uses: akhileshns/heroku-deploy@v3.12.12
with:
heroku_api_key: ${{ secrets.HEROKU_API_KEY }}
heroku_app_name: your-heroku-app-name
heroku_email: your-email@example.com
app_directory: .
run_on: python app/main.py
This GitHub Action does the following:
1. It triggers on push and pull request events on the main branch.
2. It runs on an Ubuntu environment and sets up Python 3.9.
3. It checks out the code repository.
4. It installs the project dependencies from the requirements.txt file.
5. It runs linting using pylint on the app and tests folders.
6. It runs the test suite using pytest.
7. It builds a Docker image and tags it with "your-image-name:latest".
8. It deploys the app to Heroku using the heroku-deploy action, running the app with python app/main.py
Note that this GitHub Action assumes that you have configured the following:
- A Heroku app with a unique name that you want to deploy to.
- An API key for Heroku, which you should store as a GitHub secret named HEROKU_API_KEY.
You can create a new GitHub Action by adding a new YAML file to your project's .github/workflows directory. For example, you can create a file named ci.yml and copy the above code into it. Once you've pushed the changes to your repository, the GitHub Action will run automatically.
小结
ChatGPT提供了卓越的代码生成能力，一定会成为开发人员的工具箱。然而，它无法提供（尚没有）推理或优化复杂问题的解决方案。如果出现了什么问题，不能完全依赖ChatGPT来修复错误代码，还需要有经验的开发人员参与。
内容来源
- How I Used ChatGPT To Automate These 6 Tasks In My Data Science Role
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