You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
ML-For-Beginners/4-Classification/4-Applied/README.md

337 lines
15 KiB

# Build a Cuisine Recommender Web App
4 years ago
In this lesson, you will build a classification model using some of the techniques you have learned in previous lessons and with the delicious cuisine dataset used throughout this series. In addition, you will build a small web app to use a saved model, leveraging Onnx's web runtime.
4 years ago
One of the most useful practical uses of machine learning is building recommendation systems, and you can take the first step in that direction today!
4 years ago
[![Recommendation Systems Introduction](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Recommendation Systems Introduction")
4 years ago
> 🎥 Click the image above for a video: Andrew Ng introduces recommendation system design
3 years ago
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
4 years ago
In this lesson you will learn:
3 years ago
3 years ago
- How to build a model and save it as an Onnx model
- How to use Netron to inspect the model
- How to use your model in a web app for inference
4 years ago
3 years ago
## Build your model
4 years ago
3 years ago
Building applied ML systems is an important part of leveraging these technologies for your business systems. You can use models within your web applications (and thus use them in an offline context if needed) by using Onnx.
In a [previous lesson](../../3-Web-App/1-Web-App/README.md), you built a Regression model about UFO sightings, "pickled" it, and used it in a Flask app. While this architecture is very useful to know, it is a full-stack Python app, and your requirements may include the use of a JavaScript application.
In this lesson, you can build a basic JavaScript-based system for inference. First, however, you need to train a model and convert it for use with Onnx.
## Exercise - train classification model
First, train a classification model using the cleaned cuisines dataset we used.
1. Start by importing useful libraries:
```python
!pip install skl2onnx
3 years ago
import pandas as pd
```
You need '[skl2onnx](https://onnx.ai/sklearn-onnx/)' to help convert your Scikit-learn model to Onnx format.
1. Then, work with your data in the same way you did in previous lessons, by reading a CSV file using `read_csv()`:
```python
data = pd.read_csv('../data/cleaned_cuisine.csv')
data.head()
```
1. Remove the first two unnecessary columns and save the remaining data as 'X':
4 years ago
3 years ago
```python
X = data.iloc[:,2:]
X.head()
```
4 years ago
3 years ago
1. Save the labels as 'y':
3 years ago
3 years ago
```python
y = data[['cuisine']]
y.head()
```
3 years ago
3 years ago
### Commence the training routine
3 years ago
3 years ago
We will use the 'SVC' library which has good accuracy.
3 years ago
3 years ago
1. Import the appropriate libraries from Scikit-learn:
3 years ago
3 years ago
```python
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
```
3 years ago
3 years ago
1. Separate training and test sets:
3 years ago
3 years ago
```python
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
```
3 years ago
3 years ago
1. Build an SVC Classification model as you did in the previous lesson:
3 years ago
3 years ago
```python
model = SVC(kernel='linear', C=10, probability=True,random_state=0)
model.fit(X_train,y_train.values.ravel())
```
3 years ago
3 years ago
1. Now, test your model, calling `predict()`:
4 years ago
3 years ago
```python
y_pred = model.predict(X_test)
```
3 years ago
3 years ago
1. Print out a classification report to check the model's quality:
3 years ago
3 years ago
```python
print(classification_report(y_test,y_pred))
```
3 years ago
3 years ago
As we saw before, the accuracy is good:
3 years ago
3 years ago
```output
precision recall f1-score support
chinese 0.72 0.69 0.70 257
indian 0.91 0.87 0.89 243
japanese 0.79 0.77 0.78 239
korean 0.83 0.79 0.81 236
thai 0.72 0.84 0.78 224
accuracy 0.79 1199
macro avg 0.79 0.79 0.79 1199
weighted avg 0.79 0.79 0.79 1199
```
4 years ago
3 years ago
### Convert your model to Onnx
3 years ago
3 years ago
Make sure to do the conversion with the proper Tensor number. This dataset has 380 ingredients listed, so you need to notate that number in `FloatTensorType`:
3 years ago
3 years ago
1. Convert using a tensor number of 380.
4 years ago
3 years ago
```python
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([None, 380]))]
options = {id(model): {'nocl': True, 'zipmap': False}}
```
1. Create the onx and store as a file **model.onnx**:
```python
onx = convert_sklearn(model, initial_types=initial_type, options=options)
with open("./model.onnx", "wb") as f:
f.write(onx.SerializeToString())
```
> Note, you can pass in [options](https://onnx.ai/sklearn-onnx/parameterized.html) in your conversion script. In this case, we passed in 'nocl' to be True and 'zipmap' to be False. Since this is a classification model, you have the option to remove ZipMap which produces a list of dictionaries (not necessary). `nocl` refers to class information being included in the model. Reduce your model's size by setting `nocl` to 'True'.
4 years ago
3 years ago
Running the entire notebook will now build an Onnx model and save it to this folder.
3 years ago
3 years ago
## View your model
4 years ago
3 years ago
Onnx models are not very visible in Visual Studio code, but there's a very good free software that many researchers use to visualize the model to ensure that it is properly built. Download [Netron](https://github.com/lutzroeder/Netron) and open your model.onnx file. You can see your simple model visualized, with its 380 inputs and classifier listed:
4 years ago
3 years ago
![Netron visual](images/netron.png)
4 years ago
3 years ago
Netron is a helpful tool to view your models.
Now you are ready to use this neat model in a web app. Let's build an app that will come in handy when you look in your refrigerator and try to figure out which combination of your leftover ingredients you can use to cook a given cuisine, as determined by your model.
3 years ago
3 years ago
## Build a recommender web application
You can use your model directly in a web app. This architecture also allows you to run it locally and even offline if needed. Start by creating an `index.html` file in the same folder where you stored your `model.onnx` file.
3 years ago
1. In this file _index.html_, add the following markup:
```html
<!DOCTYPE html>
<html>
<header>
<title>Cuisine Matcher</title>
</header>
<body>
...
</body>
</html>
```
1. Now, working within the `body` tags, add a little markup to show a list of checkboxes reflecting some ingredients:
```html
<h1>Check your refrigerator. What can you create?</h1>
<div id="wrapper">
<div class="boxCont">
<input type="checkbox" value="4" class="checkbox">
<label>apple</label>
</div>
<div class="boxCont">
<input type="checkbox" value="247" class="checkbox">
<label>pear</label>
</div>
<div class="boxCont">
<input type="checkbox" value="77" class="checkbox">
<label>cherry</label>
</div>
<div class="boxCont">
<input type="checkbox" value="126" class="checkbox">
<label>fenugreek</label>
</div>
<div class="boxCont">
<input type="checkbox" value="302" class="checkbox">
<label>sake</label>
</div>
<div class="boxCont">
<input type="checkbox" value="327" class="checkbox">
<label>soy sauce</label>
</div>
<div class="boxCont">
<input type="checkbox" value="112" class="checkbox">
<label>cumin</label>
</div>
3 years ago
</div>
3 years ago
<div style="padding-top:10px">
<button onClick="startInference()">What kind of cuisine can you make?</button>
</div>
```
3 years ago
3 years ago
Notice that each checkbox is given a value. This reflects the index where the ingredient is found according to the dataset. Apple, for example, in this alphabetic list, occupies the fifth column, so its value is '4' since we start counting at 0. You can consult the [ingredients spreadsheet](../data/ingredient_indexes.csv) to discover a given ingredient's index.
3 years ago
3 years ago
Continuing your work in the index.html file, add a script block where the model is called after the final closing `</div>`.
3 years ago
3 years ago
1. First, import the [Onnx Runtime](https://www.onnxruntime.ai/):
3 years ago
3 years ago
```html
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script>
```
3 years ago
3 years ago
> Onnx Runtime is used to enable running your Onnx models across a wide range of hardware platforms, including optimizations and an API to use.
3 years ago
3 years ago
1. Once the Runtime is in place, you can call it:
3 years ago
3 years ago
```javascript
<script>
const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
3 years ago
3 years ago
const checks = [].slice.call(document.querySelectorAll('.checkbox'));
// use an async context to call onnxruntime functions.
function init() {
checks.forEach(function (checkbox, index) {
checkbox.onchange = function () {
if (this.checked) {
var index = checkbox.value;
if (index !== -1) {
ingredients[index] = 1;
}
console.log(ingredients)
3 years ago
}
3 years ago
else {
var index = checkbox.value;
if (index !== -1) {
ingredients[index] = 0;
}
console.log(ingredients)
3 years ago
}
}
3 years ago
})
}
function testCheckboxes() {
for (var i = 0; i < checks.length; i++)
if (checks[i].type == "checkbox")
if (checks[i].checked)
return true;
return false;
}
async function startInference() {
let checked = testCheckboxes()
if (checked) {
try {
// create a new session and load the model.
const session = await ort.InferenceSession.create('./model.onnx');
const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
const feeds = { float_input: input };
// feed inputs and run
const results = await session.run(feeds);
// read from results
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
3 years ago
}
3 years ago
}
else alert("Please check an ingredient")
3 years ago
}
3 years ago
init();
</script>
```
3 years ago
In this code, there are several things happening:
1. You created an array of 380 possible values (1 or 0) to be set and sent to the model for inference, depending on whether an ingredient checkbox is checked.
2. You created an array of checkboxes and a way to determine whether they were checked in an `init` function that is called when the application starts. When a checkbox is checked, the `ingredients` array is altered to reflect the chosen ingredient.
3. You created a `testCheckboxes` function that checks whether any checkbox was checked.
4. You use that function when the button is pressed and, if any checkbox is checked, you start inference.
5. The inference routine includes:
3 years ago
1. Setting up an asynchronous load of the model
3 years ago
2. Creating a Tensor structure to send to the model
3. Creating 'feeds' that reflects the `float_input` input that you created when training your model (you can use Netron to verify that name)
4. Sending these 'feeds' to the model and waiting for a response
## Test your application
Open a terminal session in Visual Studio Code in the folder where your index.html file resides. Ensure that you have `[http-server](https://www.npmjs.com/package/http-server)` installed globally, and type `http-server` at the prompt. A localhost should open and you can view your web app. Check what cuisine is recommended based on various ingredients:
![ingredient web app](images/web-app.png)
3 years ago
Congratulations, you have created a 'recommendation' web app with a few fields. Take some time to build out this system!
3 years ago
## 🚀Challenge
Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
4 years ago
## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
3 years ago
4 years ago
## Review & Self Study
3 years ago
While this lesson just touched on the utility of creating a recommendation system for food ingredients, this area of ML applications is very rich in examples. Read some more about how these systems are built:
- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
3 years ago
## Assignment
3 years ago
[Build a new recommender](assignment.md)