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188 lines
11 KiB
188 lines
11 KiB
# Recipe Classifiers 1
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In this lesson, you will use the dataset you saved from the last lesson full of balanced, clean data all about recipes. You will use this dataset with a variety of classifiers to predict a given national cuisine based on a group of ingredients. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
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# Preparation
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Assuming you completed Lesson 1, make sure that a `cleaned_cuisines.csv` file exists in the root `/data` folder for these four lessons.
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Working in this lesson's `notebook.ipynb` folder, import that file along with the Pandas library:
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```python
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import pandas as pd
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recipes_df = pd.read_csv("../../data/cleaned_cuisine.csv")
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recipes_df.head()
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```
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The data looks like this:
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| | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
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| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
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| 0 | 0 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 1 | 1 | indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 2 | 2 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 3 | 3 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 4 | 4 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
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Now, import several more libraries:
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```python
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split, cross_val_score
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from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
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from sklearn.svm import SVC
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import numpy as np
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```
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Divide the X and y coordinates into two dataframes for training. `cuisine` can be the labels dataframe:
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```python
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recipes_label_df = recipes_df['cuisine']
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recipes_label_df.head()
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```
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It will look like this:
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```
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0 indian
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1 indian
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2 indian
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3 indian
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4 indian
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Name: cuisine, dtype: object
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```
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Drop that `Unnamed: 0` column and the `cuisine` column and save the rest of the data as trainable features:
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```python
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recipes_feature_df = recipes_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
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recipes_feature_df.head()
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```
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Your features look like this:
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| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | |
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| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
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| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
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Now you are ready to train your model!
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## Choosing your classifier
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Now that your data is clean and ready for training, you have to decide which algorithm to use for the job.
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Scikit-Learn groups Classification under Supervised Learning, and in that category you will find many ways to classify. [The variety](https://scikit-learn.org/stable/supervised_learning.html) is quite bewildering at first sight. The following methods all include classification techniques:
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- Linear Models
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- Support Vector Machines
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- Stochastic Gradient Descent
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- Nearest Neighbors
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- Gaussian Processes
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- Decision Trees
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- Ensemble methods (voting Classifier)
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- Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification)
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You can also use [neural networks to classify](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), but that is outside the scope of this lesson.
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So, which classifier should you choose? Often, running through several and looking for a good result is a way to test. Scikit-Learn offers a [side-by-side comparison](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) on a created dataset, comparing KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, showing the results visualized:
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> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)
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✅ Todo: knowledge check
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## Train your model
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Let's train that model. Split your data into training and testing groups:
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```python
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X_train, X_test, y_train, y_test = train_test_split(recipes_feature_df, recipes_label_df, test_size=0.3)
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```
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Use LogisticRegression with a multiclass setting and the lbfgs solver to train.
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✅ Todo: explain ravel
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```python
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lr = LogisticRegression(multi_class='ovr',solver='lbfgs')
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model = lr.fit(X_train, np.ravel(y_train))
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accuracy = model.score(X_test, y_test)
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print ("Accuracy is {}".format(accuracy))
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```
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The accuracy is good at over 80%!
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You can see this model in action by testing one row of data (#50):
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```python
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print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
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print(f'cuisine: {y_test.iloc[50]}')
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```
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The result is printed:
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```
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ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
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cuisine: indian
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```
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✅ Try a different row number!
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Digging deeper, you can check for the accuracy of this prediction:
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```python
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test= X_test.iloc[50].values.reshape(-1, 1).T
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proba = model.predict_proba(test)
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classes = model.classes_
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resultdf = pd.DataFrame(data=proba, columns=classes)
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topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
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topPrediction.head()
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```
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The result is printed - Indian cuisine is its best guess, with good probability:
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| | 0 | | | | | | | | | | | | | | | | | | | | |
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| -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| indian | 0.715851 | | | | | | | | | | | | | | | | | | | | |
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| chinese | 0.229475 | | | | | | | | | | | | | | | | | | | | |
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| japanese | 0.029763 | | | | | | | | | | | | | | | | | | | | |
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| korean | 0.017277 | | | | | | | | | | | | | | | | | | | | |
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| thai | 0.007634 | | | | | | | | | | | | | | | | | | | | |
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✅ Can you explain why the model is pretty sure this is an Indian recipe?
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Get more detail by printing a classification report, as you did in the Regression lessons:
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```python
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y_pred = model.predict(X_test)
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print(classification_report(y_test,y_pred))
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```
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| precision | recall | f1-score | support | | | | | | | | | | | | | | | | | | |
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| ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| chinese | 0.73 | 0.71 | 0.72 | 229 | | | | | | | | | | | | | | | | | |
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| indian | 0.91 | 0.93 | 0.92 | 254 | | | | | | | | | | | | | | | | | |
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| japanese | 0.70 | 0.75 | 0.72 | 220 | | | | | | | | | | | | | | | | | |
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| korean | 0.86 | 0.76 | 0.81 | 242 | | | | | | | | | | | | | | | | | |
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| thai | 0.79 | 0.85 | 0.82 | 254 | | | | | | | | | | | | | | | | | |
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| accuracy | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | |
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| macro avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | |
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| weighted avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | |
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## 🚀Challenge
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Add a challenge for students to work on collaboratively in class to enhance the project
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Optional: add a screenshot of the completed lesson's UI if appropriate
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
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## Review & Self Study
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## Assignment
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[Assignment Name](assignment.md)
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