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# Cuisine 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 cuisines.
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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://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
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# Preparation
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Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
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## Exercise - predict a national cuisine
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1. 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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cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
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cuisines_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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1. 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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1. 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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cuisines_label_df = cuisines_df['cuisine']
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cuisines_label_df.head()
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```
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It will look like this:
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```output
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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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1. Drop that `Unnamed: 0` column and the `cuisine` column, calling `drop()`. Save the rest of the data as trainable features:
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```python
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cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
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cuisines_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 | 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 | 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 | 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 | 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 | 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 data](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), but that is outside the scope of this lesson.
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### What classifier to go with?
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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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> Plots generated on Scikit-learn's documentation
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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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### A better approach
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A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:
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> A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options
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✅ Download this cheat sheet, print it out, and hang it on your wall!
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### Reasoning
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Let's see if we can reason our way through different approaches given the constraints we have:
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- **Neural networks are too heavy**. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, neural networks are too heavyweight for this task.
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- **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all.
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- **Decision tree or logistic regression could work**. A decision tree might work, or logistic regression for multiclass data.
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- **Multiclass Boosted Decision Trees solve a different problem**. The multiclass boosted decision tree is most suitable for nonparametric tasks, e.g. tasks designed to build rankings, so it is not useful for us.
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### Using Scikit-learn
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We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
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Essentially there are two important parameters `multi_class` and `solver`, that we need to specify, when we ask Scikit-learn to perform a logistic regression. The `multi_class` value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all `multi_class` values.
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According to the docs, in the multiclass case, the training algorithm:
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- **Uses the one-vs-rest (OvR) scheme**, if the `multi_class` option is set to `ovr`
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- **Uses the cross-entropy loss**, if the `multi_class` option is set to `multinomial`. (Currently the `multinomial` option is supported only by the ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)"
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> 🎓 The 'scheme' here can either be 'ovr' (one-vs-rest) or 'multinomial'. Since logistic regression is really designed to support binary classification, these schemes allow it to better handle multiclass classification tasks. [source](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
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> 🎓 The 'solver' is defined as "the algorithm to use in the optimization problem". [source](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
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Scikit-learn offers this table to explain how solvers handle different challenges presented by different kinds of data structures:
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## Exercise - split the data
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We can focus on logistic regression for our first training trial since you recently learned about the latter in a previous lesson.
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Split your data into training and testing groups by calling `train_test_split()`:
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```python
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X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
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```
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## Exercise - apply logistic regression
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Since you are using the multiclass case, you need to choose what _scheme_ to use and what _solver_ to set. Use LogisticRegression with a multiclass setting and the **liblinear** solver to train.
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1. Create a logistic regression with multi_class set to `ovr` and the solver set to `liblinear`:
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```python
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lr = LogisticRegression(multi_class='ovr',solver='liblinear')
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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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✅ Try a different solver like `lbfgs`, which is often set as default
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> Note, use Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) function to flatten your data when needed.
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The accuracy is good at over **80%**!
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1. 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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```output
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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 and check the results
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1. 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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| -------: | -------: |
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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 cuisine?
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1. 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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In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
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## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
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## Review & Self Study
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Dig a little more into the math behind logistic regression in [this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
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## Assignment
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[Study the solvers](assignment.md)
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