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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. 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/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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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_cuisine.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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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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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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```
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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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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 |
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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 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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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 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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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. We do not use a two-class classifier, so that rules out one-vs-all. A decision tree might work, or logistic regression for multiclass data. 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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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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## Train your model
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Let's train a 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(cuisines_feature_df, cuisines_label_df, test_size=0.3)
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```
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There are many ways to use the LogisticRegression library 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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According to the docs, "In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and 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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Since you are using the multiclass case, you need to choose what scheme to use and what 'solver' to set.
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Use LogisticRegression with a multiclass setting and the liblinear solver to train.
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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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```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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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 and check the results
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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 cuisine?
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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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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://jolly-sea-0a877260f.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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