# Cuisine classifiers 1 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. ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/) # Preparation 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. ## Exercise - predict a national cuisine 1. Working in this lesson's _notebook.ipynb_ folder, import that file along with the Pandas library: ```python import pandas as pd cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv") cuisines_df.head() ``` The data looks like this: | | 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 | | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- | | 0 | 0 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 1 | 1 | indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 2 | 2 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 3 | 3 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 4 | 4 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1. Now, import several more libraries: ```python from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, cross_val_score from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve from sklearn.svm import SVC import numpy as np ``` 1. Divide the X and y coordinates into two dataframes for training. `cuisine` can be the labels dataframe: ```python cuisines_label_df = cuisines_df['cuisine'] cuisines_label_df.head() ``` It will look like this: ```output 0 indian 1 indian 2 indian 3 indian 4 indian Name: cuisine, dtype: object ``` 1. Drop that `Unnamed: 0` column and the `cuisine` column, calling `drop()`. Save the rest of the data as trainable features: ```python cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1) cuisines_feature_df.head() ``` Your features look like this: | | 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 | | ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | Now you are ready to train your model! ## Choosing your classifier Now that your data is clean and ready for training, you have to decide which algorithm to use for the job. 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: - Linear Models - Support Vector Machines - Stochastic Gradient Descent - Nearest Neighbors - Gaussian Processes - Decision Trees - Ensemble methods (voting Classifier) - Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification) > 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. ### What classifier to go with? 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: ![comparison of classifiers](images/comparison.png) > Plots generated on Scikit-learn's documentation > 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) ### A better approach 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: ![cheatsheet for multiclass problems](images/cheatsheet.png) > A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options ✅ Download this cheat sheet, print it out, and hang it on your wall! ### Reasoning Let's see if we can reason our way through different approaches given the constraints we have: - **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. - **No two-class classifier**. We do not use a two-class classifier, so that rules out one-vs-all. - **Decision tree or logistic regression could work**. A decision tree might work, or logistic regression for multiclass data. - **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. ### Using Scikit-learn 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). 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. 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` - **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.)" > 🎓 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/) > 🎓 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). Scikit-learn offers this table to explain how solvers handle different challenges presented by different kinds of data structures: ![solvers](images/solvers.png) ## Exercise - split the data We can focus on logistic regression for our first training trial since you recently learned about the latter in a previous lesson. Split your data into training and testing groups by calling `train_test_split()`: ```python X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3) ``` ## Exercise - apply logistic regression 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. 1. Create a logistic regression with multi_class set to `ovr` and the solver set to `liblinear`: ```python lr = LogisticRegression(multi_class='ovr',solver='liblinear') model = lr.fit(X_train, np.ravel(y_train)) accuracy = model.score(X_test, y_test) print ("Accuracy is {}".format(accuracy)) ``` ✅ Try a different solver like `lbfgs`, which is often set as default > Note, use Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) function to flatten your data when needed. The accuracy is good at over **80%**! 1. You can see this model in action by testing one row of data (#50): ```python print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}') print(f'cuisine: {y_test.iloc[50]}') ``` The result is printed: ```output ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object') cuisine: indian ``` ✅ Try a different row number and check the results 1. Digging deeper, you can check for the accuracy of this prediction: ```python test= X_test.iloc[50].values.reshape(-1, 1).T proba = model.predict_proba(test) classes = model.classes_ resultdf = pd.DataFrame(data=proba, columns=classes) topPrediction = resultdf.T.sort_values(by=[0], ascending = [False]) topPrediction.head() ``` The result is printed - Indian cuisine is its best guess, with good probability: | | 0 | | -------: | -------: | | indian | 0.715851 | | chinese | 0.229475 | | japanese | 0.029763 | | korean | 0.017277 | | thai | 0.007634 | ✅ Can you explain why the model is pretty sure this is an Indian cuisine? 1. Get more detail by printing a classification report, as you did in the regression lessons: ```python y_pred = model.predict(X_test) print(classification_report(y_test,y_pred)) ``` | | precision | recall | f1-score | support | | ------------ | --------- | ------ | -------- | ------- | | chinese | 0.73 | 0.71 | 0.72 | 229 | | indian | 0.91 | 0.93 | 0.92 | 254 | | japanese | 0.70 | 0.75 | 0.72 | 220 | | korean | 0.86 | 0.76 | 0.81 | 242 | | thai | 0.79 | 0.85 | 0.82 | 254 | | accuracy | 0.80 | 1199 | | | | macro avg | 0.80 | 0.80 | 0.80 | 1199 | | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | ## 🚀Challenge 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. ## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/) ## Review & Self Study 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) ## Assignment [Study the solvers](assignment.md)