%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

"""
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!
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< Feature Engineering | Contents | In Depth: Linear Regression >
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In Depth: Naive Bayes Classification
The previous four sections have given a general overview of the concepts of machine learning.
In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification.
Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.
Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem.
This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets.
Bayesian Classification
Naive Bayes classifiers are built on Bayesian classification methods.
These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.
In Bayesian classification, we're interested in finding the probability of a label given some observed features, which we can write as $P(L~|~{\rm features})$.
Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:
$$
P(L~|~{\rm features}) = \frac{P({\rm features}~|~L)P(L)}{P({\rm features})}
$$
If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:
$$
\frac{P(L_1~|~{\rm features})}{P(L_2~|~{\rm features})} = \frac{P({\rm features}~|~L_1)}{P({\rm features}~|~L_2)}\frac{P(L_1)}{P(L_2)}
$$
All we need now is some model by which we can compute $P({\rm features}~|~L_i)$ for each label.
Such a model is called a generative model because it specifies the hypothetical random process that generates the data.
Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.
The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.
This is where the "naive" in "naive Bayes" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.
Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.
We begin with the standard imports:
End of explanation
"""


from sklearn.datasets import make_blobs
X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');

"""
Explanation: Gaussian Naive Bayes
Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.
In this classifier, the assumption is that data from each label is drawn from a simple Gaussian distribution.
Imagine that you have the following data:
End of explanation
"""


from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(X, y);

"""
Explanation: One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.
This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution.
The result of this naive Gaussian assumption is shown in the following figure:

figure source in Appendix
The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.
With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.
This procedure is implemented in Scikit-Learn's sklearn.naive_bayes.GaussianNB estimator:
End of explanation
"""


rng = np.random.RandomState(0)
Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)
ynew = model.predict(Xnew)

"""
Explanation: Now let's generate some new data and predict the label:
End of explanation
"""


plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')
lim = plt.axis()
plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)
plt.axis(lim);

"""
Explanation: Now we can plot this new data to get an idea of where the decision boundary is:
End of explanation
"""


yprob = model.predict_proba(Xnew)
yprob[-8:].round(2)

"""
Explanation: We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic.
A nice piece of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the predict_proba method:
End of explanation
"""


from sklearn.datasets import fetch_20newsgroups

data = fetch_20newsgroups()
data.target_names

"""
Explanation: The columns give the posterior probabilities of the first and second label, respectively.
If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach.
Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.
Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method.
Multinomial Naive Bayes
The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.
Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.
The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.
The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution.
Example: Classifying Text
One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.
We discussed the extraction of such features from text in Feature Engineering; here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories.
Let's download the data and take a look at the target names:
End of explanation
"""


categories = ['talk.religion.misc', 'soc.religion.christian',
              'sci.space', 'comp.graphics']
train = fetch_20newsgroups(subset='train', categories=categories)
test = fetch_20newsgroups(subset='test', categories=categories)

"""
Explanation: For simplicity here, we will select just a few of these categories, and download the training and testing set:
End of explanation
"""


print(train.data[5])

"""
Explanation: Here is a representative entry from the data:
End of explanation
"""


from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline

model = make_pipeline(TfidfVectorizer(), MultinomialNB())

"""
Explanation: In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.
For this we will use the TF-IDF vectorizer (discussed in Feature Engineering), and create a pipeline that attaches it to a multinomial naive Bayes classifier:
End of explanation
"""


model.fit(train.data, train.target)
labels = model.predict(test.data)

"""
Explanation: With this pipeline, we can apply the model to the training data, and predict labels for the test data:
End of explanation
"""


from sklearn.metrics import confusion_matrix
mat = confusion_matrix(test.target, labels)
sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,
            xticklabels=train.target_names, yticklabels=train.target_names)
plt.xlabel('true label')
plt.ylabel('predicted label');

"""
Explanation: Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.
For example, here is the confusion matrix between the true and predicted labels for the test data:
End of explanation
"""


def predict_category(s, train=train, model=model):
    pred = model.predict([s])
    return train.target_names[pred[0]]

"""
Explanation: Evidently, even this very simple classifier can successfully separate space talk from computer talk, but it gets confused between talk about religion and talk about Christianity.
This is perhaps an expected area of confusion!
The very cool thing here is that we now have the tools to determine the category for any string, using the predict() method of this pipeline.
Here's a quick utility function that will return the prediction for a single string:
End of explanation
"""


predict_category('sending a payload to the ISS')

predict_category('discussing islam vs atheism')

predict_category('determining the screen resolution')

"""
Explanation: Let's try it out:
End of explanation
"""


