This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).
This session will cover the basics of Scikit-Learn, a popular package containing a collection of tools for machine learning written in Python. See more at http://scikit-learn.org.
Scikit-Learn is a Python package designed to give access to well-known machine learning algorithms within Python code, through a clean, well-thought-out API. It has been built by hundreds of contributors from around the world, and is used across industry and academia.
Scikit-Learn is built upon Python's NumPy (Numerical Python) and SciPy (Scientific Python) libraries, which enable efficient in-core numerical and scientific computation within Python. As such, scikit-learn is not specifically designed for extremely large datasets, though there is some work in this area.
For this short introduction, I'm going to stick to questions of in-core processing of small to medium datasets with Scikit-learn.
In this section we will begin to explore the basic principles of machine learning. Machine Learning is about building programs with tunable parameters (typically an array of floating point values) that are adjusted automatically so as to improve their behavior by adapting to previously seen data.
Machine Learning can be considered a subfield of Artificial Intelligence since those algorithms can be seen as building blocks to make computers learn to behave more intelligently by somehow generalizing rather that just storing and retrieving data items like a database system would do.
We'll take a look at two very simple machine learning tasks here. The first is a classification task: the figure shows a collection of two-dimensional data, colored according to two different class labels. A classification algorithm may be used to draw a dividing boundary between the two clusters of points:
In [1]:
%matplotlib inline
# set seaborn plot defaults.
# This can be safely commented out
import seaborn; seaborn.set()
In [2]:
# Import the example plot from the figures directory
from fig_code import plot_sgd_separator
plot_sgd_separator()
This may seem like a trivial task, but it is a simple version of a very important concept. By drawing this separating line, we have learned a model which can generalize to new data: if you were to drop another point onto the plane which is unlabeled, this algorithm could now predict whether it's a blue or a red point.
If you'd like to see the source code used to generate this, you can either open the
code in the figures directory, or you can load the code using the %load magic command:
The next simple task we'll look at is a regression task: a simple best-fit line to a set of data:
In [3]:
from fig_code import plot_linear_regression
plot_linear_regression()
Again, this is an example of fitting a model to data, such that the model can make generalizations about new data. The model has been learned from the training data, and can be used to predict the result of test data: here, we might be given an x-value, and the model would allow us to predict the y value. Again, this might seem like a trivial problem, but it is a basic example of a type of operation that is fundamental to machine learning tasks.
Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer. Along with this, we'll build on our matplotlib examples from the previous section and show some examples of how to visualize data.
Most machine learning algorithms implemented in scikit-learn expect data to be stored in a
two-dimensional array or matrix. The arrays can be
either numpy arrays, or in some cases scipy.sparse matrices.
The size of the array is expected to be [n_samples, n_features]
The number of features must be fixed in advance. However it can be very high dimensional
(e.g. millions of features) with most of them being zeros for a given sample. This is a case
where scipy.sparse matrices can be useful, in that they are
much more memory-efficient than numpy arrays.
(Figure from the Python Data Science Handbook)
In [4]:
from IPython.core.display import Image, display
display(Image(filename='images/iris_setosa.jpg'))
print("Iris Setosa\n")
display(Image(filename='images/iris_versicolor.jpg'))
print("Iris Versicolor\n")
display(Image(filename='images/iris_virginica.jpg'))
print("Iris Virginica")
If we want to design an algorithm to recognize iris species, what might the data be?
Remember: we need a 2D array of size [n_samples x n_features].
What would the n_samples refer to?
What might the n_features refer to?
Remember that there must be a fixed number of features for each sample, and feature
number i must be a similar kind of quantity for each sample.
Scikit-learn has a very straightforward set of data on these iris species. The data consist of the following:
Features in the Iris dataset:
Target classes to predict:
scikit-learn embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:
In [5]:
from sklearn.datasets import load_iris
iris = load_iris()
In [6]:
iris.keys()
Out[6]:
In [7]:
n_samples, n_features = iris.data.shape
print((n_samples, n_features))
print(iris.data[0])
In [8]:
print(iris.data.shape)
print(iris.target.shape)
In [9]:
print(iris.target)
In [10]:
print(iris.target_names)
This data is four dimensional, but we can visualize two of the dimensions at a time using a simple scatter-plot:
In [11]:
import numpy as np
import matplotlib.pyplot as plt
x_index = 0
y_index = 1
# this formatter will label the colorbar with the correct target names
formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])
plt.scatter(iris.data[:, x_index], iris.data[:, y_index],
c=iris.target, cmap=plt.cm.get_cmap('RdYlBu', 3))
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.clim(-0.5, 2.5)
plt.xlabel(iris.feature_names[x_index])
plt.ylabel(iris.feature_names[y_index]);
They come in three flavors:
sklearn.datasets.load_*sklearn.datasets.fetch_*sklearn.datasets.make_*You can explore the available dataset loaders, fetchers, and generators using IPython's
tab-completion functionality. After importing the datasets submodule from sklearn,
type
datasets.load_ + TAB
or
datasets.fetch_ + TAB
or
datasets.make_ + TAB
to see a list of available functions.
In [12]:
from sklearn import datasets
In [13]:
# Type datasets.fetch_<TAB> or datasets.load_<TAB> in IPython to see all possibilities
# datasets.fetch_
In [14]:
# datasets.load_
In the next section, we'll use some of these datasets and take a look at the basic principles of machine learning.