Scikit-learn toy datasets

The Scikit-learn toy dataset module is embedded in the Scikit-learn package. Such datasets can easily be directly loaded into Python by the import command, and they don't require any download from any external internet repository. Some examples of this type of dataset are the Iris, Boston, and Digits datasets, to name the principal ones mentioned in uncountable publications and books, and a few other classic ones for classification and regression.

Structured in a dictionary-like object, besides the features and target variables, they offer complete descriptions and contextualization of the data itself.

For instance, to load the Iris dataset, enter the following commands:

In: from sklearn import datasets
    iris = datasets.load_iris()

After loading the dataset, we can explore the data description and understand how the features and targets are stored. All Scikit-learn datasets present the following methods:

  • .DESCR: This provides a general description of the dataset
  • .data: This contains all the features
  • .feature_names: This reports the names of the features
  • .target: This contains the target values, expressed as values or numbered classes
  • .target_names: This reports the names of the classes in the target
  • .shape: This is a method that you can apply to both .data and .target; it reports the number of observations (the first value) and features (the second value, if present) that are present

Now, let's just try to implement them (no output is reported, but the print commands will provide you with plenty of information):

In: print (iris.DESCR)
    print (iris.data)
    print (iris.data.shape)
    print (iris.feature_names)
    print (iris.target)
    print (iris.target.shape)
    print (iris.target_names)

You should know something else about the dataset—how many examples and variables are present, and what their names are. Notice that the main data structures that are enclosed in the iris object are the two arrays, data, and target:

In: print (type(iris.data))

Out: <class 'numpy.ndarray'>

Iris.data offers the numeric values of the variables named sepal length, sepal width, petal length, and petal width, arranged in a matrix form (150,4), where 150 is the number of observations and 4 is the number of features. The order of the variables is the order presented in iris.feature_names.

Iris.target is a vector of integer values, where each number represents a distinct class (refer to the content of target_names; each class name is related to its index number and setosa, which is the zero element of the list, is represented as 0 in the target vector).

The Iris flower dataset was first used in 1936 by Ronald Fisher, who was one of the fathers of modern statistical analysis, in order to demonstrate the functionality of linear discriminant analysis on a small set of empirically verifiable examples (each of the 150 data points represented iris flowers). These examples were arranged into tree-balanced species classes (each class consisted of one-third of the examples) and were provided with four metric descriptive variables that, when combined, were able to separate the classes.

The advantage of using such a dataset is that it is very easy to load, handle, and explore for different purposes, from supervised learning to a graphical representation. Modeling activities take almost no time on any computer, no matter what its specifications are. Moreover, the relationship between the classes and the role of the explicative variables are well-known. Therefore, the task is challenging, but it is not very arduous.

For example, let's just observe how classes can be easily separated when you wish to combine at least two of the four available variables by using a scatterplot matrix.

Scatterplot matrices are arranged in a matrix format, whose columns and rows are the dataset variables. The elements of the matrix contain single scatterplots whose x values are determined by the row variable of the matrix and y values by the column variable. The diagonal elements of the matrix may contain a distribution histogram or some other univariate representation of the variable at the same time in its row and column.

The pandas library offers an off-the-shelf function to quickly build scatterplot matrices and start exploring relationships and distributions between the quantitative variables in a dataset:

In: import pandas as pd
    import numpy as np
    colors = list()
    palette = {0: "red", 1: "green", 2: "blue"}

In: for c in np.nditer(iris.target): colors.append(palette[int(c)])
        # using the palette dictionary, we convert
        # each numeric class into a color string
    dataframe = pd.DataFrame(iris.data,  columns=iris.feature_names)

In: sc = pd.scatter_matrix(dataframe, alpha=0.3, figsize=(10, 10), 
    diagonal='hist', color=colors, marker='o', grid=True)

The following is the output obtained after executing the preceding code:

We encourage you to experiment a lot with this dataset and with similar ones before you work on other complex real data because the advantage of focusing on an accessible, non-trivial data problem is that it can help you to quickly build your foundations on data science.

After a while anyway, though they are useful and interesting for your learning activities, toy datasets will start limiting the variety of different experimentations that you can achieve. In spite of the insights provided, in order to progress, you'll need to gain access to complex and realistic data science topics. Consequently, we will have to resort to some external data.

上一章目录下一章