Learning Data Mining with Python

Classifying with scikit-learn Estimators
For each row in the dataset, there are 35 values. The first 34 are measurements taken
from the 17 antennas (two values for each antenna). The last is either 'g' or 'b'; that
stands for good and bad, respectively.
Start the IPython Notebook server and create a new notebook called Ionosphere
Nearest Neighbors for this chapter.
First, we load up the NumPy and csv libraries that we will need for our code:
import numpy as np
import csv
To load the dataset, we first get the filename of the dataset. First, get the folder the
dataset is stored in from your data folder:
data_filename = os.path.join(data_folder, "Ionosphere",
"ionosphere.data")
We then create the X and y NumPy arrays to store the dataset in. The sizes of these
arrays are known from the dataset. Don't worry if you don't know the size of future
datasets—we will use other methods to load the dataset in future chapters and you
won't need to know this size beforehand:
X = np.zeros((351, 34), dtype='float')
y = np.zeros((351,), dtype='bool')
The dataset is in a Comma-Separated Values (CSV) format, which is a commonly
used format for datasets. We are going to use the csv module to load this file.
Import it and set up a csv reader object:
with open(data_filename, 'r') as input_file:
reader = csv.reader(input_file)
Next, we loop over the lines in the file. Each line represents a new set of
measurements, which is a sample in this dataset. We use the enumerate
function to get the line's index as well, so we can update the appropriate
sample in the dataset (X):
for i, row in enumerate(reader):
We take the first 34 values from this sample, turn each into a float, and save that to
our dataset:
data = [float(datum) for datum in row[:-1]]
X[i] = data
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