knn.py

# import the necessary packages
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imageclassifier.preprocessing.simplepreprocessing import SimplePreprocessor
from imageclassifier.datasets.simpledataset import SimpleDatasetLoader
from imutils import paths
import argparse


ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
help="# of jobs for k-NN distance (-1 uses all available cores)")

args = vars(ap.parse_args())

# grab the list of images that we’ll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))

# initialize the image preprocessor, load the dataset from disk,
# and reshape the data matrix
sp = SimplePreprocessor(32, 32)
sdl = SimpleDatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.reshape((data.shape[0], 3072))

# show some information on memory consumption of the images
print("[INFO] features matrix: {:.1f}MB".format(
data.nbytes / (1024 * 1000.0)))


# encode the labels as integers
le = LabelEncoder()
labels = le.fit_transform(labels)

# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels,
test_size=0.25, random_state=42)


print("[INFO] evaluating k-NN classifier...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
n_jobs=args["jobs"])
model.fit(trainX, trainY)
print(classification_report(testY, model.predict(testX),
target_names=le.classes_))