Precision and recall from text file

Question

I have this chunk of code which takes in a directory and spits of 5 prediction in descending order and stores it in a text file. Any sugestions as to how may i edit this to calculate precsion and recall for the directory?

Thanks in advance.

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# change this as you see fit
image_path = sys.argv[1]

extension = ['*.jpeg', '*.jpg']
files=[]


for e in extension:
    directory = os.path.join(image_path, e)
fileList = glob.glob(directory)
for f in fileList:
    files.append(f)
    # Loads label file, strips off carriage return
    label_lines = [line.rstrip() for line
                   in tf.gfile.GFile("/tf_files/retrained_labels.txt")]
    # Unpersists graph from file  
with tf.gfile.FastGFile("/tf_files/retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:

# Feed the image_data as input to the graph and get first prediction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

# Read in the image_data
for file in files:
    image_data = tf.gfile.FastGFile(file, 'rb').read()

    predictions = sess.run(softmax_tensor, \
                   {'DecodeJpeg/contents:0': image_data})

# Sort to show labels of first prediction in order of confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
    print("Image Name: " + file)
    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))

Answer 1

Consider using precision_recall_fscore_support or confusion_matrix.

For both of these you need actual labels and the predicted labels by model.