Empirical plot of the chance values attained for the "Mean Average Precision" statistic

mapat_chance.py

# understanding chance level in MAP calculation

import ml_metrics
import numpy as np
import matplotlib.pyplot as plt

results = np.zeros((100,100))
data    = np.zeros((100, 10))
# no need to shuffle the annots since the groundtruth will be randomly generated
annots = [range(100)] * 10

for posness in range(results.shape[0]):
	print(posness)
	posprob = float(posness) / (results.shape[0]-1)
	for mapat_n in range(results.shape[0]):
		if mapat_n==0:
			results[posness, mapat_n] = 1
		else:
			gt = [list(row) for row in ((np.random.uniform(size=np.shape(annots)) < posprob) * 1)]
			results[posness, mapat_n] = ml_metrics.mapk(gt, annots, mapat_n)

plt.imshow(results * 100, origin='lower', interpolation='nearest')
plt.xlabel("Num positives in groundtruth")
plt.ylabel("map_at_what")
plt.title("Chance levels for Mean Avg Precision")
plt.colorbar()
#plt.show()
plt.savefig("mapatchance.png")