How should I generate outliers randomly?
Just generate three parts of the data independently: first non-outliers, then lower- and upper outliers, merge them together, and finally shuffle them:
def generate(median=630, err=12, outlier_err=100, size=80, outlier_size=10): errs = err * np.random.rand(size) * np.random.choice((-1, 1), size) data = median + errs lower_errs = outlier_err * np.random.rand(outlier_size) lower_outliers = median - err - lower_errs upper_errs = outlier_err * np.random.rand(outlier_size) upper_outliers = median + err + upper_errs data = np.concatenate((data, lower_outliers, upper_outliers)) np.random.shuffle(data) return data
You'll get something like this:
>>> data = generate()>>> data.shape(100,)>>> data.min()518.1635764484727>>> data.max()729.9467630423616>>> np.median(data)629.9427184256936