Bayesian histograms are a nifty tool for data mining if:
- you want to know how the event rate (probability) of a binary rare event depends on a parameter;
- you have millions or even billions of data points, but few positive samples;
- you suspect the event rate depends highly non-linearly on the parameter;
- you don't know whether you have enough data, so you need uncertainty information.
Thanks to an adaptive bin pruning algorithm, you don't even have to choose the number of bins, and you should get good results out of the box.
This is how they look in practice (see full example below):
$ pip install bayeshistAssume you have binary samples of a rare event like this:
Compute and plot a Bayesian histogram:
>>> from bayeshist import bayesian_histogram, plot_bayesian_histogram
# compute Bayesian histogram from samples
>>> bin_edges, beta_dist = bayesian_histogram(X, y, bins=100, pruning_method="bayes")
# beta_dist is a `scipy.stats.Beta` object, so we can get the
# predicted mean event rate for each histogram bin like this:
>>> bin_mean_pred = best_dist.mean()
# plot it up
>>> plot_bayesian_histogram(bin_edges, beta_dist)The result is something like this:
See also demo.ipynb for a full walkthrough of this example.
def bayesian_histogram(
x: np.ndarray,
y: np.ndarray,
bins: Union[int, Iterable] = 100,
x_range: Optional[Tuple[float, float]] = None,
prior_params: Optional[Tuple[float, float]] = None,
pruning_method: Optional[Literal["bayes", "fisher"]] = "bayes",
pruning_threshold: Optional[float] = None,
max_bin_size: Optional[float] = None,
) -> Tuple[np.ndarray, FrozenDistType]:
"""Compute Bayesian histogram for data x, binary target y.
The output is a Beta distribution over the event rate for each bin.
Parameters:
x:
1-dim array of data.
y:
1-dim array of binary labels (0 or 1).
bins:
int giving the number of equally spaced intial bins,
or array giving initial bin edges. (default: 100)
x_range:
Range spanned by binning. Not used if `bins` is an array.
(default: [min(x), max(x)])
prior_params:
Parameters to use in Beta prior. First value relates to positive,
second value to negative samples. [0.5, 0.5] represents Jeffrey's prior, [1, 1] a flat
prior. The default is a weakly informative prior based on the global event rate.
(default: `[1, num_neg / num_pos]`)
pruning_method:
Method to use to decide whether neighboring bins should be merged or not.
Valid values are "bayes" (Bayes factor), "fisher" (exact Fisher test), or None
(no pruning). (default: "bayes")
pruning_threshold:
Threshold to use in significance test specified by `pruning_method`.
(default: 2 for "bayes", 0.2 for "fisher")
max_bin_size:
Maximum size (in units of x) above which bins will not be merged
(except empty bins). (default: unlimited size)
Returns:
bin_edges: Coordinates of bin edges
beta_dist: n-dimensional Beta distribution (n = number of bins)
Example:
>>> x = np.random.randn(1000)
>>> p = 10 ** (-2 + x)
>>> y = np.random.rand() < p
>>> bins, beta_dist = bayesian_histogram(x, y)
>>> plt.plot(0.5 * (bins[1:] + bins[:-1]), beta_dist.mean())
"""def plot_bayesian_histogram(
bin_edges: np.ndarray,
data_dist: FrozenDistType,
color: Union[str, Iterable[float], None] = None,
label: Optional[str] = None,
ax: Any = None,
ci: Optional[Tuple[float, float]] = (0.01, 0.99)
) -> None:
"""Plot a Bayesian histogram as horizontal lines with credible intervals.
Parameters:
bin_edges:
Coordinates of bin edges
data_dist:
n-dimensional Beta distribution (n = number of bins)
color:
Color to use (default: use next in current color cycle)
label:
Legend label (default: no label)
ax:
Matplotlib axis to use (default: current axis)
ci:
Credible interval used for shading, use `None` to disable shading.
Example:
>>> x = np.random.randn(1000)
>>> p = 10 ** (-2 + x)
>>> y = np.random.rand() < p
>>> bins, beta_dist = bayesian_histogram(x, y)
>>> plot_bayesian_histogram(bins, beta_dist)
"""
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