import logging
import numpy as np
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import mean_absolute_error as mae # noqa
from sklearn.metrics import r2_score # noqa
from .const import EPS
logger = logging.getLogger("causalml")
[docs]def ape(y, p):
"""Absolute Percentage Error (APE).
Args:
y (float): target
p (float): prediction
Returns:
e (float): APE
"""
assert np.abs(y) > EPS
return np.abs(1 - p / y)
[docs]def mape(y, p):
"""Mean Absolute Percentage Error (MAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): MAPE
"""
filt = np.abs(y) > EPS
return np.mean(np.abs(1 - p[filt] / y[filt]))
[docs]def smape(y, p):
"""Symmetric Mean Absolute Percentage Error (sMAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): sMAPE
"""
return 2.0 * np.mean(np.abs(y - p) / (np.abs(y) + np.abs(p)))
[docs]def rmse(y, p):
"""Root Mean Squared Error (RMSE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): RMSE
"""
# check and get number of samples
assert y.shape == p.shape
return np.sqrt(mse(y, p))
[docs]def gini(y, p):
"""Normalized Gini Coefficient.
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): normalized Gini coefficient
"""
# check and get number of samples
assert y.shape == p.shape
n_samples = y.shape[0]
# sort rows on prediction column
# (from largest to smallest)
arr = np.array([y, p]).transpose()
true_order = arr[arr[:, 0].argsort()][::-1, 0]
pred_order = arr[arr[:, 1].argsort()][::-1, 0]
# get Lorenz curves
l_true = np.cumsum(true_order) / np.sum(true_order)
l_pred = np.cumsum(pred_order) / np.sum(pred_order)
l_ones = np.linspace(1 / n_samples, 1, n_samples)
# get Gini coefficients (area between curves)
g_true = np.sum(l_ones - l_true)
g_pred = np.sum(l_ones - l_pred)
# normalize to true Gini coefficient
return g_pred / g_true
[docs]def regression_metrics(
y, p, w=None, metrics={"RMSE": rmse, "sMAPE": smape, "Gini": gini}
):
"""Log metrics for regressors.
Args:
y (numpy.array): target
p (numpy.array): prediction
w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log
metrics for the treatment and control group separately
metrics (dict, optional): a dictionary of the metric names and functions
"""
assert metrics
assert y.shape[0] == p.shape[0]
for name, func in metrics.items():
if w is not None:
assert y.shape[0] == w.shape[0]
if w.dtype != bool:
w = w == 1
logger.info("{:>8s} (Control): {:10.4f}".format(name, func(y[~w], p[~w])))
logger.info("{:>8s} (Treatment): {:10.4f}".format(name, func(y[w], p[w])))
else:
logger.info("{:>8s}: {:10.4f}".format(name, func(y, p)))