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"""
Custom loss functions.
Supports training a regressor on data that includes inequalities (e.g. x < 100).
This loss assumes that the normal range for y_true and y_pred is 0 - 1. As a
hack, the implementation uses other intervals for y_pred to encode the
inequality information.
y_true is interpreted as follows:
between 0 - 1
Regular MSE loss is used. Penality (y_pred - y_true)**2 is applied if
y_pred is greater or less than y_true.
between 2 - 3:
Treated as a "<" inequality. Penality (y_pred - (y_true - 2))**2 is
applied only if y_pred is greater than y_true - 2.
between 4 - 5:
Treated as a ">" inequality. Penality (y_pred - (y_true - 4))**2 is
applied only if y_pred is less than y_true - 4.
"""
from keras import backend as K
import pandas
import numpy
LOSSES = {}
def encode_y(y, inequalities=None):
y = numpy.array(y, dtype="float32")
if y.isnan().any():
raise ValueError("y contains NaN")
if (y > 1.0).any():
raise ValueError("y contains values > 1.0")
if (y < 0.0).any():
raise ValueError("y contains values < 0.0")
if inequalities is None:
encoded = y
else:
offsets = pandas.Series(inequalities).map({
'=': 0,
'<': 2,
'>': 4,
}).values
if offsets.isnan().any():
raise ValueError("Invalid inequality. Must be =, <, or >")
encoded = y + offsets
assert not encoded.isnan().any()
return encoded
def mse_with_ineqalities(y_true, y_pred):
# Handle (=) inequalities
diff1 = y_pred - y_true
diff1 *= K.cast(y_true >= 0.0, "float32")
diff1 *= K.cast(y_true <= 1.0, "float32")
# Handle (>) inequalities
diff2 = y_pred - (y_true - 2.0)
diff2 *= K.cast(y_true >= 2.0, "float32")
diff2 *= K.cast(y_true <= 3.0, "float32")
diff2 *= K.cast(diff2 < 0.0, "float32")
# Handle (<) inequalities
diff3 = y_pred - (y_true - 4.0)
diff3 *= K.cast(y_true >= 4.0, "float32")
diff3 *= K.cast(diff3 > 0.0, "float32")
return (
K.sum(K.square(diff1), axis=-1) +
K.sum(K.square(diff2), axis=-1) +
K.sum(K.square(diff3), axis=-1))
LOSSES["mse_with_ineqalities"] = mse_with_ineqalities