Add negative binomial distribution

ngboost/distns/__init__.py

@@ -7,6 +7,7 @@
 from .laplace import Laplace
 from .lognormal import LogNormal
 from .multivariate_normal import MultivariateNormal
+from .negative_binomial import NegativeBinomial
 from .normal import Normal, NormalFixedMean, NormalFixedVar
 from .poisson import Poisson
 from .t import T, TFixedDf, TFixedDfFixedVar
@@ -23,6 +24,7 @@
     "Laplace",
     "LogNormal",
     "MultivariateNormal",
+    "NegativeBinomial",
     "Normal",
     "NormalFixedMean",
     "NormalFixedVar",

ngboost/distns/negative_binomial.py

@@ -0,0 +1,74 @@
+"""The NGBoost NegativeBinomial distribution and scores"""
+import numpy as np
+from scipy.stats import nbinom as dist
+from scipy.special import digamma
+from scipy.optimize import Bounds, minimize
+
+from ngboost.distns.distn import RegressionDistn
+from ngboost.scores import LogScore
+
+#helper function because scipy doesn't provide a fit function natively
+def negative_log_likelihood(params,k):
+    return -dist.logpmf(k = k, n = params[0], p = params[1]).sum()
+
+class NegativeBinomialLogScore(LogScore):
+
+    def score(self, Y):
+        return -self.dist.logpmf(Y)
+
+    def d_score(self, Y):
+        D = np.zeros((len(Y),2))
+        D[:,0] = -self.n * (digamma(Y + self.n) + np.log(self.p) - digamma(self.n))
+        D[:,1] = (Y * np.exp(self.z) - self.n)/(np.exp(self.z) + 1)
+        return D
+
+    def metric(self):
+        FI = np.zeros((self.n.shape[0], 2, 2))
+        FI[:, 0, 0] = (self.n * self.p)/(self.p + 1)
+        FI[:, 1, 1] = self.n * self.p
+        return FI        
+
+class NegativeBinomial(RegressionDistn):
+
+    n_params = 2
+    scores = [NegativeBinomialLogScore]
+
+    def __init__(self,params):
+        # save the parameters
+        self._params = params
+
+        self.logn = params[0]
+        self.n = np.exp(self.logn)
+        #z = log(p/(1-p)) => p = 1/(1 + e^(-z))
+        self.z = params[1]
+        self.p = 1/(1 + np.exp(-self.z))
+        self.dist = dist(n = self.n, p = self.p)
+
+    def fit(Y):
+        assert np.equal(
+            np.mod(Y, 1), 0
+        ).all(), "All Negative Binomial target data must be discrete integers"
+        assert np.all([y >= 0 for y in Y]), "Count data must be >= 0"
+
+        m = minimize(
+            negative_log_likelihood,
+            x0=np.array([np.max(Y),.5]),  # initialized value
+            args=(Y,),
+            bounds=Bounds((1e-8,1e-8),(np.inf,1-1e-8)),
+
+        )
+        return np.array([np.log(m.x[0]), np.log(m.x[1]/(1 - m.x[1]))])
+
+    def sample(self,m):
+        return np.array([self.dist.rvs() for i in range(m)])
+
+    def __getattr__(
+        self, name
+    ):  # gives us access to NegativeBinomial.mean() required for RegressionDist.predict()
+        if name in dir(self.dist):
+            return getattr(self.dist, name)
+        return None
+
+    @property
+    def params(self):
+        return {'n':self.n, 'p':self.p}

tests/test_score.py

@@ -11,6 +11,7 @@
     Gamma,
     Laplace,
     MultivariateNormal,
+    NegativeBinomial,
     Normal,
     Poisson,
     T,
@@ -100,6 +101,7 @@ def idfn(dist_score: DistScore):
     (Laplace, LogScore),
     (Poisson, LogScore),
     (Gamma, LogScore),
+    (NegativeBinomial,LogScore),
 ] + [(MultivariateNormal(i), LogScore) for i in range(2, 5)]
 # Fill in the dist, score pair to test the gradient
 # Tests all in TEST_METRIC by default