add adagrad solver for higher order factorization machines

Author: vene

polylearn/adagrad_fast.cpp

@@ -0,0 +1,188 @@
+# cython: language_level=3
+# cython: boundscheck=False
+# cython: wraparound=False
+# cython: cdivision=True
+
+
+from libc.math cimport sqrt
+from lightning.impl.dataset_fast cimport RowDataset
+
+cimport numpy as np
+import numpy as np
+
+from .kernels_fast cimport _fast_anova_kernel_grad
+from .loss_fast cimport LossFunction
+
+
+np.import_array()
+
+
+cdef inline void sync(double* param,
+                      unsigned int* last_seen,
+                      double grad_norm,
+                      double learning_rate,
+                      double beta,
+                      unsigned int t,
+                      unsigned int* dt):
+
+    dt[0] = t - last_seen[0]  # dt could be local. is that efficient?
+    if dt[0] > 0:
+        sq = sqrt(grad_norm)
+        correction = sq / (learning_rate * beta + sq + 1e-6)
+        param[0] *= correction ** dt[0]
+        last_seen[0] = t
+
+
+cdef inline void ada_update(double* param,
+                            double* grad_norm,
+                            unsigned int* last_seen,
+                            double update,
+                            double lp,
+                            double learning_rate,
+                            double beta,
+                            unsigned int t):
+    update *= lp
+
+    grad_norm[0] += update ** 2
+    sq = sqrt(grad_norm[0])
+
+    # p <- (p * sq - lr * update) / (lr * beta + sq)
+    param[0] *= sq
+    param[0] -= learning_rate * update
+    param[0] /= 1e-6 + sq + learning_rate * beta
+    last_seen[0] = t + 1
+
+
+def _fast_fm_adagrad(self,
+                     double[::1] w,
+                     double[:, ::1] P not None,
+                     RowDataset X,
+                     double[::1] y not None,
+                     unsigned int degree,
+                     double alpha,
+                     double beta,
+                     bint fit_linear,
+                     LossFunction loss,
+                     unsigned int max_iter,
+                     double learning_rate,
+                     callback,
+                     int n_calls):
+
+    cdef Py_ssize_t n_samples = X.get_n_samples()
+    cdef Py_ssize_t n_components = P.shape[0]
+    cdef Py_ssize_t n_features = P.shape[1]
+
+    cdef bint has_callback = callback is not None
+
+    cdef unsigned int it, t, dt
+    cdef Py_ssize_t i, s, j, jj
+
+    cdef double y_pred, update
+
+    # data pointers
+    cdef double* data
+    cdef int* indices
+    cdef int n_nz
+
+    # working memory and DP tables
+    cdef double[:, ::1] P_grad_data
+    cdef double[::1, :] A
+    cdef double[::1, :] Ad
+
+    # to avoid reallocating at every iteration, we allocate more than enough
+
+    P_grad_data = np.empty_like(P)
+    A = np.empty((n_features + 1, degree + 1), order='f')
+    Ad = np.empty_like(A, order='f')
+
+    # adagrad bookkeeping
+    cdef double[::1] w_grad_norms
+    cdef double[:, ::1] P_grad_norms
+    cdef unsigned int[::1] w_last_seen
+    cdef unsigned int[:, ::1] P_last_seen
+    w_grad_norms = np.zeros_like(w)
+    P_grad_norms = np.zeros_like(P)
+    w_last_seen = np.zeros_like(w, dtype=np.uint32)
+    P_last_seen = np.zeros_like(P, dtype=np.uint32)
+
+    t = 0
+    for it in range(max_iter):
+
+        for i in range(n_samples):
+            X.get_row_ptr(i, &indices, &data, &n_nz)
+
+            y_pred = 0
+
+            # catch up
+            if fit_linear:
+                for jj in range(n_nz):
+                    j = indices[jj]
+                    sync(&w[j], &w_last_seen[j], w_grad_norms[j],
+                         learning_rate, alpha, t, &dt)
+
+            for s in range(n_components):
+                for jj in range(n_nz):
+                    j = indices[jj]
+                    sync(&P[s, j], &P_last_seen[s, j], P_grad_norms[s, j],
+                         learning_rate, beta, t, &dt)
+
+            # compute predictions
+            if fit_linear:
+                for jj in range(n_nz):
+                    j = indices[jj]
+                    y_pred += w[j] * data[jj]
+
+            for s in range(n_components):
+                y_pred += _fast_anova_kernel_grad(A,
+                                                  Ad,
+                                                  P,
+                                                  s,
+                                                  indices,
+                                                  data,
+                                                  n_nz,
+                                                  degree,
+                                                  P_grad_data)
+
+            # update
+            lp = -loss.dloss(y[i], y_pred)
+
+            if fit_linear:
+                for jj in range(n_nz):
+                    j = indices[jj]
+                    ada_update(&w[j],
+                               &w_grad_norms[j],
+                               &w_last_seen[j],
+                               data[jj],  # derivative wrt w[j] is x[j]
+                               lp,
+                               learning_rate,
+                               alpha,
+                               t)
+
+            for s in range(n_components):
+                for jj in range(n_nz):
+                    j = indices[jj]
+                    ada_update(&P[s, j],
+                               &P_grad_norms[s, j],
+                               &P_last_seen[s, j],
+                               P_grad_data[s, jj],
+                               lp,
+                               learning_rate,
+                               beta,
+                               t)
+            t += 1
+        # end for n_samples
+
+        if has_callback and it % n_calls == 0:
+            ret = callback(self, it)
+            if ret is not None:
+                break
+    # end for max_iter
+
+    # finalize
+    for j in range(n_features):
+        sync(&w[j], &w_last_seen[j], w_grad_norms[j], learning_rate, alpha, t,
+             &dt)
+    for s in range(n_components):
+        for j in range(n_features):
+            sync(&P[s, j], &P_last_seen[s, j], P_grad_norms[s, j],
+                 learning_rate, beta, t, &dt)