[WIP] Adjust all algorithms to work with CuPy

dask_glm/algorithms.py

@@ -11,6 +11,7 @@
 from scipy.optimize import fmin_l_bfgs_b
 
 
+from dask.array.utils import normalize_to_array
 from dask_glm.utils import dot, normalize
 from dask_glm.families import Logistic
 from dask_glm.regularizers import Regularizer
@@ -97,7 +98,7 @@ def gradient_descent(X, y, max_iter=100, tol=1e-14, family=Logistic, **kwargs):
     stepSize = 1.0
     recalcRate = 10
     backtrackMult = firstBacktrackMult
-    beta = np.zeros(p)
+    beta = np.zeros_like(X, shape=(p,))
 
     for k in range(max_iter):
         # how necessary is this recalculation?
@@ -161,7 +162,7 @@ def newton(X, y, max_iter=50, tol=1e-8, family=Logistic, **kwargs):
     """
     gradient, hessian = family.gradient, family.hessian
     n, p = X.shape
-    beta = np.zeros(p)  # always init to zeros?
+    beta = np.zeros_like(X, shape=(p,))  # always init to zeros?
     Xbeta = dot(X, beta)
 
     iter_count = 0
@@ -178,8 +179,10 @@ def newton(X, y, max_iter=50, tol=1e-8, family=Logistic, **kwargs):
 
         # should this be dask or numpy?
         # currently uses Python 3 specific syntax
-        step, _, _, _ = np.linalg.lstsq(hess, grad)
-        beta = (beta_old - step)
+        step, _, _, _ = np.linalg.lstsq(normalize_to_array(hess), normalize_to_array(grad))
+        step_like = np.empty_like(X, shape=step.shape)
+        step_like[:] = step
+        beta = (beta_old - step_like)
 
         iter_count += 1
 
@@ -225,14 +228,19 @@ def admm(X, y, regularizer='l1', lamduh=0.1, rho=1, over_relax=1,
     def create_local_gradient(func):
         @functools.wraps(func)
         def wrapped(beta, X, y, z, u, rho):
-            return func(beta, X, y) + rho * (beta - z + u)
+            beta_like = np.empty_like(X, shape=beta.shape)
+            beta_like[:] = beta
+            return normalize_to_array(func(beta_like, X, y) + rho *
+                                      (beta_like - z + u))
         return wrapped
 
     def create_local_f(func):
         @functools.wraps(func)
         def wrapped(beta, X, y, z, u, rho):
-            return func(beta, X, y) + (rho / 2) * np.dot(beta - z + u,
-                                                         beta - z + u)
+            beta_like = np.empty_like(X, shape=beta.shape)
+            beta_like[:] = beta
+            return normalize_to_array(func(beta_like, X, y) + (rho / 2) *
+                                      np.dot(beta_like - z + u, beta_like - z + u))
         return wrapped
 
     f = create_local_f(pointwise_loss)
@@ -252,23 +260,23 @@ def wrapped(beta, X, y, z, u, rho):
     else:
         yD = [y]
 
-    z = np.zeros(p)
-    u = np.array([np.zeros(p) for i in range(nchunks)])
-    betas = np.array([np.ones(p) for i in range(nchunks)])
+    z = np.zeros_like(X, shape=(p,))
+    u = np.stack([np.zeros_like(X, shape=(p,)) for i in range(nchunks)])
+    betas = np.stack([np.ones_like(X, shape=(p,)) for i in range(nchunks)])
 
     for k in range(max_iter):
 
         # x-update step
         new_betas = [delayed(local_update)(xx, yy, bb, z, uu, rho, f=f,
                                            fprime=fprime) for
                      xx, yy, bb, uu in zip(XD, yD, betas, u)]
-        new_betas = np.array(da.compute(*new_betas))
+        new_betas = np.stack(da.compute(*new_betas), axis=0)
 
         beta_hat = over_relax * new_betas + (1 - over_relax) * z
 
         #  z-update step
         zold = z.copy()
-        ztilde = np.mean(beta_hat + np.array(u), axis=0)
+        ztilde = np.mean(beta_hat + u, axis=0)
         z = regularizer.proximal_operator(ztilde, lamduh / (rho * nchunks))
 
         # u-update step
@@ -295,11 +303,14 @@ def local_update(X, y, beta, z, u, rho, f, fprime, solver=fmin_l_bfgs_b):
     u = u.ravel()
     z = z.ravel()
     solver_args = (X, y, z, u, rho)
-    beta, f, d = solver(f, beta, fprime=fprime, args=solver_args,
+    beta, f, d = solver(f, normalize_to_array(beta),
+                        fprime=fprime, args=solver_args,
                         maxiter=200,
                         maxfun=250)
 
-    return beta
+    beta_like = np.empty_like(X, shape=beta.shape)
+    beta_like[:] = beta
+    return beta_like
 
 
 @normalize
@@ -335,21 +346,25 @@ def lbfgs(X, y, regularizer=None, lamduh=1.0, max_iter=100, tol=1e-4,
         pointwise_gradient = regularizer.add_reg_grad(pointwise_gradient, lamduh)
 
     n, p = X.shape
-    beta0 = np.zeros(p)
+    beta0 = np.zeros_like(X, shape=(p,))
 
     def compute_loss_grad(beta, X, y):
-        loss_fn = pointwise_loss(beta, X, y)
-        gradient_fn = pointwise_gradient(beta, X, y)
+        beta_like = np.empty_like(X, shape=beta.shape)
+        beta_like[:] = beta
+        loss_fn = pointwise_loss(beta_like, X, y)
+        gradient_fn = pointwise_gradient(beta_like, X, y)
         loss, gradient = compute(loss_fn, gradient_fn)
-        return loss, gradient.copy()
+        return normalize_to_array(loss), normalize_to_array(gradient.copy())
 
     with dask.config.set(fuse_ave_width=0):  # optimizations slows this down
         beta, loss, info = fmin_l_bfgs_b(
-            compute_loss_grad, beta0, fprime=None,
+            compute_loss_grad, normalize_to_array(beta0), fprime=None,
             args=(X, y),
             iprint=(verbose > 0) - 1, pgtol=tol, maxiter=max_iter)
 
-    return beta
+    beta_like = np.empty_like(X, shape=beta.shape)
+    beta_like[:] = beta
+    return beta_like
 
 
 @normalize
@@ -384,7 +399,7 @@ def proximal_grad(X, y, regularizer='l1', lamduh=0.1, family=Logistic,
     stepSize = 1.0
     recalcRate = 10
     backtrackMult = firstBacktrackMult
-    beta = np.zeros(p)
+    beta = np.zeros_like(X, shape=(p,))
     regularizer = Regularizer.get(regularizer)
 
     for k in range(max_iter):

dask_glm/cupy/datasets.py

@@ -0,0 +1,114 @@
+import numpy as np
+import cupy
+
+
+def make_classification(n_samples=1000, n_features=100, n_informative=2, scale=1.0):
+    """
+    Generate a dummy dataset for classification tasks.
+
+    Parameters
+    ----------
+    n_samples : int
+        number of rows in the output array
+    n_features : int
+        number of columns (features) in the output array
+    n_informative : int
+        number of features that are correlated with the outcome
+    scale : float
+        Scale the true coefficient array by this
+
+    Returns
+    -------
+    X : dask.array, size ``(n_samples, n_features)``
+    y : dask.array, size ``(n_samples,)``
+        boolean-valued array
+
+    Examples
+    --------
+    >>> X, y = make_classification()
+    >>> X.dtype, X.shape
+    (dtype('float64'), (1000, 100))
+    >>> y.dtype, y.shape
+    (dtype('bool'), (1000,))
+    """
+    X = cupy.random.normal(0, 1, size=(n_samples, n_features))
+    informative_idx = np.random.choice(n_features, n_informative)
+    beta = (cupy.random.random(n_features) - 1) * scale
+    z0 = X[:, informative_idx].dot(beta[informative_idx])
+    y = cupy.random.random(z0.shape) < 1 / (1 + np.exp(-z0))
+    return X, y
+
+
+def make_regression(n_samples=1000, n_features=100, n_informative=2, scale=1.0):
+    """
+    Generate a dummy dataset for regression tasks.
+
+    Parameters
+    ----------
+    n_samples : int
+        number of rows in the output array
+    n_features : int
+        number of columns (features) in the output array
+    n_informative : int
+        number of features that are correlated with the outcome
+    scale : float
+        Scale the true coefficient array by this
+
+    Returns
+    -------
+    X : dask.array, size ``(n_samples, n_features)``
+    y : dask.array, size ``(n_samples,)``
+        real-valued array
+
+    Examples
+    --------
+    >>> X, y = make_regression()
+    >>> X.dtype, X.shape
+    (dtype('float64'), (1000, 100))
+    >>> y.dtype, y.shape
+    (dtype('float64'), (1000,))
+    """
+    X = cupy.random.normal(0, 1, size=(n_samples, n_features))
+    informative_idx = cupy.random.choice(n_features, n_informative)
+    beta = (cupy.random.random(n_features) - 1) * scale
+    z0 = X[:, informative_idx].dot(beta[informative_idx])
+    y = cupy.random.random(z0.shape)
+    return X, y
+
+
+def make_poisson(n_samples=1000, n_features=100, n_informative=2, scale=1.0):
+    """
+    Generate a dummy dataset for modeling count data.
+
+    Parameters
+    ----------
+    n_samples : int
+        number of rows in the output array
+    n_features : int
+        number of columns (features) in the output array
+    n_informative : int
+        number of features that are correlated with the outcome
+    scale : float
+        Scale the true coefficient array by this
+
+    Returns
+    -------
+    X : dask.array, size ``(n_samples, n_features)``
+    y : dask.array, size ``(n_samples,)``
+        array of non-negative integer-valued data
+
+    Examples
+    --------
+    >>> X, y = make_poisson()
+    >>> X.dtype, X.shape 
+    (dtype('float64'), (1000, 100))
+    >>> y.dtype, y.shape
+    (dtype('int64'), (1000,))
+    """
+    X = cupy.random.normal(0, 1, size=(n_samples, n_features))
+    informative_idx = cupy.random.choice(n_features, n_informative)
+    beta = (cupy.random.random(n_features) - 1) * scale
+    z0 = X[:, informative_idx].dot(beta[informative_idx])
+    rate = np.exp(z0)
+    y = cupy.random.poisson(rate)
+    return X, y

dask_glm/datasets.py

@@ -1,6 +1,5 @@
 import numpy as np
 import dask.array as da
-from dask_glm.utils import exp
 
 
 def make_classification(n_samples=1000, n_features=100, n_informative=2, scale=1.0,
@@ -40,7 +39,7 @@ def make_classification(n_samples=1000, n_features=100, n_informative=2, scale=1
     informative_idx = np.random.choice(n_features, n_informative)
     beta = (np.random.random(n_features) - 1) * scale
     z0 = X[:, informative_idx].dot(beta[informative_idx])
-    y = da.random.random(z0.shape, chunks=(chunksize,)) < 1 / (1 + da.exp(-z0))
+    y = da.random.random(z0.shape, chunks=(chunksize,)) < 1 / (1 + np.exp(-z0))
     return X, y
 
 
@@ -122,6 +121,6 @@ def make_poisson(n_samples=1000, n_features=100, n_informative=2, scale=1.0,
     informative_idx = np.random.choice(n_features, n_informative)
     beta = (np.random.random(n_features) - 1) * scale
     z0 = X[:, informative_idx].dot(beta[informative_idx])
-    rate = exp(z0)
+    rate = np.exp(z0)
     y = da.random.poisson(rate, size=1, chunks=(chunksize,))
     return X, y

dask_glm/estimators.py

@@ -1,12 +1,13 @@
 """
 Models following scikit-learn's estimator API.
 """
+import numpy as np
 from sklearn.base import BaseEstimator
 
 from . import algorithms
 from . import families
 from .utils import (
-    sigmoid, dot, add_intercept, mean_squared_error, accuracy_score, exp,
+    sigmoid, dot, add_intercept, mean_squared_error, accuracy_score,
     poisson_deviance
 )
 
@@ -227,7 +228,7 @@ class PoissonRegression(_GLM):
 
     def predict(self, X):
         X_ = self._maybe_add_intercept(X)
-        return exp(dot(X_, self._coef))
+        return np.exp(dot(X_, self._coef))
 
     def get_deviance(self, X, y):
         return poisson_deviance(y, self.predict(X))

dask_glm/families.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import, division, print_function
 
-from dask_glm.utils import dot, exp, log1p, sigmoid
+from dask_glm.utils import dot, sigmoid
+import numpy as np
 
 
 class Logistic(object):
@@ -20,8 +21,8 @@ def loglike(Xbeta, y):
         Xbeta : array, shape (n_samples, n_features)
         y : array, shape (n_samples)
         """
-        enXbeta = exp(-Xbeta)
-        return (Xbeta + log1p(enXbeta)).sum() - dot(y, Xbeta)
+        enXbeta = np.exp(-Xbeta)
+        return (Xbeta + np.log1p(enXbeta)).sum() - dot(y, Xbeta)
 
     @staticmethod
     def pointwise_loss(beta, X, y):
@@ -92,7 +93,7 @@ class Poisson(object):
     """
     @staticmethod
     def loglike(Xbeta, y):
-        eXbeta = exp(Xbeta)
+        eXbeta = np.exp(Xbeta)
         yXbeta = y * Xbeta
         return (eXbeta - yXbeta).sum()
 
@@ -110,11 +111,11 @@ def pointwise_gradient(beta, X, y):
 
     @staticmethod
     def gradient(Xbeta, X, y):
-        eXbeta = exp(Xbeta)
+        eXbeta = np.exp(Xbeta)
         return dot(X.T, eXbeta - y)
 
     @staticmethod
     def hessian(Xbeta, X):
-        eXbeta = exp(Xbeta)
+        eXbeta = np.exp(Xbeta)
         x_diag_eXbeta = eXbeta[:, None] * X
         return dot(X.T, x_diag_eXbeta)