array_api with LDA

array_api_lda.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Array API example for LinearDiscriminantAnalysis"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "id": "a849ca35-90d9-4783-810d-18250bc021ee",
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   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "X_np, y_np = make_classification(random_state=0, n_samples=500_000, n_features=300)\n",
    "lda_np = LinearDiscriminantAnalysis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eefb285-86e8-4472-ac98-bd292e7e0546",
   "metadata": {},
   "source": [
    "## Fit runtime for NumPy array"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 59s, sys: 33.9 s, total: 2min 33s\n",
      "Wall time: 14.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "lda_np.fit(X_np, y_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8e7614-3b48-4955-8c02-4883c91fd327",
   "metadata": {},
   "source": [
    "## Runtime for CuPy array with array_api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea84e268-4ca3-46fe-b5b8-cb5b90799bbe",
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     "text": [
      "/tmp/ipykernel_127754/3365037576.py:2: UserWarning: The numpy.array_api submodule is still experimental. See NEP 47.\n",
      "  import cupy.array_api as xp\n"
     ]
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "import cupy.array_api as xp\n",
    "\n",
    "set_config(array_api_dispatch=True)\n",
    "\n",
    "X_cu = xp.asarray(X_np, copy=True)\n",
    "y_cu = xp.asarray(y_np, copy=True)\n",
    "lda_cu = LinearDiscriminantAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f828fcfc-ffc2-4ded-960f-d8151b624def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 720 ms, sys: 364 ms, total: 1.08 s\n",
      "Wall time: 1.08 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "lda_cu.fit(X_cu, y_cu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b73a36-a142-48c0-967c-f2e40f14df83",
   "metadata": {},
   "source": [
    "## Check coefs are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92674019-00ae-4cd7-96ab-1ada7f8dd0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np_coef_ = np.asarray(lda_np.coef_)\n",
    "\n",
    "# Is there a bettery way to convert cupy.array_api to a np.ndarray?\n",
    "cu_coef_ = lda_cu.coef_._array.get()\n",
    "\n",
    "np.testing.assert_allclose(np_coef_, cu_coef_)"
   ]
  }
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