Add all needed optimizers and compare them. Also implement Lasso regression and compare with the two other cost functions.

src/main.ipynb

@@ -6,6 +6,17 @@
    "id": "0",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import optimizers\n",
     "import datamanip\n",
@@ -20,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +213,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,21 +302,136 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
-    "RidgeMom = optimizers.RidgeMomentum(\n",
-    "    learning_rate=0.1, num_iterations=1000, lam=0.1, delta=0.5\n",
-    ")\n",
-    "RidgeMom.fit(X_tr, y_tr)\n",
-    "plt.plot(RidgeMom.cost_history)"
+    "def get_cost_history(optimizer, X, y, **kwargs):\n",
+    "    opt = optimizer(**kwargs)\n",
+    "    opt.fit(X, y)\n",
+    "    return opt.cost_history"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "13",
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=plotting.get_figsize(0.8))\n",
+    "\n",
+    "X_train = datamanip.polynomial_features(x_train, 10, False)\n",
+    "X_test = datamanip.polynomial_features(x_test, 10, False)\n",
+    "X_tr, X_te = datamanip.scale_data(X_train, X_test)\n",
+    "y_tr, y_te = datamanip.scale_data(y_train, y_test)\n",
+    "\n",
+    "num_iters = 250\n",
+    "learning_rate_ols = 0.1\n",
+    "learning_rate_ridge = 0.01\n",
+    "lam = 0.1\n",
+    "\n",
+    "optimizers_ols = [\n",
+    "    (optimizers.OLSGradientDescent, {\"learning_rate\": learning_rate_ols}),\n",
+    "    (optimizers.OLSMomentum, {\"learning_rate\": learning_rate_ols, \"delta\": 0.9}),\n",
+    "    (optimizers.OLSAdaGrad, {\"learning_rate\": learning_rate_ols}),\n",
+    "    (optimizers.OLSRMSProp, {\"learning_rate\": learning_rate_ols, \"gamma\": 0.9}),\n",
+    "    (\n",
+    "        optimizers.OLSAdam,\n",
+    "        {\"learning_rate\": learning_rate_ols, \"beta1\": 0.9, \"beta2\": 0.999},\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "optimizers_ridge = [\n",
+    "    (\n",
+    "        optimizers.RidgeGradientDescent,\n",
+    "        {\"learning_rate\": learning_rate_ridge, \"lam\": lam},\n",
+    "    ),\n",
+    "    (\n",
+    "        optimizers.RidgeMomentum,\n",
+    "        {\"learning_rate\": learning_rate_ridge, \"delta\": 0.9, \"lam\": lam},\n",
+    "    ),\n",
+    "    (optimizers.RidgeAdaGrad, {\"learning_rate\": learning_rate_ridge, \"lam\": lam}),\n",
+    "    (\n",
+    "        optimizers.RidgeRMSProp,\n",
+    "        {\"learning_rate\": learning_rate_ridge, \"gamma\": 0.9, \"lam\": lam},\n",
+    "    ),\n",
+    "    (\n",
+    "        optimizers.RidgeAdam,\n",
+    "        {\n",
+    "            \"learning_rate\": learning_rate_ridge,\n",
+    "            \"beta1\": 0.9,\n",
+    "            \"beta2\": 0.999,\n",
+    "            \"lam\": lam,\n",
+    "        },\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def plot_optimizers(ax, optimizer_list, X, y, num_iters, ylabel=\"Cost\"):\n",
+    "    for opt_class, params in optimizer_list:\n",
+    "        history = get_cost_history(opt_class, X, y, num_iterations=num_iters, **params)\n",
+    "        ax.plot(history, label=f\"{opt_class.__name__}\")\n",
+    "    ax.set_xlabel(\"Iteration\")\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    ax.legend()\n",
+    "\n",
+    "\n",
+    "plot_optimizers(ax1, optimizers_ols, X_tr, y_tr, num_iters, ylabel=\"Cost (OLS)\")\n",
+    "ax1.set_ylim(bottom=0.465, top=0.505)\n",
+    "\n",
+    "plot_optimizers(ax2, optimizers_ridge, X_tr, y_tr, num_iters, ylabel=\"Cost (Ridge)\")\n",
+    "ax2.set_ylim(bottom=0.4775, top=0.505)\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(os.path.join(FIG_DIR, \"optimizer_comparison.pdf\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=plotting.get_figsize(0.5))\n",
+    "\n",
+    "ols_adam = optimizers.OLSAdam(learning_rate=0.01, beta1=0.9, beta2=0.999)\n",
+    "ridge_adam = optimizers.RidgeAdam(learning_rate=0.01, beta1=0.9, beta2=0.999, lam=0.1)\n",
+    "lasso_adam = optimizers.LASSOAdam(learning_rate=0.01, beta1=0.9, beta2=0.999, lam=0.1)\n",
+    "ols_gd = optimizers.OLSGradientDescent(learning_rate=0.1)\n",
+    "ridge_gd = optimizers.RidgeGradientDescent(learning_rate=0.01, lam=0.1)\n",
+    "lasso_gd = optimizers.LASSOGradientDescent(learning_rate=0.01, lam=0.1)\n",
+    "\n",
+    "for i, (optimizer, label) in enumerate(\n",
+    "    zip(\n",
+    "        [ols_adam, ridge_adam, lasso_adam, ols_gd, ridge_gd, lasso_gd],\n",
+    "        [\"OLS Adam\", \"Ridge Adam\", \"LASSO Adam\", \"OLS GD\", \"Ridge GD\", \"LASSO GD\"],\n",
+    "    )\n",
+    "):\n",
+    "    optimizer.fit(X_tr, y_tr)\n",
+    "    cost_history = optimizer.cost_history\n",
+    "    ls = \"-\" if i < 3 else \"--\"\n",
+    "    c = \"C\" + str(i % 3)\n",
+    "    label = label.split()[0] if i < 3 else \"\"\n",
+    "    ax.plot(cost_history, label=label, linestyle=ls, color=c)\n",
+    "ax.set_xlabel(\"Iteration\")\n",
+    "ax.set_ylabel(\"Cost\")\n",
+    "# Add linestyles manually to legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles += [\n",
+    "    plt.Line2D([0], [0], color=\"k\", linestyle=\"-\"),\n",
+    "    plt.Line2D([0], [0], color=\"k\", linestyle=\"--\"),\n",
+    "]\n",
+    "labels += [\"Adam Opt.\", \"Grad. Desc.\"]\n",
+    "ax.legend(handles, labels)\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(os.path.join(FIG_DIR, \"cost_function_comparison.pdf\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": []

src/optimizers.py

@@ -123,3 +123,124 @@ class OLSMomentum(OLSGradientDescent):
 class RidgeMomentum(OLSMomentum, RidgeGradientDescent):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+
+
+class OLSAdaGrad(OLSGradientDescent):
+    def __init__(self, *args, learning_rate=0.1, num_iterations=1000, **kwargs):
+        super().__init__(
+            *args, learning_rate=learning_rate, num_iterations=num_iterations, **kwargs
+        )
+
+    def _precomp(self):
+        self.quad_sum = np.zeros_like(self.theta)
+        return super()._precomp()
+
+    def _update_theta(self):
+        grad = self._compute_grad()
+        self.quad_sum += np.square(grad)
+        self.theta -= (
+            self.learning_rate * grad / (np.sqrt(self.quad_sum) + 1e-10)
+        )  # to avoid division by zero
+
+
+class RidgeAdaGrad(OLSAdaGrad, RidgeGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class OLSRMSProp(OLSGradientDescent):
+    def __init__(
+        self, *args, learning_rate=0.1, num_iterations=1000, gamma=0.9, **kwargs
+    ):
+        super().__init__(
+            *args, learning_rate=learning_rate, num_iterations=num_iterations, **kwargs
+        )
+        self.gamma = gamma
+
+    def _precomp(self):
+        self.prev_v = np.zeros_like(self.theta)
+        return super()._precomp()
+
+    def _update_theta(self):
+        grad = self._compute_grad()
+        v = self.gamma * self.prev_v + (1 - self.gamma) * np.square(grad)
+        self.theta -= (
+            self.learning_rate * grad / (np.sqrt(v) + 1e-10)
+        )  # to avoid division by zero
+        self.prev_v = v
+
+
+class RidgeRMSProp(OLSRMSProp, RidgeGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class OLSAdam(OLSGradientDescent):
+    def __init__(
+        self,
+        *args,
+        learning_rate=0.1,
+        num_iterations=1000,
+        beta1=0.9,
+        beta2=0.999,
+        **kwargs,
+    ):
+        super().__init__(
+            *args, learning_rate=learning_rate, num_iterations=num_iterations, **kwargs
+        )
+        self.beta1 = beta1
+        self.beta2 = beta2
+
+    def _precomp(self):
+        self.m = np.zeros_like(self.theta)
+        self.v = np.zeros_like(self.theta)
+        return super()._precomp()
+
+    def _update_theta(self):
+        grad = self._compute_grad()
+        self.m = self.beta1 * self.m + (1 - self.beta1) * grad
+        self.v = self.beta2 * self.v + (1 - self.beta2) * np.square(grad)
+
+        m_hat = self.m / (1 - self.beta1 ** (self.num_iterations))
+        v_hat = self.v / (1 - self.beta2 ** (self.num_iterations))
+
+        self.theta -= (
+            self.learning_rate * m_hat / (np.sqrt(v_hat) + 1e-10)
+        )  # to avoid division by zero
+
+
+class RidgeAdam(OLSAdam, RidgeGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class LASSOGradientDescent(OLSGradientDescent):
+    def __init__(self, *args, lam: float = 0.1, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lam = lam
+
+    def _compute_cost(self) -> float:
+        return super()._compute_cost() + self.lam * np.sum(np.abs(self.theta))
+
+    def _compute_grad(self) -> np.ndarray:
+        return super()._compute_grad() + self.lam * np.sign(self.theta)
+
+
+class LASSOMomentum(OLSMomentum, LASSOGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class LASSOAdaGrad(OLSAdaGrad, LASSOGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class LASSORMSProp(OLSRMSProp, LASSOGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+class LASSOAdam(OLSAdam, LASSOGradientDescent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)