added dec and sym exp loops

transformer/scaling.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "98b88925",
+   "id": "5a62c0ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "042a0773",
+   "id": "c75f5208",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "35c300a9",
+   "id": "aa342b02",
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "c7b82213",
+   "id": "5aefbb4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "053971b7",
+   "id": "047f9d32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "260573cc",
+   "id": "513bbe1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "491f8c85",
+   "id": "2e484da6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed03133a",
+   "id": "b5630b79",
    "metadata": {},
    "source": [
     "## Sclaing Experiments\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "0a65866c",
+   "id": "7945eff4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d51bfbfc",
+   "id": "b0b59bb2",
    "metadata": {},
    "source": [
     "### Encoder Scaling"
@@ -246,7 +246,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "af9dc0ae",
+   "id": "681199ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,37 +296,19 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "ef47755d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "enc_metrics_out = open(\"elec_enc_metrics.pkl\", \"wb\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "855b130e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pickle.dump(enc_metrics, enc_metrics_out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "869ccbec",
+   "id": "c6cb9386",
    "metadata": {},
    "outputs": [],
    "source": [
+    "enc_metrics_out = open(\"elec_enc_metrics.pkl\", \"wb\")\n",
+    "pickle.dump(enc_metrics, enc_metrics_out)\n",
     "enc_metrics_out.close()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "02df0064",
+   "id": "513cbbbf",
    "metadata": {
     "scrolled": true
    },
@@ -367,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "95290703",
+   "id": "af5715b2",
    "metadata": {},
    "outputs": [
     {
@@ -405,8 +387,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "61cf3e7d",
+   "execution_count": 79,
+   "id": "1c64e02c",
    "metadata": {},
    "outputs": [
     {
@@ -415,13 +397,13 @@
        "Text(0.5, 1.0, 'Encoder Scaling')"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -435,24 +417,86 @@
    "source": [
     "plt.plot(\n",
     "    [metrics[\"trainable_parameters\"] for metrics in enc_metrics],\n",
-    "    [metrics[\"MSIS\"] for metrics in enc_metrics],   \n",
+    "    [metrics[\"NRMSE\"] for metrics in enc_metrics],   \n",
     ")\n",
     "plt.xlabel(\"trainable parameters\")\n",
-    "plt.ylabel(\"MSIS\")\n",
+    "plt.ylabel(\"NRMSE\")\n",
     "plt.title(\"Encoder Scaling\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4dedbf76",
+   "id": "13356916",
    "metadata": {},
    "source": [
     "### Decoder Scaling"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a175e127",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dec_metrics = []\n",
+    "for layer in layers:\n",
+    "    estimator = TransformerEstimator(\n",
+    "        freq=freq,\n",
+    "        prediction_length=prediction_length,\n",
+    "        context_length=prediction_length*7,\n",
+    "\n",
+    "        nhead=2,\n",
+    "        num_encoder_layers=6,\n",
+    "        num_decoder_layers=layer,\n",
+    "        dim_feedforward=16,\n",
+    "        activation=\"gelu\",\n",
+    "\n",
+    "        num_feat_static_cat=1,\n",
+    "        cardinality=[320],\n",
+    "        embedding_dimension=[5],\n",
+    "\n",
+    "        batch_size=128,\n",
+    "        num_batches_per_epoch=100,\n",
+    "        trainer_kwargs=dict(max_epochs=50, accelerator='auto', gpus=1),\n",
+    "    )\n",
+    "    \n",
+    "    predictor = estimator.train(\n",
+    "        training_data=train_ds,\n",
+    "        validation_data=val_ds,\n",
+    "        num_workers=8,\n",
+    "        shuffle_buffer_length=1024\n",
+    "    )\n",
+    "    \n",
+    "    forecast_it, ts_it = make_evaluation_predictions(\n",
+    "        dataset=test_ds, \n",
+    "        predictor=predictor\n",
+    "    )\n",
+    "    forecasts = list(forecast_it)\n",
+    "    if layer == layers[0]:\n",
+    "        tss = list(ts_it)\n",
+    "    \n",
+    "    evaluator = Evaluator()\n",
+    "    agg_metrics, _ = evaluator(iter(tss), iter(forecasts))\n",
+    "    agg_metrics[\"trainable_parameters\"] = summarize(estimator.create_lightning_module()).trainable_parameters\n",
+    "    dec_metrics.append(agg_metrics.copy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c439510e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dec_metrics_out = open(\"elec_dec_metrics.pkl\", \"wb\")\n",
+    "pickle.dump(dec_metrics, dec_metrics_out)\n",
+    "dec_metrics_out.close()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "abeca54b",
+   "id": "78be6dd9",
    "metadata": {},
    "source": [
     "### Symmetric Scaling"
@@ -461,10 +505,64 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "972f3216",
+   "id": "069b20d9",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sym_metrics = []\n",
+    "for layer in layers:\n",
+    "    estimator = TransformerEstimator(\n",
+    "        freq=freq,\n",
+    "        prediction_length=prediction_length,\n",
+    "        context_length=prediction_length*7,\n",
+    "\n",
+    "        nhead=2,\n",
+    "        num_encoder_layers=layer,\n",
+    "        num_decoder_layers=layer,\n",
+    "        dim_feedforward=16,\n",
+    "        activation=\"gelu\",\n",
+    "\n",
+    "        num_feat_static_cat=1,\n",
+    "        cardinality=[320],\n",
+    "        embedding_dimension=[5],\n",
+    "\n",
+    "        batch_size=128,\n",
+    "        num_batches_per_epoch=100,\n",
+    "        trainer_kwargs=dict(max_epochs=50, accelerator='auto', gpus=1),\n",
+    "    )\n",
+    "    \n",
+    "    predictor = estimator.train(\n",
+    "        training_data=train_ds,\n",
+    "        validation_data=val_ds,\n",
+    "        num_workers=8,\n",
+    "        shuffle_buffer_length=1024\n",
+    "    )\n",
+    "    \n",
+    "    forecast_it, ts_it = make_evaluation_predictions(\n",
+    "        dataset=test_ds, \n",
+    "        predictor=predictor\n",
+    "    )\n",
+    "    forecasts = list(forecast_it)\n",
+    "    if layer == layers[0]:\n",
+    "        tss = list(ts_it)\n",
+    "    \n",
+    "    evaluator = Evaluator()\n",
+    "    agg_metrics, _ = evaluator(iter(tss), iter(forecasts))\n",
+    "    agg_metrics[\"trainable_parameters\"] = summarize(estimator.create_lightning_module()).trainable_parameters\n",
+    "    sym_metrics.append(agg_metrics.copy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d48d029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sym_metrics_out = open(\"elec_sym_metrics.pkl\", \"wb\")\n",
+    "pickle.dump(sym_metrics, sym_metrics_out)\n",
+    "sym_metrics_out.close()"
+   ]
   }
  ],
  "metadata": {