From 742238bda4b75eda2c68ce2deffc05dcd6eb9366 Mon Sep 17 00:00:00 2001 From: Hilmar Lapp Date: Wed, 27 Nov 2024 19:06:14 -0500 Subject: [PATCH] Updates results of running the notebook --- Python-ML/Building-Blocks-Hyper.ipynb | 167 +++++++++++++++----------- 1 file changed, 97 insertions(+), 70 deletions(-) diff --git a/Python-ML/Building-Blocks-Hyper.ipynb b/Python-ML/Building-Blocks-Hyper.ipynb index b9216fb..d424ada 100644 --- a/Python-ML/Building-Blocks-Hyper.ipynb +++ b/Python-ML/Building-Blocks-Hyper.ipynb @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -181,28 +181,40 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 17:09:34.244632: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2\n", + "2024-11-27 17:09:34.244650: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 24.00 GB\n", + "2024-11-27 17:09:34.244665: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 8.00 GB\n", + "2024-11-27 17:09:34.244682: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2024-11-27 17:09:34.244694: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n", + "2024-11-27 17:09:34.936664: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/2\n", - "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.8830 - loss: 0.3825 - val_accuracy: 0.9796 - val_loss: 0.0682\n", - "Epoch 2/2\n", - "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 16ms/step - accuracy: 0.9815 - loss: 0.0623 - val_accuracy: 0.9856 - val_loss: 0.0474\n", - "Epoch 1/2\n", - "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.8979 - loss: 0.3201 - val_accuracy: 0.9819 - val_loss: 0.0592\n", - "Epoch 2/2\n", - "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 14ms/step - accuracy: 0.9831 - loss: 0.0549 - val_accuracy: 0.9836 - val_loss: 0.0534\n", - "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 16ms/step - accuracy: 0.8827 - loss: 0.3733 - val_accuracy: 0.9833 - val_loss: 0.0546\n", - "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 16ms/step - accuracy: 0.8821 - loss: 0.3887 - val_accuracy: 0.9819 - val_loss: 0.0613\n", - "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.8913 - loss: 0.3407 - val_accuracy: 0.9757 - val_loss: 0.0818\n", - "CPU times: user 1min 12s, sys: 37.5 s, total: 1min 49s\n", - "Wall time: 1min 41s\n" + "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.8795 - loss: 0.3822 - val_accuracy: 0.9827 - val_loss: 0.0580\n", + "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.8765 - loss: 0.3900 - val_accuracy: 0.9817 - val_loss: 0.0575\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 19ms/step - accuracy: 0.8742 - loss: 0.4156 - val_accuracy: 0.9813 - val_loss: 0.0603\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.8644 - loss: 0.4245 - val_accuracy: 0.9828 - val_loss: 0.0606\n", + "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 20ms/step - accuracy: 0.8856 - loss: 0.3725 - val_accuracy: 0.9813 - val_loss: 0.0634\n", + "Epoch 1/3\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.8706 - loss: 0.4109 - val_accuracy: 0.9813 - val_loss: 0.0615\n", + "Epoch 2/3\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 18ms/step - accuracy: 0.9785 - loss: 0.0704 - val_accuracy: 0.9849 - val_loss: 0.0515\n", + "Epoch 3/3\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 17ms/step - accuracy: 0.9844 - loss: 0.0492 - val_accuracy: 0.9858 - val_loss: 0.0479\n", + "CPU times: user 1min 16s, sys: 41.4 s, total: 1min 57s\n", + "Wall time: 1min 52s\n" ] } ], @@ -223,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -271,102 +283,117 @@ " \n", " 0\n", " 0\n", - " 0.045355\n", - " 2024-11-27 15:04:53.073742\n", - " 2024-11-27 15:05:22.764830\n", - " 0 days 00:00:29.691088\n", - " 96\n", - " 0.35\n", - " 3\n", + " 0.058012\n", + " 2024-11-27 17:09:34.281652\n", + " 2024-11-27 17:09:54.897451\n", + " 0 days 00:00:20.615799\n", + " 32\n", + " 0.40\n", + " 1\n", " COMPLETE\n", " \n", " \n", " 1\n", " 1\n", - " 0.067987\n", - " 2024-11-27 15:05:22.764992\n", - " 2024-11-27 15:05:37.271687\n", - " 0 days 00:00:14.506695\n", - " 64\n", - " 0.15\n", + " 0.057474\n", + " 2024-11-27 17:09:54.897565\n", + " 2024-11-27 17:10:15.165447\n", + " 0 days 00:00:20.267882\n", + " 32\n", + " 0.40\n", " 1\n", " COMPLETE\n", " \n", " \n", " 2\n", " 2\n", - " 0.051764\n", - " 2024-11-27 15:05:37.271814\n", - " 2024-11-27 15:05:57.435135\n", - " 0 days 00:00:20.163321\n", + " 0.060288\n", + " 2024-11-27 17:10:15.165521\n", + " 2024-11-27 17:10:25.131312\n", + " 0 days 00:00:09.965791\n", " 96\n", - " 0.35\n", - " 2\n", + " 0.20\n", + " 1\n", " COMPLETE\n", " \n", " \n", " 3\n", " 3\n", - " 0.060558\n", - " 2024-11-27 15:05:57.435226\n", - " 2024-11-27 15:06:08.352475\n", - " 0 days 00:00:10.917249\n", - " 96\n", - " 0.15\n", + " 0.060588\n", + " 2024-11-27 17:10:25.131399\n", + " 2024-11-27 17:10:38.767878\n", + " 0 days 00:00:13.636479\n", + " 64\n", + " 0.30\n", " 1\n", " COMPLETE\n", " \n", " \n", " 4\n", " 4\n", - " 0.066759\n", - " 2024-11-27 15:06:08.352579\n", - " 2024-11-27 15:06:21.581514\n", - " 0 days 00:00:13.228935\n", - " 64\n", - " 0.25\n", + " 0.063435\n", + " 2024-11-27 17:10:38.767948\n", + " 2024-11-27 17:10:48.912267\n", + " 0 days 00:00:10.144319\n", + " 96\n", + " 0.10\n", " 1\n", " COMPLETE\n", " \n", + " \n", + " 5\n", + " 5\n", + " 0.047923\n", + " 2024-11-27 17:10:48.912338\n", + " 2024-11-27 17:11:27.157779\n", + " 0 days 00:00:38.245441\n", + " 64\n", + " 0.35\n", + " 3\n", + " COMPLETE\n", + " \n", " \n", "\n", "" ], "text/plain": [ " number value datetime_start datetime_complete \\\n", - "0 0 0.045355 2024-11-27 15:04:53.073742 2024-11-27 15:05:22.764830 \n", - "1 1 0.067987 2024-11-27 15:05:22.764992 2024-11-27 15:05:37.271687 \n", - "2 2 0.051764 2024-11-27 15:05:37.271814 2024-11-27 15:05:57.435135 \n", - "3 3 0.060558 2024-11-27 15:05:57.435226 2024-11-27 15:06:08.352475 \n", - "4 4 0.066759 2024-11-27 15:06:08.352579 2024-11-27 15:06:21.581514 \n", + "0 0 0.058012 2024-11-27 17:09:34.281652 2024-11-27 17:09:54.897451 \n", + "1 1 0.057474 2024-11-27 17:09:54.897565 2024-11-27 17:10:15.165447 \n", + "2 2 0.060288 2024-11-27 17:10:15.165521 2024-11-27 17:10:25.131312 \n", + "3 3 0.060588 2024-11-27 17:10:25.131399 2024-11-27 17:10:38.767878 \n", + "4 4 0.063435 2024-11-27 17:10:38.767948 2024-11-27 17:10:48.912267 \n", + "5 5 0.047923 2024-11-27 17:10:48.912338 2024-11-27 17:11:27.157779 \n", "\n", " duration params_batch_size params_dropout params_epochs \\\n", - "0 0 days 00:00:29.691088 96 0.35 3 \n", - "1 0 days 00:00:14.506695 64 0.15 1 \n", - "2 0 days 00:00:20.163321 96 0.35 2 \n", - "3 0 days 00:00:10.917249 96 0.15 1 \n", - "4 0 days 00:00:13.228935 64 0.25 1 \n", + "0 0 days 00:00:20.615799 32 0.40 1 \n", + "1 0 days 00:00:20.267882 32 0.40 1 \n", + "2 0 days 00:00:09.965791 96 0.20 1 \n", + "3 0 days 00:00:13.636479 64 0.30 1 \n", + "4 0 days 00:00:10.144319 96 0.10 1 \n", + "5 0 days 00:00:38.245441 64 0.35 3 \n", "\n", " state \n", "0 COMPLETE \n", "1 COMPLETE \n", "2 COMPLETE \n", "3 COMPLETE \n", - "4 COMPLETE " + "4 COMPLETE \n", + "5 COMPLETE " ] }, - "execution_count": 10, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.head()" + "df" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -375,14 +402,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/_m/95yqs0kj1xsd4l92yjkfr2hm0000gn/T/ipykernel_93478/3836449081.py:1: ExperimentalWarning: plot_param_importances is experimental (supported from v2.2.0). The interface can change in the future.\n", + "/var/folders/_m/95yqs0kj1xsd4l92yjkfr2hm0000gn/T/ipykernel_95964/3836449081.py:1: ExperimentalWarning: plot_param_importances is experimental (supported from v2.2.0). The interface can change in the future.\n", " plot_param_importances(study)\n" ] }, @@ -392,13 +419,13 @@ "" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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