Add notebook that shows how to use skorch with optuna #724
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Optuna already provides a skorch callback, which is essentially the same that I also implemented.
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Update: optuna already provides a skorch callback, which is almost exactly the same that I used. I changed the notebook to use this instead. |
thomasjpfan
reviewed
Nov 25, 2020
notebooks/optuna-example.ipynb
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| " \"\"\"Model definition and hyper-parameter setting\n", | ||
| "\n", | ||
| " We set the number of units, the optimizer, the learning rate, and the number of epochs.\n", | ||
| " \n", | ||
| " To make this work efficiently with optuna, add the\n", | ||
| " `SkorchPruningCallback <https://optuna.readthedocs.io/en/stable/reference/generated/optuna.integration.OptunaSearchCV.html?highlight=optuna.integration.OptunaSearchCV>`\n", | ||
| " from the optuna library.\n", | ||
| "\n", | ||
| " \"\"\"\n", |
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Given this is a notebook, can we pull this out into an markdown block above this code block?
notebooks/optuna-example.ipynb
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| "outputs": [], | ||
| "source": [ | ||
| "def objective(trial):\n", | ||
| " \"\"\"Train the model and return the value to be maximized\"\"\"\n", |
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A markdown description before defining this function?
Next, we define an objective function that trains a
NeuralNetClassifierand returns the value to be maximized.
| " model = define_model(trial)\n", | ||
| " X, y = get_data()\n", | ||
| " model.fit(X, y)\n", | ||
| " accuracy = model.history[-1, METRIC_NAME]\n", |
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Would it be cleaner to directly place the metric name here?
Suggested change
| " accuracy = model.history[-1, METRIC_NAME]\n", | |
| " accuracy = model.history[-1, 'valid_acc']\n", |
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I would leave it as is. The idea is to set all the constants at the beginning of the notebook. This way, if I want to change the them (say, optimizing valid_loss instead), I don't need to chase through the notebook to replace all instances.
- add more markdown descriptions - set number of trials as constant - increase image size and change order
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@thomasjpfan I improved the notebook by adding more descriptions, as you correctly pointed out. Now it should be more accessible for users who don't already know optuna. Furthermore, I increased the image size, which was quite small originally. |
thomasjpfan
approved these changes
Dec 4, 2020
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| "data": { | ||
| "image/png": 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Unrelated note: In general, I have found parallel coordinate plots more useful when they are interactive. (Something like https://facebookresearch.github.io/hiplot/)
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True, maybe worth suggesting to the optuna maintainers.
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Optuna now have the interactive parallel coordinate plot.
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Nice, wish come true, thanks for letting us know.
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
thomasjpfan
reviewed
Oct 6, 2021
notebooks/optuna-example.ipynb
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Ly8uk/ZFHHsGUKVPwxhtvGNu+++47PPLII/XGJQgC5HK5Q5TWuLm51fhs7Y1CobD7GMmyOAcI4DwgzoGWzipZlUajQW5urnE7NzcXvr6+JvsolUo8//zzAKoS8xdeeAGtW7cGAKjVagCAj48P+vXrh5SUlFoT7ZiYGMTExBi3734SU3l5udk3KlZevQr9F19CUKkAjQaGggJU/utfMEx6FIrQupNthUKBysrK+o/95+v5+fnw9vY2bm/evBnfffcd9Ho9Ro8ejddeew2lpaWYOXMmMjIyIIoiZs+ejZycHGRmZuK//uu/4Ovri6+++sp47Pbt28Pb2xsnT55E3759AQDffvstPvvsM1RWVmLevHk4c+YMdDodxo4di9deew1A1XtuMBhQWVmJzp07Izk5GQDw/fffIz4+HuvWrUNubi7mzZuHtLQ0AMCyZcvQr18/s97P5lReXm73T9nik8CIc4AAzgPiHGgJ6nsypFUS7dDQUGRkZODWrVtQq9U4duwYXnrpJZN9SkpK4ObmBoVCgYMHD6Jbt25QKpXQ6XSQJAkeHh7Q6XQ4e/YsJk6c2OSYKk6ehHRX+YrJ60eOQtLpIJSUGNsknQ7l23dAjBhcax9BrYZi0KB6x9XpdBgxYgTKy8tx69YtfPHFFwCAhIQEXLt2DXv27IEkSZg6dSqOHz+O3NxcBAQE4JNPPgEAFBYWwtvbG1u2bMGXX35p/CXkTrGxsdi9ezf69u2LX375Bb6+vujYsSMA4PXXX4evry8MBgMmT56Mixcv1vpLS20WL16MGTNmoH///khLS8Njjz2GhIQEs/oSERERtTRWSbTlcjmmTZuGFStWQBRFREVFISQkBPv37wcAjBw5Emlpafjwww8hk8kQHByMZ599FgBQUFCANWvWAAAMBgMiIiLQu3dvi8csFRYCd5VlwM2tqr0J3N3dceDAAQDA6dOnMXv2bPz0009ISEhAQkICRo4cCQAoLS3FtWvX0L9/fyxfvhwrVqxATEwMBgwYcM8xxo8fjwkTJmDJkiXYvXs3JkyYYHztu+++w2effQaDwYCsrCwkJyebnWgnJibiypUrxu3i4mIUFxdDpVI15C0gIiIiahGsVpDbt29fYylDteqkEgC6dOmCDz74oEY/f39/vPvuu80ej0v//vW+LmZmQSoqgnBHsi0VFUHo3Bmuo0c3Swzh4eHIy8tDbm6usVzmySefrLHfjz/+iJ9++gnvvPMOhg0bhldeeaXe4wYFBSEkJAQ///wzfvjhB3z77bcAgBs3buCjjz7Cnj170KpVK7z88svQ6XQ1+t9ZP3/nUnqiKOLbb7/lGtZEREREZuAj2OugGBIBqbgYUlERJFGs+m9xMRRDIpptjJSUFBgMBvj6+iIyMhI7d+5EyZ+lKhkZGcZabA8PDzzyyCN49tlnce7cOQCASqVCcXFxnceeMGECli5divbt2xtrh4qKiuDh4QFvb29kZ2cbb8q8m5+fH5KTkyGKIvbu3WtsHzZsGHbs2GHcPn/+fFPfAiIiIiKnZf9LTNiIIjQUmPQoKhOP/GfVkQfH1HsjpDmqa7SBqhsQ161bB7lcjmHDhiE5ORnjx48HUHVz6IYNG3D9+nW89dZbEAQBLi4uxqX7Hn/8cTzxxBNo3bq1yc2Q1caNG4clS5Zg+fLlxrYePXrgvvvuQ1RUFNq2bVvnjYzz58/HU089hcDAQISFhRmT/+XLl2PBggWIiYlBZWUlBgwYgFWrVjXp/SAiIiJyVoJU29p7TiI9Pd1ku7S0FEql0qJjmrPqCDWNNT7HpuJd5sQ5QADnAXEOtAT1rTrC0hEiIiIiIgtg6QgREdUp48wlnPlqH1xzs6HX+KHXxFFo06trvX0qr141KbtTDIloctmdLTnT+VjrXJxtHGtpzPk423vQGPb8HsiXLl261NZBWEpRUZHJdkVFBVxcXCw6pkwmgyiKFh2jpbPG59hUSqUSpaWltg6DbMgZ5kDGmUtI2rADWUUVyJApUV5QhOKTv0DVLhheAbU/6a76YV8AILRqBRQXw/B/SRAC20BWy7r/9q6p52NP88Ban42zjdNU5s6BxpyPo7wHlmQP78HdT+m+E2u0mxlrtC2PNdrkCJxhDux9Yz1y0rKhLi+CW6UeAOBqqICXtxLdxgyttU/FqVOQysshuLkZ26q3XWzwJNmmaur5eKo8UVJccs/9rMFan42zjdNU5s6BxpyPo7wHlnTneyAL8IdMrTEuz+w+9SmrxGDzJ0MSEZHjcc/OhH9JAVylShS4qiAJACR3KCvLIQtsU3sngwGCRm2yHj9UnkBBYd197FkTz8fF2xuyJj7orNlY67NxtnGayOw50JjzcZD3wKLueA8EF9eqNk9PiFlZto3rT0y0iYioBrGwEK0qSlBWqcNl3xCUuFQ9qMpTXwa3QDVcBg+utZ8hOaX2h321bVdnH3vW1PPx1GpRZid/2bDWZ+Ns4zSVuXOgMefjKO+BJdX2HqCkBDJ/f9sFdQeuOmJl6enpePrppzF48GAMGjQIixcvhl5f9SfZnTt3YuHChbX2q15fu6H27t1r8tj0d999F4cPH27Usart3LkTzz//vElbXl4eevbsafIkybu9/PLL+P7775s0NhFZnpiXh4of9yK4RyeUePkCEiBIEjz1ZfAXytFr4qg6+1rjYV/W5EznY61zcbZxrKUx5+Ns70Fj2Pt7wETbiiRJwowZMzB69GgcPXoUiYmJKCkpMeuhL9WPUW+ouxPtuXPnYujQ2msrzfXggw/i8OHDKCsrM7Z9//33GDlyJNzuqBMjIscjZmejYt8+QCZAPe2/0eOVGQgIVKOrrAQBgWr0fnFqvauOKEJD4TrpUQheXpCysyF4ecF10qN2swJAQznT+VjrXJxtHGtpzPk423vQGPb+HrB0pB7pBeXYcjwDOcUV0Kpc8MzANgj0aXwieeTIEbi5uWHy5MkAALlcjqVLl2LgwIF47bXXqsZMT8fjjz+OGzdu4OGHH8arr74KAOjcuTOSk5MBAJs3b8Z3330HvV6P0aNHG/t++eWX+OijjwAA3bp1w3//93/jwIEDOH78ONavX4+tW7di3bp1iImJgVKpxM6dO437Hzt2DB999BE+/vhjJCQkYM2aNdDr9WjXrh3ef/99eHp6Gs/Dy8sLAwcOxP79+zFhwgQAVb8IvPTSSwCA999/HwcOHIBOp0N4eDhWrVplWj8GYMCAAfjxxx+hVqtx5swZLF++HF999RVKS0uxaNEiXLp0CZWVlZgzZw5Gjar76hkRNR9DWjoqDh2CoPSA68iREFQqtGnV6p7L+d1NERpqN/+Taw7OdD7WOhdnG8daGnM+zvYeNIY9vwdMtOuQXlCO2btSkFaoN7ZdyCjB+oc7NTrZvnLlCnr27GnS5uXlhaCgIFy7dg0AkJSUhIMHD8LDwwNjx45FdHQ0evXqZdw/ISEB165dw549eyBJEqZOnYrjx4/D19cXH3zwAXbv3g21Wo3bt2/D19cXI0aMQExMDB566CGTcYcOHYrXX3/duILHt99+i/HjxyMvLw/r16/Hzp07oVQqsXHjRmzZsgWvvPKKSf8JEybgm2++wYQJE5CZmYnff/8dg/+sB5s6dapx/xdffBEHDhzAyJEjzXqP1q9fj8GDB+O9995DQUEBxo4diyFDhtj9KiNEjs7wxx+oOHwYgrcPXEeOgODhYeuQiFqcxlzga+6LgtS8mGjXYcvxDJMkGwDSCvXYcjwDS0e1b9QxJUmqcWX37vYhQ4ZA/ee6j2PGjMHJkydrJNoJCQnGxLW0tBTXrl3DxYsXMXbsWGNfX1/femNRKBSIiorCgQMHMHbsWBw8eBCLFi3Czz//jCtXrhivVFdUVOCBBx6o0T8mJgYLFixAUVERvvvuO4wdOxZyuRxA1dXxzZs3o6ysDPn5+QgLCzM70T58+DAOHDiAf/zjHwCA8vJypKWloXPnzmb1J6KGMyQno+LYz5D5aeESEwPB1dXWIRG1OI25wGeJi4LUvJho1yGnuKL29pLa283RpUsX/PDDDyZtRUVFSE9PR/v27XH27Nkaifjd25Ik4YUXXsCTTz5p0h4XF1drEl+fcePG4eOPP0arVq3Qu3dvqFQqSJKEoUOHYtOmTfX29fDwQGRkJH788Ufs3r0b1c890ul0WLBgAX744QcEBQVh7dq1td4gqVAojA/2ufN1SZKwZcsWdOrUqUHnQkSNU3nhAipPnYYsKBAukZEQ7PxhUETOqjEX+CxxUZCaF2+GrINWVfv/bLSejf+f0JAhQ1BWVoYvv6x6gpHBYMCbb76JSZMmwePPP9MmJibi9u3bKCsrw759+9DvrgXnIyMjsXPnTpSUVC1+n5GRgZycHEREROC7775DXl4eAOD27dsAAJVKZdz3boMGDcK5c+fw2WefYdy4cQCABx54AKdOnTKWspSVleHq1au19o+NjcWWLVuQk5NjvOpdnTSr1WqUlJRgz549tfYNDg7G2bNnAcBkn2HDhmH79u2ofo7S+fPna38ziahJJElC5a//h8pTpyFv3x4uw4czySayocZc4LPERUFqXryiXYdnBrbBhYwSk98Ug7xd8czAxi8ALwgCtm3bhgULFmDdunWQJAnDhw/HvHnzjPv069cPL730Eq5fv46HH37YWDZSfbV62LBhSE5ONi73p1QqsWHDBoSFheGll17CxIkTIZPJcN9992HdunWYMGEC5s6di7i4OGzZssUkHrlcjpiYGHzxxRdYv349AECj0eD999/HrFmzjMsO/v3vf0doLTcZDBs2DC+//DKmTJlijM/HxwePPfYYYmJiEBwcbFL2cqdXX30Vc+bMwYYNG9CnTx9j+8svv4wlS5YgJiYGkiQhODgY//znPxv1ft8p48wlnPlqH1xzs6HX+KHXxFENvsHLkVVevYrKxCMQs7Ig8/eHYkiE3d44Yk8a875V97lVUAC9j0+D+lj687lzHKmiAlAq4TpgABR/GQhBxusudWENLFlDYy7wWeKiIDUvPoK9vv7VP1xLKqD1NO+HqyUewZ6Xl4fRo0fj5MmTzXpcR9XQzzHjzCUkbdiBLMkNpS7uUFbo4C+U33OZsqawp8dvV169Cv0XX0JQqQBPT6CkBFJxsV0tf2SPGvO+3dlH6eeH0uzsBvWx5OdTPQ48PSHm5UG6dQuCUgm3Z5+FSyfOg7rUVgMb5O1qdg2sPf0sINswdw40Zq41dX5S86jvEexMtJtZcyfamZmZmDhxIqZNm4Zp06Y123EdWUM+R0mScHDeauhSM6CQDJBJVXXhbgY9vL1V6D5+uEViVKk8UVxce8mOtVX8/DMknQ6Cu7uxrXrb5S9/sWFk9q0x79udfVxdXaHX6xvUx9xxmnQ+ri5AuR6ywDYQPJQQvLzgPvWpZhvH2Szddx37L9+u0T4yzNesGlgm2tSQOdCYC3yN6UPNq75Em6Ujdi4gIABHjhyxdRgORRJFSLeyYbh5A+KNGwi6dhE5ogtKXdxRIav6c1qFoIC3rgzCHeuDNyeZyguCvfwKW1YGwdvb9GZZFxegsNBi5+8UGvO+3dFHcHWF0MA+Zo/TGHfGFhAAma8akihCzMpqvjGcEGtgyZoCfdwafBNjY/qQ9TDRJodTmleAw+9sNam3DujRCWJ6OsQbNyCmpkLSlQNyGWRt2iAnqCNuFOiR7+ZlPIanvgzyQDX6RFvmiraXVotyO7mKJd68CamoCILXf85fKiqC0LEjXC10/s6gMe+bePMminLz8Us+UF4MuCk88UArwOsefazx+dQ2DkpKIPP3b7YxnBFrYImoKVrU3S9OXCXTYuhLy3D7Ugoy0/OQUukO3bUbuPnOWuS+vwEVPx2C4cZNyAKD4DJsGNwmT4ZrTAw6TZ0MtcwAT30ZBEmCp74M/kI5ek1sGU+cVAyJgFRcDKmoqOpqf1ERpOJiKIZE2Do0u9aY9+12nwE4/VsGMtPzkHa7FJnpeTj9WwZu9xnQrONY63yo6sb4IG/TdcWbemM8EbUc8qXVCyA7oaKiIpNtg8EAQRAgs+Dd9TKZzLg+NDW//IxsZJy5BNnlywgsyYHSUA6DKKGgEmj79BQoBgyAvEN7yHxbQfjzATpeAVqo2gWj5MLybLEAACAASURBVGY6/HX58PDXovfTEy266ohSqURpaanFjt8QMrUaQmAbSJlZkG7dgkythsuDY3gj5D005n1be6YYh0o8EFCah9a6fOS7e+H7kH647hOIyE6tmm0ca50PAV7uCkR08EG+rhI+Hgr0bOOJRSPamV0Da08/C8g2OAecn9edfym8S4sqHXF3d4dOp0N5eXmDH+5iLjc3t1of0EJNI1VUQCosROH+Q9DtS4Sr3AW3lL4odPWETu6KrlIJZPXcjNCmV9cWtZzf3RShoUyoGqGh71tOcQWu+wTiuo/pXFTfo57XWp8P50HjsAaWiBqrRSXagiAYHwxjKbzDvPlIkgQxPQOGy5cg3kwFAOScPocsD19keaqN+3nqy6AP9LNVmERGrOclqsK1x4mqtKhEmxyDVF4OQ0oKDJcvQyosguDuDkXP+yDv0gXBvfsjZ8MOeOrLTNbEbin11mTfLPGgKyJHU9vazhcySri2M7VITLTJbog5OTBcvgzD79cAgwEy/9ZQ9O4NWbt2xnrrNr26Ai9O/c9THgNb3lMeyX4F+rhh/cOdsOV4Bgr0gI8reCWPWpwtxzNMkmwASCvUY8vxDJbgUIvDRJtsSqqshHj9OgyXLkPMyQFcFJB3CoU8LAwytbrWPi293prsW3U9L8vIqKXi2uNE/8FEm2xCLCyE4fIViCnJkMr1EFr5VK0YEtoRgqvrvQ9A5ERYz0rOxJr3KvB7h+wdE22yGkkUIaamVd3cmJYOyATI27aDvGsYBH9/i60EQ2TPWM9KzsZa9yrwe4ccARNtsjiprAyG5GQYLl+BVFICQekBRZ/ekHfuDEGptHV4RDbFelZyNnfeq5BTUgGtp2WuNPN7hxwBE22yCEmSIN26VXVz4x9/AAYRssA2UPTvB1lwsPHmRqKWjvWs5IyssfY4v3fIEVgt0U5KSsL27dshiiKio6MRGxtr8npxcTE2b96MrKwsuLi44LnnnkPbtm3N6kv2Q6qogHjtGiovXYaUlwe4ukDepQvkXbtC5uNj6/CIGsQa9Z+sZyVqHK5bT9Xs+WebVRJtURQRFxeHRYsWQaPRYP78+QgPD0dwcLBxn127dqF9+/aYO3cu0tLSEBcXh8WLF5vVl2xPzM+vunp99Sqgr4CgVsNl0F8g69ABggt/6JHjsVb9J+tZiRqH69YTYP8/22TWGCQlJQUBAQHw9/eHQqHAoEGDcOrUKZN9UlNT0bNnTwBAUFAQsrOzkZ+fb1Zfsg3JYIDh+nXo9+2D/pvdMFy5AnlwCFwfHAPXcQ9B3qULk2xyWPXVfzan6nrWkWG+6BuswsgwX4v8D8Ja50NkLdb63iH7Zu8/26xyRTsvLw8ajca4rdFokJycbLJPu3btcOLECXTt2hUpKSnIzs5GXl6eWX2rxcfHIz4+HgCwcuVKaLVaC5xN/RQKhU3GtSaxuBjlv/2G8osXIZaUws3LC27Do+DWtStkvLkRQMuYB86uoPx67e16mPXZNmQOaLXAh6FBDQmvwZp6PtQ4/FlgWdb43mkqzgHLsvefbVZJtCVJqtF291JusbGx2LFjB+bOnYu2bduiQ4cOkMlkZvWtFhMTg5iYGOO2LR4W4SwPqcg4c+k/T1/U+KHXIyPh798KlZcvQ7xxA5AAWVAg5PffD1lQECplMpSUlgKlpbYO3S44yzxoyeq6KObjat7PFnubA009n4aw53pJa7O3ecDPxvrsbQ44G2v+bKtLYGBgna9ZJdHWaDTIzc01bufm5sLX19dkH6VSieeffx5AVWL+wgsvoHXr1tDr9ffsS80r48wlJG3YgSzJDeUKD7S59gcyV6yGywM94NU+GPLu3aEIC4Pg5WXrUIksxtnqP1kLTvxsyBnZ+89qq9Roh4aGIiMjA7du3UJlZSWOHTuG8PBwk31KSkpQWVkJADh48CC6desGpVJpVl9qXme+2ocsyQ2elTp0vf0HfMuLUCC44mJWKVwffRQu4eFMssnpOVv9J2vBiZ8NOSN7/1ltlSvacrkc06ZNw4oVKyCKIqKiohASEoL9+/cDAEaOHIm0tDR8+OGHkMlkCA4OxrPPPltvX7Ic19xslCs80LEwHcUuSmR6qqGTu6JreQnXv6YWxRprAVsT1zZu2fjZkLOy55/VVltHu2/fvujbt69J28iRI43/7tKlCz744AOz+5Ll6DV+8Lt+EwKALKUvyhRu8NSXQR/oZ+vQqJFYl0nWwrWN7Rc/GyLrs0rpCDmWXhNHIaiyCDJJhE7hCk99GfyFcvSaOMrWoVEjVNdl7r98G7+mFWP/5duYvSsF6QXltg6NnNAzA9sgyNvVpM2e6iVbMn42RNbHR7BTDQE9u8ClXw/8npyGMKEU+kA/9Jo4Cm16dbV1aNQI9dVl2uuf2shxVddLbjmegZySCmg9+RcUe8HPhsj6mGhTDVJuLlRqHzww9yHIO3a0dTjURKzLJGuz53rJlo6fDZF1sXSEahBvpgKCAFmQfT8EgMzDukwiIiLbYKJNNRjSUiFr7QfBjX9OdAasyyQiIrINlo6QCam0FFJuHuR9+9g6FGomrMskIiKyDSbaZEJMTQUAyIKDbRwJNSfWZRIREVkfS0fIhCEtDYKnEgIfc09ERETUJEy0yUgyGCCmp0MWHAxBEGwdDhEREZFDY6JNRlJWFlBRCVkQy0aIiIiImoqJNhkZUlMBuRyyNgG2DoWIiIjI4THRJiMxNQ2yAH8ILlxfmYiIiKipmGgTAEAsKIBUWAhZcIitQyEiIiJyCky0CQAgpqUBAOTBfBokERERUXNgok0AqspGBB8fCF5etg6FiIiIyCkw0SZIFRUQszL5kBoiIiKiZsREmyBmZAAGkWUjRERERM2IiTZBvJkKuLpAaN3a1qEQEREROQ0m2i2cJEkQ01IhCwyEIJfbOhwiIiIip8FEu4WT8vIglZZBzvpsIiIiombFRLuFE1OrlvWTBbE+m4iIiKg5MdFu4cS0VMi0GggeHrYOhYiIiMipMNFuwSSdDmJ2Dpf1IyIiIrIAJtotmJiWBkgSy0aIiIiILICJdgsmpqZC8PCAoNXaOhQiIiIip8NEu4WSR 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I am getting an error running with optuna=2.10.0 and matplotlib=3.4.3
optuna.visualization.matplotlib.plot_parallel_coordinate(
study, params=['num_units', 'lr', 'max_epochs', 'optimizer'])ValueError: Data has no positive values, and therefore can not be log-scaled.Are you getting this error too?
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I did not get this error when I ran the notebook. Then I upgraded optuna from 2.3.0 to 2.10.0 and now I get the same error. Seems like it's a regression in optuna?
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Feels like a regression. The function works when "lr" is removed.
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
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Closing this as it's super old :) |
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Fixes #718
This notebook shows how to use skorch in conjunction with optuna, which can be much more efficient than
GridSearchCVorRandomizedSearchCV. I don't have a deep knowledge of optuna, so there may be room for improvement. The whole example is inspired by this one.Right now, the implementation makes use of the skorch-internal train/valid split. This allows us to use optuna with very few lines of extra code.
Right now, this notebook contains an ad hoc implementation of an
OptunaCallback, which is responsible for logging and pruning trials. Possibly, this could be extended for more functionality. If so, it could find it's way into the skorch code base.Eventually, we could consider adding a section to the docs about hyper-parameter optimization using different libraries. WDYT?