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Pytorch Widedeep

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Pytorch Widedeep

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提交 d13e86f9 编写于 作者: P Pavol Mulinka
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fixed multiclass torchmetrics

上级 ef9ea277
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Showing with 127 addition and 53 deletion
examples/15_AUC_multiclass.ipynb
浏览文件 @ d13e86f9
......@@ -26,8 +26,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2021-12-10 21:26:26.784170: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
"2021-12-10 21:26:26.784221: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
"2021-12-11 16:34:55.734357: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
"2021-12-11 16:34:55.734404: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
]
}
],
......@@ -39,7 +39,7 @@
"from pytorch_widedeep import Trainer\n",
"from pytorch_widedeep.preprocessing import TabPreprocessor\n",
"from pytorch_widedeep.models import TabMlp, WideDeep\n",
"from torchmetrics import AUC\n",
"from torchmetrics import AUC, AUROC\n",
"from pytorch_widedeep.initializers import XavierNormal\n",
"from pytorch_widedeep.datasets import load_ecoli\n",
"from pytorch_widedeep.utils import LabelEncoder\n",
......@@ -53,7 +53,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 2,
"id": "07c75f0c",
"metadata": {},
"outputs": [
......@@ -163,7 +163,7 @@
"4 ADI_ECOLI 0.23 0.32 0.48 0.5 0.55 0.25 0.35 cp"
]
},
"execution_count": 26,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
......@@ -175,7 +175,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 3,
"id": "1e3f8efc",
"metadata": {},
"outputs": [
......@@ -193,7 +193,7 @@
"Name: class, dtype: int64"
]
},
"execution_count": 27,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
......@@ -205,7 +205,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 4,
"id": "e4db0d6d",
"metadata": {},
"outputs": [],
......@@ -216,7 +216,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 5,
"id": "005531a3",
"metadata": {},
"outputs": [],
......@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 6,
"id": "214b3071",
"metadata": {},
"outputs": [],
......@@ -239,7 +239,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 7,
"id": "168c81f1",
"metadata": {},
"outputs": [],
......@@ -258,7 +258,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 8,
"id": "3a7b246b",
"metadata": {},
"outputs": [],
......@@ -268,7 +268,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 9,
"id": "7a2dac24",
"metadata": {},
"outputs": [],
......@@ -298,7 +298,7 @@
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 10,
"id": "511198d4",
"metadata": {},
"outputs": [
......@@ -331,7 +331,7 @@
")"
]
},
"execution_count": 39,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
......@@ -347,7 +347,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 11,
"id": "a5359b0f",
"metadata": {},
"outputs": [
......@@ -356,49 +356,40 @@
"output_type": "stream",
"text": [
"/home/palo/miniconda3/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
" warnings.warn(*args, **kwargs)\n",
"/home/palo/miniconda3/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `AUROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.\n",
" warnings.warn(*args, **kwargs)\n"
]
}
],
"source": [
"auc = AUC()"
"auc = AUC(reorder=True)\n",
"auc.num_classes = df_enc[\"class\"].nunique()\n",
"auroc = AUROC(num_classes=df_enc[\"class\"].nunique())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 12,
"id": "34a18ac0",
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'AUC' object has no attribute 'num_classes'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_1193/1577559620.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/utils/general_utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m ] = alias\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X_wide, X_tab, X_text, X_img, X_train, X_val, val_split, target, n_epochs, validation_freq, batch_size, custom_dataloader, finetune, finetune_epochs, finetune_max_lr, finetune_deeptabular_gradual, finetune_deeptabular_max_lr, finetune_deeptabular_layers, finetune_deeptext_gradual, finetune_deeptext_max_lr, finetune_deeptext_layers, finetune_deepimage_gradual, finetune_deepimage_max_lr, finetune_deepimage_layers, finetune_routine, stop_after_finetuning, **kwargs)\u001b[0m\n\u001b[1;32m 615\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargett\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_description\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"epoch %i\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 617\u001b[0;31m \u001b[0mtrain_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargett\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 618\u001b[0m \u001b[0mprint_loss_and_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 619\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36m_train_step\u001b[0;34m(self, data, target, batch_idx)\u001b[0m\n\u001b[1;32m 1168\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1169\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1170\u001b[0;31m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1171\u001b[0m \u001b[0;31m# TODO raise exception if the loss is exploding with non scaled target values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1172\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/training/trainer.py\u001b[0m in \u001b[0;36m_get_score\u001b[0;34m(self, y_pred, y)\u001b[0m\n\u001b[1;32m 1212\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1213\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1214\u001b[0;31m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1215\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1216\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/pytorch_widedeep/metrics.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, y_pred, y_true)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTorchMetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_classes\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# type: ignore[operator]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1128\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m raise AttributeError(\"'{}' object has no attribute '{}'\".format(\n\u001b[0m\u001b[1;32m 1131\u001b[0m type(self).__name__, name))\n\u001b[1;32m 1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'AUC' object has no attribute 'num_classes'"
"name": "stderr",
"output_type": "stream",
"text": [
"epoch 1: 100%|██████████| 6/6 [00:00<00:00, 79.20it/s, loss=0.1, metrics={'AUC': 8.5, 'AUROC': 0.427}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 6.06it/s, loss=0.0961, metrics={'AUC': 6.5, 'AUROC': 0.419}]\n",
"epoch 2: 100%|██████████| 6/6 [00:00<00:00, 82.52it/s, loss=0.095, metrics={'AUC': 4.5, 'AUROC': 0.4418}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.69it/s, loss=0.0917, metrics={'AUC': 6.5, 'AUROC': 0.4351}]\n",
"epoch 3: 100%|██████████| 6/6 [00:00<00:00, 103.30it/s, loss=0.0908, metrics={'AUC': 5.5, 'AUROC': 0.4715}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.35it/s, loss=0.0875, metrics={'AUC': 6.5, 'AUROC': 0.4633}]\n",
"epoch 4: 100%|██████████| 6/6 [00:00<00:00, 90.88it/s, loss=0.0872, metrics={'AUC': 7.0, 'AUROC': 0.4767}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.44it/s, loss=0.0874, metrics={'AUC': 6.5, 'AUROC': 0.4652}]\n",
"epoch 5: 100%|██████████| 6/6 [00:00<00:00, 88.87it/s, loss=0.0866, metrics={'AUC': 6.0, 'AUROC': 0.4775}]\n",
"valid: 100%|██████████| 1/1 [00:00<00:00, 5.37it/s, loss=0.087, metrics={'AUC': 6.5, 'AUROC': 0.4524}]\n"
]
}
],
......@@ -414,11 +405,10 @@
" lr_schedulers={\"deeptabular\": deep_sch},\n",
" initializers={\"deeptabular\": XavierNormal},\n",
" optimizers={\"deeptabular\": deep_opt},\n",
" metrics=[auc],\n",
" verbose=0,\n",
" metrics=[auc, auroc],\n",
")\n",
"\n",
"trainer.fit(X_train=X_train, X_val=X_val, n_epochs=5, batch_size=10)"
"trainer.fit(X_train=X_train, X_val=X_val, n_epochs=5, batch_size=50)"
]
}
],
......
pytorch_widedeep/metrics.py
浏览文件 @ d13e86f9
import numpy as np
import torch
from torchmetrics import Metric as TorchMetric
from torchmetrics import AUC
from .wdtypes import * # noqa: F403
......@@ -38,10 +39,23 @@ class MultipleMetrics(object):
if isinstance(metric, Metric):
logs[self.prefix + metric._name] = metric(y_pred, y_true)
if isinstance(metric, TorchMetric):
if not hasattr(metric, "num_classes"):
raise ValueError(
"""TorchMetric does not have num_classes attribute.
Use metric in this library or extend the metric by num_classes attribute,
see `examples <https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`
"""
)
if metric.num_classes == 2:
metric.update(torch.round(y_pred).int(), y_true.int())
if isinstance(metric, AUC):
metric.update(torch.round(y_pred).int(), y_true.int())
else:
metric.update(y_pred, y_true.int())
if metric.num_classes > 2: # type: ignore[operator]
metric.update(torch.max(y_pred, dim=1).indices, y_true.int()) # type: ignore[attr-defined]
if isinstance(metric, AUC):
metric.update(torch.max(y_pred, dim=1).indices, y_true.int()) # type: ignore[attr-defined]
else:
metric.update(y_pred, y_true.int()) # type: ignore[attr-defined]
logs[self.prefix + type(metric).__name__] = (
metric.compute().detach().cpu().numpy()
)
......@@ -396,3 +410,62 @@ class R2Score(Metric):
y_true_avg = self.y_true_sum / self.num_examples
self.denominator += ((y_true - y_true_avg) ** 2).sum().item()
return np.array((1 - (self.numerator / self.denominator)))
class Accuracy(Metric):
r"""Class to calculate the accuracy for both binary and categorical problems
Parameters
----------
top_k: int, default = 1
Accuracy will be computed using the top k most likely classes in
multiclass problems
Examples
--------
>>> import torch
>>>
>>> from pytorch_widedeep.metrics import Accuracy
>>>
>>> acc = Accuracy()
>>> y_true = torch.tensor([0, 1, 0, 1]).view(-1, 1)
>>> y_pred = torch.tensor([[0.3, 0.2, 0.6, 0.7]]).view(-1, 1)
>>> acc(y_pred, y_true)
array(0.5)
>>>
>>> acc = Accuracy(top_k=2)
>>> y_true = torch.tensor([0, 1, 2])
>>> y_pred = torch.tensor([[0.3, 0.5, 0.2], [0.1, 0.1, 0.8], [0.1, 0.5, 0.4]])
>>> acc(y_pred, y_true)
array(0.66666667)
"""
def __init__(self, top_k: int = 1):
super(Accuracy, self).__init__()
self.top_k = top_k
self.correct_count = 0
self.total_count = 0
self._name = "acc"
def reset(self):
"""
resets counters to 0
"""
self.correct_count = 0
self.total_count = 0
def __call__(self, y_pred: Tensor, y_true: Tensor) -> np.ndarray:
num_classes = y_pred.size(1)
if num_classes == 1:
y_pred = y_pred.round()
y_true = y_true
elif num_classes > 1:
y_pred = y_pred.topk(self.top_k, 1)[1]
y_true = y_true.view(-1, 1).expand_as(y_pred)
self.correct_count += y_pred.eq(y_true).sum().item() # type: ignore[assignment]
self.total_count += len(y_pred)
accuracy = float(self.correct_count) / float(self.total_count)
return np.array(accuracy)
pytorch_widedeep/training/trainer.py
浏览文件 @ d13e86f9
......@@ -147,10 +147,14 @@ class Trainer:
<https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`__
folder in the repo
- List of objects of type :obj:`torchmetrics.Metric`. This can be any
metric from torchmetrics library `Examples
metric from torchmetrics library that has attribute num_classes `Examples
<https://torchmetrics.readthedocs.io/en/latest/references/modules.html#
classification-metrics>`_. This can also be a custom metric as
long as it is an object of type :obj:`Metric`. See `the instructions
classification-metrics>`_.
Objects of type :obj:`torchmetrics.Metric` can be extended with num_classes
attribute to be used with the Trainer object, see `examples
<https://github.com/jrzaurin/pytorch-widedeep/tree/master/examples>`.
This can also be a custom metric as long as it is an object of
type :obj:`Metric`. See `the instructions
<https://torchmetrics.readthedocs.io/en/latest/>`_.
class_weight: float, List or Tuple. optional. default=None
- float indicating the weight of the minority class in binary classification
......
tests/test_metrics/test_torchmetrics.py
浏览文件 @ d13e86f9
import numpy as np
import torch
import pytest
from torchmetrics import F1, FBeta, Recall, Accuracy, Precision
from torchmetrics import F1, FBeta, Recall, Accuracy, Precision, AUC
from sklearn.metrics import (
f1_score,
fbeta_score,
recall_score,
accuracy_score,
precision_score,
auc_score,
)
from pytorch_widedeep.metrics import MultipleMetrics
......@@ -35,9 +36,12 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
("Recall", recall_score, Recall(num_classes=2, average="none")),
("F1", f1_score, F1(num_classes=2, average="none")),
("FBeta", f2_score_bin, FBeta(beta=2, num_classes=2, average="none")),
("AUC", auc_score, AUC()),
],
)
def test_binary_metrics(metric_name, sklearn_metric, torch_metric):
if metric_name == "AUC":
torch_metric.num_classes=2
sk_res = sklearn_metric(y_true_bin_np, y_pred_bin_np.round())
wd_metric = MultipleMetrics(metrics=[torch_metric])
wd_logs = wd_metric(y_pred_bin_pt, y_true_bin_pt)
......@@ -82,11 +86,14 @@ def f2_score_multi(y_true, y_pred, average):
("Recall", recall_score, Recall(num_classes=3, average="macro")),
("F1", f1_score, F1(num_classes=3, average="macro")),
("FBeta", f2_score_multi, FBeta(beta=3, num_classes=3, average="macro")),
("AUC", auc_score, AUC()),
],
)
def test_muticlass_metrics(metric_name, sklearn_metric, torch_metric):
if metric_name == "Accuracy":
sk_res = sklearn_metric(y_true_multi_np, y_pred_muli_np.argmax(axis=1))
elif metric_name == "AUC":
torch_metric.num_classes=3
else:
sk_res = sklearn_metric(
y_true_multi_np, y_pred_muli_np.argmax(axis=1), average="macro"
......
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