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Proba_Metrics.ipynb
{
"cells": [
{
"metadata": {
"colab_type": "text",
"id": "qcMubba3e7e2"
},
"cell_type": "markdown",
"source": "# Import libraries"
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"colab_type": "code",
"id": "JIpVVG_gMx50",
"outputId": "25a0c603-f643-4734-e8b3-4c05b7ccaca6",
"trusted": false
},
"cell_type": "code",
"source": "!pip install git+https://github.com/fastai/fastcore.git@master -q\n!pip install git+https://github.com/fastai/fastai2.git@master -q",
"execution_count": 1,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " Building wheel for fastcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n Building wheel for fastai2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
}
]
},
{
"metadata": {
"colab": {},
"colab_type": "code",
"id": "F-vR9U_vRzbB",
"trusted": false
},
"cell_type": "code",
"source": "from fastai2.vision.all import *\nfrom fastai2.metrics import *\nfrom sklearn import metrics as skm",
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "text",
"id": "BOog1g8qZqjj"
},
"cell_type": "markdown",
"source": "# Scenarios\nIn sklearn there are 3 scenarios for **roc_auc_score** (each of them calculated slightly differently):\n\n* **Binary**:\n * `targets`: shape = `(n_samples, )` \n * `preds`: pass through **softmax** and then [:, -1], shape = `(n_samples,)`\n \n* **Multiclass**:\n * `targets`: shape = `(n_samples, )` \n * `preds`: pass through **softmax**, shape = `(n_samples, n_classes)`\n * `multi_class `= ‘ovr' or ‘ovo' (1)\n \n* **Multilabel**:\n * `targets`: shape = `(n_samples, n_classes)` \n * `preds`: pass through **sigmoid**, shape = `(n_samples, n_classes)`\n\n(1) ‘ovr’: average AUC of each class against the rest . 'ovo’ : average AUC of all possible pairwise combinations of classes.\n\nsklearn's **average_precision_score** implementation is restricted to binary or multilabel classification tasks. So it cannot be used in multiclass cases."
},
{
"metadata": {
"colab_type": "text",
"id": "eBH9OuqoZLZ3"
},
"cell_type": "markdown",
"source": "# Proposed code"
},
{
"metadata": {
"colab": {},
"colab_type": "code",
"id": "T5p8UL7wU2bx",
"trusted": false
},
"cell_type": "code",
"source": "class AccumMetric(Metric):\n \"Stores predictions and targets on CPU in accumulate to perform final calculations with `func`.\"\n def __init__(self, func, dim_argmax=None, sigmoid=False, softmax=False, proba=False, thresh=None, to_np=False, invert_arg=False,\n flatten=True, **kwargs):\n store_attr(self,'func,dim_argmax,sigmoid,softmax,proba,thresh,flatten')\n self.to_np,self.invert_args,self.kwargs = to_np,invert_arg,kwargs\n\n def reset(self): self.targs,self.preds = [],[]\n\n def accumulate(self, learn):\n pred = learn.pred.argmax(dim=self.dim_argmax) if (self.dim_argmax and not self.proba) else learn.pred\n if self.sigmoid: pred = torch.sigmoid(pred)\n if self.thresh: pred = (pred >= self.thresh)\n if self.softmax: \n pred = F.softmax(pred, dim=-1)\n if learn.dls.c == 2: pred = pred[:, -1]\n targ = learn.y\n pred,targ = to_detach(pred),to_detach(targ)\n if self.flatten: pred,targ = flatten_check(pred,targ)\n self.preds.append(pred)\n self.targs.append(targ)\n\n @property\n def value(self):\n if len(self.preds) == 0: return\n preds,targs = torch.cat(self.preds),torch.cat(self.targs)\n if self.to_np: preds,targs = preds.numpy(),targs.numpy()\n return self.func(targs, preds, **self.kwargs) if self.invert_args else self.func(preds, targs, **self.kwargs)\n\n @property\n def name(self): return self.func.func.__name__ if hasattr(self.func, 'func') else self.func.__name__\n\ndef skm_to_fastai(func, is_class=True, thresh=None, axis=-1, sigmoid=None, softmax=False, proba=False, **kwargs):\n \"Convert `func` from sklearn.metrics to a fastai metric\"\n dim_argmax = axis if is_class and thresh is None else None\n sigmoid = sigmoid if sigmoid is not None else (is_class and thresh is not None)\n return AccumMetric(func, dim_argmax=dim_argmax, sigmoid=sigmoid, softmax=softmax, proba=proba, thresh=thresh,\n to_np=True, invert_arg=True, **kwargs)\n\ndef APScore(axis=-1, average='macro', pos_label=1, sample_weight=None):\n \"Average Precision for binary single-label classification problems\"\n return skm_to_fastai(skm.average_precision_score, axis=axis, flatten=False, softmax=True, proba=True,\n average=average, pos_label=pos_label, sample_weight=sample_weight)\n \ndef APScoreMulti(axis=-1, average='macro', pos_label=1, sample_weight=None):\n \"Average Precision for multi-label classification problems\"\n return skm_to_fastai(skm.average_precision_score, axis=axis, flatten=False, sigmoid=True, proba=True,\n average=average, pos_label=pos_label, sample_weight=sample_weight)\n \ndef RocAuc(axis=-1, average='macro', sample_weight=None, max_fpr=None, multi_class='raise', labels=None):\n \"Area Under the Receiver Operating Characteristic Curve for single-label classification problems\"\n \"\"\"use default multi_class ('raise') for binary-class, and 'ovr'(average AUC of each class against the rest) \n or 'ovo' (average AUC of all possible pairwise combinations of classes) for multi-class tasks\"\"\"\n return skm_to_fastai(skm.roc_auc_score, axis=axis, flatten=False, softmax=True, proba=True,\n average=average, sample_weight=sample_weight, max_fpr=max_fpr, multi_class=multi_class, labels=labels)\n \ndef RocAucMulti(axis=-1, average='macro', sample_weight=None, max_fpr=None):\n \"Area Under the Receiver Operating Characteristic Curve for multi-label classification problems\"\n return skm_to_fastai(skm.roc_auc_score, axis=axis, flatten=False, sigmoid=True, proba=True,\n average=average, sample_weight=sample_weight, max_fpr=max_fpr)",
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "text",
"id": "bMBMavMhLa8l"
},
"cell_type": "markdown",
"source": "# Binary:"
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 16
},
"colab_type": "code",
"id": "uajPS5RlxjrI",
"outputId": "8e5a85ad-68de-44d6-af77-02f1aada73ce",
"trusted": false
},
"cell_type": "code",
"source": "path = untar_data(URLs.MNIST_TINY)\ndls = ImageDataLoaders.from_folder(path)",
"execution_count": 4,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 137
},
"colab_type": "code",
"id": "IYb6cStePQ9c",
"outputId": "e520c706-3633-4028-a5f7-a24e424841f7",
"trusted": false
},
"cell_type": "code",
"source": "learn = cnn_learner(dls, resnet18, pretrained=False, metrics=[APScore(), RocAuc()])\nlearn.fit_one_cycle(3, 0.1)",
"execution_count": 5,
"outputs": [
{
"data": {
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>epoch</th>\n <th>train_loss</th>\n <th>valid_loss</th>\n <th>average_precision_score</th>\n <th>roc_auc_score</th>\n <th>time</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>0</td>\n <td>1.653272</td>\n <td>1666.066528</td>\n <td>0.505007</td>\n <td>0.500000</td>\n <td>00:01</td>\n </tr>\n <tr>\n <td>1</td>\n <td>1.086577</td>\n <td>231.428848</td>\n <td>0.505007</td>\n <td>0.500000</td>\n <td>00:01</td>\n </tr>\n <tr>\n <td>2</td>\n <td>0.797317</td>\n <td>10.656080</td>\n <td>0.733888</td>\n <td>0.815029</td>\n <td>00:01</td>\n </tr>\n </tbody>\n</table>",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 16
},
"colab_type": "code",
"id": "JVldLRaEM8sE",
"outputId": "2c8705e6-61e2-4e7f-9642-0d3bb0a90246",
"trusted": false
},
"cell_type": "code",
"source": "valid_probas, valid_targets, valid_preds = learn.get_preds(dl=dls.valid, with_decoded=True)",
"execution_count": 6,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"colab_type": "code",
"id": "BaG-LDJwQncp",
"outputId": "daa82d0b-343b-4623-8317-82a3e78d8dba",
"trusted": false
},
"cell_type": "code",
"source": "# APScore and RocAuc calculated based on probas\nprint(f'avg precision : {skm.average_precision_score(valid_targets, valid_probas[:, 1]):8.6f}')\nprint(f'roc auc : {skm.roc_auc_score(valid_targets, valid_probas[:, 1]):8.6f}')",
"execution_count": 7,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "avg precision : 0.733888\nroc auc : 0.815029\n"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"colab_type": "code",
"id": "8hgemM8exHj9",
"outputId": "c25bfa28-ff7b-448d-a457-0167543a95e4",
"trusted": false
},
"cell_type": "code",
"source": "# APScore and RocAuc as they were previously on fastai2 calculated based on preds - these were wrong!!\nprint(f'avg precision : {skm.average_precision_score(valid_targets, valid_preds):8.6f}')\nprint(f'roc auc : {skm.roc_auc_score(valid_targets, valid_preds):8.6f}')",
"execution_count": 8,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "avg precision : 0.596284\nroc auc : 0.654624\n"
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "OPk9hKfsLf63"
},
"cell_type": "markdown",
"source": "# Multiclass:"
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "2pCXFq9Z-nCR",
"outputId": "ab0198d6-aab9-439f-b2f1-b4969088b7b5",
"trusted": false
},
"cell_type": "code",
"source": "bs = 64\npath = untar_data(URLs.PETS); path\nPath.BASE_PATH = path\npath_anno = path/'annotations'\npath_img = path/'images'\nfnames = get_image_files(path_img)\ndls = ImageDataLoaders.from_name_re(\n path, fnames, pat=r'(.+)_\\d+.jpg$', item_tfms=Resize(460), bs=bs,\n batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)])\ndls.c",
"execution_count": 9,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "37"
},
"execution_count": 9,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201,
"referenced_widgets": [
"7f540ad08833493db60f23aae7640818",
"60f5701faaeb4cd7bbf48ac0d9eec63c",
"a1493e86ec424e81a4486ffb11b27267",
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"b2d4ad59d12c4be6a44378bf4b39dcd7",
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"ede3695f26ce4ab0815495f04260743f",
"3693a06704f14253ba15b4e41613de2d"
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},
"colab_type": "code",
"id": "Y8IWRml8AKh6",
"outputId": "bd6d2156-f513-499f-f80f-158224354542",
"trusted": false
},
"cell_type": "code",
"source": "learn = cnn_learner(dls, resnet34, metrics=[accuracy, RocAuc(multi_class='ovo'), RocAuc(multi_class='ovr')]).to_fp16()\nlearn.fit_one_cycle(1)",
"execution_count": 10,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n"
},
{
"data": {
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"model_id": "7f540ad08833493db60f23aae7640818",
"version_major": 2,
"version_minor": 0
},
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},
"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": "\n"
},
{
"data": {
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>epoch</th>\n <th>train_loss</th>\n <th>valid_loss</th>\n <th>accuracy</th>\n <th>roc_auc_score</th>\n <th>roc_auc_score</th>\n <th>time</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>0</td>\n <td>1.248549</td>\n <td>0.344744</td>\n <td>0.899188</td>\n <td>0.997780</td>\n <td>0.997788</td>\n <td>01:04</td>\n </tr>\n </tbody>\n</table>",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. \n warnings.warn(\"The default behavior for interpolate/upsample with float scale_factor will change \"\n"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"colab_type": "code",
"id": "OR3Og5iLKjTS",
"outputId": "08d5951f-b9e0-40dd-fb26-7b4251277a3f",
"trusted": false
},
"cell_type": "code",
"source": "valid_probas, valid_targets, valid_preds = learn.get_preds(dl=dls.valid, with_decoded=True)",
"execution_count": 11,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. \n warnings.warn(\"The default behavior for interpolate/upsample with float scale_factor will change \"\n"
}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"colab_type": "code",
"id": "Fc7jATbAA6Us",
"outputId": "b6a429fe-db58-4b40-d2b2-4fb36eb65ba4",
"trusted": false
},
"cell_type": "code",
"source": "print(f'roc auc (\"ovo\") : {skm.roc_auc_score(valid_targets, valid_probas, multi_class=\"ovo\"):8.6f}')\nprint(f'roc auc (\"ovr\") : {skm.roc_auc_score(valid_targets, valid_probas, multi_class=\"ovr\"):8.6f}')",
"execution_count": 12,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "roc auc (\"ovo\") : 0.997780\nroc auc (\"ovr\") : 0.997788\n"
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "dOxwFh48LlRh"
},
"cell_type": "markdown",
"source": "# Multilabel:"
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 16
},
"colab_type": "code",
"id": "FvcXWlcMMLZh",
"outputId": "8b085400-e464-4fff-cf10-61e9c5931019",
"trusted": false
},
"cell_type": "code",
"source": "path = untar_data(URLs.PASCAL_2007)\ndf = pd.read_csv(path/'train.csv')",
"execution_count": 13,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PxJiPxLJKPgM",
"trusted": false
},
"cell_type": "code",
"source": "def splitter(df):\n train = df.index[~df['is_valid']].tolist()\n valid = df.index[df['is_valid']].tolist()\n return train,valid\n\ndef get_x(r): return path/'train'/r['fname']\ndef get_y(r): return r['labels'].split(' ')\n\ndef accuracy_multi(inp, targ, thresh=0.5, sigmoid=True):\n \"Compute accuracy when `inp` and `targ` are the same size.\"\n if sigmoid: inp = inp.sigmoid()\n return ((inp>thresh)==targ.bool()).float().mean()\n\ndblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),\n splitter=splitter,\n get_x=get_x, \n get_y=get_y,\n item_tfms = RandomResizedCrop(128, min_scale=0.35))\ndls = dblock.dataloaders(df)",
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"04c1250abdcf448cabcbbdb9b617223b",
"7a60c275ba4b4a85af07b9025d7cf0d9",
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"outputId": "b0cc7ea3-e5f7-4c34-8693-d6d305ca2879",
"trusted": false
},
"cell_type": "code",
"source": "learn = cnn_learner(dls, resnet18, metrics=[RocAucMulti(), APScoreMulti()])\nlearn.fit_one_cycle(1)",
"execution_count": 15,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n"
},
{
"data": {
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"version_minor": 0
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"output_type": "stream",
"text": "\n"
},
{
"data": {
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>epoch</th>\n <th>train_loss</th>\n <th>valid_loss</th>\n <th>roc_auc_score</th>\n <th>average_precision_score</th>\n <th>time</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>0</td>\n <td>0.928385</td>\n <td>0.686431</td>\n <td>0.803554</td>\n <td>0.365506</td>\n <td>00:31</td>\n </tr>\n </tbody>\n</table>",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
},
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}
]
},
{
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 16
},
"colab_type": "code",
"id": "7dUNtO0KN-wg",
"outputId": "4236e213-1128-4883-e163-2c7443b85682",
"trusted": false
},
"cell_type": "code",
"source": "valid_probas, valid_targets, valid_preds = learn.get_preds(dl=dls.valid, with_decoded=True)",
"execution_count": 16,
"outputs": [
{
"data": {
"text/html": "",
"text/plain": "<IPython.core.display.HTML object>"
},
"metadata": {
"tags": []
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