diff --git a/README.md b/README.md
index 29edb58e..85073ca0 100644
--- a/README.md
+++ b/README.md
@@ -100,7 +100,7 @@ from pytorch_tabular.config import (
data_config = DataConfig(
target=[
"target"
- ], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented
+ ], # target should always be a list.
continuous_cols=num_col_names,
categorical_cols=cat_col_names,
)
diff --git a/docs/gs_usage.md b/docs/gs_usage.md
index 7285c519..af3c249f 100644
--- a/docs/gs_usage.md
+++ b/docs/gs_usage.md
@@ -14,7 +14,7 @@ from pytorch_tabular.config import (
data_config = DataConfig(
target=[
"target"
- ], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented
+ ], # target should always be a list.
continuous_cols=num_col_names,
categorical_cols=cat_col_names,
)
diff --git a/docs/tutorials/01-Approaching Any Tabular Problem with PyTorch Tabular.ipynb b/docs/tutorials/01-Approaching Any Tabular Problem with PyTorch Tabular.ipynb
index af4ddbf2..ab803092 100644
--- a/docs/tutorials/01-Approaching Any Tabular Problem with PyTorch Tabular.ipynb
+++ b/docs/tutorials/01-Approaching Any Tabular Problem with PyTorch Tabular.ipynb
@@ -532,7 +532,7 @@
"data_config = DataConfig(\n",
" target=[\n",
" target_col\n",
- " ], # target should always be a list. Multi-targets are only supported for regression. Multi-Task Classification is not implemented\n",
+ " ], # target should always be a list\n",
" continuous_cols=num_col_names,\n",
" categorical_cols=cat_col_names,\n",
")\n",
diff --git a/docs/tutorials/15-Multi Target Classification.ipynb b/docs/tutorials/15-Multi Target Classification.ipynb
new file mode 100644
index 00000000..6d882cc2
--- /dev/null
+++ b/docs/tutorials/15-Multi Target Classification.ipynb
@@ -0,0 +1,2008 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.datasets import make_classification\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score, f1_score\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from pytorch_tabular.utils import make_mixed_dataset, print_metrics\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "# Load dataset\n",
+ "data, cat_col_names, num_col_names = make_mixed_dataset(task=\"classification\", n_samples=10000, n_features=8, n_categories=4, weights=[0.8], random_state=42)\n",
+ "\n",
+ "# Create a new, second target\n",
+ "data['second_target'] = 0\n",
+ "for c in cat_col_names:\n",
+ " data.second_target += data[c]\n",
+ "\n",
+ "# Correlate it to 1st target\n",
+ "data.second_target += (data.target == 'class_0')\n",
+ "\n",
+ "# Create random discrete noise to make task non-trivial\n",
+ "random_noise = np.random.normal(0, 0.8, data.shape[0]).astype(int)\n",
+ "data.second_target = (data.second_target + random_noise).mod(3) \n",
+ "\n",
+ "# Now that dataset is complete, we can perform train/test split\n",
+ "train, test = train_test_split(data, random_state=42)\n",
+ "train, val = train_test_split(train, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "target\n",
+ "class_0 0.7968\n",
+ "class_1 0.2032\n",
+ "Name: proportion, dtype: float64"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.target.value_counts(normalize=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "second_target\n",
+ "2.0 0.3364\n",
+ "1.0 0.3354\n",
+ "0.0 0.3282\n",
+ "Name: proportion, dtype: float64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.second_target.value_counts(normalize=True)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "# Importing the Library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "from pytorch_tabular import TabularModel\n",
+ "from pytorch_tabular.models import CategoryEmbeddingModelConfig\n",
+ "from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig, ExperimentConfig\n",
+ "from pytorch_tabular.models.common.heads import LinearHeadConfig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results = []"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "## Define the Configs\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "trainer_config = TrainerConfig(\n",
+ " auto_lr_find=True, # Runs the LRFinder to automatically derive a learning rate\n",
+ " batch_size=1024,\n",
+ " max_epochs=100,\n",
+ " early_stopping=\"valid_loss\", # Monitor valid_loss for early stopping\n",
+ " early_stopping_mode = \"min\", # Set the mode as min because for val_loss, lower is better\n",
+ " early_stopping_patience=5, # No. of epochs of degradation training will wait before terminating\n",
+ " checkpoints=\"valid_loss\", # Save best checkpoint monitoring val_loss\n",
+ " load_best=True, # After training, load the best checkpoint\n",
+ "# accelerator=\"cpu\"\n",
+ ")\n",
+ "optimizer_config = OptimizerConfig()\n",
+ "\n",
+ "head_config = LinearHeadConfig(\n",
+ " layers=\"\", # No additional layer in head, just a mapping layer to output_dim\n",
+ " dropout=0.1,\n",
+ " initialization=\"kaiming\"\n",
+ ").__dict__ # Convert to dict to pass to the model config (OmegaConf doesn't accept objects)\n",
+ "\n",
+ "model_config = CategoryEmbeddingModelConfig(\n",
+ " task=\"classification\",\n",
+ " layers=\"1024-512-512\", # Number of nodes in each layer\n",
+ " activation=\"LeakyReLU\", # Activation between each layers\n",
+ " head = \"LinearHead\", #Linear Head\n",
+ " head_config = head_config, # Linear Head Config\n",
+ " learning_rate = 1e-3,\n",
+ " metrics=[\"f1_score\",\"accuracy\",\"auroc\"], \n",
+ " metrics_prob_input=[True, False, True] # f1_score needs probability scores, while accuracy doesn't\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data Config For Each Target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_config_first_target = DataConfig(\n",
+ " target=['target'], #target should always be a list\n",
+ " continuous_cols=num_col_names,\n",
+ " categorical_cols=cat_col_names\n",
+ ")\n",
+ "\n",
+ "data_config_second_target = DataConfig(\n",
+ " target=['second_target'], #target should always be a list\n",
+ " continuous_cols=num_col_names,\n",
+ " categorical_cols=cat_col_names\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "## Training the Single-Target Model "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "Collapsed": "false",
+ "tags": []
+ },
+ "outputs": [
+ {
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+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
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+ "\n"
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+ ],
+ "source": [
+ "tabular_model = TabularModel(\n",
+ " data_config=data_config_first_target,\n",
+ " model_config=model_config,\n",
+ " optimizer_config=optimizer_config,\n",
+ " trainer_config=trainer_config,\n",
+ ")\n",
+ "\n",
+ "tabular_model.fit(train=train, validation=val)\n",
+ "\n",
+ "result = tabular_model.evaluate(test)\n",
+ "\n",
+ "result = {k: float(v) for k,v in result[0].items()}\n",
+ "result = pd.DataFrame({'f1':result['test_f1_score'],'auroc':result['test_auroc']},\n",
+ " index=['1st Target (single mode)'])\n",
+ "results.append(result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Now train separately on the 2nd target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "Seed set to 42\n"
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+ "2024-07-10 11:38:31,035 - {pytorch_tabular.tabular_model:338} - INFO - Preparing the Trainer \n",
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+ "Finding best initial lr: 0%| | 0/100 [00:002024-07-10 11:38:38,580 - {pytorch_tabular.tabular_model:660} - INFO - Suggested LR: 0.0002511886431509582. For \n",
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+ "plot and detailed analysis, use `find_learning_rate` method. \n"
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+ "┃ Test metric ┃ DataLoader 0 ┃\n",
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+ "\n"
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+ "metadata": {},
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+ ],
+ "source": [
+ "tabular_model = TabularModel(\n",
+ " data_config=data_config_second_target,\n",
+ " model_config=model_config,\n",
+ " optimizer_config=optimizer_config,\n",
+ " trainer_config=trainer_config,\n",
+ ")\n",
+ "\n",
+ "tabular_model.fit(train=train, validation=val)\n",
+ "\n",
+ "result = tabular_model.evaluate(test)\n",
+ "\n",
+ "result = {k: float(v) for k,v in result[0].items()}\n",
+ "result = pd.DataFrame({'f1':result['test_f1_score'],'auroc':result['test_auroc']},\n",
+ " index=['2nd Target (single mode)'])\n",
+ "\n",
+ "results.append(result)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-Target Training\n",
+ "\n",
+ "Instead of training one model for the first target, and another model for the second target, we can train a single model that will make a prediction for both targets.\n",
+ "\n",
+ "This is usually beneficial in reducing training time, but may also lead to better results since the model may have a better representation (embedding) by learning from multiple targets.\n",
+ "\n",
+ "To perform multi-target training, we only need to model the 'target' field in the data_config to include a list of multiple targets.\n",
+ "\n",
+ "Results are reported on the sum of all metrics (f1, AU-ROC, etc.), as well as a list of results for each target with the suffix '_n' (starting at n=1), for example, the f1 score for the 2nd target is test_f1_score_0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "2024-07-10 11:38:57,010 - {pytorch_tabular.tabular_model:140} - INFO - Experiment Tracking is turned off \n",
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+ "Seed set to 42\n"
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+ "2024-07-10 11:38:57,028 - {pytorch_tabular.tabular_datamodule:507} - INFO - Setting up the datamodule for \n",
+ "classification task \n",
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+ "2024-07-10 11:38:57,049 - {pytorch_tabular.tabular_model:591} - INFO - Preparing the Model: CategoryEmbeddingModel \n",
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+ "2024-07-10 11:38:57,089 - {pytorch_tabular.tabular_model:338} - INFO - Preparing the Trainer \n",
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+ "text": [
+ "GPU available: True (mps), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
+ "HPU available: False, using: 0 HPUs\n"
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+ "data": {
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+ "2024-07-10 11:38:57,115 - {pytorch_tabular.tabular_model:647} - INFO - Auto LR Find Started \n",
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+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/yony/Code/pytorch-tabular-public/docs/tutorials/saved_models exists and is not empty.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
+ ]
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+ "Finding best initial lr: 0%| | 0/100 [00:002024-07-10 11:39:06,162 - {pytorch_tabular.tabular_model:660} - INFO - Suggested LR: 4.786300923226385e-05. For \n",
+ "plot and detailed analysis, use `find_learning_rate` method. \n",
+ "\n"
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+ "plot and detailed analysis, use `find_learning_rate` method. \n"
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+ "data": {
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+ "┃ Test metric ┃ DataLoader 0 ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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+ "│ test_accuracy_0 │ 0.946399986743927 │\n",
+ "│ test_accuracy_1 │ 0.618399977684021 │\n",
+ "│ test_auroc │ 1.7456306219100952 │\n",
+ "│ test_auroc_0 │ 0.9698674082756042 │\n",
+ "│ test_auroc_1 │ 0.7757631540298462 │\n",
+ "│ test_f1_score │ 1.5648000240325928 │\n",
+ "│ test_f1_score_0 │ 0.946399986743927 │\n",
+ "│ test_f1_score_1 │ 0.618399977684021 │\n",
+ "│ test_loss │ 1.0632164478302002 │\n",
+ "│ test_loss_0 │ 0.16491228342056274 │\n",
+ "│ test_loss_1 │ 0.8983041048049927 │\n",
+ "└───────────────────────────┴───────────────────────────┘\n",
+ "
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+ "└───────────────────────────┴───────────────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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+ "\n"
+ ]
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+ "metadata": {},
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+ ],
+ "source": [
+ "data_config_multi = DataConfig(\n",
+ " target=['target','second_target'], #target should always be a list\n",
+ " continuous_cols=num_col_names,\n",
+ " categorical_cols=cat_col_names\n",
+ ")\n",
+ "\n",
+ "tabular_model = TabularModel(\n",
+ " data_config=data_config_multi,\n",
+ " model_config=model_config,\n",
+ " optimizer_config=optimizer_config,\n",
+ " trainer_config=trainer_config,\n",
+ ")\n",
+ "\n",
+ "tabular_model.fit(train=train, validation=val)\n",
+ "\n",
+ "result = tabular_model.evaluate(test)\n",
+ "\n",
+ "result = {k: float(v) for k,v in result[0].items()}\n",
+ "result1 = pd.DataFrame({'f1':result['test_f1_score_0'],'auroc':result['test_auroc_0']},\n",
+ " index=['1st Target (multi-target mode)'])\n",
+ "results.append(result1)\n",
+ "result2 = pd.DataFrame({'f1':result['test_f1_score_1'],'auroc':result['test_auroc_1']},\n",
+ " index=['2nd Target (multi-target mode)'])\n",
+ "results.append(result2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " | | \n",
+ " f1 | \n",
+ " auroc | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1st Target (single mode) | \n",
+ " 0.948400 | \n",
+ " 0.971750 | \n",
+ "
\n",
+ " \n",
+ " | 2nd Target (single mode) | \n",
+ " 0.680800 | \n",
+ " 0.813438 | \n",
+ "
\n",
+ " \n",
+ " | 1st Target (multi-target mode) | \n",
+ " 0.946400 | \n",
+ " 0.969867 | \n",
+ "
\n",
+ " \n",
+ " | 2nd Target (multi-target mode) | \n",
+ " 0.618400 | \n",
+ " 0.775763 | \n",
+ "
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+ " \n",
+ "
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "res_df = pd.concat(results)\n",
+ "res_df.style.highlight_max(color=\"lightgreen\",axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this run, we see that multi-target model performed on-par with the single-target on the 1st target, but slightly worse on the 2nd target. This may vary for this artificial dataset depending on random number generation, and in general multi-target may not outperform single-target variants. Additional tuning may be needed for multi-target, e.g. larger embedding size. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deep Learning Model\n",
+ "\n",
+ "We can also test whether a deeper model benefits from the shared embedding. In this case, we test Gandalf with multi-target classification, similar to our experiment above.\n",
+ "\n",
+ "Note: Without an accelerator (GPU), this training will take considerable time on a CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pytorch_tabular.models import GANDALFConfig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "2024-07-10 11:40:09,147 - {pytorch_tabular.tabular_model:140} - INFO - Experiment Tracking is turned off \n",
+ "\n"
+ ],
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+ "\u001b[1;36m2024\u001b[0m-\u001b[1;36m07\u001b[0m-\u001b[1;36m10\u001b[0m \u001b[1;92m11:40:09\u001b[0m,\u001b[1;36m147\u001b[0m - \u001b[1m{\u001b[0mpytorch_tabular.tabular_model:\u001b[1;36m140\u001b[0m\u001b[1m}\u001b[0m - INFO - Experiment Tracking is turned off \n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Seed set to 42\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "2024-07-10 11:40:09,160 - {pytorch_tabular.tabular_model:541} - INFO - Preparing the DataLoaders \n",
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+ "data": {
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+ "2024-07-10 11:40:09,165 - {pytorch_tabular.tabular_datamodule:507} - INFO - Setting up the datamodule for \n",
+ "classification task \n",
+ "\n"
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+ "text/plain": [
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+ "classification task \n"
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+ "data": {
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+ "2024-07-10 11:40:09,188 - {pytorch_tabular.tabular_model:591} - INFO - Preparing the Model: GANDALFModel \n",
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+ "data": {
+ "text/html": [
+ "2024-07-10 11:40:09,212 - {pytorch_tabular.tabular_model:338} - INFO - Preparing the Trainer \n",
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+ "\u001b[1;36m2024\u001b[0m-\u001b[1;36m07\u001b[0m-\u001b[1;36m10\u001b[0m \u001b[1;92m11:40:09\u001b[0m,\u001b[1;36m212\u001b[0m - \u001b[1m{\u001b[0mpytorch_tabular.tabular_model:\u001b[1;36m338\u001b[0m\u001b[1m}\u001b[0m - INFO - Preparing the Trainer \n"
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+ },
+ "metadata": {},
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+ "name": "stderr",
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+ "text": [
+ "GPU available: True (mps), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
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+ },
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+ "data": {
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+ "2024-07-10 11:40:09,240 - {pytorch_tabular.tabular_model:647} - INFO - Auto LR Find Started \n",
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+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/yony/Code/pytorch-tabular-public/docs/tutorials/saved_models exists and is not empty.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (6) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
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+ {
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+ "Finding best initial lr: 0%| | 0/100 [00:002024-07-10 11:40:22,112 - {pytorch_tabular.tabular_model:660} - INFO - Suggested LR: 0.10964781961431852. For plot \n",
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+ "and detailed analysis, use `find_learning_rate` method. \n"
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+ "2024-07-10 11:40:28,324 - {pytorch_tabular.tabular_model:680} - INFO - Training the model completed \n",
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+ "/Users/yony/Code/pytorch-tabular-public/.venv/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
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+ "┃ Test metric ┃ DataLoader 0 ┃\n",
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+ "│ test_accuracy │ 1.210800051689148 │\n",
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+ "└───────────────────────────┴───────────────────────────┘\n",
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