{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"from fastai2.basics import *\n",
"from fastai2.tabular.core import *\n",
"from fastai2.tabular.model import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nbdev.showdoc import *\n",
"from fastai2.tabular.data import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#default_exp tabular.learner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tabular learner\n",
"\n",
"> The function to immediately get a `Learner` ready to train for tabular data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The main function you probably want to use in this module is `tabular_learner`. It will automatically create a `TabulaModel` suitable for your data and infer the irght loss function. See the [tabular tutorial](http://dev.fast.ai/tutorial.tabular) for an example of use in context."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Main functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"@log_args(but_as=Learner.__init__)\n",
"class TabularLearner(Learner):\n",
" \"`Learner` for tabular data\"\n",
" def predict(self, row):\n",
" tst_to = self.dls.valid_ds.new(pd.DataFrame(row).T)\n",
" tst_to.process()\n",
" tst_to.conts = tst_to.conts.astype(np.float32)\n",
" dl = self.dls.valid.new(tst_to)\n",
" inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)\n",
" i = getattr(self.dls, 'n_inp', -1)\n",
" b = (*tuplify(inp),*tuplify(dec_preds))\n",
" full_dec = self.dls.decode((*tuplify(inp),*tuplify(dec_preds)))\n",
" return full_dec,dec_preds[0],preds[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> TabularLearner(**`dls`**, **`model`**, **`loss_func`**=*`None`*, **`opt_func`**=*`'Adam'`*, **`lr`**=*`0.001`*, **`splitter`**=*`'trainable_params'`*, **`cbs`**=*`None`*, **`metrics`**=*`None`*, **`path`**=*`None`*, **`model_dir`**=*`'models'`*, **`wd`**=*`None`*, **`wd_bn_bias`**=*`False`*, **`train_bn`**=*`True`*, **`moms`**=*`(0.95, 0.85, 0.95)`*) :: [`Learner`](/13a_learner#Learner)\n",
"\n",
"[`Learner`](/13a_learner#Learner) for tabular data"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(TabularLearner, title_level=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It works exactly as a normal `Learner`, the only difference is that it implements a `predict` method specific to work on a row of data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"@log_args(to_return=True, but_as=Learner.__init__)\n",
"@delegates(Learner.__init__)\n",
"def tabular_learner(dls, layers=None, emb_szs=None, config=None, n_out=None, y_range=None, **kwargs):\n",
" \"Get a `Learner` using `dls`, with `metrics`, including a `TabularModel` created using the remaining params.\"\n",
" if config is None: config = tabular_config()\n",
" if layers is None: layers = [200,100]\n",
" to = dls.train_ds\n",
" emb_szs = get_emb_sz(dls.train_ds, {} if emb_szs is None else emb_szs)\n",
" if n_out is None: n_out = get_c(dls)\n",
" assert n_out, \"`n_out` is not defined, and could not be infered from data, set `dls.c` or pass `n_out`\"\n",
" if y_range is None and 'y_range' in config: y_range = config.pop('y_range')\n",
" model = TabularModel(emb_szs, len(dls.cont_names), n_out, layers, y_range=y_range, **config)\n",
" return TabularLearner(dls, model, **kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If your data was built with fastai, you probably won't need to pass anything to `emb_szs` unless you want to change the default of the library (produced by `get_emb_sz`), same for `n_out` which should be automatically inferred. `layers` will default to `[200,100]` and is passed to `TabularModel` along with the `config`.\n",
"\n",
"Use `tabular_config` to create a `config` and cusotmize the model used. There is just easy access to `y_range` because this argument is often used.\n",
"\n",
"All the other arguments are passed to `Learner`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = untar_data(URLs.ADULT_SAMPLE)\n",
"df = pd.read_csv(path/'adult.csv')\n",
"cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
"cont_names = ['age', 'fnlwgt', 'education-num']\n",
"procs = [Categorify, FillMissing, Normalize]\n",
"dls = TabularDataLoaders.from_df(df, path, procs=procs, cat_names=cat_names, cont_names=cont_names, \n",
" y_names=\"salary\", valid_idx=list(range(800,1000)), bs=64)\n",
"learn = tabular_learner(dls)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"tst = learn.predict(df.iloc[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"#test y_range is passed\n",
"learn = tabular_learner(dls, y_range=(0,32))\n",
"assert isinstance(learn.model.layers[-1], SigmoidRange)\n",
"test_eq(learn.model.layers[-1].low, 0)\n",
"test_eq(learn.model.layers[-1].high, 32)\n",
"\n",
"learn = tabular_learner(dls, config = tabular_config(y_range=(0,32)))\n",
"assert isinstance(learn.model.layers[-1], SigmoidRange)\n",
"test_eq(learn.model.layers[-1].low, 0)\n",
"test_eq(learn.model.layers[-1].high, 32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"@typedispatch\n",
"def show_results(x:Tabular, y:Tabular, samples, outs, ctxs=None, max_n=10, **kwargs):\n",
" df = x.all_cols[:max_n]\n",
" for n in x.y_names: df[n+'_pred'] = y[n][:max_n].values\n",
" display_df(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted 00_torch_core.ipynb.\n",
"Converted 01_layers.ipynb.\n",
"Converted 02_data.load.ipynb.\n",
"Converted 03_data.core.ipynb.\n",
"Converted 04_data.external.ipynb.\n",
"Converted 05_data.transforms.ipynb.\n",
"Converted 06_data.block.ipynb.\n",
"Converted 07_vision.core.ipynb.\n",
"Converted 08_vision.data.ipynb.\n",
"Converted 09_vision.augment.ipynb.\n",
"Converted 09b_vision.utils.ipynb.\n",
"Converted 09c_vision.widgets.ipynb.\n",
"Converted 10_tutorial.pets.ipynb.\n",
"Converted 11_vision.models.xresnet.ipynb.\n",
"Converted 12_optimizer.ipynb.\n",
"Converted 13_callback.core.ipynb.\n",
"Converted 13a_learner.ipynb.\n",
"Converted 13b_metrics.ipynb.\n",
"Converted 14_callback.schedule.ipynb.\n",
"Converted 14a_callback.data.ipynb.\n",
"Converted 15_callback.hook.ipynb.\n",
"Converted 15a_vision.models.unet.ipynb.\n",
"Converted 16_callback.progress.ipynb.\n",
"Converted 17_callback.tracker.ipynb.\n",
"Converted 18_callback.fp16.ipynb.\n",
"Converted 18a_callback.training.ipynb.\n",
"Converted 19_callback.mixup.ipynb.\n",
"Converted 20_interpret.ipynb.\n",
"Converted 20a_distributed.ipynb.\n",
"Converted 21_vision.learner.ipynb.\n",
"Converted 22_tutorial.imagenette.ipynb.\n",
"Converted 23_tutorial.vision.ipynb.\n",
"Converted 24_tutorial.siamese.ipynb.\n",
"Converted 24_vision.gan.ipynb.\n",
"Converted 30_text.core.ipynb.\n",
"Converted 31_text.data.ipynb.\n",
"Converted 32_text.models.awdlstm.ipynb.\n",
"Converted 33_text.models.core.ipynb.\n",
"Converted 34_callback.rnn.ipynb.\n",
"Converted 35_tutorial.wikitext.ipynb.\n",
"Converted 36_text.models.qrnn.ipynb.\n",
"Converted 37_text.learner.ipynb.\n",
"Converted 38_tutorial.text.ipynb.\n",
"Converted 40_tabular.core.ipynb.\n",
"Converted 41_tabular.data.ipynb.\n",
"Converted 42_tabular.model.ipynb.\n",
"Converted 43_tabular.learner.ipynb.\n",
"Converted 44_tutorial.tabular.ipynb.\n",
"Converted 45_collab.ipynb.\n",
"Converted 46_tutorial.collab.ipynb.\n",
"Converted 50_tutorial.datablock.ipynb.\n",
"Converted 60_medical.imaging.ipynb.\n",
"Converted 61_tutorial.medical_imaging.ipynb.\n",
"Converted 65_medical.text.ipynb.\n",
"Converted 70_callback.wandb.ipynb.\n",
"Converted 71_callback.tensorboard.ipynb.\n",
"Converted 72_callback.neptune.ipynb.\n",
"Converted 73_callback.captum.ipynb.\n",
"Converted 74_callback.cutmix.ipynb.\n",
"Converted 97_test_utils.ipynb.\n",
"Converted 99_pytorch_doc.ipynb.\n",
"Converted index.ipynb.\n",
"Converted tutorial.ipynb.\n"
]
}
],
"source": [
"#hide\n",
"from nbdev.export import notebook2script\n",
"notebook2script()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}