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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "1e0cd6a7",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0,'..')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ba81c2ba",
"metadata": {},
"outputs": [],
"source": [
"from scripts.transformer_prediction_interface import TabPFNClassifier"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "0fe8a920",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/samuelmueller/TabPFN/TabPFN\r\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "fd08a53d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Caching examples at: '/Users/samuelmueller/TabPFN/TabPFN/gradio_cached_examples/670/log.csv'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/samuelmueller/opt/anaconda3/envs/TabPFN/lib/python3.7/site-packages/gradio/networking.py:59: ResourceWarning: unclosed <socket.socket fd=280, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('0.0.0.0', 0)>\n",
" s = socket.socket() # create a socket object\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/samuelmueller/opt/anaconda3/envs/TabPFN/lib/python3.7/site-packages/gradio/networking.py:59: ResourceWarning: unclosed <socket.socket fd=285, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('0.0.0.0', 0)>\n",
" s = socket.socket() # create a socket object\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7898/\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7898/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(<gradio.routes.App at 0x7fa954c66a90>, 'http://127.0.0.1:7898/', None)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import gradio as gr\n",
"import openml\n",
"\n",
"\n",
"def compute(table: np.array):\n",
" vfunc = np.vectorize(lambda s: len(s))\n",
" non_empty_row_mask = (vfunc(table).sum(1) != 0)\n",
" print(table)\n",
" table = table[non_empty_row_mask]\n",
" empty_mask = table == ''\n",
" empty_inds = np.where(empty_mask)\n",
" assert np.all(empty_inds[1][0] == empty_inds[1])\n",
" y_column = empty_inds[1][0]\n",
" eval_lines = empty_inds[0]\n",
"\n",
" train_table = np.delete(table, eval_lines, axis=0)\n",
" eval_table = table[eval_lines]\n",
"\n",
" try:\n",
" x_train = torch.tensor(np.delete(train_table, y_column, axis=1).astype(np.float32))\n",
" x_eval = torch.tensor(np.delete(eval_table, y_column, axis=1).astype(np.float32))\n",
"\n",
" y_train = train_table[:, y_column]\n",
" except ValueError:\n",
" return \"Please only add numbers (to the inputs) or leave fields empty.\", None\n",
"\n",
" classifier = TabPFNClassifier(base_path='..', device='cpu')\n",
" classifier.fit(x_train, y_train)\n",
" y_eval, p_eval = classifier.predict(x_eval, return_winning_probability=True)\n",
" print(x_train, y_train, x_eval, y_eval)\n",
"\n",
" # print(file, type(file))\n",
" out_table = table.copy().astype(str)\n",
" out_table[eval_lines, y_column] = [f\"{y_e} (p={p_e:.2f})\" for y_e, p_e in zip(y_eval, p_eval)]\n",
" return None, out_table\n",
"\n",
"\n",
"def upload_file(file):\n",
" if file.name.endswith('.arff'):\n",
" dataset = openml.datasets.OpenMLDataset('t', 'test', data_file=file.name)\n",
" X_, _, categorical_indicator_, attribute_names_ = dataset.get_data(\n",
" dataset_format=\"array\"\n",
" )\n",
" return X_\n",
" elif file.name.endswith('.csv') or file.name.endswith('.data'):\n",
" df = pd.read_csv(file.name)\n",
" return df.to_numpy()\n",
"\n",
"\n",
"example = \\\n",
" [\n",
" [1, 2, 1],\n",
" [2, 1, 1],\n",
" [1, 1, 1],\n",
" [2, 2, 2],\n",
" [3, 4, 2],\n",
" [3, 2, 2],\n",
" [2, 3, '']\n",
" ]\n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.Markdown(\"\"\"This demo allows you to play with the **TabPFN**.\n",
" You can either change the table manually (we have filled it with a toy benchmark, sum up to 3 has label 1 and over that label 2).\n",
" The network predicts fields you leave empty. Only one column can have empty entries that are predicted.\n",
" Please, provide everything but the label column as numeric values. It is ok to encode classes as integers.\n",
" \"\"\")\n",
" inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)\n",
" inp_file = gr.File(\n",
" label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')\n",
" btn = gr.Button(\"Predict Empty Table Cells\")\n",
"\n",
" inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)\n",
"\n",
" out_text = gr.Textbox()\n",
" out_table = gr.DataFrame()\n",
"\n",
" btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table])\n",
" examples = gr.Examples(examples=['./iris.csv'],\n",
" inputs=[inp_file],\n",
" outputs=[inp_table],\n",
" fn=upload_file,\n",
" cache_examples=True)\n",
"\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "c4510232",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'hi':[1,2,'j']})"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "2403f193",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1], [2], ['j']]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"sys:1: ResourceWarning: unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa9569da910>\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
}
],
"source": [
"df.to_numpy().tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adf1a91c",
"metadata": {},
"outputs": [],
"source": [
"k"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
}
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"nbformat": 4,
"nbformat_minor": 5
}
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