cleanup

Untitled.ipynb

@@ -1,260 +0,0 @@
-{
- "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}