310 lines
9.1 KiB
Plaintext
310 lines
9.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "98cddc6a-2ce1-4933-a2b7-96d2c2d197f4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"if (window.IPython && IPython.notebook.kernel) IPython.notebook.kernel.execute('jovian.utils.jupyter.get_notebook_name_saved = lambda: \"' + IPython.notebook.notebook_name + '\"')"
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import torch\n",
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"import jovian\n",
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"import torchvision\n",
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"import matplotlib\n",
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"import torch.nn as nn\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import torch.nn.functional as F\n",
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"from torchvision.datasets.utils import download_url\n",
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"from torch.utils.data import DataLoader, TensorDataset, random_split\n",
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"import random\n",
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"import os\n",
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"import sys"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7bb63556-d009-4d9f-9de0-033a30ad3fc4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(['matches', 'wins', 'draws', 'loses', 'scored', 'missed', 'pts'],\n",
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" ['position'])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#load data\n",
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"dataframe = pd.read_csv(\"understat.csv\")\n",
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"\n",
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"#choose columns\n",
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"input_cols=list(dataframe.columns)[4:11]\n",
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"output_cols = ['position']\n",
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"input_cols, output_cols"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c8151c46-c234-42b7-a786-50c73e3aa2f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def dataframe_to_arrays(dataframe):\n",
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" dataframe_loc = dataframe.copy(deep=True)\n",
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" inputs_array = dataframe_loc[input_cols].to_numpy()\n",
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" targets_array = dataframe_loc[output_cols].to_numpy()\n",
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" return inputs_array, targets_array\n",
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"\n",
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"inputs_array, targets_array = dataframe_to_arrays(dataframe)\n",
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"\n",
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"inputs = torch.from_numpy(inputs_array).type(torch.float)\n",
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"targets = torch.from_numpy(targets_array).type(torch.float)\n",
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"\n",
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"dataset = TensorDataset(inputs, targets)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8c89947b-c2fe-407d-9588-3f0087df5955",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_ds, val_ds = random_split(dataset, [548, 136])\n",
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"batch_size=50\n",
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"train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n",
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"val_loader = DataLoader(val_ds, batch_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3b1426a0-5b15-46f8-aea9-871462ca9467",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Model_xPosition(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.linear = nn.Linear(input_size,output_size) \n",
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" \n",
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" def forward(self, xb): \n",
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" out = self.linear(xb)\n",
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" return out\n",
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" \n",
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" def training_step(self, batch):\n",
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" inputs, targets = batch \n",
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" # Generate predictions\n",
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" out = self(inputs) \n",
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" # Calcuate loss\n",
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" loss = F.l1_loss(out,targets) \n",
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" return loss\n",
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" \n",
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" def validation_step(self, batch):\n",
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" inputs, targets = batch\n",
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" out = self(inputs)\n",
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" loss = F.l1_loss(out,targets) \n",
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" return {'val_loss': loss.detach()}\n",
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" \n",
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" def validation_epoch_end(self, outputs):\n",
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" batch_losses = [x['val_loss'] for x in outputs]\n",
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" epoch_loss = torch.stack(batch_losses).mean() \n",
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" return {'val_loss': epoch_loss.item()}\n",
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" \n",
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" def epoch_end(self, epoch, result, num_epochs):\n",
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" if (epoch+1) % 100 == 0 or epoch == num_epochs-1:\n",
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" print(\"Epoch {} loss: {:.4f}\".format(epoch+1, result['val_loss']))\n",
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" \n",
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" \n",
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"def evaluate(model, val_loader):\n",
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" outputs = [model.validation_step(batch) for batch in val_loader]\n",
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" return model.validation_epoch_end(outputs)\n",
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"\n",
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"def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
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" history = []\n",
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" optimizer = opt_func(model.parameters(), lr)\n",
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" for epoch in range(epochs):\n",
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" for batch in train_loader:\n",
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" loss = model.training_step(batch)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" optimizer.zero_grad()\n",
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" result = evaluate(model, val_loader)\n",
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" model.epoch_end(epoch, result, epochs)\n",
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" history.append(result)\n",
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" return history"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f2e22e9a-8724-4084-b706-0be266846c05",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_size = len(input_cols)\n",
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"output_size = len(output_cols)\n",
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"model=Model_xPosition()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "efacafe4-797a-4588-b0d8-2e4d883e639a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 100 loss: 6.2637\n",
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"Epoch 200 loss: 2.9712\n",
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"Epoch 300 loss: 1.9724\n",
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"Epoch 400 loss: 1.9376\n",
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"Epoch 500 loss: 1.9199\n",
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"Epoch 600 loss: 1.9033\n",
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"Epoch 700 loss: 1.8863\n",
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"Epoch 800 loss: 1.8703\n",
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"Epoch 900 loss: 1.8552\n",
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"Epoch 1000 loss: 1.8405\n",
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"Epoch 1100 loss: 1.8267\n",
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"Epoch 1200 loss: 1.8134\n",
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"Epoch 1300 loss: 1.8010\n",
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"Epoch 1400 loss: 1.7876\n",
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"Epoch 1500 loss: 1.7748\n",
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"Epoch 1600 loss: 1.7626\n",
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"Epoch 1700 loss: 1.7497\n",
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"Epoch 1800 loss: 1.7387\n",
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"Epoch 1900 loss: 1.7270\n",
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"Epoch 2000 loss: 1.7162\n"
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]
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}
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],
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"source": [
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"epochs = 2000\n",
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"lr = 1e-5\n",
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"learning_proccess = fit(epochs, lr, model, train_loader, val_loader)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "7007ab5a-dc79-4321-beed-cd54dd197858",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_single(input, target, model):\n",
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" inputs = input.unsqueeze(0)\n",
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" predictions = model(inputs)\n",
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" prediction = predictions[0].detach()\n",
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"\n",
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" return \"Target: \"+str(target)+\" Predicted: \"+str(prediction)+\"\\n\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "1e6ed168-2cdc-45dc-a0ff-e147ac4c46be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Target: tensor([16.]) Predicted: tensor([13.5861])\n",
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"Target: tensor([14.]) Predicted: tensor([10.1553])\n",
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"Target: tensor([19.]) Predicted: tensor([16.5709])\n",
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"Target: tensor([18.]) Predicted: tensor([18.5809])\n",
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"Target: tensor([2.]) Predicted: tensor([2.5676])\n",
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"Target: tensor([14.]) Predicted: tensor([13.4065])\n",
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"Target: tensor([11.]) Predicted: tensor([11.6196])\n",
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"Target: tensor([13.]) Predicted: tensor([13.1022])\n",
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"Target: tensor([17.]) Predicted: tensor([14.5672])\n",
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"Target: tensor([1.]) Predicted: tensor([-1.9346])\n"
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]
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}
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],
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"source": [
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"for i in random.sample(range(0, len(val_ds)), 10):\n",
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" input_, target = val_ds[i]\n",
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" print(predict_single(input_, target, model),end=\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "50c62065-5094-4595-995c-6d0b71f1f28a",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"result.txt\", \"w+\") as file:\n",
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" for i in range(0, len(val_ds), 1):\n",
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" input_, target = val_ds[i]\n",
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" file.write(str(predict_single(input_, target, model)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "8dffe789-1ad5-44f1-8f21-92b9c89ed974",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[NbConvertApp] Converting notebook ml_pytorch.ipynb to script\n",
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"[NbConvertApp] Writing 3828 bytes to ml_pytorch.py\n"
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]
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}
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],
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"source": [
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"!jupyter nbconvert --to script ml_pytorch.ipynb"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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