wehub-resource-sync
476 lines
15 KiB
Plaintext
476 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cross entropy baseline model for ordinal regression and deep learning -- cement strength dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is a regular cross entropy classifier as a baseline for comparison with ordinal regression methods."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 0 -- Obtaining and preparing the cement_strength dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will be using the cement_strength dataset from [https://github.com/gagolews/ordinal_regression_data/blob/master/cement_strength.csv](https://github.com/gagolews/ordinal_regression_data/blob/master/cement_strength.csv).\n",
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"\n",
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"First, we are going to download and prepare the and save it as CSV files locally. This is a general procedure that is not specific to CORN.\n",
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"\n",
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"This dataset has 5 ordinal labels (1, 2, 3, 4, and 5). Note that we require labels to be starting at 0, which is why we subtract \"1\" from the label column."
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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": 1,
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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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"Number of features: 8\n",
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"Number of examples: 998\n",
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"Labels: [0 1 2 3 4]\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"\n",
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"data_df = pd.read_csv(\"https://raw.githubusercontent.com/gagolews/ordinal_regression_data/master/cement_strength.csv\")\n",
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" \n",
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"data_df[\"response\"] = data_df[\"response\"]-1 # labels should start at 0\n",
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"\n",
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"data_labels = data_df[\"response\"]\n",
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"data_features = data_df.loc[:, [\"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\"]]\n",
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"\n",
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"print('Number of features:', data_features.shape[1])\n",
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"print('Number of examples:', data_features.shape[0])\n",
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"print('Labels:', np.unique(data_labels.values))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Split into training and test data"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" data_features.values,\n",
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" data_labels.values,\n",
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" test_size=0.2,\n",
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" random_state=1,\n",
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" stratify=data_labels.values)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Standardize features"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"\n",
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"sc = StandardScaler()\n",
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"X_train_std = sc.fit_transform(X_train)\n",
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"X_test_std = sc.transform(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1 -- Setting up the dataset and dataloader"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this section, we set up the data set and data loaders. This is a general procedure that is not specific to the method."
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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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"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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"Training on cuda:0\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"\n",
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"##########################\n",
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"### SETTINGS\n",
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"##########################\n",
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"\n",
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"# Hyperparameters\n",
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"random_seed = 1\n",
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"learning_rate = 0.001\n",
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"num_epochs = 50\n",
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"batch_size = 128\n",
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"\n",
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"# Architecture\n",
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"NUM_CLASSES = 5\n",
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"\n",
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"# Other\n",
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"DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"print('Training on', DEVICE)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from torch.utils.data import Dataset\n",
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"\n",
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"\n",
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"class MyDataset(Dataset):\n",
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"\n",
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" def __init__(self, feature_array, label_array, dtype=np.float32):\n",
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" \n",
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" self.features = feature_array.astype(np.float32)\n",
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" self.labels = label_array\n",
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"\n",
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" def __getitem__(self, index):\n",
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" inputs = self.features[index]\n",
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" label = self.labels[index]\n",
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" return inputs, label\n",
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"\n",
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" def __len__(self):\n",
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" return self.labels.shape[0]"
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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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"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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"Input batch dimensions: torch.Size([128, 8])\n",
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"Input label dimensions: torch.Size([128])\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"\n",
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"# Note transforms.ToTensor() scales input images\n",
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"# to 0-1 range\n",
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"train_dataset = MyDataset(X_train_std, y_train)\n",
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"test_dataset = MyDataset(X_test_std, y_test)\n",
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"\n",
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"\n",
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"train_loader = DataLoader(dataset=train_dataset,\n",
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" batch_size=batch_size,\n",
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" shuffle=True, # want to shuffle the dataset\n",
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" num_workers=0) # number processes/CPUs to use\n",
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"\n",
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"test_loader = DataLoader(dataset=test_dataset,\n",
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" batch_size=batch_size,\n",
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" shuffle=False,\n",
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" num_workers=0)\n",
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"\n",
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"# Checking the dataset\n",
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"for inputs, labels in train_loader: \n",
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" print('Input batch dimensions:', inputs.shape)\n",
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" print('Input label dimensions:', labels.shape)\n",
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" break"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2 - Implementing a simple MLP with cross entropy loss"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this section, we are implementing a simple MLP for ordinal regression. To implement the Beckham et al. method, we add the parameter layer `a` as `self.a`, which is used to compute the predictions for the loss function later in the training loop:"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"class MLP(torch.nn.Module):\n",
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"\n",
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" def __init__(self, in_features, num_classes, num_hidden_1=300, num_hidden_2=300):\n",
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" super().__init__()\n",
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" \n",
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" self.num_classes = num_classes\n",
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" self.my_network = torch.nn.Sequential(\n",
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" \n",
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" # 1st hidden layer\n",
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" torch.nn.Linear(in_features, num_hidden_1, bias=False),\n",
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" torch.nn.LeakyReLU(),\n",
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" torch.nn.Dropout(0.2),\n",
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" torch.nn.BatchNorm1d(num_hidden_1),\n",
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" \n",
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" # 2nd hidden layer\n",
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" torch.nn.Linear(num_hidden_1, num_hidden_2, bias=False),\n",
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" torch.nn.LeakyReLU(),\n",
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" torch.nn.Dropout(0.2),\n",
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" torch.nn.BatchNorm1d(num_hidden_2),\n",
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" \n",
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" # Output layer\n",
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" torch.nn.Linear(num_hidden_2, num_classes)\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" logits = self.my_network(x)\n",
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" return logits\n",
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" \n",
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" \n",
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" \n",
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"torch.manual_seed(random_seed)\n",
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"model = MLP(in_features=8, num_classes=NUM_CLASSES)\n",
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"model.to(DEVICE)\n",
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"\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3 - Using the reformulated squared error loss loss for model training"
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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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"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: 001/050 | Batch 000/007 | Cost: 1.8506\n",
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"Epoch: 002/050 | Batch 000/007 | Cost: 1.2779\n",
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"Epoch: 003/050 | Batch 000/007 | Cost: 1.0849\n",
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"Epoch: 004/050 | Batch 000/007 | Cost: 1.0136\n",
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"Epoch: 005/050 | Batch 000/007 | Cost: 1.0655\n",
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"Epoch: 006/050 | Batch 000/007 | Cost: 0.9198\n",
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"Epoch: 007/050 | Batch 000/007 | Cost: 0.9269\n",
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"Epoch: 008/050 | Batch 000/007 | Cost: 0.8566\n",
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"Epoch: 009/050 | Batch 000/007 | Cost: 0.9192\n",
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"Epoch: 010/050 | Batch 000/007 | Cost: 0.8459\n",
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"Epoch: 011/050 | Batch 000/007 | Cost: 0.8595\n",
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"Epoch: 012/050 | Batch 000/007 | Cost: 0.8126\n",
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"Epoch: 013/050 | Batch 000/007 | Cost: 0.7344\n",
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"Epoch: 014/050 | Batch 000/007 | Cost: 0.7982\n",
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"Epoch: 015/050 | Batch 000/007 | Cost: 0.7587\n",
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"Epoch: 016/050 | Batch 000/007 | Cost: 0.7278\n",
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"Epoch: 017/050 | Batch 000/007 | Cost: 0.5626\n",
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"Epoch: 018/050 | Batch 000/007 | Cost: 0.6570\n",
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"Epoch: 019/050 | Batch 000/007 | Cost: 0.6695\n",
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"Epoch: 020/050 | Batch 000/007 | Cost: 0.8091\n",
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"Epoch: 021/050 | Batch 000/007 | Cost: 0.6433\n",
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"Epoch: 022/050 | Batch 000/007 | Cost: 0.5846\n",
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"Epoch: 023/050 | Batch 000/007 | Cost: 0.6255\n",
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"Epoch: 024/050 | Batch 000/007 | Cost: 0.6438\n",
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"Epoch: 025/050 | Batch 000/007 | Cost: 0.6645\n",
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"Epoch: 026/050 | Batch 000/007 | Cost: 0.6947\n",
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"Epoch: 027/050 | Batch 000/007 | Cost: 0.5889\n",
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"Epoch: 028/050 | Batch 000/007 | Cost: 0.6015\n",
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"Epoch: 029/050 | Batch 000/007 | Cost: 0.6087\n",
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"Epoch: 030/050 | Batch 000/007 | Cost: 0.5184\n",
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"Epoch: 031/050 | Batch 000/007 | Cost: 0.5749\n",
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"Epoch: 032/050 | Batch 000/007 | Cost: 0.5191\n",
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"Epoch: 033/050 | Batch 000/007 | Cost: 0.5260\n",
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"Epoch: 034/050 | Batch 000/007 | Cost: 0.6051\n",
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"Epoch: 035/050 | Batch 000/007 | Cost: 0.5267\n",
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"Epoch: 036/050 | Batch 000/007 | Cost: 0.5485\n",
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"Epoch: 037/050 | Batch 000/007 | Cost: 0.4345\n",
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"Epoch: 038/050 | Batch 000/007 | Cost: 0.5198\n",
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"Epoch: 039/050 | Batch 000/007 | Cost: 0.4047\n",
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"Epoch: 040/050 | Batch 000/007 | Cost: 0.5052\n",
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"Epoch: 041/050 | Batch 000/007 | Cost: 0.5436\n",
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"Epoch: 042/050 | Batch 000/007 | Cost: 0.4116\n",
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"Epoch: 043/050 | Batch 000/007 | Cost: 0.4640\n",
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"Epoch: 044/050 | Batch 000/007 | Cost: 0.5765\n",
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"Epoch: 045/050 | Batch 000/007 | Cost: 0.5034\n",
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"Epoch: 046/050 | Batch 000/007 | Cost: 0.5579\n",
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"Epoch: 047/050 | Batch 000/007 | Cost: 0.4336\n",
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"Epoch: 048/050 | Batch 000/007 | Cost: 0.5188\n",
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"Epoch: 049/050 | Batch 000/007 | Cost: 0.5183\n",
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"Epoch: 050/050 | Batch 000/007 | Cost: 0.5013\n"
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]
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}
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],
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"source": [
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"for epoch in range(num_epochs):\n",
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" \n",
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" model = model.train()\n",
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" for batch_idx, (features, class_labels) in enumerate(train_loader):\n",
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"\n",
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" class_labels = class_labels.to(DEVICE)\n",
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" features = features.to(DEVICE)\n",
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" logits = model(features)\n",
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" \n",
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" logits = model(features)\n",
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" loss = torch.nn.functional.cross_entropy(logits, class_labels)\n",
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" \n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" ### LOGGING\n",
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" if not batch_idx % 200:\n",
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" print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n",
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" %(epoch+1, num_epochs, batch_idx, \n",
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" len(train_loader), loss))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4 -- Evaluate model\n",
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"\n",
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"Finally, after model training, we can evaluate the performance of the model. For example, via the mean absolute error and mean squared error measures.\n",
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"\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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"metadata": {},
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"outputs": [],
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"source": [
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"def beckham_logits_to_labels(logits, model, num_classes):\n",
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" predictions = beckham_logits_to_predictions(logits, model, num_classes)\n",
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" return torch.round(predictions).float()\n",
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" \n",
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"\n",
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"def compute_mae_and_mse(model, data_loader, device):\n",
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"\n",
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" with torch.no_grad():\n",
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" \n",
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" mae, mse, acc, num_examples = 0., 0., 0., 0\n",
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"\n",
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" for i, (features, targets) in enumerate(data_loader):\n",
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"\n",
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" features = features.to(device)\n",
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" targets = targets.float().to(device)\n",
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"\n",
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" logits = model(features)\n",
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" predicted_labels = torch.argmax(logits, dim=1)\n",
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"\n",
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" num_examples += targets.size(0)\n",
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" mae += torch.sum(torch.abs(predicted_labels - targets))\n",
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" mse += torch.sum((predicted_labels - targets)**2)\n",
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"\n",
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" mae = mae / num_examples\n",
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" mse = mse / num_examples\n",
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" return mae, mse"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"train_mae, train_mse = compute_mae_and_mse(model, train_loader, DEVICE)\n",
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"test_mae, test_mse = compute_mae_and_mse(model, test_loader, DEVICE)"
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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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"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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"Mean absolute error (train/test): 0.22 | 0.37\n",
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"Mean squared error (train/test): 0.27 | 0.41\n"
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]
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}
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],
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"source": [
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"print(f'Mean absolute error (train/test): {train_mae:.2f} | {test_mae:.2f}')\n",
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"print(f'Mean squared error (train/test): {train_mse:.2f} | {test_mse:.2f}')"
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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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"name": "ipython",
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},
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"name": "python",
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}
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