From ebf9f07985135eba2cbe24560e6ae0ceb9c27d10 Mon Sep 17 00:00:00 2001 From: ShrihanSolo Date: Mon, 15 Jul 2024 12:20:11 +0000 Subject: [PATCH] mb success --- .../fiducial/ShrihanPaperMMD_fidcheck2.ipynb | 2 +- .../multiband/ShrihanPaperMMD_mb.ipynb | 1005 ------------ .../ShrihanPaperMMD_mb_comment.ipynb | 1450 +++++++++++++++++ .../multiband/ShrihanPaperMMD_mb_fid.ipynb | 1385 ++++++++++++++++ 4 files changed, 2836 insertions(+), 1006 deletions(-) delete mode 100644 training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb.ipynb create mode 100644 training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_comment.ipynb create mode 100644 training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_fid.ipynb diff --git a/training/notebooks/MMD_paper/fiducial/ShrihanPaperMMD_fidcheck2.ipynb b/training/notebooks/MMD_paper/fiducial/ShrihanPaperMMD_fidcheck2.ipynb index a2ae443..f3d113f 100644 --- a/training/notebooks/MMD_paper/fiducial/ShrihanPaperMMD_fidcheck2.ipynb +++ b/training/notebooks/MMD_paper/fiducial/ShrihanPaperMMD_fidcheck2.ipynb @@ -1372,7 +1372,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb.ipynb b/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb.ipynb deleted file mode 100644 index 30bbb4f..0000000 --- a/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb.ipynb +++ /dev/null @@ -1,1005 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 10375, - "status": "ok", - "timestamp": 1718868666013, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", - "outputId": "9ad89b68-4fd0-4146-a087-24cd367fb09f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cuda device\n" - ] - } - ], - "source": [ - "# Imports we will use\n", - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import DataLoader, TensorDataset\n", - "from torch.autograd import Function\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random\n", - "from pathlib import Path\n", - "from sklearn.metrics import r2_score\n", - "from astropy.visualization import make_lupton_rgb\n", - "\n", - "# For matplotlib\n", - "import os\n", - "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n", - "\n", - "# Set Seed\n", - "torch.manual_seed(22)\n", - "\n", - "# Find if cuda is available\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "print(f\"Using {device} device\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7cc92062-1846-4850-8f8e-206a7c35c171", - "metadata": { - "executionInfo": { - "elapsed": 189, - "status": "ok", - "timestamp": 1718868679894, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "7cc92062-1846-4850-8f8e-206a7c35c171" - }, - "outputs": [], - "source": [ - "# Load data function\n", - "def create_dataloader(img_path, metadata_path, batch_size):\n", - " '''\n", - " Creates dataloader for training, reserving the last 10% images for validation/testing\n", - " '''\n", - " data = np.load(img_path).squeeze()\n", - " length = len(data)\n", - " data_train = torch.tensor(data[:int(.7*length)]) # 70% train\n", - " data_test = torch.tensor(data[int(.7*length):int(.9*length)]) # 20% test\n", - " data_val = torch.tensor(data[int(.9*length):]) # 10% validation\n", - "\n", - " metadata = pd.read_csv(metadata_path)\n", - " labels = metadata['PLANE_1-OBJECT_1-MASS_PROFILE_1-theta_E-g'].tolist()\n", - " labels_train = torch.tensor(labels[:int(.7*length)])\n", - " labels_test = torch.tensor(labels[int(.7*length):int(.9*length)])\n", - " labels_val = torch.tensor(labels[int(.9*length):])\n", - "\n", - " data_train.cuda()\n", - " data_test.cuda()\n", - " data_val.cuda()\n", - " labels_train.cuda()\n", - " labels_test.cuda()\n", - " labels_val.cuda()\n", - "\n", - " train_dataset = TensorDataset(data_train, labels_train)\n", - " test_dataset = TensorDataset(data_test, labels_test)\n", - " val_dataset = TensorDataset(data_val, labels_val)\n", - "\n", - " train_dataloader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n", - " test_dataloader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)\n", - " val_dataloader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=True)\n", - "\n", - " return train_dataloader, test_dataloader, val_dataloader, data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7", - "metadata": { - "executionInfo": { - "elapsed": 19938, - "status": "ok", - "timestamp": 1718868749575, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7" - }, - "outputs": [], - "source": [ - "# Load in data\n", - "head = Path.cwd().parents[3]\n", - "source_img_path = head / 'data/mb_source/mb_source.npy'\n", - "target_img_path = head / 'data/mb_target/mb_target.npy'\n", - "source_meta = head / 'data/mb_source/mb_source_metadata.csv'\n", - "target_meta = head / 'data/mb_target/mb_target_metadata.csv'\n", - "batch_size = 32\n", - "source_train_dataloader, source_test_dataloader, source_val_dataloader, source_data = create_dataloader(source_img_path, source_meta, batch_size)\n", - "target_train_dataloader, target_test_dataloader, target_val_dataloader, target_data = create_dataloader(target_img_path, target_meta, batch_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cc2641b2-6b2f-4cd7-9b29-a8ed7a595103", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_train_dataloader" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a3045daa-2e71-4335-8259-662a5c7e41a8", - "metadata": { - "executionInfo": { - "elapsed": 3, - "status": "ok", - "timestamp": 1718868749576, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "a3045daa-2e71-4335-8259-662a5c7e41a8" - }, - "outputs": [], - "source": [ - "# Define data visualization function\n", - "def visualize_data(data):\n", - " '''\n", - " visualizes 16 random images from dataset\n", - " '''\n", - " \n", - " data_length = len(data)\n", - " num_indices = 16\n", - " \n", - " # Generate 15 unique random indices using numpy\n", - " random_indices = np.random.choice(data_length, size=num_indices, replace=False)\n", - "\n", - " #plot the examples for source\n", - " fig1=plt.figure(figsize=(8,8))\n", - "\n", - " for i in range(16):\n", - " plt.subplot(4, 4, i + 1)\n", - " plt.axis(\"off\")\n", - "\n", - " img = data[random_indices[i]]\n", - " example_image = make_lupton_rgb(img[0], img[1], img[2]) #change band by switching 0:1 to 1:2 or 2:3\n", - "\n", - " plt.imshow(example_image, aspect='auto', cmap='viridis')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 673 - }, - "executionInfo": { - "elapsed": 559, - "status": "ok", - "timestamp": 1718868750133, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", - "outputId": "651cb9ac-efea-4f14-b3a0-f03648a4081a" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize source data\n", - "visualize_data(source_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 673 - }, - "executionInfo": { - "elapsed": 665, - "status": "ok", - "timestamp": 1718868750796, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", - "outputId": "eccb0d95-4566-445f-a058-b1d5b87765b0" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize target data\n", - "visualize_data(target_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f", - "metadata": { - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1718868750796, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f" - }, - "outputs": [], - "source": [ - "# Define and initialize model\n", - "class NeuralNetwork(nn.Module):\n", - " def __init__(self):\n", - " super(NeuralNetwork, self).__init__()\n", - " self.feature = nn.Sequential()\n", - " self.feature.add_module('f_conv1', nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, padding='same'))\n", - " self.feature.add_module('f_relu1', nn.ReLU(True))\n", - " self.feature.add_module('f_bn1', nn.BatchNorm2d(8))\n", - " self.feature.add_module('f_pool1', nn.MaxPool2d(kernel_size=2, stride=2))\n", - " self.feature.add_module('f_conv2', nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, padding='same'))\n", - " self.feature.add_module('f_relu2', nn.ReLU(True))\n", - " self.feature.add_module('f_bn2', nn.BatchNorm2d(16))\n", - " self.feature.add_module('f_pool2', nn.MaxPool2d(kernel_size=2, stride=2))\n", - " self.feature.add_module('f_conv3', nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding='same'))\n", - " self.feature.add_module('f_relu3', nn.ReLU(True))\n", - " self.feature.add_module('f_bn3', nn.BatchNorm2d(32))\n", - " self.feature.add_module('f_pool3', nn.MaxPool2d(kernel_size=2, stride=2))\n", - "\n", - " self.regressor = nn.Sequential()\n", - " self.regressor.add_module('r_fc1', nn.Linear(in_features=32*5*5, out_features=128))\n", - " self.regressor.add_module('r_relu1', nn.ReLU(True))\n", - " #self.regressor.add_module('r_fc2', nn.Linear(in_features=128, out_features=64))\n", - " #self.regressor.add_module('r_relu2', nn.ReLU(True))\n", - " self.regressor.add_module('r_fc3', nn.Linear(in_features=128, out_features=1))\n", - "\n", - " def forward(self, x):\n", - " x = x.view(-1, 1, 40, 40)\n", - "\n", - " features = self.feature(x)\n", - " features = features.view(-1, 32*5*5)\n", - " estimate = self.regressor(features)\n", - " estimate = F.relu(estimate)\n", - " estimate = estimate.view(-1)\n", - "\n", - " return estimate, features\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cfd79aed-d467-4d59-a44d-df05177dfd58", - "metadata": { - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1718868750796, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "cfd79aed-d467-4d59-a44d-df05177dfd58" - }, - "outputs": [], - "source": [ - "# code from https://github.com/ZongxianLee/MMD_Loss.Pytorch\n", - "\n", - "class MMD_loss(nn.Module):\n", - " def __init__(self, kernel_mul = 2.0, kernel_num = 5):\n", - " super(MMD_loss, self).__init__()\n", - " self.kernel_num = kernel_num\n", - " self.kernel_mul = kernel_mul\n", - " self.fix_sigma = None\n", - " return\n", - " def guassian_kernel(self, source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):\n", - " n_samples = int(source.size()[0])+int(target.size()[0])\n", - " total = torch.cat([source, target], dim=0)\n", - "\n", - " total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", - " total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", - " L2_distance = ((total0-total1)**2).sum(2)\n", - " if fix_sigma:\n", - " bandwidth = fix_sigma\n", - " else:\n", - " bandwidth = torch.sum(L2_distance.data) / (n_samples**2-n_samples)\n", - " bandwidth /= kernel_mul ** (kernel_num // 2)\n", - " bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]\n", - " kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]\n", - " return sum(kernel_val)\n", - "\n", - " def forward(self, source, target):\n", - " batch_size = int(source.size()[0])\n", - " kernels = self.guassian_kernel(source, target, kernel_mul=self.kernel_mul, kernel_num=self.kernel_num, fix_sigma=self.fix_sigma)\n", - " XX = kernels[:batch_size, :batch_size]\n", - " YY = kernels[batch_size:, batch_size:]\n", - " XY = kernels[:batch_size, batch_size:]\n", - " YX = kernels[batch_size:, :batch_size]\n", - " loss = torch.mean(XX + YY - XY -YX)\n", - " return loss" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ccac040a-7d18-45a4-b390-40e3dfa51756", - "metadata": { - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1718868750797, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "ccac040a-7d18-45a4-b390-40e3dfa51756" - }, - "outputs": [], - "source": [ - "# Define training loop\n", - "def train_loop(source_dataloader, target_dataloader, model, regressor_loss_fn, da_loss, optimizer, n_epoch, epoch):\n", - "\n", - " domain_error = 0\n", - " domain_classifier_accuracy = 0\n", - " estimator_error = 0\n", - " score_list = np.array([])\n", - "\n", - " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", - " data_source_iter = iter(source_dataloader)\n", - " data_target_iter = iter(target_dataloader)\n", - "\n", - " i = 0\n", - " while i < len_dataloader:\n", - "\n", - " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader\n", - " alpha = 2. / (1. + np.exp(-10 * p)) - 1\n", - "\n", - " # Source Training\n", - "\n", - " data_source = next(data_source_iter)\n", - " X, y = data_source\n", - " X = X.float()\n", - " X = X.cuda()\n", - " y = y.cuda()\n", - "\n", - " model.zero_grad()\n", - " batch_size = len(y)\n", - "\n", - " domain_label = torch.zeros(batch_size)\n", - " domain_label = domain_label.long()\n", - " domain_label = domain_label.cuda()\n", - "\n", - " estimate_output, domain_output_source = model(X)\n", - "\n", - " estimate_loss = regressor_loss_fn(estimate_output, y)\n", - "\n", - " # Target Training\n", - "\n", - " data_target = next(data_target_iter)\n", - " X_target, _ = data_target\n", - " X_target = X_target.float()\n", - " X_target = X_target.cuda()\n", - "\n", - " batch_size = len(X_target)\n", - "\n", - " _, domain_output_target = model(X_target)\n", - " domain_loss = da_loss(domain_output_source, domain_output_target)\n", - "\n", - " loss = estimate_loss + domain_loss*1.4\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # Update values\n", - "\n", - " domain_error += domain_loss.item()\n", - " #domain_classifier_accuracy +=\n", - " estimator_error += estimate_loss.item()\n", - " score = r2_score(y.cpu().detach().numpy(), estimate_output.cpu().detach().numpy())\n", - " score_list = np.append(score_list, score)\n", - "\n", - " i += 1\n", - "\n", - " score = np.mean(score_list)\n", - " domain_error = domain_error / (len_dataloader)\n", - " estimator_error /= len_dataloader\n", - "\n", - " return [domain_error, estimator_error, score]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21", - "metadata": { - "executionInfo": { - "elapsed": 6, - "status": "ok", - "timestamp": 1718868750797, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21" - }, - "outputs": [], - "source": [ - "# Define testing loop\n", - "\n", - "def test_loop(source_dataloader, target_dataloader, model, regressor_loss_fn, da_loss, n_epoch, epoch):\n", - "\n", - " with torch.no_grad():\n", - "\n", - " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", - " data_source_iter = iter(source_dataloader)\n", - " data_target_iter = iter(target_dataloader)\n", - "\n", - " domain_classifier_error = 0\n", - " domain_classifier_accuracy = 0\n", - " estimator_error = 0\n", - " estimator_error_target = 0\n", - " score_list = np.array([])\n", - " score_list_target = np.array([])\n", - "\n", - " i = 0\n", - " while i < len_dataloader:\n", - "\n", - " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader\n", - " alpha = 2. / (1. + np.exp(-10 * p)) - 1\n", - "\n", - " # Source Testing\n", - "\n", - " data_source = next(data_source_iter)\n", - " X, y = data_source\n", - " X = X.float()\n", - " X = X.cuda()\n", - " y = y.cuda()\n", - "\n", - " batch_size = len(y)\n", - "\n", - " #domain_label = torch.zeros(batch_size)\n", - " #domain_label = domain_label.long()\n", - " #domain_label = domain_label.cuda()\n", - "\n", - " estimate_output, domain_output = model(X)\n", - "\n", - " estimate_loss = regressor_loss_fn(estimate_output, y)\n", - " #domain_loss_source = classifier_loss_fn(domain_output, domain_label)\n", - "\n", - " # Target Testing\n", - "\n", - " data_target = next(data_target_iter)\n", - " X_target, y_target = data_target\n", - " X_target = X_target.float()\n", - " X_target = X_target.cuda()\n", - " y_target = y_target.cuda()\n", - "\n", - " batch_size = len(X_target)\n", - "\n", - " #domain_label = torch.ones(batch_size)\n", - " #domain_label = domain_label.long()\n", - " #domain_label = domain_label.cuda()\n", - "\n", - " estimate_output_target, domain_output = model(X_target)\n", - "\n", - " estimate_loss_target = regressor_loss_fn(estimate_output_target, y_target)\n", - " #domain_loss_target = classifier_loss_fn(domain_output, domain_label)\n", - "\n", - " # Update values\n", - "\n", - " # domain_classifier_error += domain_loss_source.item()\n", - " #domain_classifier_error += domain_loss_target.item()\n", - " #domain_classifier_accuracy +=\n", - " estimator_error += estimate_loss.item()\n", - " estimator_error_target += estimate_loss_target.item()\n", - " score = r2_score(y.cpu(), estimate_output.cpu())\n", - " score_list = np.append(score_list, score)\n", - " score_target = r2_score(y_target.cpu(), estimate_output_target.cpu())\n", - " score_list_target = np.append(score_list_target, score_target)\n", - "\n", - " i += 1\n", - "\n", - " score = np.mean(score_list)\n", - " score_target = np.mean(score_list_target)\n", - " #classifier_error = domain_classifier_error / (len_dataloader * 2)\n", - " estimator_error /= len_dataloader\n", - " estimator_error_target /= len_dataloader\n", - " classifier_error = 1\n", - " return [classifier_error, estimator_error, estimator_error_target, score, score_target]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 293833, - "status": "ok", - "timestamp": 1718869045423, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", - "outputId": "45493f2a-ea42-401e-f88b-b0ad39b969ed" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1\n", - "-------------------------------\n", - "12.33421277999878\n", - "Train Estimator Error = 0.16444933820188973\n", - "Train Estimator R2 Score = 0.6710\n", - "Train Domain Classifier Error = 0.197300594592879\n", - "Validation Source Estimator Error = 0.03957607594739859\n", - "Validation Source R2 Score = 0.9181\n", - "Validation Target Estimator Error = 0.17865040874595095\n", - "Validation Target R2 Score = 0.6406\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 2\n", - "-------------------------------\n", - "10.286649942398071\n", - "Train Estimator Error = 0.033987110668803534\n", - "Train Estimator R2 Score = 0.9313\n", - "Train Domain Classifier Error = 0.10603604664246277\n", - "Validation Source Estimator Error = 0.026627989835847334\n", - "Validation Source R2 Score = 0.9447\n", - "Validation Target Estimator Error = 0.12391905738100124\n", - "Validation Target R2 Score = 0.7497\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 3\n", - "-------------------------------\n", - "10.679370164871216\n", - "Train Estimator Error = 0.025708429421718748\n", - "Train Estimator R2 Score = 0.9480\n", - "Train Domain Classifier Error = 0.09875815365143406\n", - "Validation Source Estimator Error = 0.025580009335806224\n", - "Validation Source R2 Score = 0.9470\n", - "Validation Target Estimator Error = 0.11177382997836277\n", - "Validation Target R2 Score = 0.7764\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 4\n", - "-------------------------------\n", - "9.528148651123047\n", - "Train Estimator Error = 0.021674147663191916\n", - "Train Estimator R2 Score = 0.9560\n", - "Train Domain Classifier Error = 0.09356177005732953\n", - "Validation Source Estimator Error = 0.023202258696079635\n", - "Validation Source R2 Score = 0.9526\n", - "Validation Target Estimator Error = 0.09558532137874585\n", - "Validation Target R2 Score = 0.8068\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 5\n", - "-------------------------------\n", - "9.20451831817627\n", - "Train Estimator Error = 0.018606798048258863\n", - "Train Estimator R2 Score = 0.9622\n", - "Train Domain Classifier Error = 0.09366841838989659\n", - "Validation Source Estimator Error = 0.016288266745603578\n", - "Validation Source R2 Score = 0.9664\n", - "Validation Target Estimator Error = 0.06763043769510688\n", - "Validation Target R2 Score = 0.8619\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 6\n", - "-------------------------------\n", - "9.798243761062622\n", - "Train Estimator Error = 0.016928718104180444\n", - "Train Estimator R2 Score = 0.9657\n", - "Train Domain Classifier Error = 0.0902507189198157\n", - "Validation Source Estimator Error = 0.014676664193653188\n", - "Validation Source R2 Score = 0.9693\n", - "Validation Target Estimator Error = 0.06337754338220426\n", - "Validation Target R2 Score = 0.8730\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 7\n", - "-------------------------------\n", - "11.475250482559204\n", - "Train Estimator Error = 0.01520067899678604\n", - "Train Estimator R2 Score = 0.9690\n", - "Train Domain Classifier Error = 0.08746750692971446\n", - "Validation Source Estimator Error = 0.015763865929144392\n", - "Validation Source R2 Score = 0.9671\n", - "Validation Target Estimator Error = 0.07552005605665361\n", - "Validation Target R2 Score = 0.8486\n", - "Validation Domain Classifier Error = 1\n", - "\n", - "Epoch 8\n", - "-------------------------------\n" - ] - } - ], - "source": [ - "# Initialize dictionary for training stats\n", - "import time\n", - "model = NeuralNetwork().cuda()\n", - "# Hyper parameter presets\n", - "learning_rate = 6e-5\n", - "epochs = 30\n", - "# Define loss functions and optimizer\n", - "regressor_loss_fn = nn.MSELoss().cuda()\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", - "da_loss = MMD_loss()\n", - "\n", - "stats = {'train_domain_classifier_error':[],\n", - " 'train_estimator_error':[],\n", - " 'train_score':[],\n", - " 'val_domain_classifier_error':[],\n", - " 'val_estimator_error':[],\n", - " 'val_estimator_error_target':[],\n", - " 'val_score':[],\n", - " 'val_score_target':[]}\n", - "\n", - "# Train\n", - "for i in range(epochs):\n", - " start_time = time.time()\n", - " print(f\"Epoch {i+1}\\n-------------------------------\")\n", - " vals = train_loop(source_train_dataloader, target_train_dataloader, model,\n", - " regressor_loss_fn, da_loss, optimizer, epochs, i)\n", - "\n", - " vals_validate = test_loop(source_val_dataloader, target_val_dataloader,\n", - " model, regressor_loss_fn, da_loss, epochs, i)\n", - " print(time.time() - start_time)\n", - "\n", - " stats['train_domain_classifier_error'].append(vals[0])\n", - " stats['train_estimator_error'].append(vals[1])\n", - " stats['train_score'].append(vals[2])\n", - " stats['val_domain_classifier_error'].append(vals_validate[0])\n", - " stats['val_estimator_error'].append(vals_validate[1])\n", - " stats['val_estimator_error_target'].append(vals_validate[2])\n", - " stats['val_score'].append(vals_validate[3])\n", - " stats['val_score_target'].append(vals_validate[4])\n", - "\n", - " to_print = (\n", - " f'Train Estimator Error = {vals[1]}\\n'\n", - " f'Train Estimator R2 Score = {vals[2]:.4f}\\n'\n", - " f'Train Domain Classifier Error = {vals[0]}\\n'\n", - " f'Validation Source Estimator Error = {vals_validate[1]}\\n'\n", - " f'Validation Source R2 Score = {vals_validate[3]:.4f}\\n'\n", - " f'Validation Target Estimator Error = {vals_validate[2]}\\n'\n", - " f'Validation Target R2 Score = {vals_validate[4]:.4f}\\n'\n", - " f'Validation Domain Classifier Error = {vals_validate[0]}\\n'\n", - " )\n", - "\n", - " print(to_print)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "YfplCDIb-UU_", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "executionInfo": { - "elapsed": 649, - "status": "ok", - "timestamp": 1718869045736, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "YfplCDIb-UU_", - "outputId": "dbb362ec-4af5-4cb9-c4f9-a0a2766c26c5" - }, - "outputs": [], - "source": [ - "# Classifier\n", - "eps = np.arange(epochs)\n", - "plt.title(\"Classifier Error\")\n", - "plt.plot(eps, stats['train_domain_classifier_error'])\n", - "plt.plot(eps, stats['val_domain_classifier_error'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eYG_P_iQ_5Bv", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "executionInfo": { - "elapsed": 169, - "status": "ok", - "timestamp": 1718869045739, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "eYG_P_iQ_5Bv", - "outputId": "be450f92-eda7-4e4f-81fe-008c55b2b112" - }, - "outputs": [], - "source": [ - "# Estimator\n", - "plt.title(\"Estimator Error\")\n", - "plt.plot(eps, stats['train_estimator_error'])\n", - "plt.plot(eps, stats['val_estimator_error'])\n", - "plt.plot(eps, stats['val_estimator_error_target'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "xS9rtS-T_neg", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "executionInfo": { - "elapsed": 237, - "status": "ok", - "timestamp": 1718869045904, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "xS9rtS-T_neg", - "outputId": "d32f40ef-6042-4154-e9ee-1f4e2f90064d" - }, - "outputs": [], - "source": [ - "# R2 Scores\n", - "plt.title(\"R2 Scores\")\n", - "plt.plot(eps, stats['train_score'])\n", - "plt.plot(eps, stats['val_score'])\n", - "plt.plot(eps, stats['val_score_target'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed0a8206-7520-4a60-8e17-965a91133b92", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 428 - }, - "executionInfo": { - "elapsed": 969, - "status": "ok", - "timestamp": 1718869046858, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "ed0a8206-7520-4a60-8e17-965a91133b92", - "outputId": "7df8c563-5826-4e43-d9e6-5e686463551d" - }, - "outputs": [], - "source": [ - "# Test Source\n", - "preds = np.array([])\n", - "true = np.array([])\n", - "score_list = np.array([])\n", - "\n", - "with torch.no_grad():\n", - " for X, y in source_test_dataloader:\n", - " X = X.float()\n", - " pred, _ = model(X.cuda())\n", - " preds = np.append(preds, pred.cpu())\n", - " true = np.append(true, y.cpu())\n", - " score = r2_score(y.cpu(), pred.cpu())\n", - " score_list = np.append(score_list, score)\n", - "\n", - "score = np.mean(score_list)\n", - "print(f'Source R2 Score is {score:.4f}')\n", - "\n", - "plt.figure(figsize=(8,8),dpi=50)\n", - "plt.scatter(true, preds, color='black')\n", - "line = np.linspace(0, 4, 100)\n", - "plt.plot(line, line)\n", - "plt.rc('font', size=12)\n", - "plt.xlabel('True Theta E')\n", - "plt.ylabel('Predicted Theta E');\n", - "plt.rc('font', size=20)\n", - "plt.title('MMD - Source')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 444 - }, - "executionInfo": { - "elapsed": 1283, - "status": "ok", - "timestamp": 1718869048133, - "user": { - "displayName": "Shrihan Agarwal", - "userId": "00018416289398983661" - }, - "user_tz": 300 - }, - "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", - "outputId": "b6347093-56d9-4a8b-b515-c4c4717cdab4" - }, - "outputs": [], - "source": [ - "# Test target\n", - "preds = np.array([])\n", - "true = np.array([])\n", - "score_list = np.array([])\n", - "\n", - "with torch.no_grad():\n", - " for X, y in target_test_dataloader:\n", - " X = X.float()\n", - " pred, _ = model(X.cuda())\n", - " preds = np.append(preds, pred.cpu())\n", - " true = np.append(true, y.cpu())\n", - " score = r2_score(y.cpu(), pred.cpu())\n", - " score_list = np.append(score_list, score)\n", - "\n", - "score = np.mean(score_list)\n", - "print(f'Target R2 Score is {score:.4f}')\n", - "\n", - "plt.figure(figsize=(8,8),dpi=50)\n", - "plt.scatter(true, preds, color='black')\n", - "line = np.linspace(0, 4, 100)\n", - "plt.plot(line, line)\n", - "plt.rc('font', size=12)\n", - "plt.xlabel('True Theta E')\n", - "plt.ylabel('Predicted Theta E');\n", - "plt.rc('font', size=20)\n", - "plt.title('MMD - Target')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2", - "metadata": { - "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [ - { - "file_id": "1MFScb-3Sbugn4RNiDaeocicJUIHlh_j2", - "timestamp": 1717430435817 - }, - { - "file_id": "1wlKaSdLzleueYrwljtOcqsiOfzEy1dxP", - "timestamp": 1717429638462 - } - ] - }, - "kernelspec": { - "display_name": "Python [conda env:.conda-neural]", - "language": "python", - "name": "conda-env-.conda-neural-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_comment.ipynb b/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_comment.ipynb new file mode 100644 index 0000000..983e116 --- /dev/null +++ b/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_comment.ipynb @@ -0,0 +1,1450 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 10375, + "status": "ok", + "timestamp": 1718868666013, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", + "outputId": "9ad89b68-4fd0-4146-a087-24cd367fb09f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda device\n" + ] + } + ], + "source": [ + "# Imports we will use\n", + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from torch.autograd import Function\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "from pathlib import Path\n", + "from sklearn.metrics import r2_score\n", + "from astropy.visualization import make_lupton_rgb\n", + "\n", + "# For matplotlib\n", + "import os\n", + "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n", + "\n", + "# Set Seed\n", + "torch.manual_seed(22)\n", + "\n", + "# Find if cuda is available\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "print(f\"Using {device} device\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7cc92062-1846-4850-8f8e-206a7c35c171", + "metadata": { + "executionInfo": { + "elapsed": 189, + "status": "ok", + "timestamp": 1718868679894, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "7cc92062-1846-4850-8f8e-206a7c35c171" + }, + "outputs": [], + "source": [ + "# Load data function\n", + "def create_dataloader(img_path, metadata_path, batch_size):\n", + " '''\n", + " Creates dataloader for training, reserving the last 10% images for validation/testing\n", + " '''\n", + " data = np.load(img_path).squeeze()\n", + " length = len(data)\n", + " data_train = torch.tensor(data[:int(.7*length)]) # 70% train\n", + " data_test = torch.tensor(data[int(.7*length):int(.9*length)]) # 20% test\n", + " data_val = torch.tensor(data[int(.9*length):]) # 10% validation\n", + "\n", + " metadata = pd.read_csv(metadata_path)\n", + " labels = metadata['PLANE_1-OBJECT_1-MASS_PROFILE_1-theta_E-g'].tolist()\n", + " labels_train = torch.tensor(labels[:int(.7*length)])\n", + " labels_test = torch.tensor(labels[int(.7*length):int(.9*length)])\n", + " labels_val = torch.tensor(labels[int(.9*length):])\n", + "\n", + " data_train.cuda()\n", + " data_test.cuda()\n", + " data_val.cuda()\n", + " labels_train.cuda()\n", + " labels_test.cuda()\n", + " labels_val.cuda()\n", + "\n", + " train_dataset = TensorDataset(data_train, labels_train)\n", + " test_dataset = TensorDataset(data_test, labels_test)\n", + " val_dataset = TensorDataset(data_val, labels_val)\n", + "\n", + " train_dataloader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n", + " test_dataloader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)\n", + " val_dataloader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + " return train_dataloader, test_dataloader, val_dataloader, data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7", + "metadata": { + "executionInfo": { + "elapsed": 19938, + "status": "ok", + "timestamp": 1718868749575, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7" + }, + "outputs": [], + "source": [ + "# Load in data\n", + "head = Path.cwd().parents[3]\n", + "source_img_path = head / 'data/mb_source/mb_source.npy'\n", + "target_img_path = head / 'data/mb_target/mb_target.npy'\n", + "source_meta = head / 'data/mb_source/mb_source_metadata.csv'\n", + "target_meta = head / 'data/mb_target/mb_target_metadata.csv'\n", + "batch_size = 32\n", + "source_train_dataloader, source_test_dataloader, source_val_dataloader, source_data = create_dataloader(source_img_path, source_meta, batch_size)\n", + "target_train_dataloader, target_test_dataloader, target_val_dataloader, target_data = create_dataloader(target_img_path, target_meta, batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a3045daa-2e71-4335-8259-662a5c7e41a8", + "metadata": { + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1718868749576, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "a3045daa-2e71-4335-8259-662a5c7e41a8" + }, + "outputs": [], + "source": [ + "# Define data visualization function\n", + "def visualize_data(data):\n", + " '''\n", + " visualizes 16 random images from dataset\n", + " '''\n", + " \n", + " data_length = len(data)\n", + " num_indices = 16\n", + " \n", + " # Generate 15 unique random indices using numpy\n", + " random_indices = np.random.choice(data_length, size=num_indices, replace=False)\n", + "\n", + " #plot the examples for source\n", + " fig1=plt.figure(figsize=(8,8))\n", + "\n", + " for i in range(16):\n", + " plt.subplot(4, 4, i + 1)\n", + " plt.axis(\"off\")\n", + "\n", + " img = data[random_indices[i]]\n", + " example_image = make_lupton_rgb(img[0], img[1], img[2]) #change band by switching 0:1 to 1:2 or 2:3\n", + "\n", + " plt.imshow(example_image, aspect='auto', cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673 + }, + "executionInfo": { + "elapsed": 559, + "status": "ok", + "timestamp": 1718868750133, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", + "outputId": "651cb9ac-efea-4f14-b3a0-f03648a4081a" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize source data\n", + "visualize_data(source_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673 + }, + "executionInfo": { + "elapsed": 665, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", + "outputId": "eccb0d95-4566-445f-a058-b1d5b87765b0" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize target data\n", + "visualize_data(target_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f" + }, + "outputs": [], + "source": [ + "# Define and initialize model\n", + "class NeuralNetwork(nn.Module):\n", + " def __init__(self):\n", + " super(NeuralNetwork, self).__init__()\n", + " self.feature = nn.Sequential()\n", + " self.feature.add_module('f_conv1', nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu1', nn.ReLU(True))\n", + " self.feature.add_module('f_bn1', nn.BatchNorm2d(8))\n", + " self.feature.add_module('f_pool1', nn.MaxPool2d(kernel_size=2, stride=2))\n", + " self.feature.add_module('f_conv2', nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu2', nn.ReLU(True))\n", + " self.feature.add_module('f_bn2', nn.BatchNorm2d(16))\n", + " self.feature.add_module('f_pool2', nn.MaxPool2d(kernel_size=2, stride=2))\n", + " self.feature.add_module('f_conv3', nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu3', nn.ReLU(True))\n", + " self.feature.add_module('f_bn3', nn.BatchNorm2d(32))\n", + " self.feature.add_module('f_pool3', nn.MaxPool2d(kernel_size=2, stride=2))\n", + "\n", + " self.regressor = nn.Sequential()\n", + " self.regressor.add_module('r_fc1', nn.Linear(in_features=32*5*5, out_features=128))\n", + " self.regressor.add_module('r_relu1', nn.ReLU(True))\n", + " #self.regressor.add_module('r_fc2', nn.Linear(in_features=128, out_features=64))\n", + " #self.regressor.add_module('r_relu2', nn.ReLU(True))\n", + " self.regressor.add_module('r_fc3', nn.Linear(in_features=128, out_features=1))\n", + "\n", + " def forward(self, x):\n", + " x = x.view(-1, 3, 40, 40)\n", + "\n", + " features = self.feature(x)\n", + " features = features.view(-1, 32*5*5)\n", + " estimate = self.regressor(features)\n", + " estimate = F.relu(estimate)\n", + " estimate = estimate.view(-1)\n", + "\n", + " return estimate, features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "cfd79aed-d467-4d59-a44d-df05177dfd58", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "cfd79aed-d467-4d59-a44d-df05177dfd58" + }, + "outputs": [], + "source": [ + "# Code from https://github.com/ZongxianLee/MMD_Loss.Pytorch\n", + "# Comments by Shrihan Agarwal\n", + "\n", + "class MMD_loss(nn.Module):\n", + " \"\"\"\n", + " Calculate the MMD Loss using a Gaussian Kernel.\n", + " MMD is the distance between the mean embeddings of the source/target dataset. \n", + " The distances are determined using a Gaussian kernel \n", + " k(x, y) ~ exp(-(x-y)^2 / (2 * sigma)).\n", + "\n", + " The bandwidth sigma is either input as fixed in `fix_sigma` or determined dynamically.\n", + " One bandwidth is insufficient - small sigma leads to localization, and large leads to spread.\n", + " Need to capture similarities at various scales.\n", + "\n", + " E.g. kernel_mul = 2, kernel_num = 5, fix_sigma = 1 creates:\n", + " bandwidth_list = [1, 2, 4, 8, 16]\n", + "\n", + " Then uses pairwise kernel distances to compute MMD loss:\n", + " Loss = Mean(source pairwise + target pairwise - source/target - target/source)\n", + " \n", + " \"\"\"\n", + " def __init__(self, kernel_mul = 2.0, kernel_num = 5):\n", + " super(MMD_loss, self).__init__()\n", + " self.kernel_num = kernel_num # Number of kernels to use\n", + " self.kernel_mul = kernel_mul # How much to multiply the kernel by to get a new kernel\n", + " self.fix_sigma = None\n", + " return\n", + " \n", + " def gaussian_kernel(self, source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):\n", + " n_samples = int(source.size()[0])+int(target.size()[0])\n", + "\n", + " # Concatenate source and target catalogs along batch dimension\n", + " # Source: (n, d), Target: (m, d), Total: (n + m, d)\n", + " total = torch.cat([source, target], dim=0)\n", + "\n", + " # Replicate and calculate L2 distances between \n", + " # all samples, independent of source/target\n", + " total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", + " total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", + "\n", + " # L2 distance is of shape (n + m, n + m)\n", + " L2_distance = ((total0-total1)**2).sum(2)\n", + " \n", + " if fix_sigma:\n", + " bandwidth = fix_sigma\n", + " else:\n", + " bandwidth = torch.sum(L2_distance.data) / (n_samples**2-n_samples)\n", + " bandwidth /= kernel_mul ** (kernel_num // 2)\n", + "\n", + " # Create bandwidth list as described\n", + " bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]\n", + "\n", + " # Calculate kernel based distance using list of bandwidths and aggregate\n", + " kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]\n", + "\n", + " # Return the kernel matrix which is of shape (n + m, n + m), summing over bandwidths\n", + " return sum(kernel_val)\n", + "\n", + " def forward(self, source, target):\n", + " batch_size = int(source.size()[0])\n", + " kernels = self.gaussian_kernel(source, target, kernel_mul=self.kernel_mul, kernel_num=self.kernel_num, fix_sigma=self.fix_sigma)\n", + " XX = kernels[:batch_size, :batch_size] # source pairwise kernel dists\n", + " YY = kernels[batch_size:, batch_size:] # target pairwise kernel dists\n", + " XY = kernels[:batch_size, batch_size:] # between source and target samples\n", + " YX = kernels[batch_size:, :batch_size] # between source and target samples\n", + " loss = torch.mean(XX + YY - XY - YX) # definition of MMD loss\n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "ccac040a-7d18-45a4-b390-40e3dfa51756", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750797, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "ccac040a-7d18-45a4-b390-40e3dfa51756" + }, + "outputs": [], + "source": [ + "# Define training loop\n", + "def train_loop(source_dataloader, \n", + " target_dataloader, \n", + " model, \n", + " regressor_loss_fn,\n", + " da_loss,\n", + " optimizer,\n", + " n_epoch,\n", + " epoch):\n", + " \"\"\"\n", + " Trains the Neural Network on Source/Target Domains with the following loss:\n", + " Loss = Source Regression Loss + 1.4 * DA MMD Loss\n", + " \n", + " source_dataloader: DataLoader for the source domain data.\n", + "\ttarget_dataloader: DataLoader for the target domain data.\n", + "\tmodel: The neural network model to be trained.\n", + "\tregressor_loss_fn: Loss function for the regression task (e.g., MSELoss).\n", + "\tda_loss: Loss function for domain adaptation (e.g., MMD loss).\n", + "\toptimizer: Optimizer for the model parameters.\n", + "\tn_epoch: Total number of epochs for training.\n", + "\tepoch: Current epoch number.\n", + " \"\"\"\n", + "\n", + " domain_error = 0\n", + " domain_classifier_accuracy = 0\n", + " estimator_error = 0\n", + " score_list = np.array([])\n", + "\n", + " # Iteration length is shorter of the two datasets\n", + " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", + " data_source_iter = iter(source_dataloader)\n", + " data_target_iter = iter(target_dataloader)\n", + "\n", + " # Iterate over the two datasets\n", + " i = 0\n", + " while i < len_dataloader:\n", + "\n", + " # Time-varying hyperparameter, p 0 -> infty, alpha 0 -> 1\n", + " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader # UNUSED\n", + " alpha = 2. / (1. + np.exp(-10 * p)) - 1 # UNUSED\n", + "\n", + " # Source Training\n", + "\n", + " # Load a batch of source data, move to GPU\n", + " data_source = next(data_source_iter)\n", + " X, y = data_source\n", + " X = X.float()\n", + " X = X.cuda()\n", + " y = y.cuda()\n", + "\n", + " # Zero model gradients and labels\n", + " model.zero_grad()\n", + " batch_size = len(y)\n", + "\n", + " domain_label = torch.zeros(batch_size)\n", + " domain_label = domain_label.long()\n", + " domain_label = domain_label.cuda()\n", + "\n", + " # Apply data to model and get predictions, embeddings, apply gradients\n", + " estimate_output, domain_output_source = model(X)\n", + "\n", + " # Calculate source regression loss based on predictions\n", + " estimate_loss = regressor_loss_fn(estimate_output, y)\n", + "\n", + " # Target Training\n", + "\n", + " data_target = next(data_target_iter)\n", + " X_target, _ = data_target\n", + " X_target = X_target.float()\n", + " X_target = X_target.cuda()\n", + "\n", + " batch_size = len(X_target)\n", + "\n", + " _, domain_output_target = model(X_target)\n", + "\n", + " # Calculate the DA Loss between source and target, MMD loss\n", + " domain_loss = da_loss(domain_output_source, domain_output_target)\n", + "\n", + " # Hyperparameter of 1.4 set to weight domain loss vs source loss\n", + " # Perhaps this is where alpha was initially used\n", + " loss = estimate_loss + domain_loss*1.4\n", + "\n", + " # Backpropagation, update optimizer lr\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Update values\n", + " \n", + " # Domain loss is the DA loss or MMD loss between embedding outputs\n", + " domain_error += domain_loss.item()\n", + "\n", + " # Estimator loss is the source data loss on regression\n", + " estimator_error += estimate_loss.item()\n", + "\n", + " # Calculate the R2 score of the predictions vs. labels\n", + " score = r2_score(y.cpu().detach().numpy(), estimate_output.cpu().detach().numpy())\n", + " score_list = np.append(score_list, score)\n", + "\n", + " i += 1\n", + "\n", + " # Calculate average scores/errors of batches for this epoch\n", + " score = np.mean(score_list)\n", + " domain_error = domain_error / (len_dataloader)\n", + " estimator_error /= len_dataloader\n", + "\n", + " return [domain_error, estimator_error, score]" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750797, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21" + }, + "outputs": [], + "source": [ + "# Define testing loop\n", + "\n", + "def test_loop(source_dataloader, \n", + " target_dataloader, \n", + " model, \n", + " regressor_loss_fn, \n", + " da_loss, \n", + " n_epoch, \n", + " epoch):\n", + " \"\"\"\n", + " Tests the model accuracy.\n", + " \n", + " source_dataloader: DataLoader for the source domain data.\n", + "\ttarget_dataloader: DataLoader for the target domain data.\n", + "\tmodel: The neural network model to be trained.\n", + "\tregressor_loss_fn: Loss function for the regression task (e.g., MSELoss).\n", + "\tda_loss: Loss function for domain adaptation (e.g., MMD loss). UNUSED\n", + "\tn_epoch: Total number of epochs for training.\n", + "\tepoch: Current epoch number.\n", + " \"\"\"\n", + "\n", + " \n", + " # Evaluating without gradient computation in bg for validation\n", + " with torch.no_grad():\n", + " \n", + " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", + " data_source_iter = iter(source_dataloader)\n", + " data_target_iter = iter(target_dataloader)\n", + "\n", + " \n", + " domain_classifier_error = 0\n", + " domain_classifier_accuracy = 0\n", + " estimator_error = 0\n", + " estimator_error_target = 0\n", + " score_list = np.array([])\n", + " score_list_target = np.array([])\n", + "\n", + " i = 0\n", + " while i < len_dataloader:\n", + "\n", + " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader\n", + " alpha = 2. / (1. + np.exp(-10 * p)) - 1\n", + "\n", + " # Source Testing\n", + "\n", + " data_source = next(data_source_iter)\n", + " X, y = data_source\n", + " X = X.float()\n", + " X = X.cuda()\n", + " y = y.cuda()\n", + "\n", + " batch_size = len(y)\n", + "\n", + " estimate_output, domain_output = model(X)\n", + "\n", + " estimate_loss = regressor_loss_fn(estimate_output, y)\n", + "\n", + " # Target Testing\n", + "\n", + " data_target = next(data_target_iter)\n", + " X_target, y_target = data_target\n", + " X_target = X_target.float()\n", + " X_target = X_target.cuda()\n", + " y_target = y_target.cuda()\n", + "\n", + " batch_size = len(X_target)\n", + "\n", + " estimate_output_target, domain_output = model(X_target)\n", + "\n", + " estimate_loss_target = regressor_loss_fn(estimate_output_target, y_target)\n", + "\n", + " # Update values\n", + "\n", + " # Regression loss on validation testing\n", + " estimator_error += estimate_loss.item()\n", + " estimator_error_target += estimate_loss_target.item()\n", + "\n", + " # R2 Scores on validation testing\n", + " score = r2_score(y.cpu(), estimate_output.cpu())\n", + " score_list = np.append(score_list, score)\n", + " score_target = r2_score(y_target.cpu(), estimate_output_target.cpu())\n", + " score_list_target = np.append(score_list_target, score_target)\n", + "\n", + " i += 1\n", + "\n", + " score = np.mean(score_list)\n", + " score_target = np.mean(score_list_target)\n", + " estimator_error /= len_dataloader\n", + " estimator_error_target /= len_dataloader\n", + " \n", + " classifier_error = 1\n", + " return [classifier_error, estimator_error, estimator_error_target, score, score_target]" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "97d4a095-7e54-4e74-9700-3971a034160c", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize dictionary for training stats\n", + "import time\n", + "model = NeuralNetwork().cuda()\n", + "# Hyper parameter presets\n", + "learning_rate = 6e-5\n", + "epochs = 30\n", + "# Define loss functions and optimizer\n", + "regressor_loss_fn = nn.MSELoss().cuda()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", + "da_loss = MMD_loss()" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "02fd1d1f-d9ae-496e-b2f1-d59dfea3ba72", + "metadata": {}, + "outputs": [], + "source": [ + "def print_epoch_scores(stats, epoch, t):\n", + " \"\"\" Prints all relevant scores for each epoch. \"\"\"\n", + " train_stats = [i for i in stats.keys() if \"train\" in i]\n", + " val_stats = [i for i in stats.keys() if \"val\" in i]\n", + " fmt = lambda k: \" \".join([i.capitalize() for i in k.split('_')]) + \": \"\n", + " \n", + " print(\"\\nEpoch {0}: {1:.2f}s\".format(epoch, t) + \"\\n-------------------------------\")\n", + " print(\" Training Statistics:\")\n", + " for s in train_stats:\n", + " print(\"\\t\" + fmt(s) + \": {:.4f}\".format(stats[s][-1]))\n", + " print(\" Validation Statistics:\")\n", + " for s in val_stats:\n", + " print(\"\\t\" + fmt(s) + \": {:.4f}\".format(stats[s][-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 293833, + "status": "ok", + "timestamp": 1718869045423, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", + "outputId": "45493f2a-ea42-401e-f88b-b0ad39b969ed", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beginning Training...\n", + "\n", + "Epoch 0: 12.36s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.2037\n", + "\tTrain Regression Loss: : 0.2505\n", + "\tTrain R2 Score: : 0.4932\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0562\n", + "\tVal Target Regression Loss: : 0.3287\n", + "\tVal Source R2 Score: : 0.8845\n", + "\tVal Target R2 Score: : 0.3357\n", + "\n", + "Epoch 1: 10.50s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.1194\n", + "\tTrain Regression Loss: : 0.0476\n", + "\tTrain R2 Score: : 0.9035\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0347\n", + "\tVal Target Regression Loss: : 0.1922\n", + "\tVal Source R2 Score: : 0.9286\n", + "\tVal Target R2 Score: : 0.6082\n", + "\n", + "Epoch 2: 10.49s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.1074\n", + "\tTrain Regression Loss: : 0.0339\n", + "\tTrain R2 Score: : 0.9312\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0283\n", + "\tVal Target Regression Loss: : 0.1532\n", + "\tVal Source R2 Score: : 0.9408\n", + "\tVal Target R2 Score: : 0.6876\n", + "\n", + "Epoch 3: 10.62s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.1015\n", + "\tTrain Regression Loss: : 0.0269\n", + "\tTrain R2 Score: : 0.9457\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0245\n", + "\tVal Target Regression Loss: : 0.1272\n", + "\tVal Source R2 Score: : 0.9496\n", + "\tVal Target R2 Score: : 0.7429\n", + "\n", + "Epoch 4: 10.39s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0972\n", + "\tTrain Regression Loss: : 0.0230\n", + "\tTrain R2 Score: : 0.9534\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0186\n", + "\tVal Target Regression Loss: : 0.0948\n", + "\tVal Source R2 Score: : 0.9616\n", + "\tVal Target R2 Score: : 0.8109\n", + "\n", + "Epoch 5: 10.55s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0952\n", + "\tTrain Regression Loss: : 0.0200\n", + "\tTrain R2 Score: : 0.9596\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0177\n", + "\tVal Target Regression Loss: : 0.0870\n", + "\tVal Source R2 Score: : 0.9635\n", + "\tVal Target R2 Score: : 0.8228\n", + "\n", + "Epoch 6: 10.46s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0917\n", + "\tTrain Regression Loss: : 0.0178\n", + "\tTrain R2 Score: : 0.9639\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0153\n", + "\tVal Target Regression Loss: : 0.0886\n", + "\tVal Source R2 Score: : 0.9684\n", + "\tVal Target R2 Score: : 0.8237\n", + "\n", + "Epoch 7: 10.83s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0878\n", + "\tTrain Regression Loss: : 0.0161\n", + "\tTrain R2 Score: : 0.9674\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0168\n", + "\tVal Target Regression Loss: : 0.0832\n", + "\tVal Source R2 Score: : 0.9656\n", + "\tVal Target R2 Score: : 0.8335\n", + "\n", + "Epoch 8: 10.32s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0836\n", + "\tTrain Regression Loss: : 0.0151\n", + "\tTrain R2 Score: : 0.9694\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0135\n", + "\tVal Target Regression Loss: : 0.0609\n", + "\tVal Source R2 Score: : 0.9722\n", + "\tVal Target R2 Score: : 0.8771\n", + "\n", + "Epoch 9: 10.23s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0729\n", + "\tTrain Regression Loss: : 0.0152\n", + "\tTrain R2 Score: : 0.9692\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0158\n", + "\tVal Target Regression Loss: : 0.0601\n", + "\tVal Source R2 Score: : 0.9675\n", + "\tVal Target R2 Score: : 0.8800\n", + "\n", + "Epoch 10: 10.47s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0606\n", + "\tTrain Regression Loss: : 0.0151\n", + "\tTrain R2 Score: : 0.9694\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0163\n", + "\tVal Target Regression Loss: : 0.0579\n", + "\tVal Source R2 Score: : 0.9662\n", + "\tVal Target R2 Score: : 0.8849\n", + "\n", + "Epoch 11: 10.36s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0495\n", + "\tTrain Regression Loss: : 0.0151\n", + "\tTrain R2 Score: : 0.9693\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0161\n", + "\tVal Target Regression Loss: : 0.0629\n", + "\tVal Source R2 Score: : 0.9666\n", + "\tVal Target R2 Score: : 0.8729\n", + "\n", + "Epoch 12: 14.51s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0418\n", + "\tTrain Regression Loss: : 0.0150\n", + "\tTrain R2 Score: : 0.9694\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0135\n", + "\tVal Target Regression Loss: : 0.0534\n", + "\tVal Source R2 Score: : 0.9718\n", + "\tVal Target R2 Score: : 0.8935\n", + "\n", + "Epoch 13: 14.45s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0423\n", + "\tTrain Regression Loss: : 0.0139\n", + "\tTrain R2 Score: : 0.9719\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0126\n", + "\tVal Target Regression Loss: : 0.0535\n", + "\tVal Source R2 Score: : 0.9744\n", + "\tVal Target R2 Score: : 0.8928\n", + "\n", + "Epoch 14: 13.73s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0372\n", + "\tTrain Regression Loss: : 0.0131\n", + "\tTrain R2 Score: : 0.9736\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0147\n", + "\tVal Target Regression Loss: : 0.0519\n", + "\tVal Source R2 Score: : 0.9699\n", + "\tVal Target R2 Score: : 0.8965\n", + "\n", + "Epoch 15: 13.32s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0351\n", + "\tTrain Regression Loss: : 0.0124\n", + "\tTrain R2 Score: : 0.9748\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0118\n", + "\tVal Target Regression Loss: : 0.0542\n", + "\tVal Source R2 Score: : 0.9755\n", + "\tVal Target R2 Score: : 0.8911\n", + "\n", + "Epoch 16: 24.89s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0359\n", + "\tTrain Regression Loss: : 0.0121\n", + "\tTrain R2 Score: : 0.9754\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0097\n", + "\tVal Target Regression Loss: : 0.0486\n", + "\tVal Source R2 Score: : 0.9800\n", + "\tVal Target R2 Score: : 0.9011\n", + "\n", + "Epoch 17: 19.56s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0388\n", + "\tTrain Regression Loss: : 0.0105\n", + "\tTrain R2 Score: : 0.9786\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0108\n", + "\tVal Target Regression Loss: : 0.0444\n", + "\tVal Source R2 Score: : 0.9777\n", + "\tVal Target R2 Score: : 0.9109\n", + "\n", + "Epoch 18: 13.12s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0336\n", + "\tTrain Regression Loss: : 0.0102\n", + "\tTrain R2 Score: : 0.9794\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0092\n", + "\tVal Target Regression Loss: : 0.0448\n", + "\tVal Source R2 Score: : 0.9810\n", + "\tVal Target R2 Score: : 0.9092\n", + "\n", + "Epoch 19: 14.62s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0322\n", + "\tTrain Regression Loss: : 0.0100\n", + "\tTrain R2 Score: : 0.9798\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0096\n", + "\tVal Target Regression Loss: : 0.0463\n", + "\tVal Source R2 Score: : 0.9800\n", + "\tVal Target R2 Score: : 0.9068\n", + "\n", + "Epoch 20: 13.68s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0317\n", + "\tTrain Regression Loss: : 0.0094\n", + "\tTrain R2 Score: : 0.9810\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0108\n", + "\tVal Target Regression Loss: : 0.0451\n", + "\tVal Source R2 Score: : 0.9774\n", + "\tVal Target R2 Score: : 0.9094\n", + "\n", + "Epoch 21: 13.55s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0285\n", + "\tTrain Regression Loss: : 0.0096\n", + "\tTrain R2 Score: : 0.9804\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0087\n", + "\tVal Target Regression Loss: : 0.0427\n", + "\tVal Source R2 Score: : 0.9819\n", + "\tVal Target R2 Score: : 0.9130\n", + "\n", + "Epoch 22: 13.28s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0322\n", + "\tTrain Regression Loss: : 0.0090\n", + "\tTrain R2 Score: : 0.9816\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0086\n", + "\tVal Target Regression Loss: : 0.0421\n", + "\tVal Source R2 Score: : 0.9820\n", + "\tVal Target R2 Score: : 0.9157\n", + "\n", + "Epoch 23: 13.55s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0266\n", + "\tTrain Regression Loss: : 0.0093\n", + "\tTrain R2 Score: : 0.9812\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0116\n", + "\tVal Target Regression Loss: : 0.0436\n", + "\tVal Source R2 Score: : 0.9764\n", + "\tVal Target R2 Score: : 0.9117\n", + "\n", + "Epoch 24: 13.60s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0288\n", + "\tTrain Regression Loss: : 0.0090\n", + "\tTrain R2 Score: : 0.9816\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0080\n", + "\tVal Target Regression Loss: : 0.0447\n", + "\tVal Source R2 Score: : 0.9832\n", + "\tVal Target R2 Score: : 0.9112\n", + "\n", + "Epoch 25: 13.54s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0275\n", + "\tTrain Regression Loss: : 0.0094\n", + "\tTrain R2 Score: : 0.9803\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0076\n", + "\tVal Target Regression Loss: : 0.0411\n", + "\tVal Source R2 Score: : 0.9841\n", + "\tVal Target R2 Score: : 0.9181\n", + "\n", + "Epoch 26: 13.18s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0273\n", + "\tTrain Regression Loss: : 0.0086\n", + "\tTrain R2 Score: : 0.9827\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0075\n", + "\tVal Target Regression Loss: : 0.0390\n", + "\tVal Source R2 Score: : 0.9846\n", + "\tVal Target R2 Score: : 0.9203\n", + "\n", + "Epoch 27: 13.45s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0250\n", + "\tTrain Regression Loss: : 0.0082\n", + "\tTrain R2 Score: : 0.9833\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0077\n", + "\tVal Target Regression Loss: : 0.0399\n", + "\tVal Source R2 Score: : 0.9839\n", + "\tVal Target R2 Score: : 0.9200\n", + "\n", + "Epoch 28: 13.67s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0291\n", + "\tTrain Regression Loss: : 0.0086\n", + "\tTrain R2 Score: : 0.9826\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0077\n", + "\tVal Target Regression Loss: : 0.0394\n", + "\tVal Source R2 Score: : 0.9843\n", + "\tVal Target R2 Score: : 0.9200\n", + "\n", + "Epoch 29: 12.14s\n", + "-------------------------------\n", + " Training Statistics:\n", + "\tTrain Da Loss: : 0.0262\n", + "\tTrain Regression Loss: : 0.0079\n", + "\tTrain R2 Score: : 0.9839\n", + " Validation Statistics:\n", + "\tVal Source Regression Loss: : 0.0078\n", + "\tVal Target Regression Loss: : 0.0399\n", + "\tVal Source R2 Score: : 0.9836\n", + "\tVal Target R2 Score: : 0.9192\n" + ] + } + ], + "source": [ + "stats = {'train_DA_loss':[],\n", + " 'train_regression_loss':[],\n", + " 'train_r2_score':[],\n", + " 'val_source_regression_loss':[],\n", + " 'val_target_regression_loss':[],\n", + " 'val_source_r2_score':[],\n", + " 'val_target_r2_score':[]}\n", + "\n", + "print(\"Beginning Training...\")\n", + "# Train\n", + "for i in range(epochs):\n", + " start_time = time.time()\n", + " vals = train_loop(source_train_dataloader, target_train_dataloader, model,\n", + " regressor_loss_fn, da_loss, optimizer, epochs, i)\n", + "\n", + " vals_validate = test_loop(source_val_dataloader, target_val_dataloader,\n", + " model, regressor_loss_fn, da_loss, epochs, i)\n", + "\n", + " stats['train_DA_loss'].append(vals[0])\n", + " stats['train_regression_loss'].append(vals[1])\n", + " stats['train_r2_score'].append(vals[2])\n", + " stats['val_source_regression_loss'].append(vals_validate[1])\n", + " stats['val_target_regression_loss'].append(vals_validate[2])\n", + " stats['val_source_r2_score'].append(vals_validate[3])\n", + " stats['val_target_r2_score'].append(vals_validate[4])\n", + "\n", + " print_epoch_scores(stats, i, time.time() - start_time)\n", + " \n", + " # to_print = (\n", + " # f'Train Estimator Error = {vals[1]}\\n'\n", + " # f'Train Estimator R2 Score = {vals[2]:.4f}\\n'\n", + " # f'Train Domain Classifier Error = {vals[0]}\\n'\n", + " # f'Validation Source Estimator Error = {vals_validate[1]}\\n'\n", + " # f'Validation Source R2 Score = {vals_validate[3]:.4f}\\n'\n", + " # f'Validation Target Estimator Error = {vals_validate[2]}\\n'\n", + " # f'Validation Target R2 Score = {vals_validate[4]:.4f}\\n'\n", + " # f'Validation Domain Classifier Error = {vals_validate[0]}\\n'\n", + " # )\n", + "\n", + " # print(to_print)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "6d4e20b3-9589-4e23-a05b-893e51ddec62", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "illegal target for annotation (2919055091.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[139], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m 'train_DA_loss':[],\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m illegal target for annotation\n" + ] + } + ], + "source": [ + "'train_DA_loss':[],\n", + " 'train_regression_loss':[],\n", + " 'train_r2_score':[],\n", + " 'val_source_regression_loss':[],\n", + " 'val_target_regression_loss':[],\n", + " 'val_source_r2_score':[],\n", + " 'val_target_r2_score':[]}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "YfplCDIb-UU_", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 649, + "status": "ok", + "timestamp": 1718869045736, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "YfplCDIb-UU_", + "outputId": "dbb362ec-4af5-4cb9-c4f9-a0a2766c26c5" + }, + "outputs": [], + "source": [ + "# Classifier\n", + "eps = np.arange(epochs)\n", + "plt.title(\"DA Loss Error\")\n", + "plt.plot(eps, stats['train_DA_loss'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eYG_P_iQ_5Bv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 169, + "status": "ok", + "timestamp": 1718869045739, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "eYG_P_iQ_5Bv", + "outputId": "be450f92-eda7-4e4f-81fe-008c55b2b112" + }, + "outputs": [], + "source": [ + "# Estimator\n", + "plt.title(\"Estimator Error\")\n", + "plt.plot(eps, stats['train_estimator_error'])\n", + "plt.plot(eps, stats['val_estimator_error'])\n", + "plt.plot(eps, stats['val_estimator_error_target'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "xS9rtS-T_neg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 237, + "status": "ok", + "timestamp": 1718869045904, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "xS9rtS-T_neg", + "outputId": "d32f40ef-6042-4154-e9ee-1f4e2f90064d" + }, + "outputs": [], + "source": [ + "# R2 Scores\n", + "plt.title(\"R2 Scores\")\n", + "plt.plot(eps, stats['train_score'])\n", + "plt.plot(eps, stats['val_score'])\n", + "plt.plot(eps, stats['val_score_target'])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "ed0a8206-7520-4a60-8e17-965a91133b92", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 + }, + "executionInfo": { + "elapsed": 969, + "status": "ok", + "timestamp": 1718869046858, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "ed0a8206-7520-4a60-8e17-965a91133b92", + "outputId": "7df8c563-5826-4e43-d9e6-5e686463551d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source R2 Score is 0.9778\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MMD | Source | R2: 0.978')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Test Source\n", + "preds = np.array([])\n", + "true = np.array([])\n", + "score_list = np.array([])\n", + "\n", + "with torch.no_grad():\n", + " for X, y in source_test_dataloader:\n", + " X = X.float()\n", + " pred, _ = model(X.cuda())\n", + " preds = np.append(preds, pred.cpu())\n", + " true = np.append(true, y.cpu())\n", + " score = r2_score(y.cpu(), pred.cpu())\n", + " score_list = np.append(score_list, score)\n", + "\n", + "score = np.mean(score_list)\n", + "print(f'Source R2 Score is {score:.4f}')\n", + "\n", + "plt.figure(figsize=(8,8),dpi=50)\n", + "plt.scatter(true, preds, color='black', alpha = 0.1)\n", + "line = np.linspace(0, 4, 100)\n", + "plt.plot(line, line)\n", + "plt.rc('font', size=12)\n", + "plt.xlabel('True Theta E')\n", + "plt.ylabel('Predicted Theta E');\n", + "plt.rc('font', size=20)\n", + "plt.title('MMD | Source | R2: {0:.3f}'.format(score))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "executionInfo": { + "elapsed": 1283, + "status": "ok", + "timestamp": 1718869048133, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", + "outputId": "b6347093-56d9-4a8b-b515-c4c4717cdab4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Target R2 Score is 0.9597\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MMD | Target | R2: 0.960')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Test target\n", + "preds = np.array([])\n", + "true = np.array([])\n", + "score_list = np.array([])\n", + "\n", + "with torch.no_grad():\n", + " for X, y in target_test_dataloader:\n", + " X = X.float()\n", + " pred, _ = model(X.cuda())\n", + " preds = np.append(preds, pred.cpu())\n", + " true = np.append(true, y.cpu())\n", + " score = r2_score(y.cpu(), pred.cpu())\n", + " score_list = np.append(score_list, score)\n", + "\n", + "score = np.mean(score_list)\n", + "print(f'Target R2 Score is {score:.4f}')\n", + "\n", + "plt.figure(figsize=(8,8),dpi=50)\n", + "plt.scatter(true, preds, color='black', alpha = 0.1)\n", + "line = np.linspace(0, 4, 100)\n", + "plt.plot(line, line)\n", + "plt.rc('font', size=12)\n", + "plt.xlabel('True Theta E')\n", + "plt.ylabel('Predicted Theta E');\n", + "plt.rc('font', size=20)\n", + "plt.title('MMD | Target | R2: {0:.3f}'.format(score))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2", + "metadata": { + "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [ + { + "file_id": "1MFScb-3Sbugn4RNiDaeocicJUIHlh_j2", + "timestamp": 1717430435817 + }, + { + "file_id": "1wlKaSdLzleueYrwljtOcqsiOfzEy1dxP", + "timestamp": 1717429638462 + } + ] + }, + "kernelspec": { + "display_name": "Python [conda env:.conda-neural]", + "language": "python", + "name": "conda-env-.conda-neural-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_fid.ipynb b/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_fid.ipynb new file mode 100644 index 0000000..750256b --- /dev/null +++ b/training/notebooks/MMD_paper/multiband/ShrihanPaperMMD_mb_fid.ipynb @@ -0,0 +1,1385 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 10375, + "status": "ok", + "timestamp": 1718868666013, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "a8aa3fe5-4277-47fc-b26d-baa137256f17", + "outputId": "9ad89b68-4fd0-4146-a087-24cd367fb09f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda device\n" + ] + } + ], + "source": [ + "# Imports we will use\n", + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from torch.autograd import Function\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "from pathlib import Path\n", + "from sklearn.metrics import r2_score\n", + "from astropy.visualization import make_lupton_rgb\n", + "\n", + "# For matplotlib\n", + "import os\n", + "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n", + "\n", + "# Set Seed\n", + "torch.manual_seed(22)\n", + "\n", + "# Find if cuda is available\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "print(f\"Using {device} device\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7cc92062-1846-4850-8f8e-206a7c35c171", + "metadata": { + "executionInfo": { + "elapsed": 189, + "status": "ok", + "timestamp": 1718868679894, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "7cc92062-1846-4850-8f8e-206a7c35c171" + }, + "outputs": [], + "source": [ + "# Load data function\n", + "def create_dataloader(img_path, metadata_path, batch_size):\n", + " '''\n", + " Creates dataloader for training, reserving the last 10% images for validation/testing\n", + " '''\n", + " data = np.load(img_path).squeeze()\n", + " length = len(data)\n", + " data_train = torch.tensor(data[:int(.7*length)]) # 70% train\n", + " data_test = torch.tensor(data[int(.7*length):int(.9*length)]) # 20% test\n", + " data_val = torch.tensor(data[int(.9*length):]) # 10% validation\n", + "\n", + " metadata = pd.read_csv(metadata_path)\n", + " labels = metadata['PLANE_1-OBJECT_1-MASS_PROFILE_1-theta_E-g'].tolist()\n", + " labels_train = torch.tensor(labels[:int(.7*length)])\n", + " labels_test = torch.tensor(labels[int(.7*length):int(.9*length)])\n", + " labels_val = torch.tensor(labels[int(.9*length):])\n", + "\n", + " data_train.cuda()\n", + " data_test.cuda()\n", + " data_val.cuda()\n", + " labels_train.cuda()\n", + " labels_test.cuda()\n", + " labels_val.cuda()\n", + "\n", + " train_dataset = TensorDataset(data_train, labels_train)\n", + " test_dataset = TensorDataset(data_test, labels_test)\n", + " val_dataset = TensorDataset(data_val, labels_val)\n", + "\n", + " train_dataloader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n", + " test_dataloader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)\n", + " val_dataloader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + " return train_dataloader, test_dataloader, val_dataloader, data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7", + "metadata": { + "executionInfo": { + "elapsed": 19938, + "status": "ok", + "timestamp": 1718868749575, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "3efc6755-daeb-48ca-bbc7-c5a3b539c5b7" + }, + "outputs": [], + "source": [ + "# Load in data\n", + "head = Path.cwd().parents[3]\n", + "source_img_path = head / 'data/mb_source/mb_source.npy'\n", + "target_img_path = head / 'data/mb_target/mb_target.npy'\n", + "source_meta = head / 'data/mb_source/mb_source_metadata.csv'\n", + "target_meta = head / 'data/mb_target/mb_target_metadata.csv'\n", + "batch_size = 32\n", + "source_train_dataloader, source_test_dataloader, source_val_dataloader, source_data = create_dataloader(source_img_path, source_meta, batch_size)\n", + "target_train_dataloader, target_test_dataloader, target_val_dataloader, target_data = create_dataloader(target_img_path, target_meta, batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a3045daa-2e71-4335-8259-662a5c7e41a8", + "metadata": { + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1718868749576, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "a3045daa-2e71-4335-8259-662a5c7e41a8" + }, + "outputs": [], + "source": [ + "# Define data visualization function\n", + "def visualize_data(data):\n", + " '''\n", + " visualizes 16 random images from dataset\n", + " '''\n", + " \n", + " data_length = len(data)\n", + " num_indices = 16\n", + " \n", + " # Generate 15 unique random indices using numpy\n", + " random_indices = np.random.choice(data_length, size=num_indices, replace=False)\n", + "\n", + " #plot the examples for source\n", + " fig1=plt.figure(figsize=(8,8))\n", + "\n", + " for i in range(16):\n", + " plt.subplot(4, 4, i + 1)\n", + " plt.axis(\"off\")\n", + "\n", + " img = data[random_indices[i]]\n", + " example_image = make_lupton_rgb(img[0], img[1], img[2]) #change band by switching 0:1 to 1:2 or 2:3\n", + "\n", + " plt.imshow(example_image, aspect='auto', cmap='viridis')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673 + }, + "executionInfo": { + "elapsed": 559, + "status": "ok", + "timestamp": 1718868750133, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "b72c4588-acb2-478c-96e9-cb09a0380ecd", + "outputId": "651cb9ac-efea-4f14-b3a0-f03648a4081a" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize source data\n", + "visualize_data(source_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 673 + }, + "executionInfo": { + "elapsed": 665, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "6d6e4147-ce23-4fca-b1aa-42122b0e2501", + "outputId": "eccb0d95-4566-445f-a058-b1d5b87765b0" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize target data\n", + "visualize_data(target_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "7b706147-6d5c-4319-a7b0-87decc1e6a7f" + }, + "outputs": [], + "source": [ + "# Define and initialize model\n", + "class NeuralNetwork(nn.Module):\n", + " def __init__(self):\n", + " super(NeuralNetwork, self).__init__()\n", + " self.feature = nn.Sequential()\n", + " self.feature.add_module('f_conv1', nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu1', nn.ReLU(True))\n", + " self.feature.add_module('f_bn1', nn.BatchNorm2d(8))\n", + " self.feature.add_module('f_pool1', nn.MaxPool2d(kernel_size=2, stride=2))\n", + " self.feature.add_module('f_conv2', nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu2', nn.ReLU(True))\n", + " self.feature.add_module('f_bn2', nn.BatchNorm2d(16))\n", + " self.feature.add_module('f_pool2', nn.MaxPool2d(kernel_size=2, stride=2))\n", + " self.feature.add_module('f_conv3', nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding='same'))\n", + " self.feature.add_module('f_relu3', nn.ReLU(True))\n", + " self.feature.add_module('f_bn3', nn.BatchNorm2d(32))\n", + " self.feature.add_module('f_pool3', nn.MaxPool2d(kernel_size=2, stride=2))\n", + "\n", + " self.regressor = nn.Sequential()\n", + " self.regressor.add_module('r_fc1', nn.Linear(in_features=32*5*5, out_features=128))\n", + " self.regressor.add_module('r_relu1', nn.ReLU(True))\n", + " #self.regressor.add_module('r_fc2', nn.Linear(in_features=128, out_features=64))\n", + " #self.regressor.add_module('r_relu2', nn.ReLU(True))\n", + " self.regressor.add_module('r_fc3', nn.Linear(in_features=128, out_features=1))\n", + "\n", + " def forward(self, x):\n", + " x = x.view(-1, 3, 40, 40)\n", + "\n", + " features = self.feature(x)\n", + " features = features.view(-1, 32*5*5)\n", + " estimate = self.regressor(features)\n", + " estimate = F.relu(estimate)\n", + " estimate = estimate.view(-1)\n", + "\n", + " return estimate, features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cfd79aed-d467-4d59-a44d-df05177dfd58", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750796, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "cfd79aed-d467-4d59-a44d-df05177dfd58" + }, + "outputs": [], + "source": [ + "# code from https://github.com/ZongxianLee/MMD_Loss.Pytorch\n", + "\n", + "class MMD_loss(nn.Module):\n", + " def __init__(self, kernel_mul = 2.0, kernel_num = 5):\n", + " super(MMD_loss, self).__init__()\n", + " self.kernel_num = kernel_num\n", + " self.kernel_mul = kernel_mul\n", + " self.fix_sigma = None\n", + " return\n", + " def guassian_kernel(self, source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):\n", + " n_samples = int(source.size()[0])+int(target.size()[0])\n", + " total = torch.cat([source, target], dim=0)\n", + "\n", + " total0 = total.unsqueeze(0).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", + " total1 = total.unsqueeze(1).expand(int(total.size(0)), int(total.size(0)), int(total.size(1)))\n", + " L2_distance = ((total0-total1)**2).sum(2)\n", + " if fix_sigma:\n", + " bandwidth = fix_sigma\n", + " else:\n", + " bandwidth = torch.sum(L2_distance.data) / (n_samples**2-n_samples)\n", + " bandwidth /= kernel_mul ** (kernel_num // 2)\n", + " bandwidth_list = [bandwidth * (kernel_mul**i) for i in range(kernel_num)]\n", + " kernel_val = [torch.exp(-L2_distance / bandwidth_temp) for bandwidth_temp in bandwidth_list]\n", + " return sum(kernel_val)\n", + "\n", + " def forward(self, source, target):\n", + " batch_size = int(source.size()[0])\n", + " kernels = self.guassian_kernel(source, target, kernel_mul=self.kernel_mul, kernel_num=self.kernel_num, fix_sigma=self.fix_sigma)\n", + " XX = kernels[:batch_size, :batch_size]\n", + " YY = kernels[batch_size:, batch_size:]\n", + " XY = kernels[:batch_size, batch_size:]\n", + " YX = kernels[batch_size:, :batch_size]\n", + " loss = torch.mean(XX + YY - XY -YX)\n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ccac040a-7d18-45a4-b390-40e3dfa51756", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750797, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "ccac040a-7d18-45a4-b390-40e3dfa51756" + }, + "outputs": [], + "source": [ + "# Define training loop\n", + "def train_loop(source_dataloader, target_dataloader, model, regressor_loss_fn, da_loss, optimizer, n_epoch, epoch):\n", + "\n", + " domain_error = 0\n", + " domain_classifier_accuracy = 0\n", + " estimator_error = 0\n", + " score_list = np.array([])\n", + "\n", + " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", + " data_source_iter = iter(source_dataloader)\n", + " data_target_iter = iter(target_dataloader)\n", + "\n", + " i = 0\n", + " while i < len_dataloader:\n", + "\n", + " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader\n", + " alpha = 2. / (1. + np.exp(-10 * p)) - 1\n", + "\n", + " # Source Training\n", + "\n", + " data_source = next(data_source_iter)\n", + " X, y = data_source\n", + " X = X.float()\n", + " X = X.cuda()\n", + " y = y.cuda()\n", + "\n", + " model.zero_grad()\n", + " batch_size = len(y)\n", + "\n", + " domain_label = torch.zeros(batch_size)\n", + " domain_label = domain_label.long()\n", + " domain_label = domain_label.cuda()\n", + "\n", + " estimate_output, domain_output_source = model(X)\n", + "\n", + " estimate_loss = regressor_loss_fn(estimate_output, y)\n", + "\n", + " # Target Training\n", + "\n", + " data_target = next(data_target_iter)\n", + " X_target, _ = data_target\n", + " X_target = X_target.float()\n", + " X_target = X_target.cuda()\n", + "\n", + " batch_size = len(X_target)\n", + "\n", + " _, domain_output_target = model(X_target)\n", + " domain_loss = da_loss(domain_output_source, domain_output_target)\n", + "\n", + " loss = estimate_loss + domain_loss*1.4\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Update values\n", + "\n", + " domain_error += domain_loss.item()\n", + " #domain_classifier_accuracy +=\n", + " estimator_error += estimate_loss.item()\n", + " score = r2_score(y.cpu().detach().numpy(), estimate_output.cpu().detach().numpy())\n", + " score_list = np.append(score_list, score)\n", + "\n", + " i += 1\n", + "\n", + " score = np.mean(score_list)\n", + " domain_error = domain_error / (len_dataloader)\n", + " estimator_error /= len_dataloader\n", + "\n", + " return [domain_error, estimator_error, score]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21", + "metadata": { + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1718868750797, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "98583af6-1fbb-4091-bc22-b1ce362e8f21" + }, + "outputs": [], + "source": [ + "# Define testing loop\n", + "\n", + "def test_loop(source_dataloader, target_dataloader, model, regressor_loss_fn, da_loss, n_epoch, epoch):\n", + "\n", + " with torch.no_grad():\n", + "\n", + " len_dataloader = min(len(source_dataloader), len(target_dataloader))\n", + " data_source_iter = iter(source_dataloader)\n", + " data_target_iter = iter(target_dataloader)\n", + "\n", + " domain_classifier_error = 0\n", + " domain_classifier_accuracy = 0\n", + " estimator_error = 0\n", + " estimator_error_target = 0\n", + " score_list = np.array([])\n", + " score_list_target = np.array([])\n", + "\n", + " i = 0\n", + " while i < len_dataloader:\n", + "\n", + " p = float(i + epoch * len_dataloader) / n_epoch / len_dataloader\n", + " alpha = 2. / (1. + np.exp(-10 * p)) - 1\n", + "\n", + " # Source Testing\n", + "\n", + " data_source = next(data_source_iter)\n", + " X, y = data_source\n", + " X = X.float()\n", + " X = X.cuda()\n", + " y = y.cuda()\n", + "\n", + " batch_size = len(y)\n", + "\n", + " #domain_label = torch.zeros(batch_size)\n", + " #domain_label = domain_label.long()\n", + " #domain_label = domain_label.cuda()\n", + "\n", + " estimate_output, domain_output = model(X)\n", + "\n", + " estimate_loss = regressor_loss_fn(estimate_output, y)\n", + " #domain_loss_source = classifier_loss_fn(domain_output, domain_label)\n", + "\n", + " # Target Testing\n", + "\n", + " data_target = next(data_target_iter)\n", + " X_target, y_target = data_target\n", + " X_target = X_target.float()\n", + " X_target = X_target.cuda()\n", + " y_target = y_target.cuda()\n", + "\n", + " batch_size = len(X_target)\n", + "\n", + " #domain_label = torch.ones(batch_size)\n", + " #domain_label = domain_label.long()\n", + " #domain_label = domain_label.cuda()\n", + "\n", + " estimate_output_target, domain_output = model(X_target)\n", + "\n", + " estimate_loss_target = regressor_loss_fn(estimate_output_target, y_target)\n", + " #domain_loss_target = classifier_loss_fn(domain_output, domain_label)\n", + "\n", + " # Update values\n", + "\n", + " # domain_classifier_error += domain_loss_source.item()\n", + " #domain_classifier_error += domain_loss_target.item()\n", + " #domain_classifier_accuracy +=\n", + " estimator_error += estimate_loss.item()\n", + " estimator_error_target += estimate_loss_target.item()\n", + " score = r2_score(y.cpu(), estimate_output.cpu())\n", + " score_list = np.append(score_list, score)\n", + " score_target = r2_score(y_target.cpu(), estimate_output_target.cpu())\n", + " score_list_target = np.append(score_list_target, score_target)\n", + "\n", + " i += 1\n", + "\n", + " score = np.mean(score_list)\n", + " score_target = np.mean(score_list_target)\n", + " #classifier_error = domain_classifier_error / (len_dataloader * 2)\n", + " estimator_error /= len_dataloader\n", + " estimator_error_target /= len_dataloader\n", + " classifier_error = 1\n", + " return [classifier_error, estimator_error, estimator_error_target, score, score_target]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "97d4a095-7e54-4e74-9700-3971a034160c", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize dictionary for training stats\n", + "import time\n", + "model = NeuralNetwork().cuda()\n", + "# Hyper parameter presets\n", + "learning_rate = 2e-5\n", + "epochs = 30\n", + "# Define loss functions and optimizer\n", + "regressor_loss_fn = nn.MSELoss().cuda()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", + "da_loss = MMD_loss()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 293833, + "status": "ok", + "timestamp": 1718869045423, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "1dfe3810-672c-4a28-b606-b3079a40fca4", + "outputId": "45493f2a-ea42-401e-f88b-b0ad39b969ed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1\n", + "-------------------------------\n", + "11.67203664779663\n", + "Train Estimator Error = 0.3891782359964124\n", + "Train Estimator R2 Score = 0.2147\n", + "Train Domain Classifier Error = 0.20768008760467743\n", + "Validation Source Estimator Error = 0.08470896778592638\n", + "Validation Source R2 Score = 0.8262\n", + "Validation Target Estimator Error = 0.3546692273419374\n", + "Validation Target R2 Score = 0.2803\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 2\n", + "-------------------------------\n", + "11.13405466079712\n", + "Train Estimator Error = 0.069630926333613\n", + "Train Estimator R2 Score = 0.8617\n", + "Train Domain Classifier Error = 0.12060150971986912\n", + "Validation Source Estimator Error = 0.05495980645346034\n", + "Validation Source R2 Score = 0.8874\n", + "Validation Target Estimator Error = 0.17194434703820072\n", + "Validation Target R2 Score = 0.6507\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 3\n", + "-------------------------------\n", + "11.815532922744751\n", + "Train Estimator Error = 0.04804452671030948\n", + "Train Estimator R2 Score = 0.9033\n", + "Train Domain Classifier Error = 0.10781731483759784\n", + "Validation Source Estimator Error = 0.04093024796646112\n", + "Validation Source R2 Score = 0.9163\n", + "Validation Target Estimator Error = 0.1052956548252493\n", + "Validation Target R2 Score = 0.7884\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 4\n", + "-------------------------------\n", + "11.529789447784424\n", + "Train Estimator Error = 0.03742249261747903\n", + "Train Estimator R2 Score = 0.9247\n", + "Train Domain Classifier Error = 0.1021630066928796\n", + "Validation Source Estimator Error = 0.03397743518399016\n", + "Validation Source R2 Score = 0.9296\n", + "Validation Target Estimator Error = 0.06810495981080517\n", + "Validation Target R2 Score = 0.8614\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 5\n", + "-------------------------------\n", + "11.406839370727539\n", + "Train Estimator Error = 0.03026721713948702\n", + "Train Estimator R2 Score = 0.9389\n", + "Train Domain Classifier Error = 0.09911061770587451\n", + "Validation Source Estimator Error = 0.0262824724885119\n", + "Validation Source R2 Score = 0.9460\n", + "Validation Target Estimator Error = 0.04898795769045687\n", + "Validation Target R2 Score = 0.9024\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 6\n", + "-------------------------------\n", + "12.253169775009155\n", + "Train Estimator Error = 0.025267921251515557\n", + "Train Estimator R2 Score = 0.9490\n", + "Train Domain Classifier Error = 0.09586759520459655\n", + "Validation Source Estimator Error = 0.02350287835238276\n", + "Validation Source R2 Score = 0.9520\n", + "Validation Target Estimator Error = 0.043256576552654906\n", + "Validation Target R2 Score = 0.9139\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 7\n", + "-------------------------------\n", + "10.71419620513916\n", + "Train Estimator Error = 0.023192157467888493\n", + "Train Estimator R2 Score = 0.9529\n", + "Train Domain Classifier Error = 0.09584758205295045\n", + "Validation Source Estimator Error = 0.021949789872404875\n", + "Validation Source R2 Score = 0.9547\n", + "Validation Target Estimator Error = 0.03923684822478492\n", + "Validation Target R2 Score = 0.9219\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 8\n", + "-------------------------------\n", + "10.844721794128418\n", + "Train Estimator Error = 0.020332480160182367\n", + "Train Estimator R2 Score = 0.9588\n", + "Train Domain Classifier Error = 0.09166281744905865\n", + "Validation Source Estimator Error = 0.019413879111551555\n", + "Validation Source R2 Score = 0.9597\n", + "Validation Target Estimator Error = 0.03399869693431315\n", + "Validation Target R2 Score = 0.9317\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 9\n", + "-------------------------------\n", + "11.043089151382446\n", + "Train Estimator Error = 0.019280604596745216\n", + "Train Estimator R2 Score = 0.9609\n", + "Train Domain Classifier Error = 0.09347905429383191\n", + "Validation Source Estimator Error = 0.01709837580669173\n", + "Validation Source R2 Score = 0.9648\n", + "Validation Target Estimator Error = 0.03085407970627402\n", + "Validation Target R2 Score = 0.9379\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 10\n", + "-------------------------------\n", + "11.37652850151062\n", + "Train Estimator Error = 0.01844694849377808\n", + "Train Estimator R2 Score = 0.9630\n", + "Train Domain Classifier Error = 0.09145641151536427\n", + "Validation Source Estimator Error = 0.01627967611643349\n", + "Validation Source R2 Score = 0.9666\n", + "Validation Target Estimator Error = 0.030169435284414868\n", + "Validation Target R2 Score = 0.9391\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 11\n", + "-------------------------------\n", + "10.977824926376343\n", + "Train Estimator Error = 0.017298324282197672\n", + "Train Estimator R2 Score = 0.9652\n", + "Train Domain Classifier Error = 0.09081279168882743\n", + "Validation Source Estimator Error = 0.016463570139566615\n", + "Validation Source R2 Score = 0.9658\n", + "Validation Target Estimator Error = 0.026147935308136378\n", + "Validation Target R2 Score = 0.9480\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 12\n", + "-------------------------------\n", + "11.538264989852905\n", + "Train Estimator Error = 0.016345275334428172\n", + "Train Estimator R2 Score = 0.9670\n", + "Train Domain Classifier Error = 0.08806485555030855\n", + "Validation Source Estimator Error = 0.015452750400895146\n", + "Validation Source R2 Score = 0.9684\n", + "Validation Target Estimator Error = 0.025766917053538903\n", + "Validation Target R2 Score = 0.9486\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 13\n", + "-------------------------------\n", + "10.990694046020508\n", + "Train Estimator Error = 0.015545775231494955\n", + "Train Estimator R2 Score = 0.9685\n", + "Train Domain Classifier Error = 0.08901393925004947\n", + "Validation Source Estimator Error = 0.016945357726602134\n", + "Validation Source R2 Score = 0.9648\n", + "Validation Target Estimator Error = 0.026284849585573766\n", + "Validation Target R2 Score = 0.9471\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 14\n", + "-------------------------------\n", + "10.80895709991455\n", + "Train Estimator Error = 0.015184039930205185\n", + "Train Estimator R2 Score = 0.9693\n", + "Train Domain Classifier Error = 0.08986377877811519\n", + "Validation Source Estimator Error = 0.014697374301446471\n", + "Validation Source R2 Score = 0.9702\n", + "Validation Target Estimator Error = 0.0245086281162918\n", + "Validation Target R2 Score = 0.9509\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 15\n", + "-------------------------------\n", + "11.04081654548645\n", + "Train Estimator Error = 0.01427897860481654\n", + "Train Estimator R2 Score = 0.9710\n", + "Train Domain Classifier Error = 0.08750017689816804\n", + "Validation Source Estimator Error = 0.013485473091269184\n", + "Validation Source R2 Score = 0.9723\n", + "Validation Target Estimator Error = 0.022387957858858974\n", + "Validation Target R2 Score = 0.9555\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 16\n", + "-------------------------------\n", + "10.82071590423584\n", + "Train Estimator Error = 0.013812179233235269\n", + "Train Estimator R2 Score = 0.9721\n", + "Train Domain Classifier Error = 0.08632869272062069\n", + "Validation Source Estimator Error = 0.014277979737491744\n", + "Validation Source R2 Score = 0.9710\n", + "Validation Target Estimator Error = 0.02099753267600373\n", + "Validation Target R2 Score = 0.9572\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 17\n", + "-------------------------------\n", + "11.386169910430908\n", + "Train Estimator Error = 0.013344964414317385\n", + "Train Estimator R2 Score = 0.9730\n", + "Train Domain Classifier Error = 0.08473388459090776\n", + "Validation Source Estimator Error = 0.01278173420756201\n", + "Validation Source R2 Score = 0.9737\n", + "Validation Target Estimator Error = 0.021278422029249986\n", + "Validation Target R2 Score = 0.9576\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 18\n", + "-------------------------------\n", + "10.975096464157104\n", + "Train Estimator Error = 0.012997516384518534\n", + "Train Estimator R2 Score = 0.9736\n", + "Train Domain Classifier Error = 0.08501919150570411\n", + "Validation Source Estimator Error = 0.01293849247466227\n", + "Validation Source R2 Score = 0.9729\n", + "Validation Target Estimator Error = 0.020143746003911944\n", + "Validation Target R2 Score = 0.9601\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 19\n", + "-------------------------------\n", + "10.649860143661499\n", + "Train Estimator Error = 0.012610840526242593\n", + "Train Estimator R2 Score = 0.9745\n", + "Train Domain Classifier Error = 0.0874298877666757\n", + "Validation Source Estimator Error = 0.012515157722173983\n", + "Validation Source R2 Score = 0.9742\n", + "Validation Target Estimator Error = 0.019748232410449512\n", + "Validation Target R2 Score = 0.9600\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 20\n", + "-------------------------------\n", + "10.81557583808899\n", + "Train Estimator Error = 0.012205780995413798\n", + "Train Estimator R2 Score = 0.9752\n", + "Train Domain Classifier Error = 0.08463458424022037\n", + "Validation Source Estimator Error = 0.012325004081295174\n", + "Validation Source R2 Score = 0.9746\n", + "Validation Target Estimator Error = 0.019116343289708636\n", + "Validation Target R2 Score = 0.9621\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 21\n", + "-------------------------------\n", + "11.603881359100342\n", + "Train Estimator Error = 0.012184964865999437\n", + "Train Estimator R2 Score = 0.9752\n", + "Train Domain Classifier Error = 0.08551613857945806\n", + "Validation Source Estimator Error = 0.01236360755031276\n", + "Validation Source R2 Score = 0.9738\n", + "Validation Target Estimator Error = 0.01867004647957766\n", + "Validation Target R2 Score = 0.9626\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 22\n", + "-------------------------------\n", + "10.94370985031128\n", + "Train Estimator Error = 0.011733011241705997\n", + "Train Estimator R2 Score = 0.9763\n", + "Train Domain Classifier Error = 0.08554728992514898\n", + "Validation Source Estimator Error = 0.01137116261324875\n", + "Validation Source R2 Score = 0.9764\n", + "Validation Target Estimator Error = 0.01831504662823715\n", + "Validation Target R2 Score = 0.9633\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 23\n", + "-------------------------------\n", + "18.06373620033264\n", + "Train Estimator Error = 0.011419840791676418\n", + "Train Estimator R2 Score = 0.9769\n", + "Train Domain Classifier Error = 0.08511144389148113\n", + "Validation Source Estimator Error = 0.013395314339168702\n", + "Validation Source R2 Score = 0.9723\n", + "Validation Target Estimator Error = 0.01829975693943394\n", + "Validation Target R2 Score = 0.9627\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 24\n", + "-------------------------------\n", + "10.949392557144165\n", + "Train Estimator Error = 0.01124005260731739\n", + "Train Estimator R2 Score = 0.9772\n", + "Train Domain Classifier Error = 0.08437608700872176\n", + "Validation Source Estimator Error = 0.010781187756924302\n", + "Validation Source R2 Score = 0.9776\n", + "Validation Target Estimator Error = 0.01911643360427041\n", + "Validation Target R2 Score = 0.9609\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 25\n", + "-------------------------------\n", + "10.812219619750977\n", + "Train Estimator Error = 0.011162436417843704\n", + "Train Estimator R2 Score = 0.9773\n", + "Train Domain Classifier Error = 0.0841802812523636\n", + "Validation Source Estimator Error = 0.010849304716725638\n", + "Validation Source R2 Score = 0.9775\n", + "Validation Target Estimator Error = 0.017798513624888317\n", + "Validation Target R2 Score = 0.9642\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 26\n", + "-------------------------------\n", + "10.78961730003357\n", + "Train Estimator Error = 0.011024486007708613\n", + "Train Estimator R2 Score = 0.9778\n", + "Train Domain Classifier Error = 0.08254390428186661\n", + "Validation Source Estimator Error = 0.01079819270166432\n", + "Validation Source R2 Score = 0.9776\n", + "Validation Target Estimator Error = 0.017061652586006434\n", + "Validation Target R2 Score = 0.9653\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 27\n", + "-------------------------------\n", + "11.075653314590454\n", + "Train Estimator Error = 0.010674274028738589\n", + "Train Estimator R2 Score = 0.9784\n", + "Train Domain Classifier Error = 0.08253127733548554\n", + "Validation Source Estimator Error = 0.011198129487123079\n", + "Validation Source R2 Score = 0.9769\n", + "Validation Target Estimator Error = 0.017826482618025913\n", + "Validation Target R2 Score = 0.9642\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 28\n", + "-------------------------------\n", + "11.014330387115479\n", + "Train Estimator Error = 0.010593715743153225\n", + "Train Estimator R2 Score = 0.9784\n", + "Train Domain Classifier Error = 0.08215534362015765\n", + "Validation Source Estimator Error = 0.010194582484994724\n", + "Validation Source R2 Score = 0.9790\n", + "Validation Target Estimator Error = 0.017515959956677287\n", + "Validation Target R2 Score = 0.9652\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 29\n", + "-------------------------------\n", + "11.108861207962036\n", + "Train Estimator Error = 0.010514938445136946\n", + "Train Estimator R2 Score = 0.9786\n", + "Train Domain Classifier Error = 0.08331436429928708\n", + "Validation Source Estimator Error = 0.011087410442626022\n", + "Validation Source R2 Score = 0.9770\n", + "Validation Target Estimator Error = 0.016971729743252895\n", + "Validation Target R2 Score = 0.9659\n", + "Validation Domain Classifier Error = 1\n", + "\n", + "Epoch 30\n", + "-------------------------------\n", + "10.851420402526855\n", + "Train Estimator Error = 0.010488009098593752\n", + "Train Estimator R2 Score = 0.9788\n", + "Train Domain Classifier Error = 0.07883671006967588\n", + "Validation Source Estimator Error = 0.010881695215691142\n", + "Validation Source R2 Score = 0.9775\n", + "Validation Target Estimator Error = 0.018756618195326084\n", + "Validation Target R2 Score = 0.9625\n", + "Validation Domain Classifier Error = 1\n", + "\n" + ] + } + ], + "source": [ + "stats = {'train_domain_classifier_error':[],\n", + " 'train_estimator_error':[],\n", + " 'train_score':[],\n", + " 'val_domain_classifier_error':[],\n", + " 'val_estimator_error':[],\n", + " 'val_estimator_error_target':[],\n", + " 'val_score':[],\n", + " 'val_score_target':[]}\n", + "\n", + "# Train\n", + "for i in range(epochs):\n", + " start_time = time.time()\n", + " print(f\"Epoch {i+1}\\n-------------------------------\")\n", + " vals = train_loop(source_train_dataloader, target_train_dataloader, model,\n", + " regressor_loss_fn, da_loss, optimizer, epochs, i)\n", + "\n", + " vals_validate = test_loop(source_val_dataloader, target_val_dataloader,\n", + " model, regressor_loss_fn, da_loss, epochs, i)\n", + " print(time.time() - start_time)\n", + "\n", + " stats['train_domain_classifier_error'].append(vals[0])\n", + " stats['train_estimator_error'].append(vals[1])\n", + " stats['train_score'].append(vals[2])\n", + " stats['val_domain_classifier_error'].append(vals_validate[0])\n", + " stats['val_estimator_error'].append(vals_validate[1])\n", + " stats['val_estimator_error_target'].append(vals_validate[2])\n", + " stats['val_score'].append(vals_validate[3])\n", + " stats['val_score_target'].append(vals_validate[4])\n", + "\n", + " to_print = (\n", + " f'Train Estimator Error = {vals[1]}\\n'\n", + " f'Train Estimator R2 Score = {vals[2]:.4f}\\n'\n", + " f'Train Domain Classifier Error = {vals[0]}\\n'\n", + " f'Validation Source Estimator Error = {vals_validate[1]}\\n'\n", + " f'Validation Source R2 Score = {vals_validate[3]:.4f}\\n'\n", + " f'Validation Target Estimator Error = {vals_validate[2]}\\n'\n", + " f'Validation Target R2 Score = {vals_validate[4]:.4f}\\n'\n", + " f'Validation Domain Classifier Error = {vals_validate[0]}\\n'\n", + " )\n", + "\n", + " print(to_print)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "YfplCDIb-UU_", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 649, + "status": "ok", + "timestamp": 1718869045736, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "YfplCDIb-UU_", + "outputId": "dbb362ec-4af5-4cb9-c4f9-a0a2766c26c5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Classifier\n", + "eps = np.arange(epochs)\n", + "plt.title(\"Classifier Error\")\n", + "plt.plot(eps, stats['train_domain_classifier_error'])\n", + "plt.plot(eps, stats['val_domain_classifier_error'])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "eYG_P_iQ_5Bv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 169, + "status": "ok", + "timestamp": 1718869045739, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "eYG_P_iQ_5Bv", + "outputId": "be450f92-eda7-4e4f-81fe-008c55b2b112" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Estimator\n", + "plt.title(\"Estimator Error\")\n", + "plt.plot(eps, stats['train_estimator_error'])\n", + "plt.plot(eps, stats['val_estimator_error'])\n", + "plt.plot(eps, stats['val_estimator_error_target'])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "xS9rtS-T_neg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "executionInfo": { + "elapsed": 237, + "status": "ok", + "timestamp": 1718869045904, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "xS9rtS-T_neg", + "outputId": "d32f40ef-6042-4154-e9ee-1f4e2f90064d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# R2 Scores\n", + "plt.title(\"R2 Scores\")\n", + "plt.plot(eps, stats['train_score'])\n", + "plt.plot(eps, stats['val_score'])\n", + "plt.plot(eps, stats['val_score_target'])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "ed0a8206-7520-4a60-8e17-965a91133b92", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 + }, + "executionInfo": { + "elapsed": 969, + "status": "ok", + "timestamp": 1718869046858, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "ed0a8206-7520-4a60-8e17-965a91133b92", + "outputId": "7df8c563-5826-4e43-d9e6-5e686463551d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source R2 Score is 0.9778\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MMD | Source | R2: 0.978')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Test Source\n", + "preds = np.array([])\n", + "true = np.array([])\n", + "score_list = np.array([])\n", + "\n", + "with torch.no_grad():\n", + " for X, y in source_test_dataloader:\n", + " X = X.float()\n", + " pred, _ = model(X.cuda())\n", + " preds = np.append(preds, pred.cpu())\n", + " true = np.append(true, y.cpu())\n", + " score = r2_score(y.cpu(), pred.cpu())\n", + " score_list = np.append(score_list, score)\n", + "\n", + "score = np.mean(score_list)\n", + "print(f'Source R2 Score is {score:.4f}')\n", + "\n", + "plt.figure(figsize=(8,8),dpi=50)\n", + "plt.scatter(true, preds, color='black', alpha = 0.1)\n", + "line = np.linspace(0, 4, 100)\n", + "plt.plot(line, line)\n", + "plt.rc('font', size=12)\n", + "plt.xlabel('True Theta E')\n", + "plt.ylabel('Predicted Theta E');\n", + "plt.rc('font', size=20)\n", + "plt.title('MMD | Source | R2: {0:.3f}'.format(score))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "executionInfo": { + "elapsed": 1283, + "status": "ok", + "timestamp": 1718869048133, + "user": { + "displayName": "Shrihan Agarwal", + "userId": "00018416289398983661" + }, + "user_tz": 300 + }, + "id": "fc047cd7-bc92-4a30-9beb-7af607da141f", + "outputId": "b6347093-56d9-4a8b-b515-c4c4717cdab4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Target R2 Score is 0.9597\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MMD | Target | R2: 0.960')" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Test target\n", + "preds = np.array([])\n", + "true = np.array([])\n", + "score_list = np.array([])\n", + "\n", + "with torch.no_grad():\n", + " for X, y in target_test_dataloader:\n", + " X = X.float()\n", + " pred, _ = model(X.cuda())\n", + " preds = np.append(preds, pred.cpu())\n", + " true = np.append(true, y.cpu())\n", + " score = r2_score(y.cpu(), pred.cpu())\n", + " score_list = np.append(score_list, score)\n", + "\n", + "score = np.mean(score_list)\n", + "print(f'Target R2 Score is {score:.4f}')\n", + "\n", + "plt.figure(figsize=(8,8),dpi=50)\n", + "plt.scatter(true, preds, color='black', alpha = 0.1)\n", + "line = np.linspace(0, 4, 100)\n", + "plt.plot(line, line)\n", + "plt.rc('font', size=12)\n", + "plt.xlabel('True Theta E')\n", + "plt.ylabel('Predicted Theta E');\n", + "plt.rc('font', size=20)\n", + "plt.title('MMD | Target | R2: {0:.3f}'.format(score))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2", + "metadata": { + "id": "14a94f1e-758e-4a64-b0c7-0f3a5781f7c2" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [ + { + "file_id": "1MFScb-3Sbugn4RNiDaeocicJUIHlh_j2", + "timestamp": 1717430435817 + }, + { + "file_id": "1wlKaSdLzleueYrwljtOcqsiOfzEy1dxP", + "timestamp": 1717429638462 + } + ] + }, + "kernelspec": { + "display_name": "Python [conda env:.conda-neural]", + "language": "python", + "name": "conda-env-.conda-neural-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}