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{
"cells": [
{
"cell_type": "markdown",
"id": "74bd5ceb-afa1-4bfd-ba39-10af717cf2a5",
"metadata": {},
"source": [
"Remember to change the test and model Path!\n",
"Since I'm using Embedding to encode headlines to vector, it takes 10+ min. to encode information for test set which I cannot do it on my end since I do not have access to hiddne test set! "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a458f2b7-3ab1-479f-9627-ef7ef8ef76b4",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import Dataset, DataLoader, random_split, SubsetRandomSampler\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import random\n",
"import os\n",
"import copy\n",
"from torch.utils.data import TensorDataset\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d7943628-3454-4d21-a95d-ca53acd9b6dc",
"metadata": {},
"outputs": [],
"source": [
"class LabelSmoothingBCELoss(nn.Module):\n",
" def __init__(self, smoothing=0.1):\n",
" \"\"\"\n",
" Label Smoothing Binary Cross Entropy Loss\n",
" \n",
" Args:\n",
" smoothing (float): Amount of label smoothing to apply\n",
" \"\"\"\n",
" super(LabelSmoothingBCELoss, self).__init__()\n",
" self.smoothing = smoothing\n",
" \n",
" def forward(self, predictions, targets):\n",
" \"\"\"\n",
" Compute label-smoothed binary cross entropy loss\n",
" \n",
" Args:\n",
" predictions (torch.Tensor): Model predictions\n",
" targets (torch.Tensor): Binary labels\n",
" \n",
" Returns:\n",
" torch.Tensor: Smoothed loss\n",
" \"\"\"\n",
" # Apply label smoothing\n",
" smooth_targets = targets * (1 - self.smoothing) + 0.5 * self.smoothing\n",
" \n",
" # Standard Binary Cross Entropy Loss\n",
" loss = nn.functional.binary_cross_entropy(predictions, smooth_targets)\n",
" \n",
" return loss\n",
"\n",
"class EarlyStoppingCallback:\n",
" def __init__(self, patience=5, min_delta=0.001):\n",
" \"\"\"\n",
" Early stopping mechanism\n",
" \n",
" Args:\n",
" patience (int): Number of epochs to wait for improvement\n",
" min_delta (float): Minimum change to qualify as an improvement\n",
" \"\"\"\n",
" self.patience = patience\n",
" self.min_delta = min_delta\n",
" self.counter = 0\n",
" self.best_loss = float('inf')\n",
" self.early_stop = False\n",
" self.best_model_state = None\n",
" \n",
" def __call__(self, val_loss, model):\n",
" \"\"\"\n",
" Check if training should stop\n",
" \n",
" Args:\n",
" val_loss (float): Current validation loss\n",
" model (nn.Module): Current model state\n",
" \n",
" Returns:\n",
" bool: Whether to stop training\n",
" \"\"\"\n",
" if val_loss < self.best_loss - self.min_delta:\n",
" self.best_loss = val_loss\n",
" self.counter = 0\n",
" # Save the best model state\n",
" self.best_model_state = copy.deepcopy(model.state_dict())\n",
" else:\n",
" self.counter += 1\n",
" if self.counter >= self.patience:\n",
" self.early_stop = True\n",
" \n",
" return self.early_stop\n",
"\n",
"class EnsembleMLPClassifier(nn.Module):\n",
" def __init__(self, \n",
" input_dim=1024, # BGE embedding dimension\n",
" hidden_layers=None,\n",
" dropout_rate=0.2,\n",
" activation=nn.ReLU(), # Allow passing activation functions dynamically\n",
" device=None):\n",
" super(EnsembleMLPClassifier, self).__init__()\n",
" \n",
" # Default configuration if not provided\n",
" if hidden_layers is None:\n",
" hidden_layers = [512, 256, 128]\n",
" \n",
" # Set device (GPU if available, else CPU)\n",
" self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
" \n",
" # Store initialization parameters\n",
" self.input_dim = input_dim\n",
" self.hidden_layers = hidden_layers\n",
" self.dropout_rate = dropout_rate\n",
" self.activation = activation\n",
" \n",
" # Add linear gate mechanism\n",
" self.gate = nn.Linear(input_dim, input_dim, bias=False)\n",
" \n",
" # Create layers dynamically based on hidden_layers specification\n",
" layers = []\n",
" prev_dim = input_dim\n",
" for hidden_dim in hidden_layers:\n",
" # Dense Layer with dynamic activation and BatchNorm\n",
" layers.extend([\n",
" nn.Linear(prev_dim, hidden_dim),\n",
" nn.BatchNorm1d(hidden_dim),\n",
" activation,\n",
" nn.Dropout(dropout_rate)\n",
" ])\n",
" prev_dim = hidden_dim\n",
" \n",
" # Final output layer for binary classification\n",
" layers.append(nn.Linear(prev_dim, 1))\n",
" layers.append(nn.Sigmoid())\n",
" \n",
" # Create the model and move to device\n",
" self.model = nn.Sequential(*layers)\n",
" self.to(self.device)\n",
"\n",
" def forward(self, x):\n",
" \"\"\"Forward pass through the network\"\"\"\n",
" # Apply gating mechanism\n",
" x = self.gate(x) * x\n",
" return self.model(x)\n",
"\n",
"class EnsembleClassifier:\n",
" def __init__(self, num_models=5, label_smoothing=0.1):\n",
" self.models = self._create_diverse_models(num_models)\n",
" self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
" self.label_smoothing = label_smoothing\n",
" self.model_weights = None \n",
" \n",
" def _create_diverse_models(self, num_models):\n",
" models = []\n",
" \n",
" # Predefined configurations for consistency across runs\n",
" architectures = [\n",
" {'hidden_layers': [512, 256, 128], 'dropout_rate': 0.2, 'activation': nn.ReLU()},\n",
" {'hidden_layers': [1024, 512], 'dropout_rate': 0.3, 'activation': nn.LeakyReLU()},\n",
" {'hidden_layers': [256, 128, 64], 'dropout_rate': 0.1, 'activation': nn.GELU()},\n",
" {'hidden_layers': [512, 128], 'dropout_rate': 0.25, 'activation': nn.SELU()},\n",
" {'hidden_layers': [256, 128], 'dropout_rate': 0.15, 'activation': nn.Tanh()}\n",
" ]\n",
" \n",
" # Optimizer strategies\n",
" optimizers = [optim.Adam, optim.AdamW, optim.SGD]\n",
" \n",
" for i in range(num_models):\n",
" # Use predefined architectures in a consistent order\n",
" config = architectures[i % len(architectures)]\n",
" optimizer_fn = optimizers[i % len(optimizers)]\n",
" \n",
" model = EnsembleMLPClassifier(\n",
" input_dim=1024,\n",
" hidden_layers=config['hidden_layers'],\n",
" dropout_rate=config['dropout_rate'],\n",
" activation=config['activation']\n",
" )\n",
" \n",
" # Custom weight initialization\n",
" def init_weights(m):\n",
" if isinstance(m, nn.Linear):\n",
" init_methods = [\n",
" nn.init.xavier_uniform_,\n",
" nn.init.kaiming_normal_,\n",
" nn.init.orthogonal_\n",
" ]\n",
" init_method = init_methods[i % len(init_methods)] # Consistent initialization\n",
" init_method(m.weight)\n",
" if m.bias is not None:\n",
" nn.init.zeros_(m.bias)\n",
" \n",
" model.model.apply(init_weights)\n",
" \n",
" # Attach optimizer to model instance for flexibility\n",
" model.optimizer_fn = optimizer_fn\n",
" \n",
" # Add L2 regularization to the model (Weight Decay)\n",
" model.regularization = {\n",
" 'weight_decay': 1e-5 # Example regularization value\n",
" }\n",
" \n",
" models.append(model)\n",
" \n",
" return models\n",
" \n",
" def train(self, train_dataset, batch_size=32, num_epochs=20):\n",
" for model_idx, model in enumerate(tqdm(self.models, desc=\"Training Models\", position=0)):\n",
" print(f\"Starting training for Model {model_idx + 1}/{len(self.models)}\")\n",
" \n",
" # Randomly split 80% for training and 20% for validation\n",
" total_size = len(train_dataset)\n",
" train_size = int(0.8 * total_size)\n",
" val_size = total_size - train_size\n",
" \n",
" train_subset, val_subset = random_split(train_dataset, [train_size, val_size])\n",
" \n",
" # Create data loaders for training and validation\n",
" train_loader = DataLoader(train_subset, batch_size=batch_size, shuffle=True)\n",
" val_loader = DataLoader(val_subset, batch_size=batch_size, shuffle=False)\n",
" \n",
" # Optimizer with learning rate scheduler\n",
" optimizer = optim.AdamW(model.parameters(), lr=1e-3)\n",
" scheduler = optim.lr_scheduler.CosineAnnealingLR(\n",
" optimizer, \n",
" T_max=num_epochs, \n",
" eta_min=1e-5\n",
" )\n",
" \n",
" # Label Smoothing Loss\n",
" criterion = LabelSmoothingBCELoss(smoothing=self.label_smoothing)\n",
" \n",
" # Early stopping\n",
" early_stopping = EarlyStoppingCallback(patience=4, min_delta=0.001)\n",
" \n",
" model.train()\n",
" epoch_progress = tqdm(range(num_epochs), desc=f\"Model {model_idx} Training\", position=1, leave=False)\n",
" \n",
" best_val_loss = float('inf')\n",
" for epoch in epoch_progress:\n",
" total_loss = 0\n",
" \n",
" # Training phase\n",
" for batch in train_loader:\n",
" inputs, labels = batch\n",
" inputs, labels = inputs.to(model.device), labels.to(model.device)\n",
" \n",
" optimizer.zero_grad()\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, labels.float().unsqueeze(1))\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" total_loss += loss.item()\n",
" avg_train_loss = total_loss / len(train_loader)\n",
" \n",
" # Validation phase\n",
" model.eval()\n",
" val_loss = 0\n",
" with torch.no_grad():\n",
" for val_batch in val_loader:\n",
" val_inputs, val_labels = val_batch\n",
" val_inputs, val_labels = val_inputs.to(model.device), val_labels.to(model.device)\n",
" val_outputs = model(val_inputs)\n",
" val_loss += criterion(val_outputs, val_labels.float().unsqueeze(1)).item()\n",
" \n",
" avg_val_loss = val_loss / len(val_loader)\n",
" epoch_progress.set_postfix({\n",
" 'train_loss': avg_train_loss,\n",
" 'val_loss': avg_val_loss\n",
" })\n",
" \n",
" # Early stopping check\n",
" if early_stopping(avg_val_loss, model):\n",
" if early_stopping.best_model_state:\n",
" model.load_state_dict(early_stopping.best_model_state)\n",
" print(f\"Early stopping triggered for Model {model_idx}\")\n",
" break\n",
" \n",
" # Learning rate adjustment\n",
" scheduler.step()\n",
" \n",
" # Reset to training mode\n",
" model.train()\n",
" \n",
" # Store model's final state after training\n",
" model.eval()\n",
" \n",
" def compute_test_weights(self, test_loader):\n",
" \"\"\"\n",
" Compute model weights based on test accuracy while emphasizing distinctions.\n",
" \"\"\"\n",
" model_accuracies = []\n",
" for model_idx, model in enumerate(self.models):\n",
" correct = 0\n",
" total = 0\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for inputs, labels in test_loader:\n",
" inputs, labels = inputs.to(model.device), labels.to(model.device)\n",
" outputs = model(inputs)\n",
" preds = (outputs > 0.5).float()\n",
" correct += (preds == labels).sum().item()\n",
" total += labels.size(0)\n",
" accuracy = correct / total\n",
" model_accuracies.append(accuracy)\n",
" \n",
" # Apply a power transformation for distinction\n",
" accuracies = np.array(model_accuracies)\n",
" print(f\"Raw model accuracies: {accuracies}\")\n",
" \n",
" # Use power scaling to exaggerate differences (e.g., square the accuracies)\n",
" power_scaling_factor = 2 # Choose 2 for squaring, can experiment with higher values\n",
" scaled_accuracies = accuracies ** power_scaling_factor\n",
" \n",
" # Smooth the accuracies slightly to avoid over-reliance on any single model\n",
" smoothed_accuracies = scaled_accuracies * (1 - 0.1) + 0.1 * np.mean(scaled_accuracies)\n",
" \n",
" # Normalize weights so they sum to 1\n",
" weights = smoothed_accuracies / smoothed_accuracies.sum()\n",
" \n",
" # Store model weights\n",
" self.model_weights = torch.tensor(weights, dtype=torch.float32).to(self.device)\n",
" print(f\"Model weights after scaling: {self.model_weights}\")\n",
"\n",
"\n",
" def predict(self, test_loader, confidence_threshold=0.5, return_raw_scores=True):\n",
" \"\"\"\n",
" Prediction with confidence-weighted voting, optionally returning raw scores.\n",
" \"\"\"\n",
" if self.model_weights is None:\n",
" raise ValueError(\"Model weights not computed. Call compute_test_weights first.\")\n",
" \n",
" all_predictions = []\n",
" for model_idx, model in enumerate(self.models):\n",
" model.eval()\n",
" model_preds = []\n",
" with torch.no_grad():\n",
" for batch in test_loader:\n",
" inputs, _ = batch\n",
" inputs = inputs.to(model.device)\n",
" outputs = model(inputs)\n",
" model_preds.append(outputs)\n",
" \n",
" # Concatenate predictions for this model\n",
" all_predictions.append(torch.cat(model_preds))\n",
" \n",
" # Stack predictions and compute weighted average\n",
" stacked_preds = torch.stack(all_predictions, dim=1).squeeze(-1)\n",
" weighted_preds = (stacked_preds * self.model_weights.view(1, -1)).sum(dim=1)\n",
" \n",
" # Final prediction with thresholding\n",
" final_preds = (weighted_preds > confidence_threshold).float()\n",
" \n",
" # Optionally return raw probabilities for debugging\n",
" if return_raw_scores:\n",
" return final_preds, weighted_preds.cpu().numpy()\n",
" \n",
" return final_preds\n",
"\n",
"\n",
" def save_models(self, save_dir='ensemble_models/model_test_4'):\n",
" \"\"\"\n",
" Save ensemble model weights and model weights with progress tracking\n",
" \"\"\"\n",
" os.makedirs(save_dir, exist_ok=True)\n",
"\n",
" save_data = {\n",
" 'models': {},\n",
" 'model_weights': self.model_weights.cpu().numpy() if self.model_weights is not None else None\n",
" }\n",
"\n",
" for i, model in tqdm(enumerate(self.models), desc=\"Saving Models\", total=len(self.models)):\n",
" save_data['models'][i] = model.state_dict()\n",
"\n",
" torch.save(save_data, os.path.join(save_dir, 'ensemble_checkpoint.pth'))\n",
"\n",
" def load_models(self, save_dir='ensemble_models/model_test_4'):\n",
" \"\"\"\n",
" Load ensemble model weights and model weights with progress tracking\n",
" \"\"\"\n",
" checkpoint_path = os.path.join(save_dir, 'ensemble_checkpoint.pth')\n",
"\n",
" save_data = torch.load(checkpoint_path)\n",
"\n",
" for i, model in tqdm(enumerate(self.models), desc=\"Loading Models\", total=len(self.models)):\n",
" model.load_state_dict(save_data['models'][i])\n",
" model.eval() # Set to evaluation mode\n",
"\n",
" if save_data['model_weights'] is not None:\n",
" self.model_weights = torch.tensor(save_data['model_weights'], dtype=torch.float32).to(self.device)\n",
" \n",
" def evaluate(self, test_loader):\n",
" \"\"\"\n",
" Evaluate ensemble performance with weighted voting, supporting both CPU and GPU.\n",
" \"\"\"\n",
" # Collect ground truth labels\n",
" all_labels = torch.cat([labels for _, labels in test_loader], dim=0).to(self.device)\n",
" \n",
" # Get predictions for the entire test set\n",
" test_preds = self.predict(test_loader, return_raw_scores=True)\n",
" \n",
" # Ensure predictions and labels are on the same device\n",
" all_labels = all_labels.cpu().numpy().ravel() # Flatten to 1D\n",
" test_preds, raw_probs = test_preds\n",
" test_preds = test_preds.cpu().numpy().ravel() # Flatten to 1D\n",
" \n",
" # Print debug information\n",
" # print(\"Ground truth labels (all_labels):\", all_labels)\n",
" # print(\"Predicted classes (test_preds):\", test_preds)\n",
" # print(\"Raw probabilities (raw_probs):\", raw_probs) \n",
" \n",
" # Calculate metrics\n",
" accuracy = np.mean(test_preds == all_labels)\n",
" precision = precision_score(all_labels, test_preds, zero_division=0)\n",
" recall = recall_score(all_labels, test_preds, zero_division=0)\n",
" \n",
" return {\n",
" \"accuracy\": accuracy,\n",
" \"precision\": precision,\n",
" \"recall\": recall\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a95bb0eb-48ba-4c46-9cc5-4f6a1ee19dee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
"Requirement already satisfied: FlagEmbedding in /opt/conda/lib/python3.11/site-packages (1.3.3)\n",
"Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (2.2.2+cu121)\n",
"Requirement already satisfied: transformers==4.44.2 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (4.44.2)\n",
"Requirement already satisfied: datasets==2.19.0 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (2.19.0)\n",
"Requirement already satisfied: accelerate>=0.20.1 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (1.2.0)\n",
"Requirement already satisfied: sentence-transformers in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (3.3.1)\n",
"Requirement already satisfied: peft in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (0.14.0)\n",
"Requirement already satisfied: ir-datasets in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (0.5.9)\n",
"Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (0.2.0)\n",
"Requirement already satisfied: protobuf in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (4.25.3)\n",
"Requirement already satisfied: filelock in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (3.9.0)\n",
"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (1.26.4)\n",
"Requirement already satisfied: pyarrow>=12.0.0 in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (15.0.2)\n",
"Requirement already satisfied: pyarrow-hotfix in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (0.6)\n",
"Requirement already satisfied: dill<0.3.9,>=0.3.0 in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (0.3.8)\n",
"Requirement already satisfied: pandas in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (2.2.2)\n",
"Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (2.31.0)\n",
"Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (4.66.2)\n",
"Requirement already satisfied: xxhash in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (3.5.0)\n",
"Requirement already satisfied: multiprocess in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (0.70.16)\n",
"Requirement already satisfied: fsspec<=2024.3.1,>=2023.1.0 in /opt/conda/lib/python3.11/site-packages (from fsspec[http]<=2024.3.1,>=2023.1.0->datasets==2.19.0->FlagEmbedding) (2024.3.1)\n",
"Requirement already satisfied: aiohttp in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (3.11.10)\n",
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"Requirement already satisfied: packaging in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (24.0)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a24dee20be054f138b75c100ab2e6a36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 30 files: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Encoding titles...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 20/20 titles\n"
]
}
],
"source": [
"!pip install FlagEmbedding\n",
"from FlagEmbedding import BGEM3FlagModel\n",
"model = BGEM3FlagModel('BAAI/bge-m3')\n",
"\n",
"# Remember to change the test path\n",
"test_data_path = \"/home/jovyan/work/test_data_random_subset.csv\"\n",
"\n",
"data = data = pd.read_csv(test_data_path)\n",
"titles = data['title'].tolist()\n",
"labels = data['labels'].tolist()\n",
"\n",
"batch_size = 32\n",
"embeddings = []\n",
"\n",
"print('Encoding titles...')\n",
"for i in range(0, len(titles), batch_size):\n",
" batch = titles[i:i + batch_size]\n",
" batch_embeddings = model.encode(batch, batch_size=batch_size, max_length=512)['dense_vecs']\n",
" embeddings.extend(batch_embeddings)\n",
" print(f\"Processed {i + len(batch)}/{len(titles)} titles\")\n",
"\n",
"embeddings_df = pd.DataFrame(embeddings)\n",
"embeddings_df['label'] = labels\n",
"\n",
"# Convert embeddings and labels to PyTorch tensors\n",
"X_test = torch.FloatTensor(embeddings_df.iloc[:, :-1].values) # Features\n",
"y_test = torch.FloatTensor(embeddings_df['label'].values).view(-1, 1) # Labels\n",
"\n",
"# Create DataLoader for the test dataset\n",
"test_dataset = TensorDataset(X_test, y_test)\n",
"test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c6bcf956-4e26-4278-a6fe-9955322cf06a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading Models: 100%|██████████| 5/5 [00:00<00:00, 1799.05it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'accuracy': 0.9, 'precision': 0.9, 'recall': 0.9}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from sklearn.metrics import precision_score, recall_score\n",
"ensemble = EnsembleClassifier(5) \n",
"\n",
"# Load saved model weights\n",
"# Be sure to change to the actual path\n",
"ensemble.load_models(save_dir='/home/jovyan/work/ensemble_models/model_test_4')\n",
"\n",
"# Evaluate the ensemble\n",
"results = ensemble.evaluate(test_loader)\n",
"print(results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77da0a63-cf76-4cbb-8b48-da115e124946",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|