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interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, ] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.10,<3.11" +content-hash = "479a222d6ad71692a588b9ba1dd7f4391d41cd4d737ef25f626eb7e6c4a7467b" diff --git a/pyproject.toml b/pyproject.toml index e9db685..4e6d196 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ hydra-core = "^1.2.0" black = "23.1.0" mypy = "1.5.0" statsmodels = "^0.14.0" +torch = "^2.5.1" [tool.poetry.dev-dependencies] black = "^23.1.0" diff --git a/sandbox/mdn.py b/sandbox/mdn.py new file mode 100644 index 0000000..00d10f0 --- /dev/null +++ b/sandbox/mdn.py @@ -0,0 +1,172 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import TensorDataset, DataLoader +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +# Set random seeds for reproducibility +np.random.seed(42) +torch.manual_seed(42) + +# Generate 1D training data +def generate_data(n_samples=200): + x = np.random.uniform(-3, 3, n_samples) + x = np.sort(x) # Sort for better visualization + y = np.zeros_like(x) + + for i in range(n_samples): + if np.random.rand() > 0.5: + y[i] = 0.5 * np.sin(x[i]) + 5 + 0.5 * np.random.randn() + else: + y[i] = 0.5 * np.sin(x[i]) - 5 + 0.5 * np.random.randn() + + return x, y + +# MDN Model +class MDN(nn.Module): + def __init__(self, input_dim=1, hidden_dim=15, n_gaussians=2): + super(MDN, self).__init__() + self.n_gaussians = n_gaussians + + self.hidden = nn.Sequential( + nn.Linear(input_dim, hidden_dim), + nn.ReLU(), + nn.Linear(hidden_dim, hidden_dim), + nn.ReLU() + ) + + self.pi = nn.Linear(hidden_dim, n_gaussians) + self.mu = nn.Linear(hidden_dim, n_gaussians) + self.sigma = nn.Linear(hidden_dim, n_gaussians) + + def forward(self, x): + hidden = self.hidden(x) + pi = torch.softmax(self.pi(hidden), dim=1) + mu = self.mu(hidden) + sigma = torch.exp(self.sigma(hidden)) + return pi, mu, sigma + +# Generate data +x_train, y_train = generate_data(500) +layout_dummy = np.random.randint(0, 2, 500) +component = [1]*500 +data = np.column_stack((x_train, y_train)) # Combine x_train and y_train column-wise +data = np.column_stack((data, layout_dummy)) # Add layout column +data = np.column_stack((data, component)) # Add component column +df = pd.DataFrame(data, columns=["X_train", "y_train", "LAYOUT", "COMPONENTS"]) +df.to_csv("data/ARCADE/test_data.csv", index=False) + +X = torch.FloatTensor(x_train).reshape(-1, 1) +Y = torch.FloatTensor(y_train).reshape(-1, 1) + +# Create data loader +dataset = TensorDataset(X, Y) +loader = DataLoader(dataset, batch_size=32, shuffle=True) + +# Initialize model and optimizer +model = MDN(input_dim=1, hidden_dim=15, n_gaussians=2) +optimizer = optim.Adam(model.parameters(), lr=0.01) + +# Loss function +def mdn_loss(pi, mu, sigma, y): + gaussian_prob = (1.0 / (sigma * np.sqrt(2*np.pi))) * \ + torch.exp(-0.5 * ((y.unsqueeze(1) - mu) / sigma)**2) + weighted_prob = pi * gaussian_prob + return -torch.log(torch.sum(weighted_prob, dim=1) + 1e-6).mean() + +# Training loop +n_epochs = 200 +losses = [] + +for epoch in range(n_epochs): + epoch_loss = 0 + for batch_x, batch_y in loader: + optimizer.zero_grad() + pi, mu, sigma = model(batch_x) + loss = mdn_loss(pi, mu, sigma, batch_y) + loss.backward() + optimizer.step() + epoch_loss += loss.item() + + losses.append(epoch_loss / len(loader)) + + if (epoch + 1) % 100 == 0: + print(f'Epoch {epoch+1}/{n_epochs}, Loss: {losses[-1]:.4f}') + +# Generate predictions for visualization +model.eval() +X_test, y_test = generate_data(200) +X_test = torch.FloatTensor(X_test).reshape(-1, 1) +y_test = torch.FloatTensor(y_test).reshape(-1, 1) + +with torch.no_grad(): + pi, mu, sigma = model(X_test) + pi = pi.numpy() + mu = mu.numpy() + sigma = sigma.numpy() + +# Calculate R2 score +from sklearn.metrics import r2_score +y_pred = np.sum(pi * mu, axis=1) +print(y_pred) +r2 = r2_score(y_test, y_pred) +print(f"\nR2 Score: {r2:.4f}") +X_test = X_test.numpy() + +# Plotting +plt.figure(figsize=(15, 10)) + +# Plot 1: Training Data and Predictions +plt.subplot(2, 1, 1) + +# Plot training data +plt.scatter(x_train, y_train, alpha=0.5, label='Training Data', color='blue', s=20) + +# Plot predicted means and uncertainties for each Gaussian component +colors = ['red', 'green'] +for i in range(model.n_gaussians): + # Plot mean + plt.plot(X_test, mu[:, i], color=colors[i], + label=f'Mean (Gaussian {i+1})', linewidth=2) + + # Plot uncertainty bounds (±2 sigma) + plt.fill_between(X_test.flatten(), + mu[:, i] - 2*sigma[:, i], + mu[:, i] + 2*sigma[:, i], + color=colors[i], alpha=0.2, + label=f'Uncertainty (Gaussian {i+1})') + +plt.xlabel('Input (x)') +plt.ylabel('Output (y)') +plt.title('Training Data with Predicted Means and Uncertainties') +plt.legend() +plt.grid(True, alpha=0.3) + +# Plot 2: Mixing Coefficients +plt.subplot(2, 1, 2) +for i in range(model.n_gaussians): + plt.plot(X_test, pi[:, i], color=colors[i], + label=f'π{i+1}', linewidth=2) +plt.xlabel('Input (x)') +plt.ylabel('Mixing Coefficient (π)') +plt.title('Mixing Coefficients vs Input') +plt.legend() +plt.grid(True, alpha=0.3) + +plt.tight_layout() +plt.show() +plt.savefig('mdn_results.png') + +# Print example predictions for specific x values +test_points = [-1, 0, 1] +print("\nExample predictions at specific points:") +for x_val in test_points: + x = torch.tensor([[x_val]], dtype=torch.float32) + with torch.no_grad(): + pi, mu, sigma = model(x) + print(f"\nx = {x_val}") + print(f"Mixing coefficients (π): {pi.numpy().flatten()}") + print(f"Means (μ): {mu.numpy().flatten()}") + print(f"Standard deviations (σ): {sigma.numpy().flatten()}") \ No newline at end of file diff --git a/sandbox/mdn_results.png b/sandbox/mdn_results.png new file mode 100644 index 0000000..4f617a9 Binary files /dev/null and b/sandbox/mdn_results.png differ diff --git a/src/conf/cs/models/mdn.yaml b/src/conf/cs/models/mdn.yaml index 1a5fdd1..da44a2b 100644 --- a/src/conf/cs/models/mdn.yaml +++ b/src/conf/cs/models/mdn.yaml @@ -1,25 +1,19 @@ -mlp: +mdn: continuous: - alpha: + learning_rate: type: float - range: [0.0001, 1] + range: [0.001, 0.01] search: log + discrete: - activation: - - identity - - logistic - - tanh - - relu - hidden_layer_sizes: - - (5,) - - (5,5) - - (5,10) - - (25,) - - (25, 25) - - (25, 50) - - (50,) - - (50, 25) - - (50, 50) + hidden_dim: + - 16 + num_gaussians: + - 2 + output_dim: + - 1 + static: - solver: lbfgs - max_iter: 1000 + epochs: 500 + + diff --git a/src/conf/cs/test.yaml b/src/conf/cs/test.yaml new file mode 100644 index 0000000..80899fd --- /dev/null +++ b/src/conf/cs/test.yaml @@ -0,0 +1,16 @@ +defaults: + - models/mdn + - _self_ +experiments: + TEST-MDN: + files: + data: test_data.csv + paths: + log: ${hydra:runtime.cwd}/logs/ARCADE/test/ + data: ${hydra:runtime.cwd}/data/ARCADE/ + results: ${hydra:runtime.cwd}/results/ARCADE/test/ +data: + features: + - X_train + response: + - y_train diff --git a/src/conf/cs/topological_0_metric_8.yaml b/src/conf/cs/topological_0_metric_8.yaml index 1530a75..d7e1ca0 100644 --- a/src/conf/cs/topological_0_metric_8.yaml +++ b/src/conf/cs/topological_0_metric_8.yaml @@ -1,8 +1,5 @@ defaults: - - models/mlr - - models/rf - - models/svr - - models/mlp + - models/mdn - _self_ experiments: COUPLED-C: diff --git a/src/config.py b/src/config.py index 8d9977c..f77f844 100644 --- a/src/config.py +++ b/src/config.py @@ -52,7 +52,17 @@ def main(config: DictConfig) -> None: experiment.save_train_test() experiment.save_manifest() experiment.run() - - + model = experiment._best_models['MDN'].model.model + test_points = [-1, 0, 1] + print("\nExample predictions at specific points:") + import torch + for x_val in test_points: + x = torch.tensor([[x_val]], dtype=torch.float32) + with torch.no_grad(): + pi, mu, sigma = model.forward(x) + print(f"\nx = {x_val}") + print(f"Mixing coefficients (π): {pi.numpy().flatten()}") + print(f"Means (μ): {mu.numpy().flatten()}") + print(f"Standard deviations (σ): {sigma.numpy().flatten()}") if __name__ == "__main__": main() diff --git a/src/config_utils/assign_models.py b/src/config_utils/assign_models.py index b949933..24addcf 100644 --- a/src/config_utils/assign_models.py +++ b/src/config_utils/assign_models.py @@ -4,10 +4,11 @@ from permutation.models.rf import RF from permutation.models.svr import SVReg from permutation.models.mlp import MLP +from permutation.models.mdn import MDNReg from permutation.models.modelprotocol import Model from permutation.models.hyperparameters import HParams -MODEL_DEPENDENCIES = {"mlr": MLR, "rf": RF, "svr": SVReg, "mlp": MLP} +MODEL_DEPENDENCIES = {"mlr": MLR, "rf": RF, "svr": SVReg, "mlp": MLP, "mdn": MDNReg} def _get_correct_model(model_type: str, hparams: HParams) -> Model: diff --git a/src/permutation/models/mdn.py b/src/permutation/models/mdn.py index 7b9e4c1..a65d587 100644 --- a/src/permutation/models/mdn.py +++ b/src/permutation/models/mdn.py @@ -1,42 +1,106 @@ from typing import Optional, Iterable -from ast import literal_eval +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim -from sklearn.neural_network import MLPRegressor -from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.base import TransformerMixin +from sklearn.metrics import r2_score from permutation.models.modelprotocol import Model from permutation.models.sklearnmodel import AbstractSKLearnModel from permutation.models.hyperparameters import HParams -class MLP(AbstractSKLearnModel): - """ - Multi-Layer Perceptron model +class MDN(nn.Module): + def __init__(self, hidden_dim=1, output_dim=1, num_gaussians=1, learning_rate=0.001, epochs=1000): + super(MDN, self).__init__() + self.hidden = nn.Sequential( + nn.LazyLinear(hidden_dim), + nn.ReLU(), + nn.Linear(hidden_dim, hidden_dim), + nn.ReLU(), + ) + self.pi_layer = nn.Linear(hidden_dim, num_gaussians) # Mixing coefficients + self.mu_layer = nn.Linear(hidden_dim, num_gaussians * output_dim) # Means + self.sigma_layer = nn.Linear(hidden_dim, num_gaussians * output_dim) # Std deviations + self.num_gaussians = num_gaussians + self.output_dim = output_dim + self.optimizer = optim.Adam(self.parameters(), lr=learning_rate) + self.epochs = epochs + + def forward(self, x): + hidden = self.hidden(x) + pi = torch.softmax(self.pi_layer(hidden), dim=1) # Mixing coefficients + mu = self.mu_layer(hidden).view(-1, self.num_gaussians, self.output_dim) # Means + sigma = torch.exp(self.sigma_layer(hidden)).view(-1, self.num_gaussians, self.output_dim) # Variances + return pi, mu, sigma + + def _mdn_loss(self, pi, mu, sigma, y): + """MDN negative log-likelihood loss""" + y = y.unsqueeze(1) if len(y.shape) == 2 else y.unsqueeze(1).unsqueeze(-1) + gaussian_prob = torch.exp(-0.5 * ((y - mu) / sigma) ** 2) / (sigma * np.sqrt(2 * np.pi)) + weighted_prob = pi * torch.prod(gaussian_prob, dim=2) + loss = -torch.log(torch.sum(weighted_prob, dim=1) + 1e-6).mean() # Avoid log(0) with 1e-6 + return loss + + + def fit(self, X, y): + """Train the MDN using PyTorch.""" + X = torch.tensor(X, dtype=torch.float32) + y = torch.tensor(y.to_numpy(), dtype=torch.float32) + + for _ in range(self.epochs): + self.train() + self.optimizer.zero_grad() + pi, mu, sigma = self.forward(X) + loss = self._mdn_loss(pi, mu, sigma, y) + loss.backward() + self.optimizer.step() - Methods - ------- - set_model(cls, model_dependency, hparams, preprocessing_dependencies): - Set up the model, preprocessing pipeline and read in hyperparameters + return self + + def predict(self, X): + """Predict using the trained MDN.""" + X = torch.tensor(X, dtype=torch.float32) if not isinstance(X, torch.Tensor) else X + self.eval() + with torch.no_grad(): + pi, mu, sigma = self.forward(X) + pi = pi.unsqueeze(-1) + predictions = torch.sum(pi * mu, dim=1) + return predictions.numpy() + + def score(self, X, y, sample_weight=None): + """Compute the R^2 score (default for regression in scikit-learn).""" + predictions = self.predict(X) # Call the predict method + return r2_score(y, predictions, sample_weight=sample_weight) # Use R^2 as the scoring metric + + +class MDNReg(AbstractSKLearnModel): + """ + Mixture Density Network Regressor compatible with scikit-learn. """ - algorithm_name = "Multi-layer Perceptron Regressor" - algorithm_abv = "MLP" + algorithm_name = "Mixture Density Network Regressor" + algorithm_abv = "MDN" algorithm_type = "Regression" + def __init__(self): + """ + """ + self.model = None + + @classmethod def set_model( cls, - model_dependency: BaseEstimator = MLPRegressor, + model_dependency: nn.Module = MDN, hparams: Optional[HParams] = None, preprocessing_dependencies: Optional[Iterable[tuple[str, TransformerMixin]]] = None, ) -> Model: - """Set up model from config files and super class""" + """Set up model from config files and superclass.""" if preprocessing_dependencies is None: preprocessing_dependencies = [] - - if hparams is not None: - _fix_tuple_type(hparams) - return super()._set_model( model_dependency=model_dependency, hparams=hparams, @@ -44,12 +108,4 @@ def set_model( ) -def _fix_tuple_type( - hparams: HParams, - change_arg: str = "hidden_layer_sizes", -) -> None: - """Makes sure config parameter value is evaluated as correct type""" - for i, arg_tuple in enumerate(zip(hparams.args, hparams.values)): - arg, val = arg_tuple - if arg == change_arg: - hparams.values[i] = literal_eval(val) + \ No newline at end of file diff --git a/src/permutation/models/sklearnmodel.py b/src/permutation/models/sklearnmodel.py index cb246e9..887362d 100644 --- a/src/permutation/models/sklearnmodel.py +++ b/src/permutation/models/sklearnmodel.py @@ -67,7 +67,6 @@ def _set_model( model.model = model_dependency() pipeline_list.append((model.algorithm_name, model.model)) # pylint: disable=no-member - model.pipeline = Pipeline(pipeline_list) return model