integrate_steps.py

# Integration of Curriculum Design, Implementation, and Geometric Analysis

import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset

class SyntheticDataset(Dataset):
    def __init__(self, num_samples, num_features):
        self.data = torch.randn(num_samples, num_features)
        self.targets = torch.randn(num_samples)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.targets[idx]


# 1. Initial Data & Model Fitting
# --------------------------------
# Dummy model simulating a regression model

class DummyModel(nn.Module):
    def __init__(self, coefficients):
        super(DummyModel, self).__init__()
        self.coefficients = nn.Parameter(torch.tensor(coefficients, dtype=torch.float32))

    def forward(self, data):
        return torch.matmul(data, self.coefficients)

    def jacobian(self, data):
        # Return data as is if it's already a NumPy array
        if isinstance(data, np.ndarray):
            return data
        # Convert PyTorch tensors to NumPy arrays
        return data.detach().numpy()


# Initialize the DummyModel
coefficients = [0.5, 1.2, -0.7, 0.3, 0.9, -1.1, 0.8, 0.6, -0.2, 0.4]
best_model = DummyModel(coefficients)
print("Using dummy best-fitting regression model for testing.")

# 2. Information Geometric Analysis
# -----------------------------------
# Implementation in `geometric-analysis.py` needs to connect with Fisher Information
from geometric_analysis import calculate_fisher_matrix, compute_geometric_structures

# Load data specific for Fisher Information Matrix calculation
def load_fMRI_data(path):
    # Dummy data loader for testing
    print(f"Loading fMRI data from {path}.")
    return np.random.rand(10, 10)  # Example 10x10 random data for testing

data = load_fMRI_data("path_to_data")

# Ensure calculate_fisher_matrix correctly handles linear and non-linear models
try:
    fisher_matrix = calculate_fisher_matrix(best_model, data)
    print("Fisher Information Matrix calculated for dummy model.")
except Exception as e:
    print(f"Error in Fisher Information Matrix calculation: {e}")
    raise

# Ensure compute_geometric_structures derives all geometric properties
try:
    geo_structures = compute_geometric_structures(fisher_matrix)
    if not all(key in geo_structures for key in ["metric_tensor", "christoffel_symbols", "riemann_tensor"]):
        raise ValueError("Missing required geometric properties in computed structures.")
    print("Calculated geometric structures:", geo_structures)
except Exception as e:
    print(f"Error in computing geometric structures: {e}")
    raise

# 3. Geometric Curriculum Design
# --------------------------------
# This step depends on geometric structures calculated earlier.
# Modify `curriculum-design.py` to incorporate critical points and optimal paths.
from curriculum_design import create_curriculum

curriculum = create_curriculum(geo_structures)
print("Curriculum design stages:", curriculum)

# 4. Implementation
# ------------------
# `curriculum-implementation.py` partially handles this.
# Integrate curriculum stages with the training framework.
from curriculum_implementation import setup_training_framework, train_with_curriculum

training_framework = setup_training_framework("framework_config.json")
training_results = train_with_curriculum(training_framework, curriculum)
print("Training results:", training_results)

# 5. Validation
# --------------
# Validation involves comparing outcomes, as outlined in `validation.py`.
from validation import validate_model

def load_baseline_results(path):
    # Dummy baseline results loader
    print(f"Loading baseline results from {path}.")
    return {
        "overall_metrics": {"loss": 1.0, "accuracy": 0.8},
        "stages": [
            {"stage_id": 1, "metrics": {"loss": 1.2, "accuracy": 0.75}},
            {"stage_id": 2, "metrics": {"loss": 1.0, "accuracy": 0.78}},
        ]
    }

# Load baseline results for comparison
try:
    baseline_results = load_baseline_results("path_to_baseline_results.json")
    validation_results = validate_model(training_results, baseline_results)
    print("Validation Results:", validation_results)
except Exception as e:
    print(f"Error in validation process: {e}")
    raise

# Ensure steps are logically linked to allow easy updates or debugging between steps.