From 4934a5792cc2aa1925ee93bc8f48c6208ae95e4e Mon Sep 17 00:00:00 2001 From: Wenbo Gong Date: Fri, 15 Mar 2024 15:50:18 +0000 Subject: [PATCH] Update causica version to 0.4.1 (#100) Update to "Update causica version to 0.4.1" Co-authored-by: Wenbo Gong --- Dockerfile | 11 +- poetry.lock | 2783 +++++++------ pyproject.toml | 2 +- research_experiments/scotch/README.md | 61 + research_experiments/scotch/poetry.lock | 3520 +++++++++++++++++ research_experiments/scotch/pyproject.toml | 20 + .../example_sdes/lorenz_sde.py | 99 + .../example_sdes/yeast_glycolysis.py | 96 + .../generate_and_save_data.py | 195 + .../generate_trajectories.py | 58 + .../scotch/src/scotch/experiments/dream3.py | 171 + .../scotch/src/scotch/experiments/lorenz.py | 167 + .../scotch/src/scotch/experiments/netsim.py | 148 + .../scotch/src/scotch/experiments/yeast.py | 165 + .../functional_relationship.py | 75 + .../latent_learning/graph_distribution.py | 120 + .../latent_learning/scotch_data_module.py | 130 + .../scotch/latent_learning/scotch_module.py | 439 ++ .../src/scotch/latent_learning/scotch_nns.py | 200 + .../src/scotch/scotch_utils/graph_metrics.py | 81 + .../src/scotch/scotch_utils/scotch_utils.py | 12 + .../scotch/src/scotch/sdes/__init__.py | 0 .../scotch/src/scotch/sdes/scotch_sdes.py | 187 + .../scotch/src/scotch/sdes/sde_modules.py | 60 + .../scotch/src/scotch/sdes/sdes_core.py | 45 + src/causica/data_generation/generate_data.py | 26 +- src/causica/lightning/callbacks.py | 16 +- .../data_modules/variable_spec_data.py | 32 +- src/causica/training/auglag.py | 128 +- test/data_generation/test_generate_data.py | 26 + test/lightning/test_variable_spec_data.py | 17 +- test/sem/test_sem.py | 43 +- test/training/test_auglag.py | 15 +- 33 files changed, 7681 insertions(+), 1467 deletions(-) create mode 100644 research_experiments/scotch/README.md create mode 100644 research_experiments/scotch/poetry.lock create mode 100644 research_experiments/scotch/pyproject.toml create mode 100644 research_experiments/scotch/src/scotch/dataset_generation/example_sdes/lorenz_sde.py create mode 100644 research_experiments/scotch/src/scotch/dataset_generation/example_sdes/yeast_glycolysis.py create mode 100644 research_experiments/scotch/src/scotch/dataset_generation/generate_and_save_data.py create mode 100644 research_experiments/scotch/src/scotch/dataset_generation/generate_trajectories.py create mode 100644 research_experiments/scotch/src/scotch/experiments/dream3.py create mode 100644 research_experiments/scotch/src/scotch/experiments/lorenz.py create mode 100644 research_experiments/scotch/src/scotch/experiments/netsim.py create mode 100644 research_experiments/scotch/src/scotch/experiments/yeast.py create mode 100644 research_experiments/scotch/src/scotch/latent_learning/functional_relationship.py create mode 100644 research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py create mode 100644 research_experiments/scotch/src/scotch/latent_learning/scotch_data_module.py create mode 100644 research_experiments/scotch/src/scotch/latent_learning/scotch_module.py create mode 100644 research_experiments/scotch/src/scotch/latent_learning/scotch_nns.py create mode 100644 research_experiments/scotch/src/scotch/scotch_utils/graph_metrics.py create mode 100644 research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py create mode 100644 research_experiments/scotch/src/scotch/sdes/__init__.py create mode 100644 research_experiments/scotch/src/scotch/sdes/scotch_sdes.py create mode 100644 research_experiments/scotch/src/scotch/sdes/sde_modules.py create mode 100644 research_experiments/scotch/src/scotch/sdes/sdes_core.py diff --git a/Dockerfile b/Dockerfile index 9834aa5..528fe6d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -9,22 +9,19 @@ RUN conda install anaconda-clean && \ rm -rf /opt/miniconda RUN apt-get update && \ - apt-get install -y graphviz-dev python3-dev && \ + apt-get install -y graphviz-dev python3-dev python3-pip && \ apt-get clean -y && \ rm -rf /var/lib/apt/lists/* && \ ln -s /usr/bin/python3 /usr/bin/python && \ python -c 'import sys; assert sys.version_info[:2] == (3, 10)' - -# Install Poetry to default env (conda) ENV POETRY_CACHE_DIR="/root/.cache/pypoetry" \ - POETRY_HOME="/root/.local/share/pypoetry" \ POETRY_NO_INTERACTION=1 \ POETRY_VIRTUALENVS_CREATE=false \ POETRY_VIRTUALENVS_IN_PROJECT=false \ - POETRY_VERSION=1.7.1 -ENV PATH="$PATH:$POETRY_HOME/bin" -RUN curl -sSL https://install.python-poetry.org | python3 - + POETRY_VERSION=1.7.1 +RUN python -m pip install -U pip setuptools wheel +RUN python -m pip install poetry==$POETRY_VERSION # Install dependencies separately to allow dependency caching # Note: Temporarily create dummy content to allow installing the dev dependencies. diff --git a/poetry.lock b/poetry.lock index 206632d..5838d48 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,98 +2,98 @@ [[package]] name = "absl-py" 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mode 100644 index 0000000..fdf90bd --- /dev/null +++ b/research_experiments/scotch/README.md @@ -0,0 +1,61 @@ +# Neural Structure Learning with Stochastic Differential Equations (SCOTCH) +[![Static Badge](https://img.shields.io/badge/paper-SCOTCH-brightgreen?style=plastic&label=Paper&labelColor=violet) +](https://openreview.net/forum?id=V1GM9xDvIY) +[![Static Badge](https://img.shields.io/badge/Team-Causica-blue?style=plastic&label=Team&labelColor=purple) +]((https://www.microsoft.com/en-us/research/project/project_azua/people/)) + +This repo implements the SCOTCH proposed in the ICLR 2024 paper "Neural Structure Learning with Stochastic Differential Equations". + +SCOTCH is a structure learning method using neural stochastic differential equations (SDEs) for temporal data. SCOTCH, designed for the continuous-time processes, outperforms traditional discrete-time models, and is compatible with irregular sampling intervals. SCOTCH combines neural SDEs with variational inference over structures with theoretical guarantees regarding structure identifiability, establishing a new standard for structure learning. + +## Dependency +We use [Poetry](https://python-poetry.org/) to manage the project dependencies, they are specified in [pyproject](pyproject.toml) file. To install poetry, run: + +```console + curl -sSL https://install.python-poetry.org | python3 - +``` +To install the environment, run `poetry install` in the directory of SCOTCH project. + +## Prepare the data +To reproduce the experiment results in the [paper](https://openreview.net/forum?id=V1GM9xDvIY), you need to either generate the synthetic data (Lorenz and Yeast glycolysis dataset) or download the raw data and process them (DREAM3 and Netsim dataset). +### Lorenz and Yeast data generation +To generate the synthetic data, run the following command: +```console + python -m scotch.dataset_generation.generate_and_save_data +``` +This executes the [generate_and_save_data.py](src/scotch/dataset_generation/generate_and_save_data.py) to generate Lorenz and Yeast datasets with 5 seeds, both normalized and unnormalized data, different sub-sampling rates and different missing data probabilities to mimic irregular sampling intervals. The generated data will be saved in the `./data/lorenz96_processed`. + +### DREAM3 +We use the DREAM3 dataset from [DREAM challenge](https://gnw.sourceforge.net/dreamchallenge.html#dream3challenge). One can download the zip file and extract the content to the `./data`. + +### Netsim +One can download the Netsim dataset from [Netsim](https://www.fmrib.ox.ac.uk/datasets/netsim/), and unzip the file to the `./data`. Then, specify the path to the `sim3.mat` in the file [generate_and_save_data.py](src/scotch/dataset_generation/generate_and_save_data.py#L59) and set `gen_netsim_data = True`. Run the following command to process the data: +```console + python -m scotch.dataset_generation.generate_and_save_data +``` +The processed data will be saved in `./data/netsim_processed`. If this already exists, it will override the data in `./data/netsim_processed`. + +## Run experiments +In the [src/scotch/experiments](src/scotch/experiments/) directory, we provide scripts to run the experiments for each datasets reported in the paper (Lorenz, Yeast, DREAM3 and Netsim). + +For `Ecoli1` dataset of DREAM3, run the following command: +```console + python -m scotch.experiments.dream3 --dimension 100 --name Ecoli1 --epoch 40000 --lr 0.001 --sparsity 200 --dt 0.05 --seed 0 --normalize --experiment_name Ecoli1_exp --deci_diffusion --res_connection --sigmoid_output --lr_warmup 100 +``` +For other DREAM3 datasets, please refer to paper for hyperparameters. + +For `Netsim` dataset, run the following command: +```console + python -m scotch.experiments.netsim --epoch 20000 --lr 0.001 --sparsity 1000 --dt 0.05 --seed 0 --res_connection --deci_diffusion --lr_warmup 100 --sigmoid_output --missing_prob 0.1 --experiment_name Netsim_missing_0.1 +``` + +For `Lorenz` dataset, run the following command: +```console + python -m scotch.experiments.lorenz --epoch 40000 --lr 0.003 --sparsity 500 --dt 1 --seed 0 --experiment_name Lorenz_missing_0.3 --res_connection --deci_diffusion --lr_warmup 100 --sigmoid_output --missing_prob 0.3 --num_time_points 100 --train_size 10 +``` + +For `Yeast` dataset, run the following command: +```console + python -m scotch.experiments.yeast --epoch 40000 --lr 0.001 --sparsity 200 --dt 1 --seed 0 --experiment_name Yeast_exp --res_connection --deci_diffusion --lr_warmup 100 --sigmoid_output --missing_prob 0.0 --num_time_points 100 --train_size 10 --normalize +``` + diff --git a/research_experiments/scotch/poetry.lock b/research_experiments/scotch/poetry.lock new file mode 100644 index 0000000..2d76dce --- /dev/null +++ b/research_experiments/scotch/poetry.lock @@ -0,0 +1,3520 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "absl-py" +version = "2.1.0" +description = 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+license = "MIT" + +[tool.poetry.dependencies] +python = "~3.10" +causica = "0.3.4" +torchsde = "0.2.5" +isort = "^5.10.1" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/lorenz_sde.py b/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/lorenz_sde.py new file mode 100644 index 0000000..a00a064 --- /dev/null +++ b/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/lorenz_sde.py @@ -0,0 +1,99 @@ +import torch +from scotch.sdes.sdes_core import SDE + + +class LorenzSDE(SDE): + """Implementation of Lorenz System of SDEs. + + System of SDE consisting of the Lorenz system of ODEs for the drift coefficient and identity diffusion coefficient. + + Attributes: + noise_type: type of SDE noise, required for BaseSDE; always "diagonal" for Lorenz + sde_type: type of SDE, required for BaseSDE; always "ito" for Lorenz + sigma: Lorenz parameter sigma + rho: Lorenz parameter rho + beta: Lorenz parameter beta + + Methods: + f: drift coefficient for Lorenz system + g: diffusion coefficient for Lorenz system + """ + + noise_type = "diagonal" + sde_type = "ito" + + @staticmethod + def graph() -> torch.Tensor: + return torch.tensor([[1, 1, 1], [1, 1, 1], [0, 1, 1]], dtype=torch.long) + + def __init__(self, sigma: float = 10.0, rho: float = 8.0 / 3, beta: float = 28.0, noise_scale: float = 1.0): + super().__init__() + self.sigma = sigma + self.rho = rho + self.beta = beta + self.noise_scale = noise_scale + + def f(self, t: float, y: torch.Tensor) -> torch.Tensor: + _ = t + dxdt = self.sigma * (y[:, 1] - y[:, 0]) + dydt = y[:, 0] * (self.rho - y[:, 2]) - y[:, 1] + dzdt = y[:, 0] * y[:, 1] - self.beta * y[:, 2] + return torch.stack([dxdt, dydt, dzdt], dim=1) + + def g(self, t: float, y: torch.Tensor) -> torch.Tensor: + _ = t + return self.noise_scale * torch.ones_like(y) + + +class Lorenz96SDE(SDE): + """Implementation of Lorenz System of SDEs. + + System of SDE consisting of the Lorenz 96 system of ODEs for the drift coefficient and identity diffusion + coefficient. The Lorenz 96 system of SDEs is given by (for i = 0, ... N-1): + + dx_i = ((x_{i+1} - x_{i-2}) * x_{i-1} - x_i + F) dt + noise_scale * dw_i + + where the indices are taken modulo N. + + Attributes: + noise_type: type of SDE noise, required for BaseSDE; always "diagonal" for Lorenz + sde_type: type of SDE, required for BaseSDE; always "ito" for Lorenz + F: Lorenz 96 parameter F + noise_scale: diffusion coefficient constant + + Methods: + f: drift coefficient for Lorenz system + g: diffusion coefficient for Lorenz system + """ + + noise_type = "diagonal" + sde_type = "ito" + + @staticmethod + def graph(state_size: int) -> torch.Tensor: + """Returns the graph of the Lorenz 96 system of SDEs. + + Args: + state_size: number of variables in the system + """ + vec = torch.zeros(state_size, dtype=torch.long) + vec[1] = 1 + vec[-2] = 1 + vec[-1] = 1 + vec[0] = 1 + + vecs = [torch.roll(vec, i, 0) for i in range(state_size)] + return torch.stack(vecs, dim=1) # stack along column dimension + + def __init__(self, F: float, noise_scale: float): + super().__init__() + self.F = F + self.noise_scale = noise_scale + + def f(self, t: float, y: torch.Tensor) -> torch.Tensor: + _ = t + return (torch.roll(y, -1, 1) - torch.roll(y, 2, 1)) * torch.roll(y, 1, 1) - y + self.F + + def g(self, t: float, y: torch.Tensor) -> torch.Tensor: + _ = t + return self.noise_scale * torch.ones_like(y) diff --git a/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/yeast_glycolysis.py b/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/yeast_glycolysis.py new file mode 100644 index 0000000..6a4365c --- /dev/null +++ b/research_experiments/scotch/src/scotch/dataset_generation/example_sdes/yeast_glycolysis.py @@ -0,0 +1,96 @@ +import torch +from scotch.sdes.sdes_core import SDE + + +class YeastGlycolysisSDE(SDE): + """Implementation of yeast glycolysis SDE (Daniels & Nemenman 2015, Bellot et al. 2022); see paper for more details. + + Attributes: + noise_type: type of SDE noise, required for BaseSDE; always "diagonal" for glycolysis + sde_type: type of SDE, required for BaseSDE; always "ito" for glycolysis + noise_scale: diffusion coefficient constant + others: parameters for yeast glycolysis SDE + + Methods: + f: drift coefficient for glycolysis system + g: diffusion coefficient for glycolysis system + """ + + noise_type = "diagonal" + sde_type = "ito" + + def __init__( + self, + noise_scale, + k1=0.52, + K1=100, + K2=6, + K3=16, + K4=100, + K5=1.28, + K6=12, + K=1.8, + kappa=13, + phi=0.1, + q=4, + A=4, + N=1, + J0=2.5, + ): + super().__init__() + self.noise_scale = noise_scale + self.k1 = k1 + self.K1 = K1 + self.K2 = K2 + self.K3 = K3 + self.K4 = K4 + self.K5 = K5 + self.K6 = K6 + self.K = K + self.kappa = kappa + self.phi = phi + self.q = q + self.A = A + self.N = N + self.J0 = J0 + + @staticmethod + def graph(): + return torch.tensor( + [ + [1, 1, 0, 0, 0, 1, 0], + [0, 1, 1, 0, 1, 0, 0], + [0, 0, 1, 1, 0, 1, 0], + [0, 0, 0, 1, 1, 0, 1], + [0, 1, 1, 1, 1, 0, 0], + [1, 1, 1, 1, 0, 1, 0], + [0, 0, 0, 1, 0, 0, 1], + ], + dtype=torch.long, + ) + + def f(self, t, y): + _ = t + dydt = torch.zeros_like(y) + dydt[:, 0] = self.J0 - (self.K1 * y[:, 0] * y[:, 5]) / (1 + (y[:, 5] / self.k1) ** self.q) + dydt[:, 1] = ( + (2 * self.K1 * y[:, 0] * y[:, 5]) / (1 + (y[:, 5] / self.k1) ** self.q) + - self.K2 * y[:, 1] * (self.N - y[:, 4]) + - self.K6 * y[:, 1] * y[:, 4] + ) + dydt[:, 2] = self.K2 * y[:, 1] * (self.N - y[:, 4]) - self.K3 * y[:, 2] * (self.A - y[:, 5]) + dydt[:, 3] = ( + self.K3 * y[:, 2] * (self.A - y[:, 5]) - self.K4 * y[:, 3] * y[:, 4] - self.kappa * (y[:, 3] - y[:, 6]) + ) + dydt[:, 4] = self.K2 * y[:, 1] * (self.N - y[:, 4]) - self.K4 * y[:, 3] * y[:, 4] - self.K6 * y[:, 1] * y[:, 4] + dydt[:, 5] = ( + (-2 * self.K1 * y[:, 0] * y[:, 5]) / (1 + (y[:, 5] / self.k1) ** self.q) + + 2 * self.K3 * y[:, 2] * (self.A - y[:, 5]) + - self.K5 * y[:, 5] + ) + dydt[:, 6] = self.phi * self.kappa * (y[:, 3] - y[:, 6]) - self.K * y[:, 6] + return dydt + + def g(self, t, y): + _ = t + return self.noise_scale * torch.ones_like(y) diff --git a/research_experiments/scotch/src/scotch/dataset_generation/generate_and_save_data.py b/research_experiments/scotch/src/scotch/dataset_generation/generate_and_save_data.py new file mode 100644 index 0000000..ce2aff5 --- /dev/null +++ b/research_experiments/scotch/src/scotch/dataset_generation/generate_and_save_data.py @@ -0,0 +1,195 @@ +"""This script is to generate synthetic dataset for SCOTCH experiments.""" +import os +import sys +from argparse import Namespace + +import pandas as pd +import torch +from scipy.io import loadmat +from scotch.dataset_generation.example_sdes.lorenz_sde import Lorenz96SDE +from scotch.dataset_generation.example_sdes.yeast_glycolysis import YeastGlycolysisSDE +from scotch.dataset_generation.generate_trajectories import generate_and_return_trajectories + + +def subsample(ts, data, samp_every): + subsamp_ts = ts[::samp_every] + subsamp_data = data[:, ::samp_every, :] + return subsamp_ts, subsamp_data + + +def generate_missing_data(ts, data, missing_probability): + mask = torch.rand(data.shape[1]) > missing_probability + missing_ts = ts[mask] + missing_data = data[:, mask, :] + return missing_ts, missing_data + + +def load_data_from_file(arguments): + match arguments.dataset: + case "lorenz": + z0_train = torch.randn(size=(arguments.train_size, arguments.dimension), device="cuda") + ts, training_data, _ = generate_and_return_trajectories( + Lorenz96SDE, + z0_train, + num_time_points=arguments.num_time_points, + t_max=arguments.t_max, + noise_scale=arguments.noise_scale, + F=arguments.forcing, + return_raw=True, + dt=0.005, + ) + case "yeast": + + variable_ranges = torch.tensor( + [[0.15, 1.60], [0.19, 2.16], [0.04, 0.20], [0.10, 0.35], [0.08, 0.30], [0.14, 2.67], [0.05, 0.10]], + device="cuda", + ) + unif_samples = torch.rand(size=(arguments.train_size, 7), device="cuda") + z0_train = variable_ranges[:, 0] + (variable_ranges[:, 1] - variable_ranges[:, 0]) * unif_samples + ts, training_data, _ = generate_and_return_trajectories( + YeastGlycolysisSDE, + z0_train, + num_time_points=arguments.num_time_points, + t_max=arguments.t_max, + noise_scale=arguments.noise_scale, + return_raw=True, + dt=0.01, + ) + case "netsim": + training_data_dict = loadmat("") + ts = torch.linspace(0, 10, 200) + training_data = torch.tensor(training_data_dict["ts"], device="cuda").view(50, 200, 15)[1:6, :, :] + + return ts, training_data + + +def load_graph_from_file(arguments): + match arguments.dataset: + case "netsim": + training_data_dict = loadmat("") + true_graph = (torch.tensor(training_data_dict["net"], device="cuda")[0] != 0).long() + case "lorenz": + true_graph = Lorenz96SDE.graph(arguments.dimension).to("cuda") + case "yeast": + true_graph = YeastGlycolysisSDE.graph().to("cuda") + + return true_graph + + +def save_data_and_graph_to_file(arguments, ts, training_data, true_graph): + match arguments.dataset: + case "netsim": + subf = "norm" if arguments.normalize else "unnorm" + os.makedirs(f"data/netsim_processed_new/{subf}", exist_ok=True) + torch.save(ts, f"data/netsim_processed_new/{subf}/times_{str(arguments.missing_prob)}_{arguments.seed}.pt") + torch.save( + training_data, + f"data/netsim_processed_new/{subf}/data_{str(arguments.missing_prob)}_{arguments.seed}.pt", + ) + torch.save(true_graph, f"data/netsim_processed_new/{subf}/true_graph.pt") + case "lorenz": + subf = "norm" if arguments.normalize else "unnorm" + os.makedirs(f"data/lorenz96_processed/{arguments.dimension}/{subf}", exist_ok=True) + torch.save( + ts, + f"data/lorenz96_processed/{arguments.dimension}/{subf}/times_{arguments.num_subsamp}_{str(arguments.missing_prob)}_{arguments.seed}.pt", + ) + torch.save( + training_data, + f"data/lorenz96_processed/{arguments.dimension}/{subf}/data_{arguments.num_subsamp}_{str(arguments.missing_prob)}_{arguments.seed}.pt", + ) + torch.save(true_graph, f"data/lorenz96_processed/{arguments.dimension}/{subf}/true_graph.pt") + case "yeast": + subf = "norm" if arguments.normalize else "unnorm" + os.makedirs(f"data/yeast_processed/{arguments.dimension}/{subf}", exist_ok=True) + torch.save( + ts, + f"data/yeast_processed/{arguments.dimension}/{subf}/times_{arguments.num_subsamp}_{str(arguments.missing_prob)}_{arguments.seed}.pt", + ) + torch.save( + training_data, + f"data/yeast_processed/{arguments.dimension}/{subf}/data_{arguments.num_subsamp}_{str(arguments.missing_prob)}_{arguments.seed}.pt", + ) + torch.save(true_graph, f"data/yeast_processed/{arguments.dimension}/{subf}/true_graph.pt") + + +def generate_data_standard(arguments, save_data=False): + torch.manual_seed(arguments.seed) + + ts, training_data = load_data_from_file(arguments) + _, num_time_points, _ = training_data.shape + true_graph = load_graph_from_file(arguments) + + training_data = ( + (training_data - training_data.mean(dim=(0, 1))) / training_data.std(dim=(0, 1)) + if arguments.normalize + else training_data + ) + + # create missing data + if arguments.dataset in ("lorenz", "yeast"): + ts, training_data = subsample(ts, training_data, num_time_points // arguments.num_subsamp) + + ts, training_data = generate_missing_data(ts, training_data, arguments.missing_prob) + + if save_data: + save_data_and_graph_to_file(arguments, ts, training_data, true_graph) + + return ts, training_data, true_graph + + +if __name__ == "__main__": + sys.setrecursionlimit(2000) + gen_netsim_data = False + print("Generating Yeast Glycolysis Dataset") + for seed in [0, 1, 2, 3, 4]: + for normalize in [False, True]: + for num_subsamp in [100, 200, 500, 1000]: + for missing_prob in [0.0, 0.1, 0.2, 0.5]: + print( + f"Seed: {seed}, Normalize: {normalize}, Num Subsamp: {num_subsamp}, Missing Prob: {missing_prob}" + ) + args = Namespace( + normalize=normalize, + dataset="yeast", + missing_prob=missing_prob, + seed=seed, + train_size=100, + num_time_points=1000, + t_max=100.0, + noise_scale=0.01, + dimension=7, + num_subsamp=num_subsamp, + ) + generate_data_standard(args, save_data=True) + + print("Generating Lorenz Dataset") + for seed in [0, 1, 2, 3, 4]: + for normalize in [False, True]: + for num_subsamp in [1000, 500, 200, 100]: + for missing_prob in [0.0, 0.3, 0.6]: + print( + f"Seed: {seed}, Normalize: {normalize}, Num Subsamp: {num_subsamp}, Missing Prob: {missing_prob}" + ) + args = Namespace( + normalize=normalize, + dataset="lorenz", + missing_prob=missing_prob, + seed=seed, + forcing=10, + train_size=100, + num_time_points=1000, + t_max=100.0, + noise_scale=0.5, + dimension=10, + num_subsamp=num_subsamp, + ) + generate_data_standard(args, save_data=True) + if gen_netsim_data: + print("Generating Netsim Dataset") + for seed in [0, 1, 2, 3, 4]: + for normalize in [False, True]: + for missing_prob in [0.0, 0.1, 0.2]: + print(f"Seed: {seed}, Normalize: {normalize}, Missing Prob: {missing_prob}") + args = Namespace(normalize=normalize, dataset="netsim", missing_prob=missing_prob, seed=seed) + generate_data_standard(args, save_data=True) diff --git a/research_experiments/scotch/src/scotch/dataset_generation/generate_trajectories.py b/research_experiments/scotch/src/scotch/dataset_generation/generate_trajectories.py new file mode 100644 index 0000000..a6bafbd --- /dev/null +++ b/research_experiments/scotch/src/scotch/dataset_generation/generate_trajectories.py @@ -0,0 +1,58 @@ +from typing import Tuple + +import torch +import torchsde +from scotch.sdes.sdes_core import SDE +from tensordict import TensorDict +from torch import Tensor + + +def generate_and_return_trajectories( + sde_class: SDE, + z0: Tensor, + num_time_points: int, + t_max: float, + device: str = "cuda", + normalize: bool = False, + dt: float = 1e-3, + return_raw: bool = False, + **kwargs, +) -> Tuple[Tensor, TensorDict, torchsde.BrownianInterval]: + """Generate synthetic trajectories. + z0 = torch.full(size=(n, state_size), fill_value=0.0, device=device) + Args: + sde_class: SDE class to generate trajectories for. + z0: Tensor of shape (n, state_size) of initial points. + num_time_points: Number of time points to generate for each trajectory. + t_max: Maximum time point to generate for each trajectory. + return_bm: Whether to return the Brownian motion used to generate the trajectories. + device: Device to generate trajectories on. + normalize: Whether to normalize the trajectories per variable. + dt: Time step to use for SDE integration. + return_raw: Whether to return the raw trajectories or TensorDict version. + **kwargs: Any additional arguments to pass to the SDE class. + + Returns: + ts: Time points of generated trajectories; shape (num_time_points,). + zs_td: TensorDict of generated trajectories. + bm: Brownian motion used to generate trajectories; returned if return_bm. + """ + n, state_size = z0.shape + + ts = torch.linspace(0, t_max, num_time_points) + bm = torchsde.BrownianInterval( + t0=0.0, t1=t_max, size=(n, state_size), levy_area_approximation="space-time", device=device + ) + zs = torchsde.sdeint(sde_class(**kwargs), z0, ts, bm=bm, dt=dt, method="euler") # (t_size, batch_size, state_size) + zs = zs.permute(1, 0, 2) # reshape into format (batch_size, t_size, state_size) + print(zs.shape) + zs = (zs - zs.mean(dim=(0, 1))) / zs.std(dim=(0, 1)) if normalize else zs + + zs_td = TensorDict( + {f"x{i}": zs[:, :, i].unsqueeze(dim=2) for i in range(state_size)}, + batch_size=[n], + ) + + if return_raw: + return ts, zs, bm + return ts, zs_td, bm diff --git a/research_experiments/scotch/src/scotch/experiments/dream3.py b/research_experiments/scotch/src/scotch/experiments/dream3.py new file mode 100644 index 0000000..5788c74 --- /dev/null +++ b/research_experiments/scotch/src/scotch/experiments/dream3.py @@ -0,0 +1,171 @@ +import argparse + +import pandas as pd +import pytorch_lightning as pl +import torch +from pytorch_lightning import seed_everything +from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar +from pytorch_lightning.loggers import MLFlowLogger +from scotch.latent_learning.scotch_data_module import SCOTCHDataModule +from scotch.latent_learning.scotch_module import SCOTCHModule +from tensordict import TensorDict + +from causica.datasets.causica_dataset_format import Variable, VariablesMetadata +from causica.datasets.variable_types import VariableTypeEnum + +if __name__ == "__main__": + + parser = argparse.ArgumentParser(description="Run DREAM3 experiments.") + parser.add_argument("-d", "--dimension", type=int, help="dimension of dream3 dataset (10, 50, or 100)") + parser.add_argument("-n", "--name", type=str, help="name of the dataset (Yeast1, Yeast2, Yeast3, Ecoli1, Ecoli2)") + parser.add_argument("-e", "--epoch", type=int, help="max number of epochs", default=20000) + parser.add_argument("-l", "--lr", type=float, help="learning rate", default=3e-3) + parser.add_argument("-s", "--sparsity", type=float, help="sparsity penalty", default=500) + parser.add_argument("-t", "--dt", type=float, help="dt", default=0.05) + parser.add_argument("-nor", "--normalize", action="store_true", help="whether to normalize") + parser.add_argument("-sd", "--seed", type=int, help="random seed", required=True) + parser.add_argument("-en", "--experiment_name", type=str, help="experiment name", required=True) + parser.add_argument("-res", "--res_connection", action="store_true", help="whether to use res_connection") + parser.add_argument("-ln", "--layer_norm", action="store_true", help="whether to use layer_norm") + parser.add_argument("-warm", "--lr_warmup", type=int, default=10000, help="warmup epochs") + parser.add_argument("-deci", "--deci_diffusion", action="store_true", help="whether to use deci diffusion function") + parser.add_argument( + "-sig", + "--sigmoid_output", + action="store_true", + help="whether to use sigmoid output for deci diffusion function", + ) + + args = parser.parse_args() + # set seed + seed_everything(args.seed) + + # HParams + experiment_name = args.experiment_name + max_epochs = args.epoch + default_lr = args.lr + lrs = { + "graph": default_lr, # changed from 1e-2 + "qz0_mean_net": default_lr, + "qz0_logstd_net": default_lr, + "pz0_mean": default_lr, + "pz0_logstd": default_lr, + "prior_drift_fn": default_lr, + "diffusion_fn": default_lr, + "posterior_drift_fn": default_lr, + "trajectory_encoder": default_lr, + } + prior_sparsity_lambda = args.sparsity + + t_max = 1.05 + num_time_points = 21 + + dt = args.dt # sde solver dt = observation interval + normalize = args.normalize + lr_warmup_iters = 100 + res_connection = args.res_connection + layer_norm = args.layer_norm + deci_diffusion = args.deci_diffusion + sigmoid_output = args.sigmoid_output + + hparams = { + "seed": args.seed, + "epoch": args.epoch, + "normalize": args.normalize, + "prior_sparsity_lambda": prior_sparsity_lambda, + "t_max": t_max, + "num_time_points": num_time_points, + "dt": dt, + "default_lr": default_lr, + "lr_warmup_iters": lr_warmup_iters, + "res_connection": res_connection, + "layer_norm": layer_norm, + "deci_diffusion": deci_diffusion, + "sigmoid_output": sigmoid_output, + } + + # DREAM3 + state_size = args.dimension + variables_metadata = VariablesMetadata( + [Variable(name=f"x{i}", type=VariableTypeEnum.CONTINUOUS, group_name=f"x{i}") for i in range(state_size)] + ) + + ts = torch.linspace(0, t_max, num_time_points) + training_data_df = pd.read_csv( + f"data/DREAM3 in silico challenge/Size{args.dimension}/DREAM3 data/InSilicoSize{args.dimension}-{args.name}-trajectories.tsv", + sep="\t", + ).drop("Time", axis=1) + + training_data = torch.tensor(training_data_df.values, device="cuda").view( + -1, num_time_points, state_size + ) # (batch, t, vars) + + training_data = ( + (training_data - training_data.mean(dim=(0, 1))) / training_data.std(dim=(0, 1)) if normalize else training_data + ) + + training_data = TensorDict( + {f"x{i}": training_data[:, :, i].unsqueeze(dim=2) for i in range(state_size)}, + batch_size=[training_data.shape[0]], + ) + + edges_df = pd.read_csv( + f"data/DREAM3 in silico challenge/Size{args.dimension}/Networks/InSilicoSize{args.dimension}-{args.name}.tsv", + sep="\t", + header=None, + ).drop(2, axis=1) + true_graph = torch.zeros((state_size, state_size), dtype=torch.int64, device="cuda") + + edges_df = edges_df.applymap(lambda x: int(x[1:])) + edges = torch.tensor(edges_df.values, device="cuda") + + for edge in edges: + true_graph[edge[0] - 1, edge[1] - 1] = 1 # 0-indexed + + validation_data = training_data + ##### + + scotch_data = SCOTCHDataModule( + ts=ts, + training_data=training_data, + validation_data=validation_data, + true_graph=true_graph, + variables_metadata=variables_metadata, + batch_size=1024, + ) + + # SCOTCH Module + scotch = SCOTCHModule( + learning_rates=lrs, + prior_sparsity_lambda=prior_sparsity_lambda, + dt=dt, + record_graph_logits=args.dimension < 20, + lr_warmup_iters=lr_warmup_iters, + ignore_self_connections=True, + res_connections=res_connection, + layer_norm=layer_norm, + deci_diffusion=deci_diffusion, + add_diffusion_self_connections=True, + sigmoid_output=sigmoid_output, + ) + + mlf_logger = MLFlowLogger( + experiment_name=experiment_name, + tracking_uri="file:./mlflow_logs/mlruns", + ) + + mlf_logger.log_hyperparams(hparams) + + trainer = pl.Trainer( + accelerator="gpu", + max_epochs=max_epochs, + fast_dev_run=False, + callbacks=[ + TQDMProgressBar(refresh_rate=19), + ModelCheckpoint(every_n_epochs=500, dirpath="./outputs", save_last=True), + ], + check_val_every_n_epoch=50, + logger=mlf_logger, + ) + + trainer.fit(scotch, datamodule=scotch_data) diff --git a/research_experiments/scotch/src/scotch/experiments/lorenz.py b/research_experiments/scotch/src/scotch/experiments/lorenz.py new file mode 100644 index 0000000..cb11ef5 --- /dev/null +++ b/research_experiments/scotch/src/scotch/experiments/lorenz.py @@ -0,0 +1,167 @@ +"""This scripts is for Lorenz experiments.""" +import argparse +import os + +import pytorch_lightning as pl +import torch +from pytorch_lightning import seed_everything +from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar +from pytorch_lightning.loggers import MLFlowLogger +from scotch.latent_learning.scotch_data_module import SCOTCHDataModule +from scotch.latent_learning.scotch_module import SCOTCHModule +from tensordict import TensorDict + +from causica.datasets.causica_dataset_format import Variable, VariablesMetadata +from causica.datasets.variable_types import VariableTypeEnum + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run Lorenz experiments.") + parser.add_argument("-e", "--epoch", type=int, help="max number of epochs", default=5000) + parser.add_argument("-l", "--lr", type=float, help="learning rate", default=3e-3) + parser.add_argument("-s", "--sparsity", type=float, help="sparsity penalty", default=500) + parser.add_argument("-t", "--dt", type=float, help="dt", default=1) + parser.add_argument("-nor", "--normalize", action="store_true", help="whether to normalize") + parser.add_argument("-sd", "--seed", type=int, help="random seed", required=True) + parser.add_argument("-en", "--experiment_name", type=str, help="experiment name", required=True) + + parser.add_argument("-res", "--res_connection", action="store_true", help="whether to use res_connection") + parser.add_argument("-ln", "--layer_norm", action="store_true", help="whether to use layer_norm") + parser.add_argument("-warm", "--lr_warmup", type=int, default=100, help="warmup epochs") + parser.add_argument("-deci", "--deci_diffusion", action="store_true", help="whether to use deci diffusion function") + parser.add_argument( + "-sig", + "--sigmoid_output", + action="store_true", + help="whether to use sigmoid output for deci diffusion function", + ) + # ADDED + parser.add_argument("-p", "--missing_prob", type=float, help="missing probability", default=0.3) + # Specific to lorenz + parser.add_argument("-d", "--dimension", type=int, help="dimension of lorenz dataset", default=10) + + parser.add_argument( + "-Nt", "--num_time_points", type=int, help="number of time points, must be 100, 200, 500, or 1000", default=100 + ) + parser.add_argument("-tmax", "--t_max", type=float, help="max time", default=100) + parser.add_argument( + "-Ns", "--train_size", type=int, help="number of training time series, must be <= 100", default=10 + ) + + args = parser.parse_args() + + seed_everything(args.seed) + # HParams + experiment_name = args.experiment_name + max_epochs = args.epoch + default_lr = args.lr + res_connection = args.res_connection + layer_norm = args.layer_norm + deci_diffusion = args.deci_diffusion + sigmoid_output = args.sigmoid_output + missing_prob = args.missing_prob + num_time_points = args.num_time_points + t_max = args.t_max + train_size = args.train_size + lrs = { + "graph": default_lr, # changed from 1e-2 + "qz0_mean_net": default_lr, + "qz0_logstd_net": default_lr, + "pz0_mean": default_lr, + "pz0_logstd": default_lr, + "prior_drift_fn": default_lr, + "diffusion_fn": default_lr, + "posterior_drift_fn": default_lr, + "trajectory_encoder": default_lr, + } + prior_sparsity_lambda = args.sparsity + val_size = -1 + dt = args.dt # sde solver dt = observation interval + + normalize = args.normalize + lr_warmup_iters = args.lr_warmup + + layer_norm = False + + hparams = { + "seed": args.seed, + "epoch": args.epoch, + "dt": args.dt, + "default_lr": default_lr, + "train_size": train_size, + "val_size": val_size, + "prior_sparsity_lambda": prior_sparsity_lambda, + "t_max": t_max, + "num_time_points": num_time_points, + "normalize": normalize, + "lr_warmup_iters": lr_warmup_iters, + "res_connection": res_connection, + "layer_rnorm": layer_norm, + "deci_diffusion": deci_diffusion, + "sigmoid_output": sigmoid_output, + "missing_prob": missing_prob, + "dimension": args.dimension, + } + + # lorenz + state_size = args.dimension + variables_metadata = VariablesMetadata( + [Variable(name=f"x{i}", type=VariableTypeEnum.CONTINUOUS, group_name=f"x{i}") for i in range(state_size)] + ) + + subf = "norm" if args.normalize else "unnorm" + ts = torch.load( + f"data/lorenz96_processed/{args.dimension}/{subf}/times_{args.num_time_points}_{str(args.missing_prob)}_{args.seed}.pt" + ) + training_data = torch.load( + f"data/lorenz96_processed/{args.dimension}/{subf}/data_{args.num_time_points}_{str(args.missing_prob)}_{args.seed}.pt" + ) + true_graph = torch.load(f"data/lorenz96_processed/{args.dimension}/{subf}/true_graph.pt").to("cuda") + + training_data = training_data[: args.train_size, :, :] # reduce training data. + + training_data = TensorDict( + {f"x{i}": training_data[:, :, i].unsqueeze(dim=2) for i in range(state_size)}, + batch_size=[train_size], + ) + + validation_data = training_data + + scotch_data = SCOTCHDataModule( + ts=ts, + training_data=training_data, + validation_data=validation_data, + true_graph=true_graph, + variables_metadata=variables_metadata, + batch_size=1024, + ) + + # SCOTCH Module + scotch = SCOTCHModule( + learning_rates=lrs, + prior_sparsity_lambda=prior_sparsity_lambda, + dt=dt, + layer_norm=layer_norm, + res_connections=res_connection, + deci_diffusion=True, + add_diffusion_self_connections=True, + sigmoid_output=sigmoid_output, + ) + + mlf_logger = MLFlowLogger( + experiment_name=experiment_name, + tracking_uri="file:./mlflow_logs/mlruns", + ) + mlf_logger.log_hyperparams(hparams) + trainer = pl.Trainer( + accelerator="auto", + max_epochs=max_epochs, + fast_dev_run=False, + callbacks=[ + TQDMProgressBar(refresh_rate=19), + ModelCheckpoint(every_n_epochs=50), + ], + check_val_every_n_epoch=50, + logger=mlf_logger, + ) + + trainer.fit(scotch, datamodule=scotch_data) diff --git a/research_experiments/scotch/src/scotch/experiments/netsim.py b/research_experiments/scotch/src/scotch/experiments/netsim.py new file mode 100644 index 0000000..2f6c418 --- /dev/null +++ b/research_experiments/scotch/src/scotch/experiments/netsim.py @@ -0,0 +1,148 @@ +"""This file is for conducting the experiments on netsim dataset""" +import argparse + +import pytorch_lightning as pl +import torch +from pytorch_lightning import seed_everything +from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar +from pytorch_lightning.loggers import MLFlowLogger +from scotch.latent_learning.scotch_data_module import SCOTCHDataModule +from scotch.latent_learning.scotch_module import SCOTCHModule +from tensordict import TensorDict + +from causica.datasets.causica_dataset_format import Variable, VariablesMetadata +from causica.datasets.variable_types import VariableTypeEnum + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run Netsim experiments.") + parser.add_argument("-e", "--epoch", type=int, help="max number of epochs", default=20000) + parser.add_argument("-l", "--lr", type=float, help="learning rate", default=3e-3) + parser.add_argument("-s", "--sparsity", type=float, help="sparsity penalty", default=500) + parser.add_argument("-t", "--dt", type=float, help="dt", default=0.05) + parser.add_argument("-nor", "--normalize", action="store_true", help="whether to normalize") + parser.add_argument("-sd", "--seed", type=int, help="random seed", required=True) + parser.add_argument("-en", "--experiment_name", type=str, help="experiment name", required=True) + + parser.add_argument("-res", "--res_connection", action="store_true", help="whether to use res_connection") + parser.add_argument("-ln", "--layer_norm", action="store_true", help="whether to use layer_norm") + parser.add_argument("-warm", "--lr_warmup", type=int, default=10000, help="warmup epochs") + parser.add_argument("-deci", "--deci_diffusion", action="store_true", help="whether to use deci diffusion function") + parser.add_argument( + "-sig", + "--sigmoid_output", + action="store_true", + help="whether to use sigmoid output for deci diffusion function", + ) + # ADDED + parser.add_argument("-p", "--missing_prob", type=float, help="missing probability", required=True) + + args = parser.parse_args() + + seed_everything(args.seed) + # HParams + experiment_name = args.experiment_name + max_epochs = args.epoch + default_lr = args.lr + res_connection = args.res_connection + layer_norm = args.layer_norm + deci_diffusion = args.deci_diffusion + sigmoid_output = args.sigmoid_output + missing_prob = args.missing_prob + + lrs = { + "graph": default_lr, + "qz0_mean_net": default_lr, + "qz0_logstd_net": default_lr, + "pz0_mean": default_lr, + "pz0_logstd": default_lr, + "prior_drift_fn": default_lr, + "diffusion_fn": default_lr, + "posterior_drift_fn": default_lr, + "trajectory_encoder": default_lr, + } + prior_sparsity_lambda = args.sparsity + train_size = 5 + val_size = -1 + t_max = 10.0 + num_time_points = 200 + + dt = args.dt # sde solver dt = observation interval + normalize = args.normalize + lr_warmup_iters = args.lr_warmup + + hparams = { + "seed": args.seed, + "epoch": args.epoch, + "dt": args.dt, + "default_lr": default_lr, + "train_size": train_size, + "val_size": val_size, + "prior_sparsity_lambda": prior_sparsity_lambda, + "t_max": t_max, + "num_time_points": num_time_points, + "normalize": normalize, + "lr_warmup_iters": lr_warmup_iters, + "res_connection": res_connection, + "layer_rnorm": layer_norm, + "deci_diffusion": deci_diffusion, + "sigmoid_output": sigmoid_output, + "missing_prob": missing_prob, + } + + # netsim + state_size = 15 + variables_metadata = VariablesMetadata( + [Variable(name=f"x{i}", type=VariableTypeEnum.CONTINUOUS, group_name=f"x{i}") for i in range(state_size)] + ) + + subf = "norm" if args.normalize else "unnorm" + ts = torch.load(f"data/netsim_processed/{subf}/times_{str(args.missing_prob)}_{args.seed}.pt") + training_data = torch.load(f"data/netsim_processed/{subf}/data_{str(args.missing_prob)}_{args.seed}.pt") + true_graph = torch.load(f"data/netsim_processed/{subf}/true_graph.pt") + + training_data = TensorDict( + {f"x{i}": training_data[:, :, i].unsqueeze(dim=2) for i in range(state_size)}, + batch_size=[train_size], + ) + + validation_data = training_data + + scotch_data = SCOTCHDataModule( + ts=ts, + training_data=training_data, + validation_data=validation_data, + true_graph=true_graph, + variables_metadata=variables_metadata, + batch_size=1024, + ) + + # SCOTCH Module + scotch = SCOTCHModule( + learning_rates=lrs, + prior_sparsity_lambda=prior_sparsity_lambda, + dt=dt, + layer_norm=layer_norm, + res_connections=res_connection, + deci_diffusion=True, + add_diffusion_self_connections=True, + sigmoid_output=sigmoid_output, + ) + + mlf_logger = MLFlowLogger( + experiment_name=experiment_name, + tracking_uri="file:./mlflow_logs/mlruns", + ) + mlf_logger.log_hyperparams(hparams) + trainer = pl.Trainer( + accelerator="auto", + max_epochs=max_epochs, + fast_dev_run=False, + callbacks=[ + TQDMProgressBar(refresh_rate=19), + ModelCheckpoint(every_n_epochs=50), + ], + check_val_every_n_epoch=50, + logger=mlf_logger, + ) + + trainer.fit(scotch, datamodule=scotch_data) diff --git a/research_experiments/scotch/src/scotch/experiments/yeast.py b/research_experiments/scotch/src/scotch/experiments/yeast.py new file mode 100644 index 0000000..6c46e20 --- /dev/null +++ b/research_experiments/scotch/src/scotch/experiments/yeast.py @@ -0,0 +1,165 @@ +"""This script is for Yeast Glycolysis experiment.""" +import argparse + +import pytorch_lightning as pl +import torch +from pytorch_lightning import seed_everything +from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar +from pytorch_lightning.loggers import MLFlowLogger +from scotch.latent_learning.scotch_data_module import SCOTCHDataModule +from scotch.latent_learning.scotch_module import SCOTCHModule +from tensordict import TensorDict + +from causica.datasets.causica_dataset_format import Variable, VariablesMetadata +from causica.datasets.variable_types import VariableTypeEnum + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run yeast experiments.") + parser.add_argument("-e", "--epoch", type=int, help="max number of epochs", default=30000) + parser.add_argument("-l", "--lr", type=float, help="learning rate", default=1e-3) + parser.add_argument("-s", "--sparsity", type=float, help="sparsity penalty", default=200) + parser.add_argument("-t", "--dt", type=float, help="dt", default=1) + parser.add_argument("-nor", "--normalize", action="store_true", help="whether to normalize") + parser.add_argument("-sd", "--seed", type=int, help="random seed", required=True) + parser.add_argument("-en", "--experiment_name", type=str, help="experiment name", required=True) + + parser.add_argument("-res", "--res_connection", action="store_true", help="whether to use res_connection") + parser.add_argument("-ln", "--layer_norm", action="store_true", help="whether to use layer_norm") + parser.add_argument("-warm", "--lr_warmup", type=int, default=100, help="warmup epochs") + parser.add_argument("-deci", "--deci_diffusion", action="store_true", help="whether to use deci diffusion function") + parser.add_argument( + "-sig", + "--sigmoid_output", + action="store_true", + help="whether to use sigmoid output for deci diffusion function", + ) + parser.add_argument("-d", "--dimension", type=int, help="dimension of yeast dataset", default=7) + parser.add_argument("-p", "--missing_prob", type=float, help="missing probability", default=0.0) + parser.add_argument( + "-Nt", "--num_time_points", type=int, help="number of time points, must be 100, 200 ,500, or 1000", default=100 + ) + parser.add_argument("-tmax", "--t_max", type=float, help="max time", default=100) + parser.add_argument( + "-Ns", "--train_size", type=int, help="number of training time series, must be <=100", default=10 + ) + + args = parser.parse_args() + + seed_everything(args.seed) + # HParams + experiment_name = args.experiment_name + max_epochs = args.epoch + default_lr = args.lr + res_connection = args.res_connection + layer_norm = args.layer_norm + deci_diffusion = args.deci_diffusion + sigmoid_output = args.sigmoid_output + missing_prob = args.missing_prob + num_time_points = args.num_time_points + t_max = args.t_max + train_size = args.train_size + + lrs = { + "graph": default_lr, # changed from 1e-2 + "qz0_mean_net": default_lr, + "qz0_logstd_net": default_lr, + "pz0_mean": default_lr, + "pz0_logstd": default_lr, + "prior_drift_fn": default_lr, + "diffusion_fn": default_lr, + "posterior_drift_fn": default_lr, + "trajectory_encoder": default_lr, + } + prior_sparsity_lambda = args.sparsity + + val_size = -1 + + dt = args.dt # sde solver dt = observation interval + normalize = args.normalize + lr_warmup_iters = args.lr_warmup + + hparams = { + "seed": args.seed, + "epoch": args.epoch, + "dt": args.dt, + "default_lr": default_lr, + "train_size": train_size, + "val_size": val_size, + "prior_sparsity_lambda": prior_sparsity_lambda, + "t_max": t_max, + "num_time_points": num_time_points, + "normalize": normalize, + "lr_warmup_iters": lr_warmup_iters, + "res_connection": res_connection, + "layer_rnorm": layer_norm, + "deci_diffusion": deci_diffusion, + "sigmoid_output": sigmoid_output, + "missing_prob": missing_prob, + "dimension": args.dimension, + } + + # yeast + state_size = args.dimension + variables_metadata = VariablesMetadata( + [Variable(name=f"x{i}", type=VariableTypeEnum.CONTINUOUS, group_name=f"x{i}") for i in range(state_size)] + ) + + subf = "norm" if args.normalize else "unnorm" + ts = torch.load( + f"data/yeast_processed/{args.dimension}/{subf}/times_{args.num_time_points}_{str(args.missing_prob)}_{args.seed}.pt" + ) + training_data = torch.load( + f"data/yeast_processed/{args.dimension}/{subf}/data_{args.num_time_points}_{str(args.missing_prob)}_{args.seed}.pt" + ) + true_graph = torch.load(f"data/yeast_processed/{args.dimension}/{subf}/true_graph.pt").to("cuda") + + training_data = training_data[: args.train_size, :, :] # reduce training data. + + training_data = TensorDict( + {f"x{i}": training_data[:, :, i].unsqueeze(dim=2) for i in range(state_size)}, + batch_size=[train_size], + ) + + validation_data = training_data + + ##### + + scotch_data = SCOTCHDataModule( + ts=ts, + training_data=training_data, + validation_data=validation_data, + true_graph=true_graph, + variables_metadata=variables_metadata, + batch_size=1024, + ) + + # CRhino Module + scotch = SCOTCHModule( + learning_rates=lrs, + prior_sparsity_lambda=prior_sparsity_lambda, + dt=dt, + layer_norm=layer_norm, + res_connections=res_connection, + deci_diffusion=True, + add_diffusion_self_connections=True, + sigmoid_output=sigmoid_output, + ) + + mlf_logger = MLFlowLogger( + experiment_name=experiment_name, + tracking_uri="file:./mlflow_logs/mlruns", + ) + mlf_logger.log_hyperparams(hparams) + trainer = pl.Trainer( + accelerator="auto", + max_epochs=max_epochs, + fast_dev_run=False, + callbacks=[ + TQDMProgressBar(refresh_rate=19), + ModelCheckpoint(every_n_epochs=50), + ], + check_val_every_n_epoch=50, + logger=mlf_logger, + ) + + trainer.fit(scotch, datamodule=scotch_data) diff --git a/research_experiments/scotch/src/scotch/latent_learning/functional_relationship.py b/research_experiments/scotch/src/scotch/latent_learning/functional_relationship.py new file mode 100644 index 0000000..11befdb --- /dev/null +++ b/research_experiments/scotch/src/scotch/latent_learning/functional_relationship.py @@ -0,0 +1,75 @@ +"""This file implements the functional relationship required for SCOTCH""" +from typing import Optional, Type + +import torch +from torch import nn + +from causica.functional_relationships.icgnn import FGNNI +from causica.functional_relationships.icgnn import ICGNN as DECIEmbedFunctionalRelationships +from causica.functional_relationships.icgnn import generate_fully_connected + + +class SCOTCHFunctionalRelationships(DECIEmbedFunctionalRelationships): + """This adapts from DECIEmbedFunctionalRelationships for SCOTCH by incorporting sigmoid output activation, res_connection and norm_layer""" + + def __init__( + self, + shapes: dict[str, torch.Size], + embedding_size: Optional[int] = None, + out_dim_g: Optional[int] = None, + norm_layer: Optional[Type[nn.LayerNorm]] = None, + res_connection: bool = False, + sigmoid_output: bool = False, + ): + super().__init__( + shapes=shapes, + embedding_size=embedding_size, + out_dim_g=out_dim_g, + norm_layer=norm_layer, + res_connection=res_connection, + ) + # Overwrite the nn to include sigmoid output + self.sigmoid_output = sigmoid_output + self.nn = SCOTCHFGNNI( + self.stacked_key_masks, + embedding_size, + out_dim_g, + norm_layer, + res_connection, + sigmoid_output=self.sigmoid_output, + ) + + +class SCOTCHFGNNI(FGNNI): + """This adapts from FGNNI for SCOTCH by incorporting sigmoid output activation""" + + def __init__( + self, + group_mask: torch.Tensor, + embedding_size: Optional[int] = None, + out_dim_g: Optional[int] = None, + norm_layer: Optional[Type[nn.LayerNorm]] = None, + res_connection: bool = False, + sigmoid_output: bool = False, + ): + super().__init__( + group_mask=group_mask, + embedding_size=embedding_size, + out_dim_g=out_dim_g, + norm_layer=norm_layer, + res_connection=res_connection, + ) + # Overwrite the f function to include sigmoid output + out_dim_g = self.embedding_size if out_dim_g is None else out_dim_g + in_dim_f = self.embedding_size + out_dim_g # input dim of f + a = max(4 * self.processed_dim_all, self.embedding_size, 64) + self.f = generate_fully_connected( + input_dim=in_dim_f, + output_dim=self.processed_dim_all, + hidden_dims=[a, a], + non_linearity=nn.LeakyReLU, + activation=nn.Identity if not sigmoid_output else nn.Sigmoid, + device=group_mask.device, + normalization=norm_layer, + res_connection=res_connection, + ) diff --git a/research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py b/research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py new file mode 100644 index 0000000..3cdce0a --- /dev/null +++ b/research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py @@ -0,0 +1,120 @@ +from typing import Optional + +import torch +import torch.distributions as td +import torch.nn.functional as F +from torch import nn +from torch.distributions.utils import logits_to_probs + +from causica.distributions.adjacency.adjacency_distributions import AdjacencyDistribution +from causica.distributions.distribution_module import DistributionModule +from causica.distributions.gumbel_binary import gumbel_softmax_binary + + +class BernoulliDigraphDistribution(AdjacencyDistribution): + """Directed graph distribution, with independent Bernoulli distributions for each edge. Allows for self-loops. + + The probability of an edge existing is pᵢⱼ = σ(γᵢⱼ). where γᵢⱼ is the logit the edge exists and is independent for + each i, j. + """ + + arg_constraints = {} + + def __init__(self, logits: torch.Tensor, validate_args: Optional[bool] = None): + num_nodes = logits.shape[-1] + super().__init__(num_nodes, validate_args=validate_args) + + self.logits = logits + + @staticmethod + def base_dist(logits: torch.Tensor) -> td.Distribution: + """A matrix of independent Bernoulli distributions. + + Args: + logits: The logits for the existence of an edge; shape == batch_shape + (num_nodes, num_nodes) + """ + return td.Independent(td.Bernoulli(logits=logits), 2) + + def relaxed_sample(self, sample_shape: torch.Size = torch.Size(), temperature: float = 0.0) -> torch.Tensor: + """ + Sample from a relaxed distribution. We use a Gumbel Softmax. + + Args: + sample_shape: the shape of the samples to return + Returns: + A tensor of shape sample_shape + batch_shape + (num_nodes, num_nodes) + """ + expanded_logits = self.logits.expand(*(sample_shape + self.logits.shape)) + return gumbel_softmax_binary(logits=expanded_logits, tau=temperature, hard=True) + + def sample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor: + """ + Sample from the underyling independent Bernoulli distribution. + + Gradients will not flow through this method, use relaxed_sample instead. + + Args: + sample_shape: the shape of the samples to return + Returns: + A tensor of shape sample_shape + batch_shape + (num_nodes, num_nodes) + """ + return self.base_dist(self.logits).sample(sample_shape=sample_shape) + + def entropy(self) -> torch.Tensor: + """ + Get the entropy of the independent Bernoulli distribution. + + Returns: + A tensor of shape batch_shape, with the entropy of the distribution + """ + entropy = F.binary_cross_entropy_with_logits( + self.logits, logits_to_probs(self.logits, is_binary=True), reduction="none" + ) + return torch.sum(entropy, dim=(-2, -1)) + + @property + def mean(self) -> torch.Tensor: + """ + Return the mean of the underlying independent Bernoulli distribution. + + This will be a matrix with all entries in the interval [0, 1]. + + Returns: + A tensor of shape batch_shape + (num_nodes, num_nodes) + """ + return self.base_dist(self.logits).mean + + @property + def mode(self) -> torch.Tensor: + """ + Return the mode of the underlying independent Bernoulli distribution. + + This will be an adjacency matrix. + + Returns: + A tensor of shape batch_shape + (num_nodes, num_nodes) + """ + # bernoulli mode can be nan for very small logits, favour sparseness and set to 0 + return torch.nan_to_num(self.base_dist(self.logits).mode, nan=0.0) + + def log_prob(self, value: torch.Tensor) -> torch.Tensor: + """ + Get the log probability of each tensor from the sample space + + Args: + value: a binary matrix of shape value_shape + batch_shape + (num_nodes, num_nodes) + Returns: + A tensor of shape value_shape + batch_shape, with the log probabilities of each tensor in the batch. + """ + return self.base_dist(self.logits).log_prob(value) + + +class BernoulliDigraphDistributionModule(DistributionModule[BernoulliDigraphDistribution]): + """Represents an `BernoulliDigraphDistribution` distribution with learnable logits.""" + + def __init__(self, num_nodes: int) -> None: + super().__init__() + self.logits = nn.Parameter(torch.zeros(num_nodes, num_nodes), requires_grad=True) # prob 0.5 + + def forward(self) -> BernoulliDigraphDistribution: + return BernoulliDigraphDistribution(logits=self.logits) diff --git a/research_experiments/scotch/src/scotch/latent_learning/scotch_data_module.py b/research_experiments/scotch/src/scotch/latent_learning/scotch_data_module.py new file mode 100644 index 0000000..90f2c91 --- /dev/null +++ b/research_experiments/scotch/src/scotch/latent_learning/scotch_data_module.py @@ -0,0 +1,130 @@ +from collections import defaultdict +from collections.abc import Iterable + +import torch +from pytorch_lightning.utilities.types import EVAL_DATALOADERS +from scotch.scotch_utils.scotch_utils import check_temporal_tensordict_shapes, temporal_tensordict_shapes +from tensordict import TensorDict +from torch import Tensor +from torch.utils.data import DataLoader +from torchsde import BrownianInterval + +from causica.datasets.causica_dataset_format import VariablesMetadata +from causica.datasets.tensordict_utils import identity +from causica.datasets.variable_types import VariableTypeEnum +from causica.lightning.data_modules.deci_data_module import DECIDataModule + + +class SCOTCHDataModule(DECIDataModule): + """A SCOTCH datamodule interface for a dataframe and variable specification.""" + + def __init__( + self, + ts: list[int], + training_data: TensorDict, + validation_data: TensorDict, + true_graph: Tensor, + variables_metadata: VariablesMetadata, + batch_size: int, + validation_bm: BrownianInterval = None, + dataset_name: str = "anonymous_dataset", + ): + """Constructor for SCOTCHDataModule, given a dataframe containing the temporal data, and variable specification. + + At present it is assumed that all trajectories in the dataset are observed at the same time points; however, + in future it is planned to extend to heterogenous time points (which will be encoded as separate rows in the + dataframe, with a column indicating the time point for each row). + + Args: + ts (list[int]): An iterable containing the time points; assumed to be the same for each observation. + training_data (TensorDict): A TensorDict containing the temporal data. In particular, each entry is of shape + (num_observations, num_time_points, num_variables_in_group), and the keys are the variable group names. + validation_data (TensorDict): Like training_data, but used for validation. + validation_bm (BrownianInterval): Brownian motion from the SDE trajectories in the validation data; used + to fix the stochasticity in SDE for computing test MSE. Should have size (val_size, state_size) + If not available (for non synthetic data), then default to None. + true_graph (Tensor): The true graph adjacency matrix, of shape (state_size, state_size). + others: See BasicDECIDataModule. + """ + super().__init__() + self._dataset_name = dataset_name + self.batch_size = batch_size + + self._true_graph = true_graph + + self._dataset_train = training_data + self._dataset_val = validation_data + + # Check that shapes are valid + assert check_temporal_tensordict_shapes(len(ts), self._dataset_train) + self._variable_shapes = temporal_tensordict_shapes(self._dataset_train) + self._variable_types = {var.group_name: var.type for var in variables_metadata.variables} + self._column_names = defaultdict(list) + for variable in variables_metadata.variables: + self._column_names[variable.group_name].append(variable.name) + + # normalization removed (see BasicDECIDataModule) + + self._ts = ts + self._bm_val = validation_bm + + @property + def ts(self) -> Iterable[int]: + return self._ts + + @property + def bm_validation(self) -> BrownianInterval: + return self._bm_val + + @property + def true_graph(self) -> Tensor: + return self._true_graph + + @property + def dataset_name(self) -> TensorDict: + return self._dataset_name + + @property + def dataset_train(self) -> TensorDict: + return self._dataset_train + + @property + def dataset_val(self) -> TensorDict: + return self._dataset_val + + @property + def dataset_test(self) -> TensorDict: + raise RuntimeError("Not defined") + + @property + def variable_shapes(self) -> dict[str, torch.Size]: + """Get the shape of each variable in the dataset.""" + return self._variable_shapes + + @property + def variable_types(self) -> dict[str, VariableTypeEnum]: + """Get the type of each variable in the dataset.""" + return self._variable_types + + @property + def column_names(self) -> dict[str, list[str]]: + """Get a map of the variable names and the corresponding columns of the original dataset.""" + return self._column_names + + def train_dataloader(self): + return DataLoader( + dataset=self.dataset_train, + collate_fn=identity, + batch_size=self.batch_size, + shuffle=True, + drop_last=False, # allow for incomplete batches + ) + + def val_dataloader(self) -> EVAL_DATALOADERS: + return DataLoader( + dataset=self.dataset_val, + collate_fn=identity, + batch_size=self.batch_size, + shuffle=False, + drop_last=False, # allow for incomplete batches + ) diff --git a/research_experiments/scotch/src/scotch/latent_learning/scotch_module.py b/research_experiments/scotch/src/scotch/latent_learning/scotch_module.py new file mode 100644 index 0000000..de24d66 --- /dev/null +++ b/research_experiments/scotch/src/scotch/latent_learning/scotch_module.py @@ -0,0 +1,439 @@ +from typing import Optional, Union + +import numpy as np +import torch +import torchsde +from pytorch_lightning import LightningModule +from pytorch_lightning.trainer.states import TrainerFn +from pytorch_lightning.utilities.types import STEP_OUTPUT +from scotch.latent_learning.functional_relationship import SCOTCHFunctionalRelationships +from scotch.latent_learning.graph_distribution import BernoulliDigraphDistributionModule +from scotch.latent_learning.scotch_nns import ( + DECIEmbedNNCoefficient, + NeuralContextualDriftCoefficient, + NeuralDiffusionCoefficient, + NeuralTrajectoryGraphEncoder, +) +from scotch.scotch_utils.graph_metrics import ( + confusion_matrix_batched, + f1_score, + false_discovery_rate, + true_positive_rate, +) +from scotch.sdes.scotch_sdes import AugmentedSCOTCHSDE, SCOTCHPriorSDE, swap_t_and_batch_dimensions +from sklearn.metrics import roc_auc_score +from torch import Tensor, nn + +from causica.distributions import GibbsDAGPrior +from causica.distributions.transforms import TensorToTensorDictTransform, shapes_to_slices + + +class LinearScheduler: + def __init__(self, iters, maxval=1.0): + self._iters = max(1, iters) + self._val = maxval / self._iters + self._maxval = maxval + + def step(self): + self._val = min(self._maxval, self._val + self._maxval / self._iters) + + @property + def val(self): + return self._val + + +class SCOTCHModule(LightningModule): + """PyTorch Lightning module implementing the graph-based continuous time series model.""" + + def __init__( + self, + embedding_size: int = 32, + out_dim_g: int = 32, + prior_sparsity_lambda: float = 0.05, + gumbel_temp: float = 0.25, + context_size: int = 64, + hidden_size: int = 128, + kl_anneal_iters: int = 1000, + learning_rates: Optional[dict] = None, + use_adjoint: bool = False, + sde_method: str = "euler", + dt: float = 1e-2, + noise_scale: Union[float, Tensor] = 0.01, + record_graph_logits: bool = True, + lr_warmup_iters: int = 1, + ignore_self_connections: bool = False, + layer_norm: bool = False, + res_connections: bool = False, + deci_diffusion: bool = False, + compute_auroc=True, + add_diffusion_self_connections: bool = False, + sigmoid_output: bool = False, + ): + """Initialize the SCOTCH module. Some of the arguments are similar to arguments of the DeciModule constructor, + while others are specific to SCOTCH. + + Args: + embedding_size, out_dim_g: Hyperparameters of the prior diffusion coefficient + network, implemented by a DECIEmbedNN. + prior_sparsity_lambda: Sparsity parameter for the prior directed graph distribution. + gumbel_temp: Temperature for the Gumbel-Softmax distribution used to sample directed graphs during training. + context_size: Dimension of the context vector. Given an observed trajectory, an encoder is used to predict + this vector, which is then used as input to the NN modelling the posterior SDE (given trajectory). + hidden_size: Size of hidden NN layers in the encoder and posterior SDE NN. + kl_anneal_iters: Number of iterations to anneal the KL term for the SDE ELBO. + learning_rates: Learning rates for different parts of the components of the model. Keys are ["graph", + "qz0_mean_net", "qz0_logstd_net", "pz0_mean", "pz0_logstd", "prior_drift_fn", "diffusion_fn", + "posterior_drift_fn", "trajectory_encoder"]. + use_adjoint: Whether to use the adjoint method for computing gradients through the (augmented) SDE during + training. + sde_method: Method to use for solving SDE. + dt: Time step to use for solving SDE. + noise_scale: Noise scale of the (Laplace) noise distribution for the observed trajectory given the latent + trajectory. + graph_constrained_posterior: Whether to use the constrain the posterior distribution to respect graphs (in + the same manner as the prior); if True, we use a DECIEmbedNN for the posterior + record_graph_logits: Whether to record the logits of the graph distribution in the metrics. + lr_warmup_iters: Number of iterations to linearly increase the learning rate from 0 to the specified value. + ignore_self_connections: If True, the diagonal logits of the predicted adjacency matrix are set to -inf + during computation of graph metrics (auroc, f1, tpr, fdr) only. + layer_norm: Whether to activate layer_norm for DECI neural networks + res_connections: Whether to add residual connections for DECI neural networks + deci_diffusion: whether to use DECI for diffusion coefficient (True), or independent nns for each dimension (False) + compute_auroc: whether to compute auroc + add_diffusion_self_connections: whether to add self-connections to the diffusion coefficient graph. only used if + deci_diffusion is True. + sigmoid_output: whether to apply sigmoid to the output of the diffusion coefficient nn. Only used if deci_diffusion + """ + super().__init__() + + self.embedding_size = embedding_size + + self.gumbel_temp = gumbel_temp + self.is_setup = False + self.out_dim_g = out_dim_g + self.prior_sparsity_lambda = prior_sparsity_lambda + + self.context_size = context_size + self.hidden_size = hidden_size + self.kl_scheduler = LinearScheduler(kl_anneal_iters) + if learning_rates is None: + learning_rates = {"lambda": 3e-3} + self.learning_rates = learning_rates + self.use_adjoint = use_adjoint + self.sde_method = sde_method + self.dt = dt + self.noise_scale = noise_scale + + self.record_graph_logits = record_graph_logits + self.lr_warmup_iters = lr_warmup_iters + self.ignore_self_connections = ignore_self_connections + + self.layer_norm = layer_norm + self.res_connections = res_connections + self.deci_diffusion = deci_diffusion + self.add_diffusion_self_connections = add_diffusion_self_connections + + self.compute_auroc = compute_auroc + self.sigmoid_output = sigmoid_output + + def setup(self, stage: Optional[str] = None): + """Set up all components of the SCOTCH SDE model.""" + if self.is_setup: + return # Already setup + if stage not in {TrainerFn.TESTING, TrainerFn.FITTING}: + raise ValueError(f"Model can only be setup during the {TrainerFn.FITTING} and {TrainerFn.TESTING} stages.") + + # Replaces adjacency distribution in DECIModule, necessary to encode directed graph (with self-loop) distrn. + + variable_group_shapes = self.trainer.datamodule.variable_shapes # type: ignore + self.num_nodes = len(variable_group_shapes) + self.adjacency_dist_module = BernoulliDigraphDistributionModule(self.num_nodes) + + self.graph_prior: GibbsDAGPrior = GibbsDAGPrior( + num_nodes=self.num_nodes, sparsity_lambda=self.prior_sparsity_lambda + ) + + self.observed_size, _ = shapes_to_slices(variable_group_shapes) + self.tensor_to_td = TensorToTensorDictTransform(variable_group_shapes) + + self.latent_size = self.observed_size # in the SCOTCH model, we use the same dimension for observed/latents + + if self.layer_norm: + deci_drift = SCOTCHFunctionalRelationships( + shapes=variable_group_shapes, + embedding_size=self.embedding_size, + out_dim_g=self.out_dim_g, + norm_layer=nn.LayerNorm, + res_connection=self.res_connections, + ) + deci_diffusion = SCOTCHFunctionalRelationships( + shapes=variable_group_shapes, + embedding_size=self.embedding_size, + out_dim_g=self.out_dim_g, + norm_layer=nn.LayerNorm, + res_connection=self.res_connections, + sigmoid_output=self.sigmoid_output, + ) + else: + deci_drift = SCOTCHFunctionalRelationships( + shapes=variable_group_shapes, + embedding_size=self.embedding_size, + out_dim_g=self.out_dim_g, + res_connection=self.res_connections, + ) + deci_diffusion = SCOTCHFunctionalRelationships( + shapes=variable_group_shapes, + embedding_size=self.embedding_size, + out_dim_g=self.out_dim_g, + res_connection=self.res_connections, + sigmoid_output=self.sigmoid_output, + ) + # SDE components + self.qz0_mean_net = nn.Linear(self.context_size, self.latent_size) + self.qz0_logstd_net = nn.Linear(self.context_size, self.latent_size) + self.pz0_mean = nn.Parameter(torch.zeros(1, self.latent_size)) + self.pz0_logstd = nn.Parameter(torch.zeros(1, self.latent_size)) + + self.prior_drift_fn = DECIEmbedNNCoefficient(deci_drift.nn) + if self.deci_diffusion: + self.diffusion_fn = DECIEmbedNNCoefficient( + deci_diffusion.nn, add_self_connections=self.add_diffusion_self_connections + ) + else: + self.diffusion_fn = NeuralDiffusionCoefficient(latent_size=self.latent_size, hidden_size=self.hidden_size) + self.posterior_drift_fn = NeuralContextualDriftCoefficient( + latent_size=self.latent_size, hidden_size=self.hidden_size, context_size=self.context_size + ) + self.trajectory_encoder = NeuralTrajectoryGraphEncoder( + observed_size=self.observed_size, hidden_size=self.hidden_size, context_size=self.context_size + ) + + self.ts = self.trainer.datamodule.ts # type: ignore + + self.is_setup = True + + def training_step(self, *args, **kwargs) -> STEP_OUTPUT: + """Compute the loss function (ELBO) for SCOTCH for the batch; involves integrating an augmented SDE.""" + _ = kwargs + batch, *_ = args + # TensorDict mapping variable name to Tensor of shape (batch_size, variable_group_size, num_time_points) + batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True)) + batch_unrolled = self.tensor_to_td.inv(batch) + + # Step 1: Sample a graph from variational posterior over graphs + adjacency_distribution = self.adjacency_dist_module() + sampled_graphs = adjacency_distribution.relaxed_sample( + torch.Size([batch_unrolled.shape[0]]), temperature=self.gumbel_temp + ) # hard gumbel-softmax samples, shape (batch_size, num_nodes, num_nodes) + + # Step 2: For each observed trajectory, compute context for the variational posterior (over latent trajectories) + # Context vectors are Tensors of shape (batch_size, num_time_points, context_size) + context_vectors = self.trajectory_encoder( + torch.flip(batch_unrolled, dims=(1,)), sampled_graphs + ) # feed into encoder with reversed time + context_vectors = torch.flip(context_vectors, dims=(1,)) # unflip time dimension + ts_context_vectors = (self.ts, context_vectors) + + # Step 3: Sample initial latent state + qz0_mean, qz0_logstd = self.qz0_mean_net(context_vectors[:, 0, :]), self.qz0_logstd_net( + context_vectors[:, 0, :] + ) + qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp()) + z0 = qz0.rsample() # shape (batch_size, latent_size) + + # Step 4: Compute loss components + + # Compute KL (t = 0) + + pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp()) + logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1) # KL (t = 0) + + # Compute KL (path) and log-likelihood of observed trajectories + aug_z0 = nn.functional.pad(z0, (0, 1), value=0.0) + + aug_sde = AugmentedSCOTCHSDE( + posterior_drift_net=self.posterior_drift_fn, + diffusion_net=self.diffusion_fn, + prior_drift_net=self.prior_drift_fn, + ts_context_vectors=ts_context_vectors, + graphs=sampled_graphs, + ) + # aug_zs_t_first has shape (num_time_points, batch_size, latent_size + 1) + if self.use_adjoint: + # Adjoint method requires parameters to be explicitly passed in. + aug_zs_t_first = torchsde.sdeint_adjoint( + aug_sde, + aug_z0, + self.ts, + adjoint_params=self.parameters(), + dt=self.dt, + method=self.sde_method, + names={"drift": "f", "diffusion": "g"}, + ) + else: + aug_zs_t_first = torchsde.sdeint( + aug_sde, aug_z0, self.ts, dt=self.dt, method=self.sde_method, names={"drift": "f", "diffusion": "g"} + ) + aug_zs = swap_t_and_batch_dimensions(aug_zs_t_first) + zs, logqp_path = aug_zs[:, :, :-1], aug_zs[:, -1, -1] + + if isinstance(self.noise_scale, Tensor): + reshaped_noise_scale = self.noise_scale.repeat(zs.shape[0], zs.shape[1], 1) + xs_dist = torch.distributions.Laplace(loc=zs, scale=reshaped_noise_scale) + else: + xs_dist = torch.distributions.Laplace(loc=zs, scale=self.noise_scale) + + log_pxs_tensor = xs_dist.log_prob(batch_unrolled) + log_pxs = log_pxs_tensor.sum() # sum over batch, time points, and variables + + logqp = (logqp0 + logqp_path).sum(dim=0) + + # Compute expected log-prior and entropy terms for posterior graph distribution + log_graph_prior = ( + self.graph_prior.log_prob(sampled_graphs) / sampled_graphs.shape[0] + ) # expected log-prior graph probability under posterior adjacency_distribution + graph_entropy = adjacency_distribution.entropy() + + # Compute overall loss + nll = -log_pxs + logqp * self.kl_scheduler.val - log_graph_prior - graph_entropy + + step_output = { + "loss": nll, + "log_pxs": log_pxs, + "logqp": logqp, + "log_graph_prior": log_graph_prior, + "graph_entropy": graph_entropy, + } + if self.record_graph_logits: + for i in range(self.num_nodes): + for j in range(self.num_nodes): + step_output[f"graph_logits_{i}_{j}"] = adjacency_distribution.logits[i, j] + self.log_dict(step_output, prog_bar=True) + + self.kl_scheduler.step() + + return step_output + + def validation_step(self, *args, **kwargs): + """Compute validation metrics: test_mse, tpr (true positive rate), fdr (false discovery rate).""" + + _ = kwargs + batch, *_ = args + # TensorDict mapping variable name to Tensor of shape (batch_size, variable_group_size, num_time_points) + batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True)) + batch_unrolled = self.tensor_to_td.inv(batch) + + # Extract fixed brownian motion and true graph from the data module + bm_validation = self.trainer.datamodule.bm_validation + true_graph = self.trainer.datamodule.true_graph + + # Step 1: Sample a graph from variational posterior over graphs + adjacency_distribution = self.adjacency_dist_module() + sampled_graphs = adjacency_distribution.sample( + torch.Size([batch_unrolled.shape[0]]) + ) # samples (w/o gumbel-softmax), shape (batch_size, num_nodes, num_nodes) + if self.ignore_self_connections: + # remove self-connections for evaluation + for i, _ in enumerate(sampled_graphs): + sampled_graphs[i] = sampled_graphs[i] - torch.diag(torch.diag(sampled_graphs[i])) + + # Step 2: Compute metrics + logits = adjacency_distribution.logits.cpu().numpy() + if self.ignore_self_connections: + np.fill_diagonal(logits, -100000) + # print("Logits: ", logits) + # print("True graph: ", true_graph) + if self.compute_auroc: + auroc = roc_auc_score(y_true=true_graph.flatten().cpu().numpy(), y_score=logits.flatten()) + print("AUROC: ", auroc) + conf_matrix = confusion_matrix_batched(true_graph, sampled_graphs) + tpr = true_positive_rate(conf_matrix[1, 1], conf_matrix[1, 0]) + fdr = false_discovery_rate(conf_matrix[1, 1], conf_matrix[0, 1]) + f1 = f1_score(conf_matrix[1, 1], conf_matrix[0, 1], conf_matrix[1, 0]) + + step_output = {"tpr": tpr, "fdr": fdr, "f1": f1} + if self.compute_auroc: + step_output["auroc"] = auroc + + # If brownian motions provided for validation trajectories. + if bm_validation is not None: + # Initial latent states + z0 = batch_unrolled[:, 0, :] + + prior_sde = SCOTCHPriorSDE( + prior_drift_net=self.prior_drift_fn, diffusion_net=self.diffusion_fn, graphs=sampled_graphs + ) + + # Compute predicted trajectory, given prior SDE, initial latent state, and fixed brownian motion + if self.use_adjoint: + # Adjoint method requires parameters to be explicitly passed in. + pred_zs_t_first = torchsde.sdeint_adjoint( + prior_sde, + z0, + self.ts, + adjoint_params=self.parameters(), + dt=self.dt, + bm=bm_validation, + method=self.sde_method, + names={"drift": "f", "diffusion": "g"}, + ) + else: + pred_zs_t_first = torchsde.sdeint( + prior_sde, + z0, + self.ts, + dt=self.dt, + bm=bm_validation, + method=self.sde_method, + names={"drift": "f", "diffusion": "g"}, + ) + pred_zs = swap_t_and_batch_dimensions(pred_zs_t_first) + step_output["test_mse"] = ((pred_zs[:, 1:, :] - batch_unrolled[:, 1:, :]) ** 2).mean() + + self.log_dict(step_output) + + return step_output + + def configure_optimizers(self): + modules = { + "graph": self.adjacency_dist_module, + "qz0_mean_net": self.qz0_mean_net, + "qz0_logstd_net": self.qz0_logstd_net, + "prior_drift_fn": self.prior_drift_fn, + "diffusion_fn": self.diffusion_fn, + "posterior_drift_fn": self.posterior_drift_fn, + "trajectory_encoder": self.trajectory_encoder, + } + other_parameters = { + "pz0_mean": self.pz0_mean, + "pz0_logstd": self.pz0_logstd, + } + parameter_list = [ + {"params": module.parameters(), "lr": self.learning_rates[name], "name": name} + for name, module in modules.items() + ] + [ + {"params": [parameter], "lr": self.learning_rates[name], "name": name} + for name, parameter in other_parameters.items() + ] + + optimizer = torch.optim.Adam(parameter_list) + + return { + "optimizer": optimizer, + "lr_scheduler": torch.optim.lr_scheduler.LambdaLR( + optimizer, lambda epoch: min(1, epoch / self.lr_warmup_iters) + ), + } + + def sample(self, batch_size, ts, bm=None, z0=None): + adjacency_distribution = self.adjacency_dist_module() # self.sem_module.adjacency_module() + # hard sample + sampled_graphs = adjacency_distribution.sample(torch.Size([batch_size])) + + # Sample initial positions + if z0 is None: + eps = torch.randn(size=(batch_size, *self.pz0_mean.shape[1:]), device=self.pz0_mean.device) + z0 = self.pz0_mean + self.pz0_logstd.exp() * eps + + sde = SCOTCHPriorSDE(self.prior_drift_fn, self.diffusion_fn, sampled_graphs) + return sde.sample(z0, ts, bm, dt=self.dt) diff --git a/research_experiments/scotch/src/scotch/latent_learning/scotch_nns.py b/research_experiments/scotch/src/scotch/latent_learning/scotch_nns.py new file mode 100644 index 0000000..e2e80fb --- /dev/null +++ b/research_experiments/scotch/src/scotch/latent_learning/scotch_nns.py @@ -0,0 +1,200 @@ +import torch +from scotch.sdes.sde_modules import ( + ContextualDriftCoefficient, + DiffusionCoefficient, + GraphCoefficient, + TrajectoryGraphEncoder, +) +from torch import Tensor, nn, vmap +from torch.func import stack_module_state + +from causica.functional_relationships.icgnn import FGNNI as DECIEmbedNN + + +class NeuralTrajectoryGraphEncoder(TrajectoryGraphEncoder): + """Encodes trajectories and graphs into context vectors. For each time point in the trajectory, the context vector + for that time point depends on the observed trajectory up to that point, and the graph. + + Attributes: + gru (nn.GRU): GRU encoder for the trajectories + lin (nn.Linear): Linear layer that maps a GRU hidden state and a flattened graph to context vectors. + observed_size (int): Dimension of observed (X) variables in trajectory (input to encoder) + hidden_size (int): Dimension of hidden state of GRU encoder + context_size (int): Dimension of context vector (output of encoder) + """ + + def __init__(self, observed_size: int, hidden_size: int, context_size: int): + """Constructor for TrajectoryEncoder.""" + super().__init__() + self.observed_size, self.hidden_size, self.context_size = observed_size, hidden_size, context_size + + self.gru = nn.GRU(input_size=observed_size, hidden_size=hidden_size, batch_first=True) + self.lin = nn.Linear(hidden_size + observed_size * observed_size, context_size) + + def forward(self, trajectories: Tensor, graphs: Tensor) -> Tensor: + """Encode trajectories and graphs into context vectors. + + Args: + trajectories: Observed trajectories, shape (batch_size, num_time_points, observed_size) + graphs: Weighted adjacency matrix, shape (batch_size, observed_size, observed_size) + + Returns: + Tensor: Context vectors, shape (batch_size, num_time_points, context_size) + """ + assert ( + trajectories.shape[-1] == self.observed_size + ), "Last dimension of trajectories must match observed size of NeuralTrajectoryGraphEncoder" + assert ( + graphs.shape[-1] == graphs.shape[-2] == self.observed_size + ), "Last two dimensions of graphs must match observed size of NeuralTrajectoryGraphEncoder" + + num_time_points = trajectories.shape[-2] + # shape (batch_size, num_time_points, hidden_size) + encoded_trajectories, _ = self.gru(trajectories) + # shape (batch_size, num_time_points, observed_size * observed_size) + encoded_graphs = graphs.reshape(-1, 1, graphs.shape[-1] * graphs.shape[-2]).expand(-1, num_time_points, -1) + + return self.lin(torch.cat([encoded_trajectories, encoded_graphs], dim=-1)) + + +class NeuralContextualDriftCoefficient(ContextualDriftCoefficient): + """Implements a (family of) time-independent drift coefficient using a NN. + + The NN also takes as input a context vector, i.e. the diffusion coefficient is given by + f(z, u): R^latent_size x R^context_size -> R^latent_size + + In SCOTCH, the context vector is used to encode the observed trajectory and graph; the drift coefficient is then + interpreted as being the drift coefficient of the posterior SDE. + + Attributes: + drift_fn (nn.Sequential): NN that computes drift coefficient + latent_size (int): Dimension of latent (Z) variables + hidden_size (int): Dimension of hidden layers of NN + context_size (int): Dimension of context vector + """ + + def __init__(self, latent_size: int, hidden_size: int, context_size: int): + """Constructor for NeuralDriftCoefficient.""" + super().__init__() + self.latent_size, self.hidden_size, self.context_size = latent_size, hidden_size, context_size + + self.drift_fn = nn.Sequential( + nn.Linear(latent_size + context_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, latent_size), + ) + + def forward(self, latent_states: Tensor, context_vectors: Tensor): + """Compute drift coefficient, given (batched) input latent states and context vectors. + + Args: + latent_states: Latent states, shape (batch_size, latent_size) + context_vectors: Context vectors, shape (batch_size, context_size) + + Returns: + Tensor: Drift coefficient, shape (batch_size, latent_size) + """ + assert ( + latent_states.shape[-1] == self.latent_size + ), "Last dimension of latent states must match latent size of NeuralContextualDriftCoefficient" + assert ( + context_vectors.shape[-1] == self.context_size + ), "Last dimension of context vectors must match context size of NeuralContextualDriftCoefficient" + return self.drift_fn(torch.cat([latent_states, context_vectors], dim=-1)) + + +class NeuralDiffusionCoefficient(DiffusionCoefficient): + """Implements time-independent, diagonal (that is, elementwise) diffusion coefficient using NNs. + + g(z) = (g_1(Z_1), ..., g_d(Z_d)) : R^latent_size -> R^latent_size + + Attributes: + diffusion_fns (nn.ModuleList): List of NNs that compute diffusion coefficient for each latent variable + latent_size (int): Dimension of latent (Z) variables + hidden_size (int): Dimension of hidden layers of NNs + """ + + def __init__(self, latent_size: int, hidden_size: int): + """Constructor for NeuralDiffusionCoefficient. + + Args: + latent_size: Dimension of latent (Z) variables + hidden_size: Dimension of hidden layers of NNs + """ + super().__init__() + self.latent_size, self.hidden_size = latent_size, hidden_size + + self.diffusion_fns = nn.ModuleList( + [ + nn.Sequential(nn.Linear(1, hidden_size), nn.Softplus(), nn.Linear(hidden_size, 1), nn.Sigmoid()).to( + "cuda" + ) + for _ in range(latent_size) + ] + ) + + self.params, self.buffers = stack_module_state(self.diffusion_fns) + + def _diffusion_fn_wrapper(params, buffers, data): + return torch.func.functional_call(self.diffusion_fns[0], (params, buffers), data.unsqueeze(1)) + + self.diffusion_fns_vmap = vmap( + _diffusion_fn_wrapper, + ( + 0, + 0, + 1, + ), + out_dims=(1,), + ) + + def forward(self, latent_states: Tensor): + """Compute diffusion coefficient, given (batched) input latent states. + + Args: + latent_states: Latent states, shape (batch_size, latent_size) + + Returns: + Tensor: Diffusion coefficient, shape (batch_size, latent_size) + """ + assert ( + latent_states.shape[-1] == self.latent_size + ), "Last dimension of latent trajectories must match latent size of NeuralDiffusionCoefficient" + + out = self.diffusion_fns_vmap(self.params, self.buffers, latent_states).squeeze() + return out + + +class DECIEmbedNNCoefficient(GraphCoefficient): + """Defines a diffusion coefficient using DECIEmbedNN (the DECI NN model). + + The main implementational difference between this class and DECIEmbedNN is that, in the forward pass, we assume that the + input latent_states (samples in DECIEmbedNN) and graphs have the same batch dimension; and the output also has this batch + dimension. + """ + + def __init__(self, deci_nn: DECIEmbedNN, add_self_connections: bool = False): + """Constructor for DECIEmbedNNDiffusionCoefficient. + . + + Args: + deci_nn: DECI NN model + """ + super().__init__() + self.deci_nn = deci_nn + self.f = torch.func.vmap(self.deci_nn.forward, in_dims=(0, 0), out_dims=0) + self.add_self_connections = add_self_connections + + def forward(self, latent_states: torch.Tensor, graphs: torch.Tensor) -> torch.Tensor: + assert latent_states.shape[:-1] == graphs.shape[:-2], "batch shapes of latent_states and graphs must match" + + if self.add_self_connections: + final_graphs = graphs.clone() + mask = torch.eye(final_graphs.shape[-1]).repeat(final_graphs.shape[0], 1, 1).bool() + final_graphs[mask] = 1 + else: + final_graphs = graphs + + return self.f(latent_states, final_graphs) diff --git a/research_experiments/scotch/src/scotch/scotch_utils/graph_metrics.py b/research_experiments/scotch/src/scotch/scotch_utils/graph_metrics.py new file mode 100644 index 0000000..0732ae0 --- /dev/null +++ b/research_experiments/scotch/src/scotch/scotch_utils/graph_metrics.py @@ -0,0 +1,81 @@ +import torch +from torch import Tensor + + +def confusion_matrix(true_graph: torch.Tensor, pred_graph: torch.Tensor) -> Tensor: + """Evaluate metrics for the difference between a true and predicted graph. + + Args: + true_graph: The true graph; Tensor of shape (state_size, state_size). + pred_graph: The predicted graph; Tensor of shape (state_size, state_size). + + Returns: + confusion matrix: Tensor of shape (2, 2) reprsenting confusion matrix: + Entry (0, 0) is the number of true negatives. + Entry (0, 1) is the number of false positives. + Entry (1, 0) is the number of false negatives. + Entry (1, 1) is the number of true positives. + """ + vec1 = torch.abs(true_graph) > 0 + vec2 = torch.abs(pred_graph) > 0 + + tp = (vec1 & vec2).sum() + tn = ((~vec1) & (~vec2)).sum() + fp = ((~vec1) & (vec2)).sum() + fn = ((vec1) & (~vec2)).sum() + + return torch.stack([tn, fp, fn, tp]).view(2, 2) + + +def confusion_matrix_batched(true_graph: torch.Tensor, pred_graphs: torch.Tensor) -> Tensor: + """Evaluate metrics for the difference between a true and a set of predicted graphs. + + Args: + true_graph: The true graph; Tensor of shape (state_size, state_size). + pred_graphs: Set of predicted graphs; Tensor of shape (batch_size, state_size, state_size). + + Returns: + confusion matrix: Tensor of shape (2, 2) reprsenting confusion matrix acrpss all predicted graphs. + """ + batched_confusion_matrix = torch.vmap(confusion_matrix, in_dims=(None, 0), out_dims=0)(true_graph, pred_graphs) + return torch.sum(batched_confusion_matrix, dim=0) + + +def true_positive_rate(tp: int, fn: int) -> float: + """Compute true positive rate given number of true positives and false negatives. + + Args: + tp: Number of true positives. + fn: Number of false negatives. + + Returns: + True positive rate; -1 if tp + fn == 0. + """ + return -1 if tp + fn == 0 else tp / (tp + fn) + + +def false_discovery_rate(tp: int, fp: int) -> float: + """Compute false discovery rate given number of true positives and false positives. + + Args: + tp: Number of true positives. + fp: Number of false positives. + + Returns: + False discovery rate; -1 if tp + fp == 0. + """ + return -1 if tp + fp == 0 else fp / (tp + fp) + + +def f1_score(tp: int, fp: int, fn: int) -> float: + """Compute F1 score given number of true positives, false positives, and false negatives. + + Args: + tp: Number of true positives. + fp: Number of false positives. + fn: Number of false negatives. + + Returns: + F1 score; -1 if tp + fp + fn == 0. + """ + return -1 if tp + fp + fn == 0 else 2 * tp / (2 * tp + fp + fn) diff --git a/research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py b/research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py new file mode 100644 index 0000000..5f6ea42 --- /dev/null +++ b/research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py @@ -0,0 +1,12 @@ +import torch +from tensordict import TensorDict + + +def check_temporal_tensordict_shapes(num_timepoints: int, tds: TensorDict) -> bool: + """Check that the shapes within the TensorDict are consistent with the number of time points.""" + return all(val.shape[len(tds.batch_size)] == num_timepoints for val in tds.values()) + + +def temporal_tensordict_shapes(tds: TensorDict) -> dict[str, torch.Size]: + """Return the shapes within the TensorDict without batch and time dimensions.""" + return {key: val.shape[len(tds.batch_size) + 1 :] for key, val in tds.items()} diff --git a/research_experiments/scotch/src/scotch/sdes/__init__.py b/research_experiments/scotch/src/scotch/sdes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/research_experiments/scotch/src/scotch/sdes/scotch_sdes.py b/research_experiments/scotch/src/scotch/sdes/scotch_sdes.py new file mode 100644 index 0000000..166d794 --- /dev/null +++ b/research_experiments/scotch/src/scotch/sdes/scotch_sdes.py @@ -0,0 +1,187 @@ +import torch +import torchsde +from scotch.sdes.sde_modules import ContextualDriftCoefficient, DiffusionCoefficient, GraphCoefficient +from scotch.sdes.sdes_core import SDE +from torch import Tensor, nn + + +def _stable_division(a, b, epsilon=1e-7): + """This function is used to avoid division by zero.""" + b = torch.where(b.abs().detach() > epsilon, b, torch.full_like(b, fill_value=epsilon) * b.sign()) + return a / b + + +def swap_t_and_batch_dimensions(xs): + """This function is used to swap the time and batch dimensions of a tensor. Assume the input tensor has shape + [time, batch, ...]""" + return xs.transpose(0, 1) + + +class SCOTCHPriorSDE(SDE, nn.Module): + """Implements the prior SDE for SCOTCH (which takes graphs as input). + + The prior SDE is over latent_size variables. + dZ_t = h(Z_t, G) dt + g(Z_t) dW_t or dZ_t = h(Z_t, G) dt + g(Z_t, G) dW_t + where h is the prior drift coefficient and g is the diffusion coefficient. + + Requires that the graphs are specified in the constructor, such that the batch size is fixed. As such, this class + should usually be used for transient computation, e.g. a single call of torchsde.sdeint. + """ + + noise_type = "diagonal" + sde_type = "ito" + + def __init__(self, prior_drift_net: nn.Module, diffusion_net: nn.Module, graphs: Tensor): + """Constructor for SCOTCHPriorSDE. + + Args: + prior_drift_net: NN that computes drift coefficient of prior SDE; takes as input latent trajectories of + shape (batch_size, latent_size) and graphs of shape (batch_size, num_nodes, num_nodes) + diffusion_net: NN that computes diffusion coefficient of posterior SDE; takes as input latent trajectories + of shape (batch_size, latent_size) + graphs: Graphs used as input to prior_drift_net; shape (batch_size, num_nodes, num_nodes) + """ + super().__init__() + + self.prior_drift_net = prior_drift_net + self.diffusion_net = diffusion_net + self.graphs = graphs + + def f(self, t: float, y: Tensor): + """Computes the drift coefficient of the SDE. + + Args: + y: Latent state of shape (batch_size, latent_size) + t: Time point at which to evaluate drift coefficient. + + Returns: + Drift coefficient of shape (batch_size, latent_size) + """ + return self.prior_drift_net(y, self.graphs) + + def g(self, t: float, y: Tensor): + """Computes the diffusion coefficient of the SDE. + + Args: + y: Latent state of shape (batch_size, latent_size) + t: Time point at which to evaluate diffusion coefficient. + + Returns: + Diffusion coefficient of shape (batch_size, latent_size) + """ + return self.diffusion_net(y, self.graphs) + + @torch.no_grad() + def sample(self, z0: Tensor, ts: Tensor, bm: torchsde.BrownianInterval = None, dt: float = 1e-3): + """Samples from the prior SDE. + + Args: + z0: Initial latent state; shape (batch_size, latent_size) + ts: Time points at which to sample; shape (num_time_points,) + bm: Optionally, a precomputed Brownian motion process to control the randomness of the sampling + dt: Step-size; defaults to 1e-3 + + Returns: + Latent trajectories sampled from the prior SDE; shape (batch_size, num_time_points, latent_size) + """ + zs = torchsde.sdeint(self, z0, ts, names={"drift": "f"}, dt=dt, bm=bm) + + return swap_t_and_batch_dimensions(zs) + + +class AugmentedSCOTCHSDE(SDE, nn.Module): + """Implements the augmented SDE used for training SCOTCH. + + The augmented SDE is over latent_size + 1 variables, with an auxiliary/augmented variable. + dZ_t = f_aug(Z_t, c_t, G) dt + g_aug(Z_t, G) dW_t + where f_aug = (f, 0.5|u|^2), g_aug = (g, 0), c_t is the context vector at time t, and: + Posterior drift f(z, c): R^latent_size x R^context_size -> R^latent_size + Diffusion g(z): R^latent_size -> R^latent_size + Prior drift h(z, G): R^latent_size x R^num_nodes x R^num_nodes -> R^latent_size + Auxiliary term u(z, c, G) = (f(z, c) - h(z, G))/g(z) + + Requires that the context vectors and graphs are specified in the constructor, such that the batch size is fixed. + As such, this class should usually be used for transient computation, e.g. a single call of torchsde.sdeint. + """ + + noise_type = "diagonal" + sde_type = "ito" + + def __init__( + self, + posterior_drift_net: ContextualDriftCoefficient, + diffusion_net: DiffusionCoefficient, + prior_drift_net: GraphCoefficient, + ts_context_vectors: Tensor, + graphs: Tensor, + ): + """Constructor for AugmentedSCOTCHSDE. + + Args: + posterior_drift_net: NN that computes drift coefficient of posterior SDE; takes as input latent trajectories + of shape (batch_size, latent_size) and context vectors of shape (batch_size, context_size) + diffusion_net: NN that computes shared diffusion coefficient of prior and posterior SDE; takes as input + latent trajectories of shape (batch_size, latent_size) + prior_drift_net: NN that computes drift coefficient of prior SDE; takes as input latent trajectories of + shape (batch_size, latent_size) and graphs of shape (batch_size, num_nodes, num_nodes) + ts_context_vectors: Tuple (ts, context_vectors), where ts has shape (num_time_points,) and context_vectors + has shape (batch_size, num_time_points, context_size) and contains the context vectors at each time. + graphs: Graphs used as input to prior_drift_net; Tensor of shape (batch_size, num_nodes, num_nodes) + """ + super().__init__() + + self.posterior_drift_net = posterior_drift_net + self.diffusion_net = diffusion_net + self.prior_drift_net = prior_drift_net + + self.ts_context_vectors = ts_context_vectors + self.graphs = graphs + + _, context_vectors = ts_context_vectors + assert context_vectors.shape[0] == graphs.shape[0], "Batch size of context vectors and graphs must match" + + def f(self, t: float, y: Tensor): + """Implement drift coefficient of augmented SDE. + + Args: + t: Time point at which to evaluate drift coefficient. + y: Latent state of shape (batch_size, latent_size + 1). + + Returns: + Drift coefficient of shape (batch_size, latent_size + 1). + """ + # Remove augmented/auxiliary term + y = y[:, :-1] + + # Retrieve context vector at first time in ts that is greater than t. As the RNN encoder (c.f. + # TrajectoryEncoder) takes in trajectories in reverse time order, this context vector only depends on the + # observed trajectory in the future relative to t. + ts, context_vectors = self.ts_context_vectors + ts_idx = min(torch.searchsorted(ts, t, right=True).item(), len(ts) - 1) + posterior_drift = self.posterior_drift_net(y, context_vectors[:, ts_idx, :]) + + # Compute diffusion and prior drift coefficients + diffusion = self.diffusion_net(y, self.graphs) # CHANGED with new implementatino of diffusion + prior_drift = self.prior_drift_net(y, self.graphs) + u = _stable_division(posterior_drift - prior_drift, diffusion) + logqp = 0.5 * (u**2).sum(dim=1, keepdim=True) + + return torch.cat([posterior_drift, logqp], dim=-1) + + def g(self, t: float, y: Tensor): + """Implement diffusion coefficient of augmented SDE. + + Args: + y: Latent state of shape (batch_size, latent_size + 1). + t: Time point at which to evaluate diffusion coefficient. + + Returns: + Diffusion coefficient of shape (batch_size, latent_size + 1). + """ + # Remove augmented/auxiliary term + y = y[:, :-1] + + # Compute and return (augmented) diffusion coefficient + diffusion = self.diffusion_net(y, self.graphs) + + return torch.nn.functional.pad(diffusion, (0, 1), value=0) diff --git a/research_experiments/scotch/src/scotch/sdes/sde_modules.py b/research_experiments/scotch/src/scotch/sdes/sde_modules.py new file mode 100644 index 0000000..df16220 --- /dev/null +++ b/research_experiments/scotch/src/scotch/sdes/sde_modules.py @@ -0,0 +1,60 @@ +"""This implements the abstract classes for the drift and diffusion coefficients used in SCOTCH""" +from abc import ABC, abstractmethod + +from torch import Tensor, nn + + +class TrajectoryGraphEncoder(ABC, nn.Module): + @abstractmethod + def forward(self, trajectories: Tensor, graphs: Tensor) -> Tensor: + """Encode trajectories and graphs into context vectors for posterior SDE. + + Args: + trajectories (Tensor): Observed trajectories, shape (batch_size, num_time_points, observed_size) + graphs (Tensor): Weighted adjacency matrix, shape (batch_size, observed_size, observed_size) + + Returns: + Tensor: Context vectors, shape (batch_size, num_time_points, context_size) + """ + + +class ContextualDriftCoefficient(ABC, nn.Module): + @abstractmethod + def forward(self, latent_states: Tensor, context_vectors: Tensor) -> Tensor: + """Compute drift coefficient, given (batched) input latent states and context vectors. This will be used as + the drift coefficient of the posterior SDE. + + Args: + latent_states: Latent states, shape (batch_size, latent_size) + context_vectors: Context vectors, shape (batch_size, context_size) + + Returns: + Tensor: Drift coefficient, shape (batch_size, latent_size) + """ + + +class GraphCoefficient(ABC, nn.Module): + @abstractmethod + def forward(self, latent_states: Tensor, graphs: Tensor): + """Compute drift (or diffusion) coefficient. The difference is that it supports the graph as input. + + Args: + latent_states: Latent states, shape (batch_size, latent_size) + graphs: Context vectors, shape (batch_size, latent_size, latent_size) + + Returns: + Tensor: Drift (or diffusion) coefficient, shape (batch_size, latent_size) + """ + + +class DiffusionCoefficient(ABC, nn.Module): + @abstractmethod + def forward(self, latent_states: Tensor): + """Compute diffusion coefficient, given (batched) input latent states. + + Args: + latent_states: Latent states, shape (batch_size, latent_size) + + Returns: + Tensor: Diffusion coefficient, shape (batch_size, latent_size) + """ diff --git a/research_experiments/scotch/src/scotch/sdes/sdes_core.py b/research_experiments/scotch/src/scotch/sdes/sdes_core.py new file mode 100644 index 0000000..79b4adb --- /dev/null +++ b/research_experiments/scotch/src/scotch/sdes/sdes_core.py @@ -0,0 +1,45 @@ +"""This implements the abstract template for stochastic differential equations""" +from abc import ABC, abstractmethod + +import torch + + +class SDE(ABC): + """Abstract base class for stochastic differential equations. + + Any concrete class must implement the f and g methods, and noise_type and sde_type properties. + """ + + @property + @abstractmethod + def noise_type(self) -> str: + """Type of SDE noise. + + Must be one of "diagonal", "additive", "scalar". or "general". + """ + + @property + @abstractmethod + def sde_type(self): + """Type of SDE. + + Must be one of "ito" or "stratonovich". + """ + + @abstractmethod + def f(self, t: float, y: torch.Tensor) -> torch.Tensor: + """Drift coefficient of the SDE. + + Args: + t: time point; scalar + y: variable values; tensor of size (batch_size, state_size) + """ + + @abstractmethod + def g(self, t: float, y: torch.Tensor) -> torch.Tensor: + """Diffusion coefficient of the SDE. + + Args: + t: time point; scalar + y: variable values; tensor of size (batch_size, state_size) + """ diff --git a/src/causica/data_generation/generate_data.py b/src/causica/data_generation/generate_data.py index 7b2b7aa..d236ab1 100644 --- a/src/causica/data_generation/generate_data.py +++ b/src/causica/data_generation/generate_data.py @@ -52,6 +52,7 @@ def get_graph_distribution( graph_type: str, num_nodes: int | None = None, num_edges: int | None = None, + probs: float | None = None, graph_file: str | None = None, **storage_options, ) -> AdjacencyDistribution: @@ -61,6 +62,7 @@ def get_graph_distribution( graph_type: The type of graph to generate. Either "er" for Erdos-Renyi or "numpy" for a numpy graph. num_nodes: The number of nodes in the graph. Not used if using a numpy graph. num_edges: The number of edges in the graph. Not used if using a numpy graph. + probs: A float of the probability that an edge exists between 2 nodes graph_file: The path to a graph file if using a numpy graph. storage_options: The storage options to pass to fsspec. @@ -68,9 +70,15 @@ def get_graph_distribution( The adjacency matrix of the graph. """ if graph_type == "er": - if num_edges is None or num_nodes is None: - raise ValueError("Must provide num_edges and num_nodes for Erdos-Renyi graph.") - return ErdosRenyiDAGDistribution(num_nodes=num_nodes, num_edges=torch.tensor(num_edges, dtype=torch.float32)) + if num_nodes is None: + raise ValueError("Must provide num_nodes for Erdos-Renyi graph.") + num_edges_tensor = None + if num_edges is not None: + num_edges_tensor = torch.tensor(num_edges, dtype=torch.float32) + probs_tensor = None + if probs is not None: + probs_tensor = torch.tensor(probs) + return ErdosRenyiDAGDistribution(num_nodes=num_nodes, num_edges=num_edges_tensor, probs=probs_tensor) if graph_type == "numpy": if graph_file is None: raise ValueError("Must provide graph_file for numpy graph.") @@ -230,8 +238,7 @@ def sample_treatment_and_effect( descendants = nx.descendants(nx_graph, treatment) effects = np.random.choice(list(descendants), size=num_effects, replace=False) return treatment, effects.tolist() - else: - warnings.warn("No edges found. Defaulting to random sampling.") + warnings.warn("No edges found. Defaulting to random sampling.") samples = np.random.choice(node_names, size=1 + num_effects, replace=False) treatment = samples[0] @@ -422,6 +429,7 @@ def generate_sem_sampler( num_edges: int | None, graph_type: str, function_type: str, + probs: float | None = None, storage_options: dict[str, str] | None = None, ) -> SEMSampler: """Generates a SEM according to specifications @@ -431,6 +439,7 @@ def generate_sem_sampler( num_nodes: The number of nodes in the graph. Not used if using a numpy graph. graph_file: The path to a graph file if using a numpy graph. This can be any fsspec compatible url. num_edges: The number of edges in the graph. Not used if using a numpy graph. + probs: A float of the probability that an edge exists between 2 nodes graph_type: The type of graph to generate. Either "er" for Erdos-Renyi or "numpy" for a numpy graph. function_type: The type of function to generate. Either "linear" or "rff". storage_options: The storage options to pass to fsspec. @@ -446,7 +455,7 @@ def generate_sem_sampler( num_nodes = len(size_dict) print("Generating SEM sampler...") - adjacency_dist = get_graph_distribution(graph_type, num_nodes, num_edges, graph_file, **storage_options) + adjacency_dist = get_graph_distribution(graph_type, num_nodes, num_edges, probs, graph_file, **storage_options) functional_relationships_sampler = get_functional_relationship_sampler(function_type, shapes_dict) joint_noise_dist_sampler = get_noise_module_sampler(variables) @@ -516,6 +525,7 @@ def generate_save_plot_synthetic_data( num_nodes: int | None = None, graph_file: str | None = None, num_edges: int | None = None, + probs: float | None = None, overwrite: bool = False, plot_kind: str = "", plot_num: int = 10, @@ -538,6 +548,7 @@ def generate_save_plot_synthetic_data( num_nodes: The number of nodes in the graph. Not used if using a numpy graph. graph_file: The path to a graph file if using a numpy graph. This can be any fsspec compatible url. num_edges: The number of edges in the graph. Not used if using a numpy graph. + probs: A float of the probability that an edge exists between 2 nodes overwrite: Whether to overwrite the dataset if it already exists. plot_kind: Type of joint plot to create. plot_num: Maximum number of variables to plot. @@ -551,6 +562,7 @@ def generate_save_plot_synthetic_data( variables=variables, graph_file=graph_file, num_edges=num_edges, + probs=probs, graph_type=graph_type, function_type=function_type, storage_options=storage_options, @@ -592,6 +604,7 @@ def main() -> None: parser.add_argument("--num-interventions", "-i", type=int, default=1) parser.add_argument("--num-variables", "-nv", type=int) parser.add_argument("--num-edges", "-ne", type=int) + parser.add_argument("--edge-probabilities", "-p", type=float) parser.add_argument( "--graph-type", "-g", @@ -642,6 +655,7 @@ def main() -> None: num_nodes=args.num_variables, graph_file=args.graph_file, num_edges=args.num_edges, + probs=args.edge_probabilities, graph_type=args.graph_type, datadir=args.datadir, function_type=args.function_type, diff --git a/src/causica/lightning/callbacks.py b/src/causica/lightning/callbacks.py index e16ee26..55c1d8a 100644 --- a/src/causica/lightning/callbacks.py +++ b/src/causica/lightning/callbacks.py @@ -42,7 +42,7 @@ def on_train_batch_end( else: self.scheduler.enable(auglag_loss) - is_converged = self.scheduler.step( + is_converged, convergence_reasons = self.scheduler.step( optimizer=optimizer, loss=auglag_loss, loss_value=outputs["loss"], @@ -51,6 +51,20 @@ def on_train_batch_end( # Notify trainer to stop if the auglag algorithm has converged if is_converged: + pl_module.log( + "auglag_inner_convergence_reason", + convergence_reasons.inner_convergence_reason.value, + on_epoch=True, + rank_zero_only=True, + prog_bar=False, + ) + pl_module.log( + "auglag_outer_convergence_reason", + convergence_reasons.outer_convergence_reason.value, + on_epoch=True, + rank_zero_only=True, + prog_bar=False, + ) trainer.should_stop = True def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None: diff --git a/src/causica/lightning/data_modules/variable_spec_data.py b/src/causica/lightning/data_modules/variable_spec_data.py index 74e0d97..a726b32 100644 --- a/src/causica/lightning/data_modules/variable_spec_data.py +++ b/src/causica/lightning/data_modules/variable_spec_data.py @@ -10,6 +10,7 @@ from torch.utils.data import DataLoader from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, VariablesMetadata, load_data +from causica.datasets.interventional_data import CounterfactualData, InterventionData from causica.datasets.normalization import ( FitNormalizerType, Normalizer, @@ -93,7 +94,7 @@ def __init__( self.default_offset = default_offset self.log_normalize_min_margin = log_normalize_min_margin - self.normalize_data = standardize or log_normalize + self.use_normalizer = standardize or log_normalize self.normalizer: Optional[Normalizer] = None self._dataset_train: TensorDictBase self._dataset_test: TensorDictBase @@ -229,19 +230,38 @@ def prepare_data(self): for variable in variables_metadata.variables: self._column_names[variable.group_name].append(variable.name) - if self.normalize_data: + if self.use_normalizer: # Only applied to continuous variables normalization_variables = {k for k, v in self._variable_types.items() if v == VariableTypeEnum.CONTINUOUS} self.normalizer = self.create_normalizer(normalization_variables)( self._dataset_train.select(*normalization_variables) ) - self._dataset_train = self.normalizer(self._dataset_train) - self._dataset_test = self.normalizer(self._dataset_test) - if self.load_validation: - self._dataset_valid = self.normalizer(self._dataset_valid) + self.normalize_data() else: self.normalizer = JointTransformModule({}) + def normalize_data(self): + self._dataset_train = self.normalizer(self._dataset_train) + self._dataset_test = self.normalizer(self._dataset_test) + if self.load_validation: + self._dataset_valid = self.normalizer(self._dataset_valid) + + if self.load_interventional: + for intervention in self.interventions: + for i in intervention: + if isinstance(i, InterventionData): + i.intervention_data = self.normalizer(i.intervention_data) + i.intervention_values = self.normalizer(i.intervention_values) + i.condition_values = self.normalizer(i.condition_values) + + if self.load_counterfactual: + for cf in self.counterfactuals: + for c in cf: + if isinstance(c, CounterfactualData): + c.counterfactual_data = self.normalizer(c.counterfactual_data) + c.factual_data = self.normalizer(c.factual_data) + c.intervention_values = self.normalizer(c.intervention_values) + def train_dataloader(self): return DataLoader( dataset=self.dataset_train, diff --git a/src/causica/training/auglag.py b/src/causica/training/auglag.py index 569b09b..292d8ec 100644 --- a/src/causica/training/auglag.py +++ b/src/causica/training/auglag.py @@ -1,12 +1,58 @@ -from collections import deque +from collections import deque, namedtuple from dataclasses import dataclass, field -from typing import Any, Optional, Union +from enum import Enum +from typing import Any, Optional, Tuple, Union import torch from dataclasses_json import dataclass_json from torch.optim import Optimizer +class AugLagOuterConvergenceReason(Enum): + """ + Enum class to represent different reasons for outer loop convergence in AugLag learning rate scheduler. + + Attributes: + MAX_OUTER_STEPS_REACHED: Number of outer steps has reached `max_outer_steps`. + PENALTY_TOLERANCE_REACHED: The constraint has been below the `penalty_tolerance` for more than `patience_penalty_reached` steps. + PATIENCE_MAX_RHO_REACHED: Rho has been over `safety_rho` for more than `patience_max_rho` steps. + NOT_CONVERGED: Not converged. + """ + + MAX_OUTER_STEPS_REACHED = 1 + + PENALTY_TOLERANCE_REACHED = 2 + + PATIENCE_MAX_RHO_REACHED = 3 + + NOT_CONVERGED = 4 + + +class AugLagInnerConvergenceReason(Enum): + """ + Enum class to represent different reasons for inner loop convergence in AugLag learning rate scheduler. + + Attributes: + MAX_INNER_STEPS_REACHED: Number of inner steps has reached `max_inner_steps`. + NUM_LR_UPDATES_REACHED: Number of learning rate updates has reached `max_lr_down`. + EARLY_STOPPING_REACHED: The current step counter is equal to or greater than the sum of the last best step and the inner early stopping patience. + NOT_CONVERGED: Not converged. + """ + + MAX_INNER_STEPS_REACHED = 1 + + NUM_LR_UPDATES_REACHED = 2 + + EARLY_STOPPING_REACHED = 3 + + NOT_CONVERGED = 4 + + +AugLagConvergenceReasonTuple = namedtuple( + "AugLagConvergenceReasonTuple", ["inner_convergence_reason", "outer_convergence_reason"] +) + + class AugLagLossCalculator(torch.nn.Module): def __init__(self, init_alpha: float, init_rho: float): super().__init__() @@ -106,19 +152,31 @@ def _init_new_inner_optimisation(self) -> None: self.loss_tracker_sum: Optional[torch.Tensor] = None self.step_counter = 0 - def _is_inner_converged(self) -> bool: + def _is_inner_converged(self) -> Tuple[bool, AugLagInnerConvergenceReason]: """Check if the inner optimization loop has converged, based on maximum number of inner steps, number of lr updates. Returns: bool: Return True if converged, else False. + reason: Reason for convergence. """ - return ( - self.step_counter >= self.config.max_inner_steps - or self.num_lr_updates >= self.config.max_lr_down - or self.last_best_step + self.config.inner_early_stopping_patience <= self.step_counter - ) - - def _is_outer_converged(self) -> bool: + # Define a dictionary mapping conditions to convergence reasons + conditions = { + AugLagInnerConvergenceReason.MAX_INNER_STEPS_REACHED: self.step_counter >= self.config.max_inner_steps, + AugLagInnerConvergenceReason.NUM_LR_UPDATES_REACHED: self.num_lr_updates >= self.config.max_lr_down, + AugLagInnerConvergenceReason.EARLY_STOPPING_REACHED: self.last_best_step + + self.config.inner_early_stopping_patience + <= self.step_counter, + } + + # Check each condition + for reason, condition in conditions.items(): + if condition: + return True, reason + + # If none of the conditions were met, return False and NOT_CONVERGED + return False, AugLagInnerConvergenceReason.NOT_CONVERGED + + def _is_outer_converged(self) -> Tuple[bool, AugLagOuterConvergenceReason]: """Check if the outer loop has converged. Determined as converged if any of the below conditions are true. If `force_not_converged` is true, only (1) is checked. @@ -126,16 +184,29 @@ def _is_outer_converged(self) -> bool: 2. The constraint has been below the `penalty_tolerance` for more than `patience_penalty_reached` steps. 3. Rho has been over `safety_rho` for more than `patience_max_rho` steps. Returns: - True if outer loop has converged + bool: Return True if outer loop is converged, else False. + reason: Reason for convergence. """ + conditions = { + AugLagOuterConvergenceReason.MAX_OUTER_STEPS_REACHED: self.outer_opt_counter >= self.config.max_outer_steps, + AugLagOuterConvergenceReason.PENALTY_TOLERANCE_REACHED: self.outer_below_penalty_tol + >= self.config.patience_penalty_reached, + AugLagOuterConvergenceReason.PATIENCE_MAX_RHO_REACHED: self.outer_max_rho >= self.config.patience_max_rho, + } + if self.config.force_not_converged: - return self.outer_opt_counter >= self.config.max_outer_steps + return ( + conditions[AugLagOuterConvergenceReason.MAX_OUTER_STEPS_REACHED], + AugLagOuterConvergenceReason.MAX_OUTER_STEPS_REACHED, + ) + + # Check each condition + for reason, condition in conditions.items(): + if condition: + return True, reason - return ( - self.outer_opt_counter >= self.config.max_outer_steps - or self.outer_below_penalty_tol >= self.config.patience_penalty_reached - or self.outer_max_rho >= self.config.patience_max_rho - ) + # If none of the conditions were met, return False and NOT_CONVERGED + return False, AugLagOuterConvergenceReason.NOT_CONVERGED def _enough_steps_since_last_lr_update(self) -> bool: """Check if enough steps have been taken since the previous learning rate update, based on the previous one. @@ -218,7 +289,9 @@ def _update_lagrangian_params(self, loss: AugLagLossCalculator): loss.alpha = torch.min(loss.alpha, torch.full_like(loss.alpha, self.config.safety_alpha)) loss.rho = torch.min(loss.rho, torch.full_like(loss.rho, self.config.safety_rho)) - def _is_auglag_converged(self, optimizer: Union[Optimizer, list[Optimizer]], loss: AugLagLossCalculator) -> bool: + def _is_auglag_converged( + self, optimizer: Union[Optimizer, list[Optimizer]], loss: AugLagLossCalculator + ) -> Tuple[bool, AugLagConvergenceReasonTuple]: """Checks if the inner and outer loops have converged. If inner loop is converged, it initilaizes the optimisation parameters for a new inner loop. If both are converged, it returns True. @@ -228,11 +301,14 @@ def _is_auglag_converged(self, optimizer: Union[Optimizer, list[Optimizer]], los Returns: bool: Returns True if both inner and outer have converged, else False + dict: Dictionary of convergence reasons for inner and outer loop """ - if self._is_inner_converged(): - if self._is_outer_converged(): - return True + is_inner_converged, inner_convergence_reason = self._is_inner_converged() + is_outer_converged, outer_convergence_reason = self._is_outer_converged() + if is_inner_converged: + if is_outer_converged: + return True, AugLagConvergenceReasonTuple(inner_convergence_reason, outer_convergence_reason) self._update_lagrangian_params(loss) self.outer_opt_counter += 1 self._init_new_inner_optimisation() @@ -240,7 +316,7 @@ def _is_auglag_converged(self, optimizer: Union[Optimizer, list[Optimizer]], los elif self._enough_steps_since_last_lr_update() and self._enough_steps_since_best_model(): self._update_lr(optimizer) - return False + return False, AugLagConvergenceReasonTuple(inner_convergence_reason, outer_convergence_reason) def _update_loss_tracker(self, loss_value: torch.Tensor): """Update the loss tracker with the current loss value. @@ -306,7 +382,7 @@ def step( loss: AugLagLossCalculator, loss_value: torch.Tensor, lagrangian_penalty: torch.Tensor, - ) -> bool: + ) -> Tuple[bool, AugLagConvergenceReasonTuple]: """The main update method to take one auglag inner step. Args: @@ -317,9 +393,13 @@ def step( Returns: bool: if the auglag has converged (False) or not (True) + reason: named tuple (`AugLagConvergenceReasonTuple`) of convergence reasons for inner and outer loop, see + also `AugLagInnerConvergenceReason` and `AugLagOuterConvergenceReason`. """ if self.disabled: - return False + return False, AugLagConvergenceReasonTuple( + AugLagInnerConvergenceReason.NOT_CONVERGED, AugLagOuterConvergenceReason.NOT_CONVERGED + ) assert torch.all(lagrangian_penalty >= 0), "auglag penalty must be non-negative" self._update_loss_tracker(loss_value.detach()) self._cur_lagrangian_penalty = lagrangian_penalty.detach() diff --git a/test/data_generation/test_generate_data.py b/test/data_generation/test_generate_data.py index bac9118..60551e8 100644 --- a/test/data_generation/test_generate_data.py +++ b/test/data_generation/test_generate_data.py @@ -204,6 +204,32 @@ def test_generate_sem_numpy(five_node_graph: torch.Tensor, five_variable_one_dim torch.testing.assert_close(sem.graph, five_node_graph) +def test_generate_sem_er_with_probs(five_variable_one_dim_metadata: VariablesMetadata): + num_nodes = 5 + num_sem_samples = 1000 + probs = 0.5 + expected_num_edges = num_nodes * (num_nodes - 1) / 2 * probs + + variables = get_variable_definitions(num_nodes=num_nodes) + sem_sampler = generate_sem_sampler( + variables=variables, + graph_file=None, + num_edges=None, + probs=probs, + graph_type="er", + function_type="linear", + ) + num_edges_list = [] + for _ in range(num_sem_samples): + num_edges_list.append(sem_sampler.sample().graph.sum().item()) + + avg_num_edges = sum(num_edges_list) / num_sem_samples + + assert len(variables.variables) == 5 + assert variables == five_variable_one_dim_metadata + assert np.isclose(avg_num_edges, expected_num_edges, atol=0.05 * expected_num_edges) + + def test_sample_dataset(mixture_sem_sampler_and_shapes: tuple[SEMSampler, dict[str, torch.Size]]): mixture_sem_sampler, shapes_dict = mixture_sem_sampler_and_shapes sem = mixture_sem_sampler.sample() diff --git a/test/lightning/test_variable_spec_data.py b/test/lightning/test_variable_spec_data.py index a45b55e..74f5b11 100644 --- a/test/lightning/test_variable_spec_data.py +++ b/test/lightning/test_variable_spec_data.py @@ -5,6 +5,7 @@ import pytest import torch from pytorch_lightning.trainer import Trainer +from tensordict import TensorDict from causica.datasets.causica_dataset_format import Variable, VariablesMetadata from causica.lightning.data_modules.variable_spec_data import VariableSpecDataModule @@ -131,9 +132,11 @@ def test_variable_spec_data(tmp_path, normalize): json.dump(INTERVENTIONS, f) if normalize: - data_module = VariableSpecDataModule(tmp_path, batch_size=2, standardize=True, exclude_standardization=["x1"]) + data_module = VariableSpecDataModule( + tmp_path, batch_size=2, standardize=True, exclude_standardization=["x1"], load_interventional=True + ) else: - data_module = VariableSpecDataModule(tmp_path, batch_size=2) + data_module = VariableSpecDataModule(tmp_path, batch_size=2, load_interventional=True) data_module.prepare_data() @@ -147,6 +150,16 @@ def test_variable_spec_data(tmp_path, normalize): else: assert torch.all(data_module.dataset_train["x0"].mean(0) != torch.zeros(2)) + intervention_a, intervention_b, _ = data_module.interventions[0] + intervention_td = TensorDict({"x0": torch.ones(2)}, batch_size=[]) + samples_td = TensorDict({"x0": torch.ones(5, 2), "x1": torch.zeros(5, 2)}, batch_size=(5,)) + + torch.testing.assert_close(intervention_a.intervention_values, data_module.normalizer(intervention_td)) + torch.testing.assert_close(intervention_b.intervention_values, data_module.normalizer(intervention_td)) + + torch.testing.assert_close(intervention_a.intervention_data, data_module.normalizer(samples_td)) + torch.testing.assert_close(intervention_b.intervention_data, data_module.normalizer(samples_td)) + @pytest.mark.parametrize("log_normalize", [True, False]) def test_save_load_variable_spec_data(tmp_path, log_normalize): diff --git a/test/sem/test_sem.py b/test/sem/test_sem.py index 2bfa470..e116415 100644 --- a/test/sem/test_sem.py +++ b/test/sem/test_sem.py @@ -35,14 +35,14 @@ def test_do_linear_sem(graph, two_variable_dict): else: expected_mean = torch.tensor([0.0]) expected_log_probs = td.Independent(td.Normal(expected_mean, 1.0), 1).log_prob(array) - assert torch.allclose(log_probs, expected_log_probs) + torch.testing.assert_close(log_probs, expected_log_probs) noise = sem.sample_noise((10,)) do_sample = do_sem.noise_to_sample(noise) sample = sem.noise_to_sample(noise) if graph[1, 0] == 1.0: - assert torch.allclose(do_sample["x1"], expected_mean + noise["x1"]) + torch.testing.assert_close(do_sample["x1"], expected_mean + noise["x1"]) else: - assert torch.allclose(do_sample["x1"], sample["x1"]) + torch.testing.assert_close(do_sample["x1"], sample["x1"]) # Test multi-dim interventions do_sem = sem.do( @@ -57,9 +57,9 @@ def test_do_linear_sem(graph, two_variable_dict): do_sample = do_sem.noise_to_sample(noise) sample = sem.noise_to_sample(noise) if graph[1, 0] == 1.0: - assert torch.allclose(do_sample["x1"], expected_mean + noise["x1"]) + torch.testing.assert_close(do_sample["x1"], expected_mean + noise["x1"]) else: - assert torch.allclose(do_sample["x1"], sample["x1"]) + torch.testing.assert_close(do_sample["x1"], sample["x1"]) @pytest.mark.parametrize("graph", [torch.tensor([[0, 0], [1, 0.0]]), torch.tensor([[0, 1], [0, 0.0]])]) @@ -95,10 +95,7 @@ def test_batched_intervention_2d_graph(graph, intervention_variable, interventio .squeeze(1) ) non_batch_sample = torch.stack(non_batch_sample_list, dim=1) - diff = torch.abs(do_sample[sampled_variable] - non_batch_sample[sampled_variable]) - assert torch.allclose( - do_sample[sampled_variable], non_batch_sample[sampled_variable], atol=1e-5, rtol=1e-4 - ), f"{diff.max()}" + torch.testing.assert_close(do_sample[sampled_variable], non_batch_sample[sampled_variable], atol=1e-5, rtol=1e-4) def test_batched_intervention_3d_graph(three_variable_dict): @@ -119,7 +116,8 @@ def test_batched_intervention_3d_graph(three_variable_dict): noise = batched_do_sem.sample_noise((10,)) do_sample = batched_do_sem.noise_to_sample(noise) inferred_noise = batched_do_sem.sample_to_noise(do_sample) - assert all(torch.allclose(noise[key], inferred_noise[key]) for key in noise.keys() if key != "x2") + torch.testing.assert_close(noise["x1"], inferred_noise["x1"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(noise["x3"], inferred_noise["x3"], atol=1e-5, rtol=1e-4) non_batch_sample_list = [] for i, intervention in enumerate(intervention_value): @@ -129,8 +127,8 @@ def test_batched_intervention_3d_graph(three_variable_dict): non_batch_sample = torch.stack(non_batch_sample_list, dim=1) - assert torch.allclose(do_sample["x1"], non_batch_sample["x1"], atol=1e-5, rtol=1e-4) - assert torch.allclose(do_sample["x3"], non_batch_sample["x3"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(do_sample["x1"], non_batch_sample["x1"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(do_sample["x3"], non_batch_sample["x3"], atol=1e-5, rtol=1e-4) def test_intervention_batched_3d_graph(three_variable_dict): @@ -144,9 +142,8 @@ def test_intervention_batched_3d_graph(three_variable_dict): regular_noise = sem.sample_noise(torch.Size([10])) samples = sem.noise_to_sample(regular_noise) inferred_noise = sem.sample_to_noise(samples) - assert all( - torch.allclose(regular_noise[key], inferred_noise[key], atol=1e-5, rtol=1e-4) for key in regular_noise.keys() - ) + + torch.testing.assert_close(regular_noise, inferred_noise, atol=1e-5, rtol=1e-4) intervention_value = torch.tensor([1.42, 0.42]) do_sem = sem.do( @@ -158,7 +155,8 @@ def test_intervention_batched_3d_graph(three_variable_dict): noise = do_sem.sample_noise(torch.Size([10])) do_sample = do_sem.noise_to_sample(noise) inferred_noise = do_sem.sample_to_noise(do_sample) - assert all(torch.allclose(noise[key], inferred_noise[key]) for key in noise.keys() if key != "x2") + torch.testing.assert_close(noise["x1"], inferred_noise["x1"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(noise["x3"], inferred_noise["x3"], atol=1e-5, rtol=1e-4) def test_batched_intervention_batched_3d_graph(three_variable_dict): @@ -181,7 +179,8 @@ def test_batched_intervention_batched_3d_graph(three_variable_dict): do_sample = batched_do_sem.noise_to_sample(noise) assert do_sample.batch_size == torch.Size([10, 3, 2]) # 10 samples, 3 interventions, 2 graphs inferred_noise = batched_do_sem.sample_to_noise(do_sample) - assert all(torch.allclose(noise[key], inferred_noise[key]) for key in noise.keys() if key != "x2") + torch.testing.assert_close(noise["x1"], inferred_noise["x1"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(noise["x3"], inferred_noise["x3"], atol=1e-5, rtol=1e-4) non_batch_sample_list = [] for i, intervention in enumerate(intervention_value): non_batch_sample_list.append( @@ -189,8 +188,8 @@ def test_batched_intervention_batched_3d_graph(three_variable_dict): ) non_batch_sample = torch.stack(non_batch_sample_list, dim=1) - assert torch.allclose(do_sample["x1"], non_batch_sample["x1"], atol=1e-5, rtol=1e-4) - assert torch.allclose(do_sample["x3"], non_batch_sample["x3"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(do_sample["x1"], non_batch_sample["x1"], atol=1e-5, rtol=1e-4) + torch.testing.assert_close(do_sample["x3"], non_batch_sample["x3"], atol=1e-5, rtol=1e-4) def test_linear_sem_3d_graph_do_1_node(three_variable_dict): @@ -204,7 +203,7 @@ def test_linear_sem_3d_graph_do_1_node(three_variable_dict): log_probs = do_sem.log_prob(TensorDict({"x2": test_val[:, 0:2], "x3": test_val[:, 2:]}, batch_size=[100])) expected_mean = torch.einsum("i,ij->j", intervention_value, coef_matrix[:2, 2:]) expected_log_probs = td.Independent(td.Normal(expected_mean, 1.0), 1).log_prob(test_val) - assert torch.allclose(log_probs, expected_log_probs) + torch.testing.assert_close(log_probs, expected_log_probs) def test_linear_sem_3d_graph_condition_1_node(three_variable_dict): @@ -225,7 +224,7 @@ def test_linear_sem_3d_graph_condition_1_node(three_variable_dict): log_probs = condition_sem.log_prob(TensorDict({"x3": test_val}, batch_size=[100])) expected_mean = torch.einsum("i,ij->j", torch.cat([condition_value, condition_value]), coef_matrix[:4, 4:]) expected_log_probs = td.Independent(td.Normal(expected_mean, 1.0), 1).log_prob(test_val) - assert torch.allclose(log_probs, expected_log_probs) + torch.testing.assert_close(log_probs, expected_log_probs) def test_linear_sem_3d_graph_do_2_nodes(three_variable_dict): @@ -238,7 +237,7 @@ def test_linear_sem_3d_graph_do_2_nodes(three_variable_dict): log_probs = do_sem.log_prob(TensorDict({"x3": test_val}, batch_size=[100])) expected_mean = torch.einsum("i,ij->j", torch.cat((int_data["x1"], int_data["x2"])), coef_matrix[:4, 4:]) expected_log_probs = td.Independent(td.Normal(expected_mean, 1.0), 1).log_prob(test_val) - assert torch.allclose(log_probs, expected_log_probs) + torch.testing.assert_close(log_probs, expected_log_probs) @pytest.mark.parametrize("graph", [torch.tensor([[0, 0], [1, 0.0]]), torch.tensor([[0, 1], [0, 0.0]])]) diff --git a/test/training/test_auglag.py b/test/training/test_auglag.py index 5378594..def1423 100644 --- a/test/training/test_auglag.py +++ b/test/training/test_auglag.py @@ -2,7 +2,14 @@ from torch.nn import Parameter from torch.optim import Adam -from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig +from causica.training.auglag import ( + AugLagConvergenceReasonTuple, + AugLagInnerConvergenceReason, + AugLagLossCalculator, + AugLagLR, + AugLagLRConfig, + AugLagOuterConvergenceReason, +) def _get_auglag_config(lr_init_dict: dict[str, float]): @@ -104,10 +111,14 @@ def test_solve_auglag(): auglag_loss_tensor = auglag_loss(loss, constraint) auglag_loss_tensor.backward() optimizer.step() - converged = scheduler.step( + converged, convergence_reason = scheduler.step( optimizer=optimizer, loss=auglag_loss, loss_value=loss, lagrangian_penalty=constraint ) if converged: + assert convergence_reason == AugLagConvergenceReasonTuple( + AugLagInnerConvergenceReason.MAX_INNER_STEPS_REACHED, + AugLagOuterConvergenceReason.PENALTY_TOLERANCE_REACHED, + ) break step_counter += 1