diff --git a/docs/api/models/pyhealth.models.GAN.rst b/docs/api/models/pyhealth.models.GAN.rst index 1511b7406..896931dcf 100644 --- a/docs/api/models/pyhealth.models.GAN.rst +++ b/docs/api/models/pyhealth.models.GAN.rst @@ -1,7 +1,27 @@ pyhealth.models.GAN =================================== -The GAN model (pyhealth trainer does not apply to GAN, refer to the example/ChestXray-image-generation-GAN.ipynb for examples of using GAN model). +A Generative Adversarial Network built from a dataset with a single input +feature, whose processor picks the mode. With a ``multi_hot`` feature (for +example from :class:`~pyhealth.tasks.EHRCodeSetGenerationMIMIC3`), the GAN +learns to generate synthetic patients as sets of codes with MLP networks. With +a ``tensor`` feature of continuous values (for example from +:class:`~pyhealth.tasks.CARTVitalsGenerationEICU`), it generates new vectors in +the data's original units. With an ``image`` feature whose ``image_size`` is 32, 64 or 128 (for example from +:class:`~pyhealth.tasks.COVID19CXRClassification`), it generates small images +with CNN networks. ``examples/gan_tutorial.ipynb`` walks through both. + +The PyHealth ``Trainer`` does not apply to GAN: train it with +:meth:`~pyhealth.models.GAN.train_model`, then call +:meth:`~pyhealth.models.GAN.generate`, which returns synthetic patients in +code-set mode and a tensor in continuous and image mode. + +Reference: + Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., + Ozair, S., Courville, A., & Bengio, Y. (2014). + *Generative Adversarial Nets.* + In Advances in Neural Information Processing Systems (NeurIPS) 2014. + https://arxiv.org/abs/1406.2661 .. autoclass:: pyhealth.models.GAN :members: diff --git a/docs/api/tasks.rst b/docs/api/tasks.rst index b250cd271..e3844c2cc 100644 --- a/docs/api/tasks.rst +++ b/docs/api/tasks.rst @@ -217,6 +217,7 @@ Available Tasks DKA Prediction (MIMIC-IV) Drug Recommendation EHR Generation + Vital-Sign Generation (eICU) Length of Stay Prediction Length of Stay Prediction (StageNet MIMIC-IV) Medical Transcriptions Classification diff --git a/docs/api/tasks/pyhealth.tasks.vitals_generation_eicu.rst b/docs/api/tasks/pyhealth.tasks.vitals_generation_eicu.rst new file mode 100644 index 000000000..88f0ec740 --- /dev/null +++ b/docs/api/tasks/pyhealth.tasks.vitals_generation_eicu.rst @@ -0,0 +1,27 @@ +pyhealth.tasks.vitals_generation_eicu +=========================================== + +A task that turns eICU into static vital-sign vectors for generating new +patients: each patient's first ICU stay becomes ``[age, heart rate, +respiration rate, diastolic BP, mean BP]``, with each vital averaged over the +stay's first 24 hours. The values are continuous, so the task pairs with +:class:`~pyhealth.models.GAN`'s continuous mode; see +``examples/gan_tutorial.ipynb``. + +The task reads the ``vitalperiodic`` and ``vitalaperiodic`` tables: + +.. code-block:: python + + from pyhealth.datasets import eICUDataset + from pyhealth.tasks import CARTVitalsGenerationEICU + + dataset = eICUDataset( + root="https://physionet.org/files/eicu-crd-demo/2.0.1", + tables=["vitalperiodic", "vitalaperiodic"], + ) + samples = dataset.set_task(CARTVitalsGenerationEICU()) + +.. autoclass:: pyhealth.tasks.vitals_generation_eicu.CARTVitalsGenerationEICU + :members: + :undoc-members: + :show-inheritance: diff --git a/examples/gan_mimic3.ipynb b/examples/gan_mimic3.ipynb new file mode 100644 index 000000000..36505cc5a --- /dev/null +++ b/examples/gan_mimic3.ipynb @@ -0,0 +1,736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f292a0db", + "metadata": {}, + "source": [ + "# Generating Synthetic Patients with a GAN on MIMIC-III\n", + "\n", + "This notebook trains PyHealth's `GAN` to generate synthetic patients, each one a\n", + "set of diagnosis codes, from MIMIC-III data. It is a small example: training\n", + "takes under a minute on a CPU.\n", + "\n", + "A GAN (Generative Adversarial Network; Goodfellow et al., 2014) trains two\n", + "networks against each other:\n", + "\n", + "- the **generator** turns random noise into a fake patient;\n", + "- the **discriminator** looks at real and fake patients and learns to tell them\n", + " apart.\n", + "\n", + "Each network improves by beating the other: as the discriminator gets better at\n", + "spotting fakes, the generator has to produce more realistic patients to fool it.\n", + "\n", + "**Data.** We use Synthetic MIMIC-III, a public, artificial copy of MIMIC-III\n", + "hosted by PyHealth, so no credentials are needed. It has the same table format\n", + "as the real database: to use your own MIMIC-III copy, change `root` in step 2." + ] + }, + { + "cell_type": "markdown", + "id": "f897d85a", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4f7b8fae", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-16T22:22:35.036191Z", + "iopub.status.busy": "2026-10-16T22:22:35.035999Z", + "iopub.status.idle": "2026-10-16T22:22:50.876702Z", + "shell.execute_reply": "2026-10-16T22:22:50.875059Z" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "from pyhealth.datasets import MIMIC3Dataset, split_by_patient\n", + "from pyhealth.models import GAN\n", + "from pyhealth.tasks import EHRCodeSetGenerationMIMIC3\n", + "\n", + "torch.manual_seed(42)\n", + "# Keep PyHealth's progress logging out of the way.\n", + "logging.getLogger(\"pyhealth\").setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "ee589804", + "metadata": {}, + "source": [ + "## 2. Load MIMIC-III\n", + "\n", + "The GAN only needs the diagnoses table. The first run downloads it and caches\n", + "processed copies in `pyhealth_cache/`, which takes a few minutes; later runs\n", + "read the cache." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ae0a0838", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-16T22:22:50.879841Z", + "iopub.status.busy": "2026-10-16T22:22:50.878925Z", + "iopub.status.idle": "2026-10-16T22:22:50.894188Z", + "shell.execute_reply": "2026-10-16T22:22:50.893071Z" + } + }, + "outputs": [], + "source": [ + "base_dataset = MIMIC3Dataset(\n", + " root=\"https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III\",\n", + " tables=[\"diagnoses_icd\"],\n", + " cache_dir=\"pyhealth_cache\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cd2574c8", + "metadata": {}, + "source": [ + "## 3. Turn each patient into a set of codes\n", + "\n", + "The `EHRCodeSetGenerationMIMIC3` task pools each patient's diagnosis codes from\n", + "all of their hospital admissions into one set, keeping patients with at least\n", + "two admissions.\n", + "\n", + "Its `multi_hot` processor then turns each set into a vector with one slot per\n", + "code in the vocabulary: 1 if the patient has that code, 0 otherwise. These\n", + "vectors are what the GAN learns to generate." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a4fde199", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-16T22:22:50.896608Z", + "iopub.status.busy": "2026-10-16T22:22:50.896376Z", + "iopub.status.idle": "2026-10-16T22:25:23.162726Z", + "shell.execute_reply": "2026-10-16T22:25:23.161901Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/49993 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3.5))\n", + "series = [\n", + " (\"d_loss\", \"Discriminator\", \"#2a78d6\"),\n", + " (\"g_loss\", \"Generator\", \"#eb6834\"),\n", + "]\n", + "for key, label, color in series:\n", + " ax.plot(history[key], color=color, linewidth=2, label=label)\n", + " ax.annotate(\n", + " label,\n", + " xy=(len(history[key]) - 1, history[key][-1]),\n", + " xytext=(6, 0),\n", + " textcoords=\"offset points\",\n", + " va=\"center\",\n", + " color=\"#52514e\",\n", + " )\n", + "ax.set_xlabel(\"Epoch\")\n", + "ax.set_ylabel(\"Mean loss\")\n", + "ax.set_title(\"GAN training losses\", loc=\"left\")\n", + "ax.grid(axis=\"y\", color=\"#e4e3df\", linewidth=0.8)\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "ax.legend(frameon=False, loc=\"upper right\")\n", + "ax.set_xlim(0, len(history[\"d_loss\"]) + 4)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "656c2837", + "metadata": {}, + "source": [ + "Unlike a classifier's loss, these curves do not both settle downward: each\n", + "network's progress shows up as the other's loss rising. Ups and downs are\n", + "normal.\n", + "\n", + "Longer training is not automatically better. If the discriminator's loss drops\n", + "toward 0, it has won outright, and the generator typically collapses to\n", + "producing nearly identical patients (\"mode collapse\"). A short run is enough for\n", + "this example." + ] + }, + { + "cell_type": "markdown", + "id": "af8e01ba", + "metadata": {}, + "source": [ + "## 7. Generate synthetic patients\n", + "\n", + "The generator gives every code a probability. `generate` turns those into code\n", + "sets; with `random_sampling=True` it flips a weighted coin per code. The\n", + "default, keeping codes at or above 0.5, would rarely include uncommon codes, so\n", + "most synthetic patients would look alike." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cbea4a81", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-16T22:25:49.064858Z", + "iopub.status.busy": "2026-10-16T22:25:49.064671Z", + "iopub.status.idle": "2026-10-16T22:25:49.122962Z", + "shell.execute_reply": "2026-10-16T22:25:49.121373Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "synthetic_0: 20 codes, e.g. ['2762', '2875', '29181', '30560', '4019', '4160']\n", + "synthetic_1: 21 codes, e.g. ['2765', '29181', '30560', '4019', '41091', '41410']\n", + "synthetic_2: 25 codes, e.g. ['1173', '1580', '2851', '2875', '30560', '30981']\n" + ] + } + ], + "source": [ + "synthetic = model.generate(num_samples=1000, random_sampling=True)\n", + "\n", + "for patient in synthetic[:3]:\n", + " codes = patient[\"visits\"][0]\n", + " print(f\"{patient['patient_id']}: {len(codes)} codes, e.g. {codes[:6]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "1ae90bb6", + "metadata": {}, + "source": [ + "## 8. Compare with real patients\n", + "\n", + "Two quick checks against the held-out real patients:\n", + "\n", + "- **How many codes does a patient have?**\n", + "- **Are common real codes also common in synthetic patients?** For each of the\n", + " most frequent real codes, compare the share of real and synthetic patients\n", + " who have it, and correlate those shares across the whole vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aff058d9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-16T22:25:49.126555Z", + "iopub.status.busy": "2026-10-16T22:25:49.126018Z", + "iopub.status.idle": "2026-10-16T22:25:49.297975Z", + "shell.execute_reply": "2026-10-16T22:25:49.296767Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Codes per patient: real 16.7, synthetic 19.5\n", + "Correlation of per-code shares across all codes: 0.44\n" + ] + }, + { + "data": { + "text/html": [ + "
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ICD-9 codeReal patientsSynthetic patients
0401949%99%
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" + ], + "text/plain": [ + " ICD-9 code Real patients Synthetic patients\n", + "0 4019 49% 99%\n", + "1 4280 35% 92%\n", + "2 25000 34% 6%\n", + "3 41401 31% 3%\n", + "4 42731 31% 4%\n", + "5 53081 21% 7%\n", + "6 2449 20% 1%\n", + "7 5990 19% 76%\n", + "8 5849 18% 1%\n", + "9 51881 17% 7%" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor = samples.input_processors[\"visits\"]\n", + "real = torch.stack([test_dataset[i][\"visits\"] for i in range(len(test_dataset))])\n", + "fake = torch.stack([processor.process(p[\"visits\"][0]) for p in synthetic])\n", + "\n", + "print(f\"Codes per patient: real {real.sum(1).mean():.1f}, synthetic {fake.sum(1).mean():.1f}\")\n", + "\n", + "real_share = real.mean(0)\n", + "fake_share = fake.mean(0)\n", + "correlation = torch.corrcoef(torch.stack([real_share, fake_share]))[0, 1]\n", + "print(f\"Correlation of per-code shares across all codes: {correlation:.2f}\")\n", + "\n", + "index_to_code = {index: code for code, index in vocab.items()}\n", + "top = real_share.topk(10).indices.tolist()\n", + "pd.DataFrame(\n", + " {\n", + " \"ICD-9 code\": [index_to_code[i] for i in top],\n", + " \"Real patients\": [f\"{real_share[i]:.0%}\" for i in top],\n", + " \"Synthetic patients\": [f\"{fake_share[i]:.0%}\" for i in top],\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b0c627a3", + "metadata": {}, + "source": [ + "The GAN gets the overall shape roughly right, with a similar number of codes\n", + "per patient, but not the details: a few common codes appear in almost every\n", + "synthetic patient, while others that are common among real patients are rare.\n", + "Over-producing a handful of patterns like this is typical of a small GAN\n", + "trained briefly." + ] + }, + { + "cell_type": "markdown", + "id": "a196e5e3", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "We loaded MIMIC-III diagnoses, turned each patient into a set of codes, trained\n", + "a GAN for 30 epochs, and generated synthetic patients whose code counts and\n", + "common codes can be compared with real ones.\n", + "\n", + "This is a minimal example rather than a strong generator: the networks are\n", + "small, training is short, and the source data is itself synthetic. For a GAN\n", + "designed specifically for synthetic patient records, see `MedGAN` and\n", + "`examples/medgan_mimic3.py`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "4195e58e29174ed28dfaeb4605f98308": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "671dbb670a3e48788299581ac5cb126a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "VBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [], + "layout": "IPY_MODEL_4195e58e29174ed28dfaeb4605f98308", + "tabbable": null, + "tooltip": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyhealth/datasets/configs/eicu.yaml b/pyhealth/datasets/configs/eicu.yaml index be60087e2..576bd3728 100644 --- a/pyhealth/datasets/configs/eicu.yaml +++ b/pyhealth/datasets/configs/eicu.yaml @@ -195,5 +195,57 @@ tables: - "admitdxname" - "admitdxtext" + vitalperiodic: + file_path: "vitalPeriodic.csv" + patient_id: "uniquepid" + timestamp: null + join: + - file_path: "patient.csv" + "on": "patientunitstayid" + how: "inner" + columns: + - "uniquepid" + - "patienthealthsystemstayid" + - "unitvisitnumber" + - "hospitaladmitoffset" + - "hospitaldischargeoffset" + - "unitdischargeoffset" + - "hospitaldischargeyear" + - "hospitaldischargestatus" + attributes: + - "patientunitstayid" + - "observationoffset" + - "temperature" + - "sao2" + - "heartrate" + - "respiration" + - "systemicsystolic" + - "systemicdiastolic" + - "systemicmean" + + vitalaperiodic: + file_path: "vitalAperiodic.csv" + patient_id: "uniquepid" + timestamp: null + join: + - file_path: "patient.csv" + "on": "patientunitstayid" + how: "inner" + columns: + - "uniquepid" + - "patienthealthsystemstayid" + - "unitvisitnumber" + - "hospitaladmitoffset" + - "hospitaldischargeoffset" + - "unitdischargeoffset" + - "hospitaldischargeyear" + - "hospitaldischargestatus" + attributes: + - "patientunitstayid" + - "observationoffset" + - "noninvasivesystolic" + - "noninvasivediastolic" + - "noninvasivemean" + diff --git a/pyhealth/models/gan.py b/pyhealth/models/gan.py index 31318732d..9614a3f38 100644 --- a/pyhealth/models/gan.py +++ b/pyhealth/models/gan.py @@ -1,14 +1,12 @@ -import functools -from typing import Dict, List, Optional, Tuple -import pickle -import numpy as np - import torch -import torch.nn as nn import torch.nn.functional as F +from torch import nn +from tqdm import tqdm -from pyhealth.datasets import BaseSignalDataset +from pyhealth.datasets import get_dataloader from pyhealth.models import BaseModel, ResBlock2D +from pyhealth.processors import ImageProcessor, MultiHotProcessor, TensorProcessor + class Flatten(nn.Module): def forward(self, input): @@ -21,39 +19,162 @@ def forward(self, input): out = input.view(batch_size,-1) return out # (batch_size, *size) -class GAN(nn.Module): - """GAN model (take 128x128 or 64x64 or 32x32 images) +class GAN(BaseModel): + """GAN model for synthetic patient code sets, continuous vectors or small images. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. - Note: - We use CNN models as the encoder and decoder layers for now. + A generator turns random noise into a fake sample, and a discriminator + learns to tell fake samples from real ones; each network trains against + the other. ``dataset`` must have exactly one input feature, and that + feature's processor picks the mode: + + * **Code-set mode** (``multi_hot``): each sample is one multi-hot vector + over the code vocabulary, e.g. a MIMIC-III patient's pooled diagnosis + codes from :class:`~pyhealth.tasks.EHRCodeSetGenerationMIMIC3`. Both + networks are MLPs, and :meth:`generate` decodes synthetic patients. + * **Continuous mode** (``tensor``): each sample is one fixed-length vector of + continuous values, e.g. an ICU stay's mean vital signs from + :class:`~pyhealth.tasks.CARTVitalsGenerationEICU`. Both networks are + MLPs. :meth:`train_model` standardizes each feature with the training + data's mean and standard deviation, and :meth:`generate` returns a + tensor in the original units. + * **Image mode** (``image``): CNN networks for 32x32, 64x64 or 128x128 + images, sized from the processor's ``image_size`` and the images' + channels. :meth:`generate` returns a tensor of images. + + The PyHealth ``Trainer`` does not apply: the two networks are updated by + separate optimizers with opposing objectives, which :meth:`train_model` + handles. Args: - dataset: the dataset to train the model. It is used to query certain - information such as the set of all tokens. - feature_keys: list of keys in samples to use as features, - e.g. ["conditions", "procedures"]. - label_key: key in samples to use as label (e.g., "drugs"). - mode: one of "binary", "multiclass", or "multilabel". - embedding_dim: the embedding dimension. Default is 128. - hidden_dim: the hidden dimension. Default is 128. - **kwargs: other parameters for the Deepr layer. + dataset: a ``SampleDataset`` with exactly one input feature, backed by + a ``MultiHotProcessor``, a ``TensorProcessor`` or an + ``ImageProcessor``. + hidden_dim: the noise dimension, also the width of the hidden layers. + Default is 128. Examples: + >>> from pyhealth.datasets import create_sample_dataset + >>> from pyhealth.models import GAN + >>> samples = [ + ... {"patient_id": "p1", "visits": ["A", "B", "C"]}, + ... {"patient_id": "p2", "visits": ["A", "C", "D"]}, + ... ] + >>> dataset = create_sample_dataset( + ... samples=samples, + ... input_schema={"visits": "multi_hot"}, + ... output_schema={}, + ... ) + >>> model = GAN(dataset=dataset, hidden_dim=16) + >>> history = model.train_model(dataset, epochs=1, batch_size=2) + >>> len(model.generate(num_samples=3)) + 3 """ - def __init__( - self, - input_channel: int, - input_size: int, - hidden_dim: int = 128, - **kwargs, - ): - super(GAN, self).__init__() + def __init__(self, dataset=None, hidden_dim: int = 128, **kwargs): + # Before PyHealth 2.0, GAN was built from input_channel and input_size. + if ( + dataset is None + or isinstance(dataset, int) + or "input_channel" in kwargs + or "input_size" in kwargs + ): + raise TypeError( + "GAN(input_channel, input_size, ...) is deprecated: GAN now " + "takes a dataset whose single input feature is 'multi_hot' " + "(code sets), 'tensor' (continuous vectors) or 'image' (images), " + "e.g. " + "GAN(dataset=samples, hidden_dim=128)." + ) + super().__init__(dataset) self.hidden_dim = hidden_dim + if len(self.feature_keys) != 1: + raise ValueError( + "GAN expects exactly one input feature, got " + f"{self.feature_keys}." + ) + self.feature_key = self.feature_keys[0] + processor = dataset.input_processors[self.feature_key] + if isinstance(processor, MultiHotProcessor): + self.gan_mode = "code_set" + self._init_code_set_mode(processor) + elif isinstance(processor, TensorProcessor): + self.gan_mode = "continuous" + self._init_continuous_mode(dataset) + elif isinstance(processor, ImageProcessor): + self.gan_mode = "image" + self._init_image_mode(dataset, processor) + else: + raise TypeError( + f"GAN expects '{self.feature_key}' to use a MultiHotProcessor " + "(input_schema 'multi_hot'), a TensorProcessor ('tensor') or an " + f"ImageProcessor ('image'), got {type(processor).__name__}." + ) + + def _init_code_set_mode(self, processor: MultiHotProcessor) -> None: + """Builds MLP networks over the multi-hot code vocabulary.""" + self.input_dim = processor.size() + self._idx_to_code: list[str | None] = [None] * self.input_dim + for code, idx in processor.label_vocab.items(): + self._idx_to_code[idx] = code + + self._build_mlp_networks(bounded=True) + + def _init_continuous_mode(self, dataset) -> None: + """Builds MLP networks over fixed-length vectors of continuous values.""" + example = dataset[0][self.feature_key] + if example.dim() != 1: + raise ValueError( + "GAN's continuous mode expects each sample to be a 1-D vector, got " + f"shape {tuple(example.shape)}." + ) + self.input_dim = example.shape[0] + # Fitted by train_model(); identity scaling until then. + self.register_buffer("feature_mean", torch.zeros(self.input_dim)) + self.register_buffer("feature_std", torch.ones(self.input_dim)) + self._build_mlp_networks(bounded=False) + + def _build_mlp_networks(self, bounded: bool) -> None: + """MLP networks for vectors; ``bounded`` ends the generator in [0, 1].""" + self.discriminator = nn.Sequential( + nn.Linear(self.input_dim, self.hidden_dim), + nn.LeakyReLU(0.2), + nn.Linear(self.hidden_dim, self.hidden_dim), + nn.LeakyReLU(0.2), + nn.Linear(self.hidden_dim, 1), + nn.Sigmoid(), + ) + generator = [ + nn.Linear(self.hidden_dim, self.hidden_dim), + nn.ReLU(), + nn.Linear(self.hidden_dim, self.hidden_dim), + nn.ReLU(), + nn.Linear(self.hidden_dim, self.input_dim), + ] + if bounded: + generator.append(nn.Sigmoid()) + self.generator = nn.Sequential(*generator) + + def _init_image_mode(self, dataset, processor: ImageProcessor) -> None: + """Builds CNN networks sized to the dataset's images.""" + input_size = processor.image_size + if input_size not in (32, 64, 128): + raise ValueError( + "GAN's image mode supports an image_size of 32, 64 or 128, got " + f"{input_size}; set it in the input_schema, e.g. " + "(\"image\", {\"image_size\": 64})." + ) + if not processor.to_tensor or processor.normalize: + raise ValueError( + "GAN's image mode needs images as tensors in [0, 1], the " + "generator's output range: use to_tensor=True and " + "normalize=False." + ) + input_channel = dataset[0][self.feature_key].shape[0] + # encoder part if input_size == 128: self.discriminator = nn.Sequential( @@ -126,8 +247,10 @@ def __init__( def discriminate(self, x) -> torch.Tensor: y = self.discriminator(x) return y - + def sampling(self, n_samples, device) -> torch.Tensor: + if self.gan_mode != "image": + return torch.randn(n_samples, self.hidden_dim, device=device) eps = torch.randn(n_samples, self.hidden_dim, 1, 1).to(device) return eps @@ -135,26 +258,184 @@ def generate_fake(self, n_samples, device) -> torch.Tensor: eps = self.sampling(n_samples, device) fake_images = self.generator(eps) return fake_images - -if __name__ == "__main__": - - """ simple test """ - model = GAN( - input_channel=3, - input_size=128, - hidden_dim = 256, - ) - - # test generation - n_samples = 10 - device = "cpu" - fake_images = model.generate_fake(n_samples, device) - print (fake_images.shape) - - # test discriminate - y = model.discriminate(fake_images) - print (y.shape) - - - \ No newline at end of file + @staticmethod + def _bce(prob: torch.Tensor, target: float) -> torch.Tensor: + """Binary cross-entropy of discriminator outputs against one label.""" + return F.binary_cross_entropy(prob, torch.full_like(prob, target)) + + def _discriminator_loss( + self, real: torch.Tensor, fake: torch.Tensor + ) -> torch.Tensor: + """Rewards calling real samples real (1) and fakes fake (0). + + ``fake`` is detached so this loss only reaches the discriminator. + """ + real_loss = self._bce(self.discriminator(real), 1.0) + fake_loss = self._bce(self.discriminator(fake.detach()), 0.0) + return (real_loss + fake_loss) / 2 + + def _prepare_real(self, values: torch.Tensor) -> torch.Tensor: + """Moves a real batch to the model, standardized in continuous mode.""" + real = values.to(self.device).float() + if self.gan_mode == "continuous": + real = (real - self.feature_mean) / self.feature_std + return real + + def _fit_scaling(self, train_dataset) -> None: + """Fits continuous mode's per-feature mean and std on the training data.""" + values = torch.stack( + [sample[self.feature_key] for sample in train_dataset] + ).float() + self.feature_mean.copy_(values.mean(0)) + # A constant feature keeps unit scale rather than dividing by zero. + self.feature_std.copy_(values.std(0).clamp_min(1e-6)) + + def _generator_loss(self, fake: torch.Tensor) -> torch.Tensor: + """Rewards fakes that the discriminator calls real (1).""" + return self._bce(self.discriminator(fake), 1.0) + + def forward(self, **kwargs) -> dict[str, torch.Tensor]: + """Scores a batch of real samples against as many fresh fakes. + + Only computes the losses; :meth:`train_model` runs the optimization. + + Args: + **kwargs: a batch from ``get_dataloader``, holding the input + feature as a ``(batch_size, input_dim)`` multi-hot tensor in + code-set mode, a ``(batch_size, input_dim)`` vector in continuous + mode, or ``(batch_size, channels, size, size)`` images in + image mode. + + Returns: + A dict with the scalar ``d_loss`` and ``g_loss``, the + discriminator's ``real_prob`` and ``fake_prob`` (each + ``(batch_size, 1)``), and the generated ``fake`` samples, shaped + like the real batch (standardized, in continuous mode). + """ + real = self._prepare_real(kwargs[self.feature_key]) + fake = self.generate_fake(real.shape[0], self.device) + return { + "d_loss": self._discriminator_loss(real, fake), + "g_loss": self._generator_loss(fake), + "real_prob": self.discriminator(real), + "fake_prob": self.discriminator(fake), + "fake": fake, + } + + def train_model( + self, + train_dataset, + epochs: int = 50, + batch_size: int = 64, + lr: float = 2e-4, + device=None, + ) -> dict[str, list[float]]: + """Trains the GAN, alternating the two networks on every batch. + + Each batch takes one discriminator step (tell the real batch from a + fresh batch of fakes) and then one generator step (make those fakes + pass as real). Named ``train_model`` so it does not shadow + ``nn.Module.train()``. Call ``torch.manual_seed`` first for a + reproducible run. In continuous mode it first fits each feature's mean + and standard deviation on ``train_dataset``. + + Args: + train_dataset: a ``SampleDataset`` with the same input feature + as the one the model was built from, e.g. its training split. + epochs: number of passes over ``train_dataset``. Default is 50. + batch_size: samples per step. Default is 64. + lr: Adam learning rate for both networks. Default is 2e-4. + device: device to train on. Default is CUDA when available, + otherwise CPU. + + Returns: + The mean loss of each epoch, as ``{"d_loss": [...], + "g_loss": [...]}``. + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + self.to(device) + if self.gan_mode == "continuous": + self._fit_scaling(train_dataset) + + loader = get_dataloader(train_dataset, batch_size, shuffle=True) + + # beta1 = 0.5 is the usual Adam setting for GANs (Radford et al., 2016). + opt_d = torch.optim.Adam( + self.discriminator.parameters(), lr=lr, betas=(0.5, 0.999) + ) + opt_g = torch.optim.Adam( + self.generator.parameters(), lr=lr, betas=(0.5, 0.999) + ) + + history: dict[str, list[float]] = {"d_loss": [], "g_loss": []} + for _ in tqdm(range(epochs), desc="GAN training"): + d_total, g_total, n_batches = 0.0, 0.0, 0 + for batch in loader: + real = self._prepare_real(batch[self.feature_key]) + fake = self.generate_fake(real.shape[0], self.device) + + opt_d.zero_grad() + d_loss = self._discriminator_loss(real, fake) + d_loss.backward() + opt_d.step() + + opt_g.zero_grad() + g_loss = self._generator_loss(fake) + g_loss.backward() + opt_g.step() + + d_total += d_loss.item() + g_total += g_loss.item() + n_batches += 1 + history["d_loss"].append(d_total / max(n_batches, 1)) + history["g_loss"].append(g_total / max(n_batches, 1)) + return history + + def generate( + self, num_samples: int, random_sampling: bool = False + ) -> list[dict] | torch.Tensor: + """Generates synthetic samples. + + In code-set mode the generator gives each code a probability; a + patient has the codes at or above 0.5, or, with ``random_sampling``, + a coin flip per code weighted by its probability. + + Args: + num_samples: number of synthetic samples. + random_sampling: code-set mode only; sample each code instead of + thresholding. Default is False. + + Returns: + In code-set mode, one + ``{"patient_id": "synthetic_i", "visits": [[code, ...]]}`` dict + per patient, the format of :meth:`pyhealth.models.MedGAN.generate`: + a single visit holding the patient's codes, possibly none. In + continuous mode, a ``(num_samples, input_dim)`` tensor in the + training data's units. In image mode, a + ``(num_samples, channels, size, size)`` tensor of images in + ``[0, 1]``. + """ + if self.gan_mode != "code_set" and random_sampling: + raise ValueError("random_sampling only applies to code-set mode.") + with torch.no_grad(): + probs = self.generate_fake(num_samples, self.device) + if self.gan_mode == "continuous": + return (probs * self.feature_std + self.feature_mean).cpu() + if self.gan_mode == "image": + return probs.cpu() + if random_sampling: + rows = torch.bernoulli(probs) + else: + rows = (probs >= 0.5).float() + + results = [] + for i, row in enumerate(rows.cpu()): + codes = [ + self._idx_to_code[idx] + for idx in row.nonzero(as_tuple=True)[0].tolist() + if self._idx_to_code[idx] not in (None, "", "") + ] + results.append({"patient_id": f"synthetic_{i}", "visits": [codes]}) + return results diff --git a/pyhealth/tasks/__init__.py b/pyhealth/tasks/__init__.py index 2e7bd6bab..7e5ff570d 100644 --- a/pyhealth/tasks/__init__.py +++ b/pyhealth/tasks/__init__.py @@ -87,6 +87,9 @@ VariantClassificationClinVar, ) from .patient_linkage_mimic3 import PatientLinkageMIMIC3Task +from .vitals_generation_eicu import ( + CARTVitalsGenerationEICU as CARTVitalsGenerationEICU, +) def __getattr__(name: str): diff --git a/pyhealth/tasks/vitals_generation_eicu.py b/pyhealth/tasks/vitals_generation_eicu.py new file mode 100644 index 000000000..246cbf973 --- /dev/null +++ b/pyhealth/tasks/vitals_generation_eicu.py @@ -0,0 +1,147 @@ +"""Static vital-sign vectors per eICU ICU stay, for tabular generators. + +Each patient contributes one sample: their first ICU stay, summarized as age +plus the mean of four vital signs over the stay's first 24 hours. The task +only extracts and cleans values; generators such as +:class:`~pyhealth.models.GAN` scale them, fitting the scale on the training +split. +""" + +from typing import Any, ClassVar + +from pyhealth.data.data import Patient + +from .base_task import BaseTask + + +def _to_float(value: Any) -> float | None: + """Parses one eICU value; missing or non-numeric values become None.""" + if value is None: + return None + try: + return float(value) + except (TypeError, ValueError): + return None + + +def _parse_age(value: Any) -> float | None: + """Parses eICU's age, which is a string and reads "> 89" for 90 and over.""" + if isinstance(value, str) and value.strip() == "> 89": + return 90.0 + return _to_float(value) + + +class CARTVitalsGenerationEICU(BaseTask): + """One vital-sign vector per eICU patient, for generating new patients. + + The features are those of the Cardiac Arrest Risk Triage (CART) score + (Churpek et al., 2012) as used for eICU by Zhang et al. (2024): age, + respiratory rate, heart rate and diastolic blood pressure, plus mean + blood pressure. Unlike that work, which models vitals over time, each + sample here is one static snapshot of a stay. + + Takes each patient's first ICU stay (earliest discharge year, then + health-system stay, then unit visit number) and averages each vital over + the stay's first 24 hours (``0 <= observationoffset < 1440`` minutes). + Readings outside plausible ranges are treated as charting errors and + dropped before averaging. Stays without a heart rate, respiration rate or + non-invasive blood pressure in the window, or without an age, are skipped. + + The sample's ``vitals`` is ``[age, heart rate, respiration rate, + diastolic BP, mean BP]``, in the units eICU records (years, beats/min, + breaths/min, mmHg, mmHg); see :attr:`feature_names`. + + References: + Churpek, M. M., et al. (2012). Derivation of a cardiac arrest + prediction model using ward vital signs. *Critical Care Medicine*, + 40(7), 2102-2108. + + Zhang, X., Pu, Y., et al. (2024). Trajectory Flow Matching with + Applications to Clinical Time Series Modeling. NeurIPS 2024. + https://arxiv.org/abs/2410.21154 + + Examples: + >>> from pyhealth.datasets import eICUDataset + >>> from pyhealth.tasks import CARTVitalsGenerationEICU + >>> dataset = eICUDataset( # doctest: +SKIP + ... root="https://physionet.org/files/eicu-crd-demo/2.0.1", + ... tables=["vitalperiodic", "vitalaperiodic"], + ... ) + >>> samples = dataset.set_task(CARTVitalsGenerationEICU()) # doctest: +SKIP + >>> samples[0]["vitals"].shape # doctest: +SKIP + torch.Size([5]) + """ + + task_name: str = "CARTVitalsGenerationEICU" + input_schema: ClassVar[dict[str, str]] = {"vitals": "tensor"} + output_schema: ClassVar[dict[str, str]] = {} + + feature_names: ClassVar[tuple[str, ...]] = ( + "age", + "heartrate", + "respiration", + "noninvasivediastolic", + "noninvasivemean", + ) + # (table, attribute) -> plausible (low, high) range. + vitals: ClassVar[dict[tuple[str, str], tuple[float, float]]] = { + ("vitalperiodic", "heartrate"): (20, 250), + ("vitalperiodic", "respiration"): (4, 60), + ("vitalaperiodic", "noninvasivediastolic"): (20, 150), + ("vitalaperiodic", "noninvasivemean"): (30, 200), + } + window_minutes: int = 24 * 60 + + @staticmethod + def _stay_order(stay: Any) -> tuple[float, float, float]: + keys = ("hospitaldischargeyear", "patienthealthsystemstayid", "unitvisitnumber") + return tuple( + value if (value := _to_float(getattr(stay, key, None))) is not None + else float("inf") + for key in keys + ) + + def _window_mean( + self, events: list, attr: str, low: float, high: float + ) -> float | None: + """Means one vital's plausible readings within the first window.""" + values = [] + for event in events: + offset = _to_float(getattr(event, "observationoffset", None)) + value = _to_float(getattr(event, attr, None)) + if offset is None or value is None: + continue + if 0 <= offset < self.window_minutes and low <= value <= high: + values.append(value) + return sum(values) / len(values) if values else None + + def __call__(self, patient: Patient) -> list[dict]: + """Summarize the patient's first ICU stay as one vital-sign vector.""" + stays = patient.get_events(event_type="patient") + if not stays: + return [] + stay = min(stays, key=self._stay_order) + age = _parse_age(getattr(stay, "age", None)) + if age is None: + return [] + + stay_id = str(getattr(stay, "patientunitstayid", "")) + events = { + table: patient.get_events( + event_type=table, filters=[("patientunitstayid", "==", stay_id)] + ) + for table in {table for table, _ in self.vitals} + } + means = [ + self._window_mean(events[table], attr, low, high) + for (table, attr), (low, high) in self.vitals.items() + ] + if any(mean is None for mean in means): + return [] + return [ + { + "patient_id": patient.patient_id, + "visit_id": stay_id, + "vitals": [age, *means], + } + ] diff --git a/test-resources/core/eicudemo/create_demo_data.py b/test-resources/core/eicudemo/create_demo_data.py index 70b095cd0..5ce50002a 100644 --- a/test-resources/core/eicudemo/create_demo_data.py +++ b/test-resources/core/eicudemo/create_demo_data.py @@ -125,8 +125,29 @@ def main(): print(f" {f.name}: {len(df)} rows") +def create_vital_tables(source_root: Path = EICU_DEMO_ROOT) -> None: + """Adds vitalPeriodic and vitalAperiodic for the stays in patient.csv. + + Keeps each stay's first 24 hours, the window CARTVitalsGenerationEICU + reads. vitalPeriodic is charted every few minutes, so only the first row + per stay per hour is kept, to keep the file small. + """ + stay_ids = set(pd.read_csv(OUTPUT_DIR / "patient.csv")["patientunitstayid"]) + for table_name, hourly in [("vitalPeriodic", True), ("vitalAperiodic", False)]: + df = load_csv_gz(source_root / f"{table_name}.csv.gz") + df = df[ + df["patientunitstayid"].isin(stay_ids) + & df["observationoffset"].between(0, 24 * 60 - 1) + ].sort_values(["patientunitstayid", "observationoffset"]) + if hourly: + hour = df["observationoffset"] // 60 + df = df.groupby([df["patientunitstayid"], hour]).head(1) + save_csv(df, OUTPUT_DIR / f"{table_name}.csv") + + if __name__ == "__main__": main() + create_vital_tables() diff --git a/test-resources/core/eicudemo/vitalAperiodic.csv b/test-resources/core/eicudemo/vitalAperiodic.csv new file mode 100644 index 000000000..d4614a08c --- /dev/null +++ b/test-resources/core/eicudemo/vitalAperiodic.csv @@ -0,0 +1,6594 @@ +vitalaperiodicid,patientunitstayid,observationoffset,noninvasivesystolic,noninvasivediastolic,noninvasivemean,paop,cardiacoutput,cardiacinput,svr,svri,pvr,pvri +3661417,141764,77,176.0,87.0,107.0,,,,,,, +3661418,141764,81,171.0,90.0,116.0,,,,,,, +3661419,141764,165,173.0,106.0,128.0,,,,,,, +3661420,141764,195,181.0,102.0,143.0,,,,,,, +3661421,141764,255,182.0,103.0,133.0,,,,,,, +3661422,141764,262,180.0,101.0,143.0,,,,,,, +3661423,141764,264,166.0,80.0,132.0,,,,,,, +3661424,141764,334,153.0,78.0,103.0,,,,,,, +5600883,141765,11,142.0,87.0,108.0,,,,,,, +5600884,141765,71,144.0,70.0,99.0,,,,,,, +5600885,141765,131,139.0,67.0,97.0,,,,,,, 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Path(__file__).parent.parent.parent / "test-resources/core/eicudemo" + # litdata keeps its chunk files memory-mapped; on Windows the handles + # are still open when the directory is removed. + cls.cache = tempfile.TemporaryDirectory( + ignore_cleanup_errors=sys.platform == "win32" + ) + cls.dataset = eICUDataset( + root=str(root), + tables=["vitalperiodic", "vitalaperiodic"], + cache_dir=cls.cache.name, + ) + cls.task = CARTVitalsGenerationEICU() + cls.samples = cls.dataset.set_task(cls.task) + + @classmethod + def tearDownClass(cls): + cls.cache.cleanup() + + def test_schema(self): + """A generative task: one tensor input and no label.""" + self.assertEqual(self.task.input_schema, {"vitals": "tensor"}) + self.assertEqual(self.task.output_schema, {}) + + def test_one_sample_per_patient(self): + """Each patient contributes at most their first stay.""" + self.assertGreater(len(self.samples), 0) + patient_ids = [ + self.samples[i]["patient_id"] for i in range(len(self.samples)) + ] + self.assertEqual(len(patient_ids), len(set(patient_ids))) + + def test_vitals_shape_and_ranges(self): + """Every sample is age plus four vitals within the plausible ranges.""" + ranges = list(self.task.vitals.values()) + for i in range(len(self.samples)): + vitals = self.samples[i]["vitals"] + self.assertEqual(vitals.shape, (len(self.task.feature_names),)) + self.assertTrue(0 <= vitals[0] <= 90) + for value, (low, high) in zip(vitals[1:].tolist(), ranges): + self.assertTrue(low <= value <= high) + + def test_first_stay_is_chosen(self): + """A patient with several stays keeps the earliest one.""" + stays = [ + SimpleNamespace( + hospitaldischargeyear="2015", + patienthealthsystemstayid="20", + unitvisitnumber="1", + ), + SimpleNamespace( + hospitaldischargeyear="2014", + patienthealthsystemstayid="30", + unitvisitnumber="2", + ), + SimpleNamespace( + hospitaldischargeyear="2014", + patienthealthsystemstayid="30", + unitvisitnumber="1", + ), + ] + self.assertIs(min(stays, key=self.task._stay_order), stays[2]) + + def test_window_mean_drops_out_of_window_and_implausible(self): + """Only plausible readings in the first 24 hours are averaged.""" + events = [ + SimpleNamespace(observationoffset="10", heartrate="80"), + SimpleNamespace(observationoffset="20", heartrate="100"), + SimpleNamespace(observationoffset="30", heartrate="0"), + SimpleNamespace(observationoffset="2000", heartrate="150"), + SimpleNamespace(observationoffset="40", heartrate=None), + ] + self.assertEqual(self.task._window_mean(events, "heartrate", 20, 250), 90) + self.assertIsNone(self.task._window_mean(events[2:], "heartrate", 20, 250)) + + def test_parse_age(self): + """eICU's "> 89" becomes 90; blanks are missing.""" + self.assertEqual(_parse_age("> 89"), 90.0) + self.assertEqual(_parse_age("67"), 67.0) + self.assertIsNone(_parse_age("")) + self.assertIsNone(_parse_age(None)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/core/test_gan.py b/tests/core/test_gan.py new file mode 100644 index 000000000..272a45e5c --- /dev/null +++ b/tests/core/test_gan.py @@ -0,0 +1,318 @@ +import os +import unittest + +import torch + +from pyhealth.datasets import create_sample_dataset, get_dataloader +from pyhealth.models import GAN + + +class TestGAN(unittest.TestCase): + """Test cases for the GAN model.""" + + def setUp(self): + """Bag-of-codes generative dataset (no labels) and a tiny model.""" + torch.manual_seed(0) + self.samples = [ + {"patient_id": "patient-0", "visits": ["A05B", "A05C", "A11D", "C129"]}, + {"patient_id": "patient-1", "visits": ["A05B", "A04A", "B035"]}, + {"patient_id": "patient-2", "visits": ["C129", "A11D", "A05C", "A04A"]}, + {"patient_id": "patient-3", "visits": ["B035", "A05B", "C129"]}, + ] + self.input_schema = {"visits": "multi_hot"} + self.output_schema = {} + + self.dataset = create_sample_dataset( + samples=self.samples, + input_schema=self.input_schema, + output_schema=self.output_schema, + dataset_name="test_gan", + ) + + self.model = GAN(dataset=self.dataset, hidden_dim=8) + + def test_model_initialization(self): + """Code-set mode sizes both networks from the code vocabulary.""" + self.assertIsInstance(self.model, GAN) + self.assertEqual(self.model.feature_key, "visits") + self.assertEqual(self.model.label_keys, []) + self.assertEqual(self.model.hidden_dim, 8) + + vocab_size = self.dataset.input_processors["visits"].size() + self.assertEqual(self.model.input_dim, vocab_size) + self.assertEqual(self.model.generator[-2].out_features, vocab_size) + self.assertEqual(self.model.discriminator[0].in_features, vocab_size) + + def test_model_forward(self): + """A forward pass scores a real batch against as many fakes.""" + train_loader = get_dataloader(self.dataset, batch_size=2, shuffle=True) + data_batch = next(iter(train_loader)) + + with torch.no_grad(): + ret = self.model(**data_batch) + + for key in ("d_loss", "g_loss", "real_prob", "fake_prob", "fake"): + self.assertIn(key, ret) + self.assertEqual(ret["d_loss"].dim(), 0) + self.assertEqual(ret["g_loss"].dim(), 0) + self.assertEqual(ret["real_prob"].shape, (2, 1)) + self.assertEqual(ret["fake_prob"].shape, (2, 1)) + self.assertEqual(ret["fake"].shape, (2, self.model.input_dim)) + + # The discriminator and generator both end in a sigmoid. + for key in ("real_prob", "fake_prob", "fake"): + self.assertTrue(((ret[key] >= 0) & (ret[key] <= 1)).all()) + + def test_model_backward(self): + """d_loss reaches only the discriminator; g_loss reaches the generator.""" + train_loader = get_dataloader(self.dataset, batch_size=2, shuffle=True) + data_batch = next(iter(train_loader)) + + ret = self.model(**data_batch) + ret["d_loss"].backward() + for param in self.model.generator.parameters(): + self.assertIsNone(param.grad) + self.assertTrue( + any(p.grad is not None for p in self.model.discriminator.parameters()) + ) + + ret["g_loss"].backward() + self.assertTrue( + all(p.grad is not None for p in self.model.generator.parameters()) + ) + + def test_train_model_runs(self): + """train_model returns one mean loss per epoch for each network.""" + history = self.model.train_model( + self.dataset, epochs=2, batch_size=2, device="cpu" + ) + self.assertEqual(len(history["d_loss"]), 2) + self.assertEqual(len(history["g_loss"]), 2) + for loss in history["d_loss"] + history["g_loss"]: + self.assertTrue(torch.isfinite(torch.tensor(loss))) + + def test_train_model_updates_weights(self): + """A training epoch changes both networks' weights.""" + gen_before = [p.clone() for p in self.model.generator.parameters()] + disc_before = [p.clone() for p in self.model.discriminator.parameters()] + + self.model.train_model(self.dataset, epochs=1, batch_size=2, device="cpu") + + gen_after = list(self.model.generator.parameters()) + disc_after = list(self.model.discriminator.parameters()) + self.assertTrue( + any(not torch.equal(a, b) for a, b in zip(gen_before, gen_after)) + ) + self.assertTrue( + any(not torch.equal(a, b) for a, b in zip(disc_before, disc_after)) + ) + + def test_generate(self): + """generate() decodes synthetic patients into vocabulary codes.""" + vocab = {code for sample in self.samples for code in sample["visits"]} + + synthetic = self.model.generate(num_samples=4) + self.assertEqual(len(synthetic), 4) + for i, patient in enumerate(synthetic): + self.assertEqual(patient["patient_id"], f"synthetic_{i}") + # One aggregate visit per patient, matching MedGAN's format. + self.assertEqual(len(patient["visits"]), 1) + for code in patient["visits"][0]: + self.assertIn(code, vocab) + + def test_generate_random_sampling(self): + """random_sampling=True still produces well-formed patients.""" + synthetic = self.model.generate(num_samples=3, random_sampling=True) + self.assertEqual(len(synthetic), 3) + for patient in synthetic: + self.assertIn("patient_id", patient) + self.assertEqual(len(patient["visits"]), 1) + + def test_custom_hyperparameters(self): + """hidden_dim sets the noise size and the hidden layer widths.""" + model = GAN(dataset=self.dataset, hidden_dim=32) + self.assertEqual(model.hidden_dim, 32) + self.assertEqual(model.sampling(5, "cpu").shape, (5, 32)) + self.assertEqual(model.generator[0].in_features, 32) + self.assertEqual(model.discriminator[0].out_features, 32) + + def test_non_multi_hot_feature_raises(self): + """Code-set mode rejects a feature that is not multi-hot.""" + bad = create_sample_dataset( + samples=[ + {"patient_id": "p1", "visits": ["A", "B"]}, + {"patient_id": "p2", "visits": ["B"]}, + ], + input_schema={"visits": "sequence"}, + output_schema={}, + ) + with self.assertRaises(TypeError): + GAN(dataset=bad) + + def test_multiple_features_raise(self): + """Code-set mode generates exactly one feature.""" + bad = create_sample_dataset( + samples=[ + {"patient_id": "p1", "visits": ["A"], "procedures": ["X"]}, + {"patient_id": "p2", "visits": ["B"], "procedures": ["Y"]}, + ], + input_schema={"visits": "multi_hot", "procedures": "multi_hot"}, + output_schema={}, + ) + with self.assertRaises(ValueError): + GAN(dataset=bad) + + +class TestGANContinuousMode(unittest.TestCase): + """A tensor feature puts the GAN in continuous mode.""" + + def setUp(self): + torch.manual_seed(0) + # [age, heart rate, respiration rate] per patient. + self.values = [ + [64.0, 85.0, 18.0], + [71.0, 102.0, 24.0], + [45.0, 76.0, 16.0], + [83.0, 91.0, 20.0], + ] + self.dataset = create_sample_dataset( + samples=[ + {"patient_id": f"patient-{i}", "vitals": v} + for i, v in enumerate(self.values) + ], + input_schema={"vitals": "tensor"}, + output_schema={}, + dataset_name="test_gan_continuous", + ) + self.model = GAN(dataset=self.dataset, hidden_dim=8) + + def test_model_initialization(self): + """Continuous mode sizes MLPs to the vector, with an unbounded output.""" + self.assertEqual(self.model.gan_mode, "continuous") + self.assertEqual(self.model.input_dim, 3) + self.assertIsInstance(self.model.generator[-1], torch.nn.Linear) + self.assertEqual(self.model.generator[-1].out_features, 3) + + def test_train_model_fits_scaling(self): + """train_model standardizes with the training data's mean and std.""" + self.model.train_model(self.dataset, epochs=1, batch_size=2, device="cpu") + values = torch.tensor(self.values) + self.assertTrue(torch.allclose(self.model.feature_mean, values.mean(0))) + self.assertTrue(torch.allclose(self.model.feature_std, values.std(0))) + + def test_model_forward(self): + """A forward pass scores real vectors against fakes of the same shape.""" + data_batch = next(iter(get_dataloader(self.dataset, batch_size=2))) + with torch.no_grad(): + ret = self.model(**data_batch) + self.assertEqual(ret["real_prob"].shape, (2, 1)) + self.assertEqual(ret["fake"].shape, (2, 3)) + + def test_generate_returns_original_units(self): + """generate() undoes the standardization.""" + self.model.train_model(self.dataset, epochs=1, batch_size=2, device="cpu") + torch.manual_seed(1) + generated = self.model.generate(num_samples=5) + torch.manual_seed(1) + with torch.no_grad(): + standardized = self.model.generate_fake(5, "cpu") + expected = standardized * self.model.feature_std + self.model.feature_mean + self.assertEqual(generated.shape, (5, 3)) + self.assertTrue(torch.allclose(generated, expected)) + with self.assertRaises(ValueError): + self.model.generate(num_samples=1, random_sampling=True) + + def test_non_vector_tensor_raises(self): + """Continuous mode generates one flat vector per sample.""" + grid = create_sample_dataset( + samples=[ + {"patient_id": "p1", "vitals": [[1.0, 2.0], [3.0, 4.0]]}, + {"patient_id": "p2", "vitals": [[5.0, 6.0], [7.0, 8.0]]}, + ], + input_schema={"vitals": "tensor"}, + output_schema={}, + ) + with self.assertRaises(ValueError): + GAN(dataset=grid) + + +class TestGANImageMode(unittest.TestCase): + """An image feature puts the GAN in image mode.""" + + def setUp(self): + torch.manual_seed(0) + image_path = os.path.join( + os.path.dirname(__file__), "../../test-resources/core/cameraman.tif" + ) + self.samples = [ + {"patient_id": f"patient-{i}", "image": image_path} for i in range(4) + ] + self.dataset = self._image_dataset({"image_size": 32, "mode": "L"}) + self.model = GAN(dataset=self.dataset, hidden_dim=8) + + def _image_dataset(self, processor_kwargs): + return create_sample_dataset( + samples=self.samples, + input_schema={"image": ("image", processor_kwargs)}, + output_schema={}, + dataset_name="test_gan_image", + ) + + def test_model_initialization(self): + """Image mode sizes the CNNs from the processor and the images.""" + self.assertEqual(self.model.gan_mode, "image") + self.assertEqual(self.model.feature_key, "image") + self.assertEqual(self.model.discriminator[0].conv1.in_channels, 1) + + def test_model_forward(self): + """A forward pass scores real images against fakes of the same shape.""" + train_loader = get_dataloader(self.dataset, batch_size=2, shuffle=True) + data_batch = next(iter(train_loader)) + + with torch.no_grad(): + ret = self.model(**data_batch) + + self.assertEqual(ret["real_prob"].shape, (2, 1)) + self.assertEqual(ret["fake"].shape, (2, 1, 32, 32)) + + def test_train_model_runs(self): + """train_model takes an image SampleDataset.""" + history = self.model.train_model( + self.dataset, epochs=1, batch_size=2, device="cpu" + ) + self.assertEqual(len(history["d_loss"]), 1) + self.assertTrue(torch.isfinite(torch.tensor(history["g_loss"][0]))) + + def test_generate(self): + """generate() returns a tensor of images in [0, 1].""" + images = self.model.generate(num_samples=3) + self.assertEqual(images.shape, (3, 1, 32, 32)) + self.assertTrue(((images >= 0) & (images <= 1)).all()) + with self.assertRaises(ValueError): + self.model.generate(num_samples=1, random_sampling=True) + + def test_unsupported_image_size_raises(self): + """Image sizes other than 32, 64 and 128 are rejected up front.""" + with self.assertRaises(ValueError): + GAN(dataset=self._image_dataset({"image_size": 48})) + + def test_normalized_images_raise(self): + """The generator outputs [0, 1], so normalized images are rejected.""" + dataset = self._image_dataset( + {"image_size": 32, "normalize": True, "mean": [0.5], "std": [0.5]} + ) + with self.assertRaises(ValueError): + GAN(dataset=dataset) + + def test_pre_2_0_arguments_raise(self): + """The pre-2.0 constructor explains the dataset form.""" + with self.assertRaisesRegex(TypeError, "deprecated"): + GAN(1, 32) + with self.assertRaisesRegex(TypeError, "deprecated"): + GAN(input_channel=1, input_size=32) + with self.assertRaisesRegex(TypeError, "deprecated"): + GAN() + + +if __name__ == "__main__": + unittest.main()