Merge pull request #554 from alan-turing-institute/rm_cnp

rm CNPs

Author: radka-j

README.md

@@ -7,7 +7,7 @@
 [![Documentation](https://img.shields.io/badge/documentation-blue)](https://alan-turing-institute.github.io/autoemulate/)
 
 <!-- SPHINX-START -->
-Simulations of physical systems are often slow and need lots of compute, which makes them unpractical for real-world applications like digital twins, or when they have to run thousands of times for sensitivity analyses. The goal of `AutoEmulate` is to make it easy to replace simulations with fast, accurate emulators. To do this, `AutoEmulate` automatically fits and compares various emulators, ranging from simple models like Radial Basis Functions and Second Order Polynomials to more complex models like Support Vector Machines, Gaussian Processes and Conditional Neural Processes to find the best emulator for a simulation. 
+Simulations of physical systems are often slow and need lots of compute, which makes them unpractical for real-world applications like digital twins, or when they have to run thousands of times for sensitivity analyses. The goal of `AutoEmulate` is to make it easy to replace simulations with fast, accurate emulators. To do this, `AutoEmulate` automatically fits and compares various emulators, ranging from simple models like Radial Basis Functions and Second Order Polynomials to more complex models like Support Vector Machines and  Gaussian Processes to find the best emulator for a simulation. 
 
 ⚠️ Warning: This is an early version of the package and is still under development. We are working on improving the documentation and adding more features. If you have any questions or suggestions, please open an issue or a pull request.
 

autoemulate/emulators/__init__.py

@@ -1,8 +1,5 @@
 from ..model_registry import ModelRegistry
-from .conditional_neural_process import ConditionalNeuralProcess
-from .conditional_neural_process_attn import AttentiveConditionalNeuralProcess
 from .gaussian_process import GaussianProcess
-from .gaussian_process_mogp import GaussianProcessMOGP
 from .gaussian_process_mt import GaussianProcessMT
 from .gaussian_process_sklearn import GaussianProcessSklearn
 from .gradient_boosting import GradientBoosting
@@ -33,17 +30,8 @@
 model_registry.register_model(
     GaussianProcess().model_name, GaussianProcess, is_core=True
 )
-model_registry.register_model(
-    ConditionalNeuralProcess().model_name, ConditionalNeuralProcess, is_core=True
-)
-
 
 # non-core models
-model_registry.register_model(
-    AttentiveConditionalNeuralProcess().model_name,
-    AttentiveConditionalNeuralProcess,
-    is_core=False,
-)
 model_registry.register_model(
     GaussianProcessMT().model_name, GaussianProcessMT, is_core=False
 )

docs/getting-started/index.md

@@ -31,10 +31,9 @@ Here, we'll show you how to install `AutoEmulate` and get started with the basic
 
 :::
 
-:::{grid-item} <img src="https://pytorch.org/assets/images/pytorch-logo.png" height="16"/> **Deep Learning**
+:::{grid-item} <img src="https://pytorch.org/assets/images/pytorch-logo.png" height="16"/> **PyTorch models**
 
 - Multi-output / Multi-task Gaussian Processes
-- Conditional Neural Processes
 
 :::
 ::::

docs/getting-started/quickstart.ipynb

@@ -1741,7 +1741,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

docs/tutorials/02_speed.ipynb

@@ -1560,7 +1560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

docs/tutorials/03_emulation_sensitivity.ipynb

@@ -574,8 +574,6 @@
     "- lgbm: LightGBM\n",
     "- svm: SupportVectorMachines\n",
     "- gp: GaussianProcess\n",
-    "- cnp: ConditionalNeuralProcess\n",
-    "- acnp: AttentiveConditionalNeuralProcess\n",
     "- gpmt: GaussianProcessMT\n",
     "- gps: GaussianProcessSklearn\n",
     "- nns: NeuralNetSk\n",
@@ -687,7 +685,7 @@
    "source": [
     "em = AutoEmulate()\n",
     "parameter_names = list(parameters_range.keys())\n",
-    "em.setup(sample_df[parameter_names], Y, models = ['gp','cnp','svm','lgbm'])\n",
+    "em.setup(sample_df[parameter_names], Y, models = ['gp', 'svm','lgbm'])\n",
     "best_model = em.compare()\n"
    ]
   },

tests/models/test_cnp.py

@@ -6,7 +6,6 @@
 from autoemulate.emulators.neural_networks.cnp_module import Decoder
 from autoemulate.emulators.neural_networks.cnp_module import Encoder
 
-
 # encoder ----------------------------
 
 

tests/test_estimators.py

@@ -14,10 +14,7 @@
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.estimator_checks import set_random_state
 
-from autoemulate.emulators import AttentiveConditionalNeuralProcess
-from autoemulate.emulators import ConditionalNeuralProcess
 from autoemulate.emulators import GaussianProcess
-from autoemulate.emulators import GaussianProcessMOGP
 from autoemulate.emulators import GaussianProcessMT
 from autoemulate.emulators import GaussianProcessSklearn
 from autoemulate.emulators import GradientBoosting
@@ -27,6 +24,10 @@
 from autoemulate.emulators import RandomForest
 from autoemulate.emulators import SecondOrderPolynomial
 from autoemulate.emulators import SupportVectorMachines
+from autoemulate.emulators.conditional_neural_process import ConditionalNeuralProcess
+from autoemulate.emulators.conditional_neural_process_attn import (
+    AttentiveConditionalNeuralProcess,
+)
 
 
 @parametrize_with_checks(

tests/test_model_registry.py

@@ -1,8 +1,8 @@
 import pytest
 
-from autoemulate.emulators import ConditionalNeuralProcess
 from autoemulate.emulators import GaussianProcessSklearn
 from autoemulate.emulators import RadialBasisFunctions
+from autoemulate.emulators.conditional_neural_process import ConditionalNeuralProcess
 from autoemulate.model_registry import ModelRegistry