From c24fec350c3c25b6311238b8a8c5ff802f8c49a7 Mon Sep 17 00:00:00 2001 From: Thomas Gessey-Jones Date: Fri, 19 Apr 2024 12:12:25 +0100 Subject: [PATCH] Add Foregrounds To Simulated Data (#15) * Add analytic foreground from Hills et al. 2018 to both models (with and without signal) * Add foreground parameter to the configuration file * Reduce default noise to 0.015K due to signal now being harder to detect * Allow polychord verification to be run in batches * Increase live points for more challenging evidence calculation * Remove nlike tracking as not used * Add option for preprocessing simulated data in Evidence Network * Add whitening transform preprocessing functions (Cholesky default) * Change the default network to a deeper and broader network * Clipnorm gradients for stability * Fix overflow bug in blind coverage test * Update default network hyperparameters for the new problem * Update verification data to version with foregrounds * Update figure and results outputs to version with foregrounds * Stored old non-foreground figures for reference * Extend additive combiner to support an arbitrary number of simulators * Wording corrections and updated README * Add scikit-learn to requirements * Add anesthetic as a requirement * Update to latest polychordlite version --- LICENSE | 2 +- README.rst | 35 +- configuration.yaml | 100 +++-- evidence_networks/evidence_network.py | 203 +++++---- fbf_utilities.py | 281 +++++++++++-- .../en_blind_coverage_noise_0.015.pdf | Bin 0 -> 102925 bytes .../detectability_2_sigma_noise_0.0150_K.txt | 1 + .../detectability_3_sigma_noise_0.0150_K.txt | 1 + .../detectability_5_sigma_noise_0.0150_K.txt | 1 + ...bility_triangle_2_sigma_noise_0.0150_K.pdf | Bin 0 -> 174891 bytes ...bility_triangle_3_sigma_noise_0.0150_K.pdf | Bin 0 -> 174776 bytes ...bility_triangle_5_sigma_noise_0.0150_K.pdf | Bin 0 -> 174107 bytes .../polychord_verification_noise_0.015.pdf | Bin 0 -> 226431 bytes ...chord_verification_results_noise_0.015.txt | 25 ++ .../timing_data/timing_data_noise_0.015.pkl | Bin 0 -> 8735 bytes .../detectability_2_sigma_noise_0.0790_K.txt | 0 .../detectability_3_sigma_noise_0.0790_K.txt | 0 .../detectability_5_sigma_noise_0.0790_K.txt | 0 ...bility_triangle_2_sigma_noise_0.0790_K.pdf | Bin ...bility_triangle_3_sigma_noise_0.0790_K.pdf | Bin ...bility_triangle_5_sigma_noise_0.0790_K.pdf | Bin .../en_blind_coverage_noise_0.079.pdf} | Bin .../polychord_verification_noise_0.079.pdf} | Bin ...hord_verification_results_noise_0.079.txt} | 0 .../timing_data_noise_0.079.pkl} | Bin .../global_signal_en_noise_0.015.h5 | Bin 0 -> 981272 bytes models/en_noise_0.015/preprocessing_means.npz | Bin 0 -> 1208 bytes .../preprocessing_whitenings.npz | Bin 0 -> 111664 bytes .../global_signal_en_noise_0.079.h5} | Bin priors/priors.py | 23 + requirements.txt | 6 +- simulators/core.py | 36 +- simulators/noise.py | 30 +- simulators/twenty_one_cm.py | 115 +++++ train_evidence_network.py | 88 ++-- verification_data/en_log_k_noise_0.015.npz | Bin 0 -> 8286 bytes .../verification_data_noise_0.015.npz | Bin 0 -> 1920770 bytes .../en_log_k_noise_0.079.npz} | Bin .../verification_data_noise_0.079.npz} | Bin verification_with_polychord.py | 393 +++++++++++------- visualize_forecast.py | 29 +- 41 files changed, 983 insertions(+), 386 deletions(-) create mode 100644 figures_and_results/blind_coverage_tests/en_blind_coverage_noise_0.015.pdf create mode 100644 figures_and_results/detectability_triangle_plots/detectability_2_sigma_noise_0.0150_K.txt create mode 100644 figures_and_results/detectability_triangle_plots/detectability_3_sigma_noise_0.0150_K.txt create mode 100644 figures_and_results/detectability_triangle_plots/detectability_5_sigma_noise_0.0150_K.txt create mode 100644 figures_and_results/detectability_triangle_plots/detectability_triangle_2_sigma_noise_0.0150_K.pdf create mode 100644 figures_and_results/detectability_triangle_plots/detectability_triangle_3_sigma_noise_0.0150_K.pdf create mode 100644 figures_and_results/detectability_triangle_plots/detectability_triangle_5_sigma_noise_0.0150_K.pdf create mode 100644 figures_and_results/polychord_verification/polychord_verification_noise_0.015.pdf create mode 100644 figures_and_results/polychord_verification/polychord_verification_results_noise_0.015.txt create mode 100644 figures_and_results/timing_data/timing_data_noise_0.015.pkl rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_2_sigma_noise_0.0790_K.txt (100%) rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_3_sigma_noise_0.0790_K.txt (100%) rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_5_sigma_noise_0.0790_K.txt (100%) rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_triangle_2_sigma_noise_0.0790_K.pdf (100%) rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_triangle_3_sigma_noise_0.0790_K.pdf (100%) rename figures_and_results/{detectability_triangle_plots => without_foregrounds}/detectability_triangle_5_sigma_noise_0.0790_K.pdf (100%) rename figures_and_results/{blind_coverage_tests/en_noise_0.0790_blind_coverage.pdf => without_foregrounds/en_blind_coverage_noise_0.079.pdf} (100%) rename figures_and_results/{polychord_verification/polychord_verification_en_noise_0.0790_K.pdf => without_foregrounds/polychord_verification_noise_0.079.pdf} (100%) rename figures_and_results/{polychord_verification/polychord_verification_en_noise_0.0790_K_results.txt => without_foregrounds/polychord_verification_results_noise_0.079.txt} (100%) rename figures_and_results/{timing_data/en_noise_0.0790_timing_data.pkl => without_foregrounds/timing_data_noise_0.079.pkl} (100%) create mode 100644 models/en_noise_0.015/global_signal_en_noise_0.015.h5 create mode 100644 models/en_noise_0.015/preprocessing_means.npz create mode 100644 models/en_noise_0.015/preprocessing_whitenings.npz rename models/{en_noise_0.0790/global_signal_en.h5 => without_foregrounds/en_noise_0.0790/global_signal_en_noise_0.079.h5} (100%) create mode 100644 verification_data/en_log_k_noise_0.015.npz create mode 100644 verification_data/verification_data_noise_0.015.npz rename verification_data/{noise_0.0790_en_log_k.npz => without_foregrounds/en_log_k_noise_0.079.npz} (100%) rename verification_data/{noise_0.0790_verification_data.npz => without_foregrounds/verification_data_noise_0.079.npz} (100%) diff --git a/LICENSE b/LICENSE index 1423668..c799856 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 Thomas Gessey-Jones +Copyright (c) 2024 Thomas Gessey-Jones Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.rst b/README.rst index cb5f659..538b860 100644 --- a/README.rst +++ b/README.rst @@ -7,7 +7,7 @@ Overview :Name: Fully Bayesian Forecast Example :Author: Thomas Gessey-Jones -:Version: 0.1.3 +:Version: 0.2.0 :Homepage: https://github.com/ThomasGesseyJones/FullyBayesianForecastsExample :Letter: https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G @@ -31,7 +31,7 @@ reproducible analysis pipeline for the letter. The overall goal of the code is to produce a fully Bayesian forecast of the chance of a `REACH `__-like experiment -making a significant detection of the 21-cm global signal, given a noise level. It also produces +making a significant detection of the 21-cm global signal from within foregrounds and noise. It also produces figures showing how this conclusion changes with different astrophysical parameter values and validates the forecast through blind coverage tests and comparison to `PolyChord `__. @@ -74,17 +74,19 @@ There are three modules included in the repository: that take a number of data simulations to run and return that number of mock data simulations alongside the values of any parameters that were used in the simulations. Submodules of this module define functions to generate specific - simulators for models with noise only and models with a noisy 21-cm global signal. + simulators for noise, foregrounds, and the 21-cm signal. These three modules are used in the three analysis scripts: - verification_with_polychord.py: This script generates a range of mock data - sets from both the noise-only model and the noisy-signal model, and then + sets from both the no-signal model and the with-signal model, and then performs a Bayesian analysis on each of them. Evaluating the Bayes ratio between the two models of the data using Polychord. These results are then stored in the verification_data directory for later comparison with the results from the evidence network to - verify its accuracy. It should be run first, ideally in parallel. + verify its accuracy. It should be run first, ideally with a large number of + versions in parallel as it is very computationally expensive but + splits simply into one task per data set. - train_evidence_network.py: This script builds the evidence network object and the data simulator functions, then trains the evidence network. Once trained it stores the evidence network in the models directory, then runs a blind @@ -106,11 +108,11 @@ scripts can be run from the terminal using the following commands: .. code:: bash - python verification_with_polychord.py + python verification_with_polychord.py 0 python train_evidence_network.py python visualize_forecasts.py -to run with the default noise level of 79 mK and replicate the +to run with the default noise level of 15 mK and replicate the analysis from `Gessey-Jones et al. (2023) `__. Alternatively you can pass the scripts a command line argument to specify the experiments noise level in K. For example @@ -118,28 +120,29 @@ to run with a noise level of 100 mK you would run the following commands: .. code:: bash - python verification_with_polychord.py 0.1 + python verification_with_polychord.py 0 0.1 python train_evidence_network.py 0.1 python visualize_forecasts.py 0.1 Two other files of interest are: - fbf_utilities.py: which defines IO functions - needed by the three scripts and a utility function to assemble the data - simulators for the noise-only and noisy-signal model. + needed by the three scripts, utility functions to assemble the data + simulators for the noise-only and noisy-signal model, and standard + whitening transforms. - configuration.yaml: which defines several parameters used in the code including the experimental frequency resolution, the priors on the - astrophysical parameters of the global 21-cm signal model, and parameters - that control which astrophysical parameters are plotted in the forecast - figures. If you change the priors or resolution the entire pipeline - needs to be rerun to get accurate results. + astrophysical and foreground parameters, and the astrophysical parameters + which are plotted in the forecast figures. If you change the priors or + resolution the entire pipeline needs to be rerun to get accurate results. The various figures produced in the analysis are stored in the figures_and_results directory alongside the timing_data to assess the performance of the methodology and some summary statistics of the evidence networks performance. The figures and data generated in the analysis for `Gessey-Jones et al. (2023) `__ are provided in this -repository for reference. +repository for reference, alongside the figures generated for an earlier +version of the letter which did not model foregrounds. Licence and Citation -------------------- @@ -194,6 +197,8 @@ To run the code you will need to following additional packages: - `pypolychord `__ - `scipy `__ - `mpi4py `__ +- `scikit-learn `__ +- `anesthetic `__ The code was developed using python 3.8. It has not been tested on other versions of python. Exact versions of the packages used in our analysis diff --git a/configuration.yaml b/configuration.yaml index c32951d..d1e38b5 100644 --- a/configuration.yaml +++ b/configuration.yaml @@ -21,49 +21,81 @@ frequency_resolution: 1.0 # parameters of the prior. low and high are used in place of min and # max to avoid clashing with python keywords. priors: - f_star: - type: log_uniform - low: emu_min # If given emu_min or emu_max, the value is taken from the - high: emu_max # minimum or maximum value of GlobalEmu was trained on. - v_c: - type: log_uniform - low: emu_min - high: 30.0 - f_x: - type: log_uniform - low: 0.001 - high: emu_max - tau: - type: truncated_gaussian - mean: 0.054 - std: 0.007 - low: emu_min - high: emu_max - alpha: - type: uniform - low: emu_min - high: emu_max - nu_min: - type: log_uniform - low: emu_min - high: emu_max - R_mfp: - type: uniform - low: emu_min - high: emu_max + global_signal: + f_star: + type: log_uniform + low: emu_min # If given emu_min or emu_max, the value is taken from the + high: emu_max # minimum or maximum value of GlobalEmu was trained on. + v_c: + type: log_uniform + low: emu_min + high: 30.0 + f_x: + type: log_uniform + low: 0.001 + high: emu_max + tau: + type: truncated_gaussian + mean: 0.054 + std: 0.007 + low: emu_min + high: emu_max + alpha: + type: uniform + low: emu_min + high: emu_max + nu_min: + type: log_uniform + low: emu_min + high: emu_max + R_mfp: + type: uniform + low: emu_min + high: emu_max + foregrounds: + d0: + type: uniform + low: 1500.0 # K + high: 2000.0 # K + d1: + type: uniform + low: -1.0 + high: 1.0 + d2: + type: uniform + low: -0.05 + high: 0.05 + tau_e: + type: uniform + low: 0.005 + high: 0.200 + t_e: + type: uniform + low: 200.0 # K + high: 2000.0 # K +# +# +# PREPROCESSING +# ============= +# Settings to control the preprocessing of the data before being fed into the +# neural network. +whitening_transform: 'Cholesky' # None, ZCA, PCA, Cholesky, ZCA-cor or PCA-cor +covariance_samples: 100_000 # Number of samples to use when calculating the + # covariance matrix for the whitening transform. # # # VERIFICATION # ============ -# Number of data sets generate from each model to use when verifying the -# network against PolyChord. Each method is used to evaluate log K and then -# the results are compared. +# Number of data sets generated from each model to use when verifying the +# network against PolyChord. Evaluated in batches of fixed size due to +# HPC scheduling limitations. verification_data_sets_per_model: 1000 +verification_data_set_batch_size: 5 # # # PLOTTING # ======== -# Parameters that control details of the plots used to visualise the results. +# Parameters that control details of the plots used to visualize the results. br_evaluations_for_forecast: 1000000 detection_thresholds: ["2 sigma", "3 sigma", "5 sigma"] parameters_to_plot: ["f_star", "f_x", "tau"] diff --git a/evidence_networks/evidence_network.py b/evidence_networks/evidence_network.py index 366486e..08b1f29 100644 --- a/evidence_networks/evidence_network.py +++ b/evidence_networks/evidence_network.py @@ -7,10 +7,12 @@ # Required imports from typing import Callable, Tuple, Optional import numpy as np +import os import tensorflow as tf import keras from keras import layers from tensorflow.keras.optimizers.schedules import ExponentialDecay +from tensorflow.keras.callbacks import ModelCheckpoint from keras import backend as k_backend import matplotlib.pyplot as plt @@ -34,6 +36,9 @@ class EvidenceNetwork: alpha: float, default = 2.0 The exponent of the leaky parity-odd transformation. See arXiv:2305.11241 for details. + data_preprocessing: Callable + Optional preprocessing function to use on simulated data before + it is passed to the network. If None no preprocessing is used. Attributes ---------- @@ -52,7 +57,7 @@ class EvidenceNetwork: """ def __init__(self, simulator_0: Callable, simulator_1: Callable, - alpha: float = 2.0): + alpha: float = 2.0, data_preprocessing: Callable = None): """Initialize an EvidenceNetwork object. Parameters @@ -70,6 +75,10 @@ def __init__(self, simulator_0: Callable, simulator_1: Callable, alpha: float, default = 2.0 The exponent of the leaky parity-odd transformation used in the loss function and output transformation. + data_preprocessing: Callable + Optional preprocessing function to use on simulated data before + it is passed to the network. If None no preprocessing is used. + Function must be invertible for network to give accurate results. """ # Check models are compatible sample_data_0, _ = simulator_0(1) @@ -81,6 +90,11 @@ def __init__(self, simulator_0: Callable, simulator_1: Callable, raise ValueError("Mock data from both simulators must have the " "same type.") + # Set-up preprocessing + self.data_preprocessing = lambda x: x + if data_preprocessing is not None: + self.data_preprocessing = data_preprocessing + # Set attributes self.simulator_0 = simulator_0 self.simulator_1 = simulator_1 @@ -97,9 +111,10 @@ def __init__(self, simulator_0: Callable, simulator_1: Callable, @staticmethod def default_nn_model(input_size: int) -> keras.Model: - """Return a default neural network model. + """Return a neural network model. - This is the model from the appendix of arXiv:2305.11241. + This is a modified version of the model from the appendix of + arXiv:2305.11241. Parameters ---------- @@ -111,26 +126,35 @@ def default_nn_model(input_size: int) -> keras.Model: keras.Model The default neural network model """ + # Settings + for_network_width = 256 + back_network_width = 64 + additional_for_layers = 0 + additional_back_layers = 2 + inputs = layers.Input(shape=(input_size,)) - x = layers.Dense(130)(inputs) - x = layers.LeakyReLU()(x) - x = layers.BatchNormalization()(x) - x = layers.Dense(16)(x) - x = layers.LeakyReLU()(x) - x_batch_norm_1 = layers.BatchNormalization()(x) # Save for skip - x = layers.Dense(16)(x_batch_norm_1) - x = layers.LeakyReLU()(x) + x = inputs + for _ in range(additional_for_layers+1): + x = layers.Dense(for_network_width)(x) + x = layers.BatchNormalization()(x) + x = layers.LeakyReLU()(x) + x = layers.Dense(back_network_width)(x) x = layers.BatchNormalization()(x) - x = layers.Dense(16)(x) x = layers.LeakyReLU()(x) + x_batch_norm_1 = x # Save for skip + for _ in range(2): + x = layers.Dense(back_network_width)(x) + x = layers.BatchNormalization()(x) + x = layers.LeakyReLU()(x) x = layers.Add()([x, x_batch_norm_1]) # Skip connection - x = layers.BatchNormalization()(x) - x = layers.Dense(16)(x) - x = layers.LeakyReLU()(x) + for _ in range(additional_back_layers+1): + x = layers.Dense(back_network_width)(x) + x = layers.BatchNormalization()(x) + x = layers.LeakyReLU()(x) outputs = layers.Dense(1)(x) model = keras.Model(inputs=inputs, outputs=outputs, - name="jeffrey_wandelt_23_network") + name="default_network") return model def get_simulated_data(self, @@ -175,14 +199,15 @@ def train(self, initial_learning_rate: float = 1e-4, decay_steps: int = 1000, decay_rate: float = 0.95, - roll_back: bool = False) -> None: + roll_back: bool = False, + checkpoint_file: str = "best_weights.h5") -> None: """Train the Bayes ratio network. Parameters ---------- nn_model: keras.Model The neural network model to train. If None, a default model - from arXiv:2305.11241 appendix is used. + is used. train_data_samples_per_model: int, default=1_000_000 The number of data samples to simulate from each model for training validation_data_samples_per_model: int, default=200_000 @@ -201,6 +226,9 @@ def train(self, roll_back: bool, default=False Whether to roll back the network to validation loss minimum at the end of training + checkpoint_file: str, default="best_weights.h5" + The filename to save the best weights to during training. Has + no effect if roll_back is False. """ # Set-up NN, default from arXiv:2305.11241 appendix if not given if nn_model is None: @@ -215,7 +243,8 @@ def train(self, initial_learning_rate=initial_learning_rate, decay_steps=decay_steps, decay_rate=decay_rate, - )), + ), + clipnorm=1), metrics=["accuracy"], ) @@ -225,6 +254,11 @@ def train(self, validation_sample_data, validation_labels_data = \ self.get_simulated_data(validation_data_samples_per_model) + # Preprocess data + sample_data = self.data_preprocessing(sample_data) + validation_sample_data = self.data_preprocessing( + validation_sample_data) + # Store simulated data in case useful later on self.training_data = sample_data self.training_labels = labels_data @@ -232,56 +266,71 @@ def train(self, self.validation_labels = validation_labels_data # Train model and set trained flag - if not roll_back: - self.nn_model.fit(sample_data, labels_data, - batch_size=batch_size, - epochs=epochs, - verbose=2, - validation_data=(validation_sample_data, - validation_labels_data)) - self.trained = True - return - - # Training with roll back, train for one epoch at a time and check - # validation loss after each epoch. If validation loss is lower than - # the previous minimum, save the weights. At the end of training, roll - # back to the weights with the lowest validation loss. - minimum_val_loss = np.inf - minimum_val_loss_weights = None - minimum_epoch_num = 0 - for epoch_num in range(epochs): - # Train for one epoch - self.nn_model.fit(sample_data, labels_data, - batch_size=batch_size, - epochs=1, - verbose=2, - validation_data=(validation_sample_data, - validation_labels_data)) - - # Check validation loss against previous minimum, and save weights - # if new minimum - val_loss = self.nn_model.evaluate(validation_sample_data, - validation_labels_data, - verbose=0)[0] - if val_loss < minimum_val_loss: - minimum_val_loss = val_loss - minimum_val_loss_weights = self.nn_model.get_weights() - minimum_epoch_num = epoch_num + 1 - - # Roll back to weights with the lowest validation loss - print(f"Reverting to minimum validation loss model, which was after " - f"epoch {minimum_epoch_num}.") - self.nn_model.set_weights(minimum_val_loss_weights) + if roll_back: + checkpoint = ModelCheckpoint( + checkpoint_file, + monitor="val_loss", + verbose=1, + save_best_only=True, + mode="min") + callbacks = [checkpoint] + else: + callbacks = None + + self.nn_model.fit( + sample_data, + labels_data, + batch_size=batch_size, + epochs=epochs, + verbose=2, + validation_data=( + validation_sample_data, + validation_labels_data), + callbacks=callbacks) self.trained = True - return - def evaluate_log_bayes_ratio(self, data: np.ndarray) -> np.ndarray: + if roll_back: + self.nn_model.load_weights(checkpoint_file) + os.remove(checkpoint_file) + + def calculate_testing_loss( + self, + num_test_samples: int = 200_000, + batch_size: int = 100 + ): + """Calculate the testing loss for the network. + + Parameters + ---------- + num_test_samples: int, default=200_000 + The number of test samples to use + batch_size: int, default=100 + The batch size to use for testing + + Returns + ------- + testing_loss: float + The testing loss + """ + # Create a preprocessed test data set + test_data, test_labels = self.get_simulated_data(num_test_samples) + test_data = self.data_preprocessing(test_data) + test_loss = self.nn_model.evaluate( + test_data, test_labels, batch_size=batch_size, verbose=0)[0] + return test_loss + + def evaluate_log_bayes_ratio( + self, + data: np.ndarray, + **kwargs) -> np.ndarray: """Evaluate the log Bayes ratio between model 1 and 0. Parameters ---------- data: np.ndarray A dataset or batch of data sets to feed to the network. + **kwargs + Additional keyword arguments to pass to predict Returns ------- @@ -294,16 +343,25 @@ def evaluate_log_bayes_ratio(self, data: np.ndarray) -> np.ndarray: if len(data.shape) == 1: data = data.reshape(1, -1) - nn_output = self.nn_model(tf.constant(data), training=False) + prepped_data = self.data_preprocessing(data) + + kwargs["batch_size"] = kwargs.get("batch_size", 100_000) + kwargs["verbose"] = kwargs.get("verbose", 0) + + nn_output = self.nn_model.predict( + tf.constant(prepped_data), + **kwargs) return leaky_parity_odd_transformation(nn_output, self.alpha) - def evaluate_bayes_ratio(self, data: np.ndarray) -> np.ndarray: + def evaluate_bayes_ratio(self, data: np.ndarray, **kwargs) -> np.ndarray: """Evaluate the Bayes ratio between model 1 and 0. Parameters ---------- data: np.ndarray A dataset or batch of data sets to feed to the network. + **kwargs + Additional keyword arguments to pass to predict Returns ------- @@ -313,7 +371,7 @@ def evaluate_bayes_ratio(self, data: np.ndarray) -> np.ndarray: if not self.trained: raise ValueError("Network has not been trained yet.") - return np.exp(self.evaluate_log_bayes_ratio(data)) + return np.exp(self.evaluate_log_bayes_ratio(data, **kwargs)) def save(self, filename: str): """Save the network to file. @@ -385,8 +443,8 @@ def blind_coverage_test(self, self.get_simulated_data(num_validation_samples) # Evaluate Bayes ratio - bayes_ratio = self.evaluate_bayes_ratio(validation_data) - model_1_posterior = bayes_ratio / (1 + bayes_ratio) + log_bayes_ratio = self.evaluate_log_bayes_ratio(validation_data) + model_1_posterior = 1 / (1 + np.exp(-log_bayes_ratio)) # Bin data num_model_1_in_bin = np.zeros(num_probability_bins) @@ -400,14 +458,19 @@ def blind_coverage_test(self, else: num_model_0_in_bin[bin_index] += 1 + # Drop any bins where there are no samples + bins_to_drop = np.where(num_model_1_in_bin + num_model_0_in_bin < + min_in_bin) + num_model_1_in_bin = np.delete(num_model_1_in_bin, bins_to_drop) + num_model_0_in_bin = np.delete(num_model_0_in_bin, bins_to_drop) + posterior_bin_edges = np.delete(posterior_bin_edges, bins_to_drop) + posterior_bin_midpoints = np.delete(posterior_bin_midpoints, + bins_to_drop) + # Calculate bin proportions bin_model_1_proportions = num_model_1_in_bin / ( num_model_1_in_bin + num_model_0_in_bin) - # Set bins with too few samples to NaN - bin_model_1_proportions[ - num_model_1_in_bin + num_model_0_in_bin < min_in_bin] = np.nan - # Optionally plot results if plotting_ax is None: return posterior_bin_edges, bin_model_1_proportions diff --git a/fbf_utilities.py b/fbf_utilities.py index c7ef13a..96b0702 100644 --- a/fbf_utilities.py +++ b/fbf_utilities.py @@ -12,20 +12,23 @@ from simulators import additive_simulator_combiner, Simulator from simulators.noise import generate_white_noise_simulator from simulators.twenty_one_cm import load_globalemu_emulator, \ - GLOBALEMU_INPUTS, GLOBALEMU_PARAMETER_RANGES, \ + GLOBALEMU_PARAMETER_RANGES, \ global_signal_experiment_measurement_redshifts, \ - generate_global_signal_simulator + generate_global_signal_simulator, generate_foreground_simulator from priors import generate_uniform_prior_sampler, \ generate_log_uniform_prior_sampler, \ generate_gaussian_prior_sampler, \ - generate_truncated_gaussian_prior_sampler + generate_truncated_gaussian_prior_sampler, \ + generate_delta_prior_sampler from copy import deepcopy import yaml import os import pickle as pkl +import numpy as np +from sklearn.decomposition import PCA # Parameters -NOISE_DEFAULT = 0.079 # K, taken from REACH mission paper +NOISE_DEFAULT = 0.015 # K # IO @@ -38,7 +41,7 @@ def get_noise_sigma() -> float: The noise sigma in K. """ parser = argparse.ArgumentParser( - description="Train the Evidence Network." + description="Forecast with Evidence Network." ) parser.add_argument( "noise", @@ -63,7 +66,9 @@ def load_configuration_dict() -> dict: return yaml.safe_load(file) -def timing_filename(noise_sigma: float) -> str: +def timing_filename( + noise_sigma: float +) -> str: """Get the filename for the timing data. Parameters @@ -78,7 +83,7 @@ def timing_filename(noise_sigma: float) -> str: """ folder = os.path.join('figures_and_results', 'timing_data') os.makedirs(folder, exist_ok=True) - return os.path.join(folder, f'en_noise_{noise_sigma:.4f}_timing_data.pkl') + return os.path.join(folder, f'timing_data_noise_{noise_sigma}.pkl') def clear_timing_data(timing_file: str): @@ -115,14 +120,44 @@ def add_timing_data(timing_file: str, entry_name: str, time_s: float): pkl.dump(timing_data, file) +def _get_prior_generator(prior_type: str) -> Callable: + """Get the appropriate prior generator given a prior type. + + Parameters + ---------- + prior_type : str + String ID of the prior type + + Returns + ------- + prior_generator : Callable + Prior generator corresponding to the given prior type + """ + if prior_type == 'uniform': + return generate_uniform_prior_sampler + elif prior_type == 'log_uniform': + return generate_log_uniform_prior_sampler + elif prior_type == 'gaussian': + return generate_gaussian_prior_sampler + elif prior_type == 'truncated_gaussian': + return generate_truncated_gaussian_prior_sampler + else: + raise ValueError("Unknown prior type.") + + # Priors -def create_globalemu_prior_samplers(config_dict: dict) -> Collection[Callable]: - """Create a prior sampler over the globalemu parameters. +def create_prior_samplers( + config_dict: dict, + prior_subset: str) -> Collection[Callable]: + """Create a prior samplers over a subset of the parameters. Parameters ---------- config_dict : dict Dictionary containing the configuration parameters. + prior_subset : str + The subset of parameters to create priors for (e.g. 'global_signal', + or 'foregrounds'). Returns ------- @@ -132,30 +167,22 @@ def create_globalemu_prior_samplers(config_dict: dict) -> Collection[Callable]: """ # Loop over parameters constructing individual priors individual_priors = [] - for param in GLOBALEMU_INPUTS: + for param in config_dict['priors'][prior_subset].keys(): # Get prior info - prior_info = deepcopy(config_dict['priors'][param]) + prior_info = deepcopy(config_dict['priors'][prior_subset][param]) # Replace emu_min and emu_max with the min and max value globalemu - # can take for this parameter - for k, v in prior_info.items(): - if v == 'emu_min': - prior_info[k] = GLOBALEMU_PARAMETER_RANGES[param][0] - elif v == 'emu_max': - prior_info[k] = GLOBALEMU_PARAMETER_RANGES[param][1] - - # Get prior type + # can take for this parameter (if applicable) + if prior_subset == 'global_signal': + for k, v in prior_info.items(): + if v == 'emu_min': + prior_info[k] = GLOBALEMU_PARAMETER_RANGES[param][0] + elif v == 'emu_max': + prior_info[k] = GLOBALEMU_PARAMETER_RANGES[param][1] + + # Get prior the type prior_type = prior_info.pop('type') - if prior_type == 'uniform': - prior_generator = generate_uniform_prior_sampler - elif prior_type == 'log_uniform': - prior_generator = generate_log_uniform_prior_sampler - elif prior_type == 'gaussian': - prior_generator = generate_gaussian_prior_sampler - elif prior_type == 'truncated_gaussian': - prior_generator = generate_truncated_gaussian_prior_sampler - else: - raise ValueError("Unknown prior type.") + prior_generator = _get_prior_generator(prior_type) # Generate prior sampler prior_sampler = prior_generator(**prior_info) @@ -168,38 +195,200 @@ def create_globalemu_prior_samplers(config_dict: dict) -> Collection[Callable]: # Assemble simulators def assemble_simulators( config_dict: dict, - sigma_noise: float -) -> Tuple[Simulator, Simulator]: + noise_sigma: float) -> Tuple[Simulator, Simulator]: """Assemble the simulator functions for the Evidence Network. Parameters ---------- config_dict : dict Dictionary containing the configuration parameters. - sigma_noise : float - The noise sigma in K. + noise_sigma : float + The noise sigma is fixed to this value (in K). Returns ------- - noise_only_simulator : Simulator - Function that generates data from noise only model. - noisy_signal_simulator : Simulator - Function that generates data from noise + signal model. + no_signal_simulator : Simulator + Function that generates data from noise + foreground model. + with_signal_simulator : Simulator + Function that generates data from noise + foreground + signal model. """ # Set-up globalemu - globalemu_priors = create_globalemu_prior_samplers(config_dict) + globalemu_priors = create_prior_samplers(config_dict, 'global_signal') globalemu_redshifts = global_signal_experiment_measurement_redshifts( config_dict['frequency_resolution']) globalemu_predictor = load_globalemu_emulator(globalemu_redshifts) - # Build simulators - noise_only_simulator = generate_white_noise_simulator( - len(globalemu_redshifts), sigma_noise) - noise_for_signal = generate_white_noise_simulator( - len(globalemu_redshifts), sigma_noise) + # Set-up foreground model + foreground_priors = create_prior_samplers(config_dict, 'foregrounds') + + # Set-up noise model + sigma_prior = generate_delta_prior_sampler(noise_sigma) + + # Build no signal simulator + noise_simulator = generate_white_noise_simulator( + len(globalemu_redshifts), sigma_prior) + foreground_simulator = generate_foreground_simulator( + globalemu_redshifts, *foreground_priors) + no_signal_simulator = additive_simulator_combiner(noise_simulator, + foreground_simulator) + + # Build with signal simulator + noise_simulator = generate_white_noise_simulator( + len(globalemu_redshifts), sigma_prior) + foreground_simulator = generate_foreground_simulator( + globalemu_redshifts, *foreground_priors) signal_simulator = generate_global_signal_simulator( globalemu_predictor, *globalemu_priors) - noisy_signal_simulator = additive_simulator_combiner( - signal_simulator, noise_for_signal) + with_signal_simulator = additive_simulator_combiner( + noise_simulator, foreground_simulator, signal_simulator + ) + + return no_signal_simulator, with_signal_simulator + + +# Preprocessing function for the data +def generate_preprocessing_function( + config_dict: dict, + noise_sigma: float, + model_dir: str, + overwrite: bool = False +) -> Callable: + """Generate (or load) our preprocessing function for the data. - return noise_only_simulator, noisy_signal_simulator + This function needs to be invertible, otherwise the transform will change + the Bayes ratio, and the network will not give the correct answer. + + We use a whitening transform to do this as it both normalizes the data + and aids the network in identifying the most important features in the + data. Which whitening transform to use is set in the configuration file + using the 'whitening_transform' key, options are 'ZCA', 'PCA', + 'Cholesky', 'ZCA-cor', and 'PCA-cor' (or None). + + See arXiv:1512.00809 + + Parameters + ---------- + config_dict : dict + Dictionary containing the configuration parameters. + noise_sigma : float + The noise sigma in K. + model_dir : str + Directory to save the preprocessing function to (normally use the + same directory as the model). + overwrite : bool, optional + If True, overwrite the preprocessing function if it already exists. + + Returns + ------- + preprocessing_function : Callable + Function that preprocesses the data. + """ + # Check if the model directory exists + os.makedirs(model_dir, exist_ok=True) + + # Load or generate the means vector and whitening matrix that are the basis + # of the preprocessing function + means_file = os.path.join(model_dir, 'preprocessing_means.npz') + whitenings_file = os.path.join(model_dir, 'preprocessing_whitenings.npz') + if os.path.isfile(means_file) and os.path.isfile(whitenings_file) and \ + not overwrite: + # Loading if it exists and reusing it + with np.load(means_file) as file: + means = file['means'] + with np.load(whitenings_file) as file: + whitening = file['whitening'] + + else: + # Create a new means and whitening matrix + whitening_setting = config_dict['whitening_transform'] + covariance_samples = config_dict['covariance_samples'] + + # Get simulated data + no_signal_sim, with_signal_sim = assemble_simulators( + config_dict, noise_sigma) + simulated_data, _ = no_signal_sim(covariance_samples) + simulated_data_2, _ = with_signal_sim(covariance_samples) + simulated_data = np.concatenate((simulated_data, simulated_data_2)) + + # Calculate the means and covariance matrix + means = np.mean(simulated_data, axis=0) + covariance = np.cov(simulated_data, rowvar=False) + + # Calculate the whitening (sphering) matrix + if whitening_setting is None: + # No whitening + whitening = np.eye(covariance.shape[0]) + means = np.zeros_like(means) + + elif whitening_setting == 'Cholesky': + # Cholesky decomposition whitening + whitening = np.linalg.inv(np.linalg.cholesky(covariance)) + + elif whitening_setting == 'ZCA': + # Zero-phase component analysis (ZCA) whitening + lambda_mat, u_mat = np.linalg.eigh(covariance) + whitening = np.dot( + u_mat, np.dot(np.diag(1.0 / np.sqrt(lambda_mat)), u_mat.T)) + + elif whitening_setting == 'PCA': + # Principal component analysis (PCA) whitening + pca = PCA(n_components=simulated_data.shape[1]) + pca.fit(simulated_data - means) + unscaled_whitening = pca.components_ + variance = pca.explained_variance_ + variance_matrix = np.diag(1 / np.sqrt(variance)) + whitening = np.dot(variance_matrix, unscaled_whitening) + + elif whitening_setting == 'ZCA-cor': + # Zero-phase component analysis (ZCA) whitening with correlation + diagonal_variance = np.diag(covariance) + v_isqrt_mat = np.diag(1.0 / np.sqrt(diagonal_variance)) + correlation = np.dot(v_isqrt_mat, np.dot(covariance, v_isqrt_mat)) + theta_mat, g_mat = np.linalg.eigh(correlation) + p_isqrt = np.dot( + np.dot(g_mat, np.diag(1.0 / np.sqrt(theta_mat))), g_mat.T) + whitening = np.dot(p_isqrt, v_isqrt_mat) + + elif whitening_setting == 'PCA-cor': + # Principal component analysis (PCA) whitening with correlation + diagonal_variance = np.diag(covariance) + standardization = 1.0 / np.sqrt(diagonal_variance) + standardized_data = np.einsum( + 'ij,j->ij', simulated_data - means, standardization) + pca = PCA(n_components=simulated_data.shape[1]) + pca.fit(standardized_data) + unscaled_whitening = pca.components_ + variance = pca.explained_variance_ + variance_matrix = np.diag(1 / np.sqrt(variance)) + standardized_whitening = np.dot(variance_matrix, + unscaled_whitening) + whitening = np.dot( + standardized_whitening, + np.diag(standardization)) + + else: + raise ValueError("Unknown whitening transform.") + + # Save the means and whitening matrix for future use + np.savez(means_file, means=means) + np.savez(whitenings_file, whitening=whitening) + + def preprocessing_function(data: np.ndarray) -> np.ndarray: + """Preprocess the data using the PCA. + + Parameters + ---------- + data : np.ndarray + The data to preprocess. + + Returns + ------- + preprocessed_data : np.ndarray + The preprocessed data. + """ + centered_data = data - means + preprocessed_data = np.einsum( + 'ij,pj->pi', whitening, centered_data) + return preprocessed_data + + return preprocessing_function diff --git a/figures_and_results/blind_coverage_tests/en_blind_coverage_noise_0.015.pdf b/figures_and_results/blind_coverage_tests/en_blind_coverage_noise_0.015.pdf new file mode 100644 index 0000000000000000000000000000000000000000..bd44b82d7fcc71350f31c0fbe418013f29415509 GIT binary patch literal 102925 zcmafaWl)^k5^b>H?mkFxhr!+5-QC^YA-EHQy9IZ5_W;42;O08o9cfIokt(e?>S(6-y&iD|-t7`+uy?4kjvQt^gfST2XOODQ2Fo07fZ0Pzl2S 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0000000..6769b26 --- /dev/null +++ b/figures_and_results/polychord_verification/polychord_verification_results_noise_0.015.txt @@ -0,0 +1,25 @@ +Polychord Verification Results +----------------------------- + +Mean log K error: 0.8847 +RMSE in log K: 9.4485 + +2 sigma detection statistics: +57.10% of signals were detectable according to Polychord +56.30% of signals were detectable according to the network +-0.80% difference in detection rate +4.00% of signals changed detection status + +3 sigma detection statistics: +47.60% of signals were detectable according to Polychord +46.00% of signals were detectable according to the network +-1.60% difference in detection rate +3.40% of signals changed detection status + +5 sigma detection statistics: +30.90% of signals were detectable according to Polychord +33.00% of signals were detectable according to the network +2.10% difference in detection rate +4.90% of signals changed detection status + +Network testing loss: 0.5868 diff --git 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index fbbdb2f..7f83e60 100644 --- a/priors/priors.py +++ b/priors/priors.py @@ -8,6 +8,29 @@ import numpy as np +# Delta function priors +def generate_delta_prior_sampler( + value: float +) -> Callable: + """Generate a delta function prior sampler function. + + Parameters + ---------- + value : float + The single value allowed by the prior. + + Returns + ------- + prior_sampler : Callable + Function that takes a number of samples and returns the sampled + values as an array. + """ + def _prior_sampler(num_samples: int) -> np.ndarray: + """Sample from a uniform prior.""" + return np.full(num_samples, value) + return _prior_sampler + + # Uniform priors def generate_uniform_prior_sampler( low: float, diff --git a/requirements.txt b/requirements.txt index 042af6e..aefabec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,6 +13,8 @@ matplotlib == 3.7.1 nvidia-cudnn-cu11 == 8.6.0.163 pandas == 2.0.3 PyYAML == 6.0.1 -pypolychord == 1.20.2 +pypolychord == 1.22.1 scipy == 1.10.1 -mpi4py == 3.1.4 \ No newline at end of file +mpi4py == 3.1.4 +scikit-learn == 1.3.2 +anesthetic == 2.7.0 \ No newline at end of file diff --git a/simulators/core.py b/simulators/core.py index a07b0d5..9a3a227 100644 --- a/simulators/core.py +++ b/simulators/core.py @@ -15,31 +15,37 @@ # Combine simulators def additive_simulator_combiner( - simulator_1: Simulator, - simulator_2: Simulator + *simulators: Simulator ): - """Combine two simulators by adding their data outputs. + """Combine simulators by adding their data outputs. + + Simulator parameters must have different names. Parameters ---------- - simulator_1 : Simulator - The first simulator to combine. - simulator_2 : Simulator - The second simulator to combine. + simulators : Iterable of Simulator + The simulators to combine Returns ------- combined_simulator : Simulator The combined simulator. """ + # Catch edge-cases + if len(simulators) == 0: + raise ValueError('No simulators given.') + elif len(simulators) == 1: + return simulators[0] + def _combined_simulator(num_data_simulations: int ) -> Tuple[np.ndarray, DataFrame]: - """Combine two simulators by adding their data outputs.""" - data_1, params_1 = simulator_1(num_data_simulations) - data_2, params_2 = simulator_2(num_data_simulations) - - # Combine parameters - params = params_1.join(params_2, how='outer', - lsuffix='_1', rsuffix='_2') - return data_1 + data_2, params + """Combine simulators by adding their data outputs.""" + data_plus_params = [simulator(num_data_simulations) for simulator in + simulators] + combined_data = data_plus_params[0][0] + combined_params = data_plus_params[0][1] + for data, params in data_plus_params[1:]: + combined_params = combined_params.join(params, how='outer') + combined_data += data + return combined_data, combined_params return _combined_simulator diff --git a/simulators/noise.py b/simulators/noise.py index e7fdf4e..90fa7a6 100644 --- a/simulators/noise.py +++ b/simulators/noise.py @@ -5,22 +5,25 @@ # Import libraries from __future__ import annotations -from typing import Tuple +from typing import Tuple, Callable import numpy as np from pandas import DataFrame from .core import Simulator # White noise -def white_noise(shape: np.ndarray | int, sigma_noise: float) -> np.ndarray: +def white_noise( + shape: np.ndarray | int, + sigma_noise: np.ndarray +) -> np.ndarray: """Return white noise with given sigma value. Parameters ---------- shape : np.ndarray The shape of the noise array to generate. - sigma_noise : float - The noise standard deviation. + sigma_noise : np.ndarray + The noise standard deviation(s). Returns ------- @@ -29,13 +32,16 @@ def white_noise(shape: np.ndarray | int, sigma_noise: float) -> np.ndarray: """ if isinstance(shape, int): shape = (shape,) - noise = np.random.normal(loc=0.0, scale=sigma_noise, size=shape) - return noise + normalized_noise = np.random.normal(loc=0.0, size=shape) + if len(sigma_noise) == 1: + return normalized_noise * sigma_noise + return np.einsum('i,ij...->ij...', sigma_noise, + normalized_noise) def generate_white_noise_simulator( noise_size: int, - sigma_noise: float + sigma_sampler: Callable ) -> Simulator: """Return a simulator function for white noise. @@ -43,8 +49,8 @@ def generate_white_noise_simulator( ---------- noise_size : int Size of noise vector for each data simulation. - sigma_noise : float - Noise standard deviation. + sigma_sampler : Callable + Function that returns samples of the noise standard deviation. Returns ------- @@ -56,7 +62,9 @@ def generate_white_noise_simulator( def _noise_simulator(num_data_simulations: int ) -> Tuple[np.ndarray, DataFrame]: """Return a simulator for a white noise model.""" + sigma_samples = sigma_sampler(num_data_simulations) noise = white_noise(np.array([num_data_simulations, noise_size]), - sigma_noise) - return noise, DataFrame() + sigma_samples) + return noise, DataFrame(sigma_samples[:, np.newaxis], + columns=['sigma']) return _noise_simulator diff --git a/simulators/twenty_one_cm.py b/simulators/twenty_one_cm.py index 6400657..61d1245 100644 --- a/simulators/twenty_one_cm.py +++ b/simulators/twenty_one_cm.py @@ -97,6 +97,62 @@ def global_signal_experiment_measurement_redshifts( return redshifts +# Foreground model +def foreground_model( + frequencies_mhz: np.ndarray, + coefficients: np.ndarray +) -> np.ndarray: + r"""Foreground model for the sky-averaged radio temperature. + + Implements the physical model for the sky-averaged radio temperature + foreground from Hills et al. (2018), + T = d_{0} (nu / nu_{c})^{-2.5 + d_{1} + d_{2} + log(nu / nu_{c})} e^{-tau_{e} (nu / nu_{c})^{-2}} + + T_{e} (1 - e^{-\tau_{e} (\nu / \nu_{c})^{-2}}) + where \nu_{\rm c} is the normalization frequency 75 MHz. + + Parameters + ---------- + frequencies_mhz : np.ndarray + The frequencies at which to calculate the foreground model in MHz. + coefficients : np.ndarray + The coefficients for the foreground model (d_0, d_1, d_2, \tau_{e}, + T_{e}). + + Returns + ------- + for_temp : np.ndarray + The foreground model for the sky-averaged radio temperature in K. + Frequency is in the same order as the input frequencies, and is indexed + along the second axis of the output array. + """ + # Unpack and Scale the frequencies + d0 = np.squeeze(coefficients[..., 0]) + d1 = np.squeeze(coefficients[..., 1]) + d2 = np.squeeze(coefficients[..., 2]) + tau_e = np.squeeze(coefficients[..., 3]) + t_e = np.squeeze(coefficients[..., 4]) + nu_norm = frequencies_mhz / 75.0 + + # Reshape arrays for outer product + d0 = d0[..., np.newaxis] + d1 = d1[..., np.newaxis] + d2 = d2[..., np.newaxis] + tau_e = tau_e[..., np.newaxis] + t_e = t_e[..., np.newaxis] + nu_norm = nu_norm[np.newaxis, ...] + + # Find the principal exponent + exponent = -2.5 + d1 + d2 * np.log(nu_norm) + galactic_term = d0 * nu_norm**exponent + + # Ionosphere effects + absorption = np.exp(-tau_e * nu_norm**-2) + emission = t_e * (1 - np.exp(-tau_e * nu_norm**-2)) + + return np.squeeze(galactic_term * absorption + emission) + + # Simulators def generate_global_signal_simulator( global_emu_predictor: evaluate, @@ -178,3 +234,62 @@ def signal_simulator(num_sims: int) -> Tuple[np.ndarray, DataFrame]: return signals, params return signal_simulator + + +def generate_foreground_simulator( + redshifts: np.ndarray, + *coefficient_samplers: Callable +) -> Simulator: + """Return a simulator function for the foreground model. + + Parameters + ---------- + redshifts : np.ndarray + The redshifts at which the foreground model will be evaluated. + coefficient_samplers : Iterable[Callable] + Functions, that return samples of the coefficients for the foreground + model. The order of the foreground model is set implicitly by the size + of this iterable. + + Returns + ------- + foreground_simulator : Callable + Function that takes a number of data simulations and returns + that number of foreground models (in K) as an array + plus the corresponding parameters as a dataframe. + """ + # Convert redshifts to frequencies in MHz + frequencies_mhz = FREQ_21CM_MHZ / (1 + redshifts) + + # Set-up prior sampler + prior_sampler = generate_composite_prior_sampler(*coefficient_samplers) + + # Define the simulator function + def foreground_simulator(num_sims: int) -> Tuple[np.ndarray, DataFrame]: + """Simulate the foreground model. + + Parameters + ---------- + num_sims : int + The number of simulations to run + + Returns + ------- + foregrounds : np.ndarray + The simulated foreground models (in K) as an array + parameters : DataFrame + The corresponding foreground coefficients as a dataframe. + """ + # Sample the coefficients + coefficients = prior_sampler(num_sims) + + # Run the foreground model + foregrounds = foreground_model(frequencies_mhz, coefficients) + + # Convert coefficients to DataFrame + params = DataFrame(coefficients, + columns=['d0', 'd1', 'd2', 'tau_e', 't_e']) + + return foregrounds, params + + return foreground_simulator diff --git a/train_evidence_network.py b/train_evidence_network.py index 86a9362..905ff68 100644 --- a/train_evidence_network.py +++ b/train_evidence_network.py @@ -2,7 +2,7 @@ Script to train the Evidence Networks for the paper. The created Evidence Network is trained to predict the Bayes ratio between a model -with a noisy 21-cm signal and a model with only noise. The network is +with a 21-cm signal and a model with only noise + foreground. The network is saved to the `models` directory after training is complete. After training the network is tested using the blind coverage test and the results are saved to the `figures_and_results` directory. The network is also evaluated @@ -11,17 +11,17 @@ `figures_and_results` directory. If using this script it is recommended to train on a GPU for speed. -Plus some CPUs will not have enough memory to train the network. The script can take an optional command line argument to specify the -noise sigma in K. The default is 0.079 K. +noise sigma in K. The default is 0.015 K. """ # Required imports import numpy as np from evidence_networks import EvidenceNetwork -from fbf_utilities import load_configuration_dict, get_noise_sigma, \ - assemble_simulators, timing_filename, add_timing_data +from fbf_utilities import load_configuration_dict, \ + assemble_simulators, timing_filename, add_timing_data, \ + generate_preprocessing_function, get_noise_sigma import os import matplotlib.pyplot as plt import time @@ -53,56 +53,71 @@ def sigma_to_log_k(sigma: float) -> float: def main(): """Train the Evidence Network.""" # IO - sigma_noise = get_noise_sigma() config_dict = load_configuration_dict() - timing_file = timing_filename(sigma_noise) + noise_sigma = get_noise_sigma() + timing_file = timing_filename(noise_sigma) # Set-up simulators start = time.time() - noise_only_simulator, noisy_signal_simulator = assemble_simulators( - config_dict, sigma_noise) + no_signal_simulator, signal_simulator = assemble_simulators( + config_dict, noise_sigma) end = time.time() add_timing_data(timing_file, 'simulator_assembly', end - start) + # Generate our preprocessing function + model_dir = os.path.join("models", f'en_noise_{noise_sigma}') + data_preprocessing = generate_preprocessing_function( + config_dict, + noise_sigma, + model_dir, + overwrite=True) + # Create and train evidence network start = time.time() - en = EvidenceNetwork(noise_only_simulator, noisy_signal_simulator, - alpha=EN_ALPHA) - en.train(epochs=20, - train_data_samples_per_model=2_000_000, - initial_learning_rate=2e-4, + en = EvidenceNetwork(no_signal_simulator, + signal_simulator, + alpha=EN_ALPHA, + data_preprocessing=data_preprocessing) + en.train(epochs=900, + train_data_samples_per_model=32_000_000, + validation_data_samples_per_model=12_800_000, + initial_learning_rate=1e-3, decay_steps=100_000, - batch_size=1000, - roll_back=True) + batch_size=32_768, + roll_back=True, + checkpoint_file=os.path.join(model_dir, 'best_weights.h5')) end = time.time() add_timing_data(timing_file, 'network_training', end - start) # Save the network - network_folder = os.path.join("models", f'en_noise_{sigma_noise:.4f}') - os.makedirs(network_folder, exist_ok=True) - network_file = os.path.join(network_folder, "global_signal_en.h5") + os.makedirs(model_dir, exist_ok=True) + network_file = os.path.join( + model_dir, + f"global_signal_en_noise_{noise_sigma}.h5") en.save(network_file) # Perform blind coverage test start = time.time() plt.style.use(os.path.join('figures_and_results', 'mnras_single.mplstyle')) fig, ax = plt.subplots() - _ = en.blind_coverage_test(plotting_ax=ax, num_validation_samples=100_000) + _ = en.blind_coverage_test(plotting_ax=ax, + num_validation_samples=1_000_000) figure_folder = os.path.join('figures_and_results', 'blind_coverage_tests') os.makedirs(figure_folder, exist_ok=True) - fig.savefig(os.path.join(figure_folder, - f'en_noise_{sigma_noise:.4f}_blind_coverage.pdf')) + fig.savefig(os.path.join( + figure_folder, + f'en_blind_coverage_noise_{noise_sigma}.pdf')) end = time.time() add_timing_data(timing_file, 'bct', end - start) # Load verification data verification_data_file = os.path.join( - 'verification_data', f'noise_{sigma_noise:.4f}_verification_data.npz') + 'verification_data', + f'verification_data_noise_{noise_sigma}.npz') verification_file_contents = np.load(verification_data_file) pc_log_bayes_ratios = verification_file_contents['log_bayes_ratios'] v_data = verification_file_contents['data'] v_labels = verification_file_contents['labels'] - pc_nlike = verification_file_contents['nlike'] # Evaluate network on verification data start = time.time() @@ -113,7 +128,8 @@ def main(): # In case useful save the log bayes ratios computed by the network en_bayes_ratio_file = os.path.join( - 'verification_data', f'noise_{sigma_noise:.4f}_en_log_k.npz') + 'verification_data', + f'en_log_k_noise_{noise_sigma}.npz') np.savez(en_bayes_ratio_file, log_bayes_ratios=en_log_bayes_ratios) # Create output directory for results of verification comparison @@ -122,8 +138,7 @@ def main(): numeric_results_filename = os.path.join( "figures_and_results", "polychord_verification", - f"polychord_verification_" - f"en_noise_{sigma_noise:.4f}_K_results.txt") + f"polychord_verification_results_noise_{noise_sigma}.txt") numeric_results_file = open(numeric_results_filename, 'w') # Print results @@ -153,10 +168,10 @@ def main(): numeric_results_file.write( f"{detection_sigma} sigma detection statistics:\n") numeric_results_file.write( - f"{en_percent_detected:.2f}% of signals were detectable " + f"{pc_percent_detected:.2f}% of signals were detectable " f"according to Polychord\n") numeric_results_file.write( - f"{pc_percent_detected:.2f}% of signals were detectable " + f"{en_percent_detected:.2f}% of signals were detectable " f"according to the network\n") numeric_results_file.write( f"{percent_difference:.2f}% difference in detection rate\n") @@ -164,12 +179,10 @@ def main(): f"{percent_changed:.2f}% of signals changed detection status\n") numeric_results_file.write("\n") - # And mean and total number of live point - pc_nlike = np.array(pc_nlike) - numeric_results_file.write(f"Mean number of likelihood evaluations: " - f"{np.mean(pc_nlike):.4f}\n") - numeric_results_file.write(f"Total number of likelihood evaluations: " - f"{np.sum(pc_nlike):.4f}\n") + # Network loss + test_loss = en.calculate_testing_loss( + 12_800_000, 32_768) + numeric_results_file.write(f"Network testing loss: {test_loss:.4f}\n") numeric_results_file.close() # Plot results @@ -181,7 +194,7 @@ def main(): s=2, marker='x', zorder=1, alpha=0.8) ax.scatter(en_log_bayes_ratios[v_labels == 1], pc_log_bayes_ratios[v_labels == 1], - c='C1', label='Noisy Signal', + c='C1', label='With Signal', s=2, marker='x', zorder=1, alpha=0.8) min_log_z = np.min([np.min(en_log_bayes_ratios), np.min(pc_log_bayes_ratios)]) @@ -212,8 +225,7 @@ def main(): filename = os.path.join( "figures_and_results", "polychord_verification", - 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