This notebook is used to use KIM to perform inverse modeling for the Advanced Terrestrial Simulator (ATS).
# Libraries
from pathlib import Path
import pandas as pd
import numpy as np
from kim.map import KIM
from kim.data import Data
from kim.mapping_model import MLP
import os
import jax
%load_ext autoreload
%autoreload 2
Read the data#
The
Output_para.csvfile includes the ensemble of the seven parameters to be estimated, represented by \(\mathbf{Y}\).The
Input_logq.csvfile includes the ensemble of the streamflow simulations generated by ATS, represented by \(\mathbf{X}\).
# File and folder paths
f_para = Path("./data/Output_para.csv")
f_state = Path("./data/Input_logq.csv")
df_para, df_state = pd.read_csv(f_para, index_col=0),pd.read_csv(f_state, index_col=0)
y_keys, x_keys = df_para.keys(), df_state.keys()
y, x = df_para.values, df_state.values
x.shape, y.shape
((396, 1461), (396, 8))
Configurations#
Preliminary analysis configuration#
seed_shuffle = 1234
f_data_save = Path("./results/data")
# Data configuration
data_params = {
"xscaler_type": "minmax",
"yscaler_type": "minmax",
}
# Sensitivity analysis configuration
sensitivity_params = {
"method": "pc", "metric": "it-knn",
"sst": True, "ntest": 100, "alpha": 0.05, "k": 3,
"n_jobs": min(os.cpu_count(), 100), "seed_shuffle": seed_shuffle, # cap to available CPU cores; raise for HPC nodes
"verbose": 1
}
Ensemble learning configuration#
Ns_train = 300
Ns_val = 50
hidden_activation = 'sigmoid'
final_activation = 'leaky_relu'
seed_ens = 1024
seed_predict = 3636
seed_dl = 10
seed_model = 100
training_verbose = 1
n_models = 100
n_jobs = min(os.cpu_count(), 20) # cap to available CPU cores; raise for HPC nodes
f_kim_save1 = Path("./results/map_many2many")
f_kim_save2 = Path("./results/map_many2one")
f_kim_save3 = Path("./results/map_many2one_cond")
# Mapping parameters for each test below
map_configs = {
"model_type": MLP,
'n_model': n_models,
'ensemble_type': 'ens_random',
'model_hp_choices': {
"depth": [1,3,5,6],
"width_size": [3,6,10]
},
'model_hp_fixed': {
"hidden_activation": hidden_activation,
"final_activation": final_activation,
"model_seed": seed_model
},
'optax_hp_choices': {
'learning_rate': [0.01, 0.005, 0.003],
},
'optax_hp_fixed': {
'nsteps': 300,
'optimizer_type': 'adam',
},
'dl_hp_choices': {
},
'dl_hp_fixed': {
'dl_seed': seed_dl,
'num_train_sample': Ns_train,
'num_val_sample': Ns_val,
'batch_size': 64
},
'ens_seed': seed_ens,
'training_parallel': True,
'parallel_config': {
'n_jobs': n_jobs,
'backend': 'loky',
'verbose': 1
},
'device': None,
}
Exploratory data analysis#
Expected runtime: about 5 minutes on a 16-core laptop for the 1461 inputs x 8 outputs with ntest=100 shuffles (it scales with the number of cores given by n_jobs). The ensemble training further below takes another ~7 minutes. The results are saved to f_data_save at the end of this notebook, so on a repeated run you can skip the analysis by uncommenting the loading cell below and skipping the calculate_sensitivity cell.
# # Load the exploratory analysis result if available
# data = Data(x, y)
# data.load(f_data_save)
# Perform the sensitivity analysis if not done
data = Data(x, y, **data_params, Ns_train=Ns_train, Ns_val=Ns_val)
data.calculate_sensitivity(**sensitivity_params)
Using the kNN-based information theoretic metrics ...
Performing pairwise analysis to remove insensitive inputs ...
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Performing conditional independence testing to remove redundant inputs ...
# data.sensitivity_mask
data.cond_sensitivity_mask
array([[False, False, False, ..., False, True, False],
[False, False, False, ..., False, True, False],
[False, False, False, ..., False, True, False],
...,
[False, False, False, ..., False, True, False],
[False, False, False, ..., False, True, False],
[False, True, False, ..., False, True, False]], shape=(1461, 8))
Train the inverse mapping#
Now, let’s train the inverse mappings via ensemble learning. We are training three types of inverse mappings:
kim1: The naive inverse mapping from all \(\mathbf{Y}\) to all \(\mathbf{X}\)kim2: The knowledge-informed inverse mapping from sensitive \(\mathbf{Y}\) to each of \(\mathbf{X}\) using global sensitivity analysiskim3: The knowledge-informed inverse mapping from sensitive \(\mathbf{Y}\) to each of \(\mathbf{X}\) using global sensitivity analysis + redundancy filtering check
# Initialize three diffferent KIMs
kim1 = KIM(data, map_configs, map_option='many2many')
kim2 = KIM(data, map_configs, mask_option="sensitivity", map_option='many2one')
kim3 = KIM(data, map_configs, mask_option="cond_sensitivity", map_option='many2one')
# Train the mappings
kim1.train()
kim2.train()
kim3.train()
Performing ensemble training in parallel with 100 model configurations...
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 24.9s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 21.2s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 15.6s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
Training completes.
Performing ensemble training in parallel with 100 model configurations...
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 20.7s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 23.9s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
Training completes.
Performing ensemble training in parallel with 100 model configurations...
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 16.9s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 19.6s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 22.2s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 18.8s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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warnings.warn(
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 16.7s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
Training completes.
Performing ensemble training in parallel with 100 model configurations...
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 14.6s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 18.7s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 17.0s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 16.0s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 14.2s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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Training completes.
Performing ensemble training in parallel with 100 model configurations...
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[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 19.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
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Training completes.
[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed: 18.0s finished
Save all the results to disk#
# Preliminary analysis results
data.save(f_data_save)
# Inverse mapping results
kim1.save(f_kim_save1)
kim2.save(f_kim_save2)
kim3.save(f_kim_save3)