From bd79fde19018349404c5d813457dd448c9184132 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 1 Oct 2021 10:40:06 +0200
Subject: [PATCH] Implemented training with cross-validation.
---
climax/main/config.py | 3 +
climax/main/downscale_infer.py | 5 +-
climax/main/downscale_train.py | 123 ++++++++++++++-------
climax/main/downscale_train_cv.py | 174 ------------------------------
4 files changed, 92 insertions(+), 213 deletions(-)
delete mode 100644 climax/main/downscale_train_cv.py
diff --git a/climax/main/config.py b/climax/main/config.py
index 42baad4..7e86890 100644
--- a/climax/main/config.py
+++ b/climax/main/config.py
@@ -55,6 +55,9 @@ DEM_FEATURES = False
# stratify training/validation set for precipitation by number of wet days
STRATIFY = True
+# whether to train using cross-validation
+CV = False
+
# -----------------------------------------------------------------------------
# Observations ----------------------------------------------------------------
# -----------------------------------------------------------------------------
diff --git a/climax/main/downscale_infer.py b/climax/main/downscale_infer.py
index e61269c..9f57fd8 100644
--- a/climax/main/downscale_infer.py
+++ b/climax/main/downscale_infer.py
@@ -24,7 +24,7 @@ from climax.core.utils import split_date_range
from climax.core.loss import BernoulliGammaLoss
from climax.main.config import (ERA5_PREDICTORS, ERA5_PLEVELS, PREDICTAND, NET,
VALID_PERIOD, BATCH_SIZE, NORM, DOY, NYEARS,
- DEM, DEM_FEATURES, LOSS)
+ DEM, DEM_FEATURES, LOSS, CV)
from climax.main.io import ERA5_PATH, DEM_PATH, MODEL_PATH, TARGET_PATH
# module level logger
@@ -51,6 +51,9 @@ if __name__ == '__main__':
state_file = state_file.replace('.pt', '_{}.pt'.format(
repr(LOSS).strip('()')))
+ # add suffix for training with cross-validation
+ state_file = state_file.replace('.pt', '_cv.pt') if CV else state_file
+
# path to model state
state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
diff --git a/climax/main/downscale_train.py b/climax/main/downscale_train.py
index 79758be..b985de3 100644
--- a/climax/main/downscale_train.py
+++ b/climax/main/downscale_train.py
@@ -12,7 +12,7 @@ from logging.config import dictConfig
# externals
import torch
import xarray as xr
-from sklearn.model_selection import train_test_split
+from sklearn.model_selection import train_test_split, TimeSeriesSplit
from torch.utils.data import DataLoader
# locals
@@ -26,7 +26,7 @@ from climax.main.config import (ERA5_PLEVELS, ERA5_PREDICTORS, PREDICTAND,
CALIB_PERIOD, DOY, SHUFFLE, BATCH_SIZE, LR,
LAMBDA, NORM, TRAIN_CONFIG, NET, LOSS, FILTERS,
OVERWRITE, DEM, DEM_FEATURES, STRATIFY,
- WET_DAY_THRESHOLD)
+ WET_DAY_THRESHOLD, CV)
from climax.main.io import ERA5_PATH, OBS_PATH, DEM_PATH, MODEL_PATH
# module level logger
@@ -53,6 +53,9 @@ if __name__ == '__main__':
state_file = state_file.replace('.pt', '_{}.pt'.format(
repr(LOSS).strip('()')))
+ # add suffix for training with cross-validation
+ state_file = state_file.replace('.pt', '_cv.pt') if CV else state_file
+
# path to model state
state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
@@ -112,36 +115,8 @@ if __name__ == '__main__':
# add dem to set of predictor variables
Era5_ds = xr.merge([Era5_ds, dem])
- # initialize training data
- LogConfig.init_log('Initializing training data.')
-
- # split calibration period into training and validation period
- if PREDICTAND == 'pr' and STRATIFY:
- # stratify training and validation dataset by number of observed
- # wet days for precipitation
- wet_days = (Obs_ds.sel(time=CALIB_PERIOD).mean(dim=('y', 'x')) >=
- WET_DAY_THRESHOLD).to_array().values.squeeze()
- train, valid = train_test_split(
- CALIB_PERIOD, stratify=wet_days, test_size=0.1)
- train, valid = sorted(train), sorted(valid) # sort chronologically
- else:
- train, valid = train_test_split(CALIB_PERIOD, shuffle=False,
- test_size=0.1)
-
- # training and validation dataset
- Era5_train, Obs_train = Era5_ds.sel(time=train), Obs_ds.sel(time=train)
- Era5_valid, Obs_valid = Era5_ds.sel(time=valid), Obs_ds.sel(time=valid)
-
- # create PyTorch compliant dataset and dataloader instances for model
- # training
- train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM,
- doy=DOY)
- valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM,
- doy=DOY)
- train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=SHUFFLE,
- drop_last=False)
- valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE, shuffle=SHUFFLE,
- drop_last=False)
+ # initialize network and optimizer
+ LogConfig.init_log('Initializing network and optimizer.')
# define number of output fields
# check whether modelling pr with probabilistic approach
@@ -150,18 +125,90 @@ if __name__ == '__main__':
outputs = 3
# instanciate network
- net = NET(state_file, train_ds.X.shape[1], outputs, filters=FILTERS)
+ net = NET(state_file, len(Era5_ds.data_vars), outputs, filters=FILTERS)
# initialize optimizer
optimizer = torch.optim.Adam(net.parameters(), lr=LR,
weight_decay=LAMBDA)
- # initialize network trainer
- trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
- valid_dl, loss_function=LOSS, **TRAIN_CONFIG)
+ # initialize training data
+ LogConfig.init_log('Initializing training data.')
+ if CV:
+ # split calibration period using cross-validation TimeSeriesSplit
+ cv = TimeSeriesSplit()
+ for i, (train_idx, valid_idx) in enumerate(cv.split(CALIB_PERIOD)):
+
+ # time steps for training and validation set
+ train = CALIB_PERIOD[train_idx]
+ valid = CALIB_PERIOD[valid_idx]
+ LogConfig.init_log('Fold {}/{}: {} - {}'.format(
+ i + 1, cv.n_splits, str(train[0]), str(train[-1])))
+
+ # training and validation dataset
+ Era5_train, Obs_train = (Era5_ds.sel(time=train),
+ Obs_ds.sel(time=train))
+ Era5_valid, Obs_valid = (Era5_ds.sel(time=valid),
+ Obs_ds.sel(time=valid))
+
+ # create PyTorch compliant dataset and dataloader instances for
+ # model training
+ train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM,
+ doy=DOY)
+ valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM,
+ doy=DOY)
+ train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+ valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+
+ # initialize network trainer
+ trainer = NetworkTrainer(
+ net, optimizer, net.state_file, train_dl, valid_dl,
+ loss_function=LOSS, **TRAIN_CONFIG)
+
+ # train model
+ state = trainer.train()
- # train model
- state = trainer.train()
+ else:
+ # split calibration period into training and validation period
+ if PREDICTAND == 'pr' and STRATIFY:
+ # stratify training and validation dataset by number of
+ # observed wet days for precipitation
+ wet_days = (Obs_ds.sel(time=CALIB_PERIOD).mean(dim=('y', 'x'))
+ >= WET_DAY_THRESHOLD).to_array().values.squeeze()
+ train, valid = train_test_split(
+ CALIB_PERIOD, stratify=wet_days, test_size=0.1)
+
+ # sort chronologically
+ train, valid = sorted(train), sorted(valid)
+ else:
+ train, valid = train_test_split(CALIB_PERIOD, shuffle=False,
+ test_size=0.1)
+
+ # training and validation dataset
+ Era5_train, Obs_train = (Era5_ds.sel(time=train),
+ Obs_ds.sel(time=train))
+ Era5_valid, Obs_valid = (Era5_ds.sel(time=valid),
+ Obs_ds.sel(time=valid))
+
+ # create PyTorch compliant dataset and dataloader instances for model
+ # training
+ train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM,
+ doy=DOY)
+ valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM,
+ doy=DOY)
+ train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+ valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE,
+ shuffle=SHUFFLE, drop_last=False)
+
+ # initialize network trainer
+ trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
+ valid_dl, loss_function=LOSS,
+ **TRAIN_CONFIG)
+
+ # train model
+ state = trainer.train()
# log execution time of script
LogConfig.init_log('Execution time of script {}: {}'
diff --git a/climax/main/downscale_train_cv.py b/climax/main/downscale_train_cv.py
deleted file mode 100644
index 5aaec33..0000000
--- a/climax/main/downscale_train_cv.py
+++ /dev/null
@@ -1,174 +0,0 @@
-"""Dynamical climate downscaling using deep convolutional neural networks."""
-
-# !/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-# builtins
-import time
-import logging
-from datetime import timedelta
-from logging.config import dictConfig
-
-# externals
-import torch
-import xarray as xr
-from sklearn.model_selection import TimeSeriesSplit
-from torch.utils.data import DataLoader
-
-# locals
-from pysegcnn.core.utils import search_files
-from pysegcnn.core.trainer import NetworkTrainer, LogConfig
-from pysegcnn.core.models import Network
-from pysegcnn.core.logging import log_conf
-from climax.core.dataset import ERA5Dataset, NetCDFDataset
-from climax.core.loss import BernoulliGammaLoss
-from climax.main.config import (ERA5_PLEVELS, ERA5_PREDICTORS, PREDICTAND,
- CALIB_PERIOD, DOY, SHUFFLE, BATCH_SIZE, LR,
- LAMBDA, NORM, TRAIN_CONFIG, NET, LOSS, FILTERS,
- OVERWRITE, DEM, DEM_FEATURES)
-from climax.main.io import ERA5_PATH, OBS_PATH, DEM_PATH, MODEL_PATH
-
-# module level logger
-LOGGER = logging.getLogger(__name__)
-
-
-if __name__ == '__main__':
-
- # initialize timing
- start_time = time.monotonic()
-
- # initialize network filename
- state_file = ERA5Dataset.state_file(
- NET, PREDICTAND, ERA5_PREDICTORS, ERA5_PLEVELS, dem=DEM,
- dem_features=DEM_FEATURES, doy=DOY)
-
- # adjust statefile name for precipitation
- if PREDICTAND == 'pr':
- if isinstance(LOSS, BernoulliGammaLoss):
- state_file = state_file.replace('.pt', '_{}mm_{}.pt'.format(
- str(LOSS.min_amount).replace('.', ''),
- repr(LOSS).strip('()')))
- else:
- state_file = state_file.replace('.pt', '_{}.pt'.format(
- repr(LOSS).strip('()')))
-
- # add suffix for training with cross-validation
- state_file = state_file.replace('.pt', '_cv.pt')
-
- # path to model state
- state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
-
- # initialize logging
- log_file = MODEL_PATH.joinpath(PREDICTAND,
- state_file.name.replace('.pt', '_log.txt'))
- if log_file.exists():
- log_file.unlink()
- dictConfig(log_conf(log_file))
-
- # initialize downscaling
- LogConfig.init_log('Initializing downscaling for period: {}'.format(
- ' - '.join([str(CALIB_PERIOD[0]), str(CALIB_PERIOD[-1])])))
-
- # check if model exists
- if state_file.exists() and not OVERWRITE:
- # load pretrained network
- net, _ = Network.load_pretrained_model(state_file, NET)
- else:
- # initialize ERA5 predictor dataset
- LogConfig.init_log('Initializing ERA5 predictors.')
- Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
- plevels=ERA5_PLEVELS)
- Era5_ds = Era5.merge(chunks=-1)
-
- # initialize OBS predictand dataset
- LogConfig.init_log('Initializing observations for predictand: {}'
- .format(PREDICTAND))
-
- # check whether to joinlty train tasmin and tasmax
- if PREDICTAND == 'tas':
- # read both tasmax and tasmin
- tasmax = xr.open_dataset(
- search_files(OBS_PATH.joinpath('tasmax'), '.nc$').pop())
- tasmin = xr.open_dataset(
- search_files(OBS_PATH.joinpath('tasmin'), '.nc$').pop())
- Obs_ds = xr.merge([tasmax, tasmin])
- else:
- # read in-situ gridded observations
- Obs_ds = search_files(OBS_PATH.joinpath(PREDICTAND), '.nc$').pop()
- Obs_ds = xr.open_dataset(Obs_ds)
-
- # whether to use digital elevation model
- if DEM:
- # digital elevation model: Copernicus EU-Dem v1.1
- dem = search_files(DEM_PATH, '^eu_dem_v11_stt.nc$').pop()
-
- # read elevation and compute slope and aspect
- dem = ERA5Dataset.dem_features(
- dem, {'y': Era5_ds.y, 'x': Era5_ds.x},
- add_coord={'time': Era5_ds.time})
-
- # check whether to use slope and aspect
- if not DEM_FEATURES:
- dem = dem.drop_vars(['slope', 'aspect'])
-
- # add dem to set of predictor variables
- Era5_ds = xr.merge([Era5_ds, dem])
-
- # initialize network and optimizer
- LogConfig.init_log('Initializing network and optimizer.')
-
- # define number of output fields
- # check whether modelling pr with probabilistic approach
- outputs = len(Obs_ds.data_vars)
- if PREDICTAND == 'pr' and isinstance(LOSS, BernoulliGammaLoss):
- outputs = 3
-
- # instanciate network
- net = NET(state_file, len(Era5_ds.data_vars), outputs, filters=FILTERS)
-
- # initialize optimizer
- optimizer = torch.optim.Adam(net.parameters(), lr=LR,
- weight_decay=LAMBDA)
-
- # initialize training data
- LogConfig.init_log('Initializing training data.')
-
- # split calibration period using cross-validation TimeSeriesSplit
- cv = TimeSeriesSplit()
- for i, (train_idx, valid_idx) in enumerate(cv.split(CALIB_PERIOD)):
-
- # time steps for training and validation set
- train = CALIB_PERIOD[train_idx]
- valid = CALIB_PERIOD[valid_idx]
- LogConfig.init_log('Fold {}/{}: {} - {}'.format(
- i + 1, cv.n_splits, str(train[0]), str(train[-1])))
-
- # training and validation dataset
- Era5_train, Obs_train = (Era5_ds.sel(time=train),
- Obs_ds.sel(time=train))
- Era5_valid, Obs_valid = (Era5_ds.sel(time=valid),
- Obs_ds.sel(time=valid))
-
- # create PyTorch compliant dataset and dataloader instances for
- # model training
- train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM,
- doy=DOY)
- valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM,
- doy=DOY)
- train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE,
- shuffle=SHUFFLE, drop_last=False)
- valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE,
- shuffle=SHUFFLE, drop_last=False)
-
- # initialize network trainer
- trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
- valid_dl, loss_function=LOSS,
- **TRAIN_CONFIG)
-
- # train model
- state = trainer.train()
-
- # log execution time of script
- LogConfig.init_log('Execution time of script {}: {}'
- .format(__file__, timedelta(seconds=time.monotonic() -
- start_time)))
--
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