-
Notifications
You must be signed in to change notification settings - Fork 1
/
experiment.py
364 lines (256 loc) · 11.6 KB
/
experiment.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
import sys
sys.path.insert(0, "../models/")
import gr4h
from anns import constructor
import pandas as pd
import numpy as np
from scipy.optimize import differential_evolution
import keras
from keras.models import load_model
### IMPORTANT ###
# It is highly recommended to use any modern GPU
# (e.g., NVIDIA 1080Ti, P100, V100, 2060, 2070, 2080)
# for running this script.
# The average time needed to perform the entire set
# of experiments is almost two weeks when using 1080Ti
# or P100 GPUs.
# Probably, running this script on a standard CPU will take forever.
### uncomment when using GPU ###
#import os
#import tensorflow as tf
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
#config = tf.ConfigProto(log_device_placement=True)
#config.gpu_options.per_process_gpu_memory_fraction = 0.5
#config.gpu_options.allow_growth = True
#session = tf.Session(config=config)
# 1. Reading the data
# uncomment if you requested the data from the authors
#data = pd.read_pickle("../data/data.pkl")
# placeholder for input data
# comment out with # mark if you use the observational data
# which was provided to you by the authors
data = pd.read_pickle("../data/data_dummy.pkl")
# split data for calibration and validation periods
data_cal = data["1968":"1987"]
data_val = data["1988":"2004"]
# 2. Utils
def mse(y_true, y_pred):
return np.nanmean((y_true - y_pred) ** 2, axis=0)
def nse(y_true, y_pred):
return 1 - np.nansum((y_true-y_pred)**2)/np.nansum((y_true-np.nanmean(y_true))**2)
def data_generator(data_instance, model_type, history=720, mode="calibration", runoff_threshold=0.0):
"""
Data generator function for efficient training of neural networks
Input:
data_instance: pandas dataframe with Q, P, and PE columns represent
discharge, precipitation, and potential evapotranspiration timeseries, respectively
model_type: one of "GR4H", "MLP", "RNN", "LSTM", "GRU"
history: the number of antecedent timesteps to consider, hours (default=720, aka one month)
mode: "calibration" or "validation"
runoff threshold: the value below which discharge is not considered for calibration, float (default=0.0)
Output:
list of variables needed for model calibration / validation
"""
if model_type == "GR4H":
_Q = data_instance["Q"].values
_P = data_instance["P"].values
_PE= data_instance["PE"].values
# add warmup
# simply add a full period as a warm-up
Qobs = np.concatenate([_Q, _Q])
P = np.concatenate([_P, _P])
PE = np.concatenate([_PE, _PE])
output = [Qobs, P, PE]
elif model_type in ["RNN", "GRU", "LSTM", "MLP"]:
X_matrix = data_instance[["P", "PE"]].values
y_matrix = data_instance[["Q"]].values
X, y = [], []
for i in range(history, len(data_instance)):
X_chunk = X_matrix[i-history:i, ::]
y_chunk = y_matrix[i, ::]
if mode == "training":
# check for NaNs and non-zero runoff
if np.isnan(np.sum(X_chunk)) or np.isnan(np.sum(y_chunk)) or y_chunk<runoff_threshold:
pass
else:
X.append(X_chunk)
y.append(y_chunk)
else:
X.append(X_chunk)
y.append(y_chunk)
# from lists to np.array
X, y = np.array(X), np.array(y)
# normalization
X_mean = np.nanmean(X)
X_std = np.nanstd(X)
y_mean = np.nanmean(y)
y_std = np.nanstd(y)
X -= X_mean
X /= X_std
y -= y_mean
y /= y_std
if model_type == "MLP":
X = X.reshape(X.shape[0], -1)
else:
pass
output = [X, np.squeeze(y), y_mean, y_std]
return output
def calibration(data_instance, model_type, history=720):
"""
Calibration routine
Input:
data_instance: pandas dataframe (the same that for data_generator func)
model_type: one of "GR4H", "MLP", "RNN", "LSTM", "GRU"
history: the number of antecedent timesteps to consider, hours (default=720, aka one month)
Output:
list of: (1) optimal parameters (or Keras model instance) and pandas dataframe
with simulation results
"""
if model_type == "GR4H":
Qobs, P, PE = data_generator(data_instance=data_instance, model_type=model_type)
def loss_gr4h(params):
# calculate runoff
Qsim = gr4h.run(P, PE, params)
# mse on peiod with cropped warm-up
return np.nanmean((Qobs[-len(data_instance):] - Qsim[-len(data_instance):]) ** 2, axis=0)
# optimization
opt_par = differential_evolution(loss_gr4h, bounds=gr4h.bounds(), maxiter=100, polish=True, disp=False, seed=42).x
# calculate runoff with optimal parameters
Qsim = gr4h.run(P, PE, opt_par)
# cut the warmup period + history (for consistency with DL)
Qobs = Qobs[-len(data_instance)+history:]
Qsim = Qsim[-len(data_instance)+history:]
print(f"NSE on calibration is {np.round(nse(Qobs, Qsim), 2)}")
# save results from calibration period separately
calib_res = pd.DataFrame({"Qobs": Qobs, "Qsim": Qsim})
return opt_par, calib_res
elif model_type in ["RNN", "GRU", "LSTM", "MLP"]:
# generate data
X, y, y_mean, y_std = data_generator(data_instance=data_instance, model_type=model_type)
# create a model instance
model = constructor(model_type=model_type)
print(model.summary())
# set callbacks
# interrupt training if there is no improvement for 100 epochs
# save the best model on disk
callbacks_list = [keras.callbacks.EarlyStopping(patience=100),
keras.callbacks.ModelCheckpoint(filepath=f"../models/{model_type}.h5", save_best_only=True)]
# set up training parameters
validation_split = 0.25
epochs = 1000
batch_size=4096
# fit the model
model.fit(X, y,
validation_split=validation_split,
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks_list)
# load the best model
model = load_model(f"../models/{model_type}.h5")
# prediction
Qsim = model.predict(X, batch_size=batch_size).reshape(-1)
# postprocessing
Qsim *= y_std
Qsim += y_mean
Qsim = np.where(Qsim < 0, 0, Qsim)
Qobs = y.copy()
Qobs *= y_std
Qobs += y_mean
print(f"NSE on calibration is {np.round(nse(Qobs, Qsim), 2)}")
# save results from calibration period separately
calib_res = pd.DataFrame({"Qobs": Qobs, "Qsim": Qsim})
return model, calib_res
def validation(data_instance, model_type, model_instance, history=720):
"""
Validation routine
Input:
data_instance: pandas dataframe (the same that for data_generator func)
model_type: one of "GR4H", "MLP", "RNN", "LSTM", "GRU"
history: the number of antecedent timesteps to consider, hours (default=720, aka one month)
model_instance: optimal parameters of GR4H or Keras model instance of pretrained model
Output:
simulated discharge timeseries
"""
if model_type == "GR4H":
# generate data
Qobs, P, PE = data_generator(data_instance=data_instance, model_type=model_type, history=history, mode="validation")
# calculate runoff with optimal parameters
Qsim = gr4h.run(P, PE, model_instance)
# cut the warmup period + history (for consistency with DL)
Qobs = Qobs[-len(data_instance)+history:]
Qsim = Qsim[-len(data_instance)+history:]
elif model_type in ["RNN", "GRU", "LSTM", "MLP"]:
# generate data
X, y, y_mean, y_std = data_generator(data_instance=data_instance, model_type=model_type, history=history, mode="validation")
# prediction
Qsim = model_instance.predict(X, batch_size=1024).reshape(-1)
# postprocessing
Qsim *= y_std
Qsim += y_mean
Qsim = np.where(Qsim < 0, 0, Qsim)
Qobs = y.copy()
Qobs *= y_std
Qobs += y_mean
print(f"NSE on validation is {np.round(nse(Qobs, Qsim), 2)}")
return Qsim
def periods_constructor(duration, year_start, year_end, stride=1):
"""
Construction of individual calibration periods
Input:
duration: the required duration in calender years, int
year_start: the first year considered, int
year_end: the last year considered, int
stride: default=1
Output:
the list of considered calender years
"""
duration = duration - 1
periods=[]
while year_end - duration >= year_start:
period = [year_end-duration, year_end]
periods.append(period)
year_end = year_end - stride
return periods
# 3. The main function describes the experiment
# about the evaluation of the effect of calibration data length
# on the performance of different hydrological models
def experiment(calibration_instance, validation_instance, model_type, history=720):
"""
Input:
calibtration_instance: pandas dataframe from the respective data generator
validation_instance: pandas dataframe from the respective data generator
model_type: one of "GR4H", "MLP", "RNN", "LSTM", "GRU"
history: the number of antecedent timesteps to consider, hours (default=720, aka one month)
"""
# loop over different possible caibration period duration
for period_duration in range(1,21):
# create an instance of available periods
periods = periods_constructor(period_duration, calibration_instance.index.year[0], calibration_instance.index.year[-1])
# initialize a container for storing
# simulated runoff on validation
Qsim_container = []
colnames_container = []
for period in periods:
print(period, model_type)
# set up years for slicing
year_start, year_end = period
# create chunk for calibration
calibration_chunck = calibration_instance[str(year_start):str(year_end)]
# calibrate our model
model, calib_results = calibration(data_instance=calibration_chunck, model_type=model_type, history=history)
# save calibration results separately
calib_results.to_pickle(f"../results/calibration/{model_type}_duration_{period_duration}_period_{year_start}_{year_end}.pkl")
# run model on validation period
Qsim = validation(data_instance=validation_instance, model_type=model_type, model_instance=model, history=history)
# store results in a container
Qsim_container.append(Qsim)
# create a respective colname
colnames_container.append(f"Qsim_{year_start}_{year_end}")
Qsim_container = np.moveaxis(np.array(Qsim_container), 0, -1)
results = pd.DataFrame(data=Qsim_container, index=validation_instance.index[history:], columns=colnames_container)
results["Qobs"] = validation_instance["Q"].iloc[history:]
results.to_pickle(f"../results/validation/{model_type}_duration_{period_duration}.pkl")
# 4. The Run
for model_type in ["GR4H", "MLP", "RNN", "LSTM", "GRU"]:
runoff = experiment(data_cal, data_val, model_type, 720)