[1]:
%matplotlib inline
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Comparison of deep learning architectures
[1]:
from ai4water.datasets import MtropicsLaos
from ai4water.utils.utils import get_version_info
from ai4water.experiments import DLRegressionExperiments
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll
warnings.warn("loaded more than 1 DLL from .libs:"
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\sklearn\experimental\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.
warnings.warn(
[2]:
for k,v in get_version_info().items():
print(f"{k} version: {v}")
python version: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:16) [MSC v.1916 64 bit (AMD64)]
os version: nt
ai4water version: 1.06
lightgbm version: 3.3.1
tcn version: 3.4.0
catboost version: 0.26
xgboost version: 1.5.0
easy_mpl version: 0.21.2
SeqMetrics version: 1.3.3
tensorflow version: 2.7.0
keras.api._v2.keras version: 2.7.0
numpy version: 1.21.0
pandas version: 1.3.4
matplotlib version: 3.4.3
h5py version: 3.5.0
sklearn version: 1.0.1
shapefile version: 2.3.0
xarray version: 0.20.1
netCDF4 version: 1.5.7
optuna version: 2.10.1
skopt version: 0.9.0
hyperopt version: 0.2.7
plotly version: 5.3.1
lime version: NotDefined
seaborn version: 0.11.2
[3]:
dataset = MtropicsLaos(save_as_nc=True, # if set to True, then netcdf must be installed
convert_to_csv=False,
path="F:\\data\\MtropicsLaos", # path where the data is saved, if not availble leave it to None
)
Not downloading the data since the directory
F:\data\MtropicsLaos already exists.
Use overwrite=True to remove previously saved files and download again
Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
(3691, 9)
[5]:
data.head()
[5]:
| air_temp | rel_hum | wind_speed | sol_rad | water_level | pcp | susp_pm | Ecoli_source | Ecoli_mpn100 | |
|---|---|---|---|---|---|---|---|---|---|
| 2011-05-25 14:54:00 | 22.538134 | 96.169885 | 4.3 | 167.8 | 133.000000 | 0.0 | 0.9 | 2.868160e+15 | NaN |
| 2011-05-25 15:00:00 | 22.538271 | 96.170115 | 4.0 | 158.0 | 133.000000 | 0.0 | 0.9 | 2.868160e+15 | NaN |
| 2011-05-25 15:06:00 | 22.538407 | 96.170345 | 3.7 | 150.0 | 133.000000 | 0.0 | 0.9 | 2.868160e+15 | NaN |
| 2011-05-25 15:12:00 | 22.538544 | 96.170575 | 3.4 | 142.0 | 132.988889 | 0.0 | 0.9 | 2.868160e+15 | NaN |
| 2011-05-25 15:18:00 | 22.538680 | 96.170805 | 3.1 | 134.0 | 132.977778 | 0.0 | 0.9 | 2.868160e+15 | NaN |
[6]:
print(data.isna().sum())
air_temp 0
rel_hum 0
wind_speed 0
sol_rad 0
water_level 0
pcp 0
susp_pm 0
Ecoli_source 0
Ecoli_mpn100 3433
dtype: int64
[7]:
inputs = data.columns.tolist()[0:-1]
inputs
[7]:
['air_temp',
'rel_hum',
'wind_speed',
'sol_rad',
'water_level',
'pcp',
'susp_pm',
'Ecoli_source']
[8]:
outputs = data.columns.tolist()[-1:]
outputs
[8]:
['Ecoli_mpn100']
[9]:
comparisons = DLRegressionExperiments(
input_features=inputs,
output_features=outputs,
x_transformation="minmax",
y_transformation={'method':'log', 'treat_negatives': True, 'replace_zeros': True},
epochs=500,
batch_size=16,
ts_args={"lookback": lookback},
verbosity=0,
save=False,
)
[10]:
comparisons.fit(data=data,
include=['MLP',
'LSTM',
"CNN",
'CNNLSTM',
'TCN',
"TFT",
"LSTMAutoEncoder",
])
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
running MLP model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
running LSTM model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
running CNN model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
running CNNLSTM model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
running TCN model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
overflow encountered in square
running TFT model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
running LSTMAutoEncoder model
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
[11]:
best_models = comparisons.compare_errors('r2', data=data)
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
WARNING:tensorflow:5 out of the last 17 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000251233FBC10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002511A513EE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
[12]:
best_models = comparisons.compare_errors(
'nse',
data=data,
)
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
[13]:
train test
CNN 0.225706 0.693526
MLP 0.129064 0.296447
CNNLSTM 0.423204 0.277023
LSTMAutoEncoder 0.481236 0.130555
TFT 0.264884 0.032711
LSTM 0.065246 -0.000764
TCN 0.045221 -0.050504
[18]:
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
[19]:
_ = comparisons.compare_edf_plots(data=data)
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (144, 18, 8)
target shape: (144, 1)
********** Removing Examples with nan in labels **********
***** Validation *****
input_x shape: (36, 18, 8)
target shape: (36, 1)
********** Removing Examples with nan in labels **********
***** Test *****
input_x shape: (78, 18, 8)
target shape: (78, 1)
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
argument test is deprecated and will be removed in future. Please
use 'predict_on_test_data' method instead.
[20]:
_ = comparisons.loss_comparison()
[21]:
_ = comparisons.loss_comparison(loss_name="val_loss")
