4. Conditional

import site
site.addsitedir(r"E:\AA\AI4Water")
site.addsitedir(r"E:\AA\easy_mpl")

from ai4water import Model
from ai4water.preprocessing import Transformations
from utils import prepare_data, eval_model

lookback = 12
batch_size = 64
units = 64
lr = 0.0001

tr_x, tr_y, val_x, val_y, test_x, test_y = prepare_data(
    target="TP", lookback=lookback, batch_size=batch_size)

    Not downloading the data since the directory
    /home/docs/checkouts/readthedocs.org/user_builds/catchment-aware-lstm/envs/latest/lib/python3.7/site-packages/ai4water/datasets/data/Quadica already exists.
    Use overwrite=True to remove previously saved files and download again
(21420, 113)
(21420, 92)

Training
x shape:  (9856, 12, 5)
x shape:  (9856, 92)
y shape:  (9856, 1)

Validation
x shape:  (4288, 12, 5)
x shape:  (4288, 92)
y shape:  (4288, 1)

Test
x shape:  (6208, 12, 5)
x shape:  (6208, 92)
y shape:  (6208, 1)

transformer = Transformations('box-cox')

x_mean = tr_x[0].mean()
x_std = tr_x[0].std()
x_mean_st = tr_x[1].mean()
x_std_st = tr_x[1].std()

y_mean = tr_y.mean()
y_std = tr_y.std()

tr_x[0] = (tr_x[0] - x_mean) / x_std
tr_x[1] = (tr_x[1] - x_mean_st) / x_std_st
#tr_y = (tr_y - y_mean) / y_std
tr_y = transformer.fit_transform(tr_y)

val_x[0] = (val_x[0] - x_mean) / x_std
val_x[1] = (val_x[1] - x_mean_st) / x_std_st
#val_y = (val_y - y_mean) / y_std
val_y = transformer.transform(val_y)

test_x[0] = (test_x[0] - x_mean) / x_std
test_x[1] = (test_x[1] - x_mean_st) / x_std_st
#test_y = (test_y - y_mean) / y_std
test_y = transformer.transform(test_y)

num_dyn_inputs = tr_x[0].shape[-1]
num_static_inputs = tr_x[1].shape[-1]

layers = {
    "Input_dyn": {"batch_shape": (batch_size, lookback, num_dyn_inputs)},
    "Input_cat": {"batch_shape": (batch_size, num_static_inputs)} ,
    "Conditionalize": {"config": {"units": units},
                       "inputs": ["Input_cat"],
                       "outptus": "cond_out"},

    "LSTM": {"config": {"units": units}, "inputs": "Input_dyn",
             'call_args': {'initial_state': ['Conditionalize', 'Conditionalize']}},

    "Dense": 1
}

model = Model(model = {"layers": layers},
              batch_size=batch_size,
              epochs=100,
              lr=lr)

            building DL model for
            regression problem using Model
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 Input_cat (InputLayer)         [(64, 92)]           0           []

 Input_dyn (InputLayer)         [(64, 12, 5)]        0           []

 conditional_dense (Dense)      (64, 64)             5952        ['Input_cat[0][0]']

 LSTM (LSTM)                    (64, 64)             17920       ['Input_dyn[0][0]',
                                                                  'conditional_dense[0][0]',
                                                                  'conditional_dense[0][0]']

 Dense (Dense)                  (64, 1)              65          ['LSTM[0][0]']

==================================================================================================
Total params: 23,937
Trainable params: 23,937
Non-trainable params: 0
__________________________________________________________________________________________________
dot plot of model could not be plotted due to ('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')

h = model.fit(x=tr_x, y=tr_y, validation_data=(val_x, val_y))

Epoch 1/100
assigning name Input_dyn to IteratorGetNext:0 with shape (64, 12, 5)
assigning name Input_cat to IteratorGetNext:1 with shape (64, 92)
assigning name Input_dyn to IteratorGetNext:0 with shape (64, 12, 5)
assigning name Input_cat to IteratorGetNext:1 with shape (64, 92)

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149/154 [============================>.] - ETA: 0s - loss: 0.0158assigning name Input_dyn to IteratorGetNext:0 with shape (64, 12, 5)
assigning name Input_cat to IteratorGetNext:1 with shape (64, 92)

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Epoch 2/100

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Epoch 3/100

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Epoch 4/100

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Epoch 5/100

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Epoch 6/100

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Epoch 7/100

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Epoch 8/100

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Epoch 9/100

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Epoch 10/100

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Epoch 11/100

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Epoch 12/100

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Epoch 13/100

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Epoch 14/100

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Epoch 15/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0088 - val_loss: 0.0049
Epoch 16/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0088 - val_loss: 0.0057
Epoch 17/100

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Epoch 18/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0087 - val_loss: 0.0058
Epoch 19/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0087 - val_loss: 0.0055
Epoch 20/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0087 - val_loss: 0.0051
Epoch 21/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0039
Epoch 22/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0087 - val_loss: 0.0040
Epoch 23/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0087 - val_loss: 0.0044
Epoch 24/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0047
Epoch 25/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0038
Epoch 26/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0041
Epoch 27/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0046
Epoch 28/100

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154/154 [==============================] - ETA: 0s - loss: 0.0086
154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0051
Epoch 29/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0054
Epoch 30/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0042
Epoch 31/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0051
Epoch 32/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0086 - val_loss: 0.0039
Epoch 33/100

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Epoch 34/100

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Epoch 35/100

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Epoch 36/100

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Epoch 37/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0051
Epoch 38/100

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Epoch 39/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0042
Epoch 40/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0051
Epoch 41/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0054
Epoch 42/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0041
Epoch 43/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0054
Epoch 44/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0042
Epoch 45/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0085 - val_loss: 0.0054
Epoch 46/100

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154/154 [==============================] - ETA: 0s - loss: 0.0084
154/154 [==============================] - 1s 7ms/step - loss: 0.0084 - val_loss: 0.0051
Epoch 47/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0046
Epoch 48/100

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154/154 [==============================] - ETA: 0s - loss: 0.0083
154/154 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0054
Epoch 49/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0061
Epoch 50/100

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Epoch 51/100

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Epoch 52/100

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Epoch 53/100

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Epoch 54/100

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Epoch 55/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0082 - val_loss: 0.0046
Epoch 56/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0082 - val_loss: 0.0056
Epoch 57/100

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Epoch 58/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0081 - val_loss: 0.0057
Epoch 59/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0081 - val_loss: 0.0056
Epoch 60/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0081 - val_loss: 0.0053
Epoch 61/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0080 - val_loss: 0.0073
Epoch 62/100

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Epoch 63/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0079 - val_loss: 0.0065
Epoch 64/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0080 - val_loss: 0.0067
Epoch 65/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0079 - val_loss: 0.0053
Epoch 66/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0078 - val_loss: 0.0061
Epoch 67/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0079 - val_loss: 0.0052
Epoch 68/100

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Epoch 69/100

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Epoch 70/100

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Epoch 71/100

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Epoch 72/100

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Epoch 73/100

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Epoch 74/100

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Epoch 75/100

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Epoch 76/100

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Epoch 77/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0078 - val_loss: 0.0048
Epoch 78/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0076 - val_loss: 0.0056
Epoch 79/100

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Epoch 80/100

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Epoch 81/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0076 - val_loss: 0.0054
Epoch 82/100

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154/154 [==============================] - 1s 8ms/step - loss: 0.0075 - val_loss: 0.0061
Epoch 83/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0076 - val_loss: 0.0064
Epoch 84/100

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154/154 [==============================] - 1s 7ms/step - loss: 0.0075 - val_loss: 0.0061
Epoch 85/100

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Epoch 86/100

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Epoch 87/100

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Epoch 88/100

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Epoch 89/100

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Epoch 90/100

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Epoch 91/100

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Epoch 92/100

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Epoch 93/100

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Epoch 94/100

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Epoch 95/100

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Epoch 97/100

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Epoch 98/100

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Epoch 99/100

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Epoch 100/100

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********** Successfully loaded weights from weights_025_0.00377.hdf5 file **********

eval_model(model, tr_x, tr_y, batch_size=batch_size, prefix="Training")

assigning name Input_dyn to IteratorGetNext:0 with shape (64, 12, 5)
assigning name Input_cat to IteratorGetNext:1 with shape (64, 92)

 Training
nse 0.267
rmse 0.0937
r2 0.2846
mape 56.0352

eval_model(model, val_x, val_y, batch_size=batch_size, prefix="Validation")

 Validation
nse -0.3561
rmse 0.0614
r2 0.1403
mape 57.9175

eval_model(model, test_x, test_y, batch_size=batch_size, prefix="Test")

 Test
nse 0.2621
rmse 0.0714
r2 0.2866
mape 64.3927

model.predict(test_x, test_y, plots=['residual', 'edf', 'regression', 'prediction'
                                     ])

assigning name Input_dyn to IteratorGetNext:0 with shape (32, 12, 5)
assigning name Input_cat to IteratorGetNext:1 with shape (32, 92)

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/home/docs/checkouts/readthedocs.org/user_builds/catchment-aware-lstm/envs/latest/lib/python3.7/site-packages/scipy/stats/stats.py:283: RuntimeWarning: invalid value encountered in log
  log_a = np.log(a)

array([[0.05996177],
       [0.0535172 ],
       [0.0655101 ],
       ...,
       [0.13098806],
       [0.12118447],
       [0.1214162 ]], dtype=float32)