def f(**kwargs):
kwargs['objective'] = 'reg:squarederror'
kwargs = Jsonize(kwargs)()
model = Model(inputs=inputs,
outputs=outputs,
lookback=1,
batches="2d",
val_data="same",
test_fraction=0.3,
model={"xgboostregressor": kwargs},
transformation=None,
data=data,
prefix='testing',
verbosity=0)
model.fit(indices="random")
t, p = model.predict(indices=model.test_indices, prefix='test')
mse = RegressionMetrics(t, p).mse()
print(f"Validation mse {mse}")
return mse
def test_ml_random_indices(self):
model = Model(inputs=data_reg['feature_names'],
outputs=["target"],
lookback=1,
batches="2d",
val_fraction=0.0,
val_data="same",
test_fraction=0.3,
category="ML",
problem="regression",
model={"xgboostregressor": {}},
transformation=None,
data=df_reg,
verbosity=0)
model.fit(indices="random")
trtt, trp = model.predict(indices=model.train_indices, prefix='train')
t, p = model.predict(indices=model.test_indices, prefix='test')
self.assertGreater(len(t), 1)
self.assertGreater(len(trtt), 1)
return
def fn(**suggestion):
model = Model(inputs=inputs,
outputs=outputs,
model={"xgboostregressor": suggestion},
data=data,
prefix=f'test_{algorithm}_xgboost_{backend}',
verbosity=0)
model.fit(indices="random")
t, p = model.predict(indices=model.test_indices, prefix='test')
mse = RegressionMetrics(t, p).mse()
return mse
def fn(**suggestion):
model = Model(inputs=inputs,
outputs=outputs,
model={"xgboostregressor": suggestion},
data=data,
prefix='test_tpe_xgboost',
verbosity=0)
model.fit(indices="random")
t, p = model.predict(indices=model.test_indices, prefix='test')
mse = RegressionMetrics(t, p).mse()
print(f"Validation mse {mse}")
return mse
def build_and_run(outputs, transformation=None, indices=None):
model = Model(model={"layers": make_layers(len(outputs['inp_1d']))},
lookback=lookback,
inputs={
"inp_1d": inp_1d,
"inp_2d": inp_2d
},
outputs=outputs,
data={
'inp_1d': make_1d(outputs['inp_1d']),
'inp_2d': data_2d
},
transformation=transformation,
epochs=2,
verbosity=0)
model.fit(indices=indices)
return model.predict(indices=model.test_indices if indices else None)
def test_datetimeindex(self):
# makes sure that using datetime_index=True during prediction, the returned values are in correct order
model = Model(data=data1,
inputs=in_cols,
outputs=out_cols,
epochs=2,
model={
'layers': {
"LSTM": {
"config": {
"units": 2
}
},
"Dense": {
"config": {
"units": 1
}
},
"Reshape": {
"config": {
"target_shape": (1, 1)
}
}
}
},
lookback=lookback,
verbosity=0)
model.fit(indices="random")
t, p = model.predict(indices=model.train_indices,
use_datetime_index=True)
# the values in t must match the corresponding indices after adding 10000, because y column starts from 100000
for i in range(100):
self.assertEqual(int(t[i]), model.train_indices[i] + 10000)
return
"Dense_0": {
'units': 64,
'activation': 'relu'
},
"Flatten": {},
"Dense_3": {
'units': 1
},
}
}
df = arg_beach()
input_features = list(df.columns)[0:-1]
# column in dataframe to bse used as output/target
outputs = list(df.columns)[-1]
model = Model(data=df,
batch_size=16,
lookback=1,
model=mlp_model,
inputs=input_features,
outputs=[outputs],
lr=0.0001)
history = model.fit(indices='random')
y, obs = model.predict()
model.view_model(st=0)
# this example shows how to build the Models from `from_checkout` class method
# first we will train and save a simple model and load it from config file
import os
from AI4Water import Model
from AI4Water.utils.datasets import load_nasdaq
from AI4Water.utils.utils import find_best_weight
df = load_nasdaq()
model = Model(lookback=1, epochs=2,
data=df,
)
history = model.fit(indices='random')
w_path = model.path
# for clarity, delete the model, although it is overwritten
del model
# Load the `Model` from checkpoint, provide the checkpoint
cpath = os.path.join(w_path, "config.json") # "provide complete path of config file"
model = Model.from_config(cpath, data=df)
w_file = find_best_weight(os.path.join(w_path, "weights")) # The file name of weights
model.load_weights(w_file)
x, y = model.predict(indices=model.test_indices, use_datetime_index=False)
