def test_random_idx_with_nan_inputs_outputs(self):
"""
Test that when nans are present in inputs and outputs and we use random indices, then x,y data is correctly made.
"""
df = get_df_with_nans(inputs=True, outputs=True, frac=0.1)
model = Model(inputs=['in1', 'in2'],
outputs=['out1'],
transformation=None,
val_data='same',
test_fraction=0.3,
epochs=1,
data=df,
input_nans={'fillna': {
'method': 'bfill'
}},
verbosity=1)
model.fit(indices='random')
x, _, y = model.train_data(indices=model.train_indices)
# for i in range(100):
# idx = model.train_indices[i]
# df_x = df[['in1', 'in2']].iloc[idx]
# if idx > model.lookback and int(df_x.isna().sum()) == 0:
# self.assertAlmostEqual(float(df['out1'].iloc[idx]), y[i], 6)
# self.assertTrue(np.allclose(df[['in1', 'in2']].iloc[idx], x[0][i, -1]))
assert np.max(model.test_indices) < (
model.data.shape[0] - int(model.data[model.out_cols].isna().sum()))
assert np.max(model.train_indices) < (
model.data.shape[0] - int(model.data[model.out_cols].isna().sum()))
return
def run_same_train_val_data(**kwargs):
model = Model(data=nasdaq_df,
val_data="same",
test_fraction=0.2,
epochs=1,
verbosity=0)
model.fit(**kwargs)
x, _, y = model.train_data(indices=model.train_indices)
return x, y
def test_random_idx_with_nan_in_outputs(self):
# testing that if output contains nans and we use random indices, then correct examples are assinged
# for training and testing given val_data is 'same'.
df = get_df_with_nans(inputs=False, outputs=True, frac=0.8)
model = Model(inputs=['in1', 'in2'],
outputs=['out1'],
transformation=None,
val_data='same',
test_fraction=0.3,
epochs=1,
data=df,
verbosity=0)
model.fit(indices='random')
idx5 = [50, 0, 72, 153, 39, 31, 170, 8] # last 8 train indices
self.assertTrue(np.allclose(idx5, model.train_indices[-8:]))
x, _, y = model.train_data(indices=model.train_indices)
eighth_non_nan_val_4m_st = df['out1'][df['out1'].notnull()].iloc[8]
# the last training index is 8, so the last y value must be 8th non-nan value
self.assertAlmostEqual(float(y[-1]), eighth_non_nan_val_4m_st)
# checking that x values are also correct
eighth_non_nan_val_4m_st = df[['in1',
'in2']][df['out1'].notnull()].iloc[8]
self.assertTrue(
np.allclose(df[['in1', 'in2']].iloc[86], eighth_non_nan_val_4m_st))
self.assertTrue(np.allclose(x[0][-1, -1], eighth_non_nan_val_4m_st))
xx, _, yy = model.test_data(indices=model.test_indices)
# the second test index is 9, so second value of yy must be 9th non-nan value
self.assertEqual(model.test_indices[2], 10)
self.assertAlmostEqual(float(yy[2]),
df['out1'][df['out1'].notnull()].iloc[10])
self.assertTrue(
np.allclose(xx[0][2, -1],
df[['in1', 'in2']][df['out1'].notnull()].iloc[10]))
assert np.max(model.test_indices) < (
model.data.shape[0] - int(model.data[model.out_cols].isna().sum()))
assert np.max(model.train_indices) < (
model.data.shape[0] - int(model.data[model.out_cols].isna().sum()))
return
#How to use AI4Water for regression problems
import pandas as pd
import numpy as np
from sklearn.datasets import load_diabetes
from AI4Water import Model
data_class = load_diabetes()
cols = data_class['feature_names'] + ['target']
df = pd.DataFrame(np.concatenate(
[data_class['data'], data_class['target'].reshape(-1, 1)], axis=1),
columns=cols)
model = Model(
data=df,
inputs=data_class['feature_names'],
outputs=['target'],
lookback=1,
batches="2d",
val_fraction=0.0,
model={'DecisionTreeRegressor': {
"max_depth": 3,
"criterion": "mae"
}},
transformation=None)
h = model.fit()
x, _, y = model.train_data()
