MoReLUDNN Regression

Installation

# To install the required package, use the following command:
# !pip install modeva

Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')

Import required modules

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoReLUDNNRegressor

Load and prepare dataset

ds = DataSet()
ds.load(name="BikeSharing")
ds.set_random_split()
ds.set_target("cnt")

ds.scale_numerical(features=("cnt",), method="log1p")
ds.scale_numerical(method="minmax")
ds.preprocess()

Train model

model = MoReLUDNNRegressor(max_epochs=100, verbose=True)
model.fit(ds.train_x, ds.train_y)

#### MoReLUDNN Training ####
Epoch 0: Train loss 0.2111, Validation loss 0.0746
Epoch 1: Train loss 0.0530, Validation loss 0.0471
Epoch 2: Train loss 0.0414, Validation loss 0.0372
Epoch 3: Train loss 0.0343, Validation loss 0.0312
Epoch 4: Train loss 0.0300, Validation loss 0.0284
Epoch 5: Train loss 0.0278, Validation loss 0.0271
Epoch 6: Train loss 0.0268, Validation loss 0.0265
Epoch 7: Train loss 0.0263, Validation loss 0.0263
Epoch 8: Train loss 0.0261, Validation loss 0.0261
Epoch 9: Train loss 0.0258, Validation loss 0.0258
Epoch 10: Train loss 0.0255, Validation loss 0.0255
Epoch 11: Train loss 0.0252, Validation loss 0.0248
Epoch 12: Train loss 0.0244, Validation loss 0.0238
Epoch 13: Train loss 0.0233, Validation loss 0.0225
Epoch 14: Train loss 0.0221, Validation loss 0.0218
Epoch 15: Train loss 0.0211, Validation loss 0.0206
Epoch 16: Train loss 0.0201, Validation loss 0.0197
Epoch 17: Train loss 0.0194, Validation loss 0.0191
Epoch 18: Train loss 0.0189, Validation loss 0.0185
Epoch 19: Train loss 0.0184, Validation loss 0.0181
Epoch 20: Train loss 0.0180, Validation loss 0.0183
Epoch 21: Train loss 0.0177, Validation loss 0.0182
Epoch 22: Train loss 0.0175, Validation loss 0.0173
Epoch 23: Train loss 0.0172, Validation loss 0.0171
Epoch 24: Train loss 0.0170, Validation loss 0.0169
Epoch 25: Train loss 0.0168, Validation loss 0.0168
Epoch 26: Train loss 0.0165, Validation loss 0.0166
Epoch 27: Train loss 0.0166, Validation loss 0.0164
Epoch 28: Train loss 0.0163, Validation loss 0.0165
Epoch 29: Train loss 0.0161, Validation loss 0.0162
Epoch 30: Train loss 0.0160, Validation loss 0.0162
Epoch 31: Train loss 0.0158, Validation loss 0.0161
Epoch 32: Train loss 0.0157, Validation loss 0.0158
Epoch 33: Train loss 0.0155, Validation loss 0.0157
Epoch 34: Train loss 0.0154, Validation loss 0.0156
Epoch 35: Train loss 0.0152, Validation loss 0.0154
Epoch 36: Train loss 0.0150, Validation loss 0.0155
Epoch 37: Train loss 0.0150, Validation loss 0.0154
Epoch 38: Train loss 0.0148, Validation loss 0.0151
Epoch 39: Train loss 0.0146, Validation loss 0.0151
Epoch 40: Train loss 0.0143, Validation loss 0.0145
Epoch 41: Train loss 0.0140, Validation loss 0.0143
Epoch 42: Train loss 0.0137, Validation loss 0.0142
Epoch 43: Train loss 0.0134, Validation loss 0.0135
Epoch 44: Train loss 0.0128, Validation loss 0.0129
Epoch 45: Train loss 0.0124, Validation loss 0.0125
Epoch 46: Train loss 0.0120, Validation loss 0.0120
Epoch 47: Train loss 0.0115, Validation loss 0.0115
Epoch 48: Train loss 0.0110, Validation loss 0.0110
Epoch 49: Train loss 0.0105, Validation loss 0.0105
Epoch 50: Train loss 0.0100, Validation loss 0.0099
Epoch 51: Train loss 0.0094, Validation loss 0.0095
Epoch 52: Train loss 0.0090, Validation loss 0.0090
Epoch 53: Train loss 0.0085, Validation loss 0.0088
Epoch 54: Train loss 0.0080, Validation loss 0.0081
Epoch 55: Train loss 0.0076, Validation loss 0.0079
Epoch 56: Train loss 0.0072, Validation loss 0.0075
Epoch 57: Train loss 0.0069, Validation loss 0.0070
Epoch 58: Train loss 0.0066, Validation loss 0.0069
Epoch 59: Train loss 0.0063, Validation loss 0.0065
Epoch 60: Train loss 0.0060, Validation loss 0.0062
Epoch 61: Train loss 0.0059, Validation loss 0.0061
Epoch 62: Train loss 0.0057, Validation loss 0.0061
Epoch 63: Train loss 0.0056, Validation loss 0.0058
Epoch 64: Train loss 0.0054, Validation loss 0.0058
Epoch 65: Train loss 0.0053, Validation loss 0.0056
Epoch 66: Train loss 0.0053, Validation loss 0.0056
Epoch 67: Train loss 0.0052, Validation loss 0.0055
Epoch 68: Train loss 0.0051, Validation loss 0.0054
Epoch 69: Train loss 0.0051, Validation loss 0.0054
Epoch 70: Train loss 0.0050, Validation loss 0.0052
Epoch 71: Train loss 0.0049, Validation loss 0.0053
Epoch 72: Train loss 0.0049, Validation loss 0.0052
Epoch 73: Train loss 0.0048, Validation loss 0.0051
Epoch 74: Train loss 0.0047, Validation loss 0.0051
Epoch 75: Train loss 0.0047, Validation loss 0.0052
Epoch 76: Train loss 0.0047, Validation loss 0.0052
Epoch 77: Train loss 0.0047, Validation loss 0.0050
Epoch 78: Train loss 0.0046, Validation loss 0.0050
Epoch 79: Train loss 0.0047, Validation loss 0.0051
Epoch 80: Train loss 0.0046, Validation loss 0.0051
Epoch 81: Train loss 0.0046, Validation loss 0.0049
Epoch 82: Train loss 0.0045, Validation loss 0.0048
Epoch 83: Train loss 0.0045, Validation loss 0.0050
Epoch 84: Train loss 0.0045, Validation loss 0.0048
Epoch 85: Train loss 0.0045, Validation loss 0.0049
Epoch 86: Train loss 0.0044, Validation loss 0.0053
Epoch 87: Train loss 0.0047, Validation loss 0.0052
Epoch 88: Train loss 0.0045, Validation loss 0.0048
Epoch 89: Train loss 0.0045, Validation loss 0.0047
Epoch 90: Train loss 0.0043, Validation loss 0.0048
Epoch 91: Train loss 0.0045, Validation loss 0.0055
Epoch 92: Train loss 0.0044, Validation loss 0.0047
Epoch 93: Train loss 0.0042, Validation loss 0.0047
Epoch 94: Train loss 0.0042, Validation loss 0.0046
Epoch 95: Train loss 0.0042, Validation loss 0.0046
Epoch 96: Train loss 0.0041, Validation loss 0.0045
Epoch 97: Train loss 0.0041, Validation loss 0.0046
Epoch 98: Train loss 0.0041, Validation loss 0.0046
Epoch 99: Train loss 0.0041, Validation loss 0.0045
Training is terminated as max_epoch is reached.

MoReLUDNNRegressor(device='cpu', max_epochs=100, name='MoReLUDNNRegressor',
                   verbose=True)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Basic accuracy analysis

ts = TestSuite(ds, model)
results = ts.diagnose_accuracy_table()
results.table

# Global feature importance
# ----------------------------------------------------------
results = ts.interpret_fi()
results.plot()

LLM summary table

results = ts.interpret_llm_summary(dataset="train")
results.table

	Count	Response Mean	Response Std	Local MSE	Global MSE
0	269.0	0.8577	0.0790	3.3054e-03	0.0633
1	201.0	0.7509	0.0989	1.7391e-03	0.4963
2	177.0	0.8294	0.0749	2.5354e-03	0.0633
3	161.0	0.7857	0.0772	3.3079e-03	0.0636
4	113.0	0.3766	0.1163	3.0773e-03	2.0247
...	...	...	...	...	...
4082	1.0	0.5198	NaN	5.8006e-03	1.3755
4083	1.0	0.5166	NaN	1.0704e-03	0.0769
4084	1.0	0.8524	NaN	3.7623e-05	0.0870
4085	1.0	0.6994	NaN	9.2278e-03	0.0771
4086	1.0	0.3872	NaN	4.4448e-03	0.9584

4087 rows × 5 columns

LLM parallel coordinate plot

results = ts.interpret_llm_pc(dataset="train")
results.plot()

LLM profile plot against a feature

results = ts.interpret_llm_profile(feature="hr", dataset="train")
results.plot()

Local feature importance analysis

results = ts.interpret_local_linear_fi(dataset="train", sample_index=15, centered=True)
results.plot()

Extract the last hidden layer outputs

model.predict_last_hidden_layer(ds.train_x)

array([[0.        , 0.1710586 , 0.        , ..., 0.18726286, 0.        ,
        0.        ],
       [0.        , 0.17478107, 0.        , ..., 0.13044491, 0.        ,
        0.        ],
       [0.        , 0.18033722, 0.        , ..., 0.07552771, 0.        ,
        0.        ],
       ...,
       [0.        , 0.3858271 , 0.        , ..., 0.        , 0.        ,
        0.6165768 ],
       [0.        , 0.39269155, 0.        , ..., 0.        , 0.        ,
        0.66937524],
       [0.        , 0.35866153, 0.        , ..., 0.        , 0.        ,
        0.7024171 ]], dtype=float32)