LAMDA-TALENT
Tutorials
How to Use TALENT
1. Cloning the Repository
2. Running Experiments
3. Adding New Methods
4. Configuring Hyperparameters
5. Troubleshooting
Conclusion
Methods
Methods in TALENT
Deep Learning Methods
Classical Methods
Methodology Summary
Dependencies
Dependencies
Python Libraries
Optional Dependencies
Installation
Additional Notes
Benchmark_Datasets
Benchmark Datasets
Available Datasets
Downloading Datasets
Dataset Structure
Placing Datasets
Using Datasets
Custom Datasets
Task Types
Conclusion
Experimental_Results
Experimental Results
Evaluation Metrics
Results Summary
Conclusion
API Docs
LAMDA-TALENT Utils Module
Classes
Functions
LAMDA-TALENT Data Module
Classes
Functions
LAMDA-TALENT Base Method Module
Classes
Functions
Training and Evaluation
Performance Metrics
Acknowledgements
Acknowledgments
LAMDA-TALENT
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Index
Index
A
|
C
|
D
|
F
|
G
|
I
|
L
|
M
|
N
|
P
|
R
|
S
|
T
|
V
|
Y
A
add() (TALENT.model.utils.Averager method)
Averager (class in TALENT.model.utils)
C
C (TALENT.model.lib.data.Dataset attribute)
check_softmax() (in module TALENT.model.methods.base)
D
data_enc_process() (in module TALENT.model.lib.data)
data_format() (TALENT.model.methods.base.Method method)
,
[1]
data_label_process() (in module TALENT.model.lib.data)
data_loader_process() (in module TALENT.model.lib.data)
data_nan_process() (in module TALENT.model.lib.data)
data_norm_process() (in module TALENT.model.lib.data)
dataname_to_numpy() (in module TALENT.model.lib.data)
Dataset (class in TALENT.model.lib.data)
F
fit() (TALENT.model.methods.base.Method method)
,
[1]
G
get_categories() (in module TALENT.model.lib.data)
get_dataset() (in module TALENT.model.lib.data)
I
info (TALENT.model.lib.data.Dataset attribute)
is_binclass (TALENT.model.lib.data.Dataset property)
is_multiclass (TALENT.model.lib.data.Dataset property)
is_regression (TALENT.model.lib.data.Dataset property)
item() (TALENT.model.utils.Averager method)
L
load_json() (in module TALENT.model.lib.data)
M
measure() (TALENT.model.utils.Timer method)
Method (class in TALENT.model.methods.base)
,
[1]
metric() (TALENT.model.methods.base.Method method)
,
[1]
module
TALENT.model.lib.data
TALENT.model.methods.base
N
N (TALENT.model.lib.data.Dataset attribute)
n_cat_features (TALENT.model.lib.data.Dataset property)
n_features (TALENT.model.lib.data.Dataset property)
n_num_features (TALENT.model.lib.data.Dataset property)
num_enc_process() (in module TALENT.model.lib.data)
P
predict() (TALENT.model.methods.base.Method method)
,
[1]
R
raise_unknown() (in module TALENT.model.lib.data)
reset_stats_withconfig() (TALENT.model.methods.base.Method method)
,
[1]
S
size() (TALENT.model.lib.data.Dataset method)
T
TALENT.model.lib.data
module
TALENT.model.methods.base
module
Timer (class in TALENT.model.utils)
to_tensors() (in module TALENT.model.lib.data)
train_epoch() (TALENT.model.methods.base.Method method)
,
[1]
V
validate() (TALENT.model.methods.base.Method method)
,
[1]
Y
y (TALENT.model.lib.data.Dataset attribute)