Comments (3)
Hi Mukish, thanks for using neuropredict, and reporting it works well and produces the figures. Just double checked the results, and it appears the latest release does not export the CSV files yet, but all you need is actually stored in the results_neuropredict.pkl
file.
I'd work on adding on documentation on using that file shortly - but this is what you need to know:
there is a dictionary of input options and output results stored in results_neuropredict.pkl
file, and it can be loaded easily with:
import pickle
with open('results_neuropredict.pkl', 'rb') as rf:
results = pickle.load(rf)
which is a dictionary with the following keys:
neuropredict/neuropredict/config.py
Line 141 in 8cd4b6f
the metrics you are looking for, such as accuracy etc, are available in this dictionary results['results'].metric_val
the misclassified samplet IDs are stored in the dictionary results['results'].misclfd_samplets
let me know if you have any other questions.
from neuropredict.
were you able to get what you need, @mukishchezian ?
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Related Issues (20)
- Logging: Produce neuropredict own log
- Unable to launch multi-class classifier HOT 4
- Best options choice for classification of small and unbalanced dataset HOT 5
- Error when launching xgboost HOT 1
- Add new classifier: need of probability output? HOT 12
- Produce ROC curves for binary experiments HOT 1
- Show confidence intervals in the violin plots for BalACC, AUC etc
- List of subjects with high frequency of large residuals
- Make predictions in a new or held out dataset HOT 1
- Attribute-contrained performance estimates HOT 2
- update all docs and graphics to indicate users can try different models also HOT 1
- Classification blocked with multiple 1-dimensional features HOT 6
- Add docs for all API, esp. CVResults
- Allow user to use arbitrary Estimator via API
- ImportError: cannot import name 'MultiDatasetClassify' HOT 13
- Differences betw. frequency misclassified vs frequently correctly classified
- R version HOT 2
- Enabling plug-in user-chosen models or hyper-parameters
- ENH: adopt more sophisticated hyper param opt toolboxes
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