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@@ -78,30 +78,38 @@ You can find the details about evaluation process and the evaluation results.
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  Use the code below to get started with the model.
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  ```python
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- import joblib
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- import json
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  import pandas as pd
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- clf = joblib.load(MLstructureMining_model.bin)
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- with open("config.json") as f:
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- config = json.load(f)
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- clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
 
 
 
 
 
 
 
 
 
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  ```
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  # Model Card Authors
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  This model card is written by following authors:
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-
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- [More Information Needed]
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  # Model Card Contact
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  You can contact the model card authors through following channels:
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- [More Information Needed]
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  # Citation
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  Below you can find information related to citation.
 
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  **BibTeX:**
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  ```
 
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  Use the code below to get started with the model.
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  ```python
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+ import xgboost as xgb
 
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  import pandas as pd
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+
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+ N_CPU = 8 # Number of CPUs used
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+
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+ # Load model
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+ bst = xgb.Booster({'nthread': N_CPU})
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+ bst.load_model("MLstructureMining_model.bin")
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+
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+ # Load your data
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+ # data = pd.read_csv("your_data.csv")
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+ # data_xgb = xgb.DMatrix(data)
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+
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+ # Do inference
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+ pred = bst.predict(data_xgb)
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  ```
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  # Model Card Authors
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  This model card is written by following authors:
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+ Emil T. S. Kjær
 
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  # Model Card Contact
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  You can contact the model card authors through following channels:
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+ emil.thyge.kjæ[email protected]
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  # Citation
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  Below you can find information related to citation.
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+ In review.
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  **BibTeX:**
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  ```