Update README.md

README.md

@@ -15,71 +15,21 @@ language:
 ---
 
 # Model description
-MLStructureMining is a tree-based machine learning classifier designed to rapidly match X-ray pair distribution function (PDF) data to prototype patterns from a large database of crystal structures, providing real-time structure characterization by screening vast quantities of data in seconds.
+MLStructureMining is a tree-based machine learning classifier designed to rapidly match X-ray pair distribution function (PDF) data to prototype 
+patterns from a large database of crystal structures, providing real-time structure characterization by screening vast quantities of data in seconds.
 
-## Intended uses & limitations
-
-[More Information Needed]
-
-## Training Procedure
-
-### Hyperparameters
-
-The model is trained with below hyperparameters.
-
-<details>
-<summary> Click to expand </summary>
-
-| Hyperparameter          | Value           |
-|-------------------------|-----------------|
-| objective               | binary:logistic |
-| use_label_encoder       | True            |
-| base_score              | 0.5             |
-| booster                 | gbtree          |
-| colsample_bylevel       | 1               |
-| colsample_bynode        | 1               |
-| colsample_bytree        | 1               |
-| enable_categorical      | False           |
-| gamma                   | 0               |
-| gpu_id                  | -1              |
-| importance_type         |                 |
-| interaction_constraints |                 |
-| learning_rate           | 0.300000012     |
-| max_delta_step          | 0               |
-| max_depth               | 6               |
-| min_child_weight        | 1               |
-| missing                 | nan             |
-| monotone_constraints    | ()              |
-| n_estimators            | 100             |
-| n_jobs                  | 8               |
-| num_parallel_tree       | 1               |
-| predictor               | auto            |
-| random_state            | 0               |
-| reg_alpha               | 0               |
-| reg_lambda              | 1               |
-| scale_pos_weight        |                 |
-| subsample               | 1               |
-| tree_method             | auto            |
-| validate_parameters     | 1               |
-| verbosity               |                 |
-
-</details>
-
-### Model Plot
-
-The model plot is below.
-
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-text-repr-fallback {display: none;}</style><div id="sk-f64fd6a0-a686-4957-adf1-8209c466f428" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e5865982-53b6-475b-9bbf-6ee40514c813" type="checkbox" checked><label for="e5865982-53b6-475b-9bbf-6ee40514c813" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre></div></div></div></div></div>
+The code used to train the model can be found [HERE](https://github.com/EmilSkaaning/MLstructureMining-workflow), and the Python implementation can be found 
+[HERE](https://github.com/EmilSkaaning/MLstructureMining/tree/main) or the wheel file ´mlstructuremining-4.1.0-py3-none-any.whl´.
 
 ## Evaluation Results
 
+MLstructureMining has been trained PDFs from on 10,833 crystal structures obtained from [Crystallography Open Database](https://www.crystallography.net/cod/) (COD). 
+The Pearson Correlation Coefficient (PCC) was used to cluster structures with similar PDF, resulting in a total of 6,062 labels. 100 PDFs were simluated per structures,
+and the data were split into training, validation and testing set with a 80/10/10 ratio.
+
 We evaluate this model based an accuracy and to test the robustness of MLstructureMining, we deploy zeroth-order optimization (ZOO) 
 from the [Adversarial Robustness Toolbox](https://github.com/Trusted-AI/adversarial-robustness-toolbox) (ART) library to perform adversarial attacks.
 
-You can find the details about evaluation process and the evaluation results.
-
-
-
 | Metric   | Value   |
 |----------|---------|
 | Accuracy | 91% |