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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_file: MLstructureMining_model.bin |
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--- |
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# Model description |
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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. |
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## Intended uses & limitations |
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[More Information Needed] |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-------------------------|-----------------| |
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| objective | binary:logistic | |
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| use_label_encoder | True | |
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| base_score | 0.5 | |
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| booster | gbtree | |
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| colsample_bylevel | 1 | |
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| colsample_bynode | 1 | |
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| colsample_bytree | 1 | |
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| enable_categorical | False | |
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| gamma | 0 | |
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| gpu_id | -1 | |
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| importance_type | | |
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| interaction_constraints | | |
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| learning_rate | 0.300000012 | |
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| max_delta_step | 0 | |
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| max_depth | 6 | |
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| min_child_weight | 1 | |
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| missing | nan | |
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| monotone_constraints | () | |
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| n_estimators | 100 | |
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| n_jobs | 8 | |
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| num_parallel_tree | 1 | |
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| predictor | auto | |
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| random_state | 0 | |
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| reg_alpha | 0 | |
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| reg_lambda | 1 | |
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| scale_pos_weight | | |
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| subsample | 1 | |
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| tree_method | auto | |
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| validate_parameters | 1 | |
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| verbosity | | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-f64fd6a0-a686-4957-adf1-8209c466f428 {color: black;background-color: white;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 pre{padding: 0;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-toggleable {background-color: white;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-estimator:hover {background-color: #d4ebff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-item {z-index: 1;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-parallel-item:only-child::after {width: 0;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-f64fd6a0-a686-4957-adf1-8209c466f428 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,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=None, subsample=1,tree_method='auto', 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='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,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=None, subsample=1,tree_method='auto', validate_parameters=1, verbosity=None)</pre></div></div></div></div></div> |
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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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|----------|---------| |
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# How to Get Started with the Model |
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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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[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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[More Information Needed] |
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``` |
