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| import pickle | |
| import numpy as np | |
| import gradio as gr | |
| import sklearn | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.experimental import enable_iterative_imputer | |
| from sklearn.impute import IterativeImputer | |
| from sklearn.model_selection import KFold | |
| from sklearn.ensemble import ExtraTreesRegressor | |
| from sklearn.ensemble import GradientBoostingRegressor | |
| from sklearn.ensemble import StackingRegressor | |
| from sklearn.ensemble import RandomForestRegressor | |
| filename = 'DatabaseFinal0.csv' | |
| names0 = ['LL',"IP" ,"e0",'w', 'cc'] | |
| dataset=pd.read_csv(filename, names=names0) | |
| y = dataset['cc'] | |
| X0 = dataset.drop('cc', axis=1) | |
| impute_it = IterativeImputer() | |
| X2=impute_it.fit_transform(X0) | |
| X = pd.DataFrame(X2, columns=['LL',"IP" ,"e0",'w']) | |
| validation_size = 0.2 | |
| seed = 10 | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=validation_size, random_state=seed) | |
| model1 =ExtraTreesRegressor(max_depth=15, max_features=None, n_estimators=500,random_state=100,min_samples_split=12) | |
| model1= model1.fit(X_train, y_train) | |
| model2 =GradientBoostingRegressor(learning_rate=0.007, max_depth=2,n_estimators=1650, random_state=100,min_samples_split=9,max_features= 'log2') | |
| model2= model2.fit(X_train, y_train) | |
| model3 =RandomForestRegressor(n_estimators= 1000,min_samples_split= 11, min_samples_leaf= 1, | |
| max_features= None,max_depth= 6,bootstrap= True,random_state=100) | |
| model3= model3.fit(X_train, y_train) | |
| level1 = list() | |
| level1.append(('ET', model1)) | |
| level1.append(('GBR', model2)) | |
| level2 = model3 | |
| cv = KFold(n_splits=10, random_state=100,shuffle=True) | |
| modelodef = StackingRegressor(estimators=level1, final_estimator=level2, cv=cv, passthrough=True) | |
| modelodef.fit(X_train, y_train) | |
| pickle.dump(modelodef, open("modelodef.pkl", "wb")) | |
| def cc(LL,IP,e0,w): | |
| modelodef = pickle.load(open("modelodef.pkl", "rb")) | |
| prediction0 = modelodef.predict([[LL,IP,e0,w]]) | |
| prediction = np.round(prediction0,3) | |
| return prediction | |
| title = "A SUPER-LEARNER MACHINE LEARNING MODEL FOR A GLOBAL PREDICTION OF COMPRESSION INDEX IN CLAYS" | |
| #####################description = "This app corresponds to the research paper: A super-learner machine learning model for a global prediction of compression index in clays" | |
| description = "" | |
| article = """ | |
| Notes: | |
| - Click submit/enviar button to obtain the Compression index prediction | |
| - Click clear/limpiar button to refresh text | |
| - Please note the application ranges of the variables in the above-referenced paper (in publication process). Outside these ranges, the predictions may not be reliable | |
| - As a decimal separator you can use either a point or a comma | |
| """ | |
| #my_theme = gr.Theme.from_hub("bethecloud/storj_theme") | |
| my_theme = gr.Theme.from_hub("derekzen/stardust") | |
| app = gr.Interface( | |
| cc, | |
| inputs=[ | |
| gr.Number(value=30, label="Liquid limit (%)"), | |
| gr.Number(value=15, label="Plasticity index (%)"), | |
| gr.Number(value=0.800, label="Initial void ratio"), | |
| gr.Number(value=25, label="Natural water content (%)"), | |
| ], | |
| outputs=[gr.Text(label="Compression index")], | |
| title=title, | |
| description=description, | |
| article = article, | |
| theme=my_theme | |
| ) | |
| app.launch() |