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import pickle |
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import numpy as np |
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import gradio as gr |
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import sklearn |
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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from sklearn.ensemble import ExtraTreesRegressor |
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filename = 'Dataset_RCS_3.csv' |
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names0 = ['JET', "Suelo",'SPT', 'WtoC', 'Presion', 'Velocidad','RCS'] |
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dataset=pd.read_csv(filename, names=names0) |
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y = dataset['RCS'] |
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X = dataset.drop('RCS', axis=1) |
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categorical_cols = ['JET', "Suelo"] |
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df = pd.get_dummies(X, columns = categorical_cols) |
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validation_size = 0.20 |
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seed = 10 |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=validation_size, random_state=seed) |
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modelodef=ExtraTreesRegressor( |
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n_estimators=1000, |
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max_depth=9, |
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min_samples_leaf=1, |
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random_state=seed) |
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modelodef.fit(X_train, y_train) |
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pickle.dump(modelodef, open("modelodef.pkl", "wb")) |
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def RCS(JET, Suelo,SPT, WtoC, Presion, Velocidad): |
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modelodef = pickle.load(open("modelodef.pkl", "rb")) |
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prediction0 = modelodef.predict([[JET, Suelo,SPT, WtoC, Presion, Velocidad]]) |
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prediction = np.round(prediction0,2) |
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return prediction |
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title = "ASSESSMENT OF UNIAXIAL COMPRESSIVE STRENGTH OF JET GROUTING" |
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description = "This app corresponds to the research paper Machine learning as a solution to discover complex patterns: assessment of compressive strength of jet grouting" |
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article = """ |
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Notes: |
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- Click submit/enviar button to obtain the UCS prediction |
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- Click clear/limpiar button to refresh text |
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- Please note the application ranges of the variables in the above referenced paper |
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- As decimal separator you can use either a point or a comma |
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""" |
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app = gr.Interface( |
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RCS, |
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inputs=[ |
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gr.Radio(['1', '2', '3'], label="Jet system. 1:Single. 2:Double. 3:Triple",value="1"), |
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gr.Radio(['1', '2', '3', '4'], label="Soil type. 1:Coarse without fines. 2:Coarse with fines. 3:Fine. 4:Organic",value="1"), |
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gr.Number(value=1, label="Nspt"), |
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gr.Number(value=1, label="W/C"), |
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gr.Number(value=1, label="Grout pressure (MPa)"), |
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gr.Number(value=1, label="Rotation speed (rpm)"), |
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], |
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outputs=[gr.Text(label="UCS (MPa)")], |
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title=title, |
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description=description, |
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article = article, |
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theme="dark-seafoam" |
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) |
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app.launch() |