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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= "auto",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" | |
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 | |
""" | |
app = gr.Interface( | |
cc, | |
inputs=[ | |
gr.Number(value=1, label="Liquid limit (%)"), | |
gr.Number(value=1, label="Plasticity index (%)"), | |
gr.Number(value=1, label="Initial void ratio"), | |
gr.Number(value=1, label="Natural water content (%)"), | |
], | |
outputs=[gr.Text(label="Compression index")], | |
title=title, | |
description=description, | |
article = article, | |
theme="dark-seafoam" | |
) | |
app.launch() |