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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() |