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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve
from sklearn.metrics import precision_score, recall_score
def main():
st.title("Binary Classification Web App")
st.sidebar.title("Binary Classification Web App")
st.markdown("Are your mushroom is editable or poisionous? ")
st.sidebar.markdown("Are your mushroom is editable or poisionous? ")
def load_data():
data = pd.read_csv('/home/rhyme/Desktop/Project/mushrooms.csv')
label = LabelEncoder()
for col in data.columns:
data[col]= label.fit_transform(data[col])
return data
@st.cache(persist=True)
def split(df):
y = df.type
x = df.drop(columns=['type'])
x_train , x_test, y_train,y_test = train_test_split(x,y,test_size=0.3, random_state=0)
return x_train,x_test, y_train,y_test
def plot_metrics(metrics_list):
if 'Confusion Matrix' in metrics_list:
st.subheader("Confusion Matrix")
plot_confusion_matrix(model, x_test,y_test,display_labels=class_names)
st.pyplot()
if 'ROC Curve' in metrics_list:
st.subheader("ROC Curve")
plot_roc_curve(model,x_test,y_test)
st.pyplot()
if 'Precision-Recall Curve' in metrics_list:
st.subheader("Precision-Recall Curve")
plot_precision_recall_curve(model,x_test,y_test)
st.pyplot()
df = load_data()
x_train, x_test, y_train, y_test = split(df)
class_names = ['edible', 'poisionous']
st.sidebar.subheader("Chosse Classifiers")
classifier = st.sidebar.selectbox("Classifier", ("Support Vector Machine(SVM)", "Logostics Regression", "Random Forest"))
if classifier == "Support Vector Machine(SVM)":
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)", 0.01,10.0,step=0.01,key='C')
kernel = st.sidebar.radio("kernel", ("rbf", "linear"), key='kernal')
gamma = st.sidebar.radio("Gamma (Kernel Coefficient)", ("scale","auto"),key='gamma')
metrics = st.sidebar.multiselect("What metrics to plot?", ('Confusion Matrix','ROC Curve','Precision-Recall Curve' ))
if st.sidebar.button("Classify", key = 'classify'):
st.subheader("Support Vector Machine (SVM)")
model = SVC(C=C,kernel=kernel, gamma=gamma)
model.fit(x_train,y_train)
accuracy = model.score(x_test,y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ",accuracy.round(2))
st.write("Precision : ", precision_score(y_test,y_pred, labels=class_names).round(2))
st.write("Recall: ", recall_score(y_test, y_pred, labels= class_names).round(2))
plot_metrics(metrics)
if classifier == "Logostics Regression":
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularization parameter)", 0.01,10.0,step=0.01,key='C_LR')
max_iter = st.sidebar.slider("Maximum number of iterations", 100, 500, key='max_iter')
# kernel = st.sidebar.radio("kernel", ("rbf", "linear"), key='kernal')
# gamma = st.sidebar.radio("Gamma (Kernel Coefficient)", ("scale","auto"),key='gamma')
metrics = st.sidebar.multiselect("What metrics to plot?", ('Confusion Matrix','ROC Curve','Precision-Recall Curve' ))
if st.sidebar.button("Classify", key = 'classify'):
st.subheader("Logistics Regression")
model = LogisticRegression(C=C,max_iter =max_iter)
model.fit(x_train,y_train)
accuracy = model.score(x_test,y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ",accuracy.round(2))
st.write("Precision : ", precision_score(y_test,y_pred, labels=class_names).round(2))
st.write("Recall: ", recall_score(y_test, y_pred, labels= class_names).round(2))
plot_metrics(metrics)
#Random Forest
if classifier == "Random Forest":
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.number_input("The number of trees in the forest", 100, 500, step=10,key='n_estmators')
max_depth = st.sidebar.number_input("The maximum depth of the tree", 1, 20 , step=1, key='max_depth')
bootstrap = st.sidebar.radio("Bootstrap samples when builoding trees", ('True','False'),key='bootstrap')
# C = st.sidebar.number_input("C (Regularization parameter)", 0.01,10.0,step=0.01,key='C_LR')
# max_iter = st.sidebar.slider("Maximum number of iterations", 100, 500, key='max_iter')
# kernel = st.sidebar.radio("kernel", ("rbf", "linear"), key='kernal')
# gamma = st.sidebar.radio("Gamma (Kernel Coefficient)", ("scale","auto"),key='gamma')
metrics = st.sidebar.multiselect("What metrics to plot?", ('Confusion Matrix','ROC Curve','Precision-Recall Curve' ))
if st.sidebar.button("Classify", key = 'classify'):
st.subheader("Random Forest")
model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth,bootstrap=bootstrap)
model.fit(x_train,y_train)
accuracy = model.score(x_test,y_test)
y_pred = model.predict(x_test)
st.write("Accuracy: ",accuracy.round(2))
st.write("Precision : ", precision_score(y_test,y_pred, labels=class_names).round(2))
st.write("Recall: ", recall_score(y_test, y_pred, labels= class_names).round(2))
plot_metrics(metrics)
if st.sidebar.checkbox("show raw data",False):
st.subheader("Mushroom data Set (Classifications)")
st.write(df)
if __name__ == '__main__':
main()
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