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app.py
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app.py
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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 mushrooms edible or poisonous? 🍄")
st.sidebar.markdown("Are your mushrooms edible or poisonous? 🍄")
@st.cache(persist=True) #To not load dataset everytime
def load_data():
data = pd.read_csv("C:/Users/pandit/Projects/Web App/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','poisonous']
st.sidebar.subheader("Choose Classifier")
classifier = st.sidebar.selectbox("Classifier",("Support Vector Machine","Logistic Regression","Random Forest"))
if classifier == "Support Vector Machine":
st.sidebar.subheader("Model Hyperparamaters")
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="kernel")
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 Results")
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 == "Logistic Regression":
st.sidebar.subheader("Model Hyperparamaters")
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')
metrics = st.sidebar.multiselect("What metrics to plot?",("Confusion Matrix","ROC Curve","Precision-Recall Curve"))
if st.sidebar.button('Classify',key='classify'):
st.subheader("Logistic Regression Results")
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)
if classifier == "Random Forest":
st.sidebar.subheader("Model Hyperparamaters")
n_estimators = st.sidebar.number_input("Number of Trees in the forest",100,500,steps=10,key="n_estimators")
max_depth = st.sidebar.number_input("Max depth of the tree ",1,20,step=1,key="max_depth")
bootstrap = st.sidebar.radio("Bootstrap Samples when Building Trees",('True','False'),key="bootstrap")
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 Classifier Results")
model=RandomForestClassifier(n_estimators=n_estimators,max_depth=max_depth,n_jobs=-1)
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 Dataset (Classification)")
st.write(df)
if __name__ == '__main__':
main()