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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
st.title("AHD Classification Web App")
st.sidebar.title("Binary Classification Web App")
st.markdown("AHD yes or no?")
st.sidebar.markdown("AHD yes or no?")
@st.cache(persist=True)
def load_data():
data = pd.read_csv('Heart.csv')
data = data.dropna()
label = LabelEncoder()
for col in data[['ChestPain', 'Thal', 'AHD']].columns:
data[col] = label.fit_transform(data[col])
return data
@st.cache(persist=True)
def split(df):
y = df.AHD
x = df.drop(columns=['AHD'])
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(model, 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()
def choose_classifier(classifier):
if classifier == 'Support Vector Machine(SVM)':
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularisatoin 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(SVM) 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(model, metrics)
if classifier == 'Logistic Regression':
st.sidebar.subheader("Model Hyperparameters")
C = st.sidebar.number_input("C (Regularisatoin 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(model, metrics)
if classifier == 'Random Forest':
st.sidebar.subheader("Model Hyperparameters")
n_estimators = st.sidebar.number_input("The number of trees in the forest", 100, 5000, step=10, key='n_estimators')
max_depth = st.sidebar.number_input("The maximum depth of the tree", 1, 20, step=1, key='n_estimators')
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 Results")
model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, bootstrap=bootstrap, 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(model, metrics)
if st.sidebar.checkbox("Show raw data", False):
st.subheader("Heart Dataset")
st.write(pd.read_csv('Heart.csv'))
df = load_data()
x_train, x_test, y_train, y_test = split(df)
class_names = ['Yes', 'No']
st.sidebar.subheader("Choose Classifier")
classifier = st.sidebar.selectbox("Classifier", ("Support Vector Machine(SVM)", "Logistic Regression", "Random Forest"))
choose_classifier(classifier)