Create app.py

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 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()
+
+