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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 | |
| 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() | |