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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.set_option('deprecation.showPyplotGlobalUse', False) | |
| 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) | |
| def load_data(): | |
| data = pd.read_csv("mushrooms.csv") | |
| labelencoder=LabelEncoder() | |
| for col in data.columns: | |
| data[col] = labelencoder.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() | |
| class_names = ['edible', 'poisonous'] | |
| x_train, x_test, y_train, y_test = split(df) | |
| st.sidebar.subheader("Choose Classifier") | |
| classifier = st.sidebar.selectbox("Classifier", ("Support Vector Machine (SVM)", "Logistic Regression", "Random Forest")) | |
| if classifier == 'Support Vector Machine (SVM)': | |
| st.sidebar.subheader("Model Hyperparameters") | |
| #choose parameters | |
| C = st.sidebar.number_input("C (Regularization parameter)", 0.01, 10.0, step=0.01, key='C_SVM') | |
| 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(metrics) | |
| if classifier == 'Logistic 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') | |
| 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, penalty='l2', 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 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(metrics) | |
| if st.sidebar.checkbox("Show raw data", False): | |
| st.subheader("Mushroom Data Set (Classification)") | |
| st.write(df) | |
| st.markdown("This [data set](https://archive.ics.uci.edu/ml/datasets/Mushroom) includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms " | |
| "in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, " | |
| "or of unknown edibility and not recommended. This latter class was combined with the poisonous one.") | |
| if __name__ == '__main__': | |
| main() | |