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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)
def load_data():
data = pd.read_csv("path\\to\\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()