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import matplotlib.pyplot as plt | ||
import streamlit as st | ||
import numpy as np | ||
from sklearn.model_selection import train_test_split | ||
from sklearn.datasets import make_classification,make_blobs | ||
from sklearn.linear_model import LogisticRegression | ||
from sklearn.metrics import accuracy_score | ||
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def load_initial_graph(dataset,ax): | ||
if dataset == "Binary": | ||
X, y = make_blobs(n_features=2, centers=2,random_state=6) | ||
ax.scatter(X.T[0], X.T[1], c=y, cmap='rainbow') | ||
return X,y | ||
elif dataset == "Multiclass": | ||
X,y = make_blobs(n_features=2, centers=3,random_state=2) | ||
ax.scatter(X.T[0], X.T[1], c=y, cmap='rainbow') | ||
return X,y | ||
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def draw_meshgrid(): | ||
a = np.arange(start=X[:, 0].min() - 1, stop=X[:, 0].max() + 1, step=0.01) | ||
b = np.arange(start=X[:, 1].min() - 1, stop=X[:, 1].max() + 1, step=0.01) | ||
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XX, YY = np.meshgrid(a, b) | ||
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input_array = np.array([XX.ravel(), YY.ravel()]).T | ||
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return XX, YY, input_array | ||
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plt.style.use('fivethirtyeight') | ||
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st.sidebar.markdown("# Logistic Regression Classifier") | ||
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dataset = st.sidebar.selectbox( | ||
'Select Dataset', | ||
('Binary','Multiclass') | ||
) | ||
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penalty = st.sidebar.selectbox( | ||
'Regularization', | ||
('l2', 'l1','elasticnet','none') | ||
) | ||
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c_input = float(st.sidebar.number_input('C',value=1.0)) | ||
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solver = st.sidebar.selectbox( | ||
'Solver', | ||
('newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga') | ||
) | ||
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max_iter = int(st.sidebar.number_input('Max Iterations',value=100)) | ||
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multi_class = st.sidebar.selectbox( | ||
'Multi Class', | ||
('auto', 'ovr', 'multinomial') | ||
) | ||
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l1_ratio = int(st.sidebar.number_input('l1 Ratio')) | ||
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# Load initial graph | ||
fig, ax = plt.subplots() | ||
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# Plot initial graph | ||
X,y = load_initial_graph(dataset,ax) | ||
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) | ||
orig = st.pyplot(fig) | ||
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if st.sidebar.button('Run Algorithm'): | ||
orig.empty() | ||
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clf = LogisticRegression(penalty=penalty,C=c_input,solver=solver,max_iter=max_iter,multi_class=multi_class,l1_ratio=l1_ratio) | ||
clf.fit(X_train,y_train) | ||
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y_pred = clf.predict(X_test) | ||
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XX, YY, input_array = draw_meshgrid() | ||
labels = clf.predict(input_array) | ||
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ax.contourf(XX, YY, labels.reshape(XX.shape), alpha=0.5, cmap='rainbow') | ||
plt.xlabel("Col1") | ||
plt.ylabel("Col2") | ||
orig = st.pyplot(fig) | ||
st.subheader("Accuracy for Decision Tree " + str(round(accuracy_score(y_test, y_pred), 2))) |