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svm_classifier.py
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#! important libraries
import streamlit as st
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
from sklearn.datasets import make_moons
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
#! seaborn theme applied
sns.set_theme()
#! site configurations
st.set_page_config(
page_title="SVM in Action",
page_icon="📈",
initial_sidebar_state="collapsed",
)
#! Clean Footer
hide_default_format = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_default_format, unsafe_allow_html=True)
#! info section
st.title('Linear SVM classifier in action')
st.write("""
#### Checking real-time implementation of Linear SVM classifier using polynomial features on the sklearn generated dataset: `make_moons`.""")
#! matplotlib colormap selection dropdown
color_maps_list = ('Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'winter', 'winter_r')
st.sidebar.header("Select contour color-map")
color_map = st.sidebar.selectbox("", color_maps_list)
#! sample size & noise control sliders
st.sidebar.header("Data points distribution:")
n_samples = st.sidebar.slider("Samples", 2, 100)
noise = st.sidebar.slider("Noise", 0.01, 1.00)
#! x & y variables assignment
X, y = make_moons(n_samples=n_samples, noise=noise, random_state=42)
#! prediction pipeline
polynomial_svm_clf = Pipeline([
("poly_features", PolynomialFeatures(degree=3)),
("scaler", StandardScaler()),
("svm_clf", LinearSVC(C=10, loss="hinge", random_state=42))])
polynomial_svm_clf.fit(X, y)
#! prediction & decision function
x0s = np.linspace(-1.5, 2.5, 100)
x1s = np.linspace(-1.0, 1.5, 100)
x0, x1 = np.meshgrid(x0s, x1s)
X_concat = np.c_[x0.ravel(), x1.ravel()]
y_prediction = polynomial_svm_clf.predict(X_concat).reshape(x0.shape)
y_decision = polynomial_svm_clf.decision_function(X_concat).reshape(x0.shape)
#! contour alpha value sliders
st.sidebar.header("Contour alpha sliders:")
alpha_prediction = st.sidebar.slider("Prediction", 0.0, 1.0)
alpha_decision = st.sidebar.slider("Decision Boundary", 0.0, 1.0)
#! plotting section
fig = plt.figure()
plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], "bo")
plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], "rs")
plt.axis([-1.5, 2.5, -1, 1.5])
plt.grid(True, which='both')
plt.xlabel(r"$x_1$", fontsize=20)
plt.ylabel(r"$x_2$", fontsize=20, rotation=0)
plt.contourf(x0, x1, y_prediction, cmap=color_map, alpha=alpha_prediction)
plt.contourf(x0, x1, y_decision, cmap=color_map, alpha=alpha_decision)
#! streamlit pyplot show
st.pyplot(fig)