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day60-logistic-regression-contd/app.py

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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
+
+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
+
+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)
+
+    XX, YY = np.meshgrid(a, b)
+
+    input_array = np.array([XX.ravel(), YY.ravel()]).T
+
+    return XX, YY, input_array
+
+
+plt.style.use('fivethirtyeight')
+
+st.sidebar.markdown("# Logistic Regression Classifier")
+
+dataset = st.sidebar.selectbox(
+    'Select Dataset',
+    ('Binary','Multiclass')
+)
+
+penalty = st.sidebar.selectbox(
+    'Regularization',
+    ('l2', 'l1','elasticnet','none')
+)
+
+c_input = float(st.sidebar.number_input('C',value=1.0))
+
+solver = st.sidebar.selectbox(
+    'Solver',
+    ('newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga')
+)
+
+max_iter = int(st.sidebar.number_input('Max Iterations',value=100))
+
+multi_class = st.sidebar.selectbox(
+    'Multi Class',
+    ('auto', 'ovr', 'multinomial')
+)
+
+l1_ratio = int(st.sidebar.number_input('l1 Ratio'))
+
+# Load initial graph
+fig, ax = plt.subplots()
+
+# 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)
+
+if st.sidebar.button('Run Algorithm'):
+    orig.empty()
+
+    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)
+
+    y_pred = clf.predict(X_test)
+
+    XX, YY, input_array = draw_meshgrid()
+    labels = clf.predict(input_array)
+
+    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)))