init

Author: sinchan-s

basic-ml.py

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+import streamlit as st
+from sklearn import datasets
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.decomposition import PCA
+from matplotlib import pyplot as plt
+
+# frontend ui section
+st.title('Basic ML Visualizer')
+
+st.write("""
+## Exploring different classifiers
+""")
+
+d_name = st.sidebar.selectbox("Select dataset", ("iris", "breast cancer", "wine"))
+c_name = st.sidebar.selectbox("Select classifer", ("KNN", "SVM", "RF"))
+
+# classifier building section
+def get_dataset(d_name):
+    if d_name == "iris":
+        data = datasets.load_iris()
+    elif d_name == "breast cancer":
+        data = datasets.load_breast_cancer()
+    else:
+        data = datasets.load_wine()
+    X = data.data
+    y = data.target
+    return X,y
+
+X,y = get_dataset(d_name)
+st.write("dataset shape: ", X.shape)
+st.write("no. of classes: ", len(np.unique(y)))
+
+def add_parameters_ui(c_name):
+    params = dict()
+    if c_name == "KNN":
+        K = st.sidebar.slider("K", 1, 15)
+        params["K"] = K
+    elif c_name == "SVM":
+        C = st.sidebar.slider("C", 0.01, 10.0)
+        params["C"] = C
+    else:
+        max_depth = st.sidebar.slider("max_depth", 2, 15)
+        n_estimators = st.sidebar.slider("n_estimators", 1, 100)
+        params["max_depth"] = max_depth
+        params["n_estimators"] = n_estimators
+    return params
+
+params = add_parameters_ui(c_name)
+
+def get_classifier(c_name, params):
+    if c_name == "KNN":
+        clf = KNeighborsClassifier(n_neighbors=params["K"])
+    elif c_name == "SVM":
+        clf = SVC(C=params["C"])
+    else:
+        clf = RandomForestClassifier(n_estimators=params["n_estimators"], max_depth=params["max_depth"], random_state=42)
+    return clf
+
+clf = get_classifier(c_name, params)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+
+acc = accuracy_score(y_test, y_pred)
+st.write(f"Classifier = {c_name}")
+st.write(f"Accuracy = {acc}")
+
+# adding 2 PCA components
+pca = PCA(2)
+X_projected = pca.fit_transform(X)
+
+x1 = X_projected[:, 0]
+x2 = X_projected[:, 1]
+
+# plotting section
+fig = plt.figure()
+plt.scatter(x1, x2, c=y, alpha=0.7, cmap="viridis")
+plt.xlabel("Principle component 1")
+plt.ylabel("Principle component 2")
+plt.colorbar()
+
+st.pyplot(fig)