Clustering model deployment

app.py

@@ -0,0 +1,110 @@
+import pickle
+import pandas as pd
+import streamlit as st
+from models import  KMeans
+from models import  KMedoids
+import matplotlib.pyplot as plt
+from models import EnsembleClustering 
+from sklearn.decomposition import PCA
+
+st.title("Customer Purchase Behavior")
+
+if "form_submitted" not in st.session_state:
+    st.session_state["form_submitted"] = False
+
+# Reading csv file with clustering
+@st.cache_data
+def read_csv():
+    df = pd.read_csv("dataset/clustered_dataset.csv")
+    return df
+
+# Loading the model
+@st.cache_resource
+def load_model():
+    model = pickle.load(open("model/clustering.pkl", 'rb'))
+    return model
+
+# Prediction of cluster on the given feature
+@st.cache_data
+def predict(_model, features):
+    label = _model.predict(features)
+    return label.item()
+
+# Form submission
+def form_submission():
+    st.session_state["form_submitted"] = True
+
+# Data transformation
+def transform_data():
+    df = read_csv().drop(['Cluster'], axis=1)
+    input_data = [st.session_state["Revenue_given"], st.session_state["Frequency"], st.session_state["Recency"], 0 if st.session_state["uk"] == "No" else 1]
+    df.loc[len(df)] = input_data
+    pca = PCA(n_components = 2, random_state=42)
+    features = pca.fit_transform(df)
+    return features
+
+# Features clustering centroid points
+@st.cache_data
+def clusterPointsMean(df):
+    clusters = df['Cluster']
+    df = df.drop(['Cluster'], axis=1)
+    pca = PCA(n_components = 2, random_state=42)
+    features = pca.fit_transform(df)
+
+    x1 = [features[i][0] for i in range(len(features))]
+    x2 = [features[i][1] for i in range(len(features))]
+
+    cluster = clusters
+    data = {
+        "X1": x1,
+        "X2": x2,
+        "Cluster": cluster
+    }
+    data = pd.DataFrame(data)
+    return data
+
+# Display plot
+@st.cache_data
+def displayPoints(data, feature):
+    c1_x, c1_y = data[data['Cluster'] == 0].drop(['Cluster'], axis=1).mean()
+    c2_x, c2_y = data[data['Cluster'] == 1].drop(['Cluster'], axis=1).mean()
+
+    fig, ax = plt.subplots()
+    ax.scatter(c1_x, c1_y, color="red", label="Cluster-1", s = 150)
+    ax.scatter(c2_x, c2_y, color="blue", label="Cluster-2", s = 150)
+    ax.scatter(feature[0], feature[1], color="green", label="Data point", s = 150)
+    ax.legend()
+    st.pyplot(fig=fig)
+
+def main():
+    form = st.form(border=False, key="data_form")
+
+    with form:
+        c1, c2 = st.columns(2)
+
+        c1.number_input(label="Revenue Contribution", key="Revenue_given")
+        c1.number_input(label="Days past since last buy", min_value=0, value=0, key="Recency")
+
+        c2.number_input(label="How Frequently bought", min_value=0, value=0, key="Frequency")
+        c2.selectbox(label="From United Kingdom?", options=["Yes", "No"], index=0, key="uk")
+
+        st.form_submit_button(label="Cluster Customer", type='primary', on_click=form_submission)
+
+    if st.session_state["form_submitted"]:
+
+        features = transform_data()
+
+        feature = features[len(features)-1]
+
+        model = load_model()
+
+        st.markdown(f"## The customer falls within Cluster - {predict(model, feature.reshape(1, 2))+1}")
+
+        df = read_csv()
+
+        data = clusterPointsMean(df=df)
+
+        displayPoints(data, feature)
+
+if __name__ == "__main__":
+    main()

dataset/final_dataset.csv

@@ -0,0 +1,3617 @@
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model/clustering.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77ae769ab061f6ea9bc6401879d708c928c0c02158375ec7875f49eb1aa9ae5c
+size 58299

models.py

@@ -0,0 +1,134 @@
+import numpy as np
+
+class KMeans:
+  # Initialization and properties
+  def __init__(self):
+    self.centroids = np.empty(1)
+
+  # Fit method
+  def fit(self, data, clusters, epochs = 1, random_seed = 42):
+    np.random.seed(random_seed)
+    N = len(data)
+    centroids = data[np.random.choice(N, clusters, replace=False), :]
+
+    labels = np.empty(N)
+    old_labels = np.empty(N)
+
+    while True:
+      distances = np.linalg.norm(data[:, None, :] - centroids, axis=2)
+      labels = np.argmin(distances, axis=1)
+      for j in range(clusters):
+            centroids[j] = np.mean(data[labels == j], axis=0)
+
+      if np.all(labels == old_labels):
+            break
+
+      old_labels = labels
+    self.centroids = centroids
+    return labels
+
+  # Predict method
+  def predict(self, data):
+      distances = np.linalg.norm(data[:, None, :] - self.centroids, axis=2)
+      labels = np.argmin(distances, axis=1)
+      return labels
+  
+
+class KMedoids:
+  # Initialization and properties
+  def __init__(self, data, clusters):
+    self.medoids = np.empty(1)
+    self.data = data
+    self.N = len(data)
+    self.clusters = clusters
+
+  # Fit
+  def fit(self, random_seed = 42):
+    np.random.seed(random_seed)
+    data = self.data
+    N = self.N
+    clusters = self.clusters
+    medoids_idx = np.random.choice(N, clusters, replace=False)
+    medoids = data[medoids_idx].copy()
+    distances = np.zeros((N, clusters))
+
+    for i in range(clusters):
+        distances[:, i] = np.sum(np.abs(data - medoids[i]), axis=1)
+
+    labels = np.argmin(distances, axis=1)
+    old_labels = np.empty(N)
+    all_idxs = np.arange(N)
+
+    while True:
+      best_swap = (-1, -1, 0)
+      best_distances = np.zeros(N)
+      for i in range(clusters):
+          non_medoids_idx = all_idxs[np.logical_not(np.isin(all_idxs, medoids_idx))]
+          for j in non_medoids_idx:
+              new_medoid = data[j]
+              new_distances = np.sum(np.abs(data - new_medoid), axis=1)
+              cost_change = np.sum(new_distances[labels == i]) - np.sum(
+                  distances[labels == i, i]
+              )
+              if cost_change < best_swap[2]:
+                  best_swap = (i, j, cost_change)
+                  best_distances = new_distances
+
+      if best_swap == (-1, -1, 0):
+            break
+      i, j, _ = best_swap
+      distances[:, i] = best_distances
+      medoids[i] = data[j]
+
+      labels = np.argmin(distances, axis=1)
+
+      old_labels = labels
+    self.medoids = medoids
+    return labels
+
+  # Predict
+  def predict(self, data):
+      distances = np.zeros((len(data), self.clusters))
+      for i in range(self.clusters):
+        distances[:, i] = np.sum(np.abs(data - self.medoids[i]), axis=1)
+      labels = np.argmin(distances, axis=1)
+      return labels
+  
+  
+class EnsembleClustering:
+  # Initialization
+  def __init__(self, data, clusters):
+    self.data = data
+    self.clusters = clusters
+    self.kmeans = None
+    self.kmedoids = None
+
+  # Fit method
+  def fit(self):
+    kmeans = KMeans()
+    kmeans_labels = kmeans.fit(self.data, self.clusters)
+    self.kmeans = kmeans
+
+    kmedoids = KMedoids(data = self.data, clusters = self.clusters)
+    kmedoids_labels = kmedoids.fit()
+    self.kmedoids = kmedoids
+
+    labels = self.maximumVoting(kmeans_labels, kmedoids_labels)
+    return labels
+
+  # Maximum voting method
+  def maximumVoting(self, labels1, labels2):
+    labels = np.zeros(len(labels1), dtype=int)
+    for i in range(len(labels1)):
+      voting = np.zeros(self.clusters, dtype=int)
+      voting[labels1[i]] += 1
+      voting[labels2[i]] += 1
+      labels[i] = voting.argmax()
+    return labels
+
+  # Predict method
+  def predict(self, data):
+    kmeans_labels = self.kmeans.predict(data)
+    kmedoids_labels = self.kmedoids.predict(data)
+    labels = self.maximumVoting(kmeans_labels, kmedoids_labels)
+    return labels