Add cluster stats (#13)
Co-authored-by: bastien ollier <bastien.ollier@etu.uca.fr> Reviewed-on: https://codefirst.iut.uca.fr/git/clement.freville2/miner/pulls/13 Reviewed-by: Hugo PRADIER <hugo.pradier2@etu.uca.fr> Reviewed-by: Clément FRÉVILLE <clement.freville2@etu.uca.fr> Co-authored-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr> Co-committed-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr>
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63
frontend/clusters.py
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63
frontend/clusters.py
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@@ -0,0 +1,63 @@
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from sklearn.cluster import DBSCAN, KMeans
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import numpy as np
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class DBSCAN_cluster():
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def __init__(self, eps, min_samples,data):
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self.eps = eps
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self.min_samples = min_samples
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self.data = data
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self.labels = np.array([])
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def run(self):
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dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
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self.labels = dbscan.fit_predict(self.data)
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return self.labels
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def get_stats(self):
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unique_labels = np.unique(self.labels)
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stats = []
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for label in unique_labels:
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if label == -1:
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continue
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cluster_points = self.data[self.labels == label]
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num_points = len(cluster_points)
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density = num_points / (np.max(cluster_points, axis=0) - np.min(cluster_points, axis=0)).prod()
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stats.append({
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"cluster": label,
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"num_points": num_points,
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"density": density
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})
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return stats
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class KMeans_cluster():
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def __init__(self, n_clusters, n_init, max_iter, data):
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self.n_clusters = n_clusters
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self.n_init = n_init
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self.max_iter = max_iter
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self.data = data
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self.labels = np.array([])
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self.centers = []
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def run(self):
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kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
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self.labels = kmeans.fit_predict(self.data)
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self.centers = kmeans.cluster_centers_
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return self.labels
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def get_stats(self):
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unique_labels = np.unique(self.labels)
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stats = []
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for label in unique_labels:
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cluster_points = self.data[self.labels == label]
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num_points = len(cluster_points)
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center = self.centers[label]
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stats.append({
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'cluster': label,
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'num_points': num_points,
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'center': center
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})
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return stats
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@@ -1,10 +1,9 @@
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import streamlit as st
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import matplotlib.pyplot as plt
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from sklearn.cluster import DBSCAN
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from clusters import DBSCAN_cluster
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st.header("Clustering: dbscan")
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if "data" in st.session_state:
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data = st.session_state.data
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@@ -17,8 +16,9 @@ if "data" in st.session_state:
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if len(data_name) >= 2 and len(data_name) <=3:
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x = data[data_name].to_numpy()
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dbscan = DBSCAN(eps=eps, min_samples=min_samples)
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y_dbscan = dbscan.fit_predict(x)
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dbscan = DBSCAN_cluster(eps,min_samples,x)
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y_dbscan = dbscan.run()
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st.table(dbscan.get_stats())
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fig = plt.figure()
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if len(data_name) == 2:
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@@ -28,8 +28,5 @@ if "data" in st.session_state:
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ax = fig.add_subplot(projection='3d')
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ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_dbscan, s=50, cmap="viridis")
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st.pyplot(fig)
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else:
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st.error("file not loaded")
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@@ -1,10 +1,9 @@
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import streamlit as st
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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from clusters import KMeans_cluster
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st.header("Clustering: kmeans")
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if "data" in st.session_state:
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data = st.session_state.data
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@@ -23,21 +22,22 @@ if "data" in st.session_state:
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if len(data_name) >= 2 and len(data_name) <=3:
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x = data[data_name].to_numpy()
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kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
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y_kmeans = kmeans.fit_predict(x)
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kmeans = KMeans_cluster(n_clusters, n_init, max_iter, x)
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y_kmeans = kmeans.run()
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st.table(kmeans.get_stats())
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centers = kmeans.centers
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fig = plt.figure()
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if len(data_name) == 2:
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ax = fig.add_subplot(projection='rectilinear')
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plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
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centers = kmeans.cluster_centers_
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plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
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else:
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ax = fig.add_subplot(projection='3d')
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ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_kmeans, s=50, cmap="viridis")
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centers = kmeans.cluster_centers_
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ax.scatter(centers[:, 0], centers[:, 1],centers[:, 2], c="black", s=200, marker="X")
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ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
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st.pyplot(fig)
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else:
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