debut dbscan
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18
frontend/pages/clustering:_dbscan.py
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18
frontend/pages/clustering:_dbscan.py
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@@ -0,0 +1,18 @@
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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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import numpy as np
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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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with st.form("my_form"):
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data_name = st.multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=2)
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st.form_submit_button('launch')
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else:
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st.error("file not loaded")
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@@ -2,7 +2,7 @@ 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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st.header("Clustering")
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st.header("Clustering: kmeans")
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if "data" in st.session_state:
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@@ -27,8 +27,9 @@ if "data" in st.session_state:
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y_kmeans = kmeans.fit_predict(x)
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fig, ax = plt.subplots(figsize=(12,8))
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plt.scatter(x[:, 0], x[:, 1], s=100, c=kmeans.labels_, cmap='Set1')
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plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=400, marker='*', color='k')
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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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st.pyplot(fig)
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else:
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