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トップに関する質問Pythonクラスタリング方法について

編集履歴

質問編集履歴

4

コード囲み忘れ

2021/11/07 13:20

投稿

barobaro
barobaro

スコア1286

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@@ -158,6 +158,8 @@
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+ ```python
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  df4 = df.copy()
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@@ -186,6 +188,8 @@
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  sns.scatterplot(x='ta', y='m', hue='cluster', data=df4)
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+ ```
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  ![イメージ説明](32a22c50bf19936ab2aa15e0abc9e2e8.png)

3

LinearRegressionを追加

2021/11/07 13:20

投稿

barobaro
barobaro

スコア1286

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+ #### LinearRegression
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+ df4 = df.copy()
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+ from sklearn.linear_model import LinearRegression
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+ lr = LinearRegression()
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+ lr.fit(df4['ta'].values.reshape(-1, 1), df4['m'].values.reshape(-1, 1))
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+ pred_y = lr.predict(df4['ta'].values.reshape(-1, 1)).reshape(-1)
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+ df4['cluster'] = (df4['m'] < pred_y).astype(int)
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+ for name, dfg in df4.groupby('cluster'):
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+ lr.fit(dfg['ta'].values.reshape(-1, 1), dfg['m'].values.reshape(-1, 1))
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+ print(name, lr.coef_, lr.intercept_)
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+ sns.scatterplot(x='ta', y='m', hue='cluster', data=df4)
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+ ![イメージ説明](32a22c50bf19936ab2aa15e0abc9e2e8.png)
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  ### 補足情報（FW/ツールのバージョンなど）
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2

df3を追加

2021/11/07 13:19

投稿

barobaro
barobaro

スコア1286

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  ```python
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+ df3 = df.copy()
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  from sklearn import cluster
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1

サンプルデータ追加、GaussianMixtureとSpectralClusteringを追加

2021/11/06 23:02

投稿

barobaro
barobaro

スコア1286

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@@ -38,6 +38,18 @@
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  ```python
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+ import pandas as pd
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+ url = "https://docs.google.com/spreadsheets/d/e/2PACX-1vSA9NhTNG6rcb1BAdVzC2RYgPPCCd0ryo1YconlDj7TK15IAO8rIi3uY9FzRCkXsj48BO4hWtceriKq/pub?gid=0&single=true&output=csv"
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+
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+ df = pd.read_csv(url)
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+
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  sns.scatterplot(x='ta', y='m', data=df)
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  ```
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+ #### KMeans
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  ```python
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+ df1 = df.copy()
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+
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  from sklearn.cluster import KMeans
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  kmeans = KMeans(n_clusters=2, random_state=0)
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- clusters = kmeans.fit(df)
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+ clusters = kmeans.fit(df1)
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+ df1['cluster'] = clusters.labels_
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+ sns.scatterplot(x='ta', y='m', hue='cluster', data=df1)
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+ ```
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- df['cluster'] = clusters.labels_
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+ ![イメージ説明](e2b2395c12b50c3ac062d8305b8cda6a.png)
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+ #### GaussianMixture
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+ ```python
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+ df2 = df.copy()
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+ from sklearn.mixture import GaussianMixture
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+ model = GaussianMixture(n_components=2)
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+ model.fit(df2)
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+ df2['cluster'] = model.predict(df2)
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+
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- sns.scatterplot(x='ta', y='m', hue='cluster', data=df)
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+ sns.scatterplot(x='ta', y='m', hue='cluster', data=df2)
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  ```
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+ ![イメージ説明](7ee8a9a1129e77289039593222a2f45b.png)
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+ #### SpectralClustering
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+ ```python
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+ from sklearn import cluster
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+ spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity='nearest_neighbors')
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+ clusters = spectral.fit(df3)
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+ df3['cluster'] = clusters.labels_
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+ sns.scatterplot(x='ta', y='m', hue='cluster', data=df3)
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+ ```
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+ ![イメージ説明](72b2494b8fffbf8c2194355d3d655215.png)
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