From 20469b87a97b464b805d974250d4cf089b5aea92 Mon Sep 17 00:00:00 2001
From: Christof Kaufmann <christof.kaufmann@hs-bochum.de>
Date: Tue, 20 May 2025 14:25:47 +0000
Subject: [PATCH] Notebooks from applied-cs/data-science@bfb5dcac
---
06-clustering/04-chain-clusters.ipynb | 98 +
...istency.ipynb => 05-knn-consistency.ipynb} | 0
06-clustering/chain.csv | 3600 +++++++++++++++++
06-clustering/demo/clustering_demo.py | 25 +-
.../solutions/04-chain-clusters-sol.ipynb | 240 ++
...sol.ipynb => 05-knn-consistency-sol.ipynb} | 0
06-clustering/solutions/chain.csv | 3600 +++++++++++++++++
.../solutions/demo/clustering_demo.py | 25 +-
8 files changed, 7552 insertions(+), 36 deletions(-)
create mode 100644 06-clustering/04-chain-clusters.ipynb
rename 06-clustering/{04-knn_consistency.ipynb => 05-knn-consistency.ipynb} (100%)
create mode 100644 06-clustering/chain.csv
create mode 100644 06-clustering/solutions/04-chain-clusters-sol.ipynb
rename 06-clustering/solutions/{04-knn_consistency-sol.ipynb => 05-knn-consistency-sol.ipynb} (100%)
create mode 100644 06-clustering/solutions/chain.csv
diff --git a/06-clustering/04-chain-clusters.ipynb b/06-clustering/04-chain-clusters.ipynb
new file mode 100644
index 0000000..4d82536
--- /dev/null
+++ b/06-clustering/04-chain-clusters.ipynb
@@ -0,0 +1,98 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Kettenglied Cluster\n",
+ "\n",
+ "Im Startcode werden die Kettenglied-Daten geladen.\n",
+ "\n",
+ "- Versuchen Sie mit einem Plot über $\\varepsilon$ herauszukriegen, wie\n",
+ " viele Cluster es gibt\n",
+ "- Clustern Sie die Daten anschließend mit `DBSCAN` mit den gefundenen\n",
+ " Parametern. Speichern Sie die Labels in `labels`.\n",
+ "\n",
+ "Hier ist Ihr Startcode"
+ ],
+ "id": "0003-63fa5c481c78bbdf737c04550a38235a19937a13865b411b2a2f4b0f35b"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "from sklearn.cluster import DBSCAN, HDBSCAN\n",
+ "from plotly import graph_objects as go\n",
+ "\n",
+ "X = np.loadtxt('chain.csv', delimiter=',')"
+ ],
+ "id": "0004-172ec8d8337c3342d2de94c7d0b95e0d58501261b0b2b053c81d4df3ebb"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Tests\n",
+ "\n",
+ "Wir geben hier die Anzahl gefundener Cluster und den Anteil an Rauschen\n",
+ "aus."
+ ],
+ "id": "0006-d062c7158819e9aa5c6c8293c56a7e567d66fc80cdc9e14a22c7dbcbf3f"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "n_clusters: 18, noise_lvl: 0.0%"
+ ]
+ }
+ ],
+ "source": [
+ "noise_lvl = np.mean(labels < 0)\n",
+ "n_clusters = labels.max() + 1\n",
+ "print(f'n_clusters: {n_clusters}, noise_lvl: {noise_lvl:.1%}')"
+ ],
+ "id": "0007-e7e44b3c247a9f8e162855e55d1ae41b0da010167820ecc81de8d505942"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Außerdem plotten wir die Daten mit den gefundenen Labels."
+ ],
+ "id": "0008-02aa760645262825d3fe0fac5bed63b93cdb9024bd5568fbd9c82870756"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "fig = go.Figure(data=[\n",
+ " go.Scatter3d(x=X[:, 0], y=X[:, 1], z=X[:, 2], marker=dict(size=3, color=labels), mode='markers', hovertemplate='label: %{marker.color}<br>x: %{x}<br>y: %{y}<br>z: %{z}<extra></extra>')\n",
+ "])\n",
+ "fig.show()"
+ ],
+ "id": "0009-b26d9437b5037dc028a307b8f9e18e3423e9bdd9d750ce887210662533a"
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/06-clustering/04-knn_consistency.ipynb b/06-clustering/05-knn-consistency.ipynb
similarity index 100%
rename from 06-clustering/04-knn_consistency.ipynb
rename to 06-clustering/05-knn-consistency.ipynb
diff --git a/06-clustering/chain.csv b/06-clustering/chain.csv
new file mode 100644
index 0000000..28dd32f
--- /dev/null
+++ b/06-clustering/chain.csv
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diff --git a/06-clustering/demo/clustering_demo.py b/06-clustering/demo/clustering_demo.py
index a770258..17dafe8 100644
--- a/06-clustering/demo/clustering_demo.py
+++ b/06-clustering/demo/clustering_demo.py
@@ -32,13 +32,12 @@ from sklearn.metrics import confusion_matrix
from scipy.optimize import linear_sum_assignment
# # Install these with from terminal with:
-# mamba install hdbscan scikit-learn-extra
-# pip install hdbscan scikit-learn-extra
+# mamba install hdbscan
+# pip install hdbscan
# # Install these with from this script with:
-# !mamba install -y hdbscan scikit-learn-extra
-# !pip install hdbscan scikit-learn-extra
+# !mamba install -y hdbscan
+# !pip install hdbscan
-from sklearn_extra.cluster import CommonNNClustering
# from sklearn.cluster import HDBSCAN # does not have soft clustering and tree plot
from hdbscan import HDBSCAN
import hdbscan
@@ -188,6 +187,7 @@ fig_dbscan = plt.figure('DBSCAN', figsize=(8, 8))
ax_dbscan = fig_dbscan.add_subplot(2, 1, 1)
sc_dbscan = ax_dbscan.scatter(*X.T, alpha=0.8)
sc_dbscan.set_edgecolor(cm((y + 1) % cm.N))
+ax_dbscan.set_title('DBSCAN')
ax_dbscan.set_xticks([])
ax_dbscan.set_yticks([])
@@ -204,14 +204,11 @@ dbscan_samples_slider = Slider(ax=ax_dbscan_samples_slider, label='min_samples',
ax_dbscan_show = fig_dbscan.add_axes([0.99 - 0.3, 0.95 - 0.05, 0.3, 0.05])
dbscan_show_button = CheckButtons(ax=ax_dbscan_show, labels=['Show Ground Truth'])
-ax_dbscan_button = fig_dbscan.add_axes([0.99 - 0.25, 0.95 - 0.05 - 0.05, 0.25, 0.05])
-dbscan_button = CheckButtons(ax=ax_dbscan_button, labels=['DBSCAN / CNNC'])
-
ax_dbscan_bars = fig_dbscan.add_subplot(2, 1, 2)
ax_dbscan_bars.bar([0,1], [0, 1])
ax_dbscan_bars.set_xticks([])
-# merge small clusters to the nearest large cluster (useful for CommonNNClustering)
+# merge small clusters to the nearest large cluster (was useful for CommonNNClustering)
def merge_small_clusters(X, labels, limit=15, inplace=False):
noise_labels = labels == -1
clusters, counts = np.unique(labels[~noise_labels], return_counts=True)
@@ -236,14 +233,7 @@ def merge_small_clusters(X, labels, limit=15, inplace=False):
def update_dbscan(val=None):
- if dbscan_button.get_status()[0]:
- alg = CommonNNClustering(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
- alg_name = 'Common Nearest Neighbor Clustering'
- else:
- alg = DBSCAN(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
- alg_name = 'DBSCAN'
-
- ax_dbscan.set_title(alg_name)
+ alg = DBSCAN(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
labels = alg.fit_predict(X)
merge_small_clusters(X, labels, inplace=True)
matched_labels = match_labels(labels)
@@ -261,7 +251,6 @@ def update_dbscan(val=None):
dbscan_eps_slider.on_changed(update_dbscan)
dbscan_samples_slider.on_changed(update_dbscan)
-dbscan_button.on_clicked(update_dbscan)
dbscan_show_button.on_clicked(update_dbscan)
update_dbscan()
plt.show()
diff --git a/06-clustering/solutions/04-chain-clusters-sol.ipynb b/06-clustering/solutions/04-chain-clusters-sol.ipynb
new file mode 100644
index 0000000..1d9d4af
--- /dev/null
+++ b/06-clustering/solutions/04-chain-clusters-sol.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Kettenglied Cluster\n",
+ "\n",
+ "Im Startcode werden die Kettenglied-Daten geladen.\n",
+ "\n",
+ "- Versuchen Sie mit einem Plot über $\\varepsilon$ herauszukriegen, wie\n",
+ " viele Cluster es gibt\n",
+ "- Clustern Sie die Daten anschließend mit `DBSCAN` mit den gefundenen\n",
+ " Parametern. Speichern Sie die Labels in `labels`.\n",
+ "\n",
+ "Hier ist Ihr Startcode"
+ ],
+ "id": "0003-63fa5c481c78bbdf737c04550a38235a19937a13865b411b2a2f4b0f35b"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "from sklearn.cluster import DBSCAN, HDBSCAN\n",
+ "from plotly import graph_objects as go\n",
+ "\n",
+ "X = np.loadtxt('chain.csv', delimiter=',')"
+ ],
+ "id": "0004-172ec8d8337c3342d2de94c7d0b95e0d58501261b0b2b053c81d4df3ebb"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Lösung\n",
+ "\n",
+ "Zunächst definieren wir die Bereiche für die Parameter `eps` und\n",
+ "`min_samples`. Für jede Kombination von `eps` und `min_samples` führen\n",
+ "wir `DBSCAN` aus und speichern die Anzahl der gefundenen Cluster\n",
+ "zusammen mit `eps` und `min_samples` in einer Liste. Diese Liste wandeln\n",
+ "wir anschließend in ein DataFrame um."
+ ],
+ "id": "0006-1e1baaf19132e8389263dc7101c2161704a6858a2a3509b8b9784e04a23"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "min_samples_range = np.arange(2, 10)\n",
+ "eps_range = np.arange(0.16, 0.39, 0.02)\n",
+ "records = []\n",
+ "for min_samples in min_samples_range:\n",
+ " for eps in eps_range:\n",
+ " clusterer = DBSCAN(eps=eps, min_samples=min_samples)\n",
+ " labels = clusterer.fit_predict(X)\n",
+ " noise_lvl = np.mean(labels < 0)\n",
+ " n_clusters = labels.max() + 1\n",
+ "\n",
+ " records.append((min_samples, eps, n_clusters, noise_lvl))\n",
+ "\n",
+ "df = pd.DataFrame.from_records(records, columns=['min_samples', 'eps', 'n_clusters', 'noise_lvl'])"
+ ],
+ "id": "0007-03594f919e9a4e5881b9b1e6714a3597c78e3e46cebe932b3748e93124f"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dann lässt sich das z. B. mit Seaborns `lineplot` plotten. Wir\n",
+ "verschieben die y-Achse um den Wert von `min_samples` und multiplizieren\n",
+ "mit einem Faktor, damit die Kurven nicht übereinander liegen. Außerdem\n",
+ "setzen wir die Grenzen der x- und y-Achse, damit wir uns auf den\n",
+ "interessanten Bereich konzentrieren."
+ ],
+ "id": "0008-1c28662b4a9b50bbbcf045db605e433969f0abb64225270c4c3d8df12b3"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure()\n",
+ "df_plt = df.copy()\n",
+ "df_plt['n_clusters'] = df_plt['n_clusters'] + (df_plt['min_samples'] - 5.5) * 0.07\n",
+ "ax = sns.lineplot(df_plt, x='eps', y='n_clusters', hue='min_samples', palette='rainbow', legend='full')\n",
+ "ax.set_ylim(bottom=0, top=25) # zoom in y-Achse\n",
+ "ax.set_xlim(left=0.16, right=0.38) # zoom in x-Achse"
+ ],
+ "id": "0009-18c3413612249d6d54e5becb70cce1c4583afe097ca75b232a62eadb882"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternativ lassen sich die Daten auch als Heatmap darstellen. Hierbei\n",
+ "ist `vmax` auf 25 gesetzt, damit die Ausreißer die Farben nicht\n",
+ "beeinflussen. Außerdem setzen wir `annot=True`, damit die Anzahl der\n",
+ "Cluster in die Zellen geschrieben wird."
+ ],
+ "id": "0010-e08d4bcdda9e99f64a3fde59e80ff5c65cfce2c7645844a3edf9115ae2d"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "df_clusters = df.pivot(index='eps', columns='min_samples', values='n_clusters')\n",
+ "\n",
+ "plt.figure()\n",
+ "sns.heatmap(df_clusters, vmax=25, annot=True, fmt='d', cbar_kws={'label': 'n_clusters'}, yticklabels=df_clusters.index.values.round(2))"
+ ],
+ "id": "0011-dfcc852ce6bddb3c8367b16667188a4905ce5926e84840a6a47458df55d"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Wir sehen, dass ein `eps` zwischen 0.22 und 0.32 gewählt werden kann und\n",
+ "wir 18 Cluster erhalten sollten. Wir wählen `eps=0.32` um möglichst\n",
+ "wenig Rauschen zu erhalten und `min_samples=5` und führen `DBSCAN` aus.\n",
+ "Die Labels werden in der Variable `labels` gespeichert."
+ ],
+ "id": "0012-11a2a41b09d2c97379afb6ee828f545b345a9f604a9e223c2274921b3ce"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "clusterer = DBSCAN(eps=0.32, min_samples=5)\n",
+ "labels = clusterer.fit_predict(X)"
+ ],
+ "id": "0013-a4adbdcc1b97ce2d982515e21498cee223de6225681964c629986667b42"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternativ können wir auch `HDBSCAN` verwenden. Hierbei ist die Wahl von\n",
+ "`min_samples` nicht so wichtig, da `HDBSCAN` die Parameter automatisch\n",
+ "anpasst. Wir verwenden hier den Standardwert von 5. In der Tat erhalten\n",
+ "wir mit `HDBSCAN` ohne Einstellung der Parameter die gleiche Anzahl an\n",
+ "Clustern wie mit `DBSCAN`."
+ ],
+ "id": "0014-93102c00a0113d350231e3596456f2b8581bfdade70ec4ad4e642129496"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "clusterer = HDBSCAN()\n",
+ "labels = clusterer.fit_predict(X)"
+ ],
+ "id": "0015-10e195334248e3113087be5a111beaf458fbde346a80448174f3230cea8"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Tests\n",
+ "\n",
+ "Wir geben hier die Anzahl gefundener Cluster und den Anteil an Rauschen\n",
+ "aus."
+ ],
+ "id": "0017-d062c7158819e9aa5c6c8293c56a7e567d66fc80cdc9e14a22c7dbcbf3f"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "n_clusters: 18, noise_lvl: 0.0%"
+ ]
+ }
+ ],
+ "source": [
+ "noise_lvl = np.mean(labels < 0)\n",
+ "n_clusters = labels.max() + 1\n",
+ "print(f'n_clusters: {n_clusters}, noise_lvl: {noise_lvl:.1%}')"
+ ],
+ "id": "0018-e7e44b3c247a9f8e162855e55d1ae41b0da010167820ecc81de8d505942"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Außerdem plotten wir die Daten mit den gefundenen Labels."
+ ],
+ "id": "0019-02aa760645262825d3fe0fac5bed63b93cdb9024bd5568fbd9c82870756"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "style": "python"
+ },
+ "outputs": [],
+ "source": [
+ "fig = go.Figure(data=[\n",
+ " go.Scatter3d(x=X[:, 0], y=X[:, 1], z=X[:, 2], marker=dict(size=3, color=labels), mode='markers', hovertemplate='label: %{marker.color}<br>x: %{x}<br>y: %{y}<br>z: %{z}<extra></extra>')\n",
+ "])\n",
+ "fig.show()"
+ ],
+ "id": "0020-b26d9437b5037dc028a307b8f9e18e3423e9bdd9d750ce887210662533a"
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/06-clustering/solutions/04-knn_consistency-sol.ipynb b/06-clustering/solutions/05-knn-consistency-sol.ipynb
similarity index 100%
rename from 06-clustering/solutions/04-knn_consistency-sol.ipynb
rename to 06-clustering/solutions/05-knn-consistency-sol.ipynb
diff --git a/06-clustering/solutions/chain.csv b/06-clustering/solutions/chain.csv
new file mode 100644
index 0000000..28dd32f
--- /dev/null
+++ b/06-clustering/solutions/chain.csv
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diff --git a/06-clustering/solutions/demo/clustering_demo.py b/06-clustering/solutions/demo/clustering_demo.py
index a770258..17dafe8 100644
--- a/06-clustering/solutions/demo/clustering_demo.py
+++ b/06-clustering/solutions/demo/clustering_demo.py
@@ -32,13 +32,12 @@ from sklearn.metrics import confusion_matrix
from scipy.optimize import linear_sum_assignment
# # Install these with from terminal with:
-# mamba install hdbscan scikit-learn-extra
-# pip install hdbscan scikit-learn-extra
+# mamba install hdbscan
+# pip install hdbscan
# # Install these with from this script with:
-# !mamba install -y hdbscan scikit-learn-extra
-# !pip install hdbscan scikit-learn-extra
+# !mamba install -y hdbscan
+# !pip install hdbscan
-from sklearn_extra.cluster import CommonNNClustering
# from sklearn.cluster import HDBSCAN # does not have soft clustering and tree plot
from hdbscan import HDBSCAN
import hdbscan
@@ -188,6 +187,7 @@ fig_dbscan = plt.figure('DBSCAN', figsize=(8, 8))
ax_dbscan = fig_dbscan.add_subplot(2, 1, 1)
sc_dbscan = ax_dbscan.scatter(*X.T, alpha=0.8)
sc_dbscan.set_edgecolor(cm((y + 1) % cm.N))
+ax_dbscan.set_title('DBSCAN')
ax_dbscan.set_xticks([])
ax_dbscan.set_yticks([])
@@ -204,14 +204,11 @@ dbscan_samples_slider = Slider(ax=ax_dbscan_samples_slider, label='min_samples',
ax_dbscan_show = fig_dbscan.add_axes([0.99 - 0.3, 0.95 - 0.05, 0.3, 0.05])
dbscan_show_button = CheckButtons(ax=ax_dbscan_show, labels=['Show Ground Truth'])
-ax_dbscan_button = fig_dbscan.add_axes([0.99 - 0.25, 0.95 - 0.05 - 0.05, 0.25, 0.05])
-dbscan_button = CheckButtons(ax=ax_dbscan_button, labels=['DBSCAN / CNNC'])
-
ax_dbscan_bars = fig_dbscan.add_subplot(2, 1, 2)
ax_dbscan_bars.bar([0,1], [0, 1])
ax_dbscan_bars.set_xticks([])
-# merge small clusters to the nearest large cluster (useful for CommonNNClustering)
+# merge small clusters to the nearest large cluster (was useful for CommonNNClustering)
def merge_small_clusters(X, labels, limit=15, inplace=False):
noise_labels = labels == -1
clusters, counts = np.unique(labels[~noise_labels], return_counts=True)
@@ -236,14 +233,7 @@ def merge_small_clusters(X, labels, limit=15, inplace=False):
def update_dbscan(val=None):
- if dbscan_button.get_status()[0]:
- alg = CommonNNClustering(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
- alg_name = 'Common Nearest Neighbor Clustering'
- else:
- alg = DBSCAN(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
- alg_name = 'DBSCAN'
-
- ax_dbscan.set_title(alg_name)
+ alg = DBSCAN(eps=dbscan_eps_slider.val, min_samples=int(dbscan_samples_slider.val))
labels = alg.fit_predict(X)
merge_small_clusters(X, labels, inplace=True)
matched_labels = match_labels(labels)
@@ -261,7 +251,6 @@ def update_dbscan(val=None):
dbscan_eps_slider.on_changed(update_dbscan)
dbscan_samples_slider.on_changed(update_dbscan)
-dbscan_button.on_clicked(update_dbscan)
dbscan_show_button.on_clicked(update_dbscan)
update_dbscan()
plt.show()
--
GitLab