2584 lines
468 KiB
Plaintext
2584 lines
468 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div style=\"\n",
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" border: 2px solid #4CAF50; \n",
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" padding: 15px; \n",
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" background-color: #f4f4f4; \n",
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" border-radius: 10px; \n",
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" align-items: center;\">\n",
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"\n",
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"<h1 style=\"margin: 0; color: #4CAF50;\">Unsupervised ML Modelle: K-Means und Affinity Propagation</h1>\n",
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"<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
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"<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
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"\n",
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"<div style=\"flex-shrink: 0;\">\n",
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" <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
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"</div>\n",
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"<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
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"</div>\n",
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"<div style=\"flex: 1;\">\n",
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"</div> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## k-Means Clustering"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir sehen uns nun den $k$-Means Algorithmus an. Er ist ein sehr effizienter und weit verbreiteter *Clustering* Algorithmus, welcher häufig im Unsupervised Learning Bereich eingesetzt wird."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Info:** Als **Clustering** bezeichnet man jenen Vorgang in Machine Learning, der Datenpunkte, je nach Ähnlichkeit, in Gruppen (=Cluster) zuweist."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Der $k$-means Algorithmus $\\ldots$\n",
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"* $\\ldots$ wird zum Clustering verwendet\n",
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"* $\\ldots$ arbeitet iterativ um den Datenpunkten den entsprechenden Cluster zuzuordnen\n",
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"* $\\ldots$ besitzt hauptsächlich die Hpyerparameter $k$ als die Anzahl der Cluster $k$ und die Distanzfunktion $d(x,y)$."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"(von https://blog.flyingwhales.io/machinelearning/2021/05/05/kmeans-clustering)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Beschreibung des Algorithmus"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir erklären den Algorithmus nun anhand von einer beispielhaften Visualisierung mit 17 2-dimensionalen Datenpunkten und $k=3$ gewünschte Cluster. Der Algorithmus funktioniert natürlich auch für andere Kombinationen genau gleich. In diesem Beispiel wird außerdem die euklidische Distanz, sprich der Betrag vom Richtungsvektor zweier Punkte verwendet."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Ausgangssituation:** Wir haben (in diesem Fall 17) Datenpunkte, welche in dieser Grafik rot dargestellt sind. Diese Datenpunkte sind in diesem Fall 2-dimensional (2 Features). \n",
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"\n",
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"**Schritt 1:** Nun werden 3 Puntke ($k=3$) zufällig platziert. Sie haben hier die Farben \"Gelb, Grün und Blau\".\n",
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"\n",
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"**Schritt 2:** Im nächsten Schritt wird von jedem Datenpunkt der Abstand zu den jeweiligen Clustern berechnet. Anschließend wird jedem Datenpunkt die Farbe (entspricht dem Cluster) zugewiesen, der dem Punkt am nähesten ist. Zur Distanzberechnung wird die *Distanz-Funktion* verwendet.\n",
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"\n",
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"**Schritt 3:** Die 3 platzierten Cluster werden nun verschoben. Dabei ist die neue Position die mittlere Position der bisherigen Punkte, welche diesem Cluster angehören.\n",
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"\n",
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"**Schritt 4-5:** Nun wird wie in Schritt $2$ erneut berechnet, wie groß der Abstand von jedem Punkt zu den 3 Cluster-Punkten ist. Die Punkte werden umgefärbt, falls ein neuer Cluster nun näher ist. Dies ist bei den beiden, rot eingekreisten, blauen Punkten der Fall. Die Cluster-Punkte werden nun erneut verschoben, genauso wie in Schritt $3$.\n",
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"\n",
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"**Ende:** Da beim erneuten Distanzausrechnen keine Punkte mehr umgefärbt werden müssen, ist können wir den Algorithmus beenden."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Wichtig:** In der Praxis kann es vorkommen, dass der Algorithmus nicht (früh genug) konvergiert. Sprich es verschieben sich die Clusterpunkte nach wie vor bei jeder Iteration. In diesem Fall bricht der Algorithmus nach einer gewissen Anzahl an Iterationen automatisch ab. Dieser Wert ist standardmäßig in `scikit-learn` auf 300 gesetzt, kann aber von uns geändert werden. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Eine weitere, sehr informative, interaktive Demonstration kann hier gefunden werden:\n",
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"http://alekseynp.com/viz/k-means.html"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Der Hyperparameter $k$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* Legt fest, wie viele Cluster wir wollen.\n",
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"* Bestimmt somit die Anzahl der zufälligen Punkte, die am Anfang des Algorithmus verwendet werden."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Die Distanz Funktion"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Ähnlich wie die meisten Algorithmen hier, wird zu den $k$ Cluster-Zentren der Abstand, sprich die Distanz (vergleiche zum Beispiel kNN), berechnet. Je nach gewählter Distanzfunktion ergibt dies ein anderes Verhalten des Algorithmus. Standardmäßig wird die euklidische Distanz verwendet, sprich der Betrag des Richtungsvektors zwischen den beiden Punkte.\n",
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"\n",
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"Wiederholung: Welche anderen Distanzfunktionen gibt es?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Vorteile und Nachteile von k-Means"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"**Vorteile:**\n",
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"* Sehr schnell und effizient für große Datenmengen\n",
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"* Leicht verständlich und einfach zu implementieren\n",
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"* Wahl von $k$\n",
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"\n",
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"**Nachteile:**\n",
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"* Wahl von $k$\n",
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"* Funktionert nur für numerische Attribute (gut)\n",
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"* Stark abhängig von der gewählten Distanz Funktion, somit auch Distanz-Funktion $d(x,y)$ entscheidend\n",
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"* Sensibel gegenüber Ausreißer (neues Cluster-Zentrum sehr verzerrt)\n",
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"* Clustering basiert auf Distanz und nicht auf semantische Unterschiede. Warum ist das ein Nachteil? Wo ist dies ein Nachteil (Word Embeddings)?"
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||
]
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},
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||
{
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||
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tipps und Tricks"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"* Es gibt noch weitere Parameter, welche die Initialisierung der zufälligen Punkte beinflussen. Viele verwenden *k-means++*, default ist *auto*\n",
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"* Standardisieren der Daten ist notwendig (Ausreißer), ansonsten kann es sein, dass ungewünschte Ergebnisse resultieren\n",
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"* Bei kleinen Datasets kann, ähnlich wie bei *kNN* der Wert von $k$ probiert werden. Dazu benötigt man Metriken, welche die Qualität bewerten. Eine weitere Methode ist zum Beispiel die Elbow-Method (siehe Referate)."
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
|
||
"## k-Means in Python"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"import matplotlib.pyplot as plt\n",
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||
"from sklearn.datasets import load_iris, load_wine\n",
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||
"from sklearn.cluster import KMeans\n",
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||
"from sklearn.preprocessing import StandardScaler\n",
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||
"import pandas as pd"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 2,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
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||
"X, y_true = load_iris(return_X_y=True, as_frame=False)"
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||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
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||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
"text/plain": [
|
||
"array([[5.1, 3.5, 1.4, 0.2],\n",
|
||
" [4.9, 3. , 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.3, 0.2],\n",
|
||
" [4.6, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.6, 1.4, 0.2],\n",
|
||
" [5.4, 3.9, 1.7, 0.4],\n",
|
||
" [4.6, 3.4, 1.4, 0.3],\n",
|
||
" [5. , 3.4, 1.5, 0.2],\n",
|
||
" [4.4, 2.9, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.1],\n",
|
||
" [5.4, 3.7, 1.5, 0.2],\n",
|
||
" [4.8, 3.4, 1.6, 0.2],\n",
|
||
" [4.8, 3. , 1.4, 0.1],\n",
|
||
" [4.3, 3. , 1.1, 0.1],\n",
|
||
" [5.8, 4. , 1.2, 0.2],\n",
|
||
" [5.7, 4.4, 1.5, 0.4],\n",
|
||
" [5.4, 3.9, 1.3, 0.4],\n",
|
||
" [5.1, 3.5, 1.4, 0.3],\n",
|
||
" [5.7, 3.8, 1.7, 0.3],\n",
|
||
" [5.1, 3.8, 1.5, 0.3],\n",
|
||
" [5.4, 3.4, 1.7, 0.2],\n",
|
||
" [5.1, 3.7, 1.5, 0.4],\n",
|
||
" [4.6, 3.6, 1. , 0.2],\n",
|
||
" [5.1, 3.3, 1.7, 0.5],\n",
|
||
" [4.8, 3.4, 1.9, 0.2],\n",
|
||
" [5. , 3. , 1.6, 0.2],\n",
|
||
" [5. , 3.4, 1.6, 0.4],\n",
|
||
" [5.2, 3.5, 1.5, 0.2],\n",
|
||
" [5.2, 3.4, 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.6, 0.2],\n",
|
||
" [4.8, 3.1, 1.6, 0.2],\n",
|
||
" [5.4, 3.4, 1.5, 0.4],\n",
|
||
" [5.2, 4.1, 1.5, 0.1],\n",
|
||
" [5.5, 4.2, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.2, 1.2, 0.2],\n",
|
||
" [5.5, 3.5, 1.3, 0.2],\n",
|
||
" [4.9, 3.6, 1.4, 0.1],\n",
|
||
" [4.4, 3. , 1.3, 0.2],\n",
|
||
" [5.1, 3.4, 1.5, 0.2],\n",
|
||
" [5. , 3.5, 1.3, 0.3],\n",
|
||
" [4.5, 2.3, 1.3, 0.3],\n",
|
||
" [4.4, 3.2, 1.3, 0.2],\n",
|
||
" [5. , 3.5, 1.6, 0.6],\n",
|
||
" [5.1, 3.8, 1.9, 0.4],\n",
|
||
" [4.8, 3. , 1.4, 0.3],\n",
|
||
" [5.1, 3.8, 1.6, 0.2],\n",
|
||
" [4.6, 3.2, 1.4, 0.2],\n",
|
||
" [5.3, 3.7, 1.5, 0.2],\n",
|
||
" [5. , 3.3, 1.4, 0.2],\n",
|
||
" [7. , 3.2, 4.7, 1.4],\n",
|
||
" [6.4, 3.2, 4.5, 1.5],\n",
|
||
" [6.9, 3.1, 4.9, 1.5],\n",
|
||
" [5.5, 2.3, 4. , 1.3],\n",
|
||
" [6.5, 2.8, 4.6, 1.5],\n",
|
||
" [5.7, 2.8, 4.5, 1.3],\n",
|
||
" [6.3, 3.3, 4.7, 1.6],\n",
|
||
" [4.9, 2.4, 3.3, 1. ],\n",
|
||
" [6.6, 2.9, 4.6, 1.3],\n",
|
||
" [5.2, 2.7, 3.9, 1.4],\n",
|
||
" [5. , 2. , 3.5, 1. ],\n",
|
||
" [5.9, 3. , 4.2, 1.5],\n",
|
||
" [6. , 2.2, 4. , 1. ],\n",
|
||
" [6.1, 2.9, 4.7, 1.4],\n",
|
||
" [5.6, 2.9, 3.6, 1.3],\n",
|
||
" [6.7, 3.1, 4.4, 1.4],\n",
|
||
" [5.6, 3. , 4.5, 1.5],\n",
|
||
" [5.8, 2.7, 4.1, 1. ],\n",
|
||
" [6.2, 2.2, 4.5, 1.5],\n",
|
||
" [5.6, 2.5, 3.9, 1.1],\n",
|
||
" [5.9, 3.2, 4.8, 1.8],\n",
|
||
" [6.1, 2.8, 4. , 1.3],\n",
|
||
" [6.3, 2.5, 4.9, 1.5],\n",
|
||
" [6.1, 2.8, 4.7, 1.2],\n",
|
||
" [6.4, 2.9, 4.3, 1.3],\n",
|
||
" [6.6, 3. , 4.4, 1.4],\n",
|
||
" [6.8, 2.8, 4.8, 1.4],\n",
|
||
" [6.7, 3. , 5. , 1.7],\n",
|
||
" [6. , 2.9, 4.5, 1.5],\n",
|
||
" [5.7, 2.6, 3.5, 1. ],\n",
|
||
" [5.5, 2.4, 3.8, 1.1],\n",
|
||
" [5.5, 2.4, 3.7, 1. ],\n",
|
||
" [5.8, 2.7, 3.9, 1.2],\n",
|
||
" [6. , 2.7, 5.1, 1.6],\n",
|
||
" [5.4, 3. , 4.5, 1.5],\n",
|
||
" [6. , 3.4, 4.5, 1.6],\n",
|
||
" [6.7, 3.1, 4.7, 1.5],\n",
|
||
" [6.3, 2.3, 4.4, 1.3],\n",
|
||
" [5.6, 3. , 4.1, 1.3],\n",
|
||
" [5.5, 2.5, 4. , 1.3],\n",
|
||
" [5.5, 2.6, 4.4, 1.2],\n",
|
||
" [6.1, 3. , 4.6, 1.4],\n",
|
||
" [5.8, 2.6, 4. , 1.2],\n",
|
||
" [5. , 2.3, 3.3, 1. ],\n",
|
||
" [5.6, 2.7, 4.2, 1.3],\n",
|
||
" [5.7, 3. , 4.2, 1.2],\n",
|
||
" [5.7, 2.9, 4.2, 1.3],\n",
|
||
" [6.2, 2.9, 4.3, 1.3],\n",
|
||
" [5.1, 2.5, 3. , 1.1],\n",
|
||
" [5.7, 2.8, 4.1, 1.3],\n",
|
||
" [6.3, 3.3, 6. , 2.5],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [7.1, 3. , 5.9, 2.1],\n",
|
||
" [6.3, 2.9, 5.6, 1.8],\n",
|
||
" [6.5, 3. , 5.8, 2.2],\n",
|
||
" [7.6, 3. , 6.6, 2.1],\n",
|
||
" [4.9, 2.5, 4.5, 1.7],\n",
|
||
" [7.3, 2.9, 6.3, 1.8],\n",
|
||
" [6.7, 2.5, 5.8, 1.8],\n",
|
||
" [7.2, 3.6, 6.1, 2.5],\n",
|
||
" [6.5, 3.2, 5.1, 2. ],\n",
|
||
" [6.4, 2.7, 5.3, 1.9],\n",
|
||
" [6.8, 3. , 5.5, 2.1],\n",
|
||
" [5.7, 2.5, 5. , 2. ],\n",
|
||
" [5.8, 2.8, 5.1, 2.4],\n",
|
||
" [6.4, 3.2, 5.3, 2.3],\n",
|
||
" [6.5, 3. , 5.5, 1.8],\n",
|
||
" [7.7, 3.8, 6.7, 2.2],\n",
|
||
" [7.7, 2.6, 6.9, 2.3],\n",
|
||
" [6. , 2.2, 5. , 1.5],\n",
|
||
" [6.9, 3.2, 5.7, 2.3],\n",
|
||
" [5.6, 2.8, 4.9, 2. ],\n",
|
||
" [7.7, 2.8, 6.7, 2. ],\n",
|
||
" [6.3, 2.7, 4.9, 1.8],\n",
|
||
" [6.7, 3.3, 5.7, 2.1],\n",
|
||
" [7.2, 3.2, 6. , 1.8],\n",
|
||
" [6.2, 2.8, 4.8, 1.8],\n",
|
||
" [6.1, 3. , 4.9, 1.8],\n",
|
||
" [6.4, 2.8, 5.6, 2.1],\n",
|
||
" [7.2, 3. , 5.8, 1.6],\n",
|
||
" [7.4, 2.8, 6.1, 1.9],\n",
|
||
" [7.9, 3.8, 6.4, 2. ],\n",
|
||
" [6.4, 2.8, 5.6, 2.2],\n",
|
||
" [6.3, 2.8, 5.1, 1.5],\n",
|
||
" [6.1, 2.6, 5.6, 1.4],\n",
|
||
" [7.7, 3. , 6.1, 2.3],\n",
|
||
" [6.3, 3.4, 5.6, 2.4],\n",
|
||
" [6.4, 3.1, 5.5, 1.8],\n",
|
||
" [6. , 3. , 4.8, 1.8],\n",
|
||
" [6.9, 3.1, 5.4, 2.1],\n",
|
||
" [6.7, 3.1, 5.6, 2.4],\n",
|
||
" [6.9, 3.1, 5.1, 2.3],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [6.8, 3.2, 5.9, 2.3],\n",
|
||
" [6.7, 3.3, 5.7, 2.5],\n",
|
||
" [6.7, 3. , 5.2, 2.3],\n",
|
||
" [6.3, 2.5, 5. , 1.9],\n",
|
||
" [6.5, 3. , 5.2, 2. ],\n",
|
||
" [6.2, 3.4, 5.4, 2.3],\n",
|
||
" [5.9, 3. , 5.1, 1.8]])"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(150, 4)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# normalisieren der Daten\n",
|
||
"X = StandardScaler().fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# k-means clustering\n",
|
||
"kmeans = KMeans(n_clusters=2, random_state=42)\n",
|
||
"kmeans_labels = kmeans.fit_predict(X) # fit_predict führt fit und predict zusammen aus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1,\n",
|
||
" 1, 2, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"kmeans_labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "Sepal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Sepal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "kmeans_labels",
|
||
"rawType": "int32",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "true_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
}
|
||
],
|
||
"conversionMethod": "pd.DataFrame",
|
||
"ref": "e31bee49-0d4b-45e5-862b-0cef04b83a6d",
|
||
"rows": [
|
||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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[
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"12",
|
||
"-1.2641847816287635",
|
||
"-0.1319794793216258",
|
||
"-1.3402265266227635",
|
||
"-1.4470764792980415",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"13",
|
||
"-1.87002413384702",
|
||
"-0.1319794793216258",
|
||
"-1.5107388073635728",
|
||
"-1.4470764792980415",
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||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"14",
|
||
"-0.052506077192250644",
|
||
"2.169988183264839",
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||
"-1.4539013804499696",
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||
"-1.3154442950077407",
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||
"1",
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||
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|
||
],
|
||
[
|
||
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|
||
"-0.1736739476359015",
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||
"3.0907752482994253",
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||
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||
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||
"1",
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||
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||
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||
[
|
||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
|
||
"17",
|
||
"-0.9006811702978099",
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||
"1.0190043519716065",
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||
"-1.3402265266227635",
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||
"-1.18381211071744",
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||
"1",
|
||
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|
||
],
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||
[
|
||
"18",
|
||
"-0.1736739476359015",
|
||
"1.7095946507475455",
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||
"-1.169714245881954",
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||
"-1.18381211071744",
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||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"19",
|
||
"-0.9006811702978099",
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||
"1.7095946507475455",
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||
"-1.2833890997091604",
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||
"-1.18381211071744",
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||
"1",
|
||
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||
],
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||
[
|
||
"20",
|
||
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||
"0.7888075857129598",
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||
"-1.169714245881954",
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||
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||
"1",
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||
"0"
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||
],
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||
[
|
||
"21",
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||
"-0.9006811702978099",
|
||
"1.4793978844888998",
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||
"-1.2833890997091604",
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||
"-1.052179926427139",
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||
"1",
|
||
"0"
|
||
],
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||
[
|
||
"22",
|
||
"-1.5065205225160663",
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||
"1.2492011182302531",
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||
"-1.567576234277176",
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||
"-1.3154442950077407",
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||
"1",
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||
"0"
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||
],
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||
[
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||
"23",
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||
"-0.9006811702978099",
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||
"0.5586108194543131",
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||
"-1.169714245881954",
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||
"-0.9205477421368382",
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||
"1",
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||
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||
],
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||
[
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||
"24",
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||
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||
"0.7888075857129598",
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||
"-1.056039392054748",
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||
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||
"1",
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||
"0"
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||
],
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||
[
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||
"25",
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||
"-1.0218490407414607",
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||
"-0.1319794793216258",
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||
"-1.2265516727955572",
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||
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||
"1",
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||
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||
],
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||
[
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||
"26",
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||
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||
"0.7888075857129598",
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||
"-1.2265516727955572",
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||
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||
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||
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||
],
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||
[
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||
"27",
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
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||
"31",
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||
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||
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||
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||
"-1.052179926427139",
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||
"1",
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||
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||
],
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||
[
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||
"32",
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||
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||
"2.400184949523484",
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
|
||
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||
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||
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||
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||
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||
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||
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|
||
],
|
||
[
|
||
"36",
|
||
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|
||
"1.0190043519716065",
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||
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||
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||
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|
||
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|
||
],
|
||
[
|
||
"37",
|
||
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|
||
"1.2492011182302531",
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||
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||
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|
||
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||
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|
||
],
|
||
[
|
||
"38",
|
||
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||
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||
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||
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||
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|
||
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|
||
],
|
||
[
|
||
"39",
|
||
"-0.9006811702978099",
|
||
"0.7888075857129598",
|
||
"-1.2833890997091604",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
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|
||
],
|
||
[
|
||
"40",
|
||
"-1.0218490407414607",
|
||
"1.0190043519716065",
|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
[
|
||
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|
||
"-1.6276883929597172",
|
||
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||
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||
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|
||
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||
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||
],
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||
[
|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
[
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||
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||
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||
"text/plain": [
|
||
" Sepal length Sepal width Petal length Petal width kmeans_labels \\\n",
|
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"0 -0.900681 1.019004 -1.340227 -1.315444 1 \n",
|
||
"1 -1.143017 -0.131979 -1.340227 -1.315444 1 \n",
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|
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||
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||
"1 0 \n",
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||
"2 0 \n",
|
||
"3 0 \n",
|
||
"4 0 \n",
|
||
".. ... \n",
|
||
"145 2 \n",
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||
"146 2 \n",
|
||
"147 2 \n",
|
||
"148 2 \n",
|
||
"149 2 \n",
|
||
"\n",
|
||
"[150 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kombinieren der Clusterlabels mit den wahren Labels\n",
|
||
"df = pd.DataFrame(X, columns=['Sepal length', 'Sepal width', 'Petal length', 'Petal width'])\n",
|
||
"df['kmeans_labels'] = kmeans_labels\n",
|
||
"df['true_labels'] = y_true\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Funktioniert das gut oder schlecht (und wieso)?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualisierung der Ergebnisse\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=kmeans_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# vergleichen wir das nun mit den echten Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Wichtig** Den 2. Plot haben wir normalerweise *NICHT* zur Verfügung. Im Unsupervised Learning ist kein Label vorhanden!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Was passiert, wenn wir die Anzahl der Cluster ändern?\n",
|
||
"Wie gut funktioniert das ganze bei dem *Wine* Dataset?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Affinity Propagation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wir sehen uns nun den *Affinity-Propagation* Clustering Algorithmus an. Im Gegensatz zu $k$-Means ist dieser ein Clustering-Algorithmus, der **keine vorherige Festlegung der Clusteranzahl** benötigt. Stattdessen basiert er auf einem Message-Passing-Verfahren zur Identifikation der optimalen Clusterzentren."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Der Affinity Propagation Algorithmus $\\ldots$\n",
|
||
"* $\\ldots$ wird zur Clusterbildung verwendet\n",
|
||
"* $\\ldots$ nutzt ein Ähnlichkeitsmaß zwischen Datenpunkten, anstatt einer Distanzmetrik\n",
|
||
"* $\\ldots$ **wählt Datenpunkte** als Cluster Centers"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"\n",
|
||
"(von https://python.plainenglish.io/the-mechanics-of-affinity-propagation-clustering-eb199cc7a7c2?gi=4a1093a86978)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Beschreibung des Algorithmus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"1. **Punkte als mögliche Clusterzentren betrachten:** \n",
|
||
" Jeder Datenpunkt wird zunächst als potenzielles Clusterzentrum angesehen.\n",
|
||
" \n",
|
||
"2. **Austausch von \"Botschaften\" zwischen den Punkten:** \n",
|
||
" Die Punkte kommunizieren miteinander, um herauszufinden, welche Punkte gute Clusterzentren (sogenannte **Exemplare**) sein könnten. Dabei werden zwei Arten von Informationen ausgetauscht:\n",
|
||
" - **\"Verantwortung\"**: Ein Punkt teilt einem anderen Punkt mit, wie gut er sich als dessen Clusterzentrum eignet.\n",
|
||
" - **\"Verfügbarkeit\"**: Ein Punkt teilt mit, wie sehr er bereit ist, ein Clusterzentrum zu sein.\n",
|
||
"\n",
|
||
"3. **Iterativer Prozess:** \n",
|
||
" Diese Kommunikation wird in mehreren Runden durchgeführt, bis sich die Clusterzentren stabilisiert haben.\n",
|
||
"\n",
|
||
"4. **Zuordnung der Punkte:** \n",
|
||
" Am Ende ordnet der Algorithmus alle Punkte den endgültigen Clusterzentren zu."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wir geben nun auch noch ein **Beispiel** an, um sich das besser vorstellen zu können."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Ein Beispiel zur Veranschaulichung**\n",
|
||
"\n",
|
||
"Stellen wir uns vor, du arbeitest in einem Unternehmen und möchtest Teams basierend auf den Fähigkeiten und Interessen der Mitarbeiter bilden.\n",
|
||
"\n",
|
||
"1. **Jeder Mitarbeiter ist ein potenzielles Team-Leader-Kandidat.** \n",
|
||
" Anfangs wird niemand als Teamleiter festgelegt – jeder könnte einer sein.\n",
|
||
"\n",
|
||
"2. **Mitarbeiter tauschen Informationen aus.** \n",
|
||
" Sie bewerten einander: \n",
|
||
" - *Verantwortung:* \"Passt Person X als Teamleiter zu mir?\" \n",
|
||
" - *Verfügbarkeit:* \"Bin ich überhaupt gut als Teamleiter geeignet?\" \n",
|
||
"\n",
|
||
"3. **Nach mehreren Feedback-Runden stehen die besten Teamleiter fest.** \n",
|
||
" Mitarbeiter, die am meisten Unterstützung als Teamleiter erhalten, werden zu Clusterzentren.\n",
|
||
"\n",
|
||
"4. **Alle anderen schließen sich dem nächstgelegenen Teamleiter an.** \n",
|
||
" Jetzt sind klare Teams entstanden, ohne dass vorher festgelegt wurde, wie viele es sein müssen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Hinweis:** Die obige Beschreibung ist eher oberflächlich, jedoch würde die genaue Beschreibung dieses Algorithmus hier etwas über das Ziel hinaus schießen. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Vorteile und Nachteile"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Vorteile**:\n",
|
||
"* Kein Wert $k$ als Anzahl der Clusterzentren notwendig\n",
|
||
"* Schnelle konvergenz\n",
|
||
"\n",
|
||
"**Nachteile:**\n",
|
||
"* Kein Wert $k$ als Anzahl der Clusterzentren notwendig\n",
|
||
"* Sehr teuer (rechenintensiv)\n",
|
||
"* Schwer zu interpretieren, was genau passiert"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Tipps und Tricks\n",
|
||
"\n",
|
||
"* Es können Präferenzwerte für gewisse Punkte vergeben werden, falls diese stärker/schwächer als potenzielle Cluster-Zentren herangezogen werden sollen\n",
|
||
"* Da auch dieser Algorithmus iterativ funktioniert, kann es bei schlechter Performance helfen, Parameter, die das Algorithmus- bzw. das Konvergenzverhalten beinflussen, zu ändern"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Affinity Propagation in Python"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.datasets import load_iris, load_wine\n",
|
||
"from sklearn.cluster import AffinityPropagation\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X, y_true = load_iris(return_X_y=True, as_frame=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
||
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
||
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_true"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[5.1, 3.5, 1.4, 0.2],\n",
|
||
" [4.9, 3. , 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.3, 0.2],\n",
|
||
" [4.6, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.6, 1.4, 0.2],\n",
|
||
" [5.4, 3.9, 1.7, 0.4],\n",
|
||
" [4.6, 3.4, 1.4, 0.3],\n",
|
||
" [5. , 3.4, 1.5, 0.2],\n",
|
||
" [4.4, 2.9, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.1],\n",
|
||
" [5.4, 3.7, 1.5, 0.2],\n",
|
||
" [4.8, 3.4, 1.6, 0.2],\n",
|
||
" [4.8, 3. , 1.4, 0.1],\n",
|
||
" [4.3, 3. , 1.1, 0.1],\n",
|
||
" [5.8, 4. , 1.2, 0.2],\n",
|
||
" [5.7, 4.4, 1.5, 0.4],\n",
|
||
" [5.4, 3.9, 1.3, 0.4],\n",
|
||
" [5.1, 3.5, 1.4, 0.3],\n",
|
||
" [5.7, 3.8, 1.7, 0.3],\n",
|
||
" [5.1, 3.8, 1.5, 0.3],\n",
|
||
" [5.4, 3.4, 1.7, 0.2],\n",
|
||
" [5.1, 3.7, 1.5, 0.4],\n",
|
||
" [4.6, 3.6, 1. , 0.2],\n",
|
||
" [5.1, 3.3, 1.7, 0.5],\n",
|
||
" [4.8, 3.4, 1.9, 0.2],\n",
|
||
" [5. , 3. , 1.6, 0.2],\n",
|
||
" [5. , 3.4, 1.6, 0.4],\n",
|
||
" [5.2, 3.5, 1.5, 0.2],\n",
|
||
" [5.2, 3.4, 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.6, 0.2],\n",
|
||
" [4.8, 3.1, 1.6, 0.2],\n",
|
||
" [5.4, 3.4, 1.5, 0.4],\n",
|
||
" [5.2, 4.1, 1.5, 0.1],\n",
|
||
" [5.5, 4.2, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.2, 1.2, 0.2],\n",
|
||
" [5.5, 3.5, 1.3, 0.2],\n",
|
||
" [4.9, 3.6, 1.4, 0.1],\n",
|
||
" [4.4, 3. , 1.3, 0.2],\n",
|
||
" [5.1, 3.4, 1.5, 0.2],\n",
|
||
" [5. , 3.5, 1.3, 0.3],\n",
|
||
" [4.5, 2.3, 1.3, 0.3],\n",
|
||
" [4.4, 3.2, 1.3, 0.2],\n",
|
||
" [5. , 3.5, 1.6, 0.6],\n",
|
||
" [5.1, 3.8, 1.9, 0.4],\n",
|
||
" [4.8, 3. , 1.4, 0.3],\n",
|
||
" [5.1, 3.8, 1.6, 0.2],\n",
|
||
" [4.6, 3.2, 1.4, 0.2],\n",
|
||
" [5.3, 3.7, 1.5, 0.2],\n",
|
||
" [5. , 3.3, 1.4, 0.2],\n",
|
||
" [7. , 3.2, 4.7, 1.4],\n",
|
||
" [6.4, 3.2, 4.5, 1.5],\n",
|
||
" [6.9, 3.1, 4.9, 1.5],\n",
|
||
" [5.5, 2.3, 4. , 1.3],\n",
|
||
" [6.5, 2.8, 4.6, 1.5],\n",
|
||
" [5.7, 2.8, 4.5, 1.3],\n",
|
||
" [6.3, 3.3, 4.7, 1.6],\n",
|
||
" [4.9, 2.4, 3.3, 1. ],\n",
|
||
" [6.6, 2.9, 4.6, 1.3],\n",
|
||
" [5.2, 2.7, 3.9, 1.4],\n",
|
||
" [5. , 2. , 3.5, 1. ],\n",
|
||
" [5.9, 3. , 4.2, 1.5],\n",
|
||
" [6. , 2.2, 4. , 1. ],\n",
|
||
" [6.1, 2.9, 4.7, 1.4],\n",
|
||
" [5.6, 2.9, 3.6, 1.3],\n",
|
||
" [6.7, 3.1, 4.4, 1.4],\n",
|
||
" [5.6, 3. , 4.5, 1.5],\n",
|
||
" [5.8, 2.7, 4.1, 1. ],\n",
|
||
" [6.2, 2.2, 4.5, 1.5],\n",
|
||
" [5.6, 2.5, 3.9, 1.1],\n",
|
||
" [5.9, 3.2, 4.8, 1.8],\n",
|
||
" [6.1, 2.8, 4. , 1.3],\n",
|
||
" [6.3, 2.5, 4.9, 1.5],\n",
|
||
" [6.1, 2.8, 4.7, 1.2],\n",
|
||
" [6.4, 2.9, 4.3, 1.3],\n",
|
||
" [6.6, 3. , 4.4, 1.4],\n",
|
||
" [6.8, 2.8, 4.8, 1.4],\n",
|
||
" [6.7, 3. , 5. , 1.7],\n",
|
||
" [6. , 2.9, 4.5, 1.5],\n",
|
||
" [5.7, 2.6, 3.5, 1. ],\n",
|
||
" [5.5, 2.4, 3.8, 1.1],\n",
|
||
" [5.5, 2.4, 3.7, 1. ],\n",
|
||
" [5.8, 2.7, 3.9, 1.2],\n",
|
||
" [6. , 2.7, 5.1, 1.6],\n",
|
||
" [5.4, 3. , 4.5, 1.5],\n",
|
||
" [6. , 3.4, 4.5, 1.6],\n",
|
||
" [6.7, 3.1, 4.7, 1.5],\n",
|
||
" [6.3, 2.3, 4.4, 1.3],\n",
|
||
" [5.6, 3. , 4.1, 1.3],\n",
|
||
" [5.5, 2.5, 4. , 1.3],\n",
|
||
" [5.5, 2.6, 4.4, 1.2],\n",
|
||
" [6.1, 3. , 4.6, 1.4],\n",
|
||
" [5.8, 2.6, 4. , 1.2],\n",
|
||
" [5. , 2.3, 3.3, 1. ],\n",
|
||
" [5.6, 2.7, 4.2, 1.3],\n",
|
||
" [5.7, 3. , 4.2, 1.2],\n",
|
||
" [5.7, 2.9, 4.2, 1.3],\n",
|
||
" [6.2, 2.9, 4.3, 1.3],\n",
|
||
" [5.1, 2.5, 3. , 1.1],\n",
|
||
" [5.7, 2.8, 4.1, 1.3],\n",
|
||
" [6.3, 3.3, 6. , 2.5],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [7.1, 3. , 5.9, 2.1],\n",
|
||
" [6.3, 2.9, 5.6, 1.8],\n",
|
||
" [6.5, 3. , 5.8, 2.2],\n",
|
||
" [7.6, 3. , 6.6, 2.1],\n",
|
||
" [4.9, 2.5, 4.5, 1.7],\n",
|
||
" [7.3, 2.9, 6.3, 1.8],\n",
|
||
" [6.7, 2.5, 5.8, 1.8],\n",
|
||
" [7.2, 3.6, 6.1, 2.5],\n",
|
||
" [6.5, 3.2, 5.1, 2. ],\n",
|
||
" [6.4, 2.7, 5.3, 1.9],\n",
|
||
" [6.8, 3. , 5.5, 2.1],\n",
|
||
" [5.7, 2.5, 5. , 2. ],\n",
|
||
" [5.8, 2.8, 5.1, 2.4],\n",
|
||
" [6.4, 3.2, 5.3, 2.3],\n",
|
||
" [6.5, 3. , 5.5, 1.8],\n",
|
||
" [7.7, 3.8, 6.7, 2.2],\n",
|
||
" [7.7, 2.6, 6.9, 2.3],\n",
|
||
" [6. , 2.2, 5. , 1.5],\n",
|
||
" [6.9, 3.2, 5.7, 2.3],\n",
|
||
" [5.6, 2.8, 4.9, 2. ],\n",
|
||
" [7.7, 2.8, 6.7, 2. ],\n",
|
||
" [6.3, 2.7, 4.9, 1.8],\n",
|
||
" [6.7, 3.3, 5.7, 2.1],\n",
|
||
" [7.2, 3.2, 6. , 1.8],\n",
|
||
" [6.2, 2.8, 4.8, 1.8],\n",
|
||
" [6.1, 3. , 4.9, 1.8],\n",
|
||
" [6.4, 2.8, 5.6, 2.1],\n",
|
||
" [7.2, 3. , 5.8, 1.6],\n",
|
||
" [7.4, 2.8, 6.1, 1.9],\n",
|
||
" [7.9, 3.8, 6.4, 2. ],\n",
|
||
" [6.4, 2.8, 5.6, 2.2],\n",
|
||
" [6.3, 2.8, 5.1, 1.5],\n",
|
||
" [6.1, 2.6, 5.6, 1.4],\n",
|
||
" [7.7, 3. , 6.1, 2.3],\n",
|
||
" [6.3, 3.4, 5.6, 2.4],\n",
|
||
" [6.4, 3.1, 5.5, 1.8],\n",
|
||
" [6. , 3. , 4.8, 1.8],\n",
|
||
" [6.9, 3.1, 5.4, 2.1],\n",
|
||
" [6.7, 3.1, 5.6, 2.4],\n",
|
||
" [6.9, 3.1, 5.1, 2.3],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [6.8, 3.2, 5.9, 2.3],\n",
|
||
" [6.7, 3.3, 5.7, 2.5],\n",
|
||
" [6.7, 3. , 5.2, 2.3],\n",
|
||
" [6.3, 2.5, 5. , 1.9],\n",
|
||
" [6.5, 3. , 5.2, 2. ],\n",
|
||
" [6.2, 3.4, 5.4, 2.3],\n",
|
||
" [5.9, 3. , 5.1, 1.8]])"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(150, 4)"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Normalisieren der Daten\n",
|
||
"X = StandardScaler().fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# affinity propagation clustering\n",
|
||
"affinity_prop = AffinityPropagation(random_state=42)\n",
|
||
"affinity_prop_labels = affinity_prop.fit_predict(X) # fit_predict führt fit und predict zusammen aus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,\n",
|
||
" 1, 0, 1, 0, 1, 0, 3, 3, 3, 2, 7, 4, 3, 2, 3, 4, 2, 4, 2, 7, 4, 3,\n",
|
||
" 4, 4, 2, 2, 7, 4, 7, 4, 3, 3, 3, 3, 7, 2, 2, 2, 4, 7, 4, 3, 3, 2,\n",
|
||
" 4, 2, 4, 7, 4, 2, 4, 4, 4, 4, 2, 4, 8, 7, 5, 7, 8, 5, 2, 5, 7, 6,\n",
|
||
" 8, 7, 8, 7, 7, 8, 3, 6, 5, 2, 8, 7, 5, 7, 8, 5, 7, 7, 7, 5, 5, 6,\n",
|
||
" 7, 7, 7, 5, 8, 3, 7, 8, 8, 8, 7, 8, 8, 8, 7, 8, 8, 7])"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"affinity_prop_labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"9"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Anzahl der Cluster in Affinity Propagation\n",
|
||
"n_clusters = len(np.unique(affinity_prop_labels))\n",
|
||
"n_clusters"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "Sepal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Sepal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "affinity_prop_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "true_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
}
|
||
],
|
||
"conversionMethod": "pd.DataFrame",
|
||
"ref": "92265ec4-3eff-4106-a1a7-a4590b2e6e30",
|
||
"rows": [
|
||
[
|
||
"0",
|
||
"-0.9006811702978099",
|
||
"1.0190043519716065",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"1",
|
||
"-1.1430169111851116",
|
||
"-0.1319794793216258",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"2",
|
||
"-1.3853526520724144",
|
||
"0.3284140531956675",
|
||
"-1.3970639535363667",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"3",
|
||
"-1.5065205225160663",
|
||
"0.09821728693702086",
|
||
"-1.2833890997091604",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"4",
|
||
"-1.0218490407414607",
|
||
"1.2492011182302531",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
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|
||
"1 -1.143017 -0.131979 -1.340227 -1.315444 \n",
|
||
"2 -1.385353 0.328414 -1.397064 -1.315444 \n",
|
||
"3 -1.506521 0.098217 -1.283389 -1.315444 \n",
|
||
"4 -1.021849 1.249201 -1.340227 -1.315444 \n",
|
||
".. ... ... ... ... \n",
|
||
"145 1.038005 -0.131979 0.819596 1.448832 \n",
|
||
"146 0.553333 -1.282963 0.705921 0.922303 \n",
|
||
"147 0.795669 -0.131979 0.819596 1.053935 \n",
|
||
"148 0.432165 0.788808 0.933271 1.448832 \n",
|
||
"149 0.068662 -0.131979 0.762758 0.790671 \n",
|
||
"\n",
|
||
" affinity_prop_labels true_labels \n",
|
||
"0 1 0 \n",
|
||
"1 0 0 \n",
|
||
"2 0 0 \n",
|
||
"3 0 0 \n",
|
||
"4 1 0 \n",
|
||
".. ... ... \n",
|
||
"145 8 2 \n",
|
||
"146 7 2 \n",
|
||
"147 8 2 \n",
|
||
"148 8 2 \n",
|
||
"149 7 2 \n",
|
||
"\n",
|
||
"[150 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kombinieren der Clusterlabels mit den wahren Labels\n",
|
||
"df = pd.DataFrame(X, columns=['Sepal length', 'Sepal width', 'Petal length', 'Petal width'])\n",
|
||
"df['affinity_prop_labels'] = affinity_prop_labels\n",
|
||
"df['true_labels'] = y_true\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wie bewerten wir das?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualisierung der Ergebnisse\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=affinity_prop_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Vergleichen wir das nun mit den echten Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 3000x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Vergleich von affinity propagation und k-means nebeneinander und echten Labels\n",
|
||
"\n",
|
||
"plt.figure(figsize=(30, 5))\n",
|
||
"plt.subplot(1, 3, 1)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=affinity_prop_labels)\n",
|
||
"plt.title(\"Affinity Propagation\")\n",
|
||
"plt.subplot(1, 3, 2)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=kmeans_labels)\n",
|
||
"plt.title(\"K-Means\")\n",
|
||
"plt.subplot(1, 3, 3)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.title(\"Echte Labels\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Was sind hier die Unterschiede?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Sonstiges\n",
|
||
"\n",
|
||
"* Es gibt noch viele weitere Clustering Methoden\n",
|
||
"* Wir werden darauf aber nicht mehr genauer eingehen (Referat?)\n",
|
||
"* Haben alle ihre Vor- und Nachteile"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Aufgabe"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"* Verwende nun die folgenden Datasets und versuche die Aufgabenstellungen zu beantworten.\n",
|
||
"* Lade dazu das Dataset mit den bekannten Methoden (Laden mit Hilfe von `pd.read_csv`).\n",
|
||
"* Verwende für jede Aufgabe eigene Code-Zellen und dokumentiere für die verschiedenen Durchläufe die Ergebnisse.\n",
|
||
"\n",
|
||
"**Aufgaben**\n",
|
||
"1) HELP International konnte rund 10 Millionen Dollar aufbringen. Nun muss der Geschäftsführer der NRO entscheiden, wie er dieses Geld strategisch und effektiv einsetzen will. Der CEO muss also entscheiden, welche Länder die Hilfe am dringendsten benötigen. Ihre Aufgabe als Datenwissenschaftler besteht also darin, die Länder anhand einiger sozioökonomischer und gesundheitlicher Faktoren zu kategorisieren, die die Gesamtentwicklung des Landes bestimmen. Sie müssen also Länder vorschlagen, auf die sich der CEO am meisten konzentrieren soll.\n",
|
||
"\n",
|
||
" *Dataset:* Country `country.csv` (verwendet von https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data?select=Country-data.csv)\n",
|
||
"\n",
|
||
"2) Nach der HTL-Karriere beschließen Sie, für eine gewisse Zeit ins Ausland zu gehen. Dabei wollen Sie in ein Land ziehen, in welchem die Länder besonders glücklich sind. Versuchen Sie nun das nachfolgende Dataset zu gruppieren und herauszufinden, in welchen Ländern (Gruppen) die Menschen ähnlich glücklick zu sein scheinen.\n",
|
||
"\n",
|
||
" *Dataset:* Happiness 2017 `happiness_2017.csv` (verwendet von https://www.kaggle.com/datasets/unsdsn/world-happiness?select=2017.csv)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"19\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.datasets import load_iris, load_wine\n",
|
||
"from sklearn.cluster import AffinityPropagation\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"ds = pd.read_csv(\"../../_data/country.csv\") # ggf. etwas anders als unser bisheriges Dataset\n",
|
||
"ds = ds.dropna()\n",
|
||
"X = ds.select_dtypes(include=[np.number])\n",
|
||
"\n",
|
||
"X = StandardScaler().fit_transform(X)\n",
|
||
"affinity_prop = AffinityPropagation(random_state=42)\n",
|
||
"affinity_prop_labels = affinity_prop.fit_predict(X) # fit_predict führt fit und predict zusammen aus\n",
|
||
"n_clusters = len(np.unique(affinity_prop_labels))\n",
|
||
"print(n_clusters)\n",
|
||
"df = pd.DataFrame(X, )\n",
|
||
"df['affinity_prop_labels'] = affinity_prop_labels\n",
|
||
"\n",
|
||
"# Plot mit Affinity-Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=affinity_prop_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"16\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.datasets import load_iris, load_wine\n",
|
||
"from sklearn.cluster import AffinityPropagation\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"ds = pd.read_csv(\"../../_data/happiness_2017.csv\") # ggf. etwas anders als unser bisheriges Dataset\n",
|
||
"ds = ds.dropna()\n",
|
||
"X = ds.select_dtypes(include=[np.number])\n",
|
||
"\n",
|
||
"X = StandardScaler().fit_transform(X)\n",
|
||
"affinity_prop = AffinityPropagation(random_state=42)\n",
|
||
"affinity_prop_labels = affinity_prop.fit_predict(X) # fit_predict führt fit und predict zusammen aus\n",
|
||
"n_clusters = len(np.unique(affinity_prop_labels))\n",
|
||
"print(n_clusters)\n",
|
||
"df = pd.DataFrame(X, )\n",
|
||
"df['affinity_prop_labels'] = affinity_prop_labels\n",
|
||
"\n",
|
||
"# Plot mit Affinity-Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 2], X[:, 3], c=affinity_prop_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "dsai",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|