diff --git a/.idea/misc.xml b/.idea/misc.xml index 9a61020..f3460bf 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,4 @@ - + diff --git a/.idea/ml-programmiereprojekt.iml b/.idea/ml-programmiereprojekt.iml index e6ddb7f..842f4fe 100644 --- a/.idea/ml-programmiereprojekt.iml +++ b/.idea/ml-programmiereprojekt.iml @@ -4,7 +4,7 @@ - + diff --git a/experiments/Experiments.ipynb b/experiments/Experiments.ipynb index 0ecda6f..a698cde 100644 --- a/experiments/Experiments.ipynb +++ b/experiments/Experiments.ipynb @@ -8,18 +8,22 @@ } }, "source": [ - "# Initialschätzung von Kurswechselpositionen eines Segelboots auf einer Karte anhang con Wind, Start, und Zielpunkt\n", + "# Initialschätzung von Kurswechselpositionen eines Segelboots auf einer Karte anhang con Wind, Start und Zielpunkt\n", "\n", "## Motivation\n", "\n", - "Ziel dieser Semesterabschließenden Schriftlichen Ausrbeitung im Fach \"Maschine Learning\" an der Fachhochschule Südwestfalen ist das Genereieren einer Heatmap von Kurswechselpositionen eines Segelbootes zu einer Karte abhängig von Wind und der Zielpostion. Dies soll das finden einer guten Route vereinfachen indem die Qualität einer ersten Route die danach über ein Quotientenabstigsverfahren optimiert werden soll verbessern. Da ein solches Quotientenabsiegsverfahren sehr gerne in einem Lokalen minimum festhängt müssen mehrere routen gefunden und optimiert werden. Hier soll untersucht werden ob dies durch eine Ersteinschätzung der Lage durch KI verbessert weren kann.\n", + "Ziel dieser Semester abschließenden schriftlichen Ausarbeitung im Fach \"Maschine Learning\" an der Fachhochschule Südwestfalen ist das Generieren einer Heatmap von Kurswechselpositionen eines Segelbootes zu einer Karte abhängig von Wind und der Zielpostion. Dies soll das Finden einer guten Route vereinfachen, indem die Qualität einer ersten Route, die danach über ein Quotientenabstiegsverfahren optimiert werden soll verbessern. Da ein solches Quotientenabstiegsverfahren sehr gerne in einem Lokalen minimum festhängt, müssen mehrere routen gefunden und optimiert werden. Hier soll untersucht werden, ob dies durch eine Ersteinschätzung der Lage durch KI verbessert werden kann.\n", "\n", - "Eingesetzt werden soll die so erstellte KI in dem Segelrobotters des [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/). Einer Hochschulgruppe an der TU-Darmstadt welche den [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) ein voll autonomos segelboot welches eines Tages den Atlantik überqueren soll. [Eine Technische Herrausvorderung welche zuerst von einem Norwegischen Team erfolgreich abgeschlossen wurde](https://www.microtransat.org/)." + "Eingesetzt werden soll die so erstellte KI in dem Segelroboter des [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/). Einer Hochschulgruppe an der TU-Darmstadt welche den [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) ein vollautonomes Segelboot welches eines Tages den Atlantik überqueren soll. [Eine technische Herausforderung welche zuerst von einem norwegischen Team erfolgreich abgeschlossen wurde](https://www.microtransat.org/)." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Inhaltsverzeichnis\n", "\n", @@ -49,104 +53,109 @@ "\n", "### Situation\n", "\n", - "Eine Routenplanung für ein Segelboot hat ein Problem welches man sonst so eher nicht kennt. Eine relativ Freie fläche auf der Sich das Schiff bewegen kann. Dies verändert die Wegfindung wie man sie von der Straße kennt fundermental.\n", + "Eine Routenplanung für ein Segelboot hat ein Problem, welches man sonst so eher nicht kennt. Eine relativ freie Fläche auf der Sich das Schiff bewegen kann. Dies verändert die Wegfindung wie man sie von der Straße kennt fundamental.\n", "\n", - "Navigiert man auf Straßen hat man zumindest nach einer ersten abstracktion relativ wenige Freiheitsgrade für den Weg.\n", + "Navigiert man auf Straßen, hat man zumindest nach einer ersten abstraction relativ wenige Freiheitsgrade für den Weg.\n", "Die Richtung kann nur an Kreuzungen gewechselt werden und dort nur in Richtungen in die es Straßen gibt. Beim Segeln auf dem freien Meer ist jeder Ort ein Potenzieller Wendpunkt von dem aus Potenziell in jede Richtung gesegelt werden kann.\n", "\n", - "Dennoch ist es oft auch ohne hinderniss zwischen Boot und Ziel oft nicht möglich das Ziel direkt anzufahren das sich die Maximalgeschwindigkeiten relativ zur Windrichtung verändern.\n", - "Das Folgende Diagramm zeigt die Geschwindgkeiten eines Katamarans.\n", + "Dennoch ist es oft auch ohne Hindernisse zwischen Boot und Ziel oft nicht möglich das Ziel direkt anzufahren das sich die Maximalgeschwindigkeiten relativ zur Windrichtung verändern.\n", + "Das folgende Diagramm zeigt die Segelgeschwindigkeiten an einem Katamaran.\n", "\n", "\"Ship\n", "\n", - "Da der roBOOTer anders als an Katamaran nicht auf Geschwindichkeit sondern auf Mechanische Belastbarkeit ausgelegt wurde hat der Fahrtwind einen geringeren einfluss auf das Fahrtverhalten des Segelboots dies und eine andere Maximalgeschwindigkeit sorgen für ein etwas anderes Fahrverhalten. Die ungefähre Form der Kurven trifft aber auch auf den roBOOTer zu. Man kann deutlich erkennen das auch wenn man nicht direkt gegen den Wind fahren kann man schräg gegen den wind immer noch erstaunlich schnell ist.\n", + "Da der roBOOTer anders als an Katamaran nicht auf Geschwindigkeit, sondern auf mechanische Belastbarkeit ausgelegt wurde hat der Fahrtwind einen geringeren einfluss auf das Fahrtverhalten des Segelboots dies und eine andere Maximalgeschwindigkeit sorgen für ein etwas anderes Fahrverhalten. Die ungefähre Form der Kurven trifft aber auch auf den roBOOTer zu. Man kann deutlich erkennen das auch, wenn man nicht direkt gegen den Wind fahren kann man schräg gegen den wind immer noch erstaunlich schnell ist.\n", "\n", - "Das aktuelle Verfahren zum finden einer Route läuf folgendermaßen ab:\n", + "Das aktuelle Verfahren zum Finden einer Route läuft folgendermaßen ab:\n", "\n", - "Eine direkte Route wird berechnet. Die Route wird an jedem Hinderniss geteilt und rechts und links um jedes hindernis herum gelegt. Bei folgenden hindernissen werden die Routen wieder geteilt somit erhält man $2^n$ vorschläge für Routen wobei $n$ die Anzahl der Hindernisse auf der Route ist. Jeder Abschnitt der Route wird noch einmal zerteilt um der Route mehr Flexibilität zu geben.\n", + "Eine direkte Route wird berechnet. Die Route wird an jedem Hindernisse geteilt und rechts und links um jedes hindernis herum gelegt. Bei folgenden hindernissen werden die Routen wieder geteilt somit erhält man $2^n$ Vorschläge für Routen wobei $n$ die Anzahl der Hindernisse auf der Route ist. Jeder Abschnitt der Route wird noch einmal zerteilt, um der Route mehr Flexibilität zu geben.\n", "\n", - "Die Routen werden dann simmuliert um die Kosten der Route zu berechnen. Die so simulierte Route wird danach über die Kosten in einem Gradientenabstiegverfahren Optimiert.\n", + "Die Routen werden dann simuliert, um die Kosten der Route zu berechnen. Die so simulierte Route wird danach über die Kosten in einem Gradientenabstiegsverfahren optimiert.\n", "\n", - "Das ganze oben beschriebene Verfahren ist relativ schnell sehr rechenaufwendig und findet nicht immer ein Ergbniss. Wird kein Ergebnis gefunden wird eine mehr oder weniger zufällige Route optimiert.\n", + "Das ganze oben beschriebene Verfahren ist relativ schnell sehr rechenaufwendig und findet nicht immer ein Ergebnis. Wird kein Ergebnis gefunden wird eine mehr oder weniger zufällige Route optimiert.\n", "\n", - "Diese Ausarbeitung soll wenigstens bei der alternativen Routenfindung helfen. Im idealfall kann es aber auch genutzt wereden um die auswahl der Routen um Hindernisse frühzeitig zu reduzieren und den Rechenaufwand unter $2^n$ zu senken wobei $n$ die Anzahl von Hindernissen auf der Route ist.\n", + "Diese Ausarbeitung soll wenigstens bei der alternativen Routenfindung helfen. Im idealfall kann es aber auch genutzt werden, um die auswahl der Routen um Hindernisse frühzeitig zu reduzieren und den Rechenaufwand unter $2^n$ zu senken wobei $n$ die Anzahl von Hindernissen auf der Route ist.\n", "\n", - "### Vorgehen zur Unterstützenden KI\n", + "### Vorgehen zur unterstützenden KI\n", "\n", - "#### Eingaben und Ausageben\n", + "#### Eingaben und Ausgeben\n", "\n", - "Die Algoritmen zur Wegfindung vom Sailingteam Darmstadt e.V. arbeiten intern mit Polygonen als Hindernissen. Diese werden durch die Schapely bibiliotek implementiert. Da eine variable Anzahl an Polygonen mit einer variablen Form und Position eine Relative komplexer Input muss dieser in eine normiertere Form gebracht werden. Ein binärfarbenes Bild ist dafür die einfachste Form.\n", + "Die Algorithm zur Wegfindung vom Sailing Team Darmstadt e.V. arbeiten intern mit Polygonen als Hindernissen. Diese werden durch die Shapely Bibliothek implementiert. Da eine variable Anzahl an Polygonen mit einer variablen Form und Position eine Relative komplexer Input muss dieser in eine normierte Form gebracht werden. Ein binärfärbens Bild ist dafür die einfachste Form.\n", "\n", "Für den Computer spielen sowohl Zentrierung, Skalierung und Ausrichtung der Karte keine Rolle.\n", - "Wir rotieren also die Karte immer so das der Wind von *Norden* kommt und das Boot / die Startposition in der *Mitte* der Karte liegt. Da distanz Linar ist wird davon ausgegangen das Scenario einfach skaliert passend skaliert werden kann.\n", + "Wir rotieren also die Karte immer so das der Wind von *Norden* kommt und das Boot / die Startposition in der *Mitte* der Karte liegt. Da distanz Liner ist, wird davon ausgegangen das Scenario einfach skaliert passend skaliert werden kann.\n", "\n", - "Die nächste eingabe ist die Zielposition relativ zum Startpunkt. Diese kann endweder durch einen einzelnen Pixel in einem zweiten Farbkanal oder aber in abstrakterer Form an die KI übergeben werden.\n", + "Die nächste eingabe ist die Zielposition relativ zum Startpunkt. Diese kann entweder durch ein einzelnes Pixel in einem zweiten Farbkanal oder aber in abstrakterer Form an die KI übergeben werden.\n", "\n", - "Als ausgabe wird eine Heatmap erwarted. Zwei alternative Heatmaps sind relative einfach denkbar.\n", + "Als ausgabe wird eine Heatmap erwartet. Zwei alternative Heatmaps sind relative einfach denkbar.\n", "\n", - " 1. Eine Headmap der Kurswechselpositionen\n", - " 2. Eine Headmap des Kursverlaufes\n", + "1. Eine Headmap der Kurswechselpositionen\n", + "2. Eine Headmap des Kursverlaufes\n", "\n", - "Headmaps sind in gewisserweise Bilder. Das Problem wird daher wie ein Bild zu Bild KI Problem betrachted. Diese werden Normalerweise durch ANNs gelöst.\n", + "Headmaps sind in gewisser Weise Bilder. Das Problem wird daher wie ein Bild zu Bild KI Problem betrachtet. Diese werden normalerweise durch ANNs gelöst.\n", "\n", - "Um eine ANN zu trennieren gibt es immer die Wahl zwischen drei Primären prinzipien. Dem unüberwachten Lernen, dem reinforcement Learning und dem überwachten Lernen. Letzteres ist dabei meist am einfachsten wenn auch nicht immer möglich.\n", + "Um eine ANN zu trenntieren gibt es immer die Wahl zwischen drei Primären prinzipien. Dem unüberwachten Lernen, dem reinforcement Learning und dem überwachten Lernen. Letzteres ist dabei meist am einfachsten wenn auch nicht immer möglich.\n", "\n", - "Der Wegfindealgoritmus des Sailing Team Darmstadt e.V. ist zwar noch in der Entwicklung, funktioniert aber hinriechend gut um auf einem normalen PC Scenarien mit Routen zu paaren oder auch diese zu *labeln* um beim KI lingo zu bleiben. Um anpassungsfähig an andere Scenarien zu sein wird eine große menge unterschiedlicher Scenarien und Routen benötigt.\n", + "Der Wegfindealgorithmus des Sailing Team Darmstadt e.V. ist zwar noch in der Entwicklung, funktioniert aber hinreichend gut, um auf einem normalen PC Scenarios mit Routen zu paaren oder auch diese zu *labeln*, um beim KI lingo zu bleiben. Um anpassungsfähig an andere Scenarios zu sein wird eine große Menge unterschiedlicher Scenarios und Routen benötigt.\n", "Da das Haupteinsatzgebiet das Meer ist gehen wir von einer Insellandschaft oder Küstenlandschaft aus.\n", "\n", - "Zum Finden von Scenarien gibt es zwei Möglichkeiten.\n", + "Zum Finden von Scenarios gibt es zwei Möglichkeiten.\n", "\n", - " 1. Das auswählen von umgebungen von der Weltkarte und das bestimmen eines Zielpunktes.\n", - " 2. Das generieren von künstlichen Scenarien.\n", + "1. Das Auswählen von umgebungen von der Weltkarte und das Bestimmen eines Zielpunktes.\n", + "2. Das Generieren von künstlichen Scenarios.\n", " \n", - "Hier wird die Annaheme getroffen das sich ANNs von einem Datensatz auf den anderen Übetragen lassen.\n", - "Der Aufwand für künstliche Scenarien wird hierbei als geringer eingestuft und daher gewhält." + "Hier wird die Annahme getroffen das sich ANNs von einem Datensatz auf dem anderen Übertragen lassen.\n", + "Der Aufwand für künstliche Scenarios wird hierbei als geringer eingestuft und daher gewählt." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Vorbereitungen\n", "\n", - "Folgende Python Libararies weren verwendet:\n", + "Folgende Python Bibliotheken werden verwendet:\n", "\n", - " 1. Tensorflow\n", - " Die Tensorflow libaray ist das Werkzeug welches verwendet wurde um neuronale Netz zu modelieren, zu trainieren, zu analysieren und auszuführen.\n", - " \n", - " 2. pyrate\n", - " The pyrate programm is part of the ROS system controlling the roBOOTer. It is used to find routes.\n", - " \n", - " 3. Shapley\n", - " The shapley libarary is able to work with geometricle bodys. Used to generate sceneria and to save them in a vectorised / compact format.\n", - " \n", - " 4. pandas\n", - " A library to save data in tables. Contains lots of functions to analyze and modify the data contained.\n", - " \n", - " 5. numpy\n", - " A library to do mathematiel operations on multidimensional arrays.\n", - " \n", - " 6. matplotlib\n", - " A library to plot diagrams and images.\n", - " \n", - " 6. PIL\n", - " A library to draw and show images.\n", - " \n", - " 7. mapply\n", - " A libarary to use multiprocessing to edit a pandas DataFrame\n", - " \n", - " 8. humanize\n", - " Converts numbers into easily human readable formats.\n", - " \n", - " 9. tqdm\n", - " Used to add process bars to many things." + "1. `Tensorflow`\n", + " Die `Tensorflow` Bibliothek ist das Werkzeug welches verwendet wurde, um neuronale Netz zu modellieren, zu trainieren, zu analysieren und auszuführen.\n", + "\n", + "2. `pyrate`\n", + " Die `Pyrate` Bibliothek ist Teil des ROS Operating Systems, welches den roBOOTer betreibt. Kann Routen zu Scenarios finden.\n", + "\n", + "3. `Shapley`\n", + " Die `shapley` Bibliothek wird genutzt, um geometrische Körper zu generieren, zu mergen und an den Roboter zum Labeln weiterzugeben.\n", + "\n", + "4. `pandas`\n", + " Die `pandas` Bibliothek verwaltet, speichert und analysiert daten.\n", + "\n", + "5. `numpy`\n", + " Eine Bibliothek um Mathematische operations an multidimensionalen Arrays auszuführen.\n", + "\n", + "6. `matplotlib`\n", + " Wird genutzt um Diagramme zu plotted.\n", + "\n", + "6. `PIL`\n", + " Eine Library um Bilder manuell zu zeichnen.\n", + "\n", + "7. `humanize`\n", + " Konvertiert Zahlen, Daten und Zeitabstände in ein für menschen einfach leserliches Format.\n", + "\n", + "8. `tqdm`\n", + " Fügt einen Fortschrittsbalken zu vielen Problemen hinzu." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ - "Sets the path this notebook is executed in to be relative to other the pyrate entrypoints." + "Definiert den Pfad an dem der Jupyter notebook ausgeführt werden soll." ] }, { @@ -176,10 +185,14 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "#### Imports\n", - "Imports the necessary packages from python and pypi." + "Importiert die Imports the necessary packages from python and pypi." ] }, { @@ -214,22 +227,32 @@ "\n", "import os\n", "from typing import Optional, Final, Literal\n", - "from math import pi\n", "import glob\n", "import pickle\n", "\n", - "from tqdm.notebook import trange, tqdm\n", - "import matplotlib\n", + "from tqdm.notebook import tqdm\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from PIL import ImageDraw, Image\n", "from shapely.geometry import Polygon, Point, LineString\n", - "from shapely.ops import unary_union, split\n", - "import mapply\n", - "import tensorflow as tf" + "from shapely.ops import unary_union\n", + "import tensorflow as tf\n", + "import humanize" ] }, + { + "cell_type": "markdown", + "source": [ + "Importiert die pyrate module. Wird nur ausgeführt, wenn innerhalb des Pyrate Containers ausgeführt." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", "execution_count": 3, @@ -245,9 +268,9 @@ }, "outputs": [], "source": [ - "import experiments\n", - "from pyrate.plan.nearplanner.timing_frame import TimingFrame\n", - "from pyrate.plan.geometry.route import CartesianRoute" + "if os.getenv(\"PYRATE\"):\n", + " import experiments\n", + " from pyrate.plan.nearplanner.timing_frame import TimingFrame" ] }, { @@ -257,6 +280,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:19.587273Z", "start_time": "2022-07-11T18:34:19.580531Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -266,19 +292,16 @@ ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T18:34:19.598079Z", - "start_time": "2022-07-11T18:34:19.592567Z" - } - }, - "outputs": [], + "cell_type": "markdown", "source": [ - "# Enables and configures multiprocessing for pandas\n", - "mapply.init(n_workers=-1, chunk_size=1000, progressbar=True, max_chunks_per_worker=100)" - ] + "Einige umgebungsvariablen werden gesetzt, wenn innerhalb des Pyrate Containers ausgeführt." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } }, { "cell_type": "code", @@ -294,17 +317,22 @@ }, "outputs": [], "source": [ - "# Sets the maximum number of optimization steps that can be performed to find a route.\n", - "# Significantly lowered for more speed.\n", - "experiments.optimization_param.n_iter_grad = 50\n", + "if os.getenv(\"PYRATE\"):\n", + " # Sets the maximum number of optimization steps that can be performed to find a route.\n", + " # Significantly lowered for more speed.\n", + " experiments.optimization_param.n_iter_grad = 50\n", "\n", - "# Disbales verbose outputs from the pyrate library.\n", - "experiments.optimization_param.verbose = False" + " # Disables verbose outputs from the pyrate library.\n", + " experiments.optimization_param.verbose = False" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "#### Paramter settings" ] @@ -326,25 +354,27 @@ "# The scale the route should lie in. Only a mathematical limit.\n", "SIZE_ROUTE: Final[int] = 100\n", "\n", - "# The outer limit in with the goal need to be palced.\n", - "# Should be smaller than \n", + "# The outer limit in with the goal need to be placed.\n", + "# Should be smaller than\n", "SIZE_INNER: Final[int] = 75\n", "assert SIZE_ROUTE > SIZE_INNER, \"The goal should be well inside the limit placed \"\n", - " \n", - "# The minimum destance from the start that should \n", + "\n", + "# The minimum distance from the start that should\n", "MIN_DESTINATION_DISTANCE: Final[int] = 25\n", - "assert SIZE_INNER > MIN_DESTINATION_DISTANCE, \"The goal should be well closer to the outer limit the\"\n", + "assert (\n", + " SIZE_INNER > MIN_DESTINATION_DISTANCE\n", + "), \"The goal should be well closer to the outer limit the\"\n", "\n", "# The size the ANN input has. Equal to the image size. Should be an on of $n^2$ to be easier compatible with ANNs.\n", "IMG_SIZE: Final[int] = 128\n", "\n", "# The size an image should be in to be easily visible by eye.\n", "IMG_SHOW_SIZE: Final[int] = 400\n", - " \n", + "\n", "# The number of Files that should be read to train the ANNs\n", "NUMBER_OF_FILES_LIMIT: Final[int] = 1000\n", "\n", - "# \n", + "#\n", "NO_SHOW = False\n", "GENERATE_NEW = True" ] @@ -659,25 +689,27 @@ " plt.figure(figsize=(17.5, 25))\n", " for seed in tqdm(range(12)):\n", " plt.subplot(4, 3, seed + 1)\n", - " wind_dir = 180\n", - " obstacles = generate_obstacles(seed)\n", - " destination = generate_destination(obstacles, seed)\n", - " route = None\n", + " generated_obstacles = generate_obstacles(seed)\n", + " generated_destination = generate_destination(generated_obstacles, seed)\n", + " route_generated = None\n", + "\n", + " # noinspection PyBroadException\n", " try:\n", - " route, _ = experiments.generate_route(\n", + " route_generated, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", - " goal=destination,\n", - " obstacles=obstacles,\n", - " wind=(18, wind_dir),\n", + " goal=generated_destination,\n", + " obstacles=generated_obstacles,\n", + " wind=(18, 180),\n", " )\n", - " except Exception as e:\n", - " route = None\n", + " except Exception:\n", + " route_generated = None\n", + "\n", " plot_situation(\n", - " obstacles=obstacles,\n", - " destination=destination,\n", + " obstacles=generated_obstacles,\n", + " destination=generated_destination,\n", " obstacle_color=\"RED\",\n", - " route=route,\n", - " title=f\"Seed: {seed}, Cost: {route.cost:.3f}\",\n", + " route=route_generated,\n", + " title=f\"Seed: {seed}, Cost: {route_generated.cost:.3f}\",\n", " legend=seed == 0,\n", " )\n", " plt.show()" @@ -741,8 +773,7 @@ " for point in route[1:]:\n", " try:\n", " img.putpixel(point, (0, 0, 0xFF))\n", - " except IndexError as err:\n", - " print(point)\n", + " except IndexError:\n", " if seed:\n", " print(f\"Seed: {seed}, Point: {point}\")\n", " return None\n", @@ -775,17 +806,14 @@ "outputs": [], "source": [ "def generate_example_image(route_type: Literal[\"line\", \"dot\"]):\n", - " seed = 42\n", - " wind_dir = 180\n", - " obstacles = generate_obstacles(seed)\n", - " destination = generate_destination(obstacles, seed)\n", - " route = None\n", + " obstacles = generate_obstacles(42)\n", + " destination = generate_destination(obstacles, 42)\n", " try:\n", " route, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", " goal=destination,\n", " obstacles=obstacles,\n", - " wind=(18, wind_dir),\n", + " wind=(18, 180),\n", " )\n", " except Exception as e:\n", " route = None\n", @@ -804,6 +832,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:19.798844Z", "start_time": "2022-07-11T18:34:19.789736Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -842,6 +873,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:19.822370Z", "start_time": "2022-07-11T18:34:19.815008Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -849,13 +883,13 @@ "if not NO_SHOW:\n", " seed = 42\n", " wind_dir = 180\n", - " obstacles = generate_obstacles(seed)\n", - " destination = generate_destination(obstacles, seed)\n", - " route = None\n", - " route, _ = experiments.generate_route(\n", + " generated_obstacles = generate_obstacles(seed)\n", + " generated_destination = generate_destination(generated_obstacles, seed)\n", + " route_generated = None\n", + " route_generated, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", - " goal=destination,\n", - " obstacles=obstacles,\n", + " goal=generated_destination,\n", + " obstacles=generated_obstacles,\n", " wind=(18, wind_dir),\n", " )" ] @@ -867,6 +901,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:19.834989Z", "start_time": "2022-07-11T18:34:19.827279Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -875,24 +912,24 @@ " for seed in tqdm([42]):\n", " plt.figure(figsize=(8, 8))\n", " wind_dir = 180\n", - " obstacles = generate_obstacles(seed)\n", - " destination = generate_destination(obstacles, seed)\n", - " route = None\n", + " generated_obstacles = generate_obstacles(seed)\n", + " generated_destination = generate_destination(generated_obstacles, seed)\n", + " route_generated = None\n", " try:\n", - " route, _ = experiments.generate_route(\n", + " route_generated, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", - " goal=destination,\n", - " obstacles=obstacles,\n", + " goal=generated_destination,\n", + " obstacles=generated_obstacles,\n", " wind=(18, wind_dir),\n", " )\n", " except Exception as e:\n", - " route = None\n", + " route_generated = None\n", " plot_situation(\n", - " obstacles=obstacles,\n", - " destination=destination,\n", + " obstacles=generated_obstacles,\n", + " destination=generated_destination,\n", " obstacle_color=\"RED\",\n", - " route=route,\n", - " title=f\"Seed: {seed}, Cost: {route.cost:.3f}\",\n", + " route=route_generated,\n", + " title=f\"Seed: {seed}, Cost: {route_generated.cost:.3f}\",\n", " legend=seed == 0,\n", " )\n", " plt.show()" @@ -997,14 +1034,9 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "is_executing": true, - "name": "#%%\n" - }, - "scrolled": false - }, + "cell_type": "code", + "execution_count": null, + "outputs": [], "source": [ "```python\n", "save_frequency = int(os.getenv(\"save_frequency\", \"50\"))\n", @@ -1031,7 +1063,13 @@ " if not continues:\n", " break\n", "```" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "code", @@ -1285,7 +1323,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# find and drop all routes that exit the map!" ] @@ -1298,7 +1340,10 @@ "end_time": "2022-07-11T18:34:23.421419Z", "start_time": "2022-07-11T18:34:22.961997Z" }, - "scrolled": false + "scrolled": false, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -1356,7 +1401,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# find and drop all routes with errors!\n" ] @@ -1368,6 +1417,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:23.847712Z", "start_time": "2022-07-11T18:34:23.424364Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1413,7 +1465,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# distribution over costs and points in rutes routes!" ] @@ -1425,6 +1481,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:23.863906Z", "start_time": "2022-07-11T18:34:23.852977Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1455,6 +1514,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:24.022327Z", "start_time": "2022-07-11T18:34:23.867460Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1486,7 +1548,10 @@ "end_time": "2022-07-11T18:34:26.440028Z", "start_time": "2022-07-11T18:34:24.025987Z" }, - "scrolled": false + "scrolled": false, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -1531,9 +1596,9 @@ " plt.subplot(5, 3, count + 1)\n", " plot_situation(\n", " destination=Point(row.destination_x, row.destination_y),\n", - " obstacles=row.obstacles,\n", + " obstacles=row.generated_obstacles,\n", " obstacle_color=\"RED\",\n", - " route=row.route,\n", + " route=row.route_generated,\n", " title=f\"Cost: {row.cost}\",\n", " )\n", "plt.show()" @@ -1546,6 +1611,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:26.499749Z", "start_time": "2022-07-11T18:34:26.450483Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1755,6 +1823,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:26.837920Z", "start_time": "2022-07-11T18:34:26.504277Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1784,7 +1855,10 @@ "end_time": "2022-07-11T18:34:28.494500Z", "start_time": "2022-07-11T18:34:26.842407Z" }, - "scrolled": false + "scrolled": false, + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -1813,9 +1887,9 @@ " plt.subplot(4, 3, count + 1)\n", " plot_situation(\n", " destination=Point(row.destination_x, row.destination_y),\n", - " obstacles=row.obstacles,\n", + " obstacles=row.generated_obstacles,\n", " obstacle_color=\"RED\",\n", - " route=row.route,\n", + " route=row.route_generated,\n", " title=f\"Cost: {row.cost:.3f}\",\n", " legend=count == 0,\n", " )\n", @@ -1824,7 +1898,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Visualize Complexity" ] @@ -1836,6 +1914,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.506644Z", "start_time": "2022-07-11T18:34:28.497676Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -1856,6 +1937,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.623493Z", "start_time": "2022-07-11T18:34:28.510989Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1884,6 +1968,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.634493Z", "start_time": "2022-07-11T18:34:28.627196Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1912,6 +1999,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.773895Z", "start_time": "2022-07-11T18:34:28.638505Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1940,6 +2030,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.789074Z", "start_time": "2022-07-11T18:34:28.777273Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -1975,7 +2068,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Dropping routes that are to easy " ] @@ -1987,6 +2084,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.801868Z", "start_time": "2022-07-11T18:34:28.793309Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2019,6 +2119,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.945307Z", "start_time": "2022-07-11T18:34:28.806425Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2053,6 +2156,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:28.962854Z", "start_time": "2022-07-11T18:34:28.949690Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2093,6 +2199,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:29.030033Z", "start_time": "2022-07-11T18:34:28.966774Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2301,6 +2410,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:29.039493Z", "start_time": "2022-07-11T18:34:29.033596Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2310,7 +2422,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# Memory consumption" ] @@ -2322,6 +2438,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:29.105061Z", "start_time": "2022-07-11T18:34:29.043526Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2530,6 +2649,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:29.149676Z", "start_time": "2022-07-11T18:34:29.111848Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2545,17 +2667,13 @@ } ], "source": [ - "import humanize\n", - "import sys\n", - "\n", - "\n", "def generate_image_maps(row, route_type: Literal[\"dot\", \"line\"]):\n", " img = np.expand_dims(\n", " np.asarray(\n", " generate_image_from_map(\n", - " obstacles=row.obstacles,\n", + " obstacles=row.generated_obstacles,\n", " destination=Point(row.destination_x, row.destination_y),\n", - " route=row.route,\n", + " route=row.route_generated,\n", " route_type=route_type,\n", " seed=row.name,\n", " )\n", @@ -2577,6 +2695,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:29.160218Z", "start_time": "2022-07-11T18:34:29.154190Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2592,6 +2713,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:30.030972Z", "start_time": "2022-07-11T18:34:29.164607Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2610,6 +2734,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.027458Z", "start_time": "2022-07-11T18:34:30.033852Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2645,6 +2772,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.085664Z", "start_time": "2022-07-11T18:34:35.032718Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2660,6 +2790,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.095020Z", "start_time": "2022-07-11T18:34:35.088433Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2685,6 +2818,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.107314Z", "start_time": "2022-07-11T18:34:35.099032Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2710,6 +2846,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.124351Z", "start_time": "2022-07-11T18:34:35.111145Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2754,6 +2893,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.811285Z", "start_time": "2022-07-11T18:34:35.128881Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2767,7 +2909,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "[Pix2Pix Tensorflow](https://www.tensorflow.org/tutorials/generative/pix2pix)" ] @@ -2779,6 +2925,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.846820Z", "start_time": "2022-07-11T18:34:35.815078Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2839,6 +2988,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.859119Z", "start_time": "2022-07-11T18:34:35.852272Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2864,6 +3016,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.873070Z", "start_time": "2022-07-11T18:34:35.862530Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2889,6 +3044,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.948246Z", "start_time": "2022-07-11T18:34:35.877228Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2917,6 +3075,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:35.959098Z", "start_time": "2022-07-11T18:34:35.953165Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -2954,6 +3115,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:36.012492Z", "start_time": "2022-07-11T18:34:35.963668Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -2981,6 +3145,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:36.888819Z", "start_time": "2022-07-11T18:34:36.015415Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -3001,7 +3168,7 @@ " OUTPUT_CHANNELS: Final[int] = 1\n", " inputs = tf.keras.layers.Input(shape=[IMG_SIZE, IMG_SIZE, 2])\n", "\n", - " #down_stack = [\n", + " # down_stack = [\n", " # downsample(64, 4, apply_batchnorm=False), # (batch_size, 64, 64, 128)\n", " # downsample(128, 4), # (batch_size, 8, 8, 512)\n", " # downsample(512, 4), # (batch_size, 4, 4, 512)\n", @@ -3009,17 +3176,17 @@ " # downsample(512, 4), # (batch_size, 1, 1, 512)\n", " # downsample(512, 4), # (batch_size, 1, 1, 512)\n", " # downsample(512, 4), # (batch_size, 1, 1, 512)\n", - " #]\n", - "#\n", - " #up_stack = [\n", + " # ]\n", + " #\n", + " # up_stack = [\n", " # upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " # upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " # upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " # upsample(512, 4), # (batch_size, 16, 16, 1024)\n", " # upsample(128, 4), # (batch_size, 32, 32, 512)\n", " # upsample(64, 4), # (batch_size, 64, 64, 256)\n", - " #]\n", - " \n", + " # ]\n", + "\n", " down_stack = [\n", " downsample(64, 4, apply_batchnorm=False), # (batch_size, 64, 64, 128)\n", " downsample(128, 4), # (batch_size, 8, 8, 512)\n", @@ -3076,7 +3243,11 @@ { "cell_type": "code", "execution_count": 58, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -3096,7 +3267,11 @@ { "cell_type": "code", "execution_count": 59, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -3120,6 +3295,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:36.909543Z", "start_time": "2022-07-11T18:34:36.893252Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3145,6 +3323,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:36.922590Z", "start_time": "2022-07-11T18:34:36.913165Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [ @@ -3198,6 +3379,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.387799Z", "start_time": "2022-07-11T18:34:36.925978Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3220,6 +3404,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.398038Z", "start_time": "2022-07-11T18:34:37.391964Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3234,6 +3421,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.769969Z", "start_time": "2022-07-11T18:34:37.402840Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3251,6 +3441,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.780494Z", "start_time": "2022-07-11T18:34:37.772901Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3265,6 +3458,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.789908Z", "start_time": "2022-07-11T18:34:37.784588Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3281,6 +3477,9 @@ "ExecuteTime": { "end_time": "2022-07-11T18:34:37.800259Z", "start_time": "2022-07-11T18:34:37.794044Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3295,6 +3494,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:34:10.831132Z", "start_time": "2022-07-11T18:34:37.804670Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3306,14 +3508,18 @@ " use_multiprocessing=True,\n", " workers=5,\n", " callbacks=[early_stop, tf_board],\n", - " # tqdm_callback, \n", + " # tqdm_callback,\n", ")" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] }, @@ -3324,6 +3530,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:35:09.893719Z", "start_time": "2022-07-11T20:35:09.785795Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3338,6 +3547,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:36:41.233512Z", "start_time": "2022-07-11T20:36:41.228150Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3352,6 +3564,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:40:42.106870Z", "start_time": "2022-07-11T20:38:39.288499Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3375,6 +3590,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:40:42.284261Z", "start_time": "2022-07-11T20:40:42.275481Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3389,6 +3607,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:40:42.419205Z", "start_time": "2022-07-11T20:40:42.290807Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3404,12 +3625,18 @@ "ExecuteTime": { "end_time": "2022-07-11T20:40:42.270774Z", "start_time": "2022-07-11T20:40:42.111264Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], "source": [ "for pos in range(5):\n", - " plt.imshow(predicted[pos, :, :, 0] * 0xFF + collected_routes[pos, :, :, 0] * 20, interpolation=\"nearest\")\n", + " plt.imshow(\n", + " predicted[pos, :, :, 0] * 0xFF + collected_routes[pos, :, :, 0] * 20,\n", + " interpolation=\"nearest\",\n", + " )\n", " plt.show()" ] }, @@ -3420,6 +3647,9 @@ "ExecuteTime": { "end_time": "2022-07-11T20:34:11.274201Z", "start_time": "2022-07-11T20:34:11.274188Z" + }, + "pycharm": { + "name": "#%%\n" } }, "outputs": [], @@ -3433,6 +3663,9 @@ "ExecuteTime": { "end_time": "2022-07-11T16:47:19.020872Z", "start_time": "2022-07-11T16:47:17.607427Z" + }, + "pycharm": { + "name": "#%% raw\n" } }, "source": [ @@ -3442,14 +3675,22 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "@article{article,\n", @@ -3510,4 +3751,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/experiments/ML Example.ipynb b/experiments/ML Example.ipynb new file mode 100644 index 0000000..bc17499 --- /dev/null +++ b/experiments/ML Example.ipynb @@ -0,0 +1,5137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "xR0XwjUsW0QA", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "26TEe8B7Ub36", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Kurzfassung\n", + "\n", + "Das Spritzgießen ist ein häufig in der Kunststoffverarbeitung eingesetztes Verfahren und ist deshalb von großer ökologischer und ökonomischer Bedeutung. Ziel dieser Arbeit war es, die üblicherweise von Spritzgussmaschinen bereitgestellten Daten zu nutzen, um Fehlteile – also Teile mit unzulässigen Qualitätsmängeln – unmittelbar zu erkennen und damit aussortieren zu können. Es hat sich gezeigt, dass dies für die untersuchten Qualitätsmängel mit einer Genauigkeit von *98,0 - 99,0 %* unter Anwendung linearer Machine-Learning-Modelle möglich ist. Darüber hinaus konnte gezeigt werden, dass durch die Auswahl repräsentativer Datenpunkte der dafür erforderliche Aufwand beim Labeling auf einen Bruchteil des ursprünglichen Aufwands reduziert werden kann. Diese repräsentative Auswahl erfolgte auf der Basis unüberwachter Clustering-Algorithmen." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dwceHoM5nLeL", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Inhaltsverzeichnis\n", + "1. Einleitung\n", + "2. Vorbereitung\n", + "3. Einführung des vorliegenden Datensatzes\n", + "4. Klassifizierung anhand eines Merkmals\n", + "5. Klassifizierung anhand mehrerer Merkmale\n", + "6. Teilüberwachtes Lernen\n", + "7. Ergebnisse und Evaluation\n", + "8. Ausblick\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8AVm8Hww3HMw", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 1\\. Einleitung\n", + "Das Spritzgießen ist ein Verfahren aus der Kunststoffverarbeitung, um Rohmaterial (Kunststoffgranulat) in eine gewünschte Form zu bringen (sog. Urformverfahren). Technisch handelt es sich dabei um einen komplexen Prozess, dessen Resultat von zahlreichen Variablen abhängt. Aus diesem Grund kommt es hin und wieder vor, dass die gespritzten Teile nicht den Qualitätsstandards eines Herstellers entsprechen und aussortiert werden müssen.\n", + "\n", + "Ziel der vorliegenden Arbeit ist es, diese fehlerhaften Teile anhand der internen Messwerte der Spritzgussmaschine (sog. Prozessdaten) automatisch auszusortieren. Grundlage dafür ist ein Datensatz, welcher in einer vorherigen Hausarbeit [1] erarbeitet wurde. Im Zuge dessen sollen auch Pipelines erarbeitet und Methoden gefunden werden, sodass eine Übertragung der Ergebnisse auf andere Fehler und Produkte erleichtert wird.\n", + "\n", + "Motiviert wird diese Zielsetzung aus verschiedenen Richtungen. Die untersuchten Produkte werden nach ihrer Fertigung vollautomatisch weiterverarbeitet und gehen anschließend direkt in den Verkauf. Es existiert bisher kein System, welches fehlerhafte Teile automatisch aussortiert. Aufgrund der vollautomatischen Abläufe fallen diese Teile auch den Mitarbeitern nicht immer auf und erreichen somit teilweise den Endkunden. Dies sorgt für Unzufriedenheit und unter Umständen einen Imageschaden. Außerdem entsteht sowohl beim Kunden als auch Hersteller ein Mehraufwand für den Austausch des Produkts. Hinzu kommt die zeitliche Verzögerung für den Endkunden.\n", + "\n", + "Des Weiteren sind fehlerhafte und damit unbrauchbare Teile für ein Unternehmen sowohl aus ökologischer als auch ökonomischer Sicht zu vermeiden. Insbesondere für die Fertigungsplanung ist es außerdem wichtig, fehlerhafte Teile unmittelbar zu erkennen, damit die geplante Anzahl an (fehlerfreien) Teilen produziert werden kann. \n", + "\n", + "Im nachfolgenden Kapitel 3 wird zunächst der vorliegende Datensatz eingeführt. Der Hauptteil beginnt in Kapitel 4 mit dem Versuch, den Datensatz anhand eines einzigen Merkmals linear zu separieren. Im anschließenden Kapitel 5 werden komplexerere Algorithmen untersucht, welche mehrere Merkmale gleichzeitig berücksichtigen können.\n", + "\n", + "Kapitel 6 konzentriert sich darauf, wie die Berücksichtigung weiterer Fehler und Produkte vereinfacht werden kann. Dazu wird der Ansatz des teilüberwachten Lernens verfolgt. Dieser erfordert zunächst eine Dimensionsreduktion der Daten. Anschließend können mit Hilfe von Clustering-Algorithmen repräsentative Datenpunkte ausgewählt und gelabelt werden. Zum Abschluss wird die Qualität der Klassifikatoren untersucht, welche auf daraus resultierenden Daten trainiert wurden.\n", + "\n", + "Im Schlussteil der Arbeit werden die Ergebnisse zusammengefasst und kritisch bewertet. Außerdem wird ein Ausblick gegeben." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W_BwbI4O7C0e", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 2\\. Vorbereitung" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kn36BjyK7C0e", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Zunächst müssen einige allgemeine Vorbereitungen getroffen werden, um nachfolgend die Daten verarbeiten zu können. Grundlage dieser Arbeit bildet die Programmiersprache Python und insb. die Module *Numpy*, *Pandas*, *SciKit-Learn* sowie *Matplotlib*." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "mfjPwL3m7C0f", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Kontrolle der Python-Version\n", + "import sys\n", + "\n", + "assert sys.version_info >= (3, 5)\n", + "\n", + "# Import von Scikit-Learn und Kontrolle der Version\n", + "import sklearn\n", + "\n", + "assert sklearn.__version__ >= \"0.20\"\n", + "\n", + "# Weitere Imports\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "\n", + "# Imports und Einstellungen um Abbildungen mit matplotlib erzeugen und im\n", + "# Notebook darstellen zu können\n", + "%matplotlib inline\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "mpl.rc(\"axes\", labelsize=14)\n", + "mpl.rc(\"xtick\", labelsize=12)\n", + "mpl.rc(\"ytick\", labelsize=12)\n", + "\n", + "# Dictionary zur Abspeicherung der Zwischenergebnisse für den Ergebnis-Teil\n", + "results = []" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XqospT0cW3PY", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Damit die Ausgaben des Notebooks vergleichbar sind wird außerdem der entsprechende Seed gesetzt." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "5YcZM9azkWI-", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "np.random.seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fEMrPGqJYM4K", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Im nächsten Schritt kann der Datensatz eingelesen werden. Dieser kann entweder im aktuellen Verzeichnis liegen oder über einen entsprechenden Link aus Google Drive geladen werden." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NqQCpW1xesNp", + "outputId": "604474b0-7b40-44b3-af87-28d8b531b57d", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1r5OzQmxj2TIZUMm3UwKOA-9znGQoe31j\n", + "To: /content/df.pkl\n", + "\r", + "0.00B [00:00, ?B/s]\r", + "3.91MB [00:00, 61.7MB/s]\n", + "Anzahl Spalten: 107\n", + "Anzahl Zeilen: 4548\n" + ] + } + ], + "source": [ + "# Datei via Link aus Google Drive laden\n", + "# Alternativ können Sie die Datei 'df.pkl' auch manuell dem aktuellen Unterordner\n", + "# hinzufügen\n", + "!gdown --id 1r5OzQmxj2TIZUMm3UwKOA-9znGQoe31j\n", + "\n", + "# Daten einlesen\n", + "import pickle\n", + "\n", + "with open(\"df.pkl\", \"rb\") as file:\n", + " df = pickle.load(file)\n", + "\n", + "# Kontrolle\n", + "assert len(df.columns) == 107\n", + "assert len(df.index) == 4548\n", + "print(\"Anzahl Spalten:\", len(df.columns))\n", + "print(\"Anzahl Zeilen:\", len(df.index))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L67a7NpkPS1r", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 3\\. Einführung des vorliegenden Datensatzes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tnuqkm7uZ_KI", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.1 Überblick\n", + "Wie bereits in der Einleitung erwähnt, wird in dieser Arbeit ein Datensatz untersucht, welcher in einer vorherigen Hausarbeit [1] erarbeitet wurde.\n", + "Er umfasst die nachfolgende Anzahl an Datenpunkten, Merkmalen und Zielwerten:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "crBeVX_pgXtf", + "outputId": "fc7cb2a4-7245-4dbd-ad4b-0fef132d6975", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Übersicht\n", + "- Anzahl Datenpunkte: 4547\n", + "- Anzahl Merkmale: 104\n", + "- Anzahl Zielwerte: 3\n" + ] + } + ], + "source": [ + "print(\"Übersicht\")\n", + "print(\"- Anzahl Datenpunkte:\", len(df.index) - 1)\n", + "print(\"- Anzahl Merkmale:\", len(df.drop([\"Labels\"], axis=1).columns))\n", + "print(\"- Anzahl Zielwerte:\", len(df[\"Labels\"].columns))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7cmoVfo5oOTH", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Jeder Datenpunkt beschreibt ein gespritztes Teil. Konkret handelt es sich dabei um das Unterteil des Kabelabzweigkastens DK 0200 G der Gustav Hensel GmbH & Co. KG. Abbildung 1 zeigt ein Exemplar:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DJnstmwCU0_H", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![_Abbildung 1 - DK 0200 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nHyL8Wjvp3CC", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "**Abbildung 1: DK 0200 G nach [2]**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RvYZ__pjfbCn", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die nachfolgende Abbildung 2 zeigt exemplarisch ein Fotos eines fehlerfreien DK 0200 G." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qcVhnWtxVU5N", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Exemplarisches 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PdLUuyfIQl8F", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "**Abbildung 2: Foto eines fehlerfreien DK 0200 G**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aEFyUWr5ryVZ", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.2 Merkmale\n", + "Da moderne Spritzgussmaschinen eine Vielzahl an Daten bereitstellen, wird jeder Datenpunkt durch mehr als 100 Merkmale beschrieben. Es wurde bewusst darauf verzichtet, auf Grundlage domänenspezifischen Wissens bereits vorab Merkmale auszusortieren. Es wird zunächst davon ausgegangen, dass sämtliche Merkmale potenziell relevant sind.\n", + "\n", + "Alle Merkmale im Detail zu beschreiben wäre nicht zielführend. Stattdessen wird auf die relevanten Merkmale an den entsprechenden Stelle der Arbeit eingegangen. Für das Arbeiten mit einer solchen Vielzahl an Merkmalen ist jedoch eine Gruppierung hilfreich. Diese kann auf Grundlage des Fertigungsprozesses erfolgen. Der DK 0200 G wird aus zwei Komponenten – also zwei unterschiedlichen Kunststoffen – gespritzt. Zunächst wird der Grundkörper aus Polypropylen gespritzt. In einem zweiten Fertigungsschritt werden die Membranen zur Einführung von Kabeln aus TPE (Thermoplastischen Elastomeren) ergänzt. Beide Fertigungsschritte laufen prinzipiell ähnlich ab und werden deshalb durch dieselben Merkmale beschrieben. Insgesamt ergeben sich die nachfolgenden Gruppen an Merkmalen:\n", + "\n", + "1. `Internal`: komponentenunabhängige Messwerte der Spritzgussmaschine (z.B. Zykluszeit)\n", + "\n", + "2. `Internal_C1`: Messwerte der Spritzgussmaschine an Komponente *1* (z.B. Einspritzvolumen)\n", + "\n", + "3. `Internal_C2`: Messwerte der Spritzgussmaschine an Komponente *2* (z.B. Einspritzvolumen)\n", + "\n", + "4. `External`: Messwerte externer Sensoren (z.B. Umgebungstemperatur)\n", + "\n", + "5. `Time related`: Abgeleitete Merkmale aus den Zeitstempeln der Teile (z.B. Zeit seit letzter Wartung)\n", + "\n", + "Für eine detaillierte Beschreibung des Spritzgussprozesses und der einzelnen Merkmale siehe [1]. Die Merkmale teilen sich folgendermaßen auf die Gruppen auf:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zuhy1ZAr_yke", + "outputId": "782b5e4d-dcab-45c4-80e9-7169879550d2", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Group \n", + "Internal 59\n", + "Internal_C2 21\n", + "Internal_C1 20\n", + "Time related 2\n", + "External 2\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Im DataFrame wurde diese Gruppierung mit Hilfe eines MultiIndex umgesetzt\n", + "pd.DataFrame(df.drop([\"Labels\"], axis=1).droplevel(1, axis=1).columns).value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zzXjYIEk_U3z", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die meisten Merkmale sind interne Messwerte der Spritzgussmaschine, wobei jeweils ca. 20 % einer einzelnen Komponente zugeordnet werden können. Um die Daten nachfolgend einheitlich darzustellen, ist es sinnvoll, zunächst eine entsprechende Funktion zu definieren:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "GnFDhS24-VKu", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def formatForPlotting(df):\n", + " for number in range(10):\n", + " df.columns = df.columns.str.replace(r\" \" + str(number), \"_\" + str(number))\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zMACJ9bTuXzY", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die ersten Daten der Gruppe `Internal` sehen dann bspw. so aus:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "id": "PhuLmRKFFjP0", + "outputId": "ece7fe81-dddd-4e88-9551-93ab778274c8", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameAusschuss total [ASZ]Ausschussursache [ASU]Düse [H27x]Düse [H2x]Düse [Pakt_27]Entformzeit [ZEx]Flansch_1 [H28x]Flansch_1 [Pakt_28]Flansch_2 [H29x]Flansch_2 [Pakt_29]Formschutzzeit Istwert [ZFx]Formöffnungshub Spitzenwert [SFs]Schließkraft Spitzenwert [SKs]Schließkraft gespeichert [SKg]Schuss Gutteile [SZGx]Schusszähler Istwert [SZx]Stillstandszeit vor Zyklusstart [ZUaxs]Traverse [H34x]Traverse [H6x]Traverse [Pakt_34]Traverse [Pakt_6]Trockenlauf [ZDry]Werkzeug_1 [H7x]Werkzeug_1 [Pakt_7]Werkzeug_2 [H8x]Werkzeug_2 [Pakt_8]Werkzeug_3 [H9x]Werkzeug_3 [Pakt_9]Werkzeug_4 [H13x]Werkzeug_4 [Pakt_13]Werkzeug_5 [H14x]Werkzeug_5 [Pakt_14]Werkzeug_6 [H15x]Werkzeug_6 [Pakt_15]Werkzeug_7 [H16x]Werkzeug_7 [Pakt_16]Werkzeug_8 [H17x]Werkzeug_8 [Pakt_17]Werkzeug_9 [H18x]Werkzeug_9 [Pakt_18]Zeit Schließkraftaufbau [ZSKa]Zyklus Kühlzeit [Z4x]Zykluszeit Formschließen [ZSchl]Zykluszeit Formöffnen [ZOeff]Zykluszeit Schließen [ZEsch]Zykluszeit bis Ende Entformen [ZUs]Zykluszeit Öffnen [ZEoef]Zylinderzone keramisch_1 [H30x]Zylinderzone keramisch_1 [H3x]Zylinderzone keramisch_1 [Pakt_30]Zylinderzone keramisch_1 [Pakt_3]Zylinderzone keramisch_2 [H31x]Zylinderzone keramisch_2 [H4x]Zylinderzone keramisch_2 [Pakt_31]Zylinderzone keramisch_2 [Pakt_4]Zylinderzone keramisch_3 [H32x]Zylinderzone keramisch_3 [H5x]Zylinderzone keramisch_3 [Pakt_32]Zylinderzone keramisch_3 [Pakt_5]
2021-01-07 17:38:1173.0-214.9219.913.110.77215.031.2210.036.30.94575.11222.71214.318489.018562.00.035.439.8-100.00.04.06215.08.9215.016.8215.021.1200.013.4199.913.8199.515.2199.516.6220.922.4200.020.60.7619.012.584.271.836.061.48200.1219.92.45.1185.0209.92.55.6170.0199.97.415.1
2021-01-07 17:38:4773.0-214.9220.113.010.82215.031.2210.137.10.94575.01221.81213.218490.018563.00.035.440.0-100.00.04.06215.09.1215.016.5215.020.9200.015.7199.712.6199.517.5199.515.0221.022.7200.016.20.7619.012.594.251.836.041.48200.0219.92.34.5185.0210.13.03.9170.0200.18.014.6
2021-01-07 17:39:2373.0-215.0219.912.210.78214.837.4209.940.60.94575.11222.71214.018491.018564.00.035.340.0-100.0-100.04.06215.010.0215.018.5214.927.1200.015.7199.614.6199.417.6199.417.3220.923.6200.016.20.7619.012.594.261.836.061.48200.0220.12.01.4185.0210.23.23.7170.0200.27.714.2
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" + ], + "text/plain": [ + "Name Ausschuss total [ASZ] ... Zylinderzone keramisch_3 [Pakt_5]\n", + "2021-01-07 17:38:11 73.0 ... 15.1\n", + "2021-01-07 17:38:47 73.0 ... 14.6\n", + "2021-01-07 17:39:23 73.0 ... 14.2\n", + "\n", + "[3 rows x 59 columns]" + ] + }, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "formatForPlotting(df[\"Internal\"]).head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nsSQZgvQ-tcf", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Merkmale der Gruppe `Internal_C1` sind wie bereits erwähnt ähnlich der Gruppe `Internal_C2` und sehen so aus:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + }, + "id": "N_VS3-uK-tE9", + "outputId": "ae2bfce4-71a3-4917-d6eb-b9e62947bc47", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDosierleistung [iwdls_1]Dosiervolumen Istwert [ASSx_1]Dosierzeit Istwert [ZDx_1]Drehmoment Mittelwert laufender Zyklus [Mm_1]Drehmoment Spitzenwert laufender Zyklus [Ms_1]Drehzahl Spitzenwert [DZs_1]Einspritzarbeit [EA_1]Integral Überwachung_1 Micrograph [IDKi1_Mic_1]Massepolster Ende Nachdruck [ACPv_1]Massepolster kleinster Wert [ACPx_1]Massepolster nach Nachdruck [ACPn_1]Schussvolumen [Svo_1]Spezifischer Druck beim Umschalten [APHu_1]Spezifischer Einspritzdruck Spitzenwert [APVs_1]Spezifischer Nachdruck Spitzenwert [APNs_1]Spezifischer Staudruck Spitzenwert [APSs_1]Spritzzeit Istwert [ZSx_1]Umschaltvolumen [AC3u_1]Zykluszeit Düse vor [ZDvo_1]Zykluszeit Nachdruck [ZNach_1]
2021-01-07 17:38:117.0170.687.40166.5177.00.2941.00250534.376.014.326.2362.77511.5875.3620.994.01.2112.491.152.0
2021-01-07 17:38:476.8070.617.65163.5175.50.2940.99450484.745.603.925.8163.19503.0877.6611.293.31.2112.491.162.0
2021-01-07 17:39:236.8270.617.69165.6177.60.2941.00350598.795.914.216.1162.89508.2884.4618.193.31.2112.491.152.0
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" + ], + "text/plain": [ + "Name Dosierleistung [iwdls_1] ... Zykluszeit Nachdruck [ZNach_1]\n", + "2021-01-07 17:38:11 7.01 ... 2.0\n", + "2021-01-07 17:38:47 6.80 ... 2.0\n", + "2021-01-07 17:39:23 6.82 ... 2.0\n", + "\n", + "[3 rows x 20 columns]" + ] + }, + "execution_count": 8, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "formatForPlotting(df[\"Internal_C1\"]).head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IZUHOXzsBfE0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die restlichen Merkmale werden nachfolgend bei Bedarf dargestellt. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SdKFQh6EPaFA", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.3 Zielwerte\n", + "Der Datensatz ist gelabelt und umfasst somit Zielwerte:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "id": "q9b4YNesCHYx", + "outputId": "57dc31e8-a76e-4642-de80-88978aa90fef", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Name0_leak_corner_tl0_leak_corner_tr1_hole_bottom
2021-01-13 17:00:28230
2021-01-13 17:01:04130
2021-01-13 17:01:40130
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" + ], + "text/plain": [ + "Name 0_leak_corner_tl 0_leak_corner_tr 1_hole_bottom\n", + "2021-01-13 17:00:28 2 3 0\n", + "2021-01-13 17:01:04 1 3 0\n", + "2021-01-13 17:01:40 1 3 0" + ] + }, + "execution_count": 9, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Die Zielwerte bilden eine eigene Gruppe im MultiIndex\n", + "formatForPlotting(df[\"Labels\"][\"2021-01-13 17\"]).head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bw9oneNuDwNy", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Jedes Label beschreibt einen Fehler, welcher im beobachteten Zeitraum aufgetreten ist. Die nachfolgende Abbildung 3 zeigt ein Teil, welches alle diese Fehler gleichzeitig aufweist. Dies muss nicht zwingend der Fall sein." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pL-RDZKYWGQ0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "![Exemplarisches Schlechtteil - markiert - Kopie.jpg](data:image/jpeg;base64,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\n", 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z8NPb/5a1Gf2s77/AKLF8Df/AAXN/wDLWvpq9uBFlcEtiqMs3ybsMfwoXB/EH/Q2n/4BE0WY0X9j8Kf/AMrPnH/hrC+/6LH8Dv8AwXt/8taQ/tYXu3H/AAuP4G/+C9v/AJa19GrA02Dt781J9iYIGC5H1qv9TeIf+htP/wAAiT/alBf8u/wh/wDKz5tb9q28cf8AJYvgd+Ont/8ALWnJ+1beoePjF8Df/Bef/lrX0xBYtM4XadvHI71uW+nWnhnbcX7bR95YT95/TiqXBvEH/Q3n/wCARM/7Wof8+/wh/wDKz5h8IftPeINe1mHT9H+J3wV1bUJ8+Vb22kSXE0mAWO1F1XJwAScDoDXoPwn+NOrfE/wRHeeIX006pa6jqGnytYW729tN9mvp7dXWN3kZdyxKSC7ck84xVb9qTxQ3iPxl8M23bUXxRN5can5UH9j6mP8ACvOfgjrW3wzqNmxCn/hI9cMTZ/6i13x+dcXDuIzTBcSVcqx+KdeCpKSukrNyXbyOnEU41sMpxilfle0brWaavFR00R78b43ETbTz6g9PwrivG8i6rFJb3OEkA+VveotB8ZSRyruX5ozscY6112r/AA+bx3oTapaY22Y8xnPGfav0+VRTVjzI03D3jxnRry6spHjuY5ERGwHZSAa6W3mjli3ZB9DXTWrQ+INONrcxxs69D61l6v4TWEZh3KsY+761zxi0dUqibsxljMuR/PPSrhbYQo7nOc1j6fuRlWQZ9q144NwVf71VzFqCLumh5nAK/wAWQPUV1iSrZWnpleMVhaPZCABj1xxmrsJe/vI413Nzt2itFOyuR7NvQ9G+A2hR3mrz6lebls9NQzyNjsO31PT8a6J/ES2Fnq/iXUmEcNqr3LkdPUKP0Aqa80d/BvgjSfDUKFtU1ry7y7VfvKh/1UZ+pyx9tteVftl/ECHw7pFr4G06aOSS3VZdXKt0fbkR59ec/wDfNfL5tmioU5VevQ+kynKXXrRpvbqfGH7V/jy+8QQapdXUnOuXhnID52kH7v0HavnZnDv1/OvWf2ndRt4Z7W2tPnj3GRiX3FCMAivHzKwG4ke4r8fxNWVSTnLqfqHs1TXJHZEk5zC3zfdrO8PBQdQmPQ5GfpVi+nItJCvcdKz9Jdrfw3dTZHzFsCuGtZx1LpuzIvAcLT6/6nd6/Wuk+M96bXwq6nK5wB71l/Cy2FzfNJt+6wpv7QN75Vjbwo27zJNx57Cs6ErysOpqzy/yJP7y/lRUnmQ/7X5mit9Q5UaEE7wbfLkkjbP8Jxg1rWPjTULT/loJgp/iHX8awd/lnO3dzViHlCO+cnPQVtUw8J7o5alFSep2mm/EmFFC3FvJG3co2RWtpnjfS71sLcruzjDcGvOfPTdtU726Z9KSKwSBuijcc561xSy+m9Uc9TL4S1PYba9W4t/kZWUjjBpsEvzfKOnpXl8F7LZvujuJEx2VuKv2fjTUrE/LMsy+ki8fpzXHLLnbQ4amXy+ydxrQafTbhRlSyEAqcEcV1P8AwT2/bP1D9mD4mrJqd9eTaLdHyp4WlLLGAfvAH0z0HWvLH+JkqxFbixDDu0TZ/Q157q0udTmdFkjjdyygjpn/APXXr5HUqYWpdHZl+FjFtVYn9BemeI9H+K3ge38TeH7iG+0fVEEkhiO4QMed4x29RXK6osmkTMvHIyD2P0r8t/8Agnp/wUY1r9knxPHYXrSap4Ru323FkWy0Oerxg8fVTgH2PNfqr4a8beE/j/4B/wCEj8Hapa6lprIXkhjkzLZkcn5TztHGQRkZ9+P1bLc2g0lfQ8HMMpablE57U51mi4+ZiMYA5rFl8MX1/IEhgYr1L46VuXHim18KMyLYzXVyCctIpRV9MZ/OuN8UfEjVtezH5y2sbH/VwLtr255jSp9dTwf7PrVPI3IvhzHZ/vptSsLeRW3bWl+bNXte0G68V2kcX2yzuhGdy7ZPm/KvPbTw01vaG4mvJlk5csSrY+uRXG6j8WdYtfEZj0y3t761h4dnDIzfQr/hXRHGxsnLqefUy+TbS6HufhTRo/CFrdLcrJHMyn5SOGHPSuIHhb7TqUlxcL8pcsie3vWl8NfjXD4otfsOq+mCH/1kHuDxuX9a6DXdBkhuY44TGyzYMUrH5WB6V6EHGSvE8u7pycJHC+M9Hj1bSntY2aC5kG1ZEHzKPTPpXhv7N+mf2nf/AAZjzhl+G1y68Zzzoor6nj+GLrMW+2wSTHqpyOfSvkL4TeJ5vCFn8G7m3/1jfDi5jDY+5n+xju/SvzfjBqOe5W/78/8A0lHqZe+eNvN/+kTPo51bS7pre4zHKvJBNaFp4yi0UBY5GlkXn5e3414nbeM9S8Xa3J++eYyn5pScmu60rSzFbpGctkYZj3r9Eo4pvVHm1sMtmehXHxvmv/MW4s4Zt/3WZcSA4AySPQKPzrN/4T4mTf5HJzx2xWXoXhy88QXvk2Nu9xM3UL/ngV6L4Q/Zrkv1ZtW1CSEBN/k2kXmt75YkAfkRWeIzKnS+JhRyupUXuo5MfEGJU+aFvp2qSLxvaFBut2wetdd4g8A+F/Adrvvms7aMNjzb+8CY/ElR61xGvfFjwDpt4trHrmhic/eSFvOx/wB8g159TiTCw1nNL1aOynw9Xl8EWy9B4lh1G58qzhd5FGSoXnHtWpHry24USaa44/vHmuKHjfS9S1e3bTtQhfJwWjiZG/UDit+4nvGcHzpHX1B4rswOdYbGX+rzUrb2dzix2U1sN/Gi437mwPFtnGzB7GTpwPMxUq+LLNh/x63CngcODisRr2RT+8VJPqKZuhn52+S3p1DV6vNpc4Pq8bnRQ+I9Oku087z1jU5Pylv5V1UPxU0WCFYY/ORV6DyT/hXmXktu2+p6CmqjYbpwcD3o9oTPCxk7I9TT4r6TGGHmTfMP+ebVFdfFDRby3aORnZWGDiI/4V5sqbR/s+9I0Hln5dv/AOujmI+ow3udO3iDSY55PL891B4wpWo18ZacDn7Dc89Pn61zyRMYyv3V6kipIlhj+6vmHsT0FO7H9XijdPi+xkP/ACD7j/vulPiC3uFxHpcxxzncaxxdyudsarH6bVFbWitrtjp0slqoKtgFpEXgc9CeM1y4rGU8PTdWrKyXVm1PC87sjL1PxFBpt2HuoJYY5B8iH73H/wCuoJvHGloQypN+Iq7qvhvUNZuVnliinkx1ZlYj+lZV/wCC75Fz/Z/y56oAc/XFePR4ryyTsq8X80afUdbIlf4gWo+7bzbPU45qrc+P4EePFu7KCCRnqO9YbS2xlZQuGU4IV87TVZtkjYWRc88Hg17UMdTnrF6E1MulHdM7d/jKtvZBLfS4I2XP7wkswBUjj6Zz+Arl9S8evrd+0tzKxkLEEnt9KoRHzC6kYJ55rC8VWpEjFcqxHOBitJV7K6MaeFjczvjZHnxZ8M5N3yt4nlH1/wCJRqVc38FfAUmueF57p5hFDJ4l1wEj7wxq95/9eqXiLxPPceOfhzpM7FzD4kmmRj1AGkakD/MV6d+zD4WuNb+Ek7Q28sqt4k19QVX/AKjN73r8ywklU42rP/pzH/0o97EXpYWKXaP/AKVUOi1Hw7DPpqxxqFeIfIcct9a7T4axG4+GV1aeYkUhZwysfu1z40q8025+z3UMkMnQbl616j8N/hAt1YfatQk8i1f5gMcuewx1J9gPyr9MlyU43Z4vLOfuo8ksvB88txGqW8knlsOUGQcGuy8U6VqHiLSoreHQzCseDvCYJArtPGHxr8MfC7dYwwp9oj6pFF58y+/BCr9CTWPbfHCHxlpnnWK3bBeGjcpCyn3GD/OuCOZR5uVHof2bOUVJnkeueC7jTpfMaGSNupBFQpalcSYHHGD2rpNb+LF5p+svFqFgjWmcjJ3HH1wM1as9a8OeMIGjhmW1mYZEbnbzWkMVTm+XZ9ipYWrTXM1deRzgvMKNvzew7/5/lmvS/gV4Vtrb7R4k1Zd2k6SA7DO37VKc7Il92Yc+igmuO8H+AZtc8QyQ7o4baDLz3ErbYYIx1Zj+fuTgDk13fiLxjoOl+Fxqmp3M2kfD/wAMcx7v+PjVp+nyj+KWQ8ADhF98k+XmOOjTThf1PYyvL5VGptehJ8Uvjjbfs8fCzW/ip4oaGTVrzfHoVkxx9quCDhlH/PNAB06AH0r8+B8TdW+J2s/2xqF5NeSaw32q5nQYaRm5Oe/BOB6AYFc5+3N+1b4v/ai+NH2PUNPXSdGjuFstFslBaOyhU7fkYfKzNkFjzz+u9otjD4a8Px+WzSNaxKpKjqF4HH4V+X55mft5qnF6I/TMpwCoQba1Z5X8fvL0/wAcSWsDM8axqw5PBYZNcKhc9NrZ7muo+Jes/wDCR+Lry7YbWc4AUdAOBXOIoC8eteFUkjplHmdyj4gd49MdsbeOlQX7LaeEIY/u+ZgH6VL4mkZo1j67mAqt4ybyoLOD5lYDOOMY7Vx1tbJC5bM6T4Q2G228zpk5ya4/496of+Eht4WB+7uGO2eK9D+Hdp9j0Qsf7vAzXl3jiX/hKfiTFF13TrEOOFGaeHSTbDqH/CBS/wB1qK9f/sGP/pl+VFV7QDxC/sHgVWBj+Yg7c5OKglzM3y/Kq8YFa1zZtF8oXoMYqB9O2qR79a9Dm0NpU9dCjGQCAetWFYKfXnkVI1iwk6c8c4ojtGG5vvVm9g5dCWNVI5/n1qeOPePlX7tQRRYZePcVdt4/NJ46VJPs7ke1ih2ru+tU75WR/u7t3WteMLJwv0pbqwW5GD94dx3rSM0txRp66sw1tY3Pzwp7nbXX/C/4x+Ivg7rX9oeGtZ1DSZ8jd9nlO2QejJyrD2IrIj0Nrhc/Nt9KadEk3Asi/L0OK66eIcXeJdSinoz6x8K/8FePGFlpSWviDSdF8SCNQEllUwytj1K9TVXxz/wVO1bUdM3aH4N0rT9QbpPNeNMifRNo/U18qT6XIpXCnaP0/wA5qCa3dSuA24nn9K7P7UrtWucjwNNvVH0x+zz+2t4u8deOBovjDUlax1RisUscMcawuTwMqPunOOa+r9N0CHSLIpEvzN94+v8A9avy9sLt7K8V9xDKc7u/tX11+yl+2F9uitfDXiiZVmH7u0vnf/WeiPn8MN9c19RkWbQqP2eIfvdGfNZzlrhFyorTqe3a9A2kyR3lv+7khfIwK9c+H3in/hK/BzRg7WiTz4eehHVfzrzHWIpNSmW3hXJk6kenrXXfCWy/srUYbJdz7S6nPcEf419vh5O/kfGYqmnC7Wpm63qGqajqjSLfXkbKcHbMyqce2cV8+/DZ1/sv4Nxsu7zvhxcxYHuNHFfT1/4istG8O6hZyxq14WPlER9DkZ5r5v8AgmAW+CuRu2/Dq5I4/wCwNXwPGEX/AG7laf8APP8A9JReX+S6v/03M9X8I+FE0WwjCRLG38QArqdI09r26jhXjzHCgD/P+ear28DP833ecg+taOlu1vepIv3lIIJ7Gv0Jw5INx3OGMk6ic9rnR+MfjlpvwE8Nrb6fYSatq0y5jtoWEe49mkf+Eeg5OO3WvBfGX7SPxX8f3k0cviX/AIRewnUg2ujgxuF9DL94n3DY9q67x38MLvUtcuNSt2a4gvG8xs8tCT1U98A8AjsKzLrwRY+HYRc61qFjptv1ZrudI1x9WP6/pX8kcbcaZ3Qx08K4uGumm5+7ZDkmWVKEaqalp3PONH8B2dvefa2WfUL6T5nubmQzSMT1OT39+tdN4X+Ddxrt+0scO3zG3s23GT6msHxv+2f8LPhKrJDLfeKruPgJYW7+Sh7ZlYBSP93d06V4l8UP+Ckup+PUks9PE3hrS2Uq0FrC/mMP9qTGT+G0e1fnSwef5lO8uaMX1d/yPoMRi8FgoWpRTfkfSXj74yeEf2dbJoQ6654gUYW0gYFY2/226Ae3euO+EH/BQC6OuSw+LrOFrG6kLRvaLt+zAngY7getfI7fFXRnYtJdyeY53HzInyc/UU6H4l6G7KBfRAehUjH0zX6VwfRxnD9T21Ftt732Z+b8QYipmXuzjZLY/VvwpqGnfEjRo77QryG+tZBnMbbsH0Pp+NWNV8I3dtp7tJC0Sx9/X6V+cXwF/a8ufg34ojl0XUFnSZgslmc7Z/bHUH3FfpF8PvjzH8WfhR9q1Dw9rWk6tcbPIjudixDuSdp3sCOxCnn2r+lMh4kjmFHZqXVH5vjcqnRk29jPsomkjVmJzirRtg6nK9e/pV2x1bSLyP8A01bixk4DGNfNj/DHzfpWnaXXh3TdQhZprjUrdxhwIXjK+h5xX1WtjxZVFE53ysE9OlEcY3A/wtxXVave+GdTuIYbWOXTo2JMs0oYgY7AZrNu5vD+nDMd5eXzLxthtygP4tj9DVruRGbbuV9C8G33iy7KwDbax/ekPAroI/hFPEGkmmhjhhUvI7MBHGo5JLdAPc15H8bL3xjFY6frvgi4ntbjRHaabSBIGj1OMgDDA4ywxkDjOTyeh+Hv2q/2zNf+O9pPofiLx14p8PWyt5Vzo0NkkVquOzhWWRsf3X3Yz2r5DO+KI4CTpyi7vbTQ+ky3KIYlKTnp1R+iPgb9of4S6r4vk0PS/E1nrOsKxRFBKWjt0wsxwrHPZc/WvUP7Av8AUR86RJGwyqBxtA+lfgT/AMJRefD3XI5dF15dTGdylEdGBHqG/Doa+9v2Hv8AgqL/AGn4X/sHxZcStdWY2wP96UjHTBIyOPwr+dfEvEZ7mdL2mGm3D+RaM93H8Oyp0vaYTX8z9BIvCkluo3tDtUdm618l/wDBUT9tWP8AZq8EL4X8O3cZ8Wa1F80sbZawhIxu9mbPHtWh42/4KQ+F/C/h+8vP+Jl/okRcZhCqzdlzn1Ir8qPjH8WtY/aA+Kuo69qEkt1eapcEomc7VPCoMenQD6V+a8FcJ4yrjPbY1OMI69dTnyHI606/PXjZLuT/AAz8Q+N/EXj6G38MX2uNrmozEolpcSCSRjyS2Dz3yTwO9fqt+yp8EdYuPhI0Pj7xdJe+KCNyLDbLttRj7juOXPcnA69+tfHH7K3xB8N/si+DxdzaDdal4n1T5Li73KpiU8hEznCjjPHzEemBXqtn+3xcX97G2n+HZWuJSFjAu/mZj0GNnU1+lZtn2f8AtlRypOMI/ie1nOHr1fcoQtFdT3iwTUND8TXOiakil4Y/Pt5l6SoDz/MVa1PTvt1o3Lb85Ge4pvgC71zxDpEOpeIbGGxvJAfLgDFniQ4PzZAwT6Y6Ad8gbj2m/wDhwme9f0dwfiMwr5ZCWZRtU6n57jo2qabo8P8AiDov9n/Fr4cyspy2vXAPv/xKdQrvv2cvjHq/hH4WS6fBa2xtofEev4ldjlidZvCeB7kisX4zQgePvhq+Mf8AFSTKD7f2RqVXv2SNNtfEvg7XrW8kaOOz1/XJPlxuP/E2u+Oa+awcHHjWsl/z5j/6UdldqphYuXaP/pVQ+gfhlqK/EvV4768TyrezGcHnn/I4q18Xvilc3V4NH02RoQq4kKHDRj+6Meo5NZHw/mXQvCVvHF8qzTMWPcgdK52e0ktdbmvZMt9qcvuzX3eK5pWuTg6dOLbRND4fhiiJZVlkkO4lvmz+fesiTw9deHNV+1aduAYjeucBhmusjZSildp3dD715z+0d+0npP7OfhlpJvLvvEV2hNhp+7GzPSWbuqDsOC2B268uJVKlT55aWPSw9OrUqcsdbnM/tc/tSeH/AIOzWOiXul3F7rV1As1xslCixDHjOMliQM4yOCPWuZ8E/FrwV4pS3kXxJpto0ihytxOIzH7HJ618ZfET4g6n8TPFd9rOsXTXWoX0hkkdumT0wOgAAAGKwRJ5knQLj5ulfBYjPpyqN7o+4oZJSjTSe5+jeq/tx/DX4Z+El0O3vtQ8X6tNcLttrMmOB3zhd7t94ZPGAe+K+bP2zP2pdc8eeNdbE1yqaboN19k06xQ7baDY2CcDlidpJJ5rxz4HQx3vxf0Frj5re2vFu5cj/lnF+9f8NqGrzeIbGGa6utat/ta3wnl8o/xFvkGMcgjcW49DXh47NKtZ6nr4XA0qK90yPhj8Ur7xb480Gz1Y28kdjqO6AsSQqscswz6nnH/1q+hvG17pttozNKkyOqnaqsFVj796+fvDvhmy0v4qXSaEzXFqp8yD7YB5qoTjkY46dsHpXq3xdih0zwcTc3UzX21WVMYVsnnGPr3rxYq87s74bHkOoyLPPJIqld7FiM+9UhJs+796j7VuAGMDHJNOjkxuGcL6ilUldGGlzL1GVrrWraP7x3ZK+1VvEZS/1uOPO1lAQD0qzpsgm8RSNn/VrVXTR/anizcoyvmcn0Fc03Z2G431PQNOtW0TwO3zbp153e3NeafDVG1X4ks3l+YylpBmvRfHl4dF8GTEjkJt6dzXK/s96V5+pX14wHyrtQ+nXNK9otmPLqd1/aF9/wA8V/KitLzV/wCey0Vz8wch5MFt79FeOQMGHr0ok03L7e/Wuh/bt/Zwk/Yi+PeveBxrFtq154dvprKaWF8pJtYFWX2KsDzyOnavP/h/4wvvFFzJbpbieSKMyN2IUda9hSUo3jsd7jbQ2DpzPF8u5ec01rLy2zztb2rVtrtXm8to2jbo2e3tV/7Arxf3936Uhximc6LDcN3LCpY9Pyeh+btW3Jo2cKoOV5po08jkBsZzTZPK7mb/AGcx+6D+XWrFpabivyHHQ49a0Y7fy/m4Y+laPhzRptUvEt7WGS4nY5EcYLNWdSooxcmaUqadRX7kdj4Qu5LUyR28zRLySF6DvTP7MzFjbnuD6V91/sQfslP8WvhL4tk1K6s7G802zmSNZVIkD7CV/wBk88c14zL+wn4w1bUHt9Et11SRVLlVBXYAOSe2PT1Jr5/DcTYOdWVJzV0fYZ3w7SoUqc8DJz5ldrsfOr6UoT5gMnoKzrvQTI+7aFx6V1+o6TNZtIkkXlyxMUZHGGRgcEH3HTHtVEx+ZHt2898V9H7X3bo+LlFrdHn+pWZtLhh0P061b0y3ad1bPzLzx2q/4jsT55OOh4NR6EmJl+XnPPuK1pzd00cNaz32Pq79iT4j6sug6ta6pcXl5a2qoLRpm3eX6qCeQPavp74NSzX+o3urONsNrEcDPGTXgf7M+nR3Pwcs5I4QGaZi5x948V9T+EfDCeGfhzb27rtluMSSDHWv2LI5VZUI8zufmOc8iqu2lzjLjSH1C+eZt20sT9ec14V+ztbfarz4Mrj7vw3uW/XRa+oJLQL8qKNvoB2r5s/Zai+0at8G1/6ppdH686J/jXzPF6/4Xcrf9+f/AKSjjwcnbTu//Tcz3a30/aq5Xa3pVxNPIH3cDrn0rSisxv3HCkdfX/630qX7Hx0O09OK/SFZ6HkyvfUoWqNAV2nb2BFcF8Tv2UfBPxZv5LrUtMa3vpBzc2chiY/Ucp/47zXpy2bAA/0pyW7MSP6V5+MyfBYt3xFNSfdo7MPmGJoL9zJo+Utf/wCCXuk3EzPpfie8t+u2O5s1lK/8CVh/KsP/AIdb37P/AMjRp+3+H/R33fzr7KigyC2ORxnFCIwbv/hXjVOD8tk9I2+Z6EeJsdHRs+P7L/gllMz7rnxVbr6+TZF/5sP5103h7/gmL4PsJFbVNS1LVGU5KIiQxt/6EfyNfTZXcO60kkQY+/0rWhwjl1N83Lf1ZjU4ixsly8x538PP2a/BXwqlE2ieHNOs7xRgXJi8yb8JGyw/A120duEVuxPGRVqW1JX5V2574oWwKH5vrX0OHwdCgrUkl6I8WviKlV3qNsqpalW+78vSrHk5UY6D2qaGAlfm+XPb1p7RMf8AZ454rp5kcepWki+Taxx/WmwWgf8AE56VcEO6P5uaFi2rxn0o5kaRuVRBt+7u+XjNed/Gj9ljwP8AtAQt/wAJJoNrdXmMLfQ/ubpPT51+9j0bNenGH93xuqNLXeBx+VcuIweHxEeWsk0b0cVXoz56TaZ8Q+Ov+COdnPf/AGrwx4mNum0jyL+Etz2+dM/qBXnvjv8A4JefE7w9e6XeeH7PTdQurXK3EsN9HGJiGypwxXtX6RR2wHT5j06VNBlF28bhxivl8RwZgajvFtH0OH4tx0FaVmj85fi5+xT8YPGnhCzs7Xw2PtEpD3Sm/t1AwOOd/NQ/Af8A4JgfELQ9WXUNbsNPsZoz+5WS9jkCcYydhY/pX6SMjO+cYPtzTfKK1yx4Ewajy8zszq/10xdrRgj5LtP+CbUniFof7d8QLDBG25orGElm/wCBNjH/AHyetez/AA0/Zo8I/CBY5NH0pPtsfH2y4YzT/UE8Ln/ZAr0kxszelN+zYOOvevZy/hfL8LZqKb7s8fGZ9jcQrTlp5aGVJa7224+gx0qKbT2kDbu3pWwLVmfpnnrTWtsAZ/i619DolboeNzM8a+PVp5Hi/wCGbHv4ml/9M+p1jfAzQ5JPAGoXEBaNm8R67vKsRuA1e8H9BXUftGQrF4k+GOBz/wAJPLyT/wBQfU6n/Ze0g3vwpuW2nbJ4j1/8f+JxeV+aYXXjav8A9eI/+lHrzv8AVo37R/8ASqh6fotzj4Z6dIN2YSVcCrehrDrVmsRU9MnPFL4M0AyaZqGntwpXzIie1Jpdk+mzKyNtZT27V93iJOPu9CcHGM07PqfJX7Vv/BSaH4I+N9V8JeFdF+06tp8pglv9Q/1MTY/gj/i+pOPavka68ear8Rr+71rWr2e+1LUJjNNNKx3Oe35dOO2K7n/gqDoEOkftX6g0OzfdWsU8wAxliO/qa8z0aLydDt1K7Sq1+XZ5j606rpyeiP1PK8LSp0YzitWhzHLMP7pJC9jVd2YuBztxj6VYP3Dn7w5HtVVztB3H3r57lPV5jsPgxF9hn8QahIT/AMS/SZVjx1MkzpAB/wB8yOfwqDV/DmoajbW99pzo01iflhADmXCtJIwB4IUAEg9d1aPg+0bT/g/qE2f3mrapFaDHULDGzvj6tNF/3zXP6b4itdM1i8/tCS+jVbeR7TyR+7kkI2jP+zhSOOv4muOctWbRktmdx8DPDUniqe/1zUbjbLqEpLRqm3ocnA7DJ/SrXx41V9EtI7WSLNvcP8pzn5fb9K6L4O3ljB8NtNmkjIkkVi6Z2Y+Ynp/h7V5r8ePHEPiTxAsccMcUdugEYXOF9epzU0ou1zSTUY6HKzwq8ZktJGkjXqv8Q/Cqp1dVgYfxAcjPSsmXVX0yRpI3x7A9akM0Xiaw8w/6PJypwOtC8zllrqLo95stbyZPmZiQCe3+c1p/C+0a51Ddt4/vGufLLoeiPD5m5WbqOp+td58I9L8vThMx+9zx2rnrJXuU7xRV+OWqtFYR2qsQJCCcHrWt8GNNGmeBfOKlftBLk/XP9K434rXban4ujt927ZhQB6mvUbCyXSPDVrZruHyKp9zjFY1l7mhMe7K32xf+ea0UfbbX/JorDlYcx87+IrvWvin4mu7y5kvNV1jUpWmkkctJJNIzZJJOTyTnJ961PgrcXHgb4qaet5BJEs0n2eXzBtyG+Xv35r9CP2Uf+CdR17wpNrWqLD4V8K2LLNPrt4RGx2ZJ25+9n0B9a8g/bh8ceEfitrlj4C+E3hp7qHRdSM0+uTRFbjUZvmHyjqqcnAPoDXr83K7M7oLm1R4b+0Z4buvBWsLLC8ix7scVwekfEy/glUygTQ+vQ17B+2PoXiPRfDuiS6lbRwrGFiuAvzMrAdSf6e9eKjQwNO3Ddt6jHSpoyUlobzi4vU7nRPivaXhWORjHI3BJ/wAa6XT/ABFZ3kX3lbPo1eETqbe42joOgrV0nVZoE3IxX2zWugpLse821vb3C/u2Vd3Tviuq+C/jP/hUuv3mqblj/dYEmzcQB1A788dK8C0vxlc2ir+8yewzW3pvj66muUhO1t3GG5FcWMw6q0+RuxVGs6NRVOx9sfB7/go1pur6jLYwtbR3Fz8jBlMLyepx0bHPFfoN8WPjr8Pf2VP2bvDesaPA2rX3jdUNgq48++dtuSx/hRSQCP4eR1r8MbfVtPTUDJcWxWWMjlRyDzXuOlftLL4m8G6Rpmqao902iRPBZLOcrbRsxcqvplmJP4egr4DMOB6NWvCdN+7e8l3PqMLxG/ZtS37k/wAant9c+I+sajFAIJNRu5riaFf9XFI7sWVfUAnGa4aWwX5l9DjIrobnUIdafzI5kbuSGzzVUaPumJ67h+tfoNGn7OkqfY+XxFTmk5dzite0bz0Zc/TisbT9Na2udp78V6Bq2j4HPPasaXRP324c7TwK6acrHl1oXPpH9irxXC/hy402ct/osqyqo7g9f5V9ZPq0ms2qSLIGhwNoXsK/PP4R+LJfAXiSG8UM0X3JVHdT1r7K+GPxAim0qC5hZrjTbjk45aE/Sv1XhfNIzpey6o/P+Istkpe1Wx6RbQh0wx6D0z/n/P1Hyr4Q+EniiD4d+DLHWPhj8RYdZ8K6JFpP2vR/E1jYHHlQLMokg1OJmjZoIzhv7inANfWFl5d5Ck1u6yQsM7l5/A1oW22Q4ILV1cRcKYTOXSnXnKLp3cXB8r1snr6Hy9Os6OiWt073aasmtGmt02fKP/CtPELDB8D/AB0wOgPxE6f+Vml/4Vt4h4H/AAg/x0/8OIP/AJc19YvpfnpuXr2HrUI06WAt614P/EN8L/0FV/8AwY/8jZ5hVtfX/wACn/8AJHyqfhv4gx/yI/xy/wDDij/5c03/AIVt4gC/8iP8csf9lF/+/NfVUaE984Jpzp29elV/xDbCf9BVf/wY/wDIzeY1bdf/AAKf/wAkfKY+G3iDH/Ij/HL/AMOKP/lzQfhv4hx/yI/xz/8ADij/AOXNfVn2UqvNMEDEfSn/AMQ0wj/5iq//AIMf+RP9qVfP/wACn/8AJHyuvw18QHn/AIQf45f+HFH/AMuaU/DTxAB/yI/xy/8ADij/AOXNfVH2dtu6nNDk45Ht60v+Ia4Rf8xVf/wY/wDIn+1Kr2v/AOBT/wDkj5T/AOFceIG6eB/jl+HxFH/y5oPw28QH/mRvjl/4cQf/AC5r6t+yfJ8p2/1pkkODgn6cUf8AENcL/wBBVf8A8GP/ACHLNKqX/wBtP/5I+Vl+GfiE/wDMjfHLP/ZRB/8ALmk/4Vz4gI/5Ef45f+HEHP8A5Wa+rI4iF46d6Y8GX+5uqv8AiGmF/wCgqv8A+DH/AJEf2pVS/wDtp/8AyR8sf8K28RKv/Ij/AB0XP/VRQP8A3M0f8K38RH/mSPjp3/5qMP8A5c19ULbMRnp7elOWAr7fWj/iGmF/6Cq//gx/5CWbVfP/AMCn/wDJHymPhv4hI/5Ef45/+HFH/wAuaP8AhW3iFB/yI/xz/D4iD/5c19U+SQ3qM+tKYCqdO9P/AIhnhf8AoLr/APgx/wCQ/wC1q39Sn/8AJHyqvw48RHp4H+Of/hxB/wDLmnf8K28RA/8AIj/HTP8A2UUf/LmvqmO2cjpj+dSG3x0z+NL/AIhphf8AoLr/APgx/wCQlm1Z9/8AwKf/AMkfKf8AwrjxF/0I/wAdP/Dij/5c0n/CtPER6+B/jp/4cYf/AC5r6u8jCc//AK6cLZnQN+mKP+IaYX/oLr/+DH/kVHNaz7/+BT/+SPk8/DTxEP8AmRvjn/4cUf8Ay5pU+GviMnjwP8dP/Dij/wCXNfWItcITz7+1OW3+UfLwT1qf+IbYT/oKr/8Agx/5D/tSt5/+BT/+SPk0fDXxI3P/AAg/x0/H4jD/AOXNH/CsfEn/AEIvx0/8OKP/AJc19axWnmvt/i9auQeH5Jj83T0pPw3wn/QVX/8ABj/yB5pVXf8A8Cn/APJHyHpvwr8QWuu6fqH/AArT4u6ldaXM09oNR8Z2+oRwyNFJEXWOfVnTd5cki525Ac8ivd/2T/h1q3hP4QwR69pU2i6pPq2rX72UssMslvHcaldTxBmid48mOVCQrsASRmvWLbSYbVcsvQdaSSZriULGDmvQyTg3CZVipYylUnOco8rc5X0vft3JqYt1ocs1rda3k3pfTVvuytYwLY6ujRr8rHac+9V5tGI1eRFjPlqdxPtXSab4X+zW/wBrumKr1AH3m+g/Kvn7/goR+1xZfs1fB6+WOeP/AISPX4WtdOtlYblBGGlPcBQT+OK9/GYqMYczex1ZbgpzqKKW5+cX7dvj+D4n/tX+JLuzbzLWG5FpG394R/KT+YrmyvlWqqW6AVxfh6STVtfW4maSR5pTI5c8sSSSfzrtLngBd3PfivyTGVvaVZT8z9ao0VTpxiiHG/3Y9wKju4SjN029qnjYqO2akFlJd3UMKJvluHVEHqxOAPxrilrqdEYnaasn9h+AvDdk20NHZS6jJzjLSu23P/bNY684sfFx1q+XT76xQSW5iEcitu3L3A/M/nXpfxYmhXxNc28a+Zb2Ji0+Fc/6xIVCH8wp/OuO8JeBvN8bWHnWsym4VbiOMDDYIyP0yfyrz5WZpKne1j3TwNpEVnAq3Ui3CIpwG+76185+MtPn1nxlqUyr+7adgoHTGeK+l/GFnceDvAc001v5MUcfyS4+92wfevn668Qwq7+XE0jA9xitIysrlVo6WMe08EMXG7kN1zV5vD9vpu1WI2445xzTm1y4nXoseBngVhyCbUNdXe7ERjdjNZ87aM4WKviO1W7uo4VHcZx3r0zwzaro3hfcw2qqd+1ef6Sn9o+JtvVQ+APSu68ean/Y/hCRfusy7Rx1rF6tJE1DgfDEX/CV/ExWwWVZS5J9BXq3iG9+yxOfurAhYH+X864b4CaSWnu76Rf9hT+ea6Hx5qa2+mSE/fkbgZ7VNS7lZEyskcv/AGo395qKxd8v979BRQTyo+vPjR+0n8Rv+Cjvi6HT7K3Xwv4B0/ItNKtgY7W2jXnLY4eQjHXIzjvivdvgb+zPofwr8PadcXEcNra3V20N1fSr++maOPzJQuemNyD1JYfSuw0H4Pab8J9EktJ7KO0u9LuNMhtdJgHz3Mcym4cnvnYqqc9GlHQYra+IV7/ar3V9rbr52s202taLp0DFkD3N2IiMeoSJjn0jX1oxFbm0PqMPh4xifLf7Z/w6n+InwklLWi6fDaxR2rOw+aSZt8yt9CuPwA9a+EdGdZPDOWHzQ5jk9iP8/pX6dfH+6OuaJNdakn7zVlme1sICdsbxqsMWffgjP+wfWvzP+LOn3Hg/4katazQfY4biQukYPAI4P45FRl8rNxZGOjZc5wWr5+0kr6nApdOuDG3vUOqOxkb61Alx5X4V62j3PL5nudDZ3SuMN371es7o2d1G+4/KwNcxFqRgK9MfzqymsfI3rj161FSmpJjlU5lZnrkE8ctk020MJAG/ya4/xS90sh+zoys3TYK1vh14mt5dGt1usmO3l2Sc8hR3/n+VdF438beGVdIdHFxN3MjrjJ44/wA+tcOHpuMm2zmp6NpnF+Gh4qmWZrdZisKmV8NztA5/z7VoaL+0FrGl/LJIsiR9BIOf88VpeH/HVxpF/wDaLWMZXPyOeHHf9M1xuueHp9Tlke1hJW4cuxA+UBjnj8zXoSlc35l0PQ9L/aiguxtvLVT6lDzW3pvxh0HUiGaRoGbswrxO3+GN0xJkMabT37it7TPhc01jMYp2kmiTKp2eq5Ykyjfc9y0DxvpN3xHfW+egG7FesfBr4wP4EvV8uaO5s5CPMi3bvxHvzXw7a2U6zjdmNYzjng1rW2qXensDDeXUeOm1yK6sHjXhqnPB6nDjKMKkHTlqmfrj8O/Gket2Ed/olwjrId0kDcqfXjsa9F0DxNa6qVjcfY7rGCkvAJ+tfjz8PP2p/Fvwc1m3uLHVHbDgNbSfMko75H9a+9P2c/25PC/xusYNP1potD1pgAIp22rI3+w/A59Dz9a/Usn4go4mKjKVpdj89zTI5Urziro+rQrWrKWX5ccH1qzbtHOPmwueDmuT0nUtQ0aFfs8i6hbnkRy9QPZq3tO8V6bqW2O48zT7jO0rKOM/XpX0q5Wr3Pl5RknZmhLokb/dP5VDNoDLgqW4rQgsZkhWSJlmXnlDkVJHdPENsin16VRjK6MWWweJumajNqy/1yK6SGaGQ5bhugqVtPt51/hz6UzNy6HKLbYB3Hgnjini22j5V3bjg10kvh6OT7ufXpUaeHGZj5e0r6mglSRz0lvs+8vT3pfsyy8rzW0/huYtu2555xSPoMyD7vy9sUInmMVbcpuA/PFBtdp3fe5xitSXQ5geI2P4Up0iXzPmjYd6rmJZlfZPlJWj7Nz83atV9HlkfGw+tC6LMP8Alm2O/HSjmJMwWYK8ClNqdvSthNIuHXiE7alXw3cS7Rs27u+aOYJNGHHCGHzcU5bTKD+9zXRQ+D5d21tq+5qxD4SG7DNwPSlzMcZJHK/Ytzg5/Cp4rIsOn0rq08MQwdfm+tTG2t7OL+DPvSbY4ySOXt9ClkYfLtFaEXhMN8zH8K1GvUHyqFJ7YqNDdXrYijbHTI5zU6mik3sV49Lt7L+7u69KZPdIp/dru3Y5rR/4Rpjl7qdLdeN241q6RYaVbJ5mJLpuAoVcKfqx4A+gasZ1VHVm1PDuT1MDTfDV5rU6rHGxGeoGa6KDw/Z+GYWaXZdXa/8ALJT8qH/abt9OtHiTxvD4b0S5vLy8s9F0m1QtPI8oijiQdWeVv15A9q/Pb9tT/gtNovhO2vfDvwvVdVvtrRPrjri2tye8KHlyP7xAXpgHrXk4vGRhHmm7Hv5blU6srQjc+hf22v29PDH7Kvh9m1CaPVPE1xGTaaTEwDKSOGf+4o45PJr8cfjv8efEH7RPxFvvEXiC8a6vLtsBQf3cC9kQdlHHH4965PxX4+1j4h+JbvVta1C71LUL6TzZrid9zOx/z2qGxtiXUL+Rr4HNs6db93D4T9Dy3KqeGV1qztPAFqXnDZ+6veulmUbm/vZ7VkeC7dbayaQ8tnFajlmIZR8rHJNfOuV3c9KXcdGMc8/411vwnt1ufHdhNIoaPTg18+fSFTIPzZVH41zEZV4f5V23wotFtfD2uai+5SyxafE2PvF23tj6BAPxrOo/dKpu5m+M7R10NtUEsn277eqxKhwxJBJP5/qK6r4H+J9V8XeOrjVrzypJDaQ2QEcI3ARKqKQcccKMkY71hWnjVfAXje1ur/T11HS5sOkZHJkTcq7fpJgkdwDXrHwc0RvCvgu3a4hWO8k3yvnkqWYnbkemcYrz0joje5yX7S/iC+FktldSy4kO7YWyCK8XsLEMN5/XsK7j46+I28R+K3AfzFjGwc9Mf/rrkbRNgzjoMVty6WM61TUZPGsUDNuY7eayrNVS0urpiVZhgVr6xKsFid2Pm4FUtRtxHpUcePvcmsaklFWMdd0HwrsfO1N5JMtg5+pq18aNZLRQ2q4+9kr69K2PA9mthpjSj+L5q4vX5m8WeOFQLiNZB+QogmtSW7s9H+HunHw/4LgUjbLMMn6mue8fP9su0t1bP8P411yXKxWS/wDTFOnbAFcp4btW8Q+Mdzfdjbewrnu9WRLVjP8AhX8nq35UV6Z9lT3/AEorn5ma8qP0d1bVF8L6xHrl9cR6t4ourrVIrqNzujgCRrBbyfTe0jD2jWuR8batJ8O7ryWH9peKfD95bFVILRw20UDyyL14VXZQf9xjXdy2Vv8AbYdF0Mre33iTTrOzuLqY8W1zJcLOUB9cIiE+m/3ryH4va1FpsO7Td7XYF1bazesuflndYQSen3c/9/KTlfQ+qWhzPjS6S1WbT4XW/wBYkitrlJuPLgTy/PkHtjIB/Gvgn9tPw9HY+IFvLdpLpn2s838IZxuYE/l+INfZOv6iJYNQs9PkRbfR5rj7VfFvnuYXZIVx+H6NXzh+1F4cj8QeHtQ8hvs+m2MjyWoYEvcgkIDn8M/jTp+5URnWjzQZ8m39wo+6dzVVjbzX+8V7mtCPw3czysuzbGrYMjdBUN7a2djIFW58984IQcCvd5tLnjS7EYsY512+c3rz2qQaM3ylbiMsxwBnFNe6hjOF3t36USQ/aIVk9+OaPMXL2NfRRJpNlM00ipHOvy/NnP8An+tRpqkyQ7oVO3P3m6Vs+EvgF4o8a6GNUs7CT+zhuxMzYRtoJOPXp+taVl8OpNMtliumUFeNp9eaiMU2yXprYtfs+69p8njlbfXmaO1u18sP2jbt+frXt2p+HNB8PaXN9nuIdQjmJ8nyVz2wFH09q8h8E+C9P1LxPYw3s/2e0mkVZnVeVUn5sfhXvH7Ta6P8M/h/a2th5MEdmEa3kTqx46HuDirq2UUkVTs7s8E1bxfp1rcERzK0ZY7Sfr3rm5fi5Na3DG3jXCHbnPauX1jfrmrXFxGvlrMxbA9+ar2ujykbdp69fahbajlsb1347uNRu2meOLdIc4HAqKXxDd352ho4V/2RyK6r4R/BtvHuoiJRNJ5K+ZKEAwij1Y9P1qPx34Ri8DhpGjzbzMyD5tzwsOzcDnms5KOxnKipK5l6VbW8Ei3EvmTSHlTJ/St601RPNUqfm/lXDat4qF/Hbr5ewwKVJXvSWuvzJynXtSVKalzR0OKrhnLR7H1B8GP2+vG3wTEVuLtda0mMhTa3hLbV9Fb7y/qPavsL4K/8FKvh98WYYbTWZP8AhH9QkIHl3v8AqifaTp+eD7V+VtnPfai3yxtJsBZgo6CtKHRrryLaVo2jW45U9Bn0J6e9fYZbxRicMlCo+ZHk1+HcLWjroz9xvDj2mqWyXWi6p+7m5UxyeZGR/Kugh1/VLIKtzbW99GvG+M7XNfiP8PP2ivGfwL8TRW2j69qWmm3kUSRJPmJgcfwtleQT2r7E+Hf/AAVs1jTLxrLVPD39vNHtWMWTFJ5Fxy5OWU/QKPrX2mD4qwlXSb5X5nyuN4RxENafvL8T7+j8R6Xdv+/W4sm7h4zgfjWlaafBfL/ot5DMewDf/XrwLwR/wUD+G/ib4f6Nr+paoui2usGVIhfQsNjx43KzKCB94dSK9P8AB/jHwT8ULZLrQ9c0nVI2G7zLG7WTH/fJz69a+ho4qjWV6ck/mfM4rLcRRf7yDR3A0i/i6KW4xwetEctxa5DRMfw6Vl22kyLt+y6xdQKOQPMDD9c1pRnxBCFMN1b3S9xNDyfxBrflvsefyakkWpMpwI+fepBrKJ8rRinf2nrAXbJpmnzepSQqac2s7V/faDKuO6TLyfxo5WT7MUaxEv8AD+vSnf2nDI46fKaij12zK5l0TUo/XGG/lUker6Uzk/2bqiDqT5WaOUnlJWu4dy/d9zSSajAON3fHFNGqaOcf6DqjKT2h/wDr0+PVtK8vjTdUkxxxFt/xpcrM5RQiajGB/eUcU06ku77nFWItVtzjytB1GY9t0iLxUzaldSDbB4fSP0Ms4/pS1D2elyg99I2VVD7Gnww3d0w2RuzAcZHWrom1yVVVbLSrdc/e+dz+WB/OnrpWrXassurrbr3EEIU/1pOVio09bFVNBvLwbpMRqByS2MVVks9Psg32m+i3L/Cp3NWg3g+3fCXFzeXbHu0h/pxWL4j8eeC/hsG/tTVtD0tkGDHNcoZz/wBs1zIf++TWFTFUqa5pySR24bAVa0uWnFtlq2vrcj/RNPurg9mcbF/M1aS01S6Ta00FjG3RYQGcfQ+v4HvXH6j+1X4Ng0KS606bUtZuAP3Nrb2j2wlIYD/WSAFeCTnaeleRaL+2J8QE+N3hq81Cy8L6P4JttSjOpW6bp7ue23fPuZzhmAyRtCjgGvmcx4uy7DRb5rvyPqcLwfmElzOFkfQHia90X4daO2reIL6y0+0UZF3qNx5Suevyluv0AzXzP8Yf+Crnhnw/ctY+BdNm8TXu4KLydDb2KEnHCnEkn0+Ue9ebftgeNJP2rvjBfeItWuprzzGMVlZwtmGwgH3UQDgY/nk9xjg4vAXh3wZZb7owWrKNyxjEkxYY5xwFP+fWvi8Vx5WrQtQp2b7n2mW8G4eilPEy17Hkn/BRf4m/Ej4m6tZya94nbUdJugDHp8GYba0kULuHlD5SctwxycDrXyc3hKSS6jWWbazt8xHJr6W/aR1xPHeo2kNvCtutlEUkKOX81yxO7OBjtx04rxGfRJINZWM8so3Zz1rxP7QxNePNiJantyoUKT5aCsiHTfANiiht8rMRk59auX3h+106FfL+81aUOmunzfgRiq9xC8lxtKn5OB75rDmuy9ErF/RYzb6Ym37zc/SrmxljUZ/Wo41WGNYzxtUZFPU7No+8WPStJPQx0LUasQv3WC8n29P1r0u4tG0T4faFZiP95dGTUnVTzuPyIPoQoP8AwKuD0bQptReGGFd0lzMsKe5Y4H6kV3fxH8T2vhjxPJJKrNZ2bR2UQTGVSNQAR78Z/TvXPUeg1o7I818C2cmv/FrSIrmS6Nqt0geF3LKhDZJ/PtX1nqmt6T4K8OPceS1xIiN5aM+0DjHT+leZfCTw74bf4j6hPp0kk1pFFFBDct0unK5kI44BJB5wRjHY17RqnwFuPjRpq2Ol32i6LFbyoLi81OYxpHkN0VA7kfL1Cn6VyymoO72Ommnax8da/M1/r00g+USMSOenNRQ2rsCfl+XoM19HfGz/AIJ2a58L/h3deLtN8VeD/HGj6WU/tP8AsW5l+0aWHYKjyxTRo4jLELvUMM4BxkV4SlgqSMo49sYP+etV7Tn95dTjlGz5Wc7qFo1/eQx9e5zTdRsptQvVjQfLHxXTW+jMl4JVXcccEjNamm+E5Lss3l+XuPJIxmsakdbkxOXWCX7H5RbyhjacHmoPD/g5be9ZkWR5M53HvmvQ4/A0ER3TbW781aulgs49qKobGMqKJVLKxSitziNb83S9Ek3NtklOznsKvfCvR1t7Ca7k+9JkD6VneM521XVY7VADghTXbWWljTdGgtlUZ2gZWuetJLQnRvQj87/ppRVn+xV/2qKxuUfpPqDxvf6hoOgzLHptrdNqiX75DuLW0csqt/wNyPdlrw/4vXlvrDxrHmw8PXX2aC+nPWaVd0rH8wf++RXr3iSOTVPDsyxstjp2jx3N5aLIpWS6imnji6nqPlAHHRDXjnxvnjmF1NPE1nod0J5dOt0XmSZIwq5/Fh+ZqXJc2h9VzJo8VOu/b9Ntbi9XydPs1is57dDtkuQWeXJ9e3X0Fea+OVn1KxaO7wJYVWOK3HzKISGbJ/P+VdTq+syJqUlzdqsk1xvhW1OQIWSMIrE+o4P1Brl/ESyWt1Mqt5lzlorl+ojXAA/I8VpN63MY22R8y/FvR7nwbr0lh5m6Fgrgjp8wBrH8L/Dy48V3kscH7tY03tI4wtevftL+CZJfDUOpRrza/KzEffycD8sV4bFrt5HbNCLqaMN/dYgV6OHqc9M8+vDlkV7+1+w3zRfe8tihYHg1q/DrwdN8QvGem6Lb/wCs1C4WIEc7ATyfw6/lWJMsgI53An73rX03+zl4f0j4HN4c1PVFzqWrXUSqzdYwxHA9gMEn3pYnEOlDTc9LJct+tYhRlpFWv957L4k8W6d+zx4fHgmPT7zULe1tR5bxICylhk7s9zya+WfGvxBu7nVbjbo9zbwrIdm/lgvbNehf8FENb17wZ+0ldi3unh0/UraC6tcAbXUxgE5x6g14fa/GTXLObdJLDcKf4WTdiscHVqSpqbS1PYzvB4Gni50U3Gz7FiX4gXaStst9mDnkE4967Cz8DePf2kPDdqwQ/wBj6KxiSWT5VBbGfcgZB/GsnSf2iWitts+k6Y8ineGEI5+ua7L4a/tu6h4Utrqxm0+BtPuDkRxnbsOMccd+Pyr0Kcqkt0eHiMLhoQ5qVS7fkcNB8M5PCAmt7yRXmiYpJjkKw4IFTRWdsiDK/Ljriszx38bZvFWv3NylqkEczE7WYsSe5zxXPv42vJMABVycDFXzdzzeXmdlqfZX7Jni7w1oPw+1Szkjjg1ZmaUyPgecuOg9Mc18w/GzxrH4v8ZahDazf6O05w2fvc9f51ofDL4WeJvHkiytejSrNx808rbDj6f/AF69s8A/s5/DDwX5cmvX8+tXmc7YwSpP/Afr61xVsTCm+bdn1WW8J4zFpS0hHu2fLtv4W3xjylZiepwTWja+DLkurfZZpFXp+7PP6V+hvw88HaTq9vGvhT4U32pR8Bbma2WKM492Br1HRfg94skVfO8F+FdJhYZInulYgfRVrjebPpE+uoeFsqi5p1l9zZ8T/CL9nfULz4V3GuPbratMSIkKctGO/PPOa8h+MOpr4WihtbeZRb3Dvuts7pIHGAxH+yePxBHav038X+FZrDSmguI9CCqMbILjao+h2ivlH4w/Br4e3epyy6xHFZ3B4EsN2Dt/AEjvRTzDmldxPJzTw9qYeNoVU/wPh+S8mmu2mZ3d2OdxPJrq7L4gXmjaPIlpG8DSDbIy/e6dj17npXpvir9lXQtWUzeHvE0Nxty3lSFWYDqOhz+lef8Aij4Ra34Z82Py/tCoTkod2a9WniIy02PisRk+Jw/xR+53O0074w/8JF+zdp/hmS1g8zS7+S5FwqAMwYchj39s9MmuIn8ex6bpiSafNdWl6vzCSMlXX6MOareD4rjSdOmguopI90mQpHrVW6WTTy0TwttbIwV6jnoa66eInGV6cmvQ86rh1JWqL7z0D4ff8FAfi58OpIxp3jrXHjjGFjuZ/tK49MSbhX0F8Pf+Cy3xU8OeQuqxaHqkbKNzXNn5Uh98oVH5CvlH4RfD2TWbz7Y9s00MeWVD6CvXPg7+y94y/al8ZtZ6Xo01vaqdpkljKxog74+nOT6171HPMfTtabPna2X4Ko2pwR9XfB3/AILj6r4p1q5s9S8HWTG2G5ZLbUHQMP8AdZT/ADr0iD/guF4R0vUFtdU8M6/C+fmELxyj9cV85237Ofwt/ZjvF0rVNat9W8WOh863t33snscEgV4R+0BrXhq48Xw/YbHyfLGdyEMfxPtXof62Y6HxpHDHhvAVn7kbH6V3P/BbL4S6TefZ76PxBbytGjZFnFIBkZ5Kv1rQsf8AgtP8D7hAJdW1aE46Ppb/ANCa/G34j6ba6Zrq/YZ2mt2jVnVmyYpSORn2Nc6bplZvm57V0Q40r/yomXBuDfc/b7/h9B8C7bn+27zp0Glyg/yqrd/8Ftvgja5xfa1N6eXpbjd+ZFfiSt2zZ3FuD0z1oN3l6p8aVv5TNcF4Pq2fs1qv/Beb4S2ETC10/wAVXTL6WUS5H4uK47Xv+Dg3wnabhpvgnXrpuxmvI4P/AEEN/WvyW+2MVwuRk01rgo2F6+o61zz4wxTVoxR1U+D8DHVps/SvWv8Ag4F8R6xfrDovgHSLdpmCoLu7luWYk4A+XZk+1aGv/wDBRP47eIreZpLrS/B8eeY4tOQS49hJvYfXivzY8ESKni7TWyy7biM5A5+8Pxr7T8UXkmqwLOBLKZQG3lTzmvFx3FuOS5U7XO2nw/gabvGCudTe/HHxv42spJte8Z+Iby2UfvHu7xjGx9EhXCfpWDZfHCw8HzPJ9n+TjNxcfvZDz1I6fhjisnx1Jh7e1jfFrHEixnoGJAyfxrg/HujXD6fPBEuZOAc8cfzr5jEZhiMQ/wB5Ns9fB01QlekkfQXh74wL8T7Rlh1IyW8PzOfNMMcY568gDjNbmmahotnZKsdxFfbe8cx2D/gXBNeE/BPRdP8AFfhlvDT3lxa3dvHLdTiJQDctkAZJ7AEY+h9TXfeGvA1vY+GpPLuJJDdqCgeVY9p2gdSMdQce1eY8PyvRs96nj5TXvRTPQF+I2l6tpptEe1a3zzHDLg8+uPoetcD428P+FfGUNxa2eraxoOog7UaSUS2jv1xtwGXjvk/SvPdK+EmlfD3Xl1HVp2hWM7hBNOspmYHIwqgHH1p2oTnwv8TLrS5JfL03WMX1tLKpKRxyLvX5uy5wvtisPq9T2l4yYVMwg4ezcEchd6ZPopktZ5Fmmt3MburBlcgnkHvXH2VqLzVryZl+VX2D3xmruueM8ySsuSWJPJ61n+G73bpm8nc0shdse9e6otRPD5Unoa32ULkj5qy7m2+ZZMAb24AHpV43JVSxZV/uj17U27tWjvBH94woAcdPWimmmOo9NSBv3Z6ZycYHap1Zd42ksRxTVCq33fl9+9TQW/mzrjK7fQV0S2ucnMeg/AbQZL3xxHdSBjb6PbyXzj1YLhB/30QfwrK8cy6beXz2t3JIt9sMsYBwo39WJ9Rgfma9N+B3h+TS/hJq2qSKVbWLgW8bEf8ALKPg4/4GX/75rnfCngjRviN4st9UaC+VrKSTzHmjzbzchUUKVH3cNzz1HpXNWk0kdVGzlfseq/Bz4WW+meBtPtZLiDGyOZ+AWDfeJ/Alj+FdF4E8Dap4n+PEUei+ZfLI8Nvb2zsfLuyzDIYDnBJx/kVk+J7mDwd4at0ikkWa4HyANwgA6+uMYFdt8CNQ1L4Z6NZ+P1WRNMh1BLQ3UWJJIWwCxCEjkKep4ywGeePOxU3y8i6ndRlC7bZ9Gf8ABWj4AR/sX+BtGh8MaxE//Ca6O2i6lp0iZkNurLN5m8fe/ecbiM8DnHA/NW08FLE25mYr2ULivob/AIKQ/tnW/wC0l+0JfahpV3d3vh/T40s9LM427YVUAEj1POa+W9V8aXV1frbxt5a5+YIe1dkkoU4049Dz6kfaVOc6iWOy0ELuMaHryfmpYddhkt5HjOQMkE964OeSTU9bVWYtzwCa6PxFdL4e8MEBcSSDGR2rmlLmdkTKNtWc3J4g1Dxb4rW1SZlt1f5lXpj3rrNVP2G32sy7oU/WsX4T+H2ijn1CT/locAn8as+NrvePJ+6ZjkkdvaodROVkVZGd4G0VtZ8RTXTLlVJJruEg8694Hypxz2qj4S0hdH0X73zSDPTmtzTrQQ2hdvvN3rmldzuEYRsS/Z4/7y0UzzP9laKd2HIu5+hHiyRvLs7rUjG7aRFZ2UVky4a5hZpJST7EE/8AfQrwf48eY00hmjLXV/H9s0mCI5WESXByOvorjHuPavob40/8lPt/+vXTv/SSOvn34hf8jH4Z/wCvaH/0Y1fMwzqb15fx/wCAV/bk7fD+P/APm7xdavc6ixWVZLrVN7TyMOLZjJ2Pb/69c40KzQSbtywowS5mDZM7bsium8Rf8eeqf9fC/wDoYrmLr/kCr9U/m1brOJv7P4mP9uTWvL+P/ANX4gWmn33wZmnvWjQ20fliJurnnB/GvinWrDyrqRtu0MSQAO1fTHxp/wCRah/3l/kK8F1j/j4k+pr1cvzByu+UxqZ3Obs4/ic9p1x5c0asqtHuBIIr2n9qHVIWt/CrWkzIILUSoVHygnGDXk8P+vH416J8Yv8AkR/C/wD17CujEYy9andHsZfnlSlhaqiu3U9U8QWP/DZ3wT0xrNlm8beF4fLWLPN7EOoHvnkV8v3fhTUNHvbqG9t5La4tyQ8UiEMp9CK+g/8Agn9/yWO2/wCug/mtJ+2t/wAln1n/AK+D/Ja4aeazo4v6rGPutX9D0M0zP6zg4Y2cff2bvufNk1syH7uGxycdaWyYWqyPJGZGx8vH3TXQy/0qC6/49Gr3I5hJ9D5X+1p32OfvB+9TywSZANxx3r3X4Cfs7td6ede1aMQwwr5paUHy7df9r1b2968k0v8A5CMH/XRf5ivsLUv+Ta7r/r/i/lXn5lm06MVCMd/M+w4VxUJVpVKkOblV1r1MHwj8GL/416uqx3Eml+H42+aRjtlmUdTjoB7ZFe3aF8VPhB+zLpqQ7rHWtWteBISJmQ/UggGuY0X/AJN71T6N/wCgivjfxJ/yMSfUV5uFx8q7aatY/QMRxU8HGNSNJOUure3orH3Pr/8AwUi8RePoGs/CdpdxLgqn2W23HH1PFed+JdO+N3xPlaa717W7GzY/6rzsEj6DGK7/APZB/wCRRsfpXp3iz/j2b615suIHTlaFNfefRQzyviqCqVb+idj5Ck+Ds1jqscfiTVPEV1JI2W/fE7vXvXReKf2ZNBuNDa40/Q9evgFzvMuSfwzXUeN/+Rhj/wB4V6v4M/5FmH8aupxBVVtPx/4B40cwpVG4zp39Xf8AQ+DPFnw4bQLiXbY6tY7QcBgcj8q5+/8AEOuaZEVtbi+YZ5DgsB+dfY3xo+9L/wAC/lXhmv8ASeu+hxBUdrx/H/gHwWc4tUW+SP4nkGjeIdUvdRY3qtIqnIJj703U7e/vPEW6ZZI9zYA2/wBO3Wu1uP8Aj9j+h/pVTxb/AMjen/XRf/QRXsUM6k5r3fxPh8wziSj8P4n0z+y58C/+EttLXQtMVftV0qtqN6eI7OPqyg4645Jr6p1lv7J8A3HhP4fzp4c8L2SeXrHiJyI5r5v4gj/wp1xjk15N+xB/yRLxt/16N/Kuk/aI/wCTI4f98fyr6ZZxyR5uRP5/8A+AlnlR1bNfj/wD5q8YeJ/hPoXjYaLpEF1rOqXDlZL91+Qv3O4nJz615T8V7bSfD2ubraKO6uLgbSic7B3zz19PxqbwT/yM8f8Aur/WqXjf/kdY/wDgX/oD142Mz6pJ25fx/wCAfQYPNuT7H4/8A88+JzafLdWbWcbW9xFAEukOQskoJ+ZfwxXISSN5pAH4Yr0P4g/8ftr9P8K5u6/4+PxFRSzKTV+U9Onmrmubl/Ew0keX5I1JbOOlXLTSLp1I8teD3rQsv+Pn/gVbCfdb61VTNJLTl/EUsyknbl/E50+Hbhl6r68dqpXEbWspRu3612MH3m+lYt3/AMfjUqeaSfT8Qjmcn0/Es/B6zbUvidosQB+e7TqM45r718e6B4t+F/gc69deCfEH9jMoRdRudOdLLLfd2ykbefY18XfAf/ksPhv/AK/Y/wD0Kv6A/wDgq7/yiR03/sHab/KvPzDNJe0S5fxIqZpKOnL+J+Gc3xj1nxJ4gmkuMSR+YypbgACPnt+XpX0R+z7+y945/aL+G114l0fw/cX2k6TIYb68Z1UREANg5PXBHQd6+Z/B3/Iaj/32/mK+/P2Bv+TVPG3/AGGv/aSVlLNWnyqP4kxztxVuT8f+AfL8Kap8Ff2jdI0/UFs1jmlSCUwyGQqkuVAPHUFhX2B8G/2GvGv7RNlrmjeEJ9D0+40SNb27l1afyVijlkfZ5YwxbAAHTj8q+L/jn/ydzpf/AF/2v/ocdfql8NP+Rx8V/wDYsj/0IVx4vOJxkoqP4mmFzttv3Px/4B+b37X/AMPNU/Y78dyaT4gXRNY1LaGE9vqaXiFf+An5fYHBHoK7f44fEDS9U/Yz+F3ib+z7GTVNW8P32j3rrCvyiO7kZD067LpQD1+QelfJ/wC15/yVW9/6+G/nXqHjn/kyz4ff7tz/AOhx16EcZGEVLl1fmVPOpL3lH8f+AeC6xq0c6tHCgXdwuR+Fa2kS+TbQoE7Acd6wb7/j7H+9WxY9Y/8AeraebSSso/iL+2pR+z+P/ANuJFu5o45A3DbsAdMVOknnq7KG3Oc/hVXTv+P1v9xqfD90fQVnHOJX+H8TCtnkt+X8f+ATpl0YkbccAHvWvoGltdwsqqz3EmFiUfxMTgADvmsi0+5+Ndz8G/8AkqnhT/sL2n/o5K2nnU0vh/H/AIBzSzuXN8P4/wDAPpfxR4Gh8J+GfDvhNt23TbBDdlR992HzZ+p3H8awtavLX4d6OsNuEZoTiOEjn15/OvQfi9/yUzWvpH/I14T4+/5G26+q1jiM6nouX8Tpp55Oz938f+Ac3458WXepFpJpG8yU5GP4BzwPzrmU8f60bBbJb67W1XOIRKdgB9vT2q/42/13/Af8a5iD74rh/tiTXNy/iR/bU2r8v4/8AuwWzuW3YVRz6Cq1pAtuJ7hvovf1qaX/AI9W+lV7r/kCfiKxlnM3ry/iKOeS/k/H/gGl4E0X7TctK3POckUzx/dNrWtx2EJ+7gELWn8PP+PB/wDPrWVpf/I/fjWlHN5X+H8SY55J/Z/H/gHawaevh/RYbZR0TLmuZsrJvEfiPp+7U59q6bxB/qZP9w1j+AP+Pmb8P61zyzmS+z+JX9uP+T8f+AdP9lWSRI1I2qBwBV+ZMQrGufQgCs+w/wCPn8a0G/11YrOp/wAv4/8AAK/tyX8v4/8AAI/7Loqain/bU/5fxD+3Jfy/j/wD/9k=)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vhW7txZIFShp", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "**Abbildung 3: Beobachtete Fehler am DK 0200 G**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MLBb8nmsFtm0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Fehler `0_leak_corner_tl` und `0_leak_corner_tr` sind links bzw. rechts oben zu sehen. An diesen Stellen wird zu viel Material der Komponente 2 (TPE) in das Innere des Gehäuses gesprizt. Der Fehler `1_hole_bottom` ist unten zu beobachten: Hier fehlt das entsprechende Material. Die Häufigkeit dieser Fehler ist sehr unterschiedlich:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JbhgE7dOGwUQ", + "outputId": "84d1fadb-fbac-43ca-a33d-c235fb859858", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anzahl Datenpunkte: 4548\n", + "Anzahl Fehler:\n", + "Name\n", + "0_leak_corner_tl 2103\n", + "0_leak_corner_tr 2138\n", + "1_hole_bottom 5\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(\"Anzahl Datenpunkte:\", len(df))\n", + "print(\"Anzahl Fehler:\")\n", + "# Wenn der Wert einer Zielvariable nicht 0 ist lag ein Fehler vor\n", + "print(df[\"Labels\"].astype(bool).sum(axis=0))\n", + "# Quelle: https://stackoverflow.com/questions/26053849/counting-non-zero-values-in-each-column-of-a-dataframe-in-python" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YrQ_N0AjjCZk", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\\*) Anmerkung: Diese Zahlen wirken auf den ersten Blick sehr hoch. Allerdings wurde, um einen vollständigen Datensatz zu erhalten, jegliche Abweichung vom Optimum als Fehler eingestuft. Dem Endkunden würden diese in der Regel nicht auffallen. Außerdem wurde bewusst ein extrem fehlerlastiger Zeitraum gewählt. In anderen Zeiträumen treten wochenlang quasi gar keine Fehler auf. Bei diesen hohen Zahlen handelt es sich deshalb wahrscheinlich um einen – zumindest im Sinne dieser Arbeit – \"glücklichen\" Zufall." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_BYme0kPIvlK", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Fehler `0_leak_corner_tl` und `0_leak_corner_tr` sind mit Abstand am häufigsten aufgetreten und stehen deshalb im Fokus dieser Arbeit. Im Datensatz werden die Fehler als eine Ganzzahl zwischen 0 bis 3 codiert. Die Zahlen haben nachfolgende Bedeutungen:\n", + "- `0`: kein Fehler\n", + "- `1`: schwacher Fehler\n", + "- `2`: mittlerer Fehler\n", + "- `3`: starker Fehler\n", + "\n", + "Für eine exakte Beschreibung, wie diese Einteilung erfolgt ist, siehe [1]. \n", + "\n", + "In der Regel lag entweder kein Fehler vor oder dieser war sehr stark:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "E2vHjvrrSO2N", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def plotLabelHist(labels):\n", + " fig, ax = plt.subplots()\n", + "\n", + " # Daten\n", + " bins = [0 - 0.5, 1 - 0.5, 2 - 0.5, 3 - 0.5, 4 - 0.5]\n", + " ax.hist(labels.to_numpy(), bins=bins, label=labels.columns, edgecolor=\"black\")\n", + "\n", + " # Achsen\n", + " plt.xlabel(\"Stärke der Ausprägung\", size=18)\n", + " plt.ylabel(\"Anzahl\", size=18)\n", + " plt.xticks([0, 1, 2, 3])\n", + "\n", + " # Titel und Legende\n", + " plt.title(\"Übersicht der beobachteten Fehler\", size=18, pad=10)\n", + " plt.legend(prop={\"size\": 10})\n", + "\n", + " fig.set_size_inches(8, 6)\n", + " fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "ci8pBv6lkBr2", + "outputId": "f059ab8e-914d-4cea-a105-aeb43c00c02d", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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PbxDPZZddxiuvvMLIkSMZOXJko/umuUxwJEmqskWLFrHjjjuue9+7d28WLVrUYJvXX3+dyZMn88ADD/D4448zZMgQLrnkEgDOOOMM/vjHPzJ37lzeffdd7rnnH6fcVq9ezYknnsiuu+7K5MmTKy578eLFnHrqqdx2223MmTOHW265BYDzzjuPwYMH89RTT/Hd736XL37xi+vazJs3jwceeICbbroJgGeeeYZ7772Xxx57jPPPP5/333+f+fPnc/PNN/OHP/yBJ598kvbt2zN16lQAVqxYwbBhw5gzZw6f+MQnNojpq1/9KjvssAPTp09n+vTpTdmtzeIYHEmSNgGPPPII8+bN44ADDgDgvffeY//99wdg+vTpfP/73+edd95h6dKl9O/fnyOPPBKA0047jWOPPZZvf/vbDS77oIMOWneTvK5duwIwc+ZMbrvtNgAOPvhglixZwttvvw3AZz/7WbbYYot1yzjiiCPo2LEjHTt2pEePHvztb3/jt7/9LbNnz2a//fYD4N1336VHjx4AtG/fnjFjxlRt/zSXCY4kSVXWq1cvXn755XXvFy5cSK9evRpsk1LisMMOW9djUmflypVMnDiRWbNmseOOOzJp0qT1bnj38Y9/nOnTp3PWWWfRqVOnqm3DVltttd77jh07rnvdvn17Vq9eTUqJL33pS1x44YUbtO/UqRPt27evWjzNZYKjjWqHHXfg1YWv1mz9PXv35JWXX6nZ+iXV3va9dmzylU9NXV5j9ttvP/7yl7/wwgsv0KtXL37xi19w4403Nthm+PDhnH766Tz33HPssssurFixgkWLFq3rEenWrRvLly/n1ltv5XOf+9y6dieffDIPPvggxx57LLfffjsdOmz41T58+HAmTpzICy+8QL9+/Vi6dCldu3blwAMPZOrUqZx77rnMmDGDbt26sfXWWzd5XxxyyCEcddRRnHnmmfTo0YOlS5eybNky+vTp06T2Xbp0YdmyZVW5aqqcCY42qlcXvsqAKQNqtv654+bWbN2SNg2vLnypzdfZoUMHLr/8ckaNGsWaNWsYP348/fv3b7BN9+7dmTJlCscffzyrVq0CYPLkyey2226ceuqpDBgwgO23337d6aBS3/jGN3jrrbcYO3YsU6dOpV27dhss+6qrrmL06NGsXbuWHj16cP/9968bPDxw4EC23HJLrr322mZt55577snkyZM5/PDDWbt2LZttthlXXHFFkxOcCRMm8KlPfWrdWJxqivKR2B9WQ4YMSbNmzap1GIUTETVPcPyMSx8u8+fPZ4899qh1GNoIKh3biJidUtrgRkBeRSVJkgrHU1SSJLWBY445hhdeeGG9sosuuohRo0ZVdT3Dhg1bd4qrzvXXX89ee+1V1fU0R1tteykTHEmS2sAdd9zRJut59NFH22Q9zdFW217KU1SSJKlwTHAkSVLhmOBIkqTCMcGRJBVa3949iYiqTX1796z1JqkJHGQsSSq0Fxe9Rjqv6XfnbUyc/1qT6k2bNo2vfe1rrFmzhlNOOYWzzz67Yr0RI0Zw8cUXM2TIBrdyaVRr2m4KLr30UiZMmMCWW25Z9WXbgyNJUpWtWbOG008/nd/85jfMmzePm266iXnz5tU6rBZZvXp1q5exZs2aiuWXXnop77zzTrPaNJUJjiRJVfbYY4+xyy67sPPOO7P55ptz3HHHcddddzXa7r777mP//fdnn3324fOf/zzLly8H4IILLmC//fZjwIABTJgwYYM7tK9du5Zx48Zxzjnn1LvsadOmsc8++7D33ntzyCGHALB06VKOPvpoBg4cyPDhw3nqqacAmDRpEmPHjuWAAw5g7Nix6x7pMGLECHbeeWcuu+yydcu94YYbGDp0KIMGDeK0005bl5h07tyZs846i7333puHH354g3guu+wyXnnlFUaOHMnIkSOb1KY5THAkSaqyRYsWseOO/3goZ+/evVm0aFGDbV5//XUmT57MAw88wOOPP86QIUO45JJLADjjjDP44x//yNy5c3n33Xe555571rVbvXo1J554IrvuuiuTJ0+uuOzFixdz6qmncttttzFnzhxuueUWAM477zwGDx7MU089xXe/+12++MUvrmszb948HnjggXVPN3/mmWe49957eeyxxzj//PN5//33mT9/PjfffDN/+MMfePLJJ2nfvj1Tp04FYMWKFQwbNow5c+bwiU98YoOYvvrVr657BlXdc6gaa9McjsGRJGkT8MgjjzBv3jwOOOAAAN577z32339/AKZPn873v/993nnnHZYuXUr//v058sgjATjttNM49thj+fa3v93gsg866CD69esHQNeuXQGYOXMmt912GwAHH3wwS5Ys4e233wbgs5/9LFtsscW6ZRxxxBF07NiRjh070qNHD/72t7/x29/+ltmzZ697AOi777677unn7du3Z8yYMc3aBy1pUx8THEmSqqxXr168/PLL694vXLiQXr16NdgmpcRhhx22rsekzsqVK5k4cSKzZs1ixx13ZNKkSaxcuXLd/I9//ONMnz6ds846i06dOlVtG7baaqv13nfs2HHd6/bt27N69WpSSnzpS1/iwgsv3KB9p06daN++fbPW2ZI29THBkSQVWp9e2zf5yqemLq8x++23H3/5y1944YUX6NWrF7/4xS+48cYbG2wzfPhwTj/9dJ577jl22WUXVqxYwaJFi9b1iHTr1o3ly5dz66238rnPfW5du5NPPpkHH3yQY489lttvv50OHTb8ah8+fDgTJ07khRdeoF+/fixdupSuXbty4IEHMnXqVM4991xmzJhBt27d2Hrrpl9xdsghh3DUUUdx5pln0qNHD5YuXcqyZcvo06dPk9p36dKFZcuW0a1btyavs6lMcCRJhbZg4attvs4OHTpw+eWXM2rUKNasWcP48ePp379/g226d+/OlClTOP7449c9LHPy5MnstttunHrqqQwYMIDtt99+3emgUt/4xjd46623GDt2LFOnTqVdu3YbLPuqq65i9OjRrF27lh49enD//fevGzw8cOBAttxyS6699tpmbeeee+7J5MmTOfzww1m7di2bbbYZV1xxRZMTnAkTJvCpT31q3VicaorykdgfVkOGDEmzZs2qdRiFExEMmDKgZuufO27uBlcbSCq2+fPns8cee9Q6DG0ElY5tRMxOKW1wIyCvopIkSYXjKSpJktrAMcccwwsvvLBe2UUXXcSoUaOqup5hw4atO8VV5/rrr2evvfaq6nqao622vZQJjiRJbeCOO+5ok/U8+uijbbKe5mirbS/lKSpJklQ4JjiSJKlwTHAkSVLhmOBIkgpthx13ICKqNu2w4w613iQ1gYOMJUmF9urCV6t6P6654+Y2Wmf8+PHcc8899OjRg7lz668/YsQILr74YoYM2eA2LhXNmDGDiy++eL2HbTbFlClTmDVrFpdffnmT6r/55pvceOONTJw4EYAFCxbwf//3f5xwwgnNWm8t2YMjSVKVjRs3jmnTptU6jBZ78803ufLKK9e9X7BgQaOPmtjUmOBIklRlBx100LondjfmlltuYejQoey222489NBDQPaAzS9/+cvstddeDB48uOJjDFasWMH48eMZOnQogwcP5q677mpwPS+//DIjRoxg11135fzzz19XfskllzBgwAAGDBjApZdeCsDZZ5/N888/z6BBg/jmN7/J2WefzUMPPcSgQYP40Y9+VG98U6ZM4eijj+awww6jb9++XH755VxyySUMHjyY4cOHs3Tp0ibtk2rwFJUkSTW0evVqHnvsMX79619z/vnn88ADD3DFFVcQEfzpT3/imWee4fDDD+fZZ59dr913vvMdDj74YH7+85/z5ptvMnToUA499NANngJe57HHHmPu3LlsueWW7LfffhxxxBFEBNdccw2PPvooKSWGDRvGJz/5Sb73ve8xd+5cnnzySWDDU2M//OEP641v7ty5PPHEE6xcuZJddtmFiy66iCeeeIIzzzyT6667jq9//esbcW/+gz04kiTV0OjRowHYd999WbBgAQAzZ87kpJNOAmD33XenT58+GyQ49913H9/73vcYNGgQI0aMYOXKlbz00kv1ruewww5ju+22Y4sttmD06NHMnDmTmTNncswxx7DVVlvRuXNnRo8eva4XqSENxTdy5Ei6dOlC9+7d2WabbTjyyCMB2GuvvdZtX1uwB0eSpBrq2LEjAO3bt2f16tVNbpdS4rbbbuNjH/tYk+pHRIPvq6VuewDatWu37n27du2atX2tZYIjSSq0nr17NunKp+Ysb2M78MADmTp1KgcffDDPPvssL730Eh/72Md4+OGH19UZNWoUP/nJT/jJT35CRPDEE08wePDgepd5//33s3TpUrbYYgvuvPNOfv7zn9OuXTvGjRvH2WefTUqJO+64g+uvv54uXbqwbNmydW3L39cX3+OPP75xdkgLeIpKklRor7z8Cimlqk2vvPxKo+s8/vjj2X///fnzn/9M7969ufrqq5sV88SJE1m7di177bUXX/jCF5gyZcp6PSMA5557Lu+//z4DBw6kf//+nHvuuQ0uc+jQoYwZM4aBAwcyZswYhgwZwj777MO4ceMYOnQow4YN45RTTmHw4MFst912HHDAAQwYMIBvfvObDBw4kPbt27P33nvzox/9qEnx1VqklGodwyZhyJAhadasWbUOo3Aioqr3n2iuuePm4mdc+nCZP38+e+yxR63D0EZQ6dhGxOyU0gY3ErIHR5IkFc4mkeBExK4RsTIibigpOyEiXoyIFRFxZ0R0LZnXNSLuyOe9GBEnlC2v3raSJLW1008/nUGDBq03XXPNNVVfz7333rvBeo455piqr+eDYFMZZHwF8Me6NxHRH/j/gCOAx4GrgCuB40rqvwd8BBgE/Coi5qSUnm5CW0lSwaWUNtpVQi1xxRVXtMl6Ro0axahRo9pkXW2tucMNat6DExHHAW8Cvy0pPhH435TSgyml5cC5wOiI6BIRWwFjgHNTSstTSjOBu4GxjbVtq22SJNVOp06dWLJkiePvCiSlxJIlS+jUqVOT29S0BycitgYuAA4GTimZ1R/4v7o3KaXnI+I9YDdgLbA6pVR6x6M5wCeb0HZ22fonABMAdtpppyptlSSplnr37s3ChQtZvHhxrUNRFXXq1InevXs3uX6tT1H9F3B1SmlhWVdiZ+CtsrpvAV2ANcDb9cxrrO16UkpXkZ3CYsiQIab6klQAm222Gf369at1GKqxmiU4ETEIOBSodFei5cDWZWVbA8vIenDqm9dYW0mS9CFQyx6cEUBf4KW896Yz0D4i9gSmAXvXVYyInYGOwLNkCU6HiNg1pfSXvMrewNP566cbaCtJkj4EapngXAX8ouT9v5IlPF8BegAPR8SBZFdCXQDcnlJaBhARtwMXRMQpZFdRHQV8PF/O1IbaSpKk4qvZVVQppXdSSq/VTWSnllamlBanlJ4G/oUsWfk72fiZiSXNJwJb5PNuAr6St6EJbSVJUsHVepDxOimlSWXvbwRurKfuUuDoBpZVb1tJklR8Nb8PjiRJUrWZ4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqnJomOBFxQ0S8GhFvR8SzEXFKybxDIuKZiHgnIqZHRJ+SeR0j4ud5u9ci4htly623rSRJKr5a9+BcCPRNKeTijFMAABywSURBVG0NfBaYHBH7RkQ34HbgXKArMAu4uaTdJGBXoA8wEvi3iPgUQBPaSpKkgutQy5WnlJ4ufZtPHwX2BZ5OKd0CEBGTgNcjYveU0jPAl4BxKaU3gDci4mfAOGAaMLqRtpIkqeBq3YNDRFwZEe8AzwCvAr8G+gNz6uqklFYAzwP9I2JboGfp/Px1//x1vW0rrHtCRMyKiFmLFy+u6nZJkqTaqXmCk1KaCHQBDiQ7tbQK6Ay8VVb1rbxe55L35fNopG35uq9KKQ1JKQ3p3r17azZDkiRtQmqe4ACklNaklGYCvYGvAMuBrcuqbQ0sy+dRNr9uHo20lSRJHwKbRIJTogPZGJyngb3rCiNiq7ryfNzNq6Xz89d143nqbbtRI5ckSZuMmiU4EdEjIo6LiM4R0T4iRgHHA78F7gAGRMSYiOgE/CfwVMkg4euAcyJi24jYHTgVmJLPa6ytJEkquFr24CSy01ELgTeAi4Gvp5TuTiktBsYA38nnDQOOK2l7HtnA4ReB3wM/SClNA2hCW0mSVHA1u0w8T0Q+2cD8B4Dd65m3ChifT81qK0mSim9TG4MjSZLUaiY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMKp92GbEbGW7InfzZFSSjV7gKckSRI0/DTx62h+giNJklRz9SY4KaVxbRiHJElS1TgGR5IkFU6LxstExJbAdkCUz0spvdTaoCRJklqjyQlORLQD/g34f8D2DVRt39qgJEmSWqM5PTjfA/4VeBq4DViyUSKSJElqpeYkOCcB01JKn95YwUiSJFVDcwYZbwvctbECkSRJqpbmJDh/AnpurEAkSZKqpTkJzvnAv0TEjhsrGEmSpGpo6FEN/1mh+EVgXkTcAbwArCmbn1JK/1XF+CRJkpqtoUHGkxqYd1I95QkwwZEkSTXVUILTr82ikCRJqqKGnkX1YlsGIkmSVC0+i0qSJBVOs55FFREdgKOBYWT3xSlPkFJK6eQqxSZJktQizXkWVVdgOjCA7CGbiX88bDOVlJngSJKkmmrOKarJwO7AKcBHyRKaUcAewE3AH8meMC5JklRTzUlwjgCuSyldA7ydl61JKf05pXQS8C5wYbUDlCRJaq7mJDjbk/XSAKzOf3YqmX8n8NlqBCVJktQazUlwlgJb5a+XAe8DpY9teJ9s4LHK9Oy9ExFRk6ln751qvfmSJLW55lxF9SywJ0BKaW1EPAGMi4gpQHvgi8Bfqx5hAby26GX6fOuemqz7xYs+U5P1SpJUS83pwbkP+FxEdMzfX0J2ufhS4O/AEOBH1Q1PkiSp+ZrTg/Nd4OKU0iqAlNIvI2I12XOp1gC3ppRu3ggxqhU6toeIaLyiJEkF0uQEJ6WUgFVlZbcDt1c7KFXPqjWQztu6ZuuP899uvJIkSVXW5FNUEfHFiBjVwPx+EfHF6oQlSZLUcs0ZgzMF+HVE/LCe+R8Hrml1RJIkSa3U3IdtPgWcGRF3RsSWGyMgSZKk1mpugvMD4HSyuxo/FBE9qx+SJElS6zQ3wSGl9FPgSLLnUT0WEYOqHpUkSVIrNDvBAUgpTQMOBNaS9eT4iAZJkrTJaFGCA5BS+hMwFHiG7FLxk6sVlCRJUmu0OMEBSCn9DTgIuBsYUY2AJEmSWqs5dzIeCcwvL0wpvQuMjoivAD2qFZgkSVJLNedOxr9vZP5PWx+OJElS6zWnBweA/P43fYHtgA0ecpRSerD1YUmSJLVckxOcPLG5BPhyPe0CSED76oQmSZLUMs3pwfkx2ZVSvwZ+ByzZKBFJkiS1UnMSnGOAm1JKJ26sYCRJkqqhOZeJdwJmbKQ4JEmSqqY5Cc4sYNeNFYgkSVK1NCfBORv4ckQM2VjBSJIkVUNzxuBMABYCj0TEw8BfgTVldVJKyUc2SJKkmmpOgjOu5PUB+VQu4TOpJElSjTXnTsaNns6KiE6tC0eSJKn1WvWwzToRsW9EXAksqsbyJEmSWqPZj2qoExFdgZOA8cBeZHcyfrZKcUmSJLVYs3twImJURNxM1lvzI6AjcD6wV0pp9yrHJ0mS1GxN6sGJiL5kPTVfAnoDrwO3AicA304p3b6R4pMkSWq2BntwIuLEiPgt8BzwLbKb/R0D9AImUeFp4k0VER0j4uqIeDEilkXEkxHxzyXzD4mIZyLinYiYHhF9ytr+PCLejojXIuIbZcuut60kSSq+xk5RXQ/0Ab4O7JBSGpNSujultLoK6+4AvAx8EtgGOAf4ZUT0jYhuwO3AuUBXssTq5pK2k8juqtwHGAn8W0R8CqAJbSVJUsE1dopqFdAXOAp4IyJuTym9W40Vp5RWkCUqde6JiBeAfYHtgKdTSrcARMQk4PWI2D2l9AzZqbJxKaU38rh+RnafnmnA6EbaSpKkgmusB6cnWe/NdmS9Oa/lp5UOohWnpyqJiI8AuwFPA/2BOXXz8mToeaB/RGybxzWnpPmcvA0Nta2wzgkRMSsiZi1evLiamyNJkmqowQQnpfRmSunylNI+wBDgBrIxONOBmWR3Lt6mtUFExGbAVODavJelM/BWWbW3gC75PMrm182jkbbrSSldlVIaklIa0r1799ZthCRJ2mQ0+TLxlNLjKaXTyXpPxpL1tAD8Tz5A+JyI2KCXpDER0Y6sd+g94Iy8eDmwdVnVrYFl+TzK5tfNa6ytJEn6EGj2fXBSSqtSSjemlA4BPgp8B9gWuID1Txs1KiICuBr4CDAmpfR+PutpYO+Selvl63o6H3fzaun8/PXTjbVtTmySJOmDq1WPakgpLUgp/SfZQORPk1291Bw/BfYAjiwbvHwHMCAixuTPt/pP4KmSQcLXAedExLYRsTtwKjCliW0lSVLBVeVZVCkzLaV0bFPb5PemOQ0YRDZ4eXk+nZhSWgyMIesdegMYBhxX0vw8soHDLwK/B36QUpqWx9JYW0mSVHAtfhZVa6WUXqSBK7FSSg8AFR/9kFJaRXZn5fHNbStJUi307d2TFxe9VpN19+m1PQsWvlqTdddKzRIcSZI+TF5c9BrpvPJrYNpGnF+bxKqWqnKKSpIkaVNigiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKp0OtA5AkqS307L0Try16udZhqI2Y4EiSPhReW/Qyfb51T83W/+JFn6nZuj+MPEUlSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKp6YJTkScERGzImJVREwpm3dIRDwTEe9ExPSI6FMyr2NE/Dwi3o6I1yLiG01tK0mSiq/WPTivAJOBn5cWRkQ34HbgXKArMAu4uaTKJGBXoA8wEvi3iPhUE9tKkqSCq2mCk1K6PaV0J7CkbNZo4OmU0i0ppZVkCc3eEbF7Pv9LwH+llN5IKc0HfgaMa2JbSZJUcLXuwalPf2BO3ZuU0grgeaB/RGwL9Cydn7/u31jbjRyzJEnaRGyqCU5n4K2ysreALvk8yubXzWus7XoiYkI+BmjW4sWLWx20JEnaNGyqCc5yYOuysq2BZfk8yubXzWus7XpSSlellIaklIZ079691UFLkqRNw6aa4DwN7F33JiK2Aj5KNrbmDeDV0vn566cba7uRY5YkSZuIWl8m3iEiOgHtgfYR0SkiOgB3AAMiYkw+/z+Bp1JKz+RNrwPOiYht88HDpwJT8nmNtZUkSQVX6x6cc4B3gbOBk/LX56SUFgNjgO8AbwDDgONK2p1HNnD4ReD3wA9SStMAmtBWkiQVXIdarjylNInsMu5K8x4AKl7anVJaBYzPp2a1lSRJxVfrHhxJkqSqM8GRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOJIkqXBMcCRJUuGY4EiSpMIxwZEkSYVjgiNJkgrHBEeSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiSV6dl7JyKiJlPP3jvVevOlQuhQ6wAkaVPz2qKX6fOte2qy7hcv+kxN1isVjT04kiSpcExwJElS4ZjgSJKkwjHBkSRJhWOCI0mSCscER5IkFY4JjiRJKhwTHEmSVDgmOFIL1fJut97xVpIa5p2MpRaq5d1uwTveSlJD7MGRJEmFY4IjSZIKxwRHkiQVjgmOJEkqHBMcSZJUOCY4kiSpcLxMXJI2IR3bQ0TUbP19em3PgoWv1mz9UrWY4EjSJmTVGkjnbV2z9cf5r9Vs3VI1eYpKkiQVjgmOJEkqHBMcSZJUOCY4kiSpcExwJElS4XgVlfQB5eXEklQ/ExzpA8rLiSWpfoU9RRURXSPijohYEREvRsQJtY5JkiS1jSL34FwBvAd8BBgE/Coi5qSUnq5tWJIkaWMrZA9ORGwFjAHOTSktTynNBO4GxtY2MkmS1BYipVTrGKouIgYDf0gpbVlS9q/AJ1NKR5aUTQAm5G8/Bvy5TQP9YOgGvF7rILRReGyLyeNaXB7byvqklLqXFxb1FFVn4O2ysreALqUFKaWrgKvaKqgPooiYlVIaUus4VH0e22LyuBaXx7Z5CnmKClgOlF9esjWwrAaxSJKkNlbUBOdZoENE7FpStjfgAGNJkj4ECpngpJRWALcDF0TEVhFxAHAUcH1tI/tA8hRecXlsi8njWlwe22Yo5CBjyO6DA/wcOAxYApydUrqxtlFJkqS2UNgER5IkfXgV8hSVJEn6cDPBkSRJhWOCo4p8llcxRcQZETErIlZFxJRax6PqiIiOEXF1/ru6LCKejIh/rnVcqo6IuCEiXo2ItyPi2Yg4pdYxfRAU9UZ/aj2f5VVMrwCTgVHAFjWORdXTAXgZ+CTwEvBp4JcRsVdKaUEtA1NVXAicnFJaFRG7AzMi4omU0uxaB7YpswdHG/BZXsWVUro9pXQn2ZWFKoiU0oqU0qSU0oKU0tqU0j3AC8C+tY5NrZdSejqltKrubT59tIYhfSCY4KiS3YDVKaVnS8rmAP1rFI+kZoiIj5D9HtvjWhARcWVEvAM8A7wK/LrGIW3yTHBUSZOe5SVp0xMRmwFTgWtTSs/UOh5VR0ppItnf4APJbmS7quEWMsFRJT7LS/oAioh2ZHdsfw84o8bhqMpSSmvyIQO9ga/UOp5NnQmOKvFZXtIHTEQEcDXZhQFjUkrv1zgkbTwdcAxOo0xwtAGf5VVcEdEhIjoB7YH2EdEpIryashh+CuwBHJlSerfWwag6IqJHRBwXEZ0jon1EjAKOB35b69g2dT6qQRX5LK9iiohJwHllxeenlCa1fTSqlojoAywgG5exumTWaSmlqTUJSlUREd2BW8l60dsBLwKXpZR+VtPAPgBMcCRJUuF4ikqSJBWOCY4kSSocExxJklQ4JjiSJKlwTHAkSVLhmOBIkqTCMcGRNlERkSJiRv56Uv5+RDOXMa4l7aopIkbkMYyrVQxFERELImJB/nqc+1Wqn3cwlaooInYGzgYOAnYiu/Haa8BjwJSU0vSSupOAJ1NKd9azuLHA3/LXtwPPAfM3TuTFFhE3A8cCv0spHVLreFrh6yWvHyT7jPxfjWKRNmkmOFKVRMQQ4PfA+8B1ZM/u2gLYFTic7GGl00uanAdcC1RMcFJKN5S8fgp4aqMEXnARsR1wNPA8MDIi+qaUFtQ2qpYpTYZTSn8F/lrDcKRNmgmOVD3nAVsCg1JKc8pnRsT2bRVIRHRJKRX26e/5gyW3Siktb0L1k4DNgC8ADwNfZsPHVWySIqI90DGl9E6tY5E+aByDI1XPrsCSSskNQErpNYCI6BsRdc9I+VI+jiKVlBERJ0TE3RHxUkSsiojXI+LOiBhYvtx8XMaMiBgcEfdGxFs00tsTEd/O1/mTiGiXl/WMiJ/m63wvIl6JiKsiokdTd0BEHBURT0TEyoh4OSL+iyy5qFS3Y0T8R0Q8ndd/MyL+NyIGl9VbN4YnIk6PiHnASuBfmxjWycCMlNJs4B5gXN02l61nRt34lrLyvvn6J5WUtYuIr0fEUxGxLCLejog/R8TVEbFZSb26Y7NPRPwuIpZHxNKIuLZ8v5aMqTk0Is6NiOfz7Tw2n/+piLg5Iv4aEe/m++u+iPhkPft3TETMyfftSxFxXr7s9cbtlIzv6lthGQvqxoGVlKWImBIR+0fE7yNiRUQsiYj/iYjOFZbxyYh4OI/5tYj4cUT0L9+nUrXZgyNVz/PAxyJidErp9gbqLSYbO3E98BBwVYU6/49s/M1VZGN4dgFOBf4QEfuklP5SVn8n4HfALcBtwAZfNLCuR+By4F+Af08pfS8v34msd2Nz4Op8W3YBvkJ2WmdISumthjY+Io7J170AuIDsoY9fBo6oUHczYBrw8Xw/XA5sU7KNB6WUZpU1+zqwHfCzfJ+83FA8+Xr2A/YCxuVFU4BjgEOB+xpr34Bvk23j/wL/DawB+gGfBTqSnaas05vsyc+3kT00cR9gPDAkIvar0DtzMVlS+DPgbeDPefl4YFuy058LgV7AKcBvI2JkSumhku3+AnAT2XE8n+xYfAk4shXbXGoQWbJ4DXAjMIIskVwLTCiJ4xNk+/kN4HvAm2QJ2wFVikOqX0rJycmpChOwP/AekIBnyZ7G/hVgj3rqJ7KBx5XmbVWhbA+yQctXlpUvyJd1SoU24/J5I8jGA92Rx/jFsnp3AX8HepeVDyH7cpzUyLa3B14CXge6lZRvQ/b04wSMKyk/My8bVbacrfPlzCgpG5HXXQr0aOYx+SmwHOicv98s386bK9SdASyoUN43X/+kkrLHgXlNWH/dsfl6WXnd9p9d4Vj9GdiyiZ+Jj+T7/NclZR2ARWQJ8rYl5Z3JxuyUH4tJeVnfeuKfUVaWyBKZYWXlvyJL7DqXlD1G1gu1c0nZZsAfyvepk1O1J09RSVWSUnoY2Jds4PA2ZL0XVwLzIuLByK6wauqyVkA21iQito6IbmQ9P38GhlVospTsv+n6dAXuJ+u5ODKldF3djIjYBvgMcDewMiK61U1kX3DPkQ2Sbsi+wI7ANSml10u24y2yHo5yJwHPALPL1rd5HucnImKLsjbXpZT+3kgc6+TtjwduS/lYnZTS+8BU4KiI6NrUZVXwFtAr76FozNtkn4NSV+blx1So/9NUYcxN3WcCICI6RzZ4eg3wKOt/JvYFdiBLnt8oab+cyseiJR5OKT1aVvY7suSqbx7jR4D9gLtSNiC6Lo73gR9XKQ6pXp6ikqoopfQn8tMhEdEH+CTZaYQDgbsiYt+U0nuNLScfh/JfZL0XW5XNfqFCk+dTSmsaWOQUsv/gD0opzSyb9zGy8Xgn51MljV2tU5e8PVNh3rwKZXuQ9SgtbmCZ3Vj/NNSzjcRQ7nNkiebvI2KXkvIHyU53nQRc1sxl1vkPsqvfHoqIV8h6f34F3Frh+P61vCyltCoi/so/9lupitsZER8FvgOMAv6pbHYqed0v//lnNlSprCUqfR6W5D+3a8M4pHqZ4EgbSUrpReC6iKgba3MAMBQoTzDWk4+HeZDsP/z/IvsyWEH2JXYplcfXNHaVzc1kPUrnRsTRKaV3S1eZ/7yBrPepknfrKW+pAP4EfKOBOuXJT3OvJKpL1q6uZ/541k9wUj31Nvg7mVJ6OE84RgEj8+kE4JyI+ERKaWkzYy21wXbmg3cfJEt2LyXbd8vIThX9O3BwK9ZX33ZD/d8RDSXT0cA8qc2Y4EgbWUopRcSjZAlOryY0OYYsiflsKrkxIKy7p8uqFoQxlWyg6/XAPRFxZMlpkOfIvuQ2Tyk90IJlwz/+o9+9wrw9K5T9BehOduO9tS1cZ73y5OMgsu2udJ+hQ4B/yXvUZudlS8lO75SreGoxP+VzWz4REROBK8gSqx+Uto+IzUt7cSKiY77cSj1elRxCdtppfEppvVORETG5rO6C/OfHKiynUlldMta1pC0R0QnoSfb5aInmxiFVlWNwpCqJiMMiYoN/GvKxIHVjWEpP1ywn+1IpV/ff8Xr/CUfEqUCL76WTUvoF2ZiUA4Hf1F3Sm1JaAvwaGB0RwyvEHxHRvZHFzya7sufL+ViaurZbk12xVe46sm2p2IOTj99ojfFk+++SlNKt5RNwUUm9Os8CXSJiaEkc7cgGBJfH1628jGzgMWx4TLcGJpaVTczL67uLdbn6PhOHs+GYrFnAq2SXw29bUrczlY9F3SmxQ8vKz6QV3xEpuy3CLLLxTuuSxPwKuq+1dLlSU9mDI1XPj4DtIuJuslMI75ANvD0B2I1skOyfSuo/AhwaEd8iu3Io5UnIb/K210fE5WSX2B4AfJrsst8W/96mlG6NiPeBXwL3RsQ/p5TeJrvaaybwYERcBzxB9uW2M3AUWUIyqYHlromIM/PlPhYRPyO7+mo82diMncqa/Bg4DPhBRBxMNkD17bzeIWRX3oxsyTbml8KPI7si6vFKdVJKCyJiNnBCRJyVUlpJdkn+WcAdEfFjsqvNPkfl/T0/Ih4hG+D7CllPx4S8zS/K6j4PnBcRA8gSwX3J9sszNH0M0EyyS+N/mN+vZiHZpdpjyT5re5Vs2+qI+Fey3qvHIuJqsmMxjuxY9GP901IPkJ0GvSDvIXwB+AQwnOwKrdb4V7JB4/8XEVeSDc4+lmwwOTR8ekxqnVpfxuXkVJSJrJfmCmAO2RfDarIvlOlkX2jtyurvSnaPkLfJ/tCnknkHkX2pLSO7d8ivgAFUuJSZCpfylswbly97RFn5EWRJxCPANnlZN7JTK8/m894k+/L8MbBnE/fBaOBJstNoL5ONITqMskuT87odgK8CfyQbY7SC7NTVVODwknojKrVvIIYj8vo/bKTev+f1Tigp+3RJ/K+Q9fR8jA0vEz+bbEzM30u29RZgn0rHhuzeN7/Lt/ENslOFH2nKsSqZP5Ds3kFv5J+LGWS9cVNKPzsl9T9PdsPHVWQJ9Hlkpz8TcGxZ3d3yZb+TH/dfkp1O3eCzRT23N2jgs3Zw/jlbSZak/Zis1ykB/1br31un4k6Rkgm0JG0Mkd0ZeUFKaUSNQwEgIs4iu5Hg/imlR2oYxxiymx4en7JeS6nqHIMjSQUTEZvnp+pKyzoDp5P1KlY8dbcR4oh8sHJp2WZkY69Wk/VCSRuFY3AkqXh2JhtI/guyMTU9yR7V0A/4SmrCvZiqpCPwYkRMJRvnsx3ZQ08HAhel/Pls0sZggiNJxbOYbNzLiUAPst6SP5E9GuKXbRjH+2Tjx44iS7KCLNE5PaVUfndnqaocgyNJkgrHMTiSJKlwTHAkSVLhmOBIkqTCMcGRJEmFY4IjSZIK5/8H+3B/wQoeD+IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotLabelHist(df[\"Labels\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8e30f0-cMC6F", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Zusammenfassend kann festgehalten werden, dass der Datensatz ca. 4500 Teile umfasst welche durch ca. 100 Merkmale beschrieben werden. Diese Merkmale ergeben sich überwiegend aus den internen Messungen der Spritzgussmaschine. Interessant sind vor Allem die Zielwerte `0_leak_corner_tl` und `0_leak_corner_tr`, welche in der Regel entweder gar nicht oder sehr stark auftreten." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XY8DibNEInpC", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.4 Aufteilen der Daten\n", + "Zunächst wird der Datensatz in Trainings- und Testdaten unterteilt. Wie im vorherigen Unterkapitel gezeigt können manche Fehler sehr selten sein. Deshalb wird eine stratifizierte anstatt einer rein zufälligen Stichprobe gezogen, vgl. [3]." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wsUtJOqkHUGB", + "outputId": "b725b743-0000-4b5e-fc63-a30159c2e7e9", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anzahl Datenpunkte:\n", + "Trainingsdaten: 3638\n", + "Testdaten: 910\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import StratifiedShuffleSplit\n", + "\n", + "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n", + "for train_idx, test_idx in split.split(df, df[\"Labels\"][\"0_leak_corner_tr\"]):\n", + " df_train = df.iloc[train_idx]\n", + " df_test = df.iloc[test_idx]\n", + "\n", + "print(\"Anzahl Datenpunkte:\")\n", + "print(\"Trainingsdaten:\", len(df_train))\n", + "print(\"Testdaten:\", len(df_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1T_YoGSPfRrz", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Im nächsten Schritt werden die Merkmale und Zielwerte voneinander getrennt:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "b7aEBkCf7C0x", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Merkmale\n", + "X_train = df_train.drop(\"Labels\", axis=1)\n", + "X_test = df_test.drop(\"Labels\", axis=1)\n", + "\n", + "# Zielwerte\n", + "y_train = df_train[\"Labels\"].copy()\n", + "y_test = df_test[\"Labels\"].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "onyggdQoPsyr", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.5 Erkunden der Daten\n", + "Nun können die Trainingsdaten erkundet werden. Ein gängiges Hilfsmittel dazu ist die sog. Korrelationsmatrix. Diese enthält den Pearson-Korrelationskoeffizienten für jedes einzelne Merkmal mit jedem anderen. Um einen Überblick zu bekommen wird diese zunächst grafisch dargestellt:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "_WzzR-APzWsy", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def plotCorrMatrix(df):\n", + " fig = plt.figure(figsize=(7, 7))\n", + "\n", + " # Korrelationsmatrix\n", + " ax = fig.add_subplot(111)\n", + " cax = ax.matshow(df.corr())\n", + " fig.colorbar(cax)\n", + "\n", + " # Titel und Achsenbeschriftung\n", + " ax.set_title(\"Korrelationsmatrix\", size=18, pad=18)\n", + " ax.set_xlabel(\"Merkmale\", labelpad=20)\n", + " ax.set_ylabel(\"Merkmale\", labelpad=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433 + }, + "id": "HQTMDZdcV-Tf", + "outputId": "070cf3e6-7472-4803-8d0e-efb5b846460d", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotCorrMatrix(X_train)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZkREVkrP8IZ", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In der Grafik sind sowohl stark positive (nahe *1*) als auch stark negative (nahe *-1*) Korrelationen zu erkennen. Insbesondere rechts unten sind zusammenhängende Bereiche stark korrelierender Merkmale sichtbar. Diese Beobachtungen deuten darauf hin, dass eine Dimensionsreduktion nachfolgend ein hilfreicher Zwischenschritt sein könnte.\n", + "\n", + "Interessant können auch die Korrelationen der Merkmale mit den Zielwerten sein. Nachfolgend werden die am stärksten mit dem Fehler `0_leak_corner_tr` korrelierenden Merkmale aufgelistet." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "ib_iBopHPjyC", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "fault_corr = df.corr()[\"Labels\"][\"0_leak_corner_tr\"].sort_values(ascending=False)\n", + "\n", + "# Korrelationen der Fehler untereinander entfernen\n", + "fault_corr = fault_corr.drop(\"Labels\", level=0, axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ezW_i1xpekYx", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Diese 5 Merkmale besitzen die stärkste positive Korrelation mit dem Fehler:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qvy5wPm8Q0ww", + "outputId": "656da227-76f5-4b21-b778-075cc07a903d", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Group Name \n", + "Internal_C2 Spezifischer Einspritzdruck Spitzenwert [APVs 2] 0.936937\n", + " Einspritzarbeit [EA 2] 0.920485\n", + " Schussvolumen [Svo 2] 0.857009\n", + " Spezifischer Druck beim Umschalten [APHu 2] 0.818221\n", + " Integral Überwachung 1 Micrograph [IDKi1_Mic 2] 0.812907\n", + "Name: 0_leak_corner_tr, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "fault_corr.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jzRhX6mEe8HU", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Diese 5 Merkmale besitzen die stärkste negative Korrelation:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jLSQL2MLPssC", + "outputId": "1a3076aa-9bb6-4721-c25b-df5c47b0fdc5", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Group Name \n", + "Internal Werkzeug 8 [Pakt 17] -0.373270\n", + "Internal_C2 Spritzzeit Istwert [ZSx 2] -0.466767\n", + " Massepolster kleinster Wert [ACPx 2] -0.620924\n", + " Massepolster Ende Nachdruck [ACPv 2] -0.851165\n", + " Massepolster nach Nachdruck [ACPn 2] -0.857009\n", + "Name: 0_leak_corner_tr, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "fault_corr.tail(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sYTlG_GRfCg-", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Grundsätzlich existieren stark korrelierende Merkmale (> 0,8). Diese entstammen größtenteils der Gruppe `Internal_C2`. Nachfolgend werden die Histogramme der beiden am stärksten korrelierenden Merkmale dargestellt:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "4WO7CTnmSXYQ", + "outputId": "5bdb500f-f7b1-4f86-c738-471af5ca07ca", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2)\n", + "fig.set_size_inches(14, 6)\n", + "fig.suptitle(\"Histogramme der am stärksten korrelierenden Merkmale\", fontsize=16)\n", + "\n", + "# Histogramme\n", + "df.hist(fault_corr.index[0], bins=50, ax=axes[0])\n", + "df.hist(fault_corr.index[1], bins=50, ax=axes[1])\n", + "axes[0].set_title(fault_corr.index[0][1], pad=8)\n", + "axes[1].set_title(fault_corr.index[1][1], pad=8)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O-i5j3xXSLk_", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In beiden Histogramm sind zwei klar getrennte Cluster erkennbar. Es liegt die Vermutung nahe, dass es sich dabei um die fehlerfreien und fehlerhaften Teile handelt. Möglicherweise können diese bereits anhand eines einzigen Merkmals separiert werden. \n", + "\n", + "Das Erkunden der Daten hat gezeigt, dass relativ starke lineare Zusammenhänge im Datensatz existieren sowie Cluster erkennbar sind. Diese Erkenntnisse können nachfolgend bspw. bei der Auswahl von Algorithmen für bestimmte Probleme hilfreich sein. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-9FqxChxkKon", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 4\\. Klassifizierung anhand eines Merkmals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R9ejLXL8kKh2", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Am einfachsten könnten fehlerhafte Teile mit Hilfe der vorhandenen Funktionen der Spritzgussmaschine aussortiert werden. Bei diesen Maschinen können üblicherweise Grenzwerte für einzelne interne Messwerte vorgegeben werden. Werden diese Grenzwerte über- bzw. unterschritten sortiert das sog. Handling (der \"Roboterarm\") das Teil automatisch aus. Das Finden passender Grenzwerte ist dabei die größte Herausforderung.\n", + "\n", + "Um diese Aufgabe zu automatisieren, bietet sich der *CART* (Classification and Regression Trees)-Algorithmus an. Dieser wird genutzt um Entscheidungsbäume zu generieren. Wichtig dabei ist, dass er ausschließlich Binärbäume erzeugt. Bei jeder Abzweigungen von einem Knoten versucht der Algorithmus, den gewichteten Informationsgehalt der nachfolgenden Knoten zu maximieren. Je höher dieser Informationsgehalt, desto genauer können die Datenpunkte in der nachfolgenden Ebene klassifiziert werden, vgl. [4]. Der Algorithmus arbeitet dabei in jedem Schritt \"greedy\". Das bedeutet, er versucht in jedem einzelnen Schritt das optimale Ergebnis zu erzielen. Ein größerer Kontext wird nicht beachtet. Aufgrund dieser Eigenschaften (binär und \"greedy\") eignet sich der CART optimal für das Finden der Grenzwerte der Spritzgussmaschine.\n", + "\n", + "Die Spritzgussmaschine kann ein Teil entweder aussortieren oder nicht. Folglich müssen zunächst die Zielwerte angepasst werden. Nach Rücksprache mit dem Fachpersonal werden die Fehlerausprägungen *0* und *1* als fehlerfreies sowie *2* und *3* als fehlerhaftes Teil eingestuft:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "o4meu5t8u5co", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def convertLabelsToBinary(labels):\n", + " labels_bin = labels.copy()\n", + " labels_bin.replace(1, 0, inplace=True)\n", + " labels_bin.replace(2, 3, inplace=True)\n", + "\n", + " # Zur besseren Lesbarkeit wird die Stufe 3 abschließend in 1 \"umbenannt\"\n", + " labels_bin.replace(3, 1, inplace=True)\n", + "\n", + " return labels_bin" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "h-51kMU0vZj0", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "y_train_01 = convertLabelsToBinary(y_train)\n", + "y_test_01 = convertLabelsToBinary(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q-ZPOizyv8nW", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Außerdem kann der CART (wie auch die Spritzgussmaschine) nur mit numerischen Merkmalen umgehen. Die text-basierten Merkmale werden nachfolgend entfernt." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "apfQMyI_Qf5-", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "X_train_num = X_train.drop(X_train.select_dtypes(exclude=np.number), axis=1)\n", + "X_test_num = X_test.drop(X_train.select_dtypes(exclude=np.number), axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lahznp99tefh", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Da für den CART keine Skalierung der Merkmale erforderlich ist kann der Algorithmus unmittelbar trainiert werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P9zZH2GfRasJ", + "outputId": "d0ed80fc-9031-4f42-d9f0-ca52301f4cb5", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n", + " max_depth=1, max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort='deprecated',\n", + " random_state=42, splitter='best')" + ] + }, + "execution_count": 24, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "# max_depth = 1 da nur der beste Grenzwert gesucht ist\n", + "tree_clf = DecisionTreeClassifier(max_depth=1, random_state=42)\n", + "tree_clf.fit(X_train_num, y_train_01[\"0_leak_corner_tr\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ajqlffhd2tMN", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Um das Ergebnis darzustellen kann das Modul `graphviz` genutzt werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "id": "tmRYgZ0mRakg", + "outputId": "4a41bf28-814d-4037-dc65-337450d6a397", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "Spezifischer Einspritzdruck Spitzenwert [APVs 2] <= 397.95\n", + "gini = 0.496\n", + "samples = 3638\n", + "value = [1978, 1660]\n", + "class = Gut\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "gini = 0.007\n", + "samples = 1956\n", + "value = [1949, 7]\n", + "class = Gut\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "gini = 0.034\n", + "samples = 1682\n", + "value = [29, 1653]\n", + "class = Schlecht\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import export_graphviz\n", + "from graphviz import Source\n", + "\n", + "# .dot-Datei exportieren\n", + "export_graphviz(\n", + " tree_clf,\n", + " out_file=\"tree_clf.dot\",\n", + " feature_names=X_train_num.droplevel(0, axis=1).columns,\n", + " class_names=[\"Gut\", \"Schlecht\"],\n", + " rounded=True,\n", + " filled=True,\n", + ")\n", + "\n", + "# .dot-Datei in Graph umwandeln und darstellen\n", + "Source.from_file(\"tree_clf.dot\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TyO7Vumikrzm", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Das Ergebnis sieht vielversprechend aus: In nur einem Schritt wurden zwei Gruppen gebildet, welche zu einem sehr großen Teil entweder nur aus Gut- oder nur aus Schlechtteilen bestehen. Der Informationsgehalt wurde somit stark erhöht.\n", + "\n", + "An dieser Stelle offenbart sich ein weiterer großer Vorteil des CART. Bei ihm handelt es sich im Gegensatz zu vielen anderen ML-Algorithmen um einen sog. *White-Box-Algorithmus*. Das bedeutet, dass seine Entscheidungsfindung sehr einfach nachvollzogen werden kann. In diesem Fall scheint der *Spezifische Einspritzdruck Spitzenwert* entscheidend zu sein. Übersteigt dieser einen Wert von ca. 400, werden fehlerhafte Teile produziert. Hieraus könnten vom Fachpersonal weitere Erkenntnisse abgeleitet werden.\n", + "\n", + "Um für die quantitative Beurteilung noch nicht auf die Testdaten zurückgreifen zu müssen wird zur Bewertung eine Kreuzvalidierung, implementiert durch die Klasse `cross_val_score`, durchgeführt. Da fehlerhafte Teile nicht selten sind kann die Genauigkeit als Bewertungskriterium genutzt werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "EASyI4UXkmbh", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "tree_cv = cross_val_score(\n", + " tree_clf, X_train_num, y_train_01[\"0_leak_corner_tr\"], cv=10, scoring=\"accuracy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "52-U5OdEkmbh", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def outputCVResults(cv):\n", + " print(\"Genauigkeit bei der Kreuzvalidierung\")\n", + " print(\"- Mittelwert:\", \"{:.2f}\".format(100 * cv.mean()), \"%\")\n", + " print(\"- Standardabw.:\", \"{:.2f}\".format(100 * cv.std()), \"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gBTvASlCmQ76", + "outputId": "13835fc9-c122-41ba-e4a0-56394d391cb1", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 98.93 %\n", + "- Standardabw.: 0.52 %\n" + ] + } + ], + "source": [ + "outputCVResults(tree_cv)\n", + "results.append((\"Tree_cv\", 100 * tree_cv.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hm1ku6_J4Yfu", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Tatsächlich lassen sich die Trainingsdaten mit einer Genauigkeit von ca. 98,9 % anhand eines einzigen Merkmals klassifizieren. Auf den Testdaten sieht das Ergebnis ähnlich aus, es scheint kein Overfitting vorzuliegen:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "vd5EQ_l14Wqs", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "tree_cv = cross_val_score(\n", + " tree_clf, X_test_num, y_test_01[\"0_leak_corner_tr\"], cv=10, scoring=\"accuracy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9vFBO_rdnBUW", + "outputId": "15c2a723-e016-4863-c274-5d7d9f8cbf82", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 98.90 %\n", + "- Standardabw.: 0.98 %\n" + ] + } + ], + "source": [ + "outputCVResults(tree_cv)\n", + "results.append((\"Tree_test\", 100 * tree_cv.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WVtYmg-34UMf", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Diese Lösung kann weiter verbessert werden, indem auch die Grenzwerte für das 2., 3. usw. beste Merkmale ermittelt und in die Spritzgussmaschine eingetragen werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "darO5JMn7xDO", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Einstellungen\n", + "n_features = 10\n", + "label = \"0_leak_corner_tr\"\n", + "\n", + "# Kopie der Daten erstellen da in jedem Durchlauf das \"beste\" Merkmal entfernt wird\n", + "X_train_cpy = X_train_num.copy()\n", + "X_test_cpy = X_test_num.copy()\n", + "\n", + "# Initialisierungen\n", + "tree_clf_tmp = DecisionTreeClassifier(max_depth=1, random_state=42)\n", + "features, train_scores, test_scores = [], [], []\n", + "for _ in range(n_features):\n", + " # Entscheidungsbaum trainieren\n", + " tree_clf_tmp.fit(X_train_cpy, y_train_01[label])\n", + "\n", + " # Ergebnis abspeichern\n", + " features.append(X_train_cpy.columns[tree_clf_tmp.feature_importances_.argmax()][1])\n", + " train_scores.append(\n", + " 100\n", + " * cross_val_score(\n", + " tree_clf_tmp, X_train_cpy, y_train_01[label], cv=5, scoring=\"accuracy\"\n", + " ).mean()\n", + " )\n", + " test_scores.append(\n", + " 100\n", + " * cross_val_score(\n", + " tree_clf_tmp, X_test_cpy, y_test_01[label], cv=5, scoring=\"accuracy\"\n", + " ).mean()\n", + " )\n", + "\n", + " # Bestes Merkmal für den nächsten Durchlauf entfernen\n", + " X_train_cpy = X_train_cpy.drop(\n", + " X_train_cpy.columns[tree_clf_tmp.feature_importances_.argmax()], axis=1\n", + " )\n", + " X_test_cpy = X_test_cpy.drop(\n", + " X_test_cpy.columns[tree_clf_tmp.feature_importances_.argmax()], axis=1\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250 + }, + "id": "mniU2F-5DaxL", + "outputId": "07a1f92d-bb0b-4c1c-c389-34356a8eab7b", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Merkmal Training [%] Test [%]
Spezifischer Einspritzdruck Spitzenwert [APVs 2]98.9398.90
Massepolster Ende Nachdruck [ACPv 2]97.3696.04
Schussvolumen [Svo 2]97.3996.04
Massepolster nach Nachdruck [ACPn 2]97.3996.04
Spezifischer Druck beim Umschalten [APHu 2]96.7896.48
Einspritzarbeit [EA 2]96.3295.71
Integral Überwachung 1 Micrograph [IDKi1_Mic 2]94.2594.40
Integral Überwachung 1 Micrograph [IDKi1_Mic 1]91.2391.43
Spezifischer Nachdruck Spitzenwert [APNs 2]90.2792.75
Schusszähler Istwert [SZx]80.3583.96
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Zur besseren Darstellung in DataFrame umwandeln\n", + "data = {\"Merkmal\": features, \"Training [%]\": train_scores, \"Test [%]\": test_scores}\n", + "df_features = pd.DataFrame(data)\n", + "style = df_features.style\n", + "style = style.format({\"Training [%]\": \"{:.2f}\"})\n", + "style = style.format({\"Test [%]\": \"{:.2f}\"})\n", + "style = style.background_gradient(cmap=\"viridis\")\n", + "style = style.set_properties(**{\"text-align\": \"right\"})\n", + "style = style.set_properties(**{\"text-align\": \"left\"}, subset=[\"Merkmal\"])\n", + "style = style.hide_index()\n", + "style" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JR0m-KcqEC-i", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Neben dem *Spezifischen Einspritzdruck Spitzenwert* erzielen fünf weitere Merkmale eine Genauigkeit von über 95 % auf den Testdaten. Wenn das beste Merkmal nicht mehr ausreichen sollte könnten aus diesen Merkmalen zukünftig weitere Grenzwerte abgeleitet werden. Dazu muss der entsprechende Entscheidungsbaum betrachtet werden. Durch eine Verschiebung der ermittelten Grenzwerte in Richtung der Gut- bzw. Schlechtteile kann außerdem entweder die Sensitivität oder die Präzision des\n", + "tems entsprechend der spezifischen Anforderungen angepasst werden." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "olhWzAqyECjI", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 5\\. Klassifizierung anhand mehrerer Merkmale" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SWwMNx5EEH_l", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die bei der Firma Gustav Hensel GmbH & Co. KG verwendeten Spritzgussmaschinen stellen ihre internen Messwerte unmittelbar nach Fertigstellung eines Teils über einen USB-Anschluss zur Verfügung. Dies eröffnet die Möglichkeit, auf externer Hardware einen komplexeren Klassifikator laufen zu lassen. Die Ausgabe des Klassifikators kann von den Maschinen über einen digitalen Eingang eingelesen und das gespritzte Teil bei Bedarf aussortiert werden.\n", + "\n", + "Die meisten dieser \"komplexeren\" Klassifikatoren erfordern eine Vorverarbeitung der Daten. Deshalb wird im nachfolgenden Unterkapitel zunächst eine Pipeline zur Datenvorverarbeitung aufgebaut." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9FYl0BmpUNbQ", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 5.1 Pipeline\n", + "Grundsätzlich haben die Merkmale sehr unterschiedliche Wertebereiche:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 230 + }, + "id": "zL3OAuf3liRh", + "outputId": "29e0cd8c-993d-4672-c8b1-c92bd78ba750", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameDosierleistung [iwdls_1]Dosiervolumen Istwert [ASSx_1]Dosierzeit Istwert [ZDx_1]Drehmoment Mittelwert laufender Zyklus [Mm_1]Drehmoment Spitzenwert laufender Zyklus [Ms_1]Drehzahl Spitzenwert [DZs_1]Einspritzarbeit [EA_1]Integral Überwachung_1 Micrograph [IDKi1_Mic_1]Massepolster Ende Nachdruck [ACPv_1]Massepolster kleinster Wert [ACPx_1]Massepolster nach Nachdruck [ACPn_1]Schussvolumen [Svo_1]Spezifischer Druck beim Umschalten [APHu_1]Spezifischer Einspritzdruck Spitzenwert [APVs_1]Spezifischer Nachdruck Spitzenwert [APNs_1]Spezifischer Staudruck Spitzenwert [APSs_1]Spritzzeit Istwert [ZSx_1]Umschaltvolumen [AC3u_1]Zykluszeit Düse vor [ZDvo_1]Zykluszeit Nachdruck [ZNach_1]
mean6.78663870.5983957.644107166.944667178.0812530.2939261.0286151273.1541376.2000664.4216716.40563262.594368528.619351904.122787628.06217793.6423861.17442812.5684191.1552141.995052
min5.99000070.3700001.99000084.500000133.5000000.2920000.9700046882.5600003.7000002.1400003.91000059.690000500.000000768.300000595.0000007.4000001.12000012.4800000.0000000.000000
max26.98000070.7700008.510000178.700000192.2000000.2980001.1160055403.3900009.1100006.1600009.31000065.090000605.7000001042.300000672.80000095.7000001.23000012.6900003.5700002.000000
\n", + "
" + ], + "text/plain": [ + "Name Dosierleistung [iwdls_1] ... Zykluszeit Nachdruck [ZNach_1]\n", + "mean 6.786638 ... 1.995052\n", + "min 5.990000 ... 0.000000\n", + "max 26.980000 ... 2.000000\n", + "\n", + "[3 rows x 20 columns]" + ] + }, + "execution_count": 33, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "formatForPlotting(X_train[\"Internal_C1\"].describe()).iloc[[1, 3, 7]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VSRgigCD7C0z", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Außerdem sind alle bis auf ein Merkmal numerisch. Das einzige text-basierte Merkmal ist dabei nicht sehr aussagekräftig, da es fast immer einen Bindestrich enthält:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UjDdpVgS3Nd2", + "outputId": "3ad66fe3-5a8e-4de7-f017-cc0ff5309935", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(Internal, Ausschussursache [ASU]) \n", + "- 3615\n", + "3- Polster 2 zu klein (3/2/0/0/0) 11\n", + "3- Polster 1 zu klein (3/1/0/0/0) 8\n", + "4- Durchwärmungsprüfung 2 war aktiv (4/2/0/0/0) 4\n", + "dtype: int64" + ] + }, + "execution_count": 34, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.select_dtypes(exclude=[np.number]).value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gCq25XXl4HKU", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Deshalb wird dieses Merkmal von der nachfolgend aufgebauten Pipeline aussortiert. \n", + "\n", + "Zunächst wird eine Pipeline für die numerischen Merkmale aufgebaut. Diese übernimmt im Wesentlichen die Skalierung der Merkmale. Der Datensatz besitzt einige Ausreißer. Aus diesem Grund wird der `StandardScaler` verwendet, welcher wesentlich robuster gegenüber Ausreißern ist als der `MinMaxScaler`." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "DIwepaEY7C05", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "num_pipeline = Pipeline([(\"scaler\", StandardScaler())])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_0XyssT9ru7", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Im nächsten Schritt wird die vollständige Pipeline definiert. Diese sortiert das text-basierte Merkmal aus. Durch die Verwendung der Klasse `ColumnTransformer` können diese Merkmale zukünftig sehr einfach mit Hilfe einer eigenen Pipeline vorverarbeitet werden." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "uFKjRiqq7C05", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.compose import ColumnTransformer\n", + "\n", + "num_features = list(X_train.select_dtypes(include=[np.number]))\n", + "text_features = list(X_train.select_dtypes(exclude=[np.number]))\n", + "\n", + "pipeline = ColumnTransformer(\n", + " [(\"num\", num_pipeline, num_features), (\"text\", \"drop\", text_features)]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74LxhoOiSzhq", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Nun kann die Pipeline an die Trainingsdaten angepasst und anschließend auf alle Daten angewendet werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "BMCX2zKmScB5", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Anpassen an die Trainingsdaten\n", + "pipeline.fit(X_train)\n", + "\n", + "# Transformation der Trainings- und Testdaten\n", + "X_train_tr = pipeline.transform(X_train)\n", + "X_test_tr = pipeline.transform(X_test)\n", + "\n", + "# Wiederherstellung der DataFrames\n", + "X_train_tr = pd.DataFrame(\n", + " X_train_tr, X_train[num_features].index, X_train[num_features].columns\n", + ")\n", + "X_test_tr = pd.DataFrame(\n", + " X_test_tr, X_test[num_features].index, X_test[num_features].columns\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J_CgKgMOivjE", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die vorverarbeiteten Daten können im nächsten Schritt zum Trainieren der Klassifikatioren genutzt werden." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DSp1Loop7C07", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 5.2 Klassifikatoren\n", + "Wie bereits in den vorherigen Kapitel wird stellvertretend der häufigste Fehler `0_leak_corner_tr` untersucht." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "_28GhSGGVfUQ", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "label = \"0_leak_corner_tr\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O23OE2YJUOsu", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 5.3 Logistische Regression\n", + "In der Regel ist es sinnvoll, mit möglichst wenigen Annahmen zu starten. Deshalb wird im ersten Versuch ein lineares Modell trainiert. Dieses wird durch die Klasse `LogisticRegeression` implementiert." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FFBISqtJrpsQ", + "outputId": "74b80903-9355-4bdf-97ad-011e95ef84c6", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='auto', n_jobs=None, penalty='l2',\n", + " random_state=42, solver='lbfgs', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 39, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "log_clf = LogisticRegression(random_state=42)\n", + "log_clf.fit(X_train_tr, y_train_01[label])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nMcOHZvIXGgV", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Auch an dieser Stelle wird wieder auf die Kreuzvalidierung mit der Genauigkeit als Bewertungskritierium zurückgegriffen, siehe Kapitel 4." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "ovMrIh8JrpsR", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "log_clf_cv = cross_val_score(\n", + " log_clf, X_train_tr, y_train_01[label], cv=10, scoring=\"accuracy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VFmpuvicbL8Q", + "outputId": "4079433c-f5dc-479a-d46c-0077fadea363", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 99.20 %\n", + "- Standardabw.: 0.48 %\n" + ] + } + ], + "source": [ + "outputCVResults(log_clf_cv)\n", + "results.append((\"Log_reg_cv\", 100 * log_clf_cv.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fOv1BcGhjOa2", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Das Ergebnis ist im Durchschnitt mit *99,2 %* nur etwas besser als mit einem sehr einfachen Entscheidungsbaum mit ca. *98,9 %*. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7TZYtZIzYT7k", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 5.4 Random Forest\n", + "Möglicherweise ist ein komplexeres Modell erforderlich. Aufgrund des Erfolgs des einfachen Entscheidungsbaums wirkt ein *Random Forest*-Modell vielversprechend. Ein Random Forest besteht aus mehreren - möglichst unkorrelierten - Entscheidungsbäumen und kann sowohl für die Klassifikation als auch Regression eingesetzt werden. Die Klassifikation erfolgt durch einen Mehrheitsentscheid der einzelnen Bäume. Um unkorrelierte Entscheidungsbäume zu erhalten werden diese bspw. auf zufällig aufgewählten Teilmengen der Merkmale trainiert, vgl. [5]. In Scikit-Learn wird dieses Modell durch die Klasse `RandomForestRegressor` implementiert." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f7octDFerpsd", + "outputId": "0de01a80-5a97-4f3b-e40d-54834bf7320f", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n", + " criterion='gini', max_depth=None, max_features='auto',\n", + " max_leaf_nodes=None, max_samples=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=100,\n", + " n_jobs=None, oob_score=False, random_state=42, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 42, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "forest_clf = RandomForestClassifier(random_state=42)\n", + "forest_clf.fit(X_train_tr, y_train_01[label])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XjCU7qMmczq1", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Wie zuvor kann nun die Kreuzvalidierung zur Bewertung genutzt werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "aHxL5cpdbe2t", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "forest_clf_cv = cross_val_score(\n", + " forest_clf, X_train_tr, y_train_01[label], cv=10, scoring=\"accuracy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ug-rytSfjlnH", + "outputId": "09a61f6d-5f47-40eb-9a35-82cfed6d7636", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 99.26 %\n", + "- Standardabw.: 0.37 %\n" + ] + } + ], + "source": [ + "outputCVResults(forest_clf_cv)\n", + "results.append((\"Forest_cv\", 100 * forest_clf_cv.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A6gw_I-Ru6z3", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Mit *99,26 %* ist der Random Forest nur minimal besser als die Logistische Regression mit *99,20 %*. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m1pf9pOycySK", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 5.5 Analyse der Fehler\n", + "Dies ist ein guter Zeitpunkt um die Fehler des Modells genauer zu untersuchen. Dazu sind die \"ehrlichen\" Schätzungen des Klassifikators erforderlich. Das sind Schätzungen, bei denen der Klassifikator den zu klassifizierenden Datenpunkt noch nicht gesehen hat, also nicht auf diesem trainiert wurde. Auch hier bietet Scikit-Learn mit der Klasse `cross_val_predict` eine passende Implementierung, welche auf der Kreuzvalidierung aufbaut: " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "LJb6y8tyeMbK", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_predict\n", + "\n", + "y_train_01_pred = cross_val_predict(forest_clf, X_train_tr, y_train_01[label])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9MKdOsPdhVmc", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Fehler können im nächsten Schritt als Konfusionsmatrix dargestellt werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-xpYKoP9eaeN", + "outputId": "47854d75-5f69-4d35-c21a-bbb2735561e6", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix\n", + "[[1952 26]\n", + " [ 6 1654]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "print(\"Konfusionsmatrix\")\n", + "print(confusion_matrix(y_train_01[label], y_train_01_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VWXaHTQchy9e", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die allermeisten Teile wurden entweder korrekt positiv (1952) oder korrekt negativ (1654) klassifiziert. Allerdings wurden einige fehlerfreie Teile als fehlerhaft klassifiziert (26) und ein paar fehlerhafte Teile als fehlerfrei (6). Um diese fehlerhaften Klassifizierungen zu erklären werden nachfolgend die ursprünglichen Zielwerte (0 - 3) für ein paar dieser Teile dargestellt.\n", + "\n", + "Fehlerfreie Teile die als fehlerhaft klassifiziert wurden:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "oCD4Ox8z_-nt", + "outputId": "2da81e6b-8443-4d60-af60-8ff9c90ef587", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Name 0_leak_corner_tl 0_leak_corner_tr 1_hole_bottom\n", + "2021-01-11 15:10:02 1 1 0\n", + "2021-01-11 16:11:52 3 1 0\n", + "2021-01-11 16:30:29 2 1 0\n", + "2021-01-11 16:32:17 3 1 0\n", + "2021-01-11 16:41:53 1 1 0" + ] + }, + "execution_count": 47, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "y_train[\n", + " np.logical_and(y_train_01[label] == 0, y_train_01_pred == 1)\n", + "].sort_index().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dknVvtppAQMz", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Fehlerhafte Teile die als fehlerfrei klassifiziert wurden:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "2HxvVt50-GLV", + "outputId": "fe654aff-3f25-4e49-d530-ce8c1baf3047", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Name 0_leak_corner_tl 0_leak_corner_tr 1_hole_bottom\n", + "2021-01-11 14:53:13 1 2 0\n", + "2021-01-11 14:58:01 1 2 0\n", + "2021-01-11 16:12:29 1 2 0\n", + "2021-01-12 05:57:11 1 2 0\n", + "2021-01-12 12:55:29 1 2 0" + ] + }, + "execution_count": 48, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "y_train[\n", + " np.logical_and(y_train_01[label] == 1, y_train_01_pred == 0)\n", + "].sort_index().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qMo-Fm-JmIwx", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In der Spalte `0_leak_corner_tr` beider Tabellen ist erkennbar, dass die falsch klassifizierten Teile mit einer Fehlerstärke von 1 bzw. 2 jeweils am Rand der binären Entscheidungsgrenze (zwischen 1 und 2) liegen. Vermutlich handelt es sich bei diesen Teilen um Grenzfälle, welche sowohl der Fehlerstärke 1 als auch 2 hätten zugeordnet werden können. Die Fotos zweier dieser Teile - dargestellt in Abbildung 4 - unterstützen diese Vermutung:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nDWrRKOoqw4Q", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6-OuSEHAqloL", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "**Abbildung 4: Ein falsch positiv (links) und ein falsch negativ (rechts) klassifiziertes Teil**\n", + "\n", + "Im Vergleich zu den übrigen Teilen sind beide dargestellten Fehler weder besonders schwach noch besonders stark. \n", + "\n", + "Auf Grundlage dieser Erkenntnis könnte im nächsten Schritt der Grenzwert des Klassifikators so festgelegt werden, dass dessen Sensitivität und Präzision für den vorliegenden Anwendungsfall optimal sind. Da die positive Kategorie bei diesem Datensatz nicht selten ist, bietet sich in diesem Fall die Verwendung einer ROC-Kurve an, um dieses Ziel zu erreichen, vgl. [3]." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M7ctLex2tJYq", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 5.3 Zusammenfassung\n", + "Die Genauigkeit der beiden trainierten Klassifikatoren ist mit über 99,00 % bei der Kreuzvalidierung ausreichend hoch. Die fehlerhaft klassifizierten Teile liegen vermutlich nahe der binären Entscheidungsgrenze. Diese Fehler sind bei einem binären Klassifikator auf Basis analoger Merkmale zu erwarten. Abschließend werden die Klassifikatoren anhand der Testdaten überprüft:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7HI3I54PtPyZ", + "outputId": "eb1ed7c0-2b1d-4632-ea83-228f30f121db", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit auf den Testdaten\n", + "- Logistische Regression: 99.23 %\n", + "- Random Forest: 99.34 %\n" + ] + } + ], + "source": [ + "print(\"Genauigkeit auf den Testdaten\")\n", + "\n", + "# Logistische Regression\n", + "log_clf_test = log_clf.score(X_test_tr, y_test_01[label])\n", + "print(\"- Logistische Regression:\", \"{:.2f}\".format(100 * log_clf_test), \"%\")\n", + "results.append((\"Log_reg_test\", 100 * log_clf_test))\n", + "\n", + "# Random Forest\n", + "forest_clf_test = forest_clf.score(X_test_tr, y_test_01[label])\n", + "print(\"- Random Forest:\", \"{:.2f}\".format(100 * forest_clf_test), \"%\")\n", + "results.append((\"Forest_test\", 100 * forest_clf_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "392pFc5IxDA7", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Auch die Genauigkeit auf den Testdaten ist mit jeweils über *99,2 %* ausreichend hoch und sehr ähnlich zur Genauigkeit bei der Kreuzvalidierung. Von einem Under- oder Overfitting ist deshalb nicht auszugehen. Aufgrund der hohen Genauigkeit und der erklärbaren Fehler wird kein zusätzlicher Aufwand in die Optimierung der Hyperparamter investiert.\n", + "\n", + "Zusammenfassend hat dieses Kapitel gezeigt, dass mit Klassifikatoren auf Basis mehrerer Merkmale sehr gute Ergebnisse erzielt werden können. Der große Nachteil dabei ist, dass dafür ca. 3600 Fotos manuell gelabelt werden mussten. Im nachfolgenden Kapitel wird deshalb untersucht, ob der Ansatz des teilüberwachten Lernens genutzt werden kann, um diesen Aufwand zu minimieren." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Uv0Fg6010f59", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 6\\. Teilüberwachtes Lernen" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kYomnHlD0jg0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die vorherigen Kapitel haben gezeigt, dass ML-Algorithmen mit Hilfe gelabelter Daten fehlerhafte Teile sehr gut erkennen können. Ziel dieses Kapitel ist, den Aufwand zu minimieren, um einen solchen Algorithmus zu trainieren. Dieser Aufwand besteht hauptsächlich im Labeln der Fotos. Beim Erkunden der Daten hat sich bereits gezeigt, dass diese eine Cluster-Struktur aufweisen. Ein vielversprechender Ansatz zur Minimierung des Label-Aufwands ist deshalb das *teilüberwachte Lernen*. \n", + "\n", + "Beim teilüberwachten Lernen wird nur ein geringer Teil der Datenpunkte gelabelt. Es wird versucht, dafür repräsentative Datenpunkte zu finden. Dies könnten bspw. die Mittelpunkte der beobachteten Cluster sein. Die Label der repräsentativen Datenpunkte können auch auf andere – z.B. demselben Cluster angehörige – Datenpunkte übertragen werden, bevor ein ML-Algorithmus trainiert wird, vgl. [3]. In diesem Fall soll die Auswahl der repräsentativen Datenpunkte auf Basis der beobachteten Cluster erfolgen. Wie diese erfasst werden beschreibt das nachfolgende Unterkapitel." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8-HwAmLI-HZx", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 6.1 Clustering der Daten" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gc1rUXB-8ns8", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.1.1 Auswahl relevanter Merkmale\n", + "In einem hochdimensionalen Merkmalsraum sind Datenpunkte relativ weit voneinander entfernt, wodurch deren Clustering erschwert wird, vgl. [3]. Eine einfache Möglichkeit diesen Merkmalsraum zu verkleinern ist das Entfernen von Merkmalen, welche für das untersuchte Problem wenig relevant erscheinen. Dies könnte z.B. auf Grundlage von Fachwissen erfolgen. In diesem Fall eröffnet jedoch der in Kap. 5 trainierte `RandomForestClassifier` eine einfachere Möglichkeit. Dieser besitzt das Attribut `feature_importances_`, welches Aufschluss über diejenigen Merkmale gibt, welche bei seinen Entscheidungen am wichtigsten sind. Nachfolgend die 10 wichtigsten Merkmale:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d0QfRc917C1D", + "outputId": "d8211c48-be96-4fd4-e053-f28701fbe77b", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Group Name \n", + "Internal_C2 Spezifischer Einspritzdruck Spitzenwert [APVs 2] 0.135442\n", + " Spezifischer Druck beim Umschalten [APHu 2] 0.128987\n", + " Massepolster Ende Nachdruck [ACPv 2] 0.126340\n", + " Schussvolumen [Svo 2] 0.102903\n", + " Massepolster nach Nachdruck [ACPn 2] 0.095072\n", + " Integral Überwachung 1 Micrograph [IDKi1_Mic 2] 0.072568\n", + " Einspritzarbeit [EA 2] 0.068275\n", + " Spezifischer Nachdruck Spitzenwert [APNs 2] 0.043516\n", + "Internal Schusszähler Istwert [SZx] 0.034663\n", + "Internal_C2 Dosierzeit Istwert [ZDx 2] 0.028120\n", + "Name: Feature importance, dtype: float64" + ] + }, + "execution_count": 50, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "feature_importances = forest_clf.feature_importances_\n", + "features_df = pd.DataFrame(\n", + " feature_importances.T, X_train_tr.columns, [\"Feature importance\"]\n", + ")\n", + "features_df[\"Feature importance\"].sort_values(ascending=False).head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JM7XGnQiOb_9", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Noch aufschlussreicher ist ein Graph, welche die sortierten Wichtigkeiten der Merkmale über deren Indizes darstellt:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 417 + }, + "id": "BbzyBNHSNJj_", + "outputId": "2fbbe40b-9fc1-4d03-c3fe-ff10928e7bcb", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.plot(features_df[\"Feature importance\"].sort_values(ascending=False).to_numpy())\n", + "\n", + "# Horizontale Linie\n", + "xticks = range(0, 101, 5)\n", + "hline = 0.01\n", + "plt.plot(xticks, np.array([hline for tick in xticks]), \"r--\", label=\"1 %\")\n", + "\n", + "# Formatierung\n", + "plt.title(\"Wichtigkeit der Merkmale im Random Forest\", fontsize=16, pad=10)\n", + "plt.xlabel(\"Merkmale\")\n", + "plt.ylabel(\"Relative Wichtigkeit [%]\")\n", + "plt.xlim(0, 100)\n", + "plt.ylim(0, 0.15)\n", + "plt.xticks(xticks)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gpRLii5gDw26", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Der Graph zeigt, dass ca. ab dem *15. Merkmal* die relative Wichtigkeit unter 1 % sinkt. Deshalb werden nachfolgend nur die ersten 15 Merkmale betrachtet. An dieser Stelle kann selbstverständlich auch domänenspezifisches Wissen berücksichtigt werden." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "ob3w_u2RXtRu", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "n_features = 15\n", + "X_train_cl = X_train_tr.iloc[:, feature_importances.argsort()[-n_features:]]\n", + "X_test_cl = X_test_tr.iloc[:, feature_importances.argsort()[-n_features:]]\n", + "\n", + "# Reihenfolge der Spaltennamen wiederherstellen\n", + "X_train_cl = X_train_cl.sort_index(axis=1)\n", + "X_test_cl = X_test_cl.sort_index(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l0CfQVvlUIeS", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.1.2 Dimensionsreduktion\n", + "Über das Entfernen der unwichtigen Merkmale hinaus kann die Dimension des Merkmalsraums weiter reduziert werden. Dies erleichtert das Clustering und ermöglicht eine visuelle Darstellung der Cluster.\n", + "\n", + "Häufig wird dafür die Hauptkomponentenanalyse (Principal Component Analysis, PCA) eingesetzt. Bei dieser handelt sich um ein mathematisches Verfahren, bei dem der ursprüngliche Merkmalsraum unter Beibehaltung einer möglichst großen Varianz auf einen niedriger dimensionalen Unterraum projiziert wird, vgl. [6]. Die Dimension des Unterraums kann dabei beliebig gewählt werden.\n", + "\n", + "Die Achsen des Unterraums werden als Hauptkomponenten bezeichnet und ergeben sich aus Linearkombinationen der ursprünglchen Merkmale. Beim Erkunden der Daten in Kap. 3.5 konnten starke lineare Korrelationen im Datensatz beobachtet werden. Aus diesem Grund wird nachfolgend die PCA zur Dimensionreduktion genutzt. Auch für dieses Verfahren liefert SciKit-Learn mit der Klasse `PCA` eine Implementierung. Der nach nachfolgende Programmcode reduziert die Dimension der 15 wichtigsten Merkmale auf 3: \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "nctLJMIsS6bv", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca_3D = PCA(n_components=3)\n", + "X3D_train = pca_3D.fit_transform(X_train_cl)\n", + "X3D_test = pca_3D.fit_transform(X_test_cl)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qsoPTVoXS6b0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die erklärte Varianz der reduzierten Daten beträgt:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-2NfWFdcS6b1", + "outputId": "2096114d-2a9a-43d8-9af4-5e3402c29ea8", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anteil erklärter Varianz (3D): 95.03 %\n" + ] + } + ], + "source": [ + "print(\n", + " \"Anteil erklärter Varianz (3D):\",\n", + " \"{:.2f}\".format(100 * pca_3D.explained_variance_ratio_.sum()),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kUn_p7jzS6b1", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Bei der Reduktion *von 15 auf 3 Dimensionen* sind folglich nur ca. 5 % der Varianz verloren gegangen. Deshalb wird nachfolgend eine Reduktion auf 2 Dimensionen ausprobiert:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "C3ntagonaH8Q", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "pca_2D = PCA(n_components=2)\n", + "X2D_train = pca_2D.fit_transform(X_train_cl)\n", + "X2D_test = pca_2D.fit_transform(X_test_cl)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qm8yWefgaH8a", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die erklärte Varianz beträgt in diesem Fall:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WXyin6wGaH8b", + "outputId": "4ca4f756-670f-41bd-9425-c3177ca54f15", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anteil erklärter Varianz (2D): 86.50 %\n" + ] + } + ], + "source": [ + "print(\n", + " \"Anteil erklärter Varianz (2D):\",\n", + " \"{:.2f}\".format(100 * pca_2D.explained_variance_ratio_.sum()),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EHTwUsGzaqZ9", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Auch wenn in 2 Dimensionen mit ca. *13,5 %* deutlich mehr Informationen verloren gegangen sind ist der zweidimensionale Datensatz für das Clustering potenziell noch sehr gut geeignet. Die nachfolgenden Visualisierungen sollen bei der Auswahl der Anzahl an Dimensionen unterstützen. Zunächst wird der dreidimensionale Datensatz dargestellt." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "2sjCc-BQcdTq", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "\n", + "def plotX3D(X3D, color, anomalies=None):\n", + " # X3D bei Bedarf in np.ndarray umwandeln\n", + " if isinstance(X3D, pd.DataFrame):\n", + " X3D = X3D.to_numpy()\n", + "\n", + " # Initialisierung\n", + " fig = plt.figure(figsize=(14, 11))\n", + " ax = fig.add_subplot(111, projection=\"3d\")\n", + "\n", + " # Über alle Fehlerausprägungen (0 - 3) iterieren\n", + " colors = {0: \"green\", 1: \"yellow\", 2: \"orange\", 3: \"red\"}\n", + " labels = {\n", + " 0: \"kein Fehler\",\n", + " 1: \"schwacher Fehler\",\n", + " 2: \"mittlerer Fehler\",\n", + " 3: \"starker Fehler\",\n", + " }\n", + " for c in np.sort(np.unique(color)):\n", + " ax.plot(\n", + " X3D_train[color == c, 0],\n", + " X3D_train[color == c, 1],\n", + " X3D_train[color == c, 2],\n", + " \".\",\n", + " label=labels[c],\n", + " c=colors[c],\n", + " alpha=0.2,\n", + " )\n", + "\n", + " # Formatierung\n", + " ax.legend()\n", + " ax.set_xlabel(\"Hauptkomponente $x_1$\", fontsize=14, labelpad=10)\n", + " ax.set_ylabel(\"Hauptkomponente $x_2$\", fontsize=14, labelpad=10)\n", + " ax.set_zlabel(\"Hauptkomponente $x_3$\", fontsize=14, labelpad=10)\n", + " ax.set_zlim(-6, 6)\n", + " ax.view_init(50, 145)\n", + "\n", + " # Optional können Anomalien dargestellt werden\n", + " if isinstance(anomalies, np.ndarray):\n", + " ax.scatter(\n", + " anomalies[:, 0], anomalies[:, 1], anomalies[:, 2], marker=\"x\", s=80, c=\"red\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 629 + }, + "id": "1Y9l32ZBn9P-", + "outputId": "007deb5c-9b3c-496c-9020-fdd90e028134", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX3D(X3D_train, y_train[label])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nkR9pfzgoL1d", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Es sind eindeutig Cluster zu erkennen. Sowohl für die Gut- als auch Schlechtteile existieren mehrere Cluster. Die Cluster der Schlechtteile (rot) sind deutlich weniger kompakt und länglicher gezogen als die Cluster der Gutteile (grün). Um zu überprüfen ob diese Informationen auch im zweidimensionalen Datensatz erhalten geblieben sind wird dieser als nächstes dargestellt." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "0eZsc_fepVYJ", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def plotX2D(X2D, color, anomalies=None):\n", + " # X2D bei Bedarf in np.ndarray umwandeln\n", + " if isinstance(X2D, pd.DataFrame):\n", + " X2D = X2D.to_numpy()\n", + "\n", + " # Initialisierung\n", + " fig = plt.figure(figsize=(12, 8))\n", + " ax = fig.add_subplot(111)\n", + "\n", + " # Über alle Fehlerausprägungen (0 - 3) iterieren\n", + " colors = {0: \"green\", 1: \"yellow\", 2: \"orange\", 3: \"red\"}\n", + " labels = {\n", + " 0: \"kein Fehler\",\n", + " 1: \"schwacher Fehler\",\n", + " 2: \"mittlerer Fehler\",\n", + " 3: \"starker Fehler\",\n", + " }\n", + " for c in np.sort(np.unique(color)):\n", + " ax.plot(\n", + " X2D[color == c, 0],\n", + " X2D[color == c, 1],\n", + " \".\",\n", + " label=labels[c],\n", + " c=colors[c],\n", + " alpha=0.2,\n", + " )\n", + "\n", + " # Formatierung\n", + " ax.legend()\n", + " ax.set_xlabel(\"Hauptkomponente $x_1$\", fontsize=14, labelpad=10)\n", + " ax.set_ylabel(\"Hauptkomponente $x_2$\", fontsize=14, labelpad=10)\n", + "\n", + " # Optional können Anomalien dargestellt werden\n", + " if isinstance(anomalies, np.ndarray):\n", + " ax.scatter(anomalies[:, 0], anomalies[:, 1], marker=\"x\", s=80, c=\"red\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "gdsiFCHmqF_4", + "outputId": "ee4485db-6ba4-4830-fda0-5a1c9d77e8a0", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX2D(X2D_train, y_train[label])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dVHPCI6Fb36e", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Aus dieser Darstellung lassen sich sehr ähnliche Erkenntnisse ableiten wie aus der dreidimensionalen. Für das teilüberwachte Lernen wird aus Gründen der Einfachheit der zweidimensionale Datensatz verwendet." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X1uAqkoxvqVk", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.1.3 Durchführung des Clustering\n", + "Auf Basis des zweidimensionalen Datensatz `X2D_train` kann im nächsten Schritt das Clustering durchgeführt werden. Die Darstellungen im vorherigen Unterkapitel haben gezeigt, dass die Cluster typischerweise elliptisch sind und in ihrer Dichte stark variieren können. Der Algorithmus *K-Means* kommt deshalb nicht in Frage, vgl. [3]. Stattdessen wird ein *Gaußsches Mischverteilungsmodell* (Gaussian Mixture Model, GMM) genutzt, welches diese Art von Clustern sehr gut erzeugen kann. Das Modell versucht, die Datenpunkte im Datensatz durch eine Mischung verschiedener gaußscher Verteilungen zu erzeugen, vgl. [7] Die entsprechende Klasse SciKit-Learn heißt `GaussianMixture`:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "77mESL39S7CF", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.mixture import GaussianMixture\n", + "\n", + "# n_components = 7 auf Grundlage der Darstellungen im vorherigen Unterkapitel\n", + "gaus_mix = GaussianMixture(n_components=7, random_state=42).fit(X2D_train)\n", + "y_pred_cluster = gaus_mix.predict(X2D_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bPz16EgYzuJ6", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Es ist hilfreich, diese Cluster darstellen zu können." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "YkRcz4TQaKYo", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def plotX2DClustered(X2D, cluster, anomalies=None, cluster_centers=[]):\n", + " # X2D bei Bedarf in np.ndarray umwandeln\n", + " if isinstance(X2D, pd.DataFrame):\n", + " X2D = X2D.to_numpy()\n", + "\n", + " # Initialisierung\n", + " fig = plt.figure(figsize=(12, 8))\n", + " ax = fig.add_subplot(111)\n", + "\n", + " # Über alle Cluster iterieren\n", + " for c in np.sort(np.unique(cluster)):\n", + " ax.plot(X2D[cluster == c, 0], X2D[cluster == c, 1], \".\", label=c, alpha=0.2)\n", + "\n", + " # Formatierung\n", + " ax.legend()\n", + " ax.set_xlabel(\"Hauptkomponente $x_1$\", fontsize=14, labelpad=10)\n", + " ax.set_ylabel(\"Hauptkomponente $x_2$\", fontsize=14, labelpad=10)\n", + "\n", + " # Optional können Anomalien dargestellt werden\n", + " if isinstance(anomalies, np.ndarray):\n", + " ax.scatter(anomalies[:, 0], anomalies[:, 1], marker=\"x\", s=80, c=\"red\")\n", + "\n", + " # Optional können die Zentren der Cluster dargestellt werden\n", + " if len(cluster_centers) > 0:\n", + " ax.plot(\n", + " cluster_centers[:, 0],\n", + " cluster_centers[:, 1],\n", + " \"o\",\n", + " c=\"red\",\n", + " markersize=8,\n", + " markeredgecolor=\"black\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "TiJX23EmuCWF", + "outputId": "310d9c5f-74a5-4c5a-c44e-df6d01be91c0", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX2DClustered(X2D_train, y_pred_cluster, cluster_centers=gaus_mix.means_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DEW7GvQU0AhA", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Der Algorithmus hat die Cluster sehr gut erkannt. Die jeweiligen Mittelwerte werden als roter Punkt dargestellt. Das Gaußsche Mischverteilungsmodell wird nachfolgend die Grundlage für das teilüberwachte Lernen bilden.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jsi9AU7FS7Cu", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 6.2 Durchführung des teilüberwachten Lernens\n", + "Auf Basis des Clustering kann nun der Einfluss des teilüberwachten Lernens auf die Genauigkeit eines Klassifikators ermittelt werden. Stellvertretend wird nachfolgend der Klassifikator `LogisticRegression` eingesetzt, welcher in Kap. 5.3 bereits eine Genauigkeit von ca. *99,0 %* auf dem gesamten Datensatz erreicht hat." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ij76pqvJ4sXq", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.2.1 Referenz\n", + "Für den späteren Vergleich ist zunächst eine Referenzgenauigkeit mit allen Labeln zu ermitteln:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fHUI9YzR4P5T", + "outputId": "25ba78c3-4b6b-486b-b4e9-a0d0ece37ff4", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='auto', n_jobs=None, penalty='l2',\n", + " random_state=42, solver='lbfgs', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 64, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "log_clf_full = LogisticRegression(random_state=42)\n", + "log_clf_full.fit(X2D_train, y_train_01[label])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GNZDW3yf4P5j", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Um noch nicht auf die Testdaten zurückgreifen zu müssen wird zur Bewertung eine Kreuzvalidierung durchgeführt:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "id": "yL-2uQOr4P5k", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "log_full_cv = cross_val_score(\n", + " log_clf_full, X2D_train, y_train_01[label], cv=10, scoring=\"accuracy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "aSGbfuF6NQLz", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def outputResults(scores):\n", + " print(\"Genauigkeit bei der Kreuzvalidierung\")\n", + " print(\"- Mittelwert:\", \"{:.2f}\".format(100 * scores.mean()), \"%\")\n", + " print(\"- Standardabw.:\", \"{:.2f}\".format(100 * scores.std()), \"%\")\n", + " print(\"- 10. Perzentil:\", \"{:.2f}\".format(100 * np.percentile(scores, 10), \"%\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "z9W5Bu9n4P5k", + "outputId": "96b6344e-be28-4dc3-8abe-d3e9ccb92876", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 98.82 %\n", + "- Standardabw.: 0.51 %\n", + "- 10. Perzentil: 98.04\n" + ] + } + ], + "source": [ + "outputResults(log_full_cv)\n", + "results.append((\"Log_reg_full_cv\", 100 * log_full_cv.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "586sZH1a4a5K", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Interessanterweise hat dieser Klassifikator mit ca. *98,8 %* auf dem zweidimensionalen Datensatz eine quasi identische Genauigkeit wie auf dem gesamten Datensatz mit über 100 Merkmalen.\n", + "\n", + "Eine weitere Referenz ist die Genauigkeit mit rein zufällig ausgewählten Datenpunkten. Um diese zu ermitteln werden nachfolgend mehrfach Datenpunkte zufällig ausgewählt, das Modell mit ihnen trainiert und abschließend anhand der gesamten Trainingsdaten bewertet." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "id": "PiBzIHvVS7C1", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "n_labeled = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "8LT-so_XS7C1", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def randomSemisupervisedLearning(X, y, n_labeled, runs=1000):\n", + " scores = []\n", + "\n", + " # Initialisierung des Klassifikators\n", + " clf = LogisticRegression(random_state=42)\n", + "\n", + " for i in range(runs):\n", + " # Auswahl zufälliger Datenpunkte\n", + " idx_rnd = pd.DataFrame(X).sample(n=n_labeled, random_state=i).index\n", + "\n", + " # Kontrolle ob sowohl Gut- als auch Schlechtteile ausgewählt wurden\n", + " if y[idx_rnd].nunique() > 1:\n", + " # Trainieren des Modells\n", + " clf.fit(pd.DataFrame(X).iloc[idx_rnd], y.iloc[idx_rnd])\n", + "\n", + " # Genauigkeit des Modells abspeichern\n", + " scores.append(clf.score(pd.DataFrame(X), y))\n", + "\n", + " outputResults(np.array(scores))\n", + " return np.array(scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mYA8ijYxHfs7", + "outputId": "1c0846a9-393a-4d04-8950-62673f1b008e", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit bei der Kreuzvalidierung\n", + "- Mittelwert: 97.40 %\n", + "- Standardabw.: 2.46 %\n", + "- 10. Perzentil: 95.93\n" + ] + } + ], + "source": [ + "scores = randomSemisupervisedLearning(X2D_train, y_train_01[label], n_labeled=n_labeled)\n", + "results.append((\"Log_reg_rnd\", 100 * scores.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tyx-1M9IHeMr", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Auch mit nur 10 zufällig ausgewählten Datenpunkten erreicht der Klassifikator im Durchschnitt eine Genauigkeit von immerhin *97,4 %*. Allerdings liegen 10 % der Klassifikatoren mit ihrer Genauigkeit unter 96,0 %. Folglich ist das Ziel des nachfolgenden Kapitels, mit 10 repräsentativ ausgewählten Datenpunkten eine Genauigkeit von min. 97,4 % und im Idealfall 99,0 % zu erreichen." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BWKiWwwN6OSZ", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.2.2 Auswahl durch Clustering\n", + "Nun kann das teilüberwachte Lernen durchgeführt werden. Zunächst werden durch den Clustering-Algorithmus die Cluster und deren Mittelpunkte bestimmt:" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "id": "BlajxYsES7C5", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.mixture import GaussianMixture\n", + "\n", + "# Genauso viele Cluster wie Datenpunkte gelabelt werden sollen\n", + "gaus_mix = GaussianMixture(n_components=n_labeled, random_state=42)\n", + "gaus_mix.fit(X2D_train)\n", + "y_pred_cluster = gaus_mix.predict(X2D_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zwQeFSrOjSeJ", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Als nächstes werden die Datenpunkte ausgewählt, welche den Mittelpunkten der Cluster am nächsten liegen:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "qPQ_7IoFYkrV", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import pairwise_distances_argmin_min\n", + "\n", + "closest, _ = pairwise_distances_argmin_min(gaus_mix.means_, X2D_train)\n", + "X2D_train_repr = X2D_train[closest]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v5vufckWjo85", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Das Ergebnis stellt die nachfolgende Abbildung dar. Die gefundenen Datenpunkte sind als rote Punkte eingezeichnet:" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "Jpg48_u35FTu", + "outputId": "d3caa956-35ad-4485-d31d-8559fae58c21", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX2DClustered(X2D_train, y_pred_cluster, cluster_centers=X2D_train_repr)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GOzxFEA5kCD3", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Im nächsten Schritt kann der Klassifikator mit diesen Datenpunkten trainiert und ausgewertet werden." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u9B8CO_8rgtO", + "outputId": "53442a36-eb76-48ae-c934-30bdc54898a8", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='auto', n_jobs=None, penalty='l2',\n", + " random_state=42, solver='lbfgs', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 74, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "log_clf_repr = LogisticRegression(random_state=42)\n", + "log_clf_repr.fit(X2D_train[closest], y_train_01[label].iloc[closest])" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dlqem3HWldbB", + "outputId": "844c6e0b-6f7a-4b52-eb5e-a345f763fa13", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit nach Auswahl durch Clustering\n", + "- 98.52 %\n" + ] + } + ], + "source": [ + "print(\"Genauigkeit nach Auswahl durch Clustering\")\n", + "print(\"-\", \"{:.2f}\".format(100 * log_clf_repr.score(X2D_train, y_train_01[label])), \"%\")\n", + "results.append((\"Log_reg_repr\", 100 * log_clf_repr.score(X2D_train, y_train_01[label])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T2G_E-QBmXzb", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Das Ergebnis ist mit *98,5 %* deutlich besser als bei der rein zufälligen Auswahl mit *97,4 %*, erreicht jedoch nicht die 99,0 %. Ein Propagieren der repräsentativen Label auf die anderen Datenpunkte im Cluster könnte eine weitere Verbesserung bringen:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "0e3jqYdiS7C7", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "y_train_01_prop = np.empty(len(y_train_01), dtype=np.int32)\n", + "for c in range(n_labeled):\n", + " y_train_01_prop[y_pred_cluster == c] = y_train_01[label].iloc[closest][c]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "czgMyEnFoDt8", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die aus den 10 repräsentativen Datenpunkten für den gesamten Datensatz abgeleiteten Label zeigt die nachfolgende Abbildung:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "ARhwgNOin2Tt", + "outputId": "2d67d974-b834-43b5-c144-1106c8e22771", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX2DClustered(X2D_train, y_train_01_prop, cluster_centers=X2D_train_repr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xy1iyde3oit9", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Nun kann erneut ein Klassifikator trainiert und ausgewertet werden:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ggbcaVEES7C7", + "outputId": "699377cd-f6ed-4543-d335-c53ea7d2381a", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit nach Auswahl durch Clustering und Propagieren\n", + "- 98.65 %\n" + ] + } + ], + "source": [ + "log_clf_prop = LogisticRegression(random_state=42)\n", + "log_clf_prop.fit(X2D_train, y_train_01_prop)\n", + "\n", + "print(\"Genauigkeit nach Auswahl durch Clustering und Propagieren\")\n", + "print(\"-\", \"{:.2f}\".format(100 * log_clf_prop.score(X2D_train, y_train_01[label])), \"%\")\n", + "results.append((\"Log_reg_prop\", 100 * log_clf_prop.score(X2D_train, y_train_01[label])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FbY9sAcMxQDf", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Das Propagieren hat eine geringfügige Verbesserung auf *98,65 %* gebracht, was nun beinahe der Referenz von *98,82 %* entspricht. Abschließend wird dieses Ergebnis anhand der Testdaten überprüft:" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y6pQRkMUwN5N", + "outputId": "c546b731-84e0-40a6-9ca3-b5e7f803ed7a", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit auf den Testdaten\n", + "- Überwachtes Lernen: 98.79 %\n", + "- Teilüberwachtes Lernen: 98.57 %\n" + ] + } + ], + "source": [ + "print(\"Genauigkeit auf den Testdaten\")\n", + "print(\n", + " \"- Überwachtes Lernen:\",\n", + " \"{:.2f}\".format(100 * log_clf_full.score(X2D_test, y_test_01[label])),\n", + " \"%\",\n", + ")\n", + "print(\n", + " \"- Teilüberwachtes Lernen:\",\n", + " \"{:.2f}\".format(100 * log_clf_prop.score(X2D_test, y_test_01[label])),\n", + " \"%\",\n", + ")\n", + "\n", + "results.append(\n", + " (\"Log_reg_prop_test\", 100 * log_clf_prop.score(X2D_test, y_test_01[label]))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WfFfUJcNNmlD", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Genauigkeit auf den Testdaten ist beim überwachten und teilüberwachten ähnlich hoch und mit jeweils über 98,5 % ausreichend. Grundsätzlich stellt das teilüberwachte Lernen somit einen vielversprechenden Ansatz für Aufgaben dieser Art dar, denn in diesem Fall musste nur ca. 0,3 % (10 / 3600) des ursprünglichen Aufwands für das Labeln investiert werden." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AMPtGbLW2LKr", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 6.2.3 Analyse der Fehler\n", + "Abschließend werden die Teile analysiert, welche beim Propagieren innerhalb der Cluster ein falsches Label erhalten haben:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "id": "y8t_p6rCyy3G", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "false_pos = np.logical_and(y_train_01[label] == 0, y_train_01_prop == 1)\n", + "false_neg = np.logical_and(y_train_01[label] == 1, y_train_01_prop == 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "3-ltpG7Oyy0T", + "outputId": "a55b30ad-d064-4a77-bdd4-9a76e66459a0", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plotX2D(X2D_train, y_train[label])\n", + "\n", + "# Falsch positiv bzw. negativ klassifizierte Datenpunkte darstellen\n", + "plt.plot(\n", + " X2D_train[false_pos][:, 0],\n", + " X2D_train[false_pos][:, 1],\n", + " \"o\",\n", + " c=\"red\",\n", + " markeredgecolor=\"black\",\n", + " label=\"Falsch positiv\",\n", + ")\n", + "plt.plot(\n", + " X2D_train[false_neg][:, 0],\n", + " X2D_train[false_neg][:, 1],\n", + " \"o\",\n", + " c=\"green\",\n", + " markeredgecolor=\"black\",\n", + " label=\"Falsch negativ\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HtsteQ-XRXC0", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In der Abbildung sind als schwarz umrandete Punkte diejenigen Teile markiert, welche beim propagieren der repräsentativen Label ein falsches Label erhalten haben. Falsch positive Teile sind dabei rot, falsch negative Teile grün ausgefüllt. Grundsätzliche liegen diese Teile wie erwartet im Grenzbereich zwischen Gut- und Schlechtteilen. Auf den ersten Blick wirkt die dargestellte Klassifizierung nachvollziehbar. Bspw. erscheint es sinnvoll, dass das große Cluster der falsch positiven Teile (untere Hälfte der Abbildung) scheinbar dem rechten unteren Cluster an Fehlteilen zugeordnet wurde. Interessant ist das originale Label (0 - 3) dieser falsch klassifizierten Teile:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EPk8vO9_TjRC", + "outputId": "d7c4ff5d-ed19-402d-b9b8-76bb1f9b4dc0", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ursprüngliches Label der falsch positiven Klassifizierungen\n", + "1 34\n", + "0 7\n", + "Name: 0_leak_corner_tr, dtype: int64\n" + ] + } + ], + "source": [ + "print(\"Ursprüngliches Label der falsch positiven Klassifizierungen\")\n", + "print(y_train[label][false_pos].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Uv9m62JYPzBX", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die meisten dieser Teile hatten einen leichten Fehler (Stufe 1). Es ist nachvollziehbar, dass diese ähnlich wie die anderen Fehlteile (Stufe 2 und 3) eingeschätzt wurden. Teile mit einem leichten Fehler unterscheiden sich grundsätzlich von den vollständig fehlerfreien Teilen. Dies deuten auch die Cluster in der Abbildung an. Nachfolgend wird der Klassifikator aus dem teilüberwachten nochmal ausgewertet, diesmal werden jedoch auch leichte Fehler der positiven Kategorie zugeordnet:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "id": "5Eb1wtDqr9lx", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "y_train_01_alt = y_train.copy()\n", + "y_train_01_alt.replace(1, 1, inplace=True)\n", + "y_train_01_alt.replace(2, 1, inplace=True)\n", + "y_train_01_alt.replace(3, 1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SvVV1hy7WMCb", + "outputId": "2c2054bf-59a9-4a5c-b68b-6952f81ec873", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit mit alternativem Label:\n", + "- 99.15 %\n" + ] + } + ], + "source": [ + "print(\"Genauigkeit mit alternativem Label:\")\n", + "print(\n", + " \"-\",\n", + " \"{:.2f}\".format(100 * log_clf_prop.score(X2D_train, y_train_01_alt[label])),\n", + " \"%\",\n", + ")\n", + "results.append(\n", + " (\"Log_reg_prop_1-3\", 100 * log_clf_prop.score(X2D_train, y_train_01_alt[label]))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lvk-UgVX6hY2", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Auf den Testdaten ist das Ergebnis ebenfalls besser:" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "id": "TmHf8tVu6f9O", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "y_test_01_alt = y_test.copy()\n", + "y_test_01_alt.replace(1, 1, inplace=True)\n", + "y_test_01_alt.replace(2, 1, inplace=True)\n", + "y_test_01_alt.replace(3, 1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FFcndOHA6f9R", + "outputId": "f78b28e1-1bbe-447c-de95-e36b936278c7", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genauigkeit mit alternativem Label:\n", + "- 98.90 %\n" + ] + } + ], + "source": [ + "print(\"Genauigkeit mit alternativem Label:\")\n", + "print(\n", + " \"-\", \"{:.2f}\".format(100 * log_clf_prop.score(X2D_test, y_test_01_alt[label])), \"%\"\n", + ")\n", + "results.append(\n", + " (\"Log_reg_prop_1-3_test\", 100 * log_clf_prop.score(X2D_test, y_test_01_alt[label]))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y7-jxZLwYR85", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Je nach Problemstellung sollte somit gut abgewogen werden, ob bei einer binären Klassifikation Teile mit einem leichten Fehler als fehlerfrei oder fehlerhaft eingestuft werden." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VJcSmf5Lf9_p", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 7\\. Ergebnisse und Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pswHcwWV1Ro7", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Abschließend können die Ergebnisse zusammengefasst und kritisch bewertet werden. Dazu werden nachfolgend die Genauigkeiten der im Rahmen dieser Arbeit trainierten Klassifikatoren dargestellt:" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + }, + "id": "S3KQ3HrRuKWb", + "outputId": "7ed58eaf-50b4-440f-a442-6985ed0664bf", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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Forest99.399.3
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Für die Darstellung in DataFrame umwandeln\n", + "results_data = {\n", + " \"Klassifikator\": np.array(results)[:, 0],\n", + " \"Genauigkeit [%]\": np.array(results)[:, 1].astype(float),\n", + "}\n", + "results_df = pd.DataFrame(results_data).set_index(\"Klassifikator\")\n", + "\n", + "# Spalte für das Ergebnis auf den Testdaten hinzufügen\n", + "results_test = results_df[results_df.index.str.contains(\"_test\")]\n", + "results_test.index = results_test.index.str.replace(\"_test\", \"\")\n", + "results_test.columns = [\"Test [%]\"]\n", + "\n", + "# Spalte für das Ergebnis auf den Trainingsdaten anpassen\n", + "results_training = results_df[np.invert(results_df.index.str.contains(\"_test\"))]\n", + "results_training.index = results_training.index.str.replace(\"_cv\", \"\")\n", + "results_training.columns = [\"Training [%]\"]\n", + "\n", + "# Spalten kombinieren\n", + "results_df = pd.concat([results_training, results_test], axis=1)\n", + "results_df = results_df.reset_index()\n", + "results_df = results_df.rename(columns={\"index\": \"Klassifikator\"})\n", + "\n", + "# Formatierung\n", + "style = results_df.style\n", + "style = style.format({\"Test [%]\": \"{:.1f}\"})\n", + "style = style.format({\"Training [%]\": \"{:.1f}\"})\n", + "style = style.set_properties(**{\"text-align\": \"left\"})\n", + "style = style.set_properties(**{\"text-align\": \"right\"}, subset=[\"Test [%]\"])\n", + "style = style.set_properties(**{\"text-align\": \"right\"}, subset=[\"Training [%]\"])\n", + "style = style.background_gradient(cmap=\"plasma\", subset=[\"Training [%]\"])\n", + "style = style.hide_index()\n", + "style" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Ns5uynMKTWX", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Genauigkeit auf den Trainingsdaten wurde mittels Kreuzvalidierung bestimmt und ist deshalb stets ähnlich hoch wie die Genauigkeit auf den Testdaten. Grundsätzlich erreichen alle Klassifikatoren mit über 97,0 % eine relativ hohe Genauigkeit. Die untersuchten Fehler lassen sich sehr gut durch ML-Algorithmen erkennen.\n", + "\n", + "Bereits ein Entscheidungsbaum (`Tree`, Kap. 4) erreicht mit einer einzigen Aufteilung eine Genauigkeit von 98,9 % auf den Testdaten. Mit Hilfe logistischer Regression (`Log_reg`, Kap. 5.3) bzw. eines Random Forests (`Forest`, Kap. 5.4) lässt sich diese Genauigkeit auf über 99,2 % steigern. Problematisch waren dabei vor allen diejenigen Teile, welche sich am Rand der binären Entscheidungsgrenze zwichen Gut- und Schlechtteil befanden." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jOGOQBgl_w8T", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In Kap. 6.1 hat sich gezeigt, dass sich die Dimension des Merkmalsraums für diesen Datensatz unter Beibehaltung eines Großteils seiner Varianz auf zwei oder drei reduzieren lässt. Auch in diesem reduzierten Merkmalsraum erzielte die logistische Regression (`Log_reg_full`) noch eine Genauigkeit von 98,8 %. Mit Hilfe eines Gaußschen Mischverteilungsmodells ließen sich anschließend die zuvor beobachteten Cluster gut erkennen und für das teilüberwachte Lernen nutzen.\n", + "\n", + "Das Kapitel 6.2 zum teilüberwachten Lernen hat gezeigt, dass es für diese Art von Daten nicht notwendig ist, sämtliche Datenpunkte zu labeln. So erreicht eine logistische Regression, welche auf 10 zufällig ausgewählten Datenpunkten trainiert wurde (`Log_reg_rnd`) im Mittel eine Genauigkeit von 97,4 %. Werden auf Basis der erkannten Cluster 10 repräsentative Datenpunkte gelabelt (`Log_reg_repr`), steigt diese Genauigkeit auf 98,2 %. Ein Propagieren der Label auf alle weiteren Datenpunkte im jeweiligen Cluster (`Log_reg_prop`) erhöhte die Genauigkeit weiter auf 98,6 %. Dieses Ergebnis liegt nur noch minimal unter den Ergebnissen der auf dem gesamten Datensatz trainierten Klassifikatoren." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3k9WxAGRJ3sV", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Zusammenfassend kann festgehalten werden, dass sich die untersuchten Fehler durch teilüberwachtes Lernen mit einem sehr geringen Aufwand und einer dennoch sehr hohen Genauigkeit von über 98,5 % erkennen lassen. Dafür waren relativ einfache lineare Machine-Learning-Modelle ausreichend." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EiITZLJBJwYl", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Die Untersuchung hat jedoch auch gezeigt, dass sowohl mehrere Cluster mit Gut- als auch mit Schlechtteilen entstehen können. Es ist ungewiss, ob auch komplett neue Cluster den Regeln folgen, welche die im Rahmen dieser Arbeit trainierten Algorithmen gelernet haben. Allerdings stellt auch bei diesem Problem das teilüberwachte Lernen einen vielversprechenden Lösungsansatz dar. \n", + "\n", + "Ungewiss ist ebenfalls, wie gut sich diese Vorgehensweise auf andere Fehler übertragen lässt. Die untersuchten Fehler traten relativ häufig und in Clustern auf. Seltene Fehler, welche darüber hinaus sehr stark verstreut sind, würden vom Clustering und damit vom teilüberwachten Lernen nicht möglicherweise nicht erfasst. Ein Beispiel ist der Fehler `1_hole_bottom` im Datensatz, welcher nur ein paar Mal aufgetreten ist.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FPd1nXbxKQby", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Eine Herausforderung für den beschriebenen Lösungsansatz wäre es außerdem, wenn ein neuer Datensatz sich nicht auf relativ wenige Dimensionen reduzieren lässt, ohne einen zu großen Anteil seiner Varianz zu verlieren. Die resultierende geringe Dichte an Datenpunkten im Merkmalsraum könnte das Clustering erschweren. Zu demselben Problem würde eine zu geringe Anzahl an Datenpunkten führen. Dies ist im Spritzguss jedoch in der Regel kein Problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rrWnU-1ZYrEg", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# 8\\. Ausblick\n", + "\n", + "Grundsätzlich bildet diese Arbeit die Grundlage für eine Vielzahl möglicher Weiterentwicklungen. Mit einem System, welches die Prozessdaten regelmäßig auf neue Cluster untersucht und Fotos repräsentativer Teile zur Beurteilung an das Fachpersonal schickt, könnte der in dieser Arbeit beschriebenen Lösungsansatz in die Praxis überführt werden. Dabei könnten auch Aspekte des *aktiven Lernens* berücksichtigt werden. Im Zuge dessen könnte das System ebenfalls auf weitere Fehlertypen und Produkte übertragen werden. Es sind jedoch die in der kritischen Auseinandersetzung beschriebenen Aspekte zu berücksichtigen.\n", + "\n", + "In der kritischen Auseinandersetzung wurde ebenfalls das Problem angesprochen, dass andere Fehlertypen selten und weit verstreut sein könnten. Um auch diese zu erkennen könnte die erarbeitete Lösung um eine Anomalieerkennung ergänzt werden. Das bereits implementierte Clustering mittels Gaußschem Mischverteilungsmodell bietet durch die bereitgestellten Wahrscheinlichkeitsdichtefunktionen eine sehr gute Grundlage dafür.\n", + "\n", + "Des Weiteren könnten Zeitreihenanalysen auf dem vorliegenden Datensatz durchgeführt werden, um den Ursprung der Fehler zu finden und diese gar nicht erst entstehen zu lassen." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s4Gm1_Bvgdtk", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Literaturverzeichnis\n", + "\n", + "[1] L. Schauerte, *Vorstudie zum Potenzial des Data Mining im Spritzguss zur\n", + "Verringerung von Produktionsfehlern*, 2021\n", + "\n", + "[2] Gustav Hensel GmbH & Co. KG, Available: https://www.hensel-electric.de, 2021\n", + "\n", + "[3] Aurélien Géron, *Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow*, 2018, O'Reilly Verlag\n", + "\n", + "[4] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone: *CART: Classification and Regression Trees*, 1984.\n", + "\n", + "[5] L. Breiman, *Random forests. In: Machine Learning*, 2001, Seite 5–32\n", + "\n", + "[6] G. H. Dunteman: *Principal Component Analysis*, 1989, Sage Publications\n", + "\n", + "[7] Ch. Fraley, A. Raftery, *Normal Mixture Modeling and Model-Based Clustering*, 2015\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hNHXCtrsMDiR", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + 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)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "01_preprocessing.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + }, + "nav_menu": { + "height": "279px", + "width": "309px" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 1cbdc97..21e9ad0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,7 +1,7 @@ [metadata] lock-version = "1.1" python-versions = ">=3.9, <3.11" -content-hash = "ec16222486b3250704b376cffdeb2bae938d6208a6adfd754237f980800d1a69" +content-hash = "a8120cd93696a4141262b7267be42706ec2821d04f4a30db0682d7bce5419059" [metadata.files] absl-py = [ @@ -311,6 +311,7 @@ h5py = [ {file = "h5py-3.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:9fd8a14236fdd092a20c0bdf25c3aba3777718d266fabb0fdded4fcf252d1630"}, {file = "h5py-3.6.0.tar.gz", hash = "sha256:8752d2814a92aba4e2b2a5922d2782d0029102d99caaf3c201a566bc0b40db29"}, ] +humanize = [] idna = [ {file = "idna-3.3-py3-none-any.whl", hash = "sha256:84d9dd047ffa80596e0f246e2eab0b391788b0503584e8945f2368256d2735ff"}, {file = "idna-3.3.tar.gz", hash = "sha256:9d643ff0a55b762d5cdb124b8eaa99c66322e2157b69160bc32796e824360e6d"}, @@ -1654,6 +1655,17 @@ python-versions = ">=3.7" [package.dependencies] numpy = ">=1.14.5" +[[package]] +name = "humanize" +version = "4.2.3" +description = "Python humanize utilities" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +tests = ["freezegun", "pytest", "pytest-cov"] + [[package]] name = "idna" version = "3.3" diff --git a/pyproject.toml b/pyproject.toml index 512380f..9ab1b0a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ tensorflow = "^2.8.0" scipy = "^1.8.0" Shapely = "^1.8.2" tqdm = "^4.64.0" +humanize = "^4.2.3" [tool.poetry.dev-dependencies] black = {extras = ["jupyter"], version = "^22.3.0"}