Notebooks from applied-cs/data-science@7318d552

03-numpy-und-matplotlib/06-hidden-message-sol.ipynb

@@ -9,23 +9,14 @@
     "Man kann Bilder so manipulieren, dass sie Informationen enthalten, die\n",
     "man beim Betrachten höchstens als Rauschen wahrnehmen kann.\n",
     "\n",
-    "<figure>\n",
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-    "alt=\"Prinzipbild einer in einem Bild versteckten Nachricht mit einem Bit pro Farbwert\" />\n",
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-    "<figcaption>Prinzipbild einer in einem Bild versteckten Nachricht mit\n",
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\" /></p>\n",
     "\n",
     "Versuchen Sie aus dem Bild `zwinkersmiley.bmp` eine Nachricht zu\n",
     "extrahieren und sie auszugeben! Das Bild wird schon als 3D-NumPy-Array\n",
     "geladen (Höhe x Breite x Kanäle)."
    ],
-   "id": "0003-aaa5ddb6637d60757bd46091f1a2eca1cc7f981da853b1aa1465e25d06b"
+   "id": "0003-70f0a6f0520e15b69826b38de72cdcaab8bc2c340308e4121e7ec5c4142"
   },
   {
    "cell_type": "code",

05-skalierung-und-optimierung/folien-code/folien-code.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Code zu Folien\n",
+    "\n",
+    "\n",
+    "\n",
+    " Dieses Skript bzw. Jupyter-Notebook enthält den Code, der auch auf den Folien \"Skalierung & Optimierung\" enthalten ist. Zum Vorbereiten, Mitmachen oder Nacharbeiten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
+    "df = pd.read_csv('autos.csv')\n",
+    "X = df.drop(columns=['Marke', 'Modell', 'Fahrzeugklasse'])\n",
+    "y = df['Fahrzeugklasse']\n",
+    "\n",
+    "def plot_split(y_train, y_test):\n",
+    "    y = pd.concat((y_train, y_test), axis='index')\n",
+    "    counts = pd.concat((y.value_counts(), y_train.value_counts(), y_test.value_counts()), axis='columns').sort_index()\n",
+    "    counts.columns = ['full', 'train', 'test']\n",
+    "    ax = (counts * 100 / counts.max()).plot.bar(ylim=(0, 100))\n",
+    "    for container, col in zip(ax.containers, counts):\n",
+    "        ax.bar_label(container, counts[col].astype(str), rotation=70)\n",
+    "    ax.yaxis.set_ticklabels([])\n",
+    "    ax.yaxis.set_ticks([])\n",
+    "    plt.legend(loc='center')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs_test_size = int(round(len(df) * 0.2))           # 20% entsprechen 123 samples\n",
+    "df_idx = df.index\n",
+    "test_idx = df_idx[:abs_test_size]                   # erste 20% der Daten\n",
+    "train_idx = df_idx[abs_test_size:]                  # letzte 80% der Daten\n",
+    "X_train, X_test = X.loc[train_idx], X.loc[test_idx] # kein Zufall!\n",
+    "y_train, y_test = y.loc[train_idx], y.loc[test_idx]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs_test_size = int(round(len(df) * 0.2))           # 20% entsprechen 123 samples\n",
+    "df_idx_shuffled = rng.permutation(df.index)         # rng: random number generator\n",
+    "test_idx = df_idx_shuffled[:abs_test_size]          # zufällige 20% der Daten\n",
+    "train_idx = df_idx_shuffled[abs_test_size:]         # zufällige 80% der Daten\n",
+    "X_train, X_test = X.loc[train_idx], X.loc[test_idx]\n",
+    "y_train, y_test = y.loc[train_idx], y.loc[test_idx]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(8264)  # wähle richtig schlechte \"zufällige\" Aufteilung\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "plot_split(y_train=y_train, y_test=y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(8264)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
+    "plot_split(y_train=y_train, y_test=y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KNeighborsClassifier(weights='uniform')\n",
+    "# model = KNeighborsClassifier(weights='distance')\n",
+    "model.fit(X_train, y_train)\n",
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "sns.set_theme()\n",
+    "con = pd.crosstab(y_test, y_pred)                       # absolute Zahlen\n",
+    "# con = pd.crosstab(y_test, y_pred, normalize='index')  # normalisiert über Spalten\n",
+    "con.index.name = 'Testdaten'\n",
+    "con.columns.name = 'Vorhergesagt'\n",
+    "ax = sns.heatmap(con, cmap='YlGn', annot=True, cbar=False)\n",
+    "ax.xaxis.set_label_position('top')\n",
+    "ax.xaxis.set_ticks_position('top')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "sns.reset_orig()\n",
+    "cmd = ConfusionMatrixDisplay.from_predictions(y_test, y_pred, cmap='YlGn',\n",
+    "                                              colorbar=False, normalize='true')\n",
+    "cmd.ax_.xaxis.set_label_position('top')\n",
+    "cmd.ax_.xaxis.set_ticks_position('top')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unskaliert:\n",
+    "df = pd.DataFrame(columns=['unskaliert', 'normiert', 'standardisiert', 'robust skaliert'], index=['d(x_29, x_200)', 'd(x_29, x_540)', 'd(x_200, x_540)'])\n",
+    "d29_200 = np.linalg.norm(X.loc[29] - X.loc[200], ord=1)\n",
+    "d29_540 = np.linalg.norm(X.loc[29] - X.loc[540], ord=1)\n",
+    "d200_540 = np.linalg.norm(X.loc[200] - X.loc[540], ord=1)\n",
+    "df['unskaliert'] = [d29_200, d29_540, d200_540]\n",
+    "\n",
+    "# normiert:\n",
+    "X_scaled = (X - X.min()) / (X.max() - X.min())\n",
+    "# oder:\n",
+    "scaler = MinMaxScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)\n",
+    "d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)\n",
+    "d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)\n",
+    "df['normiert'] = [d29_200, d29_540, d200_540]\n",
+    "\n",
+    "# standardisiert:\n",
+    "X_scaled = (X - X.mean()) / X.std()\n",
+    "# oder:\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)\n",
+    "d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)\n",
+    "d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)\n",
+    "df['standardisiert'] = [d29_200, d29_540, d200_540]\n",
+    "\n",
+    "# robust skaliert:\n",
+    "iqr = X.quantile(0.75) - X.quantile(0.25)\n",
+    "X_scaled = (X - X.median()) / iqr\n",
+    "# oder:\n",
+    "scaler = RobustScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)\n",
+    "d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)\n",
+    "d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)\n",
+    "df['robust skaliert'] = [d29_200, d29_540, d200_540]\n",
+    "\n",
+    "display(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xmin, xmax = X_train.min(), X_train.max()   # Pandas DataFrames\n",
+    "# xmin, xmax = X_train.min(axis=0), X_train.max(axis=0)   # NumPy Arrays\n",
+    "X_train_scaled = (X_train - xmin) / (xmax - xmin)\n",
+    "X_test_scaled = (X_test - xmin) / (xmax - xmin)\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_test_scaled  = scaler.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score, KFold\n",
+    "folds = KFold(5, shuffle=True)    # zufällig mischen\n",
+    "scores = cross_val_score(model, X, y, cv=folds, scoring='accuracy')\n",
+    "print(100 * scores.round(3), end=' ')\n",
+    "print(f'mean: {scores.mean():.2%}, std.dev.: {scores.std():.2%}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_records = []\n",
+    "for k in range(1, 9):\n",
+    "    for p in (1, 2, 4, float('inf')):\n",
+    "        # model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')\n",
+    "        model = KNeighborsClassifier(n_neighbors=k, p=p, weights='uniform')\n",
+    "        scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds,\n",
+    "                                    scoring='accuracy')\n",
+    "        score_cv = scores_cv.mean()\n",
+    "        score_records.append((k, p, score_cv))\n",
+    "scores = pd.DataFrame.from_records(score_records, columns=['k', 'p', 'score'])\n",
+    "# display(scores)\n",
+    "display(scores.sort_values('score'))\n",
+    "\n",
+    "scr2d = scores.pivot(index='k', columns='p', values='score')\n",
+    "display(scr2d)\n",
+    "sns.heatmap(scr2d, annot=True, cbar=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_records = []\n",
+    "for trial in range(30):                      # 30 Durchläufe\n",
+    "    k = rng.integers(1, 9)                   # Integer zwischen 1 und 9\n",
+    "    p = rng.choice([1, 2, 4, float('inf')])  # einer der Werte\n",
+    "    model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')\n",
+    "    scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds, scoring='accuracy')\n",
+    "    score_cv = scores_cv.mean()\n",
+    "    score_records.append((k, p, score_cv))\n",
+    "scores = pd.DataFrame.from_records(score_records, columns=['k', 'p', 'score'])\n",
+    "display(scores.sort_values('score'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import optuna\n",
+    "from plotly.offline import plot\n",
+    "\n",
+    "def objective(trial):                 # Zielfunktion (Genauigkeit) soll maximiert werden\n",
+    "    k = trial.suggest_int('k', 1, 9)  # Zielfunktion hängt von Hyperparametern ab\n",
+    "    p = trial.suggest_categorical('p', [1, 2, 4, float('inf')])\n",
+    "    model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')\n",
+    "    scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds, scoring='accuracy')\n",
+    "    return scores_cv.mean()\n",
+    "study = optuna.create_study(direction='maximize')  # maximieren, nicht minimieren (default)\n",
+    "study.optimize(objective, n_trials=30)             # Anzahl Parameterkombinationen\n",
+    "\n",
+    "scores = study.trials_dataframe(attrs=('params', 'value'))\n",
+    "scores = scores.rename(columns={'params_k': 'k', 'params_p': 'p', 'value': 'score'})\n",
+    "scores = scores.groupby(['k', 'p']).mean().reset_index()\n",
+    "scr2d = scores.pivot(index='k', columns='p', values='score')\n",
+    "\n",
+    "# heatmap\n",
+    "sns.heatmap(scr2d, annot=True, cbar=False)\n",
+    "\n",
+    "# parallel coordinates plot\n",
+    "fig = optuna.visualization.plot_parallel_coordinate(study)\n",
+    "fig.show()\n",
+    "plot(fig, auto_open=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

05-skalierung-und-optimierung/folien-code/folien-code.py

+# %% [markdown]
+# # Code zu Folien
+#
+# Dieses Skript bzw. Jupyter-Notebook enthält den Code, der auch auf den Folien "Skalierung & Optimierung" enthalten ist. Zum Vorbereiten, Mitmachen oder Nacharbeiten.
+
+# %% imports
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from IPython.display import display
+import seaborn as sns
+from sklearn.metrics import ConfusionMatrixDisplay
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
+rng = np.random.default_rng()
+
+df = pd.read_csv('autos.csv')
+X = df.drop(columns=['Marke', 'Modell', 'Fahrzeugklasse'])
+y = df['Fahrzeugklasse']
+
+def plot_split(y_train, y_test):
+    y = pd.concat((y_train, y_test), axis='index')
+    counts = pd.concat((y.value_counts(), y_train.value_counts(), y_test.value_counts()), axis='columns').sort_index()
+    counts.columns = ['full', 'train', 'test']
+    ax = (counts * 100 / counts.max()).plot.bar(ylim=(0, 100))
+    for container, col in zip(ax.containers, counts):
+        ax.bar_label(container, counts[col].astype(str), rotation=70)
+    ax.yaxis.set_ticklabels([])
+    ax.yaxis.set_ticks([])
+    plt.legend(loc='center')
+
+# %% Daten in Trainings- und Testmenge aufteilen, kein Zufall
+abs_test_size = int(round(len(df) * 0.2))           # 20% entsprechen 123 samples
+df_idx = df.index
+test_idx = df_idx[:abs_test_size]                   # erste 20% der Daten
+train_idx = df_idx[abs_test_size:]                  # letzte 80% der Daten
+X_train, X_test = X.loc[train_idx], X.loc[test_idx] # kein Zufall!
+y_train, y_test = y.loc[train_idx], y.loc[test_idx]
+
+# %% Daten in Trainings- und Testmenge aufteilen, mit Zufall
+abs_test_size = int(round(len(df) * 0.2))           # 20% entsprechen 123 samples
+df_idx_shuffled = rng.permutation(df.index)         # rng: random number generator
+test_idx = df_idx_shuffled[:abs_test_size]          # zufällige 20% der Daten
+train_idx = df_idx_shuffled[abs_test_size:]         # zufällige 80% der Daten
+X_train, X_test = X.loc[train_idx], X.loc[test_idx]
+y_train, y_test = y.loc[train_idx], y.loc[test_idx]
+
+# %% Daten in Trainings- und Testmenge aufteilen, mit Zufall per Scikit-Learn
+np.random.seed(8264)  # wähle richtig schlechte "zufällige" Aufteilung
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+plot_split(y_train=y_train, y_test=y_test)
+
+# %% Daten in Trainings- und Testmenge aufteilen, mit Zufall und Stratifikation
+np.random.seed(8264)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)
+plot_split(y_train=y_train, y_test=y_test)
+
+# %% Vorhersage mit k-NN Modell für Konfusionsmatrix
+model = KNeighborsClassifier(weights='uniform')
+# model = KNeighborsClassifier(weights='distance')
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# %% Konfusionsmatrix mit Pandas und Seaborn
+fig = plt.figure()
+sns.set_theme()
+con = pd.crosstab(y_test, y_pred)                       # absolute Zahlen
+# con = pd.crosstab(y_test, y_pred, normalize='index')  # normalisiert über Spalten
+con.index.name = 'Testdaten'
+con.columns.name = 'Vorhergesagt'
+ax = sns.heatmap(con, cmap='YlGn', annot=True, cbar=False)
+ax.xaxis.set_label_position('top')
+ax.xaxis.set_ticks_position('top')
+
+# %% Konfusionsmatrix mit Scikii-Learn
+fig = plt.figure()
+sns.reset_orig()
+cmd = ConfusionMatrixDisplay.from_predictions(y_test, y_pred, cmap='YlGn',
+                                              colorbar=False, normalize='true')
+cmd.ax_.xaxis.set_label_position('top')
+cmd.ax_.xaxis.set_ticks_position('top')
+
+# %% Skalierung
+# unskaliert:
+df = pd.DataFrame(columns=['unskaliert', 'normiert', 'standardisiert', 'robust skaliert'], index=['d(x_29, x_200)', 'd(x_29, x_540)', 'd(x_200, x_540)'])
+d29_200 = np.linalg.norm(X.loc[29] - X.loc[200], ord=1)
+d29_540 = np.linalg.norm(X.loc[29] - X.loc[540], ord=1)
+d200_540 = np.linalg.norm(X.loc[200] - X.loc[540], ord=1)
+df['unskaliert'] = [d29_200, d29_540, d200_540]
+
+# normiert:
+X_scaled = (X - X.min()) / (X.max() - X.min())
+# oder:
+scaler = MinMaxScaler()
+X_scaled = scaler.fit_transform(X)
+d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)
+d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)
+d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)
+df['normiert'] = [d29_200, d29_540, d200_540]
+
+# standardisiert:
+X_scaled = (X - X.mean()) / X.std()
+# oder:
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)
+d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)
+d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)
+df['standardisiert'] = [d29_200, d29_540, d200_540]
+
+# robust skaliert:
+iqr = X.quantile(0.75) - X.quantile(0.25)
+X_scaled = (X - X.median()) / iqr
+# oder:
+scaler = RobustScaler()
+X_scaled = scaler.fit_transform(X)
+d29_200 = np.linalg.norm(X_scaled[29] - X_scaled[200], ord=1)
+d29_540 = np.linalg.norm(X_scaled[29] - X_scaled[540], ord=1)
+d200_540 = np.linalg.norm(X_scaled[200] - X_scaled[540], ord=1)
+df['robust skaliert'] = [d29_200, d29_540, d200_540]
+
+display(df)
+
+# %% Skalierung durchführen
+xmin, xmax = X_train.min(), X_train.max()   # Pandas DataFrames
+# xmin, xmax = X_train.min(axis=0), X_train.max(axis=0)   # NumPy Arrays
+X_train_scaled = (X_train - xmin) / (xmax - xmin)
+X_test_scaled = (X_test - xmin) / (xmax - xmin)
+
+scaler = MinMaxScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled  = scaler.transform(X_test)
+
+# %% Kreuzvalidierung
+from sklearn.model_selection import cross_val_score, KFold
+folds = KFold(5, shuffle=True)    # zufällig mischen
+scores = cross_val_score(model, X, y, cv=folds, scoring='accuracy')
+print(100 * scores.round(3), end=' ')
+print(f'mean: {scores.mean():.2%}, std.dev.: {scores.std():.2%}')
+
+# %% Manueller Grid Search
+score_records = []
+for k in range(1, 9):
+    for p in (1, 2, 4, float('inf')):
+        # model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')
+        model = KNeighborsClassifier(n_neighbors=k, p=p, weights='uniform')
+        scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds,
+                                    scoring='accuracy')
+        score_cv = scores_cv.mean()
+        score_records.append((k, p, score_cv))
+scores = pd.DataFrame.from_records(score_records, columns=['k', 'p', 'score'])
+# display(scores)
+display(scores.sort_values('score'))
+
+scr2d = scores.pivot(index='k', columns='p', values='score')
+display(scr2d)
+sns.heatmap(scr2d, annot=True, cbar=False)
+
+# %% Manueller Random Search
+score_records = []
+for trial in range(30):                      # 30 Durchläufe
+    k = rng.integers(1, 9)                   # Integer zwischen 1 und 9
+    p = rng.choice([1, 2, 4, float('inf')])  # einer der Werte
+    model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')
+    scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds, scoring='accuracy')
+    score_cv = scores_cv.mean()
+    score_records.append((k, p, score_cv))
+scores = pd.DataFrame.from_records(score_records, columns=['k', 'p', 'score'])
+display(scores.sort_values('score'))
+
+# %% Hyperparameteroptimierung mit Optuna
+import optuna
+from plotly.offline import plot
+
+def objective(trial):                 # Zielfunktion (Genauigkeit) soll maximiert werden
+    k = trial.suggest_int('k', 1, 9)  # Zielfunktion hängt von Hyperparametern ab
+    p = trial.suggest_categorical('p', [1, 2, 4, float('inf')])
+    model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')
+    scores_cv = cross_val_score(model, X_train_scaled, y_train, cv=folds, scoring='accuracy')
+    return scores_cv.mean()
+study = optuna.create_study(direction='maximize')  # maximieren, nicht minimieren (default)
+study.optimize(objective, n_trials=30)             # Anzahl Parameterkombinationen
+
+scores = study.trials_dataframe(attrs=('params', 'value'))
+scores = scores.rename(columns={'params_k': 'k', 'params_p': 'p', 'value': 'score'})
+scores = scores.groupby(['k', 'p']).mean().reset_index()
+scr2d = scores.pivot(index='k', columns='p', values='score')
+
+# heatmap
+sns.heatmap(scr2d, annot=True, cbar=False)
+
+# parallel coordinates plot
+fig = optuna.visualization.plot_parallel_coordinate(study)
+fig.show()
+plot(fig, auto_open=True)

06-clustering/03-mosaic-sol.ipynb

@@ -23,7 +23,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.neighbors import NearestNeighbors\n",
-    "from tensorflow import keras\n",
+    "import keras\n",
     "rng = np.random.default_rng()\n",
     "\n",
     "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()\n",
@@ -31,7 +31,7 @@
     "\n",
     "target = plt.imread('frochte.webp')"
    ],
-   "id": "0002-6e8318925ecf9f50d56d73b2ed5ffb3d3ba92762c1fbfa2ad105e6b65fe"
+   "id": "0002-a43f4956ced121f01ca3eb39b2e6ddc8401d6838f37a1e7845c63020e34"
   },
   {
    "cell_type": "markdown",

06-clustering/03-mosaic.ipynb

@@ -23,7 +23,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.neighbors import NearestNeighbors\n",
-    "from tensorflow import keras\n",
+    "import keras\n",
     "rng = np.random.default_rng()\n",
     "\n",
     "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()\n",
@@ -31,7 +31,7 @@
     "\n",
     "target = plt.imread('frochte.webp')"
    ],
-   "id": "0002-6e8318925ecf9f50d56d73b2ed5ffb3d3ba92762c1fbfa2ad105e6b65fe"
+   "id": "0002-a43f4956ced121f01ca3eb39b2e6ddc8401d6838f37a1e7845c63020e34"
   },
   {
    "cell_type": "markdown",