From cf67b23197ab39b547ef8d87a3fb6284a69d7ad4 Mon Sep 17 00:00:00 2001
From: Christof Kaufmann <christof.kaufmann@hs-bochum.de>
Date: Wed, 8 May 2024 10:46:18 +0000
Subject: [PATCH] Notebooks from applied-cs/data-science@b0c6376a
---
04-pandas-und-seaborn/{ => folien-code}/folien-code.ipynb | 0
04-pandas-und-seaborn/{ => folien-code}/folien-code.py | 0
.../01-autoklassen-sklearn-sol.ipynb | 8 ++++----
.../01-autoklassen-sklearn.ipynb | 8 ++++----
4 files changed, 8 insertions(+), 8 deletions(-)
rename 04-pandas-und-seaborn/{ => folien-code}/folien-code.ipynb (100%)
rename 04-pandas-und-seaborn/{ => folien-code}/folien-code.py (100%)
diff --git a/04-pandas-und-seaborn/folien-code.ipynb b/04-pandas-und-seaborn/folien-code/folien-code.ipynb
similarity index 100%
rename from 04-pandas-und-seaborn/folien-code.ipynb
rename to 04-pandas-und-seaborn/folien-code/folien-code.ipynb
diff --git a/04-pandas-und-seaborn/folien-code.py b/04-pandas-und-seaborn/folien-code/folien-code.py
similarity index 100%
rename from 04-pandas-und-seaborn/folien-code.py
rename to 04-pandas-und-seaborn/folien-code/folien-code.py
diff --git a/05-skalierung-und-optimierung/01-autoklassen-sklearn-sol.ipynb b/05-skalierung-und-optimierung/01-autoklassen-sklearn-sol.ipynb
index 39bdbb7..a5d621b 100644
--- a/05-skalierung-und-optimierung/01-autoklassen-sklearn-sol.ipynb
+++ b/05-skalierung-und-optimierung/01-autoklassen-sklearn-sol.ipynb
@@ -327,13 +327,13 @@
"\n",
"def objective(trial):\n",
" k = trial.suggest_int('k', 1, 9)\n",
- " p = trial.suggest_categorical('p', [1, 2, 4, np.inf])\n",
+ " p = trial.suggest_categorical('p', [1, 2, 4, float('inf')])\n",
" scale_factores = [trial.suggest_float('scale_' + col, 1e-2, 1e2, log=True) for col in X.columns]\n",
" model = KNeighborsClassifier(n_neighbors=k, p=p, weights='distance')\n",
" scores_cv = cross_val_score(model, X_train_scaled * scale_factores, y_train, cv=folds, scoring='accuracy')\n",
" return scores_cv.mean()"
],
- "id": "0035-a2dc6bde9454a074f712141df254855d6bc6d43503890bc2db31962e1a8"
+ "id": "0035-2239964036f73944f5196e8c13aab9159de4669ccec74a6d83ea087ec8d"
},
{
"cell_type": "markdown",
@@ -353,9 +353,9 @@
"source": [
"study = optuna.create_study(direction='maximize', sampler=optuna.samplers.GPSampler(deterministic_objective=True)) # less duplicate trials\n",
"study.optimize(objective, n_trials=100, n_jobs=4)\n",
- "optuna.visualization.plot_parallel_coordinate(study)"
+ "fig = optuna.visualization.plot_parallel_coordinate(study)"
],
- "id": "0037-8565d9b2658fda0d997f9c5e7b432ba8c45ae399d1222bad654c1e3608d"
+ "id": "0037-79487c80368ad85001b24281bc990d3a53e0e926e8d11a2d56ee7b43d52"
}
],
"nbformat": 4,
diff --git a/05-skalierung-und-optimierung/01-autoklassen-sklearn.ipynb b/05-skalierung-und-optimierung/01-autoklassen-sklearn.ipynb
index 2319890..15a76b6 100644
--- a/05-skalierung-und-optimierung/01-autoklassen-sklearn.ipynb
+++ b/05-skalierung-und-optimierung/01-autoklassen-sklearn.ipynb
@@ -148,12 +148,12 @@
"\n",
"def objective(trial):\n",
" k = trial.suggest_int('k', 1, 9)\n",
- " p = trial.suggest_categorical('p', [1, 2, 4, np.inf])\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()"
],
- "id": "0016-5b5d432c8107554b190e9b5f88594153f66d68e9ed8cb2d394aa224898d"
+ "id": "0016-91d49960a264a20d3d42fc074dda98db8fa9710f976efc5e31c705f5c9f"
},
{
"cell_type": "markdown",
@@ -173,9 +173,9 @@
"source": [
"study = optuna.create_study(direction='maximize', sampler=optuna.samplers.GPSampler(deterministic_objective=True)) # less duplicate trials\n",
"study.optimize(objective, n_trials=100, n_jobs=4)\n",
- "optuna.visualization.plot_parallel_coordinate(study)"
+ "fig = optuna.visualization.plot_parallel_coordinate(study)"
],
- "id": "0018-8565d9b2658fda0d997f9c5e7b432ba8c45ae399d1222bad654c1e3608d"
+ "id": "0018-79487c80368ad85001b24281bc990d3a53e0e926e8d11a2d56ee7b43d52"
}
],
"nbformat": 4,
--
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