From cd5581ada4efc5dba91fe58fe1c964543bff42fc Mon Sep 17 00:00:00 2001
From: aaron <aaron@0x29a.ch>
Date: Thu, 4 Jun 2026 13:54:33 +0200
Subject: [PATCH] feature(workshop): add workshop5 decision tree tuning

---
 SL/aufgaben/workshop5/README.md               |  103 +
 SL/aufgaben/workshop5/data/bank_data_prep.csv | 9861 +++++++++++++++++
 SL/aufgaben/workshop5/devenv.lock             |   65 +
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 .../__pycache__/bfh_cas_pml.cpython-313.pyc   |  Bin 0 -> 6049 bytes
 SL/aufgaben/workshop5/src/bfh_cas_pml.py      |  193 +
 .../workshop5/src/decisiontreeclassifier.py   |   58 +
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diff --git a/SL/aufgaben/workshop5/README.md b/SL/aufgaben/workshop5/README.md
new file mode 100644
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--- /dev/null
+++ b/SL/aufgaben/workshop5/README.md
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+# Workshop 05 — Decision Tree Classifier: `min_impurity_decrease` Tuning
+
+## Aufgabe
+
+Untersuchen, wie verschiedene Werte von `min_impurity_decrease` beim
+`DecisionTreeClassifier` die erreichbare **Test-Accuracy** beeinflussen.
+
+- Wertebereich **schrittweise eingrenzen** (grob → fein).
+- Resultate darstellen:
+  - grafisch als **Liniendiagramm** (Parameter vs. Accuracy),
+  - in der **Konsole**: bester Score + zugehöriger Parameterwert.
+
+Hinweis aus den Folien: `range()` liefert Ganzzahlen, `np.arange()` liefert
+Gleitkommawerte. `min_impurity_decrease` ist ein Float → `np.arange`.
+
+## Setup
+
+```bash
+devenv shell        # python + venv (pandas, numpy, sklearn, matplotlib, seaborn)
+```
+
+Datengrundlage = vorbereiteter Bank-Datensatz aus W4. Geladen über das
+kursinterne Modul (muss auf dem `PYTHONPATH` / im Projektordner liegen):
+
+```python
+from bfh_cas_pml import prep_data
+X_train, X_test, y_train, y_test = prep_data('bank_data_prep.csv', 'y', seed=1234)
+```
+
+Baseline zum Abgleich (voll ausgewachsener Baum):
+
+```python
+DecisionTreeClassifier(random_state=1234)  # train=1.0, test≈0.8296 → overfit
+```
+
+> Falls `bfh_cas_pml` / `bank_data_prep.csv` nicht zur Hand sind: als Fallback
+> tut es jeder `train_test_split` auf einem sklearn-Datensatz — die Mechanik
+> des Sweeps bleibt identisch.
+
+## Theorie — was `min_impurity_decrease` tut
+
+Ein Split wird **nur** ausgeführt, wenn er die (gewichtete, auf den ganzen
+Baum normierte) Impurity um mindestens diesen Wert senkt:
+
+```
+ΔI_norm = (N_node / N_total) * ( I_parent
+                                 - (N_left/N_node)  * I_left
+                                 - (N_right/N_node) * I_right )
+```
+
+Kernpunkte fürs Verständnis:
+
+- Es ist eine **Pre-Pruning**-Schwelle: schwache Splits werden gar nicht erst
+  gemacht → der Baum bleibt kleiner → weniger Overfitting.
+- Der Wert ist mit dem **Anteil der Beobachtungen im Knoten** gewichtet
+  (`N_node / N_total`). Tiefe Knoten betreffen wenige Samples → ihr ΔI_norm ist
+  winzig. Darum liegen sinnvolle Schwellen im Bereich **~1e-4 bis ~1e-2**, nicht
+  bei 0.1+. (Vgl. das Folien-Rechenbeispiel: ein *guter* Split nahe der Wurzel
+  ergab 0.0421.)
+- `=0` → kein Pruning → Baseline-Baum (overfit).
+
+## Vorgehen — Eingrenzung (der eigentliche Lerninhalt)
+
+1. **Grob**: weiter Bereich, grobe Schritte, um die Region des Maximums zu
+   lokalisieren — z. B. `np.arange(0, 0.02, 0.001)`.
+2. **Fein**: um das gefundene Maximum herum zoomen — z. B.
+   `np.arange(0, 0.004, 0.0002)`.
+3. Wiederholen, bis Lage/Wert des Peaks stabil sind.
+
+Jede Iteration ist ein eigener Sweep (gleicher Loop, anderer `np.arange`).
+Im README/Notes die drei (o. ä.) Ranges + jeweils Peak dokumentieren — das
+*ist* die geforderte „schrittweise Eingrenzung“.
+
+## Erwartetes Verhalten (Sanity-Check)
+
+- Bei `0` startest du auf der Baseline (~0.83).
+- Mit steigendem Wert zunächst Plateau / leichter Bump (Rausch-Splits werden
+  entfernt), dann **Kante nach unten**, sobald nützliche Splits wegfallen.
+- Im Extrem degeneriert der Baum zum Stumpf → Accuracy = Mehrheitsklasse.
+  Der Bank-Datensatz wurde in W4 ~balanciert resampled → Floor liegt nahe
+  ~0.5. Wenn deine Kurve dort hin abstürzt, ist das korrekt, kein Bug.
+
+## Deliverables
+
+- [ ] Loop über `np.arange`-Range, `set_params(min_impurity_decrease=p)`,
+      `fit`, `score(X_test, y_test)`, sammeln.
+- [ ] `sns.lineplot(x=params, y=scores)` + Scatter-Marker auf `max(scores)`,
+      Achsen beschriftet (`min_impurity_decrease` / `accuracy`).
+- [ ] Konsole: `best score` + zugehöriger Parameterwert.
+- [ ] Mind. 2 Eingrenzungs-Stufen (grob + fein) dokumentiert.
+
+## Caveats / Vertiefung (optional)
+
+- **Optimistic bias** (wie in W4): hier wird `min_impurity_decrease` direkt
+  gegen `X_test` getunt — derselbe Trap wie die manuelle Grid-Search ohne CV.
+  Der gemeldete „beste“ Score ist dadurch optimistisch verzerrt. Die Folien
+  machen es so; fürs Deliverable also bewusst übernehmen, aber als Deviation
+  notieren. Sauber wäre Tuning auf einem Validation-Split bzw. `GridSearchCV`.
+- **Verwandter Faden, falls Zeit/Interesse**: `ccp_alpha` (Cost-Complexity /
+  Minimal-Cost-Complexity-Pruning) ist sklearns „eigentlicher“ Pruning-Knopf
+  und liefert via `cost_complexity_pruning_path()` direkt eine sinnvolle
+  Kandidatenliste statt manueller `np.arange`-Rate-Erei — nur als Zeiger, nicht
+  Teil der Aufgabe.
diff --git a/SL/aufgaben/workshop5/data/bank_data_prep.csv b/SL/aufgaben/workshop5/data/bank_data_prep.csv
new file mode 100644
index 0000000..058b611
--- /dev/null
+++ b/SL/aufgaben/workshop5/data/bank_data_prep.csv
@@ -0,0 +1,9861 @@
+age,education,housing,contact_cellular,month,day_of_week,duration,campaign,pdays,previous,emp_var_rate,cons_price_idx,cons_conf_idx,euribor3m,nr_employed,y,job_blue_collar,job_entrepreneur,job_housemaid,job_management,job_retired,job_self_employed,job_services,job_student,job_technician,job_unemployed,marital_married,marital_single,loan_unknown,loan_yes
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diff --git a/SL/aufgaben/workshop5/devenv.lock b/SL/aufgaben/workshop5/devenv.lock
new file mode 100644
index 0000000..16304ee
--- /dev/null
+++ b/SL/aufgaben/workshop5/devenv.lock
@@ -0,0 +1,65 @@
+{
+  "nodes": {
+    "devenv": {
+      "locked": {
+        "dir": "src/modules",
+        "lastModified": 1780543372,
+        "narHash": "sha256-FCGxk82Lc4koWcFw5xgr+W5vbwLVFLCnSMwm2gQOgr0=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "f693b472c731e7dda69402daa88c06369d54fd3a",
+        "type": "github"
+      },
+      "original": {
+        "dir": "src/modules",
+        "owner": "cachix",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "inputs": {
+        "nixpkgs-src": "nixpkgs-src"
+      },
+      "locked": {
+        "lastModified": 1778507786,
+        "narHash": "sha256-HzSQCKMsMr8r55LwM1JuzIOB+8bzk0FEv6sItKvsfoY=",
+        "owner": "cachix",
+        "repo": "devenv-nixpkgs",
+        "rev": "8f24a228a782e24576b155d1e39f0d914b380691",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "rolling",
+        "repo": "devenv-nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-src": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1778274207,
+        "narHash": "sha256-I4puXmX1iovcCHZlRmztO3vW0mAbbRvq4F8wgIMQ1MM=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "b3da656039dc7a6240f27b2ef8cc6a3ef3bccae7",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "devenv": "devenv",
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
\ No newline at end of file
diff --git a/SL/aufgaben/workshop5/devenv.nix b/SL/aufgaben/workshop5/devenv.nix
new file mode 100644
index 0000000..22ff0ea
--- /dev/null
+++ b/SL/aufgaben/workshop5/devenv.nix
@@ -0,0 +1,31 @@
+{ pkgs, ... }:
+
+{
+  # Native libs that the pip-wheel-installed numpy/scipy/matplotlib stack
+  # dlopen()s at runtime. zlib war schon in W3/W4 nötig (libz.so.1),
+  # stdenv.cc.cc.lib liefert libstdc++ für die scipy/sklearn-Wheels.
+  packages = [
+    pkgs.zlib
+    pkgs.stdenv.cc.cc.lib
+  ];
+
+  languages.python = {
+    enable = true;
+    venv.enable = true;
+    venv.requirements = ''
+      pandas
+      numpy
+      scikit-learn
+      matplotlib
+      seaborn
+    '';
+  };
+
+  # Loader-Pfad für die obigen nativen Libs. Wenn beim Import trotzdem ein
+  # "ImportError: libXYZ.so.N" auftaucht: das bereitstellende pkgs.<paket>
+  # zu packages UND hier ergänzen — gleiches Muster wie der W3-Fix.
+  env.LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath [
+    pkgs.zlib
+    pkgs.stdenv.cc.cc.lib
+  ];
+}
diff --git a/SL/aufgaben/workshop5/dtc_tuning_coarse.png b/SL/aufgaben/workshop5/dtc_tuning_coarse.png
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diff --git a/SL/aufgaben/workshop5/src/bfh_cas_pml.py b/SL/aufgaben/workshop5/src/bfh_cas_pml.py
new file mode 100644
index 0000000..e2f94d0
--- /dev/null
+++ b/SL/aufgaben/workshop5/src/bfh_cas_pml.py
@@ -0,0 +1,193 @@
+"""
+    Useful functions for example notebooks and workshop solutions
+    of course Practical Machine Learning - Supervised Learning
+    Bern University of Applied Sciences (BFH)
+"""
+
+
+# ========== Packages ==========
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# ========== Functions ==========
+
+def prep_data(dataset, target, train_ratio = 2 / 3, seed = None, sep = ','):
+    """ read and prepare real data from the current directory
+    performs 
+        read data
+        features - target - split
+        train - test - split
+
+    Parameters
+    ----------
+    dataset: name of dataset in csv format
+    target: name of target column
+    train_ratio (2 / 3): (optional)
+    seed (None): random seet for split (optional)
+    sep (,): separator of csv file (optional)
+
+    Returns
+    -------
+    X_train: feature matrix of train set
+    X_test: target vector of train set
+    y_train: feature matrix of test set
+    y_test: target vector of train set
+    """
+
+    ## load data
+    data = pd.read_csv(dataset, sep = sep)
+
+    ## features - target - split
+    X = data.drop(target, axis=1)
+    y = data[target]
+
+    ## train - test - split
+    from sklearn.model_selection import train_test_split
+    return train_test_split(
+        X,
+        y,
+        train_size=train_ratio,
+        random_state=seed)
+    
+
+
+def prep_demo_data(dataset, target):
+    """ read demo data from the current directory
+    performs 
+        read data
+        features - target - split
+
+    Parameters
+    ----------
+    dataset: name of dataset in csv format, ',' separated
+    target: name of target column
+
+    Returns
+    -------
+    X: feature matrix
+    y: target vector
+    """
+
+    ## load data
+    data = pd.read_csv(dataset)
+    
+    ## features - target - split
+    X = data.drop(target, axis=1)
+    y = data[target]
+    
+    return X, y   
+
+
+
+def inspect_decision_tree_model(model_def, features, target, figsize=(6, 6)):
+    """ train a DecisionTreeClassifier and visualize the tree
+    
+    prints some motel attributes from within the function
+    
+    Parameters
+    ----------
+    model_def: DecisionTreeClassifier object with set parameters
+    features: feature matrix
+    target: target vector
+    figsize: size of image, optional, default = (6, 6)
+    
+    Returns
+    -------
+    visualization of the trained tree
+    prints model attributes
+    """
+    
+    from sklearn.tree import plot_tree
+    
+    model = model_def
+    model.fit(features, target)
+
+    print('TREE DIAGNOSTICS:')
+    print('depth  :', model.get_depth())
+    print('leaves :', model.get_n_leaves())
+    print('score  :', model.score(features, target))
+
+    plt.figure(figsize=figsize)
+    plot_tree(model,
+              feature_names=features.columns,
+              class_names=model.classes_,
+              filled=True);
+
+
+
+def test_regression_model(model, X_train, y_train, X_test, y_test, show_plot=True):
+    
+    """ shows behavoiur of univariate ML regression on synthetic dataset
+    
+    performs
+    -   training on train data
+    -   prediction on test data
+    -   calculate performance measures
+ 
+    Parameters
+    ----------
+    model: a parametrized regression model
+    X_train, y_train: train data
+    X_test, y_test: test data
+    show_plot: show scatterplot ov pred vs true, optional, default=True
+    
+
+    Returns
+    -------
+    shows a scatterplot von X_test vs X_pred with a diagonal line, indicating identity
+    prints r2_score and mean_squared_error
+    
+    """
+
+    from sklearn.metrics import r2_score
+    from sklearn.metrics import mean_squared_error
+
+    model = model
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    print('R2 = %0.4f' %(r2_score(y_test, y_pred)))
+    
+    if show_plot == True:
+        plt.figure(figsize=(6,6))
+        ax = sns.scatterplot(x=y_test, y=y_pred)
+        ax.set(xlabel='y_test', ylabel='y_pred')
+        ls = np.linspace(min(y_test), max(y_test), 100)
+        plt.plot(ls, ls, color='black', linestyle='dashed')
+        ax.set_title(model.__class__.__name__)
+        plt.show()
+    
+    return (model)
+    
+
+
+def show_pred_on_synth(model, X, y, X_synth, param_str):
+    """ shows behavoiur of univariate ML regression on synthetic dataset
+ 
+    Parameters
+    ----------
+    model: a parametrized regression model
+    X, y: data for univariate regression
+    X_synth: synthetic Feature
+    param_str: parameter description for title
+    seed (None): random seet for split
+
+    Returns
+    -------
+    a scatterplot von X, y, with the prediction values for X_synth
+    
+    """
+    
+    model.fit(X.to_numpy(), y)
+    y_pred = model.predict(X_synth)
+
+    ax = sns.scatterplot(x=X['X'], y=y)
+    ax = sns.lineplot(x=X_synth[:,0], y=y_pred, color='orange')
+    ax.set_title(model.__class__.__name__ + ' : ' + param_str)
+    ax.set(xlabel='X', ylabel='y')
+    plt.show()
+
+
diff --git a/SL/aufgaben/workshop5/src/decisiontreeclassifier.py b/SL/aufgaben/workshop5/src/decisiontreeclassifier.py
new file mode 100644
index 0000000..0ca66d6
--- /dev/null
+++ b/SL/aufgaben/workshop5/src/decisiontreeclassifier.py
@@ -0,0 +1,58 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+from bfh_cas_pml import prep_data
+from pathlib import Path
+from sklearn.tree import DecisionTreeClassifier
+
+
+DATA = Path(__file__).resolve().parent.parent / "data" / "bank_data_prep.csv"
+
+
+def sweep(X_train, y_train, X_test, y_test, params):
+    """Ein Sweep über eine min_impurity_decrease-Range → Liste der Test-Scores."""
+    model = DecisionTreeClassifier(random_state=1234)
+    scores = []
+    for p in params:
+        model.set_params(min_impurity_decrease=p)
+        model.fit(X_train, y_train)
+        scores.append(model.score(X_test, y_test))
+    return scores
+
+
+def report(params, scores, name):
+    """Besten Score + Parameter in die Konsole, Kurve + Peak-Marker plotten."""
+    # find best score and best param
+    best_p = params[scores.index(max(scores))]
+    best_s = max(scores)
+    print(f"best_score: {name} -> {best_s}")
+    print(f"best_param: {name} -> {best_p}")
+
+    # plot
+    plt.figure() # eigene Figur pro Sweep
+    sns.lineplot(x=params, y=scores)
+    plt.scatter(best_p, best_s, color="black", zorder=5) # Peak markieren
+    plt.xlabel("min_impurity_decrease")
+    plt.ylabel("accuracy")
+    plt.title(name)
+    plt.savefig(f"{name}.png", dpi=120, bbox_inches="tight")
+
+
+def main():
+    X_train, X_test, y_train, y_test = prep_data(str(DATA), "y", seed=1234)
+
+    sweeps = [
+        # 1) grob
+        ("dtc_tuning_coarse", np.arange(0, 0.02, 0.001)),
+        # 2) fein (Range nach Befund anpassen)
+        ("dtc_tuning_fine",   np.arange(0, 0.004, 0.0002)),
+    ]
+
+    for name, params in sweeps:
+        scores = sweep(X_train, y_train, X_test, y_test, params)
+        report(params, scores, name)
+
+
+if __name__ == "__main__":
+    main()