Add diet model notebook

docs/requirements-examples.txt

@@ -3,6 +3,7 @@ ipykernel>=6.15
 jupytext>=1.14
 matplotlib>=3.5
 nbconvert>=6.5
+openpyxl>=3.0
 scikit-learn>=1.0
 seaborn==0.13.2
 gurobipy>=10.0

docs/requirements.txt

@@ -10,6 +10,7 @@ myst-parser==0.19.2
 nbconvert==7.2.5
 nbsphinx==0.8.9
 numpydoc==1.6.0
+openpyxl==3.1.5
 scikit-learn==1.5.0
 seaborn==0.13.2
 sphinx-copybutton==0.5.2

docs/source/examples.rst

@@ -14,6 +14,7 @@ the notebooks.
 .. toctree::
    :maxdepth: 1
 
+   examples/diet
    examples/projects
    examples/regression
    examples/unit-commitment

docs/source/examples/diet.md

@@ -0,0 +1,86 @@
+---
+jupytext:
+  formats: ipynb,md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.1
+---
+
+# The Stigler Diet Problem
+
+```{code-cell}
+import pandas as pd
+import gurobipy as gp
+import gurobipy_pandas as gppd
+from gurobipy import GRB
+
+gppd.set_interactive()
+```
+
+```{code-cell}
+foods = pd.read_excel("data/diet.xlsx", sheet_name="Foods")
+foods.head()
+```
+
+```{code-cell}
+categories = pd.read_excel("data/diet.xlsx", sheet_name="Categories")
+categories.head()
+```
+
+```{code-cell}
+nutrition = pd.melt(
+    pd.read_excel("data/diet.xlsx", sheet_name="Nutrition"),
+    id_vars=["food"],
+    var_name="nutrient",
+)
+nutrition.head()
+```
+
+```{code-cell}
+env = gp.Env()
+model = gp.Model(env=env)
+```
+
+```{code-cell}
+buy = gppd.add_vars(model, foods.set_index("food"), name="buy")
+buy.head()
+```
+
+```{code-cell}
+amounts = (
+    nutrition.join(buy, on="food")
+    .set_index(["food", "nutrient"])
+    .pipe(lambda df: df["value"] * df["buy"])
+    .groupby("nutrient").sum()
+)
+
+limits = categories.set_index("category")
+
+gppd.add_constrs(
+    model,
+    amounts,
+    GRB.GREATER_EQUAL,
+    limits["minimum"],
+)
+
+gppd.add_constrs(
+    model,
+    amounts[limits["maximum"].notnull()],
+    GRB.LESS_EQUAL,
+    limits["maximum"].dropna(),
+)
+```
+
+```{code-cell}
+model.setObjective((buy * foods.set_index("food")["cost"]).sum())
+```
+
+```{code-cell}
+model.optimize()
+```
+
+```{code-cell}
+buy.gppd.X.pipe(lambda s: s[s >0])
+```