Applying Logarithmic Transformations for Demand Prediction

Answered

Florencia Vidal

May 11, 2023 16:25

• 
• 

Hi,

I'm currently working on modifying an existing notebook titled "Part One: Avocado Pricing and Supply Using Mathematical Optimization" available at https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/price_optimization/price_optimization_gcl.ipynb

My objective is to apply logarithmic transformations to the price and units_sold variables, but I'm facing an issue when trying to create an expression for the predicted demand. I would greatly appreciate any assistance or suggestions on how to resolve this issue. 

 

My code:

d = {r: np.exp(coef_dict['Intercept'] + coef_dict['log_price']*np.log(p[r]) + coef_dict['C(region)[T.%s]'%r] + coef_dict['year_index']*(year-2015) + coef_dict['peak']*peak_or_not) for r in R}

 

for r in R:

    print(d[r])

 

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

src/gurobipy/var.pxi in gurobipy.Var.__getattr__()

 

src/gurobipy/var.pxi in gurobipy.Var.getAttr()

 

src/gurobipy/attrutil.pxi in gurobipy.__getattr()

 

src/gurobipy/attrutil.pxi in gurobipy.__getattrinfo()

 

AttributeError: 'gurobipy.Var' object has no attribute 'log'

 

The above exception was the direct cause of the following exception:

 

TypeError                                 Traceback (most recent call last)

<ipython-input-45-741ec6d3f402> in <cell line: 2>()

      1 # Calcular la demanda utilizando el modelo ajustado y las variables transformadas

----> 2 d = {r: np.exp(coef_dict['Intercept'] + coef_dict['log_price']*np.log(p[r]) + coef_dict['C(region)[T.%s]'%r] + coef_dict['year_index']*(year-2015) + coef_dict['peak']*peak_or_not) for r in R}

      3 

      4 for r in R:

      5     print(d[r])

 

<ipython-input-45-741ec6d3f402> in <dictcomp>(.0)

      1 # Calcular la demanda utilizando el modelo ajustado y las variables transformadas

----> 2 d = {r: np.exp(coef_dict['Intercept'] + coef_dict['log_price']*np.log(p[r]) + coef_dict['C(region)[T.%s]'%r] + coef_dict['year_index']*(year-2015) + coef_dict['peak']*peak_or_not) for r in R}

      3 

      4 for r in R:

      5     print(d[r])

 

TypeError: loop of ufunc does not support argument 0 of type Var which has no callable log method

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  November 13, 2026 01:43

  This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
• 

  Riley Clement

  • Gurobi Staff

  May 14, 2023 11:27

  Hi Florencia,

  In the code, p[r] is a Gurobi variable, and in general cannot be passed as an argument to non-Gurobi functions such as numpy.log - there are of course exceptions such as str, type etc.

  If you wish to create the log of a Gurobi variable then you need to introduce a new variable for this value, and link it using a addGenConstrLog constraint.

  - Riley

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