How To Fix The Typeerror: G Must Be A 'd' Matrix?

Objective: trying to run toy dataset through an optimization process. I am encountering the following error: -----------------------------------------------------------------------

Solution 1:

Try casting TRANSFORMER to a float array.

For what it's worth, the code works in CVXPY v 1.0.11 (with some minor modifications, included below):

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import numpy as np
import cvxpy

X, y = make_classification(n_samples=1000, n_features=20, n_classes=8,n_informative=5,
                           class_sep=0.2, random_state=2)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=1)

model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train)
test_probs = model.predict_proba(X_test)

#clipping so that we don't take log of 0 or 1
test_probs = np.clip(test_probs, 0.0001, 0.9999)

#turning into costs
model_costs = -np.log10(test_probs)

# Our allocation cannot exceed our supply# 150 flyers, 80 pamphlets, 25 bumper stickers
supply = np.array([150, 80, 25])

# Creating our cvxpy variable of assignments
selection = cvxpy.Variable(shape=test_probs.shape, boolean=True)

# Constant matrix that counts how many of each# material we sent to each constituent
TRANSFORMER = np.array([[1,0,0],
                        [0,1,0],
                        [0,0,1],
                        [1,1,0],
                        [1,0,1],
                        [0,1,1],
                        [1,1,1],
                        [0,0,0]])

supply_constraint = cvxpy.sum(selection * TRANSFORMER, axis=0) <= supply

# We must make our choice per constituent# remember that the last column is for "no materials"
feasibility_constraint = cvxpy.sum(selection, axis=1) == 1
constraints = [supply_constraint, feasibility_constraint]

# Take the negative log of our probabilities to turn them into costs
cost = cvxpy.sum(cvxpy.multiply(model_costs, selection))

# Solving the problem
problem = cvxpy.Problem(cvxpy.Minimize(cost), constraints=constraints)
problem.solve(solver=cvxpy.GLPK_MI)
print(problem.value)
print(selection.value[0:5])
print(np.dot(selection.value, TRANSFORMER).sum(axis=0))