Title: ValueError: 'p' must be 1-dimensional
Tags: python,pandas,feature-selection

Question: I am trying to do feature selection using Ant colony optimization (ACO) for a rainfall dataset. The implementation of the code is below
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier


X = x
y = df_cap['PRECTOTCORR_SUM'] 

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define ACO feature selection function
def aco_feature_selection(X_train, X_test, y_train, y_test, num_ants=10, max_iter=50, alpha=1.0, beta=2.0, evaporation=0.5, q0=0.9):
    num_features = X_train.shape[1]
    pheromone = np.ones(num_features)
    best_solution = None
    best_accuracy = 0.0
    
    # Run ACO algorithm
    for i in range(max_iter):
        ant_solutions = []
        ant_accuracies = []
        
        # Generate ant solutions
        for ant in range(num_ants):
            features = np.random.choice([0, 1], size=num_features, p=[1-pheromone,pheromone])
            X_train_selected = X_train[:, features == 1]
            X_test_selected = X_test[:, features == 1]
            knn = KNeighborsClassifier()
            knn.fit(X_train_selected, y_train)
            y_pred = knn.predict(X_test_selected)
            accuracy = accuracy_score(y_test, y_pred)
            ant_solutions.append(features)
            ant_accuracies.append(accuracy)
            
            # Update best solution
            if accuracy > best_accuracy:
                best_solution = features
                best_accuracy = accuracy
        
        # Update pheromone levels
        pheromone *= evaporation
        for ant in range(num_ants):
            features = ant_solutions[ant]
            accuracy = ant_accuracies[ant]
            if accuracy >= np.mean(ant_accuracies):
                pheromone[features == 1] += alpha
            else:
                pheromone[features == 1] += beta
        
        # Apply elitism
        if best_solution is not None:
            pheromone[best_solution == 1] += q0
        
    return best_solution

# Run ACO feature selection
selected_features = aco_feature_selection(X_train, X_test, y_train, y_test)

# Print selected features
print("Selected features:", np.where(selected_features == 1)[0])

but I get this error
ValueError                             
Input In [175], in aco_feature_selection(X_train, X_test, y_train, y_test, num_ants, max_iter, alpha, beta, evaporation, q0)
     26 # Generate ant solutions
     27 for ant in range(num_ants):
---> 28     features = np.random.choice([0, 1], size=num_features, p=[1-pheromone,pheromone])
     29     X_train_selected = X_train[:, features == 1]
     30     X_test_selected = X_test[:, features == 1]

File mtrand.pyx:930, in numpy.random.mtrand.RandomState.choice()

ValueError: 'p' must be 1-dimensional

I suspect the issue comes list inside a list because it makes it 2-dimentional instead of 1-dimensional using something like flatten() throws this error
ValueError: 'a' and 'p' must have same size

how do I fix this?

Answer: The issue is that p is an array of probabilities and you are passing a 1 - array and an array into that argument. Without getting into the detail of the algorithm I can suggest that you need to choose a specific pheromone value for this feature.
And if you want to generate a series of 0 and 1 with given probabilities you need to iterate over pheromone