Warum kann der folgende Code den Wert von n_fold = 1 nicht erhalten?

Question

Wenn der folgende Code verwendet wird, müssen die n_folds 2 oder mehr sein. Wie kann ich es ändern, dass es mit n_folds = 1 funktioniert? Bei Änderung auf 1, jedoch für n_falten = 2 und mehr funktioniert es. Etwas scheint, dass das in den Funktionen nicht funktioniert. Für n_folds = 1, gibt es folgende Fehler:

Traceback (most recent call last): 
    File "GX.py", line 266, in <module> 
    scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features) 
    File "GX.py", line 92, in evaluate_algorithm 
    predicted = algorithm(train_set, test_set, *args) 
    File "GX.py", line 223, in random_forest 
    tree = build_tree(sample, max_depth, min_size, n_features) 
    File "GX.py", line 183, in build_tree 
    root = get_split(train, n_features) 
    File "GX.py", line 137, in get_split 
    index = randrange(len(dataset[0]) - 1) 
IndexError: list index out of range 

Hier ist der Code:

# Select the best split point for a dataset 
def get_split(dataset, n_features): 
    class_values = list(set(row[-1] for row in dataset)) 
    b_index, b_value, b_score, b_groups = 999, 999, 999, None 
    features = list() 
    while len(features) < n_features: 
     index = randrange(len(dataset[0])-1) 
     if index not in features: 
      features.append(index) 
    for index in features: 
     for row in dataset: 
      groups = test_split(index, row[index], dataset) 
      gini = gini_index(groups, class_values) 
      if gini < b_score: 
       b_index, b_value, b_score, b_groups = index, row[index], gini, groups 
    return {'index':b_index, 'value':b_value, 'groups':b_groups} 


# Random Forest Algorithm on Sonar Dataset 
from random import seed 
from random import randrange 
from csv import reader 
from math import sqrt 


# Load a CSV file 
def load_csv(filename): 
    dataset = list() 
    with open(filename, 'r') as file: 
     csv_reader = reader(file) 
     for row in csv_reader: 
      if not row: 
       continue 
      dataset.append(row) 
    return dataset 


# Convert string column to float 
def str_column_to_float(dataset, column): 
    for row in dataset: 
     row[column] = float(row[column].strip()) 


# Convert string column to integer 
def str_column_to_int(dataset, column): 
    class_values = [row[column] for row in dataset] 
    unique = set(class_values) 
    lookup = dict() 
    for i, value in enumerate(unique): 
     lookup[value] = i 
    for row in dataset: 
     row[column] = lookup[row[column]] 
    return lookup 


# Split a dataset into k folds 
def cross_validation_split(dataset, n_folds): 
    dataset_split = list() 
    dataset_copy = list(dataset) 
    fold_size = int(len(dataset)/n_folds) 
    for i in range(n_folds): 
     fold = list() 
     while len(fold) < fold_size: 
      index = randrange(len(dataset_copy)) 
      fold.append(dataset_copy.pop(index)) 
     dataset_split.append(fold) 
    return dataset_split 


# Calculate accuracy percentage 
def accuracy_metric(actual, predicted): 
    correct = 0 
    for i in range(len(actual)): 
     if actual[i] == predicted[i]: 
      correct += 1 
    return correct/float(len(actual)) * 100.0 


# Evaluate an algorithm using a cross validation split 
def evaluate_algorithm(dataset, algorithm, n_folds, *args): 
    folds = cross_validation_split(dataset, n_folds) 
    scores = list() 
    for fold in folds: 
     train_set = list(folds) 
     train_set.remove(fold) 
     train_set = sum(train_set, []) 
     test_set = list() 
     for row in fold: 
      row_copy = list(row) 
      test_set.append(row_copy) 
      row_copy[-1] = None 
     predicted = algorithm(train_set, test_set, *args) 
     actual = [row[-1] for row in fold] 
     accuracy = accuracy_metric(actual, predicted) 
     scores.append(accuracy) 
    return scores 


# Split a dataset based on an attribute and an attribute value 
def test_split(index, value, dataset): 
    left, right = list(), list() 
    for row in dataset: 
     if row[index] < value: 
      left.append(row) 
     else: 
      right.append(row) 
    return left, right 


# Calculate the Gini index for a split dataset 
def gini_index(groups, classes): 
    # count all samples at split point 
    n_instances = float(sum([len(group) for group in groups])) 
    # sum weighted Gini index for each group 
    gini = 0.0 
    for group in groups: 
     size = float(len(group)) 
     # avoid divide by zero 
     if size == 0: 
      continue 
     score = 0.0 
     # score the group based on the score for each class 
     for class_val in classes: 
      p = [row[-1] for row in group].count(class_val)/size 
      score += p * p 
     # weight the group score by its relative size 
     gini += (1.0 - score) * (size/n_instances) 
    return gini 


# Select the best split point for a dataset 
def get_split(dataset, n_features): 
    class_values = list(set(row[-1] for row in dataset)) 
    b_index, b_value, b_score, b_groups = 999, 999, 999, None 
    features = list() 
    while len(features) < n_features: 
     index = randrange(len(dataset[0]) - 1) 
     if index not in features: 
      features.append(index) 
    for index in features: 
     for row in dataset: 
      groups = test_split(index, row[index], dataset) 
      gini = gini_index(groups, class_values) 
      if gini < b_score: 
       b_index, b_value, b_score, b_groups = index, row[index], gini, groups 
    return {'index': b_index, 'value': b_value, 'groups': b_groups} 


# Create a terminal node value 
def to_terminal(group): 
    outcomes = [row[-1] for row in group] 
    return max(set(outcomes), key=outcomes.count) 


# Create child splits for a node or make terminal 
def split(node, max_depth, min_size, n_features, depth): 
    left, right = node['groups'] 
    del (node['groups']) 
    # check for a no split 
    if not left or not right: 
     node['left'] = node['right'] = to_terminal(left + right) 
     return 
    # check for max depth 
    if depth >= max_depth: 
     node['left'], node['right'] = to_terminal(left), to_terminal(right) 
     return 
    # process left child 
    if len(left) <= min_size: 
     node['left'] = to_terminal(left) 
    else: 
     node['left'] = get_split(left, n_features) 
     split(node['left'], max_depth, min_size, n_features, depth + 1) 
    # process right child 
    if len(right) <= min_size: 
     node['right'] = to_terminal(right) 
    else: 
     node['right'] = get_split(right, n_features) 
     split(node['right'], max_depth, min_size, n_features, depth + 1) 


# Build a decision tree 
def build_tree(train, max_depth, min_size, n_features): 
    root = get_split(train, n_features) 
    split(root, max_depth, min_size, n_features, 1) 
    return root 


# Make a prediction with a decision tree 
def predict(node, row): 
    if row[node['index']] < node['value']: 
     if isinstance(node['left'], dict): 
      return predict(node['left'], row) 
     else: 
      return node['left'] 
    else: 
     if isinstance(node['right'], dict): 
      return predict(node['right'], row) 
     else: 
      return node['right'] 


# Create a random subsample from the dataset with replacement 
def subsample(dataset, ratio): 
    sample = list() 
    n_sample = round(len(dataset) * ratio) 
    while len(sample) < n_sample: 
     index = randrange(len(dataset)) 
     sample.append(dataset[index]) 
    return sample 


# Make a prediction with a list of bagged trees 
def bagging_predict(trees, row): 
    predictions = [predict(tree, row) for tree in trees] 
    return max(set(predictions), key=predictions.count) 


# Random Forest Algorithm 
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features): 
    trees = list() 
    for i in range(n_trees): 
     sample = subsample(train, sample_size) 
     tree = build_tree(sample, max_depth, min_size, n_features) 
     trees.append(tree) 
    predictions = [bagging_predict(trees, row) for row in test] 
    return (predictions) 

seed(1) 
import pandas as pd 
file_path ='https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data' 
dataset2 = pd.read_csv(file_path, header=None, sep=',') 

from sklearn.ensemble import RandomForestClassifier 

from sklearn.model_selection import train_test_split 
from sklearn import preprocessing 
dataset1 = pd.DataFrame(dataset2) 
dataset1 = dataset1.drop(0, axis=1) 

train, test = train_test_split(dataset1, test_size=0.1, random_state = 0) ###<----- 
df = dataset1.astype('str') 
dataset = df.values.tolist() 

train1 = train.astype('str') 
train = train1.values.tolist() 

test1 = test.astype('str') 
test = test1.values.tolist() 

target_index = 0 
for i in range(0, len(dataset[0])): 
     if i != target_index: 
      str_column_to_float(dataset, i) 
# convert class column to integers 
str_column_to_int(dataset, target_index) 

# evaluate algorithm 
n_folds = 1 ##<---- 
max_depth = 10 
min_size = 1 
sample_size = 1.0 
n_features = int(sqrt(len(dataset[0]) - 1)) 


for n_trees in [5]: 
    scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features) 
    print('Trees: %d' % n_trees) 
    print('Scores: %s' % scores) 
    print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores)))) 

Answer 1

Verstehen Sie das Konzept der cross validation? Bei der k-fachen Kreuzvalidierung werden Ihre Daten in k Stichproben aufgeteilt und eine Stichprobe wird verwendet, um das Modell zu testen, und k-1 Stichproben werden verwendet, um das Modell zu trainieren.

Wenn Sie k = 1 (oder n = 1 in Ihrem Beispiel) setzen, können Sie Ihre Daten nicht abfragen und die Kreuzvalidierung durchführen.