python - 在 Python 中创建随机森林预测模型时遇到错误

Question

我正在尝试使用本文 https://machinelearningmastery.com/implement-random-forest-scratch-python/ 中的脚本在 Python 中实现随机森林算法并根据我的数据集修改它，但是当我运行代码时出现以下错误

Traceback (most recent call last):
  File "C:----\scratch.py", line 211, in <module>
    str_column_to_float(dataset, i)
  File "C:----\scratch.py", line 31, in str_column_to_float
    row[column] = float(row[column].strip())
ValueError: could not convert string to float: male

有什么好的办法可以解决吗？

我尝试在这部分代码中将我的属性male转换为数值

def replace_non_numeric(df):
df["Gender"] = df["Gender"].apply(lambda gender: 0 if gender == "male" else 1)
return df

train_df = replace_non_numeric(pd.read_csv("datatrain.csv"))

但还是出现错误

这是我的数据集

Id  Age Gender  Race           Result

50  15  male    Bi-Racial           1                                                      

51  14  female  African-American    1

52  16  male    African-American    0

53  18  male    African-American    0

54  19  male    African-American    1

55  16  male    Caucasian           1

56  15  female  African-American    1

57  15  male    African-American    1

这是完整的代码

import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.cross_validation import cross_val_score
from random import seed
from random import randrange
from csv import reader
from math import sqrt

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

def replace_non_numeric(df):
    df["Gender"] = df["Gender"].apply(lambda gender: 0 if gender == "male" else 1)
    return df

train_df = replace_non_numeric(pd.read_csv("datatrain.csv"))

# 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)

# Test the random forest algorithm
seed(2)
# load and prepare data
filename = 'datatrain.csv'
dataset = load_csv(filename)
# convert string attributes to integers
for i in range(0, len(dataset[0])-1):
    str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# evaluate algorithm
n_folds = 5
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = int(sqrt(len(dataset[0])-1))
for n_trees in [1, 5, 10]:
    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))))

我正在尝试获得一个模型，该模型可以显示一个人(Id)根据其人口统计数据产生 0 或 1 的可能性有多大。如果我做错了什么，请指导我，或者也许我应该打印一些不同的内容为了看到更好的输出

Answer 1

调用df["Gender"]将不起作用，因为您的csv文件的分隔符是空格，而您没有在train_df = Replace_non_numeric(pd.read_csv("datatrain .csv"))。默认情况下，read_csv 假定 , 将用于分离。

如果要使用可变的空间量进行分隔，则应使用正则表达式\s+。相应的代码如下:

def replace_non_numeric(df):
    print(df)
    df["Gender"] = df["Gender"].apply(lambda gender: 0 if gender == "male" else 1)
    print(df)
    return df

train_df = replace_non_numeric(pd.read_csv("datatrain.csv", sep="\s+"))

这将返回:

   Id  Age  Gender              Race  Result
0  50   15    male         Bi-Racial       1
1  51   14  female  African-American       1
2  52   16    male  African-American       0
3  53   18    male  African-American       0
4  54   19    male  African-American       1
5  55   16    male         Caucasian       1
6  56   15  female  African-American       1
7  57   15    male  African-American       1

   Id  Age  Gender              Race  Result
0  50   15       0         Bi-Racial       1
1  51   14       1  African-American       1
2  52   16       0  African-American       0
3  53   18       0  African-American       0
4  54   19       0  African-American       1
5  55   16       0         Caucasian       1
6  56   15       1  African-American       1
7  57   15       0  African-American       1