ValueError：形状（120,6）和（7，）未对齐：6（dim 1）！= 7（dim 0）

Question

我正在尝试在来自 Kaggle 的 Iris.csv 数据集上使用 逻辑回归 实现多类分类。这是我的代码。

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

def standardize(X_tr): # (x-Mean(x))/std(X) Normalizes data
    for i in range(X_tr.shape[1]):
        X_tr[:, i] = (X_tr[:, i] - np.mean(X_tr[:, i])) / np.std(X_tr[:, i])
    return X_tr

def sigmoid(z): #Sigmoid/Logistic function
    sig = 1 / (1 + np.exp(-z))
    return sig

def cost(theta, X, y):
    z = np.dot(X, theta)
    cost0 = y.T.dot(np.log(sigmoid(z)))
    cost1 = (1 - y).T.dot(np.log(1 - sigmoid(z)))
    cost = -((cost1 + cost0)) / len(y)
    return cost

def initialize(X): #Initializing X feature matrix and Theta vector
    thetas = np.zeros((X.shape[1] + 1, len(np.unique(y))))
    X = np.c_[np.ones((X.shape[0], 1)), X]  # adding 691 rows of ones as the first column in X
    return thetas, X

def fit(X, y, alpha=0.01, iterations=1000): # Gradient Descent
    thetas_list = []
    X = np.c_[np.ones((X.shape[0], 1)), X]
    for i in range(len(np.unique(y))):
        y_one_vs_all = np.where(y == np.unique(y)[i], 1, 0)
        thetas, _ = initialize(X)
        for j in range(iterations):
            z = np.dot(X, thetas[:, i])
            h = sigmoid(z)
            gradient = np.dot(X.T, (h - y_one_vs_all)) / len(y)
            thetas[:, i] -= alpha * gradient
        thetas_list.append(thetas[:, i])
    global gthetas
    gthetas = thetas_list
    return None

def predict(X):
    X = np.c_[np.ones((X.shape[0], 1)), X]
    predictions = []
    for sample in X:
        probs = []
        for thetas in gthetas:
            z = np.dot(sample, thetas)
            probs.append(sigmoid(z))
        predictions.append(np.argmax(probs) + 1)
    return predictions

# load data
df = pd.read_csv("Iris.csv")
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# convert class categorical values to numerical values
df['Species'].replace('Iris-setosa', 1, inplace=True)
df['Species'].replace('Iris-versicolor', 2, inplace=True)
df['Species'].replace('Iris-virginica', 3, inplace=True)

# prepare one-vs-all labels for multiclass classification
y1 = pd.DataFrame(np.zeros((len(y), len(np.unique(y)))))
for i in range(len(np.unique(y))):
    for j in range(len(y1)):
        if y[j] == np.unique(y)[i]:
            y1.iloc[j, i] = 1 
        else:
            y1.iloc[j, i] = 0

# split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y1, test_size=0.2, random_state=0)

# standardize features
X_train = standardize(X_train)
X_test = standardize(X_test)

# fit logistic regression model
fit(X_train, y_train, alpha=0.01, iterations=400)

# make predictions on test set
predictions = predict(X_test)
print(predictions)

以下是我遇到的错误。

ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_14368/3997569506.py in <module>
      1 # standardize features
      2 X_train = standardize(X_train)
----> 3 thetas_list = fit(X_train, y_train)
      4 plt.scatter(range(len(cost_list)), cost_list, c="blue")
      5 plt.show()

~\AppData\Local\Temp/ipykernel_14368/3827160719.py in fit(X, y, alpha, iter)
      6         thetas, _ = initialize(X)
      7         for j in range(iter):
----> 8             z = dot(X, thetas[:, i])
      9             h = sigmoid(z)
     10             gradient = dot(X.T, (h - y_one_vs_all)) / len(y)

<__array_function__ internals> in dot(*args, **kwargs)

ValueError: shapes (120,6) and (7,) not aligned: 6 (dim 1) != 7 (dim 0)

任何修复此错误的帮助将不胜感激。我已经查看了有关堆栈溢出的其他答案，但我仍然无法弄清楚。

我想要完成的事情：以下代码用作为每个类在 y（结果，物种类）中创建列的基础。因此，y1 中的列数（如下所示）将等于类别总数。鸢尾花数据集为 3。例如，对于第一列（类别为 1，即 Iris-setosa），数据集中的任何一行为“Iris-setosa”都将在 y1 的对应行中标记为 1。任何其他类别（第 2 类和第 3 类：Iris-versicolor 和 Iris-virginica）将在 y1 的第一列中标记为 0。

for i in range(len(np.unique(y))):
    for j in range(len(y1)):
        if y[j] == np.unique(y)[i]:
            y1.iloc[j, i] = 1  #one vs. all
        else:
            y1.iloc[j, i] = 0 #all others will be 0

Answer 1

您应该检查 theta 数组是如何构建的。您正在

initialize

函数中使用

(X.shape[1] + 1, nrows)

的形状对其进行初始化。错误告诉您，您无法计算形状为

X

的数组

(a, b)

和形状为

(b+1, c)

的 theta 之间的点积。您可以尝试删除 theta 定义中的

+1

。然后你会发现另一个关于梯度的问题，你试图再次计算两个不兼容数组之间的点积。我希望这对你有帮助，祝你好运！