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

I’m trying to implement multiclass classification with logistic regression on an Iris.csv dataset from Kaggle. This is my code.

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)

and below is the error I’m getting.

ValueError                                Traceback (most recent call last)
~AppDataLocalTemp/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()

~AppDataLocalTemp/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)

Any help in fixing this error will be appreciated. I’ve looked into other answers on stack overflow and I still can’t figure this out.

What I’m trying to accomplish: The following code is used as a base to create columns in y (outcome, Species class) for each class. So, the number of columns in y1 (given below) will be equal to the total number classes. That is 3 for iris flower dataset. For instance, for the first column (class is 1 which is Iris-setosa), whichever row in dataset is ‘Iris-setosa’ will be marked as 1 in y1’s corresponding row. Any other class (classes 2 and 3: Iris-versicolor and Iris-virginica) will be marked as 0 in y1’s first column.

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