Sharpen your NumPy skills while learning Logistic Regression
What’s our plan for implementing Logistic Regression in NumPy?
Let’s first think of the underlying math that we want to use.
There are many ways to define a loss function and then find the optimal parameters for it, among them, here we will implement in our
LogisticRegression class the following 3 ways for learning the parameters:
- We will rewrite the logistic regression equation so that we turn it into a least-squares linear regression problem with different labels and then, we use the closed-form formula to find the weights:
- Like above, we turn logistic into least-squares linear regression, but instead of the closed-form formula, we use stochastic gradient descent with the following gradient:
- We use the maximum likelihood estimation (MLE) method, write the likelihood function, play around with it, restate it as a minimization problem, and apply SGD with the following gradient:
In the above equations, X is the input matrix that contains observations on the row axis and features on the column axis; y is a column vector that contains the classification labels (0 or 1); f is the sum of squared errors loss function; h is the loss function for the MLE method.
To find out more about the above methods check out this article:
So, this is our goal: translate the above equations into code. And we’ll use NumPy for that.
We plan to use an object-oriented approach for implementation. We’ll create a
LogisticRegression class with 3 public methods:
Among fit’s parameters, one will determine how our model learns. This parameter is named method (not to be confused with a method as a function of a class) and it can take the following strings as values: ‘ols_solve’ (OLS stands for Ordinary Least Squares), ‘ols_sgd’, and ‘mle_sgd’.
To not make the
fit() method too long, we would like to split the code into 3 different private methods, each one responsible for one way of finding the parameters.
We will have the
__ols_solve() private method for applying the closed-form formula.
In this method and in the other methods that use the OLS approach, we will use the constant EPS to make sure the labels are not exactly 0 or 1, but something in between. That’s to avoid getting plus or minus infinity for the logarithm in the equations above.
__ols_solve() we first check if X has full column rank so that we can apply this method. Then we force y to be between EPS and 1-EPS. The
ols_y variable holds the labels of the ordinary least-squares linear regression problem that’s equivalent to our logistic regression problem. Basically, we transform the labels that we have for logistic regression so that they are compliant with the linear regression equations. After that, we apply the closed-form formula using NumPy functions.
For the 2 SGD-based algorithms, it would be redundant to have them as 2 separate methods since they will have almost all the code the same except for the part where we compute the gradient, as we have 2 different gradient formulas for them.
What we’ll do is to create a generic
__sgd() method that does not rely on a particular way of computing the gradient. Instead, it will expect as a parameter a function responsible for computing the gradient which the
__sgd() method will use.
In this method, we first initialize the weights to a random column vector with values drawn from a normal distribution with mean 0 and a standard deviation of 1/(# of features). The intuition for this std dev is that if we have more features, then we need smaller weights to be able to converge (and not blow up our gradients). Then we go through all the dataset for
iterations times. At the start of each such iteration, we randomly shuffle our dataset, then for each batch of data, we compute the gradient and update the weights.
For ‘ols_sgd’ and ‘mle_sgd’ we’ll create 2 private methods:
__mle_grad() that compute and return the gradient for these 2 different techniques.
For these 2 methods, we simply apply the formulas for ∇f and ∇h using NumPy.
fit() is called with
method=‘ols_solve’ we call
method=‘ols_sgd’ we call
grad_fn=self.__sse_grad, and when
method=’mle_sgd’ we call
predict() we first check if
fit() was called previously by looking for the weights attribute (the fit method is the only method that creates it). Then we check if the shapes of the input matrix x and weights vector allow multiplication. Otherwise, return error messages. If everything is OK, we do the multiplication and pass the result through the logistic function.
accuracy() we make predictions using the above method. Then check if the shape of the predictions matches that of the true labels, otherwise, we show an error message. After that we make sure that both predictions and the true labels have values of either 0 or 1 by a simple rule: if the value is >= 0.5 consider it a 1, otherwise a 0.
To compute the accuracy, we check for equality between y and y_hat. This will return a vector of Boolean values. Then cast these Booleans to float (False becomes 0.0, and True becomes 1.0). Then, the accuracy is simply the mean of these values.
Here is the full code of the
Now, we would like to test our
LogisticRegression class with some real-world data. For that, we will use this heart disease dataset from Kaggle. You can read more about this dataset on Kaggle, but the main idea is to predict the “target” column (which is 0 if healthy or 1 if has heart disease) based on the others.
Below is the code which shows our
LogisticRegression class in action (cells 1 & 2 are not shown below to avoid repetition; it was shown in the code snippet above).
As you can see, we were able to obtain a decent 80%+ accuracy both in training and testing with our from-scratch implementation.
You can see the full notebook on Kaggle.
I hope you found this information useful and thanks for reading!
This article is also posted on Medium here. Feel free to have a look!