# Logistic Regression

### Contents

### What is Logistic Regression

Logistic regression is a type of linear regression. However, it is used for classification only. Huh.. that’s confusing, right ? Let’s dive in.

Let’s take the simple iris data set. The target variable as you know by now ( from day 9 – Introduction to Classification in Python, where we discussed classification using K Nearest neighbors ) is categorical in nature. Let’s load the data first.

from sklearn import datasets iris = datasets.load_iris() iris_data = iris.data

iris_data[0:5,:]

array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2], [5. , 3.6, 1.4, 0.2]])

And the target of course is the *species*.

iris_target = iris.target iris_target

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

Let’s simplify the dataset to just 2 species –

- 0 – setosa
- 1 – versi-color

Let’s just take data for 2 of the species ( say setosa and versi-color ) with just the sepal data ( sepal length and sepal width ) and plot it.

iris_data = iris_data[0:100] iris_target = iris_target[0:100]

import matplotlib.pyplot as plt %matplotlib inline plt.scatter(iris_data[:,0],iris_data[:,1], c=iris_target) plt.xlabel("Sepal Length") plt.ylabel("Sepal Width")

Text(0, 0.5, 'Sepal Width')

Let’s simplify this further – say, we wanted to predict the species based on a single parameter – **Sepal Length**. Let’s first plot it.

plt.scatter(iris_data[:,0], iris_target) plt.savefig("iris_target.png")

We know that regression is used to predict a continous variable. What about a categorical variable like this ? (species). If we can draw a curve like this,

and for all target values predicted with value > 0.5 put it in one category, and for all target values less than 0.5, put it in the other category – like this.

A linear regression (multilinear in this case) equation looks like this.

Logistic regression is almost similar to linear regression. The difference lies in how the predictor is calculated. Let’s see it in the next section.

### Math

The name logistic regression is derived from the **logit** function. This function is based on odds.

### logit function

Let’s take an example. A standard dice roll has 6 outcomes. So, what is the probability of landing a 4 ?

Now, what about odds ? The odds of landing a 4 is

So, when we substitute p into the odds equation, it becomes

OK. Now that we understand **Probability** and **Odds**, let’s get to the **log** of odds.

How exactly is the logistic regression similar to linear regression ? Like so.

To understand this better, let’s plot the log of odds between a probabilty value of 0 and 1.

import numpy as np x = np.linspace (0, 0.999, num=100) y = np.log (x/(1-x) )

c:\program files (x86)\python37-32\lib\site-packages\ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log after removing the cwd from sys.path.

plt.plot(x,y) plt.grid()

This is the logistic regression curve. It maps a probability value ( 0 to 1 ) to a number ( -∞ to +∞ ). However, we are not looking for a continous variable, right ? The predictor we are looking for is a categorical variable – in our case, we said we would be able to predict this based on probability.

- p >= 0.5 – Category 1
- p < 0.5 – Category 2

In order to calculate those probabilities, we would have to calculate the inverse function of the **logit** function.

### sigmoid function

The inverse of the logit curve is the *inverse-logit* or **sigmoid** function ( or **expit** function as sklearn calls it). The **sigmoid** function transforms the numbers ( -∞ to +∞ ) back to values between 0 and 1. Here is the formula for the **sigmoid** function.

from scipy.special import expit x_new = y y_new = expit (x_new) plt.plot(x_new,y_new) plt.grid()

Essentially, if we flip the logit function 90^{0}, you get the sigmoid function.

Here is the trick – As long as we are able to find a curve like the one below, although the target (predictor) is a value between 0 and 1 ( probabilities), we can say that all values below 0.5 ( half way mark ) belongs to one category and the remaining ( values above 0.5 ) belong to the next category. This is the essence of logistic regression.

### Implementation

Let’s try to implement the logistic regression function in Python step by step.

### Data & Modeling

Just to keep the same example going, let’s try to fit the sepal length data to try and predict the species as either *Setosa* or *Versicolor*.

from sklearn import linear_model from scipy.special import expit model = linear_model.LogisticRegression(C=1e5, solver='lbfgs') model.fit(iris_data[:,0].reshape(-1,1), iris_target)

LogisticRegression(C=100000.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='warn', n_jobs=None, penalty='l2', random_state=None, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)

Let’s plot the data set first ( like we have already done before ).

plt.scatter(iris_data[:,0],iris_target)

**Question** – For the data above, which of the following techniques is a better fit ?

### Visualization

Now, let’s plot the linear equation (sigmoid curve actually) and see the fit visually. Let’s create dummy x axis data. The values range between 4.0 and 7.0.

x_test = np.linspace(4.0,7.0,100) # predict dummy y_test data based on the logistic model y_test = x_test * model.coef_ + model.intercept_ sigmoid = expit(y_test)

plt.scatter(iris_data[:,0],iris_target, c=iris_target,label = "sepal length") # ravel to convert the 2-d array to a flat array plt.plot(x_test,sigmoid.ravel(),c="green", label = "logistic fit") plt.yticks([0, 0.2, 0.4, 0.5, 0.6, 0.7, 1]) plt.axhline(.5, color="red", label="cutoff") plt.legend(loc="lower right")

### Basic Evaluation

Let’s split up the data into training and test data and model it. As usual, to evaluate categorical target data, we use a confusion matrix.

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split (iris_data[:,0], iris_target)

from sklearn import linear_model model = linear_model.LogisticRegression().fit(X_train.reshape(-1,1), y_train)

c:\program files (x86)\python37-32\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning)

y_predict = model.predict(X_test.reshape(-1,1))

As usual, to evaluate categorical target data, we use a confusion matrix.

from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score print ( "confusion matrix = \n" , confusion_matrix(y_test, y_predict) ) print ( "accuracy score = ",accuracy_score(y_test,y_predict) ) # or use the score function of LogisticRegression class # model.score(X_test.reshape(-1,1),y_test)

confusion matrix = [[ 6 9] [ 0 10]] accuracy score = 0.64

That’s a 84% score. Pretty decent, given the fact that we are just using 1 parameter – Sepal length. We are ofcourse not restricted to just 1 predictor – We can use all of the predictors available ( Sepal Length/Width , Petal Length/Width ). Let’s follow our standard train/test split process and try to predict the accuracy.

X_train, X_test, y_train, y_test = train_test_split (iris_data[:,0].reshape(-1,1), iris_target)

from sklearn import linear_model from scipy.special import expit model = linear_model.LogisticRegression().fit(X_train, y_train)

c:\program files (x86)\python37-32\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning)

y_predict = model.predict(X_test)

from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score print ( confusion_matrix(y_test, y_predict) ) print ( accuracy_score(y_test,y_predict) )

[[11 1] [ 1 12]] 0.92

### Optimization

Let’s plot the logistic regression curve for the test data set.

# 1. Plot the Species on y-axis and Sepal Length on x-axis plt.scatter(iris_data[:,0],iris_target, c=iris_target,label = "sepal length") # 2. Plot the logistic regression curve based on the sigmoid function # ravel to convert the 2-d array to a flat array plt.plot(x_test,sigmoid.ravel(),c="green", label = "logistic fit") # plt.scatter(X_test[:,0],probabilities) plt.yticks([0, 0.2, 0.4, 0.5, 0.6, 0.7, 1]) # Draw a horizontal line (in red) indicating the threshold (cutoff) probability plt.axhline(.5, color="red", label="cutoff") # Draw a vertical line (in purple) indicating the threshold (cutoff) sepal length plt.axvline(5.4, color="purple", label="") # Use text to show the Negative and positive values plt.text(5.50,0.9,"<--True Negative-->") plt.text(4.3,0.9,"<--False Negative-->") plt.text(4.4,0.05,"<--True Positive-->") plt.text(5.5,0.05,"<--False Positive-->")

Text(5.5, 0.05, '<--False Positive-->')

As you can see, still there are quite a bit of mis-classifications. All the **false negatives** and **false positives** in the plot above are examples of mis-classification. Irrespective of the algorithm used to calculate the fit, there is only so much that can be done in increasing the classification accuracy given the data as-is.

However, there is a specific optimization that can be done – and that is to specifically increase accuracy of one segment of the confusion matrix at the expense of the other segments. For example, if you look at a visual of the confusion matrix for our dataset.

For this dataset, classifying the species as “setosa” is positive and “versi-color” as negative

- setosa – positive
- versi-color – negative

Let’s actuall calculate the accuracy values.

cm = confusion_matrix(y_test, y_predict) print ( cm )

[[11 1] [ 1 12]]

tp = float(cm[0,0]) / float(cm[0,0] + cm[1,0]) fp = float(cm[1,0]) / float(cm[0,0] + cm[1,0]) tn = float(cm[0,1]) / float(cm[0,1] + cm[1,1]) fn = float(cm[1,1]) / float(cm[1,1] + cm[0,1]) print ("True Positive = ", tp) print ("False Positive = ", fp) print ("True Negative = ", tn) print ("False Negative = ", fn)

True Positive = 0.9166666666666666 False Positive = 0.08333333333333333 True Negative = 0.07692307692307693 False Negative = 0.9230769230769231

What if we want to predict 100% of setosa ( or a much more accurate classification than 0.9 ). Of course, like we discussed earlier, it will come at a cost. However, there is a usecase for this scenario. For example, if getting a particular classification right is extremely important, then we focus more on that particular classification than the others. Have you seen the Brad Pitt’s movie “World War Z” ? A plague emerges all around the world and an asylum is set up in Israel with a high wall. However, when you enter the wall, they make absolutely sure that you do not have the plague. Say, if you have the plague and if you call that as positive, then essentially you maximize the green box in the picture above.

Or another example would be, if you were to diagonize cancer patients, you would rather want to increase the odds of predicting a cancer patient if he/she really has it (True positive). Even it it comes at a cost of wrongly classifying a non-cancer patient as positive ( false positive ). The former can save a life while the later will just cost the company a patient.

Unfortunately, there is no such parameter that we can pass to the **LogisticRegression** object. However, it does help us with the probabilities that is has predicted.

p = model.predict_proba(X_test[:,0].reshape(-1,1)) p = p[:,1]

Once we have the probabilities, we can set a new threshold and say that all values above a particular probability value be set to 1 and all others (below that value) be set to 0.

from sklearn.preprocessing import Binarizer b = Binarizer(0.47) new_classification = b.fit_transform(p.reshape(-1,1))

Here is the old confusion matrix.

cm = confusion_matrix(y_test, y_predict) print ( cm ) tp = float(cm[0,0]) / float(cm[0,0] + cm[1,0]) fp = float(cm[1,0]) / float(cm[0,0] + cm[1,0]) tn = float(cm[0,1]) / float(cm[0,1] + cm[1,1]) fn = float(cm[1,1]) / float(cm[1,1] + cm[0,1]) print ("True Positive = ", tp) print ("False Positive = ", fp) print ("True Negative = ", tn) print ("False Negative = ", fn)

[[11 1] [ 1 12]] True Positive = 0.9166666666666666 False Positive = 0.08333333333333333 True Negative = 0.07692307692307693 False Negative = 0.9230769230769231

And here is the new confusion matrix with the transformed threshold values.

new_cm = confusion_matrix(y_test, new_classification) print ( new_cm ) cm = new_cm tp = float(cm[0,0]) / float(cm[0,0] + cm[1,0]) fp = float(cm[1,0]) / float(cm[0,0] + cm[1,0]) tn = float(cm[0,1]) / float(cm[0,1] + cm[1,1]) fn = float(cm[1,1]) / float(cm[1,1] + cm[0,1]) print ("True Positive = ", tp) print ("False Positive = ", fp) print ("True Negative = ", tn) print ("False Negative = ", fn)

[[ 5 7] [ 0 13]] True Positive = 1.0 False Positive = 0.0 True Negative = 0.35 False Negative = 0.65

We are able to increase the “setosa” species much more predictably. Of course, the threshold value for cut-off should have to be decided in a much more robust way ( after a bit of trail and error ) based on many more sets of training/test sets rather than just one. You should be able to do it using a wrapper program that repeatedly does the split, modeling and optimization until you find the best threshold that most accurately predicts the true positive.

### Evaluation

### ROC Curve

Receiver Operating Characteristics – also called ROC Curve is a measure of how good the classification is. Scikit Learn has an easy way to create ROC curve and calculate the area under the ROC curve. First off, let’s start with a classifier like Logistic Regression and let it predict all the probabilities (thresholds).

**Step 1** – Get the data

from sklearn import datasets iris = datasets.load_iris() # Get only the setosa and versicolor data iris_data = iris.data[0:100,:] iris_target = iris.target[0:100]

**Step 2** – Model the data using a classifier

# split the data into train and test datasets from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split (iris_data[:,0], iris_target) # Model the data using Logistic Regression from sklearn import linear_model model = linear_model.LogisticRegression(C=1e5, solver='lbfgs') model.fit(iris_data[:,0].reshape(-1,1), iris_target)

LogisticRegression(C=100000.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='warn', n_jobs=None, penalty='l2', random_state=None, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)

**Step 3** – Use *roc_curve* function to create the True Positive Rate and False positive Rate.

from sklearn.metrics import roc_curve, auc probabilities = model.predict_proba(X_test.reshape(-1,1))[:,1] fpr, tpr, thresholds = roc_curve(y_test, probabilities)

**Step 4** – Plot the ROC curve

import matplotlib.pyplot as plt %matplotlib inline plt.plot(fpr,tpr) plt.plot([0,1],[0,1],color="black",linestyle="--") plt.xlabel("False Positive Rate - FPR") plt.ylabel("True Positive Rate - TPR ") plt.title("Receiver Operating Characteristics - ROC Curve") plt.text(0.6,0.5,"Baseline") plt.text(0.3,0.8,"ROC Curve")

Text(0.3, 0.8, 'ROC Curve')

### Area under ROC Curve

**Step 5** – Calculate *Area under the Curve*

from sklearn.metrics import roc_auc_score roc_auc_score(y_test, probabilities)

0.9097222222222223