Open Machine Learning Course. Topic 4. Linear Classification and Regression
Welcome to the 4-th week of our course! Now we will present our most important topic — linear models. If you have your data prepared and want to start training models, then you will most probably first try either linear or logistic regression, depending on your task (regression or classification).
This week’s material covers both theory of linear models and practical aspects of their usage in real-world tasks. There’s going to be a lot of math in this topic, and we won’t even try to render all the formulas on Medium. Instead, we provide a Jupyter Notebook for each part of this article. In the assignment, you’ll beat two simple benchmarks in a Kaggle competition solving a problem of identifying a user based on her session of visited websites.
Part1. Regression, nbviewer
- Ordinary Least Squares
- Maximum Likelihood Estimation
- Bias-Variance Decomposition
- Regularization of Linear Regression
Part 2. Linear Classification, nbviewer
- Linear Classifier
- Logistic Regression as a Linear Classifier
- Maximum Likelihood Estimation and Logistic Regression
- L2-Regularization of Logistic Loss
Part 3. An Illustrative Example of Logistic Regression Regularization, nbviewer
Part 4. Where Logistic Regression Is Good and Where It’s Not, nbviewer
- Analysis of IMDB movie reviews
Part 5. Validation and Learning Curves, nbviewer
- How much model complexity is needed?
- How much data is needed?
Part 6. Kaggle Inclass competition “Catch Me If You Can”
- Data Downloading and Transformation
- Sparse Matrices
- Training the first model
Part 6. Kaggle Inclass competition “Catch Me If You Can”
We will be solving the intruder detection problem analyzing users’ behavior on the Internet. It is a complicated and interesting problem combining data analysis and behavioral psychology. As an illustration of one of such tasks, Yandex solves the mailbox intruder detection problem based on the user’s behavior patterns. In a nutshell, intruder’s behavior patterns may differ from those of the mailbox owner:
- the breaker may not delete emails right after they are read as the mailbox owner may do;
- the intruder may mark emails and even move the cursor differently;
So the intruder could be detected and thrown out from the mailbox forcing the user to authenticate via SMS-code.
Similar approaches are being developed in Google Analytics and described in scientific research papers. You can find more on this topic by searching “Traversal Pattern Mining” and “Sequential Pattern Mining”.
In this competition we are going to solve a similar problem: our algorithm is supposed to analyze the sequence of websites consequently visited by a particular person and predict whether this person is a user named Alice or an intruder (somebody else). As a metric, we will use ROC AUC.
Data Downloading and Transformation
First, load the training and test sets. Then explore the data and perform a couple of simple exercises:
The training dataset contains the following features:
- site1 — id of the first visited website in the session;
- time1 — visiting time for the first website in the session;
- site10 — id of the tenth visited website in the session;
- time10 — visiting time for the tenth website in the session;
- target — target variable, takes the value of 1 for Alice’s sessions, and 0 for the other users’ sessions.
User sessions are chosen in such a way that they are not longer than half an hour and/or contain more than ten websites; i.e. a session is considered as ended either if the user has visited ten websites or if the session has lasted over thirty minutes.
There are some empty values in the table, which means that some sessions contain less than ten websites. Replace empty values with 0 and change column types to integer. Also, load the website dictionary and see how it looks:
# Websites total: 48371
In order to train our first model, we need to prepare the data. First of all, exclude the target variable from the training set. Now both training and test sets have the same number of columns, and we can aggregate them into one dataframe. Thus, all transformations will be performed simultaneously on both the training and test datasets.
On the one hand, it leads to the fact that both of our datasets have one feature space (so you don’t have to worry that you may have forgotten to transform a feature in one of the datasets). On the other hand, the processing time will increase. In case of enormously large sets, it may turn out that it is impossible to transform both datasets simultaneously (and sometimes you have to split your transformations into several stages, separately for the train/test dataset). In our case, we are going to perform all the transformations for the united dataframe at once; and, before training the model or making predictions, we will just use the corresponding part of it.
For the sake of simplicity, we will use only the visited websites in the session (and we will not take into account the timestamp features). The point behind this data selection is: Alice has her favorite sites, and the more often you see these sites in the session, the higher the probability that this is an Alice’s session, and vice versa.
Let’s prepare the data. We will keep only the features
site1, site2, … , site10 in the dataframe. Keep in mind that missing values were replaced with zeros. Here is how the first rows of the dataframe look like:
Sessions are the sequences of website indices, and such representation of data is inconvenient for linear methods. According to our hypothesis (Alice has favorite websites), we need to transform this dataframe so that each website has the corresponding feature (column) which value is equal to the number of visits on this website within the session. All of this can be done in two lines:
If you understand what just happened here, then you can skip the next section (perhaps, you can handle logistic regression too?). If not, then let us figure it out.
Let’s estimate how much memory it would require to store our data in the example above. Our united dataframe contains 336 thousand samples of 48 thousand integer features in each. It’s easy to calculate the required amount of memory, roughly:
336K * 48K * 8 bytes = 16M * 8 bytes = 128 GB
Obviously, mere mortals don’t have such volumes of memory (strictly speaking, Python may allow you to create such a matrix, but it would not be easy to do anything with it). An interesting fact is that most of the elements of our matrix are zeros. If we counted non-zero elements, then it would make out about 1.8 million, i.е. slightly more than 10% of all matrix elements. Such a matrix, where most elements are zeros, is called sparse, and the ratio between the number of zero elements and the total number of elements is called the sparseness of the matrix.
To work with such matrices, you can use
scipy.sparse library, check the documentation to understand what possible types of sparse matrices are, how to work with them and in which cases their usage is most effective. You can learn how they are arranged, for example, be reading the Wikipedia article. Note that a sparse matrix contains only non-zero elements. Finally, you can get the allocated memory size (significant memory savings are obvious):
1866898 elements * 8 bytes = 14935184 bytes
sparse_matrix_size = 14935184 bytes
Let’s explore how the matrix with the websites has been formed using a mini example. Suppose we have the following table with user sessions:
There are 3 sessions, and no more than 3 websites in each. Users visited four different sites in total (there are numbers from 1 to 4 in the table cells). And let us assume that:
If the user has visited less than 3 websites during the session, the last few values will be zero. We want to convert the original dataframe in such a way that each session would have the corresponding row which shows the number of visits on each particular site; i.e. we want to transform the previous table into the following form:
To do this, use the constructor:
csr_matrix ((data, indices, indptr)) and create a frequency table (see examples, code and comments on the links above to see how it works). Here, we set all the parameters explicitly for greater clarity:
matrix([[2, 1, 0, 0, 0],
[0, 2, 0, 1, 0],
[0, 0, 1, 1, 1]])
As you might have noticed, the number of the columns in the resulting matrix is not four (by the number of different websites), but five. A zero column has been added, which shows on how many sites the session was shorter (in our mini example we took sessions of length 3). This column is excessive and it should be removed from the dataframe.
Another benefit of using sparse matrices is that there are special implementations of both matrix operations and machine learning algorithms for them, which sometimes allows to significantly accelerate operations due to the data structure peculiarities. This applies to logistic regression as well. Now, everything is ready to build our first model.
3. Training the first model
Let’s build our first model, using logistic regression implementation from
Sklearn with default parameters. We will use the first 90% of the data for training (the training data set is sorted by time), and the remaining 10% for validation. Let's write a simple function that returns the quality of the model, and then train our first classifier:
CPU times: user 138 ms, sys: 77.1 ms, total: 216 ms
Wall time: 2.74 s
The first model demonstrated the quality of approximately 0.92 ROC AUC on the validation set. Let’s take it as the first baseline and a starting point. To make a prediction on the test set, we need to train the model again on the entire training dataset (until this moment, our model used only part of the data for training), which will increase its generalizing ability:
If you follow these steps and upload the answer to the competition page, then you should get the quality of
ROC AUC = 0.91707 on the public leaderboard.
- A nice and concise overview of linear models is given in the book “Deep Learning” (I. Goodfellow, Y. Bengio, and A. Courville).
- Linear models are covered practically in every ML book. We recommend “Pattern Recognition and Machine Learning” (C. Bishop) and “Machine Learning: A Probabilistic Perspective” (K. Murphy).
- If you prefer a thorough overview of linear model from a statistician’s viewpoint, then look at “The elements of statistical learning” (T. Hastie, R. Tibshirani, and J. Friedman).
- The book “Machine Learning in Action” (P. Harrington) will walk you through implementations of classic ML algorithms in pure Python.
- Scikit-learn library. These guys work hard on writing really clear documentation.
- Scipy 2017 scikit-learn tutorial by Alex Gramfort and Andreas Mueller.
- One more ML course with very good materials.
- Implementations of many ML algorithms. Search for linear regression and logistic regression.
- And many others, feel free to share in comments.