In a previous post, I described very basic code to get into a world of machine learning competitions, Numerai. This one will be a continuation, so if you haven’t read it I recommend to do it- here. In this post, we will add little more complexity to the whole process. We will split out 20% of training data as validation set so we can train different models and compare performance. And we will dive into deep neural nets as predicting model.
Ok, let’s do some machine learning…
Let’s start with importing what will be required, this step is similar to what we have done in the first model. Apart from Pandas, we import “StandardScaler” to preprocess data before feeding them into neural net. We will use “train_test_split” to split out 20% of data as a test set. “roc_auc_score” is a useful metric to check and compare performance of the model, we will also need neural net itself – that will be classifier from ‘scikit-neuralnetwork’ (sknn).
import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score from sknn.mlp import Classifier, Layer
As we have all required imports, we can load the data from csv(remember to update the system path to downloaded files):
train = pd.read_csv("/home/m/Numerai/numerai_datasets/numerai_training_data.csv") test = pd.read_csv("/home/m/Numerai/numerai_datasets/numerai_tournament_data.csv") sub = pd.read_csv("/home/m/Numerai/numerai_datasets/example_predictions.csv")
Some basic data manipulation required:
sub["t_id"]=test["t_id"] test.drop("t_id", axis=1,inplace=True) labels=train["target"] train.drop("target", axis=1,inplace=True) train=train.values labels=labels.values
In next four lines we will do what is called standardization. The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0 and σ=1.
scaler = StandardScaler() scaler.fit(train) train = scaler.transform(train) test = scaler.transform(test)
Next line of code will split original downloaded train set to train and test set, basically we set aside 20% of original train data to make sure we can check the out of the sample performance – to avoid overfitting.
X_train, X_test, y_train, y_test = train_test_split(train,labels, test_size=0.2, random_state=35)
Having all data preprocessed we are ready to define model, set number of layers in neural network, and a number of neurons in each layer. Below few lines of code to do it:
nn = Classifier( layers=[ Layer("Tanh", units=50), Layer("Tanh", units=200), Layer("Tanh", units=200), Layer("Tanh", units=50), Layer("Softmax")], learning_rule='adadelta', learning_rate=0.01, n_iter=5, verbose=1, loss_type='mcc')
“units=50” – states a number of neurons in each layer, number of neurons in first layer is determined by a number of features in data we will feed in.
“Tahn” – this is kind of activation function, you can use other as well eg. rectifier, expLin, sigmoid, or convolution. In last layer the activation function is Softmax – that’s usual output layer function for classification tasks. In our network we have five layers with different number of neurons, there are no strict rules about number of neurons and layers so it is more art than science, you just need to try different versions and check what works best.
In our network we have five layers with a different number of neurons, there are no strict rules about a number of neurons and layers so it is more art than science, you just need to try different versions and check what works best. After layers we set learning rule to ‘adadelta’ again more choice available: sgd, momentum, nesterov, adagrad or rmsprop just try and check what works best.
“learning_rule=’adadelta'” – sets learning algorithm to ‘adadelta’, more choice available: sgd, momentum, nesterov, adagrad or rmsprop just try and check what works best, you can mix them for different layers.
“learning_rate=0.01” – learning rate, often as rule of thumb you start with ‘default’ value of 0.01, but other values can be used, mostly anything from 0.001 to 0.1.
“n_iter=5” – number of iterations ‘epochs’, the higher the number the longer process of learning will take, 5 is as example only, one need to look at error after each epoch, at some point it will stop dropping, I have seen anything from 50 to 5000 so feel free to play with it.
“verbose=1” – this parameter will let us see progress on screen.
“loss_type=’mcc‘ “ – loss function, ‘mcc’ typical for classification tasks.
As the model is set, we can feed data and train it, depending on how powerful your pc is it can take from seconds to days. It is recommended to use GPU computing for naural networks training.
Below line validates model against 20% of data we have set aside before.
print('Overall AUC:', roc_auc_score(y_test, nn.predict_proba(X_test)[:,1]))
Using above code we can play around with different settings and neural networks architectures, checking the performance. After finding the best settings, they can be applied for prediction to be uploaded to Numerai, just run last three lines(just remember to update system path to save the file):
y_pred = nn.predict_proba(test) sub["probability"]=y_pred[:,1] sub.to_csv("/home/m/Numerai/numerai_datasets/Prediction.csv", index=False)
I hope above text was useful and you can now start playing around with deep learning for trading predictions for Numerai. If you have any comments or questions please feel free to contact me via a contact form.
Full code below:
import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score from sknn.mlp import Classifier, Layer train = pd.read_csv("/home/m/Numerai/numerai_datasets/numerai_training_data.csv") test = pd.read_csv("/home/m/Numerai/numerai_datasets/numerai_tournament_data.csv") sub = pd.read_csv("/home/m/Numerai/numerai_datasets/example_predictions.csv") sub["t_id"]=test["t_id"] test.drop("t_id", axis=1,inplace=True) labels=train["target"] train.drop("target", axis=1,inplace=True) train=train.values labels=labels.values scaler = StandardScaler() scaler.fit(train) train = scaler.transform(train) test = scaler.transform(test) X_train, X_test, y_train, y_test = train_test_split(train,labels, test_size=0.2, random_state=35) nn = Classifier( layers=[ Layer("Tanh", units=50), Layer("Tanh", units=200), Layer("Tanh", units=200), Layer("Tanh", units=50), Layer("Softmax")], learning_rule='adadelta', learning_rate=0.01, n_iter=5, verbose=1, loss_type='mcc') nn.fit(X_train, y_train) print('Overall AUC:', roc_auc_score(y_test, nn.predict_proba(X_test)[:,1])) y_pred = nn.predict_proba(test) sub["probability"]=y_pred[:,1] sub.to_csv("/home/m/Numerai/numerai_datasets/Prediction.csv", index=False)
Was the above useful? Please share with others on social media.
Recommended reading list:
|Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
|Practical Statistics for Data Scientists: 50 Essential Concepts
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that “learn” from data
Unsupervised learning methods for extracting meaning from unlabeled data
|Doing Data Science: Straight Talk from the Frontline
Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.
In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Statistical inference, exploratory data analysis, and the data science process
Spam filters, Naive Bayes, and data wrangling
Recommendation engines and causality
Social networks and data journalism
Data engineering, MapReduce, Pregel, and Hadoop
|The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
|Introduction to Machine Learning with Python: A Guide for Data Scientists
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you’ll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills