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Example classifier on Numerai data using a logistic regression classifier. To get started, install the required packages: pip install pandas, numpy, sklearn



In [10]:

    
import pandas as pd
import numpy as np
from sklearn import metrics, preprocessing, linear_model

Set paths to the Data



In [11]:

    
dirRawData = '/home/john/Projects/RepoNumerAI/data/raw/numerai_datasets/19_03_2017/'
dirOutputData = '/home/john/Projects/RepoNumerAI/data/processed/'



In [12]:

    
# Set seed for reproducibility
np.random.seed(0)

print("Loading data...")

# Load the data from the CSV files
training_data = pd.read_csv(dirRawData + 'numerai_training_data.csv', header=0)
prediction_data = pd.read_csv(dirRawData + 'numerai_tournament_data.csv', header=0)









    



Loading data...

Transform the loaded CSV data into numpy arrays



In [13]:

    
Y = training_data['target']
X = training_data.drop('target', axis=1)
t_id = prediction_data['t_id']
x_prediction = prediction_data.drop('t_id', axis=1)

This is your model that will learn to predict



In [14]:

    
model = linear_model.LogisticRegression(n_jobs=-1)

print("Training...")
# Your model is trained on the numerai_training_data
model.fit(X, Y)









    



Training...






    Out[14]:





LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=-1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)



In [15]:

    
print("Predicting...")

# Your trained model is now used to make predictions on the numerai_tournament_data
# The model returns two columns: [probability of 0, probability of 1]
# We are just interested in the probability that the target is 1.
y_prediction = model.predict_proba(x_prediction)
results = y_prediction[:, 1]
results_df = pd.DataFrame(data={'probability':results})
joined = pd.DataFrame(t_id).join(results_df)









    



Predicting...



In [16]:

    
print("Writing predictions to predictions.csv")
# Save the predictions out to a CSV file
joined.to_csv(dirOutputData + "predictions.csv", index=False)

# Now you can upload these predictions on numer.ai









    



Writing predictions to predictions.csv



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