Higgs machine learning competition

higgs_competition.py

import numpy as np
from sklearn.ensemble import GradientBoostingClassifier as GBC
import pandas as pd
import math
 
# Load training data
print('Loading training data.')
data_train = np.loadtxt( 'training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
 
# Pick a random seed for reproducible results. Choose wisely!
np.random.seed(42)
# Random number for training/validation splitting
r =np.random.rand(data_train.shape[0])
 
# Put Y(truth), X(data), W(weight), and I(index) into their own arrays
print('Assigning data to numpy arrays.')
# First 90% are training
Y_train = data_train[:,32][r<0.9]
X_train = data_train[:,1:31][r<0.9]
W_train = data_train[:,31][r<0.9]
# Last 10% are validation
Y_valid = data_train[:,32][r>=0.9]
X_valid = data_train[:,1:31][r>=0.9]
W_valid = data_train[:,31][r>=0.9]
 
# Train the GradientBoostingClassifier using our good features
print('Training classifier (this may take some time!)')
gbc = GBC(n_estimators=50, max_depth=5,min_samples_leaf=200,max_features=10,verbose=1)
gbc.fit(X_train,Y_train) 
 
# Get the probaility output from the trained method, using the 10% for testing
prob_predict_train = gbc.predict_proba(X_train)[:,1]
prob_predict_valid = gbc.predict_proba(X_valid)[:,1]
 
# Experience shows me that choosing the top 15% as signal gives a good AMS score.
# This can be optimized though!
pcut = np.percentile(prob_predict_train,85)
 
# This are the final signal and background predictions
Yhat_train = prob_predict_train > pcut 
Yhat_valid = prob_predict_valid > pcut
 
# To calculate the AMS data, first get the true positives and true negatives
# Scale the weights according to the r cutoff.
TruePositive_train = W_train*(Y_train==1.0)*(1.0/0.9)
TrueNegative_train = W_train*(Y_train==0.0)*(1.0/0.9)
TruePositive_valid = W_valid*(Y_valid==1.0)*(1.0/0.1)
TrueNegative_valid = W_valid*(Y_valid==0.0)*(1.0/0.1)
 
# s and b for the training 
s_train = sum ( TruePositive_train*(Yhat_train==1.0) )
b_train = sum ( TrueNegative_train*(Yhat_train==1.0) )
s_valid = sum ( TruePositive_valid*(Yhat_valid==1.0) )
b_valid = sum ( TrueNegative_valid*(Yhat_valid==1.0) )
 
# Now calculate the AMS scores
print('Calculating AMS score for a probability cutoff pcut=',pcut)
def AMSScore(s,b): return  math.sqrt (2.*( (s + b + 10.)*math.log(1.+s/(b+10.))-s))
print('   - AMS based on 90% training   sample:',AMSScore(s_train,b_train))
print('   - AMS based on 10% validation sample:',AMSScore(s_valid,b_valid))

# Generate predictions.csv
data = pd.read_csv("test.csv")
X_test = data.values[:, 1:]
ids = data.EventId
d = gbc.predict_proba(X_test)[:, 1]
r = np.argsort(d) + 1
p = np.empty(len(X_test), dtype=np.object)
p[d > pcut] = 's'
p[d <= pcut] = 'b'
df = pd.DataFrame({"EventId": ids, "RankOrder": r, "Class": p})
df.to_csv("predictions.csv", index=False, cols=["EventId", "RankOrder", "Class"])