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ford.py
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ford.py
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import pandas
from pandas.tools.plotting import parallel_coordinates
import sklearn
import numpy
from sklearn import svm
import matplotlib.pyplot as plt
import pickle
from sklearn import neighbors
import copy
import ml_metrics as metrics
cols = numpy.r_[3, 4, 6, 7, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 27]
def trainSVR(infile):
print "training"
print "reading data from file"
tData = numpy.genfromtxt(infile, skip_header=1, delimiter=',', max_rows=400000)
print "finished reading file"
inc = 10
z = []
for i in range(inc):
z.append(tData[i::inc])
z = numpy.array(z)
z = z.mean(0)
#x = tData[::50,numpy.r_[3:33]]
#y = tData[::50,2]
x = z[:,numpy.r_[3:33]]
y = z[:,2]
print "N: " + str(x.shape[0])
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
#maxs = x.max(0)
#ranges = maxs - mins
#x = 2 * (x - mins) / ranges - 1;
print "creating model"
model = svm.SVR(kernel='rbf', C=10, gamma=0.01, verbose=3)
model.cache_size = 2048
print "fitting data"
model.fit(x,y)
print "saving model to model.pkl"
pickle.dump(model, open('model.pkl', 'wb'))
return {'model':model, 'x':x, 'y':y}
def trainSVC(infile):
print "reading data from file"
tData = numpy.genfromtxt(infile, skip_header=1, delimiter=',', max_rows=400000)
print "finished reading file"
#inc = 1
#z = []
#for i in range(inc):
# z.append(tData[i::inc])
#z = numpy.array(z)
#z = z.mean(0)
#x = z[:,cols]
#y = z[:,2]>0.5
x = tData[::1,cols]
y = tData[::1,2]
print "N: " + str(x.shape)
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
#maxs = x.max(0)
#ranges = maxs - mins
#x = 2 * (x - mins) / ranges - 1;
print "creating model"
model = svm.SVC(kernel='rbf', C=10, gamma=0.01, verbose=3)
model.cache_size = 2048
print "fitting data"
model.fit(x,y)
print "saving model to model.pkl"
pickle.dump(model, open('model.pkl', 'wb'))
return {'model':model, 'x':x, 'y':y}
def trainKNN(infile):
print "reading data from file"
tData = numpy.genfromtxt(infile, skip_header=1, delimiter=',', max_rows=400000)
print "finished reading file"
#inc = 5
#z = []
#for i in range(inc):
# z.append(tData[i::inc])
#z = numpy.array(z)
#z = z.mean(0)
x = tData[::1,cols]
y = tData[::1,2]
#x = z[:,numpy.r_[3:33]]
#y = z[:,2]>0.5
print "N: " + str(x.shape)
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
#maxs = x.max(0)
#ranges = maxs - mins
#x = 2 * (x - mins) / ranges - 1;
print "creating KNN model"
model = neighbors.KNeighborsClassifier(n_neighbors=19, weights='uniform', algorithm='auto', n_jobs=-1)
model.cache_size = 2048
print "fitting data"
model.fit(x,y)
print "saving model to model.pkl"
pickle.dump(model, open('model.pkl', 'wb'))
return {'model':model, 'x':x, 'y':y}
def trainLR(infile):
print "reading data from file"
#cols = numpy.r_[3:33]
tData = numpy.genfromtxt(infile, skip_header=1, delimiter=',', max_rows=400000)
print "finished reading file"
x = tData[::1,cols]
y = tData[::1,2]
print "N: " + str(x.shape[0])
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
#maxs = x.max(0)
#ranges = maxs - mins
#x = 2 * (x - mins) / ranges - 1;
print "creating model"
model = sklearn.linear_model.LogisticRegression(C=1, solver='sag', n_jobs=-1, verbose=3)
model.cache_size = 2048
print "fitting data"
model.fit(x,y)
print "saving model to model.pkl"
pickle.dump(model, open('model.pkl', 'wb'))
return {'model':model, 'x':x, 'y':y}
def holdout_test(modelfile, testfile, solutionsfile):
print "holdout set"
model = pickle.load(open(modelfile, 'rb'))
tData = numpy.genfromtxt(testfile, skip_header=1, delimiter=',')
sData = numpy.genfromtxt(solutionsfile, skip_header=1, delimiter=',')
print "finished reading file"
x = tData[:,cols]
y = sData[:,2]
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
py = numpy.array(y)
preds = numpy.array(model.predict(x))
savepreds = copy.deepcopy(preds)
preds[preds>0.5] = 1
preds[preds<=0.5] = 0
truepos = sum((preds == 1) * (py == 1))
trueneg = sum((preds == 0) * (py == 0))
falsepos = sum((preds == 1) * (py == 0))
falseneg = sum((preds == 0) * (py == 1))
correct = truepos + trueneg
precision = 1.0 * truepos / sum(preds == 1)
recall = 1.0 * truepos / sum(py == 1)
f1 = 2.0 * precision * recall / (precision + recall)
print "num correct: " + str(correct)
print "true positives: " + str(truepos)
print "true negatives: " + str(trueneg)
print "false positives: " + str(falsepos)
print "false negatives: " + str(falseneg)
print "overall accuracy: " + str(1.0 * correct / len(py))
print "precision: " + str(precision)
print "recall: " + str(recall)
print "f1: " + str(f1)
#tpos = [0]
#fpos = [0]
#for i in numpy.r_[0:1.001:0.1]:
# if sum(((savepreds > i) == 1)) > 0 and sum(py == 1) > 0:
# tpos.append(1.0 * sum(((savepreds > i) == 1) * (py == 1)) / sum(((savepreds > i) == 1)))
# fpos.append(1.0 * sum(((savepreds > i) == 0) * (py == 0)) / sum(py == 1))
#tpos.append(1)
#fpos.append(1)
#auc = 0.0
#for i in range(len(tpos) - 1):
# auc = auc + (tpos[i + 1] + tpos[i]) * (fpos[i + 1] - fpos[i]) / 2.0
#print "auc: " + str(auc)
#plt.plot(fpos, tpos)
#plt.xlabel('False Positive Rate')
#plt.ylabel('True Positive Rate')
#plt.title('Holdout ROC')
#plt.show()
#plt.savefig("holdoutroc.png")
def test(modelfile, testfile):
print "test set"
model = pickle.load(open(modelfile, 'rb'))
tData = numpy.genfromtxt(testfile, skip_header=400001, delimiter=',')
print "finished reading file"
x = tData[:,cols]
y = tData[:,2]
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
py = numpy.array(y)
preds = numpy.array(model.predict(x))
savepreds = copy.deepcopy(preds)
preds[preds>0.5] = 1
preds[preds<=0.5] = 0
truepos = sum((preds == 1) * (py == 1))
trueneg = sum((preds == 0) * (py == 0))
falsepos = sum((preds == 1) * (py == 0))
falseneg = sum((preds == 0) * (py == 1))
correct = truepos + trueneg
precision = 1.0 * truepos / sum(preds == 1)
recall = 1.0 * truepos / sum(py == 1)
f1 = 2.0 * precision * recall / (precision + recall)
print "num correct: " + str(correct)
print "true positives: " + str(truepos)
print "true negatives: " + str(trueneg)
print "false positives: " + str(falsepos)
print "false negatives: " + str(falseneg)
print "overall accuracy: " + str(1.0 * correct / len(py))
print "precision: " + str(precision)
print "recall: " + str(recall)
print "f1: " + str(f1)
#tpos = [0]
#fpos = [0]
#for i in numpy.r_[0:1.001:0.1]:
# if sum(((savepreds > i) == 1)) > 0 and sum(py == 1) > 0:
# tpos.append(1.0 * sum(((savepreds > i) == 1) * (py == 1)) / sum(((savepreds > i) == 1)))
# fpos.append(1.0 * sum(((savepreds > i) == 0) * (py == 0)) / sum(py == 1))
#tpos.append(1)
#fpos.append(1)
#auc = 0.0
#for i in range(len(tpos) - 1):
# auc = auc + (tpos[i + 1] + tpos[i]) * (fpos[i + 1] - fpos[i]) / 2.0
#print "auc: " + str(auc)
#plt.plot(fpos, tpos)
#plt.xlabel('False Positive Rate')
#plt.ylabel('True Positive Rate')
#plt.title('Test ROC')
#plt.show()
#plt.savefig("testroc.png")
def show_data():
tData = numpy.genfromtxt('fordTrain.csv', skip_header=1, delimiter=',', max_rows=400000)
x = tData[:,3:33]
y = tData[:,2]
mins = x.min(0)
x = x - mins
x = x - (x.max(0) - x.min(0)) / 2
maxs = x.max(0)
maxs[maxs==0] = 1
x = 2 * x / maxs
d = numpy.zeros(tData.shape)
d[:,[0,1]] = tData[:,[0,1]]
d[:,2] = y
d[:,3:33] = x
head = 'TrialID,ObsNum,IsAlert,P1,P2,P3,P4,P5,P6,P7,P8,E1,E2,E3,E4,E5,E6,E7,E8,E9,E10,E11,V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11'
numpy.savetxt('normalized_data.csv', d, delimiter=',', header=head, fmt='%1.3f')
data = pandas.read_csv('normalized_data.csv', header=0, usecols=numpy.r_[2:33], sep=',', nrows=4000)
parallel_coordinates(data, 'IsAlert', color=['r','b'])
plt.show()
if __name__ == "__main__":
ford = trainSVC('fordTrain.csv')
test('model.pkl', 'fordTrain.csv')
holdout_test('model.pkl', 'fordTest.csv', 'Solution.csv')
#show_data()