def classify(inputfile='slpdb.mat',
epoch_length=30,
make_plot=True,
save=True,
outputfile='classify.txt'):
# Load datas
dataset = loadData(inputfile)
ECG = groupByEpoch(dataset['ECG'], 250, epoch_length)
Resp_in, Resp_out = edr.main(dataset)
# Feature dictionary construction
d_test = dictionaryInitialization()
d_test = dico_construction(ECG, Resp_in, Resp_out, d_test)
Xarr_norm = createNormalizedDataframe(d_test)
# Predecition
model = joblib.load('model.pkl')
pred_res = list()
for j in range(0, len(ECG)):
l = str(model.predict(Xarr_norm[j])[0])
pred_res.append(l)
if make_plot:
# Plot result
plot(pred_res, dataset['labels'], epoch_length)
if save:
# Write prediction into text file
savePredictions(pred_res, outputfile)
def classify(inputfile='slpdb.mat', epoch_length=30, make_plot=True, save=True, outputfile='classify.txt'):
# Load datas
dataset = loadData(inputfile)
ECG = groupByEpoch(dataset['ECG'], 250, epoch_length)
Resp_in, Resp_out = edr.main(dataset)
# Feature dictionary construction
d_test = dictionaryInitialization()
d_test = dico_construction(ECG, Resp_in, Resp_out, d_test)
Xarr_norm = createNormalizedDataframe(d_test)
# Predecition
model = joblib.load('model.pkl')
pred_res = list()
for j in range(0, len(ECG)):
l = str(model.predict(Xarr_norm[j])[0])
pred_res.append(l)
if make_plot:
# Plot result
plot(pred_res, dataset['labels'], epoch_length)
if save:
# Write prediction into text file
savePredictions(pred_res, outputfile)
import numpy as np
from sklearn.cross_validation import train_test_split
from data import loadData
from featureConstruction import dictionaryInitialization,dico_construction
from models import listModels,createNormalizedDataframe,savePredictions
mat_file = 'data_challenge.mat'
# Load datas
dataset = loadData(mat_file)
# Train dictionary construction
d_train = dictionaryInitialization()
d_train = dico_construction(dataset['X_train'],d_train)
labels = np.array(dataset['y_train'])
Xarr_norm = createNormalizedDataframe(d_train)
# Test our models
X_train_train, X_train_test, y_train_train, y_train_test = train_test_split(Xarr_norm,labels,test_size=0.2,random_state=42)
X_train_train = np.nan_to_num(X_train_train)
X_train_test = np.nan_to_num(X_train_test)
# Scores
models = listModels()
for m in models:
print m.get_params()
m.fit(X_train_train,y_train_train)
print m.score(X_train_test,y_train_test)
# Real training model
import numpy as np
from sklearn.cross_validation import train_test_split
from data import loadData
from featureConstruction import dictionaryInitialization, dico_construction
from models import listModels, createNormalizedDataframe, savePredictions
mat_file = 'data_challenge.mat'
# Load datas
dataset = loadData(mat_file)
# Train dictionary construction
d_train = dictionaryInitialization()
d_train = dico_construction(dataset['X_train'], d_train)
labels = np.array(dataset['y_train'])
Xarr_norm = createNormalizedDataframe(d_train)
# Test our models
X_train_train, X_train_test, y_train_train, y_train_test = train_test_split(
Xarr_norm, labels, test_size=0.2, random_state=42)
X_train_train = np.nan_to_num(X_train_train)
X_train_test = np.nan_to_num(X_train_test)
# Scores
models = listModels()
for m in models:
print m.get_params()
m.fit(X_train_train, y_train_train)
print m.score(X_train_test, y_train_test)
