def test_applyFeatures():
dData = datautil.get_data0()
ldfFeatures = ftu.applyFeatures(dData, [featMA], [{'lLookback':2}])
print ldfFeatures
ldfFeatures = ftu.stackSyms(ldfFeatures)
print ldfFeatures
ftu.normFeatures(ldfFeatures,-1,1,False)
''' Plot feature for XOM '''
for i, fcFunc in enumerate(lfcFeatures[:-1]):
plt.clf()
plt.subplot(211)
plt.title( fcFunc.__name__ )
plt.plot( dfPrice.index, dfPrice['XOM'].values, 'r-' )
plt.subplot(212)
plt.plot( dfPrice.index, ldfFeatures[i]['XOM'].values, 'g-' )
plt.show()
''' Pick Test and Training Points '''
lSplit = int(len(ldtTimestamps) * 0.7)
dtStartTrain = ldtTimestamps[0]
dtEndTrain = ldtTimestamps[lSplit]
dtStartTest = ldtTimestamps[lSplit+1]
dtEndTest = ldtTimestamps[-1]
''' Stack all information into one Numpy array '''
naFeatTrain = ftu.stackSyms( ldfFeatures, dtStartTrain, dtEndTrain )
naFeatTest = ftu.stackSyms( ldfFeatures, dtStartTest, dtEndTest )
''' Normalize features, use same normalization factors for testing data as training data '''
ltWeights = ftu.normFeatures( naFeatTrain, -1.0, 1.0, False )
''' Normalize query points with same weights that come from test data '''
ftu.normQuery( naFeatTest[:,:-1], ltWeights )
learnerTest( naFeatTrain, naFeatTest )
{}]
''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data '''
ldfFeatures = ftu.applyFeatures(dData, lfcFeatures, ldArgs)
bPlot = False
if bPlot:
''' Plot feature for XOM '''
for i, fcFunc in enumerate(lfcFeatures[:-1]):
plt.clf()
plt.subplot(211)
plt.title(fcFunc.__name__)
plt.plot(dfPrice.index, dfPrice['XOM'].values, 'r-')
plt.subplot(212)
plt.plot(dfPrice.index, ldfFeatures[i]['XOM'].values, 'g-')
plt.show()
''' Pick Test and Training Points '''
lSplit = int(len(ldtTimestamps) * 0.7)
dtStartTrain = ldtTimestamps[0]
dtEndTrain = ldtTimestamps[lSplit]
dtStartTest = ldtTimestamps[lSplit + 1]
dtEndTest = ldtTimestamps[-1]
''' Stack all information into one Numpy array '''
naFeatTrain = ftu.stackSyms(ldfFeatures, dtStartTrain, dtEndTrain)
naFeatTest = ftu.stackSyms(ldfFeatures, dtStartTest, dtEndTest)
''' Normalize features, use same normalization factors for testing data as training data '''
ltWeights = ftu.normFeatures(naFeatTrain, -1.0, 1.0, False)
''' Normalize query points with same weights that come from test data '''
ftu.normQuery(naFeatTest[:, :-1], ltWeights)
learnerTest(naFeatTrain, naFeatTest)