Removing data in a plot

[ulrich48155]

For a project I needed to calculate and display a 10fold cross-validation on a time-series. After plotting my results look like this:

http://imgur.com/a/15wbF

As you can see, both plots also contain the first fold, which I circled green. This fold is not noteworthy and I would like to remove it. Due to the fact, that I work with time series data my 10fold cross-validation has this structure:

Train 0 - Test 1
Train 1 - Test 2
Train 1,2 - Test 3
Train 1,2,3 - Tet 4
...
Train 1,2,3,4,5,6,7,8,9 - Test 10

My code looks like this:

tscv = TimeSeriesSplit(n_splits=10

X = mergedf['AnzahlTweets']
y = mergedf['Kurs']

X=X.values.reshape(-1,1)
y=y.values.reshape(-1,1)

# Cross-validation
linreg=LinearRegression()
rmse=[]
prediction=np.zeros(y.shape)
for train_index, test_index in tscv.split(X):
   X_train, X_test = X[train_index], X[test_index]
   y_train, y_test = y[train_index], y[test_index]
   linreg.fit(X_train,y_train)
   y_pred=linreg.predict(X_test)
   prediction[test_index]=y_pred
   rmse.append(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))  
   print('RMSE: %.10f' % np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

# Plotting
fig, axes = pl.subplots()
pl.plot(y,label='Actual')
pl.plot(prediction, color='red',label='Predicted',)
pl.ylabel('Price')
pl.xlabel('Fold')
pl.gca().xaxis.grid(True)
pl.setp(axes, xticks=[51,98,145,192,239,286,333,380,427,474,521], xticklabels=['          1','          2','          3', '          4','          5','          6','          7','          8','          9','          10'])
pl.legend()
pl.show()

prediction = prediction[:,0]
y = y[:,0]

m, b = np.polyfit(prediction, y, 1)

plrange=np.arange(0,0.000001,0.00000005)

pl.plot(prediction, y,'ro')
pl.plot(prediction, m*prediction + b)
pl.xlabel('Predicted')
pl.ylabel('Actual')
pl.xlim()
pl.gca().xaxis.grid(True)
pl.show()

Now my question: Is it possible to remove the first fold (Train 0 - Test 1) before plotting?

Thanks in advance!

[zivoni]

Remove first elements from prediction (and y) with slicing when plotting. You can get length of first split either by directly computing it with

skip_size = len(X) - 10 * (len(X) // (10 + 1))   # for n_splits=10

or by using tcsv.split again (or you could do it in your for loop first iteration ...)

skip_size = len(next(tscv.split(X)[0]))

After that its just

pl.plot(y[skip_size:])
...
pl.plot(prediction[skip_size:], y[skip_size:], 'ro')

Your plot is not piecewise linear, so it seems that your time series is not a time series (= data points in time order).

[ulrich48155]

Thank you very much!

Is there a difference between the first and the second approach? When I try to implement the second one into my loop I get this message:

Error:
TypeError: 'generator' object is not subscriptable

[zivoni]

Sorry for late reply, there was misplaced ), it should be:

skip_size = len(next(tscv.split(X))[0])

tcsv.split(X) returns generator object; calling next on it returns tuple of arrays containing indices of first train and test split. We want size of first train split, so [0] is used to extract train split.