Coverage for mlinsights/timeseries/dummies.py : 100%
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1"""
2@file
3@brief Dummy auto-regressor which takes past values as predictions.
4"""
5import numpy
6from .base import BaseTimeSeries, TimeSeriesRegressorMixin
7from .utils import check_ts_X_y
10class DummyTimeSeriesRegressor(BaseTimeSeries, TimeSeriesRegressorMixin):
11 """
12 Dummy regressor for time series. Use past values as prediction.
13 """
15 def __init__(self, estimator="dummy", past=1, delay1=1, delay2=2,
16 use_all_past=False, preprocessing=None):
17 """
18 @param estimator estimator to use for regression,
19 :epkg:`sklearn:linear_model:LinearRegression`
20 implements a linear auto-regressor,
21 ``'dummy'`` use past value as predictions
22 @param past values to use to predict
23 @param delay1 the model computes the first prediction for
24 *time=t + delay1*
25 @param delay2 the model computes the last prediction for
26 *time=t + delay2* excluded
27 @param use_all_past use all past features, not only the timeseries
28 @param preprocessing preprocessing to apply before predicting,
29 only the timeseries itselves, it can be
30 a difference, it must be of type
31 @see cl BaseReciprocalTimeSeriesTransformer
32 """
33 TimeSeriesRegressorMixin.__init__(self)
34 BaseTimeSeries.__init__(self, past=past, delay1=delay1, delay2=delay2,
35 use_all_past=use_all_past, preprocessing=preprocessing)
37 def fit(self, X, y, sample_weight=None):
38 """
39 Trains the model.
41 :param X: output of
42 X may be empty (None)
43 :param y: timeseries (one single vector), array [n_obs]
44 :param sample_weight: weights None or array [n_obs]
45 :return: self
46 """
47 X, y, _ = self._base_fit_predict(X, y, sample_weight)
48 check_ts_X_y(self, X, y)
49 return self
51 def predict(self, X, y):
52 """
53 Returns the prediction
54 """
55 X, y, _ = self._base_fit_predict(X, y, None)
56 check_ts_X_y(self, X, y)
57 nbrow = X.shape[0]
58 X, y = self._applies_preprocessing(X, y, None)[:2]
59 nb = self.delay2 - self.delay1
60 pred = numpy.empty((nbrow, nb), dtype=X.dtype)
61 first = nbrow - X.shape[0]
62 pred[:first] = numpy.nan
63 for i in range(0, nb):
64 pred[first:, i] = X[:, -1]
65 return pred