BUG: Fix a future issue in ExpSmooth

Ensure inputs are sanitized to prevent pandas Series from entering

statsmodels/tsa/holtwinters.py

@@ -21,8 +21,10 @@
 from scipy.stats import boxcox
 
 from statsmodels.base.model import Results
-from statsmodels.base.wrapper import populate_wrapper, union_dicts, ResultsWrapper
-from statsmodels.tools.validation import array_like
+from statsmodels.base.wrapper import (populate_wrapper, union_dicts,
+                                      ResultsWrapper)
+from statsmodels.tools.validation import (array_like, bool_like, float_like,
+                                          string_like, int_like)
 from statsmodels.tsa.base.tsa_model import TimeSeriesModel
 from statsmodels.tsa.tsatools import freq_to_period
 import statsmodels.tsa._exponential_smoothers as smoothers
@@ -488,10 +490,14 @@ def __init__(self, endog, trend=None, damped=False, seasonal=None,
         self.endog = self.endog
         self._y = self._data = array_like(endog, 'endog', contiguous=True,
                                           order='C')
+        options = ("add", "mul", "additive", "multiplicative")
+        trend = string_like(trend, 'trend', options=options, optional=True)
         if trend in ['additive', 'multiplicative']:
             trend = {'additive': 'add', 'multiplicative': 'mul'}[trend]
         self.trend = trend
-        self.damped = damped
+        self.damped = bool_like(damped, 'damped')
+        seasonal = string_like(seasonal, 'seasonal', options=options,
+                               optional=True)
         if seasonal in ['additive', 'multiplicative']:
             seasonal = {'additive': 'add', 'multiplicative': 'mul'}[seasonal]
         self.seasonal = seasonal
@@ -504,7 +510,8 @@ def __init__(self, endog, trend=None, damped=False, seasonal=None,
         if self.damped and not self.trending:
             raise ValueError('Can only dampen the trend component')
         if self.seasoning:
-            self.seasonal_periods = seasonal_periods
+            self.seasonal_periods = int_like(seasonal_periods,
+                                             'seasonal_periods', optional=True)
             if seasonal_periods is None:
                 self.seasonal_periods = freq_to_period(self._index_freq)
             if self.seasonal_periods <= 1:
@@ -608,13 +615,15 @@ def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=Non
         """
         # Variable renames to alpha,beta, etc as this helps with following the
         # mathematical notation in general
-        alpha = smoothing_level
-        beta = smoothing_slope
-        gamma = smoothing_seasonal
-        phi = damping_slope
-        l0 = self._l0 = initial_level
-        b0 = self._b0 = initial_slope
-
+        alpha = float_like(smoothing_level, 'smoothing_level', True)
+        beta = float_like(smoothing_slope, 'smoothing_slope', True)
+        gamma = float_like(smoothing_seasonal, 'smoothing_seasonal', True)
+        phi = float_like(damping_slope, 'damping_slope', True)
+        l0 = self._l0 = float_like(initial_level, 'initial_level', True)
+        b0 = self._b0 = float_like(initial_slope, 'initial_slope', True)
+        if start_params is not None:
+            start_params = array_like(start_params, 'start_params',
+                                      contiguous=True)
         data = self._data
         damped = self.damped
         seasoning = self.seasoning
@@ -675,18 +684,22 @@ def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=Non
                 txi = txi.astype(np.bool)
                 bounds = np.array([(0.0, 1.0), (0.0, 1.0), (0.0, 1.0),
                                    (0.0, None), (0.0, None), (0.0, 1.0)] + [(None, None), ] * m)
-                args = (txi.astype(np.uint8), p, y, lvls, b, s, m, self.nobs, max_seen)
+                args = (txi.astype(np.uint8), p, y, lvls, b, s, m, self.nobs,
+                        max_seen)
                 if start_params is None and np.any(txi) and use_brute:
-                    res = brute(func, bounds[txi], args, Ns=20, full_output=True, finish=None)
+                    res = brute(func, bounds[txi], args, Ns=20,
+                                full_output=True, finish=None)
                     p[txi], max_seen, _, _ = res
                 else:
                     if start_params is not None:
-                        start_params = np.atleast_1d(np.squeeze(start_params))
                         if len(start_params) != xi.sum():
-                            raise ValueError('start_params must have {0} values but '
-                                             'has {1} instead'.format(len(xi), len(start_params)))
+                            msg = 'start_params must have {0} values but ' \
+                                  'has {1} instead'
+                            nxi, nsp = len(xi), len(start_params)
+                            raise ValueError(msg.format(nxi, nsp))
                         p[xi] = start_params
-                    args = (xi.astype(np.uint8), p, y, lvls, b, s, m, self.nobs, max_seen)
+                    args = (xi.astype(np.uint8), p, y, lvls, b, s, m,
+                            self.nobs, max_seen)
                     max_seen = func(np.ascontiguousarray(p[xi]), *args)
                 # alpha, beta, gamma, l0, b0, phi = p[:6]
                 # s0 = p[6:]

statsmodels/tsa/tests/test_holtwinters.py

@@ -374,7 +374,7 @@ def test_negative_multipliative(trend_seasonal):
 @pytest.mark.parametrize('seasonal', SEASONALS)
 def test_dampen_no_trend(seasonal):
     y = -np.ones(100)
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         ExponentialSmoothing(housing_data, trend=False, seasonal=seasonal, damped=True,
                              seasonal_periods=10)
 

statsmodels/tsa/tsatools.py

@@ -804,4 +804,4 @@ def freq_to_period(freq):
 
 __all__ = ['lagmat', 'lagmat2ds','add_trend', 'duplication_matrix',
            'elimination_matrix', 'commutation_matrix',
-           'vec', 'vech', 'unvec', 'unvech']
+           'vec', 'vech', 'unvec', 'unvech', 'freq_to_period']