From: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Date: Sat, 2 Mar 2019 14:59:35 +0000 (+0000)
Subject: Stop using no-longer-existing pd.ols
X-Git-Tag: archive/raspbian/0.11.1-2+rpi1~2^2^2~2
X-Git-Url: https://dgit.raspbian.org/%22http://www.example.com/cgi/success//%22http:/www.example.com/cgi/success/?a=commitdiff_plain;h=5481dd0e4e9e0e260183f8d30eefe816a1acf0d6;p=statsmodels.git

Stop using no-longer-existing pd.ols

Fixes
module 'pandas' has no attribute 'ols'
e.g. in the vector_ar documentation examples.

Origin: upstream https://github.com/statsmodels/statsmodels/commit/4d76116c9c3f47c629503837b3db91bec4d22a5a
Author: Kevin "bashtage" Sheppard
Forwarded: not-needed


Gbp-Pq: Name vector_ar_window_ols.patch
---

diff --git a/statsmodels/tsa/vector_ar/dynamic.py b/statsmodels/tsa/vector_ar/dynamic.py
index e48610c..7e51ccd 100644
--- a/statsmodels/tsa/vector_ar/dynamic.py
+++ b/statsmodels/tsa/vector_ar/dynamic.py
@@ -1,19 +1,128 @@
 # pylint: disable=W0201
 
-from statsmodels.compat.python import iteritems, string_types, range
 import numpy as np
-from statsmodels.tools.decorators import cache_readonly
 import pandas as pd
 
-from . import var_model as _model
-from . import util
-from . import plotting
+from statsmodels.compat.python import iteritems, string_types, range
+from statsmodels.regression.linear_model import OLS
+from statsmodels.tools.decorators import cache_readonly
+from statsmodels.tools.tools import Bunch
+from statsmodels.tsa.vector_ar import plotting
+from statsmodels.tsa.vector_ar import util
+from statsmodels.tsa.vector_ar import var_model as _model
 
 FULL_SAMPLE = 0
 ROLLING = 1
 EXPANDING = 2
 
 
+def _window_ols(y, x, window=None, window_type=None, min_periods=None):
+    """
+    Minimal replacement for pandas ols that provides the required features
+
+    Parameters
+    ----------
+    y : pd.Series
+        Endogenous variable
+    x : pd.DataFrame
+        Exogenous variables, always adds a constant
+    window: {None, int}
+        
+    window_type : {str, int}
+    min_periods : {None, int}
+
+    Returns
+    -------
+    results : Bunch
+        Bunch containing parameters (beta), R-squared (r2), nobs and
+        residuals (resid)
+    """
+    # Must return beta, r2, resid, nobs
+    if window_type == FULL_SAMPLE:
+        window_type = 'full_sample'
+    elif window_type == ROLLING:
+        window_type = 'rolling'
+    elif window_type == EXPANDING:
+        window_type = 'expanding'
+
+    if window_type in ('rolling', 'expanding') and window is None:
+        window = y.shape[0]
+    min_periods = 1 if min_periods is None else min_periods
+    window_type = 'full_sample' if window is None else window_type
+    window_type = 'rolling' if window_type is None else window_type
+    if window_type == 'rolling':
+        min_periods = window
+
+    if window_type not in ('full_sample', 'rolling', 'expanding'):
+        raise ValueError('Unknown window_type')
+
+    x = x.copy()
+    x['intercept'] = 1.0
+
+    bunch = Bunch()
+    if window_type == 'full_sample':
+        missing = y.isnull() | x.isnull().any(1)
+        y = y.loc[~missing]
+        x = x.loc[~missing]
+
+        res = OLS(y, x).fit()
+        bunch['beta'] = res.params
+        bunch['r2'] = res.rsquared
+        bunch['nobs'] = res.nobs
+        bunch['resid'] = res.resid
+        return bunch
+
+    index = y.index
+    columns = x.columns
+    n = y.shape[0]
+    k = x.shape[1]
+
+    beta = pd.DataFrame(np.zeros((n, k)),
+                        columns=columns,
+                        index=index)
+    r2 = pd.Series(np.zeros(n), index=index)
+    nobs = r2.copy().astype(np.int)
+    resid = r2.copy()
+    valid = r2.copy().astype(np.bool)
+
+    if window_type == 'rolling':
+        start = window
+    else:
+        start = min_periods
+    for i in range(start, y.shape[0] + 1):
+        # i is right edge, as in y[:i] for expanding
+        if window_type == 'rolling':
+            left = max(0, i - window)
+            sel = slice(left, i)
+        else:
+            sel = slice(i)
+        _y = y[sel]
+        _x = x[sel]
+        missing = _y.isnull() | _x.isnull().any(1)
+        if missing.any():
+            if (~missing).sum() < min_periods:
+                continue
+            else:
+                _y = _y.loc[~missing]
+                _x = _x.loc[~missing]
+        if _y.shape[0] <= _x.shape[1]:
+            continue
+        if window_type == 'expanding' and missing.values[-1]:
+            continue
+        res = OLS(_y, _x).fit()
+        valid.iloc[i - 1] = True
+        beta.iloc[i - 1] = res.params
+        r2.iloc[i - 1] = res.rsquared
+        nobs.iloc[i - 1] = int(res.nobs)
+        resid.iloc[i - 1] = res.resid.iloc[-1]
+
+    bunch['beta'] = beta.loc[valid]
+    bunch['r2'] = r2.loc[valid]
+    bunch['nobs'] = nobs.loc[valid]
+    bunch['resid'] = resid.loc[valid]
+    return bunch
+
+
 def _get_window_type(window_type):
     if window_type in (FULL_SAMPLE, ROLLING, EXPANDING):
         return window_type
@@ -112,10 +221,9 @@ class DynamicVAR(object):
     def equations(self):
         eqs = {}
         for col, ts in iteritems(self.y):
-            # TODO: Remove in favor of statsmodels implemetation
-            model = pd.ols(y=ts, x=self.x, window=self._window,
-                           window_type=self._window_type,
-                           min_periods=self._min_periods)
+            model = _window_ols(y=ts, x=self.x, window=self._window,
+                                window_type=self._window_type,
+                                min_periods=self._min_periods)
 
             eqs[col] = model