Add formula to stat_smooth

doc/changelog.rst

@@ -22,6 +22,9 @@ New Features
   makes it easy to change the ordering of a discrete variable according
   to some other variable/column.
 
+- :class:`~plotnine.stats.stat_smooth` can now use formulae for linear
+  models.
+
 
 Bug Fixes
 *********

doc/conf.py

@@ -410,7 +410,8 @@
     'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
     'sklearn': ('http://scikit-learn.org/stable/', None),
     'skmisc': ('https://has2k1.github.io/scikit-misc/', None),
-    'adjustText': ('https://adjusttext.readthedocs.io/en/latest/', None)
+    'adjustText': ('https://adjusttext.readthedocs.io/en/latest/', None),
+    'patsy': ('https://patsy.readthedocs.io/en/stable', None)
 }
 
 

plotnine/stats/smoothers.py

@@ -5,6 +5,8 @@
 import pandas as pd
 import scipy.stats as stats
 import statsmodels.api as sm
+import statsmodels.formula.api as smf
+from patsy import dmatrices
 
 from ..exceptions import PlotnineError, PlotnineWarning
 from ..utils import get_valid_kwargs
@@ -47,17 +49,25 @@ def lm(data, xseq, **params):
     """
     Fit OLS / WLS if data has weight
     """
+    if params['formula']:
+        return lm_formula(data, xseq, **params)
+
     X = sm.add_constant(data['x'])
     Xseq = sm.add_constant(xseq)
+    weights = data.get('weights', None)
 
-    if 'weight' in data:
-        init_kwargs, fit_kwargs = separate_method_kwargs(
-            params['method_args'], sm.WLS, sm.WLS.fit)
-        model = sm.WLS(data['y'], X, weights=data['weight'], **init_kwargs)
-    else:
+    if weights is None:
         init_kwargs, fit_kwargs = separate_method_kwargs(
             params['method_args'], sm.OLS, sm.OLS.fit)
         model = sm.OLS(data['y'], X, **init_kwargs)
+    else:
+        if np.any(weights < 0):
+            raise ValueError(
+                "All weights must be greater than zero."
+            )
+        init_kwargs, fit_kwargs = separate_method_kwargs(
+            params['method_args'], sm.WLS, sm.WLS.fit)
+        model = sm.WLS(data['y'], X, weights=data['weight'], **init_kwargs)
 
     results = model.fit(**fit_kwargs)
     data = pd.DataFrame({'x': xseq})
@@ -74,10 +84,60 @@ def lm(data, xseq, **params):
     return data
 
 
+def lm_formula(data, xseq, **params):
+    """
+    Fit OLS / WLS using a formula
+    """
+    formula = params['formula']
+    eval_env = params['enviroment']
+    weights = data.get('weight', None)
+
+    if weights is None:
+        init_kwargs, fit_kwargs = separate_method_kwargs(
+            params['method_args'], sm.OLS, sm.OLS.fit)
+        model = smf.ols(
+            formula,
+            data,
+            eval_env=eval_env,
+            **init_kwargs
+        )
+    else:
+        if np.any(weights < 0):
+            raise ValueError(
+                "All weights must be greater than zero."
+            )
+        init_kwargs, fit_kwargs = separate_method_kwargs(
+            params['method_args'], sm.OLS, sm.OLS.fit)
+        model = smf.wls(
+            formula,
+            data,
+            weights=weights,
+            eval_env=eval_env,
+            **init_kwargs
+        )
+
+    results = model.fit(**fit_kwargs)
+    data = pd.DataFrame({'x': xseq})
+    data['y'] = results.predict(data)
+
+    if params['se']:
+        _, predictors = dmatrices(formula, data, eval_env=eval_env)
+        alpha = 1 - params['level']
+        prstd, iv_l, iv_u = wls_prediction_std(
+            results, predictors, alpha=alpha)
+        data['se'] = prstd
+        data['ymin'] = iv_l
+        data['ymax'] = iv_u
+    return data
+
+
 def rlm(data, xseq, **params):
     """
     Fit RLM
     """
+    if params['formula']:
+        return rlm_formula(data, xseq, **params)
+
     X = sm.add_constant(data['x'])
     Xseq = sm.add_constant(xseq)
 
@@ -96,10 +156,38 @@ def rlm(data, xseq, **params):
     return data
 
 
+def rlm_formula(data, xseq, **params):
+    """
+    Fit RLM using a formula
+    """
+    eval_env = params['enviroment']
+    formula = params['formula']
+    init_kwargs, fit_kwargs = separate_method_kwargs(
+        params['method_args'], sm.RLM, sm.RLM.fit)
+    model = smf.rlm(
+        formula,
+        data,
+        eval_env=eval_env,
+        **init_kwargs
+    )
+    results = model.fit(**fit_kwargs)
+    data = pd.DataFrame({'x': xseq})
+    data['y'] = results.predict(data)
+
+    if params['se']:
+        warnings.warn("Confidence intervals are not yet implemented"
+                      "for RLM smoothing.", PlotnineWarning)
+
+    return data
+
+
 def gls(data, xseq, **params):
     """
     Fit GLS
     """
+    if params['formula']:
+        return gls_formula(data, xseq, **params)
+
     X = sm.add_constant(data['x'])
     Xseq = sm.add_constant(xseq)
 
@@ -122,10 +210,42 @@ def gls(data, xseq, **params):
     return data
 
 
+def gls_formula(data, xseq, **params):
+    """
+    Fit GLL using a formula
+    """
+    eval_env = params['enviroment']
+    formula = params['formula']
+    init_kwargs, fit_kwargs = separate_method_kwargs(
+        params['method_args'], sm.GLS, sm.GLS.fit)
+    model = smf.gls(
+        formula,
+        data,
+        eval_env=eval_env,
+        **init_kwargs
+    )
+    results = model.fit(**fit_kwargs)
+    data = pd.DataFrame({'x': xseq})
+    data['y'] = results.predict(data)
+
+    if params['se']:
+        _, predictors = dmatrices(formula, data, eval_env=eval_env)
+        alpha = 1 - params['level']
+        prstd, iv_l, iv_u = wls_prediction_std(
+            results, predictors, alpha=alpha)
+        data['se'] = prstd
+        data['ymin'] = iv_l
+        data['ymax'] = iv_u
+    return data
+
+
 def glm(data, xseq, **params):
     """
     Fit GLM
     """
+    if params['formula']:
+        return glm_formula(data, xseq, **params)
+
     X = sm.add_constant(data['x'])
     Xseq = sm.add_constant(xseq)
 
@@ -146,6 +266,29 @@ def glm(data, xseq, **params):
     return data
 
 
+def glm_formula(data, xseq, **params):
+    eval_env = params['enviroment']
+    init_kwargs, fit_kwargs = separate_method_kwargs(
+        params['method_args'], sm.GLM, sm.GLM.fit)
+    model = smf.glm(
+        params['formula'],
+        data,
+        eval_env=eval_env,
+        **init_kwargs
+    )
+    results = model.fit(**fit_kwargs)
+    data = pd.DataFrame({'x': xseq})
+    data['y'] = results.predict(data)
+
+    if params['se']:
+        df = pd.DataFrame({'x': xseq})
+        prediction = results.get_prediction(df)
+        ci = prediction.conf_int(1 - params['level'])
+        data['ymin'] = ci[:, 0]
+        data['ymax'] = ci[:, 1]
+    return data
+
+
 def lowess(data, xseq, **params):
     for k in ('is_sorted', 'return_sorted'):
         with suppress(KeyError):
@@ -284,7 +427,10 @@ def tdist_ci(x, df, stderr, level):
     Confidence Intervals using the t-distribution
     """
     q = (1 + level)/2
-    delta = stats.t.ppf(q, df) * stderr
+    if df is None:
+        delta = stats.norm.ppf(q) * stderr
+    else:
+        delta = stats.t.ppf(q, df) * stderr
     return x - delta, x + delta
 
 

plotnine/stats/stat.py

@@ -38,6 +38,9 @@ class stat(metaclass=Registry):
     # their default values.
     _aesthetics_doc = '{aesthetics_table}'
 
+    # The namespace in which the ggplot object is invoked
+    environment = None
+
     def __init__(self, mapping=None, data=None, **kwargs):
         kwargs = data_mapping_as_kwargs((mapping, data), kwargs)
         self._kwargs = kwargs  # Will be used to create the geom
@@ -107,9 +110,12 @@ def __deepcopy__(self, memo):
         old = self.__dict__
         new = result.__dict__
 
+        # don't make a _kwargs and environment
+        shallow = {'_kwargs', 'environment'}
         for key, item in old.items():
-            if key == '_kwargs':
+            if key in shallow:
                 new[key] = old[key]
+                memo[id(new[key])] = new[key]
             else:
                 new[key] = deepcopy(old[key], memo)
 
@@ -358,6 +364,7 @@ def __radd__(self, gg, inplace=False):
             ggplot object with added layer
         """
         gg = gg if inplace else deepcopy(gg)
+        self.environment = gg.environment
         gg += self.to_layer()  # Add layer
         return gg
 

plotnine/stats/stat_smooth.py

@@ -76,6 +76,12 @@ def my_smoother(data, xseq, **params):
                     data['ymax'] = high
 
                 return data
+    formula : formula_like
+        An object that can be used to construct a patsy design matrix.
+        This is usually a string. You can only use a formula if ``method``
+        is one of *lm*, *ols*, *wls*, *glm*, *rlm* or *gls*, and in the
+        :ref:`formula <patsy:formulas>` you may refer to the ``x`` and
+        ``y`` aesthetic variables.
     se : bool (default: True)
         If :py:`True` draw confidence interval around the smooth line.
     n : int (default: 80)
@@ -131,6 +137,7 @@ def my_smoother(data, xseq, **params):
     DEFAULT_PARAMS = {'geom': 'smooth', 'position': 'identity',
                       'na_rm': False,
                       'method': 'auto', 'se': True, 'n': 80,
+                      'formula': None,
                       'fullrange': False, 'level': 0.95,
                       'span': 0.75, 'method_args': {}}
     CREATES = {'se', 'ymin', 'ymax'}
@@ -168,6 +175,15 @@ def setup_params(self, data):
                     "facets".format(window), PlotnineWarning)
                 params['method_args']['window'] = window
 
+        if params['formula']:
+            allowed = {'lm', 'ols', 'wls', 'glm', 'rlm', 'gls'}
+            if params['method'] not in allowed:
+                raise ValueError(
+                    "You can only use a formula with `method` is "
+                    "one of {}".format(allowed)
+                )
+            params['enviroment'] = self.environment
+
         return params
 
     @classmethod

plotnine/tests/test_geom_smooth.py

@@ -4,7 +4,7 @@
 import statsmodels.api as sm
 
 
-from plotnine import ggplot, aes, geom_point, geom_smooth
+from plotnine import ggplot, aes, geom_point, geom_smooth, stat_smooth
 from plotnine.exceptions import PlotnineWarning
 
 
@@ -185,3 +185,69 @@ def test_init_and_fit_kwargs():
          )
 
     assert p == 'init_and_fit_kwargs'
+
+
+n = 100
+random_state = np.random.RandomState(123)
+mu = 0
+sigma = 0.065
+noise = random_state.randn(n)*sigma + mu
+df = pd.DataFrame({
+    'x': x,
+    'y': np.sin(x) + noise,
+})
+
+
+class TestFormula:
+
+    p = ggplot(df, aes('x', 'y')) + geom_point()
+
+    def test_lm(self):
+        p = (self.p
+             + stat_smooth(
+                 method='lm',
+                 formula='y ~ np.sin(x)',
+                 fill='red',
+                 se=True
+             ))
+        assert p == 'lm_formula'
+
+    def test_lm_weights(self):
+        p = (self.p
+             + aes(weight='x.abs()')
+             + stat_smooth(
+                 method='lm',
+                 formula='y ~ np.sin(x)',
+                 fill='red',
+                 se=True
+             ))
+        assert p == 'lm_formula_weights'
+
+    def test_glm(self):
+        p = (self.p
+             + stat_smooth(
+                 method='glm',
+                 formula='y ~ np.sin(x)',
+                 fill='red',
+                 se=True
+             ))
+        assert p == 'glm_formula'
+
+    def test_rlm(self):
+        p = (self.p
+             + stat_smooth(
+                 method='rlm',
+                 formula='y ~ np.sin(x)',
+                 fill='red',
+             ))
+        assert p == 'rlm_formula'
+
+    def test_gls(self):
+        p = (self.p
+             + stat_smooth(
+                 method='gls',
+                 formula='y ~ np.sin(x)',
+                 fill='red',
+                 se=True
+             ))
+        assert p == 'gls_formula'