Initial configuration commit

typings/seaborn/regression.pyi

@@ -0,0 +1,158 @@
+"""
+This type stub file was generated by pyright.
+"""
+
+from ._decorators import _deprecate_positional_args
+
+"""Plotting functions for linear models (broadly construed)."""
+_has_statsmodels = ...
+__all__ = ["lmplot", "regplot", "residplot"]
+class _LinearPlotter:
+    """Base class for plotting relational data in tidy format.
+
+    To get anything useful done you'll have to inherit from this, but setup
+    code that can be abstracted out should be put here.
+
+    """
+    def establish_variables(self, data, **kws): # -> None:
+        """Extract variables from data or use directly."""
+        ...
+    
+    def dropna(self, *vars): # -> None:
+        """Remove observations with missing data."""
+        ...
+    
+    def plot(self, ax):
+        ...
+    
+
+
+class _RegressionPlotter(_LinearPlotter):
+    """Plotter for numeric independent variables with regression model.
+
+    This does the computations and drawing for the `regplot` function, and
+    is thus also used indirectly by `lmplot`.
+    """
+    def __init__(self, x, y, data=..., x_estimator=..., x_bins=..., x_ci=..., scatter=..., fit_reg=..., ci=..., n_boot=..., units=..., seed=..., order=..., logistic=..., lowess=..., robust=..., logx=..., x_partial=..., y_partial=..., truncate=..., dropna=..., x_jitter=..., y_jitter=..., color=..., label=...) -> None:
+        ...
+    
+    @property
+    def scatter_data(self): # -> tuple[ndarray[Any, dtype[Unknown]] | NDArray[floating[Any]], ndarray[Any, dtype[Unknown]] | NDArray[floating[Any]]]:
+        """Data where each observation is a point."""
+        ...
+    
+    @property
+    def estimate_data(self): # -> tuple[list[Any], list[Unknown], list[Unknown]]:
+        """Data with a point estimate and CI for each discrete x value."""
+        ...
+    
+    def fit_regression(self, ax=..., x_range=..., grid=...): # -> tuple[Unknown, Unknown | NDArray[float64] | Any, Unknown | None]:
+        """Fit the regression model."""
+        ...
+    
+    def fit_fast(self, grid): # -> tuple[Any, None] | tuple[Any, Any]:
+        """Low-level regression and prediction using linear algebra."""
+        ...
+    
+    def fit_poly(self, grid, order): # -> tuple[Unknown, None] | tuple[Unknown, NDArray[Unknown]]:
+        """Regression using numpy polyfit for higher-order trends."""
+        ...
+    
+    def fit_statsmodels(self, grid, model, **kwargs): # -> tuple[Unknown | NDArray[float64], None] | tuple[Unknown | NDArray[float64], NDArray[Unknown]]:
+        """More general regression function using statsmodels objects."""
+        ...
+    
+    def fit_lowess(self): # -> tuple[Unknown, Unknown]:
+        """Fit a locally-weighted regression, which returns its own grid."""
+        ...
+    
+    def fit_logx(self, grid): # -> tuple[Any, None] | tuple[Any, Any]:
+        """Fit the model in log-space."""
+        ...
+    
+    def bin_predictor(self, bins): # -> tuple[Any, Any]:
+        """Discretize a predictor by assigning value to closest bin."""
+        ...
+    
+    def regress_out(self, a, b): # -> ndarray[Any, dtype[Unknown]]:
+        """Regress b from a keeping a's original mean."""
+        ...
+    
+    def plot(self, ax, scatter_kws, line_kws): # -> None:
+        """Draw the full plot."""
+        ...
+    
+    def scatterplot(self, ax, kws): # -> None:
+        """Draw the data."""
+        ...
+    
+    def lineplot(self, ax, kws): # -> None:
+        """Draw the model."""
+        ...
+    
+
+
+_regression_docs = ...
+@_deprecate_positional_args
+def lmplot(*, x=..., y=..., data=..., hue=..., col=..., row=..., palette=..., col_wrap=..., height=..., aspect=..., markers=..., sharex=..., sharey=..., hue_order=..., col_order=..., row_order=..., legend=..., legend_out=..., x_estimator=..., x_bins=..., x_ci=..., scatter=..., fit_reg=..., ci=..., n_boot=..., units=..., seed=..., order=..., logistic=..., lowess=..., robust=..., logx=..., x_partial=..., y_partial=..., truncate=..., x_jitter=..., y_jitter=..., scatter_kws=..., line_kws=..., size=...): # -> FacetGrid:
+    ...
+
+@_deprecate_positional_args
+def regplot(*, x=..., y=..., data=..., x_estimator=..., x_bins=..., x_ci=..., scatter=..., fit_reg=..., ci=..., n_boot=..., units=..., seed=..., order=..., logistic=..., lowess=..., robust=..., logx=..., x_partial=..., y_partial=..., truncate=..., dropna=..., x_jitter=..., y_jitter=..., label=..., color=..., marker=..., scatter_kws=..., line_kws=..., ax=...): # -> Axes:
+    ...
+
+@_deprecate_positional_args
+def residplot(*, x=..., y=..., data=..., lowess=..., x_partial=..., y_partial=..., order=..., robust=..., dropna=..., label=..., color=..., scatter_kws=..., line_kws=..., ax=...): # -> Axes:
+    """Plot the residuals of a linear regression.
+
+    This function will regress y on x (possibly as a robust or polynomial
+    regression) and then draw a scatterplot of the residuals. You can
+    optionally fit a lowess smoother to the residual plot, which can
+    help in determining if there is structure to the residuals.
+
+    Parameters
+    ----------
+    x : vector or string
+        Data or column name in `data` for the predictor variable.
+    y : vector or string
+        Data or column name in `data` for the response variable.
+    data : DataFrame, optional
+        DataFrame to use if `x` and `y` are column names.
+    lowess : boolean, optional
+        Fit a lowess smoother to the residual scatterplot.
+    {x, y}_partial : matrix or string(s) , optional
+        Matrix with same first dimension as `x`, or column name(s) in `data`.
+        These variables are treated as confounding and are removed from
+        the `x` or `y` variables before plotting.
+    order : int, optional
+        Order of the polynomial to fit when calculating the residuals.
+    robust : boolean, optional
+        Fit a robust linear regression when calculating the residuals.
+    dropna : boolean, optional
+        If True, ignore observations with missing data when fitting and
+        plotting.
+    label : string, optional
+        Label that will be used in any plot legends.
+    color : matplotlib color, optional
+        Color to use for all elements of the plot.
+    {scatter, line}_kws : dictionaries, optional
+        Additional keyword arguments passed to scatter() and plot() for drawing
+        the components of the plot.
+    ax : matplotlib axis, optional
+        Plot into this axis, otherwise grab the current axis or make a new
+        one if not existing.
+
+    Returns
+    -------
+    ax: matplotlib axes
+        Axes with the regression plot.
+
+    See Also
+    --------
+    regplot : Plot a simple linear regression model.
+    jointplot : Draw a :func:`residplot` with univariate marginal distributions
+                (when used with ``kind="resid"``).
+
+    """
+    ...
+