Tim b files

src/scripts/viz/__init__.py

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+# -*- coding: utf-8 -*-
+"""
+Visualization module for PyLogit.
+"""
+from __future__ import absolute_import
+
+from .utils import (progress,
+                    is_categorical,
+                    simulate_choice_vector,
+                    compute_predictive_log_likelihoods,
+                    compute_predictive_mse)
+from .sim_cdf import plot_simulated_cdfs
+from .sim_kde import plot_simulated_kdes
+from .cont_scalars import plot_continous_scalars
+from .disc_scalars import plot_discrete_scalars
+from .market import plot_simulated_market_shares
+from .reliability import plot_smoothed_reliability
+from .marginal import plot_smoothed_marginal

src/scripts/viz/cont_scalars.py

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+# -*- coding: utf-8 -*-
+"""
+Functions for plotting simulated vs observed, continuous scalars.
+"""
+from __future__ import absolute_import
+
+import numpy as np
+import seaborn as sbn
+import matplotlib.pyplot as plt
+
+from .plot_utils import _label_despine_save_and_show_plot
+from .plot_utils import _plot_single_cdf_on_axis
+
+# Set the plotting style
+sbn.set_style('darkgrid')
+
+
+def plot_continous_scalars(sim_scalars,
+                           obs_scalar,
+                           kde=True,
+                           fig_and_ax=None,
+                           figsize=(10, 6),
+                           sim_color='#a6bddb',
+                           sim_label='Simulated',
+                           obs_label='Observed',
+                           x_label='Log-Likelihood',
+                           y_label='Density',
+                           fontsize=12,
+                           title=None,
+                           output_file=None,
+                           dpi=500,
+                           show=True):
+    """
+    For a given continuous scalar, this function plots the distribution of
+    predicted scalars versus the observed scalars.
+
+    Parameters
+    ----------
+    sim_scalars : 1D ndarray of floats.
+        The array of simulated scalar values, with 1 value per simulation.
+    obs_scalar : int, float, or long.
+        The scalar value for one's model with the observed outcomes.
+    kde : bool, optional.
+        Determines whether a kernel density estimate is plotted. If
+        `kde=False`, a cumulative density plot is made.
+    fig_and_ax : list of matplotlib figure and axis, or `None`, optional.
+        Determines whether a new figure will be created for the plot or whether
+        the plot will be drawn on existing axes. If None, a new figure will be
+        created. Default is `None`.
+    figsize : 2-tuple of positive ints.
+        Determines the size of the created figure. Default == (5, 3).
+    sim_color : valid 'color' argument for matplotlib, optional.
+        The colors that will be used to plot the simulated and observed KDEs,
+        respectively. Default is `sim_color == '#a6bddb'`.
+    sim_label, obs_label, x_label, y_label, : str, or None, optional.
+        Denotes the label to be used for the lines denoting the simulated and
+        observed scalars, the x-axis, and the y-axis. Defaults are
+        `['Simulated', 'Observed', 'Log-Likelihood', 'Density']`.
+    fontsize : int or None, optional.
+        The fontsize to be used in the plot. Default is 12.
+    title : string or None, optional.
+        Denotes the title to be displayed for the plot. Default is None.
+    output_file : str, or None, optional.
+        Denotes the relative or absolute filepath (including the file format)
+        that is to be used to save the plot. If None, the plot will not be
+        saved to file. Default is None.
+    dpi : positive int, optional.
+        Denotes the number of 'dots per inch' for the saved figure. Will only
+        be used if `output_file is not None`. Default == 500.
+    show : bool, optional.
+        Determines whether the figure is shown after plotting is complete.
+        Default == True.
+
+    Returns
+    -------
+    None.
+    """
+    # Create or access the figure and axis on which the plot is to be drawn.
+    if fig_and_ax is None:
+        fig, axis = plt.subplots(1, figsize=figsize)
+        fig_and_ax = [fig, axis]
+    else:
+        fig, axis = fig_and_ax
+
+    # Plot the distribution of scalars
+    if kde:
+        # Check to make sure that we have data and no NaNs
+        msg = None
+        if sim_scalars.size == 0:
+            msg = "`sim_scalars` has no rows."
+        elif np.isnan(sim_scalars).sum() > 0:
+            msg = "`sim_scalars` contains NaNs"
+        if msg is not None:
+            raise ValueError(msg)
+        sbn.kdeplot(sim_scalars, ax=axis, label=sim_label)
+    else:
+        _plot_single_cdf_on_axis(sim_scalars, axis,
+                                 color=sim_color, linestyle='-',
+                                 label=sim_label, alpha=1.0)
+
+    # Figure out the axis boundaries
+    min_y, max_y = axis.get_ylim()
+
+    # Calculate the percentile corresponding to the observed scalar
+    simulated_frac_below_observed =\
+        (sim_scalars < obs_scalar).sum() / float(sim_scalars.size)
+
+    # Create the vertical line to show the observed scalar
+    line_label = obs_label + '\nP(samples < observed) = {:.0%}'
+    axis.vlines(obs_scalar, min_y, max_y, linestyle='dashed',
+                label=line_label.format(simulated_frac_below_observed))
+
+    # Create the plot legend
+    axis.legend(loc='best', fontsize=fontsize)
+
+    # Take care of boilerplate plotting necessities
+    _label_despine_save_and_show_plot(
+        x_label=x_label, y_label=y_label, fig_and_ax=fig_and_ax,
+        fontsize=fontsize, y_rot=0, y_pad=40, title=title,
+        output_file=output_file, show=show, dpi=dpi)
+    return None