UPD: beautify

examples/regression/2-advanced-analysis/plot_conditional_coverage.py

@@ -239,7 +239,7 @@ def plot_intervals(X, y, y_pred, intervals, title="", ax=None):
  ax.legend()
 
 
-def plot_coverage_by_width(y, intervals, num_bins, title="", ax=None):
+def plot_coverage_by_width(y, intervals, num_bins, alpha, title="", ax=None):
  """
  PLots a bar diagram of coverages by groups of interval widths.
 
@@ -251,6 +251,8 @@ def plot_coverage_by_width(y, intervals, num_bins, title="", ax=None):
  Intervals of prediction
  num_bins: int
  Number of groups of interval widths
+ alpha: float
+ The risk level
  title: str
  Title of the plot
  ax: matplotlib axes
@@ -263,6 +265,7 @@ def plot_coverage_by_width(y, intervals, num_bins, title="", ax=None):
  np.arange(num_bins),
  regression_ssc(y, intervals, num_bins=num_bins)[0]
  )
+ ax.axhline(y=1 - alpha, color='r', linestyle='-')
  ax.set_title(title)
  ax.set_xlabel("intervals grouped by size")
  ax.set_ylabel("coverage")
@@ -288,7 +291,7 @@ def plot_coverage_by_width(y, intervals, num_bins, title="", ax=None):
  )
  plot_coverage_by_width(
  y_test, intervals[strategy],
- num_bins=num_bins, title=strategy, ax=ax_hist
+ num_bins=num_bins, alpha=alpha, title=strategy, ax=ax_hist
  )
 
  ax_distr.hist(

examples/regression/4-tutorials/plot_crf_tutorial.py

@@ -85,7 +85,7 @@
 
 
 ##############################################################################
-# 1. Models
+# 2. Models
 # --------------------------------------------------------------------------
 # We will now define 4 different ways of using the CRF score. Remember that the
 # CRF score is only available in the split setup. First, the simplest one