mainly prettifying some graphs

Author: sborms

cobra/evaluation/evaluator.py

@@ -187,12 +187,14 @@ def plot_roc_curve(self, path: str=None, dim: tuple=(12, 8)):
                     label="ROC curve (area = {s:.3})".format(s=auc))
 
             ax.plot([0, 1], [0, 1], color="darkorange", linewidth=3,
-                    linestyle="--")
-            ax.set_xlabel("False Positive Rate", fontsize=15)
-            ax.set_ylabel("True Positive Rate", fontsize=15)
+                    linestyle="--", label="random selection")
+            ax.set_xlabel("False positive rate", fontsize=15)
+            ax.set_ylabel("True positive rate", fontsize=15)
             ax.legend(loc="lower right")
             ax.set_title("ROC curve", fontsize=20)
 
+            ax.set_ylim([0, 1])
+
             if path:
                 plt.savefig(path, format="png", dpi=300, bbox_inches="tight")
 
@@ -224,6 +226,8 @@ def plot_confusion_matrix(self, path: str=None, dim: tuple=(12, 8),
                          fmt="s", cmap="Blues",
                          xticklabels=labels, yticklabels=labels)
         ax.set_title("Confusion matrix", fontsize=20)
+        plt.ylabel('True labels', fontsize=15)
+        plt.xlabel('Predicted labels', fontsize=15)
 
         if path:
             plt.savefig(path, format="png", dpi=300, bbox_inches="tight")
@@ -256,13 +260,13 @@ def plot_cumulative_response_curve(self, path: str=None, dim: tuple=(12, 8)):
 
             plt.bar(x_labels[::-1], lifts, align="center",
                     color="cornflowerblue")
-            plt.ylabel("response (%)", fontsize=16)
-            plt.xlabel("decile", fontsize=16)
+            plt.ylabel("Response (%)", fontsize=15)
+            plt.xlabel("Decile", fontsize=15)
             ax.set_xticks(x_labels)
             ax.set_xticklabels(x_labels)
 
             plt.axhline(y=inc_rate*100, color="darkorange", linestyle="--",
-                        xmin=0.05, xmax=0.95, linewidth=3, label="Incidence")
+                        xmin=0.05, xmax=0.95, linewidth=3, label="incidence")
 
             # Legend
             ax.legend(loc="upper right")
@@ -305,13 +309,13 @@ def plot_lift_curve(self, path: str=None, dim: tuple=(12, 8)):
 
             plt.bar(x_labels[::-1], lifts, align="center",
                     color="cornflowerblue")
-            plt.ylabel("lift", fontsize=16)
-            plt.xlabel("decile", fontsize=16)
+            plt.ylabel("Lift", fontsize=15)
+            plt.xlabel("Decile", fontsize=15)
             ax.set_xticks(x_labels)
             ax.set_xticklabels(x_labels)
 
             plt.axhline(y=1, color="darkorange", linestyle="--",
-                        xmin=0.05, xmax=0.95, linewidth=3, label="Baseline")
+                        xmin=0.05, xmax=0.95, linewidth=3, label="baseline")
 
             # Legend
             ax.legend(loc="upper right")
@@ -354,7 +358,9 @@ def plot_cumulative_gains(self, path: str=None, dim: tuple=(12, 8)):
 
             # Format axes
             ax.set_xlim([0, 100])
-            ax.set_ylim([0, 105])
+            ax.set_ylim([0, 100])
+            plt.ylabel("Gain", fontsize=15)
+            plt.xlabel("Percentage", fontsize=15)
 
             # Format ticks
             ticks_loc_y = ax.get_yticks().tolist()

cobra/evaluation/pigs_tables.py

@@ -70,7 +70,7 @@ def compute_pig_table(basetable: pd.DataFrame,
     global_avg_target = basetable[target_column_name].mean()
 
     # group by the binned variable, compute the incidence
-    # (=mean of the target for the given bin) and compute the bin size
+    # (= mean of the target for the given bin) and compute the bin size
     # (e.g. COUNT(id_column_name)). After that, rename the columns
     res = (basetable.groupby(predictor_column_name)
            .agg({target_column_name: "mean", id_column_name: "size"})
@@ -165,9 +165,9 @@ def plot_incidence(pig_tables: pd.DataFrame,
         ax.plot(np.nan, "#939598", linewidth=6, label='bin size')
 
         # Set labels & ticks
-        ax.set_ylabel('incidence' if model_type == "classification" else "mean target value",
+        ax.set_ylabel('Incidence' if model_type == "classification" else "Mean target value",
                       fontsize=16)
-        ax.set_xlabel('{} bins' ''.format(variable), fontsize=16)
+        ax.set_xlabel("Bins", fontsize=15)
         ax.xaxis.set_tick_params(labelsize=14)
         plt.setp(ax.get_xticklabels(),
                  rotation=45, ha="right", rotation_mode="anchor")
@@ -210,13 +210,13 @@ def plot_incidence(pig_tables: pd.DataFrame,
                 align='center', color="#939598", zorder=1)
 
         # Set labels & ticks
-        ax2.set_xlabel('{} bins' ''.format(variable), fontsize=16)
+        ax2.set_xlabel("Bins", fontsize=15)
         ax2.xaxis.set_tick_params(rotation=45, labelsize=14)
 
         ax2.yaxis.set_tick_params(labelsize=14)
         ax2.yaxis.set_major_formatter(
             FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
-        ax2.set_ylabel('population size', fontsize=16)
+        ax2.set_ylabel('Population size', fontsize=15)
         ax2.tick_params(axis='y', colors="#939598")
         ax2.yaxis.label.set_color('#939598')
 
@@ -229,10 +229,11 @@ def plot_incidence(pig_tables: pd.DataFrame,
 
         # Title & legend
         if model_type == "classification":
-            title = "Incidence plot - " + variable
+            title = "Incidence plot"
         else:
-            title = "Mean target plot - " + variable
-        fig.suptitle(title, fontsize=22)
+            title = "Mean target plot"
+        fig.suptitle(title, fontsize=20)
+        plt.title(variable, fontsize=17)
         ax.legend(frameon=False, bbox_to_anchor=(0., 1.01, 1., .102),
                   loc=3, ncol=1, mode="expand", borderaxespad=0.,
                   prop={"size": 14})

cobra/evaluation/plotting_utils.py

@@ -47,13 +47,17 @@ def plot_univariate_predictor_quality(df_metric: pd.DataFrame,
 
         # Set pretty axis
         sns.despine(ax=ax, right=True)
+        plt.ylabel("Predictor", fontsize=15)
+        plt.xlabel(metric, fontsize=15)
 
         # Remove white lines from the second axis
         ax.grid(False)
 
         if path is not None:
             plt.savefig(path, format="png", dpi=300, bbox_inches="tight")
 
+        plt.gca().legend().set_title("")
+
         plt.show()
 
 def plot_correlation_matrix(df_corr: pd.DataFrame,
@@ -71,7 +75,7 @@ def plot_correlation_matrix(df_corr: pd.DataFrame,
         Path to store the figure.
     """
     fig, ax = plt.subplots(figsize=dim)
-    ax = sns.heatmap(df_corr, cmap='Blues')
+    ax = sns.heatmap(df_corr, cmap="Blues")
     ax.set_title("Correlation matrix", fontsize=20)
 
     if path is not None:
@@ -150,7 +154,7 @@ def plot_performance_curves(model_performance: pd.DataFrame,
         fig.suptitle('Performance curves forward feature selection',
                      fontsize=20)
         plt.title("Metric: "+metric_name, fontsize=15, loc="left")
-        plt.ylabel('Model performance')
+        plt.ylabel('Model performance', fontsize=15)
 
         if path is not None:
             plt.savefig(path, format="png", dpi=300, bbox_inches="tight")
@@ -184,8 +188,10 @@ def plot_variable_importance(df_variable_importance: pd.DataFrame,
         else:
             ax.set_title("Variable importance", fontsize=20)
 
-        # Set Axis - make them pretty
+        # Make pretty axis
         sns.despine(ax=ax, right=True)
+        plt.ylabel('Predictor', fontsize=15)
+        plt.xlabel('Importance', fontsize=15)
 
         # Remove white lines from the second axis
         ax.grid(False)

tutorials/tutorial_Cobra_linear_regression.ipynb

@@ -350,9 +350,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### The first part focusses on preparing the predictors for modelling by:\n",
+    "#### The first part focuses on preparing the predictors for modelling by:\n",
     "\n",
-    "1. Defining the ID column, the target, discrete and contineous variables.\n",
+    "1. Defining the ID column, the target, discrete and continuous variables.\n",
     "\n",
     "2. Splitting the dataset into training, selection and validation datasets.\n",
     "\n",