Add option to save figure to plotting_utils

Author: MatthiasRoelsPython

cobra/evaluation/evaluator.py

@@ -381,8 +381,8 @@ def _compute_optimal_cutoff(fpr: np.ndarray, tpr: np.ndarray,
     @staticmethod
     def _compute_cumulative_gains(y_true: np.ndarray,
                                   y_pred: np.ndarray) -> tuple:
-        """Compute lift of the model per decile, returns x-labels, lifts and
-        the target incidence to create cummulative response curves
+        """Compute cumulative gains of the model, returns percentages and
+        gains cummulative gains curves
 
         Code from (https://github.com/reiinakano/scikit-plot/blob/
                    2dd3e6a76df77edcbd724c4db25575f70abb57cb/
@@ -398,7 +398,7 @@ def _compute_cumulative_gains(y_true: np.ndarray,
         Returns
         -------
         tuple
-            x-labels, lifts per decile and target incidence
+            x-labels, gains
         """
 
         # make y_true a boolean vector

cobra/evaluation/plotting_utils.py

@@ -45,7 +45,8 @@ def plot_univariate_predictor_quality(df_auc: pd.DataFrame,
 
 
 def plot_correlation_matrix(df_corr: pd.DataFrame,
-                            dim: tuple=(12, 8)):
+                            dim: tuple=(12, 8),
+                            path: str=None):
     """Plot correlation matrix amongst the predictors
 
     Parameters
@@ -54,10 +55,16 @@ def plot_correlation_matrix(df_corr: pd.DataFrame,
         Correlation matrix
     dim : tuple, optional
         tuple with width and lentgh of the plot
+    path : str, optional
+        path to store the figure
     """
     fig, ax = plt.subplots(figsize=dim)
     ax = sns.heatmap(df_corr, cmap='Blues')
     ax.set_title('Correlation Matrix')
+
+    if path is not None:
+        plt.savefig(path, format="png", dpi=300, bbox_inches="tight")
+
     plt.show()
 
 
@@ -111,7 +118,8 @@ def plot_performance_curves(model_performance: pd.DataFrame,
 
 def plot_variable_importance(df_variable_importance: pd.DataFrame,
                              title: str=None,
-                             dim: tuple=(12, 8)):
+                             dim: tuple=(12, 8),
+                             path: str=None):
     """Plot variable importance of a given model
 
     Parameters
@@ -122,6 +130,8 @@ def plot_variable_importance(df_variable_importance: pd.DataFrame,
         Title of the plot
     dim : tuple, optional
         tuple with width and lentgh of the plot
+    path : str, optional
+        path to store the figure
     """
     with plt.style.context("seaborn-ticks"):
         fig, ax = plt.subplots(figsize=dim)
@@ -139,4 +149,7 @@ def plot_variable_importance(df_variable_importance: pd.DataFrame,
         # 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.show()