bring back plot_importance for compatibility (#274)

* bring back plot_importance for compatibility

* allow overwriting image in project.set_function()

mlrun/mlutils/__init__.py

@@ -12,7 +12,8 @@
                     roc_multi,
                     roc_bin,
                     precision_recall_bin,
-                    plot_roc)
+                    plot_roc,
+                    plot_importance)
 
 from .data import (get_sample,
                    get_splits,

mlrun/mlutils/plots.py

@@ -43,6 +43,47 @@ def feature_importances(model, header):
             TableArtifact("feature-importances-tbl", df=feature_imp))
 
 
+def plot_importance(
+    context,
+    model,
+    key: str = "feature-importances",
+    plots_dest: str = "plots"
+):
+    """Display estimated feature importances
+    Only works for models with attribute 'feature_importances_`
+
+    **legacy version please deprecate in functions and demos**
+
+    :param context:     function context
+    :param model:       fitted model
+    :param key:         key of feature importances plot and table in artifact
+                        store
+    :param plots_dest:  subfolder  in artifact store
+    """
+    if not hasattr(model, "feature_importances_"):
+        raise Exception(
+            "feature importaces are only available for some models")
+
+    # create a feature importance table with desired labels
+    zipped = zip(model.feature_importances_, context.header)
+    feature_imp = pd.DataFrame(sorted(zipped), columns=["freq", "feature"]).sort_values(
+        by="freq", ascending=False
+    )
+
+    gcf_clear(plt)
+    plt.figure(figsize=(20, 10))
+    sns.barplot(x="freq", y="feature", data=feature_imp)
+    plt.title("features")
+    plt.tight_layout()
+
+    fname = f"{plots_dest}/{key}.html"
+    context.log_artifact(PlotArtifact(key, body=plt.gcf()), local_path=fname)
+
+    # feature importances are also saved as a csv table (generally small):
+    fname = key + "-tbl.csv"
+    return context.log_artifact(TableArtifact(key + "-tbl", df=feature_imp), local_path=fname)
+
+
 def learning_curves(model):
     """model class dependent
     

mlrun/projects/project.py

@@ -408,6 +408,11 @@ def set_function(self, func, name='', kind='', image=None, with_repo=None):
             name, f = _init_function_from_dict(func, self)
         elif hasattr(func, 'to_dict'):
             name, f = _init_function_from_obj(func, self, name=name)
+            if image:
+                f.spec.image = image
+            if with_repo:
+                f.spec.build.source = './'
+
             if not name:
                 raise ValueError('function name must be specified')
         else:
@@ -690,6 +695,8 @@ def _init_function_from_dict(f, project):
     elif url.endswith('.yaml') or url.startswith('db://') \
             or url.startswith('hub://'):
         func = import_function(url)
+        if image:
+            func.spec.image = image
     elif url.endswith('.ipynb'):
         func = code_to_function(name, filename=url, image=image, kind=kind)
     elif url.endswith('.py'):
@@ -723,7 +730,6 @@ def _init_function_from_obj(func, project, name=None):
         func.metadata.project = project.name
     if project.tag:
         func.metadata.tag = project.tag
-
     return name or func.metadata.name, func