improve error message for required packages (#4304)

examples/python-guide/plot_example.py

@@ -6,7 +6,7 @@ import lightgbm as lgb
 if lgb.compat.MATPLOTLIB_INSTALLED:
     import matplotlib.pyplot as plt
 else:
-    raise ImportError('You need to install matplotlib for plot_example.py.')
+    raise ImportError('You need to install matplotlib and restart your session for plot_example.py.')
 
 print('Loading data...')
 # load or create your dataset

python-package/lightgbm/basic.py

@@ -2085,7 +2085,8 @@ class Dataset:
             elif isinstance(self.data, pd_DataFrame):
                 if not PANDAS_INSTALLED:
                     raise LightGBMError("Cannot add features to DataFrame type of raw data "
-                                        "without pandas installed")
+                                        "without pandas installed. "
+                                        "Install pandas and restart your session.")
                 if isinstance(other.data, np.ndarray):
                     self.data = concat((self.data, pd_DataFrame(other.data)),
                                        axis=1, ignore_index=True)
@@ -2402,7 +2403,8 @@ class Booster:
             Returns a pandas DataFrame of the parsed model.
         """
         if not PANDAS_INSTALLED:
-            raise LightGBMError('This method cannot be run without pandas installed')
+            raise LightGBMError('This method cannot be run without pandas installed. '
+                                'You must install pandas and restart your session to use this method.')
 
         if self.num_trees() == 0:
             raise LightGBMError('There are no trees in this Booster and thus nothing to parse')

python-package/lightgbm/plotting.py

@@ -80,7 +80,7 @@ def plot_importance(booster, ax=None, height=0.2,
     if MATPLOTLIB_INSTALLED:
         import matplotlib.pyplot as plt
     else:
-        raise ImportError('You must install matplotlib to plot importance.')
+        raise ImportError('You must install matplotlib and restart your session to plot importance.')
 
     if isinstance(booster, LGBMModel):
         booster = booster.booster_
@@ -197,7 +197,7 @@ def plot_split_value_histogram(booster, feature, bins=None, ax=None, width_coef=
         import matplotlib.pyplot as plt
         from matplotlib.ticker import MaxNLocator
     else:
-        raise ImportError('You must install matplotlib to plot split value histogram.')
+        raise ImportError('You must install matplotlib and restart your session to plot split value histogram.')
 
     if isinstance(booster, LGBMModel):
         booster = booster.booster_
@@ -294,7 +294,7 @@ def plot_metric(booster, metric=None, dataset_names=None,
     if MATPLOTLIB_INSTALLED:
         import matplotlib.pyplot as plt
     else:
-        raise ImportError('You must install matplotlib to plot metric.')
+        raise ImportError('You must install matplotlib and restart your session to plot metric.')
 
     if isinstance(booster, LGBMModel):
         eval_results = deepcopy(booster.evals_result_)
@@ -602,7 +602,7 @@ def plot_tree(booster, ax=None, tree_index=0, figsize=None, dpi=None,
         import matplotlib.image as image
         import matplotlib.pyplot as plt
     else:
-        raise ImportError('You must install matplotlib to plot tree.')
+        raise ImportError('You must install matplotlib and restart your session to plot tree.')
 
     if ax is None:
         if figsize is not None:

python-package/lightgbm/sklearn.py

@@ -461,7 +461,8 @@ class LGBMModel(_LGBMModelBase):
         and you should group grad and hess in this way as well.
         """
         if not SKLEARN_INSTALLED:
-            raise LightGBMError('scikit-learn is required for lightgbm.sklearn')
+            raise LightGBMError('scikit-learn is required for lightgbm.sklearn. '
+                                'You must install scikit-learn and restart your session to use this module.')
 
         self.boosting_type = boosting_type
         self.objective = objective