Chronos: modify examples of .evaluate (intel-analytics#5622)

python/chronos/src/bigdl/chronos/forecaster/base_forecaster.py

@@ -595,14 +595,14 @@ def evaluate(self, data, batch_size=32, multioutput="raw_values", quantize=False
         """
         Evaluate using a trained forecaster.
 
+        If you want to evaluate on a single node(which is common practice), please call
+        .to_local().evaluate(data, ...)
+
         Please note that evaluate result is calculated by scaled y and yhat. If you scaled
-        your data (e.g. use .scale() on the TSDataset) please follow the following code
+        your data (e.g. use .scale() on the TSDataset), please follow the following code
         snap to evaluate your result if you need to evaluate on unscaled data.
 
-        if you want to evaluate on a single node(which is common practice), please call
-        .to_local().evaluate(data, ...)
-
-        >>> from bigdl.orca.automl.metrics import Evaluator
+        >>> from bigdl.chronos.metric.forecast_metrics import Evaluator
         >>> y_hat = forecaster.predict(x)
         >>> y_hat_unscaled = tsdata.unscale_numpy(y_hat) # or other customized unscale methods
         >>> y_unscaled = tsdata.unscale_numpy(y) # or other customized unscale methods
@@ -699,8 +699,8 @@ def evaluate_with_onnx(self, data,
         your data (e.g. use .scale() on the TSDataset) please follow the following code
         snap to evaluate your result if you need to evaluate on unscaled data.
 
-        >>> from bigdl.orca.automl.metrics import Evaluator
-        >>> y_hat = forecaster.predict(x)
+        >>> from bigdl.chronos.metric.forecast_metrics import Evaluator
+        >>> y_hat = forecaster.predict_with_onnx(x)
         >>> y_hat_unscaled = tsdata.unscale_numpy(y_hat) # or other customized unscale methods
         >>> y_unscaled = tsdata.unscale_numpy(y) # or other customized unscale methods
         >>> Evaluator.evaluate(metric=..., y_unscaled, y_hat_unscaled, multioutput=...)

python/chronos/src/bigdl/chronos/metric/forecast_metrics.py

@@ -222,7 +222,7 @@ def get_latency(func, *args, num_running=100, **kwargs):
             >>> # run forecaster.predict(x.numpy()) for len(tsdata_test.df) times
             >>> # to evaluate the time cost
             >>> latency = Evaluator.get_latency(forecaster.predict, x.numpy(),\
-                          num_running = len(tsdata_test.df))
+num_running = len(tsdata_test.df))
             >>> # an example output:
             >>> # {"p50": 3.853, "p90": 3.881, "p95": 3.933, "p99": 4.107}
         """