add: silent_print df_print_nolimit

commom/__init__.py

@@ -8,8 +8,10 @@ from .logger import get_logger
 from .placeholder_func import f_fill_placeholder
 from .user_exceptions import GeneralException
 from .utils import f_get_clazz_in_module, f_clazz_to_json, f_get_date, f_get_datetime, f_save_train_df, f_format_float, \
-    f_df_to_image, f_display_images_by_side, NumpyEncoder, f_display_title, f_image_crop_white_borders, f_is_number
+    f_df_to_image, f_display_images_by_side, NumpyEncoder, f_display_title, f_image_crop_white_borders, f_is_number, \
+    silent_print, df_print_nolimit
 
 __all__ = ['f_get_clazz_in_module', 'f_clazz_to_json', 'GeneralException', 'get_logger', 'f_fill_placeholder',
            'f_get_date', 'f_get_datetime', 'f_save_train_df', 'f_format_float', 'f_df_to_image',
-           'f_display_images_by_side', 'f_display_title', 'NumpyEncoder', 'f_image_crop_white_borders', 'f_is_number']
+           'f_display_images_by_side', 'f_display_title', 'NumpyEncoder', 'f_image_crop_white_borders', 'f_is_number',
+           'silent_print','df_print_nolimit']

commom/utils.py

@@ -9,6 +9,8 @@ import base64
 import datetime
 import inspect
 import os
+import sys
+from contextlib import contextmanager
 from json import JSONEncoder
 from typing import Union
 
@@ -175,3 +177,31 @@ class NumpyEncoder(JSONEncoder):
         if isinstance(obj, np.ndarray):
             return obj.tolist()
         return super(NumpyEncoder, self).default(obj)
+
+
+@contextmanager
+def silent_print():
+    original_stdout = sys.stdout
+
+    class NullWriter:
+        def write(self, text):
+            pass
+
+    null_writer = NullWriter()
+    sys.stdout = null_writer
+    try:
+        yield
+    finally:
+        sys.stdout = original_stdout
+
+@contextmanager
+def df_print_nolimit():
+    max_columns = pd.get_option('display.max_columns')
+    max_rows = pd.get_option('display.max_rows')
+    pd.set_option('display.max_columns', None)
+    pd.set_option('display.max_rows', None)
+    try:
+        yield
+    finally:
+        pd.set_option('display.max_columns', max_columns)
+        pd.set_option('display.max_rows', max_rows)

online_learning/trainer_xgb.py

@@ -16,7 +16,7 @@ from sklearn2pmml import PMMLPipeline
 from tqdm import tqdm
 
 from commom import GeneralException, f_image_crop_white_borders, f_df_to_image, f_display_title, \
-    f_display_images_by_side
+    f_display_images_by_side, silent_print, df_print_nolimit
 from entitys import DataSplitEntity, OnlineLearningConfigEntity, MetricFucResultEntity
 from enums import ResultCodesEnum, ConstantEnum, FileEnum
 from init import init
@@ -164,10 +164,11 @@ class OnlineLearningTrainerXgb:
             random_state=self._ol_config.random_state,
         )
         self._pipeline_optimized.steps[-1] = ("classifier", model_optimized)
-        self._pipeline_optimized.fit(train_data, train_data[y_column],
-                                     classifier__verbose=False,
-                                     classifier__xgb_model=model_original.get_booster(),
-                                     )
+        with silent_print():
+            self._pipeline_optimized.fit(train_data, train_data[y_column],
+                                         classifier__verbose=False,
+                                         classifier__xgb_model=model_original.get_booster(),
+                                         )
         return ntree
 
     def train(self, ):
@@ -182,6 +183,13 @@ class OnlineLearningTrainerXgb:
             n = n + 1
             test_y_prob = self._pipeline_optimized.predict_proba(test_data, ntree_limit=n)[:, 1]
             test_y = test_data[y_column]
+
+            # auc_test = roc_auc_score(test_y, test_y_prob)
+            # auc_test = round(auc_test, 4)
+            # df = pd.DataFrame({'label': test_y, 'pred': test_y_prob})
+            # dfkslift = eva_dfkslift(df)
+            # ks_test = round(dfkslift["ks"].max(), 4)
+
             perf = sc.perf_eva(test_y, test_y_prob, show_plot=False)
             auc_test = perf["AUC"]
             ks_test = perf["KS"]
@@ -210,7 +218,8 @@ class OnlineLearningTrainerXgb:
         if self._ol_config.jupyter_print:
             from IPython import display
             f_display_title(display, "模型优化过程")
-            display.display(self._df_param_optimized)
+            with df_print_nolimit():
+                display.display(self._df_param_optimized)
 
         metric_value_dict = {}