change the print format for dataset and instance

fastNLP/core/dataset.py

@@ -300,13 +300,14 @@ from .field import FieldArray
 from .field import SetInputOrTargetException
 from .instance import Instance
 from .utils import _get_func_signature
+from .utils import pretty_table_printer
 
 
 class DataSet(object):
     """
     fastNLP的数据容器，详细的使用方法见文档  :doc:`fastNLP.core.dataset`
     """
-    
+
     def __init__(self, data=None):
         """
         
@@ -326,26 +327,26 @@ class DataSet(object):
                 for ins in data:
                     assert isinstance(ins, Instance), "Must be Instance type, not {}.".format(type(ins))
                     self.append(ins)
-            
+
             else:
                 raise ValueError("data only be dict or list type.")
-    
+
     def __contains__(self, item):
         return item in self.field_arrays
-    
+
     def __iter__(self):
         def iter_func():
             for idx in range(len(self)):
                 yield self[idx]
-        
+
         return iter_func()
-    
+
     def _inner_iter(self):
         class Iter_ptr:
             def __init__(self, dataset, idx):
                 self.dataset = dataset
                 self.idx = idx
-            
+
             def __getitem__(self, item):
                 assert item in self.dataset.field_arrays, "no such field:{} in Instance {}".format(item, self.dataset[
                     self.idx])
@@ -358,13 +359,13 @@ class DataSet(object):
 
             def __repr__(self):
                 return self.dataset[self.idx].__repr__()
-        
+
         def inner_iter_func():
             for idx in range(len(self)):
                 yield Iter_ptr(self, idx)
-        
+
         return inner_iter_func()
-    
+
     def __getitem__(self, idx):
         """给定int的index，返回一个Instance; 给定slice，返回包含这个slice内容的新的DataSet。
 
@@ -397,20 +398,20 @@ class DataSet(object):
             return dataset
         else:
             raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
-    
+
     def __getattr__(self, item):
         # Not tested. Don't use !!
         if item == "field_arrays":
             raise AttributeError
         if isinstance(item, str) and item in self.field_arrays:
             return self.field_arrays[item]
-    
+
     def __setstate__(self, state):
         self.__dict__ = state
-    
+
     def __getstate__(self):
         return self.__dict__
-    
+
     def __len__(self):
         """Fetch the length of the dataset.
 
@@ -420,16 +421,10 @@ class DataSet(object):
             return 0
         field = iter(self.field_arrays.values()).__next__()
         return len(field)
-    
-    def __inner_repr__(self):
-        if len(self) < 20:
-            return ",\n".join([ins.__repr__() for ins in self])
-        else:
-            return self[:5].__inner_repr__() + "\n...\n" + self[-5:].__inner_repr__()
-    
+
     def __repr__(self):
-        return "DataSet(" + self.__inner_repr__() + ")"
-    
+        return str(pretty_table_printer(self))
+
     def append(self, instance):
         """
         将一个instance对象append到DataSet后面。
@@ -454,7 +449,7 @@ class DataSet(object):
                 except AppendToTargetOrInputException as e:
                     logger.error(f"Cannot append to field:{name}.")
                     raise e
-    
+
     def add_fieldarray(self, field_name, fieldarray):
         """
         将fieldarray添加到DataSet中.
@@ -469,7 +464,7 @@ class DataSet(object):
             raise RuntimeError(f"The field to add must have the same size as dataset. "
                                f"Dataset size {len(self)} != field size {len(fieldarray)}")
         self.field_arrays[field_name] = fieldarray
-    
+
     def add_field(self, field_name, fields, padder=AutoPadder(), is_input=False, is_target=False, ignore_type=False):
         """
         新增一个field
@@ -481,14 +476,14 @@ class DataSet(object):
         :param bool is_target: 新加入的field是否是target
         :param bool ignore_type: 是否忽略对新加入的field的类型检查
         """
-        
+
         if len(self.field_arrays) != 0:
             if len(self) != len(fields):
                 raise RuntimeError(f"The field to add must have the same size as dataset. "
                                    f"Dataset size {len(self)} != field size {len(fields)}")
         self.field_arrays[field_name] = FieldArray(field_name, fields, is_target=is_target, is_input=is_input,
                                                    padder=padder, ignore_type=ignore_type)
-    
+
     def delete_instance(self, index):
         """
         删除第index个instance
@@ -504,7 +499,7 @@ class DataSet(object):
             for field in self.field_arrays.values():
                 field.pop(index)
         return self
-    
+
     def delete_field(self, field_name):
         """
         删除名为field_name的field
@@ -538,7 +533,7 @@ class DataSet(object):
         if isinstance(field_name, str):
             return field_name in self.field_arrays
         return False
-    
+
     def get_field(self, field_name):
         """
         获取field_name这个field
@@ -549,7 +544,7 @@ class DataSet(object):
         if field_name not in self.field_arrays:
             raise KeyError("Field name {} not found in DataSet".format(field_name))
         return self.field_arrays[field_name]
-    
+
     def get_all_fields(self):
         """
         返回一个dict，key为field_name, value为对应的 :class:`~fastNLP.FieldArray`
@@ -557,7 +552,7 @@ class DataSet(object):
         :return dict: 返回如上所述的字典
         """
         return self.field_arrays
-    
+
     def get_field_names(self) -> list:
         """
         返回一个list，包含所有 field 的名字
@@ -565,7 +560,7 @@ class DataSet(object):
         :return list: 返回如上所述的列表
         """
         return sorted(self.field_arrays.keys())
-    
+
     def get_length(self):
         """
         获取DataSet的元素数量
@@ -573,7 +568,7 @@ class DataSet(object):
         :return: int: DataSet中Instance的个数。
         """
         return len(self)
-    
+
     def rename_field(self, field_name, new_field_name):
         """
         将某个field重新命名.
@@ -587,7 +582,7 @@ class DataSet(object):
         else:
             raise KeyError("DataSet has no field named {}.".format(field_name))
         return self
-    
+
     def set_target(self, *field_names, flag=True, use_1st_ins_infer_dim_type=True):
         """
         将field_names的field设置为target
@@ -614,7 +609,7 @@ class DataSet(object):
             else:
                 raise KeyError("{} is not a valid field name.".format(name))
         return self
-    
+
     def set_input(self, *field_names, flag=True, use_1st_ins_infer_dim_type=True):
         """
         将field_names的field设置为input::
@@ -638,7 +633,7 @@ class DataSet(object):
             else:
                 raise KeyError("{} is not a valid field name.".format(name))
         return self
-    
+
     def set_ignore_type(self, *field_names, flag=True):
         """
         将field设置为忽略类型状态。当某个field被设置了ignore_type, 则在被设置为target或者input时将不进行类型检查，
@@ -655,7 +650,7 @@ class DataSet(object):
             else:
                 raise KeyError("{} is not a valid field name.".format(name))
         return self
-    
+
     def set_padder(self, field_name, padder):
         """
         为field_name设置padder::
@@ -671,7 +666,7 @@ class DataSet(object):
             raise KeyError("There is no field named {}.".format(field_name))
         self.field_arrays[field_name].set_padder(padder)
         return self
-    
+
     def set_pad_val(self, field_name, pad_val):
         """
         为某个field设置对应的pad_val.
@@ -683,7 +678,7 @@ class DataSet(object):
             raise KeyError("There is no field named {}.".format(field_name))
         self.field_arrays[field_name].set_pad_val(pad_val)
         return self
-    
+
     def get_input_name(self):
         """
         返回所有is_input被设置为True的field名称
@@ -691,7 +686,7 @@ class DataSet(object):
         :return list: 里面的元素为被设置为input的field名称
         """
         return [name for name, field in self.field_arrays.items() if field.is_input]
-    
+
     def get_target_name(self):
         """
         返回所有is_target被设置为True的field名称
@@ -699,7 +694,7 @@ class DataSet(object):
         :return list: 里面的元素为被设置为target的field名称
         """
         return [name for name, field in self.field_arrays.items() if field.is_target]
-    
+
     def apply_field(self, func, field_name, new_field_name=None, **kwargs):
         """
         将DataSet中的每个instance中的名为 `field_name` 的field传给func，并获取它的返回值。
@@ -728,16 +723,16 @@ class DataSet(object):
                 results.append(func(ins[field_name]))
         except Exception as e:
             if idx != -1:
-                logger.error("Exception happens at the `{}`th(from 1) instance.".format(idx+1))
+                logger.error("Exception happens at the `{}`th(from 1) instance.".format(idx + 1))
             raise e
         if not (new_field_name is None) and len(list(filter(lambda x: x is not None, results))) == 0:  # all None
             raise ValueError("{} always return None.".format(_get_func_signature(func=func)))
-        
+
         if new_field_name is not None:
             self._add_apply_field(results, new_field_name, kwargs)
-        
+
         return results
-    
+
     def _add_apply_field(self, results, new_field_name, kwargs):
         """
         将results作为加入到新的field中，field名称为new_field_name
@@ -769,7 +764,7 @@ class DataSet(object):
             self.add_field(field_name=new_field_name, fields=results, is_input=extra_param.get("is_input", None),
                            is_target=extra_param.get("is_target", None),
                            ignore_type=extra_param.get("ignore_type", False))
-    
+
     def apply(self, func, new_field_name=None, **kwargs):
         """
         将DataSet中每个instance传入到func中，并获取它的返回值.
@@ -801,13 +796,13 @@ class DataSet(object):
         # results = [func(ins) for ins in self._inner_iter()]
         if not (new_field_name is None) and len(list(filter(lambda x: x is not None, results))) == 0:  # all None
             raise ValueError("{} always return None.".format(_get_func_signature(func=func)))
-        
+
         if new_field_name is not None:
             self._add_apply_field(results, new_field_name, kwargs)
-        
+
         return results
 
-    def add_seq_len(self, field_name:str, new_field_name=Const.INPUT_LEN):
+    def add_seq_len(self, field_name: str, new_field_name=Const.INPUT_LEN):
         """
         将使用len()直接对field_name中每个元素作用，将其结果作为seqence length, 并放入seq_len这个field。
 
@@ -844,7 +839,7 @@ class DataSet(object):
                 return dataset
             else:
                 return DataSet()
-    
+
     def split(self, ratio, shuffle=True):
         """
         将DataSet按照ratio的比例拆分，返回两个DataSet
@@ -870,9 +865,9 @@ class DataSet(object):
         for field_name in self.field_arrays:
             train_set.field_arrays[field_name].to(self.field_arrays[field_name])
             dev_set.field_arrays[field_name].to(self.field_arrays[field_name])
-        
+
         return train_set, dev_set
-    
+
     def save(self, path):
         """
         保存DataSet.
@@ -881,7 +876,7 @@ class DataSet(object):
         """
         with open(path, 'wb') as f:
             pickle.dump(self, f)
-    
+
     @staticmethod
     def load(path):
         r"""

fastNLP/core/instance.py

@@ -3,10 +3,13 @@ instance 模块实现了Instance 类在fastNLP中对应sample。一个sample可
 便于理解的例子可以参考文档 :doc:`fastNLP.core.dataset` 中的表格
 
 """
+
 __all__ = [
     "Instance"
 ]
 
+from .utils import pretty_table_printer
+
 
 class Instance(object):
     """
@@ -20,11 +23,11 @@ class Instance(object):
         >>>ins.add_field("field_3", [3, 3, 3])
         >>>ins = Instance(**{'x1': 1, 'x2':np.zeros((3, 4))})
     """
-    
+
     def __init__(self, **fields):
-        
+
         self.fields = fields
-    
+
     def add_field(self, field_name, field):
         """
         向Instance中增加一个field
@@ -41,18 +44,15 @@ class Instance(object):
         :return: 一个迭代器
         """
         return self.fields.items()
-    
+
     def __getitem__(self, name):
         if name in self.fields:
             return self.fields[name]
         else:
             raise KeyError("{} not found".format(name))
-    
+
     def __setitem__(self, name, field):
         return self.add_field(name, field)
-    
+
     def __repr__(self):
-        s = '\''
-        return "{" + ",\n".join(
-            "\'" + field_name + "\': " + str(self.fields[field_name]) + \
-            f" type={(str(type(self.fields[field_name]))).split(s)[1]}" for field_name in self.fields) + "}"
+        return str(pretty_table_printer(self))

fastNLP/core/utils.py

@@ -1,6 +1,7 @@
 """
 utils模块实现了 fastNLP 内部和外部所需的很多工具。其中用户可以使用的是 :func:`cache_results` 修饰器。
 """
+
 __all__ = [
     "cache_results",
     "seq_len_to_mask",
@@ -12,12 +13,12 @@ import inspect
 import os
 import warnings
 from collections import Counter, namedtuple
-
 import numpy as np
 import torch
 import torch.nn as nn
 from typing import List
 from ._logger import logger
+from prettytable import PrettyTable
 
 _CheckRes = namedtuple('_CheckRes', ['missing', 'unused', 'duplicated', 'required', 'all_needed',
                                      'varargs'])
@@ -25,27 +26,27 @@ _CheckRes = namedtuple('_CheckRes', ['missing', 'unused', 'duplicated', 'require
 
 class Option(dict):
     """a dict can treat keys as attributes"""
-    
+
     def __getattr__(self, item):
         try:
             return self.__getitem__(item)
         except KeyError:
             raise AttributeError(item)
-    
+
     def __setattr__(self, key, value):
         if key.startswith('__') and key.endswith('__'):
             raise AttributeError(key)
         self.__setitem__(key, value)
-    
+
     def __delattr__(self, item):
         try:
             self.pop(item)
         except KeyError:
             raise AttributeError(item)
-    
+
     def __getstate__(self):
         return self
-    
+
     def __setstate__(self, state):
         self.update(state)
 
@@ -112,13 +113,13 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
     :param int _verbose: 是否打印cache的信息。
     :return:
     """
-    
+
     def wrapper_(func):
         signature = inspect.signature(func)
         for key, _ in signature.parameters.items():
             if key in ('_cache_fp', '_refresh', '_verbose'):
                 raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
-        
+
         def wrapper(*args, **kwargs):
             if '_cache_fp' in kwargs:
                 cache_filepath = kwargs.pop('_cache_fp')
@@ -136,7 +137,7 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
             else:
                 verbose = _verbose
             refresh_flag = True
-            
+
             if cache_filepath is not None and refresh is False:
                 # load data
                 if os.path.exists(cache_filepath):
@@ -145,7 +146,7 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
                     if verbose == 1:
                         logger.info("Read cache from {}.".format(cache_filepath))
                     refresh_flag = False
-            
+
             if refresh_flag:
                 results = func(*args, **kwargs)
                 if cache_filepath is not None:
@@ -155,11 +156,11 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
                     with open(cache_filepath, 'wb') as f:
                         _pickle.dump(results, f)
                     logger.info("Save cache to {}.".format(cache_filepath))
-            
+
             return results
-        
+
         return wrapper
-    
+
     return wrapper_
 
 
@@ -187,6 +188,7 @@ def _save_model(model, model_name, save_dir, only_param=False):
         torch.save(model, model_path)
         model.to(_model_device)
 
+
 def _move_model_to_device(model, device):
     """
     将model移动到device
@@ -211,7 +213,7 @@ def _move_model_to_device(model, device):
     """
     # if isinstance(model, torch.nn.parallel.DistributedDataParallel):
     #     raise RuntimeError("model of `torch.nn.parallel.DistributedDataParallel` is not supported right now.")
-    
+
     if device is None:
         if isinstance(model, torch.nn.DataParallel):
             model.cuda()
@@ -220,10 +222,10 @@ def _move_model_to_device(model, device):
         if not torch.cuda.is_available() and (
                 device != 'cpu' or (isinstance(device, torch.device) and device.type != 'cpu')):
             raise ValueError("There is no usable gpu. set `device` as `cpu` or `None`.")
-    
+
     if isinstance(model, torch.nn.DataParallel):
         raise RuntimeError("When model is `torch.nn.DataParallel`, the device has to be `None`.")
-    
+
     if isinstance(device, int):
         assert device > -1, "device can only be non-negative integer"
         assert torch.cuda.device_count() > device, "Only has {} gpus, cannot use device {}.".format(
@@ -267,7 +269,7 @@ def _get_model_device(model):
     """
     # TODO 这个函数存在一定的风险，因为同一个模型可能存在某些parameter不在显卡中，比如BertEmbedding. 或者跨显卡
     assert isinstance(model, nn.Module)
-    
+
     parameters = list(model.parameters())
     if len(parameters) == 0:
         return None
@@ -427,10 +429,10 @@ def _move_dict_value_to_device(*args, device: torch.device, non_blocking=False):
     """
     if not torch.cuda.is_available():
         return
-    
+
     if not isinstance(device, torch.device):
         raise TypeError(f"device must be `torch.device`, got `{type(device)}`")
-    
+
     for arg in args:
         if isinstance(arg, dict):
             for key, value in arg.items():
@@ -445,10 +447,10 @@ class _CheckError(Exception):
 
     _CheckError. Used in losses.LossBase, metrics.MetricBase.
     """
-    
+
     def __init__(self, check_res: _CheckRes, func_signature: str):
         errs = [f'Problems occurred when calling `{func_signature}`']
-        
+
         if check_res.varargs:
             errs.append(f"\tvarargs: {check_res.varargs}(Does not support pass positional arguments, please delete it)")
         if check_res.missing:
@@ -457,9 +459,9 @@ class _CheckError(Exception):
             errs.append(f"\tduplicated param: {check_res.duplicated}")
         if check_res.unused:
             errs.append(f"\tunused param: {check_res.unused}")
-        
+
         Exception.__init__(self, '\n'.join(errs))
-        
+
         self.check_res = check_res
         self.func_signature = func_signature
 
@@ -479,7 +481,7 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
     # if check_res.varargs:
     #     errs.append(f"\tvarargs: *{check_res.varargs}")
     #     suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
-    
+
     if check_res.unused:
         for _unused in check_res.unused:
             if _unused in target_dict:
@@ -490,7 +492,7 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
             unuseds.append(f"\tunused field: {_unused_field}")
         if _unused_param:
             unuseds.append(f"\tunused param: {_unused_param}")  # output from predict or forward
-    
+
     module_name = func_signature.split('.')[0]
     if check_res.missing:
         errs.append(f"\tmissing param: {check_res.missing}")
@@ -511,7 +513,7 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
                     mapped_missing.append(_miss)
             else:
                 unmapped_missing.append(_miss)
-        
+
         for _miss in mapped_missing + unmapped_missing:
             if _miss in dataset:
                 suggestions.append(f"Set `{_miss}` as target.")
@@ -524,17 +526,17 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
                 else:
                     _tmp = f'Provide `{_miss}` in DataSet or output of {prev_func_signature}.'
                 suggestions.append(_tmp)
-    
+
     if check_res.duplicated:
         errs.append(f"\tduplicated param: {check_res.duplicated}.")
         suggestions.append(f"Delete {check_res.duplicated} in the output of "
                            f"{prev_func_signature} or do not set {check_res.duplicated} as targets. ")
-    
+
     if len(errs) > 0:
         errs.extend(unuseds)
     elif check_level == STRICT_CHECK_LEVEL:
         errs.extend(unuseds)
-    
+
     if len(errs) > 0:
         errs.insert(0, f'Problems occurred when calling {func_signature}')
         sugg_str = ""
@@ -561,11 +563,11 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
 def _check_forward_error(forward_func, batch_x, dataset, check_level):
     check_res = _check_arg_dict_list(forward_func, batch_x)
     func_signature = _get_func_signature(forward_func)
-    
+
     errs = []
     suggestions = []
     _unused = []
-    
+
     # if check_res.varargs:
     #     errs.append(f"\tvarargs: {check_res.varargs}")
     #     suggestions.append(f"Does not support pass positional arguments, please delete *{check_res.varargs}.")
@@ -586,14 +588,14 @@ def _check_forward_error(forward_func, batch_x, dataset, check_level):
             #     _tmp += f"Or you might find it in `unused field:`, you can use DataSet.rename_field() to " \
             #             f"rename the field in `unused field:`."
             suggestions.append(_tmp)
-    
+
     if check_res.unused:
         _unused = [f"\tunused field: {check_res.unused}"]
         if len(errs) > 0:
             errs.extend(_unused)
         elif check_level == STRICT_CHECK_LEVEL:
             errs.extend(_unused)
-    
+
     if len(errs) > 0:
         errs.insert(0, f'Problems occurred when calling {func_signature}')
         sugg_str = ""
@@ -641,7 +643,7 @@ def seq_len_to_mask(seq_len, max_len=None):
         max_len = int(max_len) if max_len else int(seq_len.max())
         broad_cast_seq_len = np.tile(np.arange(max_len), (len(seq_len), 1))
         mask = broad_cast_seq_len < seq_len.reshape(-1, 1)
-    
+
     elif isinstance(seq_len, torch.Tensor):
         assert seq_len.dim() == 1, f"seq_len can only have one dimension, got {seq_len.dim() == 1}."
         batch_size = seq_len.size(0)
@@ -650,7 +652,7 @@ def seq_len_to_mask(seq_len, max_len=None):
         mask = broad_cast_seq_len.lt(seq_len.unsqueeze(1))
     else:
         raise TypeError("Only support 1-d numpy.ndarray or 1-d torch.Tensor.")
-    
+
     return mask
 
 
@@ -658,24 +660,25 @@ class _pseudo_tqdm:
     """
     当无法引入tqdm，或者Trainer中设置use_tqdm为false的时候，用该方法打印数据
     """
+
     def __init__(self, **kwargs):
         self.logger = logger
-    
+
     def write(self, info):
         self.logger.info(info)
-    
+
     def set_postfix_str(self, info):
         self.logger.info(info)
-    
+
     def __getattr__(self, item):
         def pass_func(*args, **kwargs):
             pass
-        
+
         return pass_func
-    
+
     def __enter__(self):
         return self
-    
+
     def __exit__(self, exc_type, exc_val, exc_tb):
         del self
 
@@ -749,3 +752,56 @@ def get_seq_len(words, pad_value=0):
     """
     mask = words.ne(pad_value)
     return mask.sum(dim=-1)
+
+
+def pretty_table_printer(dataset_or_ins) -> PrettyTable:
+    """
+    :param dataset_or_ins: 传入一个dataSet或者instance
+    ins = Instance(field_1=[1, 1, 1], field_2=[2, 2, 2], field_3=["a", "b", "c"])
+    +-----------+-----------+-----------------+
+    |  field_1  |  field_2  |     field_3     |
+    +-----------+-----------+-----------------+
+    | [1, 1, 1] | [2, 2, 2] | ['a', 'b', 'c'] |
+    +-----------+-----------+-----------------+
+    :return: 以 pretty table的形式返回根据terminal大小进行自动截断
+    """
+    x = PrettyTable()
+    try:
+        sz = os.get_terminal_size()
+        column = sz.columns
+        row = sz.lines
+    except OSError:
+        column = 144
+        row = 11
+    if type(dataset_or_ins).__name__ == "DataSet":
+        x.field_names = list(dataset_or_ins.field_arrays.keys())
+        c_size = len(x.field_names)
+        for ins in dataset_or_ins:
+            x.add_row([sub_column(ins[k], column, c_size, k) for k in x.field_names])
+            row -= 1
+            if row < 0:
+                x.add_row(["..." for _ in range(c_size)])
+                break
+    elif type(dataset_or_ins).__name__ == "Instance":
+        x.field_names = list(dataset_or_ins.fields.keys())
+        c_size = len(x.field_names)
+        x.add_row([sub_column(dataset_or_ins[k], column, c_size, k) for k in x.field_names])
+
+    else:
+        raise Exception("only accept  DataSet and Instance")
+    return x
+
+
+def sub_column(string: str, c: int, c_size: int, title: str) -> str:
+    """
+    :param string: 要被截断的字符串
+    :param c: 命令行列数
+    :param c_size: instance或dataset field数
+    :param title: 列名
+    :return: 对一个过长的列进行截断的结果
+    """
+    avg = max(int(c / c_size), len(title))
+    string = str(string)
+    if len(string) > avg:
+        string = string[:(avg - 3)] + "..."
+    return string