该文件是TPOT库的一部分。
当前版本的TPOT是由以下人员在Cedars-Sinai开发的：
- Pedro Henrique Ribeiro (https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/)
- Anil Saini (anil.saini@cshs.org)
- Jose Hernandez (jgh9094@gmail.com)
- Jay Moran (jay.moran@cshs.org)
- Nicholas Matsumoto (nicholas.matsumoto@cshs.org)
- Hyunjun Choi (hyunjun.choi@cshs.org)
- Miguel E. Hernandez (miguel.e.hernandez@cshs.org)
- Jason Moore (moorejh28@gmail.com)
TPOT的原始版本主要由宾夕法尼亚大学的以下人员开发：
- Randal S. Olson (rso@randalolson.com)
- Weixuan Fu (weixuanf@upenn.edu)
- Daniel Angell (dpa34@drexel.edu)
- Jason Moore (moorejh28@gmail.com)
- 以及许多慷慨的开源贡献者
TPOT 是免费软件：您可以根据自由软件基金会发布的 GNU 宽通用公共许可证的条款重新分发和/或修改它，许可证的版本可以是第 3 版，或者（根据您的选择）任何以后的版本。
TPOT 的发布是希望它能有用，
但没有任何保证；甚至没有对
适销性或特定用途适用性的暗示保证。更多详情请参阅
GNU 较宽松通用公共许可证。
您应该已经收到了一份GNU较宽松通用公共许可证的副本，随TPOT一起提供。如果没有，请参见http://www.gnu.org/licenses/。
PassKBinsDiscretizer
基类：BaseEstimator, TransformerMixin
Source code in tpot2/builtin_modules/passkbinsdiscretizer.py
| class PassKBinsDiscretizer(BaseEstimator, TransformerMixin):
def __init__(self, n_bins=5, encode='onehot-dense', strategy='quantile', subsample=None, random_state=None):
self.n_bins = n_bins
self.encode = encode
self.strategy = strategy
self.subsample = subsample
self.random_state = random_state
"""
Same as sklearn.preprocessing.KBinsDiscretizer, but passes through columns that are not discretized due to having fewer than n_bins unique values instead of ignoring them.
See sklearn.preprocessing.KBinsDiscretizer for more information.
"""
def fit(self, X, y=None):
# Identify columns with more than n unique values
# Create a ColumnTransformer to select and discretize the chosen columns
self.selected_columns_ = select_features(X, min_unique=10)
if isinstance(X, pd.DataFrame):
self.not_selected_columns_ = [col for col in X.columns if col not in self.selected_columns_]
else:
self.not_selected_columns_ = [i for i in range(X.shape[1]) if i not in self.selected_columns_]
enc = KBinsDiscretizer(n_bins=self.n_bins, encode=self.encode, strategy=self.strategy, subsample=self.subsample, random_state=self.random_state)
self.transformer = ColumnTransformer([
('discretizer', enc, self.selected_columns_),
('passthrough', 'passthrough', self.not_selected_columns_)
])
self.transformer.fit(X)
return self
def transform(self, X):
return self.transformer.transform(X)
|
random_state = random_state
instance-attribute
与 sklearn.preprocessing.KBinsDiscretizer 相同，但会传递那些由于唯一值少于 n_bins 而未进行离散化的列，而不是忽略它们。
有关更多信息，请参阅 sklearn.preprocessing.KBinsDiscretizer。
select_features(X, min_unique=10)
给定一个DataFrame或numpy数组，返回具有超过min_unique唯一值的列索引列表。
参数:
| 名称 |
类型 |
描述 |
默认值 |
X |
|
|
必填
|
min_unique |
|
|
10
|
返回:
| 类型 |
描述 |
list
|
具有超过 min_unique 唯一值的列索引列表
|
Source code in tpot2/builtin_modules/passkbinsdiscretizer.py
| def select_features(X, min_unique=10,):
"""
Given a DataFrame or numpy array, return a list of column indices that have more than min_unique unique values.
Parameters
----------
X: DataFrame or numpy array
Data to select features from
min_unique: int, default=10
Minimum number of unique values a column must have to be selected
Returns
-------
list
List of column indices that have more than min_unique unique values
"""
if isinstance(X, pd.DataFrame):
return [col for col in X.columns if len(X[col].unique()) > min_unique]
else:
return [i for i in range(X.shape[1]) if len(np.unique(X[:, i])) > min_unique]
|