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零计数

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/。

ZeroCount

基类：BaseEstimator, TransformerMixin

将每个样本中零的数量和非零的数量作为特征添加。

Source code in tpot2/builtin_modules/zero_count.py 
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class ZeroCount(BaseEstimator, TransformerMixin):
    """Adds the count of zeros and count of non-zeros per sample as features."""

    def fit(self, X, y=None):
        """Dummy function to fit in with the sklearn API."""
        return self

    def transform(self, X, y=None):
        """Transform data by adding two virtual features.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components
            is the number of components.
        y: None
            Unused

        Returns
        -------
        X_transformed: array-like, shape (n_samples, n_features)
            The transformed feature set
        """
        X = check_array(X)
        n_features = X.shape[1]

        X_transformed = np.copy(X)

        non_zero_vector = np.count_nonzero(X_transformed, axis=1)
        non_zero = np.reshape(non_zero_vector, (-1, 1))
        zero_col = np.reshape(n_features - non_zero_vector, (-1, 1))

        X_transformed = np.hstack((non_zero, X_transformed))
        X_transformed = np.hstack((zero_col, X_transformed))

        return X_transformed

fit(X, y=None)

用于适应sklearn API的虚拟函数。

Source code in tpot2/builtin_modules/zero_count.py 
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def fit(self, X, y=None):
    """用于与sklearn API兼容的虚拟函数。"""
    return self

transform(X, y=None)

通过添加两个虚拟特征来转换数据。

参数:

名称 类型 描述 默认值
X

新数据，其中 n_samples 是样本数量，n_components 是组件数量。

 required
y

未使用

 None

返回:

名称 类型 描述
X_transformed (array - like, shape(n_samples, n_features))

转换后的特征集
Source code in tpot2/builtin_modules/zero_count.py 
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def transform(self, X, y=None):
    """Transform data by adding two virtual features.

    Parameters
    ----------
    X: numpy ndarray, {n_samples, n_components}
        New data, where n_samples is the number of samples and n_components
        is the number of components.
    y: None
        Unused

    Returns
    -------
    X_transformed: array-like, shape (n_samples, n_features)
        The transformed feature set
    """
    X = check_array(X)
    n_features = X.shape[1]

    X_transformed = np.copy(X)

    non_zero_vector = np.count_nonzero(X_transformed, axis=1)
    non_zero = np.reshape(non_zero_vector, (-1, 1))
    zero_col = np.reshape(n_features - non_zero_vector, (-1, 1))

    X_transformed = np.hstack((non_zero, X_transformed))
    X_transformed = np.hstack((zero_col, X_transformed))

    return X_transformed