7个你可能在2018错过的开源python AI项目与机器学习库，David 9的收藏

2018也许是 AutoML （自动化机器学习）的探索元年。就让我们从AutoML聊起。

1. AdaNet — 一个基于TensorFlow的开源神经网络自动学习项目 。

也许你之前听过 Auto-Keras   和 Auto-Sklearn ，但是如果要认真去做神经网络的AutoML， AdaNet 有许多值得借鉴的地方。

如上图， AdaNet 会在网络层中尝试使用不用的候选（Candidates）结构和参数。并且自己维护一个Adanet loss（带正则）：

入门 AdaNet 可以先通读项目中的例程： https://github.com/tensorflow/adanet/blob/master/adanet/examples/tutorials/adanet_objective.ipynb ，并理解如何使用AdaNet已有类构造子网络生成器：

class SimpleDNNGenerator(adanet.subnetwork.Generator):
  """Generates a two DNN subnetworks at each iteration.

  The first DNN has an identical shape to the most recently added subnetwork
  in `previous_ensemble`. The second has the same shape plus one more dense
  layer on top. This is similar to the adaptive network presented in Figure 2 of
  [Cortes et al. ICML 2017](https://arxiv.org/abs/1607.01097), without the
  connections to hidden layers of networks from previous iterations.
  """

  def __init__(self,
               optimizer,
               layer_size=64,
               learn_mixture_weights=False,
               seed=None):
    """Initializes a DNN `Generator`.

    Args:
      optimizer: An `Optimizer` instance for training both the subnetwork and
        the mixture weights.
      layer_size: Number of nodes in each hidden layer of the subnetwork
        candidates. Note that this parameter is ignored in a DNN with no hidden
        layers.
      learn_mixture_weights: Whether to solve a learning problem to find the
        best mixture weights, or use their default value according to the
        mixture weight type. When `False`, the subnetworks will return a no_op
        for the mixture weight train op.
      seed: A random seed.

    Returns:
      An instance of `Generator`.
    """

    self._seed = seed
    self._dnn_builder_fn = functools.partial(
        _SimpleDNNBuilder,
        optimizer=optimizer,
        layer_size=layer_size,
        learn_mixture_weights=learn_mixture_weights)

  def generate_candidates(self, previous_ensemble, iteration_number,
                          previous_ensemble_reports, all_reports):
    """See `adanet.subnetwork.Generator`."""

    num_layers = 0
    seed = self._seed
    if previous_ensemble:
      num_layers = tf.contrib.util.constant_value(
          previous_ensemble.weighted_subnetworks[
              -1].subnetwork.persisted_tensors[_NUM_LAYERS_KEY])
    if seed is not None:
      seed += iteration_number
    return [
        self._dnn_builder_fn(num_layers=num_layers, seed=seed),
        self._dnn_builder_fn(num_layers=num_layers + 1, seed=seed),
    ]

2. TPOT — 贴心到要把 特征选择、模型选择和模型优化 一并做了

TPOT试图把繁琐的 特征选择、模型选择和模型优化 一并做优化并输出在另一个py文件中：

from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
                                                    train_size=0.75, test_size=0.25)

tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_mnist_pipeline.py')

运行以上代码会自动优化并输出

tpot_mnist_pipeline.py 代码文件：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
            train_test_split(features, tpot_data['target'].values, random_state=None)


exported_pipeline = KNeighborsClassifier(n_neighbors=6, weights="distance")

exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)

但是tpot基于scikit-learn，如果没有进行很高的优化，代码运行时间可能会令你无法忍受。其使用简单到是适合初学者实验。

3. SHAP — 解释模型的预测行为

SHAP比一般模型分析 工具 好用的地方有两个，

1. 支持tensorflow，pytorch，keras等深度学习框架
2. 支持深度神经网络模型的预测行为可视化，如下图，红色的像素区域表示在当前标签下的概率更大：

4. Augmentor — 简单实用的数据增强库

只需短短几行代码你就可以生成数据增强图片：

p = Augmentor.Pipeline("/path/to/images")
# Point to a directory containing ground truth data.
# Images with the same file names will be added as ground truth data
# and augmented in parallel to the original data.
p.ground_truth("/path/to/ground_truth_images")
# Add operations to the pipeline as normal:
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
p.flip_left_right(probability=0.5)
p.zoom_random(probability=0.5, percentage_area=0.8)
p.flip_top_bottom(probability=0.5)
p.sample(50)

Augmentor还支持加入图片噪声和图像扭曲等功能：

5. spaCy — 帮你构建高级的NLP自然语言应用

2018年不乏许多好的自然语言项目， spaCy 就是其中之一。spaCy 使用较新的研究成果作出产品级别的功能，包含的feature不限于以下所列：

仅spaCy的分词（ tokenization ）就支持31种语言和嵌套分词：

6. pytext — 深度学习+ NLP + PyTorch

来自facebook的开源项目pytext是基于pytorch的，自身带着一股研究性（如果你想寻找 深度学习+ NLP 的论文实现），如David 9 在之前文章（ 一维卷积在语义理解中的应用，莫斯科物理技术学院开源聊天机器人DeepPavlov解析及代码 ）提到的一维卷积：

7. flair — 另一个简单易用的自然语言框架

除了简单易用，与pytext不同的是，flair不专注于神经网络，但也对近年来一些成熟的方案给出了实现：

flair另一个亮点是有自己一套简单的方式组合不同的词嵌入（

embeddings ） ，包括

Flair embeddings , BERT embeddings 和LMo embeddings。

参考文献：

1. https://heartbeat.fritz.ai/top-7-libraries-and-packages-of-the-year-for-data-science-and-ai-python-r-6b7cca2bf000
2. https://heartbeat.fritz.ai/automated-machine-learning-in-python-5d7ddcf6bb9e
3. https://github.com/zalandoresearch/flair
4. https://github.com/mdbloice/Augmentor
5. https://github.com/facebookresearch/PyText

本文采用 署名 – 非商业性使用 – 禁止演绎 3.0 中国大陆许可协议 进行许可。著作权属于“David 9的博客”原创，如需转载，请联系微信: david9ml，或邮箱：yanchao727@gmail.com

或直接扫二维码: