人工智能和数据科学的七大 Python 库

时间:2019-01-10 作者: 来源:

本文作者Favio Vázquez从2018年开始发布《数据科学和人工智能每周文摘：Python & R》系列文章，为数据科学家介绍最好的库、repos、packages以及工具。

本文作者Favio Vázquez从2018年开始发布《数据科学家介绍最好的库、repos、packages以及工具。

一年结束，作者列出了2018年的7大最好的Python库，这些库确实地改进了研究人员的工作方式。

=1.7.0"

从源代码安装

要从源代码进行安装，首先需要安装bazel。

下一步，复制adanet和cd到它的根目录:

$ git clone && cd adanet

从adanet根目录运行测试：

$ cd adanet $ bazel test -c opt //...

确认一切正常后，将adanet安装为pip包。

现在，可以对adanet进行试验了。

import adanet

用法

14)

15exported_pipeline.fit(training_features, training_classes)

16results = exported_pipeline.predict(testing_features)

就是这样。你已经以一种简单但强大的方式为Iris数据集构建一个分类器。

现在我们来看看MNIST的数据集：

1from tpot import TPOTClassifier

2from sklearn.datasets import load_digits

3from sklearn.model_selection import train_test_split

5# load and split dataset

6digitsdigits == load_digitsload_di ()

7X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,

8 train_size=0.75, test_size=0.25)

10# Fit the TPOT classifier

11tpot = TPOTClassifier(verbosity=2, max_time_mins=5, population_size=40)

12tpot.fit(X_train, y_train)

14# Export pipeline

15tpot.export('tpot_mnist_pipeline.py')

接下来我们再次加载生成的 .py文件，你将看到：

1import numpy as np

2from sklearn.model_selection import train_test_split

3from sklearn.neighbors import KNeighborsClassifier

4# NOTE: Make sure that the class is labeled 'class' in the data file

5tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)

6features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)

7training_features, testing_features, training_classes, testing_classes =

8 train_test_split(features, tpot_data['class'], random_state=42)

9exported_pipeline = KNeighborsClassifier(n_neighbors=4, p=2, weights="distance")

10exported_pipeline.fit(training_features, training_classes)

11results = exported_pipeline.predict(testing_features)

5. SHAP ——一个解释任何机器模型输出的统一方法

df =op.load.url("

.rows.sort("product","desc")

.cols.lower(["firstName","lastName"])

.cols.date_transform("birth", "new_date", "yyyy/MM/dd", "dd-MM-YYYY")

.cols.years_between("birth", "years_between", "yyyy/MM/dd")

.cols.remove_accents("lastName")

.cols.remove_special_chars("lastName")

.cols.replace("product","taaaccoo","taco")

.cols.replace("product",["piza","pizzza"],"pizza")

.rows.drop(df["id"]7)

.cols.drop("dummyCol")

.cols.rename(str.lower)

.cols.apply_by_dtypes("product",func,"string", data_type="integer")

.cols.trim("*")

.show()

你可以将这个表格

u"Google in 2007, few people outside of the company took him "

u"seriously. “I can tell you very senior CEOs of major American "

u"car companies would shake my hand and turn away because I wasn’t "

u"worth talking to,” said Thrun, now the co-founder and CEO of "

u"online higher education startup Udacity, in an interview with "

u"Recode earlier this week.")

doc = nlp(text)

# Find named entities, phrases and concepts

for entity in doc.ents:

print(entity.text, entity.label_)

# Determine semantic similarities

doc1 = nlp(u"my fries were super gross")

doc2 = nlp(u"such disgusting fries")

similarity = doc1.similarity(doc2)

print(doc1.text, doc2.text, similarity)

在这个示例中，我们首先下载English tokenizer, tagger, parser, NER和word vectors。然后创建一些文本，打印找到的实体、短语和概念，最后确定两个短语的语义相似性。运行这段代码，你会得到：

Sebastian Thrun PERSON

earlier this week DATE

my fries were super gross such disgusting fries 0.7139701635071919

2. jupytext

重启Jupyter，即运行：

jupyter notebook

你可以在这里试试：

1.Chartify ——让数据科学家很容易创建图表的Python库

ch.set_subtitle("Represent changes in distribution.")

ch.plot.area(

data_frame=total_quantity_by_month_and_fruit,

x_column='month',

y_column='quantity',

color_column='fruit',

stacked=True)

ch.show('png')

超级容易创建一个互动的plot。