ml3 1.2.1

TechYoung Machine Learning ToolKit

# ML3

Introduction

ML3是TechYoung课程辅助工具包.

## Distribution

Run the following commands to register, build and upload the package to PYPI.

python3 setup.py sdist upload

The home page on PYPI is: https://pypi.org/project/wcc/

Install

sudo pip3 install ml3

---

Usage

After installation, run the following command:

import ml3

Methods:

plot.histplot(data, column_name, **kwargs):

• *data:* dataframe

• *column_name:* column name of dataframe, 例如 “hr_mean”

• *kwargs:* “xmin”, “xmax”

plot.gmmplot(data, column_names, k_range, **kwargs):

• *data:* dataframe

• *column_names:* list of columns name of dataframe, 例如 [“hr_mean”, “hr_std”]

• *k_range:* the range of components (k), 例如 [2, 11] or (2, 11)

• *kwargs:* “xmin”, “xmax”

plot.kmeansplot(data, column_names, k_range, **kwargs):

• *data:* dataframe

• *column_names:* list of columns name of dataframe, 例如 [“hr_mean”, “hr_std”]

• *k_range:* the range of clusters (k), 例如 [2, 11] or (2, 11)

• *kwargs:* “xmin”, “xmax”

plot.metricplot(n_clusters_range, scores, scores2=[], **kwargs):

• *n_clusters_range：* tuple or list of range，例如 (2, 10)

• *scores:* list of score

• *scores:* list of score2 (option)

• *kwargs:* “x_label”, “y_label”

plot.errorbarplot(data, x, y=[], y2=[], **kwargs):

• *data:* dataframe

• *x:* x-axis column name，例如 “ctime”

• *y:* y column name，例如 [“hr_mean”, “hr_std”]

• *y2:* y2 column name，例如 [“br_mean”, “br_std”] (option)

• *kwargs:* “X_LABEL”, “Y_LABEL”, “TITLE”, “LIMIT”

plot.pcaplot(data, column_names, **kwargs):

• *data:* dataframe

• *column_names:* list of columns name of dataframe, 例如 [“hr_mean”, “hr_std”]

• *kwargs:* “n_components”

plot.tsenplot(data, column_names, **kwargs):

• *data:* dataframe

• *column_names:* list of columns name of dataframe, 例如 [“hr_mean”, “hr_std”]

• *kwargs:* “n_components”

seaborn.boxplot(x, y, **kwargs):

此函数需要ml4进行对原始数据进行窗口化分类

• *x:* the UNIX timestamp list from ml4

• *y:* the data list from ml4

• *kwargs:* “X_LABEL”, “Y_LABEL”, “TITLE”

seaborn.violinplot(x, y, **kwargs):

此函数需要ml4进行对原始数据进行窗口化分类

• *x:* the UNIX timestamp list from ml4

• *y:* the data list from ml4

• *kwargs:* “X_LABEL”, “Y_LABEL”, “TITLE”

Example

import ml3
import ml4
import pandas as pd

data = pd.read_csv("tmp.csv")
# histogram
ml3.plot.histplot(data, "hr_mean")
# error bar
ml3.plot.errorbarplot(data, "ctime", ["hr_mean", "hr_std"], ["br_mean", "br_std"])
# single feature
ml3.plot.kmeansplot(data, "hr_mean", (2, 10))
ml3.plot.gmmplot(data, "hr_mean", (2, 10))
# multiple features
ml3.plot.gmmplot(data, ["hr_mean", "hr_std", "br_mean", "br_std", "mo_mean", "mo_std"], (2, 10))
ml3.plot.kmeansplot(data, ["hr_mean", "hr_std", "br_mean", "br_std", "mo_mean", "mo_std"], (2, 10))
# two scores metricplot
scores = [110704, 75304, 60731, 52297, 45675, 41231, 37744, 35247, 33263]
scores2 = [0.05, 0.09, 0.15, 0.2, 0.3, 0.5, 0.6, 0.9, 1]
ml3.plot.metricplot((2, 11), scores, scores2)
# boxplot and violoinplot
x, y = ml4.ml4.getWindowData(data, "ctime", "hr")
timeList = []
for i in x:
    tmp = datetime.fromtimestamp(i)
    timeList.append(tmp.strftime("%H:%M"))
ml3.seaborn.boxplot(timeList, y)
ml3.seaborn.violinplot(timeList, y)

Note

版本里的1.2.4是旧的版本。1.2.5和以后的版本是用于函数计算的版本。 1.2.5以及以后版本将去掉wcc自动框架. 目录下的子目录：libwebp-0.4.1-linux-x86-64 需要从网上下载，然后把里面的bin下的gif2webp放到/usr/bin里。这样就可以在wcc里调用了.