معرفی شرکت ها
AdvancedAnalytics-1.9
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Python support for 'The Art and Science of Data Analytics'
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | AdvancedAnalytics-1.9 |
| نام | AdvancedAnalytics |
| نسخه کتابخانه | 1.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Edward R Jones |
| ایمیل نویسنده | ejones@tamu.edu |
| آدرس صفحه اصلی | https://github.com/tandonneur/AdvancedAnalytics |
| آدرس اینترنتی | https://pypi.org/project/AdvancedAnalytics/ |
| مجوز | - |
AdvancedAnalytics
===================
A collection of python modules, classes and methods for simplifying the use of machine learning solutions. **AdvancedAnalytics** provides easy access to advanced tools in **Sci-Learn**, **NLTK** and other machine learning packages. **AdvancedAnalytics** was developed to simplify learning python from the book *The Art and Science of Data Analytics*.
Description
===========
From a high level view, building machine learning applications typically proceeds through three stages:
1. Data Preprocessing
2. Modeling or Analytics
3. Postprocessing
The classes and methods in **AdvancedAnalytics** primarily support the first and last stages of machine learning applications.
Data scientists report they spend 80% of their total effort in first and last stages. The first stage, *data preprocessing*, is concerned with preparing the data for analysis. This includes:
1. identifying and correcting outliers,
2. imputing missing values, and
3. encoding data.
The last stage, *solution postprocessing*, involves developing graphic summaries of the solution, and metrics for evaluating the quality of the solution.
Documentation and Examples
============================
The API and documentation for all classes and examples are available at https://github.com/tandonneur/AdvancedAnalytics/.
Usage
=====
Currently the most popular usage is for supporting solutions developed using these advanced machine learning packages:
* Sci-Learn
* StatsModels
* NLTK
The intention is to expand this list to other packages. This is a simple example for linear regression that uses the data map structure to preprocess data:
.. code-block:: python
from AdvancedAnalytics.ReplaceImputeEncode import DT
from AdvancedAnalytics.ReplaceImputeEncode import ReplaceImputeEncode
from AdvancedAnalytics.Tree import tree_regressor
from sklearn.tree import DecisionTreeRegressor, export_graphviz
# Data Map Using DT, Data Types
data_map = {
"Salary": [DT.Interval, (20000.0, 2000000.0)],
"Department": [DT.Nominal, ("HR", "Sales", "Marketing")]
"Classification": [DT.Nominal, (1, 2, 3, 4, 5)]
"Years": [DT.Interval, (18, 60)] }
# Preprocess data from data frame df
rie = ReplaceImputeEncode(data_map=data_map, interval_scaling=None,
nominal_encoding= "SAS", drop=True)
encoded_df = rie.fit_transform(df)
y = encoded_df["Salary"]
X = encoded_df.drop("Salary", axis=1)
dt = DecisionTreeRegressor(criterion= "gini", max_depth=4,
min_samples_split=5, min_samples_leaf=5)
dt = dt.fit(X,y)
tree_regressor.display_importance(dt, encoded_df.columns)
tree_regressor.display_metrics(dt, X, y)
Current Modules and Classes
=============================
ReplaceImputeEncode
Classes for Data Preprocessing
* DT defines new data types used in the data dictionary
* ReplaceImputeEncode a class for data preprocessing
Regression
Classes for Linear and Logistic Regression
* linreg support for linear regressino
* logreg support for logistic regression
* stepwise a variable selection class
Tree
Classes for Decision Tree Solutions
* tree_regressor support for regressor decision trees
* tree_classifier support for classification decision trees
Forest
Classes for Random Forests
* forest_regressor support for regressor random forests
* forest_classifier support for classification random forests
NeuralNetwork
Classes for Neural Networks
* nn_regressor support for regressor neural networks
* nn_classifier support for classification neural networks
Text
Classes for Text Analytics
* text_analysis support for topic analysis
* text_plot for word clouds
* sentiment_analysis support for sentiment analysis
Internet
Classes for Internet Applications
* scrape support for web scrapping
* metrics a class for solution metrics
Installation and Dependencies
=============================
**AdvancedAnalytics** is designed to work on any operating system running python 3. It can be installed using **pip** or **conda**.
.. code-block:: python
pip install AdvancedAnalytics
# or
conda install -c dr.jones AdvancedAnalytics
General Dependencies
There are dependencies. Most classes import one or more modules from
**Sci-Learn**, referenced as *sklearn* in module imports, and
**StatsModels**. These are both installed with the current version
of **anaconda**.
Installed with AdvancedAnalytics
Most packages used by **AdvancedAnalytics** are automatically
installed with its installation. These consist of the following
packages.
* statsmodels
* scikit-learn
* scikit-image
* nltk
* pydotplus
Other Dependencies
The *Tree* and *Forest* modules plot decision trees and importance
metrics using **pydotplus** and the **graphviz** packages. These
should also be automatically installed with **AdvancedAnalytics**.
However, the **graphviz** install is sometimes not fully complete
with the conda install. It may require an additional pip install.
.. code-block:: python
pip install graphviz
Text Analytics Dependencies
The *TextAnalytics* module uses the **NLTK**, **Sci-Learn**, and
**wordcloud** packages. Usually these are also automatically
installed automatically with **AdvancedAnalytics**. You can verify
they are installed using the following commands.
.. code-block:: python
conda list nltk
conda list sci-learn
conda list wordcloud
However, when the **NLTK** package is installed, it does not
install the data used by the package. In order to load the
**NLTK** data run the following code once before using the
*TextAnalytics* module.
.. code-block:: python
#The following NLTK commands should be run once
nltk.download("punkt")
nltk.download("averaged_preceptron_tagger")
nltk.download("stopwords")
nltk.download("wordnet")
The **wordcloud** package also uses a little know package
**tinysegmenter** version 0.3. Run the following code to ensure
it is installed.
.. code-block:: python
conda install -c conda-forge tinysegmenter==0.3
# or
pip install tinysegmenter==0.3
Internet Dependencies
The *Internet* module contains a class *scrape* which has some
functions for scraping newsfeeds. Some of these use the
**newspaper3k** package. It should be automatically installed with
**AdvancedAnalytics**.
However, it also uses the package **newsapi-python**, which is not
automatically installed. If you intended to use this news scraping
scraping tool, it is necessary to install the package using the
following code:
.. code-block:: python
conda install -c conda-forge newsapi
# or
pip install newsapi
In addition, the newsapi service is sponsored by a commercial company
www.newsapi.com. You will need to register with them to obtain an
*API* key required to access this service. This is free of charge
for developers, but there is a fee if *newsapi* is used to broadcast
news with an application or at a website.
Code of Conduct
---------------
Everyone interacting in the AdvancedAnalytics project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PyPA Code of Conduct: https://www.pypa.io/en/latest/code-of-conduct/ .
نیازمندی
| مقدار | نام |
|---|---|
| - | scikit-learn |
| - | scikit-image |
| - | statsmodels |
| - | nltk |
| - | pydotplus |
نحوه نصب
نصب پکیج whl AdvancedAnalytics-1.9:
pip install AdvancedAnalytics-1.9.whl
نصب پکیج tar.gz AdvancedAnalytics-1.9:
pip install AdvancedAnalytics-1.9.tar.gz