代做data编程、代写Java程序设计

日期：2024-10-29 08:47

Week 1 Practical

Introduction to WEKA

What are we doing?

• Download an open source machine learning tool “WEKA” and explore the main

features of this tool.

• Understand and practice the basic data pre-processing operations that can be

performed using WEKA.

Submission:

You are required to submit one .arff file (after completing the practical task as

instructed in this prac document) via the weekly-practical submission box.

What is WEKA?

The WEKA (The Waikato Environment for Knowledge Analysis) is a machine learning

toolkit developed at the University of Waikato in Hamilton, New Zealand. The

software provides many machine learning statistics and other data mining solutions

for various types of data mining task, such as classification, cluster detection,

association rule discovery and attribute selection. The software is also equipped with

data pre-processing and post-processing tools and visualisation tools so that

complete data mining projects can be conducted via a number of different styles of

user interface. The toolkit is written in Java and can, therefore, run on various

platforms, such as Linux, Windows and Macintosh. It is an open-source software and

distributed under the terms and conditions of the GNU General Public License.

Launching and Starting WEKA

You can find instructions for installing Weka at

https://waikato.github.io/weka-wiki/downloading_weka/

When you open Weka you should see a screen like the one below (Figure 1).

[Figure 1]  

Select the Explorer option below Applications.

Data Pre-Processing using WEKA

This example illustrates some of the basic data preprocessing operations that can be

performed using WEKA. The sample data set used for this example, unless otherwise

indicated, is the "bank data", called Bank Data.csv

The data contains the following fields

id a unique identification number

age age of customer in years (numeric)

sex MALE / FEMALE

region inner_city/rural/suburban/town

income income of customer (numeric)

married is the customer married (YES/NO)

children number of children (numeric)

car does the customer own a car (YES/NO)

save_acct does the customer have a saving account (YES/NO)

current_acct does the customer have a current account (YES/NO)

mortgage does the customer have a mortgage (YES/NO)

pep

did the customer buy a PEP (Personal Equity Plan) after the last mailing

(YES/NO)

Loading the Data

In addition to the native ARFF data file format, WEKA has the capability to read in

".csv" format files. This is fortunate since many databases or spreadsheet applications

can save or export data into flat files in this format. A usual Microsoft Excel worksheet  

can be saved as a CSV file and opened by WEKA. The first row of the spreadsheet is

used to name the attributes and the data types for the attributes are derived

automatically but not always accurately. Once opened, you can save the data set into

an ARFF file in WEKA (by clicking “Save” in the Preprocess tab).

In this example, we load the data set into WEKA, perform a series of operations using

WEKA's attribute and discretization filters. While all of these operations can be

performed from the command line, we use the GUI interface for WEKA Explorer.

Initially (in the Preprocess tab) click "open" and navigate to the directory containing

the data file (which is something like bank-data.csv). This is shown in [Figure 2].

Once the data is loaded, WEKA will recognize the attributes and during the scan of the

data will compute some basic statistics on each attribute. The left panel in [Figure 3]

shows the list of recognized attributes, while the top panels indicate the names of the

base relation (or table) and the current working relation (which are the same initially).

Note: The recent version of WEKA has an additional tab named “Edit” under

Preprocess menu to view the current contents of the dataset under working.

Whenever you apply any filter in WEKA, you can see the updated contents via this

viewer facility. (Alternatively, you can use the “Arff Viewer” tool included in WEKA.

Refer to the WEKA manual document for further details)

[Figure 2]

[Figure 3]

Clicking on any attribute in the left panel will show the basic statistics on that

attribute. For categorical attributes, the frequency for each attribute value is shown,

while for continuous attributes we can obtain min, max, mean, standard deviation,

etc. As an example, see the [Figure 4] below which show the results of selecting the

“age” attribute.  

[Figure 4]

Selecting or Filtering Attributes

In our sample data file, each record is uniquely identified by a customer id (the "id"

attribute). We need to remove this attribute before the data mining step (as this

attribute is not necessary). We can do this by using the Attribute filters in WEKA.

In the "Filter" panel, click on the "Choose" button.

This will show a popup window with a list available filters. Scroll down the list and

select the "weka.filters.unsupervised.attribute.Remove" filter as shown in [Figure 5].

Next, click on text box immediately to the right of the "Choose" button.

In the resulting dialog box enter the index of the attribute to be filtered out (this can

be a range or a list separated by commas). In this case, we enter 1 which is the index

of the "id" attribute (see the left panel). Make sure that the "invertSelection" option

is set to false (otherwise everything except attribute 1 will be filtered). Then click "OK"

(See [Figure 6]). Now, in the filter box you will see "Remove -R 1" (see [Figure 7]).  

[Figure 5]

[Figure 6]

[Figure 7]

Click the "Apply" button to apply this filter to the data. This will remove the "id"

attribute and create a new working relation (whose name now includes the details of

the filter that was applied). The result is depicted in [Figure 8].

[Figure 8]  

Discretization

Some techniques, such as association rule mining, can only be performed on

categorical data. This requires performing discretization on numeric or continuous

attributes. (There are 3 such attributes in this data set: "age", "income", and

"children"). Click on the “age” attribute. Again we activate the Filter dialog box, but

this time, we will select "Discretize" filter from the list. (see [Figure 9]).

[Figure 9]

Next, to change the defaults for this filter, click on the box to the right of the "Choose"

button. This will open the Discretize Filter dialog box.

We enter the index for the the attributes to be discretized. In this case we enter 1

corresponding to attribute "age". We also enter 3 as the number of bins (note that it

is possible to discretize more than one attribute at the same time (by using a list of

attribute indexes). Since we are doing simple binning, all of the other available options

are set to "false". The dialog box is shown in [Figure 10].

Click "Apply" in the Filter panel. This will result in a new working relation with the

selected attribute partitioned into 3 bins (shown in Figure 10).

Finally, save the file as something like "bank-data-final.arff".

Submit this final filtered arff file to prove your work for this weekly

practical.  

[Figure 10]

[Figure 11]  

Other Useful Filters in WEKA

There are more useful preprocessing filters provided in WEKA in addition to filters we

tried in this exercise. The following is briefs of some among them. You are

recommended to refer to WEKA manual for further details and have a try to apply

some to bank data for your own exercise.

In WEKA, data pre-processing is done using attribute or instance filters that can

operate supervised or unsupervised. Attribute filters are applied to attributes

(columns) and instance filters are applied to data objects (rows). Supervised filters

perform with consideration of a class attribute whereas unsupervised filters do not.

(Many unsupervised filters have a supervised counterpart. Supervised filters must be

used with care for classification tasks; test examples must be pre-processed in the

same way as the training examples.)

The many other filters for data pre-processing have not been described here due to

limitations of space. Filters in WEKA are continuously developed and new filters are

constantly added in new versions.

Add attribute filter

Using “Add” filter, we can create a new attribute (with empty value as default)

and specify the location, name and labels of the new attribute. Once created,

the value of the new attribute can be entered manually in the viewer window

for data objects.

New numeric features can be added with the “AddExpression” filter, which

applies a mathematical expression based on the values of other attributes.

Numeric transformation attribute filters

The “MathExpression” filter allows transformation with a valid mathematical

expression that uses arithmetic operators and built-in functions, such as

absolute (abs), logarithm (log), square root (sqrt), etc.

The “NumericTransform” filter only allows transformations by methods

supported by the Java math library. Unlike AddExpression, these filters do not

create new attributes but replace the current values with the transformed

values.

Transformation attribute filters

The “Normalize” filter converts the values of all numeric attributes in the

loaded data set to those within a common range. The default range is [0.1].

The user can change the normal range if needed.

The “Standardize” filter standardizes all numeric attributes to have zero mean

and unit variance.

ReplaceMissingValues filter

This rudimentary filter fills in missing values; numeric values are replaced with

the sample mean and nominal values are replaced with the sample mode. The

user can also fill in missing values manually in the viewer window (using “Edit”  

menu). For numeric attributes, the user may enter any value. For nominal

attributes, the user can only select one of the nominal labels that already exists

in the attribute domain. If the label does not exist (for instance, it is a special

code indicating unknown), the label can be added into the attribute domain by

using “AddValues” filter.

Resample instance filter

This filter selects a random sample of a certain percentage (SampleSizePercent

parameter) of the loaded data set, with or without replacement (to sample

without replacement, set the noReplacement parameter to True). The

unsupervised Resample filter draws the sample from the entire data set

reflecting the real distribution of attribute values including class values; the

supervised Resample filter draws samples according to either the real

distribution of classes (set the biasToUniformClass parameter to 0) or a

uniform distribution of classes (set the biasToUniformClass parameter to 1).