SIMPLE LINEAR REGRESSION WITH KNIME IRIS DATA SET
ABOUT KNIME:
KNIME
(pronounced /naɪm/), the Konstanz Information Miner, is an open source data
analytics, reporting and integration platform. KNIME integrates various
components for machine learning and data mining through its modular data
pipelining concept. A graphical user interface allows assembly of nodes for
data pre-processing (ETL: Extraction, Transformation, Loading), for modelling
and data analysis and visualization. To some extent KNIME can be considered as
SAS alternative.
KNIME
allows users to visually create data flows (or pipelines), selectively execute
some or all analysis steps, and later inspect the results, models, and
interactive views. KNIME is written in Java and based on Eclipse and makes use
of its extension mechanism to add plugins providing additional functionality.
The core version already includes hundreds of modules for data integration
(file I/O, database nodes supporting all common database management systems
through JDBC), data transformation (filter, converter, combiner) as well as the
commonly used methods for data analysis and visualization. With the free Report
Designer extension, KNIME workflows can be used as data sets to create report
templates that can be exported to document formats like doc, ppt, xls, pdf and
others.
CAPABILITIES OF KNIME:
KNIMEs
core-architecture allows processing of large data volumes that are only limited
by the available hard disk space (most other open source data analysis tools
work in main memory and are therefore limited to the available RAM). E.g. KNIME
allows analysis of 300 million customer addresses, 20 million cell images and
10 million molecular structures.
Additional
plugins allows the integration of methods for Text mining, Image mining, as
well as time series analysis.
KNIME
integrates various other open-source projects, e.g. machine learning algorithms
from Weka, the statistics package R project, as well as LIBSVM, JFreeChart,
ImageJ etc.
KNIME
is implemented in Java but also allows for wrappers calling other code in
addition to providing nodes that allow to run Java, Python, Perl and other code
fragments.
COMPARISON:
Python: With origination
as an open source scripting language, Python usage has grown over time. Today,
it sports libraries (numpy, scipy and matplotlib) and functions for almost any
statistical operation / model building you may want to do. Since introduction
of pandas, it has become very strong in operations on structured data.
Python is a programming
language that is popularly used for data mining types of tasks. Programming
languages require you give the computer very detailed, step-by-step
instructions of what to do. Memorizing those programming statements is a good
deal of what "learning to program" consists of. You can use its
add-on packages to minimize your programming effort, but you're still doing
some programming.
SAS: SAS has been the
undisputed market leader in commercial analytics space. The software offers
huge array of statistical functions, has good GUI (Enterprise Guide &
Miner) for people to learn quickly and provides awesome technical support.
However, it ends up being the most expensive option and is not always enriched
with latest statistical functions.
R: R is the Open source counterpart of SAS, which has
traditionally been used in academics and research. Because of its open source
nature, latest techniques get released quickly. There is a lot of documentation
available over the internet and it is a very cost-effective option. R is easy
to get started too, but needs around a week of initial reading, before you get
started
KNIME is primarily
workflow-based packages that try to give you most of the flexibility and power
of programming without having to know how to program. Their workflow style is
easy to use by dragging and dropping icons onto a drawing window that represent
steps of the analysis. What each icon does is controlled by dialog boxes rather
than having to remember commands. When finished, the workflow
1) accomplishes the tasks,
2) documents the steps for reproducibility,
3) shows you the big picture of what was done and
4) allows you to reuse the the steps on new sets of
data without resorting to any underlying programming code (as menu-based user
interfaces such as SPSS often require.)
A
particularly nice feature of KNIME is that it allow’s you to add nodes to your
workflow that contain custom programming. This allows you to combine the two
approaches, making the most of each.
NOTE:
Knime
needs a basic understanding of the dataset and some logical thinking before
getting into analysis. It helps to make our work much easier for analysis
rather than remembering the algorithms.
Iris flower data set
The
Iris flower data set or Fisher's Iris data set is a multivariate data set
introduced by the British statistician and biologist Ronald Fisher in his 1936
paper The use of multiple measurements in taxonomic problems as an example of linear
discriminant analysis. It is sometimes called Anderson's Iris data set because
Edgar Anderson collected the data to quantify the morphologic variation of Iris
flowers of three related species. Two of the three species were collected in
the Gaspé Peninsula "all from the same pasture, and picked on the same day
and measured at the same time by the same person with the same apparatus".
The
data set consists of 50 samples from each of three species of Iris (Iris
setosa, Iris virginica and Iris versicolor). Four features were measured from
each sample: the length and the width of the sepals and petals, in centimetres.
Based on the combination of these four features, Fisher developed a linear
discriminant model to distinguish the species from each other.
KNIME ANALYTICS PLATFORM
Using
the the simple regression tree for regression in knime analytics platform.
Node Repository:
The
node repository contains all KNIME nodes ordered in categories. A category can
contain another category, for example, the Read category is a subcategory of
the IO category. Nodes are added from the repository to the workflow editor by
dragging them to the workflow editor. Selecting a category displays all
contained nodes in the node description view; selecting a node displays the
help for this node. If you know the name of a node you can enter parts of the
name into the search box of the node repository. As you type, all nodes are
filtered immediately to those that contain the entered text in their names:
Drag
and drop the file reader from the node repository into the workflow Editor.
Workflow Editor
The
workflow editor is used to assemble workflows, configure and execute nodes, inspect
the results and explore your data. This section describes the interactions possible
within the editor.
File Reader:
This
node can be used to read data from an ASCII file or URL location. It can be
configured to read various formats. When you open the node's configuration
dialog and provide a filename, it tries to guess the reader's settings by
analyzing the content of the file. Check the results of these settings in the
preview table. If the data shown is not correct or an error is reported, you
can adjust the settings manually .
The
file analysis runs in the background and can be cut short by clicking the
"Quick scan", which shows if the analysis takes longer. In this case
the file is not analyzed completely, but only the first fifty lines are taken
into account. It could happen then, that the preview appears looking fine, but
the execution of the File Reader fails, when it reads the lines it didn't
analyze. Thus it is recommended you check the settings, when you cut an
analysis short. Load
the iris data set into the file reader by configure.
Configure:
When
a node is dragged to the workflow editor or is connected, it usually shows the
red status light indicating that it needs to be configured, i.e. the dialog has
to be opened. This can be done by either double-clicking the node or by
right-clicking the node to open the context menu. The first entry of the
context menu is "Configure", which opens the dialog. If the node is
selected you can also choose the related button from the toolbar above the
editor. The button looks like the icon next to the context menu entry.
Partitioning:
Then
drag and drop partitioning node from the node repository into the workflow
editor. This is done to divide the iris data set into training and testing. The
input table is split into two partitions (i.e. row-wise), e.g. train and test
data. The two partitions are available at the two output ports. The following
options are available in the dialog.
Simple Regression Tree Learner:
Drag
and drop simple regression tree learner into the workflow editor. It learns a single regression tree. The procedure follows
the algorithm described in CART ("Classification and Regression
Trees", Breiman et al, 1984), whereby the current implementation applies a
couple of simplifications, e.g. no pruning, not necessarily binary trees, etc.
The
currently used missing value handling also differs from the one used in CART.
In each split the algorithm tries to find the best direction for missing values
by sending them in each direction and selecting the one that yields the best result
(i.e. largest gain). The procedure is adapted from the well known XGBoost
algorithm and is described
Simple Regression Tree Predictor:
Applies
regression from a regression tree model by using the mean of the records in the
corresponding child node. Drag and drop simple regression tree predictor from
the node repository into the workflow editor.
Column Filter:
This
node allows columns to be filtered from the input table while only the
remaining columns are passed to the output table. Within the dialog, columns
can be moved between the Include and Exclude list Drag and drop column filter
from the node repository into the workflow editor.
Line Plot:
Plots
the numeric columns of the input table as lines. All values are mapped to a
single y coordinate. This may distort the visualization if the difference of
the values in the columns is large.
Only
columns with a valid domain are available in this view. Make sure that the
predecessor node is executed or set the domain with the Domain Calculator
node!. Drag and drop line plot from the node repository into the workflow
editor.
Numeric Scorer:
This
node computes certain statistics between the a numeric column's values (ri) and
predicted (pi) values. It computes R²=1-SSres/SStot=1-Σ(pi-ri)²/Σ(ri-1/n*Σri)²
(can be negative!), mean absolute error (1/n*Σ|pi-ri|), mean squared error
(1/n*Σ(pi-ri)²), root mean squared error (sqrt(1/n*Σ(pi-ri)²)), and mean signed
difference (1/n*Σ(pi-ri)). The computed values can be inspected in the node's
view and/or further processed using the output table. Drag and drop Numeric
Scorer from the node repository into the workflow editor.
Connection of all the nodes in the
workflow editor to perform simple linear regression:
Connections:
You
can connect two nodes by dragging the mouse from the out-port of one node to
the in-port of another node. Loops are not permitted. If a node is already
connected you can replace the existing connection by dragging a new connection
onto it. If the node is already connected you will be asked to confirm the
resulting reset of the target node. You can also drag the end of an existing
connection to a new in-port (either of the same node or to a different node).
Execute:
In
the next step, you probably want to execute the node, i.e. you want the node to
actually perform its task on the data. To achieve this right-click the node in order
to open the context menu and select "Execute". You can also choose
the related button from the toolbar. The button looks like the icon next to the
context menu entry. It is not necessary to execute every single node: if you
execute the last node of connected but not yet executed nodes, all predecessor
nodes will be executed before the last node is executed.
Execute All:
In
the toolbar above the editor there is also a button to execute all not yet
executed nodes on the workflow.
This
also works if a node in the flow is lit with the red status light due to
missing information in the predecessor node. When the predecessor node is
executed and the node with the red status light can apply its settings it is
executed as well as its successors. The underlying workflow manager also tries
to execute branches of the workflow in parallel.
Execute and Open View:
The
node context menu also contains the "Execute and open view" option.
This executes the node and immediately opens the view. If a node has more than
one views only the first view is opened.
In
workflow editor when you try to view Partitioning node you can see First
partition as training data of iris data set which contains 80% and Second
partition of testing data of iris data set which contains 20%.
In
workflow editor when you try to view Simple Regression tree node you can see
the tabulated form of decision tree which can be increased and observed when
you click on positive symbol (+) and the chart decreases when you click on
negative symbol (-) you can also adjust the zoom in or zoom out range from 60%
to 120%.
In
workflow editor when you try to view Simple Regression Tree Predictor it
displays the predicted value as output from the testing data set.
In workflow editor when you try to view column Filter you can see that the other columns are
been filtered and removed , only the petal width and prediction petal (petal
width) is kept which will be used to plot an line graph.
In
workflow editor when you try to view line plot. You can see that it draws a
line plot to visualize the performance of the simple regression tree
Fit
to size by clicking that you can fit the plot within the screen to have an
better visualization. You can also change the colour by clicking background
colour.
In
workflow editor when you try to view number score node. It displays an
statistical calculation of score prediction.
INTERPRETATION:
The
mean absolute error to prediction (petal width) is 0.147. In statistics, the mean absolute error (MAE) is a
quantity used to measure how close forecasts or predictions are to the eventual
outcomes. In statistics, the mean squared error (MSE) or mean squared deviation
(MSD) of an estimator (of a procedure for estimating an unobserved quantity)
measures the average of the squares of the errors or deviations—that is, the
difference between the estimator and what is estimated. MSE is a risk function,
corresponding to the expected value of the squared error loss or quadratic
loss. The difference occurs because of randomness or because the estimator
doesn't account for information that could produce a more accurate estimate. The
MSE is a measure of the quality of an estimator—it is always non-negative, and
values closer to zero are better. Here the MSE is 0.04. The root-mean-square deviation (RMSD) or
root-mean-square error (RMSE) is a frequently used measure of the differences
between values (sample and population values) predicted by a model or an
estimator and the values actually observed. The RMSD represents the sample
standard deviation of the differences between predicted values and observed
values. These individual differences are called residuals when the calculations
are performed over the data sample that was used for estimation, and are called
prediction errors when computed out-of-sample. The RMSD serves to aggregate the
magnitudes of the errors in predictions for various times into a single measure
of predictive power. RMSD is a measure of accuracy, to compare forecasting
errors of different models for a particular data and not between datasets, as
it is scale-dependent. Although RMSE is one of the most commonly reported
measures of disagreement, RMSD is the square root of the average of squared
errors, thus RMSD confounds information concerning average error with
information concerning variation in the errors. The effect of each error on
RMSD is proportional to the size of the squared error thus larger errors have a
disproportionately large effect on RMSD. Consequently, RMSD is sensitive to
outliers.
CONCLUSION:
KNIME allows users to visually create data
flows (or pipelines), selectively execute some or all analysis steps, and later
inspect the results, models, and interactive views. KNIME is written in Java
and based on Eclipse and makes use of its extension mechanism to add plugins
providing additional functionality. The core version already includes hundreds
of modules for data integration (file I/O, database nodes supporting all common
database management systems through JDBC), data transformation (filter, converter,
combiner) as well as the commonly used methods for data analysis and
visualization. With the free Report Designer extension, KNIME workflows can be
used as data sets to create report templates that can be exported to document
formats like doc, ppt, xls, pdf and others.
KNIMEs
core-architecture allows processing of large data volumes that are only limited
by the available hard disk space (most other open source data analysis tools
work in main memory and are therefore limited to the available RAM). E.g. KNIME
allows analysis of 300 million customer addresses, 20 million cell images and
10 million molecular structures. Additional
plugins allows the integration of methods for Text mining, Image mining, as
well as time series analysis.
KNIME
integrates various other open-source projects, e.g. machine learning algorithms
from Weka, the statistics package R project, as well as LIBSVM, JFreeChart,
ImageJ, and the Chemistry Development Kit. KNIME is implemented in Java but
also allows for wrappers calling other code in addition to providing nodes that
allow to run Java, Python, Perl and other code fragments.
Overall,
this is a very sophisticated and professional piece of software. Because of its
flexibility, it is nowadays our chief cheminformatics workhorse, and voting
with one’s feet is surely the best possible endorsement. The KNIME philosophy
and business model of mixed commercial and free (but Open) software, allows its
continued improvement while making it freely available to desktop users. Some
minor gripes relate to the fact that it seems only to read but not write .xlsx
files—we are confident that someone will write a node to let it do so soon.
There is a substantial community of users, increasing all the time, and many
training schools and the like. Because of this, I think it will continue to
grow in popularity. It is well worth a look for the GP community.
AUTHOR:G.POONURAJ NADAR
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