Basic Instructions

Input a text to receive response from Alfarvis

Enter Bye to close the program

Alfa is data exploration and analysis software that uses natural language

To begin data exploration, you should first load a dataset using the "load command". For example "load tour de france dataset" to load a cycling dataset

To get a list of datasets that can be loaded, call "list files"

Once you have loaded a dataset, you can perform different sets of commands : visualization, statistics, machine learning

You can list commands using "list commands" or "list visualization/statistics... commands" for specific command list

You can also get individual help for each of the commands by calling "help [command name]"

Apart from the above commands, there are also data handling commands that can set row labels, set reference for setting legends etc

You can get a list of data handling commands by calling "list data handling commands"

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Load the breast cancer dataset and set reference to diagnosis

User: load breast cancer data set

Alfa:

Running command in background!

Loaded breast cancer tumor

Showing Statistics for breast cancer tumor

Loaded file: breast_tumor_data.csv

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User: set reference as diagnosis

Alfa:

Setting reference to diagnosis

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You can get some summary statistics of any feature - mean, median, standard deviation ,etc.

User: what is the mean of area
Alfa: 






Feature

Mean


area

654.89



Saving result to mean.area
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 You can also do any of the summary statistics for each label/group 
User: what is the groupwise mean of area
Alfa: 






area

diagnosis


462.79

1.00


978.38

2.00



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 Similarly, you can do a summary statistic or groupwise summary of the complete dataset or a selected set of features. See below 
User: summary of breast cancer dataset
Alfa: 






features

max

mean

min

stdev


area

2501.00

654.89

143.50

351.91


compactness

0.35

0.10

0.02

0.05


concave.points

0.20

0.05

0.00

0.04


concavity

0.43

0.09

0.00

0.08


diagnosis

2.00

1.37

1.00

0.48


fractal.dimension

0.10

0.06

0.05

0.01


id

911320502.00

30371831.43

8670.00

125020585.61


perimeter

188.50

91.97

43.79

24.30


radius

28.11

14.13

6.98

3.52


smoothness

0.16

0.10

0.05

0.01


symmetry

0.30

0.18

0.11

0.03


texture

39.28

19.29

9.71

4.30



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User: groupwise summary of breast cancer dataset
Alfa: 






1_mean

1_stdev

2_mean

2_stdev

features

pValue: 0 vs 1


462.79

134.29

978.38

367.94

area

0.00


0.08

0.03

0.15

0.05

compactness

0.00


0.03

0.02

0.09

0.03

concave.points

0.00


0.05

0.04

0.16

0.08

concavity

0.00


0.06

0.01

0.06

0.01

fractal.dimension

0.77


26543824.62

116739662.03

36818050.44

137896549.64

id

0.36


78.08

11.81

115.37

21.85

perimeter

0.00


12.15

1.78

17.46

3.20

radius

0.00


0.09

0.01

0.10

0.01

smoothness

0.00


0.17

0.02

0.19

0.03

symmetry

0.00


17.91

4.00

21.60

3.78

texture

0.00



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 You can use any of the visualization commands shown in the  visualization section  to visualize and explore different characteristics of the dataset. A few examples are shown below 
User: bar plot of  fractal.dimension  and smoothness 
Alfa: 



Saving result to figure.1.bar
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User: violin plot of  symmetry and  concavity 
Alfa: 



Saving result to figure.2.violin
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User: scatter between  perimeter and  radius 
Alfa: 



Saving result to figure.3.scatter2d
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User: multiscatter between  compactness  concave.points and  texture 
Alfa: 



Saving result to figure.4.multiscatter
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User: correlation between  symmetry  smoothness  radius  perimeter  texture 
Alfa: 
Displaying the result as a heatmap



Saving result to figure.5.correlation
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User: ttest of breast cancer dataset
Alfa: 






features

pValue: 0 vs 1


area

0.00


compactness

0.00


concave.points

0.00


concavity

0.00


fractal.dimension

0.77


id

0.36


perimeter

0.00


radius

0.00


smoothness

0.00


symmetry

0.00


texture

0.00



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User: roc analysis of breast cancer dataset 
Alfa: 






AUC

features


0.94

area


0.86

compactness


0.96

concave.points


0.94

concavity


0.52

fractal.dimension


0.55

id


0.95

perimeter


0.94

radius


0.72

smoothness


0.70

symmetry


0.78

texture



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User: save notebook
Alfa:

features	max	mean	min	stdev
area	2501.00	654.89	143.50	351.91
compactness	0.35	0.10	0.02	0.05
concave.points	0.20	0.05	0.00	0.04
concavity	0.43	0.09	0.00	0.08
diagnosis	2.00	1.37	1.00	0.48
fractal.dimension	0.10	0.06	0.05	0.01
id	911320502.00	30371831.43	8670.00	125020585.61
perimeter	188.50	91.97	43.79	24.30
radius	28.11	14.13	6.98	3.52
smoothness	0.16	0.10	0.05	0.01
symmetry	0.30	0.18	0.11	0.03
texture	39.28	19.29	9.71	4.30

1_mean	1_stdev	2_mean	2_stdev	features	pValue: 0 vs 1
462.79	134.29	978.38	367.94	area	0.00
0.08	0.03	0.15	0.05	compactness	0.00
0.03	0.02	0.09	0.03	concave.points	0.00
0.05	0.04	0.16	0.08	concavity	0.00
0.06	0.01	0.06	0.01	fractal.dimension	0.77
26543824.62	116739662.03	36818050.44	137896549.64	id	0.36
78.08	11.81	115.37	21.85	perimeter	0.00
12.15	1.78	17.46	3.20	radius	0.00
0.09	0.01	0.10	0.01	smoothness	0.00
0.17	0.02	0.19	0.03	symmetry	0.00
17.91	4.00	21.60	3.78	texture	0.00

AUC	features
0.94	area
0.86	compactness
0.96	concave.points
0.94	concavity
0.52	fractal.dimension
0.55	id
0.95	perimeter
0.94	radius
0.72	smoothness
0.70	symmetry
0.78	texture