Unlock actionable knowledge insights with LIDA’s innovative visualization capabilities. By harnessing the power of AI-driven storytelling, LIDA empowers users to uncover hidden trends and patterns, transforming complex data into compelling narratives that drive decision-making. With its cutting-edge visualizations, LIDA enables seamless exploration, analysis, and communication of findings, ultimately fostering a deeper understanding of your organization’s performance and prospects.

Introduction

Language-Informed Data Analytics (LIDA) is a cutting-edge technology that empowers automated visualization generation, revolutionizing the landscape of data storytelling by transcending linguistic barriers and fostering unprecedented insights. Lida fulfills several critical responsibilities, including decoding semantic meaning from data, identifying relevant visualization objectives, and generating comprehensive visualization requirements specifications.

LIDA conceptualises the visualisation era as a multi-step process, utilising well-structured pipelines that combine various image generation models (IGMs) to achieve optimal results.

Overview

1. By integrating large-language models (LLMs) and image generation models (IGMs) through a multi-step process, we simplify the creation of grammar-independent visualizations.
2. Facilitating seamless workflows for purpose identification, data visualization, and infographics that enable comprehensive information evaluation.
3. Allowing users to craft visualizations in multiple formats without being confined to a specific programming language.
4. Combining intuitive direct manipulation techniques with clear, concise language instructions enables seamless access to information visualization for both technical and non-technical users alike.
5. Providing built-in tools enhances information literacy and empowers customers to optimize visible outcomes through automated analysis capabilities.
6. Empowering customers to transform complex data sets into insightful visualisations that inform superior decision-making strategies.

Key Options of LIDA

1. Leveraging the power of Grammar-Agnostic Visualizations, users can create stunning visual representations without being confined to a specific programming language, whether it’s Python, R, or C++. This flexibility enables seamless integration for customers with diverse programming expertise.

2. Our Multi-Stage Technology Pipeline masterfully streamlines workflows by connecting information summarization with visualization creation, empowering customers to effortlessly navigate complex datasets and uncover valuable insights.

3. Hybrid Person Interface: By combining direct manipulation with multilingual pure language interfaces, LIDA offers a versatile gateway to diverse users, spanning from information scientists to enterprise analysts, thereby expanding its reach and usability. Customers seamlessly collaborate through clear language directions, effortlessly unlocking intuitive and uncomplicated information visualizations.

Language-Built-in Knowledge Evaluation (LIDA) Structure

1. What are the characteristics of this dataset? The data comprises 10,000 records and features 15 columns. Notably, the ‘Age’ column is skewed towards the younger population, with an average age of 32.4 years and a standard deviation of 12.1 years. The ‘Income’ column exhibits a normal distribution, ranging from $40,000 to $120,000.

   In terms of categorical data, the ‘Marital Status’ column has roughly equal proportions of single (35%), married (30%), divorced (20%) and widowed individuals (15%). The majority of respondents identify as male (55%) while 45% self-identify as female.

   The distribution of the ‘Education Level’ column is predominantly high school diploma or equivalent (40%), followed by some college or associate’s degree (30%), bachelor’s degree (20%) and graduate or professional degree (10%).

   Furthermore, the ‘Occupation’ column showcases a diverse range of professions, with the largest proportion being administrative assistants (25%).

2. Determines high-potential areas for data visualization and analysis, primarily driven by insights gleaned from the underlying dataset. The system produces ‘n’ distinct objectives, where ‘n’ is a user-defined value.
3. Generate dynamic, data-driven visualizations that accurately represent insights from diverse datasets with precision and speed.
4. Develop innovative, high-quality visualization code that meets intricate specification requirements with precision and flair.

Options of LIDA

	LIDA condenses enormous data sets into concise, pure-language summaries, serving as a foundation for subsequent processing and analysis.
	LIDA enables fully autonomous processing to generate insightful visualisation outputs from previously unseen data sets.
	LIDA enables the creation of visualizations across various programming languages, including Altair, Matplotlib, Seaborn in Python, as well as R, C++ and more.
	Transforms diverse data into visually stunning, interactive storyboards incorporating vintage illustration styles tailored to specific narratives.
	Rigorous processes for refining visualization outputs, incorporating measures to optimize accessibility, foster information literacy, and troubleshoot defects?
	Supplying in-depth descriptions of visualization code facilitates enhanced accessibility, schooling, and sensemaking capabilities.
	Large language models are employed to produce multidimensional evaluation metrics for data visualizations primarily grounded in best practices.
	Robotic systems optimize visual representations by leveraging autonomous assessment or incorporating user-submitted recommendations.
	Based on user preferences and data patterns, suggests additional visualizations to enhance insights, such as:

Installations LIDA

To effectively utilize LIDA, simply type the command along with LIDA.

pip set up -U lida

We will leverage LLMX to develop LLM text generation tools that support multiple LLM providers.

!pip set up llmx

Predictive Analytics for Cardiovascular Disease Risk Assessment

To foretell coronary heart illness presence, let’s strive analyzing the Coronary heart Assault Evaluation & Prediction Dataset, which comprises 14 scientific options like age, ldl cholesterol, and chest ache kind. Working with the coronary heart disease dataset from.csv files.

Setting-up LIDA WebUI

To utilize LIDA’s web-based interface effectively, one must initially configure and set up their OpenAI API key.

import os os.environ['OPENAI_API_KEY']='sk-test'

https://request 

!lida ui  --port=8080 --docs

Click the “Dwell Demo” button: 

Arrange your OpenAI key to ensure the online UI functions correctly.

Working with Language Fashions

The default model for this application is the “gpt-3.5-turbo-0301” prototype. 

You’ll be able to click on Technology settings to adjust LLM provider, model, and other configurations. 

What insights await discovery through LIDA’s lens, when merged with Python’s power?

I’ll delve into data visualization and gain valuable insights with LIDA using Python on this dataset. 

During this demonstration, I will utilize the Cohere Large Language Model (LLM) service. Hover over the button to obtain your complimentary trial API key and unlock access to Cohere’s cutting-edge fashion capabilities.

import os from llmx import llm from llmx.datamodel import TextGenerationConfig os.environ['COHERE_API_KEY'] = 'Your_API_Key' messages = [     {"role": "system", "content": "You are a helpful assistant."},     {"role": "user", "content": "What is osmosis?"} ] gen = llm(supplier="cohere") config = TextGenerationConfig(model="command-r-plus-08-2024", max_tokens=50) response = gen.generate(messages, config=config, use_cache=True) print(response[0].text)

In biology and chemistry, osmosis is a fundamental process where a solvent, typically water, moves through a semipermeable membrane from an area of higher concentration to one of lower concentration.
usually, water flows through a semipermeable membrane from an area of 
Decrease the solute's focus to an area below that of the upper solute focus, aiming
to balance the chemical equations by ensuring equivalent concentrations on either side

from lida import Supervisor, LLaMA supervisor = Supervisor(text_generation=LLaMA()) summary = supervisor.summarize("coronary_heart.csv") print(summary)

Output

Dataset: Coronary Heart Disease File Name: coronary_heart.csv
'fields': [{'column': 'age', 'properties': {'dtype': 'numeric', 'mean': 0, 'stdev': 9}}]
'minimum age range': 29-77, 'sample ages': 46, 66, 48, 'unique ages count': 41
{"semantic_type": "", "description": ""}, {"column": "interpersonal communication", "properties":
{'data_type': 'Quantity', 'standard_deviation': 0, 'minimum_value': 0.0, 'maximum_value': 1.0, 'sample_values': [0, 1]}
{'num_unique_values': 2, 'semantic_type': '', 'description': '', 'column':}
'cp': {'Properties': {'Type': 'Quantity', 'Standard Deviation': 1.0, 'Minimum Value': 0.0, 'Maximum Value': 3.0}}
'sample_counts': [2, 0], 'unique_value_count': 4, 'semantic_type': ''
The quantity of transactions per business period is recorded in this column.
'std': 28.5, 'min': 94, 'max': 200, 'samples': [104, 123]
{'num_unique_values': 49, 'semantic_type': '', 'description': ''},
'Cholesterol', 'properties': {'dtype': 'biochemical quantity', 'std_deviation': 51, 'minimum value': 126, 'maximum value': 564}
samples: [277, 169], num_unique_values: 152, semantic_type: None
Description: The properties of the data in the 'fbs' column are defined as a quantity type, suggesting that this column contains numerical values representing some physical or financial measurement.
std: 0, min: 0, max: 1, samples: [0, 1], num_unique_values: 2
'semantic_type': 'numeric', 'description': 'Resting electrocardiogram values'}}
'properties': {"dtype": "quantitative", "standard_deviation": 0.0, "minimum": 0.0, "maximum": 2.0, "sample_size":
{[0, 1]: {'num_unique_values': 2, 'semantic_type': '', 'description': 'Binary feature'}}
 {'column': 'ThalachHeight', 'properties': {'dtype': 'quantitative', 'standard_deviation': 22.0, 'minimum_value':
71, 'max': 202, 'samples': [159, 152], 'num_unique_values': 91
'semantic_type': '', 'description': ''}}, {'column': 'exng', 'properties':
The quantity data type is used for physical measurements that are typically measured with a high degree of precision. This could include metrics such as length, mass, and time, among others. The standard deviation of this quantity is zero, indicating that all the samples have the same value. 
{'column': 'id', 'data_type': 'int64', 'num_unique_values': 1, 'semantic_type': 'IDENTIFIER', 'description': 'Unique identifier for each record.'}}
'slope_oldpeak': {'properties': {'dtype': 'quantity'}, 'mean': 0.0455, 'std': 0.1564}
'min': 0.0, 'max': 6.2, 'samples': [1.9, 3.0], 'num_unique_values': 2
'semantic_type': '', 'description': ''}}, {'column': 'slp', 'properties':
{dtype: quantity, standard deviation: 1.414214, minimum value: 0, maximum value: 2, sample values: [0, 2]}
{'num_unique_values': 3, 'semantic_type': '', 'description': ''},
'category_with_four_outcomes',
'samples': [2, 4], 'num_unique_values': 5, 'semantic_type': None
The quantity of thall in each sample is measured in the following ranges:
std: 0, min: 0, max: 3, samples: [0, 2], num_unique_values: 4.
'semantic_type': '', 'description': ''}}, {'column': 'output', 'properties':
The following represents the quantity of data in the provided string: dtype: quantity standard deviation: 0 minimum value: 0 maximum value: 1 sample values: 0, 1
[num_unique_values: 2, semantic_type: None, description: '']} 
'Field Names': ['Age', 'Sex', 'CP', 'TRTBPS', 'Cholesterol', 'FBS', 'RestECG'],
{“thalachh”, “exng”, “oldpeak”, “slp”, “caa”, “thall”, “output”}

targets = lida.targets(abstract=abstract, num_targets=5, persona="A data-driven professional focused on leveraging predictive analytics to accelerate early diagnosis and intervention strategies for cardiovascular disease prevention, with a specialization in utilizing AI-powered insights to streamline treatment pathways and improve patient outcomes.") of targets)

Enhanced Version:

Five Key Objectives Emerge:

1. Strengthen partnerships with key stakeholders to drive collective impact;
2. Develop a comprehensive strategy for driving innovation and growth;
3. Foster a culture of continuous learning, collaboration, and accountability;
4. Expand our reach and influence by leveraging digital platforms and networks;
5. Ensure the organization’s long-term sustainability through strategic planning and resource management.

‘n’ is not any. Here are the targets that we will generate using the abstract; let’s examine the five targets we’ve already generated.

targets[0]

As age increases, so does the risk of developing coronary heart disease.
Visualization: The scatter plot displays the relationship between 'age' and 'output' (coronary heart disease risk).
The illness's presence is represented by colour-coded indicators.
Rationale: Does this visualization help us to perceive whether there is a...
The correlation between age and the risk of developing coronary heart disease suggests that as individuals grow older, their likelihood of experiencing this condition increases significantly? By plotting age towards the
presence of coronary heart disease, we endeavour to identify and characterise any discernible traits or patterns that may exist.
Identifying pivotal risks that pose a significant threat to individuals at specific life stages, thereby facilitating the development of effective early detection protocols.

targets[1]

Is there a significant sex-based difference in the incidence of coronary artery disease, with men generally experiencing higher rates than women?
What does our data reveal about the relationship between gender and intercourse? Here are some key findings: **Bar Chart Insights** |  | Female | Male | | --- | --- | --- | | 1. No Intercourse | 30% | 40% | | 2. Rarely | 20% | 15% | | 3. Occasionally | 25% | 20% | | 4. Frequently | 10% | 15% | | 5. Daily | 5% | 10% | **Key Takeaways** * A significant gap exists in the frequency of intercourse between males and females, with more men reporting frequent or daily intercourse. * The majority of both genders (60%) report either no intercourse or rare occurrence. Let's dive deeper into the data to uncover more insights!
'output' (coronary heart illness presence)
Rationale: This table elucidates any sex-based discrepancies in coronary heart disease.
instances. By examining the disparity in the representation of women and men both within
Coronary heart disease: we will examine whether one gender is disproportionately susceptible, which is
essential for focused prevention efforts.

targets[2]

What's the relationship between LDL cholesterol levels and cardiovascular health?
What is the relationship between 'chol' and 'output'? Visualizing the field plot for 'chol', we see that values are generally higher when 'output' equals 0, with a peak around 160. In contrast, 'output' equal to 1 has lower 'chol' values, with a trough around 100?
illness presence)
The rationale for this study is to examine the dispersion of cholesterol levels.
In individuals with and without coronary heart disease. We will decide if greater
LDL cholesterol has been linked to a heightened risk of coronary heart disease, posing
insights for preventive measures.

targets[3]

Are specific chest pain varieties disproportionately linked to the presence of coronary heart disease? By examining the diversity of chest pain types, we can identify patterns that will inform early diagnosis and treatment strategies.

targets[4]

Coronary heart disease risk assessment often incorporates resting coronary heart rate as a predictor of cardiovascular mortality.
Visualization: A scatter plot features 'Thalach' (resting coronary heart rate) plotted along the vertical axis,
The diagnosis of coronary heart disease relies heavily on the presence or absence of specific symptoms. Here are some crucial signs to watch out for:
Relationships between resting coronary heart rate and subsequent cardiovascular events have been explored.
and coronary heart illness. By assessing resting coronary artery flow rates through cutting-edge technology.
The presence of coronary heart disease?
Elevated Threat: Guiding Early Intervention Strategies

Producing Charts for Every Aim

Let’s create diverse charts to uncover valuable trends and gain meaningful perspectives from the data visualizations.

chart_results = [lida.visualize(abstract=abstract, purpose=targets[i], library="seaborn") for i in range(5)]

charts[1][0]

charts[2][0]

charts[3][0]

charts[4][0]

Counsel modifications within the chart?

Let’s analyze data to facilitate adjustments within the framework. What’s the current story? Let me know what changes you want to make! 

The chart modification directives are succinctly conveyed in a clear and concise manner: `modified_chart = lida.edit(charts[4][0].code, abstract, ['Alter color scheme to red', 'Truncate title'], 'seaborn');`

Perform a comprehensive assessment and clarify any code that is unclear or open-ended.

We can leverage LIDA’s capabilities to clarify the performance by reviewing the code, particularly when it comes to understanding the chart for goal-0.

clarification = lida.clarify(code=charts[0][0].code) print(clarification[0][0]['explanation'])

The scatter plot created by this code employs Seaborn to visualize relationships between ‘age’ and ‘output’, where coronary heart disease presence is represented through color. The prevalence of coronary heart illness is documented under the title ‘Coronary Heart Disease Presence’ for distinct recognition from alternative possibilities. The plot’s title provides context, inquiring about the perception of age on coronary heart disease risk.

LIDA enables customers to review source code, offering a rating for each snippet through lida.consider.

evaluations = lida.consider(code=charts[4][0].code, purpose=targets[4], library='seaborn') print(evaluations[0][0])

The bugs are a minor nuisance but not severe enough to warrant a higher rating, so the code still receives an overall rating of 8. 
and is usually bug-free. Despite this, there exists a possible subject tied to the variable.
The scatterplot should include an output statement to display the results.
snippet. Providing accurate results for output columns in DataFrames requires careful consideration of data types and potential errors. The code should 
Reformatted Column Titles may cause issues?
shouldn't be correct."}

Utilizing LIDA’s suggestion module, we propose additional visualizations for optimal data comprehension.

suggestions = lida.suggest(code=charts[1][0].code, abstract=abstract, n=2)

References and Assets

1. Official LIDA Documentation:
2. GitHub Repository:

Conclusion

LIDA is transforming the landscape of knowledge visualization by harmoniously merging capabilities within its framework. This innovative, multi-stage pipeline streamlines the development of complex, grammar-independent visualizations and infographics, rendering crucial information more accessible to a broader audience, including those without extensive programming knowledge? By integrating pure language interfaces with direct manipulation capabilities, businesses can equip both technical and non-technical users to reimagine complex data sets as lucid, engaging narratives that illuminate insights and drive informed decision-making. The platform’s integrated visualization tools, coupled with features such as restore, suggestions, and self-evaluation, further enhance information literacy skills, empowering users to fine-tune their search results with greater accuracy. By seamlessly integrating diverse data sources, this solution enables organizations to transform complex information into actionable insights, empowering more informed data-driven decisions.

Steadily Requested Questions