# TOPSISX 📊
[](https://pypi.org/project/topsisx/)
[](https://pypi.org/project/topsisx/)
[](https://pepy.tech/project/topsisx)
[](https://github.com/SuvitKumar003/ranklib/blob/main/LICENSE)
**TOPSISX** is a comprehensive Python library for **Multi-Criteria Decision Making (MCDM)** with an intuitive web interface. Make data-driven decisions using proven algorithms like TOPSIS, VIKOR, AHP, and Entropy weighting.
---
## ✨ Features
- 🌐 **Web Interface** - Beautiful, user-friendly Streamlit dashboard
- 📊 **Multiple Methods** - TOPSIS, VIKOR, AHP, Entropy weighting
- 📁 **Easy Input** - CSV upload, sample data, or manual entry
- 📈 **Visualizations** - Interactive charts and rankings
- 📄 **PDF Reports** - Professional report generation
- 💻 **CLI Support** - Command-line interface for automation
- 🎯 **Simple API** - Easy integration into your projects
---
## 🚀 Quick Start
### Installation
```bash
pip install topsisx
```
### Option 1: Web Interface (Recommended)
Launch the interactive web app with a single command:
```bash
topsisx --web
```
This will open a beautiful interface in your browser where you can:
- 📤 Upload your CSV file
- 📋 Use sample datasets
- ✏️ Enter data manually
- 🎨 Configure methods and parameters
- 📊 View results and visualizations
- 💾 Download results
### Option 2: Python API
```python
from topsisx.pipeline import DecisionPipeline
import pandas as pd
# Your data
data = pd.DataFrame({
'Cost': [250, 200, 300, 275, 225],
'Quality': [16, 16, 32, 32, 16],
'Time': [12, 8, 16, 8, 16]
})
# Create pipeline
pipeline = DecisionPipeline(weights='entropy', method='topsis')
# Run analysis
result = pipeline.run(data, impacts=['-', '+', '-'])
print(result)
```
### Option 3: Command Line
```bash
# Basic usage
topsisx data.csv --impacts "+,-,+" --output results.csv
# With specific methods
topsisx data.csv --method vikor --weights entropy --impacts "+,-,+"
# Generate PDF report
topsisx data.csv --impacts "+,-,+" --report
```
---
## 📖 Supported Methods
### 1. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Ranks alternatives based on their distance from ideal and anti-ideal solutions.
```python
from topsisx.topsis import topsis
result = topsis(data, weights=[0.3, 0.3, 0.4], impacts=['+', '-', '+'])
```
**Best for:** General-purpose ranking, balanced decision-making
### 2. VIKOR (Compromise Ranking)
Finds compromise solutions considering both group utility and individual regret.
```python
from topsisx.vikor import vikor
result = vikor(data, weights=[0.3, 0.3, 0.4], impacts=['+', '-', '+'], v=0.5)
```
**Best for:** Conflicting criteria, compromise solutions
### 3. AHP (Analytic Hierarchy Process)
Calculates weights through pairwise comparisons.
```python
from topsisx.ahp import ahp
import pandas as pd
# Pairwise comparison matrix
pairwise = pd.DataFrame([
[1, 3, 5],
['1/3', 1, 4],
['1/5', '1/4', 1]
])
weights = ahp(pairwise, verbose=True)
```
**Best for:** Subjective criteria, expert judgments
### 4. Entropy Weighting
Calculates objective weights based on data variance.
```python
from topsisx.entropy import entropy_weights
weights = entropy_weights(data.values)
```
**Best for:** Objective weighting, data-driven decisions
---
## 💡 Usage Examples
### Example 1: Laptop Selection
```python
from topsisx.pipeline import DecisionPipeline
import pandas as pd
# Data
laptops = pd.DataFrame({
'Model': ['Laptop A', 'Laptop B', 'Laptop C', 'Laptop D'],
'Price': [800, 1200, 1000, 900],
'RAM_GB': [8, 16, 16, 8],
'Battery_Hours': [6, 4, 8, 7],
'Weight_KG': [2.0, 2.5, 1.8, 2.2]
})
# Analysis
pipeline = DecisionPipeline(weights='entropy', method='topsis')
result = pipeline.run(
laptops,
impacts=['-', '+', '+', '-'] # Price and Weight are costs
)
print(result)
```
### Example 2: Supplier Selection with AHP
```python
from topsisx.pipeline import DecisionPipeline
import pandas as pd
# Supplier data
suppliers = pd.DataFrame({
'Supplier': ['S1', 'S2', 'S3'],
'Cost': [250, 200, 300],
'Quality': [16, 16, 32],
'Delivery': [12, 8, 16]
})
# AHP pairwise matrix (Quality > Cost > Delivery)
ahp_matrix = pd.DataFrame([
[1, 3, 5], # Quality
['1/3', 1, 3], # Cost
['1/5', '1/3', 1] # Delivery
])
# Analysis with AHP weights
pipeline = DecisionPipeline(weights='ahp', method='topsis')
result = pipeline.run(
suppliers,
impacts=['-', '+', '-'],
pairwise_matrix=ahp_matrix
)
print(result)
```
### Example 3: Compare Methods
```python
from topsisx.pipeline import DecisionPipeline
pipeline = DecisionPipeline(weights='entropy', method='topsis')
# Compare TOPSIS vs VIKOR
comparison = pipeline.compare_methods(
data=data,
impacts=['+', '-', '+']
)
print(comparison['comparison'])
```
---
## 🌐 Web Interface Guide
### Starting the Web App
```bash
topsisx --web
```
### Features:
1. **Data Input Options:**
- 📤 Upload CSV files
- 📋 Use pre-loaded sample datasets
- ✏️ Manual data entry
2. **Configuration:**
- Choose weighting method (Entropy, AHP, Equal)
- Select ranking method (TOPSIS, VIKOR)
- Define impact directions (+/-)
- Set method parameters
3. **Results:**
- 📊 Interactive ranking tables
- 📈 Visual charts and graphs
- 🥇 Top-3 alternatives highlight
- 💾 Download results as CSV
- 📄 Generate PDF reports
---
## 🎯 CLI Reference
### Basic Commands
```bash
# Launch web interface
topsisx --web
# Basic analysis
topsisx data.csv --impacts "+,-,+"
# Specify method and weighting
topsisx data.csv --method vikor --weights equal --impacts "+,-,+"
# With ID column preservation
topsisx data.csv --impacts "+,-,+" --id-col "Model"
# Generate report
topsisx data.csv --impacts "+,-,+" --report
# AHP weighting
topsisx data.csv --weights ahp --ahp-matrix ahp.csv --impacts "+,-,+"
# VIKOR with custom v parameter
topsisx data.csv --method vikor --vikor-v 0.7 --impacts "+,-,+"
# Verbose output
topsisx data.csv --impacts "+,-,+" --verbose
```
### Full Options
```
usage: topsisx [-h] [--web] [--weights {entropy,ahp,equal}]
[--method {topsis,vikor}] [--impacts IMPACTS]
[--ahp-matrix AHP_MATRIX] [--vikor-v VIKOR_V]
[--output OUTPUT] [--report] [--id-col ID_COL]
[--verbose] [--version] [input]
Options:
--web Launch web interface
--weights Weighting method (default: entropy)
--method Decision method (default: topsis)
--impacts Impact directions (e.g., '+,-,+')
--ahp-matrix Path to AHP pairwise comparison matrix
--vikor-v VIKOR strategy weight (0-1, default: 0.5)
--output Output CSV file path
--report Generate PDF report
--id-col ID column to preserve
--verbose Show detailed information
```
---
## 📁 Data Format
### CSV Format
Your CSV should have:
- **Rows:** Alternatives/options to rank
- **Columns:** Criteria for evaluation
- **Optional:** ID column (will be preserved)
Example `data.csv`:
```csv
Model,Price,RAM,Battery,Weight
Laptop A,800,8,6,2.0
Laptop B,1200,16,4,2.5
Laptop C,1000,16,8,1.8
Laptop D,900,8,7,2.2
```
### Impact Direction
- `+` : Benefit criterion (higher is better) - e.g., Quality, Speed, RAM
- `-` : Cost criterion (lower is better) - e.g., Price, Time, Weight
---
## 📊 Output Format
Results include original data plus:
**TOPSIS:**
- `Topsis_Score`: Similarity to ideal solution (0-1, higher is better)
- `Rank`: Final ranking (1 is best)
**VIKOR:**
- `S`: Group utility measure
- `R`: Individual regret measure
- `Q`: Compromise ranking index
- `Rank`: Final ranking (1 is best)
---
## 🔧 Advanced Usage
### Custom Pipeline
```python
from topsisx.pipeline import DecisionPipeline
# Create custom pipeline
pipeline = DecisionPipeline(
weights='entropy',
method='topsis',
verbose=True # Show detailed logs
)
# Run with custom parameters
result = pipeline.run(
data=df,
impacts=['+', '-', '+'],
v=0.7 # VIKOR parameter
)
```
### Generate Reports
```python
from topsisx.reports import generate_report
generate_report(
result,
method='topsis',
filename='my_report.pdf'
)
```
### Batch Processing
```python
import glob
from topsisx.pipeline import DecisionPipeline
pipeline = DecisionPipeline(weights='entropy', method='topsis')
# Process multiple CSV files
for csv_file in glob.glob('data/*.csv'):
df = pd.read_csv(csv_file)
result = pipeline.run(df, impacts=['+', '-', '+'])
result.to_csv(f'results/{csv_file}', index=False)
```
---
## 🎓 Methodology
### TOPSIS Algorithm
1. Normalize decision matrix
2. Apply criteria weights
3. Determine ideal (A+) and anti-ideal (A-) solutions
4. Calculate Euclidean distances to A+ and A-
5. Rank by relative closeness to ideal
### VIKOR Algorithm
1. Determine ideal and anti-ideal values
2. Calculate S (group utility) and R (individual regret)
3. Compute Q values as weighted combination
4. Rank alternatives by Q values
### AHP Process
1. Create pairwise comparison matrix (1-9 scale)
2. Normalize matrix by column sums
3. Calculate priority weights (row averages)
4. Check consistency ratio (CR < 0.1)
### Entropy Weighting
1. Normalize data to probability distribution
2. Calculate entropy for each criterion
3. Derive diversity measure (1 - entropy)
4. Normalize diversity to get weights
---
## 📚 API Reference
### DecisionPipeline
```python
DecisionPipeline(weights='entropy', method='topsis', verbose=False)
```
**Methods:**
- `run(data, impacts, pairwise_matrix=None, **kwargs)` - Run analysis
- `compute_weights(data, pairwise_matrix=None)` - Calculate weights
- `compare_methods(data, impacts, pairwise_matrix=None)` - Compare TOPSIS vs VIKOR
### Individual Methods
```python
topsis(data, weights, impacts) -> DataFrame
vikor(data, weights, impacts, v=0.5) -> DataFrame
ahp(pairwise_matrix, verbose=False) -> ndarray
entropy_weights(matrix) -> ndarray
```
---
## 🤝 Contributing
Contributions are welcome! Please:
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
---
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## 👨💻 Author
**Suvit Kumar**
- GitHub: [@SuvitKumar003](https://github.com/SuvitKumar003)
- Email: suvitkumar03@gmail.com
---
## 🙏 Acknowledgments
- Based on established MCDM methodologies
- Built with Python, Pandas, NumPy, Streamlit, and Matplotlib
- Inspired by the need for accessible decision-making tools
---
## 📞 Support
- 📖 [Documentation](https://github.com/SuvitKumar003/ranklib/blob/main/README.md)
- 🐛 [Report Issues](https://github.com/SuvitKumar003/ranklib/issues)
- 💬 [Discussions](https://github.com/SuvitKumar003/ranklib/discussions)
---
## ⭐ Star History
If you find this project useful, please consider giving it a star on GitHub!
---
**Made with ❤️ for better decision making**
Raw data
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"description": "# TOPSISX \ud83d\udcca\r\n\r\n[](https://pypi.org/project/topsisx/)\r\n[](https://pypi.org/project/topsisx/)\r\n[](https://pepy.tech/project/topsisx)\r\n[](https://github.com/SuvitKumar003/ranklib/blob/main/LICENSE)\r\n\r\n**TOPSISX** is a comprehensive Python library for **Multi-Criteria Decision Making (MCDM)** with an intuitive web interface. Make data-driven decisions using proven algorithms like TOPSIS, VIKOR, AHP, and Entropy weighting.\r\n\r\n---\r\n\r\n## \u2728 Features\r\n\r\n- \ud83c\udf10 **Web Interface** - Beautiful, user-friendly Streamlit dashboard\r\n- \ud83d\udcca **Multiple Methods** - TOPSIS, VIKOR, AHP, Entropy weighting\r\n- \ud83d\udcc1 **Easy Input** - CSV upload, sample data, or manual entry\r\n- \ud83d\udcc8 **Visualizations** - Interactive charts and rankings\r\n- \ud83d\udcc4 **PDF Reports** - Professional report generation\r\n- \ud83d\udcbb **CLI Support** - Command-line interface for automation\r\n- \ud83c\udfaf **Simple API** - Easy integration into your projects\r\n\r\n---\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install topsisx\r\n```\r\n\r\n### Option 1: Web Interface (Recommended)\r\n\r\nLaunch the interactive web app with a single command:\r\n\r\n```bash\r\ntopsisx --web\r\n```\r\n\r\nThis will open a beautiful interface in your browser where you can:\r\n- \ud83d\udce4 Upload your CSV file\r\n- \ud83d\udccb Use sample datasets\r\n- \u270f\ufe0f Enter data manually\r\n- \ud83c\udfa8 Configure methods and parameters\r\n- \ud83d\udcca View results and visualizations\r\n- \ud83d\udcbe Download results\r\n\r\n### Option 2: Python API\r\n\r\n```python\r\nfrom topsisx.pipeline import DecisionPipeline\r\nimport pandas as pd\r\n\r\n# Your data\r\ndata = pd.DataFrame({\r\n 'Cost': [250, 200, 300, 275, 225],\r\n 'Quality': [16, 16, 32, 32, 16],\r\n 'Time': [12, 8, 16, 8, 16]\r\n})\r\n\r\n# Create pipeline\r\npipeline = DecisionPipeline(weights='entropy', method='topsis')\r\n\r\n# Run analysis\r\nresult = pipeline.run(data, impacts=['-', '+', '-'])\r\n\r\nprint(result)\r\n```\r\n\r\n### Option 3: Command Line\r\n\r\n```bash\r\n# Basic usage\r\ntopsisx data.csv --impacts \"+,-,+\" --output results.csv\r\n\r\n# With specific methods\r\ntopsisx data.csv --method vikor --weights entropy --impacts \"+,-,+\"\r\n\r\n# Generate PDF report\r\ntopsisx data.csv --impacts \"+,-,+\" --report\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udcd6 Supported Methods\r\n\r\n### 1. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)\r\n\r\nRanks alternatives based on their distance from ideal and anti-ideal solutions.\r\n\r\n```python\r\nfrom topsisx.topsis import topsis\r\n\r\nresult = topsis(data, weights=[0.3, 0.3, 0.4], impacts=['+', '-', '+'])\r\n```\r\n\r\n**Best for:** General-purpose ranking, balanced decision-making\r\n\r\n### 2. VIKOR (Compromise Ranking)\r\n\r\nFinds compromise solutions considering both group utility and individual regret.\r\n\r\n```python\r\nfrom topsisx.vikor import vikor\r\n\r\nresult = vikor(data, weights=[0.3, 0.3, 0.4], impacts=['+', '-', '+'], v=0.5)\r\n```\r\n\r\n**Best for:** Conflicting criteria, compromise solutions\r\n\r\n### 3. AHP (Analytic Hierarchy Process)\r\n\r\nCalculates weights through pairwise comparisons.\r\n\r\n```python\r\nfrom topsisx.ahp import ahp\r\nimport pandas as pd\r\n\r\n# Pairwise comparison matrix\r\npairwise = pd.DataFrame([\r\n [1, 3, 5],\r\n ['1/3', 1, 4],\r\n ['1/5', '1/4', 1]\r\n])\r\n\r\nweights = ahp(pairwise, verbose=True)\r\n```\r\n\r\n**Best for:** Subjective criteria, expert judgments\r\n\r\n### 4. Entropy Weighting\r\n\r\nCalculates objective weights based on data variance.\r\n\r\n```python\r\nfrom topsisx.entropy import entropy_weights\r\n\r\nweights = entropy_weights(data.values)\r\n```\r\n\r\n**Best for:** Objective weighting, data-driven decisions\r\n\r\n---\r\n\r\n## \ud83d\udca1 Usage Examples\r\n\r\n### Example 1: Laptop Selection\r\n\r\n```python\r\nfrom topsisx.pipeline import DecisionPipeline\r\nimport pandas as pd\r\n\r\n# Data\r\nlaptops = pd.DataFrame({\r\n 'Model': ['Laptop A', 'Laptop B', 'Laptop C', 'Laptop D'],\r\n 'Price': [800, 1200, 1000, 900],\r\n 'RAM_GB': [8, 16, 16, 8],\r\n 'Battery_Hours': [6, 4, 8, 7],\r\n 'Weight_KG': [2.0, 2.5, 1.8, 2.2]\r\n})\r\n\r\n# Analysis\r\npipeline = DecisionPipeline(weights='entropy', method='topsis')\r\nresult = pipeline.run(\r\n laptops,\r\n impacts=['-', '+', '+', '-'] # Price and Weight are costs\r\n)\r\n\r\nprint(result)\r\n```\r\n\r\n### Example 2: Supplier Selection with AHP\r\n\r\n```python\r\nfrom topsisx.pipeline import DecisionPipeline\r\nimport pandas as pd\r\n\r\n# Supplier data\r\nsuppliers = pd.DataFrame({\r\n 'Supplier': ['S1', 'S2', 'S3'],\r\n 'Cost': [250, 200, 300],\r\n 'Quality': [16, 16, 32],\r\n 'Delivery': [12, 8, 16]\r\n})\r\n\r\n# AHP pairwise matrix (Quality > Cost > Delivery)\r\nahp_matrix = pd.DataFrame([\r\n [1, 3, 5], # Quality\r\n ['1/3', 1, 3], # Cost\r\n ['1/5', '1/3', 1] # Delivery\r\n])\r\n\r\n# Analysis with AHP weights\r\npipeline = DecisionPipeline(weights='ahp', method='topsis')\r\nresult = pipeline.run(\r\n suppliers,\r\n impacts=['-', '+', '-'],\r\n pairwise_matrix=ahp_matrix\r\n)\r\n\r\nprint(result)\r\n```\r\n\r\n### Example 3: Compare Methods\r\n\r\n```python\r\nfrom topsisx.pipeline import DecisionPipeline\r\n\r\npipeline = DecisionPipeline(weights='entropy', method='topsis')\r\n\r\n# Compare TOPSIS vs VIKOR\r\ncomparison = pipeline.compare_methods(\r\n data=data,\r\n impacts=['+', '-', '+']\r\n)\r\n\r\nprint(comparison['comparison'])\r\n```\r\n\r\n---\r\n\r\n## \ud83c\udf10 Web Interface Guide\r\n\r\n### Starting the Web App\r\n\r\n```bash\r\ntopsisx --web\r\n```\r\n\r\n### Features:\r\n\r\n1. **Data Input Options:**\r\n - \ud83d\udce4 Upload CSV files\r\n - \ud83d\udccb Use pre-loaded sample datasets\r\n - \u270f\ufe0f Manual data entry\r\n\r\n2. **Configuration:**\r\n - Choose weighting method (Entropy, AHP, Equal)\r\n - Select ranking method (TOPSIS, VIKOR)\r\n - Define impact directions (+/-)\r\n - Set method parameters\r\n\r\n3. **Results:**\r\n - \ud83d\udcca Interactive ranking tables\r\n - \ud83d\udcc8 Visual charts and graphs\r\n - \ud83e\udd47 Top-3 alternatives highlight\r\n - \ud83d\udcbe Download results as CSV\r\n - \ud83d\udcc4 Generate PDF reports\r\n\r\n---\r\n\r\n## \ud83c\udfaf CLI Reference\r\n\r\n### Basic Commands\r\n\r\n```bash\r\n# Launch web interface\r\ntopsisx --web\r\n\r\n# Basic analysis\r\ntopsisx data.csv --impacts \"+,-,+\"\r\n\r\n# Specify method and weighting\r\ntopsisx data.csv --method vikor --weights equal --impacts \"+,-,+\"\r\n\r\n# With ID column preservation\r\ntopsisx data.csv --impacts \"+,-,+\" --id-col \"Model\"\r\n\r\n# Generate report\r\ntopsisx data.csv --impacts \"+,-,+\" --report\r\n\r\n# AHP weighting\r\ntopsisx data.csv --weights ahp --ahp-matrix ahp.csv --impacts \"+,-,+\"\r\n\r\n# VIKOR with custom v parameter\r\ntopsisx data.csv --method vikor --vikor-v 0.7 --impacts \"+,-,+\"\r\n\r\n# Verbose output\r\ntopsisx data.csv --impacts \"+,-,+\" --verbose\r\n```\r\n\r\n### Full Options\r\n\r\n```\r\nusage: topsisx [-h] [--web] [--weights {entropy,ahp,equal}] \r\n [--method {topsis,vikor}] [--impacts IMPACTS]\r\n [--ahp-matrix AHP_MATRIX] [--vikor-v VIKOR_V]\r\n [--output OUTPUT] [--report] [--id-col ID_COL]\r\n [--verbose] [--version] [input]\r\n\r\nOptions:\r\n --web Launch web interface\r\n --weights Weighting method (default: entropy)\r\n --method Decision method (default: topsis)\r\n --impacts Impact directions (e.g., '+,-,+')\r\n --ahp-matrix Path to AHP pairwise comparison matrix\r\n --vikor-v VIKOR strategy weight (0-1, default: 0.5)\r\n --output Output CSV file path\r\n --report Generate PDF report\r\n --id-col ID column to preserve\r\n --verbose Show detailed information\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udcc1 Data Format\r\n\r\n### CSV Format\r\n\r\nYour CSV should have:\r\n- **Rows:** Alternatives/options to rank\r\n- **Columns:** Criteria for evaluation\r\n- **Optional:** ID column (will be preserved)\r\n\r\nExample `data.csv`:\r\n\r\n```csv\r\nModel,Price,RAM,Battery,Weight\r\nLaptop A,800,8,6,2.0\r\nLaptop B,1200,16,4,2.5\r\nLaptop C,1000,16,8,1.8\r\nLaptop D,900,8,7,2.2\r\n```\r\n\r\n### Impact Direction\r\n\r\n- `+` : Benefit criterion (higher is better) - e.g., Quality, Speed, RAM\r\n- `-` : Cost criterion (lower is better) - e.g., Price, Time, Weight\r\n\r\n---\r\n\r\n## \ud83d\udcca Output Format\r\n\r\nResults include original data plus:\r\n\r\n**TOPSIS:**\r\n- `Topsis_Score`: Similarity to ideal solution (0-1, higher is better)\r\n- `Rank`: Final ranking (1 is best)\r\n\r\n**VIKOR:**\r\n- `S`: Group utility measure\r\n- `R`: Individual regret measure\r\n- `Q`: Compromise ranking index\r\n- `Rank`: Final ranking (1 is best)\r\n\r\n---\r\n\r\n## \ud83d\udd27 Advanced Usage\r\n\r\n### Custom Pipeline\r\n\r\n```python\r\nfrom topsisx.pipeline import DecisionPipeline\r\n\r\n# Create custom pipeline\r\npipeline = DecisionPipeline(\r\n weights='entropy',\r\n method='topsis',\r\n verbose=True # Show detailed logs\r\n)\r\n\r\n# Run with custom parameters\r\nresult = pipeline.run(\r\n data=df,\r\n impacts=['+', '-', '+'],\r\n v=0.7 # VIKOR parameter\r\n)\r\n```\r\n\r\n### Generate Reports\r\n\r\n```python\r\nfrom topsisx.reports import generate_report\r\n\r\ngenerate_report(\r\n result,\r\n method='topsis',\r\n filename='my_report.pdf'\r\n)\r\n```\r\n\r\n### Batch Processing\r\n\r\n```python\r\nimport glob\r\nfrom topsisx.pipeline import DecisionPipeline\r\n\r\npipeline = DecisionPipeline(weights='entropy', method='topsis')\r\n\r\n# Process multiple CSV files\r\nfor csv_file in glob.glob('data/*.csv'):\r\n df = pd.read_csv(csv_file)\r\n result = pipeline.run(df, impacts=['+', '-', '+'])\r\n result.to_csv(f'results/{csv_file}', index=False)\r\n```\r\n\r\n---\r\n\r\n## \ud83c\udf93 Methodology\r\n\r\n### TOPSIS Algorithm\r\n\r\n1. Normalize decision matrix\r\n2. Apply criteria weights\r\n3. Determine ideal (A+) and anti-ideal (A-) solutions\r\n4. Calculate Euclidean distances to A+ and A-\r\n5. Rank by relative closeness to ideal\r\n\r\n### VIKOR Algorithm\r\n\r\n1. Determine ideal and anti-ideal values\r\n2. Calculate S (group utility) and R (individual regret)\r\n3. Compute Q values as weighted combination\r\n4. Rank alternatives by Q values\r\n\r\n### AHP Process\r\n\r\n1. Create pairwise comparison matrix (1-9 scale)\r\n2. Normalize matrix by column sums\r\n3. Calculate priority weights (row averages)\r\n4. Check consistency ratio (CR < 0.1)\r\n\r\n### Entropy Weighting\r\n\r\n1. Normalize data to probability distribution\r\n2. Calculate entropy for each criterion\r\n3. Derive diversity measure (1 - entropy)\r\n4. Normalize diversity to get weights\r\n\r\n---\r\n\r\n## \ud83d\udcda API Reference\r\n\r\n### DecisionPipeline\r\n\r\n```python\r\nDecisionPipeline(weights='entropy', method='topsis', verbose=False)\r\n```\r\n\r\n**Methods:**\r\n- `run(data, impacts, pairwise_matrix=None, **kwargs)` - Run analysis\r\n- `compute_weights(data, pairwise_matrix=None)` - Calculate weights\r\n- `compare_methods(data, impacts, pairwise_matrix=None)` - Compare TOPSIS vs VIKOR\r\n\r\n### Individual Methods\r\n\r\n```python\r\ntopsis(data, weights, impacts) -> DataFrame\r\nvikor(data, weights, impacts, v=0.5) -> DataFrame\r\nahp(pairwise_matrix, verbose=False) -> ndarray\r\nentropy_weights(matrix) -> ndarray\r\n```\r\n\r\n---\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nContributions are welcome! Please:\r\n\r\n1. Fork the repository\r\n2. Create a feature branch\r\n3. Make your changes\r\n4. Add tests if applicable\r\n5. Submit a pull request\r\n\r\n---\r\n\r\n## \ud83d\udcdd License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n---\r\n\r\n## \ud83d\udc68\u200d\ud83d\udcbb Author\r\n\r\n**Suvit Kumar**\r\n- GitHub: [@SuvitKumar003](https://github.com/SuvitKumar003)\r\n- Email: suvitkumar03@gmail.com\r\n\r\n---\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- Based on established MCDM methodologies\r\n- Built with Python, Pandas, NumPy, Streamlit, and Matplotlib\r\n- Inspired by the need for accessible decision-making tools\r\n\r\n---\r\n\r\n## \ud83d\udcde Support\r\n\r\n- \ud83d\udcd6 [Documentation](https://github.com/SuvitKumar003/ranklib/blob/main/README.md)\r\n- \ud83d\udc1b [Report Issues](https://github.com/SuvitKumar003/ranklib/issues)\r\n- \ud83d\udcac [Discussions](https://github.com/SuvitKumar003/ranklib/discussions)\r\n\r\n---\r\n\r\n## \u2b50 Star History\r\n\r\nIf you find this project useful, please consider giving it a star on GitHub!\r\n\r\n---\r\n\r\n**Made with \u2764\ufe0f for better decision making**\r\n",
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