Data Science Agent Documentation

The DataScience Agent provide statistical modeling and machine learning, including Random Forest and Support Vector Machine (SVM) algorithms. The agent also supports a range of Design of Experiments (DoE) methods, such as General and 2-level Full-Factorial, 2-level Fractional, Plackett-Burman, Generalized Subset designs, and Response-Surface designs like Box-Behnken, Central-Composite, and Latin-Hypercube sampling.

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pyDOE2

Tool Name Description Example Prompt Example Answer
pyDOE2 Generate full factorial design for experiments with mixed factor levels. 
I am planning a full factorial experiment with two factors at two levels and one factor at three levels for a catalyst screening. Can you generate the corresponding experimental matrix?
                            
The full factorial experimental matrix for your catalyst screening with two factors at two levels and one factor at three levels is as follows:

| Factor 1 | Factor 2 | Factor 3 |
|----------|----------|----------|
|    0     |    0     |    0     |
|    1     |    0     |    0     |
|    0     |    1     |    0     |
|    1     |    1     |    0     |
|    0     |    0     |    1     |
|    1     |    0     |    1     |
|    0     |    1     |    1     |
|    1     |    1     |    1     |
|    0     |    0     |    2     |
|    1     |    0     |    2     |
|    0     |    1     |    2     |
|    1     |    1     |    2     |

Each row represents a unique combination of factor levels for your experiment.
pyDOE2 Generate a two-level full factorial design for optimization studies. 
"For an initial optimization study with three factors, I would like to apply a full two-level factorial design. Please generate the design matrix for me."
                            
The full two-level factorial design matrix for an optimization study with three factors is as follows:

\[
\begin{array}{ccc}
-1 & -1 & -1 \\
1 & -1 & -1 \\
-1 & 1 & -1 \\
1 & 1 & -1 \\
-1 & -1 & 1 \\
1 & -1 & 1 \\
-1 & 1 & 1 \\
1 & 1 & 1 \\
\end{array}
\]

Each row represents a different experimental run, with the columns corresponding to the levels of the three factors.
pyDOE2 Generate a fractional factorial design involving selected factor interactions. 
In a reaction engineering project, I want to use a fractional factorial design defined by factors A, B, C, and interaction AB. Please provide the experimental plan.
                            
The fractional factorial design for factors A, B, C, and interaction AB can be represented using a 2-level factorial design with 3 factors. Here is the experimental plan:

| Run | A  | B  | C  |
|-----|----|----|----|
| 1   | -1 | -1 | -1 |
| 2   |  1 | -1 | -1 |
| 3   | -1 |  1 | -1 |
| 4   |  1 |  1 | -1 |
| 5   | -1 | -1 |  1 |
| 6   |  1 | -1 |  1 |
| 7   | -1 |  1 |  1 |
| 8   |  1 |  1 |  1 |

In this design, each factor (A, B, C) is tested at two levels (-1 and 1), and the interaction AB is implicitly included in the design.
pyDOE2 Create a Plackett-Burman screening design for multiple parameters. 
I need a Plackett-Burman design for screening 12 process parameters for a pharmaceutical formulation. Can you create the appropriate design matrix?
                            
Here is the Plackett-Burman design matrix for screening 12 process parameters:



    
      

        Run
        Parameter 1
        Parameter 2
        Parameter 3
        Parameter 4
        Parameter 5
        Parameter 6
        Parameter 7
        Parameter 8
        Parameter 9
        Parameter 10
        Parameter 11
        Parameter 12
      

    
    
      
1-1-11-111-1-111-11

      
21-1-1-1-111-1-1111

      
3-11-1-11-11-11-111

      
4111-1-1-1-1-1-1-1-11

      
5-1-111-1-11-111-1-1

      
61-1-111-1-1-1-111-1

      
7-11-11-11-1-11-11-1

      
81111111-1-1-1-1-1

      
9-1-11-111-11-1-11-1

      
101-1-1-1-11111-1-1-1

      
11-11-1-11-111-11-1-1

      
12111-1-1-1-11111-1

      
13-1-111-1-111-1-111

      
141-1-111-1-111-1-11

      
15-11-11-11-11-11-11

      
16111111111111

    
  

  

This matrix can be used to screen the effects of 12 different parameters in your pharmaceutical formulation process.
pyDOE2 Generate a Generalized Subset Design (GSD) for multi-level factor experiments. 
Design a Generalized Subset Design (GSD) where three factors each have three levels for a solvent mixture optimization. Output the matrix.
                            
The Generalized Subset Design (GSD) for a solvent mixture optimization with three factors, each having three levels, is represented by the following matrix:

\[
\begin{array}{ccc}
0 & 0 & 0 \\
1 & 0 & 0 \\
2 & 0 & 0 \\
0 & 1 & 0 \\
1 & 1 & 0 \\
2 & 1 & 0 \\
0 & 2 & 0 \\
1 & 2 & 0 \\
2 & 2 & 0 \\
0 & 0 & 1 \\
1 & 0 & 1 \\
2 & 0 & 1 \\
0 & 1 & 1 \\
1 & 1 & 1 \\
2 & 1 & 1 \\
0 & 2 & 1 \\
1 & 2 & 1 \\
2 & 2 & 1 \\
0 & 0 & 2 \\
1 & 0 & 2 \\
2 & 0 & 2 \\
0 & 1 & 2 \\
1 & 1 & 2 \\
2 & 1 & 2 \\
0 & 2 & 2 \\
1 & 2 & 2 \\
2 & 2 & 2 \\
\end{array}
\]

Each row in the matrix represents a unique combination of the levels for the three factors.
pyDOE2 Create a Box-Behnken Design (BBD) for response surface methodology. 
I am preparing a response surface methodology using Box-Behnken Design (BBD) for three formulation variables. Please generate the experiment plan.
                            
The Box-Behnken Design (BBD) for three formulation variables has been generated. Here is the experiment plan:

| Run | Variable 1 | Variable 2 | Variable 3 |
|-----|------------|------------|------------|
| 1   | -1         | -1         | 0          |
| 2   | 1          | -1         | 0          |
| 3   | -1         | 1          | 0          |
| 4   | 1          | 1          | 0          |
| 5   | -1         | 0          | -1         |
| 6   | 1          | 0          | -1         |
| 7   | -1         | 0          | 1          |
| 8   | 1          | 0          | 1          |
| 9   | 0          | -1         | -1         |
| 10  | 0          | 1          | -1         |
| 11  | 0          | -1         | 1          |
| 12  | 0          | 1          | 1          |
| 13  | 0          | 0          | 0          |
| 14  | 0          | 0          | 0          |
| 15  | 0          | 0          | 0          |

This design includes 15 runs, with each variable set at three levels: -1, 0, and 1. The center point (0, 0, 0) is repeated three times to estimate the experimental error.
pyDOE2 Generate a Central Composite Design (CCD) for building quadratic models. 
"Generate a Central Composite Design (CCD) for three critical process parameters to build a quadratic model in chemical process optimization."
                            
Here's a step-by-step guide to generate a CCD for three factors:

    
    
  1. Install pyDOE2: If you haven't already, you need to install the pyDOE2 library. You can do this using pip:
        
pip install pyDOE2
        
    
    2. 
    
  2. Generate the CCD: Use the following Python code to generate a CCD for three factors.
        
from pyDOE2 import ccdesign
# Generate a Central Composite Design for 3 factors
ccd = ccdesign(3, center=(4, 4), alpha='o', face='ccc')
# Print the design
print(ccd)
        
    
    3. 



Explanation:


    
    
  • 3 is the number of factors.
    • 
    
  • center=(4, 4) specifies the number of center points in the factorial and axial portions of the design.
    • 
    
  • alpha='o' specifies the orthogonal design.
    • 
    
  • face='ccc' specifies the face-centered composite design.
    • 


This code will generate a CCD for three factors, which is suitable for building a quadratic model in chemical process optimization.
pyDOE2 Create a Latin Hypercube Sampling (LHS) plan for Monte Carlo simulations. 
I need to generate a Latin Hypercube Sampling (LHS) plan for three variables and ten samples for a Monte Carlo simulation. Can you prepare it?
                            
Here is the Latin Hypercube Sampling (LHS) plan for three variables and ten samples:

| Sample | Variable 1 | Variable 2 | Variable 3 |
|--------|------------|------------|------------|
| 1      | 0.3727     | 0.7617     | 0.0212     |
| 2      | 0.8621     | 0.3294     | 0.1531     |
| 3      | 0.4544     | 0.0838     | 0.2461     |
| 4      | 0.5311     | 0.4347     | 0.8341     |
| 5      | 0.0995     | 0.9543     | 0.4697     |
| 6      | 0.1985     | 0.6807     | 0.5151     |
| 7      | 0.9467     | 0.2413     | 0.7117     |
| 8      | 0.2681     | 0.5477     | 0.9572     |
| 9      | 0.7635     | 0.1338     | 0.6746     |
| 10     | 0.6719     | 0.8092     | 0.3269     |

This plan can be used for your Monte Carlo simulation.
pyDOE2 Explain insufficient input error when defining a design with missing parameters. 
I tried to set up an LHS design but only provided 'lhs' without parameters. What is wrong?
                            
The issue is that the LHS (Latin Hypercube Sampling) design requires specific parameters to be provided. When setting up an LHS design using the pyDOE2 tool, you need to specify the number of factors and the number of samples. The correct format is 'lhs ,'. For example, 'lhs 3,10' would create a Latin Hypercube Sampling design with 3 factors and 10 samples.
pyDOE2 Explain error when using an unknown or unsupported design type keyword. 
I used the command 'unknown 1,2,3' trying to generate an experimental design but received an error. What does this mean?
                            
The error you encountered is likely due to using an unsupported or incorrect design type in the command. The `pyDOE2` tool supports specific design types such as 'fullfact', 'ff2n', 'lhs', etc. The command 'unknown 1,2,3' uses 'unknown' as the design type, which is not recognized by the tool. To generate an experimental design, you need to specify a valid design type along with the appropriate parameters. For example:

- 'fullfact 2,3' for a full factorial design with 2 levels for the first factor and 3 levels for the second factor.        
- 'ff2n 3' for a 2-level full factorial design with 3 factors.
- 'lhs 3,10' for a Latin Hypercube Sampling design with 3 factors and 10 samples.

Please use one of the supported design types with the correct parameters.

pyDOE2

Tool Name Description Example Prompt Example Answer
sklearn Create a RandomForest model for molecular property prediction with 100 trees and fixed randomness. 
Build a RandomForestRegressor model to predict molecular properties, setting the number of estimators to 100 and using a random state of 42 for reproducibility.
                            
Here's a code snippet to build a RandomForestRegressor model for predicting molecular properties with 100 estimators and a random state of 42:


from sklearn.ensemble import RandomForestRegressor

# Create model with 100 trees and random_state=42
rf = RandomForestRegressor(n_estimators=100, random_state=42)

# Fit the model on training data
rf.fit(X_train, y_train)

# Predict on test data
predictions = rf.predict(X_test)

print(predictions)


Make sure to replace `X_train`, `y_train`, and `X_test` with your actual training and test datasets.
sklearn Set up an SVR model with 'rbf' kernel to model chemical reaction yield data. 
Develop an SVR (Support Vector Regression) model for estimating reaction yields, using an 'rbf' (radial basis function) kernel and a regularization parameter C set to 1.0.
                            
Here is the code to develop an SVR (Support Vector Regression) model using an 'rbf' kernel and a regularization parameter \( C \) set to 1.0:


from sklearn.svm import SVR

# Create an SVM regressor with kernel='rbf' and C=1.0
svm = SVR(kernel='rbf', C=1.0)

# Fit the model on training data
svm.fit(X_train, y_train)

# Predict on test data
predictions = svm.predict(X_test)

print(predictions)


Make sure to replace `X_train`, `y_train`, and `X_test` with your actual training and testing datasets.
sklearn Establish an SVR model for compound solubility prediction with comma-separated parameter input. 
Create an SVR model for predicting solubility of compounds, specifying the 'rbf' kernel and C=1.0, using comma-separated input format.
                            
Here is the code to create an SVR model for predicting the solubility of compounds using the 'rbf' kernel and C=1.0:


from sklearn.svm import SVR

# Create an SVM regressor with kernel='rbf' and C=1.0
svm = SVR(kernel='rbf', C=1.0)

# Fit the model on training data
svm.fit(X_train, y_train)

# Predict on test data
predictions = svm.predict(X_test)

print(predictions)


Make sure to replace `X_train`, `y_train`, and `X_test` with your actual training and testing datasets.