 pip install pgmpy

import pandas as pd
import numpy as np
from pgmpy.models import BayesianModel
from pgmpy.estimators import BayesianEstimator
from pgmpy.inference import VariableElimination

def macro_research(crude_oil_data: pd.DataFrame) -> pd.DataFrame:
    """
    Perform macro research on crude oil data using probabilistic graphical models.

    Args:
    - crude_oil_data (pd.DataFrame): DataFrame containing crude oil data.

    Returns:
    - pd.DataFrame: DataFrame with the results of the macro research.
    """
    # Indicator identification
    indicators = ['Brent Crude Price', 'WTI Crude Price', 'OPEC Production', 'Non-OPEC Production']
    
    # Dataset retrieval
    dataset = crude_oil_data[indicators]
    
    # Data cleaning process
    dataset_cleaned = dataset.dropna()
    
    # Create a Bayesian model
    model = BayesianModel([('Brent Crude Price', 'OPEC Production'),
                           ('WTI Crude Price', 'OPEC Production'),
                           ('OPEC Production', 'Non-OPEC Production')])
    
    # Estimate the parameters of the model using Bayesian estimator
    estimator = BayesianEstimator(model, dataset_cleaned)
    model.fit(dataset_cleaned, estimator=estimator)
    
    # Perform inference using Variable Elimination
    inference = VariableElimination(model)
    
    # Query the model to get the results of the macro research
    query = inference.query(variables=['Brent Crude Price', 'WTI Crude Price'],
                            evidence={'OPEC Production': 100, 'Non-OPEC Production': 90})
    
    return query

if __name__ == "__main__":
    # Example usage
    crude_oil_data = pd.read_csv('crude_oil_data.csv')
    result = macro_research(crude_oil_data)
    print(result)

created 1 year ago

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mylist=("Iphone","Pixel","Samsung")
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myTuple[1]="onePlus"
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Supported Libraries

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Name	Description
NumPy	NumPy python library helps users to work on arrays with ease
SciPy	SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learn	Scikit-learn or Scikit-learn is the most useful library for machine learning in Python
Pandas	Pandas is the most efficient Python library for data manipulation and analysis
DOcplex	DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling