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utils.py
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utils.py
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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.pipeline import make_pipeline
from linearmodels.panel import PanelOLS
import statsmodels.api as sm
model_performance = []
# Helper function to store models
def store_model_performance(y_true, y_pred, model_name):
"""
Function to compute MSE and R-squared for given predictions and actual values,
and store these metrics in a list of dictionaries.
Args:
y_true (array-like): Actual values.
y_pred (array-like): Predicted values from the model.
model_name (str): Name of the model.
Returns:
None: Appends the performance metrics to the global list 'model_performance'.
"""
mse = mean_squared_error(y_true, y_pred)
r2 = r2_score(y_true, y_pred)
performance_dict = {
'model_name': model_name,
'mse': mse,
'r2': r2
}
model_performance.append(performance_dict)