PCA_variance_plot.py

import sklearn.decomposition
pca = sklearn.decomposition.PCA()
pca.fit(X)
variances = pca.explained_variance_ratio_
def select_n_components(var_ratio, goal_var: float) -> int:
    total_variance = 0.0
    n_components = 0
    # For the explained variance of each feature:
    for explained_variance in var_ratio:
        # Add the explained variance to the total
        total_variance += explained_variance
        # Add one to the number of components
        n_components += 1
        if total_variance >= goal_var:
            # End the loop
            break
            
    # Return the number of components
    return n_components
    
pca = sklearn.decomposition.PCA(n1)
pca.fit(X)
XP=pca.components_