Mva Script __top__ May 2026

# mva_script.py import pandas as pd import numpy as np from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA from sklearn.cluster import KMeans import matplotlib.pyplot as plt import seaborn as sns def run_mva(data, labels=None, variance_threshold=0.8): """ Complete MVA pipeline. Parameters: data : pd.DataFrame or np.array labels : array-like, optional (for LDA) variance_threshold : float, cumulative variance for PCA """ # Step 1: Impute missing values imputer = SimpleImputer(strategy='median') data_imp = imputer.fit_transform(data)

scores, clusters = run_mva(X, labels=y) We tested the script on a synthetic 100×10 dataset. The PCA scree plot (Fig. 1) showed that 3 components capture 82% of the variance. The LDA projection (Fig. 2) separated the two synthetic classes almost perfectly due to the constructed differences in means. Clustering on unlabeled data suggested an optimal k of 3. mva script

# Step 5: LDA (if labels exist) if labels is not None: lda = LDA(n_components=min(2, len(np.unique(labels))-1)) lda_scores = lda.fit_transform(data_scaled, labels) print("LDA applied. Reduced shape:", lda_scores.shape) # LDA scatter plot plt.figure() for lab in np.unique(labels): subset = lda_scores[labels == lab] plt.scatter(subset[:,0], subset[:,1], label=f'Class {lab}') plt.legend() plt.title('LDA Projection') plt.savefig('lda_plot.png') # mva_script

# Step 6: Unsupervised clustering (if no labels) if labels is None: # Elbow method inertias = [] K_range = range(2, min(10, data_scaled.shape[0])) for k in K_range: km = KMeans(n_clusters=k, random_state=42) km.fit(data_scaled) inertias.append(km.inertia_) plt.figure() plt.plot(K_range, inertias, 'bo-') plt.xlabel('k') plt.ylabel('Inertia') plt.title('Elbow for k-means') plt.savefig('elbow.png') best_k = K_range[np.argmin(np.diff(inertias))] # simple heuristic km_final = KMeans(n_clusters=best_k, random_state=42) clusters = km_final.fit_predict(data_scaled) print(f"Optimal clusters: {best_k}") return pca_scores, clusters 1) showed that 3 components capture 82% of the variance