L1AnomalyGPUTSVD.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from cuml.decomposition import TruncatedSVD

import L1AnomalyBase
import L1AnomalyGPU

class L1AnomalyGPUTSVD(L1AnomalyBase):

    def __init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar = False, job=False):
        super().__init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar)
        if job == False:
            L1AnomalyGPU.gpu_notebook_install()
        else:
            L1AnomalyGPU.gpu_job_install()
        
        self.background_data = super.load_data(self, type = 0)
        self.signal_data = super.load_data(self, type = 1)
        self.blackbox_data = super.load_data(self, type = 2)
            
    def plot_explained_background_variance(self, RS = 42, train_size = 0.8):
        X_train, X_test = train_test_split(
            self.background_data.reshape(self.background_data.shape[0], -1),
            train_size=train_size,
            shuffle=True,
            random_state=RS,
        )

        print("Train Test Split Complete")

        X_train_scaled, pt_scaler = L1AnomalyBase.scale_pt(X_train)

        X_train_standard = StandardScaler().fit_transform(X_train_scaled)

        print("Train scale_pt Complete")

        X_test_scaled, _ = L1AnomalyBase.scale_pt(X_test, pt_scaler)

        X_test_standard = StandardScaler().fit_transform(X_test_scaled)

        print("Train and Test scale_pt Complete")

        nums = np.arange(1,58)
        var_ratio = []
        for num in nums:
            tsvd = TruncatedSVD(n_components=num)
            tsvd.fit(X_train_standard)
            var_ratio.append(np.sum(tsvd.explained_variance_ratio_))
        plt.clf()
        plt.figure()#figsize=(4,2),dpi=150)
        plt.grid()
        plt.plot(nums,var_ratio,marker='o')
        plt.xlabel('n_components')
        plt.ylabel('Explained variance ratio')
        plt.title('n_components vs. Explained Variance Ratio')
        plt.savefig(f"GPU_TSVD_num_components.png")