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Kristian Aalling Sørensen [email protected]

Tobias

Brief Description

Her skal vi skrive en fancy beskrivelse

Table Of Contents

Requirements

  • See requirements filde..

Install and Run

Currently, only git is supported. Later pypi will be aded. So, clone the dir.

Make reconstructions

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  1. Load module
from src.drone_em_dl import *
  1. Load data
data = data.Data()
data.load_data('../data/raw/falster_data_Kristian.csv')
data.get_features([11,12,13,22,23,24,25,26,27,28,29,30,31]) #[1,2,3,4,5,6,7,8,9,10,11,12,13,14]  #[1,2,3,4,5,6,7,11,12,13,14,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
data.train_test_split(split=0.8)
data.norm_data()
  1. Make reconstructions
#load model
model = tf.keras.models.load_model('../models/AE_falter_06_04_2023_15_1/best_model_AE_falter_06_04_2023_15_1.h5', custom_objects={'lr': get_lr_metric})

results = Reconstruction()
results.load_model(model)
results.get_reconstructions(data.norm_data.norm_train)

recionstructed_data = data.get_inv(results.reconstructions)
input_data  = data.get_inv(data.norm_data.norm_train)
latent_space = results.latent_space

3.Do PCA

pcas = Pca()
pcas.load_data(data.norm_data.norm_train,data.norm_data.norm_test)
pcas.get_pca(pca_amount=8)
train_pcs = data.get_inv(pcas.pca_train_inv)
train_org = data.get_inv(data.norm_data.norm_train)
  1. comapre results
for i in range(13):
    plt.figure(figsize=(40,10))
    plt.subplot(1,3,1)
    plt.title(f'Reconstructed {data.test.columns[i]}')
    cm = plt.scatter(data.org_test.X,data.org_test.Y,c=recionstructed_data[:,i],cmap='jet',s=4)
    plt.colorbar()

    plt.subplot(1,3,2)
    plt.title(f'True {data.test.columns[i]}')
    cm = plt.scatter(data.org_test.X,data.org_test.Y,c=input_data[:,i],cmap='jet',s=4)
    plt.colorbar()

    plt.subplot(1,3,3)
    plt.title(f'PCA {data.test.columns[i]}')
    cm = plt.scatter(data.org_test.X,data.org_test.Y,c=train_pcs[:,i],cmap='jet',s=4)
    plt.colorbar()
    plt.show()

EMI

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Acknowledgments

Us

Licence

See License file. In short:

  1. Cite us