ium_452487/validate.ipynb

import zipfile
with zipfile.ZipFile("dataset_cleaned.zip", 'r') as zip_ref:
    zip_ref.extractall("dataset_cleaned_extracted")

import pandas as pd
valid = pd.read_csv("dataset_cleaned_extracted/valid.csv")

x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',
       'PhysicalActivities', 'SleepHours', 'RemovedTeeth',
       'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',
       'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',
       'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
       'DifficultyConcentrating', 'DifficultyWalking',
       'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',
       'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',
       'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',
       'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']
y_column = 'HadHeartAttack'

valid_x = valid[x_columns]
valid_y = valid[y_column]

from tensorflow import keras
model = keras.models.load_model('model_v1.keras')

import numpy as np
predictions = model.predict(valid_x)[:,0]
true_answers = valid_y.to_numpy()
validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)
print(f"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}")

1392/1392 [==============================] - 1s 566us/step
Poprawność na zbiorze walidacyjnym: 86.15%

print(predictions[:100])

[0.08692811 0.12067404 0.31880796 0.64843357 0.15188715 0.06517262
 0.03407578 0.49311596 0.00781232 0.2089161  0.46056542 0.45341685
 0.4294767  0.25619727 0.20345858 0.2302334  0.38631877 0.36519188
 0.04014764 0.23888215 0.27519897 0.08928084 0.05204074 0.42043713
 0.19055638 0.29787344 0.23068897 0.88435644 0.03139259 0.95048493
 0.2457671  0.5858893  0.02678488 0.06240147 0.52132165 0.01431455
 0.02444405 0.07804424 0.11274771 0.12714393 0.35450152 0.01294624
 0.190797   0.07512036 0.48486376 0.06140704 0.9019506  0.08810509
 0.61831665 0.15642735 0.03310075 0.04532438 0.10763614 0.4277772
 0.20325996 0.8980398  0.7491019  0.38502344 0.03970775 0.0401529
 0.03046079 0.10123587 0.04993626 0.05702    0.18049946 0.1223311
 0.731555   0.40104443 0.18443953 0.1265702  0.07467585 0.03895461
 0.35271063 0.38039213 0.4450048  0.03670818 0.05534125 0.91664517
 0.413391   0.12545326 0.11306539 0.4350903  0.48778924 0.40804324
 0.33885244 0.21948677 0.01242744 0.02531701 0.6693964  0.15393472
 0.9307252  0.09181138 0.05571133 0.1261858  0.02687709 0.27069062
 0.22613294 0.20686075 0.47390068 0.40349996]

print(np.rint(predictions)[:100])

[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.
 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0.
 0. 0. 0. 0.]

print(true_answers[:100])

[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.
 0. 0. 0. 0.]

np.savetxt("predictions.txt",predictions)

np.savetxt("predictions_two_digits.txt",predictions, fmt='%1.2f')