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Copy pathUsingModels.py
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110 lines (108 loc) · 5.32 KB
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from keras.models import load_model
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
pred_data = ([[2, 34.0, 0, 0, 13.0, 1, False, 0, 0, 1],
[2, 31.0, 1, 1, 26.25, 0, False, 0, 0, 1],
[1, 11.0, 1, 2, 120.0, 1, False, 0, 0, 1],
[3, 0.42, 0, 1, 8.5167, 1, False, 1, 0, 0],
[3, 27.0, 0, 0, 6.975, 1, False, 0, 0, 1],
[3, 31.0, 0, 0, 7.775, 1, False, 0, 0, 1],
[1, 39.0, 0, 0, 0.0, 1, False, 0, 0, 1],
[3, 18.0, 0, 0, 7.775, 0, False, 0, 0, 1],
[2, 39.0, 0, 0, 13.0, 1, False, 0, 0, 1],
[1, 33.0, 1, 0, 53.1, 0, False, 0, 0, 1],
[3, 26.0, 0, 0, 7.8875, 1, False, 0, 0, 1],
[3, 39.0, 0, 0, 24.15, 1, False, 0, 0, 1],
[2, 35.0, 0, 0, 10.5, 1, False, 0, 0, 1],
[3, 6.0, 4, 2, 31.275, 0, False, 0, 0, 1],
[3, 30.5, 0, 0, 8.05, 1, False, 0, 0, 1],
[1, 29.69911764705882, 0, 0, 0.0, 1, True, 0, 0, 1],
[3, 23.0, 0, 0, 7.925, 0, False, 0, 0, 1],
[2, 31.0, 1, 1, 37.0042, 1, False, 1, 0, 0],
[3, 43.0, 0, 0, 6.45, 1, False, 0, 0, 1],
[3, 10.0, 3, 2, 27.9, 1, False, 0, 0, 1],
[1, 52.0, 1, 1, 93.5, 0, False, 0, 0, 1],
[3, 27.0, 0, 0, 8.6625, 1, False, 0, 0, 1],
[1, 38.0, 0, 0, 0.0, 1, False, 0, 0, 1],
[3, 27.0, 0, 1, 12.475, 0, False, 0, 0, 1],
[3, 2.0, 4, 1, 39.6875, 1, False, 0, 0, 1],
[3, 29.69911764705882, 0, 0, 6.95, 1, True, 0, 1, 0],
[3, 29.69911764705882, 0, 0, 56.4958, 1, True, 0, 0, 1],
[2, 1.0, 0, 2, 37.0042, 1, False, 1, 0, 0],
[3, 29.69911764705882, 0, 0, 7.75, 1, True, 0, 1, 0],
[1, 62.0, 0, 0, 80.0, 0, False, 0, 0, 0],
[3, 15.0, 1, 0, 14.4542, 0, False, 1, 0, 0],
[2, 0.83, 1, 1, 18.75, 1, False, 0, 0, 1],
[3, 29.69911764705882, 0, 0, 7.2292, 1, True, 1, 0, 0],
[3, 23.0, 0, 0, 7.8542, 1, False, 0, 0, 1],
[3, 18.0, 0, 0, 8.3, 1, False, 0, 0, 1],
[1, 39.0, 1, 1, 83.1583, 0, False, 1, 0, 0],
[3, 21.0, 0, 0, 8.6625, 1, False, 0, 0, 1],
[3, 29.69911764705882, 0, 0, 8.05, 1, True, 0, 0, 1],
[3, 32.0, 0, 0, 56.4958, 1, False, 0, 0, 1],
[1, 29.69911764705882, 0, 0, 29.7, 1, True, 1, 0, 0],
[3, 20.0, 0, 0, 7.925, 1, False, 0, 0, 1],
[2, 16.0, 0, 0, 10.5, 1, False, 0, 0, 1],
[1, 30.0, 0, 0, 31.0, 0, False, 1, 0, 0],
[3, 34.5, 0, 0, 6.4375, 1, False, 1, 0, 0],
[3, 17.0, 0, 0, 8.6625, 1, False, 0, 0, 1],
[3, 42.0, 0, 0, 7.55, 1, False, 0, 0, 1],
[3, 29.69911764705882, 8, 2, 69.55, 1, True, 0, 0, 1],
[3, 35.0, 0, 0, 7.8958, 1, False, 1, 0, 0],
[2, 28.0, 0, 1, 33.0, 1, False, 0, 0, 1],
[1, 29.69911764705882, 1, 0, 89.1042, 0, True, 1, 0, 0],
[3, 4.0, 4, 2, 31.275, 1, False, 0, 0, 1],
[3, 74.0, 0, 0, 7.775, 1, False, 0, 0, 1],
[3, 9.0, 1, 1, 15.2458, 0, False, 1, 0, 0],
[1, 16.0, 0, 1, 39.4, 0, False, 0, 0, 1],
[2, 44.0, 1, 0, 26.0, 0, False, 0, 0, 1],
[3, 18.0, 0, 1, 9.35, 0, False, 0, 0, 1],
[1, 45.0, 1, 1, 164.8667, 0, False, 0, 0, 1],
[1, 51.0, 0, 0, 26.55, 1, False, 0, 0, 1],
[3, 24.0, 0, 3, 19.2583, 0, False, 1, 0, 0],
[3, 29.69911764705882, 0, 0, 7.2292, 1, True, 1, 0, 0],
[3, 41.0, 2, 0, 14.1083, 1, False, 0, 0, 1],
[2, 21.0, 1, 0, 11.5, 1, False, 0, 0, 1],
[1, 48.0, 0, 0, 25.9292, 0, False, 0, 0, 1],
[3, 29.69911764705882, 8, 2, 69.55, 0, True, 0, 0, 1],
[2, 24.0, 0, 0, 13.0, 1, False, 0, 0, 1],
[2, 42.0, 0, 0, 13.0, 0, False, 0, 0, 1],
[2, 27.0, 1, 0, 13.8583, 0, False, 1, 0, 0],
[1, 31.0, 0, 0, 50.4958, 1, False, 0, 0, 1],
[3, 29.69911764705882, 0, 0, 9.5, 1, True, 0, 0, 1],
[3, 4.0, 1, 1, 11.1333, 1, False, 0, 0, 1],
[3, 26.0, 0, 0, 7.8958, 1, False, 0, 0, 1],
[1, 47.0, 1, 1, 52.5542, 0, False, 0, 0, 1],
[1, 33.0, 0, 0, 5.0, 1, False, 0, 0, 1],
[3, 47.0, 0, 0, 9.0, 1, False, 0, 0, 1],
[2, 28.0, 1, 0, 24.0, 0, False, 1, 0, 0],
[3, 15.0, 0, 0, 7.225, 0, False, 1, 0, 0],
[3, 20.0, 0, 0, 9.8458, 1, False, 0, 0, 1],
[3, 19.0, 0, 0, 7.8958, 1, False, 0, 0, 1],
[3, 29.69911764705882, 0, 0, 7.8958, 1, True, 0, 0, 1],
[1, 56.0, 0, 1, 83.1583, 0, False, 1, 0, 0],
[2, 25.0, 0, 1, 26.0, 0, False, 0, 0, 1],
[3, 33.0, 0, 0, 7.8958, 1, False, 0, 0, 1],
[3, 22.0, 0, 0, 10.5167, 0, False, 0, 0, 1],
[2, 28.0, 0, 0, 10.5, 1, False, 0, 0, 1],
[3, 25.0, 0, 0, 7.05, 1, False, 0, 0, 1],
[3, 39.0, 0, 5, 29.125, 0, False, 0, 1, 0],
[2, 27.0, 0, 0, 13.0, 1, False, 0, 0, 1],
[1, 19.0, 0, 0, 30.0, 0, False, 0, 0, 1],
[3, 29.69911764705882, 1, 2, 23.45, 0, True, 0, 0, 1],
[1, 26.0, 0, 0, 30.0, 1, False, 1, 0, 0],
[3, 32.0, 0, 0, 7.75, 1, False, 0, 1, 0]])
# This is a modified predictors
pred_data = np.asarray(pred_data)
# Load a saved model from ClassificationMdel.py
my_model = load_model("Models/model_titanic.h5")
# Predict my model based on a new predictor values
prediction = my_model.predict(pred_data, verbose=1)
# Predict() would return two columns, first is for false probabilities and second are trues.
# Here we return true ones
probability_prob_true = prediction[:, 1]
# Config print option in order to show with better format
np.set_printoptions(precision=8, threshold=np.nan, suppress=True)
# Final true probabilities
print(probability_prob_true)