ium_434784/training.py
Maciej Sobkowiak ba432415fb
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2021-05-16 22:53:52 +02:00

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Python

import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from countries_map import countries
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Activation, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
from keras.models import Sequential
from sklearn.metrics import mean_squared_error
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
EPOCHS = int(sys.argv[1])
BATCH_SIZE = int(sys.argv[2])
age = {"5-14 years": 0, "15-24 years": 1, "25-34 years": 2,
"35-54 years": 3, "55-74 years": 4, "75+ years": 5}
sex = {"male": 0, "female": 1}
# wczytanie danych
sc = pd.read_csv('who_suicide_statistics.csv')
print(sc.shape)
# Usunięcie niepełnych danych
sc.dropna(inplace=True)
# Kategoryzacja
sc = pd.get_dummies(
sc, columns=['age', 'sex', 'country'], prefix='', prefix_sep='')
# podział na train validate i test
train, validate, test = np.split(sc.sample(frac=1, random_state=42),
[int(.6*len(sc)), int(.8*len(sc))])
# podział train set
X_train = train.loc[:, train.columns != 'suicides_no']
y_train = train[['suicides_no']]
X_test = test.loc[:, train.columns != 'suicides_no']
y_test = test[['suicides_no']]
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(X_train))
first = np.array(X_train[:1])
with np.printoptions(precision=2, suppress=True):
print('First example:', first)
print()
print('Normalized:', normalizer(first).numpy())
model = tf.keras.Sequential([
normalizer,
layers.Dense(units=1)
])
model.predict(X_train[:10])
# Compile model
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')
# Train model
history = model.fit(
X_train, y_train,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_split=0.2)
model.save_weights('suicide_model.h5')
test_results = {}
test_results['model'] = model.evaluate(
X_test, y_test, verbose=0)
test_predictions = model.predict(X_test).flatten()
# a = plt.axes(aspect='equal')
# plt.scatter(y_test, test_predictions)
# plt.xlabel('True values [sucides_no]')
# plt.ylabel('Predictions values [sucides_no]')
# lims = [0, 5000]
# plt.xlim(lims)
# plt.ylim(lims)
# _ = plt.plot(lims, lims)
# plt.show()
predictions = model.predict(X_test)
pd.DataFrame(predictions).to_csv('results.csv')
model.summary()