55 lines
1.2 KiB
Python
55 lines
1.2 KiB
Python
import pandas as pd
|
|
import numpy as np
|
|
from tensorflow import keras
|
|
import matplotlib.pyplot as plt
|
|
from keras import backend as K
|
|
from sklearn.metrics import mean_squared_error
|
|
from tensorflow.keras import layers
|
|
from tensorflow.keras.layers.experimental import preprocessing
|
|
import tensorflow as tf
|
|
|
|
train = pd.read_csv('train.csv')
|
|
test = pd.read_csv('test.csv')
|
|
validate = pd.read_csv('validate.csv')
|
|
|
|
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))
|
|
|
|
model = tf.keras.Sequential([
|
|
normalizer,
|
|
layers.Dense(units=1)
|
|
])
|
|
|
|
model.summary()
|
|
|
|
model.load_weights('suicide_model.h5')
|
|
|
|
predictions = model.predict(X_test)
|
|
|
|
error = mean_squared_error(y_test, predictions)
|
|
|
|
with open('results.txt', 'a') as f:
|
|
f.write(str(error) + "\n")
|
|
|
|
with open('results.txt', 'r') as f:
|
|
lines = f.readlines()
|
|
|
|
fig = plt.figure(figsize=(10, 5))
|
|
chart = fig.add_subplot()
|
|
|
|
chart.set_ylabel("Mean Squared Error")
|
|
chart.set_xlabel("Build number")
|
|
|
|
x = np.arange(0, len(lines), 1)
|
|
|
|
y = [float(val) for val in lines]
|
|
|
|
plt.plot(x, y, "bo")
|
|
|
|
plt.savefig("plot.png")
|