Add evaluation method

This commit is contained in:
Paul Schaub 2018-06-11 16:42:13 +02:00
parent ba0b67565b
commit 0f8ebcff23
Signed by: vanitasvitae
GPG Key ID: 62BEE9264BF17311
2 changed files with 19 additions and 3 deletions

View File

@ -1,6 +1,8 @@
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as pp
import pywatts.neural
from sklearn.metrics import explained_variance_score, mean_absolute_error, median_absolute_error
from sklearn.model_selection import train_test_split
@ -33,3 +35,18 @@ def plot_training(evaluation):
loss.append(e['loss'])
pp.plot(loss)
def predict(X_pred):
pred = n.predict1h(X_pred)
predictions = np.array([p['predictions'][0] for p in pred])
return predictions
def eval_prediction(prediction):
print("The Explained Variance: %.2f" % explained_variance_score(
y_test, prediction))
print("The Mean Absolute Error: %.2f volt dc" % mean_absolute_error(
y_test, prediction))
print("The Median Absolute Error: %.2f volt dc" % median_absolute_error(
y_test, prediction))

View File

@ -24,6 +24,5 @@ class Net:
def evaluate(self, eval_data, eval_results):
return self.__regressor.evaluate(input_fn=pywatts_input_fn(eval_data, y=eval_results, num_epochs=1, shuffle=False), steps=1)
def predict1h(self, df):
df = df.drop(['month', 'day', 'hour'])
return self.__regressor.predict(input_fn=pywatts_input_fn(df, num_epochs=1, shuffle=False))
def predict1h(self, predict_data):
return self.__regressor.predict(input_fn=pywatts_input_fn(predict_data, num_epochs=1, shuffle=False))