Added 24 hour prediction

pywatts/main.py

@@ -68,6 +68,22 @@ def predict(nn, X_pred):
     return predictions
 
 
+def predict24h(nn, X_pred):
+    predictions = []
+
+    input = {'dc': X_pred['dc'].tolist()}
+
+    for i in range(24):
+        pred = nn.predict1h(pandas.DataFrame.from_dict(input))
+        predictions.extend(list([p['predictions'][0] for p in pred]))
+        # Remove first value and append predicted value
+        del input['dc'][0]
+        input['dc'].append(predictions[-1])
+        print(input)
+
+    return predictions
+
+
 def eval_prediction(prediction, result):
     print("The Explained Variance: %.2f" % explained_variance_score(
         result, prediction))

pywatts/neural.py

@@ -6,7 +6,6 @@ import tensorflow as tf
 def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
     # Create dictionary for features in hour 0 ... 335
     features = {str(idx): [] for idx in range(336)}
-    #dc_values = X['dc'].tolist()
     dc_values = X['dc']
 
     # Iterate the empty dictionary always adding the idx-th element from the dc_values list
@@ -15,7 +14,6 @@ def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
 
     labels = None
     if y is not None:
-        #labels = y['dc'].values
         labels = y['dc']
 
     if labels is None:
@@ -24,8 +22,10 @@ def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
         dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
 
     if shuffle:
-        dataset.shuffle(len(features['0']))
+        dataset.shuffle(len(features['0']*len(features)*4))
 
+        return dataset.repeat().batch(batch_size)
+    else:
         return dataset.batch(batch_size)
 
 

pywatts/test_predict24.py

@@ -0,0 +1,29 @@
+import tensorflow as tf
+import pywatts.db
+from pywatts.main import *
+import matplotlib.pyplot as pp
+
+
+PREDICT_QUERY = "query-sample_24hour.json"
+PREDICT_RESULT = PREDICT_QUERY.replace("query", "result")
+QUERY_ID = 0
+
+
+pred_query = input_query("../sample_data/" + PREDICT_QUERY, QUERY_ID)
+pred_result = input_result("../sample_data/" + PREDICT_RESULT, QUERY_ID)
+
+
+# Define feature columns and initialize Regressor
+feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)]
+n = pywatts.neural.Net(feature_cols=feature_col)
+
+prediction = predict24h(n, pred_query)
+
+print(prediction)
+print(pred_result)
+
+pp.plot(pred_result, 'black')
+pp.plot(prediction, 'red')
+pp.show()
+
+#pywatts.main.eval_prediction(prediction, pred_result)