Add prediction script

pywatts/db.py

@@ -7,7 +7,6 @@ import os.path
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 db_path = os.path.join(BASE_DIR, "pywatts.db")
-print(db_path)
 db = SqliteExtDatabase(db_path)
 
 

pywatts/kcross.py

@@ -1,6 +1,4 @@
 import random
-import itertools
-from pywatts import db
 
 
 def split(data, k):
@@ -18,7 +16,7 @@ def split(data, k):
     data_list = data['dc'].tolist()
 
     # Each sample has 337 elements
-    samples = [data_list[i:i+337] for i in range(0, len(data_list) - 337, 337)]
+    samples = [data_list[i:i+337] for i in range(0, len(data_list) - 337)]
     # Randomly shuffle samples
     random.shuffle(samples)
 

pywatts/neural.py

@@ -1,5 +1,3 @@
-import pandas
-import numpy as np
 import tensorflow as tf
 
 

pywatts/predict_for_json.py

@@ -0,0 +1,30 @@
+import os
+import sys
+
+import tensorflow as tf
+
+import pywatts.db
+from pywatts.routines import *
+
+# get rid of TF debug message
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+if len(sys.argv) != 3:
+    print("Usage: python predict_for_json.py 24h|1h <file.json>")
+    exit(1)
+
+type = sys.argv[1]  # '1h' or '24h'
+json_file = sys.argv[2]  # json file
+
+queries = input_queries(json_file)
+
+feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)]
+n = pywatts.neural.Net(feature_cols=feature_col)
+
+predictions = []
+for query in queries:
+    if type == '1h':
+        predictions.extend(predict(n, query).astype('Float64').tolist())
+    else:
+        predictions.append(predict24h(n, query))
+print(predictions)

pywatts/routines.py

@@ -36,6 +36,18 @@ def input_query(json_str, idx=0):
          'wind': tmp_df['wind'][idx]}
     )
 
+def input_queries(json_str):
+    tmp_df = pandas.read_json(json_str)
+
+    queries = []
+    for i in range(len(tmp_df)):
+        queries.append(pandas.DataFrame.from_dict(
+            {'dc': tmp_df['dc'][i],
+             'temp': tmp_df['temp'][i],
+             'wind': tmp_df['wind'][i]}
+        ))
+    return queries
+
 
 def input_result(json_str, idx=0):
     tmp_df = pandas.read_json(json_str)
@@ -81,7 +93,7 @@ def predict24h(nn, X_pred):
         # Remove first value and append predicted value
         del input['dc'][0]
         input['dc'].append(predictions[-1])
-        print("Prediction for hour %d/%d" % (i+1, 24))
+        # print("Prediction for hour %d/%d" % (i+1, 24))
 
     return predictions
 
@@ -94,3 +106,9 @@ def eval_prediction(prediction, result):
     print("The Median Absolute Error: %.2f volt dc" % median_absolute_error(
         result, prediction))
 
+def jsonify(predictions):
+    json_out = "["
+    for v in predictions:
+        json_out += "[" + str(v) + "],"
+    json_out = json_out[:-1] + "]"
+    return json_out

pywatts/test_kcross_train.py

@@ -1,5 +1,5 @@
-import peewee
 import tensorflow as tf
+
 import pywatts.db
 from pywatts import kcross
 

pywatts/test_train.py

@@ -1,5 +1,5 @@
-import peewee
 import tensorflow as tf
+
 import pywatts.db
 from pywatts.routines import *