Add eval_training script

pywatts/eval_training.py

@@ -0,0 +1,68 @@
+import tensorflow as tf
+import pywatts.db
+from pywatts.routines import *
+from pywatts import kcross
+
+NUM_STATIONS_FROM_DB = 75
+K = 2
+NUM_EVAL_STATIONS = 40
+TRAIN = True
+PLOT = True
+TRAIN_STEPS = 1
+TOTAL_STEPS = 2
+NUM_QUERIES = 1
+PREDICT_QUERY = "query-sample_24hour.json"
+PREDICT_RESULT = PREDICT_QUERY.replace("query", "result")
+FIGURE_OUTPUT_DIR = "../figures/"
+
+
+df = pywatts.db.rows_to_df(list(range(1, NUM_STATIONS_FROM_DB)))
+X = df
+y = df['dc']
+
+
+# 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)
+
+
+# Training data
+(X_train, y_train, X_eval, y_eval) = kcross.split(df, K)
+
+
+if TRAIN:
+
+    train_eval = None
+
+    color_gradient_base = (0.5, 0, 0)
+    color_step_width = (0.5/TOTAL_STEPS, 0, 0)
+
+    for i in range(TOTAL_STEPS):
+        # Train the model with the steps given
+        train_eval = kcross.train(n, X_train, y_train, X_eval, y_eval, TRAIN_STEPS)
+
+        for q in range(NUM_QUERIES):
+
+            pred_query = input_query("../sample_data/" + PREDICT_QUERY, q)
+            pred_result = input_result("../sample_data/" + PREDICT_RESULT, q)
+
+            prediction = predict24h(n, pred_query)
+
+            pp.figure(q)
+
+            if i == 0:
+                pp.plot(pred_result, 'black')
+
+            pp.plot(prediction, color=color_gradient_base)
+
+        color_gradient_base = tuple([sum(x) for x in zip(color_gradient_base, color_step_width)])
+
+    for i in range(NUM_QUERIES):
+        pp.figure(i)
+        pp.savefig(FIGURE_OUTPUT_DIR+'{}.pdf'.format(i), orientation='landscape')
+
+    if PLOT:
+        # Plot training success rate (with 'average loss')
+        pywatts.routines.plot_training(train_eval)
+
+exit()

pywatts/test_predict24.py

@@ -6,7 +6,7 @@ import matplotlib.pyplot as pp
 
 PREDICT_QUERY = "query-sample_24hour.json"
 PREDICT_RESULT = PREDICT_QUERY.replace("query", "result")
-QUERY_ID = 0
+QUERY_ID = 4
 
 
 pred_query = input_query("../sample_data/" + PREDICT_QUERY, QUERY_ID)