First version with ML prediction code

[ric-app/qp.git] / qp / prediction.py

diff --git a/qp/prediction.py b/qp/prediction.py
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+++ b/qp/prediction.py
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+# ==================================================================================
+#  Copyright (c) 2020 HCL Technologies Limited.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+# ==================================================================================
+# import pandas as pd
+import os
+import joblib
+import pandas as pd
+from qptrain import PROCESS
+
+
+def forecast(data, cid, nobs=1):
+    """
+     forecast the time series using the saved model.
+    """
+    data = data[['pdcpBytesUl', 'pdcpBytesDl']]
+    ps = PROCESS(data.copy())
+    ps.make_stationary()
+
+    if not ps.valid():
+        df_f = data.tail(1)
+    elif os.path.isfile('qp/'+cid):
+        model = joblib.load('qp/'+cid)
+        pred = model.forecast(y=ps.data.values, steps=nobs)
+
+        if pred is not None:
+            df_f = pd.DataFrame(pred, columns=data.columns)
+            df_f.index = pd.date_range(start=data.index[-1], freq='10ms', periods=len(df_f))
+            df_f = df_f[data.columns].astype(int)
+            df_f = ps.invert_transformation(data, df_f)
+    else:
+        return None
+    df_f = df_f[data.columns].astype(int)
+    return df_f