First version with ML prediction code

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

diff --git a/qp/qptrain.py b/qp/qptrain.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.
+# ==================================================================================
+
+from statsmodels.tsa.api import VAR
+from statsmodels.tsa.stattools import adfuller
+import joblib
+
+
+class PROCESS(object):
+
+    def __init__(self, data):
+        self.diff = 0
+        self.data = data
+
+    def adfuller_test(self, series, thresh=0.05, verbose=False):
+        """ADFuller test for Stationarity of given series and return True or False"""
+        r = adfuller(series, autolag='AIC')
+        output = {'test_statistic': round(r[0], 4), 'pvalue': round(r[1], 4), 'n_lags': round(r[2], 4), 'n_obs': r[3]}
+        p_value = output['pvalue']
+        if p_value <= thresh:
+            return True
+        else:
+            return False
+
+    def make_stationary(self):
+        """ call adfuller_test() to check for stationary
+            If the column is stationary, perform 1st differencing and return data"""
+        df = self.data.copy()
+        res_adf = []
+        for name, column in df.iteritems():
+            res_adf.append(self.adfuller_test(column))  # Perform ADF test
+        if not all(res_adf):
+            self.data = df.diff().dropna()
+            self.diff += 1
+
+    def invert_transformation(self, inp, forecast):
+        """Revert back the differencing to get the forecast to original scale."""
+        if self.diff == 0:
+            return forecast
+        df = forecast.copy()
+        columns = inp.columns
+        for col in columns:
+            df[col] = inp[col].iloc[-1] + df[col].cumsum()
+        self.diff = 0
+        return df
+
+    def process(self):
+        """ Filter throughput parameters, call make_stationary() to check for Stationarity time series
+        """
+        df = self.data.copy()
+        df = df[['pdcpBytesDl', 'pdcpBytesUl']]
+        self.data = df.loc[:, (df != 0).any(axis=0)]
+        self.make_stationary()  # check for Stationarity and make the Time Series Stationary
+
+    def valid(self):
+        df = self.data.copy()
+        df = df.loc[:, (df != 0).any(axis=0)]
+        if len(df) != 0 and df.shape[1] == 2:
+            return True
+        else:
+            return False
+
+
+def train(db, cid):
+    """
+     Read the input file(based on cell id received from the main program)
+     call process() to forecast the downlink and uplink of the input cell id
+     Make a VAR model, call the fit method with the desired lag order.
+    """
+    db.read_data(meas='liveCell', cellid=cid)
+    md = PROCESS(db.data)
+    md.process()
+    if md.valid():
+        model = VAR(md.data)          # Make a VAR model
+        model_fit = model.fit(10)            # call fit method with lag order
+        file_name = 'qp/'+cid.replace('/', '')
+        with open(file_name, 'wb') as f:
+            joblib.dump(model_fit, f)     # Save the model with the cell id name