Release 0.0.5

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

diff --git a/qp/qptrain.py b/src/qptrain.py
similarity index 55%
rename from qp/qptrain.py
rename to src/qptrain.py
index 2071ac3..0ce3438 100644 (file)
--- a/qp/qptrain.py
+++ b/src/qptrain.py
@@ -16,11 +16,15 @@
 
 from statsmodels.tsa.api import VAR
 from statsmodels.tsa.stattools import adfuller
+from mdclogpy import Logger
+from exceptions import DataNotMatchError
+from sklearn.metrics import mean_squared_error
+from math import sqrt
 import joblib
+import warnings
+warnings.filterwarnings("ignore")
 
-
-class DataNotMatchError(Exception):
-    pass
+logger = Logger(name=__name__)
 
 
 class PROCESS(object):
@@ -29,6 +33,14 @@ class PROCESS(object):
         self.diff = 0
         self.data = data
 
+    def input_data(self):
+        try:
+            self.data = self.data[db.thptparam]
+            self.data = self.data.fillna(method='bfill')
+        except DataNotMatchError:
+            logger.error('Parameters Downlink throughput and Uplink throughput does not exist in provided data')
+            self.data = None
+
     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')
@@ -62,39 +74,68 @@ class PROCESS(object):
         return df
 
     def process(self):
-        """ Filter throughput parameters, call make_stationary() to check for Stationarity time series
-        """
-        df = self.data.copy()
-        try:
-            df = df[['pdcpBytesDl', 'pdcpBytesUl']]
-        except DataNotMatchError:
-            print('Parameters pdcpBytesDl, pdcpBytesUl does not exist in provided data')
-            self.data = None
-        self.data = df.loc[:, (df != 0).any(axis=0)]
+        self.input_data()
         self.make_stationary()  # check for Stationarity and make the Time Series Stationary
 
-    def valid(self):
-        val = False
-        if self.data is not None:
-            df = self.data.copy()
-            df = df.loc[:, (df != 0).any(axis=0)]
-            if len(df) != 0 and df.shape[1] == 2:
-                val = True
+    def constant(self):
+        val = True
+        df = self.data.copy()
+        df = df.drop_duplicates().dropna()
+        df = df.loc[:, (df != 0).any(axis=0)]
+        if len(df) >= 10:
+            val = False
         return val
 
+    def evaluate_var(self, X, lag):
+        # prepare training dataset
+        train_size = int(len(X) * 0.75)
+        train, test = X[0:train_size], X[train_size:]
+        # make predictions
+        model = VAR(train)
+        model_fit = model.fit(lag)
+        predictions = model_fit.forecast(y=train.values, steps=len(test))
+        # calculate out of sample error
+        rmse = sqrt(mean_squared_error(test, predictions))
+        return rmse
+
+    def optimize_lag(self, df):
+        lag = range(1, 20, 1)
+        df = df.astype('float32')
+        best_score, best_lag = float("inf"), None
+        for l in lag:
+            try:
+                rmse = self.evaluate_var(df, l)
+                if rmse < best_score:
+                    best_score, best_lag = rmse, l
+            except ValueError as v:
+                print(v)
+        # print('Best VAR%s RMSE=%.3f' % (best_lag, best_score))
+        return best_lag
+
 
-def train(db, cid):
+def train_cid(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)
+    # print(f'Training for {cid}')
+    db.read_data(cellid=cid, limit=4800)
     md = PROCESS(db.data)
-    md.process()
-    if md.valid():
+    if md.data is not None and not md.constant():
+        md.process()
+        lag = md.optimize_lag(md.data)
         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
+        try:
+            model_fit = model.fit(lag)            # call fit method with lag order
+            file_name = 'src/'+cid.replace('/', '')
+            with open(file_name, 'wb') as f:
+                joblib.dump(model_fit, f)     # Save the model with the cell id name
+        except ValueError as v:
+            print("****************************************", v)
+
+
+def train(database, cid):
+    global db
+    db = database
+    train_cid(cid)