-# ==================================================================================
-# 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 warnings
import json
import hdbscan
import pandas as pd
-import numpy as np
-import joblib, os
+import joblib
+import os
from ad_model.processing import preprocess
from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, confusion_matrix,f1_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
# Ranges for input features based on excellent, good, average, & poor category
-UEKeyList = ['MeasTimestampRF','UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR','UEID']
-#UEKeyList = ['S_RSRP', 'S_RSRQ', 'S_SINR','UEID','MeasTimestampRF']
+UEKeyList = ['MeasTimestampRF', 'UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR', 'UEID']
-sigstr = {'S_RSRP': {'Excellent Signal' : [-80, 10000000000000000], 'Good Signal': [-90,-80], 'Average Signal':[-100,-90], 'Poor Signal':[-100000000000000000,-100]}, 'S_RSRQ' : {'Excellent Signal' : [-10, 10000000000000000], 'Good Signal': [-15,-10], 'Average Signal':[-20,-15], 'Poor Signal':[-100000000000000000,-20]}, 'S_SINR' : {'Excellent Signal' : [20, 10000000000000000], 'Good Signal': [13,20], 'Average Signal':[0,13], 'Poor Signal':[-100000000000000000,0]}}
+sigstr = {'S_RSRP': {'Excellent Signal': [-80, 10000000000000000], 'Good Signal': [-90, -80], 'Average Signal': [-100, -90], 'Poor Signal': [-100000000000000000, -100]}, 'S_RSRQ': {'Excellent Signal': [-10, 10000000000000000], 'Good Signal': [-15, -10], 'Average Signal': [-20, -15], 'Poor Signal': [-100000000000000000, -20]}, 'S_SINR': {'Excellent Signal': [20, 10000000000000000], 'Good Signal': [13, 20], 'Average Signal': [0, 13], 'Poor Signal': [-100000000000000000, 0]}}
-PRB = {'UEPRBUsageDL': {'Excellent Signal' : [25, 10000000000000000], 'Good Signal': [20,25], 'Average Signal':[10,20], 'Poor Signal':[-100000000000000000,10]}, 'UEPRBUsageUL' : {'Excellent Signal' : [15, 10000000000000000], 'Good Signal': [10,15], 'Average Signal':[5,10], 'Poor Signal':[-100000000000000000,5]}}
+PRB = {'UEPRBUsageDL': {'Excellent Signal': [25, 10000000000000000], 'Good Signal': [20, 25], 'Average Signal': [10, 20], 'Poor Signal': [-100000000000000000, 10]}, 'UEPRBUsageUL': {'Excellent Signal': [15, 10000000000000000], 'Good Signal': [10, 15], 'Average Signal': [5, 10], 'Poor Signal': [-100000000000000000, 5]}}
-tput = {'UEPDCPBytesDL': {'Excellent Signal' : [300000, 10000000000000000], 'Good Signal': [200000,300000], 'Average Signal':[100000,200000], 'Poor Signal':[-100000000000000000,100000]}, 'UEPDCPBytesUL' : {'Excellent Signal' : [125000, 10000000000000000], 'Good Signal': [100000,125000], 'Average Signal':[10000,100000], 'Poor Signal':[-100000000000000000,10000]}}
+tput = {'UEPDCPBytesDL': {'Excellent Signal': [300000, 10000000000000000], 'Good Signal': [200000, 300000], 'Average Signal': [100000, 200000], 'Poor Signal': [-100000000000000000, 100000]}, 'UEPDCPBytesUL': {'Excellent Signal': [125000, 10000000000000000], 'Good Signal': [100000, 125000], 'Average Signal': [10000, 100000], 'Poor Signal': [-100000000000000000, 10000]}}
-def category(df,ranges):
- """
- Based on ranges, each sample is return with category(excellent, good, average, & poor category).
- """
+def category(df, ranges):
+ # Based on ranges, each sample is return with category(excellent, good, average, & poor category).
data = df.copy()
for block in ranges:
df = data[list(block.keys())].copy()
ind = temp[(temp[key] <= bounds[1]) & (temp[key] > bounds[0])].index
df.loc[ind, key] = cat
data[df.columns] = df
- category = data[['UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL',
- 'S_RSRP', 'S_RSRQ', 'S_SINR']].mode(axis = 1)[0]
+ # Maximum category value is considered as final category value.
+ category = data[['UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR']].mode(axis=1)[0]
return category
class modelling(object):
- def __init__(self,data):
+ def __init__(self, data):
self.time = data.MeasTimestampRF
self.id = data.UEID
- self.data = data.drop(['UEID', 'MeasTimestampRF'], axis = 1)
-
+ self.data = data.drop(['UEID', 'MeasTimestampRF'], axis=1)
+
def dbscan(self):
"""
Train hdbscan for the input dataframe
save the hdbscan model
- """
+ """
+
df = self.data.copy()
- hdb = hdbscan.HDBSCAN(min_cluster_size=16000, min_samples = 5, prediction_data = True).fit(df)
- joblib.dump(hdb, '/tmp/ad/hdbscan')
- self.data['Category'] = hdb.labels_
+ hdb = hdbscan.HDBSCAN(min_cluster_size=16000, min_samples=5, prediction_data=True).fit(df)
+ joblib.dump(hdb, 'ad/hdbscan')
+ self.data['Category'] = hdb.labels_ # Stores the labels into category field
def RandomForest(self, y):
"""
df = self.data.copy()
le = LabelEncoder()
y = le.fit_transform(y)
- joblib.dump(le, '/tmp/ad/LabelEncoder')
+ joblib.dump(le, 'ad/LabelEncoder')
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, stratify=y, random_state=42)
rf = RandomForestClassifier(max_depth=9, random_state=0)
- rf.fit(X_train, y_train)
-
- joblib.dump(rf, '/tmp/ad/RF')
- print('--------------------------- Training Score------------------------------------')
- score(X_test, y_test, rf)
- print('--------------------------- Test Score------------------------------------')
- test = pd.read_csv('/tmp/ad/ue_test.csv')
- test = test[UEKeyList]
- y = category(test, [sigstr, PRB, tput])
- y =le.transform(y)
- ps = preprocess(test)
- ps.process()
- test = ps.data.drop(['UEID', 'MeasTimestampRF'], axis = 1)
- score(test, y, rf)
-
-def score(X, y, model):
- y_pred = model.predict(X)
- print('Accuracy : {}'.format(accuracy_score(y, y_pred)))
-
- print('confusion matrix : {}'.format(confusion_matrix(y, y_pred)))
- print('f1-score : {}'.format(f1_score(y, y_pred, average = 'macro')))
+ rf.fit(X_train, y_train) # Fit the RFC model
+ print("X_train cols:", X_train.columns)
+ joblib.dump(rf, 'ad/RF') # Save the RF model
def train():
"""
Main function to perform training on input files
Read all the csv file in the current path and create trained model
- """
+ """
print('Training Starts : ')
- path = '/tmp/ad/ue_data/'
+ path = 'ad/ue_data/'
df = pd.DataFrame()
# Read all the csv files and store the combined data into df
for file in os.listdir(path):
df = df.append(pd.read_csv(path + file))
-
df = df[UEKeyList]
df.index = range(len(df))
- y = category(df, [sigstr, PRB, tput])
+ y = category(df, [sigstr, PRB, tput])
seg = {}
-
- #Save the category of each UEID and save it as json file
+ # Save the category of each UEID and save it as json file
for ue in df.UEID.unique():
seg[str(ue)] = list(set(y[df[df['UEID'] == ue].index]))
-
+
with open('ue_seg.json', 'w') as outfile:
json.dump(seg, outfile)
ps.process()
df = ps.data
db = modelling(df)
-# db.dbscan()
+ # db.dbscan()
db.RandomForest(y)