+# ==================================================================================
+# 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 joblib
-import os
+import numpy as np
+import joblib, 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 = ['MeasTimestampRF','UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR','UEID']
+#UEKeyList = ['S_RSRP', 'S_RSRQ', 'S_SINR','UEID','MeasTimestampRF']
-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
- # 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]
+ 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, 'ad/hdbscan')
- self.data['Category'] = hdb.labels_ # Stores the labels into category field
+ 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_
def RandomForest(self, y):
"""
df = self.data.copy()
le = LabelEncoder()
y = le.fit_transform(y)
- joblib.dump(le, 'ad/LabelEncoder')
+ joblib.dump(le, '/tmp/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) # Fit the RFC model
- print("X_train cols:", X_train.columns)
- joblib.dump(rf, 'ad/RF') # Save the RF model
+ 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')))
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 = 'ad/ue_data/'
+ path = '/tmp/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)
Code Review / ric-app / ad.git / blobdiff