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import numpy as np
import pandas as pd
from tensorflow.data import Dataset
from tensorflow import lite,float16,random,where,cast,float32,constant,reshape
from sklearn.model_selection import train_test_split
from os.path import isdir
import os
from keras.callbacks import EarlyStopping,ModelCheckpoint
from keras.models import Model,load_model
from keras.layers import Layer,Dense,Dropout,Input,Embedding,Concatenate
from keras.optimizers import Adam
from keras.losses import mean_absolute_error
from keras.metrics import R2Score
import sys
User=sys.argv[1]
print(sys.argv[0])
Data=pd.read_csv(f"./IndoorLocalization/Data/{User}/Data.csv")
print(Data)
PhonsId=np.sort(Data["PHONEID"].unique())
phoneidMap={phoneid:i for i,phoneid in enumerate(PhonsId)}
def ReplaceId(id):
if (np.random.randint(low=0,high=20,size=1)==3):
return len(phoneidMap)
return phoneidMap[id]
Data["PHONEID"]=Data["PHONEID"].apply(ReplaceId)
Data=Data.dropna()
def CleanTrainData(df):
target=df[["SPACEID","LONGITUDE","LATITUDE"]]
df=df.drop(['LONGITUDE', 'LATITUDE',"SPACEID"], axis=1)
return df, target
DataX,TargetY=CleanTrainData(Data)
LONGITUDEMax=TargetY["LONGITUDE"].max()
LATITUDEMax=TargetY["LATITUDE"].max()
LONGITUDEMin=TargetY["LONGITUDE"].min()
LATITUDEMin=TargetY["LATITUDE"].min()
BuildingWidth=20
BuildingLength=20
def LONGITUDE_min_max_newrange(item):
return ((item-LONGITUDEMin)/(LONGITUDEMax-LONGITUDEMin))*BuildingWidth
def LATITUDE_min_max_newrange(item):
return ((item-LATITUDEMin)/(LATITUDEMax-LATITUDEMin))*BuildingLength
TargetY["LONGITUDE"]=TargetY["LONGITUDE"].apply(LONGITUDE_min_max_newrange)
TargetY["LATITUDE"]=TargetY["LATITUDE"].apply(LATITUDE_min_max_newrange)
X_train, X_test, y_train, y_test = train_test_split(DataX.values,TargetY.values[:,1:], test_size=0.2, random_state=42,shuffle=True,stratify=TargetY.values[:,0])
SPACESGroups=TargetY.groupby("SPACEID")
SPACESGroupsmean=SPACESGroups.mean()
SPACEIDPosition={f"{SPACEID}":(SPACESGroupsmean.query(f"SPACEID=={SPACEID}")["LONGITUDE"].values[0],SPACESGroupsmean.query(f"SPACEID=={SPACEID}")["LATITUDE"].values[0]) for SPACEID in list(SPACESGroups.groups.keys()) }
SPACEIDPositionArray=np.array([list(SPACEIDPosition[f"{i}"]) for i in SPACESGroups.groups.keys()])
PlacesNumber=len(np.unique(TargetY.iloc[:,0]))
PhonesNumber=np.unique(DataX["PHONEID"]).size
def ApplyNormalizationthenNois(X,Phoneid,Y):
X=cast(X,dtype=float32)
Y=cast(Y,dtype=float32)
additem=np.random.choice([0,1,2])
Nuknow=np.random.randint(0,high=5,size=1)
X=(X+100)/200
if additem ==1:
if Nuknow==0:
return (X,0),Y
return (X,Phoneid),Y
else:
noise=random.normal(shape=X.shape,mean=0,stddev=0.1,dtype=float32)
NoisedX=X+noise
NoisedX=where(NoisedX<0,x=0.0,y=NoisedX)
NoisedX=where(NoisedX>1,x=1.0,y=NoisedX)
if Nuknow==0:
return (NoisedX,0),Y
return (NoisedX,Phoneid),Y
def ApplyNormalizationOnly(X,Phoneid,Y):
X=cast(X,dtype=float32)
Y=cast(Y,dtype=float32)
X=(X+100)/200
if Phoneid ==1:
return (X,1),Y
elif Phoneid ==2:
return (X,2),Y
else:
return (X,0),Y
TrainDataPipeline=Dataset.from_tensor_slices((X_train[:,:-1],X_train[:,-1],y_train)).map(ApplyNormalizationthenNois).batch(100)
TestDataPipeline=Dataset.from_tensor_slices((X_test[:,:-1],X_test[:,-1],y_test)).map(ApplyNormalizationOnly).batch(10)
class PositionAproxmator(Layer):
def __init__(self,PlacesPosition,name="PositionAproxmator"):
super(PositionAproxmator,self).__init__()
self.PlacesPosition=constant(PlacesPosition,dtype=float32,name="PlacesPositions")
def build(self,inputs_shape):
self.W=self.add_weight(shape=(inputs_shape[1],2),trainable=True,dtype=float32,name="PlacesWeight")
def call(self,Probilites):
return Probilites@(self.PlacesPosition+self.W)
def MakeModel(SPACEIDPosition,PhonesNumber):
if isdir(f"./IndoorLocalization/IndoorModels/{User}/kerasModel"):
return load_model(f"./IndoorLocalization/IndoorModels/{User}/kerasModel")
WiFiReadings=Input(168)
Phoneid=Input(1)
Embeding=Embedding(PhonesNumber,64, embeddings_regularizer="l2")(Phoneid)
X=Dense(128,activation="relu")(WiFiReadings)
X=Dropout(0.2)(X)
z=Dense(64,activation="relu")(X)
X=z+reshape(Embeding,shape=(-1,64))
X=Concatenate()([z,X,reshape(Embeding,shape=(-1,64))])
X=Dropout(0.2)(X)
X=Dense(100,activation="relu", kernel_regularizer="l2")(X)
X=Dropout(0.1)(X)
X=Dense(64,activation="relu")(X)
X=X+z
X=Dense(64,activation="relu")(X)
X=Dropout(0.2)(X)
X=Dense(PlacesNumber,activation="softmax")(X)
X=PositionAproxmator(SPACEIDPosition)(X)
return Model(inputs=[WiFiReadings,Phoneid],outputs=[X])
model=MakeModel(SPACEIDPositionArray,PhonesNumber)
model.compile(optimizer=Adam(learning_rate=1e-4),loss=mean_absolute_error,metrics=[R2Score()])
hsitory=model.fit(TrainDataPipeline,validation_data=TestDataPipeline,epochs=5,callbacks=[EarlyStopping(patience=3),ModelCheckpoint(f"./IndoorLocalization/IndoorModels/{User}/kerasModel")])
# hsitory=model.fit(TrainDataPipeline,validation_data=TestDataPipeline,epochs=5,callbacks=[EarlyStopping(patience=3),ModelCheckpoint(r"C:\Users\mf\Desktop\AIProjects")])
converter=lite.TFLiteConverter.from_keras_model(model)
converter.optimizations=[lite.Optimize.DEFAULT]
converter.target_spec.supported_types=[float16]
tflitemodel=converter.convert()
with open(f"./IndoorLocalization/IndoorModels/{User}/FinalHistoryModel.tflite","wb") as file:
file.write(tflitemodel) |