From 4b2017447490efc788e468e70f07aba742846596 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sun, 13 Mar 2022 23:49:17 +0100 Subject: Dodat je serivs za treniranje modela. --- backend/microservice/mlservice.py | 286 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 286 insertions(+) create mode 100644 backend/microservice/mlservice.py (limited to 'backend') diff --git a/backend/microservice/mlservice.py b/backend/microservice/mlservice.py new file mode 100644 index 00000000..0780697c --- /dev/null +++ b/backend/microservice/mlservice.py @@ -0,0 +1,286 @@ +import pandas as pd +import tensorflow as tf +import keras +import numpy as np +import matplotlib.pyplot as plt + +### 1)Ucitavanje vrednosti + +#print(1) +data1=pd.read_csv('titanic.csv') +data=data1.copy() +#print(data.head()) + +### U promenjivoj kolone nalaze se nazivi svih kolona seta podataka +kolone=data.columns +#print(kolone[1]) +#print(data[kolone[1]].isnull().sum()) +#print(data[kolone[1]].head(10)) + + +### 2)Proveravanje svih kolona za null vrednosti i popunjavanje medijanom ili srednjom vrednosti ili birisanje + +#####Part2 ##### +brisanje=input("DA LI ZELITE DA IZBRSETE SVE KOLONE SA NULL VREDNOSTIMA? ") +if(brisanje=='da'): + data=data.dropna(axis=1) +elif(brisanje=='ne'): + brisanjer=input("DA LI ZELITE DA IZBRISETE SVE REDOVE SA NULL VREDNOSTINA ") + if(brisanjer=='da'): + data=data.dropna() + elif(brisanjer=='ne'): + + for i in range(len(kolone)): + if(isinstance(data[kolone[i]].dtype, pd.CategoricalDtype)): + print('cat') + + if(data[kolone[i]].isnull().any()): + tippodataka=data[kolone[i]].dtype + kolona=data[kolone[i]].copy() + + if(tippodataka==np.float64 or tippodataka==np.int64): + popunjavanje=input("UNETI NACIN POPUNJAVANJA PROMENJIVIH SA NULL VREDNOSTIMA ") + if(popunjavanje=='medijana'): + medijana=kolona.mean() + data[kolone[i]]=data[kolone[i]].fillna(medijana) + if(popunjavanje=='srednjavrednost'): + sv=data[kolone[i]].sum()/data[kolone[i]].count() + data[kolone[i]]=sv + if(popunjavanje=='brisanjekolone'): + data=data.dropna(axis=1) + + elif(tippodataka==np.object_): + najcescavrednost=kolona.value_counts().index[0] + data[kolone[i]]=data[kolone[i]].fillna(najcescavrednost) + +### 3)Izbacivanje kolona koje ne uticu na rezultat PART2 +nredova=data.shape[0] +for i in range(len(kolone)): + if((data[kolone[i]].nunique()>(nredova/2)) and( data[kolone[i]].dtype==np.object_)): + data.pop(kolone[i]) + +print(data.head(10)) + +### 4)izbor tipa enkodiranja +kolone=data.columns ### Azuriranje postojecih kolona nakon moguceg brisanja + +enc=input("UNETI TIP ENKODIRANJA ") +onehot=0 + +### 5)Enkodiranje svih kategorijskih promenjivih label-encode metodom + +if(enc=='label'): + from sklearn.preprocessing import LabelEncoder + encoder=LabelEncoder() + for k in range(len(kolone)): + if(data[kolone[k]].dtype==np.object_): + data[kolone[k]]=encoder.fit_transform(data[kolone[k]]) + print(data.head(20)) + +### 6)Enkodiranje svih kategorijskih promenjivih onehot metodom + +elif(enc=='onehot'): + ### PART2### + onehot==1 + kategorijskekolone=[] + for k in range(len(kolone)): + if(data[kolone[k]].dtype==np.object_): + + kategorijskekolone.append(kolone[k]) ###U kategorijske kolone smestaju se nazivi svih kolona sa kategorijskim podacima + + print(kategorijskekolone) + + ### Enkodiranje + data=pd.get_dummies(data,columns=kategorijskekolone,prefix=kategorijskekolone) + print(data.head(10)) + +kolone=data.columns ### Azuriranje kolona nakon moguceg dodavanja + +### 7)Podela skupa na skup za trening i skup za testiranje + +predvidetikol=input("UNETI NAZIV KOLONE ČIJU VREDNOST TREBA PREDVIDETI ") + +xkolone=[] +for k in range(len(kolone)): + if(kolone[k]!=predvidetikol): + + xkolone.append(kolone[k])###U xkolone se smestaju nazivi kolona cije vrednosti nije potrebno predvideti !!!Prefiks one-hot!!! + +### 7.1)Podela na x i y +###Dodavanje vrednosti u x +x=data[xkolone].values +###Dodavanje vrednosti u y, samo za label enkodiranje, bez prefiksa +y=data[predvidetikol].values + +print(data[xkolone].head(10)) +print(data[predvidetikol].head(10)) + +### 7.2)Unos velicina za trening i test skup +trening=int(input('UNETI VELIČINU TRENING SKUPA ')) +#test=int(input("UNETI VELICINU TESTNOG SKUPA")) + +###Provera unetih velicina +if(trening<=0 or trening>=100): + print("POGREŠAN UNOS VELIČINE SKUPA ZA TRENING") +if(trening>1): + trening=trening/100 + +### 7.3)Da li korisnik zeli nasumicno rasporedjivanje podataka? +nasumicno=input("DA LI ŽELITE NASUMIČNO RASPOREDJIVANJE PODATAKA U TRENING I TEST SKUP? ") +###!!!Dugme za nasumici izbor +if(nasumicno=='da'): + random=50 +else: + random=0 + +### 7.4)Podela podataka +from sklearn.model_selection import train_test_split +x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=trening,random_state=random) + +### 8)Skaliranje podataka +from sklearn.preprocessing import StandardScaler +scaler=StandardScaler() +scaler.fit(x_train) +x_test=scaler.transform(x_test) +x_train=scaler.transform(x_train) + +#####ZAVRSENA PRIPREMA PODATAKA##### + +#####OBUCAVANJE MODELA##### + +### 9)Inicijalizacija vestacke neuronske mreze + +classifier=tf.keras.Sequential() + +### 10)Dodavanje prvog,ulaznog sloja +aktivacijau=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU ULAZNOG SLOJA ") +brojnu=int(input("UNETI BROJ NEURONA ULAZNOG SLOJA ")) + +classifier.add(tf.keras.layers.Dense(units=brojnu,activation=aktivacijau,input_dim=x_train.shape[1])) + +### 11)Dodavanje drugog, skrivenog sloja +aktivacijas=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU SKRIVENOG SLOJA ") +brojns=int(input("UNETI BROJ NEURONA SKRIVENOG SLOJA ")) + +classifier.add(tf.keras.layers.Dense(units=brojns,activation=aktivacijas)) + +### 12) Dodavanje treceg, izlaznog sloja +aktivacijai=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU IZLAZNOG SLOJA ") + +classifier.add(tf.keras.layers.Dense(units=1,activation=aktivacijai)) + + +### 13) Kompajliranje neuronske mreze +#gubici=input("UNETI FUNKCIJU OBRADE GUBITAKA ") +optimizator=input("UNETI ŽELJENI OPTIMIZATOR ") + +### 13.1)Izbor metrike za kompajler PART2 +metrike=[] +while(1): + m=input("UNETI ZELJENE METRIKE ") + + if(m=='KRAJ'): + break + metrike.append(m) +classifier.compile(optimizer=optimizator, loss='binary_crossentropy',metrics = metrike) + +### 14) +uzorci=int(input("UNETI KOLIKO UZORAKA ĆE BITI UNETO U ISTO VREME ")) + +history=classifier.fit(x_train,y_train,batch_size=uzorci,epochs=10) + +### 14.1)Parametri grafika iz history PART2 +metrikedf=pd.DataFrame() ###DataFrame u kom se nalaze podaci o rezultatima metrika za iscrtavanje na grafiku. Svaka kolona sadrzi vrednost metrike po epohama +for i in range(len(metrike)): + metrikedf[metrike[i]]=history.history[metrike[i]] + #print(history.history[metrike[i]]) + plt.plot(history.history[metrike[i]]) +plt.show() + +#print(metrikedf) + +#metrikedf.to_csv("metrike.csv") + + +### 15) Predvidjanje +y_pred=classifier.predict(x_test) + +print(y_pred) + +### 15.1) Formatiranje podataka za metrike PART2 +y_pred=(y_pred>=0.5).astype('int') +y_pred=y_pred.flatten() + +#print(y_pred) + +#print(y_test) +### 15.2) Kreiranje DataFrame-a u kom se nalaze kolone koje predstavljaju stvarne i predvidjene vrednosti, potrebne za iscrtavanje grafika i metrike PART2 +rezultat=pd.DataFrame({"Stvarna vrednost ":y_test,"Predvidjena vrednost":y_pred}) +print(rezultat.head(20)) + +#####METRIKE##### PART2 + +import sklearn.metrics as sm + + +### 16)Tacnost +tacnost=sm.accuracy_score(y_test,y_pred) +print('tacnost ',tacnost) + +### 17)Preciznost +preciznost=sm.precision_score(y_test,y_pred) +print('preciznost ',preciznost) + +### 18)Recall +recall=sm.recall_score(y_test,y_pred) +print('recall ',recall) + +### 19)Specificity +tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() +spec = tn / (tn+fp) +print('spec ',spec) + +### 20)F1 +f1=sm.f1_score(y_test,y_pred) +print('f1 ',f1) + +### 21)Classification report +classificationreport=sm.classification_report(y_test,y_pred) +print('classification ',classificationreport) + +### 22)Mean squared error (mse) +mse=sm.mean_squared_error(y_test,y_pred) +print('mse ',mse) + +### 23)Mean absolute error (mae) +mae=sm.mean_absolute_error(y_test,y_pred) +print('mae ',mae) + +### 24)Mean absolute percentage error (mape) +mape=sm.mean_absolute_percentage_error(y_test,y_pred) +print('mape ',mape) + +### 25)Root mean square error (rmse) *** da bi se iskoristila u history, salje se u metrics preko funkcije +import numpy as np +rmse=np.sqrt(sm.mean_squared_error(y_test,y_pred)) +print("rmse ",rmse) + +### 26)Confusion matrix +cmatrix=sm.confusion_matrix(y_test,y_pred) +print('cmatrix ',cmatrix) + +### 27)ROC +fpr, tpr, _ = sm.roc_curve(y_test,y_pred) +plt.plot(fpr, tpr, color='blue') +plt.title('ROC') +plt.xlim([0.0, 1.0]) +plt.xlabel('False Positive Rate') +plt.ylim([0.0, 1.0]) +plt.ylabel('True Positive Rate') +plt.show() + + + + + + -- cgit v1.2.3 From ee8bd6d858df94263953d07b8c950b0d8de41c7a Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Mon, 14 Mar 2022 23:20:38 +0100 Subject: Izmenjen je mlservice.py fajl. Promenjive su povezane sa neuronstellar\backend\microservice\PythonServer\project\api\api.py fajlom i omogućeno je prihvatanje vrednosti sa servera. Kreirana je klasa za povratne vrednosti. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/mlservice.py | 515 +++++++++++++++++++++----------------- 1 file changed, 282 insertions(+), 233 deletions(-) (limited to 'backend') diff --git a/backend/microservice/mlservice.py b/backend/microservice/mlservice.py index 0780697c..f8e98184 100644 --- a/backend/microservice/mlservice.py +++ b/backend/microservice/mlservice.py @@ -1,286 +1,335 @@ +from typing_extensions import Self import pandas as pd import tensorflow as tf import keras import numpy as np -import matplotlib.pyplot as plt -### 1)Ucitavanje vrednosti - -#print(1) -data1=pd.read_csv('titanic.csv') -data=data1.copy() -#print(data.head()) - -### U promenjivoj kolone nalaze se nazivi svih kolona seta podataka -kolone=data.columns -#print(kolone[1]) -#print(data[kolone[1]].isnull().sum()) -#print(data[kolone[1]].head(10)) - - -### 2)Proveravanje svih kolona za null vrednosti i popunjavanje medijanom ili srednjom vrednosti ili birisanje - -#####Part2 ##### -brisanje=input("DA LI ZELITE DA IZBRSETE SVE KOLONE SA NULL VREDNOSTIMA? ") -if(brisanje=='da'): - data=data.dropna(axis=1) -elif(brisanje=='ne'): - brisanjer=input("DA LI ZELITE DA IZBRISETE SVE REDOVE SA NULL VREDNOSTINA ") - if(brisanjer=='da'): - data=data.dropna() - elif(brisanjer=='ne'): - - for i in range(len(kolone)): - if(isinstance(data[kolone[i]].dtype, pd.CategoricalDtype)): - print('cat') - - if(data[kolone[i]].isnull().any()): - tippodataka=data[kolone[i]].dtype - kolona=data[kolone[i]].copy() - - if(tippodataka==np.float64 or tippodataka==np.int64): - popunjavanje=input("UNETI NACIN POPUNJAVANJA PROMENJIVIH SA NULL VREDNOSTIMA ") - if(popunjavanje=='medijana'): - medijana=kolona.mean() - data[kolone[i]]=data[kolone[i]].fillna(medijana) - if(popunjavanje=='srednjavrednost'): - sv=data[kolone[i]].sum()/data[kolone[i]].count() - data[kolone[i]]=sv - if(popunjavanje=='brisanjekolone'): - data=data.dropna(axis=1) - - elif(tippodataka==np.object_): - najcescavrednost=kolona.value_counts().index[0] - data[kolone[i]]=data[kolone[i]].fillna(najcescavrednost) - -### 3)Izbacivanje kolona koje ne uticu na rezultat PART2 -nredova=data.shape[0] -for i in range(len(kolone)): - if((data[kolone[i]].nunique()>(nredova/2)) and( data[kolone[i]].dtype==np.object_)): - data.pop(kolone[i]) - -print(data.head(10)) - -### 4)izbor tipa enkodiranja -kolone=data.columns ### Azuriranje postojecih kolona nakon moguceg brisanja - -enc=input("UNETI TIP ENKODIRANJA ") -onehot=0 - -### 5)Enkodiranje svih kategorijskih promenjivih label-encode metodom - -if(enc=='label'): - from sklearn.preprocessing import LabelEncoder - encoder=LabelEncoder() - for k in range(len(kolone)): - if(data[kolone[k]].dtype==np.object_): - data[kolone[k]]=encoder.fit_transform(data[kolone[k]]) - print(data.head(20)) - -### 6)Enkodiranje svih kategorijskih promenjivih onehot metodom - -elif(enc=='onehot'): - ### PART2### - onehot==1 - kategorijskekolone=[] - for k in range(len(kolone)): - if(data[kolone[k]].dtype==np.object_): - - kategorijskekolone.append(kolone[k]) ###U kategorijske kolone smestaju se nazivi svih kolona sa kategorijskim podacima +from copyreg import constructor +import flask +from flask import request, jsonify, render_template +from sklearn.preprocessing import LabelEncoder +import csv +import json +class Response: + def __init__(self,history,rezultat,tacnost,preciznost,recall,spec,f1,classificationreport,mse,mae,mape,rmse,cmatrix,fpr,tpr): + self.history=history + self.rezultat=rezultat + self.tacnost=tacnost + self.preciznost=preciznost + self.recall=recall + self.spec=spec + self.f1=f1 + self.classificationreport=classificationreport + self.mse=mse + self.mae=mae + self.mape=mape + self.rmse=rmse + self.cmatrix=cmatrix + self.fpr=fpr + self.tpr=tpr + + ### 1)Ucitavanje vrednosti +def obuka(data,params): + import numpy as np + import pandas as pd + import tensorflow as tf + import matplotlib.pyplot as plt + #print(1) + #data1=pd.read_csv('titanic.csv') + #data=data1.copy() + #print(data.head()) + + ### U promenjivoj kolone nalaze se nazivi svih kolona seta podataka + kolone=data.columns + #print(kolone[1]) + #print(data[kolone[1]].isnull().sum()) + #print(data[kolone[1]].head(10)) + + + ### 2)Proveravanje svih kolona za null vrednosti i popunjavanje medijanom ili srednjom vrednosti ili birisanje + + #####Part2 ##### + ''' + brisanje=input("DA LI ZELITE DA IZBRSETE SVE KOLONE SA NULL VREDNOSTIMA? ") - print(kategorijskekolone) - - ### Enkodiranje - data=pd.get_dummies(data,columns=kategorijskekolone,prefix=kategorijskekolone) - print(data.head(10)) - -kolone=data.columns ### Azuriranje kolona nakon moguceg dodavanja - -### 7)Podela skupa na skup za trening i skup za testiranje - -predvidetikol=input("UNETI NAZIV KOLONE ČIJU VREDNOST TREBA PREDVIDETI ") - -xkolone=[] -for k in range(len(kolone)): - if(kolone[k]!=predvidetikol): + brisanje=True + if(brisanje=='da'): + data=data.dropna(axis=1) + elif(brisanje=='ne'): + brisanjer=input("DA LI ZELITE DA IZBRISETE SVE REDOVE SA NULL VREDNOSTINA ") + if(brisanjer=='da'): + data=data.dropna() + elif(brisanjer=='ne'): + + for i in range(len(kolone)): + if(isinstance(data[kolone[i]].dtype, pd.CategoricalDtype)): + print('cat') + + if(data[kolone[i]].isnull().any()): + tippodataka=data[kolone[i]].dtype + kolona=data[kolone[i]].copy() - xkolone.append(kolone[k])###U xkolone se smestaju nazivi kolona cije vrednosti nije potrebno predvideti !!!Prefiks one-hot!!! - -### 7.1)Podela na x i y -###Dodavanje vrednosti u x -x=data[xkolone].values -###Dodavanje vrednosti u y, samo za label enkodiranje, bez prefiksa -y=data[predvidetikol].values - -print(data[xkolone].head(10)) -print(data[predvidetikol].head(10)) - -### 7.2)Unos velicina za trening i test skup -trening=int(input('UNETI VELIČINU TRENING SKUPA ')) -#test=int(input("UNETI VELICINU TESTNOG SKUPA")) - -###Provera unetih velicina -if(trening<=0 or trening>=100): - print("POGREŠAN UNOS VELIČINE SKUPA ZA TRENING") -if(trening>1): - trening=trening/100 - -### 7.3)Da li korisnik zeli nasumicno rasporedjivanje podataka? -nasumicno=input("DA LI ŽELITE NASUMIČNO RASPOREDJIVANJE PODATAKA U TRENING I TEST SKUP? ") -###!!!Dugme za nasumici izbor -if(nasumicno=='da'): - random=50 -else: - random=0 + if(tippodataka==np.float64 or tippodataka==np.int64): + popunjavanje=input("UNETI NACIN POPUNJAVANJA PROMENJIVIH SA NULL VREDNOSTIMA ") + if(popunjavanje=='medijana'): + medijana=kolona.mean() + data[kolone[i]]=data[kolone[i]].fillna(medijana) + if(popunjavanje=='srednjavrednost'): + sv=data[kolone[i]].sum()/data[kolone[i]].count() + data[kolone[i]]=sv + if(popunjavanje=='brisanjekolone'): + data=data.dropna(axis=1) + + elif(tippodataka==np.object_): + najcescavrednost=kolona.value_counts().index[0] + data[kolone[i]]=data[kolone[i]].fillna(najcescavrednost) + + ''' + ### 3)Izbacivanje kolona koje ne uticu na rezultat PART2 + nredova=data.shape[0] + for i in range(len(kolone)): + if((data[kolone[i]].nunique()>(nredova/2)) and( data[kolone[i]].dtype==np.object_)): + data.pop(kolone[i]) -### 7.4)Podela podataka -from sklearn.model_selection import train_test_split -x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=trening,random_state=random) -### 8)Skaliranje podataka -from sklearn.preprocessing import StandardScaler -scaler=StandardScaler() -scaler.fit(x_train) -x_test=scaler.transform(x_test) -x_train=scaler.transform(x_train) + print(data.head(10)) -#####ZAVRSENA PRIPREMA PODATAKA##### + ### 4)izbor tipa enkodiranja + kolone=data.columns ### Azuriranje postojecih kolona nakon moguceg brisanja + + #enc=input("UNETI TIP ENKODIRANJA ") + enc=params["encoding"] + onehot=0 + + ### 5)Enkodiranje svih kategorijskih promenjivih label-encode metodom + + if(enc=='label'): + from sklearn.preprocessing import LabelEncoder + encoder=LabelEncoder() + for k in range(len(kolone)): + if(data[kolone[k]].dtype==np.object_): + data[kolone[k]]=encoder.fit_transform(data[kolone[k]]) + print(data.head(20)) + + ### 6)Enkodiranje svih kategorijskih promenjivih onehot metodom + + elif(enc=='onehot'): + ### PART2### + onehot==1 + kategorijskekolone=[] + for k in range(len(kolone)): + if(data[kolone[k]].dtype==np.object_): + + kategorijskekolone.append(kolone[k]) ###U kategorijske kolone smestaju se nazivi svih kolona sa kategorijskim podacima + + print(kategorijskekolone) -#####OBUCAVANJE MODELA##### + ### Enkodiranje + data=pd.get_dummies(data,columns=kategorijskekolone,prefix=kategorijskekolone) + print(data.head(10)) -### 9)Inicijalizacija vestacke neuronske mreze + kolone=data.columns ### Azuriranje kolona nakon moguceg dodavanja -classifier=tf.keras.Sequential() + ### 7)Podela skupa na skup za trening i skup za testiranje -### 10)Dodavanje prvog,ulaznog sloja -aktivacijau=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU ULAZNOG SLOJA ") -brojnu=int(input("UNETI BROJ NEURONA ULAZNOG SLOJA ")) + #predvidetikol=input("UNETI NAZIV KOLONE ČIJU VREDNOST TREBA PREDVIDETI ") + ###sta se cuva od promenjivih broj kolone ili naziv kolone??? + predvidetikol=params["columnToPredict"] -classifier.add(tf.keras.layers.Dense(units=brojnu,activation=aktivacijau,input_dim=x_train.shape[1])) + xkolone=[] + for k in range(len(kolone)): + if(kolone[k]!=predvidetikol): + + xkolone.append(kolone[k])###U xkolone se smestaju nazivi kolona cije vrednosti nije potrebno predvideti !!!Prefiks one-hot!!! -### 11)Dodavanje drugog, skrivenog sloja -aktivacijas=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU SKRIVENOG SLOJA ") -brojns=int(input("UNETI BROJ NEURONA SKRIVENOG SLOJA ")) + ### 7.1)Podela na x i y + ###Dodavanje vrednosti u x + x=data[xkolone].values + ###Dodavanje vrednosti u y, samo za label enkodiranje, bez prefiksa + y=data[predvidetikol].values -classifier.add(tf.keras.layers.Dense(units=brojns,activation=aktivacijas)) + print(data[xkolone].head(10)) + print(data[predvidetikol].head(10)) -### 12) Dodavanje treceg, izlaznog sloja -aktivacijai=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU IZLAZNOG SLOJA ") + ### 7.2)Unos velicina za trening i test skup + #trening=int(input('UNETI VELIČINU TRENING SKUPA ')) + #test=int(input("UNETI VELICINU TESTNOG SKUPA")) + test=params["randomTestSetDistribution"] + ###Provera unetih velicina + if(test<=0 or test>=100): + print("POGREŠAN UNOS VELIČINE SKUPA ZA TRENING") + if(test>1): + test=test/100 -classifier.add(tf.keras.layers.Dense(units=1,activation=aktivacijai)) + ### 7.3)Da li korisnik zeli nasumicno rasporedjivanje podataka? + #nasumicno=input("DA LI ŽELITE NASUMIČNO RASPOREDJIVANJE PODATAKA U TRENING I TEST SKUP? ") + nasumicno=params["randomTestSet"] + ###!!!Dugme za nasumici izbor + if(nasumicno): + random=50 + else: + random=0 + ### 7.4)Podela podataka + from sklearn.model_selection import train_test_split + x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=test,random_state=random) -### 13) Kompajliranje neuronske mreze -#gubici=input("UNETI FUNKCIJU OBRADE GUBITAKA ") -optimizator=input("UNETI ŽELJENI OPTIMIZATOR ") + ### 8)Skaliranje podataka + from sklearn.preprocessing import StandardScaler + scaler=StandardScaler() + scaler.fit(x_train) + x_test=scaler.transform(x_test) + x_train=scaler.transform(x_train) -### 13.1)Izbor metrike za kompajler PART2 -metrike=[] -while(1): - m=input("UNETI ZELJENE METRIKE ") - - if(m=='KRAJ'): - break - metrike.append(m) -classifier.compile(optimizer=optimizator, loss='binary_crossentropy',metrics = metrike) + #####ZAVRSENA PRIPREMA PODATAKA##### -### 14) -uzorci=int(input("UNETI KOLIKO UZORAKA ĆE BITI UNETO U ISTO VREME ")) + #####OBUCAVANJE MODELA##### -history=classifier.fit(x_train,y_train,batch_size=uzorci,epochs=10) + ### 9)Inicijalizacija vestacke neuronske mreze -### 14.1)Parametri grafika iz history PART2 -metrikedf=pd.DataFrame() ###DataFrame u kom se nalaze podaci o rezultatima metrika za iscrtavanje na grafiku. Svaka kolona sadrzi vrednost metrike po epohama -for i in range(len(metrike)): - metrikedf[metrike[i]]=history.history[metrike[i]] - #print(history.history[metrike[i]]) - plt.plot(history.history[metrike[i]]) -plt.show() + classifier=tf.keras.Sequential() -#print(metrikedf) + ### 10)Dodavanje prvog,ulaznog sloja + #aktivacijau=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU ULAZNOG SLOJA ") + #brojnu=int(input("UNETI BROJ NEURONA ULAZNOG SLOJA ")) -#metrikedf.to_csv("metrike.csv") + aktivacijau=params["inputLayerActivationFunction"] + brojnu=params["inputNeurons"] + classifier.add(tf.keras.layers.Dense(units=brojnu,activation=aktivacijau,input_dim=x_train.shape[1])) -### 15) Predvidjanje -y_pred=classifier.predict(x_test) + ### 11)Dodavanje drugog, skrivenog sloja + #aktivacijas=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU SKRIVENOG SLOJA ") + #brojns=int(input("UNETI BROJ NEURONA SKRIVENOG SLOJA ")) -print(y_pred) + aktivacijas=params["hiddenLayerActivationFunction"] + brojns=params["hiddenLayerNeurons"] -### 15.1) Formatiranje podataka za metrike PART2 -y_pred=(y_pred>=0.5).astype('int') -y_pred=y_pred.flatten() + classifier.add(tf.keras.layers.Dense(units=brojns,activation=aktivacijas)) -#print(y_pred) + ### 12) Dodavanje treceg, izlaznog sloja + #aktivacijai=input("UNETI ŽELJENU AKTIVACIONU FUNKCIJU IZLAZNOG SLOJA ") -#print(y_test) -### 15.2) Kreiranje DataFrame-a u kom se nalaze kolone koje predstavljaju stvarne i predvidjene vrednosti, potrebne za iscrtavanje grafika i metrike PART2 -rezultat=pd.DataFrame({"Stvarna vrednost ":y_test,"Predvidjena vrednost":y_pred}) -print(rezultat.head(20)) + aktivacijai=params["outputLayerActivationFunction"] -#####METRIKE##### PART2 + classifier.add(tf.keras.layers.Dense(units=1,activation=aktivacijai)) -import sklearn.metrics as sm - -### 16)Tacnost -tacnost=sm.accuracy_score(y_test,y_pred) -print('tacnost ',tacnost) + ### 13) Kompajliranje neuronske mreze + #gubici=input("UNETI FUNKCIJU OBRADE GUBITAKA ") + #optimizator=input("UNETI ŽELJENI OPTIMIZATOR ") -### 17)Preciznost -preciznost=sm.precision_score(y_test,y_pred) -print('preciznost ',preciznost) + optimizator=params["optimizer"] -### 18)Recall -recall=sm.recall_score(y_test,y_pred) -print('recall ',recall) + ### 13.1)Izbor metrike za kompajler PART2 + metrike=[] + while(1): + m=params['lossFunction'] + + if(m=='KRAJ'): + break + metrike.append(m) + classifier.compile(optimizer=optimizator, loss='binary_crossentropy',metrics = metrike) -### 19)Specificity -tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() -spec = tn / (tn+fp) -print('spec ',spec) + ### 14) + #uzorci=int(input("UNETI KOLIKO UZORAKA ĆE BITI UNETO U ISTO VREME ")) + #epohe=int(input("UNETI BROJ EPOHA")) + uzorci=params["batchSize"] + epohe=params["epochs"] + history=classifier.fit(x_train,y_train,batch_size=uzorci,epochs=epohe) -### 20)F1 -f1=sm.f1_score(y_test,y_pred) -print('f1 ',f1) + ### 14.1)Parametri grafika iz history PART2 + metrikedf=pd.DataFrame() ###DataFrame u kom se nalaze podaci o rezultatima metrika za iscrtavanje na grafiku. Svaka kolona sadrzi vrednost metrike po epohama + for i in range(len(metrike)): + metrikedf[metrike[i]]=history.history[metrike[i]] + #print(history.history[metrike[i]]) + plt.plot(history.history[metrike[i]]) + plt.show() -### 21)Classification report -classificationreport=sm.classification_report(y_test,y_pred) -print('classification ',classificationreport) + #print(metrikedf) -### 22)Mean squared error (mse) -mse=sm.mean_squared_error(y_test,y_pred) -print('mse ',mse) + #metrikedf.to_csv("metrike.csv") -### 23)Mean absolute error (mae) -mae=sm.mean_absolute_error(y_test,y_pred) -print('mae ',mae) -### 24)Mean absolute percentage error (mape) -mape=sm.mean_absolute_percentage_error(y_test,y_pred) -print('mape ',mape) + ### 15) Predvidjanje + y_pred=classifier.predict(x_test) -### 25)Root mean square error (rmse) *** da bi se iskoristila u history, salje se u metrics preko funkcije -import numpy as np -rmse=np.sqrt(sm.mean_squared_error(y_test,y_pred)) -print("rmse ",rmse) + print(y_pred) -### 26)Confusion matrix -cmatrix=sm.confusion_matrix(y_test,y_pred) -print('cmatrix ',cmatrix) + ### 15.1) Formatiranje podataka za metrike PART2 + y_pred=(y_pred>=0.5).astype('int') + y_pred=y_pred.flatten() -### 27)ROC -fpr, tpr, _ = sm.roc_curve(y_test,y_pred) -plt.plot(fpr, tpr, color='blue') -plt.title('ROC') -plt.xlim([0.0, 1.0]) -plt.xlabel('False Positive Rate') -plt.ylim([0.0, 1.0]) -plt.ylabel('True Positive Rate') -plt.show() + #print(y_pred) + #print(y_test) + ### 15.2) Kreiranje DataFrame-a u kom se nalaze kolone koje predstavljaju stvarne i predvidjene vrednosti, potrebne za iscrtavanje grafika i metrike PART2 + rezultat=pd.DataFrame({"Stvarna vrednost ":y_test,"Predvidjena vrednost":y_pred}) + #print(rezultat.head(20)) + #####METRIKE##### PART2 + import sklearn.metrics as sm + + ### 16)Tacnost + tacnost=sm.accuracy_score(y_test,y_pred) + print('tacnost ',tacnost) + + ### 17)Preciznost + preciznost=sm.precision_score(y_test,y_pred) + print('preciznost ',preciznost) + + ### 18)Recall + recall=sm.recall_score(y_test,y_pred) + print('recall ',recall) + + ### 19)Specificity + tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() + spec = tn / (tn+fp) + print('spec ',spec) + + ### 20)F1 + f1=sm.f1_score(y_test,y_pred) + print('f1 ',f1) + + ### 21)Classification report + classificationreport=sm.classification_report(y_test,y_pred) + print('classification ',classificationreport) + + ### 22)Mean squared error (mse) + mse=sm.mean_squared_error(y_test,y_pred) + print('mse ',mse) + + ### 23)Mean absolute error (mae) + mae=sm.mean_absolute_error(y_test,y_pred) + print('mae ',mae) + + ### 24)Mean absolute percentage error (mape) + mape=sm.mean_absolute_percentage_error(y_test,y_pred) + print('mape ',mape) + + ### 25)Root mean square error (rmse) *** da bi se iskoristila u history, salje se u metrics preko funkcije + import numpy as np + rmse=np.sqrt(sm.mean_squared_error(y_test,y_pred)) + print("rmse ",rmse) + + ### 26)Confusion matrix + cmatrix=sm.confusion_matrix(y_test,y_pred) + print('cmatrix ',cmatrix) + + ### 27)ROC + fpr, tpr, _ = sm.roc_curve(y_test,y_pred) + plt.plot(fpr, tpr, color='blue') + plt.title('ROC') + plt.xlim([0.0, 1.0]) + plt.xlabel('False Positive Rate') + plt.ylim([0.0, 1.0]) + plt.ylabel('True Positive Rate') + plt.show() + + r=Response(history,rezultat,tacnost,preciznost,recall,spec,f1,classificationreport,mse,mae,mape,rmse,cmatrix,fpr,tpr) + + return "Done" -- cgit v1.2.3 From 72005d90ce26890be13baec2435b2bf536165168 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Mon, 14 Mar 2022 23:22:24 +0100 Subject: Izmenjen je api.py fajl --- backend/microservice/PythonServer/project/api/api.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) (limited to 'backend') diff --git a/backend/microservice/PythonServer/project/api/api.py b/backend/microservice/PythonServer/project/api/api.py index 1f4afdeb..b2fb503b 100644 --- a/backend/microservice/PythonServer/project/api/api.py +++ b/backend/microservice/PythonServer/project/api/api.py @@ -7,7 +7,8 @@ import pandas as pd import keras import csv import json - +import mlservice +from mlservice import obuka app = flask.Flask(__name__) app.config["DEBUG"] = True @@ -20,13 +21,8 @@ def index(): @app.route('/data', methods = ['GET', 'POST']) def data(): if request.method == 'POST': - f = request.json['filepath'] - data = [] - with open(f) as file: - csvfile = csv.reader(file) - for row in csvfile: - data.append(row) - data = pd.DataFrame(data) + f = request.json['filepath'] + data = pd.read_csv(f) print(data) - return render_template('data.html', data = data.to_html(header=False, index=False)) + return obuka(data,request.json) app.run() \ No newline at end of file -- cgit v1.2.3 From 39228ed08db5aa56e6a1c02ddbfc6691e1d0eef0 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Tue, 15 Mar 2022 23:10:12 +0100 Subject: Druga verzija - Python socket. --- .../PythonServer/project/api/socket2/client.py | 16 +++++++++ .../PythonServer/project/api/socket2/server.py | 39 ++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 backend/microservice/PythonServer/project/api/socket2/client.py create mode 100644 backend/microservice/PythonServer/project/api/socket2/server.py (limited to 'backend') diff --git a/backend/microservice/PythonServer/project/api/socket2/client.py b/backend/microservice/PythonServer/project/api/socket2/client.py new file mode 100644 index 00000000..65e76b55 --- /dev/null +++ b/backend/microservice/PythonServer/project/api/socket2/client.py @@ -0,0 +1,16 @@ +# Import socket module +import socket + +# Create a socket object +s = socket.socket() + +# Define the port on which you want to connect +port = 12345 + +# connect to the server on local computer +s.connect(('127.0.0.1', port)) + +# receive data from the server and decoding to get the string. +print (s.recv(1024).decode()) +# close the connection +s.close() \ No newline at end of file diff --git a/backend/microservice/PythonServer/project/api/socket2/server.py b/backend/microservice/PythonServer/project/api/socket2/server.py new file mode 100644 index 00000000..c65dae78 --- /dev/null +++ b/backend/microservice/PythonServer/project/api/socket2/server.py @@ -0,0 +1,39 @@ +# first of all import the socket library +import socket + +# next create a socket object +s = socket.socket() +print ("Socket successfully created") + +# reserve a port on your computer in our +# case it is 12345 but it can be anything +port = 12345 + +# Next bind to the port +# we have not typed any ip in the ip field +# instead we have inputted an empty string +# this makes the server listen to requests +# coming from other computers on the network +s.bind(('', port)) +print ("socket binded to %s" %(port)) + +# put the socket into listening mode +s.listen(5) +print ("socket is listening") + +# a forever loop until we interrupt it or +# an error occurs +while True: + +# Establish connection with client. + c, addr = s.accept() + print ('Got connection from', addr ) + + # send a thank you message to the client. encoding to send byte type. + c.send('Thank you for connecting'.encode()) + + # Close the connection with the client + c.close() + + # Breaking once connection closed + break \ No newline at end of file -- cgit v1.2.3 From 3541c23e1cb953f9669ec07dd6eab710ee8faf1c Mon Sep 17 00:00:00 2001 From: "DESKTOP-S0O2C44\\ROG" Date: Wed, 16 Mar 2022 22:45:32 +0100 Subject: Ispravljen dataset,model i predictor kontroler. Zavrsene crud metode. Odradjeni servisi #41 --- backend/api/api/Controllers/DatasetController.cs | 119 +++++++++++++-- backend/api/api/Controllers/ModelController.cs | 90 ++++++++++-- backend/api/api/Controllers/PredictorController.cs | 159 +++++++++++++++++++++ backend/api/api/Data/UserStoreDatabaseSettings.cs | 2 +- .../api/Interfaces/IUserStoreDatabaseSettings.cs | 2 +- backend/api/api/Models/Predictor.cs | 23 +++ backend/api/api/Program.cs | 1 + backend/api/api/Services/DatasetService.cs | 7 +- backend/api/api/Services/IDatasetService.cs | 3 +- backend/api/api/Services/IModelService.cs | 3 +- backend/api/api/Services/IPredictorService.cs | 16 +++ backend/api/api/Services/ModelService.cs | 11 +- backend/api/api/Services/PredictorService.cs | 50 +++++++ backend/api/api/appsettings.json | 19 +-- 14 files changed, 459 insertions(+), 46 deletions(-) create mode 100644 backend/api/api/Controllers/PredictorController.cs create mode 100644 backend/api/api/Models/Predictor.cs create mode 100644 backend/api/api/Services/IPredictorService.cs create mode 100644 backend/api/api/Services/PredictorService.cs (limited to 'backend') diff --git a/backend/api/api/Controllers/DatasetController.cs b/backend/api/api/Controllers/DatasetController.cs index 3d008744..bc7448e1 100644 --- a/backend/api/api/Controllers/DatasetController.cs +++ b/backend/api/api/Controllers/DatasetController.cs @@ -1,6 +1,9 @@ using api.Models; using api.Services; +using Microsoft.AspNetCore.Authorization; using Microsoft.AspNetCore.Mvc; +using Microsoft.Net.Http.Headers; +using System.Net.Http.Headers; // For more information on enabling Web API for empty projects, visit https://go.microsoft.com/fwlink/?LinkID=397860 @@ -11,24 +14,64 @@ namespace api.Controllers public class DatasetController : ControllerBase { private readonly IDatasetService _datasetService; + private JwtToken jwtToken; - public DatasetController(IDatasetService datasetService) + public DatasetController(IDatasetService datasetService, IConfiguration configuration) { _datasetService = datasetService; + jwtToken = new JwtToken(configuration); } - // GET: api//{username}/datasets - [HttpGet("{username}/datasets")] - public ActionResult> Get(string username) + // GET: api//mydatasets + [HttpGet("/mydatasets")] + [Authorize(Roles = "User")] + public ActionResult> Get() { - return _datasetService.GetAllDatesets(username); + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + //ako bude trebao ID, samo iz baze uzeti + + return _datasetService.GetMyDatesets(username); + } + + // GET: api//publicdatasets + [HttpGet("/datasets")] + public ActionResult> GetPublicDS() + { + return _datasetService.GetPublicDatesets(); } - // GET api//{username}/{name} - [HttpGet("{username}/{name}")] - public ActionResult Get(string username, string name) + // GET api//{name} + //get odredjeni dataset + [HttpGet("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Get(string name) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + var dataset = _datasetService.GetOneDataset(username, name); if (dataset == null) @@ -37,10 +80,27 @@ namespace api.Controllers return dataset; } + /*za pretragu vratiti dataset koji je public + public ActionResult Get(string name) + { + + + var dataset = _datasetService.GetOneDataset(username, name); + + if (dataset == null) + return NotFound($"Dataset with name = {name} or user with username = {username} not found"); + + return dataset; + } + */ + // POST api//add [HttpPost("add")] + [Authorize(Roles = "User")] public ActionResult Post([FromBody] Dataset dataset) { + //da li ce preko tokena da se ubaci username ili front salje + //dataset.username = usernameToken; var existingDataset = _datasetService.GetOneDataset(dataset.username, dataset.name); if (existingDataset != null) @@ -53,10 +113,24 @@ namespace api.Controllers } } - // PUT api//{username}/{name} - [HttpPut("{username}/{name}")] - public ActionResult Put(string username, string name, [FromBody] Dataset dataset) + // PUT api//{name} + [HttpPut("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Put(string name, [FromBody] Dataset dataset) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + var existingDataset = _datasetService.GetOneDataset(username, name); //ne mora da se proverava @@ -64,13 +138,28 @@ namespace api.Controllers return NotFound($"Dataset with name = {name} or user with username = {username} not found"); _datasetService.Update(username, name, dataset); - return NoContent(); + + return Ok($"Dataset with name = {name} updated"); } - // DELETE api//username/name - [HttpDelete("{username}/{name}")] - public ActionResult Delete(string username, string name) + // DELETE api//name + [HttpDelete("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Delete(string name) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + var dataset = _datasetService.GetOneDataset(username, name); if (dataset == null) diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs index deb622b8..1d03d924 100644 --- a/backend/api/api/Controllers/ModelController.cs +++ b/backend/api/api/Controllers/ModelController.cs @@ -3,6 +3,8 @@ using api.Services; using Microsoft.AspNetCore.Authorization; using Microsoft.AspNetCore.Http; using Microsoft.AspNetCore.Mvc; +using Microsoft.Net.Http.Headers; +using System.Net.Http.Headers; namespace api.Controllers { @@ -13,12 +15,14 @@ namespace api.Controllers private IMlConnectionService _mlService; private readonly IModelService _modelService; - + private JwtToken jwtToken; - public ModelController(IMlConnectionService mlService, IModelService modelService) + + public ModelController(IMlConnectionService mlService, IModelService modelService, IConfiguration configuration) { _mlService = mlService; _modelService = modelService; + jwtToken = new JwtToken(configuration); } [HttpPost("sendModel")] @@ -29,18 +33,46 @@ namespace api.Controllers return Ok(result); } - // GET: api//{username}/models - [HttpGet("{username}/models")] - public ActionResult> Get(string username) + // GET: api//mymodels + [HttpGet("/mymodels")] + [Authorize(Roles = "User")] + public ActionResult> Get() { - return _modelService.GetAllModels(username); + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + return _modelService.GetMyModels(username); } - //id korisnika, name modela - // GET api//{username}/{name} - [HttpGet("{username}/{name}")] - public ActionResult Get(string username, string name) + // name modela + // GET api//{name} + [HttpGet("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Get(string name) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + var model = _modelService.GetOneModel(username, name); if (model == null) @@ -51,6 +83,7 @@ namespace api.Controllers // POST api//add [HttpPost("add")] + [Authorize(Roles = "User")] public ActionResult Post([FromBody] Model model) { var existingModel = _modelService.GetOneModel(model.username, model.name); @@ -66,9 +99,24 @@ namespace api.Controllers } // PUT api//{username}/{name} - [HttpPut("{username}/{name}")] - public ActionResult Put(string username, string name, [FromBody] Model model) + [HttpPut("{name}")] + [Authorize(Roles = "User")] + public ActionResult Put(string name, [FromBody] Model model) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + var existingModel = _modelService.GetOneModel(username, name); if (existingModel == null) @@ -79,9 +127,23 @@ namespace api.Controllers } // DELETE api//username - [HttpDelete("{username}/{name}")] - public ActionResult Delete(string username, string name) + [HttpDelete("{name}")] + [Authorize(Roles = "User")] + public ActionResult Delete(string name) { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + var model = _modelService.GetOneModel(username, name); if (model == null) diff --git a/backend/api/api/Controllers/PredictorController.cs b/backend/api/api/Controllers/PredictorController.cs new file mode 100644 index 00000000..d5a55b3c --- /dev/null +++ b/backend/api/api/Controllers/PredictorController.cs @@ -0,0 +1,159 @@ +using api.Models; +using api.Services; +using Microsoft.AspNetCore.Authorization; +using Microsoft.AspNetCore.Http; +using Microsoft.AspNetCore.Mvc; +using Microsoft.Net.Http.Headers; +using System.Net.Http.Headers; + +namespace api.Controllers +{ + [Route("api/[controller]")] + [ApiController] + public class PredictorController : Controller + { + private readonly IPredictorService _predictorService; + private JwtToken jwtToken; + + public PredictorController(IPredictorService predictorService, IConfiguration configuration) + { + _predictorService = predictorService; + jwtToken = new JwtToken(configuration); + } + + // GET: api//mypredictors + [HttpGet("mypredictors")] + [Authorize(Roles = "User")] + public ActionResult> Get() + { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + return _predictorService.GetMyPredictors(username); + } + // GET: api//publicpredictors + [HttpGet("publicpredictors")] + public ActionResult> GetPublicPredictors() + { + return _predictorService.GetPublicPredictors(); + } + + // GET api//{name} + [HttpGet("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Get(string name) + { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + var predictor = _predictorService.GetOnePredictor(username, name); + + if (predictor == null) + return NotFound($"Predictor with name = {name} or user with username = {username} not found"); + + return predictor; + } + + // POST api//add + [HttpPost("add")] + [Authorize(Roles = "User")] + public ActionResult Post([FromBody] Predictor predictor) + { + var existingModel = _predictorService.GetOnePredictor(predictor.username, predictor.name); + + if (existingModel != null) + return NotFound($"Predictor with name = {predictor.name} exisits"); + else + { + _predictorService.Create(predictor); + + return CreatedAtAction(nameof(Get), new { id = predictor._id }, predictor); + } + } + + + + // PUT api//{name} + [HttpPut("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Put(string name, [FromBody] Predictor predictor) + { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + var existingDataset = _predictorService.GetOnePredictor(username, name); + + //ne mora da se proverava + if (existingDataset == null) + return NotFound($"Predictor with name = {name} or user with username = {username} not found"); + + _predictorService.Update(username, name, predictor); + + return Ok($"Predictor with name = {name} updated"); + } + + + // DELETE api//name + [HttpDelete("/{name}")] + [Authorize(Roles = "User")] + public ActionResult Delete(string name) + { + string username; + var header = Request.Headers[HeaderNames.Authorization]; + if (AuthenticationHeaderValue.TryParse(header, out var headerValue)) + { + var scheme = headerValue.Scheme; + var parameter = headerValue.Parameter; + username = jwtToken.TokenToUsername(parameter); + if (username == null) + return null; + } + else + return BadRequest(); + + var predictor = _predictorService.GetOnePredictor(username, name); + + if (predictor == null) + return NotFound($"Predictor with name = {name} or user with username = {username} not found"); + + _predictorService.Delete(predictor.username, predictor.name); + + return Ok($"Predictor with name = {name} deleted"); + + } + + + + + } +} diff --git a/backend/api/api/Data/UserStoreDatabaseSettings.cs b/backend/api/api/Data/UserStoreDatabaseSettings.cs index 6416ab05..6841a3e0 100644 --- a/backend/api/api/Data/UserStoreDatabaseSettings.cs +++ b/backend/api/api/Data/UserStoreDatabaseSettings.cs @@ -10,7 +10,7 @@ namespace api.Data public string DatabaseName { get; set; } = String.Empty; public string CollectionName { get; set; } = String.Empty; public string DatasetCollectionName { get; set; } = String.Empty; - public string ModelCollectionName { get; set; } = String.Empty; + public string PredictorCollectionName { get; set; } = String.Empty; public string FilesCollectionName { get; set; } = String.Empty; } } diff --git a/backend/api/api/Interfaces/IUserStoreDatabaseSettings.cs b/backend/api/api/Interfaces/IUserStoreDatabaseSettings.cs index 82312649..94d3e1fc 100644 --- a/backend/api/api/Interfaces/IUserStoreDatabaseSettings.cs +++ b/backend/api/api/Interfaces/IUserStoreDatabaseSettings.cs @@ -6,7 +6,7 @@ string DatabaseName { get; set; } string CollectionName { get; set; } string DatasetCollectionName { get; set; } - string ModelCollectionName { get; } + string PredictorCollectionName { get; } string FilesCollectionName { get; set; } } } diff --git a/backend/api/api/Models/Predictor.cs b/backend/api/api/Models/Predictor.cs new file mode 100644 index 00000000..638495bd --- /dev/null +++ b/backend/api/api/Models/Predictor.cs @@ -0,0 +1,23 @@ +using System; +using MongoDB.Bson; +using MongoDB.Bson.Serialization.Attributes; + +namespace api.Models +{ + public class Predictor + { + [BsonId] + [BsonRepresentation(BsonType.ObjectId)]//mongo data type to .net + public string _id { get; set; } + public string username { get; set; } + + public string name { get; set; } + public string description { get; set; } + public string[] inputs { get; set; } + public string output { get; set; } + public bool isPublic { get; set; } + public bool accessibleByLink { get; set; } + public string dateCreated { get; set; } + } +} + diff --git a/backend/api/api/Program.cs b/backend/api/api/Program.cs index f3287b4c..65399bdf 100644 --- a/backend/api/api/Program.cs +++ b/backend/api/api/Program.cs @@ -30,6 +30,7 @@ builder.Services.AddScoped(); builder.Services.AddScoped(); builder.Services.AddScoped(); builder.Services.AddScoped(); +builder.Services.AddScoped(); builder.Services.AddScoped(); diff --git a/backend/api/api/Services/DatasetService.cs b/backend/api/api/Services/DatasetService.cs index 80c31758..27a8b3ee 100644 --- a/backend/api/api/Services/DatasetService.cs +++ b/backend/api/api/Services/DatasetService.cs @@ -26,15 +26,20 @@ namespace api.Services _dataset.DeleteOne(dataset => (dataset.username == username && dataset.name == name)); } - public List GetAllDatesets(string username) + public List GetMyDatesets(string username) { return _dataset.Find(dataset => dataset.username == username).ToList(); } + public List GetPublicDatesets() + { + return _dataset.Find(dataset => dataset.isPublic == true).ToList(); + } public Dataset GetOneDataset(string username, string name) { return _dataset.Find(dataset => dataset.username == username && dataset.name == name).FirstOrDefault(); } + //odraditi za pretragu getOne //ako je potrebno da se zameni name ili ekstenzija public void Update(string username, string name, Dataset dataset) diff --git a/backend/api/api/Services/IDatasetService.cs b/backend/api/api/Services/IDatasetService.cs index 49013e29..61a04b94 100644 --- a/backend/api/api/Services/IDatasetService.cs +++ b/backend/api/api/Services/IDatasetService.cs @@ -6,7 +6,8 @@ namespace api.Services public interface IDatasetService { Dataset GetOneDataset(string username, string name); - List GetAllDatesets(string username); + List GetMyDatesets(string username); + List GetPublicDatesets(); Dataset Create(Dataset dataset); void Update(string username, string name, Dataset dataset); void Delete(string username, string name); diff --git a/backend/api/api/Services/IModelService.cs b/backend/api/api/Services/IModelService.cs index 149afd4a..c1931ffa 100644 --- a/backend/api/api/Services/IModelService.cs +++ b/backend/api/api/Services/IModelService.cs @@ -6,7 +6,8 @@ namespace api.Services public interface IModelService { Model GetOneModel(string username, string name); - List GetAllModels(string username); + List GetMyModels(string username); + //List GetPublicModels(); Model Create(Model model); void Update(string username, string name, Model model); void Delete(string username, string name); diff --git a/backend/api/api/Services/IPredictorService.cs b/backend/api/api/Services/IPredictorService.cs new file mode 100644 index 00000000..594b233b --- /dev/null +++ b/backend/api/api/Services/IPredictorService.cs @@ -0,0 +1,16 @@ +using System; +using api.Models; + +namespace api.Services +{ + public interface IPredictorService + { + Predictor GetOnePredictor(string username, string name); + List GetMyPredictors(string username); + List GetPublicPredictors(); + Predictor Create(Predictor predictor); + void Update(string username, string name, Predictor predictor); + void Delete(string username, string name); + } +} + diff --git a/backend/api/api/Services/ModelService.cs b/backend/api/api/Services/ModelService.cs index 33dea30e..a3939b29 100644 --- a/backend/api/api/Services/ModelService.cs +++ b/backend/api/api/Services/ModelService.cs @@ -13,7 +13,7 @@ namespace api.Services public ModelService(IUserStoreDatabaseSettings settings, IMongoClient mongoClient) { var database = mongoClient.GetDatabase(settings.DatabaseName); - _model = database.GetCollection(settings.ModelCollectionName); + _model = database.GetCollection(settings.PredictorCollectionName); } public Model Create(Model model) @@ -27,11 +27,16 @@ namespace api.Services _model.DeleteOne(model => (model.username == username && model.name == name)); } - public List GetAllModels(string username) + public List GetMyModels(string username) { return _model.Find(model => model.username == username).ToList(); } - + /* + public List GetPublicModels() + { + return _model.Find(model => model.isPublic == true).ToList(); + } + */ public Model GetOneModel(string username, string name) { return _model.Find(model => model.username == username && model.name == name).FirstOrDefault(); diff --git a/backend/api/api/Services/PredictorService.cs b/backend/api/api/Services/PredictorService.cs new file mode 100644 index 00000000..69fb25c9 --- /dev/null +++ b/backend/api/api/Services/PredictorService.cs @@ -0,0 +1,50 @@ +using api.Interfaces; +using api.Models; +using MongoDB.Driver; + +namespace api.Services +{ + public class PredictorService : IPredictorService + { + private readonly IMongoCollection _predictor; + + public PredictorService(IUserStoreDatabaseSettings settings, IMongoClient mongoClient) + { + var database = mongoClient.GetDatabase(settings.DatabaseName); + _predictor = database.GetCollection(settings.PredictorCollectionName); + } + + public Predictor Create(Predictor predictor) + { + _predictor.InsertOne(predictor); + return predictor; + } + + public void Delete(string username, string name) + { + _predictor.DeleteOne(predictor => (predictor.username == username && predictor.name == name)); + } + + public List GetMyPredictors(string username) + { + return _predictor.Find(predictor => predictor.username == username).ToList(); + } + + public Predictor GetOnePredictor(string username, string name) + { + return _predictor.Find(predictor => predictor.username == username && predictor.name == name).FirstOrDefault(); + + } + + public List GetPublicPredictors() + { + return _predictor.Find(predictor => predictor.isPublic == true).ToList(); + } + + public void Update(string username, string name, Predictor predictor) + { + _predictor.ReplaceOne(predictor => predictor.username == username && predictor.name == name, predictor); + + } + } +} diff --git a/backend/api/api/appsettings.json b/backend/api/api/appsettings.json index 86363075..3ccba198 100644 --- a/backend/api/api/appsettings.json +++ b/backend/api/api/appsettings.json @@ -9,19 +9,20 @@ } }, "AllowedHosts": "*", - "UserStoreDatabaseSettings": { - /* LocalHost + "UserStoreDatabaseSettings": { + /* LocalHost "ConnectionString": "mongodb://127.0.0.1:27017/", "DatabaseName": "si_project", "CollectionName": "User", "DatasetCollectionName" : "Dataset", "ModelCollectionName" : "Model" */ - "ConnectionString": "mongodb+srv://si_user:si_user@sidatabase.twtfm.mongodb.net/myFirstDatabase?retryWrites=true&w=majority", - "DatabaseName": "si_db", - "CollectionName": "users", - "DatasetCollectionName": "Dataset", - "ModelCollectionName": "Model", - "FilesCollectionName": "Files" - } + "ConnectionString": "mongodb+srv://si_user:si_user@sidatabase.twtfm.mongodb.net/myFirstDatabase?retryWrites=true&w=majority", + "DatabaseName": "si_db", + "CollectionName": "users", + "DatasetCollectionName": "Dataset", + "ModelCollectionName": "Model", + "PredictorCollectionName": "Predictor", + "FilesCollectionName": "Files" + } } -- cgit v1.2.3