From 73a539f449f2f6ec7bc7adaa18ebbe1b1b45ad9c Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 16 Apr 2022 00:42:36 +0200 Subject: Omogućeno prikupljanje rezultata metrika nakon završenog treniranja modela. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/newmlservice.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index d19a4e44..ecadb0f4 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -21,6 +21,7 @@ from sklearn.model_selection import train_test_split from dataclasses import dataclass import statistics as s from sklearn.metrics import roc_auc_score + #from ann_visualizer.visualize import ann_viz; def returnColumnsInfo(dataset): dict=[] @@ -224,7 +225,7 @@ def train(dataset, params, callback): # # ###OPTIMIZATORI - + """ if(params['optimizer']=='adam'): opt=tf.keras.optimizers.Adam(learning_rate=params['learningRate']) @@ -276,7 +277,7 @@ def train(dataset, params, callback): activityreg=tf.keras.regularizers.l2(reg['activityRate']) elif(reg['kernelType']=='l1l2'): activityreg=tf.keras.regularizers.l1_l2(l1=reg['activityRate'][0],l2=reg['activityRate'][1]) - + """ if(problem_type=='multi-klasifikacioni'): #print('multi') @@ -293,17 +294,19 @@ def train(dataset, params, callback): classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) - + + hist=history.history + y_pred=classifier.predict(x_test) y_pred=np.argmax(y_pred,axis=1) - #print(y_pred.flatten()) - #print(y_test) + scores = classifier.evaluate(x_test, y_test) #print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100)) classifier.save("temp/"+params['name'], save_format='h5') #vizuelizacija u python-u #from ann_visualizer.visualize import ann_viz; #ann_viz(classifier, title="My neural network") + return hist elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') @@ -318,7 +321,7 @@ def train(dataset, params, callback): classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) - + hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -330,6 +333,7 @@ def train(dataset, params, callback): #ann_viz(classifier, title="My neural network") classifier.save("temp/"+params['name'], save_format='h5') + return hist elif(problem_type=='regresioni'): classifier=tf.keras.Sequential() @@ -343,9 +347,10 @@ def train(dataset, params, callback): classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) - + return hist def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): #creating a set of all the unique classes using the actual class list -- cgit v1.2.3 From d76cb349ef8d5254780e3ffb6afa7080513f2332 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Sat, 16 Apr 2022 18:17:07 +0200 Subject: Update-ovao ML kontroler za predict. --- backend/microservice/api/config.py | 2 +- backend/microservice/api/controller.py | 16 ++++++++-------- backend/microservice/api/newmlservice.py | 5 +++++ 3 files changed, 14 insertions(+), 9 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/config.py b/backend/microservice/api/config.py index 2b6fbe89..8fb088a7 100644 --- a/backend/microservice/api/config.py +++ b/backend/microservice/api/config.py @@ -1,2 +1,2 @@ api_url = "http://localhost:5283/api" - +hostIP = "127.0.0.1:5543" \ No newline at end of file diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 8e12c41d..d7564b70 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -9,7 +9,7 @@ import config app = flask.Flask(__name__) app.config["DEBUG"] = True -app.config["SERVER_NAME"] = "127.0.0.1:5543" +app.config["SERVER_NAME"] = config.hostIP class train_callback(tf.keras.callbacks.Callback): def __init__(self, x_test, y_test): @@ -33,19 +33,19 @@ def train(): paramsExperiment = json.loads(request.form["experiment"]) paramsDataset = json.loads(request.form["dataset"]) #dataset, paramsModel, paramsExperiment, callback) - result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback) + result = newmlservice.train(data, paramsModel, paramsExperiment, paramsDataset, train_callback) print(result) return jsonify(result) @app.route('/predict', methods = ['POST']) def predict(): - f = request.json['filepath'] - dataset = pd.read_csv(f) - m = request.json['modelpath'] - model = tf.keras.models.load_model(m) + h5 = request.files.get("h5file") + model = tf.keras.models.load_model(h5) + paramsExperiment = json.loads(request.form["experiment"]) + paramsPredictor = json.loads(request.form["predictor"]) print("********************************model loaded*******************************") - newmlservice.manageH5(dataset,request.json['model'],model) - return "done" + result = newmlservice.predict(paramsExperiment, paramsPredictor, model) + return result @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index ecadb0f4..74ad232e 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -432,6 +432,11 @@ def train(dataset, params, callback): micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred) ''' +def predict(experiment, predictor, model) { + #model.predict() + # ovo je pre bilo manageH5 + return "TODO" +} def manageH5(dataset,params,h5model): problem_type = params["type"] -- cgit v1.2.3 From 4a6665959aa8b17e3dd0235530b46c3315d520db Mon Sep 17 00:00:00 2001 From: Ognjen Cirkovic Date: Sat, 16 Apr 2022 18:41:19 +0200 Subject: Fix. --- backend/microservice/api/newmlservice.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 74ad232e..3427e287 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -432,11 +432,11 @@ def train(dataset, params, callback): micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred) ''' -def predict(experiment, predictor, model) { +def predict(experiment, predictor, model): #model.predict() # ovo je pre bilo manageH5 return "TODO" -} + def manageH5(dataset,params,h5model): problem_type = params["type"] -- cgit v1.2.3 From fdd1bdcbe113c568dbeef4de6b9a5ad3c9652ef8 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 16 Apr 2022 19:42:35 +0200 Subject: Dodat zahtev za čuvanje h5 fajla treniranog modela. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/controller.py | 14 +++++- backend/microservice/api/newmlservice.py | 77 +++++++++++++++++--------------- 2 files changed, 55 insertions(+), 36 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index d7564b70..d2b8ed2c 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -1,3 +1,4 @@ +from gc import callbacks import flask from flask import request, jsonify import newmlservice @@ -27,14 +28,25 @@ class train_callback(tf.keras.callbacks.Callback): @app.route('/train', methods = ['POST']) def train(): print("******************************TRAIN*************************************************") + f = request.files.get("file") data = pd.read_csv(f) paramsModel = json.loads(request.form["model"]) paramsExperiment = json.loads(request.form["experiment"]) paramsDataset = json.loads(request.form["dataset"]) #dataset, paramsModel, paramsExperiment, callback) - result = newmlservice.train(data, paramsModel, paramsExperiment, paramsDataset, train_callback) + filepath,result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback) + """ + f = request.json['filepath'] + dataset = pd.read_csv(f) + filepath,result=newmlservice.train(dataset,request.json['model'],train_callback) print(result) + """ + + url = config.api_url + "/file/h5" + files = {'file': open(filepath, 'rb')} + r=requests.post(url, files=files) + fileId=r.text return jsonify(result) @app.route('/predict', methods = ['POST']) diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 3427e287..e81192ab 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -1,5 +1,6 @@ from enum import unique from itertools import count +import os import pandas as pd from sklearn import datasets, multiclass import tensorflow as tf @@ -21,7 +22,7 @@ from sklearn.model_selection import train_test_split from dataclasses import dataclass import statistics as s from sklearn.metrics import roc_auc_score - +import matplotlib.pyplot as plt #from ann_visualizer.visualize import ann_viz; def returnColumnsInfo(dataset): dict=[] @@ -113,25 +114,25 @@ class TrainingResult: metrics: dict ''' -def train(dataset, params, callback): - problem_type = params["type"] +def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): + problem_type = paramsModel["type"] #print(problem_type) data = pd.DataFrame() #print(data) - for col in params["inputColumns"]: + for col in paramsExperiment["inputColumns"]: #print(col) data[col]=dataset[col] - output_column = params["columnToPredict"] + output_column = paramsExperiment["columnToPredict"] data[output_column] = dataset[output_column] #print(data) ###NULL - null_value_options = params["nullValues"] - null_values_replacers = params["nullValuesReplacers"] + null_value_options = paramsExperiment["nullValues"] + null_values_replacers = paramsExperiment["nullValuesReplacers"] if(null_value_options=='replace'): #print("replace null") # - dict=params['null_values_replacers'] + dict=null_values_replacers while(len(dict)>0): replace=dict.pop() col=replace['column'] @@ -154,7 +155,7 @@ def train(dataset, params, callback): data.pop(col) # ### Enkodiranje - encoding=params["encoding"] + encoding=paramsExperiment["encoding"] if(encoding=='label'): encoder=LabelEncoder() for col in data.columns: @@ -211,8 +212,8 @@ def train(dataset, params, callback): # # Podela na test i trening skupove # - test=params["randomTestSetDistribution"] - randomOrder = params["randomOrder"] + test=paramsExperiment["randomTestSetDistribution"] + randomOrder = paramsExperiment["randomOrder"] if(randomOrder): random=123 else: @@ -278,49 +279,54 @@ def train(dataset, params, callback): elif(reg['kernelType']=='l1l2'): activityreg=tf.keras.regularizers.l1_l2(l1=reg['activityRate'][0],l2=reg['activityRate'][1]) """ - + filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']) if(problem_type=='multi-klasifikacioni'): #print('multi') classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog - for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog + for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja #print(i) - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj - classifier.add(tf.keras.layers.Dense(units=5, activation=params['outputLayerActivationFunction']))#izlazni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=5, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) hist=history.history - + plt.plot(hist['accuracy']) + plt.show() y_pred=classifier.predict(x_test) y_pred=np.argmax(y_pred,axis=1) scores = classifier.evaluate(x_test, y_test) #print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100)) - classifier.save("temp/"+params['name'], save_format='h5') + + + classifier.save(filepath, save_format='h5') + #vizuelizacija u python-u #from ann_visualizer.visualize import ann_viz; #ann_viz(classifier, title="My neural network") - return hist + + return filepath,hist elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog - for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog + for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja #print(i) - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj - classifier.add(tf.keras.layers.Dense(units=1, activation=params['outputLayerActivationFunction']))#izlazni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -332,25 +338,26 @@ def train(dataset, params, callback): #print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100)) #ann_viz(classifier, title="My neural network") - classifier.save("temp/"+params['name'], save_format='h5') - return hist + classifier.save(filepath, save_format='h5') + return filepath,hist elif(problem_type=='regresioni'): classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog - for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog + for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja #print(i) - classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1)) - classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) - return hist + classifier.save(filepath, save_format='h5') + return filepath,hist def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): #creating a set of all the unique classes using the actual class list -- cgit v1.2.3 From 41bfcba0af1f375349b9fb1935aeb0e0856adff9 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Sat, 16 Apr 2022 20:05:30 +0200 Subject: Pravi se predictor na ML. --- backend/microservice/api/controller.py | 33 ++++++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index d2b8ed2c..95ceccbb 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -1,4 +1,6 @@ +from dataclasses import dataclass from gc import callbacks +from xmlrpc.client import DateTime import flask from flask import request, jsonify import newmlservice @@ -7,11 +9,27 @@ import pandas as pd import json import requests import config +from datetime import datetime app = flask.Flask(__name__) app.config["DEBUG"] = True app.config["SERVER_NAME"] = config.hostIP - + +@dataclass +class Predictor: + _id : str + username: str + inputs : list + output : str + isPublic: bool + accessibleByLink: bool + dateCreated: DateTime + experimentId: str + modelId: str + h5FileId: str + metrics: list + + class train_callback(tf.keras.callbacks.Callback): def __init__(self, x_test, y_test): self.x_test = x_test @@ -47,6 +65,19 @@ def train(): files = {'file': open(filepath, 'rb')} r=requests.post(url, files=files) fileId=r.text + predictor = Predictor() + predictor._id = "" + predictor.username = paramsModel["username"] + predictor.inputs = paramsExperiment["inputColumns"] + predictor.output = paramsExperiment["outputColumn"] + predictor.isPublic = False + predictor.accessibleByLink = False + predictor.dateCreated = datetime.now() + predictor.experimentId = paramsExperiment["_id"] + predictor.modelId = paramsModel["_id"] + predictor.h5FileId = fileId + + print(result) return jsonify(result) @app.route('/predict', methods = ['POST']) -- cgit v1.2.3 From 838139bbc7bee693cfb8d11e9a29e725bbe36ccb Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 16 Apr 2022 20:37:05 +0200 Subject: Prepravka za usaglašavanje sa frontend-om. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/api/api/Controllers/FileController.cs | 1 - backend/microservice/api/controller.py | 25 ++-- backend/microservice/api/newmlservice.py | 6 +- frontend/package-lock.json | 178 +++++++++----------------- frontend/src/app/_data/Model.ts | 4 +- 5 files changed, 83 insertions(+), 131 deletions(-) (limited to 'backend/microservice') diff --git a/backend/api/api/Controllers/FileController.cs b/backend/api/api/Controllers/FileController.cs index 6888f1c7..d9494525 100644 --- a/backend/api/api/Controllers/FileController.cs +++ b/backend/api/api/Controllers/FileController.cs @@ -44,7 +44,6 @@ namespace api.Controllers } [HttpPost("h5")] - [Authorize(Roles = "User,Guest")] public async Task> H5Upload([FromForm] IFormFile file) { diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 95ceccbb..437690ee 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -65,19 +65,22 @@ def train(): files = {'file': open(filepath, 'rb')} r=requests.post(url, files=files) fileId=r.text - predictor = Predictor() - predictor._id = "" - predictor.username = paramsModel["username"] - predictor.inputs = paramsExperiment["inputColumns"] - predictor.output = paramsExperiment["outputColumn"] - predictor.isPublic = False - predictor.accessibleByLink = False - predictor.dateCreated = datetime.now() - predictor.experimentId = paramsExperiment["_id"] - predictor.modelId = paramsModel["_id"] - predictor.h5FileId = fileId + predictor = Predictor( + _id = "", + username = paramsModel["username"], + inputs = paramsExperiment["inputColumns"], + output = paramsExperiment["outputColumn"], + isPublic = False, + accessibleByLink = False, + dateCreated = datetime.now(), + experimentId = paramsExperiment["_id"], + modelId = paramsModel["_id"], + h5FileId = fileId, + metrics=[] + ) print(result) + print(predictor) return jsonify(result) @app.route('/predict', methods = ['POST']) diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index e81192ab..585db480 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -122,7 +122,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): for col in paramsExperiment["inputColumns"]: #print(col) data[col]=dataset[col] - output_column = paramsExperiment["columnToPredict"] + output_column = paramsExperiment["outputColumn"] data[output_column] = dataset[output_column] #print(data) @@ -297,8 +297,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) hist=history.history - plt.plot(hist['accuracy']) - plt.show() + #plt.plot(hist['accuracy']) + #plt.show() y_pred=classifier.predict(x_test) y_pred=np.argmax(y_pred,axis=1) diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 82cd01e6..488653db 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -452,7 +452,6 @@ "version": "13.2.5", "resolved": "https://registry.npmjs.org/@angular/compiler-cli/-/compiler-cli-13.2.5.tgz", "integrity": "sha512-Xd8xj2Z0ilA4TJAM/JkTtA1CAa6SuebFsEEvabHCRO5MDvtdsIUP91ADUZIqDHy7qe6Qift/rAVN2PXxT2aaNA==", - "dev": true, "dependencies": { "@babel/core": "^7.17.2", "chokidar": "^3.0.0", @@ -482,7 +481,6 @@ "version": "2.1.2", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.1.2.tgz", "integrity": "sha512-hoyByceqwKirw7w3Z7gnIIZC3Wx3J484Y3L/cMpXFbr7d9ZQj2mODrirNzcJa+SM3UlpWXYvKV4RlRpFXlWgXg==", - "dev": true, "dependencies": { "@jridgewell/trace-mapping": "^0.3.0" }, @@ -494,7 +492,6 @@ "version": "7.17.5", "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.17.5.tgz", "integrity": "sha512-/BBMw4EvjmyquN5O+t5eh0+YqB3XXJkYD2cjKpYtWOfFy4lQ4UozNSmxAcWT8r2XtZs0ewG+zrfsqeR15i1ajA==", - "dev": true, "dependencies": { "@ampproject/remapping": "^2.1.0", "@babel/code-frame": "^7.16.7", @@ -524,7 +521,6 @@ "version": "6.3.0", "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.0.tgz", "integrity": "sha512-b39TBaTSfV6yBrapU89p5fKekE2m/NwnDocOVruQFS1/veMgdzuPcnOM34M6CwxW8jH/lxEa5rBoDeUwu5HHTw==", - "dev": true, "bin": { "semver": "bin/semver.js" } @@ -533,7 +529,6 @@ "version": "7.17.3", "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.17.3.tgz", "integrity": "sha512-+R6Dctil/MgUsZsZAkYgK+ADNSZzJRRy0TvY65T71z/CR854xHQ1EweBYXdfT+HNeN7w0cSJJEzgxZMv40pxsg==", - "dev": true, "dependencies": { "@babel/types": "^7.17.0", "jsesc": "^2.5.1", @@ -547,7 +542,6 @@ "version": "0.5.7", "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.5.7.tgz", "integrity": "sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w=", - "dev": true, "engines": { "node": ">=0.10.0" } @@ -787,7 +781,6 @@ "version": "7.16.12", "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.16.12.tgz", "integrity": "sha512-dK5PtG1uiN2ikk++5OzSYsitZKny4wOCD0nrO4TqnW4BVBTQ2NGS3NgilvT/TEyxTST7LNyWV/T4tXDoD3fOgg==", - "dev": true, "dependencies": { "@babel/code-frame": "^7.16.7", "@babel/generator": "^7.16.8", @@ -817,7 +810,6 @@ "version": "6.3.0", "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.0.tgz", "integrity": "sha512-b39TBaTSfV6yBrapU89p5fKekE2m/NwnDocOVruQFS1/veMgdzuPcnOM34M6CwxW8jH/lxEa5rBoDeUwu5HHTw==", - "dev": true, "bin": { "semver": "bin/semver.js" } @@ -826,7 +818,6 @@ "version": "0.5.7", "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.5.7.tgz", "integrity": "sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w=", - "dev": true, "engines": { "node": ">=0.10.0" } @@ -835,7 +826,6 @@ "version": "7.16.8", "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.16.8.tgz", "integrity": "sha512-1ojZwE9+lOXzcWdWmO6TbUzDfqLD39CmEhN8+2cX9XkDo5yW1OpgfejfliysR2AWLpMamTiOiAp/mtroaymhpw==", - 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"dev": true + "dev": true, + "requires": {} }, "json-schema-traverse": { "version": "0.4.1", @@ -20039,7 +19990,6 @@ "version": "5.0.1", "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", "integrity": "sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==", - "dev": true, "requires": { "is-number": "^7.0.0" } @@ -20108,8 +20058,7 @@ "typescript": { "version": "4.5.5", "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.5.5.tgz", - "integrity": "sha512-TCTIul70LyWe6IJWT8QSYeA54WQe8EjQFU4wY52Fasj5UKx88LNYKCgBEHcOMOrFF1rKGbD8v/xcNWVUq9SymA==", - "dev": true + "integrity": "sha512-TCTIul70LyWe6IJWT8QSYeA54WQe8EjQFU4wY52Fasj5UKx88LNYKCgBEHcOMOrFF1rKGbD8v/xcNWVUq9SymA==" }, "ua-parser-js": { "version": "0.7.31", @@ -20318,7 +20267,8 @@ "version": "3.5.2", "resolved": "https://registry.npmjs.org/ajv-keywords/-/ajv-keywords-3.5.2.tgz", "integrity": "sha512-5p6WTN0DdTGVQk6VjcEju19IgaHudalcfabD7yhDGeA6bcQnmL+CpveLJq/3hvfwd1aof6L386Ougkx6RfyMIQ==", - "dev": true + "dev": true, + "requires": {} }, "json-schema-traverse": { "version": "0.4.1", @@ -20554,7 +20504,8 @@ "version": "8.2.3", "resolved": "https://registry.npmjs.org/ws/-/ws-8.2.3.tgz", "integrity": "sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==", - "dev": true + "dev": true, + "requires": {} }, "y18n": { "version": "5.0.8", @@ -20564,8 +20515,7 @@ "yallist": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz", - "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==", - "dev": true + "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==" }, "yaml": { "version": "1.10.2", diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts index 8a85e296..1af3fe30 100644 --- a/frontend/src/app/_data/Model.ts +++ b/frontend/src/app/_data/Model.ts @@ -73,7 +73,7 @@ export enum LossFunction { HingeLoss = 'hinge_loss', // multi-class classification loss functions CategoricalCrossEntropy = 'categorical_crossentropy', - SparseCategoricalCrossEntropy = 'sparse_categorical_crosentropy', + SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy', KLDivergence = 'kullback_leibler_divergence', // regression loss functions @@ -95,7 +95,7 @@ export enum LossFunctionBinaryClassification { } export enum LossFunctionMultiClassification { CategoricalCrossEntropy = 'categorical_crossentropy', - SparseCategoricalCrossEntropy = 'sparse_categorical_crosentropy', + SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy', KLDivergence = 'kullback_leibler_divergence', } -- cgit v1.2.3 From 66c147bc3154e531cfc78591a7451d904122fc1f Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 16 Apr 2022 21:52:40 +0200 Subject: Ispravljeno obaveštavanje backend-a o epohama. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/api/api/Controllers/FileController.cs | 2 +- backend/microservice/api/controller.py | 18 ++++++++++++++---- backend/microservice/api/newmlservice.py | 10 +++++----- 3 files changed, 20 insertions(+), 10 deletions(-) (limited to 'backend/microservice') diff --git a/backend/api/api/Controllers/FileController.cs b/backend/api/api/Controllers/FileController.cs index c4a14b9a..68d2ebed 100644 --- a/backend/api/api/Controllers/FileController.cs +++ b/backend/api/api/Controllers/FileController.cs @@ -84,7 +84,7 @@ namespace api.Controllers await file.CopyToAsync(stream); } FileModel fileModel = new FileModel(); - fileModel.type = "h5"; + fileModel.type = ".h5"; fileModel.path = fullPath; fileModel.uploaderId = uploaderId; fileModel.date = DateTime.Now.ToUniversalTime(); diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 437690ee..f0f36907 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -1,4 +1,6 @@ +from cmath import log from dataclasses import dataclass +from distutils.command.upload import upload from gc import callbacks from xmlrpc.client import DateTime import flask @@ -31,16 +33,24 @@ class Predictor: class train_callback(tf.keras.callbacks.Callback): - def __init__(self, x_test, y_test): + def __init__(self, x_test, y_test,modelId): self.x_test = x_test self.y_test = y_test + self.modelId=modelId # def on_epoch_end(self, epoch, logs=None): - print(epoch) + #print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n") + + #print(epoch) + + #print(logs) + #ml_socket.send(epoch) #file = request.files.get("file") url = config.api_url + "/Model/epoch" - requests.post(url, epoch).text + r=requests.post(url, json={"Stat":str(logs),"ModelId":str(self.modelId),"EpochNum":epoch}).text + + #print(r) #print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n") #broj parametara zavisi od izabranih metrika loss je default @app.route('/train', methods = ['POST']) @@ -63,7 +73,7 @@ def train(): url = config.api_url + "/file/h5" files = {'file': open(filepath, 'rb')} - r=requests.post(url, files=files) + r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']}) fileId=r.text predictor = Predictor( _id = "", diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 585db480..a9bce3bb 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -252,7 +252,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): opt=tf.keras.optimizers.RMSprop(learning_rate=params['learningRate']) ###REGULARIZACIJA - #regularisation={'kernelType':'l1 ili l2 ili l1_l2','krenelRate':default=0.01 ili jedna od vrednosti(0.0001,0.001,0.1,1,2,3) ili neka koju je korisnik zadao,'biasType':'','biasRate':'','activityType','activityRate'} + #regularisation={'kernelType':'l1 ili l2 ili l1_l2','kernelRate':default=0.01 ili jedna od vrednosti(0.0001,0.001,0.1,1,2,3) ili neka koju je korisnik zadao,'biasType':'','biasRate':'','activityType','activityRate'} reg=params['regularisation'] ###Kernel @@ -279,7 +279,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): elif(reg['kernelType']=='l1l2'): activityreg=tf.keras.regularizers.l1_l2(l1=reg['activityRate'][0],l2=reg['activityRate'][1]) """ - filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']) + filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']+".h5") if(problem_type=='multi-klasifikacioni'): #print('multi') classifier=tf.keras.Sequential() @@ -294,7 +294,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history #plt.plot(hist['accuracy']) @@ -326,7 +326,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -352,7 +352,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) -- cgit v1.2.3 From 1849a275864cdd7d70e284127360fa211ad470d7 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Sat, 16 Apr 2022 22:23:02 +0200 Subject: Dodat zahtev addPredictor. --- backend/microservice/api/controller.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index f0f36907..88e84624 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -75,6 +75,9 @@ def train(): files = {'file': open(filepath, 'rb')} r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']}) fileId=r.text + m = [] + for attribute, value in result.items(): + m.append({"Name" : attribute, "JsonValue" : jsonify(value)}) predictor = Predictor( _id = "", username = paramsModel["username"], @@ -86,12 +89,12 @@ def train(): experimentId = paramsExperiment["_id"], modelId = paramsModel["_id"], h5FileId = fileId, - metrics=[] + metrics = m ) - - print(result) print(predictor) - return jsonify(result) + url = config.api_url + "/Predictor/add" + r = requests.post(url, json=predictor).text + return r @app.route('/predict', methods = ['POST']) def predict(): -- cgit v1.2.3 From 55b1ee5e4bd96510f8ed2505d157b655a1202a45 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Sat, 16 Apr 2022 22:53:52 +0200 Subject: Ispravljen bug pri serijalizaciji i deserijalizaciji novog predictora. --- .gitignore | 1 + backend/api/api/Controllers/PredictorController.cs | 1 + backend/microservice/api/controller.py | 56 +++++++++++----------- 3 files changed, 29 insertions(+), 29 deletions(-) (limited to 'backend/microservice') diff --git a/.gitignore b/.gitignore index afaf09d3..247afbd9 100644 --- a/.gitignore +++ b/.gitignore @@ -9,3 +9,4 @@ backend/microservice/temp/ backend/microservice/api/__pycache__/ production/app/node_modules/ production/app/dist/ +backend/microservice/api/temp/ diff --git a/backend/api/api/Controllers/PredictorController.cs b/backend/api/api/Controllers/PredictorController.cs index 481334e9..64907dac 100644 --- a/backend/api/api/Controllers/PredictorController.cs +++ b/backend/api/api/Controllers/PredictorController.cs @@ -155,6 +155,7 @@ namespace api.Controllers public async Task> Post([FromBody] Predictor predictor) { var user=_userService.GetUserByUsername(predictor.username); + predictor.dateCreated = DateTime.Now.ToUniversalTime(); var model = _modelService.GetOneModel(predictor.modelId); if (model == null || user==null) return BadRequest("Model not found or user doesnt exist"); diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 88e84624..e6515e7b 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -2,7 +2,6 @@ from cmath import log from dataclasses import dataclass from distutils.command.upload import upload from gc import callbacks -from xmlrpc.client import DateTime import flask from flask import request, jsonify import newmlservice @@ -11,25 +10,24 @@ import pandas as pd import json import requests import config -from datetime import datetime app = flask.Flask(__name__) app.config["DEBUG"] = True app.config["SERVER_NAME"] = config.hostIP -@dataclass -class Predictor: - _id : str - username: str - inputs : list - output : str - isPublic: bool - accessibleByLink: bool - dateCreated: DateTime - experimentId: str - modelId: str - h5FileId: str - metrics: list +#@dataclass +#class Predictor: +# _id : str + # username: str + # inputs : list + # output : str + # isPublic: bool + # accessibleByLink: bool + # dateCreated: DateTime + # experimentId: str + # modelId: str + # h5FileId: str + # metrics: list class train_callback(tf.keras.callbacks.Callback): @@ -77,23 +75,23 @@ def train(): fileId=r.text m = [] for attribute, value in result.items(): - m.append({"Name" : attribute, "JsonValue" : jsonify(value)}) - predictor = Predictor( - _id = "", - username = paramsModel["username"], - inputs = paramsExperiment["inputColumns"], - output = paramsExperiment["outputColumn"], - isPublic = False, - accessibleByLink = False, - dateCreated = datetime.now(), - experimentId = paramsExperiment["_id"], - modelId = paramsModel["_id"], - h5FileId = fileId, - metrics = m - ) + m.append({"Name" : attribute, "JsonValue" : value}) + predictor = { + "_id" : "", + "username" : paramsModel["username"], + "inputs" : paramsExperiment["inputColumns"], + "output" : paramsExperiment["outputColumn"], + "isPublic" : False, + "accessibleByLink" : False, + "experimentId" : paramsExperiment["_id"], + "modelId" : paramsModel["_id"], + "h5FileId" : fileId, + "metrics" : m + } print(predictor) url = config.api_url + "/Predictor/add" r = requests.post(url, json=predictor).text + print(r) return r @app.route('/predict', methods = ['POST']) -- cgit v1.2.3 From 84b53024a7e0b2c4a0ea34d559678301ad8ee750 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Sun, 17 Apr 2022 20:36:34 +0200 Subject: Razlicita enkodiranja za kolone ML. --- backend/microservice/api/newmlservice.py | 85 +++++++++++++++++--------------- 1 file changed, 45 insertions(+), 40 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index a9bce3bb..9951c25f 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -156,48 +156,53 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # ### Enkodiranje encoding=paramsExperiment["encoding"] - if(encoding=='label'): - encoder=LabelEncoder() - for col in data.columns: - if(data[col].dtype==np.object_): - data[col]=encoder.fit_transform(data[col]) + datafront=dataset.copy() + svekolone=datafront.columns + kategorijskekolone=datafront.select_dtypes(include=['object']).columns + for kolona in svekolone: + if(kolona in kategorijskekolone): + if(encoding=='label'): + encoder=LabelEncoder() + for col in data.columns: + if(data[col].dtype==np.object_): + data[col]=encoder.fit_transform(data[col]) - elif(encoding=='onehot'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - data=pd.get_dummies(data, columns=category_columns, prefix=category_columns) - - elif(encoding=='ordinal'): - encoder = OrdinalEncoder() - for col in data.columns: - if(data[col].dtype==np.object_): - data[col]=encoder.fit_transform(data[col]) - - elif(encoding=='hashing'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns)) - encoder.fit_transform(data) - elif(encoding=='binary'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.BinaryEncoder(cols=category_columns, return_df=True) - encoder.fit_transform(data) - - elif(encoding=='baseN'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) - encoder.fit_transform(data) + elif(encoding=='onehot'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + data=pd.get_dummies(data, columns=category_columns, prefix=category_columns) + + elif(encoding=='ordinal'): + encoder = OrdinalEncoder() + for col in data.columns: + if(data[col].dtype==np.object_): + data[col]=encoder.fit_transform(data[col]) + + elif(encoding=='hashing'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns)) + encoder.fit_transform(data) + elif(encoding=='binary'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.BinaryEncoder(cols=category_columns, return_df=True) + encoder.fit_transform(data) + + elif(encoding=='baseN'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) + encoder.fit_transform(data) # # Input - output # -- cgit v1.2.3 From 208394ff08cba0880746d9c7841be08e127f66d6 Mon Sep 17 00:00:00 2001 From: Ivan Ljubisavljevic Date: Mon, 18 Apr 2022 01:14:31 +0200 Subject: Izmena na frontu i ml-u(username -> userId) #71 --- backend/api/api/Controllers/DatasetController.cs | 24 +++++++++++----------- backend/api/api/Controllers/ModelController.cs | 2 +- backend/api/api/Controllers/PredictorController.cs | 2 +- backend/api/api/Services/DatasetService.cs | 12 +++++------ backend/api/api/Services/IDatasetService.cs | 6 +++--- backend/microservice/api/controller.py | 2 +- frontend/src/app/_data/Dataset.ts | 2 +- frontend/src/app/_data/Model.ts | 2 +- frontend/src/app/_data/Predictor.ts | 3 ++- .../add-new-dataset/add-new-dataset.component.ts | 2 +- .../_elements/model-load/model-load.component.ts | 2 +- .../filter-datasets/filter-datasets.component.ts | 2 +- .../app/_pages/my-models/my-models.component.html | 2 +- .../app/_pages/my-models/my-models.component.ts | 8 +++++++- frontend/src/app/_services/datasets.service.ts | 4 ++-- frontend/src/app/_services/predictors.service.ts | 2 +- 16 files changed, 42 insertions(+), 35 deletions(-) (limited to 'backend/microservice') diff --git a/backend/api/api/Controllers/DatasetController.cs b/backend/api/api/Controllers/DatasetController.cs index bdac9ed9..58a903eb 100644 --- a/backend/api/api/Controllers/DatasetController.cs +++ b/backend/api/api/Controllers/DatasetController.cs @@ -149,46 +149,46 @@ namespace api.Controllers // PUT api//{name} - [HttpPut("{name}")] + [HttpPut("{id}")] [Authorize(Roles = "User")] - public ActionResult Put(string name, [FromBody] Dataset dataset) + public ActionResult Put(string id, [FromBody] Dataset dataset) { string uploaderId = getUserId(); if (uploaderId == null) return BadRequest(); - var existingDataset = _datasetService.GetOneDataset(uploaderId, name); + var existingDataset = _datasetService.GetOneDataset(uploaderId, id); //ne mora da se proverava if (existingDataset == null) - return NotFound($"Dataset with name = {name} or user with ID = {uploaderId} not found"); + return NotFound($"Dataset with ID = {id} or user with ID = {uploaderId} not found"); dataset.lastUpdated = DateTime.UtcNow; - _datasetService.Update(uploaderId, name, dataset); + _datasetService.Update(uploaderId, id, dataset); - return Ok($"Dataset with name = {name} updated"); + return Ok($"Dataset with ID = {id} updated"); } // DELETE api//name - [HttpDelete("{name}")] + [HttpDelete("{id}")] [Authorize(Roles = "User")] - public ActionResult Delete(string name) + public ActionResult Delete(string id) { string uploaderId = getUserId(); if (uploaderId == null) return BadRequest(); - var dataset = _datasetService.GetOneDataset(uploaderId, name); + var dataset = _datasetService.GetOneDataset(uploaderId, id); if (dataset == null) - return NotFound($"Dataset with name = {name} or user with ID = {uploaderId} not found"); + return NotFound($"Dataset with ID = {id} or user with ID = {uploaderId} not found"); - _datasetService.Delete(dataset.uploaderId, dataset.name); + _datasetService.Delete(dataset.uploaderId, dataset._id); - return Ok($"Dataset with name = {name} deleted"); + return Ok($"Dataset with ID = {id} deleted"); } } diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs index 1ec01ab8..fe16507b 100644 --- a/backend/api/api/Controllers/ModelController.cs +++ b/backend/api/api/Controllers/ModelController.cs @@ -82,7 +82,7 @@ namespace api.Controllers { var model=_modelService.GetOneModel(info.ModelId); - var user = _userService.GetUserByUsername(model.uploaderId); + var user = _userService.GetUserById(model.uploaderId); if (ChatHub.CheckUser(user._id)) await _ichat.Clients.Client(ChatHub.Users[user._id]).SendAsync("NotifyEpoch",model.name,info.ModelId,info.Stat,model.epochs,info.EpochNum); diff --git a/backend/api/api/Controllers/PredictorController.cs b/backend/api/api/Controllers/PredictorController.cs index 26fe8f1d..dd5aa5fd 100644 --- a/backend/api/api/Controllers/PredictorController.cs +++ b/backend/api/api/Controllers/PredictorController.cs @@ -213,7 +213,7 @@ namespace api.Controllers } // DELETE api//name - [HttpDelete("{name}")] + [HttpDelete("{id}")] [Authorize(Roles = "User")] public ActionResult Delete(string id) { diff --git a/backend/api/api/Services/DatasetService.cs b/backend/api/api/Services/DatasetService.cs index 33026687..6c2efe14 100644 --- a/backend/api/api/Services/DatasetService.cs +++ b/backend/api/api/Services/DatasetService.cs @@ -27,9 +27,9 @@ namespace api.Services } //brisanje odredjenog name-a - public void Delete(string userId, string name) + public void Delete(string userId, string id) { - _dataset.DeleteOne(dataset => (dataset.uploaderId == userId && dataset.name == name)); + _dataset.DeleteOne(dataset => (dataset.uploaderId == userId && dataset._id == id)); } public List GetMyDatasets(string userId) @@ -62,9 +62,9 @@ namespace api.Services return _dataset.Find(dataset => dataset.isPublic == true && dataset.isPreProcess).ToList(); } - public Dataset GetOneDataset(string userId, string name) + public Dataset GetOneDataset(string userId, string id) { - return _dataset.Find(dataset => dataset.uploaderId == userId && dataset.name == name && dataset.isPreProcess).FirstOrDefault(); + return _dataset.Find(dataset => dataset.uploaderId == userId && dataset._id == id && dataset.isPreProcess).FirstOrDefault(); } //odraditi za pretragu getOne @@ -74,9 +74,9 @@ namespace api.Services } //ako je potrebno da se zameni name ili ekstenzija - public void Update(string userId, string name, Dataset dataset ) + public void Update(string userId, string id, Dataset dataset ) { - _dataset.ReplaceOne(dataset => dataset.uploaderId == userId && dataset.name == name, dataset); + _dataset.ReplaceOne(dataset => dataset.uploaderId == userId && dataset._id == id, dataset); } public void Update(Dataset dataset) { diff --git a/backend/api/api/Services/IDatasetService.cs b/backend/api/api/Services/IDatasetService.cs index b700e87c..bb06208d 100644 --- a/backend/api/api/Services/IDatasetService.cs +++ b/backend/api/api/Services/IDatasetService.cs @@ -5,15 +5,15 @@ namespace api.Services { public interface IDatasetService { - Dataset GetOneDataset(string userId, string name); + Dataset GetOneDataset(string userId, string id); Dataset GetOneDataset(string id); List SearchDatasets(string name); List GetMyDatasets(string userId); List SortDatasets(string userId, bool ascdsc, int latest); List GetPublicDatasets(); Dataset Create(Dataset dataset); - void Update(string userId, string name, Dataset dataset); - void Delete(string userId, string name); + void Update(string userId, string id, Dataset dataset); + void Delete(string userId, string id); public List GetGuestDatasets(); public void Update(Dataset dataset); string GetDatasetId(string fileId); diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index e6515e7b..9b83b8e7 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -78,7 +78,7 @@ def train(): m.append({"Name" : attribute, "JsonValue" : value}) predictor = { "_id" : "", - "username" : paramsModel["username"], + "uploaderId" : paramsModel["uploaderId"], "inputs" : paramsExperiment["inputColumns"], "output" : paramsExperiment["outputColumn"], "isPublic" : False, diff --git a/frontend/src/app/_data/Dataset.ts b/frontend/src/app/_data/Dataset.ts index 732d1c56..766040a3 100644 --- a/frontend/src/app/_data/Dataset.ts +++ b/frontend/src/app/_data/Dataset.ts @@ -10,7 +10,7 @@ export default class Dataset { public accessibleByLink: boolean = false, public dateCreated: Date = new Date(), public lastUpdated: Date = new Date(), - public username: string = '', + public uploaderId: string = '', public delimiter: string = '', public hasHeader: boolean = true, diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts index 1af3fe30..b273f56a 100644 --- a/frontend/src/app/_data/Model.ts +++ b/frontend/src/app/_data/Model.ts @@ -19,7 +19,7 @@ export default class Model { public batchSize: number = 5, public hiddenLayerActivationFunctions: string[] = ['sigmoid'], public outputLayerActivationFunction: ActivationFunction = ActivationFunction.Sigmoid, - public username: string = '', + public uploaderId: string = '', public metrics: string[] = [], // TODO add to add-model form public epochs: number = 5 // TODO add to add-model form ) { } diff --git a/frontend/src/app/_data/Predictor.ts b/frontend/src/app/_data/Predictor.ts index 7e902eae..8aa2b6cb 100644 --- a/frontend/src/app/_data/Predictor.ts +++ b/frontend/src/app/_data/Predictor.ts @@ -7,6 +7,7 @@ export default class Predictor { public output: string = '', public isPublic: boolean = false, public accessibleByLink: boolean = false, - public dateCreated: Date = new Date() + public dateCreated: Date = new Date(), + public uploaderId: string = '' ) { } } \ No newline at end of file diff --git a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts index 6ff108ce..3e1b5c73 100644 --- a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts +++ b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts @@ -90,7 +90,7 @@ export class AddNewDatasetComponent { this.modelsService.uploadData(this.files[0]).subscribe((file) => { //console.log('ADD MODEL: STEP 2 - ADD DATASET WITH FILE ID ' + file._id); this.dataset.fileId = file._id; - this.dataset.username = shared.username; + this.dataset.uploaderId = shared.userId; this.datasetsService.addDataset(this.dataset).subscribe((dataset) => { this.newDatasetAdded.emit("added"); diff --git a/frontend/src/app/_elements/model-load/model-load.component.ts b/frontend/src/app/_elements/model-load/model-load.component.ts index 9bd81f95..dbca3d17 100644 --- a/frontend/src/app/_elements/model-load/model-load.component.ts +++ b/frontend/src/app/_elements/model-load/model-load.component.ts @@ -69,7 +69,7 @@ export class ModelLoadComponent implements OnInit { uploadModel() { this.getMetrics(); - this.newModel.username = Shared.username; + this.newModel.uploaderId = Shared.userId; this.modelsService.addModel(this.newModel).subscribe((response) => { Shared.openDialog('Model dodat', 'Model je uspešno dodat u bazu.'); diff --git a/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts b/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts index c83bf208..66b3755e 100644 --- a/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts +++ b/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts @@ -33,7 +33,7 @@ export class FilterDatasetsComponent implements OnInit { newDataset._id = ""; newDataset.isPublic = false; newDataset.lastUpdated = new Date(); - newDataset.username = decodedToken.name; + newDataset.uploaderId = decodedToken.uploaderId; let name=prompt("Unesite naziv dataset-a",newDataset.name); newDataset.name=name as string; if(name!=null && name!="") diff --git a/frontend/src/app/_pages/my-models/my-models.component.html b/frontend/src/app/_pages/my-models/my-models.component.html index b0e9c4ef..9b281239 100644 --- a/frontend/src/app/_pages/my-models/my-models.component.html +++ b/frontend/src/app/_pages/my-models/my-models.component.html @@ -15,7 +15,7 @@
- diff --git a/frontend/src/app/_pages/my-models/my-models.component.ts b/frontend/src/app/_pages/my-models/my-models.component.ts index 92d3fbaa..d379fa69 100644 --- a/frontend/src/app/_pages/my-models/my-models.component.ts +++ b/frontend/src/app/_pages/my-models/my-models.component.ts @@ -1,4 +1,5 @@ import { Component, OnInit } from '@angular/core'; +import { Router } from '@angular/router'; import shared from 'src/app/Shared'; import Model from 'src/app/_data/Model'; import { ModelsService } from 'src/app/_services/models.service'; @@ -12,7 +13,7 @@ export class MyModelsComponent implements OnInit { myModels: Model[] = []; //myModel: Model; - constructor(private modelsS : ModelsService) { + constructor(private modelsS : ModelsService, private router : Router) { @@ -44,6 +45,11 @@ deleteThisModel(model: Model): void{ } +useThisModel(model: Model): void{ + + this.router.navigate(['/training']) + +} getAllMyModels(): void{ this.modelsS.getMyModels().subscribe(m => { this.myModels = m; diff --git a/frontend/src/app/_services/datasets.service.ts b/frontend/src/app/_services/datasets.service.ts index c3281be6..b2272f0a 100644 --- a/frontend/src/app/_services/datasets.service.ts +++ b/frontend/src/app/_services/datasets.service.ts @@ -29,10 +29,10 @@ export class DatasetsService { } editDataset(dataset: Dataset): Observable { - return this.http.put(`${Configuration.settings.apiURL}/dataset/`, dataset, { headers: this.authService.authHeader() }); + return this.http.put(`${Configuration.settings.apiURL}/dataset/` + dataset._id, dataset, { headers: this.authService.authHeader() }); } deleteDataset(dataset: Dataset) { - return this.http.delete(`${Configuration.settings.apiURL}/dataset/` + dataset.name, { headers: this.authService.authHeader(), responseType: "text" }); + return this.http.delete(`${Configuration.settings.apiURL}/dataset/` + dataset._id, { headers: this.authService.authHeader(), responseType: "text" }); } } diff --git a/frontend/src/app/_services/predictors.service.ts b/frontend/src/app/_services/predictors.service.ts index 9e8383aa..a24ee708 100644 --- a/frontend/src/app/_services/predictors.service.ts +++ b/frontend/src/app/_services/predictors.service.ts @@ -24,7 +24,7 @@ export class PredictorsService { } deletePredictor(predictor: Predictor) { - return this.http.delete(`${Configuration.settings.apiURL}/predictor/` + predictor.name, { headers: this.authService.authHeader(), responseType: "text" }); + return this.http.delete(`${Configuration.settings.apiURL}/predictor/` + predictor._id, { headers: this.authService.authHeader(), responseType: "text" }); } getMyPredictors(): Observable { -- cgit v1.2.3 From 3c2a0be2a169f16702ff9648dc65ef9efbae07fc Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Mon, 18 Apr 2022 21:27:40 +0200 Subject: Enkodiranje reseno. --- backend/microservice/api/newmlservice.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 9951c25f..604e4d3c 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -155,11 +155,15 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data.pop(col) # ### Enkodiranje - encoding=paramsExperiment["encoding"] + encodings=paramsExperiment["encodings"] datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns - for kolona in svekolone: + for kolonaEncoding in encodings: + + kolona = kolonaEncoding["columnName"] + encoding = kolonaEncoding["encoding"] + if(kolona in kategorijskekolone): if(encoding=='label'): encoder=LabelEncoder() -- cgit v1.2.3 From 9c0b48ed66e363928e8970f9b328c0bb3eee6c2d Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Tue, 19 Apr 2022 17:57:17 +0200 Subject: Dodate su komponente za grafik. --- backend/microservice/api/newmlservice.py | 22 +++++++-- frontend/package.json | 3 +- .../_elements/line-chart/line-chart.component.css | 0 .../_elements/line-chart/line-chart.component.html | 9 ++++ .../line-chart/line-chart.component.spec.ts | 25 ++++++++++ .../_elements/line-chart/line-chart.component.ts | 57 ++++++++++++++++++++++ frontend/src/app/app.module.ts | 4 +- 7 files changed, 114 insertions(+), 6 deletions(-) create mode 100644 frontend/src/app/_elements/line-chart/line-chart.component.css create mode 100644 frontend/src/app/_elements/line-chart/line-chart.component.html create mode 100644 frontend/src/app/_elements/line-chart/line-chart.component.spec.ts create mode 100644 frontend/src/app/_elements/line-chart/line-chart.component.ts (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 604e4d3c..219f8a20 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -155,7 +155,19 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data.pop(col) # ### Enkodiranje +<<<<<<< HEAD encodings=paramsExperiment["encodings"] +======= + + from sklearn.preprocessing import LabelEncoder + kategorijskekolone=data.select_dtypes(include=['object']).columns + encoder=LabelEncoder() + for kolona in data.columns: + if(kolona in kategorijskekolone): + data[kolona]=encoder.fit_transform(data[kolona]) + ''' + encoding=paramsExperiment["encoding"] +>>>>>>> 7d57bb9 (Dodate su komponente za grafik.) datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns @@ -207,6 +219,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): category_columns.append(col) encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) encoder.fit_transform(data) + + ''' # # Input - output # @@ -301,7 +315,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) @@ -333,7 +347,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history @@ -359,7 +373,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1)) - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history @@ -529,7 +543,7 @@ def manageH5(dataset,params,h5model): h5model.summary() #ann_viz(h5model, title="My neural network") - h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics']) + h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['accuracy','']) history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize']) diff --git a/frontend/package.json b/frontend/package.json index c02a1fb0..7df35a97 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -29,13 +29,14 @@ "@popperjs/core": "^2.10.2", "bootstrap": "^5.1.3", "chart.js": "^3.7.1", + "chartjs-plugin-datalabels": "^0.7.0", "csv-parser": "^3.0.0", "d3-graphviz": "^2.6.1", "jquery": "^3.6.0", "mdb-angular-ui-kit": "^2.0.0", "ng-multiselect-dropdown": "^0.3.8", "ng-uikit-pro-standard": "^1.0.0", - "ng2-charts": "^3.0.8", + "ng2-charts": "^3.0.9", "ng2-search-filter": "^0.5.1", "ngx-cookie-service": "^13.1.2", "popper.js": "^1.16.1", diff --git a/frontend/src/app/_elements/line-chart/line-chart.component.css b/frontend/src/app/_elements/line-chart/line-chart.component.css new file mode 100644 index 00000000..e69de29b diff --git a/frontend/src/app/_elements/line-chart/line-chart.component.html b/frontend/src/app/_elements/line-chart/line-chart.component.html new file mode 100644 index 00000000..8607aac5 --- /dev/null +++ b/frontend/src/app/_elements/line-chart/line-chart.component.html @@ -0,0 +1,9 @@ +
+ + +
\ No newline at end of file diff --git a/frontend/src/app/_elements/line-chart/line-chart.component.spec.ts b/frontend/src/app/_elements/line-chart/line-chart.component.spec.ts new file mode 100644 index 00000000..0c5e7ef5 --- /dev/null +++ b/frontend/src/app/_elements/line-chart/line-chart.component.spec.ts @@ -0,0 +1,25 @@ +import { ComponentFixture, TestBed } from '@angular/core/testing'; + +import { LineChartComponent } from './line-chart.component'; + +describe('LineChartComponent', () => { + let component: LineChartComponent; + let fixture: ComponentFixture; + + beforeEach(async () => { + await TestBed.configureTestingModule({ + declarations: [ LineChartComponent ] + }) + .compileComponents(); + }); + + beforeEach(() => { + fixture = TestBed.createComponent(LineChartComponent); + component = fixture.componentInstance; + fixture.detectChanges(); + }); + + it('should create', () => { + expect(component).toBeTruthy(); + }); +}); diff --git a/frontend/src/app/_elements/line-chart/line-chart.component.ts b/frontend/src/app/_elements/line-chart/line-chart.component.ts new file mode 100644 index 00000000..7a06ecf5 --- /dev/null +++ b/frontend/src/app/_elements/line-chart/line-chart.component.ts @@ -0,0 +1,57 @@ +import { Component, OnInit } from '@angular/core'; +import { ChartOptions } from 'chart.js'; +import { BaseChartDirective } from 'ng2-charts'; + +@Component({ + selector: 'app-line-chart', + templateUrl: './line-chart.component.html', + styleUrls: ['./line-chart.component.css'] +}) +export class LineChartComponent implements OnInit { + + constructor() { } + + ngOnInit(): void { + } + lineChartData:Array = [ + {data: [65, 59, 80, 81, 56, 55, 40], label: 'Series A'}, + {data: [28, 48, 40, 19, 86, 27, 90], label: 'Series B'}, + {data: [18, 48, 77, 9, 100, 27, 40], label: 'Series C'} + ]; + lineChartLabels: BaseChartDirective["labels"] = ['January', 'February', 'March', 'April', 'May', 'June']; + lineChartOptions = { + responsive: true, + }; + lineChartColors:Array = [ + { // grey + backgroundColor: 'rgba(148,159,177,0.2)', + borderColor: 'rgba(148,159,177,1)', + pointBackgroundColor: 'rgba(148,159,177,1)', + pointBorderColor: '#fff', + pointHoverBackgroundColor: '#fff', + pointHoverBorderColor: 'rgba(148,159,177,0.8)' + }, + { // dark grey + backgroundColor: 'rgba(77,83,96,0.2)', + borderColor: 'rgba(77,83,96,1)', + pointBackgroundColor: 'rgba(77,83,96,1)', + pointBorderColor: '#fff', + pointHoverBackgroundColor: '#fff', + pointHoverBorderColor: 'rgba(77,83,96,1)' + }, + { // grey + backgroundColor: 'rgba(148,159,177,0.2)', + borderColor: 'rgba(148,159,177,1)', + pointBackgroundColor: 'rgba(148,159,177,1)', + pointBorderColor: '#fff', + pointHoverBackgroundColor: '#fff', + pointHoverBorderColor: 'rgba(148,159,177,0.8)' + } + ]; + lineChartLegend = true; + lineChartPlugins = []; + lineChartType = 'line'; + +} + + diff --git a/frontend/src/app/app.module.ts b/frontend/src/app/app.module.ts index 948e9a2b..6a95bfe6 100644 --- a/frontend/src/app/app.module.ts +++ b/frontend/src/app/app.module.ts @@ -50,6 +50,7 @@ import { YesNoDialogComponent } from './_modals/yes-no-dialog/yes-no-dialog.comp import { Configuration } from './configuration.service'; import { PointLinechartComponent } from './point-linechart/point-linechart.component'; +import { LineChartComponent } from './_elements/line-chart/line-chart.component'; export function initializeApp(appConfig: Configuration) { return () => appConfig.load(); } @@ -89,7 +90,8 @@ export function initializeApp(appConfig: Configuration) { TrainingComponent, ItemExperimentComponent, YesNoDialogComponent, - PointLinechartComponent + PointLinechartComponent, + LineChartComponent ], imports: [ BrowserModule, -- cgit v1.2.3 From 3ee39c4a5c0dfccc4fcb429762e5a7cc026da4a0 Mon Sep 17 00:00:00 2001 From: Sonja Galovic Date: Tue, 19 Apr 2022 20:09:38 +0200 Subject: ML: resen konflikt --- backend/microservice/api/newmlservice.py | 3 --- 1 file changed, 3 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 219f8a20..f5122a06 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -155,9 +155,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data.pop(col) # ### Enkodiranje -<<<<<<< HEAD encodings=paramsExperiment["encodings"] -======= from sklearn.preprocessing import LabelEncoder kategorijskekolone=data.select_dtypes(include=['object']).columns @@ -167,7 +165,6 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data[kolona]=encoder.fit_transform(data[kolona]) ''' encoding=paramsExperiment["encoding"] ->>>>>>> 7d57bb9 (Dodate su komponente za grafik.) datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns -- cgit v1.2.3 From d1528c6739ff702efa8963dc8e2f6e097e97eb0b Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Tue, 19 Apr 2022 21:26:15 +0200 Subject: Encoding korekcija. --- backend/microservice/api/newmlservice.py | 94 +++++++++++++++++--------------- 1 file changed, 49 insertions(+), 45 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index f5122a06..9e09186f 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -155,6 +155,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data.pop(col) # ### Enkodiranje + ''' encodings=paramsExperiment["encodings"] from sklearn.preprocessing import LabelEncoder @@ -164,60 +165,63 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): if(kolona in kategorijskekolone): data[kolona]=encoder.fit_transform(data[kolona]) ''' - encoding=paramsExperiment["encoding"] + + + encodings=paramsExperiment["encoding"] datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns for kolonaEncoding in encodings: kolona = kolonaEncoding["columnName"] - encoding = kolonaEncoding["encoding"] - - if(kolona in kategorijskekolone): - if(encoding=='label'): - encoder=LabelEncoder() - for col in data.columns: - if(data[col].dtype==np.object_): - data[col]=encoder.fit_transform(data[col]) - - - elif(encoding=='onehot'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - data=pd.get_dummies(data, columns=category_columns, prefix=category_columns) - - elif(encoding=='ordinal'): - encoder = OrdinalEncoder() - for col in data.columns: - if(data[col].dtype==np.object_): - data[col]=encoder.fit_transform(data[col]) + if kolona in data.columns: + encoding = kolonaEncoding["encoding"] + + if(kolona in kategorijskekolone): + if(encoding=='label'): + encoder=LabelEncoder() + for col in data.columns: + if(data[col].dtype==np.object_): + data[col]=encoder.fit_transform(data[col]) + - elif(encoding=='hashing'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns)) - encoder.fit_transform(data) - elif(encoding=='binary'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.BinaryEncoder(cols=category_columns, return_df=True) - encoder.fit_transform(data) + elif(encoding=='onehot'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + data=pd.get_dummies(data, columns=category_columns, prefix=category_columns) + + elif(encoding=='ordinal'): + encoder = OrdinalEncoder() + for col in data.columns: + if(data[col].dtype==np.object_): + data[col]=encoder.fit_transform(data[col]) - elif(encoding=='baseN'): - category_columns=[] - for col in data.columns: - if(data[col].dtype==np.object_): - category_columns.append(col) - encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) - encoder.fit_transform(data) + elif(encoding=='hashing'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns)) + encoder.fit_transform(data) + elif(encoding=='binary'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.BinaryEncoder(cols=category_columns, return_df=True) + encoder.fit_transform(data) + + elif(encoding=='baseN'): + category_columns=[] + for col in data.columns: + if(data[col].dtype==np.object_): + category_columns.append(col) + encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) + encoder.fit_transform(data) - ''' + # # Input - output # -- cgit v1.2.3 From b25af94d6df8854129e99f77638e4013a9c57086 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Wed, 20 Apr 2022 00:04:19 +0200 Subject: Povezao metric view komponentu, chart, sa signalR tako da se iscrtavaju metrike modela kako se trenira. Ispravio neke bug-ove. --- backend/microservice/api/newmlservice.py | 20 ++++- .../_elements/line-chart/line-chart.component.ts | 87 +++++++++++++++++----- .../metric-view/metric-view.component.html | 6 +- .../_elements/metric-view/metric-view.component.ts | 55 +++++++++----- .../_elements/model-load/model-load.component.html | 4 +- .../_elements/model-load/model-load.component.ts | 15 +++- frontend/src/app/app.module.ts | 8 +- frontend/src/app/grafici/grafici.component.css | 0 frontend/src/app/grafici/grafici.component.html | 1 - frontend/src/app/grafici/grafici.component.spec.ts | 25 ------- frontend/src/app/grafici/grafici.component.ts | 15 ---- frontend/src/app/training/training.component.html | 5 +- frontend/src/app/training/training.component.ts | 35 ++++++--- 13 files changed, 165 insertions(+), 111 deletions(-) delete mode 100644 frontend/src/app/grafici/grafici.component.css delete mode 100644 frontend/src/app/grafici/grafici.component.html delete mode 100644 frontend/src/app/grafici/grafici.component.spec.ts delete mode 100644 frontend/src/app/grafici/grafici.component.ts (limited to 'backend/microservice') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 9e09186f..6cbda69c 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -129,7 +129,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ###NULL null_value_options = paramsExperiment["nullValues"] null_values_replacers = paramsExperiment["nullValuesReplacers"] - + if(null_value_options=='replace'): #print("replace null") # dict=null_values_replacers @@ -138,8 +138,13 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): col=replace['column'] opt=replace['option'] if(opt=='replace'): - replacevalue=replace['value'] - data[col]=data[col].fillna(replacevalue) + val = replace['value'] + if(data[col].dtype == 'int64'): + val = np.int64(val) + elif(data[col].dtype == 'float64'): + val = np.float64(val) + #elif(data[col].dtype == 'object'): + data[col]=data[col].fillna(val) elif(null_value_options=='delete_rows'): data=data.dropna() elif(null_value_options=='delete_columns'): @@ -167,7 +172,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ''' - encodings=paramsExperiment["encoding"] + encodings=paramsExperiment["encodings"] datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns @@ -232,6 +237,13 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #print(x_columns) x = data[x_columns].values y = data[output_column].values + print('-----------------dfghfhgfhfg-------------------------------') + print(x) + print('-----------------dfghfhgfhfg-------------------------------') + print(y) + print('-----------------dfghfhgfhfg-------------------------------') + print(output_column) + print('-----------------dfghfhgfhfg-------------------------------') # # Podela na test i trening skupove diff --git a/frontend/src/app/_elements/line-chart/line-chart.component.ts b/frontend/src/app/_elements/line-chart/line-chart.component.ts index 68663861..1a8579a0 100644 --- a/frontend/src/app/_elements/line-chart/line-chart.component.ts +++ b/frontend/src/app/_elements/line-chart/line-chart.component.ts @@ -1,6 +1,6 @@ -import { Component, OnInit,Input } from '@angular/core'; -import { BaseChartDirective } from 'ng2-charts'; -import {Chart} from 'chart.js'; +import { Component, OnInit, Input } from '@angular/core'; +import { Chart } from 'chart.js'; + @Component({ selector: 'app-line-chart', templateUrl: './line-chart.component.html', @@ -8,32 +8,81 @@ import {Chart} from 'chart.js'; }) export class LineChartComponent implements OnInit { - @Input() dataAcc=[] ; - @Input() dataEpoch=[]; - constructor() { } + + dataAcc: number[] = []; + dataMAE: number[] = []; + dataMSE: number[] = []; + dataLOSS: number[] = []; + + dataEpoch: number[] = []; + + constructor() { + /*let i = 0; + setInterval(() => { + this.dataAcc.push(0.5); + this.dataEpoch.push(i); + i++; + this.update(); + }, 200);*/ + } + + myChart!: Chart; + + update(myEpochs: number[], myAcc: number[], myLoss: number[], myMae: number[], myMse: number[]) { + this.dataAcc.length = 0; + this.dataAcc.push(...myAcc); + + this.dataEpoch.length = 0; + this.dataEpoch.push(...myEpochs); + + this.dataMAE.length = 0; + this.dataMAE.push(...myMae); + + this.dataLOSS.length = 0; + this.dataLOSS.push(...myLoss); + + this.dataMSE.length = 0; + this.dataMSE.push(...myMse); + + this.myChart.update(); + } ngOnInit(): void { - var myChart=new Chart("myChart", + this.myChart = new Chart("myChart", { type: 'line', - data: { - labels:this.dataEpoch, - datasets: [{ - label: 'Vrednost', + data: { + labels: this.dataEpoch, + datasets: [{ + label: 'Accuracy', data: this.dataAcc, borderWidth: 1 - }] - }, - options: { - scales: { + }, + { + label: 'Loss', + data: this.dataLOSS, + borderWidth: 1 + }, + { + label: 'MAE', + data: this.dataMAE, + borderWidth: 1 + }, + { + label: 'MSE', + data: this.dataMSE, + borderWidth: 1 + } + ] + }, + options: { + scales: { y: { - beginAtZero: true + beginAtZero: true } + } } - } } ); - } } - \ No newline at end of file diff --git a/frontend/src/app/_elements/metric-view/metric-view.component.html b/frontend/src/app/_elements/metric-view/metric-view.component.html index 7408547a..e7a4c547 100644 --- a/frontend/src/app/_elements/metric-view/metric-view.component.html +++ b/frontend/src/app/_elements/metric-view/metric-view.component.html @@ -1,5 +1,5 @@
- - + + -
+
\ No newline at end of file diff --git a/frontend/src/app/_elements/metric-view/metric-view.component.ts b/frontend/src/app/_elements/metric-view/metric-view.component.ts index 76dd7d20..9193a0e5 100644 --- a/frontend/src/app/_elements/metric-view/metric-view.component.ts +++ b/frontend/src/app/_elements/metric-view/metric-view.component.ts @@ -1,32 +1,49 @@ -import { Component, OnInit } from '@angular/core'; +import { Component, Input, OnInit, ViewChild } from '@angular/core'; import { SignalRService } from 'src/app/_services/signal-r.service'; +import { LineChartComponent } from '../line-chart/line-chart.component'; @Component({ selector: 'app-metric-view', templateUrl: './metric-view.component.html', styleUrls: ['./metric-view.component.css'] }) export class MetricViewComponent implements OnInit { - myAcc:[]=[]; - myMae:[]=[]; - myMse:[]=[]; - myEpochs:[]=[]; + @ViewChild(LineChartComponent) linechartComponent!: LineChartComponent; + + @Input() history!: any[]; + constructor(private signalRService: SignalRService) { } ngOnInit(): void { - if(this.signalRService.hubConnection) - { - this.signalRService.hubConnection.on("NotifyEpoch", (mName: string, mId: string, stat: string, totalEpochs: number, currentEpoch: number) => { - console.log(stat) - console.log(totalEpochs) - const data=JSON.parse(stat) - for (let key in data) - { - let value = data[key]; - console.log(value) + } + + update(history: any[]) { + const myAcc: number[] = []; + const myMae: number[] = []; + const myMse: number[] = []; + const myLoss: number[] = []; + + const myEpochs: number[] = []; + this.history = history; + this.history.forEach((metrics, epoch) => { + myEpochs.push(epoch + 1); + for (let key in metrics) { + let value = metrics[key]; + console.log(key, ':::', value, epoch); + if (key === 'accuracy') { + myAcc.push(parseFloat(value)); + } + else if (key === 'loss') { + myLoss.push(parseFloat(value)); + } + else if (key === 'mae') { + myMae.push(parseFloat(value)); + } + else if (key === 'mse') { + myMse.push(parseFloat(value)); } - }); - - } + } + }); + this.linechartComponent.update(myEpochs, myAcc, myLoss, myMae, myMse); } -} +} \ No newline at end of file diff --git a/frontend/src/app/_elements/model-load/model-load.component.html b/frontend/src/app/_elements/model-load/model-load.component.html index 1afb1ecb..1f9852d1 100644 --- a/frontend/src/app/_elements/model-load/model-load.component.html +++ b/frontend/src/app/_elements/model-load/model-load.component.html @@ -173,7 +173,7 @@
-
+
diff --git a/frontend/src/app/_elements/model-load/model-load.component.ts b/frontend/src/app/_elements/model-load/model-load.component.ts index 8bf8fd93..0799b4d4 100644 --- a/frontend/src/app/_elements/model-load/model-load.component.ts +++ b/frontend/src/app/_elements/model-load/model-load.component.ts @@ -4,6 +4,7 @@ import Experiment from 'src/app/_data/Experiment'; import Model, { ActivationFunction, LossFunction, LossFunctionBinaryClassification, LossFunctionMultiClassification, LossFunctionRegression, Metrics, MetricsBinaryClassification, MetricsMultiClassification, MetricsRegression, NullValueOptions, Optimizer, ProblemType } from 'src/app/_data/Model'; import { AuthService } from 'src/app/_services/auth.service'; import { ModelsService } from 'src/app/_services/models.service'; +import { SignalRService } from 'src/app/_services/signal-r.service'; import { GraphComponent } from '../graph/graph.component'; @@ -48,10 +49,11 @@ export class ModelLoadComponent implements OnInit { }) } - fetchModels() { + fetchModels(andSelectWithId: string | null = '') { //if (this.forExperiment == undefined) { this.modelsService.getMyModels().subscribe((models) => { - this.myModels = models; + this.myModels = models.reverse(); + this.selectThisModel(this.myModels.filter(x => x._id == andSelectWithId)[0]); }); /*} else { @@ -90,7 +92,14 @@ export class ModelLoadComponent implements OnInit { this.newModel.uploaderId = Shared.userId; this.modelsService.addModel(this.newModel).subscribe((response) => { - Shared.openDialog('Model dodat', 'Model je uspešno dodat u bazu.'); + console.log(this.newModel); + //Shared.openDialog('Model dodat', 'Model je uspešno dodat u bazu.'); + + Shared.openYesNoDialog("Model dodat", "Model je uspešno dodat u bazu. Da li želite da nastavite treniranje sa dodatim modelom?", () => { + this.fetchModels(response._id); + this.showMyModels = true; + }); + this.fetchModels(); }, (error) => { Shared.openDialog('Greška', 'Model sa unetim nazivom već postoji u Vašoj kolekciji. Promenite naziv modela i nastavite sa kreiranim datasetom.'); }); diff --git a/frontend/src/app/app.module.ts b/frontend/src/app/app.module.ts index 41aec3b5..51374bd4 100644 --- a/frontend/src/app/app.module.ts +++ b/frontend/src/app/app.module.ts @@ -49,7 +49,6 @@ import { ItemExperimentComponent } from './_elements/item-experiment/item-experi import { YesNoDialogComponent } from './_modals/yes-no-dialog/yes-no-dialog.component'; import { Configuration } from './configuration.service'; import { PointLinechartComponent } from './point-linechart/point-linechart.component'; -import { GraficiComponent } from './grafici/grafici.component'; import { MixedChartComponent } from './mixed-chart/mixed-chart.component'; import { LineChartComponent } from './_elements/line-chart/line-chart.component'; @@ -92,15 +91,14 @@ export function initializeApp(appConfig: Configuration) { GraphComponent, TrainingComponent, ItemExperimentComponent, - YesNoDialogComponent, + YesNoDialogComponent, LineChartComponent, PointLinechartComponent, - GraficiComponent, MixedChartComponent, LineChartComponent, MetricViewComponent, - - + + ], imports: [ BrowserModule, diff --git a/frontend/src/app/grafici/grafici.component.css b/frontend/src/app/grafici/grafici.component.css deleted file mode 100644 index e69de29b..00000000 diff --git a/frontend/src/app/grafici/grafici.component.html b/frontend/src/app/grafici/grafici.component.html deleted file mode 100644 index 5f987238..00000000 --- a/frontend/src/app/grafici/grafici.component.html +++ /dev/null @@ -1 +0,0 @@ -

grafici works!

diff --git a/frontend/src/app/grafici/grafici.component.spec.ts b/frontend/src/app/grafici/grafici.component.spec.ts deleted file mode 100644 index 9b5ba94d..00000000 --- a/frontend/src/app/grafici/grafici.component.spec.ts +++ /dev/null @@ -1,25 +0,0 @@ -import { ComponentFixture, TestBed } from '@angular/core/testing'; - -import { GraficiComponent } from './grafici.component'; - -describe('GraficiComponent', () => { - let component: GraficiComponent; - let fixture: ComponentFixture; - - beforeEach(async () => { - await TestBed.configureTestingModule({ - declarations: [ GraficiComponent ] - }) - .compileComponents(); - }); - - beforeEach(() => { - fixture = TestBed.createComponent(GraficiComponent); - component = fixture.componentInstance; - fixture.detectChanges(); - }); - - it('should create', () => { - expect(component).toBeTruthy(); - }); -}); diff --git a/frontend/src/app/grafici/grafici.component.ts b/frontend/src/app/grafici/grafici.component.ts deleted file mode 100644 index 749b35e2..00000000 --- a/frontend/src/app/grafici/grafici.component.ts +++ /dev/null @@ -1,15 +0,0 @@ -import { Component, OnInit } from '@angular/core'; - -@Component({ - selector: 'app-grafici', - templateUrl: './grafici.component.html', - styleUrls: ['./grafici.component.css'] -}) -export class GraficiComponent implements OnInit { - - constructor() { } - - ngOnInit(): void { - } - -} diff --git a/frontend/src/app/training/training.component.html b/frontend/src/app/training/training.component.html index 2bee3b12..fa80089e 100644 --- a/frontend/src/app/training/training.component.html +++ b/frontend/src/app/training/training.component.html @@ -33,9 +33,8 @@

Rezultati treniranja

Rezultati treniranja:

-

- {{trainingResult}} -

+ {{trainingResult}} +
diff --git a/frontend/src/app/training/training.component.ts b/frontend/src/app/training/training.component.ts index 4c38f166..6b5405cb 100644 --- a/frontend/src/app/training/training.component.ts +++ b/frontend/src/app/training/training.component.ts @@ -3,6 +3,7 @@ import { ActivatedRoute } from '@angular/router'; import Shared from '../Shared'; import Experiment from '../_data/Experiment'; import Model, { ProblemType } from '../_data/Model'; +import { MetricViewComponent } from '../_elements/metric-view/metric-view.component'; import { ModelLoadComponent } from '../_elements/model-load/model-load.component'; import { AuthService } from '../_services/auth.service'; import { ExperimentsService } from '../_services/experiments.service'; @@ -17,6 +18,7 @@ import { SignalRService } from '../_services/signal-r.service'; export class TrainingComponent implements OnInit { @ViewChild(ModelLoadComponent) modelLoadComponent?: ModelLoadComponent; + @ViewChild(MetricViewComponent) metricViewComponent!: MetricViewComponent; myExperiments?: Experiment[]; selectedExperiment?: Experiment; @@ -24,16 +26,11 @@ export class TrainingComponent implements OnInit { trainingResult: any; + history: any[] = []; + term: string = ""; constructor(private modelsService: ModelsService, private route: ActivatedRoute, private experimentsService: ExperimentsService, private authService: AuthService, private signalRService: SignalRService) { - if (this.signalRService.hubConnection) { - this.signalRService.hubConnection.on("NotifyEpoch", (mName: string, mId: string, stat: string, totalEpochs: number, currentEpoch: number) => { - if (this.selectedModel?._id == mId) { - this.trainingResult = stat; - } - }); - } } ngOnInit(): void { @@ -45,17 +42,32 @@ export class TrainingComponent implements OnInit { this.authService.loggedInEvent.subscribe(_ => { this.fetchExperiments(experimentId); - this.signalRService.startConnection() + this.signalRService.startConnection(); }); + + console.log(this.signalRService.hubConnection); + if (this.signalRService.hubConnection) { + this.signalRService.hubConnection.on("NotifyEpoch", (mName: string, mId: string, stat: string, totalEpochs: number, currentEpoch: number) => { + console.log(this.selectedModel?._id, mId); + if (this.selectedModel?._id == mId) { + stat = stat.replace(/'/g, '"'); + this.trainingResult = JSON.parse(stat); + //console.log('JSON', this.trainingResult); + this.history.push(this.trainingResult); + this.metricViewComponent.update(this.history); + } + }); + } }); } fetchExperiments(andSelectWithId: string | null = '') { this.experimentsService.getMyExperiments().subscribe((experiments) => { - this.myExperiments = experiments; + this.myExperiments = experiments.reverse(); this.selectedExperiment = this.myExperiments.filter(x => x._id == andSelectWithId)[0]; - console.log("selektovan exp u training comp: ", this.selectedExperiment); + if (this.modelLoadComponent) + this.modelLoadComponent.newModel.type = this.selectedExperiment.type; }); } @@ -82,8 +94,7 @@ export class TrainingComponent implements OnInit { } this.modelsService.trainModel(this.selectedModel._id, this.selectedExperiment._id).subscribe((response: any) => { //console.log('Train model complete!', response); - Shared.openDialog("Obaveštenje", "Treniranje modela je uspešno završeno!"); - this.trainingResult = response; + Shared.openDialog("Obaveštenje", "Treniranje modela je počelo!"); }); } } -- cgit v1.2.3 From 61682b01751369307d7777f55be98d25d7fc10a9 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Wed, 20 Apr 2022 01:04:11 +0200 Subject: Onemogućeno brisanje izlazne kolone koja ima null vrednosti ukoliko korisnik zatraži brisanje svih kolona sa null vrednostima. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/controller.py | 15 +++++++------- backend/microservice/api/newmlservice.py | 24 ++++++++++++----------- frontend/src/app/training/training.component.html | 1 - 3 files changed, 21 insertions(+), 19 deletions(-) (limited to 'backend/microservice') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 9b83b8e7..f870b2b1 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -53,7 +53,7 @@ class train_callback(tf.keras.callbacks.Callback): @app.route('/train', methods = ['POST']) def train(): - print("******************************TRAIN*************************************************") + #print("******************************TRAIN*************************************************") f = request.files.get("file") data = pd.read_csv(f) @@ -88,10 +88,11 @@ def train(): "h5FileId" : fileId, "metrics" : m } - print(predictor) + #print(predictor) + #print('\n') url = config.api_url + "/Predictor/add" r = requests.post(url, json=predictor).text - print(r) + #print(r) return r @app.route('/predict', methods = ['POST']) @@ -100,13 +101,13 @@ def predict(): model = tf.keras.models.load_model(h5) paramsExperiment = json.loads(request.form["experiment"]) paramsPredictor = json.loads(request.form["predictor"]) - print("********************************model loaded*******************************") + #print("********************************model loaded*******************************") result = newmlservice.predict(paramsExperiment, paramsPredictor, model) return result @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): - print("********************************PREPROCESS*******************************") + #print("********************************PREPROCESS*******************************") dataset = json.loads(request.form["dataset"]) file = request.files.get("file") data=pd.read_csv(file) @@ -126,8 +127,8 @@ def returnColumnsInfo(): dataset["colCount"] = preprocess["colCount"] dataset["rowCount"] = preprocess["rowCount"] dataset["isPreProcess"] = True - print(dataset) + #print(dataset) return jsonify(dataset) -print("App loaded.") +#print("App loaded.") app.run() \ No newline at end of file diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 6cbda69c..6e65c876 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -129,7 +129,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): ###NULL null_value_options = paramsExperiment["nullValues"] null_values_replacers = paramsExperiment["nullValuesReplacers"] - + kategorijskekolone=data.select_dtypes(include=['object']).columns.copy() + #print(kategorijskekolone) if(null_value_options=='replace'): #print("replace null") # dict=null_values_replacers @@ -143,11 +144,18 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): val = np.int64(val) elif(data[col].dtype == 'float64'): val = np.float64(val) - #elif(data[col].dtype == 'object'): data[col]=data[col].fillna(val) elif(null_value_options=='delete_rows'): data=data.dropna() elif(null_value_options=='delete_columns'): + if(data[output_column].isnull().sum()>0): + if(output_column in kategorijskekolone): + replace=data[output_column].value_counts().index[0] + #print(replace) + else: + replace=data[output_column].mean() + data[output_column]=data[output_column].fillna(replace) + #print(data[output_column].isnull().sum()) data=data.dropna(axis=1) #print(data.shape) @@ -175,7 +183,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): encodings=paramsExperiment["encodings"] datafront=dataset.copy() svekolone=datafront.columns - kategorijskekolone=datafront.select_dtypes(include=['object']).columns + + for kolonaEncoding in encodings: kolona = kolonaEncoding["columnName"] @@ -237,13 +246,6 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): #print(x_columns) x = data[x_columns].values y = data[output_column].values - print('-----------------dfghfhgfhfg-------------------------------') - print(x) - print('-----------------dfghfhgfhfg-------------------------------') - print(y) - print('-----------------dfghfhgfhfg-------------------------------') - print(output_column) - print('-----------------dfghfhgfhfg-------------------------------') # # Podela na test i trening skupove @@ -360,7 +362,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history diff --git a/frontend/src/app/training/training.component.html b/frontend/src/app/training/training.component.html index fa80089e..66f77c37 100644 --- a/frontend/src/app/training/training.component.html +++ b/frontend/src/app/training/training.component.html @@ -33,7 +33,6 @@

Rezultati treniranja

Rezultati treniranja:

- {{trainingResult}}
-- cgit v1.2.3