From a2180a2d37b9b75af90b511cebde1677a748681e Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Thu, 31 Mar 2022 23:07:02 +0200 Subject: Omoguceno resavanje regresionih problema. Metrike se vracaju kao recnik. --- backend/microservice/api/ml_service.py | 123 +++++++++++++++++++++++++++------ 1 file changed, 100 insertions(+), 23 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index ea562212..f1f34cb7 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -1,4 +1,5 @@ import pandas as pd +from sklearn import datasets import tensorflow as tf import keras import numpy as np @@ -11,12 +12,15 @@ from typing_extensions import Self from copyreg import constructor from flask import request, jsonify, render_template from sklearn.preprocessing import LabelEncoder +from sklearn.preprocessing import OrdinalEncoder +import category_encoders as ce from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from dataclasses import dataclass +''' @dataclass -class TrainingResult: +class TrainingResultClassification: accuracy: float precision: float recall: float @@ -26,12 +30,21 @@ class TrainingResult: tp: float specificity: float f1: float + logloss: float + fpr: float + tpr: float + metrics: dict + +@datasets +class TrainingResultRegression: mse: float mae: float mape: float rmse: float - fpr: float - tpr: float +''' +@dataclass +class TrainingResult: + metrics: dict def train(dataset, params, callback): problem_type = params["type"] @@ -66,6 +79,7 @@ def train(dataset, params, callback): data.pop(col) # # Enkodiranje + # https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/ # encoding=params["encoding"] if(encoding=='label'): @@ -79,6 +93,32 @@ def train(dataset, params, callback): 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 # @@ -101,10 +141,13 @@ def train(dataset, params, callback): # # Skaliranje vrednosti # + ''' scaler=StandardScaler() scaler.fit(x_train) x_test=scaler.transform(x_test) x_train=scaler.transform(x_train) + ''' + # # Treniranje modela # @@ -113,7 +156,10 @@ def train(dataset, params, callback): for func in params["hiddenLayerActivationFunctions"]: classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) output_func = params["outputLayerActivationFunction"] - classifier.add(tf.keras.layers.Dense(units=1,activation=output_func)) + if(problem_type!="regresioni"): + classifier.add(tf.keras.layers.Dense(units=1,activation=output_func)) + else: + classifier.add(tf.keras.layers.Dense(units=1)) optimizer = params["optimizer"] metrics=params['metrics'] loss_func=params["lossFunction"] @@ -125,11 +171,14 @@ def train(dataset, params, callback): # Test # model_name = params['_id'] - y_pred=classifier.predict(x_test) + #y_pred=classifier.predict(x_test) if(problem_type == "regresioni"): - classifier.evaluate(x_test, y_test) - elif(problem_type == "binarni-klasifikacioni"): + print(classifier.evaluate(x_test, y_test)) + y_pred=classifier.predict(x_test) + elif(problem_type == "binarni-klasifikacioni"): + y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') + y_pred=y_pred.flatten() result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred}) classifier.save("temp/"+model_name, save_format='h5') @@ -139,20 +188,48 @@ def train(dataset, params, callback): print("HELLO???") print(result) print("HELLO???") - accuracy = float(sm.accuracy_score(y_test,y_pred)) - precision = float(sm.precision_score(y_test,y_pred)) - recall = float(sm.recall_score(y_test,y_pred)) - tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() - specificity = float(tn / (tn+fp)) - f1 = float(sm.f1_score(y_test,y_pred)) - mse = float(sm.mean_squared_error(y_test,y_pred)) - mae = float(sm.mean_absolute_error(y_test,y_pred)) - mape = float(sm.mean_absolute_percentage_error(y_test,y_pred)) - rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred))) - fpr, tpr, _ = sm.roc_curve(y_test,y_pred) + if(problem_type=="binarni-klasifikacioni"): + accuracy = float(sm.accuracy_score(y_test,y_pred)) + precision = float(sm.precision_score(y_test,y_pred)) + recall = float(sm.recall_score(y_test,y_pred)) + tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() + specificity = float(tn / (tn+fp)) + f1 = float(sm.f1_score(y_test,y_pred)) + fpr, tpr, _ = sm.roc_curve(y_test,y_pred) + logloss = float(sm.log_loss(y_test, y_pred)) + metrics= {"accuracy" : accuracy, + "precision" : precision, + "recall" : recall, + "specificity" : specificity, + "f1" : f1, + "tn" : float(tn), + "fp" : float(fp), + "fn" : float(fn), + "tp" : float(tp), + "fpr" : fpr.tolist(), + "tpr" : tpr.tolist(), + "logloss" : logloss + } + elif(problem_type=="regresioni"): + # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/ + mse = float(sm.mean_squared_error(y_test,y_pred)) + mae = float(sm.mean_absolute_error(y_test,y_pred)) + mape = float(sm.mean_absolute_percentage_error(y_test,y_pred)) + rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred))) + rmsle = float(np.sqrt(sm.mean_squared_error(y_test, y_pred))) + r2 = float(sm.r2_score(y_test, y_pred)) + # n - num of observations + # k - num of independent variables + n = 40 + k = 2 + adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1))) + metrics= {"mse" : mse, + "mae" : mae, + "mape" : mape, + "rmse" : rmse, + "rmsle" : rmsle, + "r2" : r2, + "adj_r2" : adj_r2 + } # TODO upload trenirani model nazad na backend - return TrainingResult(accuracy, precision, recall, float(tn), float(fp), float(fn), float(tp), specificity, f1, mse, mae, mape, rmse, fpr.tolist(), tpr.tolist()) - - - - + return TrainingResult(metrics) \ No newline at end of file -- cgit v1.2.3 From 70c96a90a12778e06499f7d30542afae137347a4 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Fri, 1 Apr 2022 21:42:09 +0200 Subject: Sitna korekcija. --- backend/microservice/api/ml_service.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index f1f34cb7..c7082454 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -134,10 +134,10 @@ def train(dataset, params, callback): test=params["randomTestSetDistribution"] randomOrder = params["randomOrder"] if(randomOrder): - random=50 + random=123 else: random=0 - x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) + x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, shuffle=params["shuffle"], random_state=random) # # Skaliranje vrednosti # @@ -173,8 +173,8 @@ def train(dataset, params, callback): model_name = params['_id'] #y_pred=classifier.predict(x_test) if(problem_type == "regresioni"): - print(classifier.evaluate(x_test, y_test)) y_pred=classifier.predict(x_test) + print(classifier.evaluate(x_test, y_test)) elif(problem_type == "binarni-klasifikacioni"): y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') -- cgit v1.2.3 From 491db336204f911c8f0717b0b16ff345ca5ee355 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 2 Apr 2022 14:00:52 +0200 Subject: Dodata je mogućnost obučavanja modela primenom multiklasne klasifikacije. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/ml_service.py | 58 ++++++++++++++++++++++++---------- 1 file changed, 41 insertions(+), 17 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index c7082454..7b950bcd 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -13,7 +13,7 @@ from copyreg import constructor from flask import request, jsonify, render_template from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OrdinalEncoder -import category_encoders as ce +#import category_encoders as ce from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from dataclasses import dataclass @@ -98,6 +98,7 @@ def train(dataset, params, callback): 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: @@ -112,13 +113,14 @@ def train(dataset, params, callback): 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) + encoder.fit_transform(data)''' # # Input - output # @@ -151,22 +153,44 @@ def train(dataset, params, callback): # # Treniranje modela # - classifier=tf.keras.Sequential() + # hidden_layer_neurons = params["hiddenLayerNeurons"] - for func in params["hiddenLayerActivationFunctions"]: - classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) - output_func = params["outputLayerActivationFunction"] - if(problem_type!="regresioni"): - classifier.add(tf.keras.layers.Dense(units=1,activation=output_func)) - else: - classifier.add(tf.keras.layers.Dense(units=1)) - optimizer = params["optimizer"] - metrics=params['metrics'] - loss_func=params["lossFunction"] - classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) - batch_size = params["batchSize"] - epochs = params["epochs"] - history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test), validation_split=0.2) # TODO params["validationSplit"] + + if(problem_type=='multi-klasifikacioni'): + func=params['hiddenLayerActivationFunctions'] + funcFirst=func.pop(0) + inputDim = len(data.columns) - 1 + classifier=tf.keras.Sequential(units=hidden_layer_neurons,input_dim=inputDim,activation=funcFirst) + for f in func: + classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) + output_func = params["outputLayerActivationFunction"] + numberofclasses=len(output_column.unique()) + classifier.add(tf.keras.layers.Dense(units=numberofclasses,activation=output_func)) + + optimizer = params["optimizer"] + metrics=params['metrics'] + loss_func=params["lossFunction"] + classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) + batch_size = params["batchSize"] + epochs = params["epochs"] + history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test)) + else: + classifier=tf.keras.Sequential() + + for func in params["hiddenLayerActivationFunctions"]: + classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) + output_func = params["outputLayerActivationFunction"] + if(problem_type!="regresioni"): + classifier.add(tf.keras.layers.Dense(units=1,activation=output_func)) + else: + classifier.add(tf.keras.layers.Dense(units=1)) + optimizer = params["optimizer"] + metrics=params['metrics'] + loss_func=params["lossFunction"] + classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) + batch_size = params["batchSize"] + epochs = params["epochs"] + history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test), validation_split=0.2) # TODO params["validationSplit"] # # Test # -- cgit v1.2.3 From 94dcb65454e55caafa0a6e36e5766144cfb204c6 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Sat, 2 Apr 2022 22:37:38 +0200 Subject: Obrada statistike je dodata u ml_service.py fajl i kreirana je ruta u controller.py fajlu. --- backend/microservice/api/controller.py | 11 ++++++ backend/microservice/api/ml_service.py | 62 +++++++++++++++++++++++++++++----- 2 files changed, 65 insertions(+), 8 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 059af317..524b97b5 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -38,6 +38,17 @@ def predict(): # #model.predict? +@app.route('/preprocess',methods=['POST']) +def returnColumnsInfo(): + f=request.json['filepathcolinfo'] + dataset=pd.read_csv(f) + + result=ml_service.returnColumnsInfo(dataset) + + return jsonify(result) + + + print("App loaded.") ml_socket.start() app.run() \ No newline at end of file diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index 7b950bcd..21ec8fa3 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -1,3 +1,6 @@ +from cmath import nan +from enum import unique +from itertools import count import pandas as pd from sklearn import datasets import tensorflow as tf @@ -13,12 +16,55 @@ from copyreg import constructor from flask import request, jsonify, render_template from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OrdinalEncoder -#import category_encoders as ce +import category_encoders as ce from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from dataclasses import dataclass +import statistics as s + +def returnColumnsInfo(dataset): + dict=[] + datafront=dataset.copy() + svekolone=datafront.columns + kategorijskekolone=datafront.select_dtypes(include=['object']).columns + allNullCols=0 + for kolona in svekolone: + if(kolona in kategorijskekolone): + uniquevalues=datafront[kolona].unique() + mean=0 + median=0 + nullCount=datafront[kolona].isnull().sum() + if(nullCount>0): + allNullCols=allNullCols+1 + frontreturn={'columnName':kolona, + 'isNumber':False, + 'uniqueValues':uniquevalues.tolist(), + 'median':float(mean), + 'mean':float(median), + 'numNulls':float(nullCount) + } + dict.append(frontreturn) + else: + mean=datafront[kolona].mean() + median=s.median(datafront[kolona]) + nullCount=datafront[kolona].isnull().sum() + if(nullCount>0): + allNullCols=allNullCols+1 + frontreturn={'columnName':kolona, + 'isNumber':1, + 'uniqueValues':[], + 'mean':float(mean), + 'median':float(median), + 'numNulls':float(nullCount) + } + dict.append(frontreturn) + NullRows = datafront[datafront.isnull().any(axis=1)] + #print(NullRows) + #print(len(NullRows)) + allNullRows=len(NullRows) + + return {'columnInfo':dict,'allNullColl':allNullCols,'allNullRows':allNullRows} -''' @dataclass class TrainingResultClassification: accuracy: float @@ -34,18 +80,18 @@ class TrainingResultClassification: fpr: float tpr: float metrics: dict - +''' @datasets class TrainingResultRegression: mse: float mae: float mape: float rmse: float -''' + @dataclass class TrainingResult: metrics: dict - +''' def train(dataset, params, callback): problem_type = params["type"] data = pd.DataFrame() @@ -98,7 +144,7 @@ def train(dataset, params, callback): 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: @@ -120,7 +166,7 @@ def train(dataset, params, callback): 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)''' + encoder.fit_transform(data) # # Input - output # @@ -256,4 +302,4 @@ def train(dataset, params, callback): "adj_r2" : adj_r2 } # TODO upload trenirani model nazad na backend - return TrainingResult(metrics) \ No newline at end of file +#return TrainingResult(metrics) \ No newline at end of file -- cgit v1.2.3 From 87601668a7ee5173d0b11308b360d2ef01cae4d6 Mon Sep 17 00:00:00 2001 From: Nevena Bojovic Date: Sun, 3 Apr 2022 19:10:30 +0200 Subject: Korekcija ml_service. --- backend/microservice/api/ml_service.py | 35 ++++++- frontend/package-lock.json | 167 ++++++++++++--------------------- 2 files changed, 94 insertions(+), 108 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index 21ec8fa3..b264b428 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -21,6 +21,7 @@ from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from dataclasses import dataclass import statistics as s +from sklearn.metrics import roc_auc_score def returnColumnsInfo(dataset): dict=[] @@ -252,6 +253,26 @@ def train(dataset, params, callback): y_pred=y_pred.flatten() result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred}) classifier.save("temp/"+model_name, save_format='h5') + # ROC multi-klasifikacioni + def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): + + #creating a set of all the unique classes using the actual class list + unique_class = set(actual_class) + roc_auc_dict = {} + for per_class in unique_class: + + #creating a list of all the classes except the current class + other_class = [x for x in unique_class if x != per_class] + + #marking the current class as 1 and all other classes as 0 + new_actual_class = [0 if x in other_class else 1 for x in actual_class] + new_pred_class = [0 if x in other_class else 1 for x in pred_class] + + #using the sklearn metrics method to calculate the roc_auc_score + roc_auc = roc_auc_score(new_actual_class, new_pred_class, average = average) + roc_auc_dict[per_class] = roc_auc + + return roc_auc_dict # # Metrike # @@ -301,5 +322,17 @@ def train(dataset, params, callback): "r2" : r2, "adj_r2" : adj_r2 } + elif(problem_type=="multi-klasifikacioni"): + # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook + accuracy=metrics.accuracy_score(y_test, y_pred) + macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro') + micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro') + macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro') + micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro') + macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro') + micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') + roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred) + + # TODO upload trenirani model nazad na backend -#return TrainingResult(metrics) \ No newline at end of file + #return TrainingResult(metrics) \ No newline at end of file diff --git a/frontend/package-lock.json b/frontend/package-lock.json index b5ca9d02..962905b7 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -450,7 +450,6 @@ "version": "13.2.5", "resolved": "https://registry.npmjs.org/@angular/compiler-cli/-/compiler-cli-13.2.5.tgz", "integrity": "sha512-Xd8xj2Z0ilA4TJAM/JkTtA1CAa6SuebFsEEvabHCRO5MDvtdsIUP91ADUZIqDHy7qe6Qift/rAVN2PXxT2aaNA==", - 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"dev": true + "dev": true, + "requires": {} }, "json-schema-traverse": { "version": "0.4.1", @@ -20285,7 +20238,8 @@ "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", @@ -20295,8 +20249,7 @@ "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", -- cgit v1.2.3 From aa2a296ef27520f38957bf4ef103fd5571468937 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Mon, 4 Apr 2022 00:55:52 +0200 Subject: Izmena multi-klasne klasifikacije. --- backend/microservice/api/ml_service.py | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index b264b428..40166cc4 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -205,21 +205,26 @@ def train(dataset, params, callback): if(problem_type=='multi-klasifikacioni'): func=params['hiddenLayerActivationFunctions'] - funcFirst=func.pop(0) - inputDim = len(data.columns) - 1 - classifier=tf.keras.Sequential(units=hidden_layer_neurons,input_dim=inputDim,activation=funcFirst) - for f in func: - classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) output_func = params["outputLayerActivationFunction"] - numberofclasses=len(output_column.unique()) - classifier.add(tf.keras.layers.Dense(units=numberofclasses,activation=output_func)) - optimizer = params["optimizer"] metrics=params['metrics'] loss_func=params["lossFunction"] - classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) batch_size = params["batchSize"] epochs = params["epochs"] + inputDim = len(data.columns) - 1 + + classifier=tf.keras.Sequential() + + classifier.add(tf.keras.layers.Dense(units=len(data.columns),input_dim=inputDim))#input layer + + for f in func:#hidden layers + classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=f)) + + numberofclasses=len(output_column.unique()) + classifier.add(tf.keras.layers.Dense(units=numberofclasses,activation=output_func))#output layer + + classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) + history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test)) else: classifier=tf.keras.Sequential() @@ -227,10 +232,12 @@ def train(dataset, params, callback): for func in params["hiddenLayerActivationFunctions"]: classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func)) output_func = params["outputLayerActivationFunction"] + if(problem_type!="regresioni"): classifier.add(tf.keras.layers.Dense(units=1,activation=output_func)) else: classifier.add(tf.keras.layers.Dense(units=1)) + optimizer = params["optimizer"] metrics=params['metrics'] loss_func=params["lossFunction"] @@ -249,7 +256,10 @@ def train(dataset, params, callback): elif(problem_type == "binarni-klasifikacioni"): y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') - + elif(problem_type=='multi-klasifikacioni'): + y_pred=classifier.predict(x_test) + y_pred=np.argmax(y_pred,axis=1) + y_pred=y_pred.flatten() result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred}) classifier.save("temp/"+model_name, save_format='h5') -- cgit v1.2.3 From 15a842c76f2d2200b9e5d1fd88f9cc755bac92cb Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Mon, 4 Apr 2022 01:14:24 +0200 Subject: Izmena --- backend/microservice/api/ml_service.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index 40166cc4..73b191da 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -333,6 +333,9 @@ def train(dataset, params, callback): "adj_r2" : adj_r2 } elif(problem_type=="multi-klasifikacioni"): + + cr=sm.classification_report(y_test, y_pred) + cm=sm.confusion_matrix(y_test,y_pred) # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook accuracy=metrics.accuracy_score(y_test, y_pred) macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro') -- cgit v1.2.3 From 38eda7a2967d3d3739070a3b805511fac665a843 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Tue, 5 Apr 2022 00:55:56 +0200 Subject: Omogućena predikcija na osnovu h5 fajla. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/controller.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index 524b97b5..bbba4af9 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -34,9 +34,8 @@ def predict(): f = request.json['filepath'] dataset = pd.read_csv(f) m = request.json['modelpath'] - #model = tf.keras.models.load_model(m) - # - #model.predict? + model = tf.keras.models.load_model(m) + h5=ml_service.manageH5(dataset,request.json,model,train_callback) @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): -- cgit v1.2.3 From edc79f0cff16ce889a0691351d088a61c9d1c353 Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Tue, 5 Apr 2022 00:57:25 +0200 Subject: Izmena ml_service, dodato učitavanje h5 fajla. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/microservice/api/ml_service.py | 128 ++++++++++++++++++++++++++++++--- 1 file changed, 117 insertions(+), 11 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py index 73b191da..0aed3dc9 100644 --- a/backend/microservice/api/ml_service.py +++ b/backend/microservice/api/ml_service.py @@ -34,21 +34,27 @@ def returnColumnsInfo(dataset): uniquevalues=datafront[kolona].unique() mean=0 median=0 + min=0 + max=0 nullCount=datafront[kolona].isnull().sum() if(nullCount>0): allNullCols=allNullCols+1 frontreturn={'columnName':kolona, 'isNumber':False, 'uniqueValues':uniquevalues.tolist(), - 'median':float(mean), - 'mean':float(median), - 'numNulls':float(nullCount) + 'mean':float(mean), + 'median':float(median), + 'numNulls':float(nullCount), + 'min':min, + 'max':max } dict.append(frontreturn) else: mean=datafront[kolona].mean() median=s.median(datafront[kolona]) nullCount=datafront[kolona].isnull().sum() + min=min(datafront[kolona]) + max=max(datafront[kolona]) if(nullCount>0): allNullCols=allNullCols+1 frontreturn={'columnName':kolona, @@ -56,7 +62,9 @@ def returnColumnsInfo(dataset): 'uniqueValues':[], 'mean':float(mean), 'median':float(median), - 'numNulls':float(nullCount) + 'numNulls':float(nullCount), + 'min':min, + 'max':max } dict.append(frontreturn) NullRows = datafront[datafront.isnull().any(axis=1)] @@ -98,6 +106,8 @@ def train(dataset, params, callback): data = pd.DataFrame() for col in params["inputColumns"]: data[col]=dataset[col] + + print(data.head()) output_column = params["columnToPredict"] data[output_column] = dataset[output_column] # @@ -177,6 +187,8 @@ def train(dataset, params, callback): x_columns.append(col) x = data[x_columns].values y = data[output_column].values + print(x_columns) + print(x) # # Podela na test i trening skupove # @@ -186,7 +198,7 @@ def train(dataset, params, callback): random=123 else: random=0 - x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, shuffle=params["shuffle"], random_state=random) + x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5,random_state=0) # # Skaliranje vrednosti # @@ -212,17 +224,21 @@ def train(dataset, params, callback): batch_size = params["batchSize"] epochs = params["epochs"] inputDim = len(data.columns) - 1 - + ''' classifier=tf.keras.Sequential() - + classifier.add(tf.keras.layers.Dense(units=len(data.columns),input_dim=inputDim))#input layer for f in func:#hidden layers - classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=f)) + classifier.add(tf.keras.layers.Dense(hidden_layer_neurons,activation=f)) numberofclasses=len(output_column.unique()) - classifier.add(tf.keras.layers.Dense(units=numberofclasses,activation=output_func))#output layer - + classifier.add(tf.keras.layers.Dense(numberofclasses,activation=output_func))#output layer + ''' + model=tf.keras.Sequential() + model.add(tf.keras.layers.Dense(1,input_dim=x_train.shape[1]))#input layer + model.add(tf.keras.layers.Dense(1, activation='sigmoid')) + model.add(tf.keras.layers.Dense(len(output_column.unique())+1, activation='softmax')) classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics) history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test)) @@ -348,4 +364,94 @@ def train(dataset, params, callback): # TODO upload trenirani model nazad na backend - #return TrainingResult(metrics) \ No newline at end of file + #return TrainingResult(metrics) + + +def manageH5(datain,params,h5model): + dataset=datain.copy() + problem_type = params["type"] + data = pd.DataFrame() + for col in params["inputColumns"]: + data[col]=dataset[col] + output_column = params["columnToPredict"] + data[output_column] = dataset[output_column] + # + # Brisanje null kolona / redova / zamena + #nullreplace=[ + # {"column":"Embarked","value":"C","deleteRow":false,"deleteCol":true}, + # {"column": "Cabin","value":"C123","deleteRow":"0","deleteCol":"0"}] + + null_value_options = params["nullValues"] + null_values_replacers = params["nullValuesReplacers"] + + if(null_value_options=='replace'): + print("replace null") # TODO + elif(null_value_options=='delete_rows'): + data=data.dropna() + elif(null_value_options=='delete_columns'): + data=data.dropna() + # + #print(data.isnull().any()) + # + # Brisanje kolona koje ne uticu na rezultat + # + num_rows=data.shape[0] + for col in data.columns: + if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)): + data.pop(col) + # + # Enkodiranje + # https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/ + # + encoding=params["encoding"] + 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) + # + # Input - output + # + x_columns = [] + for col in data.columns: + if(col!=output_column): + x_columns.append(col) + x = data[x_columns].values + y = data[output_column].values + + + y_pred=h5model.predict_classes(x) \ No newline at end of file -- cgit v1.2.3 From 4dbaa82b1448a2cf7b69246ae20ebaae36d03f9b Mon Sep 17 00:00:00 2001 From: TAMARA JERINIC Date: Tue, 5 Apr 2022 22:15:30 +0200 Subject: Dodat je novi ml service fajl, uklonjeni su bagovi iz prethodnog. Izmenjen je i controller.py fajl --- backend/microservice/api/controller.py | 10 +- backend/microservice/api/newmlservice.py | 424 +++++++++++++++++++++++++++++++ 2 files changed, 430 insertions(+), 4 deletions(-) create mode 100644 backend/microservice/api/newmlservice.py (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py index bbba4af9..1b17f727 100644 --- a/backend/microservice/api/controller.py +++ b/backend/microservice/api/controller.py @@ -1,7 +1,7 @@ import flask from flask import request, jsonify import ml_socket -import ml_service +import newmlservice import tensorflow as tf import pandas as pd @@ -25,7 +25,7 @@ def train(): f = request.json["dataset"] dataset = pd.read_csv(f) # - result = ml_service.train(dataset, request.json["model"], train_callback) + result = newmlservice.train(dataset, request.json["model"], train_callback) print(result) return jsonify(result) @@ -35,14 +35,16 @@ def predict(): dataset = pd.read_csv(f) m = request.json['modelpath'] model = tf.keras.models.load_model(m) - h5=ml_service.manageH5(dataset,request.json,model,train_callback) + print("********************************model loaded*******************************") + newmlservice.manageH5(dataset,request.json['model'],model) + return "done" @app.route('/preprocess',methods=['POST']) def returnColumnsInfo(): f=request.json['filepathcolinfo'] dataset=pd.read_csv(f) - result=ml_service.returnColumnsInfo(dataset) + result=newmlservice.returnColumnsInfo(dataset) return jsonify(result) diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py new file mode 100644 index 00000000..50af15f8 --- /dev/null +++ b/backend/microservice/api/newmlservice.py @@ -0,0 +1,424 @@ +from enum import unique +from itertools import count +import pandas as pd +from sklearn import datasets, multiclass +import tensorflow as tf +import keras +import numpy as np +import csv +import json +import h5py +import sklearn.metrics as sm +from statistics import mode +from typing_extensions import Self +from copyreg import constructor +from flask import request, jsonify, render_template +from sklearn.preprocessing import LabelEncoder, MinMaxScaler +from sklearn.preprocessing import OrdinalEncoder +import category_encoders as ce +from sklearn.preprocessing import StandardScaler +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=[] + datafront=dataset.copy() + svekolone=datafront.columns + kategorijskekolone=datafront.select_dtypes(include=['object']).columns + allNullCols=0 + for kolona in svekolone: + if(kolona in kategorijskekolone): + uniquevalues=datafront[kolona].unique() + mean=0 + median=0 + nullCount=datafront[kolona].isnull().sum() + if(nullCount>0): + allNullCols=allNullCols+1 + frontreturn={'columnName':kolona, + 'isNumber':False, + 'uniqueValues':uniquevalues.tolist(), + 'median':float(mean), + 'mean':float(median), + 'numNulls':float(nullCount) + } + dict.append(frontreturn) + else: + mean=datafront[kolona].mean() + median=s.median(datafront[kolona]) + nullCount=datafront[kolona].isnull().sum() + if(nullCount>0): + allNullCols=allNullCols+1 + frontreturn={'columnName':kolona, + 'isNumber':1, + 'uniqueValues':[], + 'mean':float(mean), + 'median':float(median), + 'numNulls':float(nullCount) + } + dict.append(frontreturn) + NullRows = datafront[datafront.isnull().any(axis=1)] + #print(NullRows) + #print(len(NullRows)) + allNullRows=len(NullRows) + + return {'columnInfo':dict,'allNullColl':allNullCols,'allNullRows':allNullRows} + +@dataclass +class TrainingResultClassification: + accuracy: float + precision: float + recall: float + tn: float + fp: float + fn: float + tp: float + specificity: float + f1: float + logloss: float + fpr: float + tpr: float + metrics: dict +''' +@datasets +class TrainingResultRegression: + mse: float + mae: float + mape: float + rmse: float + +@dataclass +class TrainingResult: + metrics: dict +''' + +def train(dataset, params, callback): + problem_type = params["type"] + print(problem_type) + data = pd.DataFrame() + print(data) + for col in params["inputColumns"]: + print(col) + data[col]=dataset[col] + output_column = params["columnToPredict"] + data[output_column] = dataset[output_column] + print(data) + + ###NULL + null_value_options = params["nullValues"] + null_values_replacers = params["nullValuesReplacers"] + + if(null_value_options=='replace'): + print("replace null") # TODO + elif(null_value_options=='delete_rows'): + data=data.dropna() + elif(null_value_options=='delete_columns'): + data=data.dropna() + print(data.shape) + + # + # Brisanje kolona koje ne uticu na rezultat + # + num_rows=data.shape[0] + for col in data.columns: + if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)): + data.pop(col) + # + ### Enkodiranje + encoding=params["encoding"] + 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) + # + # Input - output + # + x_columns = [] + for col in data.columns: + if(col!=output_column): + x_columns.append(col) + print(x_columns) + x = data[x_columns].values + y = data[output_column].values + + # + # Podela na test i trening skupove + # + test=params["randomTestSetDistribution"] + randomOrder = params["randomOrder"] + if(randomOrder): + random=123 + else: + random=0 + x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) + print(x_train,x_test) + + # + # Treniranje modela + # + # + 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 + #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.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) + + history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + + 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") + + 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 + #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.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) + + history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) + + y_pred=classifier.predict(x_test) + y_pred=(y_pred>=0.5).astype('int') + + 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)) + #ann_viz(classifier, title="My neural network") + + classifier.save("temp/"+params['name'], save_format='h5') + + 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 + #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)) + + 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']) + y_pred=classifier.predict(x_test) + print(classifier.evaluate(x_test, y_test)) + + def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"): + + #creating a set of all the unique classes using the actual class list + unique_class = set(actual_class) + roc_auc_dict = {} + for per_class in unique_class: + + #creating a list of all the classes except the current class + other_class = [x for x in unique_class if x != per_class] + + #marking the current class as 1 and all other classes as 0 + new_actual_class = [0 if x in other_class else 1 for x in actual_class] + new_pred_class = [0 if x in other_class else 1 for x in pred_class] + + #using the sklearn metrics method to calculate the roc_auc_score + roc_auc = roc_auc_score(new_actual_class, new_pred_class, average = average) + roc_auc_dict[per_class] = roc_auc + + return roc_auc_dict + # + # Metrike + # + + if(problem_type=="binarni-klasifikacioni"): + accuracy = float(sm.accuracy_score(y_test,y_pred)) + precision = float(sm.precision_score(y_test,y_pred)) + recall = float(sm.recall_score(y_test,y_pred)) + tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel() + specificity = float(tn / (tn+fp)) + f1 = float(sm.f1_score(y_test,y_pred)) + fpr, tpr, _ = sm.roc_curve(y_test,y_pred) + logloss = float(sm.log_loss(y_test, y_pred)) + metrics= {"accuracy" : accuracy, + "precision" : precision, + "recall" : recall, + "specificity" : specificity, + "f1" : f1, + "tn" : float(tn), + "fp" : float(fp), + "fn" : float(fn), + "tp" : float(tp), + "fpr" : fpr.tolist(), + "tpr" : tpr.tolist(), + "logloss" : logloss + } + elif(problem_type=="regresioni"): + # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/ + mse = float(sm.mean_squared_error(y_test,y_pred)) + mae = float(sm.mean_absolute_error(y_test,y_pred)) + mape = float(sm.mean_absolute_percentage_error(y_test,y_pred)) + rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred))) + rmsle = float(np.sqrt(sm.mean_squared_error(y_test, y_pred))) + r2 = float(sm.r2_score(y_test, y_pred)) + # n - num of observations + # k - num of independent variables + n = 40 + k = 2 + adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1))) + metrics= {"mse" : mse, + "mae" : mae, + "mape" : mape, + "rmse" : rmse, + "rmsle" : rmsle, + "r2" : r2, + "adj_r2" : adj_r2 + } + ''' + elif(problem_type=="multi-klasifikacioni"): + + cr=sm.classification_report(y_test, y_pred) + cm=sm.confusion_matrix(y_test,y_pred) + # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook + accuracy=metrics.accuracy_score(y_test, y_pred) + macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro') + micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro') + macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro') + micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro') + macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro') + micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') + roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred) + ''' + +def manageH5(dataset,params,h5model): + problem_type = params["type"] + print(problem_type) + data = pd.DataFrame() + #print(data) + for col in params["inputColumns"]: + print(col) + data[col]=dataset[col] + output_column = params["columnToPredict"] + data[output_column] = dataset[output_column] + #print(data) + + ###NULL + null_value_options = params["nullValues"] + null_values_replacers = params["nullValuesReplacers"] + + if(null_value_options=='replace'): + print("replace null") # TODO + elif(null_value_options=='delete_rows'): + data=data.dropna() + elif(null_value_options=='delete_columns'): + data=data.dropna() + print(data.shape) + + # + # Brisanje kolona koje ne uticu na rezultat + # + num_rows=data.shape[0] + for col in data.columns: + if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)): + data.pop(col) + # + ### Enkodiranje + encoding=params["encoding"] + 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) + #print(data) + + # + # Input - output + # + x_columns = [] + for col in data.columns: + if(col!=output_column): + x_columns.append(col) + #print(x_columns) + x2 = data[x_columns] + print(x2) + print(x2.values) + x2 = data[x_columns].values + print(x2) + y2 = data[output_column].values + h5model.summary() + ann_viz(h5model, title="My neural network") + + h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics']) + + history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize']) + + y_pred2=h5model.predict(x2) + + y_pred2=np.argmax(y_pred2,axis=1) + #y_pred=h5model.predict_classes(x) + score = h5model.evaluate(x2,y_pred2, verbose=0) + print("%s: %.2f%%" % (h5model.metrics_names[1], score[1]*100)) + print(y_pred2) + print( 'done') \ No newline at end of file -- cgit v1.2.3