commit WAR

.gitattributes

@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv filter=lfs diff=lfs merge=lfs -text
+datasets/traffic_flow_forecasting/traffic_dataset.mat filter=lfs diff=lfs merge=lfs -text

README.md

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+# Representativity-based active learning for regression using Wasserstein distance and GroupSort Neural Networks
+
+You will find in this repository the codes used to test the performance of the WAR model on a fully labeled dataset
+
+**WAR-notebook** : you can run the algorithm from there and change the desired parameters
+
+
+### WAR directory
+
+**Experiment_functions.py** : functions used to vizualise information about WAR process (loss, metrics, points queried every rounds...).
+
+**Models.py**: Definition of the two neural networks h and phi.
+
+**dataset_handler.py**: Import and preprocess datasets.
+
+**full_training_process.py**: main function.
+
+**training_and_query.py**: function to run one round (one training and querying process).
+
+
+## Abstract
+This paper proposes a new active learning strategy called Wasserstein active regression (WAR) based on the principle of distribution-matching to measure the representativeness of our labeled dataset  compared to the global data distribution. We use GroupSort Neural Networks to compute the Wasserstein distance and provide theoretical foundations to justify the use of such networks with explicit bounds for their size and depth. We combine this solution with another diversity and uncertainty-based approach to sharpen our query strategy. Finally, we compare our method with other solutions and show empirically that we consistently achieve better estimations with less labeled data.
+
+

WAR-notebook.ipynb

@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook to test WAR performances on a fully labelled dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import itertools\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from WAR.Models import NN_phi,NN_h_RELU\n",
+    "from WAR.training_and_query import WAR\n",
+    "from WAR.dataset_handler import myData,import_dataset,get_dataset\n",
+    "from WAR.Experiment_functions import *\n",
+    "from WAR.full_training_process import full_training,check_num_round\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Using {device} device\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#choosing dataset and splitting it with the desired testset proportion\n",
+    "# for now dataset=\n",
+    "#\"boston\",\"airfoil\",\"energy1\",\"energy2\",\"yacht\"\n",
+    "#,\"concrete_slump\",\"concrete_flow\",\"concrete_compressive\",x_squared\",\"news_popularity\"\n",
+    "\n",
+    "X_train,X_test,y_train,y_test=get_dataset(proportion=0.2,dataset=\"boston\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2D PCA visualization of the data\n",
+    "#kmeans = KMeans(n_clusters=nb_initial_labelled_datas, init='k-means++', n_init=10).fit_predict(X_train)\n",
+    "pca = PCA(n_components=2)\n",
+    "transformed = pca.fit_transform(X=X_train)\n",
+    "print(f\"{round(sum(pca.explained_variance_),4)*100}% variance explained\")\n",
+    "plt.figure(figsize=(8.5, 6))\n",
+    "plt.scatter(x=transformed[:, 0], y=transformed[:, 1]#,c=kmeans\n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WAR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total_epoch_h=100 # number of epochs to train h each round\n",
+    "total_epoch_phi=100 # number of epochs to train phi each round \n",
+    "num_elem_queried= int(0.02*X_train.shape[0]) # number of elem queried each round  \n",
+    "nb_initial_labelled_datas = int(0.02*X_train.shape[0]) #nb of labelled datas at round 0\n",
+    "init_method=\"k_mean\" # how the initial data will be chosen. \"random\" or \"k-means\" \n",
+    "second_query_strategy=\"loss_approximation\" # query strategy assisting our distribution-matching criterion. \"loss_approximation\" or None for now\n",
+    "lr_h=0.001  # learning rate h \n",
+    "lr_phi=0.01  # learning rate phi  \n",
+    "weight_decay=0.001 # L2 regularization on h\n",
+    "\n",
+    "batch_size_train=len(X_train) # size of the batch during the training process  #len(X_train)\n",
+    "num_round=500  # number of rounds\n",
+    "num_round=check_num_round(num_round,len(y_train),nb_initial_labelled_datas,num_elem_queried)\n",
+    "\n",
+    "\n",
+    "reset_phi=False  # reset the training of phi each round or not\n",
+    "reset_h=False  # reset the training of h each round or not\n",
+    "\n",
+    "reduced=True  # if true (recommended),\n",
+    "#the heterogeneity and representativity criteria will have the same standard deviation,\n",
+    "#to give them the same weight in the query process. This give us more control on our querying strategy\n",
+    "\n",
+    "eta=3   # weight of the representativity criterion. if relatively low (<3) can lead WAR to query too many outliers\n",
+    "# cnst_t3phi>3 recommended, can be put higher if there are a lot of outliers in the data distribution \n",
+    "\n",
+    "show_losses=False   # show T1 and T2 losses each rounds in a graph\n",
+    "show_chosen_each_round=False   # show which data have been chosen each round in a 2D PCA representation of the data\n",
+    "\n",
+    "dim_input=X_train.shape[1]\n",
+    "\n",
+    "start=time.time()\n",
+    "\n",
+    "n_pool = len(y_train)\n",
+    "n_test = len(y_test)\n",
+    "idxs_lb = np.zeros(n_pool, dtype=bool)\n",
+    "idxs_tmp = np.arange(n_pool)\n",
+    "\n",
+    "\n",
+    "if init_method==\"random\":\n",
+    "    # Generate the initial labeled pool\n",
+    "    np.random.shuffle(idxs_tmp)\n",
+    "    idxs_lb[idxs_tmp[:nb_initial_labelled_datas]] = True\n",
+    "    \n",
+    "elif init_method==\"k_mean\":\n",
+    "    init_indices=[]\n",
+    "    kmeans = KMeans(n_clusters=nb_initial_labelled_datas, init='k-means++', n_init=10).fit(X_train)\n",
+    "    for i in range(nb_initial_labelled_datas):\n",
+    "        xsc = kmeans.cluster_centers_[i]\n",
+    "        ind = np.argmin(((X_train - xsc) ** 2).sum(axis=1))\n",
+    "        init_indices.append(ind)\n",
+    "    idxs_lb[init_indices] = True\n",
+    "\n",
+    "h=NN_h_RELU(dim_input)\n",
+    "opti_h = optim.Adam(h.parameters(), lr=lr_h,weight_decay=weight_decay)\n",
+    "\n",
+    "phi=NN_phi(dim_input)\n",
+    "opti_phi = optim.Adam(phi.parameters(), lr=lr_phi,maximize=True)\n",
+    "\n",
+    "strategy = WAR(X_train,y_train,X_test,y_test,idxs_lb,total_epoch_h,total_epoch_phi,batch_size_train,num_elem_queried,phi\n",
+    "                ,h,opti_phi,opti_h,second_query_strategy)\n",
+    "    \n",
+    "error_each_round,error_each_round_per,error_each_round_rmse,t1_descend_list,t2_ascend_list=full_training(\n",
+    "    strategy,num_round,show_losses,show_chosen_each_round\n",
+    "    ,reset_phi,reset_h,weight_decay,lr_h,lr_phi,reduced,eta)\n",
+    "\n",
+    "\n",
+    "stop=time.time()\n",
+    "\n",
+    "time_execution(start,stop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot the loss of h\n",
+    "\n",
+    "plt.plot(list(itertools.chain(*t1_descend_list)),c=\"green\")\n",
+    "plt.grid(True)\n",
+    "plt.yscale(\"log\")\n",
+    "plt.title(\"T1 loss evolution each batch\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot the loss of phi\n",
+    "\n",
+    "plt.plot(np.array(list(itertools.chain(*t2_ascend_list))),c=\"brown\")\n",
+    "plt.grid(True)\n",
+    "plt.title(\"T2 loss evolution each batch\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#plot RMSE\n",
+    "\n",
+    "plt.plot(error_each_round_rmse)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"RMSE of h each rounds\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot MAE\n",
+    "\n",
+    "plt.plot(error_each_round)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"mean absolute error of h each rounds\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot MAPE\n",
+    "\n",
+    "plt.plot(error_each_round_per)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"mean absolute percentage error of h each rounds\",fontsize=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

WAR/Experiment_functions.py

@@ -0,0 +1,94 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+import numpy as np
+import pandas as pd
+from sklearn.decomposition import PCA
+import torch
+
+
+#Execution duration
+
+def time_execution(start,end):
+    timespan=end-start
+    minutes=timespan//60
+    secondes=timespan%60
+    heures=minutes//60
+    minutes=minutes%60
+    print(f"{int(heures)}h {int(minutes)} min {secondes} s")
+    return(f"{int(heures)}h {int(minutes)} min {secondes} s")
+
+
+#Graphs
+
+def display_prediction(X_test,h,y_test,rd):
+    plt.figure(figsize=[9,6])
+        
+    plt.scatter(X_test,h(X_test).cpu(),label="predicted values")
+    plt.scatter(X_test,y_test,label="true_values")
+    plt.legend()
+    if rd=="final":
+        plt.title("true et predicted values at the end")
+    else:plt.title(f"true et predicted values after {rd} rounds")
+    
+
+def display_chosen_labelled_datas_PCA(X_train,idx_lb,y_train,b_idxs,rd):
+    
+    pca = PCA(n_components=2)
+    transformed = pca.fit_transform(X=X_train)
+    x_component = transformed[:, 0]
+    
+    plt.figure(figsize=[9,6])
+    plt.scatter(transformed[:, 0][~idx_lb],transformed[:, 1][~idx_lb],label="unlabelled points",c="brown")
+    plt.scatter(transformed[:, 0][idx_lb],transformed[:, 1][idx_lb],label="labelled points")
+    plt.scatter(transformed[:, 0][b_idxs],transformed[:, 1][b_idxs],label="new points added",c="yellow")
+    plt.legend()
+    plt.title(f"points selected after {rd} rounds")
+    
+def display_chosen_labelled_datas(X_train,idx_lb,y_train,b_idxs,rd):
+    plt.figure(figsize=[9,6])
+    
+    plt.scatter(X_train[~idx_lb],y_train[~idx_lb],label="unlabelled points",c="brown")   
+    plt.scatter(X_train[idx_lb],y_train[idx_lb],label="labelled points")
+    plt.scatter(X_train[b_idxs],y_train[b_idxs],label="new points added",c="yellow")
+    plt.legend()
+    plt.title(f"points selected after {rd} rounds")
+        
+def display_loss_t1(t1_descend,rd):
+    plt.figure(figsize=[9,6])
+    plt.plot(t1_descend)
+    plt.xlabel("batch")
+    plt.title(f"t1 loss evolution each batch after {rd} rounds")
+    
+
+def display_loss_t2(t2_ascend,rd):
+    plt.figure(figsize=[9,6])
+    plt.plot(t2_ascend)
+    plt.xlabel("batch")
+    plt.title(f"t2 loss evolution each batch after {rd} rounds")
+    
+def display_phi(X_train,phi,rd=None):
+    plt.figure(figsize=[9,6])
+    plt.scatter(X_train,phi(X_train))
+    plt.xlabel("X_train")
+    plt.title(f"phi function on the full trainset after {rd} rounds")
+    
+
+#Metrics        
+        
+def MAPE(X_test,y_test,h):
+    acc_per_i=sum(abs(h(X_test)-y_test)/abs(y_test))
+    acc_per_i = acc_per_i[0]/len(y_test)
+    return acc_per_i    
+
+
+def MAE(X_test,y_test,h):
+    acc_i = sum(abs((h(X_test)-y_test)))
+    acc_i = acc_i[0]/len(y_test)
+    return acc_i
+
+def RMSE(X_test,y_test,h):
+    acc_i = ((h(X_test)-y_test)**2).mean()
+    return torch.sqrt(acc_i)  
+
+

WAR/Models.py

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+import torch
+import torch.nn as nn
+import torchvision
+from monotonenorm import GroupSort, direct_norm
+
+
+# GroupSort Neural Networks created using monotonenorm package.
+#See https://github.com/niklasnolte/MonotoneNorm for more information
+
+class NN_phi(nn.Module):
+    
+    def __init__(self,dim_input):
+        
+        
+        super(NN_phi, self).__init__()
+        self.linear1=direct_norm(torch.nn.Linear(dim_input,16),kind="two-inf")
+        self.group1=GroupSort(16//2)#GroupSort with a grouping size of 2
+        self.linear2=direct_norm(torch.nn.Linear(16,32),kind="inf")
+        self.group2=GroupSort(32//2)
+        self.linear3=direct_norm(torch.nn.Linear(32,1),kind="inf")
+    
+
+    def forward(self, x):
+        x=self.linear1(x)
+        x=self.group1(x)
+        x=self.linear2(x)
+        x=self.group2(x)
+        x=self.linear3(x)
+        
+        return x
+    
+
+
+class NN_h_RELU(nn.Module):
+    def __init__(self,dim_input):
+        
+        
+        super(NN_h_RELU, self).__init__()
+        
+        
+        self.linear1=torch.nn.Linear(dim_input,16)
+        self.RELU=torch.nn.ReLU()
+        self.linear2=torch.nn.Linear(16,32)
+        self.linear3=torch.nn.Linear(32,1)
+            
+            
+            
+
+    def forward(self, x):
+        x=self.linear1(x)
+        x=self.RELU(x)
+        x=self.linear2(x)
+        x=self.RELU(x)
+        x=self.linear3(x)
+            
+        return x
+    

WAR/dataset_handler.py

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+import torch
+from torch.utils.data import Dataset
+import random
+import numpy as np
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import MinMaxScaler
+from sklearn import datasets
+
+
+def import_dataset(name):# import dataset among a list a available ones 
+    
+    if name=="boston":
+        data_url = "http://lib.stat.cmu.edu/datasets/boston"
+        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+        target = raw_df.values[1::2, 2]
+        y_boston=target
+        X_boston=data
+        y_boston=torch.Tensor(y_boston).view(len(y_boston),1).float()
+        X_boston=torch.Tensor(X_boston).float()
+        return X_boston,y_boston
+    
+    if name=="airfoil":
+        columns_names=["Frequency","Angle of attack","Chord length","Free-stream velocity","Suction side displacement thickness","sound pressure level"]
+        airfoil=pd.read_csv('datasets/airfoil_self_noise.dat',sep='\t',names=columns_names)
+        y_airfoil=airfoil["sound pressure level"]
+        X_airfoil=airfoil.drop("sound pressure level",axis=1)
+        y_airfoil=torch.Tensor(y_airfoil).view(len(y_airfoil),1).float()
+        X_airfoil=torch.Tensor(X_airfoil.values).float()
+        return X_airfoil,y_airfoil
+    
+    if name=="energy1":
+        energy=pd.read_csv('datasets/energy efficiency.csv')
+        y_energy=energy["Y1"]
+        X_energy=energy.drop(["Y2","Y1"],axis=1)
+        y_energy=torch.Tensor(y_energy).view(len(y_energy),1).float()
+        X_energy=torch.Tensor(X_energy.values).float()
+        return X_energy,y_energy
+    
+    if name=="energy2":# other target function
+        energy=pd.read_csv('datasets/energy efficiency.csv')
+        y_energy=energy["Y2"]
+        X_energy=energy.drop(["Y2","Y1"],axis=1)
+        y_energy=torch.Tensor(y_energy).view(len(y_energy),1).float()
+        X_energy=torch.Tensor(X_energy.values).float()
+        return X_energy,y_energy
+    
+    if name=="yacht":        
+        yacht=pd.read_csv('datasets/yacht_hydrodynamics.data',sep=' ',header=None)
+        y_yacht=yacht[6]
+        X_yacht=yacht.drop([6],axis=1)
+        y_yacht=torch.Tensor(y_yacht).view(len(y_yacht),1).float()
+        X_yacht=torch.Tensor(X_yacht.values).float()
+        return X_yacht,y_yacht  
+    
+    if name=="concrete_slump":        
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["SLUMP(cm)"]
+        X_concrete=concrete.drop(["No","SLUMP(cm)","FLOW(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    
+    if name=="concrete_flow":#other target function
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["FLOW(cm)"]
+        X_concrete=concrete.drop(["No","FLOW(cm)","SLUMP(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    
+    if name=="concrete_compressive":#other target function
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["Compressive Strength (28-day)(Mpa)"]
+        X_concrete=concrete.drop(["No","FLOW(cm)","SLUMP(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    if name=="x_squared":
+        
+        data_generated=100
+        x_b=torch.tensor([random.random() for i in range(data_generated)])
+        x_carré_b=x_b.view(x_b.size()[0],1)
+        y_carré_b=(x_b**2 + torch.tensor([np.random.normal(loc=0,scale=0.05) for i in range(data_generated)])).view(x_b.size()[0],1)
+        return x_carré_b,y_carré_b
+    
+    if name=="news_popularity":
+        news=pd.read_csv('datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv')
+        y_news=news[" shares"]
+        X_news=news.drop([" shares","url"," timedelta"],axis=1)
+        y_news=torch.Tensor(y_news).view(len(y_news),1).float()
+        X_news=torch.Tensor(X_news.values).float()
+        return X_news,y_news
+
+def get_dataset(proportion=0.2,dataset="boston"):# scale and process the data
+
+    scaler = MinMaxScaler()
+    X,y=import_dataset(dataset)
+    X=torch.Tensor(scaler.fit_transform(X))
+    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=proportion)
+    print(f"Shape of the training set: {X_train.shape}")
+    return X_train,X_test,y_train,y_test
+
+
+
+class myData(Dataset):
+    
+    def __init__(self,x,y):
+        self.x=x
+        self.y=y
+        self.shape=x.size(0)
+        
+    def __getitem__(self,index):
+        return self.x[index],self.y[index]
+    
+    def __len__(self):
+        return self.shape
+    
+
+
+

WAR/full_training_process.py

@@ -0,0 +1,116 @@
+import numpy as np
+import warnings
+import torch.optim as optim
+import torch
+from WAR.Models import NN_phi,NN_h_RELU
+from WAR.Experiment_functions import *
+
+
+
+def full_training(strategy,num_round,show_losses,show_chosen_each_round,
+                  reset_phi,reset_h,weight_decay,lr_h=None,lr_phi=None,reduced=False,eta=1
+                 ):
+    
+    """
+    strategy: an object of class WAR
+    num_round: total number of query rounds
+    show_losses: display graphs showing the loss of h and phi each rounds
+    show_chosen_each_round:display a graph showing the data queried each round
+    reset_phi: if True, the phi neural network is reset after each round. can avoir overfitting but increase the number of epochs required to train the model
+    reset_h:if True, the h neural network is reset after each round. can avoir overfitting but increase the number of epochs required to train the model
+    lr_h: learning rate of h
+    lr_phi: learning rate of phi
+    reduced: will divide each query criterion by their standard deviation. In the case where they don't have the same amplitude, This will give them the same weight in the querying process. Irrelevant parameter if there is only one query criterion 
+    eta:factor used to rebalance the criteria. If >1, distribution matching criterion gets more weight than the other(s). Irrelevant parameter if there is only one query criterion.
+    
+    """
+    t1_descend_list=[]
+    t2_ascend_list=[]
+    acc = []# MAE
+    acc_percentage=[] #MAPE
+    acc_rmse=[] #RMSE
+    
+    only_train=False
+    
+    for rd in range(1,num_round+1):
+
+        print('\n================Round {:d}==============='.format(rd))
+        
+        # if not enough unlabelled data to query a full batch, we will query the remaining data
+        if len(np.arange(strategy.n_pool)[~strategy.idx_lb])<=strategy.num_elem_queried:
+            only_train=True
+        
+        #reset neural networks
+        if reset_phi==True:
+            strategy.phi=NN_phi(dim_input=strategy.X_train.shape[1])
+            strategy.opti_phi = optim.Adam(strategy.phi.parameters(), lr=lr_phi,maximize=True)
+
+
+        if reset_h==True:
+            strategy.h=NN_h_RELU(dim_input=strategy.X_train.shape[1])
+            strategy.opti_h = optim.Adam(strategy.h.parameters(), lr=lr_h,weight_decay=weight_decay)
+
+        
+        t1,t2,b_idxs=strategy.train(only_train,reduced,eta)
+
+        
+        t1_descend_list.append(t1)
+        t2_ascend_list.append(t2)
+        if only_train==True:
+            strategy.idx_lb[:]= True
+        else:
+            
+            strategy.idx_lb[b_idxs] = True #"simulation" of the oracle who label the queried samples
+            
+        with torch.no_grad():
+            if show_losses:
+                display_loss_t1(t1,rd)
+                display_loss_t2(t2,rd)
+
+            if show_chosen_each_round:
+                if strategy.X_train.shape[1]==1:
+                    #display_phi(strategy.X_train,strategy.phi,rd)
+                    display_chosen_labelled_datas(strategy.X_train.cpu(),strategy.idx_lb,strategy.y_train.cpu(),b_idxs,rd)
+                    #display_prediction(strategy.X_test,strategy.h,strategy.y_test,rd)
+                    
+                else:
+                    display_chosen_labelled_datas_PCA(strategy.X_train.cpu(),strategy.idx_lb,strategy.y_train.cpu(),b_idxs,rd)
+
+
+            acc_rmse.append(RMSE(strategy.X_test,strategy.y_test,strategy.h).cpu())   
+            acc.append(MAE(strategy.X_test,strategy.y_test,strategy.h).cpu())
+            acc_percentage.append(MAPE(strategy.X_test,strategy.y_test,strategy.h).cpu())
+        
+            
+    print('\n================Final training===============')
+    
+
+    
+    t1,t2,_=strategy.train(only_train,reduced,eta)
+    
+    t1_descend_list.append(t1)
+    t2_ascend_list.append(t2)
+    
+    with torch.no_grad():
+        #display_loss_t1(t1,rd)
+        #display_prediction(strategy.X_test,strategy.h,strategy.y_test,"final")
+
+        acc.append(MAE(strategy.X_test,strategy.y_test,strategy.h).cpu())      
+        acc_percentage.append(MAPE(strategy.X_test,strategy.y_test,strategy.h).cpu())        
+        acc_rmse.append(RMSE(strategy.X_test,strategy.y_test,strategy.h).cpu()) 
+        
+    
+    return acc,acc_percentage, acc_rmse,t1_descend_list,t2_ascend_list
+
+
+
+
+
+
+def check_num_round(num_round,len_dataset,nb_initial_labelled_datas,num_elem_queried):
+    max_round=int(np.ceil((len_dataset-nb_initial_labelled_datas)/num_elem_queried))
+    if num_round>max_round:
+        warnings.warn(f"when querying {num_elem_queried} data per round, num_rounds={num_round} is exceeding"+
+        f" the maximum number of rounds (total data queried superior to number of initial unlabelled data).\nnum_round set to {max_round}")
+        num_round=max_round
+    return num_round

WAR/training_and_query.py

@@ -0,0 +1,226 @@
+import numpy as np
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import itertools
+
+from WAR.Experiment_functions import display_phi
+from WAR.dataset_handler import myData
+  
+        
+class WAR:
+
+    def __init__(self,X_train,y_train,X_test,y_test,idx_lb,total_epoch_h,total_epoch_phi,batch_size_train,num_elem_queried
+                 ,phi,h,opti_phi,opti_h,second_query_strategy=None):
+        
+        """
+        device: device on which to train the model.
+        X_train: trainset.
+        Y_train: labels of the trainset
+        idx_lb: indices of the trainset that would be considered as labelled.
+        n_pool: length of the trainset.
+        total_epoch_h: number of epochs to train h.
+        total_epoch_phi: number of epochs to train phi.
+        batch_size_train: size of the batch in the training process.
+        num_elem_queried: number of elem queried each round.
+        phi: phi neural network.
+        h: h neural network.
+        opti_phi: phi optimizer.
+        opti_h: h optimizer.
+        cost: define the cost function for both neural network. "MSE" or MAE".
+        second_query_strategy: second strategy to assist our distribution-matching criterion.
+        """
+        
+        self.device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.X_train = X_train.to(self.device)
+        self.y_train = y_train.to(self.device)
+        self.X_test=X_test.to(self.device)
+        self.y_test=y_test.to(self.device)
+        self.idx_lb  = idx_lb
+        self.n_pool  = len(y_train)
+        self.total_epoch_h=total_epoch_h
+        self.total_epoch_phi=total_epoch_phi
+        self.batch_size_train=batch_size_train
+        self.num_elem_queried=num_elem_queried
+        self.phi=phi.to(self.device)
+        self.h=h.to(self.device)
+        self.opti_phi=opti_phi
+        self.opti_h=opti_h
+        self.cost="MSE"
+        self.second_query_strategy=second_query_strategy
+        
+        
+        
+        
+    #cost function used to train both phi and h
+    def cost_func(self,predicted,true):
+        if self.cost=="MSE":
+            return (predicted-true)**2
+        elif self.cost=="MAE":
+            return abs(predicted-true)
+        else:
+            raise Exception("invalid cost function")
+      
+   
+
+    
+    def train(self,only_train=False,reduced=True,eta=3):# train function for one round
+        
+        """
+        only_train: activite when there is no more unlabelled data in the trainset. Will only train h and not train phi or query data.
+        reduced: will divide each query criterion by their standard deviation. In the case where they don't have the same amplitude, This will give them the same weight in the querying process. Irrelevant parameter if there is only one query criterion (self.second_query_strategy=None).
+        eta:factor used to rebalance the criteria. If >1, distribution matching criterion gets more weight than the other(s). Irrelevant parameter if there is only one query criterion.
+        
+        """
+        #recover loss 
+        t1_descend=[]
+        t2_ascend=[]
+        
+        # separating labelled and unlabelled data respectively
+        idx_lb_train = np.arange(self.n_pool)[self.idx_lb]
+        idx_ulb_train = np.arange(self.n_pool)[~self.idx_lb]
+        
+        
+        trainset_labelled=myData(self.X_train[idx_lb_train],self.y_train[idx_lb_train])
+        trainloader_labelled= DataLoader(trainset_labelled,shuffle=True,batch_size=self.batch_size_train)
+
+        for epoch in range(self.total_epoch_h):
+      
+            for i,data in enumerate(trainloader_labelled,0):
+                label_x, label_y=data
+                self.opti_h.zero_grad()                 
+                # T1 (train h)
+                lb_out = self.h(label_x)
+                h_descent=torch.mean(self.cost_func(lb_out,label_y))
+                t1_descend.append(h_descent.detach().cpu())
+                h_descent.backward()
+                self.opti_h.step()
+                
+              
+        b_idxs=[]# batch of queried points             
+        if not only_train: 
+            #T2 (train phi) 
+                                 
+            # temporary set of labelled data indices. Used only to retrain phi during the time oracle has not been called. 
+            #h is no retrained during this time.
+            idxs_temp=self.idx_lb.copy()
+            
+            for elem_queried in range(self.num_elem_queried):
+                
+                trainset_total=myData(self.X_train,self.y_train)
+                trainloader_total= DataLoader(trainset_total,shuffle=True,batch_size=len(trainset_total))
+                trainset_labelled=myData(self.X_train[idx_lb_train],self.y_train[idx_lb_train])
+                trainloader_labelled= DataLoader(trainset_labelled,shuffle=True,batch_size=self.batch_size_train)
+                for epoch in range(self.total_epoch_phi):  
+                    iterator_total_phi=itertools.cycle(trainloader_total)
+                    iterator_labelled_phi=itertools.cycle(trainloader_labelled)
+                    for i in range(len(trainloader_labelled)):
+                        label_x,label_y = next(iterator_labelled_phi)
+                        total_x,total_y = next(iterator_total_phi)
+                        #display_phi(self.X_train,self.phi)
+                        self.opti_phi.zero_grad()
+                        phi_ascent = (torch.mean(self.phi(total_x))-torch.mean(self.phi(label_x)))
+                        t2_ascend.append(phi_ascent.detach().cpu())
+                        phi_ascent.backward()
+                        self.opti_phi.step()
+                        
+                # Query process
+                b_queried=self.query(reduced,eta,idx_ulb_train)# query one element
+                idxs_temp[b_queried]=True #add it to the temporary set of labeled point indices indices
+                idx_ulb_train = np.arange(self.n_pool)[~idxs_temp] #update the set of unlabeled point indices
+                idx_lb_train = np.arange(self.n_pool)[idxs_temp] #update the set of labeled point indices
+                b_idxs.append(b_queried)#add the chosen point in the batch
+            self.idx_lb=idxs_temp#end of the query process: update the true set of labeled point indices indices
+        return t1_descend,t2_ascend,b_idxs
+
+    
+    
+    def query(self,reduced,eta,idx_ulb_train):# computing T3: query one point according to the chosen query criteria
+         
+                          
+        """
+        reduced:same as for function "train"
+        eta: sme as for function "train"
+        idx_ulb_train:indices of unlabeled points
+        
+        """
+
+        if self.second_query_strategy=="loss_approximation":
+            second_query_criterion = self.predict_loss(self.X_train[idx_ulb_train])
+                          
+        with torch.no_grad():
+            phi_scores =  self.phi(self.X_train[idx_ulb_train]).view(-1)
+
+            if reduced and self.second_query_strategy!=None:
+                phi_scores_reduced=phi_scores/torch.std(phi_scores)
+                second_query_criterion_reduced=second_query_criterion/torch.std(second_query_criterion)
+                total_scores =-(eta*phi_scores_reduced+second_query_criterion_reduced )  
+                          
+            elif self.second_query_strategy!=None:
+                total_scores =-(eta*phi_scores+second_query_criterion)
+                          
+            else:
+                total_scores =-eta*phi_scores
+                
+            b=torch.argmin(total_scores)
+        
+        return idx_ulb_train[b]
+    
+                          
+    def predict_loss(self,X):# Second query criterion which act as loss estimator (uncertainty and diversity-based sampling)
+        
+        """
+        X: set of unlabeled elements of the trainset
+        
+        """
+                          
+        idxs_lb=np.arange(self.n_pool)[self.idx_lb]#get labeled data indices
+        losses=[]
+        with torch.no_grad():
+            for i in X:
+                idx_nearest_Xk,dist=self.Idx_NearestP(i,idxs_lb) 
+                losses.append(self.Max_cost_B(idx_nearest_Xk,dist,i))
+        
+        return torch.Tensor(losses).to(self.device)
+    
+    def Idx_NearestP(self,Xu,idxs_lb):# Return the closest labeled point to the unlabeled point 
+        
+                          
+        """
+        Xu:unlabeled point
+        idxs_lb: indices of labeled points
+        
+        """
+                          
+        distances=[]
+        for i in idxs_lb:
+            distances.append(torch.norm(Xu-self.X_train[i]))
+            
+        return idxs_lb[distances.index(min(distances))],float(min(distances))
+    
+
+    
+    def Max_cost_B(self,idx_Xk,distance,i):#return the "maximum loss" of the unlabeled point
+       
+        """
+        idx_Xk: labeled point indice nearest to the unlabeled point
+        distance: distance between them
+        i:unlabeled point
+        
+        """
+                          
+        est_h_unl_X=self.h(i)
+        true_value_labelled_X=self.y_train[idx_Xk]
+        bound_min= true_value_labelled_X-distance
+        bound_max= true_value_labelled_X+distance
+        return max(self.cost_func(est_h_unl_X,bound_min),self.cost_func(est_h_unl_X,bound_max))[0]
+    
+        
+
+    
+    
+            
+
+    
+    
+    

datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011c4f45de56844f346f232dc455ce6fc88a7c610ac57ad16f31c6b19c2ca435
+size 16874641

datasets/OnlineNewsPopularity/OnlineNewsPopularity.names

@@ -0,0 +1,194 @@
+1. Title: Online News Popularity
+
+2. Source Information
+    -- Creators: Kelwin Fernandes (kafc ‘@’ inesctec.pt, kelwinfc ’@’ gmail.com),
+                 Pedro Vinagre (pedro.vinagre.sousa ’@’ gmail.com) and
+                 Pedro Sernadela
+   -- Donor: Kelwin Fernandes (kafc ’@’ inesctec.pt, kelwinfc '@' gmail.com)
+   -- Date: May, 2015
+
+3. Past Usage:
+    1. K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision
+       Support System for Predicting the Popularity of Online News. Proceedings
+       of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence,
+       September, Coimbra, Portugal.
+
+       -- Results: 
+          -- Binary classification as popular vs unpopular using a decision
+             threshold of 1400 social interactions.
+          -- Experiments with different models: Random Forest (best model),
+             Adaboost, SVM, KNN and Naïve Bayes.
+          -- Recorded 67% of accuracy and 0.73 of AUC.
+    - Predicted attribute: online news popularity (boolean)
+
+4. Relevant Information:
+   -- The articles were published by Mashable (www.mashable.com) and their
+      content as the rights to reproduce it belongs to them. Hence, this
+      dataset does not share the original content but some statistics
+      associated with it. The original content be publicly accessed and
+      retrieved using the provided urls.
+   -- Acquisition date: January 8, 2015
+   -- The estimated relative performance values were estimated by the authors
+      using a Random Forest classifier and a rolling windows as assessment
+      method.  See their article for more details on how the relative
+      performance values were set.
+
+5. Number of Instances: 39797 
+
+6. Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 
+                             1 goal field)
+
+7. Attribute Information:
+     0. url:                           URL of the article
+     1. timedelta:                     Days between the article publication and
+                                       the dataset acquisition
+     2. n_tokens_title:                Number of words in the title
+     3. n_tokens_content:              Number of words in the content
+     4. n_unique_tokens:               Rate of unique words in the content
+     5. n_non_stop_words:              Rate of non-stop words in the content
+     6. n_non_stop_unique_tokens:      Rate of unique non-stop words in the
+                                       content
+     7. num_hrefs:                     Number of links
+     8. num_self_hrefs:                Number of links to other articles
+                                       published by Mashable
+     9. num_imgs:                      Number of images
+    10. num_videos:                    Number of videos
+    11. average_token_length:          Average length of the words in the
+                                       content
+    12. num_keywords:                  Number of keywords in the metadata
+    13. data_channel_is_lifestyle:     Is data channel 'Lifestyle'?
+    14. data_channel_is_entertainment: Is data channel 'Entertainment'?
+    15. data_channel_is_bus:           Is data channel 'Business'?
+    16. data_channel_is_socmed:        Is data channel 'Social Media'?
+    17. data_channel_is_tech:          Is data channel 'Tech'?
+    18. data_channel_is_world:         Is data channel 'World'?
+    19. kw_min_min:                    Worst keyword (min. shares)
+    20. kw_max_min:                    Worst keyword (max. shares)
+    21. kw_avg_min:                    Worst keyword (avg. shares)
+    22. kw_min_max:                    Best keyword (min. shares)
+    23. kw_max_max:                    Best keyword (max. shares)
+    24. kw_avg_max:                    Best keyword (avg. shares)
+    25. kw_min_avg:                    Avg. keyword (min. shares)
+    26. kw_max_avg:                    Avg. keyword (max. shares)
+    27. kw_avg_avg:                    Avg. keyword (avg. shares)
+    28. self_reference_min_shares:     Min. shares of referenced articles in
+                                       Mashable
+    29. self_reference_max_shares:     Max. shares of referenced articles in
+                                       Mashable
+    30. self_reference_avg_sharess:    Avg. shares of referenced articles in
+                                       Mashable
+    31. weekday_is_monday:             Was the article published on a Monday?
+    32. weekday_is_tuesday:            Was the article published on a Tuesday?
+    33. weekday_is_wednesday:          Was the article published on a Wednesday?
+    34. weekday_is_thursday:           Was the article published on a Thursday?
+    35. weekday_is_friday:             Was the article published on a Friday?
+    36. weekday_is_saturday:           Was the article published on a Saturday?
+    37. weekday_is_sunday:             Was the article published on a Sunday?
+    38. is_weekend:                    Was the article published on the weekend?
+    39. LDA_00:                        Closeness to LDA topic 0
+    40. LDA_01:                        Closeness to LDA topic 1
+    41. LDA_02:                        Closeness to LDA topic 2
+    42. LDA_03:                        Closeness to LDA topic 3
+    43. LDA_04:                        Closeness to LDA topic 4
+    44. global_subjectivity:           Text subjectivity
+    45. global_sentiment_polarity:     Text sentiment polarity
+    46. global_rate_positive_words:    Rate of positive words in the content
+    47. global_rate_negative_words:    Rate of negative words in the content
+    48. rate_positive_words:           Rate of positive words among non-neutral
+                                       tokens
+    49. rate_negative_words:           Rate of negative words among non-neutral
+                                       tokens
+    50. avg_positive_polarity:         Avg. polarity of positive words
+    51. min_positive_polarity:         Min. polarity of positive words
+    52. max_positive_polarity:         Max. polarity of positive words
+    53. avg_negative_polarity:         Avg. polarity of negative  words
+    54. min_negative_polarity:         Min. polarity of negative  words
+    55. max_negative_polarity:         Max. polarity of negative  words
+    56. title_subjectivity:            Title subjectivity
+    57. title_sentiment_polarity:      Title polarity
+    58. abs_title_subjectivity:        Absolute subjectivity level
+    59. abs_title_sentiment_polarity:  Absolute polarity level
+    60. shares:                        Number of shares (target)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution: the class value (shares) is continuously valued. We
+                       transformed the task into a binary task using a decision
+                       threshold of 1400.
+
+   Shares Value Range:   Number of Instances in Range:
+   <  1400            18490
+   >= 1400            21154
+
+
+Summary Statistics:
+                       Feature       Min          Max         Mean           SD
+                     timedelta    8.0000     731.0000     354.5305     214.1611
+                n_tokens_title    2.0000      23.0000      10.3987       2.1140
+              n_tokens_content    0.0000    8474.0000     546.5147     471.1016
+               n_unique_tokens    0.0000     701.0000       0.5482       3.5207
+              n_non_stop_words    0.0000    1042.0000       0.9965       5.2312
+      n_non_stop_unique_tokens    0.0000     650.0000       0.6892       3.2648
+                     num_hrefs    0.0000     304.0000      10.8837      11.3319
+                num_self_hrefs    0.0000     116.0000       3.2936       3.8551
+                      num_imgs    0.0000     128.0000       4.5441       8.3093
+                    num_videos    0.0000      91.0000       1.2499       4.1078
+          average_token_length    0.0000       8.0415       4.5482       0.8444
+                  num_keywords    1.0000      10.0000       7.2238       1.9091
+     data_channel_is_lifestyle    0.0000       1.0000       0.0529       0.2239
+ data_channel_is_entertainment    0.0000       1.0000       0.1780       0.3825
+           data_channel_is_bus    0.0000       1.0000       0.1579       0.3646
+        data_channel_is_socmed    0.0000       1.0000       0.0586       0.2349
+          data_channel_is_tech    0.0000       1.0000       0.1853       0.3885
+         data_channel_is_world    0.0000       1.0000       0.2126       0.4091
+                    kw_min_min   -1.0000     377.0000      26.1068      69.6323
+                    kw_max_min    0.0000  298400.0000    1153.9517    3857.9422
+                    kw_avg_min   -1.0000   42827.8571     312.3670     620.7761
+                    kw_min_max    0.0000  843300.0000   13612.3541   57985.2980
+                    kw_max_max    0.0000  843300.0000  752324.0667  214499.4242
+                    kw_avg_max    0.0000  843300.0000  259281.9381  135100.5433
+                    kw_min_avg   -1.0000    3613.0398    1117.1466    1137.4426
+                    kw_max_avg    0.0000  298400.0000    5657.2112    6098.7950
+                    kw_avg_avg    0.0000   43567.6599    3135.8586    1318.1338
+     self_reference_min_shares    0.0000  843300.0000    3998.7554   19738.4216
+     self_reference_max_shares    0.0000  843300.0000   10329.2127   41027.0592
+    self_reference_avg_sharess    0.0000  843300.0000    6401.6976   24211.0269
+             weekday_is_monday    0.0000       1.0000       0.1680       0.3739
+            weekday_is_tuesday    0.0000       1.0000       0.1864       0.3894
+          weekday_is_wednesday    0.0000       1.0000       0.1875       0.3903
+           weekday_is_thursday    0.0000       1.0000       0.1833       0.3869
+             weekday_is_friday    0.0000       1.0000       0.1438       0.3509
+           weekday_is_saturday    0.0000       1.0000       0.0619       0.2409
+             weekday_is_sunday    0.0000       1.0000       0.0690       0.2535
+                    is_weekend    0.0000       1.0000       0.1309       0.3373
+                        LDA_00    0.0000       0.9270       0.1846       0.2630
+                        LDA_01    0.0000       0.9259       0.1413       0.2197
+                        LDA_02    0.0000       0.9200       0.2163       0.2821
+                        LDA_03    0.0000       0.9265       0.2238       0.2952
+                        LDA_04    0.0000       0.9272       0.2340       0.2892
+           global_subjectivity    0.0000       1.0000       0.4434       0.1167
+     global_sentiment_polarity   -0.3937       0.7278       0.1193       0.0969
+    global_rate_positive_words    0.0000       0.1555       0.0396       0.0174
+    global_rate_negative_words    0.0000       0.1849       0.0166       0.0108
+           rate_positive_words    0.0000       1.0000       0.6822       0.1902
+           rate_negative_words    0.0000       1.0000       0.2879       0.1562
+         avg_positive_polarity    0.0000       1.0000       0.3538       0.1045
+         min_positive_polarity    0.0000       1.0000       0.0954       0.0713
+         max_positive_polarity    0.0000       1.0000       0.7567       0.2478
+         avg_negative_polarity   -1.0000       0.0000      -0.2595       0.1277
+         min_negative_polarity   -1.0000       0.0000      -0.5219       0.2903
+         max_negative_polarity   -1.0000       0.0000      -0.1075       0.0954
+            title_subjectivity    0.0000       1.0000       0.2824       0.3242
+      title_sentiment_polarity   -1.0000       1.0000       0.0714       0.2654
+        abs_title_subjectivity    0.0000       0.5000       0.3418       0.1888
+  abs_title_sentiment_polarity    0.0000       1.0000       0.1561       0.2263
+
+   
+ Citation Request:
+ 
+ Please include this citation if you plan to use this database: 
+ 
+    K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision
+    Support System for Predicting the Popularity of Online News. Proceedings
+    of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence,
+    September, Coimbra, Portugal.

datasets/airfoil_self_noise.dat

@@ -0,0 +1,1503 @@
+800	0	0.3048	71.3	0.00266337	126.201
+1000	0	0.3048	71.3	0.00266337	125.201
+1250	0	0.3048	71.3	0.00266337	125.951
+1600	0	0.3048	71.3	0.00266337	127.591
+2000	0	0.3048	71.3	0.00266337	127.461
+2500	0	0.3048	71.3	0.00266337	125.571
+3150	0	0.3048	71.3	0.00266337	125.201
+4000	0	0.3048	71.3	0.00266337	123.061
+5000	0	0.3048	71.3	0.00266337	121.301
+6300	0	0.3048	71.3	0.00266337	119.541
+8000	0	0.3048	71.3	0.00266337	117.151
+10000	0	0.3048	71.3	0.00266337	115.391
+12500	0	0.3048	71.3	0.00266337	112.241
+16000	0	0.3048	71.3	0.00266337	108.721
+500	0	0.3048	55.5	0.00283081	126.416
+630	0	0.3048	55.5	0.00283081	127.696
+800	0	0.3048	55.5	0.00283081	128.086
+1000	0	0.3048	55.5	0.00283081	126.966
+1250	0	0.3048	55.5	0.00283081	126.086
+1600	0	0.3048	55.5	0.00283081	126.986
+2000	0	0.3048	55.5	0.00283081	126.616
+2500	0	0.3048	55.5	0.00283081	124.106
+3150	0	0.3048	55.5	0.00283081	123.236
+4000	0	0.3048	55.5	0.00283081	121.106
+5000	0	0.3048	55.5	0.00283081	119.606
+6300	0	0.3048	55.5	0.00283081	117.976
+8000	0	0.3048	55.5	0.00283081	116.476
+10000	0	0.3048	55.5	0.00283081	113.076
+12500	0	0.3048	55.5	0.00283081	111.076
+200	0	0.3048	39.6	0.00310138	118.129
+250	0	0.3048	39.6	0.00310138	119.319
+315	0	0.3048	39.6	0.00310138	122.779
+400	0	0.3048	39.6	0.00310138	124.809
+500	0	0.3048	39.6	0.00310138	126.959
+630	0	0.3048	39.6	0.00310138	128.629
+800	0	0.3048	39.6	0.00310138	129.099
+1000	0	0.3048	39.6	0.00310138	127.899
+1250	0	0.3048	39.6	0.00310138	125.499
+1600	0	0.3048	39.6	0.00310138	124.049
+2000	0	0.3048	39.6	0.00310138	123.689
+2500	0	0.3048	39.6	0.00310138	121.399
+3150	0	0.3048	39.6	0.00310138	120.319
+4000	0	0.3048	39.6	0.00310138	119.229
+5000	0	0.3048	39.6	0.00310138	117.789
+6300	0	0.3048	39.6	0.00310138	116.229
+8000	0	0.3048	39.6	0.00310138	114.779
+10000	0	0.3048	39.6	0.00310138	112.139
+12500	0	0.3048	39.6	0.00310138	109.619
+200	0	0.3048	31.7	0.00331266	117.195
+250	0	0.3048	31.7	0.00331266	118.595
+315	0	0.3048	31.7	0.00331266	122.765
+400	0	0.3048	31.7	0.00331266	125.045
+500	0	0.3048	31.7	0.00331266	127.315
+630	0	0.3048	31.7	0.00331266	129.095
+800	0	0.3048	31.7	0.00331266	129.235
+1000	0	0.3048	31.7	0.00331266	127.365
+1250	0	0.3048	31.7	0.00331266	124.355
+1600	0	0.3048	31.7	0.00331266	122.365
+2000	0	0.3048	31.7	0.00331266	122.375
+2500	0	0.3048	31.7	0.00331266	120.755
+3150	0	0.3048	31.7	0.00331266	119.135
+4000	0	0.3048	31.7	0.00331266	118.145
+5000	0	0.3048	31.7	0.00331266	115.645
+6300	0	0.3048	31.7	0.00331266	113.775
+8000	0	0.3048	31.7	0.00331266	110.515
+10000	0	0.3048	31.7	0.00331266	108.265
+800	1.5	0.3048	71.3	0.00336729	127.122
+1000	1.5	0.3048	71.3	0.00336729	125.992
+1250	1.5	0.3048	71.3	0.00336729	125.872
+1600	1.5	0.3048	71.3	0.00336729	126.632
+2000	1.5	0.3048	71.3	0.00336729	126.642
+2500	1.5	0.3048	71.3	0.00336729	124.512
+3150	1.5	0.3048	71.3	0.00336729	123.392
+4000	1.5	0.3048	71.3	0.00336729	121.762
+5000	1.5	0.3048	71.3	0.00336729	119.632
+6300	1.5	0.3048	71.3	0.00336729	118.122
+8000	1.5	0.3048	71.3	0.00336729	115.372
+10000	1.5	0.3048	71.3	0.00336729	113.492
+12500	1.5	0.3048	71.3	0.00336729	109.222
+16000	1.5	0.3048	71.3	0.00336729	106.582
+315	1.5	0.3048	39.6	0.00392107	121.851
+400	1.5	0.3048	39.6	0.00392107	124.001
+500	1.5	0.3048	39.6	0.00392107	126.661
+630	1.5	0.3048	39.6	0.00392107	128.311
+800	1.5	0.3048	39.6	0.00392107	128.831
+1000	1.5	0.3048	39.6	0.00392107	127.581
+1250	1.5	0.3048	39.6	0.00392107	125.211
+1600	1.5	0.3048	39.6	0.00392107	122.211
+2000	1.5	0.3048	39.6	0.00392107	122.101
+2500	1.5	0.3048	39.6	0.00392107	120.981
+3150	1.5	0.3048	39.6	0.00392107	119.111
+4000	1.5	0.3048	39.6	0.00392107	117.741
+5000	1.5	0.3048	39.6	0.00392107	116.241
+6300	1.5	0.3048	39.6	0.00392107	114.751
+8000	1.5	0.3048	39.6	0.00392107	112.251
+10000	1.5	0.3048	39.6	0.00392107	108.991
+12500	1.5	0.3048	39.6	0.00392107	106.111
+400	3	0.3048	71.3	0.00425727	127.564
+500	3	0.3048	71.3	0.00425727	128.454
+630	3	0.3048	71.3	0.00425727	129.354
+800	3	0.3048	71.3	0.00425727	129.494
+1000	3	0.3048	71.3	0.00425727	129.004
+1250	3	0.3048	71.3	0.00425727	127.634
+1600	3	0.3048	71.3	0.00425727	126.514
+2000	3	0.3048	71.3	0.00425727	125.524
+2500	3	0.3048	71.3	0.00425727	124.024
+3150	3	0.3048	71.3	0.00425727	121.514
+4000	3	0.3048	71.3	0.00425727	120.264
+5000	3	0.3048	71.3	0.00425727	118.134
+6300	3	0.3048	71.3	0.00425727	116.134
+8000	3	0.3048	71.3	0.00425727	114.634
+10000	3	0.3048	71.3	0.00425727	110.224
+400	3	0.3048	55.5	0.00452492	126.159
+500	3	0.3048	55.5	0.00452492	128.179
+630	3	0.3048	55.5	0.00452492	129.569
+800	3	0.3048	55.5	0.00452492	129.949
+1000	3	0.3048	55.5	0.00452492	129.329
+1250	3	0.3048	55.5	0.00452492	127.329
+1600	3	0.3048	55.5	0.00452492	124.439
+2000	3	0.3048	55.5	0.00452492	123.069
+2500	3	0.3048	55.5	0.00452492	122.439
+3150	3	0.3048	55.5	0.00452492	120.189
+4000	3	0.3048	55.5	0.00452492	118.689
+5000	3	0.3048	55.5	0.00452492	117.309
+6300	3	0.3048	55.5	0.00452492	115.679
+8000	3	0.3048	55.5	0.00452492	113.799
+10000	3	0.3048	55.5	0.00452492	112.169
+315	3	0.3048	39.6	0.00495741	123.312
+400	3	0.3048	39.6	0.00495741	125.472
+500	3	0.3048	39.6	0.00495741	127.632
+630	3	0.3048	39.6	0.00495741	129.292
+800	3	0.3048	39.6	0.00495741	129.552
+1000	3	0.3048	39.6	0.00495741	128.312
+1250	3	0.3048	39.6	0.00495741	125.802
+1600	3	0.3048	39.6	0.00495741	122.782
+2000	3	0.3048	39.6	0.00495741	120.532
+2500	3	0.3048	39.6	0.00495741	120.162
+3150	3	0.3048	39.6	0.00495741	118.922
+4000	3	0.3048	39.6	0.00495741	116.792
+5000	3	0.3048	39.6	0.00495741	115.792
+6300	3	0.3048	39.6	0.00495741	114.042
+8000	3	0.3048	39.6	0.00495741	110.652
+315	3	0.3048	31.7	0.00529514	123.118
+400	3	0.3048	31.7	0.00529514	125.398
+500	3	0.3048	31.7	0.00529514	127.548
+630	3	0.3048	31.7	0.00529514	128.698
+800	3	0.3048	31.7	0.00529514	128.708
+1000	3	0.3048	31.7	0.00529514	126.838
+1250	3	0.3048	31.7	0.00529514	124.838
+1600	3	0.3048	31.7	0.00529514	122.088
+2000	3	0.3048	31.7	0.00529514	120.088
+2500	3	0.3048	31.7	0.00529514	119.598
+3150	3	0.3048	31.7	0.00529514	118.108
+4000	3	0.3048	31.7	0.00529514	115.608
+5000	3	0.3048	31.7	0.00529514	113.858
+6300	3	0.3048	31.7	0.00529514	109.718
+250	4	0.3048	71.3	0.00497773	126.395
+315	4	0.3048	71.3	0.00497773	128.175
+400	4	0.3048	71.3	0.00497773	129.575
+500	4	0.3048	71.3	0.00497773	130.715
+630	4	0.3048	71.3	0.00497773	131.615
+800	4	0.3048	71.3	0.00497773	131.755
+1000	4	0.3048	71.3	0.00497773	131.015
+1250	4	0.3048	71.3	0.00497773	129.395
+1600	4	0.3048	71.3	0.00497773	126.645
+2000	4	0.3048	71.3	0.00497773	124.395
+2500	4	0.3048	71.3	0.00497773	123.775
+3150	4	0.3048	71.3	0.00497773	121.775
+4000	4	0.3048	71.3	0.00497773	119.535
+5000	4	0.3048	71.3	0.00497773	117.785
+6300	4	0.3048	71.3	0.00497773	116.165
+8000	4	0.3048	71.3	0.00497773	113.665
+10000	4	0.3048	71.3	0.00497773	110.905
+12500	4	0.3048	71.3	0.00497773	107.405
+250	4	0.3048	39.6	0.00579636	123.543
+315	4	0.3048	39.6	0.00579636	126.843
+400	4	0.3048	39.6	0.00579636	128.633
+500	4	0.3048	39.6	0.00579636	130.173
+630	4	0.3048	39.6	0.00579636	131.073
+800	4	0.3048	39.6	0.00579636	130.723
+1000	4	0.3048	39.6	0.00579636	128.723
+1250	4	0.3048	39.6	0.00579636	126.343
+1600	4	0.3048	39.6	0.00579636	123.213
+2000	4	0.3048	39.6	0.00579636	120.963
+2500	4	0.3048	39.6	0.00579636	120.233
+3150	4	0.3048	39.6	0.00579636	118.743
+4000	4	0.3048	39.6	0.00579636	115.863
+5000	4	0.3048	39.6	0.00579636	113.733
+1250	0	0.2286	71.3	0.00214345	128.144
+1600	0	0.2286	71.3	0.00214345	129.134
+2000	0	0.2286	71.3	0.00214345	128.244
+2500	0	0.2286	71.3	0.00214345	128.354
+3150	0	0.2286	71.3	0.00214345	127.834
+4000	0	0.2286	71.3	0.00214345	125.824
+5000	0	0.2286	71.3	0.00214345	124.304
+6300	0	0.2286	71.3	0.00214345	122.044
+8000	0	0.2286	71.3	0.00214345	118.024
+10000	0	0.2286	71.3	0.00214345	118.134
+12500	0	0.2286	71.3	0.00214345	117.624
+16000	0	0.2286	71.3	0.00214345	114.984
+20000	0	0.2286	71.3	0.00214345	114.474
+315	0	0.2286	55.5	0.00229336	119.540
+400	0	0.2286	55.5	0.00229336	121.660
+500	0	0.2286	55.5	0.00229336	123.780
+630	0	0.2286	55.5	0.00229336	126.160
+800	0	0.2286	55.5	0.00229336	127.530
+1000	0	0.2286	55.5	0.00229336	128.290
+1250	0	0.2286	55.5	0.00229336	127.910
+1600	0	0.2286	55.5	0.00229336	126.790
+2000	0	0.2286	55.5	0.00229336	126.540
+2500	0	0.2286	55.5	0.00229336	126.540
+3150	0	0.2286	55.5	0.00229336	125.160
+4000	0	0.2286	55.5	0.00229336	123.410
+5000	0	0.2286	55.5	0.00229336	122.410
+6300	0	0.2286	55.5	0.00229336	118.410
+315	0	0.2286	39.6	0.00253511	121.055
+400	0	0.2286	39.6	0.00253511	123.565
+500	0	0.2286	39.6	0.00253511	126.195
+630	0	0.2286	39.6	0.00253511	128.705
+800	0	0.2286	39.6	0.00253511	130.205
+1000	0	0.2286	39.6	0.00253511	130.435
+1250	0	0.2286	39.6	0.00253511	129.395
+1600	0	0.2286	39.6	0.00253511	127.095
+2000	0	0.2286	39.6	0.00253511	125.305
+2500	0	0.2286	39.6	0.00253511	125.025
+3150	0	0.2286	39.6	0.00253511	124.625
+4000	0	0.2286	39.6	0.00253511	123.465
+5000	0	0.2286	39.6	0.00253511	122.175
+6300	0	0.2286	39.6	0.00253511	117.465
+315	0	0.2286	31.7	0.0027238	120.595
+400	0	0.2286	31.7	0.0027238	123.635
+500	0	0.2286	31.7	0.0027238	126.675
+630	0	0.2286	31.7	0.0027238	129.465
+800	0	0.2286	31.7	0.0027238	130.725
+1000	0	0.2286	31.7	0.0027238	130.595
+1250	0	0.2286	31.7	0.0027238	128.805
+1600	0	0.2286	31.7	0.0027238	125.625
+2000	0	0.2286	31.7	0.0027238	123.455
+2500	0	0.2286	31.7	0.0027238	123.445
+3150	0	0.2286	31.7	0.0027238	123.445
+4000	0	0.2286	31.7	0.0027238	122.035
+5000	0	0.2286	31.7	0.0027238	120.505
+6300	0	0.2286	31.7	0.0027238	116.815
+400	2	0.2286	71.3	0.00293031	125.116
+500	2	0.2286	71.3	0.00293031	126.486
+630	2	0.2286	71.3	0.00293031	127.356
+800	2	0.2286	71.3	0.00293031	128.216
+1000	2	0.2286	71.3	0.00293031	128.956
+1250	2	0.2286	71.3	0.00293031	128.816
+1600	2	0.2286	71.3	0.00293031	127.796
+2000	2	0.2286	71.3	0.00293031	126.896
+2500	2	0.2286	71.3	0.00293031	127.006
+3150	2	0.2286	71.3	0.00293031	126.116
+4000	2	0.2286	71.3	0.00293031	124.086
+5000	2	0.2286	71.3	0.00293031	122.816
+6300	2	0.2286	71.3	0.00293031	120.786
+8000	2	0.2286	71.3	0.00293031	115.996
+10000	2	0.2286	71.3	0.00293031	113.086
+400	2	0.2286	55.5	0.00313525	122.292
+500	2	0.2286	55.5	0.00313525	124.692
+630	2	0.2286	55.5	0.00313525	126.842
+800	2	0.2286	55.5	0.00313525	128.492
+1000	2	0.2286	55.5	0.00313525	129.002
+1250	2	0.2286	55.5	0.00313525	128.762
+1600	2	0.2286	55.5	0.00313525	126.752
+2000	2	0.2286	55.5	0.00313525	124.612
+2500	2	0.2286	55.5	0.00313525	123.862
+3150	2	0.2286	55.5	0.00313525	123.742
+4000	2	0.2286	55.5	0.00313525	122.232
+5000	2	0.2286	55.5	0.00313525	120.472
+6300	2	0.2286	55.5	0.00313525	118.712
+315	2	0.2286	39.6	0.00346574	120.137
+400	2	0.2286	39.6	0.00346574	122.147
+500	2	0.2286	39.6	0.00346574	125.157
+630	2	0.2286	39.6	0.00346574	127.417
+800	2	0.2286	39.6	0.00346574	129.037
+1000	2	0.2286	39.6	0.00346574	129.147
+1250	2	0.2286	39.6	0.00346574	128.257
+1600	2	0.2286	39.6	0.00346574	125.837
+2000	2	0.2286	39.6	0.00346574	122.797
+2500	2	0.2286	39.6	0.00346574	121.397
+3150	2	0.2286	39.6	0.00346574	121.627
+4000	2	0.2286	39.6	0.00346574	120.227
+5000	2	0.2286	39.6	0.00346574	118.827
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datasets/conditions_based_maintenance/Features.txt

@@ -0,0 +1,18 @@
+1 - Lever position (lp) [ ]
+2 - Ship speed (v) [knots]
+3 - Gas Turbine shaft torque (GTT) [kN m]
+4 - Gas Turbine rate of revolutions (GTn) [rpm]
+5 - Gas Generator rate of revolutions (GGn) [rpm]
+6 - Starboard Propeller Torque (Ts) [kN]
+7 - Port Propeller Torque (Tp) [kN]
+8 - HP Turbine exit temperature (T48) [C]
+9 - GT Compressor inlet air temperature (T1) [C]
+10 - GT Compressor outlet air temperature (T2) [C]
+11 - HP Turbine exit pressure (P48) [bar]
+12 - GT Compressor inlet air pressure (P1) [bar]
+13 - GT Compressor outlet air pressure (P2) [bar]
+14 - Gas Turbine exhaust gas pressure (Pexh) [bar]
+15 - Turbine Injecton Control (TIC) [%]
+16 - Fuel flow (mf) [kg/s]
+17 - GT Compressor decay state coefficient.
+18 - GT Turbine decay state coefficient. 

datasets/conditions_based_maintenance/README.txt

@@ -0,0 +1,92 @@
+=================================================================================================================
+Human Activity Recognition Using Smartphones Dataset
+Version 1.0
+=================================================================================================================
+Andrea Coraddu(2), Luca Oneto(1), Alessandro Ghio(1), Stefano Savio(2), Davide Anguita(1), Massimo Figari(2)
+1 - Smartlab - Non-Linear Complex Systems Laboratory
+DITEN - Universit�  degli Studi di Genova, Genoa (I-16145), Italy. 
+{luca.oneto,alessandro.ghio,davide.anguita}@unige.it
+2 - Marine Technology Research Team
+DITEN - Universit�  degli Studi di Genova, Genoa (I-16145), Italy. 
+{andrea.coraddu,stefano.savio,massimo.figari}@unige.it
+=================================================================================================================
+
+The experiments have been carried out by means of a numerical simulator of a naval vessel (Frigate) characterized by a Gas Turbine (GT) propulsion plant. The different blocks forming the complete simulator (Propeller, Hull, GT, Gear Box and Controller) have been developed and fine tuned over the year on several similar real propulsion plants. In view of these observations the available data are in agreement with a possible real vessel.
+In this release of the simulator it is also possible to take into account the performance decay over time of the GT components such as GT compressor and turbines.
+The propulsion system behaviour has been described with this parameters:
+- Ship speed (linear function of the lever position lp).
+- Compressor degradation coefficient kMc.
+- Turbine degradation coefficient kMt.
+so that each possible degradation state can be described by a combination of this triple (lp,kMt,kMc).
+The range of decay of compressor and turbine has been sampled with an uniform grid of precision 0.001 so to have a good granularity of representation.
+In particular for the compressor decay state discretization the kMc coefficient has been investigated in the domain [1; 0.95], and the turbine coefficient in the domain [1; 0.975].
+Ship speed has been investigated sampling the range of feasible speed from 3 knots to 27 knots with a granularity of representation equal to tree knots.
+A series of measures (16 features) which indirectly represents of the state of the system subject to performance decay has been acquired and stored in the dataset over the parameter's space.
+Check the README.txt file for further details about this dataset.
+
+For each record it is provided:
+======================================
+
+- A 16-feature vector containing the GT measures at steady state of the physical asset:
+    Lever position (lp) [ ]
+    Ship speed (v) [knots]
+    Gas Turbine (GT) shaft torque (GTT) [kN m]
+    GT rate of revolutions (GTn) [rpm]
+    Gas Generator rate of revolutions (GGn) [rpm]
+    Starboard Propeller Torque (Ts) [kN]
+    Port Propeller Torque (Tp) [kN]
+    Hight Pressure (HP) Turbine exit temperature (T48) [C]
+    GT Compressor inlet air temperature (T1) [C]
+    GT Compressor outlet air temperature (T2) [C]
+    HP Turbine exit pressure (P48) [bar]
+    GT Compressor inlet air pressure (P1) [bar]
+    GT Compressor outlet air pressure (P2) [bar]
+    GT exhaust gas pressure (Pexh) [bar]
+    Turbine Injecton Control (TIC) [%]
+    Fuel flow (mf) [kg/s]
+- GT Compressor decay state coefficient
+- GT Turbine decay state coefficient
+
+The dataset includes the following files:
+=========================================
+
+- 'README.txt'
+
+- 'Features.txt': List of all features.
+
+- 'data.txt': Dataset.
+
+Notes: 
+======
+- Features are not normalized
+- Each feature vector is a row on the text file (18 elements in each row)
+
+For more information about this dataset please contact: [email protected]
+Check at www.cbm.smartlab.ws for updates on this dataset.
+
+License:
+========
+Use of this dataset in publications must be acknowledged by referencing the following publication [1] 
+
+[1] A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, M. Figari, Machine Learning Approaches for Improving Condition?Based Maintenance of Naval Propulsion Plants, Journal of Engineering for the Maritime Environment, 2014, DOI: 10.1177/1475090214540874, (In Press)
+
+@article{Coraddu2013Machine,
+    author={Coraddu, Andrea and Oneto, Luca and Ghio, Alessandro and 
+                 Savio, Stefano and Anguita, Davide and Figari, Massimo},
+    title={Machine Learning Approaches for Improving Condition?Based Maintenance of Naval Propulsion Plants},
+    journal={Journal of Engineering for the Maritime Environment},
+    volume={--},
+    number={--},
+    pages={--},
+    year={2014}
+}
+
+This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.
+
+Other Related Publications:
+===========================
+
+[2] M. Altosole, G. Benvenuto, M. Figari, U. Campora, Real-time simulation of a cogag naval ship propulsion system, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 223 (1) (2009) 47-62.
+
+=================================================================================================================
+Andrea Coraddu, Luca Oneto, Alessandro Ghio, Stefano Savio, Davide Anguita, Massimo Figari. September 2014.

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datasets/forestfires.csv

@@ -0,0 +1,518 @@
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datasets/slump_test.data

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datasets/traffic_flow_forecasting/traffic_dataset.mat

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+

requirements.txt

@@ -0,0 +1,9 @@
+numpy
+pandas
+scikit-learn
+pandas
+matplotlib
+seaborn
+torch
+torchvision
+monotonenorm