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lucaspg96/Email Example.ipynb

Created July 13, 2018 13:50
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Email Example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Email Classification Example\n",
"---\n",
"\n",
"This graph dataset contains the emails exchange inside an enterprise. Each node *u* represents an employee email, that is labeled by its department; each edge *(u,v)* says that *u* sent at least one email to *v*.\n",
"\n",
"Our objective is to predict the department in which the employee works."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition of some useful functions\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#basic imports\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import networkx as nx\n",
"from networkx import DiGraph\n",
"from node2vec import Node2Vec\n",
"import telegram\n",
"\n",
"\n",
"#Telegram Bot configurations\n",
"\n",
"my_token = '363083153:AAGFIcfkGdxRmEqYwSrq4h91t83mWMfgNgc' #token generated by @BotFather\n",
"my_chat_id = 169036135 #your telegram Id (can be obtained from @userinfobot)\n",
"\n",
"def send(msg, chat_id=my_chat_id, token=my_token):\n",
" \"\"\"\n",
" Send a mensage to a chat\n",
" \n",
" Parameters\n",
" ----------\n",
" msg : String\n",
" text that will be sent\n",
"\n",
" chat_id : int or String\n",
" id of the chat that will be sent the message.\n",
" If is a group ou user, the id MUST be integer (negative numbers for groups)\n",
" If it is a chanel, the id CAN be the chanel's tag\n",
" \n",
" token : String\n",
" Token of the bot that will send the message. \n",
" When sending message for an user, the user must has talked\n",
" at least one time with the bot (usualy, the /start command).\n",
" When sending to a group, the bot must be allowed to talk\n",
" on groups.\n",
" When sending to a chanel, the bot must be an admin.\n",
" \"\"\"\n",
" \n",
" bot = telegram.Bot(token=token)\n",
" bot.sendMessage(chat_id=chat_id, text=msg)\n",
"\n",
"\n",
"#Defining log object to notify the steps updates\n",
"\n",
"class AbstractSimpleLog():\n",
" def log(self, msg):\n",
" raise Exception(\"log method must be implemented\")\n",
"\n",
"class PrintLog(AbstractSimpleLog):\n",
" def log(self, msg):\n",
" print(msg)\n",
" \n",
"class BotLog(AbstractSimpleLog):\n",
" def __init__(self, reason=None):\n",
" if reason:\n",
" send(\"Bot started to log: {}\".format(reason))\n",
" else:\n",
" send(\"Log Started -------------------------\")\n",
" def log(self, msg):\n",
" send(msg)\n",
"\n",
"\n",
"#Default name for embeddign file\n",
"EMBEDDING_FILE = \"embeddings.emb\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing the Embedding\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we define the path to the dataset and to its edges"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#We can definine an log object. To long computations, we use BotLog to keep tracking\n",
"# by the cellphone using Telegram App\n",
"\n",
"logger = PrintLog()\n",
"# logger = BotLog()\n",
"\n",
"dataset = \"Email_dataset/\" #path to dataset\n",
"graph_file = dataset+\"edges.ssv\" #path to edges file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we process the graph embedding"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Computing transition probabilities: 0%| | 0/1005 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading graph from Email_dataset/edges.ssv\n",
"Graph loaded\n",
"Computing transition probabilities\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Computing transition probabilities: 100%|██████████| 1005/1005 [00:03<00:00, 301.44it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transitions probabilities computed\n",
"Starting Node2Vec embedding\n",
"Node2Vec embedding created\n",
"Saving embedding file\n",
"Embedding file saved\n"
]
}
],
"source": [
"logger.log(\"Loading graph from {}\".format(graph_file))\n",
"graph = nx.read_edgelist(graph_file, delimiter=\" \", create_using=DiGraph())\n",
"logger.log(\"Graph loaded\")\n",
"\n",
"logger.log(\"Computing transition probabilities\")\n",
"n2v = Node2Vec(graph, dimensions=128, walk_length=80, num_walks=50, workers=4, p=1, q=1)\n",
"logger.log(\"Transitions probabilities computed\")\n",
"\n",
"logger.log(\"Starting Node2Vec embedding\")\n",
"n2v_model = n2v.fit(window=80, min_count=1, batch_words=64)\n",
"logger.log(\"Node2Vec embedding created\")\n",
"\n",
"logger.log(\"Saving embedding file\")\n",
"n2v_model.wv.save_word2vec_format(dataset+EMBEDDING_FILE)\n",
"logger.log(\"Embedding file saved\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are going to use the embedding generated to predict the employees departments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Processing\n",
"---\n",
"\n",
"To process the data, we are going to use Numpy's functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy import array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we saved the embedding, we can load it as a Numpy matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"vectors = np.loadtxt(dataset+EMBEDDING_FILE,delimiter=' ',skiprows=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also define a function to get the node embedding representation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def to_embedded(n):\n",
" return vectors[n,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we load the data from the *labels.ssv* file and generate the dataset like:\n",
"\n",
"NODE_EMBEDDING, DEPARTMENT"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00000000e+00, -5.52356600e-01, -1.31899000e+00, ...,\n",
" 2.73627400e+00, 3.10003760e-01, 1.00000000e+00],\n",
" [ 1.30000000e+02, 1.60013030e+00, -4.14573250e-01, ...,\n",
" -1.19659230e+00, -4.13204510e-02, 1.00000000e+00],\n",
" [ 5.32000000e+02, -9.19102910e-01, -1.96323200e-01, ...,\n",
" 2.04122850e+00, -2.22895600e+00, 2.10000000e+01],\n",
" ..., \n",
" [ 7.50000000e+02, -2.84327940e-03, -2.77449560e-03, ...,\n",
" 3.72426860e-03, -9.51730650e-04, 1.00000000e+00],\n",
" [ 7.90000000e+02, -5.00332680e-04, -2.21990440e-03, ...,\n",
" -1.75051400e-03, 2.45084990e-03, 6.00000000e+00],\n",
" [ 9.44000000e+02, 6.54737760e-04, -1.85789620e-03, ...,\n",
" -1.21631000e-04, -3.08797140e-03, 2.20000000e+01]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = [] #data matrix initialized empty\n",
"\n",
"with open(\"Email_dataset/labels.ssv\") as f:\n",
" for line in f:\n",
" node,department = line.split() #get the node id and its department (class)\n",
" node_embedded = to_embedded(int(node)) #get the embedded representation of the node\n",
" data.append(np.append(node_embedded,array([department]))) #insert the embedding and the class inside the data matrix\n",
"\n",
"data = array(data,dtype=float) #transform the data matrix in a Numpy array\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We shuffle the data and split into train and test subsets, using the *train_percentage* factor"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.shuffle(data)\n",
"train_percentage = 0.8\n",
"train_size = int(len(data)*train_percentage)\n",
"\n",
"train_data = array(data[0:train_size])\n",
"test_data = array(data[train_size:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predictions\n",
"---\n",
"We are going to use the following models to predict the classes"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.linear_model import Perceptron\n",
"\n",
"from sklearn.svm import SVC\n",
"\n",
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import ExtraTreesClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"\n",
"from sklearn.gaussian_process import GaussianProcessClassifier\n",
"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"from sklearn.naive_bayes import BernoulliNB\n",
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also define a function to train the model and give the score"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train_and_eval(model):\n",
" model.fit(train_data[:,0:-1], train_data[:,-1])\n",
" return model.score(test_data[:,:-1], test_data[:,-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At least, we train and compute the scores for each model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression score: 5.970149253731343%\n",
"SGDClassifier score: 1.4925373134328357%\n",
"Perceptron score: 2.9850746268656714%\n",
"SVC score: 8.955223880597014%\n",
"MLPClassifier score: 7.960199004975125%\n",
"KNeighborsClassifier score: 5.970149253731343%\n",
"GaussianProcessClassifier score: 0.4975124378109453%\n",
"DecisionTreeClassifier score: 4.975124378109453%\n",
"BernoulliNB score: 3.482587064676617%\n",
"GaussianNB score: 6.467661691542288%\n",
"GradientBoostingClassifier score: 5.970149253731343%\n",
"RandomForestClassifier score: 7.960199004975125%\n",
"ExtraTreesClassifier score: 5.472636815920398%\n",
"AdaBoostClassifier score: 9.45273631840796%\n"
]
}
],
"source": [
"score = train_and_eval(LogisticRegression())\n",
"logger.log(\"LogisticRegression score: {}%\".format(score*100))\n",
"score = train_and_eval(SGDClassifier(max_iter=100, tol=0.001))\n",
"logger.log(\"SGDClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(Perceptron(max_iter=100, tol=0.001))\n",
"logger.log(\"Perceptron score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(SVC())\n",
"logger.log(\"SVC score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(MLPClassifier())\n",
"logger.log(\"MLPClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(KNeighborsClassifier())\n",
"logger.log(\"KNeighborsClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(GaussianProcessClassifier())\n",
"logger.log(\"GaussianProcessClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(DecisionTreeClassifier())\n",
"logger.log(\"DecisionTreeClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(BernoulliNB())\n",
"logger.log(\"BernoulliNB score: {}%\".format(score*100))\n",
"score = train_and_eval(GaussianNB())\n",
"logger.log(\"GaussianNB score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(GradientBoostingClassifier())\n",
"logger.log(\"GradientBoostingClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(RandomForestClassifier())\n",
"logger.log(\"RandomForestClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(ExtraTreesClassifier())\n",
"logger.log(\"ExtraTreesClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(AdaBoostClassifier())\n",
"logger.log(\"AdaBoostClassifier score: {}%\".format(score*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our accuracy is very low to all the models. To understand this, let's analyse the departments distribution over the data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1257084a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"Email_dataset/labels.ssv\", delimiter=\" \", names=[\"Node\",\"Dep\"])\n",
"_ = df[\"Dep\"].value_counts().plot(kind=\"bar\", figsize=(8,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analysing the graph we can see that the dataset is unbalanced, so that can explain the bad accuracies that we got.\n",
"So we are going take a subgraph that have only a balanced distribution of departments."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieving the Subgraph\n",
"---\n",
"First, we compute a dictionary with the nodes departments"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"labels_dict = {}\n",
"with open(\"Email_dataset/labels.ssv\") as f:\n",
" for line in f:\n",
" node,department = line.split()\n",
" labels_dict[node] = department"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After, we generate a subset of edges that contains only the nodes that are from the departments defined in *labels_filter*"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"labels_filter = [\"4\",\"14\"]\n",
"with open(\"Email_dataset/edges.ssv\",'r') as f_in:\n",
" with open(\"Email_dataset/edges_filtered.ssv\",'w') as f_out:\n",
" for line in f_in:\n",
" src,trg = line.split()\n",
" if labels_dict[src] in labels_filter and labels_dict[trg] in labels_filter:\n",
" f_out.write(line)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we reload the graph an compute its embedding"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Computing transition probabilities: 28%|██▊ | 54/194 [00:00<00:00, 537.90it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading graph from Email_dataset/edges.ssv\n",
"Graph loaded\n",
"Computing transition probabilities\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Computing transition probabilities: 100%|██████████| 194/194 [00:00<00:00, 1001.05it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transitions probabilities computed\n",
"Starting Node2Vec embedding\n",
"Node2Vec embedding created\n",
"Saving embedding file\n",
"Embedding file saved\n"
]
}
],
"source": [
"logger = PrintLog()\n",
"\n",
"logger.log(\"Loading graph from {}\".format(graph_file))\n",
"graph = nx.read_edgelist(\"Email_dataset/edges_filtered.ssv\", delimiter=\" \", create_using=DiGraph())\n",
"logger.log(\"Graph loaded\")\n",
"\n",
"logger.log(\"Computing transition probabilities\")\n",
"n2v = Node2Vec(graph, dimensions=128, walk_length=50, num_walks=30, workers=4, p=1, q=1)\n",
"logger.log(\"Transitions probabilities computed\")\n",
"\n",
"logger.log(\"Starting Node2Vec embedding\")\n",
"n2v_model = n2v.fit(window=50, min_count=1, batch_words=64)\n",
"logger.log(\"Node2Vec embedding created\")\n",
"\n",
"logger.log(\"Saving embedding file\")\n",
"n2v_model.wv.save_word2vec_format(dataset+EMBEDDING_FILE)\n",
"logger.log(\"Embedding file saved\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we process the data again, but we take only the employees that are at the graph"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"vectors = np.loadtxt(dataset+EMBEDDING_FILE,delimiter=' ',skiprows=1)\n",
"\n",
"def to_embedded(n):\n",
" return vectors[n,:]\n",
"\n",
"data = []\n",
"\n",
"with open(\"Email_dataset/labels.ssv\") as f:\n",
" for line in f:\n",
" node,department = line.split()\n",
" if department in labels_filter:\n",
" try:\n",
" node_embedded = to_embedded(graph.nodes().index(node))\n",
" data.append(np.append(node_embedded,array([department])))\n",
" except:\n",
" pass\n",
"\n",
"data = array(data,dtype=float)\n",
"\n",
"np.random.shuffle(data)\n",
"train_percentage = 0.7\n",
"train_size = int(len(data)*train_percentage)\n",
"\n",
"train_data = array(data[0:train_size])\n",
"test_data = array(data[train_size:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At least, we run again the models"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression score: 55.932203389830505%\n",
"SGDClassifier score: 45.76271186440678%\n",
"Perceptron score: 54.23728813559322%\n",
"SVC score: 42.3728813559322%\n",
"MLPClassifier score: 55.932203389830505%\n",
"KNeighborsClassifier score: 45.76271186440678%\n",
"GaussianProcessClassifier score: 40.67796610169492%\n",
"DecisionTreeClassifier score: 52.54237288135594%\n",
"BernoulliNB score: 45.76271186440678%\n",
"GaussianNB score: 55.932203389830505%\n",
"GradientBoostingClassifier score: 55.932203389830505%\n",
"RandomForestClassifier score: 54.23728813559322%\n",
"ExtraTreesClassifier score: 57.6271186440678%\n",
"AdaBoostClassifier score: 52.54237288135594%\n"
]
}
],
"source": [
"score = train_and_eval(LogisticRegression())\n",
"logger.log(\"LogisticRegression score: {}%\".format(score*100))\n",
"score = train_and_eval(SGDClassifier(max_iter=100, tol=0.001))\n",
"logger.log(\"SGDClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(Perceptron(max_iter=100, tol=0.001))\n",
"logger.log(\"Perceptron score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(SVC())\n",
"logger.log(\"SVC score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(MLPClassifier())\n",
"logger.log(\"MLPClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(KNeighborsClassifier())\n",
"logger.log(\"KNeighborsClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(GaussianProcessClassifier())\n",
"logger.log(\"GaussianProcessClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(DecisionTreeClassifier())\n",
"logger.log(\"DecisionTreeClassifier score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(BernoulliNB())\n",
"logger.log(\"BernoulliNB score: {}%\".format(score*100))\n",
"score = train_and_eval(GaussianNB())\n",
"logger.log(\"GaussianNB score: {}%\".format(score*100))\n",
"\n",
"score = train_and_eval(GradientBoostingClassifier())\n",
"logger.log(\"GradientBoostingClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(RandomForestClassifier())\n",
"logger.log(\"RandomForestClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(ExtraTreesClassifier())\n",
"logger.log(\"ExtraTreesClassifier score: {}%\".format(score*100))\n",
"score = train_and_eval(AdaBoostClassifier())\n",
"logger.log(\"AdaBoostClassifier score: {}%\".format(score*100))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
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},
"nbformat": 4,
"nbformat_minor": 2
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