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timlinucb.py
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timlinucb.py
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#! /usr/local/bin/python3
import logging
import pandas as pd
import numpy as np
import sys
from tqdm import tqdm
from subprocess import Popen, PIPE
import os
import matplotlib.pyplot as plt
import random
import shutil
import stat
from functools import partial
from helpers import run_ic_eff, tim, tim_parallel, tqdm_joblib, _run_timlinucb_parallel
import joblib
# --------------------------------------------------------------------------------------
# %% ------------------------------ Initial setup --------------------------------------
# --------------------------------------------------------------------------------------
# Fancy plots in matplotlib and pandas
random.seed(42)
plt.style.use("ggplot")
# Setting up logging
VERBOSE = False
LOGGING_FMT = (
"%(levelname)s | %(asctime)s | line %(lineno)s | %(funcName)s | %(message)s"
)
LOGGING_DATEFMT = "%H:%M:%S"
logger_tlu = logging.getLogger("logger_tlu")
syslog = logging.StreamHandler()
formatter = logging.Formatter(fmt=LOGGING_FMT, datefmt=LOGGING_DATEFMT)
syslog.setFormatter(formatter)
logger_tlu.addHandler(syslog)
if VERBOSE:
logger_tlu.setLevel(logging.DEBUG)
else:
logger_tlu.setLevel(logging.WARNING)
# --------------------------------------------------------------------------------------
# %% -------------------- Generating edge features (preprocessing) ---------------------
# --------------------------------------------------------------------------------------
def get_features_nodes(
df_graph,
dims=20,
epochs=1,
node2vec_path="node2vec",
tempdir_name="temp_dir",
dataset_name="facebook",
check_existing=True,
):
""" Generate node2vec features for the nodes in a graph
Parameters
----------
df_graph : pandas.DataFrame
The graph we run node2vec on, in the form of a DataFrame. A row represents
one edge in the graph, with columns containing the source and target nodes for
the edge.
dims : int, optional
Specifies the number of dimensions to pass to node2vec[1]. Default: 20
epochs : int, optional
Specifies the number of epochs to run the node2vec for. Default: 1
node2vec_path : str, optional
Path to the node2vec executable. Default: "node2vec", which means that it is
in one folder with this file
tempdir_name : str, optional
The name of a temporary directory to use for running node2vec.
Default: "temp_dir"
dataset_name : str, optional
The name of the dataset, used when naming the files in the temporary directory.
Default: "facebook"
check_existing : boolean, optional
Determines if we are checking for an alredy existing algorithm output in the
temporary directory.
Returns
-------
df_emb : pd.DataFrame
A dataframe with the contents of embeddings generated by node2vec. It is also
available as <dataset_name>-d<dims>.emb in the temporary directory.
References
----------
.. [1] Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
if not os.path.exists(tempdir_name):
os.makedirs(tempdir_name)
FNAME_IN = os.path.join(tempdir_name, f"{dataset_name}.edgelist")
FNAME_OUT = os.path.join(tempdir_name, f"{dataset_name}-d{dims}.emb")
# Checking if we already ran the function before
if check_existing and os.path.exists(FNAME_OUT):
return pd.read_csv(
FNAME_OUT,
sep=" ",
names=(["node"] + [f"feat_{i}" for i in range(dims)]),
skiprows=1,
)
# Saving the edgelist to run node2vec on
df_graph.to_csv(
FNAME_IN, index=False, sep=" ", header=False, columns=["source", "target"]
)
# Preparing to run node2vec
process = Popen(
[
os.path.join(".", node2vec_path, "node2vec"),
f"-i:{FNAME_IN}",
f"-o:{FNAME_OUT}",
f"-d:{dims}",
f"-e:{epochs}",
"-v",
],
stdout=PIPE,
stderr=PIPE,
)
(output, err) = process.communicate()
_ = process.wait() # Returns exit code
out = output.decode("utf-8")
logger_tlu.debug(f"Running node2vec, {out}")
return pd.read_csv(
FNAME_OUT,
sep=" ",
names=(["node"] + [f"feat_{i}" for i in range(dims)]),
skiprows=1,
)
def generate_node2vec_fetures(
df,
node2vec_path="node2vec",
tempdir_name="temp_dir",
dataset_name="facebook",
num_features=20,
check_existing=True,
):
""" Generate node2vec features for the edges in a graph
Runs get_features_nodes to get the node2vec[1] features for the nodes and then
multiplies them to get the edge features.
Edge feature vector Fe = source_node_feats * target_node_feats.
Parameters
----------
df : pandas.DataFrame
The graph we run node2vec on, in the form of a DataFrame. A row represents one
edge in the graph, with columns containing the source and target nodes for
the edge.
node2vec_path : str, optional
Path to the node2vec executable. Default: "os.getcwd()", which means that it is
in one folder with this
tempdir_name : str, optional
The name of a temporary directory to use for running node2vec.
Default: "temp_dir"
dataset_name : str, optional
The name of the dataset, used when naming the files in the temporary directory.
Default: "facebook"
dims : int, optional
Specifies the number of dimensions to pass to node2vec[1]. Default: 20
check_existing : boolean, optional
Determines if we are checking for an alredy existing algorithm output in the
temporary directory.
Returns
-------
df_feats : pd.DataFrame
A dataframe with edge embeddings, where one row represents one edge.
References
----------
.. [1] Grover, Aditya, and Jure Leskovec.
"node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
# Checking if we did this before
FNAME_SAVE = os.path.join(tempdir_name, f"{dataset_name}-d{num_features}-edges.emb")
if check_existing and os.path.exists(FNAME_SAVE):
df_ret = pd.read_csv(FNAME_SAVE)
df_ret[df_ret.columns[0]] = df_ret[df_ret.columns[0]].astype(int)
return df_ret.set_index(df_ret.columns[0])
# Getting node embeddings
logger_tlu.debug("Getting node embeddings...")
df_emb = get_features_nodes(
df,
dims=num_features,
node2vec_path=node2vec_path,
tempdir_name=tempdir_name,
dataset_name=dataset_name,
check_existing=check_existing,
)
df_emb = df_emb.set_index("node").sort_values(by="node")
# Generating edge features
logger_tlu.debug(f"Generating {num_features} edge features...")
df_feats = []
for row in tqdm(df.itertuples(), total=df.shape[0]):
df_feats.append(df_emb.loc[row.source].values * df_emb.loc[row.target].values)
df_feats = pd.DataFrame(df_feats)
# Saving the results
logger_tlu.debug(f"Saving the edge features to {FNAME_SAVE}")
df_feats.to_csv(FNAME_SAVE)
return df_feats
# --------------------------------------------------------------------------------------
# %% --------------------------------- Online IM --------------------------------------
# --------------------------------------------------------------------------------------
def _oim_node2vec_test(
df,
df_feats,
num_inf=10,
sigma=4,
c=0.1,
epsilon=0.4,
num_repeats=30,
num_repeats_regret_algo=20,
num_repeats_regret_true=20,
num_nodes_tim=-1,
oracle=tim,
):
""" Test of the Online IM with Node2Vec features
Online Influence Maximization using IMLinUCB [1] with node2vec [3] features. The
"test" version also compares the result obtained to the "true" seed set, generated
by an Offline IM algorithm with the actual activation probabilities. This uses the
node2vec node embeddings to speed up the processing.
Parameters
----------
df : pandas.DataFrame
The graph we run the OIM on, in the form of a DataFrame. A row represents one
edge in the graph, with columns being named "source", "target", and "probab".
"probab" column is the "true" activation probability.
df_feats : pandas.DataFrame
A dataframe with node embeddings generated by node2vec [3]. Should have n rows,
where n is the number of nodes in the graph. The number of columns is specified
when running node2vec on the graph. The smaller the number of columns is the
more uncertain the embedding, but it also speeds up processing.
num_inf : int, optional
Number of seed nodes to find. Default: 10.
sigma : float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 4
c: float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 0.1
epsilon : float, optional
A parameter used by the TIM algorithm. Refer to the TIM paper for
more details. [2] Default: 0.4
num_repeats : int, optional
Number of iterations of the IMLinUCB algorithm. The more, the better the
results. Default: 30
num_repeats_regret_algo : int, optional
Number of times to run IC at every IMLinUCB step. A larger number might
improve the results. Default: 20
num_repeats_regret_true : int, optional
Number of times to run the IC for the "true" seed set. A larger number will
provide a more accurate picture of the comparison. Default: 20
num_nodes_tim : int, optional
Number of nodes to use for the offline IM algorithm, TIM[2]. Default: -1
oracle : function, optional
A function to use as the Offline IM algorithm. Default: tim [2]
Returns
-------
return_dict : dict
A dictionary consisting of following keys:
- rewards, a list of all rewards (number of inf nodes) obtained by the algorithm
- rewards_edges, a list of edge rewards (number of inf edges) obtained
- s_best, the list of the selected seed nodes
- u_e_best, the approximated probabilities
- reward_best, the reward obtained by running IC with s_best
References
----------
.. [1] Wen, Zheng, Branislav Kveton, and Michal Valko.
"Influence maximization with semi-bandit feedback." CoRR, abs/1605.06593 (2016).
.. [2] Tang, Youze, Xiaokui Xiao, and Yanchen Shi.
"Influence maximization: Near-optimal time complexity meets practical efficiency."
Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
.. [3] Grover, Aditya, and Jure Leskovec.
"node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
logger_tlu.debug("Started Online Influence Maximization...")
logger_tlu.debug("Setting parameters")
num_feats = df_feats.shape[1]
num_edges_t = df.shape[0]
# "True" probabilities - effectively our test set
true_weights = df["probab"].copy()
# Using nodes_t[-1] because TIM wants the max node id
s_true = sorted(
oracle(
df[["source", "target", "probab"]],
num_nodes_tim,
num_edges_t,
num_inf,
epsilon,
)
)
# Gathering the stats for the "true" seed set
all_true_nodes = []
all_true_edges = []
all_true_obs = []
for k in range(num_repeats_regret_true):
true_act_nodes, true_act_edges, true_obs_edges = run_ic_eff(df, s_true)
all_true_nodes.append(true_act_nodes)
all_true_edges.append(true_act_edges)
all_true_obs.append(true_obs_edges)
# Means for nodes and activated edges
mean_true_nodes = np.mean([len(i) for i in all_true_nodes])
mean_true_edges = np.mean([len(i) for i in all_true_edges])
mean_true_obs = np.mean([len(i) for i in all_true_obs])
# b, M_inv - used by IMLinUCB
b = np.zeros((num_feats, 1))
m_inv = np.eye(num_feats, num_feats)
# Returning these
s_best = []
reward_best = 0
u_e_best = []
regrets = []
regrets_edges = []
for iter_oim in tqdm(
range(num_repeats),
desc=f"OIM iters {num_edges_t} edges",
leave=False,
file=sys.stderr,
):
# ---- Step 1 - Calculating the u_e ----
theta = (m_inv @ b) / (sigma * sigma)
# xMx = (df_feats.values @ m_inv @ df_feats.T.values).clip(min=0)
u_e = []
for i in range(num_edges_t):
x_e = df_feats.loc[i].values
xMx = x_e @ m_inv @ x_e.T # .clip(min=0)
u_e.append(np.clip(x_e @ theta + c * np.sqrt(xMx), 0, 1))
# u_e.append(expit(x_e @ theta + c * np.sqrt(xMx)))
u_e = np.array(u_e)
# ---- Step 2 - Evaluating the performance ----
# Loss function
df["probab"] = u_e
s_oracle = sorted(
oracle(
df[["source", "target", "probab"]],
num_nodes_tim,
num_edges_t,
num_inf,
epsilon,
)
)
# Observing edge-level feedback
df["probab"] = true_weights
all_algo_nodes = []
all_algo_edges = []
all_algo_obs = []
for k in range(num_repeats_regret_algo):
algo_act_nodes, algo_act_edges, algo_obs_edges = run_ic_eff(df, s_oracle)
all_algo_nodes.append(algo_act_nodes)
all_algo_edges.append(algo_act_edges)
all_algo_obs.append(algo_obs_edges)
# Mean node counts
mean_algo_nodes = np.mean([len(i) for i in all_algo_nodes])
# Mean activated edge counts
mean_algo_edges = np.mean([len(i) for i in all_algo_edges])
mean_algo_obs = np.mean([len(i) for i in all_algo_obs])
# Used for updating M and b later
all_algo_edges = np.unique(np.concatenate(all_algo_edges))
all_algo_obs = np.unique(np.concatenate(all_algo_obs))
regrets.append(mean_true_nodes - mean_algo_nodes)
regrets_edges.append(mean_true_edges - mean_algo_edges)
logger_tlu.debug(f"True seeds: {s_true}")
logger_tlu.debug(f"Algo seeds: {s_oracle}")
logger_tlu.debug(
"Diff between true and algo seeds: "
f"{len(np.setdiff1d(s_true, s_oracle))}"
)
logger_tlu.debug(f"True reward: {mean_true_nodes}")
logger_tlu.debug(f"Algo reward: {mean_algo_nodes}")
logger_tlu.debug(f"Best algo reward: {reward_best}")
logger_tlu.debug(f"Regrets: {regrets}")
logger_tlu.debug(f"Edge regrets: {regrets_edges}")
logger_tlu.debug(f"Observed diff: {mean_true_obs - mean_algo_obs}")
logger_tlu.debug(f"Algo weights {u_e[80:90]}".replace("\n", ""))
logger_tlu.debug(f"Real weights {true_weights[80:90]}".replace("\n", ""))
if mean_algo_nodes > reward_best:
reward_best = mean_algo_nodes
s_best = s_oracle
u_e_best = u_e
if mean_algo_nodes > mean_true_nodes:
logger_tlu.debug(
"The algorithm has achieved better reward than the true seed node set."
)
logger_tlu.debug("Stopping learning.")
logger_tlu.debug(f"Best algo seed node set: {s_best}")
return_dict = {
"regrets": regrets,
"regrets_edges": regrets_edges,
"s_true": s_true,
"s_best": s_best,
"u_e_best": u_e_best,
"reward_best": reward_best,
}
logger_tlu.debug(f"Returning {return_dict}")
return return_dict
# ---- Step 3 - Calculating updates ----
for i in all_algo_obs:
x_e = np.array([df_feats.loc[i].values])
m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (
x_e @ m_inv @ x_e.T + sigma * sigma
)
b += x_e.T * int(i in all_algo_edges)
return_dict = {
"regrets": regrets,
"regrets_edges": regrets_edges,
"s_true": s_true,
"s_best": s_best,
"u_e_best": u_e_best,
"reward_best": reward_best,
}
logger_tlu.debug("The algorithm has finished running.")
logger_tlu.debug(f"Returning: {return_dict}")
return return_dict
def oim_node2vec_simple(
df,
df_feats,
num_inf=10,
sigma=4,
c=0.1,
epsilon=0.4,
num_repeats=15,
num_nodes_tim=-1,
oracle=tim,
):
""" Online IM with Node2Vec features (Simple version)
Online Influence Maximization using IMLinUCB[1] with node2vec [3] features. Simple
version doesn't simulate the propagation multiple times at every time step, which
makes the algorithm faster but less accurate.
Parameters
----------
df : pandas.DataFrame
The graph we run the OIM on, in the form of a DataFrame. A row represents one
edge in the graph, with columns being named "source", "target", and "probab".
"probab" column is the "true" activation probability.
df_feats : pandas.DataFrame
A dataframe with node embeddings generated by node2vec [3]. Should have n rows,
where n is the number of nodes in the graph. The number of columns is specified
when running node2vec on the graph. The smaller the number of columns is the
more uncertain the embedding, but it also speeds up processing.
num_inf : int, optional
Number of seed nodes to find. Default: 10.
sigma : float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 4
c: float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 0.1
epsilon : float, optional
A parameter used by the TIM algorithm. Refer to the TIM paper for
more details. [2] Default: 0.4
num_repeats : int, optional
Number of iterations of the IMLinUCB algorithm. The more, the better the
results. Default: 30
num_nodes_tim : int, optional
Number of nodes to use for the offline IM algorithm, TIM [2]. Default: -1
oracle : function, optional
A function to use as the Offline IM algorithm. Default: tim [2]
Returns
-------
return_dict : dict
A dictionary consisting of following keys:
- rewards, a list of all rewards (number of inf nodes) obtained by the algorithm
- rewards_edges, a list of edge rewards (number of inf edges) obtained
- s_best, the list of the selected seed nodes
- u_e_best, the approximated probabilities
- reward_best, the reward obtained by running IC with s_best
References
----------
.. [1] Wen, Zheng, Branislav Kveton, and Michal Valko.
"Influence maximization with semi-bandit feedback." CoRR, abs/1605.06593 (2016).
.. [2] Tang, Youze, Xiaokui Xiao, and Yanchen Shi.
"Influence maximization: Near-optimal time complexity meets practical efficiency."
Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
.. [3] Grover, Aditya, and Jure Leskovec.
"node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
logger_tlu.debug("Started Online Influence Maximization...")
logger_tlu.debug("Setting parameters")
num_feats = df_feats.shape[1]
num_edges_t = df.shape[0]
# "True" probabilities - effectively our test set
true_weights = df["probab"].copy()
# b, M_inv - used by IMLinUCB
b = np.zeros((num_feats, 1))
m_inv = np.eye(num_feats, num_feats)
# Returning these
s_best = []
reward_best = 0
u_e_best = []
rewards = []
rewards_edges = []
for iter_oim in tqdm(
range(num_repeats),
desc=f"OIM iters {num_edges_t} edges",
leave=False,
file=sys.stderr,
):
# ---- Step 1 - Calculating the u_e ----
theta = (m_inv @ b) / (sigma * sigma)
# xMx = (df_feats.values @ m_inv @ df_feats.T.values).clip(min=0)
u_e = []
for i in range(num_edges_t):
x_e = df_feats.loc[i].values
xMx = x_e @ m_inv @ x_e.T # .clip(min=0)
u_e.append(np.clip(x_e @ theta + c * np.sqrt(xMx), 0, 1))
# u_e.append(expit(x_e @ theta + c * np.sqrt(xMx)))
u_e = np.array(u_e)
# ---- Step 2 - Evaluating the performance ----
# Loss function
df["probab"] = u_e
s_oracle = sorted(
oracle(
df[["source", "target", "probab"]],
num_nodes_tim,
num_edges_t,
num_inf,
epsilon,
)
)
# Observing edge-level feedback
df["probab"] = true_weights
algo_act_nodes, algo_act_edges, algo_obs_edges = run_ic_eff(df, s_oracle)
algo_num_nodes = len(algo_act_nodes)
algo_num_edges = len(algo_act_edges)
rewards.append(algo_num_nodes)
rewards_edges.append(algo_num_edges)
logger_tlu.debug(f"Algo seeds: {s_oracle}")
logger_tlu.debug(f"Algo reward: {algo_num_nodes}")
logger_tlu.debug(f"Best algo reward: {reward_best}")
logger_tlu.debug(f"Rewards: {rewards}")
logger_tlu.debug(f"Edge rewards: {rewards_edges}")
logger_tlu.debug(f"Algo weights {u_e[80:90]}".replace("\n", ""))
logger_tlu.debug(f"Real weights {true_weights[80:90]}".replace("\n", ""))
if algo_num_nodes > reward_best:
reward_best = algo_num_nodes
s_best = s_oracle
u_e_best = u_e
# ---- Step 3 - Calculating updates ----
for i in algo_obs_edges:
x_e = np.array([df_feats.loc[i].values])
m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (
x_e @ m_inv @ x_e.T + sigma * sigma
)
b += x_e.T * int(i in algo_act_edges)
return_dict = {
"rewards": rewards,
"rewards_edges": rewards_edges,
"s_best": s_best,
"u_e_best": u_e_best,
"reward_best": reward_best,
}
logger_tlu.debug("The algorithm has finished running.")
logger_tlu.debug(f"Returning: {return_dict}")
return return_dict
def oim_node2vec(
df,
df_feats,
nodes,
num_inf=10,
sigma=4,
c=0.1,
epsilon=0.4,
num_repeats=30,
num_repeats_reward=20,
oracle=tim,
persist=False,
b=None,
m_inv=None,
hide_tqdm=False,
):
""" Online IM with Node2Vec features
Run the IMLinUCB[1] algorithm with pre-trained Node2Vec [3] features.
Parameters
----------
df : pandas.DataFrame
The graph we run the OIM on, in the form of a DataFrame. A row represents one
edge in the graph, with columns being named "source", "target", and "probab".
"probab" column is the "true" activation probability.
df_feats : pandas.DataFrame
A dataframe with node embeddings generated by node2vec [3]. Should have n rows,
where n is the number of nodes in the graph. The number of columns is specified
when running node2vec on the graph. The smaller the number of columns is the
more uncertain the embedding, but it also speeds up processing.
nodes : pandas.DataFrame
A dataframe of all unique nodes in df. Is used to calculate the number of nodes
for TIM.
num_inf : int, optional
Number of seed nodes to find. Default: 10.
sigma : float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 4
c: float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 0.1
epsilon : float, optional
A parameter used by the TIM algorithm. Refer to the TIM paper for
more details. [2] Default: 0.4
num_repeats : int, optional
Number of iterations of the IMLinUCB algorithm. The more, the better the
results. Default: 30
num_repeats_reward : int, optional
Number of times to run the simulation at every step of the IMLinUCB algorithm.
A larger number should better the results. Default: 20
num_nodes_tim : int, optional
Number of nodes to use for the offline IM algorithm, TIM [2]. Default: -1
oracle : function, optional
A function to use as the Offline IM algorithm. Default: tim [2]
persist : boolean, optional
A parameter that determines if we are running this algorithm in a temporal IM
toolchain and would want to persist parameters. If True, you need to provide b
and m_inv as well. Default: False
b : pandas.DataFrame, optional
A dataframe of b taken from the previous time step in the temporal IM.
Used if we want to persist parameters through time steps. Default: None
m_inv : pandas.DataFrame, optional
A dataframe of m_inv taken from the previous time step in the temporal IM.
Used if we want to persist parameters through time steps. Default: None
hide_tqdm : boolean, optional
A paremeters used if you want to hide all tqdm progress bars. It's useful if
you want to paralellize the algorithm. Default: False
Returns
-------
return_dict : dict
A dictionary consisting of following keys:
- s_best, the list of the selected seed nodes
- u_e_best, the approximated probabilities
- reward_best, the reward obtained by running IC with s_best
- m_inv, a parameter to use in the next it of temporal OIM
(only if persist is True)
- b, a parameter to use in the next it of temporal OIM
(only if persist is True)
References
----------
.. [1] Wen, Zheng, Branislav Kveton, and Michal Valko.
"Influence maximization with semi-bandit feedback." CoRR, abs/1605.06593 (2016).
.. [2] Tang, Youze, Xiaokui Xiao, and Yanchen Shi.
"Influence maximization: Near-optimal time complexity meets practical efficiency."
Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
.. [3] Grover, Aditya, and Jure Leskovec.
"node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
logger_tlu.debug("Started Online Influence Maximization...")
logger_tlu.debug("Setting parameters")
num_feats = df_feats.shape[1]
num_edges_t = df.shape[0]
num_nodes_tim = nodes[-1] + 1 # TIM counts from 0
# "True" probabilities - effectively our test set
true_weights = df["probab"].copy()
# b, M_inv - used by IMLinUCB
if not persist:
b = np.zeros((num_feats, 1))
m_inv = np.eye(num_feats, num_feats)
# Returning these
s_best = []
reward_best = 0
std_best = 0
u_e_best = []
if hide_tqdm:
oim_iterator = range(num_repeats)
else:
oim_iterator = tqdm(
range(num_repeats),
desc=f"OIM iters {num_edges_t} edges",
leave=False,
file=sys.stderr,
)
for iter_oim in oim_iterator:
# ---- Step 1 - Calculating the u_e ----
theta = (m_inv @ b) / (sigma * sigma)
# xMx = (df_feats.values @ m_inv @ df_feats.T.values).clip(min=0)
u_e = []
for i in range(num_edges_t):
x_e = df_feats.loc[i].values
xMx = x_e @ m_inv @ x_e.T # .clip(min=0)
u_e.append(np.clip(x_e @ theta + c * np.sqrt(xMx), 0, 1))
# u_e.append(expit(x_e @ theta + c * np.sqrt(xMx)))
u_e = np.array(u_e)
# ---- Step 2 - Evaluating the performance ----
# Loss function
df["probab"] = u_e
s_oracle = sorted(
oracle(
df[["source", "target", "probab"]],
num_nodes_tim,
num_edges_t,
num_inf,
epsilon,
)
)
# Observing edge-level feedback
df["probab"] = true_weights
all_algo_nodes = []
all_algo_edges = []
all_algo_obs = []
for k in range(num_repeats_reward):
algo_act_nodes, algo_act_edges, algo_obs_edges = run_ic_eff(df, s_oracle)
all_algo_nodes.append(algo_act_nodes)
all_algo_edges.append(algo_act_edges)
all_algo_obs.append(algo_obs_edges)
# Mean node counts
num_affected_nodes = [len(i) for i in all_algo_nodes]
mean_algo_nodes = np.mean(num_affected_nodes)
std_algo_nodes = np.std(num_affected_nodes)
# Used for updating M and b later
all_algo_edges = np.unique(np.concatenate(all_algo_edges))
all_algo_obs = np.unique(np.concatenate(all_algo_obs))
logger_tlu.debug(f"Algo seeds: {s_oracle}")
logger_tlu.debug(f"Algo reward: {mean_algo_nodes}")
logger_tlu.debug(f"Best algo reward: {reward_best}")
logger_tlu.debug(f"Algo weights {u_e[80:90]}".replace("\n", ""))
logger_tlu.debug(f"Real weights {true_weights[80:90]}".replace("\n", ""))
if mean_algo_nodes > reward_best:
reward_best = mean_algo_nodes
std_best = std_algo_nodes
s_best = s_oracle
u_e_best = u_e
# ---- Step 3 - Calculating updates ----
for i in all_algo_obs:
x_e = np.array([df_feats.loc[i].values])
m_inv -= (m_inv @ x_e.T @ x_e @ m_inv) / (
x_e @ m_inv @ x_e.T + sigma * sigma
)
b += x_e.T * int(i in all_algo_edges)
if persist:
return_dict = {
"s_best": s_best,
"u_e_best": u_e_best,
"reward_best": reward_best,
"std_best": std_best,
"m_inv": m_inv,
"b": b,
}
else:
return_dict = {
"s_best": s_best,
"u_e_best": u_e_best,
"reward_best": reward_best,
"std_best": std_best,
}
logger_tlu.debug("The algorithm has finished running.")
logger_tlu.debug(f"Returning: {return_dict}")
return return_dict
# --------------------------------------------------------------------------------------
# %% ------------------------------ Temporal Online IM ---------------------------------
# --------------------------------------------------------------------------------------
def timlinucb(
df_edges,
df_feats,
times,
nodes,
num_seeds=5,
sigma=4,
c=0.1,
epsilon=0.4,
num_repeats_oim=10,
num_repeats_oim_reward=10,
style="additive",
persist=False,
hide_tqdm=False,
):
""" Temporal Online Influence Maximization
Run the IMLinUCB[1] algorithm with pre-trained Node2Vec[3] features at every time
step in the graph df_edges.
Parameters
----------
df_edges : pandas.DataFrame
The graph we run the TOIM on, in the form of a DataFrame. A row represents one
edge in the graph, with columns being named "source", "target", "probab",
and "day". "probab" column is the "true" activation probability and "day" should
correspond to the days specified in times.
df_feats : pandas.DataFrame
A dataframe with node embeddings generated by node2vec [3]. Should have n rows,
where n is the number of nodes in the graph. The number of columns is specified
when running node2vec on the graph. The smaller the number of columns is the
more uncertain the embedding, but it also speeds up processing.
times : pandas.Series, list
A series or a list of the times that we are going to iterate through. Useful
if you don't want to iterate through every day in the network.
nodes : pandas.DataFrame
A dataframe of all unique nodes in df. Is used to calculate the number of nodes
for TIM.
num_inf : int, optional
Number of seed nodes to find. Default: 5.
sigma : float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 4
c: float, optional
A parameter used by the IMLinUCB algorithm. Refer to the IMLinUCB paper for
more details. [1] Default: 0.1
epsilon : float, optional
A parameter used by the TIM algorithm. Refer to the TIM paper for
more details. [2] Default: 0.4
num_repeats_oim : int, optional
Number of iterations of the IMLinUCB algorithm. The more, the better the
results. Default: 10
num_repeats_reward_oim : int, optional
Number of times to run the simulation at every step of the IMLinUCB algorithm.
A larger number should better the results. Default: 10
style : str, optional
Determines whether we take into account all edges up to t ("additive") or just
the ones that were formed at t ("dynamic"). Default: "additive"
persist : boolean, optional
Determines if we want to persist the OIM parameters. Default: False
hide_tqdm : boolean, optional
A paremeters used if you want to hide all tqdm progress bars. It's useful if
you want to paralellize the algorithm. Default: False
Returns
-------
results : DataFrame
A dataframe with the following columns
- s_best, the list of the selected seed nodes
- u_e_best, the approximated probabilities
- reward_best, the reward obtained by running IC with s_best
- time, the time step at which everything else was obtained
References
----------
.. [1] Wen, Zheng, Branislav Kveton, and Michal Valko.
"Influence maximization with semi-bandit feedback." CoRR, abs/1605.06593 (2016).
.. [2] Tang, Youze, Xiaokui Xiao, and Yanchen Shi.
"Influence maximization: Near-optimal time complexity meets practical efficiency."
Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014.
.. [3] Grover, Aditya, and Jure Leskovec.
"node2vec: Scalable feature learning for networks."
Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. 2016.
"""
results = []
# For persistent parameters - making the b and M matrices
if persist:
b = np.zeros((df_feats.shape[1], 1))
m_inv = np.eye(df_feats.shape[1], df_feats.shape[1])
else:
b = None
m_inv = None
times_iter = (
times
if hide_tqdm
else tqdm(times, desc=f"TOIM iters", leave=False, file=sys.stderr)
)
for t in times_iter:
if style == "additive":
df_t = df_edges[df_edges["day"] <= t].sort_values("source").reset_index()
elif style == "dynamic":
df_t = df_edges[df_edges["day"] == t].sort_values("source").reset_index()
df_feats_t = df_t["index"].apply(lambda x: df_feats.loc[x])
result_oim = oim_node2vec(
df_t,
df_feats_t,
nodes,
num_inf=num_seeds,
sigma=sigma,
c=c,
epsilon=epsilon,
num_repeats=num_repeats_oim,
num_repeats_reward=num_repeats_oim_reward,
persist=persist,
m_inv=m_inv,
b=b,
)
result_oim["time"] = t
if persist:
m_inv = result_oim.pop("m_inv")
b = result_oim.pop("b")
results.append(result_oim)
return pd.DataFrame(results)
def timlinucb_parallel_oim(
df_edges,
df_feats,
times,
nodes,
num_seeds=5,
sigma=4,
c=0.1,
epsilon=0.4,
num_repeats_oim=10,
num_repeats_oim_reward=10,
style="additive",
process_id=1,