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import torch
import time
import numpy as np
import pandas as pd
import networkx as nx
import scipy.sparse as sp
from texttable import Texttable
def tab_printer(args):
"""
Function to print the logs in a nice tabular format.
:param args: Parameters used for the model.
"""
args = vars(args)
keys = sorted(args.keys())
t = Texttable()
t.add_rows([["Parameter", "Value"]] + [[k.replace("_"," ").capitalize(),args[k]] for k in keys])
t.set_precision(6)
print(t.draw())
def graph_reader(path):
"""
Function to read the graph from the path.
:param path: Path to the edge list.
:return graph: NetworkX object returned.
"""
graph = nx.from_edgelist(pd.read_csv(path).values.tolist())
return graph
def field_reader(path):
"""
Function to read the field index from the path.
:param path: Path to the field index.
:return field_index: Numpy matrix of field index.
"""
field_index = np.load(path).astype(np.int64)
return field_index
def target_reader(path):
"""
Reading the target vector from disk.
:param path: Path to the target.
:return target: Target vector.
"""
target = np.array(pd.read_csv(path).iloc[:,1]).reshape(-1,1)
return target
def label_reader(path):
"""
Reading the user_label file from the path.
:param path: Path to the label file
:return user_labels: User labels DataFrame file.
"""
user_labels = pd.read_csv(path)
return user_labels
def distr_label_attr(df, label, attr):
"""
For a given df's label (e.g. gender), compute the distribution
of a given attribute (e.g. age) for each label's class
"""
return df.groupby([label, attr])[attr].count()
def pos_preds_attr_distr(df, targets, predictions, idx_list, label, attr):
"""
Given a list of prediction, compute the given attribute's
distribution for the correct predictions of the label
"""
# Distribution of attribute's classes in test set
df_test_grouped = df.iloc[idx_list].groupby([label, attr])[attr]
dict_test_grouped = df_test_grouped.apply(list).to_dict()
for k,v in dict_test_grouped.items():
dict_test_grouped[k] = len(v)
# Distribution of attribute's classes for correct predictions
pos_preds = targets == predictions
idx_pos_preds = idx_list[pos_preds]
df_pos_preds = df.iloc[idx_pos_preds]
df_pos_preds_grouped = df_pos_preds.groupby([label, attr])[attr]
dict_pos_preds_grouped = df_pos_preds_grouped.apply(list).to_dict()
for k,v in dict_pos_preds_grouped.items():
dict_pos_preds_grouped[k] = len(v)
# Compute correct prediction percentage
dict_perc_preds = {}
for k in dict_test_grouped.keys():
try:
perc = dict_pos_preds_grouped[k] / dict_test_grouped[k]
dict_perc_preds[k] = perc
except KeyError:
dict_perc_preds[k] = 0
return dict_perc_preds |