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handler_x3.py
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handler_x3.py
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import pandas as pd
from clean_x3 import clean_x3
pc_aliases = {
"810g2": "810", "3626": "3113", "3249": "3113", "r7572": "i5420", "1229dx": "1016dx", "6787": "3435"}
cpu_model_aliases = {
"hp": {"1st gen": "620m", "3540m": "3520m", "2nd gen": "2520m", "q720": "2800m", "m520": "520m",
"3rd gen": "620m", "m640": "620m", "m620": "620m", "q820": "620m", "720qm": "620m", "640m": "620m",
"880m": "620m"},
"acer": {"1005m": "2020m"},
"lenovo": {"e-300": "hd-6310", "3rd gen": "3320m", "sl9400": "l9400"},
"asus": {},
"dell": {}
}
model_2_pcname = {
"1-6010": "15g070nr"
}
family_single = ["x200", "x200t", "x100"]
family_capacity = ["x220"]
pc_single = ["v5132", "8440p", "ux301la", "e5571", "15f009wm", "5742", "ux31a", "15g070nr",
"e1731", "p3171", "v5123", "e1532", "v3772", "e1522", "e5531", "v5573", "e5521",
"15r150nr", "15d090nr", "2339", "2320", "2338", "3448", "0622", "s7392", "v5122",
"8770w", "5547", "15g012dx", "7537", "5735", "2560p", "3444", "8570p",
"8730w", "8530p", "8530w", "2540p", "nc6400", "ux21e", "5620", "8470w",
"2170p", "e1531", "2325", "as5552", "15p030nr", "2760p", "dv6000",
"m731r", "i5420", "1016dx"]
model_single = ["3320m"]
pc_core = ["8560p", "m5481", "810", "e1572", "e1771", "v3111"]
pc_capacity = ["8540w", "8460p", "0611"]
pc_core_capacity = ["9470m", "8740w"]
solved_spec = []
unsolved_spec = []
instance_list = set()
def handle_x3(dataset: pd.DataFrame):
""" Call clean_x3.py;
Give an identification for each record according to their cleaned field values
and match records based on their identification
:param dataset: X3.csv
:return:
A DataFrame of matched pairs which contains following columns:
{left_instance_id: the left instance of a matched pair
left_instance_id: the right instance of a matched pair}
"""
dataset = clean_x3(dataset)
for index, row in dataset.iterrows():
instance_id = row['instance_id']
brand = row['brand']
cpu_core = row['cpu_core']
cpu_model = row['cpu_model']
cpu_frequency = row['cpu_frequency']
pc_name = row['pc_name']
capacity = row['ram_capacity']
family = row['family']
title = row['title']
pc = {}
if pc_name in pc_aliases.keys():
pc_name = pc_aliases[pc_name]
if (pc_name == '0') and (family in family_single):
pc_name = family
if cpu_model in model_2_pcname.keys():
pc_name = model_2_pcname[cpu_model]
if brand in cpu_model_aliases.keys():
if cpu_model in cpu_model_aliases[brand].keys():
cpu_model = cpu_model_aliases[brand][cpu_model]
instance_list.add(instance_id)
pc['id'] = instance_id
pc['title'] = title
pc['brand'] = brand
pc['pc_name'] = pc_name
pc['cpu_model'] = cpu_model
pc['capacity'] = capacity
pc['cpu_core'] = cpu_core
if pc_name == "8460p" and cpu_model == "2450m":
pc['identification'] = pc_name + ' ' + cpu_model
solved_spec.append(pc)
elif pc_name in pc_single or pc_name in family_single:
pc['identification'] = pc_name
solved_spec.append(pc)
elif pc_name in pc_capacity and capacity != '0':
pc['identification'] = pc_name + ' ' + capacity
solved_spec.append(pc)
elif cpu_model in model_single:
pc['identification'] = cpu_model
solved_spec.append(pc)
elif pc_name in pc_core and cpu_core != '0':
pc['identification'] = pc_name + ' ' + cpu_core
solved_spec.append(pc)
elif family in family_capacity and capacity != '0':
pc['identification'] = family + ' ' + capacity
solved_spec.append(pc)
elif pc_name in pc_core_capacity and cpu_core != '0' and capacity != '0':
pc['identification'] = pc_name + ' ' + cpu_core + ' ' + capacity
solved_spec.append(pc)
elif pc_name != '0' and cpu_model != '0':
pc['identification'] = pc_name + ' ' + cpu_model
solved_spec.append(pc)
elif pc_name != '0' and cpu_core != '0':
pc['identification'] = pc_name + ' ' + cpu_core
solved_spec.append(pc)
elif pc_name != '0' and cpu_frequency != '0':
pc['identification'] = pc_name + ' ' + cpu_frequency
solved_spec.append(pc)
else:
unsolved_spec.append(pc)
clusters = dict()
for s in solved_spec:
if s['identification'] in clusters.keys():
clusters[s['identification']].append(s['id'])
else:
clusters.update({s['identification']: [s['id']]})
for u in unsolved_spec:
identification = u['brand'] + ' ' + u['pc_name'] + ' ' + u['cpu_model'] + ' ' + u['capacity'] + ' ' + \
u['cpu_core']
if identification in clusters.keys():
clusters[identification].append(u['id'])
else:
clusters.update({identification: [u['id']]})
couples = set()
for c in clusters.keys():
if len(clusters[c]) > 1:
for i in clusters[c]:
for j in clusters[c]:
if i < j:
couples.add((i, j, 1))
if i > j:
couples.add((j, i, 1))
output = couples
output = pd.DataFrame(
output,
columns=[
'left_instance_id',
'right_instance_id',
'label'])
output.drop(columns=['label'], inplace=True)
return output