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runOldIterations.py
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runOldIterations.py
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from train import ModelTrainer
from collection import Collection
import pandas as pd
import logging
import traceback
import os
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# === THESIS ===
anbieter_config = {
'Construction': [
'Alpiq AG',
'Swisscom',
'Kummler + Matter AG',
'Siemens AG'
],
'IT': [
'G. Baumgartner AG',
'ELCA Informatik AG',
'Thermo Fisher Scientific (Schweiz) AG',
'Arnold AG',
],
'Other': [
'Riget AG',
'isolutions AG',
'CSI Consulting AG',
'Aebi & Co. AG Maschinenfabrik',
],
'Divers': [
'DB Schenker AG',
'IT-Logix AG',
'AVS Syteme AG',
'Sajet SA'
]
}
# === TESTING ===
#anbieter = 'Marti AG' #456
#anbieter = 'Axpo AG' #40
#anbieter = 'Hewlett-Packard' #90
#anbieter = 'BG Ingénieurs Conseils' SA #116
#anbieter = 'Pricewaterhousecoopers' #42
#anbieter = 'Helbling Beratung + Bauplanung AG' #20
#anbieter = 'Ofrex SA' #52
#anbieter = 'PENTAG Informatik AG' #10
#anbieter = 'Wicki Forst AG' #12
#anbieter = 'T-Systems Schweiz' #18
#anbieter = 'Bafilco AG' #20
#anbieter = '4Video-Production GmbH' #3
#anbieter = 'Widmer Ingenieure AG' #6
#anbieter = 'hmb partners AG' #2
#anbieter = 'Planmeca' #4
#anbieter = 'K & M Installationen AG' #4
select_anbieter = (
"anbieter.anbieter_id, "
"anbieter.institution as anbieter_institution, "
"cpv_dokument.cpv_nummer as anbieter_cpv, "
"ausschreibung.meldungsnummer"
)
# anbieter_CPV are all the CPVs the Anbieter ever won a procurement for. So all the CPVs they are interested in.
select_ausschreibung = (
"anbieter.anbieter_id, "
"auftraggeber.institution as beschaffungsstelle_institution, "
"auftraggeber.beschaffungsstelle_plz, "
"ausschreibung.gatt_wto, "
"ausschreibung.sprache, "
"ausschreibung.auftragsart_art, "
"ausschreibung.lose, "
"ausschreibung.teilangebote, "
"ausschreibung.varianten, "
"ausschreibung.projekt_id, "
# "ausschreibung.titel, "
"ausschreibung.bietergemeinschaft, "
"cpv_dokument.cpv_nummer as ausschreibung_cpv, "
"ausschreibung.meldungsnummer as meldungsnummer2"
)
attributes = ['ausschreibung_cpv', 'auftragsart_art','beschaffungsstelle_plz','gatt_wto','lose','teilangebote', 'varianten','sprache']
# attributes = ['auftragsart_art']
config = {
# ratio that the positive and negative responses have to each other
'positive_to_negative_ratio': 0.5,
# Percentage of training set that is used for testing (Recommendation of at least 25%)
'test_size': 0.25,
'runs': 100,
#'enabled_algorithms': ['random_forest'],
'enabled_algorithms': ['random_forest', 'decision_tree', 'gradient_boost'],
'random_forest': {
# Tune Random Forest Parameter
'n_estimators': 100,
'max_features': 'sqrt',
'max_depth': None,
'min_samples_split': 2
},
'decision_tree': {
'max_depth': 15,
'max_features': 'sqrt'
},
'gradient_boost': {
'n_estimators': 100,
'learning_rate': 0.1,
'max_depth': 15,
'max_features': 'sqrt'
}
}
# Prepare Attributes
def cleanData(df, filters):
# if 'beschaffungsstelle_plz' in filters:
# df[['beschaffungsstelle_plz']] = df[['beschaffungsstelle_plz']].applymap(ModelTrainer.tonumeric)
if 'gatt_wto' in filters:
df[['gatt_wto']] = df[['gatt_wto']].applymap(ModelTrainer.unifyYesNo)
if 'anzahl_angebote' in filters:
df[['anzahl_angebote']] = df[['anzahl_angebote']].applymap(ModelTrainer.tonumeric)
if 'teilangebote' in filters:
df[['teilangebote']] = df[['teilangebote']].applymap(ModelTrainer.unifyYesNo)
if 'lose' in filters:
df[['lose']] = df[['lose']].applymap(ModelTrainer.unifyYesNo)
if 'varianten' in filters:
df[['varianten']] = df[['varianten']].applymap(ModelTrainer.unifyYesNo)
if 'auftragsart_art' in filters:
auftrags_art_df = pd.get_dummies(df['auftragsart_art'], prefix='aftrgsrt',dummy_na=True)
df = pd.concat([df,auftrags_art_df],axis=1).drop(['auftragsart_art'],axis=1)
if 'sprache' in filters:
sprache_df = pd.get_dummies(df['sprache'], prefix='lang',dummy_na=True)
df = pd.concat([df,sprache_df],axis=1).drop(['sprache'],axis=1)
if 'auftragsart' in filters:
auftragsart_df = pd.get_dummies(df['auftragsart'], prefix='auftr',dummy_na=True)
df = pd.concat([df,auftragsart_df],axis=1).drop(['auftragsart'],axis=1)
if 'beschaffungsstelle_plz' in filters:
plz_df = pd.get_dummies(df['beschaffungsstelle_plz'], prefix='beschaffung_plz',dummy_na=True)
df = pd.concat([df,plz_df],axis=1).drop(['beschaffungsstelle_plz'],axis=1)
return df
class IterationRunner():
def __init__(self, anbieter_config, select_anbieter, select_ausschreibung, attributes, config, cleanData):
self.anbieter_config = anbieter_config
self.select_anbieter = select_anbieter
self.select_ausschreibung = select_ausschreibung
self.attributes = attributes
self.config = config
self.cleanData = cleanData
self.trainer = ModelTrainer(select_anbieter, select_ausschreibung, '', config, cleanData, attributes)
self.collection = Collection()
def run(self):
for label, anbieters in self.anbieter_config.items():
logger.info(label)
for anbieter in anbieters:
for attr_id in range(len(self.attributes)-1):
att_list = self.attributes[:attr_id+1]
self.singleRun(anbieter, att_list, label)
self.trainer.resetSQLData()
def runAttributesEachOne(self):
for label, anbieters in self.anbieter_config.items():
logger.info(label)
for anbieter in anbieters:
for attr in self.attributes:
att_list = [attr]
self.singleRun(anbieter, att_list, label)
self.trainer.resetSQLData()
def runSimpleAttributeList(self):
for label, anbieters in self.anbieter_config.items():
logger.info(label)
for anbieter in anbieters:
self.singleRun(anbieter, self.attributes, label)
self.trainer.resetSQLData()
def singleRun(self, anbieter, att_list, label):
logger.info('label: {}, anbieter: {}, attributes: {}'.format(label, anbieter, att_list))
try:
self.trainer.attributes = att_list
self.trainer.anbieter = anbieter
output = self.trainer.run()
output['label'] = label
self.collection.append(output)
filename = os.getenv('DB_FILE', 'dbs/auto.json')
self.collection.to_file(filename)
except Exception as e:
traceback.print_exc()
print(e)
print('one it done')
runner = IterationRunner(anbieter_config, select_anbieter, select_ausschreibung, attributes, config, cleanData)
if __name__ == '__main__':
# runner.collection.import_file('dbs/auto.json')
runner.run()
runner.runAttributesEachOne()
# label, anbieters = next(iter(runner.anbieter_config.items()))
# print(label)