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supervised_classifier.py
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supervised_classifier.py
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import csv
import logging
import math
import os
import os
import pickle
import re
import time
import warnings
from pprint import pprint
from typing import List, Tuple
import gensim
import gensim
import gensim.corpora as corpora
import jsonpickle
import matplotlib.pyplot as plt
import neptune
import neptunecontrib.monitoring.skopt as sk_utils
import nltk
import numpy as np
import pandas as pd
import regex
import skopt
from gensim.models import CoherenceModel, LdaMulticore, HdpModel
from gensim.utils import simple_preprocess
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import TomekLinks
from nltk.corpus import wordnet
from sklearn import linear_model
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import fbeta_score
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.svm import SVC, LinearSVC
from sklearn.tree import DecisionTreeClassifier
from skopt import BayesSearchCV
from skopt.space import Real, Categorical
from spellchecker import SpellChecker
import settings
import os
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
DATA_DIRECTORY = os.getenv("DATA_DIRECTORY")
OUTPUT_DIRECTORY = os.getenv("OUTPUT_DIRECTORY")
# Concepts LDA
dictionary = gensim.corpora.Dictionary.load(os.path.join(OUTPUT_DIRECTORY, 'models', 'concept', 'lda_dict.dict'))
lda = gensim.models.ldamodel.LdaModel.load(os.path.join(OUTPUT_DIRECTORY, 'models', 'concept', 'lda_10.gensim'))
# # Wiki LDA
# dictionary = gensim.corpora.Dictionary.load_from_text(os.path.join(OUTPUT_DIRECTORY ,'wikipedia','wiki_wordids.txt'))
# corpus = gensim.corpora.MmCorpus(os.path.join(OUTPUT_DIRECTORY ,'wikipedia','wiki_tfidf.mm'))
# lda = gensim.models.ldamodel.LdaModel.load(os.path.join(OUTPUT_DIRECTORY, 'models', 'wiki','lda_10.gensim'))
conceptsDir = os.path.join(OUTPUT_DIRECTORY, 'concepts', 'clean')
train_corpus = []
train_corpus_excluded = []
train_corpus_splitted = []
conceptPolysemyGS = {}
# wikiDisambiguationPages = {}
ytrainwiki = []
ytrainwiki_excluded = []
conceptInWikiOrDisambiguation = {}
cleanConceptNames = {}
def cleanString(str):
remove = regex.compile(r'[\p{C}|\p{M}|\p{P}|\p{S}|\p{Z}]+', regex.UNICODE)
str = remove.sub(u" ", str).strip()
str = str.lower()
return str
def getWikiDisambiguationPages():
filename = os.path.join(DATA_DIRECTORY, 'evaluation', 'wiki', 'wikiDisambiguationPages.json')
jsondata = open(filename, encoding='utf-8').read()
thawed = jsonpickle.decode(jsondata)
return thawed
def getConceptInWikiOrDisambiguation():
filename = os.path.join(DATA_DIRECTORY, 'evaluation', 'wiki', 'conceptInWikiOrDisambiguation.json')
jsondata = open(filename, encoding='utf-8').read()
thawed = jsonpickle.decode(jsondata)
return thawed
def saveCleanNames():
print('Saving new cleanConceptNames file...')
files = os.listdir(conceptsDir)
conceptNames = {}
for concept in files:
conceptNames[concept] = cleanString(concept.replace('.txt', ''))
json_string = jsonpickle.encode(conceptNames)
jsonFile = os.path.join(DATA_DIRECTORY, 'evaluation', 'wiki', 'cleanConceptNames.json')
with open(jsonFile, 'w') as json_file:
json_file.write(json_string)
json_file.close()
def getCleanConceptNames():
filename = os.path.join(DATA_DIRECTORY, 'evaluation', 'wiki', 'cleanConceptNames.json')
jsondata = open(filename, encoding='utf-8').read()
thawed = jsonpickle.decode(jsondata)
return thawed
def loadAllConceptFiles(search_params):
global train_corpus, ytrainwiki, train_corpus_excluded, ytrainwiki_excluded
train_corpus = []
ytrainwiki = []
train_corpus_excluded = []
ytrainwiki_excluded = []
for concept in cleanConceptNames.keys():
rawtext = loadConceptFile(concept)
text = rawtext.split()
cleanName = cleanConceptNames[concept]
if ADD_NAME_IN_DOC:
text = cleanName + ' ' + text
if (len(text) > search_params['MIN_DOC_LEN'] and len(text) <= MAX_DOC_WORDS):
train_corpus.append(text)
# train_corpus_splitted.append(text.split())
if conceptInWikiOrDisambiguation[cleanName] == 1:
ytrainwiki.append(1)
else:
ytrainwiki.append(0)
else:
train_corpus_excluded.append(text)
if conceptInWikiOrDisambiguation[cleanName] == 1:
ytrainwiki_excluded.append(1)
else:
ytrainwiki_excluded.append(0)
print(f'Documents in train_corpus: {len(train_corpus)}')
print(f'Documents in train_corpus_excluded: {len(train_corpus_excluded)}')
def loadConceptFile(concept):
file = os.path.join(conceptsDir, concept)
doc = open(file, encoding="utf8").read()
return doc
def getSingleVector(textSplits):
bow = dictionary.doc2bow(textSplits)
top_topics = lda.get_document_topics(bow, minimum_probability=0.0)
topic_vec = [x[1] for x in top_topics]
if SORT_TOPICS:
topic_vec = sorted(topic_vec, reverse=True)
if ADD_LENGTH_FEATURE:
topic_vec.extend([len(textSplits)]) # length concept text
return topic_vec
def getTrainVecs(train_corpus):
train_vecs = []
if REMOVE_DUPLICATE_DOCS:
train_corpus = list(set(train_corpus))
for i in range(len(train_corpus)):
text = train_corpus[i]
topic_vec = getSingleVector(text)
train_vecs.append(topic_vec)
print(f'Length of features {len(train_vecs[0])}')
return train_vecs
def runLogisticRegression(X_train_scale, X_val_scale, y_train, y_test):
print("Training a Logistic Regression Model...")
scikit_log_reg = LogisticRegression(
class_weight='balanced',
solver='saga',
max_iter=10000,
C=0.1,
fit_intercept=True)
model = scikit_log_reg.fit(X_train_scale, y_train)
y_pred = model.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric('LR-f1', f1)
print('Logistic Regression f1:', f1)
if TEST_EXCLUDED:
testModelonExcluded(model, 'LR')
return f1
def runModelBayesSearchCV(X_train_scale, X_val_scale, y_train, y_test):
print("Fine tuning Model...")
# Change model and params here for the model you want to fine tune
clf = LogisticRegression(max_iter=10000)
# this is our parameter grid
param_grid = {
'solver': ['liblinear', 'saga'],
'class_weight': [None, 'balanced'],
'penalty': ['l1', 'l2'],
'tol': Real(1e-5, 1e-3, 'log-uniform'),
'C': Real(1e-5, 100, 'log-uniform'),
'fit_intercept': [True, False]
}
# set up our optimiser to find the best params in 30 searches
opt = BayesSearchCV(
clf,
param_grid,
scoring='f1',
n_iter=30,
random_state=1234,
verbose=5
)
print(opt.total_iterations)
opt.fit(X_train_scale, y_train)
print('Best params achieve a test score of', opt.score(X_val_scale, y_test), ':')
print(opt.best_params_)
print('Best params achieve a test score of', opt.best_estimator_, ':')
def runSGDClassifier(X_train_scale, X_val_scale, y_train, y_test):
print("Training a Logistic Regression SGD Model...")
sgd = linear_model.SGDClassifier(
max_iter=10000,
tol=1e-3,
loss='log',
class_weight='balanced'
).fit(X_train_scale, y_train)
y_pred = sgd.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric('LRSGD-f1', f1)
print('Logistic Regression SGD f1:', f1)
if TEST_EXCLUDED:
testModelonExcluded(sgd, 'LRSGD')
return f1
def runSGDHuberClassifier(X_train_scale, X_val_scale, y_train, y_test):
print("Training a SGD Modified Huber Model...")
sgd_huber = linear_model.SGDClassifier(
max_iter=10000,
tol=1e-3,
alpha=1,
loss='modified_huber',
class_weight='balanced'
).fit(X_train_scale, y_train)
y_pred = sgd_huber.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric('LRSGDH-f1', f1)
print('SGD Modified Huber f1:', f1)
if TEST_EXCLUDED:
testModelonExcluded(sgd_huber, 'LRSGDH')
return f1
def runAllSVM(X_train_scale, X_val_scale, y_train, y_test):
kernels = ['linear', 'rbf', 'poly']
gammas = [0.1, 1, 10, 100]
degrees = [0, 1, 2, 3, 4, 5, 6]
cv_svclinear_f1 = []
for kernel in kernels:
if kernel != 'linear':
for gamma in gammas:
if kernel == 'poly':
for degree in degrees:
svm = runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test, kernel, degree=degree,
gamma=gamma)
cv_svclinear_f1.append(svm)
else:
svm = runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test, kernel, gamma=gamma)
cv_svclinear_f1.append(svm)
else:
svm = runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test, kernel)
cv_svclinear_f1.append(svm)
return cv_svclinear_f1
def runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test, kernel='linear', gamma='scale', degree=3):
print(f'Training a SVM {kernel}, {gamma}, {degree} Model...')
# Create a svm Classifier
clf = SVC(kernel=kernel, gamma=gamma, degree=degree) # Linear Kernel
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'SVM-{kernel}-{gamma}-{degree}-f1', f1)
print(f'SVM {kernel}, {gamma}, {degree}:', f1)
return clf
def runGaussianNBClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a GaussianNB Model...')
clf = GaussianNB()
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'GaussianNB-f1', f1)
print(f'GaussianNB:', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'GaussianNB')
return f1
def runLinearSVCClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a LinearSVC Model...')
clf = LinearSVC(
max_iter=100000,
tol=1e-3,
dual=False
)
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'LinearSVC-f1', f1)
print(f'LinearSVC:', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'LinearSVC')
return f1
def runRandomForestClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a RandomForestClassifier Model...')
clf = RandomForestClassifier(class_weight='balanced', n_estimators=1000)
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'RandomForest-f1', f1)
print(f'RandomForest:', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'RandomForest')
return f1
def runGradientBoostingClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a GradientBoostingClassifier Model...')
clf = GradientBoostingClassifier()
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'GradientBoosting-f1', f1)
print(f'GradientBoosting:', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'GradientBoosting')
return f1
def runKNeighborsClassifier(X_train_scale, X_val_scale, y_train, y_test, neighbors=5):
print(f'Training a KNeighborsClassifier {neighbors} Model...')
clf = KNeighborsClassifier(n_neighbors=neighbors)
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'KNeighborsClassifier-f1', f1)
print(f'KNeighborsClassifier {neighbors}:', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'KNeighborsClassifier')
return f1
def runDecisionTreeClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a KNeighborsClassifier Model...')
clf = DecisionTreeClassifier()
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'DecisionTreeClassifier-f1', f1)
print(f'DecisionTreeClassifier :', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'DecisionTreeClassifier')
return f1
def runMLPClassifier(X_train_scale, X_val_scale, y_train, y_test):
print(f'Training a MLPClassifier Model...')
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(140, 140, 140), random_state=42, max_iter=10000)
# Train the model using the training sets
clf.fit(X_train_scale, y_train)
# Predict the response for test dataset
y_pred = clf.predict(X_val_scale)
f1 = f1_score(y_test, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'MLPClassifier-f1', f1)
print(f'MLPClassifier :', f1)
if TEST_EXCLUDED:
testModelonExcluded(clf, 'MLPClassifier')
return f1
def runMLPClassifierBayesSearchCV(X_train_scale, X_val_scale, y_train, y_test):
print("Training a MLPClassifier Model...")
clf = MLPClassifier(max_iter=10000)
# this is our parameter grid
param_grid = {
'hidden_layer_sizes': Categorical[(50, 50, 50), (50, 100, 50), (100,)],
'activation': ['tanh', 'relu', 'logistic', 'identity'],
'solver': ['sgd', 'adam', 'lbfgs'],
'alpha': Real(0.0001, 0.9, 'log-uniform'),
'learning_rate': ['constant', 'adaptive', 'invscaling']}
# set up our optimiser to find the best params in 30 searches
opt = BayesSearchCV(
clf,
param_grid,
scoring='f1',
n_iter=30,
random_state=1234,
verbose=5
)
print(opt.total_iterations)
opt.fit(X_train_scale, y_train)
print('Best params achieve a test score of', opt.score(X_val_scale, y_test), ':')
print(opt.best_params_)
print('Best params achieve a test score of', opt.best_estimator_, ':')
def printSend(name, val):
try:
if (val != []):
print(f'{name} Val f1: {np.mean(val):.3f} +- {np.std(val):.3f}')
if NEPTUNE_SEND:
neptune.send_metric(f'{name}-f1-Mean', np.mean(val))
neptune.send_metric(f'{name}-f1-Std', np.std(val))
else:
print(f'{name} is []')
except:
print('Error sending values...')
def getFeatures():
features = getTrainVecs(train_corpus)
return features
### Used for testing on documents excluded from the training corpus, e.g. training on larger documents and testing on smaller
def testModelonExcluded(clf, name):
global train_corpus_excluded, ytrainwiki_excluded
X = getTrainVecs(train_corpus_excluded)
y = ytrainwiki_excluded
# Scale Data
scaler = StandardScaler()
X_train_scale = scaler.fit_transform(X)
y_pred = clf.predict(X_train_scale)
f1 = f1_score(y, y_pred, average='binary')
if NEPTUNE_SEND:
neptune.send_metric(f'{name}-ex-f1', f1)
print(f'{name}-ex f1:', f1)
def train_models(kfold=0, test_size=0.2, neighbors=5):
print("Starting model training...")
result_list = []
X = getFeatures()
y = ytrainwiki
print('Got features and data..')
if kfold == 0: # Run without K-fold
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
if SMOTE_SAMPLING:
sm = SMOTE(sampling_strategy='minority', random_state=7)
# Fit the model to generate the data.
X_train, y_train = sm.fit_sample(X_train, y_train)
elif TomekLinks_SAMPLING:
tl = TomekLinks(sampling_strategy='majority')
X_train, y_train = tl.fit_sample(X_train, y_train)
# Scale Data
scaler = StandardScaler()
X_train_scale = scaler.fit_transform(X_train)
X_val_scale = scaler.transform(X_test)
# For fine tuning the model use this method
# runModelBayesSearchCV(X_train_scale,X_val_scale, y_train, y_test)
lr = runLogisticRegression(X_train_scale, X_val_scale, y_train, y_test)
sgd = runSGDClassifier(X_train_scale, X_val_scale, y_train, y_test)
sgdh = runSGDHuberClassifier(X_train_scale, X_val_scale, y_train, y_test)
# runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test)
gnb = runGaussianNBClassifier(X_train_scale, X_val_scale, y_train, y_test)
lsvc = runLinearSVCClassifier(X_train_scale, X_val_scale, y_train, y_test)
rf = runRandomForestClassifier(X_train_scale, X_val_scale, y_train, y_test)
gb = runGradientBoostingClassifier(X_train_scale, X_val_scale, y_train, y_test)
dt = runDecisionTreeClassifier(X_train_scale, X_val_scale, y_train, y_test)
knn = runKNeighborsClassifier(X_train_scale, X_val_scale, y_train, y_test, neighbors)
mlp = runMLPClassifier(X_train_scale, X_val_scale, y_train, y_test)
result_list.append(lr)
result_list.append(sgd)
result_list.append(sgdh)
result_list.append(gnb)
result_list.append(lsvc)
result_list.append(rf)
result_list.append(gb)
result_list.append(dt)
result_list.append(knn)
result_list.append(mlp)
else:
if SMOTE_SAMPLING:
sm = SMOTE(sampling_strategy='minority', random_state=42)
X, y = sm.fit_sample(X, y)
elif TomekLinks_SAMPLING:
tl = TomekLinks(sampling_strategy='majority')
X, y = tl.fit_sample(X, y)
cv_lr_f1, cv_lrsgd_f1, cv_svcsgd_f1, cv_svclinear_f1, cv_gnb_f1, cv_lsvc_f1, cv_rndf_f1, cv_gb_f1, cv_knn_f1 = [], [], [], [], [], [], [], [], []
cv_mlp_f1, cv_dt_f1 = [], []
kf = StratifiedKFold(kfold, shuffle=True, random_state=42)
X = np.array(X)
y = np.array(y)
k = 0
for train_ind, val_ind in kf.split(X, y):
# Assign CV IDX
X_train, y_train = X[train_ind], y[train_ind]
X_val, y_test = X[val_ind], y[val_ind]
# summarize train and test composition
train_0, train_1 = len(y_train[y_train == 0]), len(y_train[y_train == 1])
test_0, test_1 = len(y_test[y_test == 0]), len(y_test[y_test == 1])
print('>Train: 0=%d, 1=%d, Test: 0=%d, 1=%d' % (train_0, train_1, test_0, test_1))
# Scale Data
scaler = StandardScaler()
X_train_scale = scaler.fit_transform(X_train)
X_val_scale = scaler.transform(X_val)
# Logisitic Regression
lr = runLogisticRegression(X_train_scale, X_val_scale, y_train, y_test)
cv_lr_f1.append(lr)
# Logistic Regression SGD
sgd = runSGDClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_lrsgd_f1.append(sgd)
# SGD Modified Huber
sgd_huber = runSGDHuberClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_svcsgd_f1.append(sgd_huber)
# # SVM
# svm = runSVMClassifier(X_train_scale, X_val_scale, y_train, y_test)
# cv_svclinear_f1.append(svm)
#
# Gaussian NB
gnb = runGaussianNBClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_gnb_f1.append(gnb)
# Linear SVC
lsvc = runLinearSVCClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_lsvc_f1.append(lsvc)
# Random Forest
rndf = runRandomForestClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_rndf_f1.append(rndf)
# Gradient Boosting
gb = runGradientBoostingClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_gb_f1.append(gb)
# Decision Trees
dt = runDecisionTreeClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_dt_f1.append(dt)
# KNN
knn = runKNeighborsClassifier(X_train_scale, X_val_scale, y_train, y_test, neighbors)
cv_knn_f1.append(knn)
# MLP
mlp = runMLPClassifier(X_train_scale, X_val_scale, y_train, y_test)
cv_mlp_f1.append(mlp)
print('\n')
printSend('LR', cv_lr_f1)
printSend('LRSGD', cv_lrsgd_f1)
printSend('LRSGDH', cv_svcsgd_f1)
printSend('SVM', cv_svclinear_f1)
printSend('GaussianNB', cv_gnb_f1)
printSend('LinearSVC', cv_lsvc_f1)
printSend('RandomForest', cv_rndf_f1)
printSend('GradientBoosting', cv_gb_f1)
printSend('KNN', cv_knn_f1)
printSend('MLP', cv_mlp_f1)
printSend('DecisionTrees', cv_dt_f1)
return [0]
return result_list
# Model params
kfold = 10 # 10 for 10 fold otherwise 0 for split
test_size = 0.2
ADD_LENGTH_FEATURE = True
ADD_NAME_IN_DOC = False
SORT_TOPICS = False
NEPTUNE_SEND = False
REMOVE_NUMBERS = True
REMOVE_DUPLICATE_DOCS = False
SMOTE_SAMPLING = False
TomekLinks_SAMPLING = True
MAX_DOC_WORDS = 1706800 # All
MIN_DOC_WORDS = 5 # doc must have at least this many words
SPACE = None
SCIKIT_OPTIMIZE = False
TEST_EXCLUDED = False
if SCIKIT_OPTIMIZE:
SPACE = [skopt.space.Integer(MIN_DOC_WORDS, MAX_DOC_WORDS, name='MIN_DOC_LEN')]
else:
SPACE = {'MIN_DOC_LEN': MIN_DOC_WORDS}
CALLS = 500
RANDOM_CALLS = 20
if SCIKIT_OPTIMIZE:
@skopt.utils.use_named_args(SPACE)
def objective(**params):
mind = params['MIN_DOC_LEN']
print(f'Loading files min doc len: {mind}...')
loadAllConceptFiles(params)
print('Files loaded...')
neptune.send_metric('MIN_DOC_LEN', mind)
model_scores = train_models(kfold=kfold)
result = max(model_scores)
print('Result: ' + str(result) + ' Params: ' + str(params['MIN_DOC_LEN']))
return -1.0 * result
if __name__ == '__main__':
start_time = time.time()
if NEPTUNE_SEND:
neptune.set_project('arshad115/thesis')
experiment_name = 'supervised_classifier_concept'
neptune.create_experiment(name=experiment_name)
# Run if the file cleanConceptNames.json is not found to create it from the concepts dir
saveCleanNames()
conceptInWikiOrDisambiguation = getConceptInWikiOrDisambiguation()
cleanConceptNames = getCleanConceptNames()
if SCIKIT_OPTIMIZE:
monitor = sk_utils.NeptuneMonitor()
results = skopt.forest_minimize(objective, SPACE, n_calls=CALLS, n_random_starts=RANDOM_CALLS,
callback=[monitor])
best_auc = -1.0 * results.fun
best_params = results.x
print('Finished processing everything, --- %s minutes ---' % ((time.time() - start_time) / 60), flush=True)
print('best result: ', best_auc, flush=True)
print('best parameters: ', best_params, flush=True)
# sk_utils.log_results(results)
neptune.stop()
else:
print(f'Loading files...')
loadAllConceptFiles(SPACE)
print('Files loaded...')
# for x in range(200, 270, 10):
x = 10
# lda = LdaMulticore.load(os.path.join(OUTPUT_DIRECTORY, 'models', 'wiki', f'lda_{x}.gensim'))
if NEPTUNE_SEND:
neptune.send_metric('MIN_DOC_LEN', SPACE['MIN_DOC_LEN'])
neptune.send_metric('lda_model_topics', x)
model_scores = train_models(kfold=kfold)
result = max(model_scores)
print('Result: ' + str(result) + ' Params: ' + str(x))