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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, fbeta_score, confusion_matrix, ConfusionMatrixDisplay
from sklearn.utils.class_weight import compute_sample_weight
import pickle as pkl
from tqdm import tqdm
import time
import os
import shutil
import json
from copy import deepcopy
from helpers.required_classes import *
def log(*args):
print(*args, flush=True)
def train_code_classifier(vecs_train_codes, vecs_test_for_groups,
labels_train_codes, labels_test_groups_codes, labels_test_groups_groups,
labels_train_groups,
models_folder, group_name, balance=None, logging=True, use_gpu=True):
"""
balance - is a type of balancing dataset:
remove - remove items per class until amount texts per clas is not the same as minimum amount
duplicate - duplicate items per class until amount texts per clas is not the same as maximum amount
weight - weighted training model
None - without any balancing method
"""
log(f"training model for codes classifiers in group {group_name}")
# create / remove folder
experiment_path = f"{models_folder}/{group_name}"
if not os.path.exists(experiment_path):
os.makedirs(experiment_path, exist_ok=True)
else:
shutil.rmtree(experiment_path)
os.makedirs(experiment_path, exist_ok=True)
labels_train_for_group = labels_train_codes[labels_train_groups==group_name]
if logging:
log(f"e.g. labels in the group: {labels_train_for_group[:3]} cng of codes: {len(np.unique(labels_train_for_group))} cnt of texts: {len(labels_train_for_group)}")
# prepare train labels
if len(np.unique(labels_train_for_group)) < 2:
# if group have only one code inside
code_name = labels_train_for_group[0]
if logging:
log(f'group {group_name} have only one code inside {code_name}')
simple_clf = SimpleModel()
simple_clf.fit([], [code_name])
pkl.dump(simple_clf, open(f"{experiment_path}/{group_name}_code_clf.pkl", 'wb'))
return {"f1_score": 'one_cls', "accuracy": 'one_cls'}
sample_weights = compute_sample_weight(
class_weight='balanced',
y=labels_train_for_group
)
# prepare other data
vecs_train_for_group = vecs_train_codes[labels_train_groups==group_name]
vecs_test_for_group = vecs_test_for_groups[labels_test_groups_groups==group_name]
labels_test_for_group = labels_test_groups_codes[labels_test_groups_groups==group_name]
labels_train_for_group, vecs_train_for_group = balance_dataset(
labels_train_for_group, vecs_train_for_group, balance=balance
)
fit_start_time = time.time()
model = CustomXGBoost(use_gpu)
if balance == 'weight':
model.fit(vecs_train_for_group, labels_train_for_group, sample_weight=sample_weights)
else:
model.fit(vecs_train_for_group, labels_train_for_group)
pkl.dump(model, open(f"{experiment_path}/{group_name}_code_clf.pkl", 'wb'))
if logging:
log(f'Trained in {time.time() - fit_start_time}s')
pred_start_time = time.time()
predictions_group = model.predict(vecs_test_for_group)
scores = {
"f1_score": fbeta_score(labels_test_for_group, predictions_group, beta=1, average='macro'),
"accuracy": accuracy_score(labels_test_for_group, predictions_group)
}
if logging:
log(scores, f'Predicted in {time.time() - pred_start_time}s')
with open(f"{experiment_path}/{group_name}_scores.json", 'w') as f:
f.write(json.dumps(scores))
conf_matrix = confusion_matrix(labels_test_for_group, predictions_group)
disp_code = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,
display_labels=model.classes_, )
fig, ax = plt.subplots(figsize=(5,5))
disp_code.plot(ax=ax)
plt.xticks(rotation=90)
plt.savefig(f"{experiment_path}/{group_name}_matrix.png")
return scores
def train_codes_for_groups(vecs_train_codes, vecs_test_groups,
labels_train_codes, labels_test_groups_codes, labels_test_groups_groups,
labels_train_groups,
output_path, logging, use_gpu=True):
all_scores = []
for group_name in tqdm(np.unique(labels_train_groups)):
row = {'group': group_name}
for balanced_method in ['weight']: # [None, 'remove', 'weight', 'duplicate']:
if logging:
log('\n', '-'*50)
scores = train_code_classifier(vecs_train_codes, vecs_test_groups,
labels_train_codes, labels_test_groups_codes, labels_test_groups_groups,
labels_train_groups,
output_path, group_name, balanced_method, logging, use_gpu)
scores = {f"{balanced_method}_{k}": v for k, v in scores.items()}
row.update(scores)
all_scores.append(row)
df = pd.DataFrame(all_scores)
columns = df.columns.tolist()
columns.remove('group')
mean_scores = {'group': 'MEAN'}
for score_name in columns:
mean_score = df[df[score_name] != 'one_cls'][score_name].mean()
mean_scores.update({score_name: float(mean_score)})
df = pd.concat([df, pd.DataFrame([mean_scores])], ignore_index=True)
return df
def make_experiment_classifier(vecs_train_codes, vecs_test_codes, vecs_test_group,
labels_train_codes, labels_test_codes,
labels_test_groups, labels_train_groups,
sample_weights_codes, sample_weights_groups,
texts_test_codes, texts_test_groups,
experiment_name, classifier_model_code, classifier_model_group, experiment_path, balance=None):
# train different models as base model for group and codes
log(f'Model: {experiment_name}')
# create / remove experiment folder
experiment_path = f"{experiment_path}/{experiment_name}"
if not os.path.exists(experiment_path):
os.makedirs(experiment_path, exist_ok=True)
else:
shutil.rmtree(experiment_path)
os.makedirs(experiment_path, exist_ok=True)
# fit the models
cls_codes = deepcopy(classifier_model_code)
cls_groups = deepcopy(classifier_model_group)
labels_train_codes_balanced, vecs_train_codes_balanced = balance_dataset(
labels_train_codes, vecs_train_codes, balance=balance
)
labels_train_groups_balanced, vecs_train_codes_balanced = balance_dataset(
labels_train_groups, vecs_train_codes, balance=balance
)
log('start training base model')
if balance == 'weight':
try:
start_time = time.time()
cls_codes.fit(vecs_train_codes_balanced, labels_train_codes_balanced, sample_weight=sample_weights_codes)
log(f'codes classify trained in {(time.time() - start_time) / 60}m')
start_time = time.time()
cls_groups.fit(vecs_train_codes_balanced, labels_train_groups_balanced, sample_weight=sample_weights_groups)
log(f'groups classify trained in {(time.time() - start_time) / 60}m')
except Exception as e:
log(str(e))
start_time = time.time()
cls_codes.fit(vecs_train_codes_balanced, labels_train_codes_balanced)
log(f'codes classify trained in {(time.time() - start_time) / 60}m')
start_time = time.time()
cls_groups.fit(vecs_train_codes_balanced, labels_train_groups_balanced)
log(f'groups classify trained in {(time.time() - start_time) / 60}m')
else:
start_time = time.time()
cls_codes.fit(vecs_train_codes_balanced, labels_train_codes_balanced)
log(f'codes classify trained in {(time.time() - start_time) / 60}m')
start_time = time.time()
cls_groups.fit(vecs_train_codes_balanced, labels_train_groups_balanced)
log(f'groups classify trained in {(time.time() - start_time) / 60}m')
pkl.dump(cls_codes, open(f"{experiment_path}/{experiment_name}_codes.pkl", 'wb'))
pkl.dump(cls_groups, open(f"{experiment_path}/{experiment_name}_groups.pkl", 'wb'))
# inference the model
predictions_code = cls_codes.predict(vecs_test_codes)
predictions_group = cls_groups.predict(vecs_test_group)
scores = {
"f1_score_code": fbeta_score(labels_test_codes, predictions_code, beta=1, average='macro'),
"f1_score_group": fbeta_score(labels_test_groups, predictions_group, beta=1, average='macro'),
"accuracy_code": accuracy_score(labels_test_codes, predictions_code),
"accuracy_group": accuracy_score(labels_test_groups, predictions_group)
}
with open(f"{experiment_path}/{experiment_name}_scores.json", 'w') as f:
f.write(json.dumps(scores))
conf_matrix = confusion_matrix(labels_test_codes, predictions_code)
disp_code = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,
display_labels=cls_codes.classes_, )
fig, ax = plt.subplots(figsize=(20,20))
disp_code.plot(ax=ax)
plt.xticks(rotation=90)
plt.savefig(f"{experiment_path}/{experiment_name}_codes_matrix.png")
conf_matrix = confusion_matrix(labels_test_groups, predictions_group)
disp_group = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,
display_labels=cls_groups.classes_, )
fig, ax = plt.subplots(figsize=(20,20))
disp_group.plot(ax=ax)
plt.xticks(rotation=90)
plt.savefig(f"{experiment_path}/{experiment_name}_groups_matrix.png")
pd.DataFrame({'codes': predictions_code, 'truth': labels_test_codes, 'text': texts_test_codes}).to_csv(f"{experiment_path}/{experiment_name}_pred_codes.csv")
pd.DataFrame({'groups': predictions_group, 'truth': labels_test_groups, 'text': texts_test_groups}).to_csv(f"{experiment_path}/{experiment_name}_pred_groups.csv")
return predictions_code, predictions_group, scores
def train_base_clfs(classifiers, vecs_train_codes, vecs_test_codes, vecs_test_group,
labels_train_codes, labels_test_codes,
labels_test_groups_codes, labels_test_groups_groups, labels_train_groups,
sample_weights_codes, sample_weights_groups,
texts_test_codes, texts_test_groups, output_path):
results = ''
for experiment_data in classifiers:
for balanced_method in ['weight']:
exp_name = experiment_data['name']
cls_model = experiment_data['model']
_, _, scores = make_experiment_classifier(vecs_train_codes, vecs_test_codes, vecs_test_group,
labels_train_codes, labels_test_codes,
labels_test_groups_groups, labels_train_groups,
sample_weights_codes, sample_weights_groups,
texts_test_codes, texts_test_groups,
exp_name, cls_model, cls_model, output_path, balance=None)
res = f"\n\n{exp_name} balanced by: {balanced_method} scores: {scores}"
results += res
log(res)
return results
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