def train_model(cls, field: DocumentField, train_data_sets: List[List[dict]]) -> ClassifierModel:
typed_field = TypedField.by(field)
df = pd.DataFrame.from_records(train_data_sets.pop(0))
# add transferred external data
for train_data in train_data_sets:
df = df.append(pd.DataFrame.from_records(train_data))
df['target_name'] = df.apply(lambda row: encode_category(
field.code,
row.value if typed_field.is_choice_field else None,
row.extraction_hint), axis=1)
df['target_index'] = df['target_name'].factorize(sort=True)[0] + 1
df = df.append(
[{'text_unit__textunittext__text': i} for i in cls.get_no_field_text_units(field.document_type, field.text_unit_type)])
df['target_index'] = df['target_index'].fillna(0).astype('int')
df['target_name'] = df['target_name'].fillna(SkLearnClassifierModel.EMPTY_CAT_NAME).astype(
'str')
df['user_input'] = df['created_by'].fillna(0).astype('bool')
res_df = pd.DataFrame()
for group_index, group_df in df.groupby('target_index'):
if group_df.shape[0] > settings.ML_TRAIN_DATA_SET_GROUP_LEN:
group_df = shuffle(
group_df.sort_values('user_input', ascending=False)[:settings.ML_TRAIN_DATA_SET_GROUP_LEN])
res_df = res_df.append(group_df)
res_df = shuffle(res_df)
target_names = sorted(res_df['target_name'].unique())
text_clf = Pipeline([('vect', CountVectorizer(strip_accents='unicode', analyzer='word',
stop_words='english',
tokenizer=word_position_tokenizer)),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier(loss='hinge', penalty='l2',
alpha=1e-3, max_iter=5, tol=None, n_jobs=-1,
class_weight='balanced')),
])
x = res_df['text_unit__textunittext__text']
y = res_df['target_index']
x_train, x_test_os, y_train, y_test_os = train_test_split(x, y, test_size=0.2, random_state=42)
_x_train, x_test_is, _y_train, y_test_is = train_test_split(x_train, y_train, test_size=0.2, random_state=42)
sklearn_model = text_clf.fit(x_train, y_train)
model = SkLearnClassifierModel(sklearn_model=sklearn_model, target_names=target_names)
classifier_model = ClassifierModel()
classifier_model.set_trained_model_obj(model)
classifier_model.document_field = field
predicted_os = text_clf.predict(x_test_os)
predicted_is = text_clf.predict(x_test_is)
classifier_model.classifier_accuracy_report_out_of_sample = classification_report(y_test_os,
predicted_os,
target_names=target_names)
classifier_model.classifier_accuracy_report_in_sample = classification_report(y_test_is,
predicted_is,
target_names=target_names)
return classifier_model
def train_document_field_detector_model(cls,
log: ProcessLogger,
field: DocumentField,
train_data_project_ids: Optional[List],
use_only_confirmed_field_values: bool = False) \
-> Optional[ClassifierModel]:
typed_field = TypedField.by(field) # type: TypedField
log.set_progress_steps_number(7)
log.info('Training model for field #{0} ({1})...'.format(
field.pk, field.code))
# Classifier: values of dependencies -> value of this field
# Field types supported: only choice fields
if not isinstance(typed_field, ChoiceField):
raise ValueError(
'Field-based ML supports only choice fields but field {0} (#{1}) is of type {2}'
.format(field.code, field.uid, typed_field.type_code))
# Lets find good values of depends-on fields suitable for using as train data.
categories = cls.get_categories(field)
train_data = cls.get_train_values(field, train_data_project_ids,
set(categories),
use_only_confirmed_field_values)
if not train_data:
raise RuntimeError(
'Not enough train data for field {0} (#{1}). '
'Need at least {2} approved or changed documents of type {3}.'.
format(field.code, field.uid,
settings.ML_TRAIN_DATA_SET_GROUP_LEN,
field.document_type.code))
depends_on_code_types = cls.get_depends_on_code_type(field)
depends_on_code_types = cls.remove_empty_fields(
depends_on_code_types, train_data)
pipeline, feature_names_funcs = cls.build_pipeline(
field, depends_on_code_types) # type: Pipeline, List[Callable]
category_names_to_indexes = {c: i for i, c in enumerate(categories)}
log.step_progress()
log.info(
'Collecting feature rows from train and test documents in dict form...'
)
# When tried to use sklearn shuffling something went wrong, leaving manual methods for a while.
random.shuffle(train_data)
# TODO: use sklearn methods for splitting train/test data and shuffling
test_size = 0.2
train_feature_data = list()
train_target_data = list()
for doc_field_values in train_data:
field_value = doc_field_values.get(field.code)
del doc_field_values[field.code]
field_value_idx = category_names_to_indexes.get(
field_value) if field_value else None
if field_value_idx is None:
field_value_idx = len(categories)
train_feature_data.append(doc_field_values)
train_target_data.append(field_value_idx)
is_index = math.floor(test_size * len(train_data))
test_oos_feature_data = train_feature_data[:is_index]
test_oos_target_data = train_target_data[:is_index]
train_feature_data = train_feature_data[is_index:]
train_target_data = train_target_data[is_index:]
test_is_feature_data = train_feature_data # [:is_index]
test_is_target_data = train_target_data # [:is_index]
log.step_progress()
log.info('Training the model...')
model = pipeline.fit(train_feature_data, train_target_data)
log.step_progress()
log.info('Testing the model...')
cm = ClassifierModel()
cm.document_field = field
predicted_oos = pipeline.predict(test_oos_feature_data)
cm.classifier_accuracy_report_out_of_sample = classification_report(
test_oos_target_data, predicted_oos, target_names=categories)
predicted_is = pipeline.predict(test_is_feature_data)
cm.classifier_accuracy_report_in_sample = classification_report(
test_is_target_data, predicted_is, target_names=categories)
log.step_progress()
log.info('Saving ClassifierModel instance...')
feature_names = []
for f in feature_names_funcs:
feature_names.extend(f())
cm.set_trained_model_obj({
'model': model,
'categories': categories,
'feature_names': feature_names
})
log.step_progress()
log.info('Finished.')
return cm
