From 39229b3113f6c0ce70a4bbd4ccffc70a5d6ce4a4 Mon Sep 17 00:00:00 2001 From: anapmt <43222877+apmt@users.noreply.github.com> Date: Thu, 2 Feb 2023 14:58:26 -0300 Subject: [PATCH] 51/new address features (#60) * #51 add WordEmbeddingConditions, Abbreviations and Emails * #51 fix test pre-processing path --------- Co-authored-by: atarchetti --- data/dicts/mexico_abbreviations.csv | 7 ++ data/dicts/mexico_context.csv | 3 + ...Ksmash_WordEmbedding_Regex_Enrichments.pkl | Bin 0 -> 1097036 bytes examples/MEXICO_retrain_predict_example.ipynb | 95 ++++++++++++------ .../feature_engineering/regex.py | 9 +- .../feature_engineering/word_embedding.py | 18 +++- .../pre_process_data/augment_data.py | 5 +- .../pre_process_data/pre_process_data.py | 24 ++++- .../feature_engineering/test_regex.py | 29 +++--- .../test_word_embedding.py | 23 +++-- .../pre_process_data/test_pre_process_data.py | 15 +++ 11 files changed, 162 insertions(+), 66 deletions(-) create mode 100644 data/dicts/mexico_abbreviations.csv create mode 100644 data/dicts/mexico_context.csv create mode 100644 data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments.pkl create mode 100644 tests/data_pipeline/pre_process_data/test_pre_process_data.py diff --git a/data/dicts/mexico_abbreviations.csv b/data/dicts/mexico_abbreviations.csv new file mode 100644 index 00000000..7f3a91e2 --- /dev/null +++ b/data/dicts/mexico_abbreviations.csv @@ -0,0 +1,7 @@ +abreviation,replace +AV,AVENUE +NO,NUMBER +NUM,NUMBER +PO,POST OFFICE +BLVD,BOULEVARD +LT,LOTE \ No newline at end of file diff --git a/data/dicts/mexico_context.csv b/data/dicts/mexico_context.csv new file mode 100644 index 00000000..67508dab --- /dev/null +++ b/data/dicts/mexico_context.csv @@ -0,0 +1,3 @@ +text,valid +pendiente,0 +pending,0 \ No newline at end of file diff --git a/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments.pkl b/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments.pkl new file mode 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+2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/anapaula/.local/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "2023-01-28 20:08:59.580652: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-01-28 20:08:59.659490: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2023-01-28 20:08:59.659504: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2023-01-28 20:09:00.126530: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", + "2023-01-28 20:09:00.126588: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", + "2023-01-28 20:09:00.126592: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + } + ], "source": [ "import pandas as pd\n", "import hygia as hg\n", @@ -73,7 +89,16 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aliases indified: \u001b[1mconcat_STREET_ADDRESS_1_STREET_ADDRESS_2 -> \u001b[22m['STREET_ADDRESS_1', 'STREET_ADDRESS_2']\n", + "handle null values in the column \u001b[1mconcat_STREET_ADDRESS_1_STREET_ADDRESS_2\u001b[22m\n" + ] + } + ], "source": [ "concatened_column_name = 'concat_STREET_ADDRESS_1_STREET_ADDRESS_2'\n", "df = pre_process_data.pre_process_data(df, ['STREET_ADDRESS_1', 'STREET_ADDRESS_2'], concatened_column_name)\n", @@ -133,7 +158,7 @@ " 'feature_re_contains_email_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_url_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_date_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_re_contains_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_is_substring_of_column_name_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_only_one_char_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_only_white_spaces_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", @@ -166,15 +191,18 @@ { "data": { "text/plain": [ - "valid 1363810\n", - "key_smash 904\n", - "contains_email 412\n", - "contains_exactly_the_word_test 182\n", - "only_special_characters 151\n", - "contains_exactly_the_word_dell 126\n", - "only_one_char 16\n", - "contains_invalid_words 10\n", - "only_white_spaces 2\n", + "valid 1346998\n", + "key_smash 739\n", + "contains_email 569\n", + "contains_exactly_the_word_test 177\n", + "only_special_characters 144\n", + "contains_exactly_the_word_dell 125\n", + "only_numbers 106\n", + "only_one_char 14\n", + "contains_exactly_invalid_words 10\n", + "is_substring_of_column_name 3\n", + "contains_date 1\n", + "empty 1\n", "Name: target, dtype: int64" ] }, @@ -204,15 +232,18 @@ { "data": { "text/plain": [ - "valid 2514911\n", - "key_smash 1770\n", + "valid 2514338\n", + "key_smash 1762\n", "only_special_characters 1291\n", - "contains_email 720\n", + "contains_email 1048\n", "contains_exactly_the_word_test 667\n", "contains_exactly_the_word_dell 553\n", "only_one_char 287\n", - "only_white_spaces 71\n", - "contains_invalid_words 26\n", + "only_numbers 239\n", + "empty 71\n", + "contains_exactly_invalid_words 26\n", + "is_substring_of_column_name 12\n", + "contains_date 2\n", "Name: target, dtype: int64" ] }, @@ -241,10 +272,10 @@ { "data": { "text/plain": [ - "{'accuracy': 0.998769987699877,\n", - " 'precision': 0.9965156794425087,\n", - " 'recall': 1.0,\n", - " 'f1': 0.9982547993019197}" + "{'accuracy': 0.9885931558935361,\n", + " 'precision': 0.9822222222222222,\n", + " 'recall': 0.9778761061946902,\n", + " 'f1': 0.9800443458980044}" ] }, "execution_count": 9, @@ -274,15 +305,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/anapaula/dell/Playground/env/lib/python3.8/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", + "/home/anapaula/.local/lib/python3.8/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ - "0.0 1362585\n", - "1.0 3028\n", + "0.0 1344545\n", + "1.0 4342\n", "Name: prediction, dtype: int64" ] }, @@ -306,25 +337,25 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "pickle.dump(clf, open('../data/models/RandomForest_Ksmash_WordEmbedding_Regex.pkl', 'wb'))" + "pickle.dump(clf, open('../data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments.pkl', 'wb'))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "df[df['prediction'] == 1][[concatened_column_name, 'prediction']].drop_duplicates(subset=[concatened_column_name]).to_csv('../data/tmp/prediction.csv')" + "df[df['prediction'] == 1][[concatened_column_name, 'prediction']].drop_duplicates(subset=[concatened_column_name]).to_csv('../data/tmp/prediction_enrichments.csv')" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +369,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.16 ('env': venv)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -357,7 +388,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "e46586f29fe5e457cab913e550e4adb40fe0e0134f73028154375029c57105e1" + "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" } } }, diff --git a/hygia/data_pipeline/feature_engineering/regex.py b/hygia/data_pipeline/feature_engineering/regex.py index 7b20df7a..c3dd6f50 100644 --- a/hygia/data_pipeline/feature_engineering/regex.py +++ b/hygia/data_pipeline/feature_engineering/regex.py @@ -18,8 +18,9 @@ def only_special_characters(self, text:str) -> bool: return bool(re.search(pattern, text, re.IGNORECASE)) def contains_email(self, text:str) -> bool: - pattern = r'\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}\b' - return bool(re.search(pattern, text, re.IGNORECASE)) + pattern_1 = r'\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}\b' + pattern_2 = r'(GMAIL|HOTMAIL|YAHOO|OUTLOOK)' + return bool(re.search(pattern_1, text, re.IGNORECASE)) or bool(re.search(pattern_2, text, re.IGNORECASE)) def contains_url(self, text:str) -> bool: pattern = r'\b(https?:\/\/|www\.)[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&//=]*)' @@ -29,7 +30,7 @@ def contains_date(self, text:str) -> bool: pattern = r'^(?P\d{1,2})(?:-|\.|/)(?P\d{1,2})(?:-|\.|/)(?P\d{4})$' return bool(re.search(pattern, text, re.IGNORECASE)) - def contains_invalid_words(self, text:str) -> bool: + def contains_exactly_invalid_words(self, text:str) -> bool: pattern = r'\b(null|undefined|dummy)\b' return bool(re.search(pattern, text, re.IGNORECASE)) @@ -57,7 +58,7 @@ def extract_regex_features(self, df:pd.DataFrame, column_name:str) -> pd.DataFra self.contains_email, self.contains_url, self.contains_date, - self.contains_invalid_words, + self.contains_exactly_invalid_words, self.is_substring_of_column_name, self.only_one_char, self.only_white_spaces, diff --git a/hygia/data_pipeline/feature_engineering/word_embedding.py b/hygia/data_pipeline/feature_engineering/word_embedding.py index 8310c25a..5c509d93 100644 --- a/hygia/data_pipeline/feature_engineering/word_embedding.py +++ b/hygia/data_pipeline/feature_engineering/word_embedding.py @@ -6,6 +6,7 @@ # from whatlies.language import Glove # from whatlies.language import BagOfWords import pandas as pd +import re class WordEmbedding: """ @@ -58,6 +59,10 @@ def _load_model(self) -> Any: raise NotImplementedError else: raise ValueError + + def _pre_embedding(self, text: str) -> str: + text = ' '.join(e for e in text.split() if e.isalpha() and len(e) >= 3 and not e.isspace()) + return text def get_embedding(self, text: str) -> np.ndarray: """ @@ -82,8 +87,17 @@ def get_embedding(self, text: str) -> np.ndarray: print(embedding) # Output: [0.5, 0.6, ..., 0.7] (a list of float values representing the word embedding vector) """ - return self.word_embedding_model[text].vector + + empty_vector = [0.0] * self.dimensions + text = self._pre_embedding(text) + + # White space string + if len(text.strip().split()) == 0: + return empty_vector + + return self.word_embedding_model[text].vector + def extract_word_embedding_features(self, df: pd.DataFrame, column_name: str, normalize: bool = False) -> pd.DataFrame: """ Extract word embedding features from a given dataframe and column. @@ -110,7 +124,7 @@ def extract_word_embedding_features(self, df: pd.DataFrame, column_name: str, no print(df.head()) """ - feature_we_tmp = df[column_name].fillna('').apply(lambda x: self.get_embedding(x) if len(x.strip().split()) > 0 else [0.0] * self.dimensions) + feature_we_tmp = df[column_name].fillna('').apply(lambda x: self.get_embedding(x)) for i in range(self.dimensions): df[f'feature_we_{i}_{column_name}'] = feature_we_tmp.apply(lambda x: x[i]) diff --git a/hygia/data_pipeline/pre_process_data/augment_data.py b/hygia/data_pipeline/pre_process_data/augment_data.py index 7e5014d2..ed6173b0 100644 --- a/hygia/data_pipeline/pre_process_data/augment_data.py +++ b/hygia/data_pipeline/pre_process_data/augment_data.py @@ -15,7 +15,6 @@ def __init__(self, country:str='MEXICO') -> None: 'south_america': 'zip_to_lat_lon_South America.pkl' } country_mappings = { - # TODO document list of supported countries # TODO implement only numbers validation in zipcode 'BRAZIL': {'code': 'BR', 'zipcode_file': continent_files['south_america'], 'length':7, 'only_numbers':True}, 'US': {'code': 'US', 'zipcode_file': continent_files['north_america'], 'length':5, 'only_numbers':True}, @@ -33,14 +32,14 @@ def validate_zipcode(self, text:str) -> bool: return text in self.country_zipcode_df['postal code'].values def validate_zipcodes(self, df:pd.DataFrame, zipcode_column_name:str) -> pd.DataFrame: - # TODO raise errors if zipcode_column_name not in df + if zipcode_column_name not in df: + return validated_column = f"{zipcode_column_name}_is_valid" indicator_column = f"{zipcode_column_name}_is_valid_indicator" df_aux = pd.merge(df, self.country_zipcode_df, how='left', left_on=zipcode_column_name, right_on='postal code', indicator=indicator_column) df_aux[validated_column] = df_aux[indicator_column] == 'both' return df_aux[[validated_column]] - def augment_data(self, df:pd.DataFrame, zipcode_column_name:str) -> pd.DataFrame: df = pd.concat([df, self.validate_zipcodes(df, zipcode_column_name)], axis=1) return df diff --git a/hygia/data_pipeline/pre_process_data/pre_process_data.py b/hygia/data_pipeline/pre_process_data/pre_process_data.py index 3cd28272..1bf24af8 100644 --- a/hygia/data_pipeline/pre_process_data/pre_process_data.py +++ b/hygia/data_pipeline/pre_process_data/pre_process_data.py @@ -1,7 +1,17 @@ import pandas as pd +import re from colorama import Style - class PreProcessData: + def __init__(self, country:str='MEXICO') -> None: + country_mappings = { + 'MEXICO': {'code': 'MX', 'abbrevitations_file': 'data/dicts/mexico_abbreviations.csv'}, + } + self.abbreviations_dict = {} + with open(country_mappings[country]['abbrevitations_file'], 'r') as f: + for line in f: + key, value = line.strip().split(',') + self.abbreviations_dict.update({key: value}) + def concatenate_columns(self, df, columns, concatenated_column_name): print(f'aliases indified: {Style.BRIGHT}{concatenated_column_name} -> {Style.NORMAL}{columns}') @@ -14,12 +24,22 @@ def handle_nulls(self, df, column_name): df[column_name].fillna('').astype(str) return df - def pre_process_data(self, df, columns_to_concat=None, column_name=None): + def _replace_abbreviation(self, text:str) -> str: + for abbreviation in self.abbreviations_dict: + text = ' '.join([re.sub(rf'(\b|(?<=[^a-zA-Z])){abbreviation}(\b|(?=[^a-zA-Z]))', self.abbreviations_dict[abbreviation], e, flags=re.IGNORECASE) for e in text.split()]) + return text + + def handle_abreviations(self, df, column_name): + df[column_name] = df[column_name].apply(lambda x: self._replace_abbreviation(x)) + return df + + def pre_process_data(self, df, columns_to_concat=None, column_name=None, zipcode_columns=None): if columns_to_concat and column_name: df = self.concatenate_columns(df, columns_to_concat, column_name) if column_name and column_name in df.columns: df = self.handle_nulls(df, column_name) + df = self.handle_abreviations(df, column_name) return df diff --git a/tests/data_pipeline/feature_engineering/test_regex.py b/tests/data_pipeline/feature_engineering/test_regex.py index 3024e3ef..5dfe4c9e 100644 --- a/tests/data_pipeline/feature_engineering/test_regex.py +++ b/tests/data_pipeline/feature_engineering/test_regex.py @@ -1,5 +1,3 @@ -import pytest -import pandas as pd from hygia.data_pipeline.feature_engineering.regex import Regex class TestRegex: @@ -28,6 +26,13 @@ def test_only_special_characters(self): def test_contains_email(self): assert self.regex.contains_email("EXAMPLE@GMAIL.COM") == True + assert self.regex.contains_email("EXAMPLE@HOTMAIL.COM") == True + assert self.regex.contains_email("EXAMPLE@YAHOO.COM") == True + assert self.regex.contains_email("EXAMPLE@OUTLOOK.COM") == True + assert self.regex.contains_email("EXAMPLE@HOTMAIL.COM") == True + assert self.regex.contains_email("EXAMPLE@HOTMAILCOM") == True + assert self.regex.contains_email("EXAMPLEHOTMAILCOM") == True + assert self.regex.contains_email("EXAMPLEHOTMAIL.COM") == True assert self.regex.contains_email("EXAMPLE") == False assert self.regex.contains_email("") == False @@ -54,16 +59,16 @@ def test_contains_date(self): assert self.regex.contains_date("LAGUNA 375-1-1001") == False assert self.regex.contains_date("") == False - def test_contains_invalid_words(self): - assert self.regex.contains_invalid_words("NULL") == True - assert self.regex.contains_invalid_words("A NULL") == True - assert self.regex.contains_invalid_words("UNDEFINED") == True - assert self.regex.contains_invalid_words("A UNDEFINED") == True - assert self.regex.contains_invalid_words("DUMMY") == True - assert self.regex.contains_invalid_words("A DUMMY") == True - assert self.regex.contains_invalid_words("ABC") == False - assert self.regex.contains_invalid_words("A ABC") == False - assert self.regex.contains_invalid_words("") == False + def test_contains_exactly_invalid_words(self): + assert self.regex.contains_exactly_invalid_words("NULL") == True + assert self.regex.contains_exactly_invalid_words("A NULL") == True + assert self.regex.contains_exactly_invalid_words("UNDEFINED") == True + assert self.regex.contains_exactly_invalid_words("A UNDEFINED") == True + assert self.regex.contains_exactly_invalid_words("DUMMY") == True + assert self.regex.contains_exactly_invalid_words("A DUMMY") == True + assert self.regex.contains_exactly_invalid_words("ABC") == False + assert self.regex.contains_exactly_invalid_words("A ABC") == False + assert self.regex.contains_exactly_invalid_words("") == False def test_is_substring_of_column_name(self): assert self.regex.is_substring_of_column_name("STREET_ADDRESS_1", "concat_STREET_ADDRESS_1_STREET_ADDRESS_2") == True diff --git a/tests/data_pipeline/feature_engineering/test_word_embedding.py b/tests/data_pipeline/feature_engineering/test_word_embedding.py index af67d938..1c1d688f 100644 --- a/tests/data_pipeline/feature_engineering/test_word_embedding.py +++ b/tests/data_pipeline/feature_engineering/test_word_embedding.py @@ -6,20 +6,21 @@ from hygia import WordEmbedding class TestWordEmbedding: + def setup_method(self): + self.word_embedding = WordEmbedding() - @pytest.fixture - def word_embedding(self): - return WordEmbedding() - - def test_load_model(self, word_embedding): - assert isinstance(word_embedding._load_model(), BytePairLanguage) - - def test_get_embedding(self, word_embedding): - embedding = word_embedding.get_embedding("This is a sample text.") + def test_load_model(self): + assert isinstance(self.word_embedding._load_model(), BytePairLanguage) + + def test_get_embedding(self): + embedding = self.word_embedding.get_embedding("This is a sample text.") assert isinstance(embedding, np.ndarray) - def test_extract_word_embedding_features(self, word_embedding): + def test_pre_embedding(self): + assert self.word_embedding._pre_embedding("A test with ABC123 AVENUE") == "test with AVENUE" + + def test_extract_word_embedding_features(self): df = pd.DataFrame({"text_column": ["This is a sample text.", "Another sample text."]}) - result = word_embedding.extract_word_embedding_features(df, "text_column") + result = self.word_embedding.extract_word_embedding_features(df, "text_column") assert isinstance(result, pd.DataFrame) assert any(col.startswith("feature_we_") for col in result.columns) \ No newline at end of file diff --git a/tests/data_pipeline/pre_process_data/test_pre_process_data.py b/tests/data_pipeline/pre_process_data/test_pre_process_data.py new file mode 100644 index 00000000..09344a65 --- /dev/null +++ b/tests/data_pipeline/pre_process_data/test_pre_process_data.py @@ -0,0 +1,15 @@ +from hygia.data_pipeline.pre_process_data.pre_process_data import PreProcessData + +class TestPreProcessData: + def setup_method(self): + self.pre_process_data = PreProcessData() + + def test_replace_abbreviation(self): + assert self.pre_process_data._replace_abbreviation("NO") == "NUMBER" + assert self.pre_process_data._replace_abbreviation("no") == "NUMBER" + assert self.pre_process_data._replace_abbreviation("no123") == "NUMBER123" + assert self.pre_process_data._replace_abbreviation("no 123") == "NUMBER 123" + assert self.pre_process_data._replace_abbreviation("123 no") == "123 NUMBER" + assert self.pre_process_data._replace_abbreviation("not") == "not" + assert self.pre_process_data._replace_abbreviation("NOT") == "NOT" + assert self.pre_process_data._replace_abbreviation("ono") == "ono" \ No newline at end of file