From 96febc0d571d126f9a5b6d8bba27e93a73667ab1 Mon Sep 17 00:00:00 2001 From: atarchetti Date: Mon, 6 Feb 2023 21:15:58 -0300 Subject: [PATCH 01/10] #9 add key_smash normalization --- ...edding_Regex_Enrichments_Normalization.pkl | Bin 0 -> 1179116 bytes data/models/normalization_absolutes.csv | 2 + examples/MEXICO_predict_example.ipynb | 8 +- examples/MEXICO_retrain_predict_example.ipynb | 650 +++++++++++++++--- .../feature_engineering/key_smash.py | 4 +- hygia/data_pipeline/model/random_forest.py | 57 +- .../pre_process_data/pre_process_data.py | 7 +- 7 files changed, 631 insertions(+), 97 deletions(-) create mode 100644 data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl create mode 100644 data/models/normalization_absolutes.csv diff --git a/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl b/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl new file mode 100644 index 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+feature_ks_count_sequence_squared_vowels,feature_ks_count_sequence_squared_consonants,feature_ks_count_sequence_squared_special_characters,feature_ks_ratio_of_numeric_digits_squared,feature_ks_average_of_char_count_squared +15.03125,30.0,30.0,10.242424242424242,30.0 diff --git a/examples/MEXICO_predict_example.ipynb b/examples/MEXICO_predict_example.ipynb index 40bdd6e8..3d9bdef8 100644 --- a/examples/MEXICO_predict_example.ipynb +++ b/examples/MEXICO_predict_example.ipynb @@ -17,7 +17,7 @@ "import pandas as pd\n", "import hygia as hg\n", "\n", - "pre_process_data = hg.PreProcessData()\n", + "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", "augment_data = hg.AugmentData(country=\"MEXICO\")\n", "feature_engineering = hg.FeatureEngineering()\n", "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_WordEmbedding_Regex.pkl')" @@ -38,7 +38,7 @@ "outputs": [], "source": [ "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", - "df = pd.read_csv(file_path, sep='¨', nrows=500_000, engine='python')" + "df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')" ] }, { @@ -188,7 +188,7 @@ } ], "source": [ - "df['prediction'] = rf_model.predict(df[ks_we_and_re_colummns].values)\n", + "df['prediction'] = rf_model.predict(df[ks_we_and_re_colummns], concatened_column_name)\n", "df['prediction'].value_counts()" ] }, @@ -206,7 +206,7 @@ "metadata": {}, "outputs": [], "source": [ - "df[['concat_STREET_ADDRESS_1_STREET_ADDRESS_2', 'prediction']].to_csv('data/tmp/prediction.csv')" + "df[['concat_STREET_ADDRESS_1_STREET_ADDRESS_2', 'prediction']].to_csv('data/tmp/pre_trained_prediction.csv')" ] } ], diff --git a/examples/MEXICO_retrain_predict_example.ipynb b/examples/MEXICO_retrain_predict_example.ipynb index f86d172c..c82ca123 100644 --- a/examples/MEXICO_retrain_predict_example.ipynb +++ b/examples/MEXICO_retrain_predict_example.ipynb @@ -2,25 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "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" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import hygia as hg\n", @@ -39,11 +23,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning feature engineering with configs below...\u001b[37m\n", + "\u001b[1mlanguage -> \u001b[22mes\n", + "\u001b[1mdimensions -> \u001b[22m25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/anapaula/dell/Playground/hygia/data_pipeline/augment_data/augment_data.py:29: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].astype(str).str.pad(country_mappings[country]['length'],fillchar='0')\n" + ] + } + ], "source": [ - "pre_process_data = hg.PreProcessData()\n", + "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", "augment_data = hg.AugmentData(country=\"MEXICO\")\n", "feature_engineering = hg.FeatureEngineering()\n", "annotate_data = hg.AnnotateData()\n", @@ -62,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -96,7 +102,8 @@ "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" + "handle null values in the column \u001b[1mconcat_STREET_ADDRESS_1_STREET_ADDRESS_2\u001b[22m\n", + "extract features from -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n" ] } ], @@ -116,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -166,7 +173,7 @@ " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -186,28 +193,493 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning annotate data with configs below...\u001b[37m\n", + "\u001b[1mthresholds -> \u001b[22m{'count_sequence_squared_vowels': 1.0, 'count_sequence_squared_consonants': 1.999, 'count_sequence_squared_special_characters': 2.2499, 'ratio_of_numeric_digits_squared': 2.9, 'average_of_char_count_squared': 2.78}\n", + "column -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n" + ] + }, { "data": { + "text/html": [ + "

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CUSTOMER_NUMCOMPANY_NUMADDRESS_SEQ_NUMADDRESS_TYPESTREET_ADDRESS_1STREET_ADDRESS_2CITYCITY_2STATESTATE_L...feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_email_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_url_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_date_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_exactly_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_is_substring_of_column_name_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_only_one_char_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_only_white_spaces_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2target
010155164737SCARRET FED 307 KM 298.8CHETUMAL- PUERTO JUAREZPLAYA DEL CARMEN SOLIDARIDADMXQROO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
1103051972393SCARRETERA YERBABUENA-PUENTECILLA KM. 1.7GUANAJUATOMXGTO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2103051972395SCARR IRAPUATO-ABASOLO KM 5COL PURISIMA DEL JARDINIRAPUATOMXGTO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
3103317515230BPRESA DE LAS VIRGENES 115DESP. 6 COL IRRIGACION DELEG.MEXICOMXDF...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
4103354057230SAV RAFAEL NAVAS SIN NUMEROCOL INDUSTRIAL EL CERRILLOLERMAMXMEX...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
..................................................................
2520291108405112230SCORDILLERA DEL MARQUEZ NO 113SAN LUIS POTOSIMXSLP...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520292104633149230SCALLE DE BEISBOL 147COL. CHURUBUSCO COUNTRY CLUBDELEGACION COYOACAN, MEXICOMXDF...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520293112184095230BAV. VICENTE GUERRERO 8951 , COL. PARTIDO DOBLADO CD.JUAREZCHIHUAHUAMXCHIH...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520294108261326230SLAGO GARDA 226AGUA AZULNEZAHUALCOYOTLMXMEX...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520295111380702336SBLVD. CONSTITUCIóN 1111COL MARGARITASTORREONMXCOAH...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
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2520296 rows × 95 columns

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" + ], "text/plain": [ - "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" + " CUSTOMER_NUM COMPANY_NUM ADDRESS_SEQ_NUM ADDRESS_TYPE \\\n", + "0 101551647 3 7 S \n", + "1 103051972 3 93 S \n", + "2 103051972 3 95 S \n", + "3 103317515 23 0 B \n", + "4 103354057 23 0 S \n", + "... ... ... ... ... \n", + "2520291 108405112 23 0 S \n", + "2520292 104633149 23 0 S \n", + "2520293 112184095 23 0 B \n", + "2520294 108261326 23 0 S \n", + "2520295 111380702 3 36 S \n", + "\n", + " STREET_ADDRESS_1 STREET_ADDRESS_2 \\\n", + "0 CARRET FED 307 KM 298.8 CHETUMAL- PUERTO JUAREZ \n", + "1 CARRETERA YERBABUENA- PUENTECILLA KM. 1.7 \n", + "2 CARR IRAPUATO-ABASOLO KM 5 COL PURISIMA DEL JARDIN \n", + "3 PRESA DE LAS VIRGENES 115 DESP. 6 COL IRRIGACION DELEG. \n", + "4 AV RAFAEL NAVAS SIN NUMERO COL INDUSTRIAL EL CERRILLO \n", + "... ... ... \n", + "2520291 CORDILLERA DEL MARQUEZ NO 113 \n", + "2520292 CALLE DE BEISBOL 147 COL. CHURUBUSCO COUNTRY CLUB \n", + "2520293 AV. VICENTE GUERRERO 8951 , CO L. PARTIDO DOBLADO CD.JUAREZ \n", + "2520294 LAGO GARDA 226 AGUA AZUL \n", + "2520295 BLVD. CONSTITUCIóN 1111 COL MARGARITAS \n", + "\n", + " CITY CITY_2 STATE STATE_L ... \\\n", + "0 PLAYA DEL CARME N SOLIDARIDAD MX QROO ... \n", + "1 GUANAJUATO MX GTO ... \n", + "2 IRAPUATO MX GTO ... \n", + "3 MEXICO MX DF ... \n", + "4 LERMA MX MEX ... \n", + "... ... ... ... ... ... \n", + "2520291 SAN LUIS POTOSI MX SLP ... \n", + "2520292 DELEGACION COYO ACAN, MEXICO MX DF ... \n", + "2520293 CHIHUAHUA MX CHIH ... \n", + "2520294 NEZAHUALC OYOTL MX MEX ... \n", + "2520295 TORREON MX COAH ... \n", + "\n", + " feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_contains_email_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_contains_url_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_contains_date_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_contains_exactly_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_is_substring_of_column_name_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_only_one_char_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_only_white_spaces_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "2520291 False \n", + "2520292 False \n", + "2520293 False \n", + "2520294 False \n", + "2520295 False \n", + "\n", + " feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 target \n", + "0 False valid \n", + "1 False valid \n", + "2 False valid \n", + "3 False valid \n", + "4 False valid \n", + "... ... ... \n", + "2520291 False valid \n", + "2520292 False valid \n", + "2520293 False valid \n", + "2520294 False valid \n", + "2520295 False valid \n", + "\n", + "[2520296 rows x 95 columns]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -227,14 +699,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "valid 2514338\n", - "key_smash 1762\n", + "valid 2514405\n", + "key_smash 1695\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -248,7 +720,7 @@ "Name: target, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -267,19 +739,32 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mtranning model...\u001b[37m\n", + "\u001b[32mdone\u001b[37m\n", + "\u001b[33mget model score...\u001b[37m\n", + "\u001b[1maccuracy -> \u001b[22m0.9937578027465668\n", + "\u001b[1mprecision -> \u001b[22m0.9794238683127572\n", + "\u001b[1mrecall -> \u001b[22m1.0\n", + "\u001b[1mf1 -> \u001b[22m0.9896049896049895\n" + ] + }, { "data": { "text/plain": [ - "{'accuracy': 0.9885931558935361,\n", - " 'precision': 0.9822222222222222,\n", - " 'recall': 0.9778761061946902,\n", - " 'f1': 0.9800443458980044}" + "{'accuracy': 0.9937578027465668,\n", + " 'precision': 0.9794238683127572,\n", + " 'recall': 1.0,\n", + " 'f1': 0.9896049896049895}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -299,32 +784,45 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning model...\u001b[37m\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/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", + "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:41: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum\n", + "/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", " warnings.warn(\n" ] }, { "data": { "text/plain": [ - "0.0 1344545\n", - "1.0 4342\n", + "0.0 1340347\n", + "1.0 5774\n", "Name: prediction, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['prediction'] = new_rf_model.predict(df[all_features_columns].values)\n", + "df['prediction'] = new_rf_model.predict(df[all_features_columns], concatened_column_name)\n", "df.drop_duplicates(subset=[concatened_column_name])['prediction'].value_counts()" ] }, @@ -338,39 +836,35 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "new_rf_model.export_model('../data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mexporting model and normalization absolutes...\u001b[37m\n" + ] + } + ], "source": [ - "df[df['prediction'] == 1][[concatened_column_name, 'prediction']].drop_duplicates(subset=[concatened_column_name]).to_csv('../data/tmp/prediction_enrichments.csv')" + "new_rf_model.export_model('../data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl',\n", + " '../data/models/normalization_absolutes.csv')" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "df['prediction'] = df['target'] == 'key_smash'\n", - "df['prediction_model'] = df['prediction']\n", - "df[df['prediction_model']!=df['prediction']][[concatened_column_name, 'target', 'prediction_model', 'prediction']] \\\n", - " .drop_duplicates(subset=[concatened_column_name]) \\\n", - " .to_csv('../data/tmp/prediction_better_with_model.csv')" + "df[df['prediction'] == 1][[concatened_column_name, 'prediction']].drop_duplicates(subset=[concatened_column_name]).to_csv('../data/tmp/prediction_RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.csv')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "env", "language": "python", "name": "python3" }, @@ -384,12 +878,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10 (default, Mar 15 2022, 12:22:08) \n[GCC 9.4.0]" + "version": "3.8.10" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" + "hash": "acd904f7927719ac3bd428a31e6feadbc6c298bbba280a82d6227cca902ecf8e" } } }, diff --git a/hygia/data_pipeline/feature_engineering/key_smash.py b/hygia/data_pipeline/feature_engineering/key_smash.py index 515f9842..3e253879 100644 --- a/hygia/data_pipeline/feature_engineering/key_smash.py +++ b/hygia/data_pipeline/feature_engineering/key_smash.py @@ -112,11 +112,11 @@ def count_sequence_squared(self, text:str, opt:str) -> float: if text[i] in char_set and text[i + 1] in char_set: count_sequence += 1 else: - if count_sequence > 1: + if count_sequence > 2: sequences.append(count_sequence ** 2) count_sequence = 1 - if count_sequence > 1: + if count_sequence > 2: sequences.append(count_sequence ** 2) return sum(sequences) / len(text) diff --git a/hygia/data_pipeline/model/random_forest.py b/hygia/data_pipeline/model/random_forest.py index 9f8c7e0e..980b52cb 100644 --- a/hygia/data_pipeline/model/random_forest.py +++ b/hygia/data_pipeline/model/random_forest.py @@ -10,9 +10,14 @@ from sklearn.metrics import precision_score class RandomForestModel: - def __init__(self, model_file=None, n_estimators=100, max_depth=None, random_state=0): + def __init__(self, model_file=None, normalization_absolutes_file=None ,n_estimators=100, max_depth=None, random_state=0): self.model = None - if model_file is not None: + self.normalization_absolutes = None + self.pre_trained = False + if normalization_absolutes_file: + self.normalization_absolutes = pd.read_csv(normalization_absolutes_file) + if model_file: + self.pre_trained = True with open(model_file, 'rb') as f: self.model = pickle.load(f) if self.model is None: @@ -21,31 +26,54 @@ def __init__(self, model_file=None, n_estimators=100, max_depth=None, random_sta self.random_state = random_state self.model = RandomForestClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth, random_state=self.random_state) + def _get_absolute_maximums(self, df, features_columns_to_normalize, concatened_column_name): + if self.normalization_absolutes: + return + absolutes_dict = {} + for column in features_columns_to_normalize: + absolute_maximum = df[column].max() + absolutes_dict[column.replace(f"_{concatened_column_name}", '')] = [absolute_maximum] if absolute_maximum else [1.0] + self.normalization_absolutes = pd.DataFrame(absolutes_dict) + + def _normalization(self, df, features_columns_to_normalize, concatened_column_name): + for features_column_to_normalize in features_columns_to_normalize: + column_absolute_maximum = self.normalization_absolutes[features_column_to_normalize.replace(f"_{concatened_column_name}", '')].values[0] + df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum + return df + def train_and_get_scores(self, df, concatened_column_name, all_features_columns, test_size=0.3): print(f'{Fore.YELLOW}tranning model...{Fore.WHITE}') + # Droping Duplicates df_not_duplicates = df.drop_duplicates(subset=[concatened_column_name]) + # Balancing KEY_SMASH_COUNT = df_not_duplicates['target'].value_counts()['key_smash'] df_valid = df_not_duplicates[df_not_duplicates['target'] == 'valid'].sample(n=KEY_SMASH_COUNT, random_state=42) df_invalid = df_not_duplicates[df_not_duplicates['target'] != 'valid'] df_balanced = pd.concat([df_valid, df_invalid]) - df_balanced['is_key_smash'] = df_balanced['target'] == 'key_smash' - - X = df_balanced[[*all_features_columns]] - y = df_balanced['is_key_smash'] - - clf=self.model + # Normalization + key_smash_features_columns = [column for column in all_features_columns if column.startswith('feature_ks')] + self._get_absolute_maximums(df_balanced, key_smash_features_columns, concatened_column_name) + df_balanced_normalized = self._normalization(df_balanced, key_smash_features_columns, concatened_column_name) + + # Train/Test split + X = df_balanced_normalized[[*all_features_columns]] + y = df_balanced_normalized['is_key_smash'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size) + # Train + clf=self.model clf.fit(X_train,y_train) + # Test y_pred = clf.predict(X_test) print(f'{Fore.GREEN}done{Fore.WHITE}') print(f'{Fore.YELLOW}get model score...{Fore.WHITE}') + # Scoring accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) @@ -65,11 +93,16 @@ def train_and_get_scores(self, df, concatened_column_name, all_features_columns, return scores - def predict(self, X): + def predict(self, X, concatened_column_name): print(f'{Fore.YELLOW}running model...{Fore.WHITE}') - return self.model.predict(X) + + key_smash_features_columns = [column for column in X.columns if column.startswith('feature_ks')] + X = self._normalization(X, key_smash_features_columns, concatened_column_name) + + return self.model.predict(X.values) - def export_model(self, export_path:str) -> None: - print(f'{Fore.YELLOW}exporting model...{Fore.WHITE}') + def export_model(self, export_path:str, normalization_absolutes_file_path:str) -> None: + print(f'{Fore.YELLOW}exporting model and normalization absolutes...{Fore.WHITE}') pickle.dump(self.model, open(export_path, 'wb')) + self.normalization_absolutes.to_csv(normalization_absolutes_file_path, index=False) 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 5a95aae3..971f2414 100644 --- a/hygia/data_pipeline/pre_process_data/pre_process_data.py +++ b/hygia/data_pipeline/pre_process_data/pre_process_data.py @@ -31,10 +31,14 @@ def handle_nulls(self, df, column_name): df[column_name].fillna('').astype(str) return df + + def handle_extra_spaces(self, df, column_name:str) -> str: + df[column_name] = df[column_name].apply(lambda x: ' '.join(x.split())) + return df 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()]) + 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): @@ -48,6 +52,7 @@ def pre_process_data(self, df, columns_to_concat=None, column_name=None): if column_name and column_name in df.columns: df = self.handle_nulls(df, column_name) + df = self.handle_extra_spaces(df, column_name) df = self.handle_abreviations(df, column_name) return df From b303a500d31071f83f1dc03464c34ba6179096ff Mon Sep 17 00:00:00 2001 From: atarchetti Date: Tue, 7 Feb 2023 12:09:30 -0300 Subject: [PATCH 02/10] #65 add regex for contex words and remove old key_smash normalization --- ...edding_Regex_Enrichments_Normalization.pkl | Bin 1179116 -> 472798 bytes data/models/normalization_absolutes.csv | 4 ++-- .../feature_engineering.py | 4 ++-- .../feature_engineering/key_smash.py | 11 +---------- 4 files changed, 5 insertions(+), 14 deletions(-) diff --git a/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl b/data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl index 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a/examples/MEXICO_predict_example.ipynb b/examples/MEXICO_predict_example.ipynb index 3d9bdef8..544584fe 100644 --- a/examples/MEXICO_predict_example.ipynb +++ b/examples/MEXICO_predict_example.ipynb @@ -10,17 +10,28 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning feature engineering with configs below...\u001b[37m\n", + "\u001b[1mlanguage -> \u001b[22mes\n", + "\u001b[1mdimensions -> \u001b[22m25\n" + ] + } + ], "source": [ "import pandas as pd\n", "import hygia as hg\n", "\n", "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", "augment_data = hg.AugmentData(country=\"MEXICO\")\n", - "feature_engineering = hg.FeatureEngineering()\n", - "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_WordEmbedding_Regex.pkl')" + "feature_engineering = hg.FeatureEngineering(country=\"MEXICO\")\n", + "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_Regex_Enrichments_Normalization.pkl',\n", + " normalization_absolutes_file='../data/models/normalization_absolutes.csv')" ] }, { @@ -33,12 +44,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", - "df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')" + "# df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')\n", + "df = pd.read_csv(file_path, sep='¨', nrows=5000, engine='python')" ] }, { @@ -51,11 +63,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "df = df.augment_data.augment_data()" + "df = augment_data.augment_data(df, zipcode_column_name='ZIP_CODE_L')" ] }, { @@ -76,7 +88,17 @@ "cell_type": "code", "execution_count": 4, "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", + "extract features from -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\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", @@ -88,12 +110,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Check new columns names" + "# Check features columns names" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -102,33 +124,8 @@ "['feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_ks_ratio_of_numeric_digits_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_0_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_1_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_2_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_3_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_4_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_5_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_6_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_7_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_8_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_9_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_10_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_11_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_12_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_13_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_14_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_15_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_16_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_17_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_18_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_19_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_20_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_21_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_22_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_23_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", - " 'feature_we_24_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", @@ -136,21 +133,24 @@ " '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", " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ks_we_and_re_colummns = [col for col in df if col.startswith('feature_ks') or col.startswith('feature_we') or col.startswith('feature_re')]\n", - "ks_we_and_re_colummns" + "all_features_columns = [col for col in df if col.startswith('feature_ks') or col.startswith('feature_we') or col.startswith('feature_re')]\n", + "model_features_columns = [col for col in all_features_columns \\\n", + " if not col.startswith('feature_we') \\\n", + " and 'ratio_of_numeric_digits_squared' not in col]\n", + "model_features_columns" ] }, { @@ -163,33 +163,36 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "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", - " warnings.warn(\n" + "\u001b[33mrunning model...\u001b[37m\n", + "AAAAAAAAAAA concatened_column_name concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAAAAAAA features_column_to_normalize feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAAAAAAA 1 feature_ks_count_sequence_squared_vowels\n" ] }, { - "data": { - "text/plain": [ - "0.0 497769\n", - "1.0 2231\n", - "Name: prediction, dtype: int64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m rf_model\u001b[39m.\u001b[39;49mpredict(df[model_features_columns], concatened_column_name)\n\u001b[1;32m 2\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mvalue_counts()\n", + "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:107\u001b[0m, in \u001b[0;36mRandomForestModel.predict\u001b[0;34m(self, X, concatened_column_name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mYELLOW\u001b[39m}\u001b[39;00m\u001b[39mrunning model...\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mWHITE\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m 106\u001b[0m key_smash_features_columns \u001b[39m=\u001b[39m [column \u001b[39mfor\u001b[39;00m column \u001b[39min\u001b[39;00m X\u001b[39m.\u001b[39mcolumns \u001b[39mif\u001b[39;00m column\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mfeature_ks\u001b[39m\u001b[39m'\u001b[39m)]\n\u001b[0;32m--> 107\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_normalization(X\u001b[39m.\u001b[39;49mcopy(), key_smash_features_columns, concatened_column_name)\n\u001b[1;32m 109\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mpredict(X\u001b[39m.\u001b[39mvalues)\n", + "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:46\u001b[0m, in \u001b[0;36mRandomForestModel._normalization\u001b[0;34m(self, df, features_columns_to_normalize, concatened_column_name)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA features_column_to_normalize\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize)\n\u001b[1;32m 45\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA 1\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m---> 46\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39;49mreplace(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mconcatened_column_name\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m)])\n\u001b[1;32m 47\u001b[0m column_absolute_maximum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)]\u001b[39m.\u001b[39mvalues[\u001b[39m0\u001b[39m]\n\u001b[1;32m 48\u001b[0m df[features_column_to_normalize] \u001b[39m=\u001b[39m df[features_column_to_normalize] \u001b[39m/\u001b[39m column_absolute_maximum\n", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + ] } ], "source": [ - "df['prediction'] = rf_model.predict(df[ks_we_and_re_colummns], concatened_column_name)\n", - "df['prediction'].value_counts()" + "df['prediction_is_key_smash'] = rf_model.predict(df[model_features_columns], concatened_column_name)\n", + "df['prediction_is_key_smash'].value_counts()" ] }, { @@ -206,7 +209,9 @@ "metadata": {}, "outputs": [], "source": [ - "df[['concat_STREET_ADDRESS_1_STREET_ADDRESS_2', 'prediction']].to_csv('data/tmp/pre_trained_prediction.csv')" + "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + " .drop_duplicates(subset=[concatened_column_name]) \\\n", + " .to_csv(f'../data/tmp/prediction_rf_ks_we_regex_enrich_normal.csv')" ] } ], diff --git a/examples/MEXICO_predict_example_no_embeddings copy.ipynb b/examples/MEXICO_predict_example_no_embeddings copy.ipynb new file mode 100644 index 00000000..544584fe --- /dev/null +++ b/examples/MEXICO_predict_example_no_embeddings copy.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports and classes instanciations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning feature engineering with configs below...\u001b[37m\n", + "\u001b[1mlanguage -> \u001b[22mes\n", + "\u001b[1mdimensions -> \u001b[22m25\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import hygia as hg\n", + "\n", + "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", + "augment_data = hg.AugmentData(country=\"MEXICO\")\n", + "feature_engineering = hg.FeatureEngineering(country=\"MEXICO\")\n", + "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_Regex_Enrichments_Normalization.pkl',\n", + " normalization_absolutes_file='../data/models/normalization_absolutes.csv')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", + "# df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')\n", + "df = pd.read_csv(file_path, sep='¨', nrows=5000, engine='python')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Augment Data with context validations" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = augment_data.augment_data(df, zipcode_column_name='ZIP_CODE_L')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add new columns\n", + "\n", + "1. Concatenate address\n", + "2. All features columns:\n", + " - Key Smash\n", + " - Regex\n", + " - Word Embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "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", + "extract features from -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\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", + "df = feature_engineering.extract_features(df, concatened_column_name)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Check features columns names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " '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_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", + " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_features_columns = [col for col in df if col.startswith('feature_ks') or col.startswith('feature_we') or col.startswith('feature_re')]\n", + "model_features_columns = [col for col in all_features_columns \\\n", + " if not col.startswith('feature_we') \\\n", + " and 'ratio_of_numeric_digits_squared' not in col]\n", + "model_features_columns" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predict using pre-trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning model...\u001b[37m\n", + "AAAAAAAAAAA concatened_column_name concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAAAAAAA features_column_to_normalize feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAAAAAAA 1 feature_ks_count_sequence_squared_vowels\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m rf_model\u001b[39m.\u001b[39;49mpredict(df[model_features_columns], concatened_column_name)\n\u001b[1;32m 2\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mvalue_counts()\n", + "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:107\u001b[0m, in \u001b[0;36mRandomForestModel.predict\u001b[0;34m(self, X, concatened_column_name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mYELLOW\u001b[39m}\u001b[39;00m\u001b[39mrunning model...\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mWHITE\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m 106\u001b[0m key_smash_features_columns \u001b[39m=\u001b[39m [column \u001b[39mfor\u001b[39;00m column \u001b[39min\u001b[39;00m X\u001b[39m.\u001b[39mcolumns \u001b[39mif\u001b[39;00m column\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mfeature_ks\u001b[39m\u001b[39m'\u001b[39m)]\n\u001b[0;32m--> 107\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_normalization(X\u001b[39m.\u001b[39;49mcopy(), key_smash_features_columns, concatened_column_name)\n\u001b[1;32m 109\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mpredict(X\u001b[39m.\u001b[39mvalues)\n", + "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:46\u001b[0m, in \u001b[0;36mRandomForestModel._normalization\u001b[0;34m(self, df, features_columns_to_normalize, concatened_column_name)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA features_column_to_normalize\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize)\n\u001b[1;32m 45\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA 1\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m---> 46\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39;49mreplace(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mconcatened_column_name\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m)])\n\u001b[1;32m 47\u001b[0m column_absolute_maximum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)]\u001b[39m.\u001b[39mvalues[\u001b[39m0\u001b[39m]\n\u001b[1;32m 48\u001b[0m df[features_column_to_normalize] \u001b[39m=\u001b[39m df[features_column_to_normalize] \u001b[39m/\u001b[39m column_absolute_maximum\n", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + ] + } + ], + "source": [ + "df['prediction_is_key_smash'] = rf_model.predict(df[model_features_columns], concatened_column_name)\n", + "df['prediction_is_key_smash'].value_counts()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save predicted data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + " .drop_duplicates(subset=[concatened_column_name]) \\\n", + " .to_csv(f'../data/tmp/prediction_rf_ks_we_regex_enrich_normal.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "acd904f7927719ac3bd428a31e6feadbc6c298bbba280a82d6227cca902ecf8e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/MEXICO_retrain_predict_example.ipynb b/examples/MEXICO_retrain_predict_example.ipynb index c82ca123..2d670ad0 100644 --- a/examples/MEXICO_retrain_predict_example.ipynb +++ b/examples/MEXICO_retrain_predict_example.ipynb @@ -8,7 +8,8 @@ "source": [ "import pandas as pd\n", "import hygia as hg\n", - "import pickle" + "import pickle\n", + "import time" ] }, { @@ -51,7 +52,7 @@ "source": [ "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", "augment_data = hg.AugmentData(country=\"MEXICO\")\n", - "feature_engineering = hg.FeatureEngineering()\n", + "feature_engineering = hg.FeatureEngineering(country=\"MEXICO\")\n", "annotate_data = hg.AnnotateData()\n", "new_rf_model = hg.RandomForestModel()" ] @@ -159,6 +160,7 @@ " 'feature_we_22_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_we_23_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_we_24_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", @@ -188,498 +190,89 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Annotate data" + "# Select Features\n", + "- remove word embeddings\n", + "- remove key smash feature: ratio_of_numeric_digits_squared" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " '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_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", + " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_features = [col for col in all_features_columns if 'ratio_of_numeric_digits_squared' not in col]\n", + "selected_features" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Annotate data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mrunning annotate data with configs below...\u001b[37m\n", - "\u001b[1mthresholds -> \u001b[22m{'count_sequence_squared_vowels': 1.0, 'count_sequence_squared_consonants': 1.999, 'count_sequence_squared_special_characters': 2.2499, 'ratio_of_numeric_digits_squared': 2.9, 'average_of_char_count_squared': 2.78}\n", + "\u001b[1mthresholds -> \u001b[22m{'count_sequence_squared_vowels': 1.0, 'count_sequence_squared_consonants': 1.999, 'count_sequence_squared_special_characters': 2.2499, 'average_of_char_count_squared': 2.78}\n", "column -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n" ] }, { "data": { - "text/html": [ - "

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CUSTOMER_NUMCOMPANY_NUMADDRESS_SEQ_NUMADDRESS_TYPESTREET_ADDRESS_1STREET_ADDRESS_2CITYCITY_2STATESTATE_L...feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_email_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_url_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_date_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_contains_exactly_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_is_substring_of_column_name_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_only_one_char_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_only_white_spaces_concat_STREET_ADDRESS_1_STREET_ADDRESS_2feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2target
010155164737SCARRET FED 307 KM 298.8CHETUMAL- PUERTO JUAREZPLAYA DEL CARMEN SOLIDARIDADMXQROO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
1103051972393SCARRETERA YERBABUENA-PUENTECILLA KM. 1.7GUANAJUATOMXGTO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2103051972395SCARR IRAPUATO-ABASOLO KM 5COL PURISIMA DEL JARDINIRAPUATOMXGTO...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
3103317515230BPRESA DE LAS VIRGENES 115DESP. 6 COL IRRIGACION DELEG.MEXICOMXDF...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
4103354057230SAV RAFAEL NAVAS SIN NUMEROCOL INDUSTRIAL EL CERRILLOLERMAMXMEX...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
..................................................................
2520291108405112230SCORDILLERA DEL MARQUEZ NO 113SAN LUIS POTOSIMXSLP...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520292104633149230SCALLE DE BEISBOL 147COL. CHURUBUSCO COUNTRY CLUBDELEGACION COYOACAN, MEXICOMXDF...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520293112184095230BAV. VICENTE GUERRERO 8951 , COL. PARTIDO DOBLADO CD.JUAREZCHIHUAHUAMXCHIH...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520294108261326230SLAGO GARDA 226AGUA AZULNEZAHUALCOYOTLMXMEX...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
2520295111380702336SBLVD. CONSTITUCIóN 1111COL MARGARITASTORREONMXCOAH...FalseFalseFalseFalseFalseFalseFalseFalseFalsevalid
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" - ], "text/plain": [ - " CUSTOMER_NUM COMPANY_NUM ADDRESS_SEQ_NUM ADDRESS_TYPE \\\n", - "0 101551647 3 7 S \n", - "1 103051972 3 93 S \n", - "2 103051972 3 95 S \n", - "3 103317515 23 0 B \n", - "4 103354057 23 0 S \n", - "... ... ... ... ... \n", - "2520291 108405112 23 0 S \n", - "2520292 104633149 23 0 S \n", - "2520293 112184095 23 0 B \n", - "2520294 108261326 23 0 S \n", - "2520295 111380702 3 36 S \n", - "\n", - " STREET_ADDRESS_1 STREET_ADDRESS_2 \\\n", - "0 CARRET FED 307 KM 298.8 CHETUMAL- PUERTO JUAREZ \n", - "1 CARRETERA YERBABUENA- PUENTECILLA KM. 1.7 \n", - "2 CARR IRAPUATO-ABASOLO KM 5 COL PURISIMA DEL JARDIN \n", - "3 PRESA DE LAS VIRGENES 115 DESP. 6 COL IRRIGACION DELEG. \n", - "4 AV RAFAEL NAVAS SIN NUMERO COL INDUSTRIAL EL CERRILLO \n", - "... ... ... \n", - "2520291 CORDILLERA DEL MARQUEZ NO 113 \n", - "2520292 CALLE DE BEISBOL 147 COL. CHURUBUSCO COUNTRY CLUB \n", - "2520293 AV. VICENTE GUERRERO 8951 , CO L. PARTIDO DOBLADO CD.JUAREZ \n", - "2520294 LAGO GARDA 226 AGUA AZUL \n", - "2520295 BLVD. CONSTITUCIóN 1111 COL MARGARITAS \n", - "\n", - " CITY CITY_2 STATE STATE_L ... \\\n", - "0 PLAYA DEL CARME N SOLIDARIDAD MX QROO ... \n", - "1 GUANAJUATO MX GTO ... \n", - "2 IRAPUATO MX GTO ... \n", - "3 MEXICO MX DF ... \n", - "4 LERMA MX MEX ... \n", - "... ... ... ... ... ... \n", - "2520291 SAN LUIS POTOSI MX SLP ... \n", - "2520292 DELEGACION COYO ACAN, MEXICO MX DF ... \n", - "2520293 CHIHUAHUA MX CHIH ... \n", - "2520294 NEZAHUALC OYOTL MX MEX ... \n", - "2520295 TORREON MX COAH ... \n", - "\n", - " feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_contains_email_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_contains_url_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_contains_date_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_contains_exactly_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_is_substring_of_column_name_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_only_one_char_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_only_white_spaces_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "2520291 False \n", - "2520292 False \n", - "2520293 False \n", - "2520294 False \n", - "2520295 False \n", - "\n", - " feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2 target \n", - "0 False valid \n", - "1 False valid \n", - "2 False valid \n", - "3 False valid \n", - "4 False valid \n", - "... ... ... \n", - "2520291 False valid \n", - "2520292 False valid \n", - "2520293 False valid \n", - "2520294 False valid \n", - "2520295 False valid \n", - "\n", - "[2520296 rows x 95 columns]" + "valid 1344306\n", + "key_smash 665\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" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -689,24 +282,24 @@ " 'count_sequence_squared_vowels': 1.00,\n", " 'count_sequence_squared_consonants': 1.999,\n", " 'count_sequence_squared_special_characters': 2.2499,\n", - " 'ratio_of_numeric_digits_squared': 2.9,\n", + " # 'ratio_of_numeric_digits_squared': 2.9,\n", " 'average_of_char_count_squared': 2.78,\n", "}\n", "\n", "df = annotate_data.annotate_data(df, concatened_column_name, key_smash_thresholds)\n", - "df" + "df.drop_duplicates(subset=[concatened_column_name])['target'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "valid 2514405\n", - "key_smash 1695\n", + "valid 2514579\n", + "key_smash 1521\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -720,7 +313,7 @@ "Name: target, dtype: int64" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -739,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -747,31 +340,21 @@ "output_type": "stream", "text": [ "\u001b[33mtranning model...\u001b[37m\n", + "AAAAAA feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", "\u001b[32mdone\u001b[37m\n", "\u001b[33mget model score...\u001b[37m\n", - "\u001b[1maccuracy -> \u001b[22m0.9937578027465668\n", - "\u001b[1mprecision -> \u001b[22m0.9794238683127572\n", + "\u001b[1maccuracy -> \u001b[22m1.0\n", + "\u001b[1mprecision -> \u001b[22m1.0\n", "\u001b[1mrecall -> \u001b[22m1.0\n", - "\u001b[1mf1 -> \u001b[22m0.9896049896049895\n" + "\u001b[1mf1 -> \u001b[22m1.0\n" ] - }, - { - "data": { - "text/plain": [ - "{'accuracy': 0.9937578027465668,\n", - " 'precision': 0.9794238683127572,\n", - " 'recall': 1.0,\n", - " 'f1': 0.9896049896049895}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "scores = new_rf_model.train_and_get_scores(df, concatened_column_name, all_features_columns)\n", - "scores" + "scores = new_rf_model.train_and_get_scores(df, concatened_column_name, selected_features)" ] }, { @@ -784,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -798,7 +381,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:41: SettingWithCopyWarning: \n", + "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:45: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -811,18 +394,18 @@ { "data": { "text/plain": [ - "0.0 1340347\n", - "1.0 5774\n", + "0.0 1345115\n", + "1.0 1006\n", "Name: prediction, dtype: int64" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['prediction'] = new_rf_model.predict(df[all_features_columns], concatened_column_name)\n", + "df['prediction'] = new_rf_model.predict(df[selected_features], concatened_column_name)\n", "df.drop_duplicates(subset=[concatened_column_name])['prediction'].value_counts()" ] }, @@ -836,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -848,7 +431,7 @@ } ], "source": [ - "new_rf_model.export_model('../data/models/RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.pkl',\n", + "new_rf_model.export_model('../data/models/RandomForest_Ksmash_Word_Embedding_Regex_Enrichments_Normalization.pkl',\n", " '../data/models/normalization_absolutes.csv')" ] }, @@ -858,7 +441,9 @@ "metadata": {}, "outputs": [], "source": [ - "df[df['prediction'] == 1][[concatened_column_name, 'prediction']].drop_duplicates(subset=[concatened_column_name]).to_csv('../data/tmp/prediction_RandomForest_Ksmash_WordEmbedding_Regex_Enrichments_Normalization.csv')" + "df[df['prediction'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + " .drop_duplicates(subset=[concatened_column_name]) \\\n", + " .to_csv(f'../data/tmp/{time.strftime(\"%Y%m%d-%H%M%S\")}prediction_rf_ks_we_regex_enrich_normal.csv')" ] } ], diff --git a/examples/MEXICO_retrain_predict_example_no_embedding.ipynb b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb new file mode 100644 index 00000000..e6198a66 --- /dev/null +++ b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import hygia as hg\n", + "import pickle\n", + "import time" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classes instanciations\n", + "\n", + "NOTE: Please check if the model_path matches your path" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning feature engineering with configs below...\u001b[37m\n", + "\u001b[1mlanguage -> \u001b[22mes\n", + "\u001b[1mdimensions -> \u001b[22m25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/anapaula/dell/Playground/hygia/data_pipeline/augment_data/augment_data.py:29: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].astype(str).str.pad(country_mappings[country]['length'],fillchar='0')\n" + ] + } + ], + "source": [ + "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", + "augment_data = hg.AugmentData(country=\"MEXICO\")\n", + "feature_engineering = hg.FeatureEngineering(country=\"MEXICO\")\n", + "annotate_data = hg.AnnotateData()\n", + "new_rf_model = hg.RandomForestModel()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Data\n", + "\n", + "NOTE: Please check if the file_path matches your data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", + "df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Add new columns\n", + "\n", + "1. Concatenate address\n", + "2. All features columns:\n", + " - Key Smash\n", + " - Regex\n", + " - Word Embedding\n", + "\n", + "NOTE: Please check if the columns names matches your data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "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", + "extract features from -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\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", + "df = feature_engineering.extract_features(df, concatened_column_name)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Check new columns names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_ratio_of_numeric_digits_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_0_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_1_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_2_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_3_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_4_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_5_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_6_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_7_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_8_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_9_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_10_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_11_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_12_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_13_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_14_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_15_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_16_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_17_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_18_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_19_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_20_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_21_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_22_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_23_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_24_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " '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_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", + " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_features_columns = [col for col in df if col.startswith('feature_ks') or col.startswith('feature_we') or col.startswith('feature_re')]\n", + "all_features_columns" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Select Features\n", + "- remove word embeddings\n", + "- remove key smash feature: ratio_of_numeric_digits_squared" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_numbers_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_re_only_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " '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_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", + " 'feature_re_empty_concat_STREET_ADDRESS_1_STREET_ADDRESS_2']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_features = [col for col in all_features_columns \\\n", + " if not col.startswith('feature_we') \\\n", + " and 'ratio_of_numeric_digits_squared' not in col]\n", + "selected_features" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Annotate data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning annotate data with configs below...\u001b[37m\n", + "\u001b[1mthresholds -> \u001b[22m{'count_sequence_squared_vowels': 1.0, 'count_sequence_squared_consonants': 1.999, 'count_sequence_squared_special_characters': 2.2499, 'average_of_char_count_squared': 2.78}\n", + "column -> concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n" + ] + }, + { + "data": { + "text/plain": [ + "valid 1344306\n", + "key_smash 665\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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "key_smash_thresholds = {\n", + " 'count_sequence_squared_vowels': 1.00,\n", + " 'count_sequence_squared_consonants': 1.999,\n", + " 'count_sequence_squared_special_characters': 2.2499,\n", + " # 'ratio_of_numeric_digits_squared': 2.9,\n", + " 'average_of_char_count_squared': 2.78,\n", + "}\n", + "\n", + "df = annotate_data.annotate_data(df, concatened_column_name, key_smash_thresholds)\n", + "df.drop_duplicates(subset=[concatened_column_name])['target'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "valid 2514579\n", + "key_smash 1521\n", + "only_special_characters 1291\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_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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['target'].value_counts()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiment: retrain model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mtranning model...\u001b[37m\n", + "AAAAAA feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "AAAAAA feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", + "\u001b[32mdone\u001b[37m\n", + "\u001b[33mget model score...\u001b[37m\n", + "\u001b[1maccuracy -> \u001b[22m1.0\n", + "\u001b[1mprecision -> \u001b[22m1.0\n", + "\u001b[1mrecall -> \u001b[22m1.0\n", + "\u001b[1mf1 -> \u001b[22m1.0\n" + ] + } + ], + "source": [ + "scores = new_rf_model.train_and_get_scores(df, concatened_column_name, selected_features)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predict using pre-trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mrunning model...\u001b[37m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:45: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum\n", + "/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", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.0 1345115\n", + "1.0 1006\n", + "Name: prediction, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['prediction'] = new_rf_model.predict(df[selected_features], concatened_column_name)\n", + "df.drop_duplicates(subset=[concatened_column_name])['prediction'].value_counts()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save model and predicted data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mexporting model and normalization absolutes...\u001b[37m\n" + ] + } + ], + "source": [ + "new_rf_model.export_model('../data/models/RandomForest_Ksmash_Regex_Enrichments_Normalization.pkl',\n", + " '../data/models/normalization_absolutes.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df[df['prediction'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + " .drop_duplicates(subset=[concatened_column_name]) \\\n", + " .to_csv(f'../data/tmp/{time.strftime(\"%Y%m%d-%H%M%S\")}prediction_rf_ks_regex_enrich_normal.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "acd904f7927719ac3bd428a31e6feadbc6c298bbba280a82d6227cca902ecf8e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b40eae52e6decfcc9bae6e4fffb1662914502cfb Mon Sep 17 00:00:00 2001 From: atarchetti Date: Wed, 8 Feb 2023 14:25:36 -0300 Subject: [PATCH 08/10] #65 save notebook outputs --- examples/MEXICO_predict_example.ipynb | 66 ++++++++++------ ...O_predict_example_no_embeddings copy.ipynb | 30 ++++---- examples/MEXICO_retrain_predict_example.ipynb | 77 +++++++++---------- ...retrain_predict_example_no_embedding.ipynb | 44 ++--------- 4 files changed, 100 insertions(+), 117 deletions(-) diff --git a/examples/MEXICO_predict_example.ipynb b/examples/MEXICO_predict_example.ipynb index 544584fe..4e461654 100644 --- a/examples/MEXICO_predict_example.ipynb +++ b/examples/MEXICO_predict_example.ipynb @@ -30,7 +30,7 @@ "pre_process_data = hg.PreProcessData(country=\"MEXICO\")\n", "augment_data = hg.AugmentData(country=\"MEXICO\")\n", "feature_engineering = hg.FeatureEngineering(country=\"MEXICO\")\n", - "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_Regex_Enrichments_Normalization.pkl',\n", + "rf_model = hg.RandomForestModel('../data/models/RandomForest_Ksmash_Word_Embedding_Regex_Enrichments_Normalization.pkl',\n", " normalization_absolutes_file='../data/models/normalization_absolutes.csv')" ] }, @@ -49,8 +49,7 @@ "outputs": [], "source": [ "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", - "# df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')\n", - "df = pd.read_csv(file_path, sep='¨', nrows=5000, engine='python')" + "df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')" ] }, { @@ -125,6 +124,31 @@ " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_0_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_1_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_2_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_3_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_4_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_5_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_6_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_7_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_8_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_9_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_10_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_11_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_12_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_13_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_14_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_15_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_16_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_17_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_18_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_19_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_20_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_21_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_22_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_23_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_24_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", @@ -147,9 +171,7 @@ ], "source": [ "all_features_columns = [col for col in df if col.startswith('feature_ks') or col.startswith('feature_we') or col.startswith('feature_re')]\n", - "model_features_columns = [col for col in all_features_columns \\\n", - " if not col.startswith('feature_we') \\\n", - " and 'ratio_of_numeric_digits_squared' not in col]\n", + "model_features_columns = [col for col in all_features_columns if 'ratio_of_numeric_digits_squared' not in col]\n", "model_features_columns" ] }, @@ -170,24 +192,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mrunning model...\u001b[37m\n", - "AAAAAAAAAAA concatened_column_name concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAAAAAAA features_column_to_normalize feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAAAAAAA 1 feature_ks_count_sequence_squared_vowels\n" + "\u001b[33mrunning model...\u001b[37m\n" ] }, { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m rf_model\u001b[39m.\u001b[39;49mpredict(df[model_features_columns], concatened_column_name)\n\u001b[1;32m 2\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mvalue_counts()\n", - "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:107\u001b[0m, in \u001b[0;36mRandomForestModel.predict\u001b[0;34m(self, X, concatened_column_name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mYELLOW\u001b[39m}\u001b[39;00m\u001b[39mrunning model...\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mWHITE\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m 106\u001b[0m key_smash_features_columns \u001b[39m=\u001b[39m [column \u001b[39mfor\u001b[39;00m column \u001b[39min\u001b[39;00m X\u001b[39m.\u001b[39mcolumns \u001b[39mif\u001b[39;00m column\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mfeature_ks\u001b[39m\u001b[39m'\u001b[39m)]\n\u001b[0;32m--> 107\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_normalization(X\u001b[39m.\u001b[39;49mcopy(), key_smash_features_columns, concatened_column_name)\n\u001b[1;32m 109\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mpredict(X\u001b[39m.\u001b[39mvalues)\n", - "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:46\u001b[0m, in \u001b[0;36mRandomForestModel._normalization\u001b[0;34m(self, df, features_columns_to_normalize, concatened_column_name)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA features_column_to_normalize\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize)\n\u001b[1;32m 45\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA 1\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m---> 46\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39;49mreplace(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mconcatened_column_name\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m)])\n\u001b[1;32m 47\u001b[0m column_absolute_maximum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)]\u001b[39m.\u001b[39mvalues[\u001b[39m0\u001b[39m]\n\u001b[1;32m 48\u001b[0m df[features_column_to_normalize] \u001b[39m=\u001b[39m df[features_column_to_normalize] \u001b[39m/\u001b[39m column_absolute_maximum\n", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] + "data": { + "text/plain": [ + "0.0 2512460\n", + "1.0 7836\n", + "Name: prediction_is_key_smash, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -205,13 +223,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'prediction_is_key_smash']] \\\n", " .drop_duplicates(subset=[concatened_column_name]) \\\n", - " .to_csv(f'../data/tmp/prediction_rf_ks_we_regex_enrich_normal.csv')" + " .to_csv(f'../data/tmp/prediction_rf_ks_regex_enrich_normal.csv')" ] } ], diff --git a/examples/MEXICO_predict_example_no_embeddings copy.ipynb b/examples/MEXICO_predict_example_no_embeddings copy.ipynb index 544584fe..df311d97 100644 --- a/examples/MEXICO_predict_example_no_embeddings copy.ipynb +++ b/examples/MEXICO_predict_example_no_embeddings copy.ipynb @@ -170,24 +170,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mrunning model...\u001b[37m\n", - "AAAAAAAAAAA concatened_column_name concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAAAAAAA features_column_to_normalize feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAAAAAAA 1 feature_ks_count_sequence_squared_vowels\n" + "\u001b[33mrunning model...\u001b[37m\n" ] }, { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m rf_model\u001b[39m.\u001b[39;49mpredict(df[model_features_columns], concatened_column_name)\n\u001b[1;32m 2\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mprediction_is_key_smash\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mvalue_counts()\n", - "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:107\u001b[0m, in \u001b[0;36mRandomForestModel.predict\u001b[0;34m(self, X, concatened_column_name)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mYELLOW\u001b[39m}\u001b[39;00m\u001b[39mrunning model...\u001b[39m\u001b[39m{\u001b[39;00mFore\u001b[39m.\u001b[39mWHITE\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[1;32m 106\u001b[0m key_smash_features_columns \u001b[39m=\u001b[39m [column \u001b[39mfor\u001b[39;00m column \u001b[39min\u001b[39;00m X\u001b[39m.\u001b[39mcolumns \u001b[39mif\u001b[39;00m column\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mfeature_ks\u001b[39m\u001b[39m'\u001b[39m)]\n\u001b[0;32m--> 107\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_normalization(X\u001b[39m.\u001b[39;49mcopy(), key_smash_features_columns, concatened_column_name)\n\u001b[1;32m 109\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mpredict(X\u001b[39m.\u001b[39mvalues)\n", - "File \u001b[0;32m~/dell/Playground/hygia/data_pipeline/model/random_forest.py:46\u001b[0m, in \u001b[0;36mRandomForestModel._normalization\u001b[0;34m(self, df, features_columns_to_normalize, concatened_column_name)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA features_column_to_normalize\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize)\n\u001b[1;32m 45\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA 1\u001b[39m\u001b[39m'\u001b[39m, features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m---> 46\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAAAAAAAAAAA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39;49mreplace(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m{\u001b[39;49;00mconcatened_column_name\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m)])\n\u001b[1;32m 47\u001b[0m column_absolute_maximum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnormalization_absolutes[features_column_to_normalize\u001b[39m.\u001b[39mreplace(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_\u001b[39m\u001b[39m{\u001b[39;00mconcatened_column_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)]\u001b[39m.\u001b[39mvalues[\u001b[39m0\u001b[39m]\n\u001b[1;32m 48\u001b[0m df[features_column_to_normalize] \u001b[39m=\u001b[39m df[features_column_to_normalize] \u001b[39m/\u001b[39m column_absolute_maximum\n", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] + "data": { + "text/plain": [ + "0.0 4984\n", + "1.0 16\n", + "Name: prediction_is_key_smash, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -205,11 +201,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'target', 'prediction']] \\\n", + "df[df['prediction_is_key_smash'] == 1][[concatened_column_name, 'prediction_is_key_smash']] \\\n", " .drop_duplicates(subset=[concatened_column_name]) \\\n", " .to_csv(f'../data/tmp/prediction_rf_ks_we_regex_enrich_normal.csv')" ] diff --git a/examples/MEXICO_retrain_predict_example.ipynb b/examples/MEXICO_retrain_predict_example.ipynb index 2d670ad0..b13f60f0 100644 --- a/examples/MEXICO_retrain_predict_example.ipynb +++ b/examples/MEXICO_retrain_predict_example.ipynb @@ -35,18 +35,6 @@ "\u001b[1mlanguage -> \u001b[22mes\n", "\u001b[1mdimensions -> \u001b[22m25\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anapaula/dell/Playground/hygia/data_pipeline/augment_data/augment_data.py:29: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].astype(str).str.pad(country_mappings[country]['length'],fillchar='0')\n" - ] } ], "source": [ @@ -207,6 +195,31 @@ " 'feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_0_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_1_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_2_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_3_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_4_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_5_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_6_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_7_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_8_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_9_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_10_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_11_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_12_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_13_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_14_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_15_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_16_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_17_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_18_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_19_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_20_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_21_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_22_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_23_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", + " 'feature_we_24_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_context_invalid_words_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_dell_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", " 'feature_re_contains_exactly_the_word_test_concat_STREET_ADDRESS_1_STREET_ADDRESS_2',\n", @@ -257,11 +270,12 @@ { "data": { "text/plain": [ - "valid 1344306\n", - "key_smash 665\n", + "valid 1344182\n", + "key_smash 661\n", "contains_email 569\n", "contains_exactly_the_word_test 177\n", "only_special_characters 144\n", + "contains_context_invalid_words 128\n", "contains_exactly_the_word_dell 125\n", "only_numbers 106\n", "only_one_char 14\n", @@ -298,8 +312,9 @@ { "data": { "text/plain": [ - "valid 2514579\n", - "key_smash 1521\n", + "valid 2511507\n", + "contains_context_invalid_words 3079\n", + "key_smash 1514\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -340,16 +355,12 @@ "output_type": "stream", "text": [ "\u001b[33mtranning model...\u001b[37m\n", - "AAAAAA feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", "\u001b[32mdone\u001b[37m\n", "\u001b[33mget model score...\u001b[37m\n", - "\u001b[1maccuracy -> \u001b[22m1.0\n", - "\u001b[1mprecision -> \u001b[22m1.0\n", - "\u001b[1mrecall -> \u001b[22m1.0\n", - "\u001b[1mf1 -> \u001b[22m1.0\n" + "\u001b[1maccuracy -> \u001b[22m0.9910256410256411\n", + "\u001b[1mprecision -> \u001b[22m0.9776536312849162\n", + "\u001b[1mrecall -> \u001b[22m0.9831460674157303\n", + "\u001b[1mf1 -> \u001b[22m0.9803921568627452\n" ] } ], @@ -377,25 +388,11 @@ "\u001b[33mrunning model...\u001b[37m\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:45: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum\n", - "/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", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ - "0.0 1345115\n", - "1.0 1006\n", + "0.0 1343185\n", + "1.0 2936\n", "Name: prediction, dtype: int64" ] }, diff --git a/examples/MEXICO_retrain_predict_example_no_embedding.ipynb b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb index e6198a66..49d27c08 100644 --- a/examples/MEXICO_retrain_predict_example_no_embedding.ipynb +++ b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb @@ -35,18 +35,6 @@ "\u001b[1mlanguage -> \u001b[22mes\n", "\u001b[1mdimensions -> \u001b[22m25\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anapaula/dell/Playground/hygia/data_pipeline/augment_data/augment_data.py:29: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].astype(str).str.pad(country_mappings[country]['length'],fillchar='0')\n" - ] } ], "source": [ @@ -259,11 +247,12 @@ { "data": { "text/plain": [ - "valid 1344306\n", - "key_smash 665\n", + "valid 1344182\n", + "key_smash 661\n", "contains_email 569\n", "contains_exactly_the_word_test 177\n", "only_special_characters 144\n", + "contains_context_invalid_words 128\n", "contains_exactly_the_word_dell 125\n", "only_numbers 106\n", "only_one_char 14\n", @@ -300,8 +289,9 @@ { "data": { "text/plain": [ - "valid 2514579\n", - "key_smash 1521\n", + "valid 2511507\n", + "contains_context_invalid_words 3079\n", + "key_smash 1514\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -342,10 +332,6 @@ "output_type": "stream", "text": [ "\u001b[33mtranning model...\u001b[37m\n", - "AAAAAA feature_ks_count_sequence_squared_vowels_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_count_sequence_squared_consonants_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_count_sequence_squared_special_characters_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", - "AAAAAA feature_ks_average_of_char_count_squared_concat_STREET_ADDRESS_1_STREET_ADDRESS_2\n", "\u001b[32mdone\u001b[37m\n", "\u001b[33mget model score...\u001b[37m\n", "\u001b[1maccuracy -> \u001b[22m1.0\n", @@ -379,25 +365,11 @@ "\u001b[33mrunning model...\u001b[37m\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anapaula/dell/Playground/hygia/data_pipeline/model/random_forest.py:45: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum\n", - "/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", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ - "0.0 1345115\n", - "1.0 1006\n", + "0.0 1345148\n", + "1.0 973\n", "Name: prediction, dtype: int64" ] }, From 975cc18cefef5195071564b8d490d22316ed66da Mon Sep 17 00:00:00 2001 From: atarchetti Date: Wed, 8 Feb 2023 19:51:44 -0300 Subject: [PATCH 09/10] #65 add new abbreviations and retrain models --- data/dicts/mexico_abbreviations.csv | 4 +++- ...Ksmash_Regex_Enrichments_Normalization.pkl | Bin 517843 -> 541900 bytes ...edding_Regex_Enrichments_Normalization.pkl | Bin 1021330 -> 1104724 bytes ...O_predict_example_no_embeddings copy.ipynb | 9 ++++---- examples/MEXICO_retrain_predict_example.ipynb | 20 +++++++++--------- ...retrain_predict_example_no_embedding.ipynb | 18 ++++++++-------- 6 files changed, 26 insertions(+), 25 deletions(-) diff --git a/data/dicts/mexico_abbreviations.csv b/data/dicts/mexico_abbreviations.csv index 6d6a168d..c0b7bf59 100644 --- 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b/examples/MEXICO_predict_example_no_embeddings copy.ipynb index df311d97..9c873d4e 100644 --- a/examples/MEXICO_predict_example_no_embeddings copy.ipynb +++ b/examples/MEXICO_predict_example_no_embeddings copy.ipynb @@ -49,8 +49,7 @@ "outputs": [], "source": [ "file_path = '../data/tmp/AI_LATA_ADDRESS_MEX_modificado.csv'\n", - "# df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')\n", - "df = pd.read_csv(file_path, sep='¨', nrows=5000, engine='python')" + "df = pd.read_csv(file_path, sep='¨', nrows=None, engine='python')" ] }, { @@ -176,8 +175,8 @@ { "data": { "text/plain": [ - "0.0 4984\n", - "1.0 16\n", + "0.0 2518008\n", + "1.0 2288\n", "Name: prediction_is_key_smash, dtype: int64" ] }, @@ -201,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/MEXICO_retrain_predict_example.ipynb b/examples/MEXICO_retrain_predict_example.ipynb index b13f60f0..0d3cc13b 100644 --- a/examples/MEXICO_retrain_predict_example.ipynb +++ b/examples/MEXICO_retrain_predict_example.ipynb @@ -270,8 +270,8 @@ { "data": { "text/plain": [ - "valid 1344182\n", - "key_smash 661\n", + "valid 1344254\n", + "key_smash 657\n", "contains_email 569\n", "contains_exactly_the_word_test 177\n", "only_special_characters 144\n", @@ -312,9 +312,9 @@ { "data": { "text/plain": [ - "valid 2511507\n", + "valid 2511527\n", "contains_context_invalid_words 3079\n", - "key_smash 1514\n", + "key_smash 1494\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -357,10 +357,10 @@ "\u001b[33mtranning model...\u001b[37m\n", "\u001b[32mdone\u001b[37m\n", "\u001b[33mget model score...\u001b[37m\n", - "\u001b[1maccuracy -> \u001b[22m0.9910256410256411\n", - "\u001b[1mprecision -> \u001b[22m0.9776536312849162\n", - "\u001b[1mrecall -> \u001b[22m0.9831460674157303\n", - "\u001b[1mf1 -> \u001b[22m0.9803921568627452\n" + "\u001b[1maccuracy -> \u001b[22m0.9922879177377892\n", + "\u001b[1mprecision -> \u001b[22m0.9903846153846154\n", + "\u001b[1mrecall -> \u001b[22m0.9809523809523809\n", + "\u001b[1mf1 -> \u001b[22m0.985645933014354\n" ] } ], @@ -391,8 +391,8 @@ { "data": { "text/plain": [ - "0.0 1343185\n", - "1.0 2936\n", + "0.0 1342240\n", + "1.0 3949\n", "Name: prediction, dtype: int64" ] }, diff --git a/examples/MEXICO_retrain_predict_example_no_embedding.ipynb b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb index 49d27c08..6263dde5 100644 --- a/examples/MEXICO_retrain_predict_example_no_embedding.ipynb +++ b/examples/MEXICO_retrain_predict_example_no_embedding.ipynb @@ -247,8 +247,8 @@ { "data": { "text/plain": [ - "valid 1344182\n", - "key_smash 661\n", + "valid 1344254\n", + "key_smash 657\n", "contains_email 569\n", "contains_exactly_the_word_test 177\n", "only_special_characters 144\n", @@ -289,9 +289,9 @@ { "data": { "text/plain": [ - "valid 2511507\n", + "valid 2511527\n", "contains_context_invalid_words 3079\n", - "key_smash 1514\n", + "key_smash 1494\n", "only_special_characters 1291\n", "contains_email 1048\n", "contains_exactly_the_word_test 667\n", @@ -334,10 +334,10 @@ "\u001b[33mtranning model...\u001b[37m\n", "\u001b[32mdone\u001b[37m\n", "\u001b[33mget model score...\u001b[37m\n", - "\u001b[1maccuracy -> \u001b[22m1.0\n", - "\u001b[1mprecision -> \u001b[22m1.0\n", + "\u001b[1maccuracy -> \u001b[22m0.9987146529562982\n", + "\u001b[1mprecision -> \u001b[22m0.9946524064171123\n", "\u001b[1mrecall -> \u001b[22m1.0\n", - "\u001b[1mf1 -> \u001b[22m1.0\n" + "\u001b[1mf1 -> \u001b[22m0.9973190348525469\n" ] } ], @@ -368,8 +368,8 @@ { "data": { "text/plain": [ - "0.0 1345148\n", - "1.0 973\n", + "0.0 1345413\n", + "1.0 776\n", "Name: prediction, dtype: int64" ] }, From 99faaaa3be83feb279b8e44f89784d6c9f96b70c Mon Sep 17 00:00:00 2001 From: atarchetti Date: Thu, 9 Feb 2023 11:42:53 -0300 Subject: [PATCH 10/10] #65 fix abbreviations file --- data/dicts/mexico_abbreviations.csv | 1 + 1 file changed, 1 insertion(+) diff --git a/data/dicts/mexico_abbreviations.csv b/data/dicts/mexico_abbreviations.csv index c0b7bf59..0a0cbf31 100644 --- a/data/dicts/mexico_abbreviations.csv +++ b/data/dicts/mexico_abbreviations.csv @@ -6,5 +6,6 @@ PO,POST OFFICE P.O,POST OFFICE BLVD,BOULEVARD LT,LOTE +MZ,MANZANA CDMX,Ciudad de México DF,Distrito Federal \ No newline at end of file

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-feature_ks_count_sequence_squared_vowels,feature_ks_count_sequence_squared_consonants,feature_ks_count_sequence_squared_special_characters,feature_ks_ratio_of_numeric_digits_squared,feature_ks_average_of_char_count_squared -15.03125,30.0,30.0,10.242424242424242,30.0 +feature_ks_count_sequence_squared_vowels,feature_ks_count_sequence_squared_consonants,feature_ks_count_sequence_squared_special_characters,feature_ks_average_of_char_count_squared +15.03125,30.0,30.0,30.0 diff --git a/hygia/data_pipeline/feature_engineering/feature_engineering.py b/hygia/data_pipeline/feature_engineering/feature_engineering.py index a19ea430..bf91debc 100644 --- a/hygia/data_pipeline/feature_engineering/feature_engineering.py +++ b/hygia/data_pipeline/feature_engineering/feature_engineering.py @@ -22,7 +22,7 @@ class FeatureEngineering: print(df) """ - def __init__(self, lang: str = 'es', dimensions: int = 25, model: str = 'bytepair'): + def __init__(self, lang:str='es', dimensions:int=25, model:str='bytepair', country:str=None, context_words_file:str=None): """ Initialize the FeatureEngineering class. @@ -41,7 +41,7 @@ def __init__(self, lang: str = 'es', dimensions: int = 25, model: str = 'bytepai self.key_smash = KeySmash() self.word_embedding = WordEmbedding(lang=lang, dimensions=dimensions, model=model) - self.regex = Regex() + self.regex = Regex(country=country, context_words_file=context_words_file) def extract_features(self, df: pd.DataFrame, text_column: str) -> pd.DataFrame: """ diff --git a/hygia/data_pipeline/feature_engineering/key_smash.py b/hygia/data_pipeline/feature_engineering/key_smash.py index 3e253879..359550f7 100644 --- a/hygia/data_pipeline/feature_engineering/key_smash.py +++ b/hygia/data_pipeline/feature_engineering/key_smash.py @@ -177,7 +177,7 @@ def _normalize_column(self, df: pd.DataFrame, column: str) -> pd.DataFrame: return df[column] / df[column].abs().max() if df[column].abs().max() != 0.0 else 0.0 - def extract_key_smash_features(self, df:pd.DataFrame, column_name:str, normalize:bool=False) -> pd.DataFrame: + def extract_key_smash_features(self, df:pd.DataFrame, column_name:str) -> pd.DataFrame: """ Extract key smash features from a given dataframe and column. @@ -209,13 +209,4 @@ def extract_key_smash_features(self, df:pd.DataFrame, column_name:str, normalize df[f'feature_ks_ratio_of_numeric_digits_squared_{column_name}'] = df[column_name].fillna('').apply(lambda x: self.ratio_of_numeric_digits_squared(x) if len(x) > 0 else 0.0) df[f'feature_ks_average_of_char_count_squared_{column_name}'] = df[column_name].fillna('').apply(lambda x: self.average_of_char_count_squared(x) if len(x) > 0 else 0.0) - if normalize: - key_smash_columns = [f'feature_ks_count_sequence_squared_vowels_{column_name}', - f'feature_ks_count_sequence_squared_consonants_{column_name}', - f'feature_ks_count_sequence_squared_special_characters_{column_name}', - f'feature_ks_ratio_of_numeric_digits_squared_{column_name}', - f'feature_ks_average_of_char_count_squared_{column_name}'] - for column in key_smash_columns: - df[column] = self._normalize_column(df, column) - return df \ No newline at end of file From 862a63453570f306b1b1d86246f6f04c5c58256e Mon Sep 17 00:00:00 2001 From: atarchetti Date: Tue, 7 Feb 2023 12:09:53 -0300 Subject: [PATCH 03/10] #65 add regex for contains_context_invalid_words --- .../feature_engineering/regex.py | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/hygia/data_pipeline/feature_engineering/regex.py b/hygia/data_pipeline/feature_engineering/regex.py index c3dd6f50..9f6f4116 100644 --- a/hygia/data_pipeline/feature_engineering/regex.py +++ b/hygia/data_pipeline/feature_engineering/regex.py @@ -1,6 +1,29 @@ import re import pandas as pd +from hygia.paths.paths import root_path class Regex: + def __init__(self, country:str=None, context_words_file:str=None): + self.context_invalid_words = [] + if not country and not context_words_file: + return + country_mappings = { + 'MEXICO': {'context_words_file': root_path + '/data/dicts/mexico_context.csv.csv'}, + } + if country: + context_words_file_path = country_mappings[country]['abbrevitations_file'] + if context_words_file: + context_words_file_path = context_words_file + + df_context = pd.read_csv(context_words_file_path) + self.context_invalid_words = df_context[df_context['valid']==0]['text'].values + + def contains_context_invalid_words(self, text:str) -> bool: + for context_invalid_word in self.context_invalid_words: + pattern = rf'{context_invalid_word}' + if bool(re.search(pattern, text, re.IGNORECASE)): + return True + return False + def contains_exactly_the_word_dell(self, text:str) -> bool: pattern = r'\bdell\b' return bool(re.search(pattern, text, re.IGNORECASE)) @@ -51,6 +74,7 @@ def empty(self, text:str) -> bool: def extract_regex_features(self, df:pd.DataFrame, column_name:str) -> pd.DataFrame: regex_features = [ + self.contains_context_invalid_words, self.contains_exactly_the_word_dell, self.contains_exactly_the_word_test, self.only_numbers, From e5a593cf934d7cefa612714de58368ab11812a2f Mon Sep 17 00:00:00 2001 From: atarchetti Date: Tue, 7 Feb 2023 12:58:58 -0300 Subject: [PATCH 04/10] #65 fix context file path and normalization param --- hygia/data_pipeline/feature_engineering/regex.py | 4 ++-- hygia/data_pipeline/model/random_forest.py | 5 ++++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/hygia/data_pipeline/feature_engineering/regex.py b/hygia/data_pipeline/feature_engineering/regex.py index 9f6f4116..da2410aa 100644 --- a/hygia/data_pipeline/feature_engineering/regex.py +++ b/hygia/data_pipeline/feature_engineering/regex.py @@ -7,10 +7,10 @@ def __init__(self, country:str=None, context_words_file:str=None): if not country and not context_words_file: return country_mappings = { - 'MEXICO': {'context_words_file': root_path + '/data/dicts/mexico_context.csv.csv'}, + 'MEXICO': {'context_words_file': root_path + '/data/dicts/mexico_context.csv'}, } if country: - context_words_file_path = country_mappings[country]['abbrevitations_file'] + context_words_file_path = country_mappings[country]['context_words_file'] if context_words_file: context_words_file_path = context_words_file diff --git a/hygia/data_pipeline/model/random_forest.py b/hygia/data_pipeline/model/random_forest.py index 980b52cb..0bd0a42a 100644 --- a/hygia/data_pipeline/model/random_forest.py +++ b/hygia/data_pipeline/model/random_forest.py @@ -10,7 +10,8 @@ from sklearn.metrics import precision_score class RandomForestModel: - def __init__(self, model_file=None, normalization_absolutes_file=None ,n_estimators=100, max_depth=None, random_state=0): + def __init__(self, model_file=None, normalization_absolutes_file=None ,n_estimators=100, max_depth=None, random_state=0, normalize=True): + self.normalize = normalize self.model = None self.normalization_absolutes = None self.pre_trained = False @@ -36,6 +37,8 @@ def _get_absolute_maximums(self, df, features_columns_to_normalize, concatened_c self.normalization_absolutes = pd.DataFrame(absolutes_dict) def _normalization(self, df, features_columns_to_normalize, concatened_column_name): + if not self.normalize: + return df for features_column_to_normalize in features_columns_to_normalize: column_absolute_maximum = self.normalization_absolutes[features_column_to_normalize.replace(f"_{concatened_column_name}", '')].values[0] df[features_column_to_normalize] = df[features_column_to_normalize] / column_absolute_maximum From 881825b9e3241cd6801a38c87d5296249da02a31 Mon Sep 17 00:00:00 2001 From: atarchetti Date: Wed, 8 Feb 2023 14:23:07 -0300 Subject: [PATCH 05/10] #65 fix tests with new key_smash threshold and normalization --- tests/data_pipeline/feature_engineering/test_key_smash.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/data_pipeline/feature_engineering/test_key_smash.py b/tests/data_pipeline/feature_engineering/test_key_smash.py index e6d31233..e72bcec6 100644 --- a/tests/data_pipeline/feature_engineering/test_key_smash.py +++ b/tests/data_pipeline/feature_engineering/test_key_smash.py @@ -15,8 +15,8 @@ def test_average_of_char_count_squared(self, data, expected_output): assert self.key_smash.average_of_char_count_squared(data) == expected_output @pytest.mark.parametrize("data, opt, expected_output", [ - ("PUENTECILLA KM. 1.7", "vowels", 0.21052631578947367), - ("ASDASD XXXX", "consonants", 2.1818181818181817) + ("PUENTECILLA KM. 1.7", "vowels", 0.0), + ("ASDASD XXXX", "consonants", 2.272727272727273) ]) def test_count_sequence_squared(self, data, opt, expected_output): assert self.key_smash.count_sequence_squared(data, opt) == expected_output @@ -31,7 +31,7 @@ def test_ratio_of_numeric_digits_squared(self, data, expected_output): def test_extract_key_smash_features(self): df = pd.DataFrame({"text_column": ["abcdefgh", "ijklmnop", "qrstuvwxyz"]}) - result = self.key_smash.extract_key_smash_features(df, "text_column", normalize=False) + result = self.key_smash.extract_key_smash_features(df, "text_column") assert 'feature_ks_count_sequence_squared_vowels_text_column' in result.columns assert 'feature_ks_count_sequence_squared_consonants_text_column' in result.columns From c51df51ff761af1f7d6a035eb913efa2d8fd3f6e Mon Sep 17 00:00:00 2001 From: atarchetti Date: Wed, 8 Feb 2023 14:24:02 -0300 Subject: [PATCH 06/10] #65 fix dataframe assignment pansas warning --- hygia/data_pipeline/augment_data/augment_data.py | 4 ++-- hygia/data_pipeline/model/random_forest.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/hygia/data_pipeline/augment_data/augment_data.py b/hygia/data_pipeline/augment_data/augment_data.py index d5485d67..6750d1fe 100644 --- a/hygia/data_pipeline/augment_data/augment_data.py +++ b/hygia/data_pipeline/augment_data/augment_data.py @@ -24,9 +24,9 @@ def __init__(self, country:str) -> None: country_code = country_mappings[country]['code'] zipcode_file = country_mappings[country]['zipcode_file'] zipcode_df = pd.read_pickle(root_path + f"/data/zipcode/{zipcode_file}") - country_zipcode_df_raw = zipcode_df[zipcode_df['country code']== country_code] + country_zipcode_df_raw = zipcode_df[zipcode_df['country code']== country_code].copy() if country_mappings[country]['length']: - country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].astype(str).str.pad(country_mappings[country]['length'],fillchar='0') + country_zipcode_df_raw['postal code'] = country_zipcode_df_raw['postal code'].str.pad(country_mappings[country]['length'],fillchar='0') self.country_zipcode_df = country_zipcode_df_raw.drop_duplicates(subset=['postal code']) def validate_zipcode(self, text:str) -> bool: diff --git a/hygia/data_pipeline/model/random_forest.py b/hygia/data_pipeline/model/random_forest.py index 0bd0a42a..6b2cc24c 100644 --- a/hygia/data_pipeline/model/random_forest.py +++ b/hygia/data_pipeline/model/random_forest.py @@ -60,10 +60,10 @@ def train_and_get_scores(self, df, concatened_column_name, all_features_columns, # Normalization key_smash_features_columns = [column for column in all_features_columns if column.startswith('feature_ks')] self._get_absolute_maximums(df_balanced, key_smash_features_columns, concatened_column_name) - df_balanced_normalized = self._normalization(df_balanced, key_smash_features_columns, concatened_column_name) - + df_balanced_normalized = self._normalization(df_balanced.copy(), key_smash_features_columns, concatened_column_name) + # Train/Test split - X = df_balanced_normalized[[*all_features_columns]] + X = df_balanced_normalized[[*all_features_columns]].values y = df_balanced_normalized['is_key_smash'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size) @@ -100,7 +100,7 @@ def predict(self, X, concatened_column_name): print(f'{Fore.YELLOW}running model...{Fore.WHITE}') key_smash_features_columns = [column for column in X.columns if column.startswith('feature_ks')] - X = self._normalization(X, key_smash_features_columns, concatened_column_name) + X = self._normalization(X.copy(), key_smash_features_columns, concatened_column_name) return self.model.predict(X.values) From e322c8556e82665f09fc471464dc9438211f4db5 Mon Sep 17 00:00:00 2001 From: atarchetti Date: Wed, 8 Feb 2023 14:25:03 -0300 Subject: [PATCH 07/10] #65 generated new models and new notebooks to use them --- ...Ksmash_Regex_Enrichments_Normalization.pkl | Bin 0 -> 517843 bytes ...edding_Regex_Enrichments_Normalization.pkl | Bin 472798 -> 0 bytes ...edding_Regex_Enrichments_Normalization.pkl | Bin 0 -> 1021330 bytes examples/MEXICO_predict_example.ipynb | 121 ++-- ...O_predict_example_no_embeddings copy.ipynb | 245 +++++++ examples/MEXICO_retrain_predict_example.ipynb | 601 +++--------------- ...retrain_predict_example_no_embedding.ipynb | 479 ++++++++++++++ 7 files changed, 880 insertions(+), 566 deletions(-) create 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