{ "cells": [ { "cell_type": "markdown", "id": "md2bfe6758", "metadata": {}, "source": [ "# BitBullet:Lessons — 04: Data Transformations\n", "### A Deep Dive into `bitbullet.transform`\n", "\n", "---\n", "\n", "Raw data does not feed models cleanly. A feature with extreme right skew pushes tree splits to the extremes. An unscaled column ranging `[0, 1,000,000]` next to one ranging `[0, 1]` biases distance-based algorithms. A categorical column with 50 unique values explodes into 50 one-hot columns unless you have a smarter strategy.\n", "\n", "`bitbullet.transform` is built around one critical constraint: **transformers must be fit only on training data** and applied identically to test data. Violating this rule is one of the most common sources of optimistic, non-reproducible models in the industry.\n", "\n", "**Dataset**: `default_of_credit_card_clients.xls` — 30,000 Taiwanese credit card clients, 23 features after cleaning, binary target: `default_payment`. \n", "**Source**: [UCI ML Repository — Default of Credit Card Clients](https://archive.uci.edu/dataset/350/default+of+credit+card+clients) \n", "**Context**: Predict whether a client will default on next month's payment based on credit limit, repayment history, bill statements, and demographics.\n", "\n", "---\n", "\n", "### Notebook Roadmap\n", "\n", "| Section | Topic |\n", "|---------|-------|\n", "| 2 | Setup and imports |\n", "| 3 | Load and clean data |\n", "| 4 | EDA with `generate_feature_stats` |\n", "| 5 | The pipeline mental model |\n", "| 6 | Train/test split |\n", "| 7a–7e | Numerical transforms: log/power, scaling, quantile, winsorize, binning |\n", "| 8a–8f | Categorical transforms: label, one-hot, frequency, target, rare-bin, hash |\n", "| 9 | Composing a production pipeline |\n", "| 10 | fit_transform on train |\n", "| 11 | Save, reload, transform test set |\n", "| 12 | Pipeline management: list, disable, enable |\n", "| 13 | Visualising transformations |\n", "| 14 | Pipeline metadata inspection |\n", "| 15 | Conclusion |" ] }, { "cell_type": "markdown", "id": "0a31a66d", "metadata": {}, "source": [ "> **Let a guided workflow carry reviewed transformations through to training.**\n", "> [BitBullet Platform](https://bitbullet.co.uk/platform/datasets) centralises datasets, profiles, managed compute, storage, and modelling work in one place. Configure and review transformations with guided controls or AI-assisted drafting, compare completed results, and export the fitted preprocessing with the trained result, metadata, and generated inference code. This lesson provides the same transformation discipline directly in the BitBullet SDK." ] }, { "cell_type": "markdown", "id": "md75f7d83a", "metadata": {}, "source": [ "## 2. Setup and Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "cd2bd599b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bitbullet version : dev\n", "All imports successful.\n" ] } ], "source": [ "import sys\n", "import os\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "\n", "# When running this notebook from bitbullet/lessons in a local clone, prefer the local SDK source.\n", "sdk_root = os.path.abspath(\"..\")\n", "if sdk_root not in sys.path:\n", " sys.path.insert(0, sdk_root)\n", "\n", "from bitbullet.transform import (\n", " TransformPipeline,\n", " BaseTransformer,\n", " TransformConfig,\n", " NumericalTransformer,\n", " CategoricalTransformer,\n", " DateTimeTransformer,\n", " generate_feature_stats,\n", ")\n", "from bitbullet.transform.eda import plot_pipeline_transformations\n", "\n", "import bitbullet\n", "print(f\"bitbullet version : {getattr(bitbullet, '__version__', 'dev')}\")\n", "print(\"All imports successful.\")\n", "\n", "plt.rcParams.update({\n", " 'figure.facecolor': 'white',\n", " 'axes.spines.top': False,\n", " 'axes.spines.right': False,\n", " 'axes.grid': True,\n", " 'grid.alpha': 0.3,\n", " 'font.size': 11,\n", "})\n", "BLUE = '#2563EB'\n", "ORANGE = '#F59E0B'\n", "GREEN = '#10B981'\n", "RED = '#EF4444'\n", "GREY = '#9CA3AF'" ] }, { "cell_type": "markdown", "id": "md95c95e0e", "metadata": {}, "source": [ "## 3. Load and Clean the Dataset\n", "\n", "**Dataset:** Default of Credit Card Clients Dataset \n", "**Source:** [UCI ML Repository — Default of Credit Card Clients](https://archive.uci.edu/dataset/350/default+of+credit+card+clients) \n", "**File:** `default_of_credit_card_clients.xls`\n", "\n", "> **Before running this cell:** download `default_of_credit_card_clients.xls` from the link above and\n", "> place it in the **same directory as this notebook**. If you store it elsewhere,\n", "> update `data_path` in the code cell below to match your chosen location." ] }, { "cell_type": "code", "execution_count": null, "id": "cd84d8218d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw shape: (30000, 25)\n", "Columns: ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'default payment next month']\n" ] } ], "source": [ "# Update this path if you stored the file in a different location.\n", "data_path = \"default_of_credit_card_clients.xls\"\n", "# The XLS file has a descriptive first row — header is on row 1 (0-indexed)\n", "df_raw = pd.read_excel(data_path, header=1)\n", "\n", "print(f\"Raw shape: {df_raw.shape}\")\n", "print(f\"Columns: {list(df_raw.columns)}\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "cd93086551", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset shape (after cleaning): (30000, 24)\n", "\n", "Target distribution:\n", " No default (0) : 23,364 (77.9%)\n", " Default (1) : 6,636 (22.1%)\n", "\n" ] }, { "data": { "text/html": [ "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default_payment
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5 rows × 24 columns

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" ], "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", "0 20000 Female University Married 24 2 2 -1 -1 \n", "1 120000 Female University Single 26 -1 2 0 0 \n", "2 90000 Female University Single 34 0 0 0 0 \n", "3 50000 Female University Married 37 0 0 0 0 \n", "4 50000 Male University Married 57 -1 0 -1 0 \n", "\n", " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", "0 -2 ... 0 0 0 0 689 0 \n", "1 0 ... 3272 3455 3261 0 1000 1000 \n", "2 0 ... 14331 14948 15549 1518 1500 1000 \n", "3 0 ... 28314 28959 29547 2000 2019 1200 \n", "4 0 ... 20940 19146 19131 2000 36681 10000 \n", "\n", " PAY_AMT4 PAY_AMT5 PAY_AMT6 default_payment \n", "0 0 0 0 1 \n", "1 1000 0 2000 1 \n", "2 1000 1000 5000 0 \n", "3 1100 1069 1000 0 \n", "4 9000 689 679 0 \n", "\n", "[5 rows x 24 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Drop the ID column — it is a row identifier with no predictive value\n", "df = df_raw.drop(columns=['ID'])\n", "\n", "# Rename target to a clean identifier\n", "df = df.rename(columns={'default payment next month': 'default_payment'})\n", "\n", "# Map integer-coded categorical columns to descriptive strings\n", "df['SEX'] = df['SEX'].map({1: 'Male', 2: 'Female'})\n", "df['EDUCATION'] = df['EDUCATION'].map({\n", " 1: 'Graduate', 2: 'University', 3: 'High_School', 4: 'Other',\n", " 5: 'Other', 6: 'Other', 0: 'Other' # values 0, 5, 6 are undocumented — treat as Other\n", "})\n", "df['MARRIAGE'] = df['MARRIAGE'].map({\n", " 1: 'Married', 2: 'Single', 3: 'Other', 0: 'Other'\n", "})\n", "\n", "TARGET = 'default_payment'\n", "\n", "print(f\"Dataset shape (after cleaning): {df.shape}\")\n", "print(f\"\\nTarget distribution:\")\n", "counts = df[TARGET].value_counts()\n", "print(f\" No default (0) : {counts[0]:,} ({counts[0]/len(df)*100:.1f}%)\")\n", "print(f\" Default (1) : {counts[1]:,} ({counts[1]/len(df)*100:.1f}%)\")\n", "print()\n", "display(df.head())" ] }, { "cell_type": "markdown", "id": "md0862fe45", "metadata": {}, "source": [ "### Column Glossary\n", "\n", "| Column | Type | Description |\n", "|--------|------|-------------|\n", "| `LIMIT_BAL` | numerical | Credit limit (NT dollar) |\n", "| `SEX` | categorical | Male / Female |\n", "| `EDUCATION` | categorical | Graduate / University / High_School / Other |\n", "| `MARRIAGE` | categorical | Married / Single / Other |\n", "| `AGE` | numerical | Age in years |\n", "| `PAY_0`–`PAY_6` | numerical | Repayment status for Sept–April (-2=no consumption, -1=paid duly, 0=revolving credit, 1–9=months delayed) |\n", "| `BILL_AMT1`–`BILL_AMT6` | numerical | Bill statement amount Sept–April (NT dollar) |\n", "| `PAY_AMT1`–`PAY_AMT6` | numerical | Previous payment amount Sept–April (NT dollar) |\n", "| **`default_payment`** | **target** | **1 = defaulted next month, 0 = did not** |" ] }, { "cell_type": "markdown", "id": "md2dc0ea52", "metadata": {}, "source": [ "## 4. EDA with `generate_feature_stats`" ] }, { "cell_type": "code", "execution_count": 6, "id": "cdadd177c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical (20): ['LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n", "Categorical (3): ['SEX', 'EDUCATION', 'MARRIAGE']\n", "\n" ] }, { "data": { "text/html": [ "
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dtypemissing_countmissing_percentunique_valueszero_countnegative_countmeanstdskewnesskurtosismean_percentilemin25%50%75%maxtopfreq
LIMIT_BALint6400.08100167484.322667129747.6615670.9928670.53626356.9810000.050000.0140000.0240000.01000000.0--
SEXobject00.0200----------Female18112
EDUCATIONobject00.0400----------University14030
MARRIAGEobject00.0300----------Single15964
AGEint6400.0560035.48559.2179040.7322460.04430356.0321.028.034.041.079.0--
PAY_0int6400.011147378445-0.01671.1238020.7319752.72071528.15-2.0-1.00.00.08.0--
PAY_2int6400.011157309832-0.1337671.1971860.7905651.57041832.773333-2.0-1.00.00.08.0--
PAY_3int6400.0111576410023-0.16621.1968680.8406822.08443633.41-2.0-1.00.00.08.0--
PAY_4int6400.0111645510035-0.2206671.1691390.9996293.49698333.45-2.0-1.00.00.08.0--
PAY_5int6400.0101694710085-0.26621.1331871.0081973.98974833.616667-2.0-1.00.00.08.0--
PAY_6int6400.0101628610635-0.29111.1499880.9480293.42653435.45-2.0-1.00.00.08.0--
BILL_AMT1int6400.022723200859051223.330973635.8605762.6638619.80628969.516667-165580.03558.7522381.567091.0964511.0--
BILL_AMT2int6400.022346250666949179.07516771173.7687832.70522110.30294668.426667-69777.02984.7521200.064006.25983931.0--
BILL_AMT3int6400.022026287065547013.154869349.3874273.0878319.78325568.073333-157264.02666.2520088.560164.751664089.0--
BILL_AMT4int6400.021548319567543262.94896764332.8561342.82196511.30932568.856667-170000.02326.7519052.054506.0891586.0--
BILL_AMT5int6400.021010350665540311.40096760797.155772.8763812.30588169.696667-81334.01763.018104.550190.5927171.0--
BILL_AMT6int6400.020604402068838871.760459554.1075372.84664512.27070569.846667-339603.01256.017071.049198.25961664.0--
PAY_AMT1int6400.07943524905663.580516563.28035414.668364415.25474377.590.01000.02100.05006.0873552.0--
PAY_AMT2int6400.07899539605921.163523040.87040230.4538171641.63191179.1333330.0833.02009.05000.01684259.0--
PAY_AMT3int6400.07518596805225.681517606.9614717.216635564.31122979.8733330.0390.01800.04505.0896040.0--
PAY_AMT4int6400.06937640804826.07686715666.15974412.904985277.33376877.6833330.0296.01500.04013.25621000.0--
PAY_AMT5int6400.06897670304799.38763315278.30567911.127417180.0639477.50.0252.51500.04031.5426529.0--
PAY_AMT6int6400.06939717305215.50256717777.46577510.640727167.1614381.9866670.0117.751500.04000.0528666.0--
\n", "
" ], "text/plain": [ " dtype missing_count missing_percent unique_values zero_count \\\n", "LIMIT_BAL int64 0 0.0 81 0 \n", "SEX object 0 0.0 2 0 \n", "EDUCATION object 0 0.0 4 0 \n", "MARRIAGE object 0 0.0 3 0 \n", "AGE int64 0 0.0 56 0 \n", "PAY_0 int64 0 0.0 11 14737 \n", "PAY_2 int64 0 0.0 11 15730 \n", "PAY_3 int64 0 0.0 11 15764 \n", "PAY_4 int64 0 0.0 11 16455 \n", "PAY_5 int64 0 0.0 10 16947 \n", "PAY_6 int64 0 0.0 10 16286 \n", "BILL_AMT1 int64 0 0.0 22723 2008 \n", "BILL_AMT2 int64 0 0.0 22346 2506 \n", "BILL_AMT3 int64 0 0.0 22026 2870 \n", "BILL_AMT4 int64 0 0.0 21548 3195 \n", "BILL_AMT5 int64 0 0.0 21010 3506 \n", "BILL_AMT6 int64 0 0.0 20604 4020 \n", "PAY_AMT1 int64 0 0.0 7943 5249 \n", "PAY_AMT2 int64 0 0.0 7899 5396 \n", "PAY_AMT3 int64 0 0.0 7518 5968 \n", "PAY_AMT4 int64 0 0.0 6937 6408 \n", "PAY_AMT5 int64 0 0.0 6897 6703 \n", "PAY_AMT6 int64 0 0.0 6939 7173 \n", "\n", " negative_count mean std skewness \\\n", "LIMIT_BAL 0 167484.322667 129747.661567 0.992867 \n", "SEX 0 - - - \n", "EDUCATION 0 - - - \n", "MARRIAGE 0 - - - \n", "AGE 0 35.4855 9.217904 0.732246 \n", "PAY_0 8445 -0.0167 1.123802 0.731975 \n", "PAY_2 9832 -0.133767 1.197186 0.790565 \n", "PAY_3 10023 -0.1662 1.196868 0.840682 \n", "PAY_4 10035 -0.220667 1.169139 0.999629 \n", "PAY_5 10085 -0.2662 1.133187 1.008197 \n", "PAY_6 10635 -0.2911 1.149988 0.948029 \n", "BILL_AMT1 590 51223.3309 73635.860576 2.663861 \n", "BILL_AMT2 669 49179.075167 71173.768783 2.705221 \n", "BILL_AMT3 655 47013.1548 69349.387427 3.08783 \n", "BILL_AMT4 675 43262.948967 64332.856134 2.821965 \n", "BILL_AMT5 655 40311.400967 60797.15577 2.87638 \n", "BILL_AMT6 688 38871.7604 59554.107537 2.846645 \n", "PAY_AMT1 0 5663.5805 16563.280354 14.668364 \n", "PAY_AMT2 0 5921.1635 23040.870402 30.453817 \n", "PAY_AMT3 0 5225.6815 17606.96147 17.216635 \n", "PAY_AMT4 0 4826.076867 15666.159744 12.904985 \n", "PAY_AMT5 0 4799.387633 15278.305679 11.127417 \n", "PAY_AMT6 0 5215.502567 17777.465775 10.640727 \n", "\n", " kurtosis mean_percentile min 25% 50% 75% \\\n", "LIMIT_BAL 0.536263 56.98 10000.0 50000.0 140000.0 240000.0 \n", "SEX - - - - - - \n", "EDUCATION - - - - - - \n", "MARRIAGE - - - - - - \n", "AGE 0.044303 56.03 21.0 28.0 34.0 41.0 \n", "PAY_0 2.720715 28.15 -2.0 -1.0 0.0 0.0 \n", "PAY_2 1.570418 32.773333 -2.0 -1.0 0.0 0.0 \n", "PAY_3 2.084436 33.41 -2.0 -1.0 0.0 0.0 \n", "PAY_4 3.496983 33.45 -2.0 -1.0 0.0 0.0 \n", "PAY_5 3.989748 33.616667 -2.0 -1.0 0.0 0.0 \n", "PAY_6 3.426534 35.45 -2.0 -1.0 0.0 0.0 \n", "BILL_AMT1 9.806289 69.516667 -165580.0 3558.75 22381.5 67091.0 \n", "BILL_AMT2 10.302946 68.426667 -69777.0 2984.75 21200.0 64006.25 \n", "BILL_AMT3 19.783255 68.073333 -157264.0 2666.25 20088.5 60164.75 \n", "BILL_AMT4 11.309325 68.856667 -170000.0 2326.75 19052.0 54506.0 \n", "BILL_AMT5 12.305881 69.696667 -81334.0 1763.0 18104.5 50190.5 \n", "BILL_AMT6 12.270705 69.846667 -339603.0 1256.0 17071.0 49198.25 \n", "PAY_AMT1 415.254743 77.59 0.0 1000.0 2100.0 5006.0 \n", "PAY_AMT2 1641.631911 79.133333 0.0 833.0 2009.0 5000.0 \n", "PAY_AMT3 564.311229 79.873333 0.0 390.0 1800.0 4505.0 \n", "PAY_AMT4 277.333768 77.683333 0.0 296.0 1500.0 4013.25 \n", "PAY_AMT5 180.06394 77.5 0.0 252.5 1500.0 4031.5 \n", "PAY_AMT6 167.16143 81.986667 0.0 117.75 1500.0 4000.0 \n", "\n", " max top freq \n", "LIMIT_BAL 1000000.0 - - \n", "SEX - Female 18112 \n", "EDUCATION - University 14030 \n", "MARRIAGE - Single 15964 \n", "AGE 79.0 - - \n", "PAY_0 8.0 - - \n", "PAY_2 8.0 - - \n", "PAY_3 8.0 - - \n", "PAY_4 8.0 - - \n", "PAY_5 8.0 - - \n", "PAY_6 8.0 - - \n", "BILL_AMT1 964511.0 - - \n", "BILL_AMT2 983931.0 - - \n", "BILL_AMT3 1664089.0 - - \n", "BILL_AMT4 891586.0 - - \n", "BILL_AMT5 927171.0 - - \n", "BILL_AMT6 961664.0 - - \n", "PAY_AMT1 873552.0 - - \n", "PAY_AMT2 1684259.0 - - \n", "PAY_AMT3 896040.0 - - \n", "PAY_AMT4 621000.0 - - \n", "PAY_AMT5 426529.0 - - \n", "PAY_AMT6 528666.0 - - " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_full = df.drop(columns=[TARGET])\n", "y_full = df[TARGET]\n", "\n", "stats_df, numerical_cols, categorical_cols = generate_feature_stats(X_full)\n", "\n", "print(f\"Numerical ({len(numerical_cols)}): {numerical_cols}\")\n", "print(f\"Categorical ({len(categorical_cols)}): {categorical_cols}\")\n", "print()\n", "display(stats_df)" ] }, { "cell_type": "code", "execution_count": 7, "id": "cd1103dd56", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical feature audit:\n" ] }, { "data": { "text/html": [ "
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dtypemissing_percentskewnesskurtosiszero_countnegative_countminmax
LIMIT_BALint640.00.9928670.5362630010000.01000000.0
AGEint640.00.7322460.0443030021.079.0
PAY_0int640.00.7319752.720715147378445-2.08.0
PAY_2int640.00.7905651.570418157309832-2.08.0
PAY_3int640.00.8406822.0844361576410023-2.08.0
PAY_4int640.00.9996293.4969831645510035-2.08.0
PAY_5int640.01.0081973.9897481694710085-2.08.0
PAY_6int640.00.9480293.4265341628610635-2.08.0
BILL_AMT1int640.02.6638619.8062892008590-165580.0964511.0
BILL_AMT2int640.02.70522110.3029462506669-69777.0983931.0
BILL_AMT3int640.03.0878319.7832552870655-157264.01664089.0
BILL_AMT4int640.02.82196511.3093253195675-170000.0891586.0
BILL_AMT5int640.02.8763812.3058813506655-81334.0927171.0
BILL_AMT6int640.02.84664512.2707054020688-339603.0961664.0
PAY_AMT1int640.014.668364415.254743524900.0873552.0
PAY_AMT2int640.030.4538171641.631911539600.01684259.0
PAY_AMT3int640.017.216635564.311229596800.0896040.0
PAY_AMT4int640.012.904985277.333768640800.0621000.0
PAY_AMT5int640.011.127417180.06394670300.0426529.0
PAY_AMT6int640.010.640727167.16143717300.0528666.0
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" ], "text/plain": [ " dtype missing_percent skewness kurtosis zero_count \\\n", "LIMIT_BAL int64 0.0 0.992867 0.536263 0 \n", "AGE int64 0.0 0.732246 0.044303 0 \n", "PAY_0 int64 0.0 0.731975 2.720715 14737 \n", "PAY_2 int64 0.0 0.790565 1.570418 15730 \n", "PAY_3 int64 0.0 0.840682 2.084436 15764 \n", "PAY_4 int64 0.0 0.999629 3.496983 16455 \n", "PAY_5 int64 0.0 1.008197 3.989748 16947 \n", "PAY_6 int64 0.0 0.948029 3.426534 16286 \n", "BILL_AMT1 int64 0.0 2.663861 9.806289 2008 \n", "BILL_AMT2 int64 0.0 2.705221 10.302946 2506 \n", "BILL_AMT3 int64 0.0 3.08783 19.783255 2870 \n", "BILL_AMT4 int64 0.0 2.821965 11.309325 3195 \n", "BILL_AMT5 int64 0.0 2.87638 12.305881 3506 \n", "BILL_AMT6 int64 0.0 2.846645 12.270705 4020 \n", "PAY_AMT1 int64 0.0 14.668364 415.254743 5249 \n", "PAY_AMT2 int64 0.0 30.453817 1641.631911 5396 \n", "PAY_AMT3 int64 0.0 17.216635 564.311229 5968 \n", "PAY_AMT4 int64 0.0 12.904985 277.333768 6408 \n", "PAY_AMT5 int64 0.0 11.127417 180.06394 6703 \n", "PAY_AMT6 int64 0.0 10.640727 167.16143 7173 \n", "\n", " negative_count min max \n", "LIMIT_BAL 0 10000.0 1000000.0 \n", "AGE 0 21.0 79.0 \n", "PAY_0 8445 -2.0 8.0 \n", "PAY_2 9832 -2.0 8.0 \n", "PAY_3 10023 -2.0 8.0 \n", "PAY_4 10035 -2.0 8.0 \n", "PAY_5 10085 -2.0 8.0 \n", "PAY_6 10635 -2.0 8.0 \n", "BILL_AMT1 590 -165580.0 964511.0 \n", "BILL_AMT2 669 -69777.0 983931.0 \n", "BILL_AMT3 655 -157264.0 1664089.0 \n", "BILL_AMT4 675 -170000.0 891586.0 \n", "BILL_AMT5 655 -81334.0 927171.0 \n", "BILL_AMT6 688 -339603.0 961664.0 \n", "PAY_AMT1 0 0.0 873552.0 \n", "PAY_AMT2 0 0.0 1684259.0 \n", "PAY_AMT3 0 0.0 896040.0 \n", "PAY_AMT4 0 0.0 621000.0 \n", "PAY_AMT5 0 0.0 426529.0 \n", "PAY_AMT6 0 0.0 528666.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Categorical feature audit:\n" ] }, { "data": { "text/html": [ "
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dtypemissing_percentunique_valuestopfreq
SEXobject0.02Female18112
EDUCATIONobject0.04University14030
MARRIAGEobject0.03Single15964
\n", "
" ], "text/plain": [ " dtype missing_percent unique_values top freq\n", "SEX object 0.0 2 Female 18112\n", "EDUCATION object 0.0 4 University 14030\n", "MARRIAGE object 0.0 3 Single 15964" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_stats = stats_df.loc[numerical_cols, ['dtype', 'missing_percent', 'skewness', 'kurtosis',\n", " 'zero_count', 'negative_count', 'min', 'max']]\n", "print(\"Numerical feature audit:\")\n", "display(num_stats)\n", "\n", "print()\n", "print(\"Categorical feature audit:\")\n", "cat_stats = stats_df.loc[categorical_cols, ['dtype', 'missing_percent', 'unique_values', 'top', 'freq']]\n", "display(cat_stats)" ] }, { "cell_type": "markdown", "id": "md3257bc00", "metadata": {}, "source": [ "### Reading the Stats Table — Key Observations for This Dataset\n", "\n", "- **`PAY_AMT1`–`PAY_AMT6`**: extreme right skew (> 10). Clients who pay large lump sums dominate the tail. `log1p` is the right choice — safe for zeros, compresses the right tail.\n", "- **`BILL_AMT1`–`BILL_AMT6`**: moderate-to-high right skew, and some **negative values** (credit balance). `yeo_johnson` handles negatives that `log` and `log1p` cannot.\n", "- **`LIMIT_BAL`**: right-skewed credit limits. `log1p` reduces the long tail.\n", "- **`AGE`**: roughly normal — `standard_scale` is appropriate.\n", "- **`PAY_0`–`PAY_6`**: ordinal integer repayment status (-2 to 9). Treat as numerical; `robust_scale` is appropriate for this bounded ordinal range.\n", "- **`SEX`, `EDUCATION`, `MARRIAGE`**: three low-cardinality categoricals, 2–4 unique values each. `label_encode` works for tree models; `onehot_encode` for linear models." ] }, { "cell_type": "markdown", "id": "md5b5c0601", "metadata": {}, "source": [ "## 5. The Pipeline Mental Model" ] }, { "cell_type": "markdown", "id": "md0a90805e", "metadata": {}, "source": [ "Before writing a single transform, we internalise one rule:\n", "\n", "> **Transformers must be fit exclusively on training data. They must then be applied — without refitting — to validation and test data.**\n", "\n", "### What `TransformPipeline` Remembers After Fitting\n", "\n", "| Transform | Fitted Parameters Stored |\n", "|-----------|-------------------------|\n", "| `standard_scale` | `mean`, `std` per column |\n", "| `robust_scale` | `median`, `IQR` per column |\n", "| `yeo_johnson` | `PowerTransformer` object per column |\n", "| `log1p` | Stateless — no fitting required |\n", "| `winsorize` | Lower and upper clip thresholds per column |\n", "| `label_encode` | `LabelEncoder` object per column |\n", "| `onehot_encode` | `OneHotEncoder` object per column |\n", "| `target_encode` | Leakage-aware target statistics: OOF values from `fit_transform(..., y=target)` for training, full-data smoothed map for `transform(...)` at inference |" ] }, { "cell_type": "markdown", "id": "md66d9d68d", "metadata": {}, "source": [ "## 6. Train/Test Split" ] }, { "cell_type": "code", "execution_count": 8, "id": "cd790460f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set : 24,000 samples (default rate: 0.221)\n", "Test set : 6,000 samples (default rate: 0.221)\n", "\n", "Test set is sealed. No fitting step will touch it until final evaluation.\n" ] } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X_full, y_full, test_size=0.20, random_state=42, stratify=y_full\n", ")\n", "\n", "print(f\"Training set : {X_train.shape[0]:,} samples (default rate: {y_train.mean():.3f})\")\n", "print(f\"Test set : {X_test.shape[0]:,} samples (default rate: {y_test.mean():.3f})\")\n", "print()\n", "print(\"Test set is sealed. No fitting step will touch it until final evaluation.\")" ] }, { "cell_type": "markdown", "id": "md4b28b500", "metadata": {}, "source": [ "---\n", "## 7. Numerical Transformations" ] }, { "cell_type": "markdown", "id": "md30ed2b2b", "metadata": {}, "source": [ "### 7a. Logarithmic and Power Transforms: `log1p`, `yeo_johnson`, `sqrt`, `boxcox`\n", "\n", "**Decision rule for this dataset:**\n", "- `PAY_AMT` columns: zeros present, right-skewed → `log1p`\n", "- `BILL_AMT` columns: possible negatives, right-skewed → `yeo_johnson`\n", "- `LIMIT_BAL`: strictly positive, right-skewed → `log1p`" ] }, { "cell_type": "code", "execution_count": 9, "id": "cd9897ea91", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Skewness reduction summary:\n", " PAY_AMT1 [log1p ] +15.312 → -1.299\n", " PAY_AMT2 [log1p ] +21.124 → -1.241\n", " BILL_AMT1 [yeo_johnson ] +2.691 → -2.029\n", " LIMIT_BAL [log1p ] +1.000 → -0.517\n" ] } ], "source": [ "fig, axes = plt.subplots(2, 4, figsize=(18, 9))\n", "fig.suptitle(\"Log / Power Transforms — Before vs After\", fontsize=14, fontweight='bold')\n", "\n", "transforms_demo = [\n", " ('PAY_AMT1', 'log1p', BLUE, GREEN),\n", " ('PAY_AMT2', 'log1p', BLUE, GREEN),\n", " ('BILL_AMT1','yeo_johnson', BLUE, ORANGE),\n", " ('LIMIT_BAL','log1p', BLUE, ORANGE),\n", "]\n", "\n", "results_power = {}\n", "for col, method, c1, c2 in transforms_demo:\n", " cfg = TransformConfig(name=f\"{method}_{col}\", transformer_type=\"numerical\",\n", " method=method, columns=[col], params={})\n", " t = NumericalTransformer(cfg)\n", " results_power[col] = t.fit_transform(X_train)\n", "\n", "for i, (col, method, c1, c2) in enumerate(transforms_demo):\n", " before = X_train[col]\n", " after = results_power[col][col]\n", "\n", " axes[0, i].hist(before.dropna(), bins=40, color=c1, alpha=0.8)\n", " axes[0, i].set_title(f\"`{col}`\\n(before)\", fontsize=10, fontweight='bold')\n", " axes[0, i].text(0.97, 0.95, f'skew={before.skew():.2f}', transform=axes[0, i].transAxes,\n", " ha='right', va='top', fontsize=9,\n", " bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor='grey', alpha=0.8))\n", "\n", " axes[1, i].hist(after.dropna(), bins=40, color=c2, alpha=0.8)\n", " axes[1, i].set_title(f\"`{method}`\\n(after)\", fontsize=10, fontweight='bold')\n", " axes[1, i].text(0.97, 0.95, f'skew={after.skew():.2f}', transform=axes[1, i].transAxes,\n", " ha='right', va='top', fontsize=9,\n", " bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor='grey', alpha=0.8))\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Skewness reduction summary:\")\n", "for col, method, _, _ in transforms_demo:\n", " before_skew = X_train[col].skew()\n", " after_skew = results_power[col][col].skew()\n", " print(f\" {col:15s} [{method:12s}] {before_skew:+.3f} \\u2192 {after_skew:+.3f}\")" ] }, { "cell_type": "markdown", "id": "md7fc3c189", "metadata": {}, "source": [ "### 7b. Scaling Transforms: `standard_scale`, `robust_scale`" ] }, { "cell_type": "code", "execution_count": 10, "id": "cd8a94930c", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Repayment status (PAY_0) range in training data:\n", " min=-2, max=8, unique values: [np.int64(-2), np.int64(-1), np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8)]\n", "\n", "Using robust_scale for PAY columns — resistant to extreme delay values (8, 9 months).\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "fig.suptitle(\"Scaling Transforms on `AGE`\", fontsize=13, fontweight='bold')\n", "\n", "axes[0].hist(X_train['AGE'], bins=30, color=GREY, alpha=0.8)\n", "axes[0].set_title(f\"Original\\nrange [{X_train['AGE'].min():.0f}, {X_train['AGE'].max():.0f}]\", fontweight='bold')\n", "\n", "for i, (method, params, color) in enumerate([\n", " ('standard_scale', {}, BLUE),\n", " ('robust_scale', {}, ORANGE),\n", "], start=1):\n", " cfg = TransformConfig(name=f\"{method}_age\", transformer_type=\"numerical\",\n", " method=method, columns=[\"AGE\"], params=params)\n", " t = NumericalTransformer(cfg)\n", " result = t.fit_transform(X_train)['AGE']\n", "\n", " axes[i].hist(result, bins=30, color=color, alpha=0.8)\n", " axes[i].set_title(f\"`{method}`\\nrange [{result.min():.2f}, {result.max():.2f}]\", fontweight='bold')\n", " axes[i].set_xlabel('Scaled value')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Show outlier effect on PAY_0 (can range from -2 to 9)\n", "print(\"Repayment status (PAY_0) range in training data:\")\n", "print(f\" min={X_train['PAY_0'].min()}, max={X_train['PAY_0'].max()}, unique values: {sorted(X_train['PAY_0'].unique())}\")\n", "print(\"\\nUsing robust_scale for PAY columns — resistant to extreme delay values (8, 9 months).\")" ] }, { "cell_type": "markdown", "id": "md2d9c9989", "metadata": {}, "source": [ "### 7c. Quantile Transforms: `quantile_normal`, `quantile_uniform`" ] }, { "cell_type": "code", "execution_count": 11, "id": "cd52795c28", "metadata": {}, "outputs": [ { "data": { "image/png": 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DUr4vevz555+GxWIxzz8jR44s0D4fe+yxPM93t956q90Fj8IUM8aMGZPnPvr27Vuo2LyKGZ6ennluZ8aMGXbxWS8SZO+zZH516tQpXzm9ePGi0bp16zz3P2TIELt1iuOP9uxxdevWzTF24sSJee4vE8UMACi4wpzbDcN5xYzczmmvvvpqrusUpJjx9ddfGwEBAbnGBQQEGF9//bW5n+znjezty6mYUbNmTYftNm/e3Ljnnnsc5nt5eRlHjhzJV46vViDIet1h9OjRdsuynucfeOCBfO0vq+IuZmRkZBjdunXL8xzdrVs3uyLCtShm5NYXefLJJ6+6v9zyRjEDmTwE4IZ34cIFHTx40Jxu2rRpjnGNGzdWiRL/G51u9+7dDjG//fabevfurTVr1mjZsmW65ZZbct2vYRjq27evLl26JEmqUqWKPvzwQy1fvly+vr46d+5cYQ9JKSkp8vX11aJFizRv3jzzcdC0tDS98847ZlyFChU0YcIELVu2TOvXr9eWLVu0evVqPfLII2Z85niMAAA4Q82aNfX5558rMDBQQ4YM0ZQpU666zvfffy/DMMzpjh072i3funWrw9eJEyfM5R06dNDbb7+tTz75RJs2bdKmTZu0cOFCVa9eXZK0fft288WOhbVs2TLz8+jRo7Vx40atWrVK06dP17333ms3NENBYvPy8ssv68MPP1RcXJy2bNmidevWaeLEiebyESNG6J9//slx3d9++009evTQmjVr1K9fP3P+Z599pgMHDlx136NGjdJXX30l6cqQX6+88ori4uLUt29fM2by5MmKi4vL17FkV758ecXHx6t3797mvIYNGyo+Pt78yskff/yhmTNn6tNPP9Wdd95pzh85cqSOHj1aqLZkjufdqVMnc16nTp3MdqxYsaJQ2wWA60Vhzu3OdOjQIY0aNUpr1qxR69atzflTp0696rorVqzQtGnT7OZl/v/+8ssvKz09Xd27d1dycrIk6eabb9ayZcu0fPly870bycnJ6t69u9LT03Pcx7FjxzR27FitX79es2fPVtmyZR1i/vnnH61YsUJjx441523btk2rV6/WhAkT9NFHHykkJESSdOnSJbu/6wsrMTHR7rpDWFiY3fLy5cubn3///fci78/Z3nnnHS1dulSS5OHhof/85z/67LPPNHToUHl4XLnku3Tp0iLlKj4+3u6dqWFhYXZ9kaw5ynTw4EG9+uqrWrt2rR5++GFz/uzZs/X1118Xqh2F7Rfh+sM7MwAoKSnJbjq3l2p7enoqODhYp06dynE9Sbr//vv13//+N1/7/eGHH+wuFvz3v//V7bffLkmqU6eObr755nxtJzfLli1T48aNJV25SPPuu+9Kkt044lWrVlWVKlU0e/Zs7dmzR+fOnXO46LFt27YitQMAcGMp6BjTQ4YMUc2aNXX33XfnGpOYmGg3nfnHfKbbbrvNYZ1JkybpP//5jySpXbt2mjx5smbPnq2DBw/qwoULdsUR6cr57oEHHihQ27MKDAw0P9esWVN16tQx/8DN/p6tgsTmpUOHDnrrrbe0fft2nTp1yuEiSmJion755Zcc+xT16tXTwoULZbFY1KlTJy1YsEAXL16UdKWvULNmzVz3axiG5s2bZ06/8MILeumllyRduci/f/9+s9Axb948xcTE5PuYMlmtVrVq1UpffPGFOS/ruNq5mTRpklmcadu2rSpVqqTk5GT9888/+uijj/T8888XuC2Z+8367pLQ0FCnvhMNANxZcZzbnalfv34aPXq0JCk4OFjNmzeXJJ08eVKpqakqXbp0rus2btxY58+ft5uX9f/3NWvWmO/UsFgs+vjjj82bIRo1aqSaNWvKZrPp+PHj+uKLL9S5c2eHfbz++usaMGCA3bzsxYGZM2eqQ4cOuv/++/Xaa6+ZbXrooYfMi+kbN27U22+/LUn5uvHgai5cuGA3nf09Hlmns+fIHWS99tKzZ09NmjRJ0pWbXhISErRgwQJJV/oi/fv3L9Q+WrVqZfe9yuyf5GXgwIF68cUXzbZ89913+u233yRduU7TsmXLArejsP0iXH94MgOA3QUFSWaxIruMjAydPXs21/UkFegiSObJTLrSKcp6EqpXr16O28+v0qVLm4UMSXZ3fmS982LkyJH6v//7P33xxRc6ffp0jndvFuUJEQAA8iMlJSXP5UFBQXbTWc/HV3Px4kW1bNlSr7zyinbv3q3z5887FDKkop/vshYhHn74YVWoUEGlS5dWixYtNHbsWLuCTEFic/P555+rdevWWrZsmY4dO5br3aC5HVe7du3Mi1MeHh52Ob5aLs6cOWO+iF1yfKFq1rti9+3bl/eBOFmLFi3Mz35+fqpfv745nbXvBQAoXlc7t0sy756XlOO5Oeu8rLHZtWvXzvyc/amHop7fs57HwsLCzEKGJFWrVk0VKlTIMTar/FwnuPXWWyVduTaQ9ZycOV/K/e/6wipVqpTddPa+RNZpPz+/Iu/P2bLm2137Ih4eHnbfQ/oiKCqezACgUqVKqWrVqjp06JAk6dtvv80x7vvvv7e72N+gQQOHmJweMXSFMmXK2E1nHR4rs0N4+fJlvfHGG+b8jh07qn///goKCtLOnTvNOxdz6lQCAJCb/PyRdvToUXXt2lVJSUnq16+f3SP4OYmKipLFYjHPSZ9//rmaNWtmLs+cHxER4TCU0KpVq8ynEj09PTV69Gi1aNFCXl5eGjt2rDZs2CBJstls5jpZ70DNXug/c+ZMjm3s3r27IiIitGjRIu3atUu//fabTp8+rW3btmnbtm1as2aNtm3bJk9PzwLF5mby5MnKyMiQJNWoUUOjR49W5cqVZbPZ1KZNGzMu63FllZ++QmFlXT+3u3mz5jW3nBZG9v3ldCzZY4qrLQBwvSiOc7skuycmshbJM2X9PzmvIRizntOyns+k4v97Nj/nvPxcJwgICDA/Zy3cZJ2f234LKygoSGXKlDELI3/++afd8pMnT5qfsxZx/g1y+77k1se71n2R7OiLIL94MgOAJKlHjx7m57i4OH333Xd2yw3DMB9bla50unIaMqEgj99mHb7BMAxt377dnN67d2+Ow1g509mzZ+0eK3399dd19913q1WrVm75CCkA4N+hevXqeX55e3vriSeeUFJSkvr27auZM2de9fwZFhZm956MKVOm6Oeff85Xe44dO2Z+btiwoYYPH6527dqpefPmdsuyynpHZEJCgvk5KSlJW7duzXEdwzB06623atq0adq6datOnTqlhIQERURESJLdEAMFic3PcT377LN6+OGHddttt+VZAHGWkJAQu7tDs4//nDVHtWvXNj9nzWvWd5qsXr06131lvaCTW2Emq2+++cb8fOHCBf3000/mdOaFmOxP+hRXWwDgelEc53ZJdsMgXrx4UV9++aU5nZSUZHd+KeowzIVVp04d8/Off/5p977NQ4cO2V3wz3rOy6qgw3RdS1mfasn63oXz589r165d5nTmkNjuJOv3xl37IoZhaMeOHeZ01qLQtWgLrj88mQFAkjR48GAtXLhQR44ckWEY6tChg1588UU1bdpUiYmJmjt3rtatW2fGjxs3zuEP4YJq2LChatWqZd4t2qdPH40fP14lS5bUqFGjirTt/ChXrpxKlSplFjTGjx+vxx57TN9//70mTJhQ7PsHANyY/P39FRYWpjvuuEOzZs3K9x/4U6dO1fbt25WYmKjk5GTdeuut6tevn6Kjo+Xn56eEhATz5ZxZVa1a1fy8Z88ezZw5UzfddJNmz55t9x6prGrVqmV+PnLkiGJjY9W4cWO99957uQ6b0bVrV0lXLgpUrFhRgYGB+uWXX+zurktLSytwbG6qVq1qtn/u3LmKiIjQuXPnNHz48DzXcwaLxaLY2FhNnjxZ0pUbIvz8/BQZGalPP/3U7mJUnz59zM+1atUyb94YPny4UlNTdfjwYYcXr2YVHBxsft6zZ48++ugjhYaGKjAwMMcLW0OHDpUkVaxYUW+//bb5M+Hp6an7779f0v9+BjPvQH3mmWfUv39/ff/99/rggw/y1Zb4+HitXbtWAQEBDsOOAMCNprDn9vvuu0/PP/+8+Tfpvffeq//7v/+Tr6+vPvroI7tz7iOPPFIsbb+aO+64Q5UqVdKJEydkGIbuv/9+jRgxQhaLRePGjTMvKFeuXFl33HGHS9pYFAMHDtTKlStlGIY2bdqkwYMHq02bNpo5c6b5fSlXrpx69uxprvP++++bL6IODw/XkSNHzGUXL15UXFycJMcnDD777DPzvWfR0dEO70ArqD59+pgFl4ULF6pSpUpq3bq1tm7dqg8//NAuLlOtWrX0ww8/SJLefPNNlS5dWikpKWafJidZz/9//PGHFixYoKpVq8rHx0e33HKLQ/zUqVNVpkwZ3XzzzVq0aJHdDSoPPfSQXVuKu1+E65ABAP/fr7/+atSpU8eQlOuXh4eHMXr0aLv1si7fvHlzjtvOLWbz5s2Gl5eXw37Cw8ONMmXKmNPz5s0z1+nVq5c5v1evXub8efPm2a2f1ahRo8xl0dHR5vxhw4bleJzt2rWzmy7o8QIAkJcLFy4YNputwOt9//33RrVq1fI8V2d+TZkyxTAMw7h48aJRvXp1h+V+fn5GkyZNcjynZmRkGHXr1nVYx2q1GjVr1sxxnQ4dOuTZnltuucU85oLE5nZ+//zzz/N1Ds96ro6Ojjbnjxo1yi634eHhOfY7cnPhwgWjdevWeR7HkCFD7NZZsmRJjnGRkZF204cPHzbX+eWXXwwPDw+HdW6//XbDMAzj8OHDdvMbNWqU4z7GjRtn15ZXXnklX23JasOGDTmu89hjj101XwBwvSvsuX3hwoVGiRIl8jyfPP300w7r5Xauy35eyHpOyevv8tz+7zcMw9i6dasREBCQa/sCAgKMr7/+Ot/bu1pMbufk3P6uz0vWc39uf7+PHz8+12Pz9fU1Nm7caBef17WH7PnP7SuntuSnrVllZGQYDz30UJ776datm5GRkWGus23btnyd/7PuPzEx0ShVqpTDOtWqVTNj8tMXyd5fKI5+Ea5/DDMFwFSzZk3t2rVLs2fP1p133qly5cqpZMmS8vPzU926ddW/f3/t3r3bqU9NtGnTRps2bVLr1q3l4+OjoKAg9ejRQ1u3bjXHwZYcX8zlLGPGjNErr7yi6tWry2q1qnbt2poxY4ZGjBhRLPsDAECSfH19CzXkQlRUlPbu3au5c+eqc+fOqlChgry8vOTl5aWwsDC1bt1aL730kuLj4zVo0CBJko+PjzZt2qSuXbuqbNmy8vPz0x133KH4+HjVrVs3x/14eHjo008/VefOneXn56dSpUrpzjvvVHx8vJo3b57jOv3791evXr1Ur149BQcHy9PTU6VKlVL9+vU1dOhQbdy40TzmgsTm5o477tAnn3yiqKgo+fj4qEKFCho4cKA+/fTTAue1MHx9fbVx40bNnDlTLVu2VEBAgEqUKKHQ0FDdddddiouL0+uvv263Trdu3fTmm2+qatWqKlmypCIiIjR69GgtX7481/3UqlVLCxcuVP369eXl5XXVdi1evFjDhw9XlSpV5OXlpTp16ujdd991eGJl8ODBevnll1WxYkWVLFlStWvX1rRp0zR16tRct92+fXu99dZbqlmzpsOY7ABwoyvsuf3hhx/Wzp071bt3b1WrVk3e3t4qWbKkKlSooC5duuiTTz7RjBkziqHF+deyZUvt2bNHAwYMUM2aNeXt7S2r1aoaNWromWee0e7du+1e+vxv8/LLL2vdunXq1KmTgoODVbJkSVWqVEm9evXSrl277IaikmR3rcLb2/taN9fk4eGhpUuXauHChWrfvr3KlCkjT09PlSlTRrfffrs+/PBDLVmyxG5opltvvVULFy5U3bp1zZ+zgQMH2g2xlV1gYKBWrVqlZs2a5et4J0+erDfeeEM1atSQl5eXbrrpJk2cOFHvvvuuXdy16Bfh+mMxDN5sC8B1DMPIscO3e/duNWzY0Jzes2ePIiMjr2HLAAAA3N+RI0d00003mdOHDx823z0CAMCNrk2bNnbDP0pSgwYN9OOPPxZ6m0899ZTeeecdSdKyZcv04IMPFnpbW7ZsUdu2bR3mb968WW3atCn0dq+1rNd1/m1tx78LT2YAcKmDBw+qffv2WrZsmfbt26cjR45o1apVduMo3nLLLRQyAAAAAACAy23YsEGS1L179yIVMgAUHM8GA3C5jRs3auPGjTkuq1ixohYtWnSNWwQAAAAAAP7tpk+fruTkZLt5fn5+hd7e4cOHdfDgQYWFhTll+K9GjRrlOMQTN3QCOaOYAcClypYtq379+umbb77R8ePHlZKSotKlS6tWrVq666679MwzzygwMNDVzQQAAAAAAP8yzi4K3HTTTXLmiP0BAQFq1aqV07YHXO94ZwYAAAAAAAAAAHBrvDMDAAAAAAAAAAC4NYoZAAAAAAAAAADArVHMAP7lRo8eLYvFIovFojZt2ri6OQAA4BqiHwAAAPKDPgOA6wHFDADXpU8//VR9+vRR/fr1VaJECbPTFhsbm2P8kSNHzJjcvubOnZvv/X/11Vfq2bOn6tWrp+DgYJUoUUJ+fn6qW7eunnrqKR04cMBhnT179mjgwIFq2bKlfH19zf1GREQUMgsAACC7S5cuady4cbrnnntUoUIFu3P9li1bCry9VatWqWvXrqpZs6aCgoJUokQJ+fv7q0GDBhoyZIj++OOPHNdLSEjQM888o2rVqsnb21tBQUFq27atli5d6hAbGxt71X6KxWLR6NGj7dY7e/asXnrpJdWrV0++vr4qVaqUGjRooAkTJujixYsFPlYAAG5EixcvVtu2bRUUFCRvb29Vq1ZNzzzzTK7n+NwcOXJEL774otq1a6eAgAC7c/jVbNu2TQ8//LCqVKkiq9WqwMBA1alTR3369NHx48cd4m02mxYvXqyOHTsqNDRUXl5eCgkJUZMmTTRo0CC72L1796p3795q0KCBQkNDVbJkSfn6+qpGjRp69NFHtXPnzgIdJ1CcSri6AQBQHP773//qk08+cdn+v/rqKy1atMhu3oULF7R//37t379fCxYs0JdffqnGjRubyzdt2qS33nrrWjcVAIAbysWLFzVy5EinbW/NmjVauXKl3bzU1FTt2bNHe/bs0YIFC/Tdd98pPDzcXL579261b99ef/31lzkvPT1dW7ZsMb9mzZpV4LaULFnS/Hzw4EG1bt3a4UJLZrs++ugjbdy4UYGBgQXeDwAAN4onnnjC4cbGQ4cO6e2339ayZcu0adMm3Xzzzfna1o8//qjXX3+9wG0YNWqUxo4dazfv0qVLSk5O1i+//KJHH31UlStXNpddvHhRXbt21WeffWa3zl9//aW//vpLO3fu1BtvvGHXrvfff98u9p9//tHvv/+u33//XYsWLdLHH3+su+++u8BtB5yNYgaA61LlypX14IMPqnHjxlq3bp02b96c73U7deqkYcOGOcyvUaNGvrdRoUIFPfXUU7r11ltVvnx5Xb58WWvWrDEvTFy8eFFTp07Vhx9+aK4TGBiojh07qnHjxvrrr7/0zjvv5Ht/AAAgfzw8PNS4cWPz6/HHHy/S9qpXr65BgwbplltuUbly5XT+/HktWrRIy5YtkySdOXNGc+bM0fjx4yVJGRkZ6tGjh1nIaNasmf7zn/9o3759GjFihGw2m9555x21bdtWDz30kCTp5ZdfzrGdmzdvNgszHh4eZrwk9e/f3yxk1KpVS+PHj5eXl5fGjBmjXbt2adeuXRo8eLDee++9Ih0/AADXq0WLFpmFDA8PD40fP1516tTRpEmT9O233+rMmTPq3r279uzZIw+Pqw9+4+vrq7Zt26px48YqWbKkJk6ceNV1FixYYBYySpUqpX79+qlly5by8/NTQkKCtm3b5nBjwjPPPGMWMipXrqwBAwbo5ptvls1m05EjR/TVV1/ZxZcpU0axsbG67bbbVL58eXl4eOirr77Sa6+9pn/++UcZGRl69dVXKWbAPRgA3E56eroxceJEIyoqyihdurRRokQJo2zZskaDBg2M3r17G9u2bTNjR40aZUgyJBnR0dHm/H379hlhYWHmsscee8zIyMgwDMMw/vjjD2Pw4MFG3bp1DV9fX8PHx8e4+eabjbFjxxrnz583t3HixAlzfW9vbyMtLc1cFh4ebkgyPD09jeTkZHN+vXr1zHU+//zzqx7rgQMHjPj4+Hx9fffdd4XKZ69evcw29erVK8eYw4cPXzXGGSIjI8393H333bnGzZs3z4wLDw8vtvYAANwP/QDn9gOuJrO9kozNmzc7bbv+/v7mdgcMGGDOX7NmjTnfYrEYx48fN5f17t3bXNa0adOr7qNdu3Zm/AMPPGDOP3/+vOHh4WEu++ijj8xlW7duNeeXLFnSOH36tJOOGABwrdFnKN4+Q+PGjc02Pv744+b8I0eO2PUf4uLiCrztzZs3220jJ5cvXzYqVapkxmzYsOGq2923b59hsVgMSUZISIiRkJBQ4LZluvvuu819R0ZGFno7gDNRzADcUNY/ZHP6mjRpkhmbU4dk3759Rrly5cz5zz//vGGz2QzDMIzvvvvOCA4OznXb9evXNxITE83t165d21z21VdfGYZhGEePHrVbZ+3atYZhGMaZM2fMk6aXl5dx8eLFqx5r1kLD1b4Ke1G/oMWMoKAgIzAw0ChZsqRRuXJl45FHHjH27NlTqH1nSkxMNBYuXGhYrVZzP2+//Xau8RQzAODGRT/Auf2Aq3FmMcNmsxmnT582pk2blmOODMMwnn/+eXN+RESE3fr//e9/7QodWS/6ZLdz5067fXz77bfmsj/++MNu2fr1681lu3fvtlu2cuXKIh0zAMB16DMUX58hKSnJbKMk4/3337dbXqVKFXPZ4MGDC7z9/BQzst6AULFiRWPEiBFGzZo1DavVapQvX97o1auXcejQIbt1xo8fb67ToUMH49FHHzUqV65seHl5GTVq1DCGDh1qV4jKSUpKihEXF2f3/X/hhRcKfIxAceAF4IAbyhyWoESJEpo+fbo2bdqklStXavLkyerQoYO8vb1zXXffvn1q27atTp06JUkaPXq03njjDVksFl26dEkPPvigzp49K0m666679Omnn2rVqlW69dZbJV0ZR/m5554zt9e+fXvz85dffmn3b/b5X331lQzDkCS1aNFCPj4+RcqDqyQmJiopKUmXL1/W8ePH9eGHH6px48Zas2ZNgbfVqlUrWSwWBQUFqWfPnkpPT1fZsmU1btw49evXrxhaDwD4t6Mf8O9UqVIleXh4KDQ0VM8++6w5791331VMTIwZd/DgQfNzWFiY3TbKly9vfjYMQ4cOHcp1f6+99pr5uW3btmrSpIk5HRoaqrJly5rTb731lk6dOqVz587plVdesdvOkSNH8nmEAAB3Q5+h+Bw6dMhso5T3Ofv3338vljbs3r3b/JyQkKBx48bpwIEDSk9P18mTJzV//nzdcsst+vnnn3NcZ/369VqwYIGOHz+uS5cu6bffftOkSZPUunVrXbhwwWF/jzzyiCwWi/z9/RUTE6OzZ8+qdOnSGjhwoMaNG1csxwgUFMUMwA1ljndYsmRJ1ahRQ5GRkbr//vs1ePBgrVu3TgMHDsxxvRMnTpidEYvFoqlTp2rUqFHm8g0bNph/sAYEBOiFF15QUFCQgoOD1b9/fzNu8eLFOn/+vCTp9ttvN+dn75A0aNBAkrRlyxa7f7Ovl5f3339fxpWnxK76VZx/bFssFjVs2FDjxo3TypUrtW7dOo0cOVK+vr6Srrxc67HHHlNaWlqR91WyZElZLJYibwcAcH2iH1C0fsBPP/2krVu32n3t3LkzX+s6m5eXl92FEEl2Fw+8vLwc4rPK/D5kd+jQIbuXjr/00kt2yz09Pe1ech4XF6ewsDAFBwdr8eLFdrHO6NsAAFyDPkPx9RmyX+zP65yd2/m6qBITE+2ma9WqpRUrVmjRokWqVKmSGTNgwIBc17n77rs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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "quantile_normal achieves near-zero skewness by construction — the most powerful normalisation available.\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "fig.suptitle(\"Quantile Transforms on `PAY_AMT1`\", fontsize=13, fontweight='bold')\n", "\n", "axes[0].hist(X_train['PAY_AMT1'], bins=50, color=GREY, alpha=0.8)\n", "axes[0].set_title(f\"Original\\nskew = {X_train['PAY_AMT1'].skew():.2f}\", fontweight='bold')\n", "\n", "for i, (method, color, label) in enumerate([\n", " ('quantile_normal', BLUE, 'quantile_normal\\n\\u2192 Gaussian output'),\n", " ('quantile_uniform', ORANGE, 'quantile_uniform\\n\\u2192 Uniform [0,1] output'),\n", "], start=1):\n", " cfg = TransformConfig(name=f\"{method}_pamt1\", transformer_type=\"numerical\",\n", " method=method, columns=[\"PAY_AMT1\"], params={\"n_quantiles\": 1000})\n", " t = NumericalTransformer(cfg)\n", " result = t.fit_transform(X_train)['PAY_AMT1']\n", "\n", " axes[i].hist(result, bins=50, color=color, alpha=0.8)\n", " axes[i].set_title(f\"{label}\\nskew = {result.skew():.4f}\", fontweight='bold')\n", " axes[i].set_xlabel('Transformed value')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "print(\"quantile_normal achieves near-zero skewness by construction — the most powerful normalisation available.\")" ] }, { "cell_type": "markdown", "id": "mde7721d9a", "metadata": {}, "source": [ "### 7d. Winsorization: `winsorize`" ] }, { "cell_type": "code", "execution_count": 12, "id": "cd02e417be", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "fig.suptitle(\"Winsorization on `LIMIT_BAL` — Clipping Extreme Values\", fontsize=13, fontweight='bold')\n", "\n", "axes[0].boxplot(X_train['LIMIT_BAL'].dropna(), vert=True, patch_artist=True,\n", " boxprops=dict(facecolor=GREY, alpha=0.7))\n", "axes[0].set_title(f\"Original\\nmax={X_train['LIMIT_BAL'].max():,}\", fontweight='bold')\n", "axes[0].set_xticks([])\n", "\n", "for i, (lower, upper, color) in enumerate([(0.01, 0.99, BLUE), (0.05, 0.95, ORANGE)], start=1):\n", " cfg = TransformConfig(name=f\"winsorize_{int(lower*100)}_{int(upper*100)}_bal\",\n", " transformer_type=\"numerical\", method=\"winsorize\",\n", " columns=[\"LIMIT_BAL\"], params={\"lower\": lower, \"upper\": upper})\n", " t = NumericalTransformer(cfg)\n", " t.fit(X_train)\n", " result = t.transform(X_train)['LIMIT_BAL']\n", " fitted = t.get_params()\n", " clip_lo, clip_hi = fitted['LIMIT_BAL_lower'], fitted['LIMIT_BAL_upper']\n", "\n", " axes[i].boxplot(result.dropna(), vert=True, patch_artist=True,\n", " boxprops=dict(facecolor=color, alpha=0.7))\n", " axes[i].set_title(f\"Winsorize ({int(lower*100)}%–{int(upper*100)}%)\\nclipped to [{clip_lo:,.0f}, {clip_hi:,.0f}]\",\n", " fontweight='bold')\n", " axes[i].set_xticks([])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "mda7e9d05a", "metadata": {}, "source": [ "### 7e. Percentile Binning: `percentile_binning`" ] }, { "cell_type": "code", "execution_count": 13, "id": "cdf97767db", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Learned age quartile edges:\n", " Young (Q1): age [21, 28) years\n", " Early-career (Q2): age [28, 34) years\n", " Mid-career (Q3): age [34, 41) years\n", " Senior (Q4): age [41, 75) years\n" ] } ], "source": [ "cfg_bin = TransformConfig(name=\"bin_age\", transformer_type=\"numerical\",\n", " method=\"percentile_binning\", columns=[\"AGE\"], params={\"n_bins\": 4})\n", "t_bin = NumericalTransformer(cfg_bin)\n", "X_binned = t_bin.fit_transform(X_train)\n", "bin_edges = t_bin.get_params()['AGE_bin_edges']\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "fig.suptitle(\"Percentile Binning on `AGE`\", fontsize=13, fontweight='bold')\n", "\n", "axes[0].hist(X_train['AGE'], bins=30, color=GREY, alpha=0.8)\n", "for edge in bin_edges[1:-1]:\n", " axes[0].axvline(edge, color=RED, linestyle='--', lw=1.5, alpha=0.7)\n", "axes[0].set_title('Original — dashed lines show bin edges', fontweight='bold')\n", "axes[0].set_xlabel('AGE')\n", "\n", "bin_counts = X_binned['AGE'].value_counts().sort_index()\n", "axes[1].bar([str(b) for b in bin_counts.index], bin_counts.values, color=BLUE, alpha=0.8)\n", "axes[1].set_title('After binning — approximately equal counts per bin', fontweight='bold')\n", "axes[1].set_xlabel('Bin (0 = youngest quartile, 3 = oldest)')\n", "axes[1].set_ylabel('Count')\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Learned age quartile edges:\")\n", "names = ['Young (Q1)', 'Early-career (Q2)', 'Mid-career (Q3)', 'Senior (Q4)']\n", "for name, (lo, hi) in zip(names, [(bin_edges[i], bin_edges[i+1]) for i in range(len(bin_edges)-1)]):\n", " print(f\" {name}: age [{lo:.0f}, {hi:.0f}) years\")" ] }, { "cell_type": "markdown", "id": "mdd0378344", "metadata": {}, "source": [ "---\n", "## 8. Categorical Transformations" ] }, { "cell_type": "markdown", "id": "mddd91e4dd", "metadata": {}, "source": [ "### 8a. Label Encoding: `label_encode`" ] }, { "cell_type": "code", "execution_count": 14, "id": "cd5844c101", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learned label encoding for SEX:\n", " 'Female' → 0 (n=14,514)\n", " 'Male' → 1 (n=9,486)\n" ] } ], "source": [ "cfg_le = TransformConfig(name=\"label_sex\", transformer_type=\"categorical\",\n", " method=\"label_encode\", columns=[\"SEX\"],\n", " params={\"unknown_strategy\": \"use_encoded_value\", \"unknown_value\": -1})\n", "t_le = CategoricalTransformer(cfg_le)\n", "X_le = t_le.fit_transform(X_train)\n", "\n", "learned_classes = t_le.get_params()['SEX_classes']\n", "print(\"Learned label encoding for SEX:\")\n", "for i, cls in enumerate(learned_classes):\n", " count = (X_train['SEX'] == cls).sum()\n", " print(f\" {cls!r:15s} \\u2192 {i} (n={count:,})\")" ] }, { "cell_type": "markdown", "id": "mdec46ec05", "metadata": {}, "source": [ "### 8b. One-Hot Encoding: `onehot_encode`" ] }, { "cell_type": "code", "execution_count": 15, "id": "cdb62fbe41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns before OHE: 23\n", "Columns after OHE: 26\n", "\n", "New columns: ['EDUCATION_Graduate', 'EDUCATION_High_School', 'EDUCATION_Other', 'EDUCATION_University']\n", " EDUCATION_Graduate mean=0.352\n", " EDUCATION_High_School mean=0.163\n", " EDUCATION_Other mean=0.016\n", " EDUCATION_University mean=0.469\n" ] } ], "source": [ "cfg_ohe = TransformConfig(name=\"ohe_education\", transformer_type=\"categorical\",\n", " method=\"onehot_encode\", columns=[\"EDUCATION\"],\n", " params={\"sparse\": False, \"drop\": None})\n", "t_ohe = CategoricalTransformer(cfg_ohe)\n", "X_ohe = t_ohe.fit_transform(X_train)\n", "\n", "print(f\"Columns before OHE: {X_train.shape[1]}\")\n", "print(f\"Columns after OHE: {X_ohe.shape[1]}\")\n", "new_cols = [c for c in X_ohe.columns if c.startswith('EDUCATION')]\n", "print(f\"\\nNew columns: {new_cols}\")\n", "for col in new_cols:\n", " print(f\" {col:30s} mean={X_ohe[col].mean():.3f}\")" ] }, { "cell_type": "markdown", "id": "md507bb2ff", "metadata": {}, "source": [ "### 8c. Frequency Encoding: `frequency_encode`" ] }, { "cell_type": "code", "execution_count": 16, "id": "cd553aef6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frequency map learned from training data:\n", " Single → 0.5336 (53.4% of training rows)\n", " Married → 0.4538 (45.4% of training rows)\n", " Other → 0.0126 (1.3% of training rows)\n" ] } ], "source": [ "cfg_fe = TransformConfig(name=\"freq_marriage\", transformer_type=\"categorical\",\n", " method=\"frequency_encode\", columns=[\"MARRIAGE\"],\n", " params={\"normalize\": True})\n", "t_fe = CategoricalTransformer(cfg_fe)\n", "X_fe = t_fe.fit_transform(X_train)\n", "freq_map = t_fe.get_params()['MARRIAGE_freq_map']\n", "\n", "print(\"Frequency map learned from training data:\")\n", "for cat, freq in sorted(freq_map.items(), key=lambda x: -x[1]):\n", " print(f\" {cat:10s} \\u2192 {freq:.4f} ({freq*100:.1f}% of training rows)\")" ] }, { "cell_type": "markdown", "id": "md1a8bc242", "metadata": {}, "source": [ "### 8d. Target Encoding: `target_encode`\n", "\n", "Target encoding is supervised: the encoder learns category-to-target statistics from the training target. In BitBullet, pass the target through `fit(..., y=...)` or `pipeline.fit_transform(..., y=...)` rather than storing `y` inside transformer params. This keeps the config declarative and the fitted state explicit.\n", "\n", "The upgraded SDK target encoder is leakage-aware. During `fit_transform` it returns out-of-fold (OOF) encodings for the training rows, so a row never sees its own target. During later `transform` calls it uses the full training-data smoothed map, which is the state saved with the pipeline for inference. It supports regression targets, binary classification probabilities, and multiclass one-vs-rest probability columns." ] }, { "cell_type": "code", "execution_count": null, "id": "cd904f387a", "metadata": {}, "outputs": [], "source": [ "target_pipeline = TransformPipeline(name=\"target_encoding_example\")\n", "target_pipeline.add(\n", " \"categorical\",\n", " \"target_encode\",\n", " columns=[\"EDUCATION\"],\n", " params={\n", " \"target_type\": \"classification\", # binary target -> P(positive_label | category)\n", " \"positive_label\": 1,\n", " \"cv_folds\": 5,\n", " \"cv_strategy\": \"stratified\",\n", " \"smoothing\": 10.0,\n", " \"random_state\": 42,\n", " },\n", ")\n", "\n", "# Training path: out-of-fold encodings, so each row is encoded by folds that did not contain it.\n", "X_train_te = target_pipeline.fit_transform(X_train[[\"EDUCATION\"]], y=y_train)\n", "\n", "# Inference path: the saved full-training-data map is applied without refitting.\n", "X_test_te = target_pipeline.transform(X_test[[\"EDUCATION\"]])\n", "\n", "target_step = target_pipeline.transformers[0]\n", "description = target_step.describe()\n", "\n", "audit = pd.DataFrame({\n", " \"education\": X_train[\"EDUCATION\"].to_numpy(),\n", " \"target\": y_train.to_numpy(),\n", " \"oof_encoded\": X_train_te[\"EDUCATION\"].to_numpy(),\n", "})\n", "\n", "summary = (\n", " audit.groupby(\"education\")\n", " .agg(\n", " n=(\"target\", \"size\"),\n", " raw_default_rate=(\"target\", \"mean\"),\n", " mean_oof_encoding=(\"oof_encoded\", \"mean\"),\n", " )\n", " .sort_values(\"mean_oof_encoding\", ascending=False)\n", ")\n", "\n", "print(\"Target encoder audit:\")\n", "print(f\" mode: {description['mode']}\")\n", "print(f\" fit_scope: {description['fit_scope']}\")\n", "print(f\" output: {description['output_columns_by_input']}\")\n", "print(f\" test dtype: {X_test_te['EDUCATION'].dtype}\")\n", "display(summary)\n", "\n", "print(\"\\nMulticlass target encoding expands one source column into one probability channel per class:\")\n", "toy_X = pd.DataFrame({\n", " \"merchant_segment\": [\"grocery\", \"grocery\", \"fuel\", \"fuel\", \"travel\", \"travel\", \"grocery\", \"fuel\", \"travel\", \"other\", \"other\", \"other\"],\n", "})\n", "toy_y = pd.Series([\"approve\", \"review\", \"approve\", \"reject\", \"review\", \"reject\", \"approve\", \"review\", \"reject\", \"approve\", \"review\", \"reject\"], name=\"decision\")\n", "\n", "multi_pipeline = TransformPipeline(name=\"multiclass_target_encoding_example\")\n", "multi_pipeline.add(\n", " \"categorical\",\n", " \"target_encode\",\n", " columns=[\"merchant_segment\"],\n", " params={\"target_type\": \"classification\", \"cv_folds\": 3, \"cv_strategy\": \"stratified\", \"smoothing\": 2.0},\n", ")\n", "toy_encoded = multi_pipeline.fit_transform(toy_X, y=toy_y)\n", "print([col for col in toy_encoded.columns if col.startswith(\"merchant_segment\")])" ] }, { "cell_type": "markdown", "id": "mdf0dd31d8", "metadata": {}, "source": [ "### 8e. Rare Category Binning: `bin_rare`" ] }, { "cell_type": "code", "execution_count": 18, "id": "cd58a37a76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EDUCATION distribution:\n", " University : 0.469 (46.9%)\n", " Graduate : 0.352 (35.2%)\n", " High_School : 0.163 (16.3%)\n", " Other : 0.016 (1.6%)\n", "\n", " threshold=0.01: 0 merged → 4 unique remain. Merged: []\n", " threshold=0.10: 1 merged → 4 unique remain. Merged: ['Other']\n", " threshold=0.20: 2 merged → 3 unique remain. Merged: ['High_School', 'Other']\n" ] } ], "source": [ "print(\"EDUCATION distribution:\")\n", "edu_dist = X_train['EDUCATION'].value_counts(normalize=True)\n", "for cat, freq in edu_dist.items():\n", " print(f\" {cat:15s}: {freq:.3f} ({freq*100:.1f}%)\")\n", "\n", "print()\n", "for threshold in [0.01, 0.10, 0.20]:\n", " cfg_br = TransformConfig(name=f\"bin_rare_{threshold}\", transformer_type=\"categorical\",\n", " method=\"bin_rare\", columns=[\"EDUCATION\"],\n", " params={\"threshold\": threshold, \"other_label\": \"other\"})\n", " t_br = CategoricalTransformer(cfg_br)\n", " X_br = t_br.fit_transform(X_train)\n", " rare_cats = t_br.get_params()['EDUCATION_rare_categories']\n", " print(f\" threshold={threshold:.2f}: {len(rare_cats)} merged \\u2192 {X_br['EDUCATION'].nunique()} unique remain. Merged: {rare_cats}\")" ] }, { "cell_type": "markdown", "id": "md01918f05", "metadata": {}, "source": [ "### 8f. Feature Hashing: `hash_encode`" ] }, { "cell_type": "code", "execution_count": 19, "id": "cd288cbf77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MARRIAGE unique values: ['Single' 'Married' 'Other']\n", " n_features= 4: 0 collision(s) among 3 categories: {'Single': 2, 'Married': 3, 'Other': 0}\n", " n_features= 8: 0 collision(s) among 3 categories: {'Single': 2, 'Married': 7, 'Other': 0}\n", " n_features= 16: 0 collision(s) among 3 categories: {'Single': 2, 'Married': 7, 'Other': 0}\n" ] } ], "source": [ "print(\"MARRIAGE unique values:\", X_train['MARRIAGE'].unique())\n", "for n_features in [4, 8, 16]:\n", " cfg_hash = TransformConfig(name=f\"hash_{n_features}\", transformer_type=\"categorical\",\n", " method=\"hash_encode\", columns=[\"MARRIAGE\"],\n", " params={\"n_features\": n_features})\n", " t_hash = CategoricalTransformer(cfg_hash)\n", " t_hash.fit(X_train)\n", " unique_cats = X_train['MARRIAGE'].unique()\n", " hash_mapping = {cat: hash(str(cat)) % n_features for cat in unique_cats}\n", " collisions = len(unique_cats) - len(set(hash_mapping.values()))\n", " print(f\" n_features={n_features:3d}: {collisions} collision(s) among {len(unique_cats)} categories: {hash_mapping}\")" ] }, { "cell_type": "markdown", "id": "mdcd4d6caf", "metadata": {}, "source": [ "---\n", "## 9. Composing a Production Pipeline\n", "\n", "We now build a complete `TransformPipeline` for the credit card default dataset, guided by the EDA in Section 4:\n", "\n", "| Feature Group | Transform | Reasoning |\n", "|---------------|-----------|-----------|\n", "| `PAY_AMT1`–`PAY_AMT6` | `log1p` | Extreme right skew, non-negative, many zeros |\n", "| `BILL_AMT1`–`BILL_AMT6` | `yeo_johnson` | Right skew, possible negative values (credit balance) |\n", "| `LIMIT_BAL` | `log1p` | Strictly positive, right-skewed credit limit |\n", "| `AGE` | `standard_scale` | Roughly normal, no outlier concern |\n", "| `PAY_0`–`PAY_6` | `robust_scale` | Ordinal integers, some extreme delay values (8, 9) |\n", "| `SEX`, `MARRIAGE` | `label_encode` | Low cardinality (2–3 values), tree models |\n", "| `EDUCATION` | `onehot_encode` | 4 categories, slightly higher cardinality |" ] }, { "cell_type": "code", "execution_count": 20, "id": "cdd31b724d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TransformPipeline(name='credit_default_pipeline', steps=6, status=not fitted)\n", "\n", "Total steps: 6\n" ] } ], "source": [ "pay_amt_cols = [f'PAY_AMT{i}' for i in range(1, 7)]\n", "bill_amt_cols = [f'BILL_AMT{i}' for i in range(1, 7)]\n", "pay_cols = ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n", "binary_cats = ['SEX', 'MARRIAGE']\n", "ohe_cats = ['EDUCATION']\n", "\n", "default_pipeline = (\n", " TransformPipeline(name=\"credit_default_pipeline\")\n", "\n", " # ── Numerical transforms ────────────────────────────────────────────────\n", " .add(\"numerical\", \"log1p\",\n", " columns=pay_amt_cols + ['LIMIT_BAL'],\n", " name=\"log1p_payment_amounts\")\n", "\n", " .add(\"numerical\", \"yeo_johnson\",\n", " columns=bill_amt_cols,\n", " name=\"yj_bill_amounts\")\n", "\n", " .add(\"numerical\", \"standard_scale\",\n", " columns=[\"AGE\"],\n", " name=\"standard_scale_age\")\n", "\n", " .add(\"numerical\", \"robust_scale\",\n", " columns=pay_cols,\n", " name=\"robust_scale_pay_status\")\n", "\n", " # ── Categorical transforms ──────────────────────────────────────────────\n", " .add(\"categorical\", \"label_encode\",\n", " columns=binary_cats,\n", " params={\"unknown_strategy\": \"use_encoded_value\", \"unknown_value\": -1},\n", " name=\"label_encode_binary_cats\")\n", "\n", " .add(\"categorical\", \"onehot_encode\",\n", " columns=ohe_cats,\n", " params={\"sparse\": False, \"drop\": None},\n", " name=\"onehot_education\")\n", ")\n", "\n", "print(default_pipeline)\n", "print(f\"\\nTotal steps: {len(default_pipeline)}\")" ] }, { "cell_type": "markdown", "id": "md3ccb2adc", "metadata": {}, "source": [ "---\n", "## 10. `fit_transform` on Training Data" ] }, { "cell_type": "code", "execution_count": 21, "id": "cd895c6c19", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting [1/6]: log1p_payment_amounts\n", "Fitting [2/6]: yj_bill_amounts\n", "Fitting [3/6]: standard_scale_age\n", "Fitting [4/6]: robust_scale_pay_status\n", "Fitting [5/6]: label_encode_binary_cats\n", "Fitting [6/6]: onehot_education\n", "Transforming [1/6]: log1p_payment_amounts\n", "Transforming [2/6]: yj_bill_amounts\n", "Transforming [3/6]: standard_scale_age\n", "Transforming [4/6]: robust_scale_pay_status\n", "Transforming [5/6]: label_encode_binary_cats\n", "Transforming [6/6]: onehot_education\n", "\n", "Input shape : (24000, 23)\n", "Output shape : (24000, 26)\n", "Pipeline fitted: True\n", "\n", "Columns AFTER transformation:\n", "['LIMIT_BAL', 'SEX', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'EDUCATION_Graduate', 'EDUCATION_High_School', 'EDUCATION_Other', 'EDUCATION_University']\n" ] } ], "source": [ "X_train_t = default_pipeline.fit_transform(X_train, verbose=True)\n", "\n", "print()\n", "print(f\"Input shape : {X_train.shape}\")\n", "print(f\"Output shape : {X_train_t.shape}\")\n", "print(f\"Pipeline fitted: {default_pipeline.is_fitted}\")\n", "print()\n", "print(\"Columns AFTER transformation:\")\n", "print(list(X_train_t.columns))" ] }, { "cell_type": "code", "execution_count": 22, "id": "cdf91db9ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spot check: PAY_AMT1 (log1p)\n", " Before: min=0, max=873552, skew=15.312\n", " After : min=0.000, max=13.680, skew=-1.299\n", "\n", "Spot check: BILL_AMT1 (yeo_johnson)\n", " Before: skew=2.691\n", " After : skew=-2.029\n", "\n", "Spot check: EDUCATION one-hot columns\n", " Columns: ['EDUCATION_Graduate', 'EDUCATION_High_School', 'EDUCATION_Other', 'EDUCATION_University']\n", " Row sums: [1.]\n" ] } ], "source": [ "print(\"Spot check: PAY_AMT1 (log1p)\")\n", "print(f\" Before: min={X_train['PAY_AMT1'].min():.0f}, max={X_train['PAY_AMT1'].max():.0f}, skew={X_train['PAY_AMT1'].skew():.3f}\")\n", "print(f\" After : min={X_train_t['PAY_AMT1'].min():.3f}, max={X_train_t['PAY_AMT1'].max():.3f}, skew={X_train_t['PAY_AMT1'].skew():.3f}\")\n", "print()\n", "print(\"Spot check: BILL_AMT1 (yeo_johnson)\")\n", "print(f\" Before: skew={X_train['BILL_AMT1'].skew():.3f}\")\n", "print(f\" After : skew={X_train_t['BILL_AMT1'].skew():.3f}\")\n", "print()\n", "print(\"Spot check: EDUCATION one-hot columns\")\n", "edu_cols = [c for c in X_train_t.columns if c.startswith('EDUCATION')]\n", "print(f\" Columns: {edu_cols}\")\n", "print(f\" Row sums: {X_train_t[edu_cols].sum(axis=1).unique()}\")" ] }, { "cell_type": "markdown", "id": "mdb881e0df", "metadata": {}, "source": [ "---\n", "## 11. Save, Load, and Transform the Test Set" ] }, { "cell_type": "code", "execution_count": 23, "id": "cd8b4d57e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline saved → credit_default_pipeline.pkl (2.3 KB)\n", "Pipeline loaded: 6 steps | fitted: True\n", "\n", "Test set: (6000, 23) → (6000, 26)\n", "Column alignment: PASSED\n" ] } ], "source": [ "pipeline_path = \"credit_default_pipeline.pkl\"\n", "\n", "default_pipeline.save(pipeline_path)\n", "file_size_kb = os.path.getsize(pipeline_path) / 1024\n", "print(f\"Pipeline saved \\u2192 {pipeline_path} ({file_size_kb:.1f} KB)\")\n", "\n", "loaded_pipeline = TransformPipeline.load(pipeline_path)\n", "print(f\"Pipeline loaded: {len(loaded_pipeline)} steps | fitted: {loaded_pipeline.is_fitted}\")\n", "\n", "X_test_t = loaded_pipeline.transform(X_test)\n", "\n", "print(f\"\\nTest set: {X_test.shape} \\u2192 {X_test_t.shape}\")\n", "assert list(X_train_t.columns) == list(X_test_t.columns), \"Column mismatch!\"\n", "print(\"Column alignment: PASSED\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "cdfa622887", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reproducibility check: loaded pipeline produces IDENTICAL results\n", "\n", "Distribution comparison — train vs test (post-transform):\n", "Column train mean test mean train std test std\n", "------------------------------------------------------------\n", "PAY_AMT1 6.6368 6.6027 3.2440 3.2755\n", "BILL_AMT1 13533.5819 13716.1056 18330.9054 17488.7793\n", "LIMIT_BAL 11.6633 11.6617 0.9400 0.9456\n", "AGE -0.0000 0.0288 1.0000 1.0120\n", "PAY_0 -0.0141 -0.0270 1.1232 1.1264\n" ] } ], "source": [ "X_train_t_verify = loaded_pipeline.transform(X_train)\n", "is_identical = X_train_t.round(10).equals(X_train_t_verify.round(10))\n", "print(f\"Reproducibility check: loaded pipeline produces {'IDENTICAL' if is_identical else 'DIFFERENT'} results\")\n", "\n", "print()\n", "print(\"Distribution comparison — train vs test (post-transform):\")\n", "print(f\"{'Column':15s} {'train mean':>12s} {'test mean':>12s} {'train std':>12s} {'test std':>12s}\")\n", "print('-' * 60)\n", "for col in ['PAY_AMT1', 'BILL_AMT1', 'LIMIT_BAL', 'AGE', 'PAY_0']:\n", " print(f\"{col:15s} {X_train_t[col].mean():12.4f} {X_test_t[col].mean():12.4f} \"\n", " f\"{X_train_t[col].std():12.4f} {X_test_t[col].std():12.4f}\")" ] }, { "cell_type": "markdown", "id": "mdbb20d218", "metadata": {}, "source": [ "---\n", "## 12. Pipeline Management: `list_steps`, `disable_step`, `enable_step`" ] }, { "cell_type": "code", "execution_count": 25, "id": "cd88d8db78", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " # Name Type Method Enabled \n", "------------------------------------------------------------------------------------------\n", " 1 log1p_payment_amounts numerical log1p Yes \n", " columns: ['PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'LIMIT_BAL']\n", " 2 yj_bill_amounts numerical yeo_johnson Yes \n", " columns: ['BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']\n", " 3 standard_scale_age numerical standard_scale Yes \n", " columns: ['AGE']\n", " 4 robust_scale_pay_status numerical robust_scale Yes \n", " columns: ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n", " 5 label_encode_binary_cats categorical label_encode Yes \n", " columns: ['SEX', 'MARRIAGE']\n", " 6 onehot_education categorical onehot_encode Yes \n", " columns: ['EDUCATION']\n" ] } ], "source": [ "steps = default_pipeline.list_steps()\n", "print(f\"{'#':>3} {'Name':40s} {'Type':12s} {'Method':20s} {'Enabled':8s}\")\n", "print('-' * 90)\n", "for i, step in enumerate(steps, 1):\n", " print(f\"{i:>3} {step['name']:40s} {step['type']:12s} {step['method']:20s} \"\n", " f\"{'Yes' if step['enabled'] else 'No':8s}\")\n", " if step['columns']:\n", " print(f\" columns: {step['columns']}\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "cd630f6301", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "With log1p enabled : PAY_AMT1 skew = -1.2993\n", "With log1p disabled : PAY_AMT1 skew = 15.3121 (raw values pass through)\n", "After re-enabling : PAY_AMT1 skew = -1.2993\n" ] } ], "source": [ "X_with_log = default_pipeline.transform(X_train)\n", "skew_with = X_with_log['PAY_AMT1'].skew()\n", "print(f\"With log1p enabled : PAY_AMT1 skew = {skew_with:.4f}\")\n", "\n", "default_pipeline.disable_step(\"log1p_payment_amounts\")\n", "X_without_log = default_pipeline.transform(X_train)\n", "skew_without = X_without_log['PAY_AMT1'].skew()\n", "print(f\"With log1p disabled : PAY_AMT1 skew = {skew_without:.4f} (raw values pass through)\")\n", "\n", "default_pipeline.enable_step(\"log1p_payment_amounts\")\n", "X_re_enabled = default_pipeline.transform(X_train)\n", "print(f\"After re-enabling : PAY_AMT1 skew = {X_re_enabled['PAY_AMT1'].skew():.4f}\")" ] }, { "cell_type": "markdown", "id": "md981ac3d0", "metadata": {}, "source": [ "---\n", "## 13. Visualising Transformations" ] }, { "cell_type": "code", "execution_count": 27, "id": "cd16eed226", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3ca1c8730a0d407886b41e0fdfe7f15c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='feature', options=('AGE', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', '…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Interactive widget — use the dropdown to explore each transformed feature\n", "plot_pipeline_transformations(default_pipeline, X_train, X_train_t)" ] }, { "cell_type": "code", "execution_count": 28, "id": "cd091235ff", "metadata": {}, "outputs": [ { "data": { "image/png": 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jZcuWadWqVVU+3qrKycnR/fffryuuuEKBgYFq0KCBOnXqpCeeeMKtrmdjtVqVkJCghIQEBQcHV+r1Y2JilJCQoHbt2lWl+l7RsWNHJSQkqE6dOtVdlQtuyZIl6t+/vyIjIxUYGKimTZvqpptu0hdffFGh7Y8ePaqxY8cqKipKAQEBatSoka677jrt3bvXjPnyyy918803q2nTpgoMDNRVV12lf/3rXy77adeunRISEhQQEHBBjw8AUDH0/UrUhr6fJD366KNm/Ro0aKBff/3VLebBBx/Uhg0b9Ntvv6lz585mX82bicTQ0FD95z//kSQ98MADHn+9qgoODjb7wlartbqr4zXNmzfX5s2b9eWXX+qpp546r301atRICQkJio6OvjCVuwhs2bJFt99+u2JiYlSvXj01bNhQ3bt31xtvvFGh7T/55BP169dPwcHBqlu3rv7whz/o/fffd4mZOHGi2rZtq+DgYAUFBSkqKkrDhw/X/v37zZjIyEglJCSoUaNGF/LwcLExAFw0evbsaUgyAgICjISEBCM2NtaQZEgy2rdvb8bt27fPLH/00UfNcmfZ0KFDy9z/2barrI0bN5r7kWTUr1/fOH78uEvMo48+6hIzdepU87kPP/zQ5bmEhATDMAxjyJAhRkJCgpGQkGA0bdrUfL5jx45m+aJFiwzDMIyrr77aCAkJcYk7efJklY/pQtu1a5fRsGFDQ5JhsViMmJgY47LLLjPr2qlTJ6OwsLDcfdjtdi/VtmqGDh1qSDJ69uxZqe1KX4f79u3zymtWB+f7uUmTJkbr1q3NY65Xr56xd+/ecrc9evSo0aJFC/P3Qfv27Y127doZQUFBxgcffGAYhmHs3r3bqFu3riHJCAsLMzp06GC+xjPPPOO2z6ioKEOS8a9//csDRwsAqAr6fjWn72cYhuFwOMy/386fJUuWuMXFxMQYkowHH3zQpdy5zYX8W+3r/UlP8sT5vJg4+81n+/1wNqV/b2zatMkjdfM05++j4OBgo0OHDsYll1xiHtPKlSvL3Xbnzp1GUFCQIclo1KiR+TvH39/feOedd8y4uLg4o1mzZkZ8fLzRqlUrc/+tW7d222dV2wI1AyNmgYtQRESEPvzwQ+3atUsjRoyQJO3evVu//PJLNdeshPNb/NjYWFmtVh0/flyrV68+a/wll1yiF198Ub/99pskad68eWZ5aWvXrtWHH36oDz/8UCNHjiy3/O2331Zubq5LnK8wDEN33XWXfvnlFwUEBOj999/Xd999p0OHDmnatGmSpE8//VRTpkwxt3GOkHjyySd10003qV69err77rvPeivR/Pnz1bRpU9WvX1+pqal69tln3W7zKuu2f2fMnDlzlJqaqgYNGqhp06aaMWOGyzHceeeduuKKK9SgQQMFBAQoKipK9913nwoKCjx23iTJZrOpa9euCg0NVVBQkFq0aKFbb71Vubm5ZcYfPXpUbdu2lcVi0bXXXqu8vDw5HA4999xz6tChgwIDAxUWFqZbb73VZeRRZGSkLBaLOQJhx44dslgs8vf3V35+vnkOLBaLkpKSqnQsQ4YMUWZmpn766Sd9/fXXevbZZyVJv/766zm/rZ8yZYr27dun9u3b6/vvv9dXX32l3bt3Ky8vT507d5ZU8j48ceKEAgIC9O233+rLL7/U5MmTJZWMWjp58mSV6g0A8D76fhd3389p8+bNZn/jmmuukSSX0a/Ofl12drYkac6cObJYLObIYae//OUvZrnTO++8o549e6pBgwYKCgrSH//4R23atMlt3xaLRYsXL9Z1112nwMBAPfbYY5U6hm+//VZ+fn6yWCzmiFqpZE5aZ18pJydHkvT111/r1ltvVXh4uKxWq9q2basXXnjBZX+nT5/W7Nmz1a5dO1mtVoWEhKhv377asmVLhepztqkM1q1bp+7du6t+/foKCgpSfHy8/vnPf5a5j/z8fA0bNkzBwcFu/d7S523JkiW64YYbVLduXbVo0UIvvfSSy3FMnDhRLVu2VGBgoEJDQxUfH+8ymvXkyZOaNGmSYmJiFBAQoIYNG+rGG2/UV199ZcYsWbLEfL1NmzYpPj7erP+HH35YoXNyvo4fP66//vWvatasmaxWqy699FJ17dpVS5cuPes2c+fONdv/lVdekXTu9l+0aJEsFouCg4N1+vRpSVJycrIsFosefPBBSVJ2drZ5Pj766KMLdowdOnTQe++9p7y8PH355Zf68MMP5edXkh577bXXyt32kUce0cmTJxUdHa29e/fq+++/V0JCgk6fPq0JEyaYcdu2bdOBAwf06aef6ttvv9Wf//xnSdKePXt85nc3fAOJWeAiduLECbPjEx4eXulb2T3h+PHj5hxpo0eP1sCBAyWp3Fuubr75Zv30009as2aNDh8+rNdff12RkZG69tprq1wPZ2LNF+3atUuff/65JOn222/XddddZz43efJks5P96quvus0Z9cgjj+g///mPWrVqddbbtd566y2NHTtWP/74o4KCgvTBBx+4JHkrYuLEidq0aZMCAwP1448/6pFHHnG5PWft2rXKzc1VTEyMmjVrpgMHDmjevHnmh0VPOHr0qG688UZ9+OGHCgkJ0ZVXXqm8vDytXr3aTJaWlpubq+uvv15ff/21rr32Wr3//vsKDQ3Vvffeq3Hjxmn37t1q1aqV/P39tXr1anXr1k1HjhyRJPXs2VOStHXrVpd/HQ6Htm/fLknmBwZnYrusuXHP/Cl9K+m4cePUtm1b8/Ef//hH8//l3YpnGIZ5+2WzZs10/fXXq169err66qu1Zs0ac1uHwyHp92S78/9SyQeQjz/++JznHADgW+j7nZ0v9/2cnOekc+fO5hQM//3vf82EonOKKuf0Qk2bNlVCQoL+8pe/KCEhwdxPy5YtlZCQoLi4OEnSypUrNWDAAP2///f/1LBhQ0VERGjLli26/vrrXZKzTn/729/0+eef64orrpC/v3+ljuGKK65Qnz59JMkl0fnvf/9bktSnTx81bdpU3377rbp06aLVq1fL4XDoyiuv1J49ezRmzBhzIIIk3XPPPZowYYKysrLUvHlz1alTR++//7569+6t//73v5Wqm9Orr76qwYMHa+vWrapfv74uu+wy7dy5UyNGjNDMmTPd4idOnKj33ntPVqu1zH6v0913363du3frkksu0ffff6+7775bX3/9tSTp//7v//T444/rwIEDat26tcLDw7V7925lZGSY2w8aNEizZs3Svn37FBMTo+LiYr3xxhvq2rWruZ/SkpKSdOLECZ06dUo7d+7U7bffrlOnTlXpnFRGenq6XnzxRR09elTt27dXaGioPv744zKvJankOhg3bpz8/f318ssv684776xQ+zv70IWFhfryyy9lGIbZz3b2vZ397fr166tTp06S3OfGLevnXPMN33LLLbr++uvN3xlxcXFq0KCBpPL74adOndKGDRskSX379lWDBg1Up04dDRo0SJL01Vdf6ccff5QkBQYGasGCBUpISNAVV1yhV199VVLJNGKXXnppufVD7UJiFrgI7d+/XxaLRfXq1dO7776rgIAAvfzyy26jDKrD66+/rl9//VWXXHKJbrvtNt15552SXDudZxo7dqykktESCxYsUHFxscaMGVMj5xWVSr4ldYqPj3d5zs/PT1dffbUk6ZdfftHPP//s8nx0dLS+//57ffHFF3r++efL3P+TTz4pSWrRooX27t2rvXv3mqMyKqpTp076/vvvlZWVZV5Xzk6IVNJJ+vnnn/X5558rOzvbHIn5xhtvVHh+3Mo6cOCAiouLVbduXWVlZemLL77QsWPH9PHHHys8PNwl9vjx40pKStIXX3yhLl266P3331dISIj27dunF198UZK0dOlSffXVV/r+++8VGRmpQ4cOmSN2nB3F0p1C54ffLVu26McffzSvZ2ds6fl+z/YTExNz1uNzjpi99NJLdfPNN5817ujRo+YI4XfffVe5ubkKCwvTrl27dMcdd5gjlG655Rb5+/vLbrfriiuu0FVXXeXyYcT5wR4A4Pvo+138Siew77zzTvXr10+NGzeWYRjmSETnyOiIiAhJ0siRI/Xhhx/q0UcfdRkt+cgjj+jDDz/U2rVrJUkPP/ywDMPQ8OHDtW/fPmVnZ+vGG2/U6dOnlZ6e7laXhIQE/fDDD/ryyy81adKkSh/LX//6V0klX9Tn5eXp1KlTWrdunSTprrvukiQ99thjys/PV4cOHXTw4EF9+eWXeuaZZyRJjz/+uAoLC7V3714zuXv//ffr22+/1d69exUVFaVTp06VWfeKcPZLExIStH//fu3bt0833nijJGnmzJk6ceKES/zVV19dbr/XadCgQdq7d68++OADSSVfgjvvWPv2228lSX/+85/1xRdf6Ntvv9Uvv/xijpjdtGmTOcL46aefVlZWlrKyslS/fn0dP35cs2bNcnu9p556Sl9//bXmzJkjqeT3wJnzUHuC81gefvhhffbZZ9q7d6+OHDlS5lzDK1eu1KhRo+Tn56dXXnlFqampkirW/ldccYWaNm0qqaR//dVXXykvL0/BwcH67LPPdPLkSTMx2717d/P3g3OdjPJ+Kjvf8D//+U/l5+fLYrGUO+r+559/Nu86a9y4sVl+2WWXmf8/cOCA+f+DBw9qx44dZrvFxcXp/fff9/kvkeBdJGaBi1BAQIASEhLMW1uKi4s1bNgwl4nEq4tzJEBycrIaNmyopKQkNWrUyKXTeab4+Hh169ZN27Zt05w5c2S1WjVq1Cgv1vrcfvrpJ3Xp0qVSP5999lmZ+yo9CrasP8rl/aEeNmyY+Q3r2UY47N69W1LJt+zOb3HLS/SV5bbbbjMXlHJ2Og4fPmw+v2HDBnXo0EFBQUGyWCxmwu/UqVM6evRopV6rotq3b6+WLVvqxIkTaty4seLj4zVs2DD9+OOPqlevnkvsp59+qo8++khRUVFav369mVT95JNPzPM/dOhQc+GNH374QZLMDz3OZOvPP/+sPXv2aOvWrerdu7euvPJKbd261eyQl/723vlhqryfRx55xO24Tp06pbvvvluvvPKK6tevr7Vr17p07sqKd2rbtq327dunvXv3mqNv58+fL0nq0qWL3nrrLXXp0kXFxcX65ZdfzA9LkvvtogAA30Xfz/suZN9P+j2BXadOHd1+++3mv1LJl8Vn3iVVUUePHjUT4P/85z/l5+cnPz8/M2lb1u3ff/3rXxUUFCTp7P3J8gwaNEiRkZEqKirSsmXLtHHjRuXm5qpBgwZmAnTHjh2SSkYQ1qtXTxaLRePGjZNUckv/rl27XPpld9xxhyQpJCREycnJkkr6bZV15MgRMzF20003mYvBOc/1yZMnzb6y07n6vU5//vOfZbFYXBbOdcbdcMMNslgsWrp0qS6//HL17t1bM2bMMPvtpe9Uch5rZGSkecdUWcfq/JKjrNfzJOeo96lTpyoqKkr9+vXTvHnzyuyfvvjii3I4HHr66aeVkpJillek/SXXu9ScAyJGjx6t3377TTt27HC7Q036/YuJ8n6cX25Mnz7d5T06ffp0t2P45z//qdGjR0uSZs+erb59+5713JztfXq2z3czZszQqVOn9PXXX6t3797auXOnUlNTzakbAEmquV9JAjWYMwEkSVlZWWrXrp0OHz6shQsXlnl7jrfs27fPTFitX79eoaGhkmSuNrt06VKlp6eXmXgcO3astm3bpoKCAg0bNsznVqa02+2VntfobPOttmnTxvz/mR14h8OhL774QpLUsGFDt/PQpEmTCr9+6fNc2c6+s+0kmd9OO/fx2muvafz48ZJKrsVmzZrp559/1t69eyXJYx2NwMBAffrpp3rllVf00UcfKTMzU6+88opefvllrVq1SrfeeqsZW69ePf3666/av3+/XnnlFf3tb39zOQapZGXmM79Nj4qKkiTzG/ycnBwtWbJEhw8fVvfu3dWwYUOtWLFCrVu3luT67f1PP/1kfhg5mwEDBrgkZwsLC/WnP/1J7777ri677DK9/fbb5xzdHB4eroCAABUXF+vqq682b3e8+uqrlZWV5TI6KSkpyWUO3OXLl5sfkp3HAADwffT9vO9C9v2k3xPYDodDV1xxhSSZc+zu27dP/+///T8zSVUZpfs2LVu2dLuLSJKKi4tdHlemP1kWf39/jRo1So8++qj+9a9/mXeA3Xrrrapbt65LvRo1alTmHUNnJoQ9MYKwovssr99bVlzpkd3OuH79+umzzz7T66+/ri+++EI7d+7U5s2btWTJErdRrpWtV1mv50l333232rRpo3Xr1unLL7/Up59+qvfee0+vv/66y3y4kswRvy+88ILuuOMO831c0fbv1auXli1bpi1btsjf31/BwcEaM2aMnnzySb355pvmnYalE7PTp093mSKiLGvXrlVERISys7Nd3selP4cZhqFHHnlEM2fOVJ06dfTSSy9p+PDh5e43PDxcQUFBOnnypEuS3DkdmlQy1diZx9q6dWuNGzdOmzZt0ubNm7Vhw4ZyE8CoXUjMAhe50n+c7XZ7hbY5ffq02+3mZyaoTp065RYTGBhY7n6XLFli1qeoqMht+/I6nTfffLMiIiL0008/mbe3+ZLo6OgL1hG66qqrdPXVV+uLL77Q8uXLdeedd5rzzM6cOdNcFML5rXxpFenIdejQQR988IHee+89/frrrwoMDDRHTVwIzg+GDRo00L59+2S1WvXXv/7VnCLAUwoKCvT111/r3nvvNa+R6667Ths3btT/+3//zyUxe8011+iPf/yjZsyYobFjx+rSSy9VSkqKrrnmGlksFhmGoWHDhun++++XVPI+2rp1q8tcfT179tSyZcvMRQqcidl//vOf5qIApTuJFfkAV7ozmJOTowEDBuiLL75Qu3btZLPZzMRwadddd51ycnJ04403atasWbrkkkvUo0cP/ec//9GuXbvMD3XOkQfOD3tSyW2kzvfbwYMHzfns2rdvrw4dOpRbVwCAb6Lv5x0Xsu9XOoHtcDjKnBt/yZIl50zMOhNCzsS3VHI7dVRUlPbv36/4+HgtX77cTOR988032r9/v/klrtOFSIKOGjVK06dP1yeffKKsrCxJcrkz59prr1VWVpZCQkJks9nMkaM///yzNmzYoC5duig8PNzsl7322mvq3Lmz8vPzZbPZJKnSU3FJJeejefPmOnDggNasWaP77rtPAQEBWrFihaSSc9i+ffvzPXw3u3btUuPGjc0vSg4ePKjmzZvr8OHD2rNnj7k4q1QyyGHcuHH64YcfzOuiKsfqKTt27FD79u3Vo0cPSdIHH3ygHj16lLng4P/93/9p4sSJ+vrrr5WUlKRNmzapfv36FWp/6fe+9A8//KC3335bXbt2VVRUlJo3b65FixbJMAyXO9QkuSVby+L83bhkyZIy57suLi7WX/7yFy1btkwhISFavXq1OXdyaRMnTtTatWvVtGlTbdiwQXXq1NF1112nt99+W++9954KCwsVGBioN998U1LJAoiXX365vv32W2VlZemGG26Qn5+fHA6H3n33XXO/pd/DAFMZABch561V11xzjflH3M/Pz7zt5FxeffVVBQUFufw4R2k6zZw50y0mLy/vrPs0DMNcgfOmm26SYRjmz+nTp83bgs62EMQll1yiPXv26Oeff3abd7UqUlNT1apVK82dO9csa9++vVq1amUuTlBdLBaLXn75ZTVs2FDFxcW6/vrrdcUVVygiIsKcS6tTp05l3mpTEc7VQL/99lu1bNlSLVq0MG8nuhCuuuoqSSWjPVu2bKmWLVuai1F50pEjR9S1a1c1bNhQV111ldq0aaONGze61Km06dOn6y9/+YsMw9DQoUP1zjvvqGXLluatkuPGjVPLli111VVXKTQ0VH/84x9dRjA7O4r5+fkKDAxUfHy8/vCHP0gqmSeudIz0+we48n5KX//Dhw8333eGYei2224r8zar7Oxs7dmzRz/99JNZNmPGDAUEBCgzM9Ns48zMTPn7+7vMFTdgwAA1btxYsbGxuuKKK/TNN9+obt265iq4AICLA32/c/Plvp8zgV2nTh398ssvLufKufr86tWrz5mscX7B+/DDD+vaa681/+Y/9thj5j4uv/xyxcXFqUmTJmrduvU5V5ivqoiICA0ePFhSSZIpOjraTORJJQmt4OBgZWdnq1mzZoqLi1NUVJSaNGmiv//975JK5gp1jlB87rnndMUVV6hly5bav3+/6tSpo6lTp1apbs7kqHNaqxYtWpiDFCZPnmyO6r2QVq1apcjISDVv3lydOnUyr+m6desqJiZGvXv3NhN/aWlpateundq1a6fjx4+rfv36mjhx4gWvU1XNnTtXTZo0UYsWLdSpUyfdcMMNkkoWoztz0armzZvrnXfeUUhIiD755BMNHjxYdru9Qu0vyWWe2fz8fLOv/Yc//MHsb5e+Q036/f1U3o9zMeWzmTNnjpYtWyapZNTvlClTzH546TvgfvrpJ+3Zs0fZ2dlm2YwZMxQUFKT9+/erZcuWio6O1o4dO+Tv72+u9ZGTk6PBgwcrJCREV199tS6//HJzsEdkZKTL4s8AiVngIlRcXKyPPvpIn376qerUqaOuXbtq5cqVVbr96UL573//a470vOWWW1ye8/Pz05AhQySV3+ls0KCBGjZseEHqk5OTo+zsbHORJEnau3evsrOzy73NzFuuuuoqffHFFxo7dqxatmypAwcOqKCgQHFxcZo1a5a2bNlirgxaWQMHDtS8efMUERGh48ePq2vXri6dPeecYlU1YsQIPfDAA2rUqJEKCwvVq1cvl9V1PaVhw4YaNmyYmjRpon379ungwYNq06aNHnvssbNO0r9w4UIlJyfrt99+0y233KKtW7fqhRde0DPPPKPY2Fj9+OOP2r9/v6Kjo5WWluaSaO3du7f5/2uvvVYBAQG68sorzQ+aZ357X1mlRzllZWXpo48+Mn9Kd/7KkpCQoI0bN6pXr146duyYioqK1KdPH3MuXKeBAweqTp062rNnj+rVq6ebbrpJ27dvV9euXatcbwCA99H3Ozdf7fuVTmAnJia6Jbac6wAcP37cXMDzbObOnavY2FgVFxfr448/1jfffCOpZM7St99+Wz179tTJkye1Z88eNWjQQHfddVe5CxmdrzFjxpj/v/POO12+9G3durW2b99uTm+we/duORwO9e/f3+UL6AULFujJJ59U27ZtdeDAAf3222/q06ePNm3a5NIvqwg/v5L0xp///Ge98cYb6tatmwoLC3Xo0CF17NhRL730krkw2IXWo0cP9e/fXw6HQ1999ZUcDocSExP1zjvvmFMSrFu3ThMnTlSLFi307bffqk6dOhoyZIi2b9/ucldVdRswYIB69OihoqIiffnllwoMDNQNN9wgm81W5hf7V111ldauXauAgABt3LhRKSkpatWqVYXaX3Id6NC9e3eXf898/kIp3Q/Pyclx6Yfv3Lmz3G2vvvpq/fe//9X111+voqIiHTt2TN26dZPNZlP//v0llSSshwwZorCwMO3Zs0e5ubmKiYnRPffco+3bt7vcpQdYDG9MUgIA8JrffvtNOTk55jfFp0+f1oABA7R+/XpFREQoJyfHp0dLfv/992rRooWk3+eB/c9//qP69etXc81qpjFjxuizzz7Tzp07VVxcrH/9618aNmxYdVcLAAD4uEOHDikiIkIWi0XffvttmXOJetqKFSuUkpIii8WiEydOnHP6Dfxu2LBhWrp0qTkP7O23324u0AXvWLx4sRYvXqzs7Gz9/PPPGjp06FnvMkDNxRyzACrM+YejLCNHjvToN/KouF9//VWtWrXSNddcoyZNmujLL780F+aaPn26Tydlz/T5559LKpn3Dp6RmZlZ6cVNAAC1A30/lOXnn3/W/fffr08++USSdOONN3osKZuRkXHW6b0++ugjcyGzP/7xjyRlq+jnn3/Wzz//bM77Cu/54Ycf6IeDxCyAiivvD4fztg1Uv8DAQCUnJ+vjjz/WZ59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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(4, 2, figsize=(14, 16))\n", "fig.suptitle(\"Numerical Transforms — Before vs After (Training Data)\", fontsize=14, fontweight='bold')\n", "\n", "num_pairs = [\n", " ('PAY_AMT1', 'log1p', BLUE, GREEN),\n", " ('BILL_AMT1', 'yeo_johnson', BLUE, ORANGE),\n", " ('LIMIT_BAL', 'log1p', BLUE, GREEN),\n", " ('AGE', 'standard_scale', BLUE, ORANGE),\n", "]\n", "\n", "for i, (col, method, c_before, c_after) in enumerate(num_pairs):\n", " before = X_train[col]\n", " after = X_train_t[col]\n", " axes[i, 0].hist(before.dropna(), bins=40, color=c_before, alpha=0.75)\n", " axes[i, 0].set_title(f\"`{col}` — Original [skew={before.skew():.2f}]\", fontweight='bold', fontsize=10)\n", " axes[i, 1].hist(after.dropna(), bins=40, color=c_after, alpha=0.75)\n", " axes[i, 1].set_title(f\"`{col}` — After `{method}` [skew={after.skew():.2f}]\", fontweight='bold', fontsize=10)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "md1697f1f5", "metadata": {}, "source": [ "---\n", "## 14. Pipeline Metadata Inspection" ] }, { "cell_type": "code", "execution_count": 29, "id": "cd9fd50dc2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline Metadata\n", "==================================================\n", " Pipeline name : credit_default_pipeline\n", " Created at : None\n", " Fitted at : 2026-05-06T14:27:18.945569\n", " Number of steps : 6\n", " Input shape : (24000, 23)\n", " Output shape : (24000, 26)\n", " Is fitted : True\n", " Column expansion : +3 columns (from one-hot encoding)\n" ] } ], "source": [ "meta = default_pipeline.metadata\n", "\n", "print(\"Pipeline Metadata\")\n", "print(\"=\" * 50)\n", "print(f\" Pipeline name : {default_pipeline.name}\")\n", "print(f\" Created at : {meta.created_at}\")\n", "print(f\" Fitted at : {meta.fitted_at}\")\n", "print(f\" Number of steps : {meta.n_transformers}\")\n", "print(f\" Input shape : {meta.input_shape}\")\n", "print(f\" Output shape : {meta.output_shape}\")\n", "print(f\" Is fitted : {default_pipeline.is_fitted}\")\n", "print(f\" Column expansion : +{meta.output_shape[1] - meta.input_shape[1]} columns (from one-hot encoding)\")" ] }, { "cell_type": "markdown", "id": "mdab9c1f76", "metadata": {}, "source": [ "---\n", "## 15. Conclusion\n", "\n", "This notebook covered `bitbullet.transform` applied to a real-world credit default dataset with a genuine mix of transformation needs.\n", "\n", "### What We Built\n", "\n", "| You Wrote | BitBullet Handled |\n", "|-----------|------------------|\n", "| `generate_feature_stats(df)` | Full audit — skewness, kurtosis, missing values, cardinality |\n", "| `pipeline.add(\"numerical\", \"log1p\", ...)` | Fit on training data, store parameters, apply consistently |\n", "| `pipeline.add(\"numerical\", \"yeo_johnson\", ...)` | Power transformer for negative-valued BILL_AMT columns |\n", "| `pipeline.fit_transform(X_train, y=y_train)` when supervised encoders are present | Sequential chaining, target-aware fitting, fit-lock after completion |\n", "| `pipeline.save(path)` | Compressed serialisation of all fitted parameters |\n", "| `TransformPipeline.load(path)` | Exact reconstruction — identical parameters, identical output |\n", "| `loaded_pipeline.transform(X_test)` | Apply exact training parameters — zero leakage |\n", "\n", "### Quick Decision Guide\n", "\n", "```\n", "PAY_AMT columns (zeros, extreme right skew) → log1p\n", "BILL_AMT columns (right skew, possible negatives) → yeo_johnson\n", "LIMIT_BAL (strictly positive, right-skewed) → log1p\n", "AGE (roughly normal) → standard_scale\n", "PAY_0–PAY_6 (ordinal integer, some extremes) → robust_scale\n", "SEX, MARRIAGE (2–3 categories, tree model) → label_encode\n", "EDUCATION (4 categories, nominal) → onehot_encode\n", "```\n", "\n", "**Continue the Academy:**\n", "- `05_Model_Management.ipynb` — versioning, comparison, and model governance" ] } ], "metadata": { "kernelspec": { "display_name": "bitbullet", "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": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }