{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Test For `DriftChecker`-`pydrift` \n", "\n", "We're going to test how it works with the famous titanic dataset\n", "\n", "# Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "from sklearn import set_config\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.compose import make_column_transformer\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import OrdinalEncoder\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "from pydrift import DriftChecker\n", "from pydrift.exceptions import ColumnsNotMatchException\n", "from pydrift.constants import PATH_DATA, RANDOM_STATE\n", "\n", "\n", "set_config(display='diagram')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read Data " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_titanic = pd.read_csv(PATH_DATA / 'titanic.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Constants " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "DATA_LENGTH = df_titanic.shape[0]\n", "TARGET = 'Survived'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Split\n", "\n", "50% sample will give us a non-drift problem\n", "\n", "We drop Ticket and Cabin features because of cardinality" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = df_titanic.drop(columns=['Ticket', 'Cabin', 'PassengerId', 'Name', TARGET])\n", "y = df_titanic[TARGET]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=.5, random_state=RANDOM_STATE, stratify=y\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test `ColumnsNotMatchException`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Different columns for left and right dataframes\n", "\n", "Columns in right dataframe but not in left one: Sex\n", "Columns in left dataframe but not in right one: None\n" ] } ], "source": [ "try:\n", " DriftChecker(X_train.drop(columns='Sex'), X_test)\n", "except ColumnsNotMatchException as exception:\n", " print(exception)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Same With Right DataFrame " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Different columns for left and right dataframes\n", "\n", "Columns in right dataframe but not in left one: None\n", "Columns in left dataframe but not in right one: SibSp\n" ] } ], "source": [ "try:\n", " DriftChecker(X_train, X_test.drop(columns='SibSp'))\n", "except ColumnsNotMatchException as exception:\n", " print(exception)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Both Dataframes With Different Columns " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Different columns for left and right dataframes\n", "\n", "Columns in right dataframe but not in left one: Fare\n", "Columns in left dataframe but not in right one: Embarked\n" ] } ], "source": [ "try:\n", " DriftChecker(X_train.drop(columns='Fare'), X_test.drop(columns='Embarked'))\n", "except ColumnsNotMatchException as exception:\n", " print(exception)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test `ml_model_can_discriminate` Feature With Different Model\n", "\n", "You can pass any model to be the discriminative ml model, for example a pipeline with logistic regression" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(transformers=[('pipeline',\n",
       "                                                  Pipeline(steps=[('simpleimputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('ordinalencoder',\n",
       "                                                                   OrdinalEncoder())]),\n",
       "                                                  Index(['Sex', 'Embarked'], dtype='object')),\n",
       "                                                 ('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='median'),\n",
       "                                                  Index(['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object'))])),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(max_iter=1000, random_state=1994))])
ColumnTransformer(transformers=[('pipeline',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder())]),\n",
       "                                 Index(['Sex', 'Embarked'], dtype='object')),\n",
       "                                ('simpleimputer',\n",
       "                                 SimpleImputer(strategy='median'),\n",
       "                                 Index(['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object'))])
Index(['Sex', 'Embarked'], dtype='object')
SimpleImputer(strategy='most_frequent')
OrdinalEncoder()
Index(['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object')
SimpleImputer(strategy='median')
LogisticRegression(max_iter=1000, random_state=1994)
" ], "text/plain": [ "Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(transformers=[('pipeline',\n", " Pipeline(steps=[('simpleimputer',\n", " SimpleImputer(strategy='most_frequent')),\n", " ('ordinalencoder',\n", " OrdinalEncoder())]),\n", " Index(['Sex', 'Embarked'], dtype='object')),\n", " ('simpleimputer',\n", " SimpleImputer(strategy='median'),\n", " Index(['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object'))])),\n", " ('logisticregression',\n", " LogisticRegression(max_iter=1000, random_state=1994))])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "categorical_pipeline = make_pipeline(\n", " SimpleImputer(strategy='most_frequent'),\n", " OrdinalEncoder()\n", ")\n", "\n", "column_transformer = make_column_transformer(\n", " (categorical_pipeline, X_train.select_dtypes(include=['category', 'object']).columns),\n", " (SimpleImputer(strategy='median'), X_train.select_dtypes(include='number').columns)\n", ")\n", "\n", "pipeline_lr = make_pipeline(column_transformer, LogisticRegression(max_iter=1000, random_state=RANDOM_STATE))\n", "\n", "display(pipeline_lr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DiftChecker Apply" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "910910d2b6b24b3b87a789afa86d4ad6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=446.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "No drift found in discriminative model step\n", "\n", "AUC drift check model: 0.50\n", "AUC threshold: .5 ± 0.10\n" ] } ], "source": [ "drift_checker_ok = DriftChecker(\n", " X_train, X_test\n", ")\n", "\n", "drift_checker_ok.ml_model_can_discriminate(ml_discriminate_model=pipeline_lr);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Same But Make It Drift\n", "\n", "`pydrift` tells you that the problem is in `Sex` feature (as is obviously in this example)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c0ea054f07fc45b6bba67250b6b255ca", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=446.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Drift found in discriminative model step, take a look on the most discriminative features (plots when minimal is set to False)\n", "\n", "AUC drift check model: 0.84\n", "AUC threshold: .5 ± 0.10\n" ] } ], "source": [ "mask = (X['Pclass'] > 1) & (X['Fare'] > 10)\n", "\n", "X_mask = X[mask]\n", "X_unmask = X[~mask]\n", "\n", "drift_checker_ko = DriftChecker(\n", " X_mask, X_unmask\n", ")\n", "\n", "drift_checker_ko.ml_model_can_discriminate(ml_discriminate_model=pipeline_lr);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checking Features Histograms\n", "\n", "`Embarked` is the most driscriminative feature!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histnorm": "probability density", "hovertemplate": "is_left=0
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