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+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Dataset: UCI Cervical Cancer\n",
+ "\n",
+ "In this Notebook we will show you how to analyse and properly preprocess the UCI Cervical Cancer dataset. \n",
+ "\n",
+ "--------"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started\n",
+ "\n",
+ "### Setup Colab environment\n",
+ "\n",
+ "If you installed the packages and requirments on your own machine, you can skip this section and start from the import section.\n",
+ "Otherwise you can follow and execute the tutorial on your browser. In order to start working on the notebook, click on the following button, this will open this page in the Colab environment and you will be able to execute the code on your own.\n",
+ "\n",
+ "[](https://colab.research.google.com/github/HelmholtzAI-Consultants-Munich/XAI-Tutorials/blob/main/data/datasets_sklearn/Dataset-CervicalCancer.ipynb)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that you opened the notebook in Colab, follow the next step:\n",
+ "\n",
+ "1. Run this cell to connect your Google Drive to Colab and install packages\n",
+ "2. Allow this notebook to access your Google Drive files. Click on 'Yes', and select your account.\n",
+ "3. \"Google Drive for desktop wants to access your Google Account\". Click on 'Allow'.\n",
+ " \n",
+ "At this point, a folder has been created in your Drive and you can navigate it through the lefthand panel in Colab, you might also have received an email that informs you about the access on your Google Drive."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Mount drive folder to dbe abale to download repo\n",
+ "# from google.colab import drive\n",
+ "# drive.mount('/content/drive')\n",
+ "\n",
+ "# Switch to correct folder'\n",
+ "# %cd /content/drive/MyDrive\n",
+ "\n",
+ "# Don't run this cell if you already cloned the repo \n",
+ "# !git clone --branch main https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials.git\n",
+ "# %cd XAI-Tutorials\n",
+ "\n",
+ "# Install additional packages\n",
+ "# %pip install ucimlrepo"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Imports\n",
+ "\n",
+ "Let's start with importing all required Python packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the required packages\n",
+ "import pickle\n",
+ "import pprint\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from ucimlrepo import fetch_ucirepo \n",
+ "from sklearn.impute import KNNImputer\n",
+ "\n",
+ "pp = pprint.PrettyPrinter(depth=4)\n",
+ "\n",
+ "import utils"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We fix the random seeds to ensure reproducible results, as we work with (pseudo) random numbers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# assert reproducible random number generation\n",
+ "seed = 1\n",
+ "np.random.seed(seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--------"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The UCI Cervical Cancer Dataset\n",
+ "\n",
+ "In this notebook, we will work with the **UCI Cervical Cancer dataset**, containing medical records of 858 patients with 803 healthy patients (class 0) and 55 patients with biopsy indicating cervical cancer (class 1). Diagnosis outcome is described by 29 features covering demographic information, habits, and historic medical records (for more details please see descrption [here](https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors)).\n",
+ "\n",
+ "
\n",
+ "\n",
+ " Source: [Link](https://www.kaggle.com/datasets/loveall/cervical-cancer-risk-classification)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the data\n",
+ "cervical_cancer_risk_factors = fetch_ucirepo(id=383) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's have a look at the description of the dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'abstract': 'This dataset focuses on the prediction of indicators/diagnosis '\n",
+ " 'of cervical cancer. The features cover demographic information, '\n",
+ " 'habits, and historic medical records.',\n",
+ " 'additional_info': {'citation': None,\n",
+ " 'funded_by': None,\n",
+ " 'instances_represent': None,\n",
+ " 'preprocessing_description': None,\n",
+ " 'purpose': None,\n",
+ " 'recommended_data_splits': None,\n",
+ " 'sensitive_data': None,\n",
+ " 'summary': \"The dataset was collected at 'Hospital \"\n",
+ " \"Universitario de Caracas' in Caracas, \"\n",
+ " 'Venezuela. The dataset comprises demographic '\n",
+ " 'information, habits, and historic medical '\n",
+ " 'records of 858 patients. Several patients '\n",
+ " 'decided not to answer some of the questions '\n",
+ " 'because of privacy concerns (missing values).',\n",
+ " 'variable_info': '(int) Age\\r\\n'\n",
+ " '(int) Number of sexual partners\\r\\n'\n",
+ " '(int) First sexual intercourse (age)\\r\\n'\n",
+ " '(int) Num of pregnancies\\r\\n'\n",
+ " '(bool) Smokes\\r\\n'\n",
+ " '(bool) Smokes (years)\\r\\n'\n",
+ " '(bool) Smokes (packs/year)\\r\\n'\n",
+ " '(bool) Hormonal Contraceptives\\r\\n'\n",
+ " '(int) Hormonal Contraceptives '\n",
+ " '(years)\\r\\n'\n",
+ " '(bool) IUD\\r\\n'\n",
+ " '(int) IUD (years)\\r\\n'\n",
+ " '(bool) STDs\\r\\n'\n",
+ " '(int) STDs (number)\\r\\n'\n",
+ " '(bool) STDs:condylomatosis\\r\\n'\n",
+ " '(bool) STDs:cervical condylomatosis\\r\\n'\n",
+ " '(bool) STDs:vaginal condylomatosis\\r\\n'\n",
+ " '(bool) STDs:vulvo-perineal '\n",
+ " 'condylomatosis\\r\\n'\n",
+ " '(bool) STDs:syphilis\\r\\n'\n",
+ " '(bool) STDs:pelvic inflammatory '\n",
+ " 'disease\\r\\n'\n",
+ " '(bool) STDs:genital herpes\\r\\n'\n",
+ " '(bool) STDs:molluscum contagiosum\\r\\n'\n",
+ " '(bool) STDs:AIDS\\r\\n'\n",
+ " '(bool) STDs:HIV\\r\\n'\n",
+ " '(bool) STDs:Hepatitis B\\r\\n'\n",
+ " '(bool) STDs:HPV\\r\\n'\n",
+ " '(int) STDs: Number of diagnosis\\r\\n'\n",
+ " '(int) STDs: Time since first '\n",
+ " 'diagnosis\\r\\n'\n",
+ " '(int) STDs: Time since last '\n",
+ " 'diagnosis\\r\\n'\n",
+ " '(bool) Dx:Cancer\\r\\n'\n",
+ " '(bool) Dx:CIN\\r\\n'\n",
+ " '(bool) Dx:HPV\\r\\n'\n",
+ " '(bool) Dx\\r\\n'\n",
+ " '(bool) Hinselmann: target variable\\r\\n'\n",
+ " '(bool) Schiller: target variable\\r\\n'\n",
+ " '(bool) Cytology: target variable\\r\\n'\n",
+ " '(bool) Biopsy: target variable'},\n",
+ " 'area': 'Health and Medicine',\n",
+ " 'characteristics': ['Multivariate'],\n",
+ " 'creators': ['Kelwin Fernandes', 'Jaime Cardoso', 'Jessica Fernandes'],\n",
+ " 'data_url': 'https://archive.ics.uci.edu/static/public/383/data.csv',\n",
+ " 'dataset_doi': '10.24432/C5Z310',\n",
+ " 'demographics': ['Age', 'Other'],\n",
+ " 'feature_types': ['Integer', 'Real'],\n",
+ " 'has_missing_values': 'yes',\n",
+ " 'index_col': None,\n",
+ " 'intro_paper': {'authors': 'Kelwin Fernandes, Jaime S. Cardoso, Jessica C. '\n",
+ " 'Fernandes',\n",
+ " 'doi': None,\n",
+ " 'published_in': 'Iberian Conference on Pattern Recognition '\n",
+ " 'and Image Analysis',\n",
+ " 'title': 'Transfer Learning with Partial Observability '\n",
+ " 'Applied to Cervical Cancer Screening',\n",
+ " 'url': 'https://www.semanticscholar.org/paper/Transfer-Learning-with-Partial-Observability-to-Fernandes-Cardoso/1c02438ba4dfa775399ba414508e9cd335b69012',\n",
+ " 'year': 2017},\n",
+ " 'last_updated': 'Sun Mar 10 2024',\n",
+ " 'missing_values_symbol': 'NaN',\n",
+ " 'name': 'Cervical Cancer (Risk Factors)',\n",
+ " 'num_features': 36,\n",
+ " 'num_instances': 858,\n",
+ " 'repository_url': 'https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors',\n",
+ " 'target_col': None,\n",
+ " 'tasks': ['Classification'],\n",
+ " 'uci_id': 383,\n",
+ " 'year_of_dataset_creation': 2017}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# metadata \n",
+ "pp.pprint(cervical_cancer_risk_factors.metadata) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before we can start analysing the dataset, we have to pre-process it to retrieve the desired features and target variable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_cervicalcancer = cervical_cancer_risk_factors.data.features\n",
+ "\n",
+ "# Drop unnecessary columns (alternative target columns for either different diagnosis (DX) or tests)\n",
+ "data_cervicalcancer = data_cervicalcancer.drop(columns=['Citology', 'Schiller', 'Hinselmann', 'Dx:Cancer', 'Dx:CIN', 'Dx:HPV', 'Dx'])\n",
+ "\n",
+ "# Convert Biopsy column to categorical labels\n",
+ "data_cervicalcancer['Biopsy'] = data_cervicalcancer['Biopsy'].map({0: '0_Healthy', 1: '1_Cancer'})\n",
+ "\n",
+ "# Define which features are categorical\n",
+ "features_categorical = ['Biopsy','Smokes','Hormonal Contraceptives','IUD','STDs','STDs:condylomatosis','STDs:cervical condylomatosis','STDs:vaginal condylomatosis','STDs:vulvo-perineal condylomatosis','STDs:syphilis','STDs:pelvic inflammatory disease','STDs:genital herpes','STDs:molluscum contagiosum','STDs:AIDS','STDs:HIV','STDs:Hepatitis B','STDs:HPV']"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exploratory Data Analysis\n",
+ "\n",
+ "Exploratory data analysis is a first important step to get an understanding of the data and to identify patterns and problems in the data. First, we will check how many samples and variables our dataset has and inspect the first few lines of our dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The dataset has 29 variables that describe 858 patients.\n",
+ "We have 1 categorical and 28 numerical variables.\n",
+ "We have the folliwng number of healthy and cancer patients in the dataset: \n",
+ " 0_Healthy 803\n",
+ "1_Cancer 55\n",
+ "Name: Biopsy, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Inspect the data\n",
+ "print(f\"The dataset has {data_cervicalcancer.shape[1]} variables that describe {data_cervicalcancer.shape[0]} patients.\")\n",
+ "print(f\"We have {data_cervicalcancer.select_dtypes(include='object').shape[1]} categorical and {data_cervicalcancer.select_dtypes(exclude='object').shape[1]} numerical variables.\")\n",
+ "print(f\"We have the folliwng number of healthy and cancer patients in the dataset: \\n {data_cervicalcancer['Biopsy'].value_counts()}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Here are the first few lines of our dataset:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "utils.plot_distributions(dataset=data_cervicalcancer, features_categorical=features_categorical, ncols=5, nrows=6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By analysing the results of the EDA we can conclude:\n",
+ "\n",
+ "- we have missing values in the dataset we need to take care of\n",
+ "- we need to encode categorical features for the classification model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Handling of Missing Values \n",
+ "\n",
+ "Based on what we saw in the explorative analysis above, we need to do some preprocessing steps before we start training the model. First, we need to take care of the missing values. There are different options how one can deal with this problem and the strategy one chooses depends heavily on the dataset and the context we are in.\n",
+ "\n",
+ "In this dataset, we cannot handle all missing values with one approach but choose accordingly:\n",
+ "\n",
+ "- the fratures \"STDs: Time since first diagnosis\" and \"STDs: Time since last diagnosis\" have more than 90% missing values, hence we remove those features entirely\n",
+ "- the features \"STDs:xx\" have more than 10% missing values, however, removing them would lead to a loss of ~20% \"cancer\" patients. Since the \"cancer\" class is already underrepresented in our dataset, we want to avoid loosing too many samples of this class. On the other hand, we have to consider that imputing those values might be difficult since we can see from the distribution plots that we have a high imbalance in no STD vs STD. This is why we decide to drop the single \"STDs:xx\" value and only keep the aggregation features \"STDs\" and STDs (number). Since we have more balanced data here, it's more reliable to impute the missing values of those features.\n",
+ "- remaining features: for the ramining features we impute the missing values to keep all \"cancer\" class samples in the dataset. We choose the KNN Imputer as it is more sophisticated than a simple mean/median/most_frequent imputer. In order to use the KNN imputer, we have to remove the target variable \"biopsy\" from the dataset beforehand as this would influence in an overly optimistic way our classification model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remove features we can't reliably impute\n",
+ "std_time = ['STDs: Time since first diagnosis', 'STDs: Time since last diagnosis']\n",
+ "std_xx = ['STDs:condylomatosis','STDs:cervical condylomatosis','STDs:vaginal condylomatosis','STDs:vulvo-perineal condylomatosis','STDs:syphilis','STDs:pelvic inflammatory disease','STDs:genital herpes','STDs:molluscum contagiosum','STDs:AIDS','STDs:HIV','STDs:Hepatitis B','STDs:HPV']\n",
+ "\n",
+ "data_cervicalcancer = data_cervicalcancer.drop(columns=std_time)\n",
+ "data_cervicalcancer = data_cervicalcancer.drop(columns=std_xx)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Age 0\n",
+ "Number of sexual partners 26\n",
+ "First sexual intercourse 7\n",
+ "Num of pregnancies 56\n",
+ "Smokes 13\n",
+ "Smokes (years) 13\n",
+ "Smokes (packs/year) 13\n",
+ "Hormonal Contraceptives 108\n",
+ "Hormonal Contraceptives (years) 108\n",
+ "IUD 117\n",
+ "IUD (years) 117\n",
+ "STDs 105\n",
+ "STDs (number) 105\n",
+ "STDs: Number of diagnosis 0\n",
+ "Biopsy 0\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(data_cervicalcancer.isnull().sum())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Age 0\n",
+ "Number of sexual partners 0\n",
+ "First sexual intercourse 0\n",
+ "Num of pregnancies 0\n",
+ "Smokes 0\n",
+ "Smokes (years) 0\n",
+ "Smokes (packs/year) 0\n",
+ "Hormonal Contraceptives 0\n",
+ "Hormonal Contraceptives (years) 0\n",
+ "IUD 0\n",
+ "IUD (years) 0\n",
+ "STDs 0\n",
+ "STDs (number) 0\n",
+ "STDs: Number of diagnosis 0\n",
+ "Biopsy 0\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# impute missing values for remaining features using KNN Imputer\n",
+ "data_cervicalcancer_imp = data_cervicalcancer.drop(columns=[\"Biopsy\"])\n",
+ "imputer = KNNImputer(n_neighbors=5, weights=\"distance\")\n",
+ "imputer.set_output(transform=\"pandas\")\n",
+ "data_cervicalcancer_imp = imputer.fit_transform(data_cervicalcancer_imp)\n",
+ "data_cervicalcancer_imp[\"Biopsy\"] = data_cervicalcancer[\"Biopsy\"]\n",
+ "\n",
+ "print(data_cervicalcancer_imp.isnull().sum())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since we also imputed missing values for categorical features, we need to convert the imputed values to categories, simply by rounding the imputed values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for col in data_cervicalcancer_imp.columns:\n",
+ " if col in features_categorical and col not in [\"Biopsy\"]:\n",
+ " data_cervicalcancer_imp[col] = round(data_cervicalcancer_imp[col])\n",
+ " data_cervicalcancer_imp[col] = data_cervicalcancer_imp[col].map({0: 'No', 1: 'Yes'})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have another look at the distribution of the features we will keep in the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "utils.plot_distributions(dataset=data_cervicalcancer_imp, features_categorical=features_categorical, ncols=5, nrows=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The distribution for the features with imputed values looks similar to the original distribution, which is a good sign for a suitable imputation. Is our dataset ready to be used for training the model? \n",
+ "Well...almost! What did we observe regarding feature transformation? Do we need to encode some of them? Yes, we do! "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Encoding of categorical variables\n",
+ "\n",
+ "Categorical features need to be encoded, i.e. turned into numerical data. This is essential because most machine learning models can only interpret numerical data and not data in a text form. As with many data preprocessing steps, there are multiple strategies one can apply to encode the categorical features. \n",
+ "\n",
+ "Here, we will use a simple **dummy encoding** for the categorical features, which will transform the categorical feature values into one-hot encoded vectors. Remeber that our goal is to predict the Biopsy outcome and hence, our target variable *\"Biopsy\"* does not need to be encoded, because it will be interepreted as different classes by the Random Forest Classifier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Age
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+ "
Number of sexual partners
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First sexual intercourse
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Num of pregnancies
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STDs (number)
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STDs: Number of diagnosis
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Biopsy
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+ "
0.0
\n",
+ "
0.0
\n",
+ "
0_Healthy
\n",
+ "
0
\n",
+ "
0
\n",
+ "
0
\n",
+ "
0
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
34.0
\n",
+ "
1.0
\n",
+ "
24.015132
\n",
+ "
1.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0_Healthy
\n",
+ "
0
\n",
+ "
0
\n",
+ "
0
\n",
+ "
0
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
52.0
\n",
+ "
5.0
\n",
+ "
16.000000
\n",
+ "
4.0
\n",
+ "
37.0
\n",
+ "
37.0
\n",
+ "
3.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0_Healthy
\n",
+ "
1
\n",
+ "
1
\n",
+ "
0
\n",
+ "
0
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
46.0
\n",
+ "
3.0
\n",
+ "
21.000000
\n",
+ "
4.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
15.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
0_Healthy
\n",
+ "
0
\n",
+ "
1
\n",
+ "
0
\n",
+ "
0
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Age Number of sexual partners First sexual intercourse \\\n",
+ "0 18.0 4.0 15.000000 \n",
+ "1 15.0 1.0 14.000000 \n",
+ "2 34.0 1.0 24.015132 \n",
+ "3 52.0 5.0 16.000000 \n",
+ "4 46.0 3.0 21.000000 \n",
+ "\n",
+ " Num of pregnancies Smokes (years) Smokes (packs/year) \\\n",
+ "0 1.0 0.0 0.0 \n",
+ "1 1.0 0.0 0.0 \n",
+ "2 1.0 0.0 0.0 \n",
+ "3 4.0 37.0 37.0 \n",
+ "4 4.0 0.0 0.0 \n",
+ "\n",
+ " Hormonal Contraceptives (years) IUD (years) STDs (number) \\\n",
+ "0 0.0 0.0 0.0 \n",
+ "1 0.0 0.0 0.0 \n",
+ "2 0.0 0.0 0.0 \n",
+ "3 3.0 0.0 0.0 \n",
+ "4 15.0 0.0 0.0 \n",
+ "\n",
+ " STDs: Number of diagnosis Biopsy Smokes_Yes \\\n",
+ "0 0.0 0_Healthy 0 \n",
+ "1 0.0 0_Healthy 0 \n",
+ "2 0.0 0_Healthy 0 \n",
+ "3 0.0 0_Healthy 1 \n",
+ "4 0.0 0_Healthy 0 \n",
+ "\n",
+ " Hormonal Contraceptives_Yes IUD_Yes STDs_Yes \n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 0 0 \n",
+ "3 1 0 0 \n",
+ "4 1 0 0 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# preprocess categorical features such that they can be used for the RF model\n",
+ "data_cervicalcancer_encoded = pd.get_dummies(data_cervicalcancer_imp, columns=['Smokes','Hormonal Contraceptives','IUD','STDs'], prefix=['Smokes','Hormonal Contraceptives','IUD','STDs'], drop_first=True)\n",
+ "data_cervicalcancer_encoded.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note, that the option ``drop_first=True`` leads to k-1 dummies out of k categorical levels by removing the first level. However, it still contains the same amount of information. \n",
+ "\n",
+ "Let's now save the dataset in a ``pickle`` file, such that we can load the preprocessed data into other notebooks later on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Save the data with joblib\n",
+ "data = data_cervicalcancer_encoded\n",
+ "\n",
+ "with open('data_cervicalcancer_preprocessed.pickle', 'wb') as handle:\n",
+ " pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "15eba238ee50506661b317f799612087b7a9e3c36911f2fe83f383722861f9ae"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/datasets_sklearn/Dataset-Penguins.ipynb b/data/datasets_sklearn/Dataset-Penguins.ipynb
index 11a2028..dd72c0e 100644
--- a/data/datasets_sklearn/Dataset-Penguins.ipynb
+++ b/data/datasets_sklearn/Dataset-Penguins.ipynb
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -78,7 +78,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -102,7 +102,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -134,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -154,7 +154,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -274,7 +274,7 @@
"4 3450.0 female 2007 "
]
},
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -298,7 +298,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -331,49 +331,20 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:3: FutureWarning: \n",
- "\n",
- "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
- "\n",
- " sns.countplot(data=penguins, x=\"species\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 0])\n",
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:3: UserWarning: The palette list has more values (8) than needed (3), which may not be intended.\n",
- " sns.countplot(data=penguins, x=\"species\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 0])\n",
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:4: FutureWarning: \n",
- "\n",
- "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
- "\n",
- " sns.countplot(data=penguins, x=\"island\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 1])\n",
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:4: UserWarning: The palette list has more values (8) than needed (3), which may not be intended.\n",
- " sns.countplot(data=penguins, x=\"island\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 1])\n",
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:9: FutureWarning: \n",
- "\n",
- "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
- "\n",
- " sns.countplot(data=penguins, x=\"sex\", palette=sns.color_palette(\"Set2\"), ax=axs[1, 2])\n",
- "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_66826/2133589459.py:9: UserWarning: The palette list has more values (8) than needed (2), which may not be intended.\n",
- " sns.countplot(data=penguins, x=\"sex\", palette=sns.color_palette(\"Set2\"), ax=axs[1, 2])\n"
+ "/var/folders/g2/lm8kzppj1r35mwcj4zmxsygw0000gn/T/ipykernel_32489/2046816302.py:12: UserWarning: The palette list has more values (3) than needed (2), which may not be intended.\n",
+ " sns.countplot(data=penguins, x=col, hue=col, palette=sns.color_palette(palette=\"Set2\", n_colors=3), ax=axs[fig_row, fig_col])\n"
]
},
{
"data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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",
"text/plain": [
""
]
@@ -383,40 +354,25 @@
}
],
"source": [
+ "categorical_features = [\"species\", \"island\", \"sex\"]\n",
+ "\n",
"fig, axs = plt.subplots(ncols=4, nrows=2, figsize=(15, 7.5))\n",
"fig.suptitle('Features', fontsize=16)\n",
- "sns.countplot(data=penguins, x=\"species\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 0])\n",
- "sns.countplot(data=penguins, x=\"island\", palette=sns.color_palette(\"Set2\"), ax=axs[0, 1])\n",
- "sns.histplot(data=penguins, x=\"bill_length_mm\", bins=20, ax=axs[0, 2])\n",
- "sns.histplot(data=penguins, x=\"bill_depth_mm\", bins=20, ax=axs[0, 3])\n",
- "sns.histplot(data=penguins, x=\"flipper_length_mm\", bins=20, ax=axs[1, 0])\n",
- "sns.histplot(data=penguins, x=\"body_mass_g\", bins=20, ax=axs[1, 1])\n",
- "sns.countplot(data=penguins, x=\"sex\", palette=sns.color_palette(\"Set2\"), ax=axs[1, 2])\n",
- "sns.histplot(data=penguins, x=\"year\", bins=20, ax=axs[1, 3])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "By examining the cells above, try to answer the following questions:"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
+ "fig_col = 0\n",
+ "fig_row = 0\n",
"\n",
- "#### Question 1: Are there missing values in the dataset? If yes, how do we deal with this?\n",
+ "for col in penguins.columns:\n",
+ " if col not in categorical_features:\n",
+ " sns.histplot(data=penguins, x=col, bins=30, ax=axs[fig_row, fig_col])\n",
+ " else:\n",
+ " sns.countplot(data=penguins, x=col, hue=col, palette=sns.color_palette(palette=\"Set2\", n_colors=3), ax=axs[fig_row, fig_col])\n",
"\n",
- "\n",
+ " fig_col += 1\n",
+ " if fig_col == 4:\n",
+ " fig_col = 0\n",
+ " fig_row += 1\n",
"\n",
- "#### Your Answer: \n",
- "\n",
- "Yes, we have missing values in the dataset. There are different options how one can deal with this problem and the chosen strategy depends heavily on the dataset and the context we are in. We can, for example, just omit the cases with missing values (if we do not loose to many samples) or use a missing value imputation method."
+ "plt.subplots_adjust(top=0.95) "
]
},
{
@@ -424,15 +380,10 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n",
- "\n",
- "#### Question 2: Do we need to preprocess some features before modelling?\n",
- "\n",
- "\n",
+ "By analysing the results of the EDA we can conclude:\n",
"\n",
- "#### Your Answer: \n",
- "\n",
- "Yes, we need to encode categorical features because sklearn models usually can't work with string values. These are the features 'island', 'sex', and the target variable 'species'. "
+ "- we have missing values in the dataset. There are different options how one can deal with this problem and the chosen strategy depends heavily on the dataset and the context we are in. We can, for example, just omit the cases with missing values (if we do not loose to many samples) or use a missing value imputation method\n",
+ "- we need to encode categorical features because sklearn models usually can't work with string values. These are the features 'island', 'sex', and the target variable 'species'. "
]
},
{
@@ -457,7 +408,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -501,7 +452,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -617,7 +568,7 @@
"5 2007 0 1 1 "
]
},
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -640,7 +591,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
diff --git a/data/datasets_sklearn/data_cervicalcancer_preprocessed.pickle b/data/datasets_sklearn/data_cervicalcancer_preprocessed.pickle
new file mode 100644
index 0000000..bef950e
Binary files /dev/null and b/data/datasets_sklearn/data_cervicalcancer_preprocessed.pickle differ
diff --git a/data/datasets_sklearn/utils.py b/data/datasets_sklearn/utils.py
new file mode 100644
index 0000000..71c3425
--- /dev/null
+++ b/data/datasets_sklearn/utils.py
@@ -0,0 +1,37 @@
+############################################################
+##### Imports
+############################################################
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+############################################################
+##### Utility Functions
+############################################################
+
+
+def plot_distributions(dataset, features_categorical, ncols, nrows):
+ fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(ncols * 4, nrows * 4))
+ fig.suptitle("Distribution Dataset", fontsize=16)
+ fig_col = 0
+ fig_row = 0
+
+ for col in dataset.columns:
+ if col not in features_categorical:
+ sns.histplot(data=dataset, x=col, bins=30, ax=axs[fig_row, fig_col])
+ else:
+ n_colors = dataset[col].nunique()
+ sns.countplot(
+ data=dataset,
+ x=col,
+ hue=col,
+ palette=sns.color_palette(palette="Set2", n_colors=n_colors),
+ ax=axs[fig_row, fig_col],
+ )
+
+ fig_col += 1
+ if fig_col == 5:
+ fig_col = 0
+ fig_row += 1
+
+ plt.subplots_adjust(top=0.95)
![](\"https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials/blob/main/docs/source/_figures/dataset_cervicalcancer.jpg?raw=true\")