Module_4_Machine_learning.ipynb

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   "source": [
    "# Module 4: Machine-learning on breast-cancer cell imaging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning objectives\n",
    "This notebook is designed to give users an introduction to:\n",
    "-  Exploratory data analysis of cell imaging features\n",
    "-  Data pre-processing and normalization\n",
    "-  Manifold-learning methods for clustering\n",
    "-  Machine-learning classifiers in Scikit-learn\n",
    " \n",
    "Users will after this notebook be able to:\n",
    "-  Analyse and interpret machine-learning datasets\n",
    "-  Perform dimensionality reduction for visualization and interpretation\n",
    "-  Clean and transform datasets for machine learning\n",
    "-  Build training and validation sets\n",
    "-  Train Sci-kit learn machine learning classifiers\n",
    "-  Perform hyperparameter optimization\n",
    "-  Validate classifiers using appropriate performance metrics"
   ]
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    "### What is a feature?\n",
    "\n",
    "In the context of machine-learning, a feature is some measurable property or observation which describes a sample. Usually one sample would correspond to a single row in a dataframe, and all features for that sample are described along the columns. Machine-learning algorithms rely on pattern recognition or identification of statistical relationships between the features that describe a sample, and the corresponding target label or target numerical value that matches with the sample.\n",
    "\n",
    "For example, the sample may be blood serum taken from malaria infected or non-infected mice, the features levels of specific proteins in the serum, and the target label whether that mouse has a malaria diagnosis or not. The machine-learning classifier would in this case try to identify which features and in which combination best predict malaria status, within the dataset.\n",
    "\n",
    "\n",
    "**Due to this, it is crucial that the features are as descriptive as possible of the sample target label or value.**\n",
    "\n",
    "When building machine-learning models, the quality of the input features in the dataset that go into the model, is in practice far more important than the machine-learning model itself. For this we argue three reasons:\n",
    "1. If there is poor or no relationship between the sample features and the target value, no machine-learning model will be able to predict an outcome. This is often summarized in computer-science and statistics as **\"Garbage in, garbage out\"** - https://en.wikipedia.org/wiki/Garbage_in,_garbage_out\n",
    "2. Swapping out choice of a machine-learning architecture in your pipeline is usually relatively easy and quick, while collection, pre-processing and annotation of training datasets is usually hard and time-consuming\n",
    "3. A good dataset will often be predicted well even by relatively simple machine-learning models. This particularly holds well for tabular data - data which can be represented as dataframes of rows and columns."
   ]
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   "source": [
    "## Case study:\n",
    "\n",
    "### Machine learning classification of imaging features from breast cancer fine-needle aspirates\n",
    "\n",
    "Breast cancer is a state of malignant growth in the breast tissue.\n",
    "If the condition is left untreated, it is likely that it will spread to other tissues, causing metastasis and cancer in other parts of the body. This eventually leads to death. \n",
    "\n",
    "According to the WHO World Cancer report 2020 [1], breast cancer is the most or second most type of cancer in the world, representing about 11.6 % of all new cancer diagnoses and had an estimated 2.1 million new cases in 2018. It is 100 times more common in women than in men. Luckily its 5-year age-adjusted net surival is relatively high at up to 90 % depending on geographical region, but this rate will be heavily dependent on how early the tumor can be detected and diagnosed.\n",
    "\n",
    "Therefore, fast diagnosis is essential in minimizing patient morbidity and mortality. \n",
    "Current methods for diagnosis involves imaging based systems, invasive surgical procedures and histologistical evaluation. \n",
    "\n",
    "On the basis of this, W. Nick Street et. al. [2] proposed in 1992  a minimally invasive procedure, involving fine needle aspirates (FNA) from patients and assessing features of the extracted cell nuclei.  The extracted features were here used for classification or beneign or malignant status of the tumor, with good success. \n",
    "\n",
    "1. Reference: WHO World Cancer report 2020, 978-92-832-0448-0, https://publications.iarc.fr/586\n",
    "2. Reference: Nick Street et al, 1992, Nuclear Feature Extraction For Breast Tumor Diagnosis. DOI: https://doi.org/10.1117/12.148698\n"
   ]
  },
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   "source": [
    "<center><b>Nuclei imaging and estimation of perimeters</b></center>\n",
    "<a href=\"img/nuclei.png\"><center> <img src=\"img/nuclei.png\" alt=\"Drawing\" style=\"width: 600px;\"/> <center/><a/>\n",
    "\n",
    "    Figure 1, adapted from Nick street et al, 1992"
   ]
  },
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    "The authors calculated 10 imaging features for each cell nuclei extracted from the fine needle aspirated, describing its shape and size. Over 569 samples or images were chosen for analysis.\n",
    "\n",
    "The chosen features were:\n",
    "1. Radius (mean of distances from nuclei center to points on the perimeter)\n",
    "2. Texture (standard deviation of pixel gray-scale values)\n",
    "3. Perimeter (distance between between snake points on the parimeter - circumference)\n",
    "4. Area (by counting pixels)\n",
    "5. Smoothness (local variation in radius lengths)\n",
    "6. Compactness ($\\frac{perimeter^2}{ area - 1.0}$)\n",
    "7. Concavity (severity of concave portions of the contour outline)\n",
    "8. Concave points (number of concave portions of the contour outline)\n",
    "9. Symmetry of nucleus\n",
    "10. Fractal dimension ($coastline\\_approximation - 1$)\n",
    "\n",
    "All features are reported with:\n",
    "1. mean of all nuclei in sample\n",
    "2. standard error\n",
    "3. \"worst\" sample (calculated mean of the three worst nuclei in an image)\n",
    "\n",
    "This gives a total of 30 features per sample that can be used for machine learning classification. \n",
    "\n",
    "For an illustration of how some of these features were calculated, the authors made these figures:"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<center><b>Nuclei smoothness</b></center>\n",
    "<a href=\"img/smooth.png\"><center> <img src=\"img/smooth.png\" alt=\"Drawing\" style=\"width: 400px;\"/> <center/><a/>\n",
    "    \n",
    "<center><b>Nuclei concavity</b></center>\n",
    "<a href=\"img/concavity.png\"><center> <img src=\"img/concavity.png\" alt=\"Drawing\" style=\"width: 340px;\"/> <center/><a/>\n",
    "    \n",
    "<center><b>Nuclei symetry</b></center>\n",
    "<a href=\"img/symetry.png\"><center> <img src=\"img/symetry.png\" alt=\"Drawing\" style=\"width: 400px;\"/> <center/><a/>\n",
    "    \n",
    "<center><b>Nuclei fractal dimension</b></center>\n",
    "<a href=\"img/fractal.png\"><center> <img src=\"img/fractal.png\" alt=\"Drawing\" style=\"width: 500px;\"/> <center/><a/>\n",
    "\n",
    "Figures from Nick street et al, 1992"
   ]
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   "source": [
    "Using a combination of these 30 variables for 569 patient fine-needle aspirate samples and a known diagnoses of the tumors, the authors were able to achieve a prediction classifier with 97.3 % accuracy (when assessed directly on the training set).\n",
    "\n",
    "In this module we will be comparing three different machine learning classifiers from the scikit-learn library, to gain an understanding of the strength and weaknesses of each. We will analyze the relative contribution of all of the features in the prediction model and try to build the best classifier possible. Performance will be evaluated on a held out validation set, and finally tested on a hidden test only accessible at the end of the module."
   ]
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   "source": [
    "### Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><b>https://scikit-learn.org/stable/</b></center>\n",
    "<a href=\"img/sklearn.png\"><center> <img src=\"img/sklearn.png\" alt=\"Drawing\" style=\"width: 600px;\"/> <center/><a/>\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn is a very popular machine-learning and data modelling library in Python, originally developed at a Google summer of code project in 2007. It is known to be used in the pipelines of many larger and well-known companies, including Spotify, OKcupid, and JP Morgan (https://scikit-learn.org/stable/testimonials/testimonials.html ).\n",
    "\n",
    "The Scikit-learn library is designed around common tasks necessary to build machine-learning models, including dataset preprocessing, modelling of data, and choice of many different machine-learning architectures for classification, regression, clustering and more. In particular it is built on top of, among other libraries, Numpy, SciPy, Pandas and Matplotlib, making their integration easy. It is possible to load a sample dataset, train a machine-learning model and estimate its performance in very few lines of code, as demonstrated in this quick tutorial from machinelearningmastery.com:\n",
    "\n",
    "https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/\n",
    "\n",
    "We encourage you to read more on Scikit-learn and the many functions it supports in the User Guide, after or during this module: https://scikit-learn.org/stable/user_guide.html\n",
    "\n",
    "In this module we will be focusing on the use of Sklearn \"fit-transformers\" for dataset normalization, dataset visualization through manifold learning, and use three different machine-learning models that we will compare the performances of on the same data. In particular, we want to highlight how the different decisions we make impact the final results, and which considerations are important when building a robust prediction model."
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Imports"
   ]
  },
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   "source": [
    "# Data science libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Plotting libraries\n",
    "from matplotlib import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sys.path.insert(0, \"scripts/\")\n",
    "import competition_model\n",
    "submit_competition_model = competition_model.competition_model_class()\n",
    "\n",
    "# This is a custom script which gives intended coloring to cells in this notebook\n",
    "from scripts.noteStyle import set_styles, background\n",
    "set_styles()"
   ]
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   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "#Show matplotlib graphs directly in jupyter notebook\n",
    "# Allow reloading of scripts\n",
    "# Automatically reload scripts if they change"
   ]
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   "source": [
    "# Part 1: Dataset overview and explorative data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we will be using was downloaded in an already pre-processed and machine-learning ready format from Kaggle at:\n",
    "https://www.kaggle.com/mercheovejero/breast-cancer-analysis-real-machine-learning\n",
    "\n",
    "This enables us to quickly get started on analysing the dataset and make our additional pre-processing and normalization steps relatively straight forward.\n",
    "\n",
    "The original dataset can also be downloaded from the University of California, Irvey machine learning datasets repository here: \n",
    "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29\n",
    "\n",
    "\n",
    "Throughout this section we will explore the dataset layout and individual features. \n",
    "Specifically we want to explore relationships in the dataset features and remove any unwanted or non-predictive features. We also want to calculate the sampling frequencies of both groups of the dataset. \n",
    "Lastly, we want to explore the dymanic ranges of feature levels in the dataset."
   ]
  },
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   "source": [
    "## Dataset loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now focus on the following tasks: loading the dataset into a data frame, inspect the type and values we have loaded, and clean it up to remove unuseful data or formatting errors. \n",
    "<b>Even though this step might seem trivial, it is a major source of errors for beginners!</b>\n",
    "\n",
    "The dataset as a csv file can be loaded using the Pandas <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html\"> .read_csv()</a> method."
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       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
       "..        ...       ...          ...           ...             ...        ...   \n",
       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
       "566    926954         M        16.60         28.08          108.30      858.1   \n",
       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
       "568     92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0    ...          17.33           184.60      2019.0           0.16220   \n",
       "1    ...          23.41           158.80      1956.0           0.12380   \n",
       "2    ...          25.53           152.50      1709.0           0.14440   \n",
       "3    ...          26.50            98.87       567.7           0.20980   \n",
       "4    ...          16.67           152.20      1575.0           0.13740   \n",
       "..   ...            ...              ...         ...               ...   \n",
       "564  ...          26.40           166.10      2027.0           0.14100   \n",
       "565  ...          38.25           155.00      1731.0           0.11660   \n",
       "566  ...          34.12           126.70      1124.0           0.11390   \n",
       "567  ...          39.42           184.60      1821.0           0.16500   \n",
       "568  ...          30.37            59.16       268.6           0.08996   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0              0.66560           0.7119                0.2654          0.4601   \n",
       "1              0.18660           0.2416                0.1860          0.2750   \n",
       "2              0.42450           0.4504                0.2430          0.3613   \n",
       "3              0.86630           0.6869                0.2575          0.6638   \n",
       "4              0.20500           0.4000                0.1625          0.2364   \n",
       "..                 ...              ...                   ...             ...   \n",
       "564            0.21130           0.4107                0.2216          0.2060   \n",
       "565            0.19220           0.3215                0.1628          0.2572   \n",
       "566            0.30940           0.3403                0.1418          0.2218   \n",
       "567            0.86810           0.9387                0.2650          0.4087   \n",
       "568            0.06444           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "0                    0.11890          NaN  \n",
       "1                    0.08902          NaN  \n",
       "2                    0.08758          NaN  \n",
       "3                    0.17300          NaN  \n",
       "4                    0.07678          NaN  \n",
       "..                       ...          ...  \n",
       "564                  0.07115          NaN  \n",
       "565                  0.06637          NaN  \n",
       "566                  0.07820          NaN  \n",
       "567                  0.12400          NaN  \n",
       "568                  0.07039          NaN  \n",
       "\n",
       "[569 rows x 33 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"data/breast-cancer-wisconsin-data.csv\")\n",
    "df_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the datatypes contained in each feature column using the DataFrame <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dtypes.html\"> .dtypes()</a> method.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "diagnosis                   object\n",
       "radius_mean                float64\n",
       "texture_mean               float64\n",
       "perimeter_mean             float64\n",
       "area_mean                  float64\n",
       "smoothness_mean            float64\n",
       "compactness_mean           float64\n",
       "concavity_mean             float64\n",
       "concave points_mean        float64\n",
       "symmetry_mean              float64\n",
       "fractal_dimension_mean     float64\n",
       "radius_se                  float64\n",
       "texture_se                 float64\n",
       "perimeter_se               float64\n",
       "area_se                    float64\n",
       "smoothness_se              float64\n",
       "compactness_se             float64\n",
       "concavity_se               float64\n",
       "concave points_se          float64\n",
       "symmetry_se                float64\n",
       "fractal_dimension_se       float64\n",
       "radius_worst               float64\n",
       "texture_worst              float64\n",
       "perimeter_worst            float64\n",
       "area_worst                 float64\n",
       "smoothness_worst           float64\n",
       "compactness_worst          float64\n",
       "concavity_worst            float64\n",
       "concave points_worst       float64\n",
       "symmetry_worst             float64\n",
       "fractal_dimension_worst    float64\n",
       "Unnamed: 32                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most features are of the type float64, which are numerical features. The id column values are of type int64, as the id's are integers. Diagnosis is of dtype object as they are  strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that each sample has been marked with an ID number, identifying the sample/patient it came from. The diagnosis field represents the Malignant (M) or Benign (B) status of the breast tumor cell nuclei. \n",
    "\n",
    "The ID field is not useful to us for data analysis or machine-learning predictions, and there is an additional \"Unnamed: 32\" column with no information. We will remove these columns. In addition, we will define the \"Diagnosis\" column as a categorical type. This means that each unique value in \"Diagnosis\" will be interpreted as separate categories, as opposed to numbers along a continous scale.\n",
    "\n",
    "Note: This is technically not necessary when the values are specified as strings (\"M\", \"B\"). However if the values were 0.0 and 1.0, and we did not specify that these were categorical labels, they would be interpreted on a continuous scale and e.g 1 would be considered one unit higher than 0, and 0.5 as being an intermediate value exactly in between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove/drop a column we are not interested in. This column will now no longer be a part of the dataframe.\n",
    "df_proc = df_raw.drop([\"id\", \"Unnamed: 32\"], axis = 1)\n",
    "\n",
    "# Set the diagnosis labels as categorical (not continous) labels\n",
    "df_proc[\"diagnosis\"] = df_proc[\"diagnosis\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  fractal_dimension_mean  ...  radius_worst  \\\n",
       "count     569.000000              569.000000  ...    569.000000   \n",
       "mean        0.181162                0.062798  ...     16.269190   \n",
       "std         0.027414                0.007060  ...      4.833242   \n",
       "min         0.106000                0.049960  ...      7.930000   \n",
       "25%         0.161900                0.057700  ...     13.010000   \n",
       "50%         0.179200                0.061540  ...     14.970000   \n",
       "75%         0.195700                0.066120  ...     18.790000   \n",
       "max         0.304000                0.097440  ...     36.040000   \n",
       "\n",
       "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       compactness_worst  concavity_worst  concave points_worst  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       symmetry_worst  fractal_dimension_worst  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dataset description generated by pandas .describe method, we observe that the features are on very different scales and dynamic ranges. For example, \"area_mean\" has a mean value of 654.889104 and a standard deviation of 351.914129, while the feature \"compactness_mean\" has a mean of 0.104341 and a standard deviation of 0.052813. These are vastly different scales.\n",
    "\n",
    "Without correcting for this bias, \"area_mean\" will likely contribute more to the final prediction than \"compactness_mean\" just because the magnitude of change in \"area_mean\" is bigger. We will come back to this problem later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types of each diagnosis\n",
    "We want to make sure that we have sampled enough patients from both group (B and M) to make good predictions... We therefore want to count the number of samples in both groups. For this we can use the Pandas Series [.value_counts()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.value_counts.html) method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that accessing a single column of the DataFrame df_proc does not return a dataframe. It returns a Pandas Series (of values)\n",
    "df_proc[\"diagnosis\"].value_counts()\n",
    "\n",
    "# If you wanted to confirm this, you could run:\n",
    "# type(df_proc[\"diagnosis\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To put these numbers into perspective, we can plot them using a barplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of benign and malignant samples in dataset')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df_proc[\"diagnosis\"]\n",
    "sns.countplot(data, label=\"Count\", order = ['B', 'M']) \n",
    "plt.title(\"Number of benign and malignant samples in dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class = 'yellow'>\n",
    "\n",
    "Accessing a single column of a DataFrame returns a Pandas Series with all the values and index label for that column. While a DataFrame can have multiple columns, a Series technically has no columns, it is only a series of values with an associated index. This is important to note as some methods (such as **.value_counts()** ) only work on Series, or on DataFrames or Numpy arrays. \n",
    "\n",
    "Return Pandas DataFrame (Can access any number of columns and/or rows at a time):\n",
    "- **df_raw[[\"diagnosis\"]]**\n",
    "- **df_raw[[\"diagnosis\", \"area_mean\"]]**\n",
    "\n",
    "Return Pandas Series (Can only access one column at a time): \n",
    "- **df_raw[\"diagnosis\"]**\n",
    "\n",
    "You can convert a DataFrame or a Pandas Series into a numpy array (only values, no named index) by using:\n",
    "- np.array(df_raw)\n",
    "- df_raw.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset preprocessing and normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine-learning relies on identifying statistical relationships in numerical data. This means that we must ensure all features in our dataset are in a numerical format, as well as normalize the data such that it is easier for the machine-learning models we will use to work with.\n",
    "\n",
    "Data normalization is a critical part of biological data science. The goal of normalization is to push numerical data onto a common scale, without distorting the measurement distributions. \n",
    "\n",
    "This is done partially due to the risk of features with a larger total value or magnitude of change, being likely to contribute more to the final prediction, regardless of whether these features are more predictive or not. This is particularly an issue when training more complex models like neural networks, which are very sensitive to large differences in the scaling of features. Additionally, many machine-learning models are able to converge/train faster when all features are on the same scale, including LogisticRegression, SupportVectorMachines and neural networks. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><b>sklearn.preprocessing.MinMaxScaler</b></center>\n",
    "<a href=\"img/min_max_scaling.png\"><center> <img src=\"img/min_max_scaling.png\" alt=\"Drawing\" style=\"width: 600px;\"/> <center/><a/>\n",
    "    \n",
    "Source: [https://medium.com/hacktive-devs/feature-engineering-in-machine-learning-part-2-ab7fe457a85a](https://medium.com/hacktive-devs/feature-engineering-in-machine-learning-part-2-ab7fe457a85a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most common and simple normalization methods used in machine learning is [**min-max scaling**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html), where all observations of a feature is scaled to a range interval, e.g. 0-1. Min-max scaling preserves the original distribution of our features, but puts them all on a common scale. This also means that the effect of outliers will be preserved, which can either be intended and beneficial, or severely shift the distribution in an unintended way. With this caveat in mind, Min-Max scaling is usually a robust choice and often the first choice of normalization method to try without having special considerations for the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rationale for our normalization strategy is as follow: \n",
    "\n",
    "We do not care about the absolute value of a feature or the absolute value of difference. This is because mesurements come in all sorts of unit, e.g. meters and millimeters. If not scaling to a common range, then a feature would count more to the prediction if it was reported in millimeters than in meters. \n",
    "\n",
    "Min-max scaling essentially squeeze the data onto a specified range. Thereby the internal distribution is preserved. \n",
    "\n",
    "One significant downside of min-max scaling is that it is sensitive to outliers. \n",
    "\n",
    "Another approach for data scaling that is more robust to outliers is Sklearn´s [**StandardScaler**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html), which uses Z-score normalization. Here the data is scaled to mean = 0 and standardized to a standard deviation = 1. \n",
    "\n",
    "An example of an implementation of a z-score normalization from sklearn can be found here:\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "\n",
    "Read more on data normalization for machine learning:\n",
    "https://medium.com/@urvashilluniya/why-data-normalization-is-necessary-for-machine-learning-models-681b65a05029\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nice trick we can use to find all the columns with numerical data, is to run the Pandas <a href='https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html'> .select_dtypes() <a/> method. \n",
    "    \n",
    "This method will return only the data (and columns) with data of the specified types. As we found out earlier, all of the features we want to explore are of dtype = float64, so we could specify that. A more general solution would be to specify np.number, which would allow us to select columns with any type of numerical encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also show the inverse - all data which is NOT numerical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>565</th>\n",
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       "      <th>567</th>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis\n",
       "0           M\n",
       "1           M\n",
       "2           M\n",
       "3           M\n",
       "4           M\n",
       "..        ...\n",
       "564         M\n",
       "565         M\n",
       "566         M\n",
       "567         M\n",
       "568         B\n",
       "\n",
       "[569 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc.select_dtypes(exclude=[np.number])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use this functionality to scale only numeric features. For data scaling we use the sklearn method MinMaxScaler. \n",
    "\n",
    "Lastly, we map the diagnosis to numeric labels instead of strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
       "       'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let´s load the data again ot make sure we are starting fresh\n",
    "df_proc = df_raw.copy()\n",
    "\n",
    "# And lets drop the columns we are not interested in\n",
    "df_proc = df_proc.drop([\"id\", \"Unnamed: 32\"], axis = 1)\n",
    " \n",
    "# Select the numerical columns ...\n",
    "numerical_columns = df_proc.select_dtypes(include=[np.number]).columns\n",
    "numerical_columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn normalization methods - known as transformers - are usually initialized with a .fit method. The initialized transformer then can transform any data with the same features (same number of columns) using the .transform method.\n",
    "\n",
    "Since we want to both fit and transform the same data, we will do both steps at once using the .fit_transform method. The MinMaxScaler is first fitted to our data. It then transforms the features using the minimum and maximum values per feature it read during initialization. The fit_transform function performs both steps at once, but it is also possible to run them in this order separately. For example if you already fitted the MinMaxScaler on the dataset, but acquired more data you wanted ot use as an external test set, you could use the already fitted MinMaxScaler to normalize your new data in the same way as your current dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52103744, 0.0226581 , 0.54598853, ..., 0.91202749, 0.59846245,\n",
       "        0.41886396],\n",
       "       [0.64314449, 0.27257355, 0.61578329, ..., 0.63917526, 0.23358959,\n",
       "        0.22287813],\n",
       "       [0.60149557, 0.3902604 , 0.59574321, ..., 0.83505155, 0.40370589,\n",
       "        0.21343303],\n",
       "       ...,\n",
       "       [0.45525108, 0.62123774, 0.44578813, ..., 0.48728522, 0.12872068,\n",
       "        0.1519087 ],\n",
       "       [0.64456434, 0.66351031, 0.66553797, ..., 0.91065292, 0.49714173,\n",
       "        0.45231536],\n",
       "       [0.03686876, 0.50152181, 0.02853984, ..., 0.        , 0.25744136,\n",
       "        0.10068215]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets scale the features using Scikit-Learn´s preprocessing.MinMaxScaler\n",
    "from sklearn import preprocessing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "X = df_proc[numerical_columns].values\n",
    "X_scaled = min_max_scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_scaled contains our normalized values, encoded as a numpy array. You can see this from the \"array\" part when printing the variable, but you can also check this by running type(X_scaled).\n",
    "\n",
    "We will now replace the un-normalized values in our dataframe with our normalized values. In order to keep track of which values we are using, we will name this new dataframe something else:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.521037</td>\n",
       "      <td>0.022658</td>\n",
       "      <td>0.545989</td>\n",
       "      <td>0.363733</td>\n",
       "      <td>0.593753</td>\n",
       "      <td>0.792037</td>\n",
       "      <td>0.703140</td>\n",
       "      <td>0.731113</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620776</td>\n",
       "      <td>0.141525</td>\n",
       "      <td>0.668310</td>\n",
       "      <td>0.450698</td>\n",
       "      <td>0.601136</td>\n",
       "      <td>0.619292</td>\n",
       "      <td>0.568610</td>\n",
       "      <td>0.912027</td>\n",
       "      <td>0.598462</td>\n",
       "      <td>0.418864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.643144</td>\n",
       "      <td>0.272574</td>\n",
       "      <td>0.615783</td>\n",
       "      <td>0.501591</td>\n",
       "      <td>0.289880</td>\n",
       "      <td>0.181768</td>\n",
       "      <td>0.203608</td>\n",
       "      <td>0.348757</td>\n",
       "      <td>0.379798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606901</td>\n",
       "      <td>0.303571</td>\n",
       "      <td>0.539818</td>\n",
       "      <td>0.435214</td>\n",
       "      <td>0.347553</td>\n",
       "      <td>0.154563</td>\n",
       "      <td>0.192971</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.233590</td>\n",
       "      <td>0.222878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>0.601496</td>\n",
       "      <td>0.390260</td>\n",
       "      <td>0.595743</td>\n",
       "      <td>0.449417</td>\n",
       "      <td>0.514309</td>\n",
       "      <td>0.431017</td>\n",
       "      <td>0.462512</td>\n",
       "      <td>0.635686</td>\n",
       "      <td>0.509596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556386</td>\n",
       "      <td>0.360075</td>\n",
       "      <td>0.508442</td>\n",
       "      <td>0.374508</td>\n",
       "      <td>0.483590</td>\n",
       "      <td>0.385375</td>\n",
       "      <td>0.359744</td>\n",
       "      <td>0.835052</td>\n",
       "      <td>0.403706</td>\n",
       "      <td>0.213433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.210090</td>\n",
       "      <td>0.360839</td>\n",
       "      <td>0.233501</td>\n",
       "      <td>0.102906</td>\n",
       "      <td>0.811321</td>\n",
       "      <td>0.811361</td>\n",
       "      <td>0.565604</td>\n",
       "      <td>0.522863</td>\n",
       "      <td>0.776263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248310</td>\n",
       "      <td>0.385928</td>\n",
       "      <td>0.241347</td>\n",
       "      <td>0.094008</td>\n",
       "      <td>0.915472</td>\n",
       "      <td>0.814012</td>\n",
       "      <td>0.548642</td>\n",
       "      <td>0.884880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>0.629893</td>\n",
       "      <td>0.156578</td>\n",
       "      <td>0.630986</td>\n",
       "      <td>0.489290</td>\n",
       "      <td>0.430351</td>\n",
       "      <td>0.347893</td>\n",
       "      <td>0.463918</td>\n",
       "      <td>0.518390</td>\n",
       "      <td>0.378283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519744</td>\n",
       "      <td>0.123934</td>\n",
       "      <td>0.506948</td>\n",
       "      <td>0.341575</td>\n",
       "      <td>0.437364</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.319489</td>\n",
       "      <td>0.558419</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>0.142595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.428813</td>\n",
       "      <td>0.678668</td>\n",
       "      <td>0.566490</td>\n",
       "      <td>0.526948</td>\n",
       "      <td>0.296055</td>\n",
       "      <td>0.571462</td>\n",
       "      <td>0.690358</td>\n",
       "      <td>0.336364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623266</td>\n",
       "      <td>0.383262</td>\n",
       "      <td>0.576174</td>\n",
       "      <td>0.452664</td>\n",
       "      <td>0.461137</td>\n",
       "      <td>0.178527</td>\n",
       "      <td>0.328035</td>\n",
       "      <td>0.761512</td>\n",
       "      <td>0.097575</td>\n",
       "      <td>0.105667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>M</td>\n",
       "      <td>0.622320</td>\n",
       "      <td>0.626987</td>\n",
       "      <td>0.604036</td>\n",
       "      <td>0.474019</td>\n",
       "      <td>0.407782</td>\n",
       "      <td>0.257714</td>\n",
       "      <td>0.337395</td>\n",
       "      <td>0.486630</td>\n",
       "      <td>0.349495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560655</td>\n",
       "      <td>0.699094</td>\n",
       "      <td>0.520892</td>\n",
       "      <td>0.379915</td>\n",
       "      <td>0.300007</td>\n",
       "      <td>0.159997</td>\n",
       "      <td>0.256789</td>\n",
       "      <td>0.559450</td>\n",
       "      <td>0.198502</td>\n",
       "      <td>0.074315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455251</td>\n",
       "      <td>0.621238</td>\n",
       "      <td>0.445788</td>\n",
       "      <td>0.303118</td>\n",
       "      <td>0.288165</td>\n",
       "      <td>0.254340</td>\n",
       "      <td>0.216753</td>\n",
       "      <td>0.263519</td>\n",
       "      <td>0.267677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.393099</td>\n",
       "      <td>0.589019</td>\n",
       "      <td>0.379949</td>\n",
       "      <td>0.230731</td>\n",
       "      <td>0.282177</td>\n",
       "      <td>0.273705</td>\n",
       "      <td>0.271805</td>\n",
       "      <td>0.487285</td>\n",
       "      <td>0.128721</td>\n",
       "      <td>0.151909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>M</td>\n",
       "      <td>0.644564</td>\n",
       "      <td>0.663510</td>\n",
       "      <td>0.665538</td>\n",
       "      <td>0.475716</td>\n",
       "      <td>0.588336</td>\n",
       "      <td>0.790197</td>\n",
       "      <td>0.823336</td>\n",
       "      <td>0.755467</td>\n",
       "      <td>0.675253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633582</td>\n",
       "      <td>0.730277</td>\n",
       "      <td>0.668310</td>\n",
       "      <td>0.402035</td>\n",
       "      <td>0.619626</td>\n",
       "      <td>0.815758</td>\n",
       "      <td>0.749760</td>\n",
       "      <td>0.910653</td>\n",
       "      <td>0.497142</td>\n",
       "      <td>0.452315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "      <td>0.036869</td>\n",
       "      <td>0.501522</td>\n",
       "      <td>0.028540</td>\n",
       "      <td>0.015907</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074351</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.266162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054287</td>\n",
       "      <td>0.489072</td>\n",
       "      <td>0.043578</td>\n",
       "      <td>0.020497</td>\n",
       "      <td>0.124084</td>\n",
       "      <td>0.036043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.257441</td>\n",
       "      <td>0.100682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0           M     0.521037      0.022658        0.545989   0.363733   \n",
       "1           M     0.643144      0.272574        0.615783   0.501591   \n",
       "2           M     0.601496      0.390260        0.595743   0.449417   \n",
       "3           M     0.210090      0.360839        0.233501   0.102906   \n",
       "4           M     0.629893      0.156578        0.630986   0.489290   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564         M     0.690000      0.428813        0.678668   0.566490   \n",
       "565         M     0.622320      0.626987        0.604036   0.474019   \n",
       "566         M     0.455251      0.621238        0.445788   0.303118   \n",
       "567         M     0.644564      0.663510        0.665538   0.475716   \n",
       "568         B     0.036869      0.501522        0.028540   0.015907   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0           0.593753          0.792037        0.703140             0.731113   \n",
       "1           0.289880          0.181768        0.203608             0.348757   \n",
       "2           0.514309          0.431017        0.462512             0.635686   \n",
       "3           0.811321          0.811361        0.565604             0.522863   \n",
       "4           0.430351          0.347893        0.463918             0.518390   \n",
       "..               ...               ...             ...                  ...   \n",
       "564         0.526948          0.296055        0.571462             0.690358   \n",
       "565         0.407782          0.257714        0.337395             0.486630   \n",
       "566         0.288165          0.254340        0.216753             0.263519   \n",
       "567         0.588336          0.790197        0.823336             0.755467   \n",
       "568         0.000000          0.074351        0.000000             0.000000   \n",
       "\n",
       "     symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.686364  ...      0.620776       0.141525         0.668310   \n",
       "1         0.379798  ...      0.606901       0.303571         0.539818   \n",
       "2         0.509596  ...      0.556386       0.360075         0.508442   \n",
       "3         0.776263  ...      0.248310       0.385928         0.241347   \n",
       "4         0.378283  ...      0.519744       0.123934         0.506948   \n",
       "..             ...  ...           ...            ...              ...   \n",
       "564       0.336364  ...      0.623266       0.383262         0.576174   \n",
       "565       0.349495  ...      0.560655       0.699094         0.520892   \n",
       "566       0.267677  ...      0.393099       0.589019         0.379949   \n",
       "567       0.675253  ...      0.633582       0.730277         0.668310   \n",
       "568       0.266162  ...      0.054287       0.489072         0.043578   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      0.450698          0.601136           0.619292         0.568610   \n",
       "1      0.435214          0.347553           0.154563         0.192971   \n",
       "2      0.374508          0.483590           0.385375         0.359744   \n",
       "3      0.094008          0.915472           0.814012         0.548642   \n",
       "4      0.341575          0.437364           0.172415         0.319489   \n",
       "..          ...               ...                ...              ...   \n",
       "564    0.452664          0.461137           0.178527         0.328035   \n",
       "565    0.379915          0.300007           0.159997         0.256789   \n",
       "566    0.230731          0.282177           0.273705         0.271805   \n",
       "567    0.402035          0.619626           0.815758         0.749760   \n",
       "568    0.020497          0.124084           0.036043         0.000000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.912027        0.598462                 0.418864  \n",
       "1                0.639175        0.233590                 0.222878  \n",
       "2                0.835052        0.403706                 0.213433  \n",
       "3                0.884880        1.000000                 0.773711  \n",
       "4                0.558419        0.157500                 0.142595  \n",
       "..                    ...             ...                      ...  \n",
       "564              0.761512        0.097575                 0.105667  \n",
       "565              0.559450        0.198502                 0.074315  \n",
       "566              0.487285        0.128721                 0.151909  \n",
       "567              0.910653        0.497142                 0.452315  \n",
       "568              0.000000        0.257441                 0.100682  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc_scaled = df_proc.copy()\n",
    "df_proc_scaled[numerical_columns] = X_scaled\n",
    "df_proc_scaled\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one more thing we need to do. Notice how we have a single, non-numerical column? The diagnosis label is assigned as strings. If we did not recognize this manually, we could also identify this by checking which of the columns are NOT the numerical columns we found. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc_scaled.columns.isin(numerical_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we will use the ~ operator to invert the result above, to give us the NON-numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc_scaled.columns[~df_proc_scaled.columns.isin(numerical_columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let´s convert these into numerical values. We can do this using the pd.DataFrame.map method. We will map \"B\" for benign to 0, and \"M\" for malignant to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set diagnosis to 0 and 1 labels (numeric)\n",
    "df_proc[\"diagnosis\"] = df_raw[\"diagnosis\"].map({\"M\":1, \"B\":0})\n",
    "df_proc_scaled[\"diagnosis\"] = df_raw[\"diagnosis\"].map({\"M\":1, \"B\":0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let´s visualize our data before and after normalization to see how the process impacted our data for one feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Before MinMax scaling')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_raw[\"radius_mean\"]\n",
    "sns.distplot(x, color = \"blue\")\n",
    "plt.title(\"Before MinMax scaling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After MinMax scaling')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df_proc_scaled[\"radius_mean\"]\n",
    "sns.distplot(y, color = \"orange\")\n",
    "plt.title(\"After MinMax scaling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After vs before MinMax scaling')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x, color = \"blue\")\n",
    "sns.distplot(y, color = \"orange\")\n",
    "plt.title(\"After vs before MinMax scaling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect! Now all values in our dataframe are numerical, and all continous values (i.e. the numerical data which were not diagnosis labels) are normalized. We may now proceed with our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explorative data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will explore feature distributions and correlations. This provides an idea of which features are likely to be good tumor status predictors, which features are co-correlated, and discover any issues the features may have in our dataset. \n",
    "\n",
    "Before we start looking into any specific distributions, we will use dimensionality reductions and manifold learning, to highlight how well the features jointly separate the benign and malignant samples. \n",
    "\n",
    "We will compare 2 different manifold learning methods for dimensionality reduction on our dataset.\n",
    "These can be read in more detail on here: https://scikit-learn.org/stable/modules/manifold.html#manifold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Principal component analysis (PCA):__\n",
    "PCA is a linear dimensionality reduction which aims to convert observations with correlated variables into linearly uncorrelated variables (principal components). The first principal component is a linear combination of the original variable that describes the largest source of variation. The second component is another linear combination of the input variables, independent from (orthogonal to) the first, that describes most of the residual variation in the data - it is therefore the second highest variation component. In a similar way, each of the following principal components describe less and less of the variability, until the last component. Intuitively, only working with the largest sources of variation reduces the redundancy and noise in the data. It also becomes a lot easier to compute on.\n",
    "\n",
    "In simple terms, PCA reduces the dimensionality of the data into linearly uncorrelated components. Components are ordered by increasing importance (how much of the data variation they explain). In some dataset, the first 2-4 components may describe a large fraction of the variance (e.g. 95%), and possibly be enough by themselves to separate groups of samples.\n",
    "It is important to underline that the principal components are obtained as linear combination of the original variables - therefore, if non-linear dependency are present among variables, those will not be fully identified by PCA.\n",
    "**Read more:** Intuition for Principal Component Analysis (PCA), https://blog.ephorie.de/intuition-for-principal-component-analysis-pca\n",
    "\n",
    "\n",
    "__T-distributed Stochastic Neighbor Embedding (t-SNE):__\n",
    "t-SNE is an unsuperviced non-linear dimensionality reduction used to embed high-dimensional data into 2 or 3 dimensions, in such a way that similar objects are put close together in the embedded space. This gives a good intuition for what objects belong to the same class. Therefore t-SNE is often used for visualization of high-dimensional data. \n",
    "This is achieved by modeling the data in low dimensional space t-SNE preserves small parwise distances. This yields a map where local structures are accurately captured, whilst the global structure may hold little to no meaning. \n",
    "**Read more:** https://towardsdatascience.com/an-introduction-to-t-sne-with-python-example-5a3a293108d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_X, data_y = df_proc_scaled.drop(\"diagnosis\", axis = 1), df_proc_scaled[\"diagnosis\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Component1</th>\n",
       "      <th>Component2</th>\n",
       "      <th>Component3</th>\n",
       "      <th>Component4</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.387021</td>\n",
       "      <td>0.426895</td>\n",
       "      <td>-0.541710</td>\n",
       "      <td>0.048481</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.462308</td>\n",
       "      <td>-0.556947</td>\n",
       "      <td>-0.205176</td>\n",
       "      <td>-0.042830</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.954621</td>\n",
       "      <td>-0.109701</td>\n",
       "      <td>-0.147849</td>\n",
       "      <td>-0.001067</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000816</td>\n",
       "      <td>1.525089</td>\n",
       "      <td>-0.053269</td>\n",
       "      <td>-0.207915</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.626828</td>\n",
       "      <td>-0.302471</td>\n",
       "      <td>-0.409339</td>\n",
       "      <td>0.238810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1.002840</td>\n",
       "      <td>-0.474785</td>\n",
       "      <td>-0.100042</td>\n",
       "      <td>0.272995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>0.620757</td>\n",
       "      <td>-0.517200</td>\n",
       "      <td>0.400363</td>\n",
       "      <td>0.182444</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>0.226311</td>\n",
       "      <td>-0.287946</td>\n",
       "      <td>0.315226</td>\n",
       "      <td>-0.011746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1.677834</td>\n",
       "      <td>0.335946</td>\n",
       "      <td>0.296114</td>\n",
       "      <td>-0.156305</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>-0.905068</td>\n",
       "      <td>-0.104109</td>\n",
       "      <td>0.382849</td>\n",
       "      <td>0.068357</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Component1  Component2  Component3  Component4  diagnosis\n",
       "0      1.387021    0.426895   -0.541710    0.048481          1\n",
       "1      0.462308   -0.556947   -0.205176   -0.042830          1\n",
       "2      0.954621   -0.109701   -0.147849   -0.001067          1\n",
       "3      1.000816    1.525089   -0.053269   -0.207915          1\n",
       "4      0.626828   -0.302471   -0.409339    0.238810          1\n",
       "..          ...         ...         ...         ...        ...\n",
       "564    1.002840   -0.474785   -0.100042    0.272995          1\n",
       "565    0.620757   -0.517200    0.400363    0.182444          1\n",
       "566    0.226311   -0.287946    0.315226   -0.011746          1\n",
       "567    1.677834    0.335946    0.296114   -0.156305          1\n",
       "568   -0.905068   -0.104109    0.382849    0.068357          0\n",
       "\n",
       "[569 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caclulate the embeddings\n",
    "from sklearn.decomposition import PCA\n",
    "model = PCA(n_components=4)\n",
    "embeddings = model.fit_transform(data_X)\n",
    "\n",
    "# Create the plotting dataframe\n",
    "plot_df = pd.DataFrame(embeddings, columns = [\"Component1\",\"Component2\", \"Component3\",\"Component4\"])\n",
    "plot_df[\"diagnosis\"] = data_y\n",
    "\n",
    "# Plot\n",
    "sns.scatterplot(x = \"Component1\", y = \"Component2\", data = plot_df,\n",
    "               hue = \"diagnosis\")\n",
    "plt.title(\"PCA embeddings\")\n",
    "\n",
    "# Show values\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.38702122e+00,  4.26895220e-01, -5.41709904e-01,\n",
       "         4.84807796e-02],\n",
       "       [ 4.62308252e-01, -5.56946760e-01, -2.05176151e-01,\n",
       "        -4.28303772e-02],\n",
       "       [ 9.54621472e-01, -1.09701157e-01, -1.47848938e-01,\n",
       "        -1.06738459e-03],\n",
       "       ...,\n",
       "       [ 2.26311313e-01, -2.87945738e-01,  3.15225932e-01,\n",
       "        -1.17461781e-02],\n",
       "       [ 1.67783369e+00,  3.35945927e-01,  2.96114061e-01,\n",
       "        -1.56305452e-01],\n",
       "       [-9.05068038e-01, -1.04108903e-01,  3.82849036e-01,\n",
       "         6.83572256e-02]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Component1</th>\n",
       "      <th>Component2</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.207458</td>\n",
       "      <td>17.896420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.806538</td>\n",
       "      <td>9.387826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.865993</td>\n",
       "      <td>13.831332</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.442743</td>\n",
       "      <td>1.541194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.587406</td>\n",
       "      <td>16.012486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>32.472515</td>\n",
       "      <td>10.770337</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>24.801754</td>\n",
       "      <td>12.309140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>13.351972</td>\n",
       "      <td>4.536528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>35.852921</td>\n",
       "      <td>16.007612</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>-25.709124</td>\n",
       "      <td>-11.532327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Component1  Component2  diagnosis\n",
       "0     34.207458   17.896420          1\n",
       "1     19.806538    9.387826          1\n",
       "2     30.865993   13.831332          1\n",
       "3     22.442743    1.541194          1\n",
       "4     21.587406   16.012486          1\n",
       "..          ...         ...        ...\n",
       "564   32.472515   10.770337          1\n",
       "565   24.801754   12.309140          1\n",
       "566   13.351972    4.536528          1\n",
       "567   35.852921   16.007612          1\n",
       "568  -25.709124  -11.532327          0\n",
       "\n",
       "[569 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caclulate the embeddings\n",
    "from sklearn import manifold\n",
    "model = manifold.TSNE(n_components=2)\n",
    "embeddings = model.fit_transform(data_X)\n",
    "\n",
    "# Create the plotting dataframe\n",
    "plot_df = pd.DataFrame(embeddings, columns = [\"Component1\", \"Component2\"])\n",
    "plot_df[\"diagnosis\"] = data_y\n",
    "\n",
    "# Plot\n",
    "sns.scatterplot(x = \"Component1\", y = \"Component2\", data = plot_df,\n",
    "               hue = \"diagnosis\")\n",
    "plt.title(\"T-SNE embeddings\")\n",
    "\n",
    "# Show values\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task: Try to run the dimensionality reduction methods again. Do the results change? Can you figure out why?\n",
    "\n",
    "You can find more dimensionality reduction / manifold learning methods to try here: **https://scikit-learn.org/stable/modules/manifold.html#manifold**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With both dimensionality reduction methods we see a clear separation by the cancer status. From this, we can infer that future machine learning models will also be able to capture spatial differences between nuclei from beneign and malignant samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a quick sense of what features may be predictive of the diagnosis, we make a pearson correlation using the pandas <a href='https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html' >.corr() <a/> method.\n",
    "    \n",
    "We here infer that that features that correlate well with the group label, will be good predictors. \n",
    "\n",
    "We do though acknowledge that also features with poor correlation to the group label, may add value and therefore better the accuracy of the final model.\n",
    "\n",
    "The Pandas DataFrame [.corr](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html) method is a powerful method to quickly compute pairwise Pearson (default), Spearman or Kendall tau correlations between all features (columns) in a dataframe. \n",
    "    \n",
    "Pearson correlation calculates the linear relationship between two numerical vectors (in our case - dataset features). Spearman correlation on the other hand calculates the relationship between the ranks of the data, thus capturing non-linear relationships better. \n",
    "    \n",
    "Read more about and see examples of Spearman and Pearson correlations:\n",
    "https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/supporting-topics/basics/a-comparison-of-the-pearson-and-spearman-correlation-methods/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diagnosis</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.732785</td>\n",
       "      <td>0.461971</td>\n",
       "      <td>0.748496</td>\n",
       "      <td>0.734122</td>\n",
       "      <td>0.371892</td>\n",
       "      <td>0.609288</td>\n",
       "      <td>0.733308</td>\n",
       "      <td>0.777877</td>\n",
       "      <td>0.332567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787933</td>\n",
       "      <td>0.476720</td>\n",
       "      <td>0.796319</td>\n",
       "      <td>0.786902</td>\n",
       "      <td>0.425513</td>\n",
       "      <td>0.606810</td>\n",
       "      <td>0.705734</td>\n",
       "      <td>0.781674</td>\n",
       "      <td>0.396843</td>\n",
       "      <td>0.311477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>0.732785</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340956</td>\n",
       "      <td>0.997802</td>\n",
       "      <td>0.999602</td>\n",
       "      <td>0.148510</td>\n",
       "      <td>0.497578</td>\n",
       "      <td>0.645728</td>\n",
       "      <td>0.759702</td>\n",
       "      <td>0.120242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978604</td>\n",
       "      <td>0.314911</td>\n",
       "      <td>0.971555</td>\n",
       "      <td>0.978863</td>\n",
       "      <td>0.125789</td>\n",
       "      <td>0.491357</td>\n",
       "      <td>0.596043</td>\n",
       "      <td>0.727265</td>\n",
       "      <td>0.174698</td>\n",
       "      <td>0.044564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>0.461971</td>\n",
       "      <td>0.340956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.348142</td>\n",
       "      <td>0.344145</td>\n",
       "      <td>0.024649</td>\n",
       "      <td>0.266499</td>\n",
       "      <td>0.342646</td>\n",
       "      <td>0.306891</td>\n",
       "      <td>0.110130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.366547</td>\n",
       "      <td>0.909218</td>\n",
       "      <td>0.375273</td>\n",
       "      <td>0.368335</td>\n",
       "      <td>0.101401</td>\n",
       "      <td>0.290917</td>\n",
       "      <td>0.339725</td>\n",
       "      <td>0.319235</td>\n",
       "      <td>0.120693</td>\n",
       "      <td>0.116144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>0.748496</td>\n",
       "      <td>0.997802</td>\n",
       "      <td>0.348142</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997068</td>\n",
       "      <td>0.182923</td>\n",
       "      <td>0.543925</td>\n",
       "      <td>0.681958</td>\n",
       "      <td>0.788629</td>\n",
       "      <td>0.150049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.981244</td>\n",
       "      <td>0.323109</td>\n",
       "      <td>0.978980</td>\n",
       "      <td>0.980864</td>\n",
       "      <td>0.156611</td>\n",
       "      <td>0.534565</td>\n",
       "      <td>0.632106</td>\n",
       "      <td>0.757526</td>\n",
       "      <td>0.199007</td>\n",
       "      <td>0.088961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>0.734122</td>\n",
       "      <td>0.999602</td>\n",
       "      <td>0.344145</td>\n",
       "      <td>0.997068</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138053</td>\n",
       "      <td>0.488988</td>\n",
       "      <td>0.642557</td>\n",
       "      <td>0.755165</td>\n",
       "      <td>0.113928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979258</td>\n",
       "      <td>0.318178</td>\n",
       "      <td>0.971822</td>\n",
       "      <td>0.980264</td>\n",
       "      <td>0.119712</td>\n",
       "      <td>0.485813</td>\n",
       "      <td>0.593736</td>\n",
       "      <td>0.723390</td>\n",
       "      <td>0.170860</td>\n",
       "      <td>0.038758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>0.371892</td>\n",
       "      <td>0.148510</td>\n",
       "      <td>0.024649</td>\n",
       "      <td>0.182923</td>\n",
       "      <td>0.138053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.678806</td>\n",
       "      <td>0.518511</td>\n",
       "      <td>0.565172</td>\n",
       "      <td>0.542228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203453</td>\n",
       "      <td>0.060645</td>\n",
       "      <td>0.226345</td>\n",
       "      <td>0.191735</td>\n",
       "      <td>0.796085</td>\n",
       "      <td>0.481384</td>\n",
       "      <td>0.429107</td>\n",
       "      <td>0.498868</td>\n",
       "      <td>0.393579</td>\n",
       "      <td>0.511457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>0.609288</td>\n",
       "      <td>0.497578</td>\n",
       "      <td>0.266499</td>\n",
       "      <td>0.543925</td>\n",
       "      <td>0.488988</td>\n",
       "      <td>0.678806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896518</td>\n",
       "      <td>0.848295</td>\n",
       "      <td>0.552203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.542626</td>\n",
       "      <td>0.255305</td>\n",
       "      <td>0.592254</td>\n",
       "      <td>0.531590</td>\n",
       "      <td>0.578902</td>\n",
       "      <td>0.901029</td>\n",
       "      <td>0.837921</td>\n",
       "      <td>0.825473</td>\n",
       "      <td>0.450333</td>\n",
       "      <td>0.688986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>0.733308</td>\n",
       "      <td>0.645728</td>\n",
       "      <td>0.342646</td>\n",
       "      <td>0.681958</td>\n",
       "      <td>0.642557</td>\n",
       "      <td>0.518511</td>\n",
       "      <td>0.896518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927352</td>\n",
       "      <td>0.446793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682316</td>\n",
       "      <td>0.335866</td>\n",
       "      <td>0.722424</td>\n",
       "      <td>0.676628</td>\n",
       "      <td>0.488775</td>\n",
       "      <td>0.849985</td>\n",
       "      <td>0.938543</td>\n",
       "      <td>0.904938</td>\n",
       "      <td>0.383667</td>\n",
       "      <td>0.541838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>0.777877</td>\n",
       "      <td>0.759702</td>\n",
       "      <td>0.306891</td>\n",
       "      <td>0.788629</td>\n",
       "      <td>0.755165</td>\n",
       "      <td>0.565172</td>\n",
       "      <td>0.848295</td>\n",
       "      <td>0.927352</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787411</td>\n",
       "      <td>0.300562</td>\n",
       "      <td>0.813960</td>\n",
       "      <td>0.780395</td>\n",
       "      <td>0.490035</td>\n",
       "      <td>0.758309</td>\n",
       "      <td>0.827281</td>\n",
       "      <td>0.937075</td>\n",
       "      <td>0.355477</td>\n",
       "      <td>0.421110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>0.332567</td>\n",
       "      <td>0.120242</td>\n",
       "      <td>0.110130</td>\n",
       "      <td>0.150049</td>\n",
       "      <td>0.113928</td>\n",
       "      <td>0.542228</td>\n",
       "      <td>0.552203</td>\n",
       "      <td>0.446793</td>\n",
       "      <td>0.423767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164552</td>\n",
       "      <td>0.118890</td>\n",
       "      <td>0.190526</td>\n",
       "      <td>0.154462</td>\n",
       "      <td>0.424230</td>\n",
       "      <td>0.440828</td>\n",
       "      <td>0.394481</td>\n",
       "      <td>0.397477</td>\n",
       "      <td>0.710359</td>\n",
       "      <td>0.410069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>-0.025903</td>\n",
       "      <td>-0.349931</td>\n",
       "      <td>-0.059303</td>\n",
       "      <td>-0.304891</td>\n",
       "      <td>-0.358425</td>\n",
       "      <td>0.588465</td>\n",
       "      <td>0.499195</td>\n",
       "      <td>0.258174</td>\n",
       "      <td>0.142659</td>\n",
       "      <td>0.428467</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.294540</td>\n",
       "      <td>-0.047791</td>\n",
       "      <td>-0.247456</td>\n",
       "      <td>-0.304927</td>\n",
       "      <td>0.493474</td>\n",
       "      <td>0.403653</td>\n",
       "      <td>0.242611</td>\n",
       "      <td>0.139152</td>\n",
       "      <td>0.295046</td>\n",
       "      <td>0.760771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>0.616912</td>\n",
       "      <td>0.550247</td>\n",
       "      <td>0.363621</td>\n",
       "      <td>0.560326</td>\n",
       "      <td>0.553388</td>\n",
       "      <td>0.334282</td>\n",
       "      <td>0.506582</td>\n",
       "      <td>0.575277</td>\n",
       "      <td>0.635054</td>\n",
       "      <td>0.337912</td>\n",
       "      <td>...</td>\n",
       "      <td>0.598030</td>\n",
       "      <td>0.283581</td>\n",
       "      <td>0.592509</td>\n",
       "      <td>0.595732</td>\n",
       "      <td>0.203760</td>\n",
       "      <td>0.339725</td>\n",
       "      <td>0.404431</td>\n",
       "      <td>0.508662</td>\n",
       "      <td>0.147213</td>\n",
       "      <td>0.111043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>0.019419</td>\n",
       "      <td>-0.144499</td>\n",
       "      <td>0.450720</td>\n",
       "      <td>-0.137578</td>\n",
       "      <td>-0.142469</td>\n",
       "      <td>0.091283</td>\n",
       "      <td>0.047766</td>\n",
       "      <td>0.051318</td>\n",
       "      <td>0.008710</td>\n",
       "      <td>0.139124</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.148594</td>\n",
       "      <td>0.496551</td>\n",
       "      <td>-0.142855</td>\n",
       "      <td>-0.147786</td>\n",
       "      <td>-0.023095</td>\n",
       "      <td>-0.090069</td>\n",
       "      <td>-0.070625</td>\n",
       "      <td>-0.097025</td>\n",
       "      <td>-0.119890</td>\n",
       "      <td>-0.048143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>0.630411</td>\n",
       "      <td>0.565520</td>\n",
       "      <td>0.386813</td>\n",
       "      <td>0.582789</td>\n",
       "      <td>0.568237</td>\n",
       "      <td>0.331360</td>\n",
       "      <td>0.583520</td>\n",
       "      <td>0.646199</td>\n",
       "      <td>0.679841</td>\n",
       "      <td>0.354888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606902</td>\n",
       "      <td>0.302553</td>\n",
       "      <td>0.626896</td>\n",
       "      <td>0.605163</td>\n",
       "      <td>0.197899</td>\n",
       "      <td>0.438416</td>\n",
       "      <td>0.490340</td>\n",
       "      <td>0.569428</td>\n",
       "      <td>0.166606</td>\n",
       "      <td>0.185534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>0.714184</td>\n",
       "      <td>0.738077</td>\n",
       "      <td>0.395139</td>\n",
       "      <td>0.745824</td>\n",
       "      <td>0.741518</td>\n",
       "      <td>0.296059</td>\n",
       "      <td>0.539511</td>\n",
       "      <td>0.644344</td>\n",
       "      <td>0.726982</td>\n",
       "      <td>0.288322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774244</td>\n",
       "      <td>0.327857</td>\n",
       "      <td>0.768336</td>\n",
       "      <td>0.775662</td>\n",
       "      <td>0.188777</td>\n",
       "      <td>0.413658</td>\n",
       "      <td>0.500307</td>\n",
       "      <td>0.619539</td>\n",
       "      <td>0.154415</td>\n",
       "      <td>0.091670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>-0.052193</td>\n",
       "      <td>-0.326385</td>\n",
       "      <td>0.037048</td>\n",
       "      <td>-0.311147</td>\n",
       "      <td>-0.327431</td>\n",
       "      <td>0.338692</td>\n",
       "      <td>0.127381</td>\n",
       "      <td>0.070321</td>\n",
       "      <td>0.016798</td>\n",
       "      <td>0.206106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.321112</td>\n",
       "      <td>-0.036290</td>\n",
       "      <td>-0.308749</td>\n",
       "      <td>-0.323724</td>\n",
       "      <td>0.372247</td>\n",
       "      <td>-0.049245</td>\n",
       "      <td>-0.063848</td>\n",
       "      <td>-0.076503</td>\n",
       "      <td>-0.067149</td>\n",
       "      <td>0.129752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>0.380666</td>\n",
       "      <td>0.264904</td>\n",
       "      <td>0.263591</td>\n",
       "      <td>0.308620</td>\n",
       "      <td>0.260362</td>\n",
       "      <td>0.392455</td>\n",
       "      <td>0.817875</td>\n",
       "      <td>0.761230</td>\n",
       "      <td>0.608388</td>\n",
       "      <td>0.435714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.285413</td>\n",
       "      <td>0.209979</td>\n",
       "      <td>0.344865</td>\n",
       "      <td>0.278844</td>\n",
       "      <td>0.320500</td>\n",
       "      <td>0.789431</td>\n",
       "      <td>0.701251</td>\n",
       "      <td>0.587471</td>\n",
       "      <td>0.265987</td>\n",
       "      <td>0.604844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>0.470338</td>\n",
       "      <td>0.364555</td>\n",
       "      <td>0.287188</td>\n",
       "      <td>0.402277</td>\n",
       "      <td>0.362308</td>\n",
       "      <td>0.354730</td>\n",
       "      <td>0.772283</td>\n",
       "      <td>0.858306</td>\n",
       "      <td>0.674668</td>\n",
       "      <td>0.367637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381860</td>\n",
       "      <td>0.235945</td>\n",
       "      <td>0.432895</td>\n",
       "      <td>0.377836</td>\n",
       "      <td>0.305368</td>\n",
       "      <td>0.731517</td>\n",
       "      <td>0.811327</td>\n",
       "      <td>0.656814</td>\n",
       "      <td>0.230690</td>\n",
       "      <td>0.505962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>0.488717</td>\n",
       "      <td>0.410576</td>\n",
       "      <td>0.238610</td>\n",
       "      <td>0.441996</td>\n",
       "      <td>0.406468</td>\n",
       "      <td>0.438826</td>\n",
       "      <td>0.732425</td>\n",
       "      <td>0.774656</td>\n",
       "      <td>0.758438</td>\n",
       "      <td>0.382736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.410221</td>\n",
       "      <td>0.157304</td>\n",
       "      <td>0.448363</td>\n",
       "      <td>0.403587</td>\n",
       "      <td>0.294840</td>\n",
       "      <td>0.585929</td>\n",
       "      <td>0.624555</td>\n",
       "      <td>0.692071</td>\n",
       "      <td>0.132782</td>\n",
       "      <td>0.357090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>-0.092303</td>\n",
       "      <td>-0.241376</td>\n",
       "      <td>0.008945</td>\n",
       "      <td>-0.228187</td>\n",
       "      <td>-0.243507</td>\n",
       "      <td>0.150740</td>\n",
       "      <td>0.098388</td>\n",
       "      <td>0.022753</td>\n",
       "      <td>-0.028353</td>\n",
       "      <td>0.384123</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.261267</td>\n",
       "      <td>-0.104702</td>\n",
       "      <td>-0.246712</td>\n",
       "      <td>-0.266705</td>\n",
       "      <td>-0.042873</td>\n",
       "      <td>-0.082054</td>\n",
       "      <td>-0.118144</td>\n",
       "      <td>-0.140795</td>\n",
       "      <td>0.283201</td>\n",
       "      <td>0.011133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>0.201492</td>\n",
       "      <td>-0.008411</td>\n",
       "      <td>0.147605</td>\n",
       "      <td>0.032429</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>0.413429</td>\n",
       "      <td>0.621121</td>\n",
       "      <td>0.513593</td>\n",
       "      <td>0.378374</td>\n",
       "      <td>0.402630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013324</td>\n",
       "      <td>0.083174</td>\n",
       "      <td>0.063012</td>\n",
       "      <td>0.007312</td>\n",
       "      <td>0.312293</td>\n",
       "      <td>0.526899</td>\n",
       "      <td>0.431677</td>\n",
       "      <td>0.331206</td>\n",
       "      <td>0.172926</td>\n",
       "      <td>0.712771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>0.787933</td>\n",
       "      <td>0.978604</td>\n",
       "      <td>0.366547</td>\n",
       "      <td>0.981244</td>\n",
       "      <td>0.979258</td>\n",
       "      <td>0.203453</td>\n",
       "      <td>0.542626</td>\n",
       "      <td>0.682316</td>\n",
       "      <td>0.787411</td>\n",
       "      <td>0.164552</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.371230</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.998891</td>\n",
       "      <td>0.218616</td>\n",
       "      <td>0.558316</td>\n",
       "      <td>0.655942</td>\n",
       "      <td>0.780632</td>\n",
       "      <td>0.257165</td>\n",
       "      <td>0.127449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>0.476720</td>\n",
       "      <td>0.314911</td>\n",
       "      <td>0.909218</td>\n",
       "      <td>0.323109</td>\n",
       "      <td>0.318178</td>\n",
       "      <td>0.060645</td>\n",
       "      <td>0.255305</td>\n",
       "      <td>0.335866</td>\n",
       "      <td>0.300562</td>\n",
       "      <td>0.118890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381022</td>\n",
       "      <td>0.372376</td>\n",
       "      <td>0.217799</td>\n",
       "      <td>0.342319</td>\n",
       "      <td>0.387009</td>\n",
       "      <td>0.365309</td>\n",
       "      <td>0.226816</td>\n",
       "      <td>0.193191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>0.796319</td>\n",
       "      <td>0.971555</td>\n",
       "      <td>0.375273</td>\n",
       "      <td>0.978980</td>\n",
       "      <td>0.971822</td>\n",
       "      <td>0.226345</td>\n",
       "      <td>0.592254</td>\n",
       "      <td>0.722424</td>\n",
       "      <td>0.813960</td>\n",
       "      <td>0.190526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.381022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992433</td>\n",
       "      <td>0.241172</td>\n",
       "      <td>0.613070</td>\n",
       "      <td>0.700572</td>\n",
       "      <td>0.812983</td>\n",
       "      <td>0.281383</td>\n",
       "      <td>0.179003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>0.786902</td>\n",
       "      <td>0.978863</td>\n",
       "      <td>0.368335</td>\n",
       "      <td>0.980864</td>\n",
       "      <td>0.980264</td>\n",
       "      <td>0.191735</td>\n",
       "      <td>0.531590</td>\n",
       "      <td>0.676628</td>\n",
       "      <td>0.780395</td>\n",
       "      <td>0.154462</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998891</td>\n",
       "      <td>0.372376</td>\n",
       "      <td>0.992433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.210063</td>\n",
       "      <td>0.550007</td>\n",
       "      <td>0.651120</td>\n",
       "      <td>0.773945</td>\n",
       "      <td>0.248358</td>\n",
       "      <td>0.118734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>0.425513</td>\n",
       "      <td>0.125789</td>\n",
       "      <td>0.101401</td>\n",
       "      <td>0.156611</td>\n",
       "      <td>0.119712</td>\n",
       "      <td>0.796085</td>\n",
       "      <td>0.578902</td>\n",
       "      <td>0.488775</td>\n",
       "      <td>0.490035</td>\n",
       "      <td>0.424230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218616</td>\n",
       "      <td>0.217799</td>\n",
       "      <td>0.241172</td>\n",
       "      <td>0.210063</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.560156</td>\n",
       "      <td>0.519490</td>\n",
       "      <td>0.543982</td>\n",
       "      <td>0.501230</td>\n",
       "      <td>0.614796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>0.606810</td>\n",
       "      <td>0.491357</td>\n",
       "      <td>0.290917</td>\n",
       "      <td>0.534565</td>\n",
       "      <td>0.485813</td>\n",
       "      <td>0.481384</td>\n",
       "      <td>0.901029</td>\n",
       "      <td>0.849985</td>\n",
       "      <td>0.758309</td>\n",
       "      <td>0.440828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.558316</td>\n",
       "      <td>0.342319</td>\n",
       "      <td>0.613070</td>\n",
       "      <td>0.550007</td>\n",
       "      <td>0.560156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.914894</td>\n",
       "      <td>0.844454</td>\n",
       "      <td>0.527102</td>\n",
       "      <td>0.762247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>0.705734</td>\n",
       "      <td>0.596043</td>\n",
       "      <td>0.339725</td>\n",
       "      <td>0.632106</td>\n",
       "      <td>0.593736</td>\n",
       "      <td>0.429107</td>\n",
       "      <td>0.837921</td>\n",
       "      <td>0.938543</td>\n",
       "      <td>0.827281</td>\n",
       "      <td>0.394481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655942</td>\n",
       "      <td>0.387009</td>\n",
       "      <td>0.700572</td>\n",
       "      <td>0.651120</td>\n",
       "      <td>0.519490</td>\n",
       "      <td>0.914894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902301</td>\n",
       "      <td>0.476179</td>\n",
       "      <td>0.623128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>0.781674</td>\n",
       "      <td>0.727265</td>\n",
       "      <td>0.319235</td>\n",
       "      <td>0.757526</td>\n",
       "      <td>0.723390</td>\n",
       "      <td>0.498868</td>\n",
       "      <td>0.825473</td>\n",
       "      <td>0.904938</td>\n",
       "      <td>0.937075</td>\n",
       "      <td>0.397477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780632</td>\n",
       "      <td>0.365309</td>\n",
       "      <td>0.812983</td>\n",
       "      <td>0.773945</td>\n",
       "      <td>0.543982</td>\n",
       "      <td>0.844454</td>\n",
       "      <td>0.902301</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460711</td>\n",
       "      <td>0.516664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>0.396843</td>\n",
       "      <td>0.174698</td>\n",
       "      <td>0.120693</td>\n",
       "      <td>0.199007</td>\n",
       "      <td>0.170860</td>\n",
       "      <td>0.393579</td>\n",
       "      <td>0.450333</td>\n",
       "      <td>0.383667</td>\n",
       "      <td>0.355477</td>\n",
       "      <td>0.710359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257165</td>\n",
       "      <td>0.226816</td>\n",
       "      <td>0.281383</td>\n",
       "      <td>0.248358</td>\n",
       "      <td>0.501230</td>\n",
       "      <td>0.527102</td>\n",
       "      <td>0.476179</td>\n",
       "      <td>0.460711</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.488439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>0.311477</td>\n",
       "      <td>0.044564</td>\n",
       "      <td>0.116144</td>\n",
       "      <td>0.088961</td>\n",
       "      <td>0.038758</td>\n",
       "      <td>0.511457</td>\n",
       "      <td>0.688986</td>\n",
       "      <td>0.541838</td>\n",
       "      <td>0.421110</td>\n",
       "      <td>0.410069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127449</td>\n",
       "      <td>0.193191</td>\n",
       "      <td>0.179003</td>\n",
       "      <td>0.118734</td>\n",
       "      <td>0.614796</td>\n",
       "      <td>0.762247</td>\n",
       "      <td>0.623128</td>\n",
       "      <td>0.516664</td>\n",
       "      <td>0.488439</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
       "diagnosis                 1.000000     0.732785      0.461971        0.748496   \n",
       "radius_mean               0.732785     1.000000      0.340956        0.997802   \n",
       "texture_mean              0.461971     0.340956      1.000000        0.348142   \n",
       "perimeter_mean            0.748496     0.997802      0.348142        1.000000   \n",
       "area_mean                 0.734122     0.999602      0.344145        0.997068   \n",
       "smoothness_mean           0.371892     0.148510      0.024649        0.182923   \n",
       "compactness_mean          0.609288     0.497578      0.266499        0.543925   \n",
       "concavity_mean            0.733308     0.645728      0.342646        0.681958   \n",
       "concave points_mean       0.777877     0.759702      0.306891        0.788629   \n",
       "symmetry_mean             0.332567     0.120242      0.110130        0.150049   \n",
       "fractal_dimension_mean   -0.025903    -0.349931     -0.059303       -0.304891   \n",
       "radius_se                 0.616912     0.550247      0.363621        0.560326   \n",
       "texture_se                0.019419    -0.144499      0.450720       -0.137578   \n",
       "perimeter_se              0.630411     0.565520      0.386813        0.582789   \n",
       "area_se                   0.714184     0.738077      0.395139        0.745824   \n",
       "smoothness_se            -0.052193    -0.326385      0.037048       -0.311147   \n",
       "compactness_se            0.380666     0.264904      0.263591        0.308620   \n",
       "concavity_se              0.470338     0.364555      0.287188        0.402277   \n",
       "concave points_se         0.488717     0.410576      0.238610        0.441996   \n",
       "symmetry_se              -0.092303    -0.241376      0.008945       -0.228187   \n",
       "fractal_dimension_se      0.201492    -0.008411      0.147605        0.032429   \n",
       "radius_worst              0.787933     0.978604      0.366547        0.981244   \n",
       "texture_worst             0.476720     0.314911      0.909218        0.323109   \n",
       "perimeter_worst           0.796319     0.971555      0.375273        0.978980   \n",
       "area_worst                0.786902     0.978863      0.368335        0.980864   \n",
       "smoothness_worst          0.425513     0.125789      0.101401        0.156611   \n",
       "compactness_worst         0.606810     0.491357      0.290917        0.534565   \n",
       "concavity_worst           0.705734     0.596043      0.339725        0.632106   \n",
       "concave points_worst      0.781674     0.727265      0.319235        0.757526   \n",
       "symmetry_worst            0.396843     0.174698      0.120693        0.199007   \n",
       "fractal_dimension_worst   0.311477     0.044564      0.116144        0.088961   \n",
       "\n",
       "                         area_mean  smoothness_mean  compactness_mean  \\\n",
       "diagnosis                 0.734122         0.371892          0.609288   \n",
       "radius_mean               0.999602         0.148510          0.497578   \n",
       "texture_mean              0.344145         0.024649          0.266499   \n",
       "perimeter_mean            0.997068         0.182923          0.543925   \n",
       "area_mean                 1.000000         0.138053          0.488988   \n",
       "smoothness_mean           0.138053         1.000000          0.678806   \n",
       "compactness_mean          0.488988         0.678806          1.000000   \n",
       "concavity_mean            0.642557         0.518511          0.896518   \n",
       "concave points_mean       0.755165         0.565172          0.848295   \n",
       "symmetry_mean             0.113928         0.542228          0.552203   \n",
       "fractal_dimension_mean   -0.358425         0.588465          0.499195   \n",
       "radius_se                 0.553388         0.334282          0.506582   \n",
       "texture_se               -0.142469         0.091283          0.047766   \n",
       "perimeter_se              0.568237         0.331360          0.583520   \n",
       "area_se                   0.741518         0.296059          0.539511   \n",
       "smoothness_se            -0.327431         0.338692          0.127381   \n",
       "compactness_se            0.260362         0.392455          0.817875   \n",
       "concavity_se              0.362308         0.354730          0.772283   \n",
       "concave points_se         0.406468         0.438826          0.732425   \n",
       "symmetry_se              -0.243507         0.150740          0.098388   \n",
       "fractal_dimension_se     -0.012688         0.413429          0.621121   \n",
       "radius_worst              0.979258         0.203453          0.542626   \n",
       "texture_worst             0.318178         0.060645          0.255305   \n",
       "perimeter_worst           0.971822         0.226345          0.592254   \n",
       "area_worst                0.980264         0.191735          0.531590   \n",
       "smoothness_worst          0.119712         0.796085          0.578902   \n",
       "compactness_worst         0.485813         0.481384          0.901029   \n",
       "concavity_worst           0.593736         0.429107          0.837921   \n",
       "concave points_worst      0.723390         0.498868          0.825473   \n",
       "symmetry_worst            0.170860         0.393579          0.450333   \n",
       "fractal_dimension_worst   0.038758         0.511457          0.688986   \n",
       "\n",
       "                         concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "diagnosis                      0.733308             0.777877       0.332567   \n",
       "radius_mean                    0.645728             0.759702       0.120242   \n",
       "texture_mean                   0.342646             0.306891       0.110130   \n",
       "perimeter_mean                 0.681958             0.788629       0.150049   \n",
       "area_mean                      0.642557             0.755165       0.113928   \n",
       "smoothness_mean                0.518511             0.565172       0.542228   \n",
       "compactness_mean               0.896518             0.848295       0.552203   \n",
       "concavity_mean                 1.000000             0.927352       0.446793   \n",
       "concave points_mean            0.927352             1.000000       0.423767   \n",
       "symmetry_mean                  0.446793             0.423767       1.000000   \n",
       "fractal_dimension_mean         0.258174             0.142659       0.428467   \n",
       "radius_se                      0.575277             0.635054       0.337912   \n",
       "texture_se                     0.051318             0.008710       0.139124   \n",
       "perimeter_se                   0.646199             0.679841       0.354888   \n",
       "area_se                        0.644344             0.726982       0.288322   \n",
       "smoothness_se                  0.070321             0.016798       0.206106   \n",
       "compactness_se                 0.761230             0.608388       0.435714   \n",
       "concavity_se                   0.858306             0.674668       0.367637   \n",
       "concave points_se              0.774656             0.758438       0.382736   \n",
       "symmetry_se                    0.022753            -0.028353       0.384123   \n",
       "fractal_dimension_se           0.513593             0.378374       0.402630   \n",
       "radius_worst                   0.682316             0.787411       0.164552   \n",
       "texture_worst                  0.335866             0.300562       0.118890   \n",
       "perimeter_worst                0.722424             0.813960       0.190526   \n",
       "area_worst                     0.676628             0.780395       0.154462   \n",
       "smoothness_worst               0.488775             0.490035       0.424230   \n",
       "compactness_worst              0.849985             0.758309       0.440828   \n",
       "concavity_worst                0.938543             0.827281       0.394481   \n",
       "concave points_worst           0.904938             0.937075       0.397477   \n",
       "symmetry_worst                 0.383667             0.355477       0.710359   \n",
       "fractal_dimension_worst        0.541838             0.421110       0.410069   \n",
       "\n",
       "                         ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "diagnosis                ...      0.787933       0.476720         0.796319   \n",
       "radius_mean              ...      0.978604       0.314911         0.971555   \n",
       "texture_mean             ...      0.366547       0.909218         0.375273   \n",
       "perimeter_mean           ...      0.981244       0.323109         0.978980   \n",
       "area_mean                ...      0.979258       0.318178         0.971822   \n",
       "smoothness_mean          ...      0.203453       0.060645         0.226345   \n",
       "compactness_mean         ...      0.542626       0.255305         0.592254   \n",
       "concavity_mean           ...      0.682316       0.335866         0.722424   \n",
       "concave points_mean      ...      0.787411       0.300562         0.813960   \n",
       "symmetry_mean            ...      0.164552       0.118890         0.190526   \n",
       "fractal_dimension_mean   ...     -0.294540      -0.047791        -0.247456   \n",
       "radius_se                ...      0.598030       0.283581         0.592509   \n",
       "texture_se               ...     -0.148594       0.496551        -0.142855   \n",
       "perimeter_se             ...      0.606902       0.302553         0.626896   \n",
       "area_se                  ...      0.774244       0.327857         0.768336   \n",
       "smoothness_se            ...     -0.321112      -0.036290        -0.308749   \n",
       "compactness_se           ...      0.285413       0.209979         0.344865   \n",
       "concavity_se             ...      0.381860       0.235945         0.432895   \n",
       "concave points_se        ...      0.410221       0.157304         0.448363   \n",
       "symmetry_se              ...     -0.261267      -0.104702        -0.246712   \n",
       "fractal_dimension_se     ...      0.013324       0.083174         0.063012   \n",
       "radius_worst             ...      1.000000       0.371230         0.993548   \n",
       "texture_worst            ...      0.371230       1.000000         0.381022   \n",
       "perimeter_worst          ...      0.993548       0.381022         1.000000   \n",
       "area_worst               ...      0.998891       0.372376         0.992433   \n",
       "smoothness_worst         ...      0.218616       0.217799         0.241172   \n",
       "compactness_worst        ...      0.558316       0.342319         0.613070   \n",
       "concavity_worst          ...      0.655942       0.387009         0.700572   \n",
       "concave points_worst     ...      0.780632       0.365309         0.812983   \n",
       "symmetry_worst           ...      0.257165       0.226816         0.281383   \n",
       "fractal_dimension_worst  ...      0.127449       0.193191         0.179003   \n",
       "\n",
       "                         area_worst  smoothness_worst  compactness_worst  \\\n",
       "diagnosis                  0.786902          0.425513           0.606810   \n",
       "radius_mean                0.978863          0.125789           0.491357   \n",
       "texture_mean               0.368335          0.101401           0.290917   \n",
       "perimeter_mean             0.980864          0.156611           0.534565   \n",
       "area_mean                  0.980264          0.119712           0.485813   \n",
       "smoothness_mean            0.191735          0.796085           0.481384   \n",
       "compactness_mean           0.531590          0.578902           0.901029   \n",
       "concavity_mean             0.676628          0.488775           0.849985   \n",
       "concave points_mean        0.780395          0.490035           0.758309   \n",
       "symmetry_mean              0.154462          0.424230           0.440828   \n",
       "fractal_dimension_mean    -0.304927          0.493474           0.403653   \n",
       "radius_se                  0.595732          0.203760           0.339725   \n",
       "texture_se                -0.147786         -0.023095          -0.090069   \n",
       "perimeter_se               0.605163          0.197899           0.438416   \n",
       "area_se                    0.775662          0.188777           0.413658   \n",
       "smoothness_se             -0.323724          0.372247          -0.049245   \n",
       "compactness_se             0.278844          0.320500           0.789431   \n",
       "concavity_se               0.377836          0.305368           0.731517   \n",
       "concave points_se          0.403587          0.294840           0.585929   \n",
       "symmetry_se               -0.266705         -0.042873          -0.082054   \n",
       "fractal_dimension_se       0.007312          0.312293           0.526899   \n",
       "radius_worst               0.998891          0.218616           0.558316   \n",
       "texture_worst              0.372376          0.217799           0.342319   \n",
       "perimeter_worst            0.992433          0.241172           0.613070   \n",
       "area_worst                 1.000000          0.210063           0.550007   \n",
       "smoothness_worst           0.210063          1.000000           0.560156   \n",
       "compactness_worst          0.550007          0.560156           1.000000   \n",
       "concavity_worst            0.651120          0.519490           0.914894   \n",
       "concave points_worst       0.773945          0.543982           0.844454   \n",
       "symmetry_worst             0.248358          0.501230           0.527102   \n",
       "fractal_dimension_worst    0.118734          0.614796           0.762247   \n",
       "\n",
       "                         concavity_worst  concave points_worst  \\\n",
       "diagnosis                       0.705734              0.781674   \n",
       "radius_mean                     0.596043              0.727265   \n",
       "texture_mean                    0.339725              0.319235   \n",
       "perimeter_mean                  0.632106              0.757526   \n",
       "area_mean                       0.593736              0.723390   \n",
       "smoothness_mean                 0.429107              0.498868   \n",
       "compactness_mean                0.837921              0.825473   \n",
       "concavity_mean                  0.938543              0.904938   \n",
       "concave points_mean             0.827281              0.937075   \n",
       "symmetry_mean                   0.394481              0.397477   \n",
       "fractal_dimension_mean          0.242611              0.139152   \n",
       "radius_se                       0.404431              0.508662   \n",
       "texture_se                     -0.070625             -0.097025   \n",
       "perimeter_se                    0.490340              0.569428   \n",
       "area_se                         0.500307              0.619539   \n",
       "smoothness_se                  -0.063848             -0.076503   \n",
       "compactness_se                  0.701251              0.587471   \n",
       "concavity_se                    0.811327              0.656814   \n",
       "concave points_se               0.624555              0.692071   \n",
       "symmetry_se                    -0.118144             -0.140795   \n",
       "fractal_dimension_se            0.431677              0.331206   \n",
       "radius_worst                    0.655942              0.780632   \n",
       "texture_worst                   0.387009              0.365309   \n",
       "perimeter_worst                 0.700572              0.812983   \n",
       "area_worst                      0.651120              0.773945   \n",
       "smoothness_worst                0.519490              0.543982   \n",
       "compactness_worst               0.914894              0.844454   \n",
       "concavity_worst                 1.000000              0.902301   \n",
       "concave points_worst            0.902301              1.000000   \n",
       "symmetry_worst                  0.476179              0.460711   \n",
       "fractal_dimension_worst         0.623128              0.516664   \n",
       "\n",
       "                         symmetry_worst  fractal_dimension_worst  \n",
       "diagnosis                      0.396843                 0.311477  \n",
       "radius_mean                    0.174698                 0.044564  \n",
       "texture_mean                   0.120693                 0.116144  \n",
       "perimeter_mean                 0.199007                 0.088961  \n",
       "area_mean                      0.170860                 0.038758  \n",
       "smoothness_mean                0.393579                 0.511457  \n",
       "compactness_mean               0.450333                 0.688986  \n",
       "concavity_mean                 0.383667                 0.541838  \n",
       "concave points_mean            0.355477                 0.421110  \n",
       "symmetry_mean                  0.710359                 0.410069  \n",
       "fractal_dimension_mean         0.295046                 0.760771  \n",
       "radius_se                      0.147213                 0.111043  \n",
       "texture_se                    -0.119890                -0.048143  \n",
       "perimeter_se                   0.166606                 0.185534  \n",
       "area_se                        0.154415                 0.091670  \n",
       "smoothness_se                 -0.067149                 0.129752  \n",
       "compactness_se                 0.265987                 0.604844  \n",
       "concavity_se                   0.230690                 0.505962  \n",
       "concave points_se              0.132782                 0.357090  \n",
       "symmetry_se                    0.283201                 0.011133  \n",
       "fractal_dimension_se           0.172926                 0.712771  \n",
       "radius_worst                   0.257165                 0.127449  \n",
       "texture_worst                  0.226816                 0.193191  \n",
       "perimeter_worst                0.281383                 0.179003  \n",
       "area_worst                     0.248358                 0.118734  \n",
       "smoothness_worst               0.501230                 0.614796  \n",
       "compactness_worst              0.527102                 0.762247  \n",
       "concavity_worst                0.476179                 0.623128  \n",
       "concave points_worst           0.460711                 0.516664  \n",
       "symmetry_worst                 1.000000                 0.488439  \n",
       "fractal_dimension_worst        0.488439                 1.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_proc_scaled.corr(method = \"spearman\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we mostly care about which features correlate the most with the diagnosis label (benign or malignant tumor), we can extract the \"diagnosis column\" and sort the correlating features by the highest values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  1.000000\n",
       "perimeter_worst            0.796319\n",
       "radius_worst               0.787933\n",
       "area_worst                 0.786902\n",
       "concave points_worst       0.781674\n",
       "concave points_mean        0.777877\n",
       "perimeter_mean             0.748496\n",
       "area_mean                  0.734122\n",
       "concavity_mean             0.733308\n",
       "radius_mean                0.732785\n",
       "area_se                    0.714184\n",
       "concavity_worst            0.705734\n",
       "perimeter_se               0.630411\n",
       "radius_se                  0.616912\n",
       "compactness_mean           0.609288\n",
       "compactness_worst          0.606810\n",
       "concave points_se          0.488717\n",
       "texture_worst              0.476720\n",
       "concavity_se               0.470338\n",
       "texture_mean               0.461971\n",
       "smoothness_worst           0.425513\n",
       "symmetry_worst             0.396843\n",
       "compactness_se             0.380666\n",
       "smoothness_mean            0.371892\n",
       "symmetry_mean              0.332567\n",
       "fractal_dimension_worst    0.311477\n",
       "fractal_dimension_se       0.201492\n",
       "texture_se                 0.019419\n",
       "fractal_dimension_mean    -0.025903\n",
       "smoothness_se             -0.052193\n",
       "symmetry_se               -0.092303\n",
       "Name: diagnosis, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations_ordered = df_proc_scaled.corr(method = \"spearman\")['diagnosis'].sort_values(ascending = False)\n",
    "correlations_ordered\n",
    "\n",
    "# Note that the sort_values method by default will return values in ascending order\n",
    "# If you want the top values first, do .sort_values(ascending=False) or do correlations_ordered[::-1] to reverse the order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = correlations_ordered\n",
    "plt.subplots(figsize = (10,10))\n",
    "sns.barplot(df_plot.values, df_plot.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We here observe that concave \"perimeter_worst\", \"radius_worst\" and \"concave area_worst\" are the features that are the features most correlated to \"diagnosis\". The feature \"diagnosis\" of course perfectly correlates with itself, giving a Pearson r value of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find features that are highly co-correlated with each other and therefore unlikely to add more useful information (only providing redundant information) to the final model, we can inspect the features co-correlations using the Seaborn [clustermap](https://seaborn.pydata.org/generated/seaborn.clustermap.html) graphing function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x165e673d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations = df_proc_scaled.corr()\n",
    "\n",
    "sns.clustermap(correlations, method = \"average\", metric = \"euclidean\",\n",
    "               figsize = (15, 15), linewidth=0.5, cmap = 'RdBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colored blocks which cluster together are blocks of features that co-correlate highly. Each feature´s correlation with another is seen along the x and y axes. Note that because the correlation between a given feature1 and feature2 is the same as feature2 and feature1, the clustermap is symmetrical.\n",
    "\n",
    "We observe that there is a high similarity between the perimeter/radius/area mean and worst values, which seem to mostly be reporting the same information. Including more than one of them in the model is therefore unlikely to increase performance. We see the same pattern for the area, radius and perimiter standard error features.\n",
    "\n",
    "Can you identify sets of features which are highly correlated with \"diagnosis\", but also highly different from each other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a map giving us which features are likely good predictiors of malignant tumors, and how co-correlated those features are. To visualize how well the selected features individually separate the two groups, we use a combination of violinplots and swarm plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>features</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.668310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.539818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.508442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.241347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.506948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5116</th>\n",
       "      <td>1</td>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5117</th>\n",
       "      <td>1</td>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.622320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5118</th>\n",
       "      <td>1</td>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.455251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5119</th>\n",
       "      <td>1</td>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.644564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5120</th>\n",
       "      <td>0</td>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.036869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      diagnosis         features     value\n",
       "0             1  perimeter_worst  0.668310\n",
       "1             1  perimeter_worst  0.539818\n",
       "2             1  perimeter_worst  0.508442\n",
       "3             1  perimeter_worst  0.241347\n",
       "4             1  perimeter_worst  0.506948\n",
       "...         ...              ...       ...\n",
       "5116          1      radius_mean  0.690000\n",
       "5117          1      radius_mean  0.622320\n",
       "5118          1      radius_mean  0.455251\n",
       "5119          1      radius_mean  0.644564\n",
       "5120          0      radius_mean  0.036869\n",
       "\n",
       "[5121 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the top 10 features\n",
    "top_features_only = df_proc_scaled[correlations_ordered.index[0:10]]\n",
    "\n",
    "plot_df = pd.melt(top_features_only,id_vars=\"diagnosis\",\n",
    "                    var_name=\"features\",\n",
    "                    value_name='value')\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x165678580>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 775.5x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=plot_df, y=\"features\", x=\"value\", \n",
    "                   kind = \"violin\", split = True, # Split violin-plot with two graphs \"in one\"\n",
    "                   hue=\"diagnosis\", palette={0:'blue', 1:'red'}, # Set colors\n",
    "                   height = 10, aspect = 1) # Determine figure size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the individual points and label the graph properly with a few added lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/opt/anaconda3/envs/biodatascience101/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 9.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 features by separation of benign from malignant tumors')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 824.33x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "sns.set_context(context = \"paper\", font_scale = 2)\n",
    "\n",
    "#Violinplot with 2 distributions in one graph\n",
    "fig1 = sns.catplot(data=plot_df, y=\"features\", x=\"value\", \n",
    "                   kind = \"violin\", split = True, # Split violin-plot with two graphs \"in one\"\n",
    "                   hue=\"diagnosis\", palette={0:'blue', 1:'red'}, # Set colors\n",
    "                   height = 10, aspect = 1) # Determine figure size\n",
    "\n",
    "# Swarmplot to see all plot_df points on top of the violinplot\n",
    "fig2 = sns.swarmplot(data=plot_df, y=\"features\", x=\"value\", hue = \"diagnosis\", size=2.5) \n",
    "\n",
    "# Remove the extract second legend\n",
    "fig2.get_legend().remove() \n",
    "plt.title(\"Top 10 features by separation of benign from malignant tumors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides looking for features that are correlated to the phenotype, we can also test for associations between phenotype and feature distributions.\n",
    "This we do using the non-parametric <a href='https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html'>Mann-Whitney U Test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "564    1\n",
       "565    1\n",
       "566    1\n",
       "567    1\n",
       "568    0\n",
       "Name: diagnosis, Length: 569, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw[\"diagnosis\"].map({\"B\":0, \"M\":1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney U test of features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "perimeter_worst            2.583004e-80\n",
       "radius_worst               1.135630e-78\n",
       "area_worst                 1.803309e-78\n",
       "concave points_worst       1.863997e-77\n",
       "concave points_mean        1.006324e-76\n",
       "perimeter_mean             3.553870e-71\n",
       "area_mean                  1.539780e-68\n",
       "concavity_mean             2.164549e-68\n",
       "radius_mean                2.692943e-68\n",
       "area_se                    5.767823e-65\n",
       "concavity_worst            1.761723e-63\n",
       "perimeter_se               5.099437e-51\n",
       "radius_se                  6.217140e-49\n",
       "compactness_mean           8.951992e-48\n",
       "compactness_worst          2.115525e-47\n",
       "concave points_se          2.370852e-31\n",
       "texture_worst              6.517718e-30\n",
       "concavity_se               3.675508e-29\n",
       "texture_mean               3.428627e-28\n",
       "smoothness_worst           3.637942e-24\n",
       "symmetry_worst             3.151237e-21\n",
       "compactness_se             1.168061e-19\n",
       "smoothness_mean            7.793007e-19\n",
       "symmetry_mean              2.268050e-15\n",
       "fractal_dimension_worst    1.144240e-13\n",
       "fractal_dimension_se       1.572165e-06\n",
       "symmetry_se                2.783664e-02\n",
       "smoothness_se              2.136316e-01\n",
       "fractal_dimension_mean     5.371856e-01\n",
       "texture_se                 6.436927e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html\n",
    "\n",
    "u_tests = pd.Series(dtype=float)\n",
    "for col in df_proc_scaled.columns[1:]:\n",
    "    b = df_raw[df_raw.diagnosis == 'B'][col]\n",
    "    m = df_raw[df_raw.diagnosis == 'M'][col]\n",
    "    u_value = mannwhitneyu(b,m)[1]\n",
    "    u_tests.loc[col] = u_value\n",
    "    \n",
    "print(\"Mann-Whitney U test of features:\")\n",
    "u_tests.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now visualized the overall distribution of our dataset using manifold learning and its ability to distinguish tumor status,\n",
    "identified co-correlating features which are unlikely to give us much additional information if including multiple of them as features in our machine-learning model, and the top features which by themselves best correlate with the tumor status.\n",
    "\n",
    "With this knowledge in mind, we are ready to choose the right machine-learning architecture for the job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Machine-learning concepts and application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine-learning is the application of algorithms that identify patterns in data. This can be done either to group unlabeled data (unsupervised learning) or to predict the class of samples based on pre-existing labels. \n",
    "\n",
    "The problem we have here is of the latter class: we want to predict if new samples belong to the \"benign\" or \"malignant\" class, only by looking at patterns in the features. The task of the algorithm is to identify such patterns from a dataset for which we already know the correct classification.\n",
    "\n",
    "Now, a quick overview of the concepts we will cover in the following. In order to build a supervised machine-learning model we need 2 main parts:\n",
    "\n",
    "- A dataset of samples with corresponding features, and target labels. The dataset must be numerically encoded, and preferably normalized.\n",
    "    - The features are a representation of each sample and what the machine-learning model will see as inputs, while the targets are what those samples/features should match to.\n",
    "    - The training part of machine-learning is finding a good statistical model that connects these, hopefully in a way that generalizes to unseen data.\n",
    "- A machine-learning model\n",
    "    - The statistical framework for which to learn and predict patterns in the dataset. Here we will be showcasing Logistic Regression, Support-Vector-Machines and Random Forests. All models will be loaded from the Scikit-Learn library\n",
    "\n",
    "In addition, in order to be able to evaluate the performance of our model, we need to split our dataset of samples and target labels into a training set and a validation set.\n",
    "\n",
    "<a href=\"img/machine_learning_workflow.png\"><center> <a href=\"img/machine_learning_workflow.png\"><img src=\"img/machine_learning_workflow.png\" alt=\"Drawing\" style=\"width: 700px;\"/> <center/><a/>\n",
    "\n",
    "Figure reference: Osman, Alexander. (2019). Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine. 10.5772/intechopen.84629. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During this section of the module we will build machine learning models of the types:\n",
    "\n",
    "__logistic regression (LR),\n",
    "support vector machines (SVM),\n",
    "and random forest (RF).__\n",
    "\n",
    "__Logistic regression:__ \n",
    "LR a statistical model, used to describe the probability of an event. In the basic implementation, parameters of a logistic function is fitted to a training dataset to model a binary (0 or 1) dependent variable. The below figure shows the how a logistic function is fitted to the data, and how it compares to a linear regression, where a straight line is being fitted.\n",
    "Thus, the output of the model is the probability of 0 or 1 given the input data (x).\n",
    "\n",
    "Therefore, LR is a simple machine learning model with good interpretability, that generally struggles with non-linearities. \n",
    "\n",
    "<a href=\"img/lr.png\"><center> <a href=\"img/lr.png\"><img src=\"img/lr.png\" alt=\"Drawing\" style=\"width: 700px;\"/> <center/><a/>\n",
    "    \n",
    "Figure reference: maithili joshi, 2019, medium.com.\n",
    "    \n",
    "Read more: [https://medium.com/@maithilijoshi6/a-comparison-between-linear-and-logistic-regression-8aea40867e2d](https://medium.com/@maithilijoshi6/a-comparison-between-linear-and-logistic-regression-8aea40867e2d)\n",
    "\n",
    "For a more detailed explanation of logistic regression from the youtube channel StatQuest:\n",
    "\n",
    "https://www.youtube.com/watch?v=yIYKR4sgzI8\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Support vector machines:__\n",
    "SVM's are non-probabilistic machine learning models used for regression and classification of data. SVM's functions by fitting a __\"hyperplane\"__ (see figure below. A \"fancy\" word for a decision boundary), that effectively separates classes as points in space. Most importantly, SVM's are able to learn non-linear trends in the data by mapping to a higher-dimensional space than the input data. \n",
    "\n",
    "SVM's are complex machine learning models sensitive to outliers, that works well with high-dimensional data where observations are separable. \n",
    "\n",
    "<a href=\"img/svm.png\"><center> <a href=\"img/svm_kernel.jpeg\"><img src=\"img/svm_kernel.jpeg\" alt=\"Drawing\" style=\"width: 700px;\"/> <center/><a/>\n",
    "    \n",
    "Figure reference: Pier Paolo Ippolito.\n",
    "        \n",
    "[https://pierpaolo28.github.io/blog/blog6/](https://pierpaolo28.github.io/blog/blog6/)\n",
    "    \n",
    "\n",
    "For a more detailed explanation of support vector machines explained by the Youtube channel StatQuest:\n",
    "\n",
    "https://www.youtube.com/watch?v=efR1C6CvhmE\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Random forests:__\n",
    "RF's are ensemble learners of many simpler decision classifiers. Random forests can be used both for for regression (predict continous values) and classification (assign labels, e.g. True and False). They work by constructing many decision trees on subsets of the data. The decision trees by themselves will overfit on the training data and produce poorly generalizable decisions. However, by combining many of them (in a \"forest\" of decision trees), the random forest model can often learn complex patterns in the data and predict very well on new data with a similar distribution.\n",
    "\n",
    "In a fully trained model, the prediction that the most decision trees will come to given the input sample (or the mean value in regression) will be outputted by the model. Decision trees are build top-down, by iteratively taking the feature that best separate the data until the tree has only pure nodes (nodes with only one category). \n",
    "By aggregating information from many decision trees it is less likely to overfit the training data and produce a generalizable model. \n",
    "\n",
    "Therefore, RF are a class of models with excellent performance on big datasets, with little chance of overfitting, but it depend on the input features being uncorrelated. \n",
    "    \n",
    "<a href=\"img/rf.png\"><center> <a href=\"img/rf.png\"><img src=\"img/rf.png\" alt=\"Drawing\" style=\"width: 700px;\"/> <center/><a/>\n",
    "    \n",
    "Figure reference: Abilash R, 2018, medium.com.\n",
    "        \n",
    "[https://medium.com/@ar.ingenious/applying-random-forest-classification-machine-learning-algorithm-from-scratch-with-real-24ff198a1c57](https://medium.com/@ar.ingenious/applying-random-forest-classification-machine-learning-algorithm-from-scratch-with-real-24ff198a1c57)\n",
    "    \n",
    "For a more detailed explanation of random forest: \n",
    "\n",
    "https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&t=8s\n",
    "        \n",
    "        \n",
    "Read more about pros and cons of various machine learning models: https://towardsdatascience.com/pros-and-cons-of-various-classification-ml-algorithms-3b5bfb3c87d6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><div class='yellow'>\n",
    "Note: One of the most difficult issues to tackle in machine-learning is the issue of over-fitting. We can define over-fitting as the event of the model learning the data \"by hearth\", including the noise and specific biases of the training set. This means that, while it may perfectly predict all samples in the training set, it will fail to generalize well to new real world data. Meanwhile, an underfitted model is one that can still improve its predictive performance by better fitting the training data, and in doing so, would also generalize better to unseen test data.\n",
    "</div><b/>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"img/over_under_fitting.png\"><center> <a href=\"img/over_under_fitting.png\"><img src=\"img/over_under_fitting.png\" alt=\"Drawing\" style=\"width: 700px;\"/> <center/><a/>\n",
    "Figure reference: [https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76](https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76)\n",
    "\n",
    "The ideal trained model lies somewhere between these two extremes, and is the model which achieves the highest predictive performance on held out test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a choice of three different machine-learning models that we can choose from. You will try them all out later. However, next we need to figure out how to define our training dataset and our validation dataset, so that we can reliably  estimate the performance of our trained models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Train-test-split__\n",
    "\n",
    "When performing machine learning we generally split our data into separate subsets. One way to do this, is to split the data into three: one training set, one validation set and one \"hidden\" test set.\n",
    "\n",
    "The training set is used to fit (train) the model. The validation set is used to estimate the performance of our model and helps us optimize the hyperparameters of the model (e.g. specific parameters in how the random forest model works) or decide which input features or what samples to use in the training set. These two together is what we will be spending most of our time working with.\n",
    "\n",
    "The test set should be a \"hidden\" and in the ideal case, an external dataset which we have not done any hyperparameter optimization against. This will give us a much more accurate estimation of our model performance, and would be what we would report the performance as being if we were to publish the model. The job of the test set is therefore to give an estimation of the \"true\" model performance on a real-world dataset, for which the prediction model cannot be optimized against.\n",
    "\n",
    "If the model underfits, it does not capture trends in the data, and will therefore not generalize to new samples. In such a case, both the training set and test set predictions will be poor.  If on the other hand the model overfits, it means that it likely has adapted to and learned \"noise\" in the training data, yielding excellent performance on the training set but generalizing very poorly to data the model has not seen (validation / test set). While the training evaluation performance may look great, this is not a very useful model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Performance metrics__\n",
    "\n",
    "To measure the performance of a model, we use different metrics. A few of the most common ones are:\n",
    "\n",
    "\n",
    "Accuracy: Accuracy is a metric often used for classification models. It is defined as:\n",
    "$$\\textrm{Accuracy} = \\frac{\\textrm{Number of correct predictions}}{\\textrm{Total number of predictions}}$$\n",
    "\n",
    "Precision & Recall: Likewise for classification models, it is also common to compute the model precision and recall:\n",
    "\n",
    "$$\\textrm{Precision} = \\frac{\\textrm{True positives}}{\\textrm{True positives + False positives}}$$\n",
    "$$\\textrm{Recall} = \\frac{\\textrm{True positives}}{\\textrm{True positives + False negatives}}$$\n",
    "\n",
    "All of these values can be dirrectly canculated from a confusion matrix:\n",
    "<a href=\"img/confusion.png\"><center> <a href=\"img/confusion.png\"><img src=\"img/confusion.png\" alt=\"Drawing\" style=\"width: 300px;\"/> <center/><a/>\n",
    "\n",
    "Figure reference: [https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have made the following function to create tainining and test set splits of userdefined sizes. Here you input your preprocessed data and labels, and the output will be training feature set, training labels, validation feature set and validation labels. \n",
    "\n",
    "__Note:__ For some dataset, with non-randomly distributed rows, it may be needed to perform random sampling, to get a valid train-test-split. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put everything together in a function that returns our training and validation X and Y sets\n",
    "def generate_train_valid_sets(df, targets, validation_set_size_fraction = 0.25):\n",
    "    if len(targets) != len(df):\n",
    "        print(\"Target labels different length than feature dataset!\\n\", len(targets), len(df))\n",
    "    \n",
    "    split_point = int(len(df) * (1-validation_set_size_fraction))\n",
    "    \n",
    "    train_x, valid_x = df[0: split_point], df[split_point: ]\n",
    "    train_y, valid_y = targets[0: split_point], targets[split_point: ]    \n",
    "    \n",
    "    print(\"Returning train_x, train_y, valid_x, valid_y\")\n",
    "    print(train_x.shape, train_y.shape, valid_x.shape, valid_y.shape)\n",
    "    return(train_x, train_y, valid_x, valid_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "import scipy as sc\n",
    "\n",
    "# Calculate how well the trained model predicts the test set\n",
    "def test_performance(model, test_x, test_y):\n",
    "    y_hat = model.predict(test_x)\n",
    "    y_true = test_y\n",
    "    \n",
    "    #Calculate accuracy\n",
    "    acc = np.round(metrics.accuracy_score(y_true, y_hat), 3)\n",
    "    \n",
    "    #Calculate confusion_matrix, with true and false positives/negatives\n",
    "    confusion_m =  metrics.confusion_matrix(y_true, y_hat)\n",
    "    tn, fp, fn, tp = confusion_m.ravel()\n",
    "    \n",
    "    # Next calculate the false true positive rates and precision\n",
    "    precision = np.round(metrics.precision_score(y_true, y_hat, average='binary'), 3)\n",
    "    recall = np.round(metrics.recall_score(y_true, y_hat), 3)\n",
    "    \n",
    "    # Calculate the ROC-AUC and Matthews Correlation Coefficient\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_hat, pos_label=1)\n",
    "    roc_auc = np.round(metrics.auc(fpr, tpr), 3)\n",
    "    prec_rec_auc = np.round(metrics.average_precision_score(y_true, y_hat, pos_label = 1), 3)\n",
    "    mcc = np.round(metrics.matthews_corrcoef(y_true, y_hat), 3) \n",
    "    \n",
    "    # Calculate pearson correlation beween predicted values (y_hat) and true values (y_true)\n",
    "    pearson = sc.stats.pearsonr(y_true, y_hat)\n",
    "    pearson_value, pearson_p_value = np.round(pearson[0], 3), pearson[1]\n",
    "    \n",
    "    print(\"Accuracy:\", acc)\n",
    "    print(\"ROC AUC:\", roc_auc)\n",
    "    print(\"MCC:\", mcc)\n",
    "    print(\"Pearson w/ p-value:\", pearson_value, pearson_p_value)\n",
    "    #print(\"\\nRecall:\", tp, \"/\", tp+fn, \"true positives\")\n",
    "    print(\"Recall\", recall)\n",
    "    print(\"Precision\", precision)\n",
    "   \n",
    "    print(\"\\nConfusion matrix:\")\n",
    "    print(\"[[tn, fp]]\")\n",
    "    print(confusion_m)    \n",
    "    print(\"[[fn tp]]\")\n",
    "    \n",
    "    \n",
    "def plot_boundary(model, valid_x, valid_y, figsize = (5,5)):\n",
    "    from mlxtend.plotting import plot_decision_regions\n",
    "    if valid_x.shape[1] > 2:\n",
    "        print(\"Dataset has more than 2 features:\", valid_x.shape, \"Unable to plot 2-dimensional plot.\")\n",
    "    else:\n",
    "        fig = plt.figure(figsize=figsize)\n",
    "        X = np.array(valid_x)\n",
    "        y = np.array(valid_y, dtype=int)\n",
    "\n",
    "        #plot\n",
    "        #ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "        fig = plot_decision_regions(X=X, y=y, clf=model, legend=2,\n",
    "                                   scatter_kwargs = {\"s\":100, \"alpha\":0.8},\n",
    "                                   zoom_factor = 7)\n",
    "\n",
    "        plt.title(\"Decision boundary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let´s import the machine learning models that we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available features:\n",
      " Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
      "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
      "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
      "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
      "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
      "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
      "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
      "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
      "       'symmetry_worst', 'fractal_dimension_worst'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Available features:\\n\", df_proc_scaled.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have here set up code to run any one of the 3 selected machine learning models. To run the cells, first uncomment the model you wish to run and select the set of features you want to use. \n",
    "When only selecting 2 features, you will also be able to visualize the decision boundary made by the model, for classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To help interpreting the model, we have included a small function, that lets you inspect the decision boundary drawn by the model, when training on two features. Drawing decision boundries in multiple dimensions is out of the scope of this module. \n",
    "\n",
    "Please try fitting the different model types on only two features, to visually inspect the difference in how the models partition the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returning train_x, train_y, valid_x, valid_y\n",
      "(512, 2) (512,) (57, 2) (57,)\n",
      "Accuracy: 0.825\n",
      "ROC AUC: 0.811\n",
      "MCC: 0.577\n",
      "Pearson w/ p-value: 0.577 2.6445253756636745e-06\n",
      "Recall 0.786\n",
      "Precision 0.611\n",
      "\n",
      "Confusion matrix:\n",
      "[[tn, fp]]\n",
      "[[36  7]\n",
      " [ 3 11]]\n",
      "[[fn tp]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decide features to use in the dataset\n",
    "model = LogisticRegression()\n",
    "#model = SVC()\n",
    "#model = RandomForestClassifier(n_estimators = 100, min_samples_split = 2, min_samples_leaf = 1)\n",
    "\n",
    "validation_set_size_fraction = 0.1\n",
    "features_to_use = [\"radius_mean\", \"texture_mean\"]\n",
    "\n",
    "# Load dataset\n",
    "dataset = df_proc_scaled[features_to_use]\n",
    "targets = df_proc_scaled[\"diagnosis\"]\n",
    "\n",
    "# Define training and validation dataset\n",
    "train_x, train_y, valid_x, valid_y = generate_train_valid_sets(dataset, targets,\n",
    "                                                               validation_set_size_fraction = validation_set_size_fraction)\n",
    "# Train model\n",
    "model.fit(train_x, train_y)\n",
    "\n",
    "# Calculate performance and show decision boundary decision between the first two features\n",
    "test_performance(model, valid_x, valid_y)\n",
    "plot_boundary(model, valid_x, valid_y, figsize = (6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To help you explore the impact of different parameters and input features on the model performance, we have formalized a few questions. Please go through these, and use them when playing with model fitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions:\n",
    "\n",
    "1. What does the decision boundary look like if you are training only with a single feature?\n",
    "2. What are the top 2 most predictive features in isolation (only 1 training feature), as judged by accuracy??\n",
    "3. Which combination of 2 features gives the best predictive model, as judged by accuracy? You may want to look at previous sections of the notebook\n",
    "4. What happens if you include \"diagnosis\" as a feature in the prediction model (in isolation or in combination with other features)?\n",
    "5. How would you describe the different decision boundaries of **LogisticRegression**, **SupportVectorMachine** and **RandomForestClassifier** models respectively (use two features at a time)?\n",
    "6. What is the effect of changing the validation set fraction parameter? How does this impact your training model?\n",
    "6. With the best model that you have so far, what is the difference in performance between the normalized dataset (**df_proc_scaled**) vs the un-normalized dataset (**df_proc**)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine-learning competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have played arround with 3 different machine learning algorithms for classification, **we believe you are ready for a challenge!**\n",
    "\n",
    "We therefore invite all workshop participants to take part in the **big machine learning competition**. \n",
    "\n",
    "The aim of the machine learning competition is for you to independently implement a machine learning model, that is able to accurately classify tumor samples from the very same dataset that we have been working with all morning. \n",
    "The winner is the participant that reaches the highest accuracy (as it was defined above). \n",
    "\n",
    "The competition rules are as follow: \n",
    "\n",
    "1. You are only allowed to submit 3 models to the competition. \n",
    "2. You are only allowed to use the specific classification algorithms taught in this workshop.\n",
    "3. You are only allowed to train on the provided dataset. \n",
    "4. Use the already scaled data (df_proc_scaled) to train your model on to ensure compatability with the test set.\n",
    "\n",
    "We would also very much appreciate if you would post a picture of your model accuracy on __social media (Facebook or Twitter) #Biodatascience101__ and tag us in it. Links for our social media pages can be found in the bottom.\n",
    "\n",
    "\n",
    "To ensure fairness, we have implement a class to handle all model submissions. It is already instantiated as \"submit_competition_model\" in the notebook.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<competition_model.competition_model_class at 0x164465100>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If module is loaded\n",
    "#Should print something like: \n",
    "#<competition_model.competition_model_class at 0x7fbf394af940>\n",
    "\n",
    "submit_competition_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It itself holds the test set, so you don't have to worry about that. \n",
    "\n",
    "The module contains two core functionalities:\n",
    "\n",
    "**submit_competition_model.Submit()**: is a method to submit a trained model for the competition. You need to pass it the following things to successfully make the submission: model (sklearn model) and features (list of features used for training e.g. [\"perimeter_worst\", \"radius_worst\"])\n",
    "\n",
    "**submit_competition_model.get_best_model()**: allows you to see the performance of your best model. This method resets every time you restart the notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of how to use submission module: \n",
    "    \n",
    "__Note__: Examples should not be run!"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "submit_competition_model.Submit(model, features = [\"perimeter_worst\", \"radius_worst\"])\n",
    "\n",
    "\n",
    "####Prints#####\n",
    "\n",
    "Accuracy: xxx\n",
    "Recall xxx\n",
    "Precision xxx\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "submit_competition_model.get_best_model()\n",
    "\n",
    "\n",
    "####Prints#####\n",
    "\n",
    "{'Accuracy': xxx,\n",
    " 'Recall': xxx,\n",
    " 'Precision': xxx,\n",
    " 'Features': \"'xxxxx', 'yyyyy'\",\n",
    " 'Params': \"LogisticRegression( prameters)\",\n",
    " 'Model': LogisticRegression( parameters )}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Good luck all!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implement your model here: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We would love to hear about your experience with the notebook. If you would like to give us 2 minutes extra of your time, we have prepared a feedback form here. Thanks a lot!\n",
    "\n",
    "## Biodatscience101 2nd event feedback form: https://forms.gle/j8LHtRe1cDgS8YKy9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Want to stay updated on our next #Biodatscience101 events? We will be posting these on our social media:__\n",
    "\n",
    "Facebook: https://www.facebook.com/biodatascience101\n",
    "\n",
    "Twitter: https://twitter.com/Biodatascience1\n",
    "\n",
    "Linkedin: https://www.linkedin.com/company/biodatascience101/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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