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 %% Cell type:markdown id:441343d0-e422-4dae-b988-9130e5a0d565 tags:

 ## Packages
 os: Operating system interface

 OpenCV: cv2 `pip install opencv-python>=4.5.5`

 %% Cell type:code id:d7e56e0e-7eec-429d-940b-c3337db4b4dc tags:

 ``` python
 %load_ext autoreload
 %autoreload 2
 import os
 import numpy as np
 import pandas as pd
 import tensorflow as tf
 import importlib as lib
 from data_processing import load_sort_data, transform
 import EDA
 import matplotlib.pyplot as plt
 %matplotlib inline
 ```

-%% Output
-
-    The autoreload extension is already loaded. To reload it, use:
-      %reload_ext autoreload
-
 %% Cell type:markdown id:caa99aac tags:

 # Introduction
 The project we are presenting is a multi-label image classification task based on the 2018 Human vs Machine skin lesion analysis toward melanoma detection hosted by the International Skin Imaging Collaboration (ISIC).

 This notebook will contain the following sections:
 1. Problem Definition & Data Description
 2. Data Preparation
 3. Exploratory Analysis
 4. Data Processing for Model Ingestion
 5. Model Creation
 6. Model Scoring & Evaluation
 7. Interpretation of Results

 %% Cell type:markdown id:830dee53 tags:

 # 1. Problem Definition & Data description

 #### Problem Definition:
 Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. With a sufficiently large and diverse collection of skin lesions we will develop a method to automate the prediction of disease classification within dermoscopic images. The project is meant to human computer computer collaboration and not intended to replace traditional forms of diagnosis.

 Possible disease categories (and abbreviation) for classification are:
 1. Melanoma (mel)
 2. Melanocytic Nevus (nv)
 3. Basal Cell Carcinoma (bcc)
 4. Actinic Keratosis / Bowen's Disease (akiec)
 5. Benign Keratosis (bkl)
 6. Dermatofibroma (df)
 7. Vascular Lesion (vasc)

 #### Data Description
 - Data images are in JPEG format using the naming scheme `ISIC_.jpg` where _ is a 7 digit unique identifier of the image.
 - There are a total of 10,015 600x450 pixel color images contained in the training data folder.
 - There are a total of 193 600x450 pixel color images contained in the validation data folder.
 - The training metadata is a 10,015x8 .csv file containing the following variables*:
  - lesion_id: Unique identifier of a legion with multiple images.
  - image_id: Unique identifier of the associated image file.
  - dx: Prediction label containing the 7 abbreviated disease categories.
  - dx_type: Method of how the diagnosis was confirmed.
  - age: Numeric year age of the patient.
  - sex: String binary value 'male' or 'female'.
  - localization: Categorical location on the body the image was taken.
  - dataset: Image source.

 *Further details of the data will be provided in the data preparation section.

 %% Cell type:markdown id:96ff082e tags:

 # 2. Data Preparation

 #### Step 1. Load and Sort
 First we will load the data using the function `load_sort_data()`.

 The `load_sort_data()` function sorts the images into folders based on the diagnosis label.  This will help reduce the overall size of the dataset and make preprocessing the images much faster.  The function will return the metadata as a pandas DataFrame and the path of the sorted image folders.

 %% Cell type:code id:b8c4f292 tags:

 ``` python
 # function takes 3 parameters: metadata filename, the folder of the raw images, and the desired name of the destination directory.
 metadata, dest_dir = load_sort_data('HAM10000_metadata', 'ISIC2018_Task3_Training_Input', 'Training_Images_')
 ```

 %% Cell type:code id:7e9702c3 tags:

 ``` python
 # The path of our training image folders sorted by label
 dest_dir
 ```

 %% Output

-    'h:\\School\\Winter 2022\\DS Projects\\2018\\hvm-image-clf/data/Training_Images_'
+    'c:\\Users\\Bennett Nolan\\Desktop\\info442\\hvm-image-clf/data/Training_Images_'

 %% Cell type:markdown id:20b8415f tags:

 #### Step 2. Tidy Metadata
 We will now take steps to tidy our metadata.
 First, subset the variables we intend on using, next analyze missingness and finally we will correct our expected datatypes.

 %% Cell type:code id:0ba9148a tags:

 ``` python
 # Subsetting into variables we will use.
 metadata = metadata[['image_id', 'dx', 'age', 'sex', 'localization']]
 # We will need to change the Dtypes of the columns into the expected types
 metadata.info()
 ```

 %% Output

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10015 entries, 0 to 10014
    Data columns (total 5 columns):
     #   Column        Non-Null Count  Dtype
    ---  ------        --------------  -----
     0   image_id      10015 non-null  object
     1   dx            10015 non-null  object
     2   age           9958 non-null   float64
     3   sex           10015 non-null  object
     4   localization  10015 non-null  object
    dtypes: float64(1), object(4)
    memory usage: 391.3+ KB

 %% Cell type:markdown id:3f4a3578 tags:

 As we can see below, we have a total of 57 NA values in age.  When looking at the distribution of NA values, only our largest quantity of labels have NA's. The age variable is only useful in providing context to our problem and will not be used as a feature in our model.  Therefore it is not necessary to do anything further to the NA values.  During exploratory analysis we can deal with the NA values as needed.

 %% Cell type:code id:e6d378d5 tags:

 ``` python
 # Sum the na values contained within each label
 print("Total number of unique labels\n",
      metadata['dx'].value_counts(),
      "\nNumber of NaN values within each label\n",
      metadata.drop('dx',1).isna().groupby(
          metadata.dx,
          dropna=False,
          observed = True
          ).sum().reset_index()
      )
 ```

 %% Output

    Total number of unique labels
     nv       6705
    mel      1113
    bkl      1099
    bcc       514
    akiec     327
    vasc      142
    df        115
    Name: dx, dtype: int64
    Number of NaN values within each label
           dx  image_id  age  sex  localization
    0  akiec         0    0    0             0
    1    bcc         0    0    0             0
    2    bkl         0   10    0             0
    3     df         0    0    0             0
    4    mel         0    2    0             0
    5     nv         0   45    0             0
    6   vasc         0    0    0             0

-    <ipython-input-5-4c0ddd092066>:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
+    C:\Users\Bennett Nolan\AppData\Local\Temp\ipykernel_10700\2944415004.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
      metadata.drop('dx',1).isna().groupby(

 %% Cell type:code id:91aa284b tags:

 ``` python
 #Changing datatypes
 dtypes = {'image_id':'string',
          'dx':'category',
          'sex':'category',
          'localization': 'category'
          }
 metadata = metadata.astype(dtypes)
 metadata.info()
 ```

 %% Output

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10015 entries, 0 to 10014
    Data columns (total 5 columns):
     #   Column        Non-Null Count  Dtype
    ---  ------        --------------  -----
     0   image_id      10015 non-null  string
     1   dx            10015 non-null  category
     2   age           9958 non-null   float64
     3   sex           10015 non-null  category
     4   localization  10015 non-null  category
    dtypes: category(3), float64(1), string(1)
    memory usage: 187.1 KB

 %% Cell type:markdown id:41f467b5 tags:

 #### Step 3. Image Processing

 In this step we will construct an NxM matrix where N is an image and M is the number of pixels in the image.

 %% Cell type:code id:05398a91 tags:

 ``` python
 #Assign vectorized images to variables
 akiec_images = transform(dest_dir + 'akiec')
 ```

 %% Cell type:code id:e8642d8d tags:

 ``` python
 bcc_images = transform(dest_dir + 'bcc')
 ```

 %% Cell type:code id:5312b5de tags:

 ``` python
 bkl_images = transform(dest_dir + 'bkl')
 ```

 %% Cell type:code id:49338970 tags:

 ``` python
 df_images = transform(dest_dir + 'df')
 ```

 %% Cell type:code id:784d69cd tags:

 ``` python
 mel_images = transform(dest_dir + 'mel')
 ```

 %% Cell type:code id:6cd167a7 tags:

 ``` python
 #This takes a really long time to run even when cutting down the images size.
 nv_images = transform(dest_dir + 'nv', size=(200, 150))
 ```

-%% Output
-
-    ---------------------------------------------------------------------------
-    KeyboardInterrupt                         Traceback (most recent call last)
-    <ipython-input-35-c19654a1d8a1> in <module>
-    ----> 1 nv_images = transform(dest_dir + 'nv', size=(200, 150))
-
-    h:\School\Winter 2022\DS Projects\2018\hvm-image-clf\data_processing.py in transform(path, size)
-         50         try:
-         51             # concatenate different images
-    ---> 52             full_mat = np.concatenate((full_mat, img_ts))
-         53         except UnboundLocalError:
-         54             # if not assigned yet, assign one
-    KeyboardInterrupt:
-
 %% Cell type:code id:4de5cec3 tags:

 ``` python
 vasc_images = transform(dest_dir + 'vasc')
 ```

 %% Cell type:code id:d92158fa tags:

 ``` python
 mel_images.shape
 ```

 %% Output

    (1113, 67500)

 %% Cell type:markdown id:3fb5f03e tags:

 # 3. Exploritory Data Analysis

 Exploritory analysis will be conducted on in two major steps.  First we will complete analysis on the metadata then the image dataset.

 %% Cell type:markdown id:6ca3d6bf tags:

 #### Step 1: Metadata EDA
 We will perform the following analysis on the metadata:
 - Summary Statistics
 - Class label distributions
 - Correlation

 %% Cell type:markdown id:686965dd tags:

 Summary Statistics

+%% Cell type:code id:5d475aed tags:
+
+``` python
+metadata.head()
+```
+
+%% Output
+
+           image_id   dx   age   sex localization
+    0  ISIC_0027419  bkl  80.0  male        scalp
+    1  ISIC_0025030  bkl  80.0  male        scalp
+    2  ISIC_0026769  bkl  80.0  male        scalp
+    3  ISIC_0025661  bkl  80.0  male        scalp
+    4  ISIC_0031633  bkl  75.0  male          ear
+
 %% Cell type:code id:6e579e93 tags:

 ``` python
+metadata.agg({
+    "age":["min", "max", "median", "mean", "skew"]
+})
+```
+
+%% Output
+
+                  age
+    min      0.000000
+    max     85.000000
+    median  50.000000
+    mean    51.863828
+    skew    -0.166802
+
+%% Cell type:code id:ea361300 tags:
+
+``` python
+metadata.groupby("sex")["sex"].count()
 ```

+%% Output
+
+    sex
+    female     4552
+    male       5406
+    unknown      57
+    Name: sex, dtype: int64
+
 %% Cell type:markdown id:e031ebf0 tags:

 Class Label Distributions

-%% Cell type:code id:bf51add8 tags:
+%% Cell type:markdown id:bf51add8 tags:
+
+Distributions for metadata including Age, Localization, Sex, and Diagnosis
+
+%% Cell type:code id:6681e88c tags:
+
+``` python
+fig =plt.figure(figsize=(15,15))
+ax1 = fig.add_subplot(221)
+metadata['sex'].value_counts().plot(kind='bar', ax=ax1)
+ax1.set_title('Sex')
+
+ax2=fig.add_subplot(222)
+metadata['localization'].value_counts().plot(kind='bar', ax=ax2)
+ax2.set_title('Localization')
+```
+
+%% Output
+
+    Text(0.5, 1.0, 'Localization')
+
+
+
+%% Cell type:code id:f6596829 tags:

 ``` python
+fig =plt.figure(figsize=(15,15))
+ax1 = fig.add_subplot(221)
+metadata['age'].value_counts().plot(kind='bar', ax=ax1)
+ax1.set_title('Age')
+
+ax2=fig.add_subplot(222)
+metadata['dx'].value_counts().plot(kind='bar', ax=ax2)
+ax2.set_title('Diagnosis')
 ```

+%% Output
+
+    Text(0.5, 1.0, 'Diagnosis')
+
+
+
 %% Cell type:markdown id:8858805d tags:

 Correlation

+%% Cell type:markdown id:fbc246dd tags:
+
+Cross Tabulation of Age and Dx (Skin Lesion)
+nv = Melanocytic nevi
+mel = Melanoma
+bkl = Benign keratosis-like lesions
+bcc = Basal cell carcinoma
+akiec = Actinic keratosis
+vas = Vascular lesions
+df = Dermatofibroma
+
 %% Cell type:code id:a0602660 tags:

 ``` python
+ct = pd.crosstab(index=metadata['age'], columns=metadata['dx'])
+print(ct)
+```
+
+%% Output
+
+    dx    akiec  bcc  bkl  df  mel    nv  vasc
+    age
+    0.0       0    0    5   0    0    30     4
+    5.0       0    0    1   0    1    81     3
+    10.0      0    0    0   0    0    39     2
+    15.0      0    0    0   0    0    73     4
+    20.0      0    1    0   0    6   158     4
+    25.0      0    3    0   2   16   221     5
+    30.0      1    4    6   4   34   410     5
+    35.0      0    5   24  12   36   668     8
+    40.0      9   23   46   9   49   846     3
+    45.0     10   26   59  14   74  1100    16
+    50.0     19   27   87  18   96   928    12
+    55.0     27   25   95  13  142   686    21
+    60.0     58   35  131   9  106   454    10
+    65.0     38   79  108  18  133   351     4
+    70.0     56   85  183   4  166   248    14
+    75.0     47   76  153   9   91   231    11
+    80.0     37   73   98   3   85    97    11
+    85.0     25   52   93   0   76    39     5
+
+%% Cell type:code id:0a82f299 tags:
+
+``` python
+ct2 = pd.crosstab(index=metadata['sex'], columns=metadata['dx'])
+print(ct2)
 ```

+%% Output
+
+    dx       akiec  bcc  bkl  df  mel    nv  vasc
+    sex
+    female     106  197  463  52  424  3237    73
+    male       221  317  626  63  689  3421    69
+    unknown      0    0   10   0    0    47     0
+
+%% Cell type:code id:ad813357 tags:
+
+``` python
+from scipy.stats import chi2_contingency
+#Sex & Localization
+chi2= chi2_contingency(ct2)
+print("PValue = " , chi2[1])
+```
+
+%% Output
+
+    PValue =  2.4464388098587195e-17
+
+%% Cell type:code id:20474e6b tags:
+
+``` python
+#Age & DX
+chi2_2= chi2_contingency(ct)
+print("PValue = " , chi2_2[1])
+```
+
+%% Output
+
+    PValue =  0.0
+
 %% Cell type:markdown id:864a5b70 tags:

 #### Step 2: Image EDA
 We will perform the following analysis on the metadata:
 - Average image of each label.
 - Contrast between the average images.
 - Principal component analysis (PCA) on each label.

 %% Cell type:markdown id:b80a2f2b tags:

 Average Image

 %% Cell type:code id:971b33b7 tags:

 ``` python
 EDA.find_mean_img(bkl_images, "Vasc Images")
 ```

 %% Output



    array([[121.11101 , 122.14468 , 122.84349 , ..., 119.93995 , 118.71974 ,
            117.54959 ],
           [122.58053 , 123.72703 , 124.56142 , ..., 121.46952 , 119.99272 ,
            119.01729 ],
           [122.78344 , 124.01911 , 124.747955, ..., 121.76524 , 120.1738  ,
            119.262054],
           ...,
           [121.69427 , 122.42311 , 123.208374, ..., 116.711555, 115.545044,
            114.91993 ],
           [121.50591 , 122.17652 , 123.054596, ..., 116.464966, 115.33849 ,
            114.60964 ],
           [120.98453 , 121.68972 , 122.56961 , ..., 115.86988 , 114.70428 ,
            113.95268 ]], dtype=float32)

 %% Cell type:markdown id:3acaca8b tags:

 Contrast Between Images

 %% Cell type:code id:39497a39 tags:

 ``` python
 ```

 %% Cell type:markdown id:e327218b tags:

 Principal Component Analysis.

 %% Cell type:code id:ee96ea64 tags:

 ``` python
 EDA.plot_pca(EDA.eigenimages(bkl_images, "bkl images"))
 ```

 %% Output

    Number of PC:  6



 %% Cell type:markdown id:3157d03a tags:

 # 4. Data Processing for Model Ingestion

 %% Cell type:markdown id:64adf033 tags:

 # 5. Model Creation

 %% Cell type:markdown id:c3114115 tags:

 # 6. Model Scoring & Evaluation

 %% Cell type:markdown id:38d22b6e tags:

 # 7. Interpretation of Results