Fix EDA Errors

Closes #11 (closed)

EDA.py

@@ -2,60 +2,74 @@ from sklearn.decomposition import PCA
 from math import ceil
 import numpy as np
 import matplotlib.pyplot as plt
+from tqdm import tqdm
 
-def find_mean_img(full_mat, title):
-    """[summary]
+def find_mean_img(full_mat):
+    """Calculates and plots the mean of each pixel in an image matrix.
 
     Args:
-        full_mat ([type]): [description]
-        title ([type]): [description]
+        full_mat (np.ndarray): Vectorized array of the image matrix.
+        title (String): Name of the title for the plot.
 
     Returns:
-        [type]: [description]
+        matplotlib.plt: A plot of the the mean pixels for each disease category.
     """
+    cols = 4
+    rows = len(full_mat)//cols + 1
+    
+    fig = plt.figure(figsize = (12,6))
+
+    for i, mat in zip(range(0,len(full_mat)),full_mat):
     # calculate the average
-    mean_img = np.mean(full_mat, axis = 0)
+        mean_img = np.mean(full_mat[mat], axis = 0)
     # reshape it back to a matrix
-    mean_img = mean_img.reshape((300,225))
-    plt.imshow(mean_img, vmin=0, vmax=255, cmap='Greys_r')
-    plt.title(f'Average {title}')
-    plt.axis('off')
+        mean_img = mean_img.reshape((200, 150))
+        ax = fig.add_subplot(rows, cols,i+1)
+        ax.imshow(mean_img, vmin=0, vmax=255, cmap='Greys_r')
+        ax.set_title('Average ' + mat)
+        plt.axis('off')
+
+    plt.tight_layout()
+    
+def plot_pca(pca, title, size = (200, 150)):
+    """Plots each decomposed PCA image and labels the amount of variability for each image.
+
+    Args:
+        pca (sklearn PCA object): A fitted PCA object.
+        title (String): Title of the plot.
+        size (tuple, optional): Shape of the image matrix. Defaults to (300,225).
+    """
+    # plot eigen images in a grid
+    n = pca.n_components_
+    print('Number of PC in ' + title + ':', n)
+    fig = plt.figure(figsize=(8, 8))
+    fig.suptitle('PCA Components of ' + title)
+    r = int(n**.5)
+    c = ceil(n/ r) 
+    for i in range(n):
+        ax = fig.add_subplot(r, c, i + 1)
+        ax.imshow(pca.components_[i].reshape(size), 
+                  cmap='Greys_r')
+        ax.set_title("Variance " + "{0:.2f}%".format(pca.explained_variance_ratio_[i] * 100) )
+        plt.axis('off')
+        plt.tight_layout()
+
     plt.show()
-    return mean_img
 
-def eigenimages(full_mat, title, n_comp = 0.7, size = (300,225)):
-    """[summary]
+
+def eigenimages(full_mat,n_comp = 0.7, size = (200, 150)):
+    """Creates creates and fits a PCA estimator from sklearn.
 
     Args:
-        full_mat ([type]): [description]
-        title ([type]): [description]
-        n_comp (float, optional): [description]. Defaults to 0.7.
-        size (tuple, optional): [description]. Defaults to (300,225).
+        full_mat (np.ndarray): A vectorized array of images.
+        n_comp (float, optional): Percentage of desired variability. Defaults to 0.7.
+        size (tuple, optional): Shape of the image matrix. Defaults to (300,225).
 
     Returns:
-        [type]: [description]
+        sklearn PCA object: Fitted PCA model.
     """
     # fit PCA to describe n_comp * variability in the class
     pca = PCA(n_components = n_comp, whiten = True)
     pca.fit(full_mat)
-    print('Number of PC: ', pca.n_components_)
-    return pca
-  
-def plot_pca(pca, size = (300,225)):
-    """[summary]
 
-    Args:
-        pca ([type]): [description]
-        size (tuple, optional): [description]. Defaults to (300,225).
-    """
-    # plot eigenimages in a grid
-    n = pca.n_components_
-    fig = plt.figure(figsize=(8, 8))
-    r = int(n**.5)
-    c = ceil(n/ r)
-    for i in range(n):
-        ax = fig.add_subplot(r, c, i + 1, xticks = [], yticks = [])
-        ax.imshow(pca.components_[i].reshape(size), 
-                  cmap='Greys_r')
-    plt.axis('off')
-    plt.show()
\ No newline at end of file
+    return pca