test

PLS.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 19 09:23:49 2021
+
+@author: Dijkhofmf
+"""
+
+# Import stuff
+
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy import stats
+#from pca import pca
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split, KFold, LeaveOneOut
+from sklearn.cross_decomposition import PLSRegression
+from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, cross_val_score
+
+from sklearn.metrics import r2_score, accuracy_score, plot_confusion_matrix, confusion_matrix, roc_curve, auc, roc_auc_score, precision_recall_fscore_support, classification_report, mean_squared_error
+
+#%%  Import data and path
+
+Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
+
+# Set path
+os.chdir(Path)
+
+#%% Create DF
+FinalDF = pd.DataFrame(pd.read_csv('FinalDataset.csv'))
+
+X = FinalDF.drop(['Pt Type'], axis=1)
+X = X.drop(['Study ID'], axis=1)
+
+# #%% 
+
+# plt.figure(dpi=720, figsize=(20,20))
+# heatmap = sns.heatmap(X.corr(), vmin=-1, vmax=1, annot=True)
+# plt.title('Pearson correlation heatmap')
+
+# plt.figure(dpi=720, figsize=(20,20))
+# heatmap = sns.heatmap(X.corr(method='spearman'), vmin=-1, vmax=1, annot=True)
+# plt.title('Spearman correlation heatmap')
+
+
+#%%
+cols = pd.DataFrame(X).columns
+y = FinalDF['Pt Type']
+
+y = y.replace('Complication', int(1))
+y = y.replace('Healthy', int(0))
+
+X = X.dropna()
+
+y = y.loc[X.index]
+y = y.astype(int)
+
+#X = X.reset_index()
+#y = y.reset_index()
+#y = y.drop('index', axis=1)
+#X = X.drop('Study ID', axis=1)
+
+X =  preprocessing.scale(X)
+
+#%%
+
+#PLS = PLSRegression(n_components=2, scale=True)
+
+# # Or reduce the data towards 2 PCs
+# model = pca(n_components=8)
+# # Fit transform
+# results = model.fit_transform(X, y, col_labels=cols)
+# # Plot explained variance
+# fig, ax = model.plot()
+# # Scatter first 2 PCs
+# fig, ax = model.scatter()
+# # Make biplot with the number of features
+# fig, ax = model.biplot(n_feat=8)
+
+#%%
+ 
+def base_pls(X_train, y_train, X_test, n_components):
+
+    # Define PLS model and fit it to the train set
+    PLS = PLSRegression(n_components=n_components)
+    PLS.fit(X_train, y_train)
+
+    # Prediction on train set
+    y_c = PLS.predict(X_train)
+
+    # Prediction on test set
+    y_p = PLS.predict(X_test)
+
+    return(y_c, y_p)
+
+#%%
+
+cval = LeaveOneOut()
+n_components = range(1,19)
+
+X = pd.DataFrame(X)
+yu = pd.DataFrame(y)
+
+rmse_c = np.zeros(len(n_components)).astype('float32') 
+rmse_cv = np.zeros(len(n_components)).astype('float32') 
+R2_cv = np.zeros(len(n_components)).astype('float32') 
+Q2_cv = np.zeros(len(n_components)).astype('float32') 
+PRESScv = np.zeros(len(n_components)).astype('float32') 
+RSS_cv =  np.zeros(len(n_components)).astype('float32') 
+WoldR = np.zeros(len(n_components)).astype('float32') 
+
+for i in n_components: 
+    
+    rmsec = []
+    rmsecv = []
+    PRESS = []
+    RSS = []
+    R2 = []
+    Q2 = []
+        
+    for train, test in cval.split(X):
+        
+        PLS = PLSRegression(n_components=i, scale=False)
+        PLS.fit_transform(X.iloc[train], y.iloc[train])
+        
+        Y_pred_train = PLS.predict(X.iloc[train])
+        Y_pred_test = PLS.predict(X.iloc[test])
+
+        rmsec.append(np.sqrt(mean_squared_error(y.iloc[train], Y_pred_train[:,0])))
+        rmsecv.append(np.sqrt(mean_squared_error(y.iloc[test], Y_pred_test[:,0])))
+       
+        y_value = y.iloc[test]
+        y_value = y_value.iloc[0]
+        
+        RSS.append((Y_pred_test - y_value)**2)
+        PRESS.append(mean_squared_error(y.iloc[test], Y_pred_test[:,0]))
+        R2Y = r2_score(y.iloc[train], Y_pred_train)
+        
+    
+    rmse_c[i-1] = np.mean(np.array(rmsec))
+    rmse_cv[i-1] = np.mean(np.array(rmsecv))
+    RSS_cv[i-1] = np.mean(np.array(RSS))
+    PRESScv[i-1] = np.mean(np.array(PRESS))
+    R2_cv[i-1] = np.mean(np.array(R2Y))
+    if i == 1:
+        Q2_cv[0] = 0  
+    else:
+        Q2_cv[i-1] = (1 - (PRESScv[i-1]/RSS_cv[i-2]))
+ 
+    
+    if i == 1:
+        WoldR[i-1] = 0
+    elif i > 1:
+        WoldR[i-1] = (PRESScv[i-1]/PRESScv[i-2])
+        
+ 
+
+with plt.style.context(('seaborn')):
+    plt.plot(rmse_c, 'o-r', label = "RMSE Calibration")
+    plt.plot(rmse_cv, 'o-b', label = "RMSE Cross Validation")
+    plt.ylabel('RMSE')
+    plt.xlabel('Number of LV included')
+plt.legend()
+plt.show()
+
+with plt.style.context(('seaborn')):
+    plt.plot(R2_cv, 'o-r', label = "R2_score")
+    plt.plot(Q2_cv, 'o-b', label = "Q2_score")
+    plt.ylabel('Score')
+    plt.xlabel('Number of LV included')
+plt.legend()
+plt.show()
+
+with plt.style.context(('seaborn')):
+    plt.plot(WoldR, 'o-r', label = "Wold R")
+    plt.plot(PRESScv, 'o-b', label = "PRESS")
+    plt.ylabel('Score')
+    plt.xlabel('Number of LV included')
+plt.legend()
+plt.show()
+
+#%% Select n_components based on LOOCV:
+
+PLS = PLSRegression(n_components=6, scale=True)
+
+
+PLS.fit_transform(X, y)
+
+Y_pred = PLS.predict(X)
+bin_pred = (PLS.predict(X)[:,0] > 0.5).astype('uint8')
+
+
+plt.figure(dpi=720)
+plt.title('Confusion Matrix PLS-DA')
+cf_rf = confusion_matrix(y, bin_pred)
+sns.heatmap(cf_rf, annot=True, cmap='Blues')
+plt.xlabel('Predicted outcomes')
+plt.ylabel('True outcomes')
+plt.show()
+
+print(classification_report(y, bin_pred))
+scores_PLS = precision_recall_fscore_support(y, bin_pred)
+
+#%%
+
+def vip(model):
+
+  t = model.x_scores_
+  w = model.x_weights_
+  q = model.y_loadings_
+  p, h = w.shape
+  
+  vips = np.zeros((p,))
+
+  s = np.diag(t.T @ t @ q.T @ q).reshape(h, -1)
+  total_s = np.sum(s)
+
+  for i in range(p):
+
+      weight = np.array([ (w[i,j] / np.linalg.norm(w[:,j]))**2 for j in range(h) ])
+      vips[i] = np.sqrt(p*(s.T @ weight)/total_s)
+
+  return vips
+
+VIP = vip(PLS)
+
+#%%
+
+cols = ['Age (years)', 'Gender', 'Daily alcohol use', 'Medication',
+              'ASA-classification', 'Recurrent disease?', 'Comorb',
+              'Independent, with others', 'Smokes cigarettes/sigar', 'BMI', 'GFI',
+              'HADS_A', 'HADS Depression', 'ADL', 'iADL', 'TUG', 'Handgrip strength',
+              'Avg. Steps/day', 'Avg. MVPA/day']
+
+VIP  = pd.DataFrame(VIP, columns=['VIP'])
+VIP['labels'] = cols
+VIP = VIP.sort_values('VIP', ascending=False)
+
+
+#%%
+
+BCT = VIP['VIP'].describe()[6] + (1.5*( VIP['VIP'].describe()[6]- VIP['VIP'].describe()[4]))
+
+#%%
+
+VIP = VIP[VIP['VIP']>1] 
+
+#%%
+import matplotlib.pylab as pylab
+
+params = {'legend.fontsize': 'x-large',
+             'axes.labelsize': 'x-large',
+             'axes.titlesize':'x-large',
+             'xtick.labelsize':'x-large',
+             'ytick.labelsize':'x-large'}
+
+pylab.rcParams.update(params)
+
+
+plt.figure(figsize=(12,7), dpi=720)
+sns.set_style('whitegrid')
+plt.bar(x=VIP['labels'], height=VIP['VIP'])
+plt.title('VIP scores')
+plt.yticks(fontsize='x-large')
+plt.xticks(rotation=-90, fontsize='x-large')
+
+#%% Compute ROC curve and ROC area for each class
+
+fpr = dict()
+tpr = dict()
+roc_auc = dict()
+
+fpr, tpr, _ = roc_curve(y, bin_pred)
+roc_auc = auc(fpr, tpr)
+
+# Compute micro-average ROC curve and ROC area
+fpr_m, tpr_m, _ = roc_curve(y.ravel(), bin_pred.ravel())
+roc_auc_m = auc(fpr_m, tpr_m)
+
+plt.figure(dpi=720)
+lw = 2
+plt.plot(fpr, tpr, color='darkorange',
+         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
+plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.05])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('ROC-Curve PLS-DA')
+plt.legend(loc="lower right")
+plt.show()
+
+plt.figure(dpi=720)
+lw = 2
+plt.plot(fpr_m, tpr_m, color='darkorange',
+         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc_m)
+plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.05])
+plt.xlabel('False Positive Rate')
+plt.ylabel('True Positive Rate')
+plt.title('Receiver operating characteristic example')
+plt.legend(loc="lower right")
+plt.show()