def PLS_Canonical(csv_data, point_index, sub_index, var_name, train=None, components=None): X_array = [] temp_array = [] for j in csv_data: temp_array = j[point_index - 1:point_index + 8] X_array.append(temp_array) X_array = np.array(X_array) if components == None: components = np.shape(X_array)[1] for i in range(7): Y_array = np.array(csv_data[:, sub_index - 1 + i]) plsca = PLSCanonical(n_components=1) plsca.fit(X_array, Y_array) print(var_name[sub_index + i]) print("R^2 =", np.around(plsca.score(X_array, Y_array), decimals=2))
#correct not accurate from sklearn.cross_validation import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics from sklearn.svm import SVC import numpy as np import pandas as pd from sklearn.cross_decomposition import PLSRegression from sklearn.cross_decomposition import PLSCanonical df=pd.read_csv('newdata.csv') x=df.drop(['tag'],axis=1) y=df.drop(['kx','ky','kz','wa','wb','wc','wd','we','wf'],axis=1) X_train , X_test , Y_train , Y_test = train_test_split(x,y , random_state=5) plsr=PLSRegression() plsr.fit(X_train,Y_train) plsc=PLSCanonical() plsc.fit(X_train,Y_train) print (plsr.score(X_test,Y_test)) print (plsc.score(X_test,Y_test))
#correct not accurate from sklearn.cross_validation import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics from sklearn.svm import SVC import numpy as np import pandas as pd from sklearn.cross_decomposition import PLSRegression from sklearn.cross_decomposition import PLSCanonical df = pd.read_csv('newdata.csv') x = df.drop(['tag'], axis=1) y = df.drop(['kx', 'ky', 'kz', 'wa', 'wb', 'wc', 'wd', 'we', 'wf'], axis=1) X_train, X_test, Y_train, Y_test = train_test_split(x, y, random_state=5) plsr = PLSRegression() plsr.fit(X_train, Y_train) plsc = PLSCanonical() plsc.fit(X_train, Y_train) print(plsr.score(X_test, Y_test)) print(plsc.score(X_test, Y_test))