| # Install on Terminal of MacOS # 1. pandas #pip3 install -U pandas # 2. NumPy #pip3 install -U numpy # 3. matplotlib #pip3 install -U matplotlib # 4. scikit-learn (sklearn) #pip3 install -U scikit-learn # 5. statsmodels #pip3 install -U statsmodels |
1_MacOS_Terminal.txt
| ########## Run Terminal on MacOS and execute ### TO UPDATE cd "YOUR_WORKING_DIRECTORY" python3 ttscv01.py python3 ttscv02.py 2 python3 ttscv03.py 2 #python3 ttscv03.py 3 |
Input data files
X.csv
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| y 151.0 75.0 141.0 206.0 135.0 97.0 138.0 63.0 110.0 310.0 101.0 69.0 179.0 185.0 118.0 171.0 166.0 144.0 97.0 168.0 68.0 49.0 68.0 245.0 184.0 202.0 137.0 85.0 131.0 283.0 129.0 59.0 341.0 87.0 65.0 102.0 265.0 276.0 252.0 90.0 100.0 55.0 61.0 92.0 259.0 53.0 190.0 142.0 75.0 142.0 155.0 225.0 59.0 104.0 182.0 128.0 52.0 37.0 170.0 170.0 61.0 144.0 52.0 128.0 71.0 163.0 150.0 97.0 160.0 178.0 48.0 270.0 202.0 111.0 85.0 42.0 170.0 200.0 252.0 113.0 143.0 51.0 52.0 210.0 65.0 141.0 55.0 134.0 42.0 111.0 98.0 164.0 48.0 96.0 90.0 162.0 150.0 279.0 92.0 83.0 128.0 102.0 302.0 198.0 95.0 53.0 134.0 144.0 232.0 81.0 104.0 59.0 246.0 297.0 258.0 229.0 275.0 281.0 179.0 200.0 200.0 173.0 180.0 84.0 121.0 161.0 99.0 109.0 115.0 268.0 274.0 158.0 107.0 83.0 103.0 272.0 85.0 280.0 336.0 281.0 118.0 317.0 235.0 60.0 174.0 259.0 178.0 128.0 96.0 126.0 288.0 88.0 292.0 71.0 197.0 186.0 25.0 84.0 96.0 195.0 53.0 217.0 172.0 131.0 214.0 59.0 70.0 220.0 268.0 152.0 47.0 74.0 295.0 101.0 151.0 127.0 237.0 225.0 81.0 151.0 107.0 64.0 138.0 185.0 265.0 101.0 137.0 143.0 141.0 79.0 292.0 178.0 91.0 116.0 86.0 122.0 72.0 129.0 142.0 90.0 158.0 39.0 196.0 222.0 277.0 99.0 196.0 202.0 155.0 77.0 191.0 70.0 73.0 49.0 65.0 263.0 248.0 296.0 214.0 185.0 78.0 93.0 252.0 150.0 77.0 208.0 77.0 108.0 160.0 53.0 220.0 154.0 259.0 90.0 246.0 124.0 67.0 72.0 257.0 262.0 275.0 177.0 71.0 47.0 187.0 125.0 78.0 51.0 258.0 215.0 303.0 243.0 91.0 150.0 310.0 153.0 346.0 63.0 89.0 50.0 39.0 103.0 308.0 116.0 145.0 74.0 45.0 115.0 264.0 87.0 202.0 127.0 182.0 241.0 66.0 94.0 283.0 64.0 102.0 200.0 265.0 94.0 230.0 181.0 156.0 233.0 60.0 219.0 80.0 68.0 332.0 248.0 84.0 200.0 55.0 85.0 89.0 31.0 129.0 83.0 275.0 65.0 198.0 236.0 253.0 124.0 44.0 172.0 114.0 142.0 109.0 180.0 144.0 163.0 147.0 97.0 220.0 190.0 109.0 191.0 122.0 230.0 242.0 248.0 249.0 192.0 131.0 237.0 78.0 135.0 244.0 199.0 270.0 164.0 72.0 96.0 306.0 91.0 214.0 95.0 216.0 263.0 178.0 113.0 200.0 139.0 139.0 88.0 148.0 88.0 243.0 71.0 77.0 109.0 272.0 60.0 54.0 221.0 90.0 311.0 281.0 182.0 321.0 58.0 262.0 206.0 233.0 242.0 123.0 167.0 63.0 197.0 71.0 168.0 140.0 217.0 121.0 235.0 245.0 40.0 52.0 104.0 132.0 88.0 69.0 219.0 72.0 201.0 110.0 51.0 277.0 63.0 118.0 69.0 273.0 258.0 43.0 198.0 242.0 232.0 175.0 93.0 168.0 275.0 293.0 281.0 72.0 140.0 189.0 181.0 209.0 136.0 261.0 113.0 131.0 174.0 257.0 55.0 84.0 42.0 146.0 212.0 233.0 91.0 111.0 152.0 120.0 67.0 310.0 94.0 183.0 66.0 173.0 72.0 49.0 64.0 48.0 178.0 104.0 132.0 220.0 57.0 |
Python files
ttscv01.py
| #################### Training/Test Split in Python #################### # #Input data files: #y.csv dependent variable (target) y #X.csv independent variables x1, x2, ... (before standardization) # #Reference #https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6 ########## import import pandas as pd from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split from matplotlib import pyplot as plt import numpy as np import statsmodels.api as sm from sklearn.preprocessing import StandardScaler ########## Load the Diabetes dataset ##columns = “age sex bmi map tc ldl hdl tch ltg glu”.split() # Declare the columns names #columns = ['age', 'sex', 'bmi', 'map', 'tc', 'ldl', 'hdl', 'tch', 'ltg', 'glu'] #print(columns) #diabetes = datasets.load_diabetes() # Call the diabetes dataset from sklearn #X = pd.DataFrame(diabetes.data, columns=columns) # load the dataset as a pandas data frame #print(X) ''' age sex bmi ... tch ltg glu 0 0.038076 0.050680 0.061696 ... -0.002592 0.019908 -0.017646 1 -0.001882 -0.044642 -0.051474 ... -0.039493 -0.068330 -0.092204 2 0.085299 0.050680 0.044451 ... -0.002592 0.002864 -0.025930 3 -0.089063 -0.044642 -0.011595 ... 0.034309 0.022692 -0.009362 4 0.005383 -0.044642 -0.036385 ... -0.002592 -0.031991 -0.046641 .. ... ... ... ... ... ... ... 437 0.041708 0.050680 0.019662 ... -0.002592 0.031193 0.007207 438 -0.005515 0.050680 -0.015906 ... 0.034309 -0.018118 0.044485 439 0.041708 0.050680 -0.015906 ... -0.011080 -0.046879 0.015491 440 -0.045472 -0.044642 0.039062 ... 0.026560 0.044528 -0.025930 441 -0.045472 -0.044642 -0.073030 ... -0.039493 -0.004220 0.003064 ''' #print(type(X)) #<class 'pandas.core.frame.DataFrame'> # #X.to_csv('X.csv', header = True, index = False) #y = diabetes.target # define the target variable (dependent variable) as y #print(y) #print(type(y)) #<class 'numpy.ndarray'> # #pd.DataFrame(y).to_csv('y.csv', header = True, index = False) X = pd.read_csv('X.csv', header=0) #print(type(X)) #print(X) y = pd.read_csv('y.csv', header=0) #y.rename(columns={'0': 'y'}, inplace = True) #y = np.array(y) #print(type(y)) #print(y) ########## Standardization of X scaler = StandardScaler() scaler.fit(X) XSTD = pd.DataFrame(scaler.transform(X), columns = X.columns) #print(XSTD.describe()) ''' age sex ... ltg glu count 4.420000e+02 4.420000e+02 ... 4.420000e+02 4.420000e+02 mean -3.215126e-17 1.607563e-17 ... 8.037814e-18 -3.617016e-17 std 1.001133e+00 1.001133e+00 ... 1.001133e+00 1.001133e+00 min -2.254290e+00 -9.385367e-01 ... -2.651046e+00 -2.896390e+00 25% -7.841722e-01 -9.385367e-01 ... -6.990157e-01 -6.975491e-01 50% 1.131724e-01 -9.385367e-01 ... -4.094666e-02 -2.265729e-02 75% 8.005001e-01 1.065488e+00 ... 6.818695e-01 5.869224e-01 max 2.327895e+00 1.065488e+00 ... 2.808758e+00 2.851075e+00 ''' ##### Note that original data of sex should be 0 or 1 (i.e., dummy variable). XSTD.to_csv('XSTD.csv', header=True, index=False) #y = pd.DataFrame(y) #y.rename(columns={0: 'y'}, inplace = True) yXSTD = pd.concat([y, XSTD], axis=1) yXSTD.to_csv('yXSTD.csv', header=True, index=False) ########## Train/Test Split # create training and testing vars XSTD_train, XSTD_test, y_train, y_test = train_test_split(XSTD, y, test_size=0.2, random_state = 0) # #print (XSTD_train.shape, ySTD_train.shape) #(353, 10) (353, 1) # #print (XSTD_test.shape, ySTD_test.shape) #(89, 10) (89, 1) #print(type(XSTD_train)) #<class 'pandas.core.frame.DataFrame'> #print(type(y_train)) #<class 'pandas.core.frame.DataFrame'> ########## multiple linear regression model fitting lm = linear_model.LinearRegression() model = lm.fit(XSTD_train, y_train) #print(model.score(XSTD_train, y_train)) #0.5539285357415583 #print(model.score(XSTD_test, y_test)) #0.3322220326906514 #print(model.intercept_[0]) #152.53813351954003 #print(model.coef_) ''' [[ -1.69126625 -11.56638059 26.7674803 14.52982275 -31.52559699 15.4242019 1.17875634 8.10179878 34.80237853 2.04665552]] ''' #print(type(model.predict(XSTD_train))) #<class 'numpy.ndarray'> # #print(model.predict(XSTD_train)[0:5]) ''' [[145.9502111 ] [100.12253043] [123.30242341] [ 77.82273702] [148.66482879]] ''' ypred_train = model.predict(XSTD_train) #print(type(ypred_train)) #<class 'numpy.ndarray'> ypred_test = model.predict(XSTD_test) ########## Output: Training Data & Predictions #y_train = pd.DataFrame(y_train) #y_train.rename(columns={0: 'y'}, inplace = True) y_train.reset_index(drop=True, inplace = True) #print(y_train) #print(XSTD_train) XSTD_train.reset_index(drop=True, inplace = True) #print(XSTD_train) ypred_train = pd.DataFrame(ypred_train) ypred_train.rename(columns={0: 'ypred'}, inplace = True) #print(ypred_train) yXSTDypred_train = pd.concat([y_train, XSTD_train, ypred_train], axis=1) #print(yXSTDypred_train) ''' y age sex bmi ... tch ltg glu ypred 0 85.0 0.265912 1.065488 0.050805 ... -0.830301 0.078035 1.544833 145.950211 1 137.0 -2.254290 -0.938537 -1.626013 ... -1.606102 -0.895027 -0.109740 100.122530 2 53.0 0.571391 1.065488 -0.742285 ... -0.054499 -0.314442 -1.067651 123.302423 3 51.0 -0.115937 1.065488 0.028145 ... -0.830301 -0.865768 -1.851396 77.822737 4 197.0 1.411458 1.065488 0.436020 ... -0.054499 0.181652 0.064426 148.664829 .. ... ... ... ... ... ... ... ... ... 348 248.0 1.487828 1.065488 -0.153132 ... 2.272906 2.712478 1.196502 221.168821 349 91.0 1.182349 1.065488 -0.651646 ... -0.830301 -0.620783 -1.241817 87.459215 350 281.0 1.258719 -0.938537 -0.447709 ... 1.497104 1.663425 2.851075 230.902621 351 142.0 -1.643332 -0.938537 -1.535374 ... -0.830301 -0.380915 -1.764314 115.024538 352 295.0 0.876870 1.065488 1.501026 ... 0.721302 1.543359 1.806082 223.952368 [353 rows x 12 columns] ''' yXSTDypred_train.to_csv('yXSTDypred_train.csv', header = True, index = False) ########## Output: Test Data & Predictions (of a model built with Training Data) #y_test = pd.DataFrame(y_test) #y_test.rename(columns={0: 'y'}, inplace = True) y_test.reset_index(drop=True, inplace = True) #print(y_test) #print(XSTD_test) XSTD_test.reset_index(drop=True, inplace = True) #print(XSTD_test) ypred_test = pd.DataFrame(ypred_test) ypred_test.rename(columns={0: 'ypred'}, inplace = True) #print(ypred_test) yXSTDypred_test = pd.concat([y_test, XSTD_test, ypred_test], axis=1) yXSTDypred_test.to_csv('yXSTDypred_test.csv', header = True, index = False) ########## plot plt.scatter(y_train, ypred_train) plt.xlabel('y (Training Data)') plt.ylabel('ypred (Training Data)') plt.savefig('Fig_1.png') plt.show() plt.scatter(y_test, ypred_test) plt.xlabel('y (Test Data)') plt.ylabel('ypred (A model built by Training Data is applied to Test Data)') plt.savefig('Fig_2.png') plt.show() ########## multiple linear regression model fitting (reprise) smXSTD_train = sm.add_constant(XSTD_train) model2 = sm.OLS(y_train, smXSTD_train) model2fit = model2.fit() #print(model2fit.summary()) ''' OLS Regression Results ============================================================================== Dep. Variable: y R-squared: 0.554 Model: OLS Adj. R-squared: 0.541 Method: Least Squares F-statistic: 42.47 Date: Tue, 14 Jul 2020 Prob (F-statistic): 4.05e-54 Time: 11:05:50 Log-Likelihood: -1897.7 No. Observations: 353 AIC: 3817. Df Residuals: 342 BIC: 3860. Df Model: 10 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 152.5381 2.836 53.789 0.000 146.960 158.116 age -1.6913 3.121 -0.542 0.588 -7.829 4.447 sex -11.5664 3.155 -3.666 0.000 -17.772 -5.361 bmi 26.7675 3.411 7.848 0.000 20.059 33.476 map 14.5298 3.529 4.117 0.000 7.588 21.471 tc -31.5256 20.866 -1.511 0.132 -72.567 9.516 ldl 15.4242 16.828 0.917 0.360 -17.675 48.524 hdl 1.1788 11.105 0.106 0.916 -20.665 23.022 tch 8.1018 8.627 0.939 0.348 -8.867 25.071 ltg 34.8024 8.729 3.987 0.000 17.633 51.972 glu 2.0467 3.359 0.609 0.543 -4.561 8.654 ============================================================================== Omnibus: 2.147 Durbin-Watson: 1.890 Prob(Omnibus): 0.342 Jarque-Bera (JB): 1.789 Skew: 0.003 Prob(JB): 0.409 Kurtosis: 2.651 Cond. No. 21.4 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ''' #print(model.intercept_[0]) #152.5381335195406 #print(model.coef_) ''' [[ -1.69126625 -11.56638059 26.7674803 14.52982275 -31.52559699 15.4242019 1.17875634 8.10179878 34.80237853 2.04665552]] ''' with open('model2fit.summary.txt', 'w') as f: print(model2fit.summary(), file=f) |
ttscv02.py
| #################### K-Folds Cross Validation in Python (Example) #################### # # Run this script as follows: # python3 ttscv02.py 2 # python3 ttscv02.py (number of subsets for K-Folds) # #Input data files: #y.csv dependent variable (target) y #X.csv independent variables x1, x2, ... (before standardization) # #Reference #https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6 ##### import import sys #print(sys.argv[0]) #ttscv02.py # n = int(sys.argv[1]) ##### ########## K-Folds Cross Validation ##### import from sklearn.model_selection import KFold import numpy as np ##### #X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) # create an array X = np.array([[0, 10], [20, 30], [40, 50], [60, 70]]) # create an array #y = np.array([1, 2, 3, 4]) # Create another array y = np.array([0, 1, 2, 3]) # Create another array #kf = KFold(n_splits=2) # Define the split - into 2 folds #kf = KFold(n_splits=n) # Define the split - into n folds kf = KFold(n_splits=n, random_state=None, shuffle=False) # Define the split - into n folds #kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator #print(kf.get_n_splits(X)) #2 # #print(kf) #KFold(n_splits=2, random_state=None, shuffle=False) #KFold(n_splits=n, random_state=None, shuffle=False) for train_index, test_index in kf.split(X): print('Training index:', train_index) print('Test index:', test_index) print('') # X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] print('X Training: \n', X_train) print('') print('X Test: \n', X_test) print('') print('y Training: \n', y_train) print('') print('y Test: \n', y_test) print('') #('TRAIN:', array([2, 3]), 'TEST:', array([0, 1])) #('TRAIN:', array([0, 1]), 'TEST:', array([2, 3])) |
ttscv03.py
| #################### K-Folds Cross Validation in Python #################### # # Run this script as follows: # python3 ttscv02.py 2 # python3 ttscv02.py (number of subsets for K-Folds) # #Input data files: #y.csv dependent variable (target) y #X.csv independent variables x1, x2, ... (before standardization) # #Reference #https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6 ##### import import sys import pandas as pd #print(sys.argv[0]) #ttscv02.py # n = int(sys.argv[1]) #i = 0 i = 1 ##### ########## loading data XSTD = pd.read_csv('XSTD.csv', header=0) X = XSTD y = pd.read_csv('y.csv', header=0) ########## K-Folds Cross Validation ##### import from sklearn.model_selection import KFold import numpy as np ##### #X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) # create an array #X = np.array([[0, 10], [20, 30], [40, 50], [60, 70]]) # create an array #y = np.array([1, 2, 3, 4]) # Create another array #y = np.array([0, 1, 2, 3]) # Create another array #kf = KFold(n_splits=2) # Define the split - into 2 folds #kf = KFold(n_splits=n) # Define the split - into n folds kf = KFold(n_splits=n, random_state=None, shuffle=False) # Define the split - into n folds #kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator #print(kf.get_n_splits(X)) #2 # #print(kf) #KFold(n_splits=2, random_state=None, shuffle=False) #KFold(n_splits=n, random_state=None, shuffle=False) #print(X.index) #print(list(X.index)) #print(X) #print(X[0:5]) #for train_index, test_index in kf.split(X): #for train_index, test_index in kf.split(np.array(X)): #print(kf.split(list(X.index))) #print(type(kf.split(list(X.index)))) # for train_index, test_index in kf.split(list(X.index)): print('Round: ', i) # #print('Training index:', train_index) #print(type(train_index)) #<class 'numpy.ndarray'> #print(train_index.shape) #(397,) # #train_max = max(train_index) #print('Training index (max):', max(train_index)) # #train_min = min(train_index) #print('Training index (min):', min(train_index)) #print('') # #print(pd.DataFrame(train_index)) # # #print('Test index:', test_index) #test_max = max(test_index) #print('Test index (max):', max(test_index)) # #test_min = min(test_index) #print('Test index (min):', min(test_index)) # #print(pd.DataFrame(test_index)) # #print('') # #print(X[train_index]) #print(X[list(train_index)]) #print(list(train_index)) #print(X[slice(train_index)]) #print(X[np.array(train_index)]) #print(X[pd.DataFrame(train_index)]) # #print(train_index.flatten()) #print(X[train_index.flatten()]) # #print(X.iloc[[0,]]) #print(train_index.shape) #print(pd.Series(train_index[:])) # X_train, X_test = X.iloc[train_index], X.iloc[test_index] #print(type(X_train)) #X_train, X_test = X[train_index], X[test_index] #X_train, X_test = X[train_index.flatten()], X[test_index.flatten()] #X_train, X_test = X[train_min:train_max+1], X[test_min:test_max+1] #X_train, X_test = X[pd.DataFrame(train_index)], X[pd.DataFrame(test_index)] #X_train, X_test = X[list(train_index)], X[list(test_index)] # y_train, y_test = y.iloc[train_index], y.iloc[test_index] #y_train, y_test = y[train_index], y[test_index] #y_train, y_test = y[train_min:train_max+1], y[test_min:test_max+1] #y_train, y_test = y[pd.DataFrame(train_index)], y[pd.DataFrame(test_index)] # #print('X Training: \n', X_train) #print('') #print('X Test: \n', X_test) #print('') #print('y Training: \n', y_train) #print('') #print('y Test: \n', y_test) #print('') # X_train.to_csv(str(i) +'_X_train_.csv', header = True, index = False) X_test.to_csv(str(i) +'_X_test_.csv', header = True, index = False) y_train.to_csv(str(i) +'_y_train_.csv', header = True, index = False) y_test.to_csv(str(i) + '_y_test_.csv', header = True, index = False) # i = i + 1 #print(i) print('***************') |
Figures
Fig_1.png
Fig_2.png
References
https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6
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