init commit

Author: noahvelasco

Final GMiS Data Analytics 2022 Presentation.pptx

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+import numpy as np
+import pandas as pd
+import math 
+import time
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import mean_squared_error 
+from sklearn.decomposition import PCA
+
+def split_train_test(X,y,percent_train=0.9,seed=None):
+    if seed!=None:
+        np.random.seed(seed)
+    ind = np.random.permutation(X.shape[0])
+    train = ind[:int(X.shape[0]*percent_train)]
+    test = ind[int(X.shape[0]*percent_train):]
+    return X[train],X[test], y[train], y[test] 
+
+
+
+def accuracy(y_true,y_pred):
+    return np.sum(y_true==y_pred)/y_true.shape[0]
+
+
+if __name__ == "__main__":
+   
+    #Noahs Data Path
+    data_path = 'C:\\Users\\Noah\\Desktop\\cahsi_data_2022\\'  # Use your own path her
+
+    df_d1 = pd.read_csv(data_path+'D1.csv')
+    df_d2 = pd.read_csv(data_path+'D2.csv')
+    
+    
+    #Train
+    X_train = df_d1[:].values
+   
+   #training 
+    X_d1 = X_train[:,:-1]
+    y_d1 = X_train[:,-1]
+    
+    #Testing
+    X_test = df_d2[:].values
+    X_d2 = X_test[:,:]
+    
+    
+    
+    pca = PCA(n_components=24)
+    pca.fit(X_d1)
+    ev = pca.explained_variance_ratio_
+    cum_ev = np.cumsum(ev)
+    cum_ev = cum_ev/cum_ev[-1]
+    
+    X_train_t = pca.transform(X_d1)
+    X_test_t = pca.transform(X_d2)
+    
+    
+    
+    
+    
+    #X_train, X_test, y_train, y_test = split_train_test(X,y,seed=20)
+    
+    
+    model = MLPClassifier(solver='adam', alpha=1e-5, batch_size = 400 ,learning_rate='adaptive',momentum=0.95,  hidden_layer_sizes=(400), verbose=True, random_state=1)
+   
+    start = time.time()
+    #model.fit(X_d1, y_d1)
+    model.fit(X_train_t, y_d1)
+    elapsed_time = time.time()-start
+    print('Elapsed_time training  {0:.6f} '.format(elapsed_time))  
+    print('Training iterations  {} '.format(model.n_iter_))  
+    
+    start = time.time()       
+    #pred = model.predict(X_d2)
+    pred = model.predict(X_test_t)
+    elapsed_time = time.time()-start
+    print('Elapsed_time testing  {0:.6f} '.format(elapsed_time))   
+    #print('Accuracy: {0:.6f}'.format(accuracy(y_test,pred)))
+    
+   # print("MSE",mean_squared_error(y_test, pred))
+   
+   
+    #export
+    with open("answer.txt", "w") as txt_file:
+        for line in pred:
+            txt_file.write(str(line) + "\n") # works with any number of elements in a line
+   
+   
+   
+   
+   
+   
+   
+   
+   
+   

MainCode.py

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+
+import numpy as np
+import pandas as pd
+import math 
+import time
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import mean_squared_error 
+
+
+def split_train_test(X,y,percent_train=0.9,seed=None):
+    if seed!=None:
+        np.random.seed(seed)
+    ind = np.random.permutation(X.shape[0])
+    train = ind[:int(X.shape[0]*percent_train)]
+    test = ind[int(X.shape[0]*percent_train):]
+    return X[train],X[test], y[train], y[test] 
+
+
+
+def accuracy(y_true,y_pred):
+    return np.sum(y_true==y_pred)/y_true.shape[0]
+
+
+if __name__ == "__main__":
+   
+    #Noahs Data Path
+    data_path = 'C:\\Users\\Noah\\Desktop\\cahsi_data_2022\\'  # Use your own path her
+
+    df_d1 = pd.read_csv(data_path+'D1.csv')
+    df_d2 = pd.read_csv(data_path+'D2.csv')
+    
+    
+    #Train
+    X = df_d1[:].values
+    X_d1 = X[:,:-1]
+    y_d1 = X[:,-1]
+    
+    X_train, X_test, y_train, y_test = split_train_test(X_d1,y_d1,seed=20)
+
+    
+    model = MLPClassifier(activation= 'relu',solver='adam', alpha=1e-5, batch_size = 100 ,learning_rate='adaptive',momentum=0.95,  hidden_layer_sizes=(200), verbose=True, random_state=1)
+   
+    start = time.time()
+    model.fit(X_train, y_train)
+    elapsed_time = time.time()-start
+    print('Elapsed_time training  {0:.6f} '.format(elapsed_time))  
+    print('Training iterations  {} '.format(model.n_iter_))  
+    
+    start = time.time()       
+    pred = model.predict(X_test)
+    elapsed_time = time.time()-start
+    print('Elapsed_time testing  {0:.6f} '.format(elapsed_time))   
+    print('Accuracy: {0:.6f}'.format(accuracy(y_test,pred)))
+    
+    print("MSE",mean_squared_error(y_test, pred))
+    
+    
+    #export
+    with open("answers.txt", "w") as txt_file:
+        for line in pred:
+            txt_file.write(str(line) + "\n") # works with any number of elements in a line
+    
+    
+    
+    
+    
+    
+    
+