added baseline trainer

trainer.py

@@ -0,0 +1,113 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Dec  5 15:08:41 2019
+
+@author: Saint8312
+"""
+
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import KFold
+from sklearn.metrics import mean_squared_error
+from scipy.stats import pearsonr
+
+import pickle
+import os
+
+def dataset_loader(filepath):
+    data = []
+    try:
+        with open(filepath, 'rb') as fr:
+            try:
+                while True:
+                    data.append(pickle.load(fr))
+            except EOFError:
+                pass            
+    except FileNotFoundError:
+        print('File is not found')
+    saved_ids = [d['id'] for d in data]
+    return data
+
+
+
+
+if __name__ == '__main__':
+    '''
+    load and split the dataset
+    '''
+    dataset = dataset_loader('dataset.pkl')
+
+    features = np.array([data['x_vector'] for data in dataset])
+    labels = np.array([data['y'] for data in dataset])
+    x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size=0.25, random_state=13)
+    print('Training Features Shape:', x_train.shape)
+    print('Training Labels Shape:', y_train.shape)
+    print('Testing Features Shape:', x_test.shape)
+    print('Testing Labels Shape:', y_test.shape)
+#    
+#    '''
+#    data regression
+#    '''
+#    rf = RandomForestRegressor(n_estimators= 1000, random_state=11, verbose=1)
+#    rf.fit(x_train, y_train)
+#    
+#    '''
+#    model saver
+#    '''
+#    with open(os.getcwd()+"/Model/rf_pp_alpha.pkl", "wb") as f:
+#        pickle.dump(rf, f)
+
+    '''
+    model loader
+    '''
+    with open(os.getcwd()+"/Model/rf_pp_alpha.pkl", "rb") as f:
+        rf = pickle.load(f)
+
+    '''
+    train set analysis
+    '''
+    #Mean Absolute Error
+    preds = rf.predict(x_train)
+    errors = abs(preds - y_train)
+    print('Mean Absolute Error:', round(np.mean(errors), 2))
+
+    #Mean Absolute Percentage Error & Accuracy
+    mape = 100 * (errors / y_train)
+    accuracy = 100 - np.mean(mape)
+    print('Accuracy:', round(accuracy, 2), '%.')
+
+    #Root Mean Squared Error
+    rmse = np.sqrt(mean_squared_error(y_train, preds))
+    print('Root Mean Squared Error :', round(rmse, 2))
+
+    #Pearson Correlation Coefficient (PCC) score
+    pcc = pearsonr(y_train, preds)
+    print('Pearson Correlation Coefficient :', round(pcc[0],2))
+    print(preds, y_train)
+
+    '''
+    test set analysis
+    '''
+    #Mean Absolute Error
+    preds = rf.predict(x_test)
+    errors = abs(preds - y_test)
+    print('Mean Absolute Error:', round(np.mean(errors), 2))
+
+    #Mean Absolute Percentage Error & Accuracy
+    mape = 100 * (errors / y_test)
+    accuracy = 100 - np.mean(mape)
+    print('Accuracy:', round(accuracy, 2), '%.')
+
+    #Root Mean Squared Error
+    rmse = np.sqrt(mean_squared_error(y_test, preds))
+    print('Root Mean Squared Error :', round(rmse, 2))
+
+    #Pearson Correlation Coefficient (PCC) score
+    pcc = pearsonr(y_test, preds)
+    print('Pearson Correlation Coefficient :', round(pcc[0],2))
+
+
+#    for i in range(len(preds)):
+#        print(preds[i], y_test[i])