Create housingprice.py

housingprice.py

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+from sklearn.metrics import accuracy_score, recall_score, precision_score
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+import pandas as pd_df
+import numpy as np
+import xgboost as xgb
+import sklearn.metrics as mets
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import VotingRegressor
+
+
+# defining  reading CSV file
+def load_analysis_data(v_in_file):
+    return pd_df.read_csv(v_in_file)
+
+
+def split_data(in_df_clean_base, in_file_process_flag):
+    if in_file_process_flag == 'Trainset':
+        X_train, X_test, y_train, y_test = train_test_split(in_df_clean_base, in_df_clean_base['SalePrice'],
+                                                            test_size=0.2, stratify=in_df_clean_base['YearRemodAdd'])
+        # X_train = pd_df.DataFrame(X_train, columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+        # X_test = pd_df.DataFrame(X_test, columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+    else:
+        X_test = pd_df.DataFrame(in_df_clean_base,
+                                 columns=['PassengerId', 'Pclass', 'gender', 'Age', 'Fare', 'Embarked_val'])
+        y_test = pd_df.DataFrame(in_df_clean_base,
+                                 columns=['PassengerId', 'Survived'])
+        X_train = [0]
+        y_train = [0]
+    return X_train, X_test, y_train, y_test
+
+
+# data analysis
+v_train_file = 'train_HousePrice.csv'
+v_house_base = load_analysis_data(v_train_file)
+v_house_corr = v_house_base.corr()
+
+# print(corr.sort_values(corr["SalesPrice"], ascending=False))
+highest_corr_features = v_house_corr.index[abs(v_house_corr["SalePrice"]) > 0.5]
+Final_columns = highest_corr_features.values
+Final_column = np.append(Final_columns, ['Id'])
+#print(Final_column)
+v_house_reduce = pd_df.DataFrame(v_house_base, columns=Final_column)
+Final_column_val = np.delete(Final_column, np.where(Final_column == 'SalePrice'))
+
+# print(v_house_reduce["SalePrice"].skew())
+
+# std_scale = StandardScaler()
+# X_s = std_scale.fit_transform(v_house_reduce.columns)
+# v_house_std = pd_df.DataFrame(X_s)   # Put the np array back into a pandas DataFrame for later
+# print(v_house_std.info())
+
+X_train, X_test, y_train, y_test = split_data(v_house_reduce, 'Trainset')
+
+X_train = pd_df.DataFrame(X_train, columns=Final_column_val)
+X_test  = pd_df.DataFrame(X_test, columns=Final_column_val)
+
+xgb_boost = xgb.XGBRegressor(eta=0.2, max_depth=5,subsample=0.8)
+xgb_model = xgb_boost.fit(X_train, y_train, eval_metric=mets.r2_score)
+
+reg1 = GradientBoostingRegressor(n_estimators=100, learning_rate=0.2)
+reg2 = RandomForestRegressor(n_estimators=100, max_depth=4)
+reg3 = LinearRegression()
+
+reg1.fit(X_train, y_train)
+reg2.fit(X_train, y_train)
+reg3.fit(X_train, y_train)
+
+ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
+ereg.fit(X_train, y_train)
+
+xt = X_test
+
+pred1 = reg1.predict(xt)
+pred2 = reg2.predict(xt)
+pred3 = reg3.predict(xt)
+pred4 = ereg.predict(xt)
+pred5 = xgb_model.predict(xt)
+
+plt.plot(pred1, 'gd', label='GradientBoostingRegressor')
+plt.plot(pred2, 'b^', label='RandomForestRegressor')
+plt.plot(pred3, 'ys', label='LinearRegression')
+plt.plot(pred5, 'xg', label='Xgboost')
+plt.plot(pred4, 'r*', ms=10, label='VotingRegressor')
+
+plt.tick_params(axis='x', which='both', bottom=False, top=False,
+                labelbottom=False)
+plt.ylabel('predicted')
+plt.xlabel('training samples')
+plt.legend(loc="best")
+plt.title('Regressor predictions and their average')
+
+#plt.show()
+
+v_train_file = 'testHousePrice.csv'
+v_house_base_test = load_analysis_data(v_train_file)
+
+#print(Final_column_val)
+v_house_base_test_red = pd_df.DataFrame(v_house_base_test, columns=Final_column_val)
+print(v_house_base_test_red.info())
+
+
+median_impute = SimpleImputer(strategy="median")
+median_impute.fit(v_house_base_test_red)
+X_val = median_impute.transform(v_house_base_test_red)
+v_in_df_data_clean = pd_df.DataFrame(X_val, columns=v_house_base_test_red.columns)
+print(v_in_df_data_clean.info())
+
+
+pred6 = ereg.predict(v_in_df_data_clean)
+pred7 = xgb_model.predict(v_in_df_data_clean)
+
+## Save Result in Output File
+submit_train = pd_df.DataFrame({"PassengerId": X_test.Id, 'calculated_voting': pred4, 'Actual': y_test,
+                           'xgboost': pred5, 'Score_voting': mets.r2_score(pred4, y_test),
+                           'Score_voting_xgb': mets.r2_score(pred5, y_test)})
+
+submit = pd_df.DataFrame({"Id": v_house_base_test_red.Id, 'SalePrice': pred6, 'SalePricexgb': pred7})
+#submit = pd_df.DataFrame(v_in_df_data_clean)
+submit.to_csv("Submission_houseprice_28.csv", index=False)
+submit_train.to_csv("Submission_houseprice_test1.csv", index=False)