Created
June 9, 2014 00:38
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Kaggle Bike Sharing Demand - Decision Tree Regressor
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| import numpy as np | |
| import pandas as pd | |
| import statsmodels.formula.api as sm #lin reg | |
| import pylab as py | |
| import matplotlib as mp | |
| from sklearn.tree import DecisionTreeRegressor | |
| from sklearn.ensemble import ExtraTreesRegressor | |
| from sklearn.ensemble import RandomForestRegressor | |
| #%pylab qt | |
| #create graphs | |
| filepath = '/Documents/Data Science/Kaggle/Bike Sharing Demand/' | |
| def read_file(path): | |
| data = pd.read_csv(path, parse_dates = ['datetime'], index_col = 'datetime') | |
| return data | |
| def describe_data(bk, test): | |
| print bk.head() | |
| print test.head() | |
| def clean_data_bk(bk): | |
| bk = bk.drop('casual', 1) | |
| bk = bk.drop('registered',1) | |
| #Adding a weekday variable | |
| bk['weekday'] = bk.index.weekday | |
| return bk | |
| def clean_data_test(test): | |
| test['weekday'] = test.index.weekday | |
| return test | |
| def decision_tree_regressor_fit(bk_columns, bk): | |
| clf = DecisionTreeRegressor() | |
| X = bk[bk_columns] | |
| y = bk['count'] | |
| clf = clf.fit(X, y) | |
| return clf | |
| def decision_tree_prediction(bk_columns, clf, test): | |
| clf_pred_1 = clf.predict(test[bk_columns]) | |
| print clf_pred_1 | |
| return clf_pred_1 | |
| def main(): | |
| bk = read_file(filepath + 'train.csv') | |
| test = read_file(filepath + 'test.csv') | |
| bk = clean_data_bk(bk) | |
| test = clean_data_test(test) | |
| describe_data(bk, test) | |
| bk_columns = bk.columns.tolist() | |
| bk_columns.remove('count') | |
| clf = decision_tree_regressor_fit(bk_columns, bk) | |
| clf_pred_1 = decision_tree_prediction(bk_columns, clf, test) | |
| clf_pred_1 = pd.DataFrame(clf_pred_1, index = test.index, columns = ['count']) | |
| results_tree_file = filepath + 'results_tree1.csv' | |
| clf_pred_1.to_csv(results_tree_file, index_label = ['datetime']) | |
| if __name__ == '__main__': | |
| main() |
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