rozwiazanie

rozwiązanie.py

@@ -0,0 +1,50 @@
+import pandas
+from sklearn.linear_model import LinearRegression
+
+r_in = './train/train.tsv'
+# r_expected= './sport-text-classification-ball-ISI-public/train/expected.tsv'
+r_ind_ev = './dev-0/in.tsv'
+
+r_ind_test_A = './test-A/in.tsv'
+
+with open('./names') as f_names:
+    names = f_names.read().rstrip('\n').split('\t')
+
+tsv_read = pandas.read_table(r_in, error_bad_lines=False, sep='\t', names=names)
+tsv_read_dev = pandas.read_table(r_ind_ev, error_bad_lines=False, sep='\t',
+                             names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+tsv_read_test_A = pandas.read_table(r_ind_test_A, error_bad_lines=False, sep='\t',
+                             names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+
+train = pandas.get_dummies(tsv_read, columns=['engineType'])
+
+categorical_cols = train.select_dtypes(include=object).columns.values
+for col in categorical_cols:
+    train[col] = train[col].astype('category').cat.codes
+
+train = train.loc[(train['price'] > 1000)]
+
+X = train.loc[:, train.columns != 'price']
+clf = LinearRegression().fit(X, train['price'])
+
+dev = pandas.get_dummies(tsv_read_dev, columns=['engineType'])
+
+categorical_cols1 = dev.select_dtypes(include=object).columns.values
+for col in categorical_cols1:
+    dev[col] = dev[col].astype('category').cat.codes
+
+predictions = clf.predict(dev)
+
+predictions.tofile("./dev-0/out.tsv", sep='\n')
+
+test = pandas.get_dummies(tsv_read_test_A, columns=['engineType'])
+
+categorical_cols2 = test.select_dtypes(include=object).columns.values
+for col in categorical_cols2:
+    test[col] = test[col].astype('category').cat.codes
+
+predictions = clf.predict(dev)
+
+predictions.tofile("./test-A/out.tsv", sep='\n')