linear sklearn

dev0_rmse.txt

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+1013603084.8408571

main.py

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+import pandas as pd
+from pandas import DataFrame
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import numpy as np
+
+df = pd.read_csv("train/train.tsv", header=None, sep="\t", error_bad_lines=False)
+dev0 = pd.read_csv("dev-0/in.tsv", header=None, sep="\t", error_bad_lines=False)
+testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False)
+expected = pd.read_csv("dev-0/expected.tsv", header=None, sep="\t", error_bad_lines=False)
+
+all_df = df.copy()
+test_dev0 = dev0.copy()
+test_dev0.insert(0, "dum", 0, True)
+test_dev0 = test_dev0.rename(columns={"dum": 0, 0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6})
+all_df = all_df.append(test_dev0, ignore_index=True)
+
+test_testA = testA.copy()
+test_testA.insert(0, "dum", 0, True)
+test_testA = test_testA.rename(columns={"dum": 0, 0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6})
+all_df = all_df.append(test_testA, ignore_index=True)
+all_df = pd.get_dummies(all_df, columns=[3, 4])
+
+min_val = np.min(all_df)
+max_val = np.max(all_df)
+all_df = (all_df - min_val) / (max_val - min_val)
+
+dummy_df = all_df[:len(df)]
+dummy_dev0 = all_df[len(df):len(df) + len(dev0)]
+dummy_testA = all_df[len(df) + len(dev0):]
+
+X = dummy_df[dummy_df.columns[1:]]
+Y = dummy_df[dummy_df.columns[:1]]
+
+model = LinearRegression().fit(X, Y)
+
+predicted_dev0 = model.predict(dummy_dev0[dummy_dev0.columns[1:]])
+predicted_testA = model.predict(dummy_testA[dummy_testA.columns[1:]])
+
+with open("dev-0/out.tsv", "w") as file:
+    for pred in predicted_dev0:
+        file.write(str(pred[0]) + "\n")
+
+with open("test-A/out.tsv", "w") as file:
+    for pred in predicted_testA:
+        file.write(str(pred[0]) + "\n")
+
+predicted_denormalized = []
+for pred in predicted_dev0:
+    predicted_denormalized.append(pred[0] * (max_val[0] - min_val[0]) + min_val[0])
+
+predicted_denormalized = DataFrame(predicted_denormalized, columns=['pred'])
+error = mean_squared_error(expected, predicted_denormalized)
+for exp, pred in zip(expected.values, predicted_denormalized.values):
+    print(exp, pred)
+    
+f = open("dev0_rmse.txt", "w")
+f.write(str(error))
+f.close()