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linear sklearn v2

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This commit is contained in:
Karolina Oparczyk 2021-05-17 17:52:41 +02:00
parent 4b141f3a8c
commit 916bb37c66
4 changed files with 2028 additions and 2030 deletions
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2000
dev-0/out.tsv
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2
dev0_rmse.txt
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@ -1 +1 @@
1013603084.8408571 1165319253.2984157

56
main.py
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@ -4,38 +4,33 @@ from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_squared_error
import numpy as np import numpy as np
df = pd.read_csv("train/train.tsv", header=None, sep="\t", error_bad_lines=False) df = pd.read_csv("train/train.tsv", header=None, sep="\t", error_bad_lines=False, names=['price', 'mileage', 'year',
dev0 = pd.read_csv("dev-0/in.tsv", header=None, sep="\t", error_bad_lines=False) 'brand', 'engineType',
testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False) 'engineCapacity'])
expected = pd.read_csv("dev-0/expected.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, names=['mileage', 'year',
'brand', 'engineType',
'engineCapacity'])
testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False, names=['mileage', 'year',
'brand', 'engineType',
'engineCapacity'])
expected = pd.read_csv("dev-0/expected.tsv", header=None, sep="\t", error_bad_lines=False, names=['price'])
all_df = df.copy() df = df[['price', 'year', 'mileage', 'engineCapacity']]
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() min_val = np.min(df)
test_testA.insert(0, "dum", 0, True) max_val = np.max(df)
test_testA = test_testA.rename(columns={"dum": 0, 0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6}) df = (df - min_val) / (max_val - min_val)
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) Y = df[['price']]
max_val = np.max(all_df) X = df[['year', 'mileage', 'engineCapacity']]
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) model = LinearRegression().fit(X, Y)
predicted_dev0 = model.predict(dummy_dev0[dummy_dev0.columns[1:]]) dev0 = (dev0[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
predicted_testA = model.predict(dummy_testA[dummy_testA.columns[1:]]) testA = (testA[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
predicted_dev0 = model.predict(dev0[['year', 'mileage', 'engineCapacity']])
predicted_testA = model.predict(testA[['year', 'mileage', 'engineCapacity']])
with open("dev-0/out.tsv", "w") as file: with open("dev-0/out.tsv", "w") as file:
for pred in predicted_dev0: for pred in predicted_dev0:
@ -45,15 +40,18 @@ with open("test-A/out.tsv", "w") as file:
for pred in predicted_testA: for pred in predicted_testA:
file.write(str(pred[0]) + "\n") file.write(str(pred[0]) + "\n")
predicted_denormalized = [] predicted_denormalized = []
for pred in predicted_dev0: for pred in predicted_dev0:
predicted_denormalized.append(pred[0] * (max_val[0] - min_val[0]) + min_val[0]) denorm = pred[0] * (max_val[0] - min_val[0]) + min_val[0]
predicted_denormalized.append(denorm)
predicted_denormalized = DataFrame(predicted_denormalized, columns=['pred']) predicted_denormalized = DataFrame(predicted_denormalized, columns=['pred'])
error = mean_squared_error(expected, predicted_denormalized) error = mean_squared_error(expected, predicted_denormalized)
for exp, pred in zip(expected.values, predicted_denormalized.values): for exp, pred in zip(expected.values, predicted_denormalized.values):
print(exp, pred) print(exp, pred)
f = open("dev0_rmse.txt", "w") f = open("dev0_rmse.txt", "w")
f.write(str(error)) f.write(str(error))
f.close() f.close()
print(error)

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test-A/out.tsv
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