rozwiazanie.py

@@ -1,109 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from sklearn.linear_model import LinearRegression
-from sklearn import preprocessing
-from sklearn.metrics import mean_squared_error
-
-data_train = pd.read_csv("train/train.tsv", header=None, sep='\t')
-#print(data_train[[1,2,5]])
-data_train[3] = data_train[3].astype('category')
-data_train[4] = data_train[4].astype('category')
-brand_codest = dict(enumerate(data_train[3].cat.categories))
-brand_codes = {y:x for x,y in brand_codest.items()}
-#data_train[3].map(data_train_codes)
-data_train.replace({3: brand_codes}, inplace=True)
-#print(brand_codes)
-#print(data_train[3])
-fuel_codest = dict(enumerate(data_train[4].cat.categories))
-fuel_codes = {y:x for x,y in fuel_codest.items()}
-#data_train[3].map(data_train_codes)
-data_train.replace({4: fuel_codes}, inplace=True)
-#print(fuel_codes)
-#print(data_train[4])
-# #Normalizacja danych
-# flcols = data_train[[0, 1, 2]].columns
-# x = data_train[[0, 1, 2]].values
-# # min_max_scaler = preprocessing.MinMaxScaler()
-# max_abs_scaler = preprocessing.MaxAbsScaler()
-# # x_scaled = min_max_scaler.fit_transform(x)
-# x_scaled = max_abs_scaler.fit_transform(x)
-# normcols = pd.DataFrame(x_scaled, columns=flcols)
-# for col in flcols:
-#     data_train[col] = normcols[col]
-
-X_train = data_train[[1,2,3,4]]
-y_train = data_train[data_train.columns[0]]
-# print(X_train[3].value_counts())
-# print(X_train[4].value_counts())
-#print(X_train)
-
-# train_columns = data_train.columns[5]
-# text_columns = [3,4]
-# data_train_dummy = pd.get_dummies(data_train[columns])
-# print(len(data_train_dummy.columns))
-data_val_X = pd.read_csv("dev-0/in.tsv", header=None, sep='\t')
-data_val_y = pd.read_csv("dev-0/expected.tsv", header=None)
-
-data_val_X.replace({2: brand_codes}, inplace=True)
-data_val_X.replace({3: fuel_codes}, inplace=True)
-#print(data_val_X[2].value_counts())
-#print(data_val_X[3].value_counts())
-X_val = data_val_X[[0,1,2,3]]
-#print(data_val_y)
-reg = LinearRegression().fit(X_train, y_train)
-print(reg.score(X_train, y_train))
-print(reg.score(X_val, data_val_y))
-print(mean_squared_error(data_val_y, reg.predict(X_val), squared=False))
-
-file = open('dev-0/out.tsv',"w") 
-for index, row in X_val.iterrows():
-    #print(np.reshape(row.to_numpy(),(-1,1)))
-    y_pred = reg.predict(np.reshape(row.to_numpy(),(1,-1)))
-    # print(y_pred)
-    file.writelines("{}\n".format(y_pred[0]))
-    # if index==10:
-    #     break  
-file.close()
-
-
-data_test_X = pd.read_csv("test-A/in.tsv", header=None, sep='\t')
-data_test_X.replace({2: brand_codes}, inplace=True)
-data_test_X.replace({3: fuel_codes}, inplace=True)
-data_test_X.replace({2: {"Fabrycznie": 90}}, inplace=True)
-# print(data_test_X[2].value_counts())
-# print(data_test_X[3].value_counts())
-X_test = data_test_X[[0,1,2,3]]
-#print(X_test)
-
-file = open('test-A/out.tsv',"w") 
-for index, row in X_test.iterrows():
-    #print(np.reshape(row.to_numpy(),(-1,1)))
-    y_pred = reg.predict(np.reshape(row.to_numpy(),(1,-1)))
-    # print(y_pred)
-    file.writelines("{}\n".format(y_pred[0]))
-    # if index==10:
-    #     break  
-file.close()
-# #Normalizacja danych
-# flcols = data_val_X[[4]].columns
-# x = data_val_X[[4]].values
-# # min_max_scaler = preprocessing.MinMaxScaler()
-# max_abs_scaler = preprocessing.MaxAbsScaler()
-# # x_scaled = min_max_scaler.fit_transform(x)
-# x_scaled = max_abs_scaler.fit_transform(x)
-# normcols = pd.DataFrame(x_scaled, columns=flcols)
-# for col in flcols:
-#     data_val_X[col] = normcols[col]
-# #Normalizacja danych
-# flcols = data_val_y[[0]].columns
-# x = data_val_y[[0]].values
-# # min_max_scaler = preprocessing.MinMaxScaler()
-# max_abs_scaler = preprocessing.MaxAbsScaler()
-# # x_scaled = min_max_scaler.fit_transform(x)
-# x_scaled = max_abs_scaler.fit_transform(x)
-# normcols = pd.DataFrame(x_scaled, columns=flcols)
-# for col in flcols:
-#     data_val_y[col] = normcols[col]
-
-