Zadanie regresja

Untitled.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[243]:
+
+
+import numpy as np
+import matplotlib
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+
+# In[244]:
+
+
+#with open('train/train.tsv') as file:
+ #   for line in file.readlines()[:10]:
+  #      print(line)
+
+
+# In[245]:
+
+
+#with open('names') as file:
+ #   for line in file.readlines():
+  #          header.append(line.strip())
+
+
+# In[246]:
+
+
+#train
+
+
+# In[247]:
+
+
+with open('names') as file:
+    header = file.read().rstrip('\n').split('\t')
+
+train_path='train/train.tsv'
+
+
+
+train = pd.read_csv(train_path, sep='\t', names=header)
+#removing discrete value
+train.drop('brand', inplace=True, axis=1)
+train.drop('engineType', inplace=True, axis=1)
+
+
+# In[ ]:
+
+
+
+
+
+# In[248]:
+
+
+
+#output
+y_train = pd.DataFrame(train['price'])
+
+
+#removing output
+train.drop('price', inplace=True, axis=1)
+x_train = pd.DataFrame(train)
+
+model = LinearRegression()
+model.fit(x_train, y_train)
+
+header=['price','year','brand','engineType','engineCapacity']
+
+
+# In[249]:
+
+
+#dev
+
+
+# In[250]:
+
+
+dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=header)
+
+
+# In[251]:
+
+
+print(dev)
+
+
+# In[252]:
+
+
+with open('dev-0/expected.tsv', 'r') as file:
+    y_dev = np.array([float(x.rstrip('\n')) for x in file.readlines()])
+
+
+# In[253]:
+
+
+dev.drop('brand', inplace=True, axis=1)
+dev.drop('engineType', inplace=True, axis=1)
+
+
+# In[254]:
+
+
+print(dev)
+
+
+# In[255]:
+
+
+
+
+x_dev = pd.DataFrame(dev)
+
+predict_dev = model.predict(x_dev)
+print(predict_dev)
+
+
+# In[256]:
+
+
+predict_dev.tofile('dev-0/out.tsv', sep='\n') 
+
+
+# # RMSE dla dev-0
+
+# In[257]:
+
+
+error = np.sqrt(mean_squared_error(y_dev, predict_dev))
+print(error)
+
+
+# In[258]:
+
+
+#test
+
+
+# In[259]:
+
+
+pd.DataFrame(predict).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
+
+
+# In[260]:
+
+
+test=pd.read_csv('test-A/in.tsv', sep='\t', names=header)
+print(test)
+
+
+# In[261]:
+
+
+test.drop('brand', inplace=True, axis=1)
+test.drop('engineType', inplace=True, axis=1)
+y_expected = pd.DataFrame(test['price'])
+
+y_expected.to_csv('test-A/expected.tsv', sep='\t', encoding='utf-8')
+
+
+# In[262]:
+
+
+print(test)
+
+
+# In[263]:
+
+
+x_test = pd.DataFrame(test)
+
+predict_test = model.predict(x_test)
+pd.DataFrame(predict_test).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)
+
+
+# In[264]:
+
+
+predict_test.tofile('test-A/out.tsv', sep='\n') 
+
+
+# # RMSE dla dev-0
+
+# In[266]:
+
+
+error = np.sqrt(mean_squared_error(y_dev, predict_dev))
+print(error)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

config.txt

@@ -1 +1 @@
---precision 1
+ --metric RMSE --precision 1