tfidf with linear regression

mean_from_train/run.py

@@ -24,7 +24,3 @@ with open('../dev-0/out.tsv','w') as f_out, open('../dev-0/in.tsv', 'r') as f_in
 with open('../test-A/out.tsv','w') as f_out, open('../test-A/in.tsv', 'r') as f_in:
     for line in f_in:
         f_out.write(mean + '\n')
-        
-        
-# test 3
-

tfidf/train_predict.py

@@ -0,0 +1,80 @@
+import pandas as pd
+import numpy as np
+import sklearn
+from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+# from xgboost import XGBRegressor
+import random
+import pickle
+
+def tokenizer_space(text):
+    return text.split(' ')
+
+
+
+def run():
+
+    # LOADING DATA
+    train_text = [a.rstrip('\n') for a in open('../train/in.tsv','r')]
+    dev_text = [a.rstrip('\n') for a in open('../dev-0/in.tsv','r')]
+    test_text = [a.rstrip('\n') for a in open('../test-A/in.tsv','r')]
+    global lowest
+
+    train_year = [  sum([float(b) for b in a.rstrip('\n').split(',')]) / 2 for a in open('../train/expected.tsv','r')]
+    dev_year = [  sum([float(b) for b in a.rstrip('\n').split(',')]) / 2 for a in open('../dev-0/expected.tsv','r')]
+
+    max_year = max(train_year)
+    min_year = min(train_year)
+
+    tfidf = TfidfVectorizer()
+    #tfidf = HashingVectorizer()
+    train_text_vectorized = tfidf.fit_transform(train_text)
+    pickle.dump(train_text_vectorized, open('text_train_tfidf_all.pickle','wb'))
+    pickle.dump(tfidf, open('tfidf_all.pickle','wb'))
+    train_text_vectorized = pickle.load(open('text_train_tfidf_all.pickle','rb'))
+    tfidf = pickle.load(open('tfidf_all.pickle','rb'))
+
+    dev_text_vectorized = tfidf.transform(dev_text)
+    test_text_vectorized = tfidf.transform(test_text)
+
+    # MODELLING
+    lr = LinearRegression( n_jobs=10)
+    #xgb = XGBRegressor(n_jobs=8)
+    #xgb_1000 = XGBRegressor(n_estimators=1000,n_jobs=8)
+    #xgb_5000 = XGBRegressor(n_estimators=5000,n_jobs=8)
+    lr.fit(train_text_vectorized, train_year)
+    #xgb.fit(text, year)
+    #xgb_1000.fit(text, year)
+    #xgb_5000.fit(text, year)
+
+
+    ##################
+    # DEV PREDICTIONS
+    predictions_lr = lr.predict(dev_text_vectorized)
+    predictions_lr = np.minimum(predictions_lr, max_year)
+    predictions_lr = np.maximum(predictions_lr, min_year)
+    print('dev-0 RMSE')
+    print(np.sqrt(sklearn.metrics.mean_squared_error(predictions_lr, dev_year)))
+    print('dev-0 MAE')
+    print(sklearn.metrics.mean_absolute_error(predictions_lr, dev_year))
+
+    f = open('../dev-0/out.tsv','w')
+    for i in predictions_lr:
+        f.write(str(i) + '\n')
+    f.close()
+
+    ##################
+    # TEST PREDICTIONS
+    predictions_lr = lr.predict(test_text_vectorized)
+    predictions_lr = np.minimum(predictions_lr, max_year)
+    predictions_lr = np.maximum(predictions_lr, min_year)
+
+    f = open('../test-A/out.tsv','w')
+    for i in predictions_lr:
+        f.write(str(i) + '\n')
+    f.close()
+
+
+run()