.gitignore

@@ -1,4 +1,5 @@
-
+in.tsv
+model.pkl
 *~
 *.swp
 *.bak
@@ -6,3 +7,4 @@
 *.o
 .DS_Store
 .token
+.idea

README.md

@@ -5,7 +5,7 @@ Classify a reddit as either from Skeptic subreddit or one of the
 "paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts,
 ,Glitch-in-the-Matrix, conspiracytheories).
 
-Output label is `S` and `P`.
+Output label is 0 (for skeptic) and  1 (for paranormal).
 
 Sources
 -------

config.txt

@@ -1 +1 @@
---metric Accuracy --precision 4  
+--metric Accuracy --metric F1 --metric F0:N<Precision> --metric F9999999:N<Recall>  --precision 4 --in-header in-header.tsv --out-header out-header.tsv

scores.txt

@@ -1,15 +0,0 @@
-0.6920
-0.6857
-0.6969
-0.6931
-0.6927
-0.6952
-0.6969
-0.6969
-0.6959
-0.6959
-0.6965
-0.6965
-0.6965
-0.6954
-0.6965

solution.py

@@ -1,20 +1,42 @@
-import re
-import sys
+import pandas as pd
+import numpy as np
+import csv
+from sklearn.linear_model import LogisticRegression
+from sklearn.feature_extraction.text import CountVectorizer
+count_vect = CountVectorizer()
 
-for line in sys.stdin:
-    if re.search(r'UFO|paranormal|UFOs|video|night|house|saw|camera|lights|light|alien|aliens|ghost|object|dream|sky|room|ufo|craft|happened|sightings|footage|dreams|sleep', line):
-        print("P")
-    else:
-        print("S")
+#load data:
+train = pd.read_csv("train/in.tsv", delimiter="\t", header=None, names=["text","date"], quoting=csv.QUOTE_NONE)
+texts = train["text"]
+y = pd.read_csv("train/expected.tsv", header=None)
 
+#print(y)
+#train
+X_train_counts = count_vect.fit_transform(texts)
+clf = LogisticRegression().fit(X_train_counts, y)
+print(texts[0])
+print(len(texts))
+print(len(y))
 
+#predict
+dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["text","date"], quoting=csv.QUOTE_NONE)["text"]
+testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["text","date"], quoting=csv.QUOTE_NONE)["text"]
 
+dev0_new_counts = count_vect.transform(dev0)
+testA_new_counts = count_vect.transform(testA)
 
+predicted_dev0 = clf.predict(dev0_new_counts)
+predicted_testA = clf.predict(testA_new_counts)
 
+print(len(dev0))
+print(len(predicted_dev0))
 
-"""
+with open("dev-0/out.tsv", "w") as out1:
+    for line in predicted_dev0:
+        out1.write(str(line))
+        out1.write("\n")
 
-
-happened|sightings|footage|dreams|sleep|videos|experiences|weird|objects|flying|strange|ET|photo|moving|fake|sighting|door|ghosts|looks|bed|spirits|paralysis|pictures|glitch|shadow|picture|space|photos|looked|phenomena|contact|spirit|stories|phenomenon|window|ufos|haunted|lol|creepy|lanterns|dark|scared|cameras|balloon|seen|beings|disclosure|story
-
-"""
+with open("test-A/out.tsv", "w") as out2:
+    for line in predicted_testA:
+        out2.write(str(line))
+        out2.write("\n")

start.sh

@@ -1,4 +0,0 @@
-xzcat dev-0/in.tsv.xz | python3 solution.py > dev-0/out.tsv
-
-xzcat test-A/in.tsv.xz | python3 solution.py > test-A/out.tsv
-geval -t dev-0 >>scores.txt

train/s.sh

@@ -1,4 +0,0 @@
-xzcat in.tsv.xz | paste expected.tsv - | egrep -o '^ P.*'| egrep -o '[[:alpha:]]+' | sort > sortedP
-xzcat in.tsv.xz | paste expected.tsv - | egrep -o '^ S.*'| egrep -o '[[:alpha:]]+' | sort > sortedS
-comm -23 sortedP sortedS > PsetsubtractionS
-cat PsetsubtractionS | uniq -c | sort -nr > PsetsubtractionS_counted.txt