???

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,24 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="main.PRAWO" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="SpellCheckingInspection" enabled="false" level="TYPO" enabled_by_default="false">
+      <option name="processCode" value="true" />
+      <option name="processLiterals" value="true" />
+      <option name="processComments" value="true" />
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (sport-text-classification-ball-isi-public)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/sport-text-classification-ball-isi-public.iml" filepath="$PROJECT_DIR$/.idea/sport-text-classification-ball-isi-public.iml" />
+    </modules>
+  </component>
+</project>

.idea/sport-text-classification-ball-isi-public.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

mian.py

@@ -0,0 +1,90 @@
+import csv
+
+import nltk
+import pandas as pd
+from sklearn.neural_network import MLPClassifier
+from nltk.tokenize import word_tokenize
+from gensim.models import Word2Vec
+nltk.download('punkt')
+
+# w pliku train.tsv w kolumnach 25706, 58881, 73761 trzeba zamienic w tekscie tabulator na 4 spacje
+train = pd.read_csv('train/train.tsv', sep='\t')
+train.columns = ["y", "x"]
+
+print(train["y"][0], train["x"][0])
+
+# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
+slowa = []
+for tekst in train["x"]:
+    pom = []
+    for slowo in word_tokenize(tekst):
+        pom.append(slowo.lower())
+    slowa.append(pom)
+print(slowa[0])
+
+# https://radimrehurek.com/gensim/models/word2vec.html
+model = Word2Vec(sentences=slowa, vector_size=100, window=5, min_count=1, workers=4)
+model.save("word2vec.model")
+
+wektor = model.wv['przyjmujący']
+print(wektor)
+
+podobne = model.wv.most_similar('przyjmujący', topn=5)
+print(podobne)
+
+teksty = []
+for tekst in train["x"]:
+    pom = None
+    for slowo in word_tokenize(tekst):
+        wektor = model.wv[slowo.lower()]
+        if pom is None:
+            pom = wektor
+        else:
+            pom = pom + wektor
+    teksty.append(wektor)
+print(teksty[0])
+
+X = teksty
+y = train["y"]
+
+clf = MLPClassifier()   # activation="tanh"
+clf.fit(X, y)
+
+# w pliku in.tsv w kolumnach 1983, 5199 trzeba zamienic w tekscie tabulator na 4 spacje
+test = pd.read_csv('test-A/in.tsv', sep='\t')
+test.columns = ["x"]
+
+print(test["x"][0])
+
+# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
+slowa = []
+for tekst in test["x"]:
+    pom = []
+    for slowo in word_tokenize(tekst):
+        pom.append(slowo.lower())
+    slowa.append(pom)
+print(slowa[0])
+
+teksty = []
+for tekst in test["x"]:
+    pom = None
+    for slowo in word_tokenize(tekst):
+        wektor = None
+        try:
+            wektor = model.wv[slowo.lower()]
+        except KeyError:
+            pass
+        if wektor is not None:
+            if pom is None:
+                pom = wektor
+            else:
+                pom = pom + wektor
+    teksty.append(wektor)
+print(teksty[0])
+
+przewidywania = clf.predict(teksty)
+print(przewidywania)
+
+with open("test-A/out.tsv", "w", encoding="utf-8") as uwu:
+    for p in przewidywania:
+        uwu.write(str(p)+"\n")