boolean similarity

datat.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "import sklearn.model_selection\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
+    "import pandas as pd\n",
+    "import scipy\n",
+    "\n",
+    "df_eng = pd.read_csv('raw_texts.csv')\n",
+    "df_akk = pd.read_csv('new.csv')\n",
+    "# akk_raw_train, akk_raw_test = sklearn.model_selection.train_test_split(df_akk, test_size=0.2, random_state=0)\n",
+    "# eng_raw_train, eng_raw_test = sklearn.model_selection.train_test_split(df_eng, test_size=0.2, random_state=0)\n",
+    "tf_vectorizer = TfidfVectorizer(analyzer='word')\n",
+    "# tf_vectorizer.fit(akk_raw_train['Text'].to_list())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf_vectorizer = TfidfVectorizer(analyzer='word')\n",
+    "save_vect = tf_vectorizer.fit_transform(df_akk['Text'].dropna().to_list())\n",
+    "# save_vect = tf_vectorizer.fit_transform(['The sun in the sky is bright', 'We can see the shining sun, the bright sun.'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tfidf_tokens = tf_vectorizer.get_feature_names_out()\n",
+    "df_tfidfvect = pd.DataFrame(data=save_vect.toarray(), columns=tfidf_tokens)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_mat = tf_vectorizer.transform(df_akk['Text'].dropna().to_list())\n",
+    "cc = cosine_similarity(save_vect,save_vect)\n",
+    "bool_similarity = cc > 0.5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Cannot set a DataFrame with multiple columns to the single column Genre",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m df_genre \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mgenre.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m df_akk[\u001b[39m\"\u001b[39;49m\u001b[39mGenre\u001b[39;49m\u001b[39m\"\u001b[39;49m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([df_genre, df_akk], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, join\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39minner\u001b[39m\u001b[39m'\u001b[39m, keys\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mProject\u001b[39m\u001b[39m'\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Saret\\Programming\\C#\\DH\\venv\\lib\\site-packages\\pandas\\core\\frame.py:3949\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   3947\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setitem_array(key, value)\n\u001b[0;32m   3948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, DataFrame):\n\u001b[1;32m-> 3949\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_set_item_frame_value(key, value)\n\u001b[0;32m   3950\u001b[0m \u001b[39melif\u001b[39;00m (\n\u001b[0;32m   3951\u001b[0m     is_list_like(value)\n\u001b[0;32m   3952\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mis_unique\n\u001b[0;32m   3953\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39m1\u001b[39m \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mget_indexer_for([key])) \u001b[39m==\u001b[39m \u001b[39mlen\u001b[39m(value)\n\u001b[0;32m   3954\u001b[0m ):\n\u001b[0;32m   3955\u001b[0m     \u001b[39m# Column to set is duplicated\u001b[39;00m\n\u001b[0;32m   3956\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setitem_array([key], value)\n",
+      "File \u001b[1;32mc:\\Users\\Saret\\Programming\\C#\\DH\\venv\\lib\\site-packages\\pandas\\core\\frame.py:4103\u001b[0m, in \u001b[0;36mDataFrame._set_item_frame_value\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   4100\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m   4102\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(value\u001b[39m.\u001b[39mcolumns) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m-> 4103\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   4104\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mCannot set a DataFrame with multiple columns to the single \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   4105\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcolumn \u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m   4106\u001b[0m     )\n\u001b[0;32m   4108\u001b[0m \u001b[39mself\u001b[39m[key] \u001b[39m=\u001b[39m value[value\u001b[39m.\u001b[39mcolumns[\u001b[39m0\u001b[39m]]\n",
+      "\u001b[1;31mValueError\u001b[0m: Cannot set a DataFrame with multiple columns to the single column Genre"
+     ]
+    }
+   ],
+   "source": [
+    "df_genre = pd.read_csv('genre.csv')\n",
+    "df_akk[\"Genre\"] = pd.concat([df_genre, df_akk], axis=1, join='inner', keys='Project')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

project_notebook.ipynb

@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "installations\n",
-    "\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -763,7 +753,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def "
+    "#def "
    ]
   },
   {
@@ -86883,7 +86873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,

projects.csv

@@ -0,0 +1,52 @@
+1,adsd
+2,aemw
+3,akklove
+4,amgg
+5,ario
+6,armep
+7,arrim
+8,asbp
+9,atae
+10,babcity
+11,blms
+12,btmao
+13,btto
+14,cams
+15,caspo
+16,ccpo
+17,cdli
+18,ckst
+19,cmawro
+20,contrib
+23,ctij
+24,dcclt
+25,dccmt
+26,doc
+27,dsst
+28,ecut
+29,eisl
+30,epsd2
+31,etcsri
+32,glass
+33,hbtin
+34,lacost
+35,lovelyrics
+36,neo
+37,nere
+38,nimrud
+39,obel
+40,obmc
+41,obta
+42,ogsl
+43,oimea
+44,pnao
+45,qcat
+46,riao
+47,ribo
+48,rimanum
+49,rinap
+50,saao
+51,suhu
+52,tcma
+53,tsae
+54,xcat