svn

.ipynb_checkpoints/bayes-checkpoint.ipynb

@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"train/in.tsv\") as f:\n",
+    "    x_train = f.readlines()\n",
+    "\n",
+    "with open(\"train/expected.tsv\") as f:\n",
+    "    y_train = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, ..., 0, 0, 1])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = LabelEncoder().fit_transform(y_train)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pipeline.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"dev-0/in.tsv\") as f:\n",
+    "    x_dev = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_dev)\n",
+    "np.savetxt(\"dev-0/out.tsv\", prediction, fmt='%d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test-A/in.tsv\") as f:\n",
+    "    x_test = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_test)\n",
+    "np.savetxt(\"test-A/out.tsv\", prediction, fmt='%d')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

.ipynb_checkpoints/svm-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

repo.txt

@@ -0,0 +1 @@
+https://git.wmi.amu.edu.pl/s434695/projekt-uma

svm.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fc8ba0a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk import pos_tag\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from collections import defaultdict\n",
+    "from nltk.corpus import wordnet as wn\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn import model_selection, naive_bayes, svm\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b082c37a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"train/in.tsv\") as f:\n",
+    "    x_train = f.readlines()\n",
+    "\n",
+    "with open(\"train/expected.tsv\") as f:\n",
+    "    y_train = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6bd3ba5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eefd7b1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"dev-0/in.tsv\") as f:\n",
+    "    x_dev = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6696a4d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, ..., 0, 0, 1])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = LabelEncoder().fit_transform(y_train)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5488f657",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = make_pipeline(TfidfVectorizer(),svm.SVC())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddbb1608",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pipeline.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b286bec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model.predict(x_dev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b1fac69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"svm/out.tsv\", prediction, fmt='%d')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}