File size: 3,876 Bytes
b444daa a51c8fe b444daa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"list_output_ALOY_MbR = []\n",
"with open(\"metricas_ALOY_MbR.csv\", \"r\") as arquivo:\n",
" arquivo_csv = csv.reader(arquivo)\n",
" for i, linha in enumerate(arquivo_csv):\n",
" list_output_ALOY_MbR.append(float(linha[0]))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
" list_output_ALOY_NEOSP_SVR = []\n",
"with open(\"metricas_ALOY_NEOSP_SVR.csv\", \"r\") as arquivo:\n",
" arquivo_csv = csv.reader(arquivo)\n",
" for i, linha in enumerate(arquivo_csv):\n",
" list_output_ALOY_NEOSP_SVR.append(float(linha[0]))"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5313348545588383"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"np.mean(list_output_ALOY_NEOSP_SVR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.dummy import DummyRegressor\n",
"from nltk.corpus import stopwords\n",
"from textblob import TextBlob\n",
"import textstat\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn import svm\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.feature_selection import SelectKBest\n",
"import pandas as pd\n",
"from util import escape_tags_and_content, escape_tags, escape_strings, escape_links, escape_hex_character_codes, escape_punctuation_boundaries, escape_odd_spaces\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.model_selection import RepeatedKFold\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.feature_selection import f_classif, f_regression\n",
"from sklearn.pipeline import Pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"project_name = \"7764\"\n",
"\n",
"df = pd.read_json(\"database\\\\neo\\\\json\\\\{}.json\".format(project_name))\n",
"\n",
"def extract_leg_features():\n",
" return \"Extract Leg, Sent, Subj Features\"\n",
" \n",
"# pipeline = Pipeline(\n",
"# [\n",
"# (\"vect\", Tfi()),\n",
"# #(\"red\", SelectKBest(f_regression, k=50)),\n",
"# (\"scaler\", StandardScaler()),\n",
"# (\"clf\", svm.SVR()),\n",
"# ]\n",
"# )\n",
"# pipeline\n",
"\n",
"# pipeline = Pipeline(\n",
"# [\n",
"# (\"vect\", extract_leg_features()),\n",
"# (\"red\", SelectKBest(f_regression, k=50)),\n",
"# (\"scaler\", StandardScaler()),\n",
"# (\"clf\", svm.SVR()),\n",
"# ]\n",
"# )\n",
"# pipeline\n",
"\n",
"\n",
"pipeline = Pipeline(\n",
" [ \n",
" (\"clf\", DummyRegressor(strategy=\"mean\")),\n",
" ]\n",
")\n",
"pipeline\n",
"\n",
"\n",
" "
]
}
],
"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": "3.10.11"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|