ultima versao

_GERADOR_METRICAS_CONSOLIDADAS.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import csv\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "def gerar_metricas(nome_projeto):\n",
+    "    list_output_MbR = []\n",
+    "    with open(\"metricas/metricas_{}_MbR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
+    "        arquivo_csv = csv.reader(arquivo)\n",
+    "        for i, linha in enumerate(arquivo_csv):\n",
+    "            list_output_MbR.append(float(linha[0]))\n",
+    "    list_output_NEOSP_SVR = []\n",
+    "    with open(\"metricas/metricas_{}_NEOSP_SVR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
+    "        arquivo_csv = csv.reader(arquivo)\n",
+    "        for i, linha in enumerate(arquivo_csv):\n",
+    "            list_output_NEOSP_SVR.append(float(linha[0]))\n",
+    "    list_output_TFIDF_SVR = []\n",
+    "    with open(\"metricas/metricas_{}_TFIDF.csv\".format(nome_projeto), \"r\") as arquivo:\n",
+    "        arquivo_csv = csv.reader(arquivo)\n",
+    "        for i, linha in enumerate(arquivo_csv):\n",
+    "            list_output_TFIDF_SVR.append(float(linha[0]))\n",
+    "            \n",
+    "    list_results = [[\"MbR Regressor\", np.mean(list_output_MbR)], [\"NEOSP-SVR Regressor\", np.mean(list_output_NEOSP_SVR)], [\"TFIDF-SVR Regressor\", np.mean(list_output_TFIDF_SVR)]]\n",
+    "    \n",
+    "    df = pd.DataFrame(list_results, columns=[\"Model\", \"MAE\"])\n",
+    "    \n",
+    "    df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=[\"MAE_MbR\"])\n",
+    "    df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP_SVR, columns=[\"MAE_NEOSP\"])\n",
+    "    df_list_output_TFIDF = pd.DataFrame(list_output_TFIDF_SVR, columns=[\"MAE_TFIDF\"])\n",
+    "    \n",
+    "    return df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#LIBRARIES_TAWOS = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
+    "#             \"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
+    "\n",
+    "LIBRARIES_NEO = [\"7764\", \n",
+    "                 \"250833\", \n",
+    "                 #\"278964\"\n",
+    "                 \"734943\", \n",
+    "                 #\"1304532\",\n",
+    "                 #\"1714548\", \n",
+    "                 \"2009901\",\n",
+    "                 \"2670515\", \n",
+    "                 \"3828396\",\n",
+    "                 \"3836952\", \n",
+    "                 \"4456656\", \n",
+    "                 \"5261717\",\n",
+    "                 \"6206924\", \n",
+    "                 \"7071551\", \n",
+    "                 \"7128869\",\n",
+    "                 \"7603319\",\n",
+    "                 \"7776928\", \n",
+    "                 \"10152778\",\n",
+    "                 \"10171263\", \n",
+    "                 \"10171270\", \n",
+    "                 \"10171280\",\n",
+    "                 \"10174980\", \n",
+    "                 \"12450835\",\n",
+    "                 \"12584701\",\n",
+    "                 \"12894267\",\n",
+    "                 \"14052249\",\n",
+    "                 \"14976868\", \n",
+    "                 \"15502567\",\n",
+    "                 \"19921167\",  \n",
+    "                 \"21149814\", \n",
+    "                 \"23285197\", \n",
+    "                 \"28419588\",\n",
+    "                 \"28644964\",  \n",
+    "                 \"28847821\"\n",
+    "                 ]\n",
+    "#RETIRADOS = [] Muito grande\n",
+    "#RETIRADO2 = [] valores NaN\n",
+    "lista = list()\n",
+    "for project_name in LIBRARIES_NEO:\n",
+    "    df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF = gerar_metricas(project_name)\n",
+    "    lista.append([project_name, df_list_output_MbR[\"MAE_MbR\"].mean(), df_list_output_NEOSP[\"MAE_NEOSP\"].mean(), df_list_output_TFIDF[\"MAE_TFIDF\"].mean()])\n",
+    "\n",
+    "df_media = pd.DataFrame(lista, columns=[\"Projeto\", \"MAE_MbR\", \"MAE_NEOSP\", \"MAE_TFIDF\"])\n",
+    "df_media.to_csv(\"_METRICAS_NEO.csv\")"
+   ]
+  }
+ ],
+ "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
+}

_GERADOR_METRICAS_PROJETOS.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gerar_metricas(project_name):\n",
+    "\n",
+    "    ########## SE FOR TAWOS DESCOMENTAR\n",
+    "    # df = pd.read_csv(\"database\\\\tawos\\\\deep\\\\{}_deep-se.csv\".format(project_name))\n",
+    "    \n",
+    "    ########## SE FOR NEODATASET, SE FOR TAWOS REMOVER\n",
+    "    df = pd.read_json(\"database\\\\neo\\\\json\\\\{}.json\".format(project_name))\n",
+    "    \n",
+    "    ########## SE FOR NEODATASET, SE FOR TAWOS REMOVER\n",
+    "    df = df.rename(columns={ \"id\": \"issuekey\", \"created_at\": \"created\", \"weight\": \"storypoint\"})\n",
+    "    \n",
+    "    # criação de uma nova coluna\n",
+    "    df[\"context\"] = df[\"title\"] + df[\"description\"]\n",
+    "\n",
+    "    # pré-processamento\n",
+    "    df['context'] = df['context'].astype(str)\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags_and_content(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_strings(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_links(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_hex_character_codes(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_punctuation_boundaries(x))\n",
+    "    df[\"context\"] = df[\"context\"].apply(lambda x: escape_odd_spaces(x))\n",
+    "\n",
+    "    # removendo stop-words\n",
+    "    stop = stopwords.words('english')\n",
+    "    df['context'] = df['context'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
+    "\n",
+    "    # SE FOR TAWOS renomeando as colunas porque senão dá um problema com a extração de features do NEOSP\n",
+    "    #df = df.rename(columns={ \"issuekey\": \"issuekey_\", \"created\": \"created_\", \"description\": \"description_\", \"title\": \"title_\", \"context\": \"context_\", \"storypoint\": \"storypoint_\"})\n",
+    "    \n",
+    "    ########## SE FOR NEODATASET, SE FOR TAWOS REMOVER\n",
+    "    df = df.rename(columns={ \"issuekey\": \"issuekey_\", \"created\": \"created_\", \"description\": \"description_\", \"title\": \"title_\", \"context\": \"context_\", \"storypoint\": \"storypoint_\"})\n",
+    "    \n",
+    "    y = df[\"storypoint_\"]\n",
+    "    df = df.drop(columns=['storypoint_'])\n",
+    "    \n",
+    "    ########## SE FOR NEODATASET, SE FOR TAWOS REMOVER\n",
+    "    df = df[[\"issuekey_\", \"created_\", \"title_\", \"description_\", \"context_\"]]\n",
+    "\n",
+    "    # 5º coluna -> extração das features para o neosp\n",
+    "    df[\"gunning_fog_\"] = df['context_'].apply(textstat.gunning_fog)\n",
+    "    df[\"flesch_reading_ease_\"] = df['context_'].apply(textstat.flesch_reading_ease)\n",
+    "    df[\"flesch_kincaid_grade_\"] = df['context_'].apply(textstat.flesch_kincaid_grade)\n",
+    "    df[\"smog_index_\"] = df['context_'].apply(textstat.smog_index)\n",
+    "    df[\"coleman_liau_index_\"] = df['context_'].apply(textstat.coleman_liau_index)\n",
+    "    df[\"automated_readability_index_\"] = df['context_'].apply(textstat.automated_readability_index)\n",
+    "    df[\"dale_chall_readability_score_\"] = df['context_'].apply(textstat.dale_chall_readability_score)\n",
+    "    df[\"difficult_words_\"] = df['context_'].apply(textstat.difficult_words)\n",
+    "    df[\"linsear_write_formula_\"] = df['context_'].apply(textstat.linsear_write_formula)\n",
+    "    df[\"polarity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.polarity)\n",
+    "    df[\"subjectivity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n",
+    "    # 16º colunas\n",
+    "\n",
+    "    # Extração das features para o TFIDF\n",
+    "    vectorizer = TfidfVectorizer()\n",
+    "    X_vec = vectorizer.fit_transform(df[\"context_\"])\n",
+    "\n",
+    "    df_vec = pd.DataFrame(data = X_vec.toarray(), columns = vectorizer.get_feature_names_out())\n",
+    "\n",
+    "    # Juntando as features do neosp com o tfidf\n",
+    "    df = df.join(df_vec)\n",
+    "    X = df\n",
+    "\n",
+    "    rkf = RepeatedKFold(n_splits=10, n_repeats=30, random_state=2652124)\n",
+    "    \n",
+    "    list_results_MAE_MbR, list_results_MAE_NEOSP, list_results_TFIDF_MbR = list(), list(), list()\n",
+    "    ### Dummy\n",
+    "    model = DummyRegressor(strategy=\"mean\")\n",
+    "    list_results_MAE_MbR = cross_val_score(model, X[X.columns[5:6]], y, cv = rkf, scoring=\"neg_mean_absolute_error\")\n",
+    "    df_results_MAE_MbR = pd.DataFrame(list_results_MAE_MbR, columns = [\"MAE\"])\n",
+    "    df_results_MAE_MbR = df_results_MAE_MbR.apply(lambda x: x*-1)\n",
+    "    df_results_MAE_MbR.to_csv(\"metricas/metricas_{}_MbR.csv\".format(project_name),index = False, header=False)\n",
+    "    \n",
+    "    ##### NEOSP\n",
+    "    model = make_pipeline(StandardScaler(), svm.SVR())\n",
+    "    list_results_MAE_NEOSP = cross_val_score(model, X[X.columns[5:16]], y, cv = rkf, scoring=\"neg_mean_absolute_error\")\n",
+    "    df_results_MAE_NEOSP = pd.DataFrame(list_results_MAE_NEOSP, columns = [\"MAE\"])\n",
+    "    df_results_MAE_NEOSP = df_results_MAE_NEOSP.apply(lambda x: x*-1)\n",
+    "    df_results_MAE_NEOSP.to_csv(\"metricas/metricas_{}_NEOSP_SVR.csv\".format(project_name), index = False, header=False)\n",
+    "    \n",
+    "    #### TFIDF\n",
+    "    model = make_pipeline(SelectKBest(f_regression, k=50), StandardScaler(), svm.SVR())\n",
+    "    list_results_TFIDF_MbR = cross_val_score(model, X[X.columns[16:]], y, cv = rkf, scoring=\"neg_mean_absolute_error\")\n",
+    "    df_results_MAE_TFIDF = pd.DataFrame(list_results_TFIDF_MbR, columns = [\"MAE\"])\n",
+    "    df_results_MAE_TFIDF = df_results_MAE_TFIDF.apply(lambda x: x*-1)\n",
+    "    df_results_MAE_TFIDF.to_csv(\"metricas/metricas_{}_TFIDF.csv\".format(project_name),index = False, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#LIBRARIES_TAWOS = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
+    "             #\"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
+    "\n",
+    "#LIBRARIES_NEO = [\"7764\", \"250833\", \"734943\", \"2009901\", \"2670515\", \"3828396\",\"3836952\", \n",
+    "#                 \"4456656\", \"5261717\", \"6206924\", \"7071551\", \"7128869\",\"7603319\", \n",
+    "#                 \"7776928\", \"10152778\",\"10171263\", \"10171270\", \"10171280\", \"10174980\", \n",
+    "#                 \"12450835\",\"12584701\",\"12894267\", \"14052249\",\"14976868\", \"15502567\",\n",
+    "#                 \"19921167\",  \"21149814\", \"23285197\", \"28419588\",\"28644964\",  \"28847821\"]\n",
+    "\n",
+    "#for project_name in LIBRARIES_NEO:\n",
+    "#    gerar_metricas(project_name)"
+   ]
+  }
+ ],
+ "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
+}

_metrics generator.ipynb → _SELEÇÃO_HIPERPARAMETROS.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,16 +16,16 @@
     "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"
+    "from sklearn.svm import SVR\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "import numpy as np\n",
+    "import util"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,46 +78,42 @@
     "    # Juntando as features do neosp com o tfidf\n",
     "    df = df.join(df_vec)\n",
     "    X = df\n",
+    "   \n",
+    "    grid = GridSearchCV(\n",
+    "            estimator=SVR(kernel='rbf'),\n",
+    "            param_grid={\n",
+    "                'C': [1.1, 5.4, 170, 1001],\n",
+    "                'epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14, 0.05, 8, 0.2, 3, 2, 7],\n",
+    "                'gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]\n",
+    "            }, \n",
+    "            cv=10, scoring='neg_mean_squared_error', verbose=0, n_jobs=-1)\n",
     "\n",
-    "    results = list()\n",
-    "\n",
-    "    rkf = RepeatedKFold(n_splits=10, n_repeats=30, random_state=2652124)\n",
-    "    \n",
-    "    #model = DummyRegressor(strategy=\"mean\")\n",
-    "    #results.append(cross_val_score(model, X[X.columns[5:16]], y, cv = rkf, scoring=\"neg_mean_absolute_error\"))\n",
-    "    \n",
-    "    model = make_pipeline(SelectKBest(f_regression, k=50), StandardScaler(), svm.SVR())\n",
-    "    results.append(cross_val_score(model, X[X.columns[16:]], y, cv = rkf, scoring=\"neg_mean_absolute_error\"))\n",
-    "    \n",
-    "    list_results_TFIDF_MbR = results[0]\n",
-    "    df_results_MAE_TFIDF = pd.DataFrame(list_results_TFIDF_MbR, columns = [\"MAE\"])\n",
-    "    df_results_MAE_TFIDF = df_results_MAE_TFIDF.apply(lambda x: x*-1)\n",
-    "    df_results_MAE_TFIDF.to_csv(\"metricas_{}_TFIDF.csv\".format(project_name),index = False, header=False)\n",
-    "    \n",
-    "    #list_results = [ [\"MbR\", results[0].mean()*-1], [\"NEOSP-SVR\", results[1].mean()*-1] ]\n",
-    "    #df = pd.DataFrame(list_results, columns = [\"Model\",\"MAE\"])\n",
-    "\n",
-    "    #list_results_MAE_MbR = results[0]\n",
-    "    #df_results_MAE_MbR = pd.DataFrame(list_results_MAE_MbR, columns = [\"MAE\"])\n",
-    "    #df_results_MAE_MbR = df_results_MAE_MbR.apply(lambda x: x*-1)\n",
-    "    #df_results_MAE_MbR.to_csv(\"metricas_{}_MbR.csv\".format(project_name),index = False, header=False)\n",
-    "\n",
-    "    #list_results_MAE_NEOSP = results[1]\n",
-    "    #df_results_MAE_NEOSP = pd.DataFrame(list_results_MAE_NEOSP, columns = [\"MAE\"])\n",
-    "    #df_results_MAE_NEOSP = df_results_MAE_NEOSP.apply(lambda x: x*-1)\n",
-    "    #df_results_MAE_NEOSP.to_csv(\"metricas_{}_NEOSP_SVR.csv\".format(project_name), index = False, header=False)"
+    "    #print the best parameters from all possible combinations\n",
+    "    grid.fit(X[X.columns[5:16]], y)\n",
+    "    print(grid.best_params_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'C': 5.4, 'epsilon': 0.2, 'gamma': 5}\n",
+      "{'C': 1.1, 'epsilon': 2, 'gamma': 0.7001}\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "LIBRARIES = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
-    "             \"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
+    "LIBRARIES = [\"ALOY\", \"CLI\"]\n",
+    "             #\"APSTUD\", \n",
+    "             #\"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
+    "             #\"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
     "\n",
     "for lp in LIBRARIES:\n",
     "    gerar_metricas(lp)"

_gerador_planilha.ipynb

@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import csv\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "def gerar_metricas(nome_projeto):\n",
-    "    list_output_MbR = []\n",
-    "    with open(\"metricas_{}_MbR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
-    "        arquivo_csv = csv.reader(arquivo)\n",
-    "        for i, linha in enumerate(arquivo_csv):\n",
-    "            list_output_MbR.append(float(linha[0]))\n",
-    "    list_output_NEOSP_SVR = []\n",
-    "    with open(\"metricas_{}_NEOSP_SVR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
-    "        arquivo_csv = csv.reader(arquivo)\n",
-    "        for i, linha in enumerate(arquivo_csv):\n",
-    "            list_output_NEOSP_SVR.append(float(linha[0]))\n",
-    "    list_output_TFIDF_SVR = []\n",
-    "    with open(\"metricas_{}_TFIDF.csv\".format(nome_projeto), \"r\") as arquivo:\n",
-    "        arquivo_csv = csv.reader(arquivo)\n",
-    "        for i, linha in enumerate(arquivo_csv):\n",
-    "            list_output_TFIDF_SVR.append(float(linha[0]))\n",
-    "            \n",
-    "    list_results = [[\"MbR Regressor\", np.mean(list_output_MbR)], [\"NEOSP-SVR Regressor\", np.mean(list_output_NEOSP_SVR)], [\"TFIDF-SVR Regressor\", np.mean(list_output_TFIDF_SVR)]]\n",
-    "    \n",
-    "    df = pd.DataFrame(list_results, columns=[\"Model\", \"MAE\"])\n",
-    "    \n",
-    "    df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=[\"MAE\"])\n",
-    "    df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP_SVR, columns=[\"MAE\"])\n",
-    "    df_list_output_TFIDF = pd.DataFrame(list_output_TFIDF_SVR, columns=[\"MAE\"])\n",
-    "    \n",
-    "    \n",
-    "    return df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          MAE\n",
-      "0    1.719630\n",
-      "1    1.619240\n",
-      "2    2.035138\n",
-      "3    2.714286\n",
-      "4    1.690284\n",
-      "..        ...\n",
-      "295  1.877688\n",
-      "296  1.825845\n",
-      "297  2.281874\n",
-      "298  1.448541\n",
-      "299  2.011329\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    3.991628\n",
-      "1    5.509540\n",
-      "2    4.276285\n",
-      "3    3.210280\n",
-      "4    4.419198\n",
-      "..        ...\n",
-      "295  4.515966\n",
-      "296  4.156574\n",
-      "297  4.072459\n",
-      "298  3.360661\n",
-      "299  4.133661\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.749430\n",
-      "1    1.615589\n",
-      "2    1.891128\n",
-      "3    1.352403\n",
-      "4    2.137931\n",
-      "..        ...\n",
-      "295  1.505617\n",
-      "296  1.497649\n",
-      "297  1.944096\n",
-      "298  1.594697\n",
-      "299  1.889890\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    6.520257\n",
-      "1    7.011882\n",
-      "2    3.508960\n",
-      "3    3.912252\n",
-      "4    6.778048\n",
-      "..        ...\n",
-      "295  5.067004\n",
-      "296  3.978298\n",
-      "297  4.770977\n",
-      "298  3.592259\n",
-      "299  3.753875\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.279750\n",
-      "1    1.747535\n",
-      "2    1.429980\n",
-      "3    1.367193\n",
-      "4    1.769231\n",
-      "..        ...\n",
-      "295  1.736029\n",
-      "296  1.297173\n",
-      "297  1.407627\n",
-      "298  1.742932\n",
-      "299  1.625247\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.484524\n",
-      "1    0.870833\n",
-      "2    1.362500\n",
-      "3    1.141270\n",
-      "4    1.118586\n",
-      "..        ...\n",
-      "295  1.162477\n",
-      "296  1.446631\n",
-      "297  1.257882\n",
-      "298  2.020915\n",
-      "299  1.351226\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.562142\n",
-      "1    1.121421\n",
-      "2    1.011506\n",
-      "3    1.309862\n",
-      "4    1.386479\n",
-      "..        ...\n",
-      "295  1.217656\n",
-      "296  1.166991\n",
-      "297  1.309732\n",
-      "298  1.546553\n",
-      "299  1.458637\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    3.183489\n",
-      "1    3.254141\n",
-      "2    2.820652\n",
-      "3    3.147516\n",
-      "4    3.724896\n",
-      "..        ...\n",
-      "295  3.343784\n",
-      "296  2.467027\n",
-      "297  2.586486\n",
-      "298  2.901351\n",
-      "299  2.793243\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    3.085758\n",
-      "1    2.960811\n",
-      "2    2.402817\n",
-      "3    3.173362\n",
-      "4    3.209479\n",
-      "..        ...\n",
-      "295  2.802939\n",
-      "296  2.611202\n",
-      "297  2.317784\n",
-      "298  3.191521\n",
-      "299  2.972561\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    0.817816\n",
-      "1    1.292632\n",
-      "2    0.798383\n",
-      "3    0.766996\n",
-      "4    0.811808\n",
-      "..        ...\n",
-      "295  0.764333\n",
-      "296  0.871069\n",
-      "297  0.830722\n",
-      "298  1.263252\n",
-      "299  0.915469\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    0.861718\n",
-      "1    1.440051\n",
-      "2    0.793965\n",
-      "3    1.079316\n",
-      "4    0.765291\n",
-      "..        ...\n",
-      "295  1.312637\n",
-      "296  0.746560\n",
-      "297  0.861487\n",
-      "298  0.844606\n",
-      "299  0.893629\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    0.651576\n",
-      "1    0.649636\n",
-      "2    0.624728\n",
-      "3    0.599720\n",
-      "4    0.670485\n",
-      "..        ...\n",
-      "295  0.625787\n",
-      "296  0.620482\n",
-      "297  0.617713\n",
-      "298  0.653711\n",
-      "299  0.671403\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.843216\n",
-      "1    1.947581\n",
-      "2    1.326613\n",
-      "3    1.818193\n",
-      "4    1.829548\n",
-      "..        ...\n",
-      "295  1.692674\n",
-      "296  1.968132\n",
-      "297  3.074359\n",
-      "298  1.361050\n",
-      "299  2.180586\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "           MAE\n",
-      "0     8.977660\n",
-      "1    13.935714\n",
-      "2    11.211688\n",
-      "3    10.565220\n",
-      "4    10.296718\n",
-      "..         ...\n",
-      "295  12.521110\n",
-      "296  11.826106\n",
-      "297   9.696185\n",
-      "298  11.501992\n",
-      "299  10.755109\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.563933\n",
-      "1    1.527785\n",
-      "2    1.514647\n",
-      "3    1.419521\n",
-      "4    1.537474\n",
-      "..        ...\n",
-      "295  1.508380\n",
-      "296  1.713129\n",
-      "297  1.520726\n",
-      "298  1.499336\n",
-      "299  1.515071\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    3.124855\n",
-      "1    2.894180\n",
-      "2    3.055756\n",
-      "3    2.742524\n",
-      "4    2.910823\n",
-      "..        ...\n",
-      "295  2.968017\n",
-      "296  2.876247\n",
-      "297  2.786590\n",
-      "298  2.969128\n",
-      "299  2.820308\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.275430\n",
-      "1    0.953375\n",
-      "2    0.943311\n",
-      "3    1.077534\n",
-      "4    0.880661\n",
-      "..        ...\n",
-      "295  0.955650\n",
-      "296  0.951646\n",
-      "297  0.953232\n",
-      "298  0.975788\n",
-      "299  1.029818\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    2.097472\n",
-      "1    1.692843\n",
-      "2    1.335077\n",
-      "3    1.525819\n",
-      "4    1.529649\n",
-      "..        ...\n",
-      "295  1.723645\n",
-      "296  1.577294\n",
-      "297  1.154258\n",
-      "298  1.946014\n",
-      "299  1.708333\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    0.933591\n",
-      "1    0.871815\n",
-      "2    0.946461\n",
-      "3    0.765251\n",
-      "4    0.945946\n",
-      "..        ...\n",
-      "295  0.565508\n",
-      "296  0.833871\n",
-      "297  1.040323\n",
-      "298  1.303763\n",
-      "299  0.954301\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    2.833315\n",
-      "1    2.558738\n",
-      "2    3.003724\n",
-      "3    2.535398\n",
-      "4    2.797720\n",
-      "..        ...\n",
-      "295  2.425558\n",
-      "296  2.596320\n",
-      "297  2.665249\n",
-      "298  2.642077\n",
-      "299  3.175072\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    2.732684\n",
-      "1    2.229971\n",
-      "2    2.475378\n",
-      "3    2.495666\n",
-      "4    2.462650\n",
-      "..        ...\n",
-      "295  2.512257\n",
-      "296  2.619655\n",
-      "297  2.390718\n",
-      "298  2.241314\n",
-      "299  2.395140\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    2.290064\n",
-      "1    2.309586\n",
-      "2    2.167920\n",
-      "3    2.191076\n",
-      "4    2.249119\n",
-      "..        ...\n",
-      "295  2.204164\n",
-      "296  2.246984\n",
-      "297  2.521996\n",
-      "298  2.094582\n",
-      "299  2.248426\n",
-      "\n",
-      "[300 rows x 1 columns]\n",
-      "          MAE\n",
-      "0    1.699304\n",
-      "1    1.956807\n",
-      "2    1.673685\n",
-      "3    1.788314\n",
-      "4    1.908642\n",
-      "..        ...\n",
-      "295  1.915542\n",
-      "296  2.274142\n",
-      "297  1.923457\n",
-      "298  1.721884\n",
-      "299  1.901150\n",
-      "\n",
-      "[300 rows x 1 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "LIBRARIES = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
-    "             \"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
-    "\n",
-    "for lp in LIBRARIES:\n",
-    "    df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF = gerar_metricas(lp)\n",
-    "    print(df_list_output_MbR)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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
-}

app.py

@@ -6,8 +6,41 @@ import csv
 from scipy.stats import wilcoxon
 import matplotlib.gridspec as gridspec
 
-LIBRARIES = ["ALOY", "APSTUD", "CLI", "CLOV", "COMPASS", "CONFCLOUD", "CONFSERVER", "DAEMON", "DM", "DNN", "DURACLOUD", "EVG", "FAB", 
-             "MDL", "MESOS" ,"MULE", "NEXUS", "SERVER", "STL", "TIDOC", "TIMOB", "TISTUD", "XD"]
+LIBRARIES = ["7764", 
+                 "250833", 
+                 #"278964"
+                 "734943", 
+                 #"1304532",
+                 #"1714548", 
+                 "2009901",
+                 "2670515", 
+                 "3828396",
+                 "3836952", 
+                 "4456656", 
+                 "5261717",
+                 "6206924", 
+                 "7071551", 
+                 "7128869",
+                 "7603319",
+                 "7776928", 
+                 "10152778",
+                 "10171263", 
+                 "10171270", 
+                 "10171280",
+                 "10174980", 
+                 "12450835",
+                 "12584701",
+                 "12894267",
+                 "14052249",
+                 "14976868", 
+                 "15502567",
+                 "19921167",  
+                 "21149814", 
+                 "23285197", 
+                 "28419588",
+                 "28644964",  
+                 "28847821", "ALOY", "APSTUD", "CLI", "CLOV", "COMPASS", "CONFCLOUD", "CONFSERVER", "DAEMON", "DM", "DNN", "DURACLOUD", "EVG", "FAB", 
+             "MDL", "MESOS" ,"MULE", "NEXUS", "SERVER", "STL", "TIDOC", "TIMOB", "TISTUD", "XD", ]
 
 def grafico(list_output_MbR, list_output_NEOSP, list_output_TFIDF, nome_projeto, pip_choices):
     list_results = [["MbR Regressor", np.mean(list_output_MbR)], ["NEOSP-SVR Regressor", np.mean(list_output_NEOSP)], ["TFIDF-SVR Regressor", np.mean(list_output_TFIDF)]]
@@ -80,28 +113,26 @@ def grafico(list_output_MbR, list_output_NEOSP, list_output_TFIDF, nome_projeto,
     
     return gr.update(value=plt, visible=True), gr.update(value=resultado, visible=True), gr.update(value=resultado2, visible=True), gr.update(value=resultado3, visible=True)
 
-
 def create_pip_plot(libraries, pip_choices):
     nome_projeto = libraries
     list_output_MbR = []
-    with open("metricas_{}_MbR.csv".format(nome_projeto), "r") as arquivo:
+    with open("metricas/metricas_{}_MbR.csv".format(nome_projeto), "r") as arquivo:
         arquivo_csv = csv.reader(arquivo)
         for i, linha in enumerate(arquivo_csv):
             list_output_MbR.append(float(linha[0]))
     list_output_NEOSP_SVR = []
-    with open("metricas_{}_NEOSP_SVR.csv".format(nome_projeto), "r") as arquivo:
+    with open("metricas/metricas_{}_NEOSP_SVR.csv".format(nome_projeto), "r") as arquivo:
         arquivo_csv = csv.reader(arquivo)
         for i, linha in enumerate(arquivo_csv):
             list_output_NEOSP_SVR.append(float(linha[0]))
     list_output_TFIDF_SVR = []
-    with open("metricas_{}_TFIDF.csv".format(nome_projeto), "r") as arquivo:
+    with open("metricas/metricas_{}_TFIDF.csv".format(nome_projeto), "r") as arquivo:
         arquivo_csv = csv.reader(arquivo)
         for i, linha in enumerate(arquivo_csv):
             list_output_TFIDF_SVR.append(float(linha[0]))
     
     return grafico(list_output_MbR, list_output_NEOSP_SVR, list_output_TFIDF_SVR, nome_projeto, pip_choices)
     
-    
 demo = gr.Blocks()
 
 with demo:

metricas/metricas_10152778_MbR.csv

@@ -0,0 +1,300 @@
+1.50020158039026
+1.5373544366081677
+1.6996883713301625
+2.1387567656224373
+1.9560849598163028
+1.45153354108578
+3.0041823847793987
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+1.3147449565360012
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+1.6527390519927834
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