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{
"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)"
]
}
],
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