file_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import pickle\n",
    "\n",
    "from scipy import stats\n",
    "import scipy.io\n",
    "from scipy.spatial.distance import pdist\n",
    "from scipy.linalg import cholesky\n",
    "from scipy.io import loadmat\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report,roc_auc_score,recall_score,precision_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.experimental import enable_iterative_imputer  \n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "from src import SMOTE\n",
    "from src import CFS\n",
    "from src import metrices_V2 as metrices\n",
    "\n",
    "import platform\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "import sys\n",
    "import os\n",
    "import copy\n",
    "import traceback\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Load and other util function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(project):\n",
    "    understand_path = 'data/understand_files_all/' + project + '_understand.csv'\n",
    "    commit_guru_path = 'data/commit_guru/' + project + '.csv'\n",
    "    understand_df = pd.read_csv(understand_path)\n",
    "    understand_df = understand_df.dropna(axis = 1,how='all')\n",
    "    cols_list = understand_df.columns.values.tolist()\n",
    "    for item in ['Kind', 'Name','commit_hash', 'Bugs']:\n",
    "        if item in cols_list:\n",
    "            cols_list.remove(item)\n",
    "            cols_list.insert(0,item)\n",
    "    understand_df = understand_df[cols_list]\n",
    "    commit_guru_df = pd.read_csv(commit_guru_path)\n",
    "    cols = understand_df.columns.tolist()\n",
    "    \n",
    "    commit_guru_df = commit_guru_df.drop(labels = ['parent_hashes','author_name','author_name',\n",
    "                                                   'author_email','fileschanged','author_date',\n",
    "                                                   'author_date_unix_timestamp', 'commit_message',\n",
    "                                                  'classification', 'fix', 'contains_bug','fixes',],axis=1)\n",
    "\n",
    "#     understand_df = understand_df.drop_duplicates(cols[4:len(cols)])\n",
    "    df = understand_df.merge(commit_guru_df,on='commit_hash')\n",
    "    cols = df.columns.tolist()\n",
    "    cols = cols[1:] + [cols[0]]\n",
    "    df = df[cols]\n",
    "    for item in ['Kind','commit_hash']:\n",
    "        if item in cols:\n",
    "            df = df.drop(labels = [item],axis=1)\n",
    "    df.dropna(inplace=True)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    return df\n",
    "\n",
    "def load_both_data(project,metric):\n",
    "    understand_path = 'data/understand_files_all/' + project + '_understand.csv'\n",
    "    understand_df = pd.read_csv(understand_path)\n",
    "    understand_df = understand_df.dropna(axis = 1,how='all')\n",
    "    cols_list = understand_df.columns.values.tolist()\n",
    "    for item in ['Kind', 'Name','commit_hash', 'Bugs']:\n",
    "        if item in cols_list:\n",
    "            cols_list.remove(item)\n",
    "            cols_list.insert(0,item)\n",
    "    understand_df = understand_df[cols_list]\n",
    "    cols = understand_df.columns.tolist()\n",
    "    understand_df = understand_df.drop_duplicates(cols[4:len(cols)])\n",
    "    understand_df['Name'] = understand_df.Name.str.rsplit('.',1).str[1]\n",
    "    \n",
    "    commit_guru_file_level_path = 'data/commit_guru_file/' + project + '.csv'\n",
    "    commit_guru_file_level_df = pd.read_csv(commit_guru_file_level_path)\n",
    "    commit_guru_file_level_df['commit_hash'] = commit_guru_file_level_df.commit_hash.str.strip('\"')\n",
    "    commit_guru_file_level_df = commit_guru_file_level_df[commit_guru_file_level_df['file_name'].str.contains('.java')]\n",
    "    commit_guru_file_level_df['Name'] = commit_guru_file_level_df.file_name.str.rsplit('/',1).str[1].str.split('.').str[0].str.replace('/','.')\n",
    "    commit_guru_file_level_df = commit_guru_file_level_df.drop('file_name',axis = 1)\n",
    "    \n",
    "    \n",
    "    df = understand_df.merge(commit_guru_file_level_df,how='left',on=['commit_hash','Name'])\n",
    "    \n",
    "    \n",
    "    cols = df.columns.tolist()\n",
    "    cols.remove('Bugs')\n",
    "    cols.append('Bugs')\n",
    "    df = df[cols]\n",
    "    file_names = df.Name\n",
    "    \n",
    "    for item in ['Kind', 'Name','commit_hash']:\n",
    "        if item in cols:\n",
    "            df = df.drop(labels = [item],axis=1)\n",
    "#     df.dropna(inplace=True)\n",
    "    df = df.drop_duplicates()\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    y = df.Bugs\n",
    "    X = df.drop('Bugs',axis = 1)\n",
    "    cols = X.columns\n",
    "    scaler = MinMaxScaler()\n",
    "    X = scaler.fit_transform(X)\n",
    "    X = pd.DataFrame(X,columns = cols)\n",
    "    imp_mean = IterativeImputer(random_state=0)\n",
    "    X = imp_mean.fit_transform(X)\n",
    "    X = pd.DataFrame(X,columns = cols)\n",
    "    \n",
    "    if metric == 'process':\n",
    "        X = X[['file_la', 'file_ld', 'file_lt', 'file_age', 'file_ddev',\n",
    "       'file_nuc', 'own', 'minor', 'file_ndev', 'file_ncomm', 'file_adev',\n",
    "       'file_nadev', 'file_avg_nddev', 'file_avg_nadev', 'file_avg_ncomm',\n",
    "       'file_ns', 'file_exp', 'file_sexp', 'file_rexp', 'file_nd', 'file_sctr']]\n",
    "    elif metric == 'product':\n",
    "        X = X.drop(['file_la', 'file_ld', 'file_lt', 'file_age', 'file_ddev',\n",
    "       'file_nuc', 'own', 'minor', 'file_ndev', 'file_ncomm', 'file_adev',\n",
    "       'file_nadev', 'file_avg_nddev', 'file_avg_nadev', 'file_avg_ncomm',\n",
    "       'file_ns', 'file_exp', 'file_sexp', 'file_rexp', 'file_nd', 'file_sctr'],axis = 1)\n",
    "    else:\n",
    "        X = X\n",
    "    X['Name'] = file_names\n",
    "    X['Bugs'] = y\n",
    "    \n",
    "    return X\n",
    "\n",
    "def load_data_release_level(project,metric):\n",
    "    understand_path = 'data/understand_files_all/' + project + '_understand.csv'\n",
    "    understand_df = pd.read_csv(understand_path)\n",
    "    understand_df = understand_df.dropna(axis = 1,how='all')\n",
    "    cols_list = understand_df.columns.values.tolist()\n",
    "    for item in ['Kind', 'Name','commit_hash', 'Bugs']:\n",
    "        if item in cols_list:\n",
    "            cols_list.remove(item)\n",
    "            cols_list.insert(0,item)\n",
    "    understand_df = understand_df[cols_list]\n",
    "    cols = understand_df.columns.tolist()\n",
    "    understand_df = understand_df.drop_duplicates(cols[4:len(cols)])\n",
    "    understand_df['Name'] = understand_df.Name.str.rsplit('.',1).str[1]\n",
    "    \n",
    "    commit_guru_file_level_path = 'data/commit_guru_file/' + project + '.csv'\n",
    "    commit_guru_file_level_df = pd.read_csv(commit_guru_file_level_path)\n",
    "    commit_guru_file_level_df['commit_hash'] = commit_guru_file_level_df.commit_hash.str.strip('\"')\n",
    "    commit_guru_file_level_df = commit_guru_file_level_df[commit_guru_file_level_df['file_name'].str.contains('.java')]\n",
    "    commit_guru_file_level_df['Name'] = commit_guru_file_level_df.file_name.str.rsplit('/',1).str[1].str.split('.').str[0].str.replace('/','.')\n",
    "    commit_guru_file_level_df = commit_guru_file_level_df.drop('file_name',axis = 1)\n",
    "    \n",
    "    release_df = pd.read_pickle('data/release/' + project + '_release.pkl')\n",
    "    release_df = release_df.sort_values('created_at',ascending=False)\n",
    "    release_df = release_df.reset_index(drop=True)\n",
    "    release_df['created_at'] = pd.to_datetime(release_df.created_at)\n",
    "    release_df['created_at'] = release_df.created_at.dt.date\n",
    "    \n",
    "    commit_guru_path = 'data/commit_guru/' + project + '.csv' \n",
    "    commit_guru_df = pd.read_csv(commit_guru_path)\n",
    "    cols = understand_df.columns.tolist()\n",
    "    commit_guru_df['created_at'] = pd.to_datetime(commit_guru_df.author_date_unix_timestamp,unit='s')\n",
    "    commit_guru_df['created_at'] = commit_guru_df.created_at.dt.date\n",
    "    \n",
    "    commit_guru_df = commit_guru_df[['commit_hash','created_at']]\n",
    "    \n",
    "    df = understand_df.merge(commit_guru_file_level_df,how='left',on=['commit_hash','Name'])\n",
    "    df = df.merge(commit_guru_df,how='left',on=['commit_hash'])\n",
    "\n",
    "\n",
    "    \n",
    "    cols = df.columns.tolist()\n",
    "    cols.remove('Bugs')\n",
    "    cols.append('Bugs')\n",
    "    df = df[cols]\n",
    "    file_names = df.Name\n",
    "    commit_hash = df.commit_hash\n",
    "    for item in ['Kind', 'Name','commit_hash']:\n",
    "        if item in cols:\n",
    "            df = df.drop(labels = [item],axis=1)\n",
    "    df = df.drop_duplicates()\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    created_at = df.created_at\n",
    "    df = df.drop('created_at',axis = 1)\n",
    "    y = df.Bugs\n",
    "    X = df.drop('Bugs',axis = 1)\n",
    "    cols = X.columns\n",
    "    scaler = MinMaxScaler()\n",
    "    X = scaler.fit_transform(X)\n",
    "    X = pd.DataFrame(X,columns = cols)\n",
    "    imp_mean = IterativeImputer(random_state=0)\n",
    "    X = imp_mean.fit_transform(X)\n",
    "    X = pd.DataFrame(X,columns = cols)\n",
    "    X['created_at'] = created_at\n",
    "    \n",
    "    if metric == 'process':\n",
    "        X = X[['file_la', 'file_ld', 'file_lt', 'file_age', 'file_ddev',\n",
    "       'file_nuc', 'own', 'minor', 'file_ndev', 'file_ncomm', 'file_adev',\n",
    "       'file_nadev', 'file_avg_nddev', 'file_avg_nadev', 'file_avg_ncomm',\n",
    "       'file_ns', 'file_exp', 'file_sexp', 'file_rexp', 'file_nd', 'file_sctr','created_at']]\n",
    "    elif metric == 'product':\n",
    "        X = X.drop(['file_la', 'file_ld', 'file_lt', 'file_age', 'file_ddev',\n",
    "       'file_nuc', 'own', 'minor', 'file_ndev', 'file_ncomm', 'file_adev',\n",
    "       'file_nadev', 'file_avg_nddev', 'file_avg_nadev', 'file_avg_ncomm',\n",
    "       'file_ns', 'file_exp', 'file_sexp', 'file_rexp', 'file_nd', 'file_sctr'],axis = 1)\n",
    "    else:\n",
    "        X = X\n",
    "    \n",
    "    df = X\n",
    "    df['Name'] = file_names\n",
    "    df['Bugs'] = y\n",
    "    \n",
    "    accepted_commit_dates = []\n",
    "    for i in range(release_df.shape[0]-1):\n",
    "        sub_df = df[df['created_at'] <= release_df.loc[i,'created_at']]\n",
    "        sub_df = sub_df[sub_df['created_at'] > release_df.loc[i+1,'created_at']]\n",
    "        if len(sub_df.created_at.unique()) > 0:\n",
    "            last_release = sub_df.created_at.unique()[0]\n",
    "            for unq_release in sub_df.created_at.unique():\n",
    "                if unq_release > last_release:\n",
    "                    last_release = unq_release\n",
    "            accepted_commit_dates.append(last_release)\n",
    "#             print(sub_df.created_at.unique(),last_release)\n",
    "            \n",
    "    df = df[df.created_at.isin(accepted_commit_dates)]\n",
    "    \n",
    "    df = df.drop('created_at',axis = 1)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def apply_smote(df):\n",
    "    cols = df.columns\n",
    "    smt = SMOTE.smote(df)\n",
    "    df = smt.run()\n",
    "    df.columns = cols\n",
    "    return df\n",
    "\n",
    "def apply_cfs(df):\n",
    "        y = df.Bugs.values\n",
    "        X = df.drop(labels = ['Bugs'],axis = 1)\n",
    "        X = X.values\n",
    "        selected_cols = CFS.cfs(X,y)\n",
    "        cols = df.columns[[selected_cols]].tolist()\n",
    "        cols.append('Bugs')\n",
    "        return df[cols],cols\n",
    "\n",
    "def get_spearmanr(df):\n",
    "    df = df.drop('Bugs',axis = 1)\n",
    "    file_corr = []\n",
    "    for name in df.Name.unique():\n",
    "        sub_df = df[df['Name'] == name]\n",
    "        if sub_df.shape[0] < 2:\n",
    "            continue\n",
    "        sub_df = sub_df.drop('Name',axis = 1)\n",
    "        rho, pval = stats.spearmanr(sub_df.values,sub_df.values,axis = 1)\n",
    "        file_corr.append(np.median(rho[:,:int(len(rho)/2)]))\n",
    "    return file_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting List of projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj_df = pd.read_csv('projects.csv')\n",
    "projects = proj_df.repo_name.tolist()\n",
    "metric = 'process+product'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "+++++++++++++++++   AlphabetIndex-Fast-Scroll-RecyclerView  +++++++++++++++++\n",
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      "+++++++++++++++++   reflections  +++++++++++++++++\n",
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      "+++++++++++++++++   databus  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   Once  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   easygcm  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   ion-java  +++++++++++++++++\n",
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      "+++++++++++++++++   SoLoader  +++++++++++++++++\n",
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      "+++++++++++++++++   arara  +++++++++++++++++\n",
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      "+++++++++++++++++   swipe-button  +++++++++++++++++\n",
      "0.8302272824609348\n",
      "+++++++++++++++++   react-native-intercom  +++++++++++++++++\n",
      "nan\n",
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      "+++++++++++++++++   KenBurnsView  +++++++++++++++++\n",
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      "nan\n",
      "+++++++++++++++++   CreditCardEntry  +++++++++++++++++\n",
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      "+++++++++++++++++   Qigsaw  +++++++++++++++++\n",
      "0.9597257939867109\n",
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      "+++++++++++++++++   DaggerMock  +++++++++++++++++\n",
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      "+++++++++++++++++   Fast-Android-Networking  +++++++++++++++++\n",
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      "+++++++++++++++++   busybox  +++++++++++++++++\n",
      "0.9543615378295016\n",
      "+++++++++++++++++   react-native-dialogs  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   android-material-design-icon-generator-plugin  +++++++++++++++++\n",
      "[Errno 2] File data/commit_guru_file/android-material-design-icon-generator-plugin.csv does not exist: 'data/commit_guru_file/android-material-design-icon-generator-plugin.csv'\n",
      "+++++++++++++++++   DataflowTemplates  +++++++++++++++++\n"
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      "nan\n",
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      "+++++++++++++++++   tut-spring-boot-oauth2  +++++++++++++++++\n",
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      "+++++++++++++++++   eo  +++++++++++++++++\n",
      "Shape of passed values is (160, 45), indices imply (160, 66)\n",
      "+++++++++++++++++   aws-ec2-ssh  +++++++++++++++++\n",
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      "+++++++++++++++++   Hacktoberfest-Mathematics  +++++++++++++++++\n",
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      "+++++++++++++++++   airline  +++++++++++++++++\n",
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      "+++++++++++++++++   ncalc  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   react-native-google-places  +++++++++++++++++\n",
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      "+++++++++++++++++   Android-Rich-text-Editor  +++++++++++++++++\n",
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      "+++++++++++++++++   FizzBuzzEnterpriseEdition  +++++++++++++++++\n",
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      "+++++++++++++++++   gpmall  +++++++++++++++++\n",
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      "+++++++++++++++++   openhab1-addons  +++++++++++++++++\n",
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      "+++++++++++++++++   Router  +++++++++++++++++\n",
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      "+++++++++++++++++   react-native-push-notification  +++++++++++++++++\n",
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      "+++++++++++++++++   UsbSerial  +++++++++++++++++\n",
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      "+++++++++++++++++   cordova-crosswalk-engine  +++++++++++++++++\n",
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      "+++++++++++++++++   ahbottomnavigation  +++++++++++++++++\n",
      "0.9516419879963971\n",
      "+++++++++++++++++   lottie-react-native  +++++++++++++++++\n",
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      "+++++++++++++++++   ThinDownloadManager  +++++++++++++++++\n",
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      "+++++++++++++++++   react-native-bottom-sheet-behavior  +++++++++++++++++\n",
      "0.9128605711752976\n",
      "+++++++++++++++++   loklak_wok_android  +++++++++++++++++\n",
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      "+++++++++++++++++   Hive-JSON-Serde  +++++++++++++++++\n",
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      "+++++++++++++++++   addressbook-level2  +++++++++++++++++\n",
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      "+++++++++++++++++   storm-yarn  +++++++++++++++++\n",
      "nan\n",
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      "+++++++++++++++++   ReactiveNetwork  +++++++++++++++++\n",
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      "Shape of passed values is (1064, 45), indices imply (1064, 66)\n",
      "+++++++++++++++++   handlebars.java  +++++++++++++++++\n",
      "0.9324118523337305\n",
      "+++++++++++++++++   stringtemplate4  +++++++++++++++++\n",
      "0.9386951262636644\n",
      "+++++++++++++++++   RxNetty  +++++++++++++++++\n",
      "Shape of passed values is (4407, 45), indices imply (4407, 66)\n",
      "+++++++++++++++++   ghprb-plugin  +++++++++++++++++\n",
      "0.8735094465811648\n",
      "+++++++++++++++++   xmemcached  +++++++++++++++++\n",
      "Shape of passed values is (3018, 45), indices imply (3018, 66)\n",
      "+++++++++++++++++   guacamole-client  +++++++++++++++++\n",
      "Shape of passed values is (1622, 45), indices imply (1622, 66)\n",
      "+++++++++++++++++   KernelAdiutor  +++++++++++++++++\n",
      "0.9048177328812932\n",
      "+++++++++++++++++   chat-sdk-android  +++++++++++++++++\n",
      "0.9268523585170481\n",
      "+++++++++++++++++   Duke  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   EhViewer  +++++++++++++++++\n",
      "0.9743864630849737\n",
      "+++++++++++++++++   goclipse  +++++++++++++++++\n",
      "0.9857062135068958\n",
      "+++++++++++++++++   Jest  +++++++++++++++++\n",
      "0.9223275299185401\n",
      "+++++++++++++++++   flume  +++++++++++++++++\n",
      "0.9447120387332679\n",
      "+++++++++++++++++   android-testdpc  +++++++++++++++++\n",
      "0.9125816633696188\n",
      "+++++++++++++++++   samourai-wallet-android  +++++++++++++++++\n",
      "0.919311325637513\n",
      "+++++++++++++++++   jbehave-core  +++++++++++++++++\n",
      "0.9751928788005293\n",
      "+++++++++++++++++   pushy  +++++++++++++++++\n",
      "0.9069069217015641\n",
      "+++++++++++++++++   boon  +++++++++++++++++\n",
      "0.9597852164119818\n",
      "+++++++++++++++++   spring-data-solr  +++++++++++++++++\n",
      "0.9099028792648202\n",
      "+++++++++++++++++   javassist  +++++++++++++++++\n",
      "0.9595022003430485\n",
      "+++++++++++++++++   uhabits  +++++++++++++++++\n",
      "Shape of passed values is (3961, 45), indices imply (3961, 66)\n",
      "+++++++++++++++++   spock  +++++++++++++++++\n",
      "0.970905036499208\n",
      "+++++++++++++++++   PageTurner  +++++++++++++++++\n",
      "0.9448446263147035\n",
      "+++++++++++++++++   heroic  +++++++++++++++++\n",
      "0.9799471744283343\n",
      "+++++++++++++++++   univocity-parsers  +++++++++++++++++\n",
      "0.9499489406549122\n",
      "+++++++++++++++++   twilio-java  +++++++++++++++++\n",
      "0.9819398403782972\n",
      "+++++++++++++++++   TrebleShot  +++++++++++++++++\n",
      "0.9221419845530975\n",
      "+++++++++++++++++   interview  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   jsprit  +++++++++++++++++\n",
      "0.9586296010284632\n",
      "+++++++++++++++++   focus-android  +++++++++++++++++\n",
      "0.9721264550934328\n",
      "+++++++++++++++++   spring-cloud-commons  +++++++++++++++++\n",
      "0.9370362565319563\n",
      "+++++++++++++++++   spring-hateoas  +++++++++++++++++\n",
      "0.9187497502667599\n",
      "+++++++++++++++++   flinkx  +++++++++++++++++\n",
      "0.9791645987569633\n",
      "+++++++++++++++++   streamex  +++++++++++++++++\n",
      "0.9704108910722522\n",
      "+++++++++++++++++   testcontainers-java  +++++++++++++++++\n",
      "0.9317188228736735\n",
      "+++++++++++++++++   connectbot  +++++++++++++++++\n",
      "0.9372096172912853\n",
      "+++++++++++++++++   json-schema  +++++++++++++++++\n",
      "0.9378715578208021\n",
      "+++++++++++++++++   msf4j  +++++++++++++++++\n",
      "0.9457630330427665\n",
      "+++++++++++++++++   spring-kafka  +++++++++++++++++\n",
      "Shape of passed values is (2731, 45), indices imply (2731, 66)\n",
      "+++++++++++++++++   HiBench  +++++++++++++++++\n",
      "Shape of passed values is (3939, 45), indices imply (3939, 66)\n",
      "+++++++++++++++++   embulk  +++++++++++++++++\n",
      "0.9497417625424216\n",
      "+++++++++++++++++   jbake  +++++++++++++++++\n",
      "0.8624098284271141\n",
      "+++++++++++++++++   gravitee-gateway  +++++++++++++++++\n",
      "0.9278969415621499\n",
      "+++++++++++++++++   jcodec  +++++++++++++++++\n",
      "0.950170184846372\n",
      "+++++++++++++++++   spring-social  +++++++++++++++++\n",
      "0.9386440913050988\n",
      "+++++++++++++++++   pebble  +++++++++++++++++\n",
      "0.9067777465243756\n",
      "+++++++++++++++++   CameraView  +++++++++++++++++\n",
      "0.9172043911841112\n",
      "+++++++++++++++++   py4j  +++++++++++++++++\n",
      "0.9311882915392152\n",
      "+++++++++++++++++   schema-registry  +++++++++++++++++\n",
      "0.9364832122749916\n",
      "+++++++++++++++++   jedis  +++++++++++++++++\n",
      "0.9384334827032693\n",
      "+++++++++++++++++   download-manager  +++++++++++++++++\n",
      "0.9411991562682559\n",
      "+++++++++++++++++   AppiumTestDistribution  +++++++++++++++++\n",
      "0.917815559475569\n",
      "+++++++++++++++++   incubator-hudi  +++++++++++++++++\n",
      "0.8935411142888796\n",
      "+++++++++++++++++   XposedInstaller  +++++++++++++++++\n",
      "Shape of passed values is (1491, 45), indices imply (1491, 66)\n",
      "+++++++++++++++++   jBrowserDriver  +++++++++++++++++\n",
      "0.9383472885620063\n",
      "+++++++++++++++++   spring-cloud-aws  +++++++++++++++++\n",
      "0.9584434205047488\n",
      "+++++++++++++++++   rtmp-rtsp-stream-client-java  +++++++++++++++++\n",
      "Shape of passed values is (2531, 45), indices imply (2531, 66)\n",
      "+++++++++++++++++   spring-data-rest  +++++++++++++++++\n",
      "0.927561909839933\n",
      "+++++++++++++++++   PokeGOAPI-Java  +++++++++++++++++\n",
      "0.9523321576527637\n",
      "+++++++++++++++++   github-api  +++++++++++++++++\n",
      "0.9270816476228956\n",
      "+++++++++++++++++   Book-Catalogue  +++++++++++++++++\n",
      "0.9483217354590652\n",
      "+++++++++++++++++   RedReader  +++++++++++++++++\n",
      "0.9227132323450541\n",
      "+++++++++++++++++   BiglyBT  +++++++++++++++++\n",
      "0.9532266888433647\n",
      "+++++++++++++++++   parquet-mr  +++++++++++++++++\n",
      "0.9843853228867907\n",
      "+++++++++++++++++   jsoup  +++++++++++++++++\n",
      "0.9179630720589707\n",
      "+++++++++++++++++   cassandra-lucene-index  +++++++++++++++++\n",
      "0.93489899406317\n",
      "+++++++++++++++++   AndroidAsync  +++++++++++++++++\n",
      "nan\n",
      "+++++++++++++++++   cyclops-integration  +++++++++++++++++\n",
      "0.9508751921175063\n",
      "+++++++++++++++++   opsu  +++++++++++++++++\n",
      "Shape of passed values is (2375, 45), indices imply (2375, 66)\n",
      "+++++++++++++++++   metrics  +++++++++++++++++\n",
      "0.9338325765129751\n",
      "+++++++++++++++++   amidst  +++++++++++++++++\n",
      "0.9546495994208064\n",
      "+++++++++++++++++   jsweet  +++++++++++++++++\n",
      "0.9402893679181326\n",
      "+++++++++++++++++   pgjdbc-ng  +++++++++++++++++\n",
      "0.9526400321717629\n",
      "+++++++++++++++++   qbit  +++++++++++++++++\n",
      "Shape of passed values is (5962, 45), indices imply (5962, 66)\n",
      "+++++++++++++++++   thingsboard  +++++++++++++++++\n",
      "0.9265137465131066\n",
      "+++++++++++++++++   blueflood  +++++++++++++++++\n",
      "0.8667869560994467\n",
      "+++++++++++++++++   androidannotations  +++++++++++++++++\n",
      "0.9456324563796512\n",
      "+++++++++++++++++   digdag  +++++++++++++++++\n",
      "0.9402167867043085\n",
      "+++++++++++++++++   openrouteservice  +++++++++++++++++\n",
      "0.9481873304708057\n",
      "+++++++++++++++++   nzbhydra2  +++++++++++++++++\n",
      "0.9450957883296989\n",
      "+++++++++++++++++   phpinspectionsea  +++++++++++++++++\n",
      "0.938098531079575\n",
      "+++++++++++++++++   phoenicis  +++++++++++++++++\n",
      "Shape of passed values is (272, 45), indices imply (272, 66)\n",
      "+++++++++++++++++   guice  +++++++++++++++++\n",
      "0.9337041588033415\n",
      "+++++++++++++++++   chunky  +++++++++++++++++\n",
      "0.9803511348457246\n",
      "+++++++++++++++++   dropwizard  +++++++++++++++++\n",
      "0.9194458942128564\n",
      "+++++++++++++++++   Digital  +++++++++++++++++\n",
      "0.9535478777710391\n",
      "+++++++++++++++++   intellij-elixir  +++++++++++++++++\n",
      "0.9600419067574646\n",
      "+++++++++++++++++   nakadi  +++++++++++++++++\n",
      "0.9238195850254093\n",
      "+++++++++++++++++   Discord4J  +++++++++++++++++\n",
      "0.9229764815650872\n",
      "+++++++++++++++++   OpenRefine  +++++++++++++++++\n",
      "0.9472736618981813\n",
      "+++++++++++++++++   nokogiri  +++++++++++++++++\n",
      "0.8944597435338664\n",
      "+++++++++++++++++   rapidoid  +++++++++++++++++\n",
      "0.9874533031106862\n",
      "+++++++++++++++++   druid  +++++++++++++++++\n",
      "0.9549305886238268\n"
     ]
    }
   ],
   "source": [
    "project_corr = {}\n",
    "for project in projects[151:]:\n",
    "    try:\n",
    "        if project == '.DS_Store':\n",
    "            continue\n",
    "#         if project != 'Pokemap':\n",
    "#             continue\n",
    "        print(\"+++++++++++++++++   \"  + project + \"  +++++++++++++++++\")\n",
    "        df = load_data_release_level(project,metric)\n",
    "        corr = get_spearmanr(df)\n",
    "        project_corr[project] = corr\n",
    "        print(np.median(corr))\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        continue\n",
    "with open('results/Performance/process+product_corr_release.pkl', 'wb') as handle:\n",
    "    pickle.dump(project_corr, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_corr = pd.read_pickle('results/Performance/Correlations/product_corr_release.pkl')\n",
    "# flat_list = [item for sublist in list(project_corr.values()) for item in sublist]\n",
    "flat_list = [np.median(sublist) for sublist in list(project_corr.values())]\n",
    "ax = sns.violinplot(y=flat_list)\n",
    "plt.savefig('results/Performance/Correlations/product_corr_release.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LGlAY7DqZMmUKCgoK8MYbb+Cjjz5CoLkGRsdImLPGwGBiwBNdSpX8iJw/DNl3HAaDgBnf/jb+7u/+Di6XS+/SBhwGu47cbjfWrVuHe++9F//93/+Nbdu2IdBcAzFtMIyO4TAOGsw2eEppmqpAbjsD2VcL2V8H0SBi9uxZWLJkCXJyOFXH5TDYB4AhQ4bgsccew7333os//elP2Lbtr/Cd+RQGowXioOthctwAgzWT7YeUEjRNgxI8D9l3AkrrKahKBJmZWbhj9mJUVFTA7XbrXeKAx2AfQPLy8vDggw/i/vvvx969e7Ft2zbs+PxzBJqORW/RlzYYxvShEO05nPedkoqmylD89ZDbzkL1n4MiBWA2m3Hb9Gm48847UVxcDFHkZ767GOwDkCiKmDx5MiZPngy/34/t27fj888/x969+xBsqoFBNMJgz4MxfQjE9CEwGBPrtl1EAKBKwWgzS9tZqIEGaKoMi8WKyZMnory8HOXl5bDb7XqXmZAY7ANcWloaZsyYgRkzZiAcDqOqqgq7du3Czp274D23CwBgtDoh2HNhtOdCtLs5oIYGJE2JQA40QPHXQw02QAn5AADZ2W6U3z4bZWVlKCkpSempAHoLgz2BWCyW2Jn86tWrUVtbiy+//BL79n2Fw4cPIeg9CggCRGsWRHsOxLRciLZsNtuQLjRVhhI8D8VfDyXQACXoBaDBZDKjZNxYjB8/HmVlZbj++ut5/aiXMdgTlCAIGDFiBEaMGIF77rkH4XAYR44cwVdffYV9+75CTc1hRBoPQTCIMFizINrd0R9bNu/qRH1CU6RokAc8UIIeqMFGaJoKg0FEYWEhJkyYgdLSUowePZpn5X2M/+FJwmKxxO7itHIl4Pf7cfDgQVRVVWH//irU1h5C5LwGQTDAYM1sF/RsuulEMF55nQBEm1aUgAdywAM16IES8gKaBoPBgJEjC1BScitKSkowduxYtpX3M35ik1RaWhpuuukm3HTTTQCAQCCAw4cP48CBA6iqOoCammpEGg8DECDaXDDY3DDacy600afmHNYXGQcNgeI/12GdAE0OQw54oAQaLgR59P4CotGIosJCFBdHe6+MHj0aNhsH2emJwZ4i7HY7Jk2aFBt6HQqFcPTo0QtBX4WjR49G2+gBiFYXDHY3RHsOjHY3BNGiZ+n9zuQsQKTxKKBKMLtLYHKm5m0NVTkUbVaJBXkzgOi0GGNHj0FJyRwUFxejqKgoZW9oMVAx2FOU1WqNNd0AQCQSiQX9/qoqHD1yBCFvNQBAtGVCtOdBTMuDaM9K+tGwgiBcmNbBBrOrQO9y+o2mKlCCHij+OiiBeijB6Bm52WxG8ZixKCkpRnFxMQoLC9lGPsAx2AlA9J+3pKQEJSUlWLZsGSRJQnV1Nfbv3489e/aiuvoIIo2HYBCNEGw5MKZFg95gHsQeDQlK0zSoYV90YJC/DmrQA02VYTCIGDNmNCZOnIvx48dj5MiRDPIEw2CnuEwmE8aNG4dx48Zh2bJl8Pv9qKqqwt69e7F7zx7U1+0BAIiWDIjpw2DMuA4Gi5MhP8BpmgY15IXUcgqq/zSUcBsAYMiQoZh0xxxMmDABxcXFvNiZ4Bjs1C1paWmx0YAAUFdXhz179mD79u2oqqpCpPEQREt6NOQHXce5bQYQTdOgBhshtZ6C2nYaSsQPg0HEhAnjMXXqVEycOJETaiUZBjtdlby8PFRUVKCiogI+nw87d+7EZ599hn37vkKk8QhEqwNG5yiYHPm8Z6hONCUCyXcCcnMNlHALRNGIG2+ciGnTpuHmm29Genq63iVSH2Gw0zVzOByYOXMmZs6ciba2Nmzfvh2bN/8RtbVfQvLsh+i4AWbXKN4pqp8oYR+kpmNQWr6BqkgoLCxERcWDKC8vT/mbPKcKBjv1qvT0dMycORMzZszA0aNHsWnTJnz66Wfwe6thdAyHxV0Cg4ntt31BjbQh7NkPueUkjEYjbr9tOubNm4dRo0bpXRr1s24F+6ZNm/D6669DlmXcf//9+M53vtNh+8GDB/Hss89CkiQMHjwY//Zv/4aMjIw+KZgSgyAIGD16NEaPHo2HHnoIf/jDH7Bp02YEWk/B5CqEOWsMR7z2Ek2JIHz+IOSmYzAajVi6dCnuuusuOBwOvUsjnRi62qG+vh4bNmzAW2+9hXfffRdvv/02ampqOuzzwgsvYM2aNXj//fcxfPhw/PKXv+yzginxZGZm4qGHHsLPf/46br1lKiKNhxCs/RPk1jN6l5bQNE2D1HISgdo/QWqqxh13fAsbN/4nVq5cyVBPcV0G+/bt21FeXg6n0wm73Y5Zs2Zhy5YtHfZRVRV+vx8AEAwGYbVyfnDqLC8vD48//jh+8pOfYOiQHARP/w3BMzugymG9S0s4qhxE6MxnCJ3ZjuH5w/DqK69g7dq1yM7O1rs0GgC6bIppaGjocCuqnJwc7N+/v8M+Tz75JB588EG8+OKLsNlseOedd3pUxIEDB3q0PyW+lStW4NNPP8XHn3yCYKAe5tzJMGUM07usAU/TNMgt3yBSvwcCFHz729/GlClT4PV64fV69S6PBogug11V1Q79kTVN67AeCoXw9NNP49e//jVKS0vxq1/9Ck888QQ2btzY7SKKi4thsaTWfCQE3HzzzVi0aBE2bNiA2tpPIbfmw5p3Y8rNTdNdqhxCuO5LyK2nUVhYiLVr1+K6667TuyzSQTgcvuIJcZdNMXl5efB4PLF1j8fTYTBDdXU1LBYLSktLAQD33nsvdu3adS01UwoZPnw4fvrTn2LZsmVQ204hWLsFUsspaJqmd2kDhqZpkHy1CNb+L7RAHe6//3786Ec/YqjTZXUZ7FOnTsWOHTvg9XoRDAaxdetWTJ8+PbY9Pz8fdXV1OH78OADgL3/5C0pKSvquYko6RqMRy5cvx09/+lNcPywv2nZ8+lOoUkDv0nSnRtoQOvUxQmd3omBEPl555WUsWbKEN3amK+qyKSY3Nxfr1q3DypUrIUkSlixZgtLSUlRWVmLNmjUoKSnBSy+9hLVr10LTNGRlZeHFF1/sj9opyYwcORIbNmzAe++9hzfeeBPB2v+FKbsYJtcoCEKX5yBJRdMURBqPQmo8BLPJiIdWrcKcOXMY6NQtgqbjd96L7URsY6dL1dXV4fXXX8eePXsgWl2w5E2GaMvqt/cPfPMXAIA9/85+e8+LZH8DIg27oYR8KC8vx8MPP9yhAwNRV9nJkac0IOXl5eH555/H9u3b8fP//E80n/gzTK5RsOSUJu3cM5oSQbhhH6Tm48h2u7H68Wdid8Ai6gkGOw1YgiBg2rRpmDhxIn77299i8x//CNV/FubcyTCmD9a7vF4ltZ6GVL8bqhzCokWLsGzZMo4HoavGYKcBz263Y9WqVZg+fTpefvkVnD31MUyuQlhyxyf83Zw0VUG4fjek5uPIz78Ba9c+hoKC1LlrE/WN1LoiRQltzJgxeO21V7FgwQJITdUIfvNXqJJf77KumhppRfDkh5Caj2Pp0qV4+eUNDHXqFQx2SihmsxmVlZV44oknYFTbEPrmQ6jhFr3L6jEl1ITgNx/CLITxzDPPYOXKlTAa+QWaegeDnRLSLbfcgp/85MewW00InfoIaqRV75K6TQk1I3TqYzgz0vHyhg28QEq9jsFOCSs/Px8vvfQibBYRodMfQ1MkvUvqkqaEET79CTIG2fDSSy9iyJAhepdESYjBTgktPz8f69c/DSXiR7hhn97ldClUtxuaEsJzzz7LUKc+w2CnhDdu3DgsuvtuSM1fQw406F3OZcltZyG3nMTy5ct5kZT6FIOdksKyZcuQ4XBAOn9Q71Li0jQNkfMH4Xa7sXjxYr3LoSTHYKekYLVasXTJEsj+esgBT9dP6GeKvw5KsBH33Xcfe79Qn2OwU9KYPXs20tLSIXmP6F1KJ5L3CFyuTHzrW9/SuxRKAQx2ShpWqxXz5s2F3HoGStindzkxSrARsr8eCxfeBZMpOee5oYGFwU5JZcGCBbBarYh4qvQuJSbiqUJ6+iDMnj1b71IoRTDYKalkZGTg7rvvhtx6ekC0tctt5yD763DvvffAbrfrXQ6lCAY7JZ27774b2dluROq+hKYqutWhqRIi9V9iyJChmDt3rm51UOphsFPSsdls+P73H4US9iGsY5NMuH4flIgfa9b8gG3r1K8Y7JSUJk2ahNmzZ0PyHoHcerbf319qOQmp+WssWrQI48aN6/f3p9TGYKekVVlZifz8GxCu2wk10tZv76uEfYjUfYGiotFYsWJFv70v0UUMdkpaZrMZTz/9FKxmI0JnPoWm9v0kYZoSQfjMp0hPt+PJJ5/gYCTSBYOdktrgwYPx5JNPQA37EDq7E31573ZNUxE6ux2QA3j6qaeQnZ3dZ+9FdCUMdkp6EydOxHe/+13IracRaTzUreeYHCNgcozo0ftEPFWQ2+qwevVqjB079mpKJeoVDHZKCQsXLsT06bfFwrcrJudwmJzDu/36UstpRBoPY/bs2Zg1a9a1lEp0zRjslBIEQcAPfvB9DLvuOkTqPocqBXvttdVIGyL1uzByZAEefvjhXntdoqvFYKeUYbVa8eQTT8AAFeG6Xb3S3q5pGsLndsJsFPHkk0+wvzoNCAx2Sin5+fn47ncfiA71bzlxza8nNdVADniwatXDyMvLu/YCiXoBg51STkVFBYqKRiPSsA+aErnq11HlEKTz+zFhwgTceeedvVgh0bVhsFPKEUURjzyyCqocRqTx6uduj5w/CGgKVq1aBUEQerFComvDYKeUVFBQgOnTp0NqqoYqh3v8fFUKQG7+GjNmzMCwYcP6oEKiq8dgp5R1zz33QFNlSM01PX6u1HQMgIalS5f2fmFE14jBTikrPz8fEydOhNJcA01Tu/08TVUg+45jypQpyM3N7cMKia4Og51S2uzZs6FIQSj+rgctXSS3nYEqhzFnzpw+rIzo6jHYKaWVlZVFb4DtO9Ht58i+E8jMzEJJSUnfFUZ0DRjslNJMJhNuuWUaVP+5bt1tSVMiUPx1mD79Voii2A8VEvUcg51S3tSpU6EqUreaY+S2s9A0FdOmTeuHyoiuDoOdUl5JSQmsVhvktjNd7iu3noHD4URhYWE/VEZ0dRjslPJMJhMmT54UbY65wvwxmqZADdTh5ptvgsHAfx0auLr16dy0aRMqKiowc+ZMvPnmm522Hz9+HCtWrMCCBQvw0EMPwefz9XqhRH2prKwMihSEGmq67D5K4DxURUJZWVk/VkbUc10Ge319PTZs2IC33noL7777Lt5++23U1PzfgA5N07B69WpUVlbi/fffx5gxY7Bx48Y+LZqot02aNAmCIEBuu/yNr+W2sxBFI8aPH9+PlRH1XJfBvn37dpSXl8PpdMJut2PWrFnYsmVLbPvBgwdht9sxffp0AMAjjzyC73znO31XMVEfcDgcGDFyJJTA5S+gqoF6jBs3FjabrR8rI+q5Lu+029DQALfbHVvPycnB/v37Y+snT55EdnY2nnrqKRw+fBgjRozAM88806MiDhw40KP9ifrC0CFD8HXN19CUCATR3GGbKgWhhJqRm1uG3bt361QhUfd0GeyqqnaYuU7TtA7rsixj165deOONN1BSUoKXX34ZP/zhD/HDH/6w20UUFxfDYrH0sHSi3mU2m/HJJ59ACXhgHDS0wzYlUA8gOuVvQUGBHuURxYTD4SueEHfZFJOXlwePxxNb93g8yMnJia273W7k5+fHRuHNmzevwxk9UaIoKiqC0WiEHGjotE0JNMBms2P48O7fB5VIL10G+9SpU7Fjxw54vV4Eg0Fs3bo11p4ORO8A7/V6ceRIdF7rbdu2Ydy4cX1XMVEfMZvNKCgYBTXU2GmbGvJizJjRHG1KCaHLppjc3FysW7cOK1euhCRJWLJkCUpLS1FZWYk1a9agpKQEP/vZz7B+/XoEg0Hk5eXhRz/6UX/UTtTrRo8uwtHqamiaCkGInvdoqgwl1IyioiKdqyPqni6DHQDmz5+P+fPnd/jdL37xi9jy+PHj8T//8z+9WxmRDoYPHw5NVaBGWiFaHAAANeyLbSNKBBw+R9ROfn4+AEANt8R+dzHYL24jGugY7ETtDB48GACgSm2x36mRNhgMBt5Ug9mCMwgAAAdESURBVBIGg52oHbvdjvT0QdAi7YJd8iMzM4sXTilhMNiJLpGdnQ1VDsbWNTmAnBz3FZ5BNLAw2IkukZ2dBSih2LqghJCVlaVjRUQ9w2AnuoTT6ewQ7Kocgsvl0rEiop7pVndHolTidDqhSKHo3OyaAlWRomFPlCAY7ESXcLlcgKYCSgSaKgEAg50SCoOd6BIXm11UJQQoDHZKPAx2oktcDHZNDkK7EOyZmZl6lkTUIwx2oktcDHFNCkJT5Q6/I0oEDHaiS1wMcVUOAqoEg0GEw+HQuSqi7mOwE13CarXCbk+DJAegqTKcTicMBvYMpsTBTytRHFnZWdGmGImjTinxMNiJ4nBnZ0NTghx1SgmJwU4UR2ZmJqCEoMpBXjilhMM2dqI4nE4nlEgAADidACUcnrETxZGRkRF3mSgRMNiJ4hg0aFBsOT09XcdKiHqOwU4Uh8ViiS1brVYdKyHqOQY7URztg91sNutYCVHPMdiJ4hAEIbbMwUmUaPiJJYqjfbATJRoGO1EciqLEllVV1bESop5jsBPFIcty3GWiRMBgJ4qj/Rl7+2WiRMBgJ4qjfZjzjJ0SDYOdKA5N0+IuEyUCBjsRUZJhsBPFIYpi3GWiRMBgJ4rDaDTGXSZKBAx2ojjaTynQfpkoETDYieKw2WyxZU4CRomGwU4Uh91ujy2npaXpWAlRzzHYieJoH+ztl4kSAYOdKA4GOyUyBjtRHO3DnL1iKNF0K9g3bdqEiooKzJw5E2+++eZl9/voo49wxx139FpxRHph33VKZF2eitTX12PDhg34/e9/D7PZjPvuuw8333wzCgoKOux3/vx5/Ou//mufFUpERN3T5Rn79u3bUV5eDqfTCbvdjlmzZmHLli2d9lu/fj2+//3v90mRRETUfV2esTc0NMDtdsfWc3JysH///g77/OY3v8HYsWMxfvz4qyriwIEDV/U8ov6we/duvUsg6pEug11V1Q63CdM0rcN6dXU1tm7dil//+teoq6u7qiKKi4s5uo8GrEmTJuldAlEH4XD4iifEXTbF5OXlwePxxNY9Hg9ycnJi61u2bIHH48HixYvx8MMPo6GhAcuXL7/GsomI6Gp1GexTp07Fjh074PV6EQwGsXXrVkyfPj22fc2aNfjggw/w3nvvYePGjcjJycFbb73Vp0UTEdHldRnsubm5WLduHVauXImFCxdi3rx5KC0tRWVlJaqqqvqjRiIi6oFujbyYP38+5s+f3+F3v/jFLzrtN2zYMGzbtq13KiMioqvCkadEREmGwU5ElGQY7ERESYbBTkSUZBjsRERJhsFORJRkGOxEREmGwU5ElGQY7ERESYbBTkSUZBjsRERJhsFORJRkGOxEREmGwU5ElGQY7ERESYbBTkSUZBjsRERJhsFORJRkGOxEREmGwU5ElGQY7ERESYbBTkSUZBjsRHGoqqp3CURXjcFOFEcwGIwtK4qiYyVEPcdgJ4ojEAjEltuHPFEiYLATxdE+2NsvEyUCBjtRHAx2SmQMdqI42je/hEIhHSsh6jkGO1Ec4XA47jJRImCwE8Uhy3LcZaJEwGAniqN9F0d2d6REw2AnikMUxdiywcB/E0os/MQSxdE+zNuHPFEiYLATxWGxWOIuEyUCBjtRHFarNe4yUSJgsBPFYbfb4y4TJQIGO1Ec7cPcZrPpWAlRz3Ur2Ddt2oSKigrMnDkTb775ZqftH374Ie666y4sWLAA3/ve9+Dz+Xq9UKL+1D7Y09LSdKyEqOe6DPb6+nps2LABb731Ft599128/fbbqKmpiW1va2vD888/j40bN+L9999HUVERXnvttT4tmqivtQ92o9GoYyVEPddlsG/fvh3l5eVwOp2w2+2YNWsWtmzZEtsuSRKee+455ObmAgCKiopw7ty5vquYqB8wzCmRdfnpbWhogNvtjq3n5ORg//79sXWXy4UZM2YAiE6WtHHjRqxYsaJHRRw4cKBH+xP1p927d+tdAlGPdBnsqqpCEITYuqZpHdYvam1txaOPPorRo0fj7rvv7lERxcXF7CtMA9akSZP0LoGog3A4fMUT4i6bYvLy8uDxeGLrHo8HOTk5HfZpaGjA8uXLUVRUhBdeeOEayiUaOHJzc3HDDTfoXQZRj3V5xj516lS89tpr8Hq9sNls2Lp1K/75n/85tl1RFDzyyCOYM2cOvve97/VpsUT96dVXX4377ZRooOsy2HNzc7Fu3TqsXLkSkiRhyZIlKC0tRWVlJdasWYO6ujocOnQIiqLggw8+ABBtWuGZOyU6DkyiRCVomqbp9eYX24nYxk5E1H1dZSdHnhIRJRkGOxFRkmGwExElGQY7EVGSYbATESUZXSfEuNghJxKJ6FkGEVFCuZiZl+vUqGuwS5IEAKiurtazDCKihCRJUtw7fOnaj11VVfj9fphMJo7wIyLqJk3TIEkS0tLSOtx4/SJdg52IiHofL54SESUZBjsRUZJhsBMRJRkGOxFRkvn/cAh3SIJ+vxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_corr = pd.read_pickle('results/Performance/Correlations/process_corr_release.pkl')\n",
    "# flat_list = [item for sublist in list(project_corr.values()) for item in sublist]\n",
    "flat_list = [np.nanmedian(sublist) for sublist in list(project_corr.values())]\n",
    "ax = sns.violinplot(y=flat_list)\n",
    "plt.savefig('results/Performance/Correlations/process_corr_release.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k26IEcOVmb0A3TAHd0PUhqHUleKvPszP3b3z88ccEBQWTlTWK7Oxs7rzzzi65c5ekSjsqLS1l586dfPzxx1d2ijJgCoy5MsKmh0yK6gCUmsLrjiXw/Y/BYMQcFIs5KBZdU1HqLuGqvsDmv37Ehx9+SERkJGPHjGHMmDH07t3b1+W2Gwn8NlZbW8vf/tbQwjj22WcNe746IrHFpGEO6Smbenc0unLzY+F3DMavjfTRvCg1F6muPs97a9fy3nvv0bNnT8aNG0d2djZRUVG+LrdNNSvwN27cyOrVq1EUhblz5zJ79uxrnt+5cyfLly8HGna7X7ZsGYGBgaxbt47nn3+eyMhIAMaMGcOiRYta+S34H03TOHz4MB999BG7d+9GURRMthCskYOwhPaSxcqE8BGD0YIltDeW0N4Ni7lVf0nh5fOsWbOGNX/4A3el3sU990wgMzMTq7XzfeJuMvCLi4tZuXIla9euxWq1MnPmTIYPH06/fv2Ahm3wFi9ezB//+Ef69evHK6+8wsqVK1myZAkFBQUsXryY+++/v83fiD8oKipi27ZtfPTRVsrKLjeMsAlOJCA0EaM9XG7uCeFHjGY71ogkiEhC89TgrTpHwfEvOHz4EAEBAYwdO5a77767Mes6gyYDf9euXWRmZhIWFgZATk4Omzdv5tFHHwUabj7GxcU1/k8ZO3Ys8+fPZ8mSJRw9epRz587x0ksvMWDAAH7+8593uskRuq5z5MgRNmzYQN7+/aDrmINiscePxBwUj8EoI2yE8HdGazC2qBT0bsmo9cV4Ks+y6cPNfPDBByQl9WfKlO8xcuTIDj/Kp8nqS0pKrunXio6O5siRI43HvXv3pqioiM8//5w77riDDz/8kMuXLwMQFRXFvHnzSEtLY8WKFSxbtoznn3++Dd5G+/N4POTm5rJu/XounD+P0WLHGnknlrA+MvNViA7KYDBgDozFHBiLrnrwVp3jzIUv+M1vfkNERCSTJt1PTk5Oh92rt8nA1zTtmq4IXdevOQ4JCeHXv/41P//5z9E0jenTp2OxNKznsmrVqsbz5s+fz4QJE1pUXEFBQYvObw+apnHkyBG2bttGbU0NJntYwyibkF7SmheiEzGYrFgj+mMJT0KtvURVxQn+8Ic/8Oabb5GVNYoRI0Y0Zl1H0WTgx8bGsn///sbj0tJSoqOjG49VVSU2NpZ33nkHgCNHjpCQkEBNTQ3vvfceP/zhD4GGXxQtXd40OTnZr2bJHT16lFdefZWzZ85gckTgSBhzZSMR6ZsXorMyGAyYg+MwB8ehuirxXD7K9u3bOXToMHPnzmHMmDF+M6bf7XbftKHcZJUjR45k9+7dlJeX43Q62bJlC9nZ2Y3PGwwG5s2bR3FxMbqus2bNGiZOnEhAQACvvvoqhw8fBuCNN95ocQvfX6iqyu9//3t+9rOfceGrYuxxmTh6TcAcFCthL0QXYrKH4eiRhaPnOKqdOitXrmTZsmXU19f7urRmabKFHxMTw6JFi5gzZw5er5dp06aRmprKggULWLhwISkpKSxbtoz58+fj8XgYMWIE//RP/4TJZOKFF15g6dKluFwuevfuzXPPPdce76lVOZ1Oli9fzr59+7CEJ2GLvktmwQrRxZkDozEFTMBbcYr8Awd44ic/4an//M9rej/8kUHXdd3XRXzT1Y8l/tCl81//9V/87W+7sMUMwRrR36e1CN+rP78Ntb608dgUEEVAr/E+rEj4mlJ7CXfhbqKjIvnd71b7dGeuprLTPzqe/FRhYSF/27ULa+RACXshxA2Zg7pjjR1KUdEl9uzZ4+tybkoC/yZyc3NB17GEd56JF0KI1mcOjsdocfDxxx/7upSbksC/iYSEBAA0T7WPKxH+Qle92O12Jk+ejN1uR1e9vi5J+AFd9aApLnr27OnrUm5KAv8m0tPTsdnseEqPoqseX5cj/ICuebnnnntYsGABEyZMQNck8Ls6XddwlxwCXScrK8vX5dyUBP5N2O12Hnvs/4GnCueFbWjeOl+XJHzMYLSwZcsWXn75ZT766CMMxo418Ua0Ll3z4vrqE5Sqczz44IMkJib6uqSbkvGFTbi6OfLTTz+D89xfMUcMxBqeJEMzuyiDyYKrvpKNGzcCYAromFPsxe3RdR2l+jzeywXoSj2PPvooOTk5vi6rSdLCb4aUlBRWrHieIXel4Ck5jPPMJjwVp9F1zdelCSHaka7rKLWXcJ7bgqtwDwlxUTzzzDMdIuxBWvjN1qNHD5YuXUpBQQGvvfYaJ0/moZQfwxSSKAumCdHJXV1ITak6g+qqJDo6hjmPPk5WVpbfLKvQHBL4LZScnMzy5cvJy8vjgw8+4MDBg3guf4Y5qDuWsL6YgrpjMHScbwAhxI3puo7mLMNTeRq15kt0TaFv375897sPMW7cuA63cBpI4N8Sg8HAsGHDGDZsGMXFxWzZsoW/btlC1VefYLTYMQXGYw5JwBQQLeEvRAei6zqaqwKl5gJq7Veo7lrsdjsTciZwzz33dPjNUCTwb1NMTAz/8A//wIMPPsj+/fvJzc1l7759OCtPN+x4FRSPOTjhyqqaEv5C+JuGkC/HW/0lWl1DyBuNJgYPvotRo0YxatQoHA6Hr8tsFRL4rcRsNpOZmUlmZiZut5sDBw7w6aefsnfvPpyVZzCarBgDYzEHxWEK7I7R7D/LPgvR1eiaglpXjFJbiFZ3CdVbj9FoYsiQwYwaNYrhw4d32E1ObkYCvw3YbDZGjBjBiBEjcLvdHDx4kH379rFvXx5VhRcAA6aAbpgCu2MOisNoC5VlloVoY5q3DqW2ELW2ELW+BF1TsdvtDB2axrBhwxg+fDhBQUG+LrNNSeC3MZvN1tjy1zSN06dPk5eXx969+zhz5gie0iOYrAEYHbGYghq2VjOYrL4uW4gOT9cU1PpSlLoitPoiVFcV0NANO3zcfQwbNow777yzQ958vVUS+O3IaDSSlJREUlISs2bNoqysjP3793PgwAEOHjyE8+IZGlr/kZgCYjEHdcdoD5e+fyGaQdd1NE8Nat0llNoiNGdDK95sNpOSnEx6ejoZGRnEx8d32U/UEvg+FBkZSU5ODjk5OaiqysmTJ8nPz2d/fj5nTh/Dc7kAo9mGMSAGc2BsQ9+/pXPcPBKiNeiqB6WuGLXuElp9MaqnYfmT7nFxDB1/H2lpaQwaNAi73e7jSv2DBL6fMJlMDBw4kIEDB/LQQw9RVVXFoUOHOHDgAPn5B6i6dKHhPHvYlV8A3TEFRMnG6aJL0XWtYdhk7SXU+iJUZxnoOna7g/S0u0hLSyMtLY2YmBhfl+qXJPD9VGhoKKNHj2b06NHous65c+caw/+zzz7DWX4Cg9GMMSAac2D3hu4fa+e+4SS6Jk1xX+mmuYRWX4SmuDEYDPTp25eM9PEMGTKEAQMGYDZLnDVF/g91AAaDgcTERBITE3nggQdwuVwcPXqUAwcOsG9fHiXF+biLwWQLbRj6Ka1/0YFdHRev1F5CrbvU0IoHgoNDyMgaSXp6OoMHDyY0NNTHlXY8EvgdUMNQsqEMHTqUH/3oRxQWFpKfn09eXh4FBQU4y09gNFkwBsRiDo7DHBSHwSTj/oX/ahwXX3MRrf4SqteJwWAgKSmJjIwcMjIy6Nu3b4dat8YfSeB3cAaDgfj4eOLj45k8eTIul4sjR46Ql5fH7j17qCrcCwYDJkcU5qC4hq3YrJ1vQonoeDTFhVJ7EbWmELW+GF1TsNsdZGZmMGzYMIYMGSKt+FbWrMDfuHEjq1evRlEU5s6dy+zZs695fufOnSxfvhyA/v37s2zZMgIDAyksLOSJJ56grKyMxMREli9fTmCgrCrZlux2e+M6P4888ginTp1i37597N69mwsXDuEuOYTJHo4pOAFLSIKEf0sZzDc/FjelKS6Umq9Qay6g1JUCOpGR3Rgx5l6GDx/OoEGDutS4+PZm0HVdv9kJxcXFPPjgg6xduxar1crMmTNZsWJF4yJC1dXV5OTk8Mc//pF+/frxyiuvUFxczJIlS3j44YeZPHky9913H6tWraK+vp4nnniiyaLcbjcFBQUkJydjs0lXRGspKipiz549fPrpp5w4cQIAkyMcU1AClpCectO3GTwVX+Auym88tsWmYw1P8mFF/k9T3Cg1X6LUfIlaVwLoxMXFkZWVxciRI0lMTOyy4+JbW1PZ2WTzZNeuXWRmZhIWFgZATk4Omzdv5tFHHwXg3LlzxMXFNf4CGDt2LPPnz+enP/0peXl5rFq1CoCpU6fy0EMPNSvwRduIjY1lypQpTJkyhZKSEnbt2kVu7id88cWVGb8BUZhDemMJSZDZvt/CEtYPT9kJ0LxYo1KwhPX1dUl+SddVlJpLKFVnUesuoesa3bt3J/v+6YwaNYpevXpJyPtAk4FfUlJCVFRU43F0dDRHjhxpPO7duzdFRUV8/vnn3HHHHXz44YdcvnyZiooKgoKCGodKRUVFUVxc3KLiCgoKWnS+aJmEhARmz55FZWUlR48e5eChQ5QX5eEpOYApqAeW0ERMgbLE89cZDIYrk98cWMM79lK5bUF1luOtOotacwFNcRMYGMhdIzJJSUkhNjYWg8FAWVkZZWVlvi61S2oy8DVNu+Y3sa7r1xyHhITw61//mp///Odomsb06dOxWCzXnQe0+De6dOm0n/Hjx6PrOidPnmTbtm3s3LmT+i/PY7IGYQrriyW0j6zwKW5I1xSU6gsoladQnOWYzWZGZmYyfnzDGHmTSYYHt5erXTrfpsnAj42NZf/+/Y3HpaWlREdHNx6rqkpsbCzvvPMOAEeOHCEhIYGIiAhqampQVRWTyXTddcL/GAwGBgwYwIABA5g/fz579+5l06ZNFBQcxlt6FFNwAtbwJIyOSPk4LtDc1Q27QVWfRVM89EhI4P77pjN69OhOv+pkR9Vk4I8cOZL/+Z//oby8HIfDwZYtW3j66acbnzcYDMybN4933nmH6Oho1qxZw8SJE7FYLGRkZLBp0yYmTZrE+vXryc7ObtM3I1qP1WolKyuLrKwszp8/z+bNm9m6dSv1589jckRiiRiAObiHdPd0Mbquo9aX4i3/HKW2EKPRxHe+M5KJEycyaNAgaQj4uSZH6UDDsMyXXnoJr9fLtGnTWLBgAQsWLGDhwoWkpKSwY8cOnn/+eTweDyNGjODJJ5/EYrFw8eJFFi9eTFlZGd27d2fFihXNGlcro3T8k9PpZPv27axbv57ioiJM1iDM4UlYQvtgMHWdoXT157cBENBrvI8raT+6rqFUf4lScQLFWU5QUDD3338fEydOJDw83NfliSuays5mBX57k8D3b6qqsm/fPtauXcfnnx/HaLZhDuuHNbw/hi7Qz9+VAl/XVLxVZ1HKP0f11NK9exzf//4Uxo0bJz+bfui2h2UK8U0mk6lxR6/PP/+cd999l71796JUnMAU2hdrxB2yjHMHp6tevJWnUSpOoHqd9EtKYsb06QwbNkyWN+jAJPDFbbnjjjtYsmQJ58+f55133iE3Nxel8hTm0D5YI+/AaJGZ1R2JrnrwVHyBUnESTXGTmprK9OnTSU1Nlf75TkACX7SKXr168fjjjzN79mzeffddtm3bRn3lacyhiVgjB8osXj+nq2485SdRKr5AUz1kZAxl5swZDBgwwNeliVYkgS9aVffu3fnxj3/MjBkzeO+99/jrX7dQX3UWc0gvrN3ulLV7/IyuuPGUn0Cp/AJN9ZKZmcmMGTMaZ86LzkUCX7SJ6OhoHnnkEaZPn857773Hh5s3U3fmHOaQXti6DZLg9zFNceEtP4FSeQpNU/jOyJHMnDmT3r17+7o00YYk8EWbioyM5Ec/+hHTpk1j7dq1bNr0IXWnz2MO6dkQ/LYQX5fYpWiKC0/Z5yiVp9B1laxRo5gxYwa9evXydWmiHUjgi3YRERHB/PnzeeCBB1i3bh0ffPABdWcuYA7pibXbnZhssu55W2oI+uMoladBV8nOzmbGjBkkJCT4ujTRjiTwRbsKDw9n3rx5TJ06lfXr17PxL3+h/sz5K8E/SIK/lX0z6MeMGcP06dPp0aOHr0sTPiCBL3wiLCyMH/7wh3z/+99n/fr1vL9xI/VnLly5uTsIk3T13JYbBf3MmTOJi4vzdTgyniIAABVFSURBVGnChyTwhU+FhoYyd+5cpkyZwrp16660+Bu6emxRyXJzt4U0xY23/DjeilMS9OI6EvjCL4SGhja2+NeuXcvGjX+h7swFLCG9sXYbJOP4m6Crnis3Y79A0xRGZ2fz4IMPEh8f7+vShB+RwBd+JTQ0lH/8x39kypQpvPvuu2zatIn6mvOYQ/s1jOM3231dol/RNRVvxRd4y4+jKW6+853vMGvWLHr27Onr0oQfksAXfik8PJwFCxbw/e9/n7feeouPPvoItfos5vABWCMHYDB2ndU5b0TXNZSqc3jLClA99aSlpTN37hz69Onj69KEH5PAF36tW7du/PjHP2bKlCm8/vrr7NmzB7XqDJaoVMwhXXNfVKWuBE/JQVRXBf2Skpj3j/9ISkqKr8sSHYAEvugQEhISePLJJzl+/DgvvfQSp0/vwVx5Cmv0EEyOSF+X1y40bx3u4kMoNV8SGdmNf1r4E0aNGtUlf+mJWyOBLzqUgQMHsmLFCrZv385ra9ZQfe4jLOH9sUWnYjB2zm9nXdfwVnyBp/QoZpORWbNmMXXqVFmPXrRY5/wJEZ2a0Wjk7rvvZuTIkfzhD39g06ZNaHWFWGMzMAfG+rq8VqW6q/AU5aHUXyY9PZ1/+Zd/kb2hxS2TwBcdVkBAAI888gjZ2dm88MJ/U3RhB9bIgVijUjrFXrveyjO4i/MJcDhY+G//xpgxY6T7RtyWjv9TIbq8QYMG8b//+z/ce++9eMqO47qwA83r9HVZt0zXFJyFe3Fd2kdK8iBWr36RsWPHStiL2yaBLzoFm83Gv/7rv/Lv//7vGJVKXBc+QnNX+7qsFtNVD84LH6NUn2PGjBksW7ZMNgkXrUYCX3QqY8aM4Te/+Q0BNjOuL7ejuip9XVKzaYoL54WPwVPJfyxezEMPPYTJZPJ1WaITaVbgb9y4kYkTJ3LPPffwpz/96brnjx07xgMPPMDkyZN5+OGHqa5uaFmtW7eOUaNG8b3vfY/vfe97rFy5snWrF+IG+vTpw3PP/ZrQ4EBcX+1A89b5uqQm6ZqC66tcjGod//mf/8nIkSN9XZLohJoM/OLiYlauXMmbb77J+vXr+fOf/8ypU6euOefZZ59l4cKFvP/++yQmJvL73/8egIKCAhYvXsyGDRvYsGEDixYtapt3IcQ39OjRg2effQaryYDr4t/QNcXXJX0rXddxXcpDdZazePFPSUtL83VJopNqMvB37dpFZmYmYWFhBAQEkJOTw+bNm685R9M06uoaWlFOpxO7vWG9k6NHj7Ju3TomTZrE448/TlVVVRu8BSFurEePHjzxxOOorgrcpUd9Xc63UqrPo1SfZ9asWQwbNszX5YhOrMlhmSUlJURFRTUeR0dHc+TIkWvOWbx4MfPmzeOXv/wlDoeDt99+G4CoqCjmzZtHWloaK1asYNmyZTz//PPNLq6goKDZ5wpxIyaTibQhQzhw8CDWsL5+t6WirnnxlB4mtnt3+vXrR35+vq9LEp1Yk4Gvado1w8F0Xb/m2OVy8eSTT7JmzRpSU1N57bXX+OlPf8rLL7/MqlWrGs+bP38+EyZMaFFxycnJMptQ3La+ffuyYMGPcJcexdHjO74u5xqe8lNoXif/tugXDBw40NfliA7O7XbftKHcZJdObGwspaWljcelpaXXzPQ7efIkNpuN1NRUAGbMmMG+ffuoqalhzZo1jefpui4jDoRPhIWFce+9Oai1F/1qfL6ua6hVp0lOTpGwF+2iycAfOXIku3fvpry8HKfTyZYtW8jOzm58vlevXhQVFXHmzBkAtm3bRkpKCgEBAbz66qscPnwYgDfeeKPFLXwhWst3v/vdhjVpqs76upRGal0JqqeW++6b6OtSRBfRZJdOTEwMixYtYs6cOXi9XqZNm0ZqaioLFixg4cKFpKSk8Ktf/YrHHnsMXdeJjIzkl7/8JSaTiRdeeIGlS5ficrno3bs3zz33XHu8JyGuExcXR7+kJM5+dRG63enrcgBQar7EarPJjVrRbgy6ruu+LuKbrvZDSR++aE3vvPMOr7/+OoH9JmO0BNzy69Sf3wZAQK/xt/wauq7jPP0+mcPSWLx48S2/jhBf11R2ykxb0WUMHz4cAKW20MeVgOYqR/U6G2sSoj1I4IsuIyEhgejoGJSai74uBaXmIgaDgfT0dF+XIroQCXzRZRgMBrKyRqHVF6Mrbp/Voes6au2XpKSkEBLiX/MCROcmgS+6lKysrIbROtUXfFaD5ipHdddcM9pNiPYggS+6lD59+pDYpw9K1Sl8NV7BU/EFNpudrKwsn3x90XVJ4IsuxWAwMHnSJFRXFWpdUbt/fc1bj1pzgfHjxxEQcOsjhYS4FRL4ossZPXo0kZHd8FwuaPdWvufyZxgNBqZOndquX1cIkMAXXZDFYuHBB2eiOstuacSOJbQPltA+Lb5O89SgVJ1hwoQJxMTEtPh6IW6XBL7oku6++256JCTgLT3U4rXyLWGJWMISW/w13cUHsdmszJo1q8XXCtEaJPBFl2QymXjkn/8Z1VOLp+x4m389peYiSm0hDz74oOxRK3xGAl90WampqWRnZ+MtO96mG57rmhdPyQESEnoyefLkNvs6QjRFAl90afPnz8fusOMu3t9mN3DdpcdQPXU8+ui/YjY3uV6hEG1GAl90aeHh4fxw7lyUuhKU6vOt/vqqqxJvxQnuuece7rzTP1bpFF2XBL7o8nJycujbrx/e0sOtutm5rut4Sg4SGBjI3LlzW+11hbhVEviiyzOZTDz8ox+hep14yk+02uuqdZdQ6op5aPZsWTNH+AUJfCGAgQMHkpmZiVL+Obrque3X03Udz+UCYmJiyMnJaYUKhbh9EvhCXDF9+nQ01Yu38va3QVSdpajOcqZNm4bFYmmF6oS4fRL4QlyRlJTEHXcMRKk6fduv5a04TWBgEGPGjLn9woRoJRL4QnzNmDGjUd3VtzUuX9dVtLpLjBw5Arvd3orVCXF7JPCF+JqrG4ordZdu+TVUZxma6mHo0KGtVZYQraJZgb9x40YmTpzIPffcw5/+9Kfrnj927BgPPPAAkydP5uGHH6a6uqF1VFhYyOzZs7n33nt55JFHqKura93qhWhlUVFRhISEoroqb/k1tCvX9u/fv7XKEqJVNBn4xcXFrFy5kjfffJP169fz5z//mVOnTl1zzrPPPsvChQt5//33SUxM5Pe//z0Av/jFL5g1axabN28mOTmZF198sW3ehRCtqHfvXuieW+/S0Tw1OBwBREREtGJVQty+JgN/165dZGZmEhYWRkBAADk5OWzevPmaczRNa2y9O51O7HY7Xq+XvLy8xiFpU6dOve46IfxRWFgYBu3Wh2bqiovQsFAMBkMrViXE7Wsy8EtKSoiKimo8jo6Opri4+JpzFi9ezJIlSxg1ahS7du1i5syZVFRUEBQU1Lh2SFRU1HXXCeGPAgMDb2ssvq55CQ4KasWKhGgdTa7kpGnaNS0VXdevOXa5XDz55JOsWbOG1NRUXnvtNX7605/y9NNPX9fCaWmLp6CgoEXnC9EaysrKbm8hNV3H5XKRn5/fekUJ0QqaDPzY2Fj279/feFxaWkp0dHTj8cmTJ7HZbKSmpgIwY8YM/vu//5uIiAhqampQVRWTyXTddc2RnJyMzWZr0TVC3K4DBw7c5ivoBAcHk56e3ir1CNFcbrf7pg3lJrt0Ro4cye7duykvL8fpdLJlyxays7Mbn+/VqxdFRUWcOXMGgG3btpGSkoLFYiEjI4NNmzYBsH79+muuE8Jf6boOt9n93t575QrRHE228GNiYli0aBFz5szB6/Uybdo0UlNTWbBgAQsXLiQlJYVf/epXPPbYY+i6TmRkJL/85S8BeOqpp1i8eDGrV6+me/furFixos3fkBC3S8JadFbN2o1h0qRJTJo06ZrHXnnllca/jx49mtGjR193XXx8PH/84x9vs0Qh2pfZbAZdu41X0GWjE+GXZKatEN9gtVrRVPXWW/q6KgumCb8kgS/ENzSsXa/DLY7FN2geQkNDW7coIVqBBL4Q3xAeHg6A5nW2+Fpd11G9TsLCwlq7LCFumwS+EN8QFxcHNCyR0FK6Uo+uqcTHx7d2WULcNgl8Ib7halhr7qoWX3v1mh49erRqTUK0Bgl8Ib7B4XAQFxeP6ipv8bWqswyDwUCfPn3aoDIhbo8EvhA3MHDgHeiuli+xoDrLSOjZE4fD0UaVCXHrJPCFuIHk5GQ0xd2ibh1dV9Gdl0lNSWnDyoS4dRL4QtzA1bWh1Prmr/CqOsvQNKXxWiH8jQS+EDcQHR1N9+7dUeuKmn2NWluE0WiUwBd+SwJfiG+Rnp6OWl+KrqnNOl+tLyYpqT+BgYFtXJkQt0YCX4hvMWTIEHRNQXWWNnmuprhRnWWkp6e1Q2VC3BoJfCG+RUpKCiaTqVndOlf7+ocMGdLWZQlxyyTwhfgWDoeDAQPuQKsvafJcta4IhyOApKSkdqhMiFsjgS/ETQwefBeKsxxddd/0PM1ZSmpqwycCIfyVBL4QN3F1xI1S/+39+Jq3DtVdI6NzhN+TwBfiJvr374/ZbEa9SeBffW7QoEHtVZYQt0QCX4ibsFgsJCUloTkvf+s5av1l7HY7vXv3br/ChLgFEvhCNOHOO+9EdVV863h83VXGgAEDpP9e+D0JfCGa0L9/f9A1NHfldc/pmoLirmTAgAE+qEyIlpHAF6IJ/fr1A0B1Xr9csuauBF1vPEcIf2ZuzkkbN25k9erVKIrC3LlzmT17duNzx48fZ/HixY3H5eXlhIaG8pe//IV169bx/PPPExkZCcCYMWNYtGhRK78FIdpWVFQUAQGBeG/QwlddDY/J+veiI2gy8IuLi1m5ciVr167FarUyc+ZMhg8f3tiiGThwIBs2bADA6XTygx/8gKVLlwJQUFDA4sWLuf/++9vuHQjRxho2NEnk+OlL1z2nuauw2x1ER0f7oDIhWqbJLp1du3aRmZlJWFgYAQEB5OTksHnz5hue+9JLLzF06FAyMjIAOHr0KOvWrWPSpEk8/vjjVFW1fMs4IfxBQkICurfmug1RNE81CQkJGAwGH1UmRPM12cIvKSkhKiqq8Tg6OpojR45cd15NTQ1vv/02GzdubHwsKiqKefPmkZaWxooVK1i2bBnPP/98s4srKCho9rlCtCVd19EUD7rqxmC2//0JTw0ORwz5+fm+K06IZmoy8DVNu6b1ouv6DVsz77//PnfffXdjfz3AqlWrGv8+f/58JkyY0KLikpOTsdlsLbpGiLagKAqbN29G99bBlcDXNRXVW8+gQYNIT0/3cYVCgNvtvmlDuckundjYWEpL/z7LsLS09Ib9lVu3bmXixImNxzU1NaxZs6bxWNd1GacsOqyYmBigYRmFq3SlHkD670WH0WTgjxw5kt27d1NeXo7T6WTLli1kZ2dfc46u6xw7duyapWEDAgJ49dVXOXz4MABvvPFGi1v4QviLq59cdcXZ+Jh25e/dunXzSU1CtFSTXToxMTEsWrSIOXPm4PV6mTZtGqmpqSxYsICFCxeSkpJCeXk5Fovlmu4Xk8nECy+8wNKlS3G5XPTu3ZvnnnuuTd+MEG0lKCgIk9l8TeDriguA8PBwX5UlRIsY9G8OO/ADV/uhpA9f+JN/+Ic51Onh2OOGAeAp/wJ3cT6vv/66hL7wC01lp8y0FaKZgkOCr1kX/+rfg4ODfVWSEC0igS9EMwUHBaFrnsZjXfNitdkwm5s1YV0In5PAF6KZAgMDMejK3x9QvQQ4AnxXkBAtJIEvRDM5HA742hLJuq40PCZEByGBL0QzORwOdM3beKxrCna7/SZXCOFfJPCFaCabzYaufa1LR1NwOCTwRcchgS9EM9lsNjRV+fsCaroqLXzRoUjgC9FMDeGug64BYNClS0d0LBL4QjTT1XC/2q0jffiio5HAF6KZAgKuDMG8cuNW15S/PyZEByCBL0QzXQ13XfM2rI+veiTwRYcigS9EM11dQkFXPaApoOuyrILoUCTwhWimoKAgoCHwr66jc/UxIToCCXwhmik0NBQAXXU1Bv7Vx4ToCCTwhWimkJAQDAYDuuJqXAtfAl90JBL4QjST2WwmKDgYXXE27nb19T2chfB3EvhCtEBkRCSa4kJXnBgMBsLCwnxdkhDNJoEvRAtERXXDoDjRvPWEhoZhMpl8XZIQzSaBL0QLdOvWDU2pR1fq6dZNunNExyKBL0QLREZGoilu8NYRFRXl63KEaBEJfCFaoFu3bgConlq5YSs6nGZtxrlx40ZWr16NoijMnTuX2bNnNz53/PhxFi9e3HhcXl5OaGgof/nLXygsLOSJJ56grKyMxMREli9fTmBgYOu/CyHaSXh4eOPfIyIifFiJEC3XZAu/uLiYlStX8uabb7J+/Xr+/Oc/c+rUqcbnBw4cyIYNG9iwYQP/93//R2hoKEuXLgXgF7/4BbNmzWLz5s0kJyfz4osvttkbEaI9fD3kvx7+QnQETQb+rl27yMzMJCwsjICAAHJycti8efMNz33ppZcYOnQoGRkZeL1e8vLyyMnJAWDq1Knfep0QHcXXJ1rJkEzR0TTZpVNSUnLNzano6GiOHDly3Xk1NTW8/fbbbNy4EYCKigqCgoIwmxu+RFRUFMXFxS0qrqCgoEXnC9HWVPXvm5hfvHgRg8Hgw2qEaJkmA1/TtGu+qXVdv+E3+fvvv8/dd9/deCPrRue19IcjOTkZm83WomuEaC9Dhw4lLi7O12UI0cjtdt+0odxkl05sbCylpaWNx6WlpURHR1933tatW5k4cWLjcUREBDU1NY0tom+7ToiOSgYgiI6mycAfOXIku3fvpry8HKfTyZYtW8jOzr7mHF3XOXbsGEOGDGl8zGKxkJGRwaZNmwBYv379ddcJ0ZE5HA5flyBEizQZ+DExMSxatIg5c+YwZcoU7r//flJTU1mwYAFHjx4FGoZiWiyW67pfnnrqKd5++20mTpzI/v37eeyxx9rmXQjRjvr06QM0NGqE6EgMuq7rvi7im672Q0kfvvBHLpcLt9stSyMLv9NUdjZr4pUQ4u/sdjt2u93XZQjRYrK0ghBCdBES+EII0UVI4AshRBchgS+EEF2EBL4QQnQREvhCCNFF+OWwzKtTAzwej48rEUKIjuNqZn7b9Cq/DHyv1wvAyZMnfVyJEEJ0PF6v94ZzRfxypq2madTV1WGxWGT5WSGEaCZd1/F6vQQGBmI0Xt9j75eBL4QQovXJTVshhOgiJPCFEKKLkMAXQoguQgJfCCG6iP8PJn2UQcuSG9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_corr = pd.read_pickle('results/Performance/Correlations/process+product_corr_release.pkl')\n",
    "# flat_list = [item for sublist in list(project_corr.values()) for item in sublist]\n",
    "flat_list = [np.nanmedian(sublist) for sublist in list(project_corr.values())]\n",
    "ax = sns.violinplot(y=flat_list,scale = 'count')\n",
    "plt.savefig('results/Performance/Correlations/process+product_corr_release.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['process','product','process+product']\n",
    "final_df = pd.DataFrame()\n",
    "for metric in metrics:\n",
    "    project_corr = pd.read_pickle('results/Performance/Correlations/' + metric + '_corr_release.pkl')\n",
    "    flat_list = [np.nanmedian(sublist) for sublist in list(project_corr.values())]\n",
    "    project_corr_df = pd.DataFrame(flat_list, columns = ['scores'])\n",
    "    project_corr_df['metrics'] = [metric]*project_corr_df.shape[0]\n",
    "    final_df = pd.concat([final_df,project_corr_df], axis = 0)\n",
    "final_df = final_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid',font_scale=1.8)\n",
    "fig_dims = (16, 12)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "order = metrics\n",
    "g = sns.violinplot(x=\"metrics\", y=\"scores\",margin_titles=True,kind=\"box\", scale = 'area',\n",
    "                   inner = 'box',\n",
    "                   order=order, data=final_df)\n",
    "plt.xlabel('Metric Types', fontsize=20)\n",
    "plt.ylabel('Correlation Scores', fontsize=20)\n",
    "ax.set_xticklabels(metrics, rotation=0, fontsize=18)\n",
    "# ax.set_yticklabels([0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1], rotation=0, fontsize=18)\n",
    "plt.savefig('results/Performance/Correlations/corr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scores</th>\n",
       "      <th>metrics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.692618</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.956465</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.981806</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.960721</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.896325</td>\n",
       "      <td>process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.940191</td>\n",
       "      <td>process+product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.975143</td>\n",
       "      <td>process+product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.900852</td>\n",
       "      <td>process+product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.987432</td>\n",
       "      <td>process+product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.938475</td>\n",
       "      <td>process+product</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1626 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      scores          metrics\n",
       "0   0.692618          process\n",
       "1   0.956465          process\n",
       "2   0.981806          process\n",
       "3   0.960721          process\n",
       "4   0.896325          process\n",
       "..       ...              ...\n",
       "40  0.940191  process+product\n",
       "41  0.975143  process+product\n",
       "42  0.900852  process+product\n",
       "43  0.987432  process+product\n",
       "44  0.938475  process+product\n",
       "\n",
       "[1626 rows x 2 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}