runs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sync"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !aws s3 sync resources/ s3://openproblems-data/resources/grn/ --delete\n",
    "# !aws s3 sync resources/grn_models/ s3://openproblems-data/resources/grn/grn_models --delete\n",
    "# !aws s3 sync resources/prior/ s3://openproblems-data/resources/grn/prior --delete\n",
    "# !aws s3 sync resources/results/ s3://openproblems-data/resources/grn/results --delete\n",
    "# !aws s3 sync resources/scores/ s3://openproblems-data/resources/grn/scores --delete\n",
    "# !aws s3 sync  resources/evaluation_datasets/ s3://openproblems-data/resources/grn/evaluation_datasets/ --delete\n",
    "# !aws s3 sync  resources/inference_datasets/ s3://openproblems-data/resources/grn/inference_datasets/ --delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delete: resources/datasets_raw/op_bulked.h5ad.dde839C8\n",
      "delete: resources/datasets_raw/op_multiome_sc_counts.h5ad.DDCAB9FD               \n",
      "delete: resources/results/.DS_Store                                              \n",
      "delete: resources/results/figs/.DS_Store                                          \n",
      "delete: resources/results/figs/consensus_regulators.png                           \n",
      "delete: resources/results/figs/raw_scores_one.png                                 \n",
      "delete: resources/results/figs/raw_scores_three.png                               \n",
      "delete: resources/results/figs/similaritys_donors.png                             \n",
      "delete: resources/results/figs/topology_stats_1.png                               \n",
      "delete: resources/results/figs/topology_stats_2.png                               \n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/nakatake_bulked.h5ad to resources/datasets_raw/nakatake_bulked.h5ad\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/adamson_sc_counts.h5ad to resources/datasets_raw/adamson_sc_counts.h5ad\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/op_multiome_sc_counts.h5ad to resources/datasets_raw/op_multiome_sc_counts.h5ad\n",
      "download: s3://openproblems-data/resources/grn/evaluation_datasets/norman_perturbation.h5ad to resources/evaluation_datasets/norman_perturbation.h5ad\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/grnboost2_0.5.csv to resources/grn_models/adamson/grnboost2_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/grnboost2_1.0.csv to resources/grn_models/adamson/grnboost2_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/pearson_corr_0.2.csv to resources/grn_models/adamson/pearson_corr_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/pearson_corr_0.5.csv to resources/grn_models/adamson/pearson_corr_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/pearson_corr_1.0.csv to resources/grn_models/adamson/pearson_corr_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/portia_0.2.csv to resources/grn_models/adamson/portia_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/portia_0.5.csv to resources/grn_models/adamson/portia_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/adamson/portia_1.0.csv to resources/grn_models/adamson/portia_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Ananse:Bone marrow.csv to resources/grn_models/global/Ananse:Bone marrow.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Ananse:Heart.csv to resources/grn_models/global/Ananse:Heart.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Ananse:Lung.csv to resources/grn_models/global/Ananse:Lung.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Ananse:Stomach.csv to resources/grn_models/global/Ananse:Stomach.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Cellnet:Bcell.csv to resources/grn_models/global/Cellnet:Bcell.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Cellnet:Heart.csv to resources/grn_models/global/Cellnet:Heart.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Cellnet:Neuron.csv to resources/grn_models/global/Cellnet:Neuron.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Cellnet:Skin.csv to resources/grn_models/global/Cellnet:Skin.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Cellnet:Tcell.csv to resources/grn_models/global/Cellnet:Tcell.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Gtex:Brain amygdala.csv to resources/grn_models/global/Gtex:Brain amygdala.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Gtex:Breast mammary tissue.csv to resources/grn_models/global/Gtex:Breast mammary tissue.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Gtex:Lung.csv to resources/grn_models/global/Gtex:Lung.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Gtex:Stomach.csv to resources/grn_models/global/Gtex:Stomach.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/Gtex:Whole blood.csv to resources/grn_models/global/Gtex:Whole blood.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/global/collectri.csv to resources/grn_models/global/collectri.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/grnboost2_0.2.csv to resources/grn_models/nakatake/grnboost2_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/grnboost2_0.5.csv to resources/grn_models/nakatake/grnboost2_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/grnboost2_1.0.csv to resources/grn_models/nakatake/grnboost2_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/pearson_corr_0.2.csv to resources/grn_models/nakatake/pearson_corr_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/pearson_corr_0.5.csv to resources/grn_models/nakatake/pearson_corr_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/pearson_corr_1.0.csv to resources/grn_models/nakatake/pearson_corr_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/portia_0.2.csv to resources/grn_models/nakatake/portia_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/portia_0.5.csv to resources/grn_models/nakatake/portia_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/nakatake/portia_1.0.csv to resources/grn_models/nakatake/portia_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/pearson_corr.csv to resources/grn_models/norman/pearson_corr.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/pearson_corr_0.2.csv to resources/grn_models/norman/pearson_corr_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/pearson_corr_0.5.csv to resources/grn_models/norman/pearson_corr_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/pearson_corr_1.0.csv to resources/grn_models/norman/pearson_corr_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/portia_0.2.csv to resources/grn_models/norman/portia_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/portia_0.5.csv to resources/grn_models/norman/portia_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/portia_1.0.csv to resources/grn_models/norman/portia_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/norman/scenic.csv to resources/grn_models/norman/scenic.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/op/pearson_corr_1.csv to resources/grn_models/op/pearson_corr_1.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/op/pearson_corr_2.csv to resources/grn_models/op/pearson_corr_2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/op/portia_1.csv to resources/grn_models/op/portia_1.csv\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/op_bulked.h5ad to resources/datasets_raw/op_bulked.h5ad\n",
      "download: s3://openproblems-data/resources/grn/grn_models/op/portia_2.csv to resources/grn_models/op/portia_2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/grnboost2_0.5.csv to resources/grn_models/replogle2/grnboost2_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/grnboost2_1.0.csv to resources/grn_models/replogle2/grnboost2_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/grnboost2_0.2.csv to resources/grn_models/replogle2/grnboost2_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/pearson_corr_0.2.csv to resources/grn_models/replogle2/pearson_corr_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/pearson_corr_1.0.csv to resources/grn_models/replogle2/pearson_corr_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/pearson_corr_0.5.csv to resources/grn_models/replogle2/pearson_corr_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/portia_0.2.csv to resources/grn_models/replogle2/portia_0.2.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/portia_0.5.csv to resources/grn_models/replogle2/portia_0.5.csv\n",
      "download: s3://openproblems-data/resources/grn/grn_models/replogle2/portia_1.0.csv to resources/grn_models/replogle2/portia_1.0.csv\n",
      "download: s3://openproblems-data/resources/grn/results/figs/boxplot_r2scores_tfactivation.png to resources/results/figs/boxplot_r2scores_tfactivation.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/centrality_in.png to resources/results/figs/centrality_in.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/centrality_in_short.png to resources/results/figs/centrality_in_short.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/consensus_all.png to resources/results/figs/consensus_all.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/consensus_ws.png to resources/results/figs/consensus_ws.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/evaluation_scores_global_models.png to resources/results/figs/evaluation_scores_global_models.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/evaluation_scores_norman_adamson.png to resources/results/figs/evaluation_scores_norman_adamson.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/evaluation_scores_opsca.png to resources/results/figs/evaluation_scores_opsca.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/evaluation_scores_other_datasets.png to resources/results/figs/evaluation_scores_other_datasets.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/gb_vs_ridge.png to resources/results/figs/gb_vs_ridge.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/jaccard_similarity_op.png to resources/results/figs/jaccard_similarity_op.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/nregulators_opsca.png to resources/results/figs/nregulators_opsca.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/opsca_perturbation_strength.png to resources/results/figs/opsca_perturbation_strength.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/perturbation_strength_celltypes.png to resources/results/figs/perturbation_strength_celltypes.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/perturbation_strength_datasets.png to resources/results/figs/perturbation_strength_datasets.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/perturbation_strength_lowscored_compounds.png to resources/results/figs/perturbation_strength_lowscored_compounds.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/raw_scores_norman_adamson.png to resources/results/figs/raw_scores_norman_adamson.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/raw_scores_others.png to resources/results/figs/raw_scores_others.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_feature_importannce_model_corr.png to resources/results/figs/reg1_feature_importannce_model_corr.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_sample_wise_cell_type.png to resources/results/figs/reg1_sample_wise_cell_type.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_sample_wise_compound.png to resources/results/figs/reg1_sample_wise_compound.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_sample_wise_compounds.png to resources/results/figs/reg1_sample_wise_compounds.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_sample_wise_model_corr.png to resources/results/figs/reg1_sample_wise_model_corr.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_sample_wise_scores_dist.png to resources/results/figs/reg1_sample_wise_scores_dist.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg1_tf_activation_stability.png to resources/results/figs/reg1_tf_activation_stability.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_feature_importance_stability.png to resources/results/figs/reg2_feature_importance_stability.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_grnderived_stability.png to resources/results/figs/reg2_grnderived_stability.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_joint_r2scores.png to resources/results/figs/reg2_joint_r2scores.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_models_corr.png to resources/results/figs/reg2_models_corr.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_nregulators_vs_r2scoes.png to resources/results/figs/reg2_nregulators_vs_r2scoes.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/reg2_tf_activation.png to resources/results/figs/reg2_tf_activation.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/relative_scores_all.png to resources/results/figs/relative_scores_all.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/robustnes_analysis.png to resources/results/figs/robustnes_analysis.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/robustnes_analysis_reg1_1.png to resources/results/figs/robustnes_analysis_reg1_1.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/robustness_reg1.png to resources/results/figs/robustness_reg1.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/robustness_reg2.png to resources/results/figs/robustness_reg2.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/similaritys_donors_reg2.png to resources/results/figs/similaritys_donors_reg2.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/skelton_guided_barplot.png to resources/results/figs/skelton_guided_barplot.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/summary_figure.pdf to resources/results/figs/summary_figure.pdf\n",
      "download: s3://openproblems-data/resources/grn/results/figs/summary_figure.png to resources/results/figs/summary_figure.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/tf_guided_barplot.png to resources/results/figs/tf_guided_barplot.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/topology_stats.png to resources/results/figs/topology_stats.png\n",
      "download: s3://openproblems-data/resources/grn/results/figs/topology_stats_short.png to resources/results/figs/topology_stats_short.png\n",
      "download: s3://openproblems-data/resources/grn/results/summary.tsv to resources/results/summary.tsv\n",
      "download: s3://openproblems-data/resources/grn/results/summary_figure.pdf to resources/results/summary_figure.pdf\n",
      "download: s3://openproblems-data/resources/grn/results/summary_figure.png to resources/results/summary_figure.png\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/replogle2_bulked.h5ad to resources/datasets_raw/replogle2_bulked.h5ad\n",
      "download: s3://openproblems-data/resources/grn/inference_datasets/norman_rna.h5ad to resources/inference_datasets/norman_rna.h5ad\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/norman_sc_counts.h5ad to resources/datasets_raw/norman_sc_counts.h5ad\n",
      "download: s3://openproblems-data/resources/grn/datasets_raw/op_perturbation_sc_counts.h5ad to resources/datasets_raw/op_perturbation_sc_counts.h5ad\n"
     ]
    }
   ],
   "source": [
    "!aws s3 sync  s3://openproblems-data/resources/grn/ resources/ --delete\n",
    "# aws s3 sync s3://openproblems-data/resources/grn/results  resources/results/ --delete\n",
    "# aws s3 sync  s3://openproblems-data/resources/grn/grn_models resources/grn_models/\n",
    "# aws s3 sync  s3://openproblems-data/resources/grn/inference_datasets/ resources/inference_datasets/\n",
    "# aws s3 sync  s3://openproblems-data/resources/grn/evaluation_datasets/ resources/evaluation_datasets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !aws s3 sync resources_test/ s3://openproblems-data/resources_test/grn/ --delete\n",
    "# !aws s3 sync resources/inference_datasets/ s3://openproblems-data/resources/grn/inference_datasets/ --delete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "import yaml\n",
    "import os\n",
    "import pandas as pd\n",
    "import anndata as ad \n",
    "import numpy as np\n",
    "import scanpy as sc \n",
    "from src.exp_analysis.helper import plot_cumulative_density\n",
    "import matplotlib.pyplot as plt\n",
    "import sys \n",
    "import subprocess\n",
    "import io\n",
    "import warnings\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Suppress all FutureWarnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "\n",
    "sys.path.append('../')\n",
    "from grn_benchmark.src.helper import surragate_names\n",
    "from src.helper import *\n",
    "par = {\n",
    "    # 'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'portia', 'ppcor', 'genie3', 'grnboost2', 'scenic', 'scglue', 'celloracle'],\n",
    "    'methods': [ 'collectri', 'negative_control', 'positive_control', 'pearson_corr', 'portia', 'ppcor', 'grnboost2', 'scenic', 'scglue', 'celloracle', 'scenicplus'],\n",
    "    'models_dir': 'resources/grn_models/',\n",
    "    'scores_dir': 'resources/scores'\n",
    "}\n",
    "\n",
    "datasets = ['op', 'replogle2', 'nakatake', 'norman', 'adamson']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_db840_row0_col0, #T_db840_row0_col1, #T_db840_row0_col2, #T_db840_row0_col3, #T_db840_row0_col4 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_db840_row1_col0, #T_db840_row1_col1, #T_db840_row1_col2, #T_db840_row1_col3, #T_db840_row1_col4 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_db840\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_db840_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_db840_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_db840_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_db840_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_db840_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_db840_level0_row0\" class=\"row_heading level0 row0\" >pearson_corr_1</th>\n",
       "      <td id=\"T_db840_row0_col0\" class=\"data row0 col0\" >0.530897</td>\n",
       "      <td id=\"T_db840_row0_col1\" class=\"data row0 col1\" >0.970276</td>\n",
       "      <td id=\"T_db840_row0_col2\" class=\"data row0 col2\" >0.568970</td>\n",
       "      <td id=\"T_db840_row0_col3\" class=\"data row0 col3\" >0.436350</td>\n",
       "      <td id=\"T_db840_row0_col4\" class=\"data row0 col4\" >0.367186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_db840_level0_row1\" class=\"row_heading level0 row1\" >pearson_corr_2</th>\n",
       "      <td id=\"T_db840_row1_col0\" class=\"data row1 col0\" >0.532218</td>\n",
       "      <td id=\"T_db840_row1_col1\" class=\"data row1 col1\" >0.983027</td>\n",
       "      <td id=\"T_db840_row1_col2\" class=\"data row1 col2\" >0.583044</td>\n",
       "      <td id=\"T_db840_row1_col3\" class=\"data row1 col3\" >0.439533</td>\n",
       "      <td id=\"T_db840_row1_col4\" class=\"data row1 col4\" >0.368858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x155273305810>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(f'resources/scores/op/subsampled.csv', index_col=0)\n",
    "df.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "theta_values = [0.2, 0.5, 1.0]\n",
    "methods = ['pearson_corr', 'portia', 'grnboost2']\n",
    "\n",
    "# Prepare the figure with subplots (one for each column in df)\n",
    "fig, axes = plt.subplots(1, len(df.columns), figsize=(15, 5), sharey=True)\n",
    "\n",
    "# Loop through each column and plot\n",
    "for i, col in enumerate(df.columns):\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Plot each method's line for the current subplot\n",
    "    for method in methods:\n",
    "        # Filter out the rows that match the current method\n",
    "        method_rows = df.index.str.contains(method)\n",
    "        \n",
    "        # Get the values corresponding to theta values (0.2, 0.5, 1.0)\n",
    "        method_data = df.loc[method_rows, col]\n",
    "        \n",
    "        # Drop missing values (NaN) and plot\n",
    "        method_data = method_data.dropna()\n",
    "        \n",
    "        # Plot only if there are available data points\n",
    "        if len(method_data) > 0:\n",
    "            ax.plot(method_data.index, method_data.values, marker='o', label=method)\n",
    "    \n",
    "    # Set labels and title for each subplot\n",
    "    ax.set_title(f'{col}')\n",
    "    ax.set_xlabel('Theta Value')\n",
    "    ax.set_ylabel('Value')\n",
    "    ax.set_xticks([])\n",
    "    ax.legend()\n",
    "\n",
    "# Adjust layout and show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prior "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False: \n",
    "    create_skeleton() # create tf2gene putative links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run grn inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False: # local runs\n",
    "    run_grn_inference()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.helper import calculate_scores\n",
    "if False: # consensus: run this after updating grns\n",
    "    run_consensus(par)\n",
    "\n",
    "if False: # run metrics/script_all.py\n",
    "    calculate_scores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OPSCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'resources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global_False.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_scores \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global_False.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# df_scores[df_scores<0] = 0\u001b[39;00m\n\u001b[1;32m      3\u001b[0m df_all_n \u001b[38;5;241m=\u001b[39m (df_scores\u001b[38;5;241m-\u001b[39mdf_scores\u001b[38;5;241m.\u001b[39mmin(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\u001b[38;5;241m/\u001b[39m(df_scores\u001b[38;5;241m.\u001b[39mmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m-\u001b[39mdf_scores\u001b[38;5;241m.\u001b[39mmin(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n",
      "File \u001b[0;32m~/miniconda3/envs/py10/lib/python3.10/site-packages/pandas/io/parsers/readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    900\u001b[0m     dialect,\n\u001b[1;32m    901\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m    909\u001b[0m )\n\u001b[1;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/py10/lib/python3.10/site-packages/pandas/io/parsers/readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/miniconda3/envs/py10/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/py10/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1660\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1662\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1663\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1664\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1665\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1666\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1667\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1668\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1669\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1670\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/miniconda3/envs/py10/lib/python3.10/site-packages/pandas/io/common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    862\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    863\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    864\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    865\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'resources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global_False.csv'"
     ]
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global_False.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_all_n = (df_scores-df_scores.min(axis=0))/(df_scores.max(axis=0)-df_scores.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6a1eb_row0_col0 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row0_col1 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row0_col2 {\n",
       "  background-color: #dddbec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row0_col3 {\n",
       "  background-color: #c4cbe3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row0_col4 {\n",
       "  background-color: #046198;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row1_col0, #T_6a1eb_row1_col1, #T_6a1eb_row2_col4, #T_6a1eb_row8_col2, #T_6a1eb_row8_col3 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row1_col2 {\n",
       "  background-color: #e6e2ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row1_col3 {\n",
       "  background-color: #bcc7e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row1_col4 {\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row2_col0, #T_6a1eb_row2_col1, #T_6a1eb_row2_col2, #T_6a1eb_row8_col4, #T_6a1eb_row12_col3 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row2_col3 {\n",
       "  background-color: #1278b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row3_col0, #T_6a1eb_row10_col3 {\n",
       "  background-color: #04649e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row3_col1 {\n",
       "  background-color: #03517e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row3_col2 {\n",
       "  background-color: #045382;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row3_col3 {\n",
       "  background-color: #328dbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row3_col4 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row4_col0 {\n",
       "  background-color: #80aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row4_col1, #T_6a1eb_row7_col4, #T_6a1eb_row9_col1 {\n",
       "  background-color: #96b6d7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row4_col2 {\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row4_col3 {\n",
       "  background-color: #84b0d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row4_col4 {\n",
       "  background-color: #73a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row5_col0 {\n",
       "  background-color: #e8e4f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row5_col1 {\n",
       "  background-color: #efe9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row5_col2, #T_6a1eb_row11_col2 {\n",
       "  background-color: #9ebad9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row5_col3, #T_6a1eb_row7_col0 {\n",
       "  background-color: #b7c5df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row5_col4 {\n",
       "  background-color: #056faf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row6_col0 {\n",
       "  background-color: #023e62;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row6_col1 {\n",
       "  background-color: #0872b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row6_col2, #T_6a1eb_row10_col2 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row6_col3 {\n",
       "  background-color: #023f64;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row6_col4 {\n",
       "  background-color: #f2ecf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row7_col1 {\n",
       "  background-color: #c1cae2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row7_col2 {\n",
       "  background-color: #0570b0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row7_col3 {\n",
       "  background-color: #0569a4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row8_col0 {\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row8_col1 {\n",
       "  background-color: #f0eaf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row9_col0 {\n",
       "  background-color: #e5e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row9_col2 {\n",
       "  background-color: #1077b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row9_col3 {\n",
       "  background-color: #abbfdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row9_col4 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row10_col0 {\n",
       "  background-color: #6ba5cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row10_col1 {\n",
       "  background-color: #6da6cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row10_col4 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row11_col0 {\n",
       "  background-color: #a7bddb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row11_col1 {\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row11_col3 {\n",
       "  background-color: #9fbad9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6a1eb_row11_col4 {\n",
       "  background-color: #4a98c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row12_col0 {\n",
       "  background-color: #0c74b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row12_col1 {\n",
       "  background-color: #529bc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row12_col2 {\n",
       "  background-color: #034a74;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6a1eb_row12_col4 {\n",
       "  background-color: #d0d1e6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6a1eb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6a1eb_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_6a1eb_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_6a1eb_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_6a1eb_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_6a1eb_level0_col4\" class=\"col_heading level0 col4\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row0\" class=\"row_heading level0 row0\" >collectri</th>\n",
       "      <td id=\"T_6a1eb_row0_col0\" class=\"data row0 col0\" >0.037785</td>\n",
       "      <td id=\"T_6a1eb_row0_col1\" class=\"data row0 col1\" >0.082205</td>\n",
       "      <td id=\"T_6a1eb_row0_col2\" class=\"data row0 col2\" >0.282480</td>\n",
       "      <td id=\"T_6a1eb_row0_col3\" class=\"data row0 col3\" >0.294742</td>\n",
       "      <td id=\"T_6a1eb_row0_col4\" class=\"data row0 col4\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row1\" class=\"row_heading level0 row1\" >negative_control</th>\n",
       "      <td id=\"T_6a1eb_row1_col0\" class=\"data row1 col0\" >0.000440</td>\n",
       "      <td id=\"T_6a1eb_row1_col1\" class=\"data row1 col1\" >0.000459</td>\n",
       "      <td id=\"T_6a1eb_row1_col2\" class=\"data row1 col2\" >0.260926</td>\n",
       "      <td id=\"T_6a1eb_row1_col3\" class=\"data row1 col3\" >0.302373</td>\n",
       "      <td id=\"T_6a1eb_row1_col4\" class=\"data row1 col4\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row2\" class=\"row_heading level0 row2\" >positive_control</th>\n",
       "      <td id=\"T_6a1eb_row2_col0\" class=\"data row2 col0\" >0.671630</td>\n",
       "      <td id=\"T_6a1eb_row2_col1\" class=\"data row2 col1\" >1.106263</td>\n",
       "      <td id=\"T_6a1eb_row2_col2\" class=\"data row2 col2\" >0.739857</td>\n",
       "      <td id=\"T_6a1eb_row2_col3\" class=\"data row2 col3\" >0.420285</td>\n",
       "      <td id=\"T_6a1eb_row2_col4\" class=\"data row2 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row3\" class=\"row_heading level0 row3\" >pearson_corr</th>\n",
       "      <td id=\"T_6a1eb_row3_col0\" class=\"data row3 col0\" >0.546758</td>\n",
       "      <td id=\"T_6a1eb_row3_col1\" class=\"data row3 col1\" >1.005020</td>\n",
       "      <td id=\"T_6a1eb_row3_col2\" class=\"data row3 col2\" >0.682991</td>\n",
       "      <td id=\"T_6a1eb_row3_col3\" class=\"data row3 col3\" >0.396617</td>\n",
       "      <td id=\"T_6a1eb_row3_col4\" class=\"data row3 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row4\" class=\"row_heading level0 row4\" >portia</th>\n",
       "      <td id=\"T_6a1eb_row4_col0\" class=\"data row4 col0\" >0.317483</td>\n",
       "      <td id=\"T_6a1eb_row4_col1\" class=\"data row4 col1\" >0.459731</td>\n",
       "      <td id=\"T_6a1eb_row4_col2\" class=\"data row4 col2\" >0.595570</td>\n",
       "      <td id=\"T_6a1eb_row4_col3\" class=\"data row4 col3\" >0.345240</td>\n",
       "      <td id=\"T_6a1eb_row4_col4\" class=\"data row4 col4\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row5\" class=\"row_heading level0 row5\" >ppcor</th>\n",
       "      <td id=\"T_6a1eb_row5_col0\" class=\"data row5 col0\" >0.097117</td>\n",
       "      <td id=\"T_6a1eb_row5_col1\" class=\"data row5 col1\" >0.117754</td>\n",
       "      <td id=\"T_6a1eb_row5_col2\" class=\"data row5 col2\" >0.396929</td>\n",
       "      <td id=\"T_6a1eb_row5_col3\" class=\"data row5 col3\" >0.306533</td>\n",
       "      <td id=\"T_6a1eb_row5_col4\" class=\"data row5 col4\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row6\" class=\"row_heading level0 row6\" >grnboost2</th>\n",
       "      <td id=\"T_6a1eb_row6_col0\" class=\"data row6 col0\" >0.653354</td>\n",
       "      <td id=\"T_6a1eb_row6_col1\" class=\"data row6 col1\" >0.819973</td>\n",
       "      <td id=\"T_6a1eb_row6_col2\" class=\"data row6 col2\" >0.658351</td>\n",
       "      <td id=\"T_6a1eb_row6_col3\" class=\"data row6 col3\" >0.493956</td>\n",
       "      <td id=\"T_6a1eb_row6_col4\" class=\"data row6 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row7\" class=\"row_heading level0 row7\" >scenic</th>\n",
       "      <td id=\"T_6a1eb_row7_col0\" class=\"data row7 col0\" >0.219697</td>\n",
       "      <td id=\"T_6a1eb_row7_col1\" class=\"data row7 col1\" >0.325873</td>\n",
       "      <td id=\"T_6a1eb_row7_col2\" class=\"data row7 col2\" >0.596756</td>\n",
       "      <td id=\"T_6a1eb_row7_col3\" class=\"data row7 col3\" >0.441442</td>\n",
       "      <td id=\"T_6a1eb_row7_col4\" class=\"data row7 col4\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row8\" class=\"row_heading level0 row8\" >granie</th>\n",
       "      <td id=\"T_6a1eb_row8_col0\" class=\"data row8 col0\" >0.084081</td>\n",
       "      <td id=\"T_6a1eb_row8_col1\" class=\"data row8 col1\" >0.110564</td>\n",
       "      <td id=\"T_6a1eb_row8_col2\" class=\"data row8 col2\" >0.172862</td>\n",
       "      <td id=\"T_6a1eb_row8_col3\" class=\"data row8 col3\" >0.211528</td>\n",
       "      <td id=\"T_6a1eb_row8_col4\" class=\"data row8 col4\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row9\" class=\"row_heading level0 row9\" >scglue</th>\n",
       "      <td id=\"T_6a1eb_row9_col0\" class=\"data row9 col0\" >0.105952</td>\n",
       "      <td id=\"T_6a1eb_row9_col1\" class=\"data row9 col1\" >0.462190</td>\n",
       "      <td id=\"T_6a1eb_row9_col2\" class=\"data row9 col2\" >0.581289</td>\n",
       "      <td id=\"T_6a1eb_row9_col3\" class=\"data row9 col3\" >0.316301</td>\n",
       "      <td id=\"T_6a1eb_row9_col4\" class=\"data row9 col4\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row10\" class=\"row_heading level0 row10\" >celloracle</th>\n",
       "      <td id=\"T_6a1eb_row10_col0\" class=\"data row10 col0\" >0.346608</td>\n",
       "      <td id=\"T_6a1eb_row10_col1\" class=\"data row10 col1\" >0.566949</td>\n",
       "      <td id=\"T_6a1eb_row10_col2\" class=\"data row10 col2\" >0.659436</td>\n",
       "      <td id=\"T_6a1eb_row10_col3\" class=\"data row10 col3\" >0.448130</td>\n",
       "      <td id=\"T_6a1eb_row10_col4\" class=\"data row10 col4\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row11\" class=\"row_heading level0 row11\" >figr</th>\n",
       "      <td id=\"T_6a1eb_row11_col0\" class=\"data row11 col0\" >0.251315</td>\n",
       "      <td id=\"T_6a1eb_row11_col1\" class=\"data row11 col1\" >0.583482</td>\n",
       "      <td id=\"T_6a1eb_row11_col2\" class=\"data row11 col2\" >0.398051</td>\n",
       "      <td id=\"T_6a1eb_row11_col3\" class=\"data row11 col3\" >0.325951</td>\n",
       "      <td id=\"T_6a1eb_row11_col4\" class=\"data row11 col4\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a1eb_level0_row12\" class=\"row_heading level0 row12\" >scenicplus</th>\n",
       "      <td id=\"T_6a1eb_row12_col0\" class=\"data row12 col0\" >0.492528</td>\n",
       "      <td id=\"T_6a1eb_row12_col1\" class=\"data row12 col1\" >0.630716</td>\n",
       "      <td id=\"T_6a1eb_row12_col2\" class=\"data row12 col2\" >0.700669</td>\n",
       "      <td id=\"T_6a1eb_row12_col3\" class=\"data row12 col3\" >0.502712</td>\n",
       "      <td id=\"T_6a1eb_row12_col4\" class=\"data row12 col4\" >4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14f81badbac0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/op/50000-skeleton_False-binarize_True-GB.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_all_n = (df_scores-df_scores.min(axis=0))/(df_scores.max(axis=0)-df_scores.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### replogle2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7d4a5_row0_col0, #T_7d4a5_row1_col2, #T_7d4a5_row4_col5 {\n",
       "  background-color: #046198;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row0_col1 {\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row0_col2, #T_7d4a5_row0_col3, #T_7d4a5_row0_col4, #T_7d4a5_row4_col0, #T_7d4a5_row4_col1, #T_7d4a5_row5_col5 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row0_col5, #T_7d4a5_row1_col0, #T_7d4a5_row1_col1, #T_7d4a5_row5_col2, #T_7d4a5_row5_col3, #T_7d4a5_row5_col4 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row1_col3 {\n",
       "  background-color: #0c74b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row1_col4 {\n",
       "  background-color: #3991c1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row1_col5 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row2_col0 {\n",
       "  background-color: #8eb3d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row2_col1 {\n",
       "  background-color: #b7c5df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row2_col2 {\n",
       "  background-color: #0566a0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row2_col3 {\n",
       "  background-color: #197db7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row2_col4 {\n",
       "  background-color: #4e9ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row2_col5 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row3_col0 {\n",
       "  background-color: #056dab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row3_col1 {\n",
       "  background-color: #2081b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row3_col2 {\n",
       "  background-color: #3f93c2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row3_col3 {\n",
       "  background-color: #4697c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row3_col4 {\n",
       "  background-color: #6fa7ce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row3_col5 {\n",
       "  background-color: #73a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row4_col2, #T_7d4a5_row4_col3 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row4_col4 {\n",
       "  background-color: #fbf4f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row5_col0 {\n",
       "  background-color: #8bb2d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row5_col1 {\n",
       "  background-color: #a7bddb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row6_col0 {\n",
       "  background-color: #056dac;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row6_col1 {\n",
       "  background-color: #1077b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7d4a5_row6_col2 {\n",
       "  background-color: #b0c2de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row6_col3 {\n",
       "  background-color: #d5d5e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row6_col4 {\n",
       "  background-color: #e5e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7d4a5_row6_col5 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7d4a5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7d4a5_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_7d4a5_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_7d4a5_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_7d4a5_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_7d4a5_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "      <th id=\"T_7d4a5_level0_col5\" class=\"col_heading level0 col5\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row0\" class=\"row_heading level0 row0\" >negative_control</th>\n",
       "      <td id=\"T_7d4a5_row0_col0\" class=\"data row0 col0\" >0.007890</td>\n",
       "      <td id=\"T_7d4a5_row0_col1\" class=\"data row0 col1\" >0.007940</td>\n",
       "      <td id=\"T_7d4a5_row0_col2\" class=\"data row0 col2\" >0.004070</td>\n",
       "      <td id=\"T_7d4a5_row0_col3\" class=\"data row0 col3\" >0.007549</td>\n",
       "      <td id=\"T_7d4a5_row0_col4\" class=\"data row0 col4\" >0.016413</td>\n",
       "      <td id=\"T_7d4a5_row0_col5\" class=\"data row0 col5\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row1\" class=\"row_heading level0 row1\" >positive_control</th>\n",
       "      <td id=\"T_7d4a5_row1_col0\" class=\"data row1 col0\" >0.014413</td>\n",
       "      <td id=\"T_7d4a5_row1_col1\" class=\"data row1 col1\" >0.019584</td>\n",
       "      <td id=\"T_7d4a5_row1_col2\" class=\"data row1 col2\" >0.070334</td>\n",
       "      <td id=\"T_7d4a5_row1_col3\" class=\"data row1 col3\" >0.051472</td>\n",
       "      <td id=\"T_7d4a5_row1_col4\" class=\"data row1 col4\" >0.043955</td>\n",
       "      <td id=\"T_7d4a5_row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row2\" class=\"row_heading level0 row2\" >pearson_corr</th>\n",
       "      <td id=\"T_7d4a5_row2_col0\" class=\"data row2 col0\" >-0.008077</td>\n",
       "      <td id=\"T_7d4a5_row2_col1\" class=\"data row2 col1\" >-0.010913</td>\n",
       "      <td id=\"T_7d4a5_row2_col2\" class=\"data row2 col2\" >0.068009</td>\n",
       "      <td id=\"T_7d4a5_row2_col3\" class=\"data row2 col3\" >0.049308</td>\n",
       "      <td id=\"T_7d4a5_row2_col4\" class=\"data row2 col4\" >0.041981</td>\n",
       "      <td id=\"T_7d4a5_row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row3\" class=\"row_heading level0 row3\" >portia</th>\n",
       "      <td id=\"T_7d4a5_row3_col0\" class=\"data row3 col0\" >0.005164</td>\n",
       "      <td id=\"T_7d4a5_row3_col1\" class=\"data row3 col1\" >0.005206</td>\n",
       "      <td id=\"T_7d4a5_row3_col2\" class=\"data row3 col2\" >0.052368</td>\n",
       "      <td id=\"T_7d4a5_row3_col3\" class=\"data row3 col3\" >0.043075</td>\n",
       "      <td id=\"T_7d4a5_row3_col4\" class=\"data row3 col4\" >0.039028</td>\n",
       "      <td id=\"T_7d4a5_row3_col5\" class=\"data row3 col5\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row4\" class=\"row_heading level0 row4\" >ppcor</th>\n",
       "      <td id=\"T_7d4a5_row4_col0\" class=\"data row4 col0\" >-0.025308</td>\n",
       "      <td id=\"T_7d4a5_row4_col1\" class=\"data row4 col1\" >-0.025607</td>\n",
       "      <td id=\"T_7d4a5_row4_col2\" class=\"data row4 col2\" >0.007153</td>\n",
       "      <td id=\"T_7d4a5_row4_col3\" class=\"data row4 col3\" >0.009689</td>\n",
       "      <td id=\"T_7d4a5_row4_col4\" class=\"data row4 col4\" >0.017616</td>\n",
       "      <td id=\"T_7d4a5_row4_col5\" class=\"data row4 col5\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row5\" class=\"row_heading level0 row5\" >grnboost2</th>\n",
       "      <td id=\"T_7d4a5_row5_col0\" class=\"data row5 col0\" >-0.007698</td>\n",
       "      <td id=\"T_7d4a5_row5_col1\" class=\"data row5 col1\" >-0.008728</td>\n",
       "      <td id=\"T_7d4a5_row5_col2\" class=\"data row5 col2\" >0.083347</td>\n",
       "      <td id=\"T_7d4a5_row5_col3\" class=\"data row5 col3\" >0.067546</td>\n",
       "      <td id=\"T_7d4a5_row5_col4\" class=\"data row5 col4\" >0.060886</td>\n",
       "      <td id=\"T_7d4a5_row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d4a5_level0_row6\" class=\"row_heading level0 row6\" >scenic</th>\n",
       "      <td id=\"T_7d4a5_row6_col0\" class=\"data row6 col0\" >0.005057</td>\n",
       "      <td id=\"T_7d4a5_row6_col1\" class=\"data row6 col1\" >0.006892</td>\n",
       "      <td id=\"T_7d4a5_row6_col2\" class=\"data row6 col2\" >0.031547</td>\n",
       "      <td id=\"T_7d4a5_row6_col3\" class=\"data row6 col3\" >0.021333</td>\n",
       "      <td id=\"T_7d4a5_row6_col4\" class=\"data row6 col4\" >0.023515</td>\n",
       "      <td id=\"T_7d4a5_row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x149cc9131210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/replogle2/50000-skeleton_False-binarize_True-ridge.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "\n",
    "df_scores_f = df_scores[['static-theta-0.0', 'static-theta-0.5', 'static-theta-1.0']]\n",
    "df_all_n = (df_scores_f-df_scores_f.min(axis=0))/(df_scores_f.max(axis=0)-df_scores_f.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### nakatake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b6302_row0_col0 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row0_col1, #T_b6302_row6_col2 {\n",
       "  background-color: #f0eaf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row0_col2, #T_b6302_row0_col3, #T_b6302_row0_col4, #T_b6302_row1_col5, #T_b6302_row5_col0, #T_b6302_row5_col1 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row0_col5, #T_b6302_row1_col2, #T_b6302_row1_col3, #T_b6302_row5_col4, #T_b6302_row6_col0, #T_b6302_row6_col1 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row1_col0 {\n",
       "  background-color: #c0c9e2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row1_col1 {\n",
       "  background-color: #b8c6e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row1_col4 {\n",
       "  background-color: #509ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col0 {\n",
       "  background-color: #2786bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col1 {\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col2, #T_b6302_row4_col5 {\n",
       "  background-color: #046198;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col3 {\n",
       "  background-color: #045585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col4, #T_b6302_row5_col2 {\n",
       "  background-color: #84b0d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row2_col5 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row3_col0 {\n",
       "  background-color: #d3d4e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row3_col1 {\n",
       "  background-color: #9ab8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row3_col2 {\n",
       "  background-color: #045788;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row3_col3 {\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row3_col4 {\n",
       "  background-color: #d2d3e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row3_col5 {\n",
       "  background-color: #73a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row4_col0 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row4_col1 {\n",
       "  background-color: #e9e5f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row4_col2 {\n",
       "  background-color: #f4edf6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row4_col3, #T_b6302_row4_col4 {\n",
       "  background-color: #fbf4f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row5_col3 {\n",
       "  background-color: #045280;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b6302_row5_col5 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row6_col3 {\n",
       "  background-color: #bbc7e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row6_col4 {\n",
       "  background-color: #89b1d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b6302_row6_col5 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b6302\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b6302_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_b6302_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_b6302_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_b6302_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_b6302_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "      <th id=\"T_b6302_level0_col5\" class=\"col_heading level0 col5\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row0\" class=\"row_heading level0 row0\" >negative_control</th>\n",
       "      <td id=\"T_b6302_row0_col0\" class=\"data row0 col0\" >-0.000815</td>\n",
       "      <td id=\"T_b6302_row0_col1\" class=\"data row0 col1\" >-0.000943</td>\n",
       "      <td id=\"T_b6302_row0_col2\" class=\"data row0 col2\" >0.000648</td>\n",
       "      <td id=\"T_b6302_row0_col3\" class=\"data row0 col3\" >0.024343</td>\n",
       "      <td id=\"T_b6302_row0_col4\" class=\"data row0 col4\" >0.047426</td>\n",
       "      <td id=\"T_b6302_row0_col5\" class=\"data row0 col5\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row1\" class=\"row_heading level0 row1\" >positive_control</th>\n",
       "      <td id=\"T_b6302_row1_col0\" class=\"data row1 col0\" >0.000333</td>\n",
       "      <td id=\"T_b6302_row1_col1\" class=\"data row1 col1\" >0.000933</td>\n",
       "      <td id=\"T_b6302_row1_col2\" class=\"data row1 col2\" >0.050172</td>\n",
       "      <td id=\"T_b6302_row1_col3\" class=\"data row1 col3\" >0.195597</td>\n",
       "      <td id=\"T_b6302_row1_col4\" class=\"data row1 col4\" >0.103299</td>\n",
       "      <td id=\"T_b6302_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row2\" class=\"row_heading level0 row2\" >pearson_corr</th>\n",
       "      <td id=\"T_b6302_row2_col0\" class=\"data row2 col0\" >0.002183</td>\n",
       "      <td id=\"T_b6302_row2_col1\" class=\"data row2 col1\" >0.006059</td>\n",
       "      <td id=\"T_b6302_row2_col2\" class=\"data row2 col2\" >0.042007</td>\n",
       "      <td id=\"T_b6302_row2_col3\" class=\"data row2 col3\" >0.177372</td>\n",
       "      <td id=\"T_b6302_row2_col4\" class=\"data row2 col4\" >0.092215</td>\n",
       "      <td id=\"T_b6302_row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row3\" class=\"row_heading level0 row3\" >portia</th>\n",
       "      <td id=\"T_b6302_row3_col0\" class=\"data row3 col0\" >0.000026</td>\n",
       "      <td id=\"T_b6302_row3_col1\" class=\"data row3 col1\" >0.001641</td>\n",
       "      <td id=\"T_b6302_row3_col2\" class=\"data row3 col2\" >0.044538</td>\n",
       "      <td id=\"T_b6302_row3_col3\" class=\"data row3 col3\" >0.094127</td>\n",
       "      <td id=\"T_b6302_row3_col4\" class=\"data row3 col4\" >0.071015</td>\n",
       "      <td id=\"T_b6302_row3_col5\" class=\"data row3 col5\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row4\" class=\"row_heading level0 row4\" >ppcor</th>\n",
       "      <td id=\"T_b6302_row4_col0\" class=\"data row4 col0\" >-0.000114</td>\n",
       "      <td id=\"T_b6302_row4_col1\" class=\"data row4 col1\" >-0.000657</td>\n",
       "      <td id=\"T_b6302_row4_col2\" class=\"data row4 col2\" >0.004380</td>\n",
       "      <td id=\"T_b6302_row4_col3\" class=\"data row4 col3\" >0.028708</td>\n",
       "      <td id=\"T_b6302_row4_col4\" class=\"data row4 col4\" >0.049842</td>\n",
       "      <td id=\"T_b6302_row4_col5\" class=\"data row4 col5\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row5\" class=\"row_heading level0 row5\" >grnboost2</th>\n",
       "      <td id=\"T_b6302_row5_col0\" class=\"data row5 col0\" >-0.001190</td>\n",
       "      <td id=\"T_b6302_row5_col1\" class=\"data row5 col1\" >-0.001845</td>\n",
       "      <td id=\"T_b6302_row5_col2\" class=\"data row5 col2\" >0.023334</td>\n",
       "      <td id=\"T_b6302_row5_col3\" class=\"data row5 col3\" >0.179273</td>\n",
       "      <td id=\"T_b6302_row5_col4\" class=\"data row5 col4\" >0.144886</td>\n",
       "      <td id=\"T_b6302_row5_col5\" class=\"data row5 col5\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6302_level0_row6\" class=\"row_heading level0 row6\" >scenic</th>\n",
       "      <td id=\"T_b6302_row6_col0\" class=\"data row6 col0\" >0.003915</td>\n",
       "      <td id=\"T_b6302_row6_col1\" class=\"data row6 col1\" >0.006797</td>\n",
       "      <td id=\"T_b6302_row6_col2\" class=\"data row6 col2\" >0.005719</td>\n",
       "      <td id=\"T_b6302_row6_col3\" class=\"data row6 col3\" >0.078472</td>\n",
       "      <td id=\"T_b6302_row6_col4\" class=\"data row6 col4\" >0.091197</td>\n",
       "      <td id=\"T_b6302_row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x149cc9131c30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/nakatake/50000-skeleton_False-binarize_True-ridge.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_scores_f = df_scores[['static-theta-0.0', 'static-theta-0.5', 'static-theta-1.0']]\n",
    "df_all_n = (df_scores_f-df_scores_f.min(axis=0))/(df_scores_f.max(axis=0)-df_scores_f.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### norman "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fdc29_row0_col0 {\n",
       "  background-color: #569dc8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row0_col1 {\n",
       "  background-color: #93b5d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row0_col2, #T_fdc29_row0_col3, #T_fdc29_row0_col4, #T_fdc29_row1_col0, #T_fdc29_row1_col1, #T_fdc29_row5_col5 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row0_col5, #T_fdc29_row1_col2, #T_fdc29_row2_col0, #T_fdc29_row4_col0, #T_fdc29_row5_col3, #T_fdc29_row5_col4, #T_fdc29_row6_col1 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row1_col3 {\n",
       "  background-color: #034369;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row1_col4 {\n",
       "  background-color: #197db7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row1_col5 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row2_col1 {\n",
       "  background-color: #0771b1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row2_col2 {\n",
       "  background-color: #023a5b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row2_col3 {\n",
       "  background-color: #045585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row2_col4 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row2_col5 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col0 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col1, #T_fdc29_row4_col4 {\n",
       "  background-color: #e4e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col2 {\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col3 {\n",
       "  background-color: #c6cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col4 {\n",
       "  background-color: #eee9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row3_col5 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row4_col1 {\n",
       "  background-color: #0d75b3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row4_col2 {\n",
       "  background-color: #2c89bd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row4_col3 {\n",
       "  background-color: #8bb2d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row4_col5 {\n",
       "  background-color: #73a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row5_col0 {\n",
       "  background-color: #2786bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row5_col1 {\n",
       "  background-color: #75a9cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row5_col2 {\n",
       "  background-color: #04639b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row6_col0 {\n",
       "  background-color: #034871;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row6_col2 {\n",
       "  background-color: #4c99c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_fdc29_row6_col3 {\n",
       "  background-color: #efe9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row6_col4 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fdc29_row6_col5 {\n",
       "  background-color: #046198;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fdc29\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fdc29_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_fdc29_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_fdc29_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_fdc29_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_fdc29_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "      <th id=\"T_fdc29_level0_col5\" class=\"col_heading level0 col5\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row0\" class=\"row_heading level0 row0\" >negative_control</th>\n",
       "      <td id=\"T_fdc29_row0_col0\" class=\"data row0 col0\" >-0.000234</td>\n",
       "      <td id=\"T_fdc29_row0_col1\" class=\"data row0 col1\" >-0.000256</td>\n",
       "      <td id=\"T_fdc29_row0_col2\" class=\"data row0 col2\" >0.014227</td>\n",
       "      <td id=\"T_fdc29_row0_col3\" class=\"data row0 col3\" >0.013110</td>\n",
       "      <td id=\"T_fdc29_row0_col4\" class=\"data row0 col4\" >0.003253</td>\n",
       "      <td id=\"T_fdc29_row0_col5\" class=\"data row0 col5\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row1\" class=\"row_heading level0 row1\" >positive_control</th>\n",
       "      <td id=\"T_fdc29_row1_col0\" class=\"data row1 col0\" >-0.002901</td>\n",
       "      <td id=\"T_fdc29_row1_col1\" class=\"data row1 col1\" >-0.003470</td>\n",
       "      <td id=\"T_fdc29_row1_col2\" class=\"data row1 col2\" >0.136180</td>\n",
       "      <td id=\"T_fdc29_row1_col3\" class=\"data row1 col3\" >0.098129</td>\n",
       "      <td id=\"T_fdc29_row1_col4\" class=\"data row1 col4\" >0.040224</td>\n",
       "      <td id=\"T_fdc29_row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row2\" class=\"row_heading level0 row2\" >pearson_corr</th>\n",
       "      <td id=\"T_fdc29_row2_col0\" class=\"data row2 col0\" >0.001870</td>\n",
       "      <td id=\"T_fdc29_row2_col1\" class=\"data row2 col1\" >0.002176</td>\n",
       "      <td id=\"T_fdc29_row2_col2\" class=\"data row2 col2\" >0.135031</td>\n",
       "      <td id=\"T_fdc29_row2_col3\" class=\"data row2 col3\" >0.092110</td>\n",
       "      <td id=\"T_fdc29_row2_col4\" class=\"data row2 col4\" >0.036152</td>\n",
       "      <td id=\"T_fdc29_row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row3\" class=\"row_heading level0 row3\" >portia</th>\n",
       "      <td id=\"T_fdc29_row3_col0\" class=\"data row3 col0\" >-0.002051</td>\n",
       "      <td id=\"T_fdc29_row3_col1\" class=\"data row3 col1\" >-0.002247</td>\n",
       "      <td id=\"T_fdc29_row3_col2\" class=\"data row3 col2\" >0.064202</td>\n",
       "      <td id=\"T_fdc29_row3_col3\" class=\"data row3 col3\" >0.037832</td>\n",
       "      <td id=\"T_fdc29_row3_col4\" class=\"data row3 col4\" >0.009061</td>\n",
       "      <td id=\"T_fdc29_row3_col5\" class=\"data row3 col5\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row4\" class=\"row_heading level0 row4\" >ppcor</th>\n",
       "      <td id=\"T_fdc29_row4_col0\" class=\"data row4 col0\" >0.001867</td>\n",
       "      <td id=\"T_fdc29_row4_col1\" class=\"data row4 col1\" >0.002047</td>\n",
       "      <td id=\"T_fdc29_row4_col2\" class=\"data row4 col2\" >0.093536</td>\n",
       "      <td id=\"T_fdc29_row4_col3\" class=\"data row4 col3\" >0.052257</td>\n",
       "      <td id=\"T_fdc29_row4_col4\" class=\"data row4 col4\" >0.011904</td>\n",
       "      <td id=\"T_fdc29_row4_col5\" class=\"data row4 col5\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row5\" class=\"row_heading level0 row5\" >grnboost2</th>\n",
       "      <td id=\"T_fdc29_row5_col0\" class=\"data row5 col0\" >0.000263</td>\n",
       "      <td id=\"T_fdc29_row5_col1\" class=\"data row5 col1\" >0.000314</td>\n",
       "      <td id=\"T_fdc29_row5_col2\" class=\"data row5 col2\" >0.114651</td>\n",
       "      <td id=\"T_fdc29_row5_col3\" class=\"data row5 col3\" >0.101651</td>\n",
       "      <td id=\"T_fdc29_row5_col4\" class=\"data row5 col4\" >0.056132</td>\n",
       "      <td id=\"T_fdc29_row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdc29_level0_row6\" class=\"row_heading level0 row6\" >scenic</th>\n",
       "      <td id=\"T_fdc29_row6_col0\" class=\"data row6 col0\" >0.001586</td>\n",
       "      <td id=\"T_fdc29_row6_col1\" class=\"data row6 col1\" >0.004122</td>\n",
       "      <td id=\"T_fdc29_row6_col2\" class=\"data row6 col2\" >0.084830</td>\n",
       "      <td id=\"T_fdc29_row6_col3\" class=\"data row6 col3\" >0.022496</td>\n",
       "      <td id=\"T_fdc29_row6_col4\" class=\"data row6 col4\" >0.005257</td>\n",
       "      <td id=\"T_fdc29_row6_col5\" class=\"data row6 col5\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x149d8a4d7ca0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/norman/X_norm-50000-skeleton_False-binarize_True-ridge-global-False.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_scores_f = df_scores[['static-theta-0.0', 'static-theta-0.5', 'static-theta-1.0']]\n",
    "df_all_n = (df_scores_f-df_scores_f.min(axis=0))/(df_scores_f.max(axis=0)-df_scores_f.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000-skeleton_False-binarize_True-ridge.csv\n",
      "X_norm-50000-skeleton_False-binarize_True-ridge-global-False.csv\n"
     ]
    }
   ],
   "source": [
    "!ls resources/scores/adamson/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adamson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4eb42_row0_col0, #T_4eb42_row1_col2 {\n",
       "  background-color: #03446a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row0_col1 {\n",
       "  background-color: #045b8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row0_col2 {\n",
       "  background-color: #bdc8e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row0_col3 {\n",
       "  background-color: #6fa7ce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row0_col4 {\n",
       "  background-color: #d2d3e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row0_col5 {\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row1_col0 {\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row1_col1, #T_4eb42_row3_col0, #T_4eb42_row3_col2, #T_4eb42_row3_col3, #T_4eb42_row3_col4, #T_4eb42_row5_col5 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row1_col3 {\n",
       "  background-color: #046096;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row1_col4 {\n",
       "  background-color: #187cb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row1_col5 {\n",
       "  background-color: #dbdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row2_col0, #T_4eb42_row2_col1, #T_4eb42_row3_col5, #T_4eb42_row5_col2, #T_4eb42_row5_col3, #T_4eb42_row5_col4 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row2_col2 {\n",
       "  background-color: #034d79;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row2_col3 {\n",
       "  background-color: #046299;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row2_col4 {\n",
       "  background-color: #1b7eb7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row2_col5 {\n",
       "  background-color: #9cb9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row3_col1 {\n",
       "  background-color: #f5eff6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row4_col0 {\n",
       "  background-color: #efe9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row4_col1 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row4_col2 {\n",
       "  background-color: #328dbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row4_col3 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row4_col4 {\n",
       "  background-color: #8bb2d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row4_col5 {\n",
       "  background-color: #4295c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4eb42_row5_col0 {\n",
       "  background-color: #d1d2e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4eb42_row5_col1 {\n",
       "  background-color: #d2d2e7;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4eb42\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4eb42_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_4eb42_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_4eb42_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_4eb42_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_4eb42_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "      <th id=\"T_4eb42_level0_col5\" class=\"col_heading level0 col5\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row0\" class=\"row_heading level0 row0\" >negative_control</th>\n",
       "      <td id=\"T_4eb42_row0_col0\" class=\"data row0 col0\" >0.022322</td>\n",
       "      <td id=\"T_4eb42_row0_col1\" class=\"data row0 col1\" >0.022322</td>\n",
       "      <td id=\"T_4eb42_row0_col2\" class=\"data row0 col2\" >0.445594</td>\n",
       "      <td id=\"T_4eb42_row0_col3\" class=\"data row0 col3\" >0.439009</td>\n",
       "      <td id=\"T_4eb42_row0_col4\" class=\"data row0 col4\" >0.413479</td>\n",
       "      <td id=\"T_4eb42_row0_col5\" class=\"data row0 col5\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row1\" class=\"row_heading level0 row1\" >positive_control</th>\n",
       "      <td id=\"T_4eb42_row1_col0\" class=\"data row1 col0\" >-0.021639</td>\n",
       "      <td id=\"T_4eb42_row1_col1\" class=\"data row1 col1\" >-0.027437</td>\n",
       "      <td id=\"T_4eb42_row1_col2\" class=\"data row1 col2\" >0.664241</td>\n",
       "      <td id=\"T_4eb42_row1_col3\" class=\"data row1 col3\" >0.482923</td>\n",
       "      <td id=\"T_4eb42_row1_col4\" class=\"data row1 col4\" >0.436312</td>\n",
       "      <td id=\"T_4eb42_row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row2\" class=\"row_heading level0 row2\" >pearson_corr</th>\n",
       "      <td id=\"T_4eb42_row2_col0\" class=\"data row2 col0\" >0.024375</td>\n",
       "      <td id=\"T_4eb42_row2_col1\" class=\"data row2 col1\" >0.030097</td>\n",
       "      <td id=\"T_4eb42_row2_col2\" class=\"data row2 col2\" >0.652753</td>\n",
       "      <td id=\"T_4eb42_row2_col3\" class=\"data row2 col3\" >0.481473</td>\n",
       "      <td id=\"T_4eb42_row2_col4\" class=\"data row2 col4\" >0.435883</td>\n",
       "      <td id=\"T_4eb42_row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row3\" class=\"row_heading level0 row3\" >portia</th>\n",
       "      <td id=\"T_4eb42_row3_col0\" class=\"data row3 col0\" >-0.022940</td>\n",
       "      <td id=\"T_4eb42_row3_col1\" class=\"data row3 col1\" >-0.023618</td>\n",
       "      <td id=\"T_4eb42_row3_col2\" class=\"data row3 col2\" >0.341579</td>\n",
       "      <td id=\"T_4eb42_row3_col3\" class=\"data row3 col3\" >0.371780</td>\n",
       "      <td id=\"T_4eb42_row3_col4\" class=\"data row3 col4\" >0.401550</td>\n",
       "      <td id=\"T_4eb42_row3_col5\" class=\"data row3 col5\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row4\" class=\"row_heading level0 row4\" >ppcor</th>\n",
       "      <td id=\"T_4eb42_row4_col0\" class=\"data row4 col0\" >-0.017781</td>\n",
       "      <td id=\"T_4eb42_row4_col1\" class=\"data row4 col1\" >-0.017927</td>\n",
       "      <td id=\"T_4eb42_row4_col2\" class=\"data row4 col2\" >0.555375</td>\n",
       "      <td id=\"T_4eb42_row4_col3\" class=\"data row4 col3\" >0.453831</td>\n",
       "      <td id=\"T_4eb42_row4_col4\" class=\"data row4 col4\" >0.423552</td>\n",
       "      <td id=\"T_4eb42_row4_col5\" class=\"data row4 col5\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4eb42_level0_row5\" class=\"row_heading level0 row5\" >grnboost2</th>\n",
       "      <td id=\"T_4eb42_row5_col0\" class=\"data row5 col0\" >-0.011154</td>\n",
       "      <td id=\"T_4eb42_row5_col1\" class=\"data row5 col1\" >-0.013385</td>\n",
       "      <td id=\"T_4eb42_row5_col2\" class=\"data row5 col2\" >0.679135</td>\n",
       "      <td id=\"T_4eb42_row5_col3\" class=\"data row5 col3\" >0.503771</td>\n",
       "      <td id=\"T_4eb42_row5_col4\" class=\"data row5 col4\" >0.451201</td>\n",
       "      <td id=\"T_4eb42_row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x149d8a510e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = pd.read_csv(f\"resources/scores/adamson/X_norm-50000-skeleton_False-binarize_True-ridge-global-False.csv\", index_col=0)\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_scores_f = df_scores[['static-theta-0.0', 'static-theta-0.5', 'static-theta-1.0']]\n",
    "df_all_n = (df_scores_f-df_scores_f.min(axis=0))/(df_scores_f.max(axis=0)-df_scores_f.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000-skeleton_False-binarize_True-GB.csv\n",
      "50000-skeleton_False-binarize_True-ridge.csv\n",
      "lognorm-50000-skeleton_False-binarize_True-ridge-global-False.csv\n",
      "nets\n",
      "X_norm-50000-skeleton_False-binarize_True-ridge-global-False.csv\n",
      "X_norm-50000-skeleton_False-binarize_True-ridge-global-True.csv\n"
     ]
    }
   ],
   "source": [
    "!ls resources/scores/op/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d946e_row0_col0, #T_d946e_row21_col0 {\n",
       "  background-color: #f3edf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row0_col1, #T_d946e_row18_col2, #T_d946e_row20_col2 {\n",
       "  background-color: #efe9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row0_col2, #T_d946e_row17_col0 {\n",
       "  background-color: #eae6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row0_col3 {\n",
       "  background-color: #d4d4e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row0_col4, #T_d946e_row1_col4, #T_d946e_row8_col1 {\n",
       "  background-color: #e4e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row0_col5, #T_d946e_row9_col1 {\n",
       "  background-color: #509ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row1_col0, #T_d946e_row1_col1, #T_d946e_row6_col5, #T_d946e_row8_col2, #T_d946e_row8_col3, #T_d946e_row8_col4, #T_d946e_row14_col0, #T_d946e_row14_col1, #T_d946e_row16_col2 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row1_col2 {\n",
       "  background-color: #f6eff7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row1_col3, #T_d946e_row10_col5, #T_d946e_row15_col3 {\n",
       "  background-color: #dedcec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row1_col5 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row2_col0, #T_d946e_row12_col3 {\n",
       "  background-color: #023c5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row2_col1, #T_d946e_row2_col2, #T_d946e_row6_col0, #T_d946e_row6_col3, #T_d946e_row6_col4, #T_d946e_row14_col5 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row2_col3, #T_d946e_row10_col3 {\n",
       "  background-color: #056caa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row2_col4 {\n",
       "  background-color: #4e9ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row2_col5, #T_d946e_row15_col2, #T_d946e_row21_col2 {\n",
       "  background-color: #f8f1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row3_col0, #T_d946e_row6_col1 {\n",
       "  background-color: #056ba9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row3_col1 {\n",
       "  background-color: #04588a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row3_col2, #T_d946e_row6_col2 {\n",
       "  background-color: #045e94;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row3_col3 {\n",
       "  background-color: #2d8abd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row3_col4, #T_d946e_row7_col0 {\n",
       "  background-color: #8eb3d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row3_col5 {\n",
       "  background-color: #e8e4f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row4_col0, #T_d946e_row10_col0, #T_d946e_row12_col1 {\n",
       "  background-color: #328dbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row4_col1 {\n",
       "  background-color: #529bc7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row4_col2, #T_d946e_row18_col5 {\n",
       "  background-color: #1379b5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row4_col3 {\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row4_col4, #T_d946e_row9_col3, #T_d946e_row17_col1 {\n",
       "  background-color: #b9c6e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row4_col5 {\n",
       "  background-color: #c4cbe3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col0 {\n",
       "  background-color: #cacee5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col1, #T_d946e_row8_col0, #T_d946e_row19_col1, #T_d946e_row21_col1 {\n",
       "  background-color: #dbdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col2 {\n",
       "  background-color: #9ab8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col3 {\n",
       "  background-color: #bfc9e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col4, #T_d946e_row14_col3 {\n",
       "  background-color: #e2dfee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row5_col5 {\n",
       "  background-color: #91b5d6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row7_col1 {\n",
       "  background-color: #9fbad9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row7_col2 {\n",
       "  background-color: #0f76b3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row7_col3 {\n",
       "  background-color: #056faf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row7_col4 {\n",
       "  background-color: #0c74b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row7_col5 {\n",
       "  background-color: #d3d4e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row8_col5 {\n",
       "  background-color: #03456c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row9_col0, #T_d946e_row16_col3 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row9_col2 {\n",
       "  background-color: #1e80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row9_col4 {\n",
       "  background-color: #d5d5e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row9_col5 {\n",
       "  background-color: #b4c4df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row10_col1 {\n",
       "  background-color: #2f8bbe;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row10_col2 {\n",
       "  background-color: #045f95;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row10_col4 {\n",
       "  background-color: #569dc8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row11_col0 {\n",
       "  background-color: #bdc8e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row11_col1 {\n",
       "  background-color: #88b1d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row11_col2 {\n",
       "  background-color: #ced0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row11_col3 {\n",
       "  background-color: #b0c2de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row11_col4 {\n",
       "  background-color: #cccfe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row11_col5 {\n",
       "  background-color: #a4bcda;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row12_col0 {\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row12_col2 {\n",
       "  background-color: #034f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row12_col4 {\n",
       "  background-color: #045687;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row12_col5, #T_d946e_row21_col4 {\n",
       "  background-color: #f1ebf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col0, #T_d946e_row16_col0 {\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col1 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col2 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col3, #T_d946e_row20_col3 {\n",
       "  background-color: #dad9ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col4, #T_d946e_row17_col4 {\n",
       "  background-color: #e5e1ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row13_col5 {\n",
       "  background-color: #056dac;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row14_col2, #T_d946e_row16_col1 {\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row14_col4 {\n",
       "  background-color: #ede8f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row15_col0 {\n",
       "  background-color: #fef6fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row15_col1 {\n",
       "  background-color: #fef6fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row15_col4 {\n",
       "  background-color: #ebe6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row15_col5 {\n",
       "  background-color: #045280;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row16_col4 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row16_col5 {\n",
       "  background-color: #05659f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row17_col2, #T_d946e_row18_col1 {\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row17_col3 {\n",
       "  background-color: #d6d6e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row17_col5 {\n",
       "  background-color: #7eadd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row18_col0 {\n",
       "  background-color: #f9f2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row18_col3 {\n",
       "  background-color: #e3e0ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row18_col4, #T_d946e_row20_col0 {\n",
       "  background-color: #f1ebf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row19_col0 {\n",
       "  background-color: #f4edf6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row19_col2 {\n",
       "  background-color: #f2ecf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row19_col3 {\n",
       "  background-color: #e0dded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row19_col4 {\n",
       "  background-color: #eee9f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row19_col5 {\n",
       "  background-color: #3991c1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row20_col1 {\n",
       "  background-color: #d2d3e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row20_col4 {\n",
       "  background-color: #e7e3f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row20_col5 {\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d946e_row21_col3 {\n",
       "  background-color: #dfddec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d946e_row21_col5 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d946e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d946e_level0_col0\" class=\"col_heading level0 col0\" >S1</th>\n",
       "      <th id=\"T_d946e_level0_col1\" class=\"col_heading level0 col1\" >S2</th>\n",
       "      <th id=\"T_d946e_level0_col2\" class=\"col_heading level0 col2\" >static-theta-0.0</th>\n",
       "      <th id=\"T_d946e_level0_col3\" class=\"col_heading level0 col3\" >static-theta-0.5</th>\n",
       "      <th id=\"T_d946e_level0_col4\" class=\"col_heading level0 col4\" >static-theta-1.0</th>\n",
       "      <th id=\"T_d946e_level0_col5\" class=\"col_heading level0 col5\" >rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row0\" class=\"row_heading level0 row0\" >collectri</th>\n",
       "      <td id=\"T_d946e_row0_col0\" class=\"data row0 col0\" >0.058315</td>\n",
       "      <td id=\"T_d946e_row0_col1\" class=\"data row0 col1\" >0.126899</td>\n",
       "      <td id=\"T_d946e_row0_col2\" class=\"data row0 col2\" >0.229779</td>\n",
       "      <td id=\"T_d946e_row0_col3\" class=\"data row0 col3\" >0.272409</td>\n",
       "      <td id=\"T_d946e_row0_col4\" class=\"data row0 col4\" >0.290769</td>\n",
       "      <td id=\"T_d946e_row0_col5\" class=\"data row0 col5\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row1\" class=\"row_heading level0 row1\" >negative_control</th>\n",
       "      <td id=\"T_d946e_row1_col0\" class=\"data row1 col0\" >-0.000923</td>\n",
       "      <td id=\"T_d946e_row1_col1\" class=\"data row1 col1\" >-0.000961</td>\n",
       "      <td id=\"T_d946e_row1_col2\" class=\"data row1 col2\" >0.193640</td>\n",
       "      <td id=\"T_d946e_row1_col3\" class=\"data row1 col3\" >0.260667</td>\n",
       "      <td id=\"T_d946e_row1_col4\" class=\"data row1 col4\" >0.290877</td>\n",
       "      <td id=\"T_d946e_row1_col5\" class=\"data row1 col5\" >19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row2\" class=\"row_heading level0 row2\" >positive_control</th>\n",
       "      <td id=\"T_d946e_row2_col0\" class=\"data row2 col0\" >0.721750</td>\n",
       "      <td id=\"T_d946e_row2_col1\" class=\"data row2 col1\" >1.188803</td>\n",
       "      <td id=\"T_d946e_row2_col2\" class=\"data row2 col2\" >0.654297</td>\n",
       "      <td id=\"T_d946e_row2_col3\" class=\"data row2 col3\" >0.411675</td>\n",
       "      <td id=\"T_d946e_row2_col4\" class=\"data row2 col4\" >0.310124</td>\n",
       "      <td id=\"T_d946e_row2_col5\" class=\"data row2 col5\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row3\" class=\"row_heading level0 row3\" >pearson_corr</th>\n",
       "      <td id=\"T_d946e_row3_col0\" class=\"data row3 col0\" >0.570333</td>\n",
       "      <td id=\"T_d946e_row3_col1\" class=\"data row3 col1\" >1.048355</td>\n",
       "      <td id=\"T_d946e_row3_col2\" class=\"data row3 col2\" >0.580583</td>\n",
       "      <td id=\"T_d946e_row3_col3\" class=\"data row3 col3\" >0.379891</td>\n",
       "      <td id=\"T_d946e_row3_col4\" class=\"data row3 col4\" >0.303594</td>\n",
       "      <td id=\"T_d946e_row3_col5\" class=\"data row3 col5\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row4\" class=\"row_heading level0 row4\" >portia</th>\n",
       "      <td id=\"T_d946e_row4_col0\" class=\"data row4 col0\" >0.465527</td>\n",
       "      <td id=\"T_d946e_row4_col1\" class=\"data row4 col1\" >0.674097</td>\n",
       "      <td id=\"T_d946e_row4_col2\" class=\"data row4 col2\" >0.513885</td>\n",
       "      <td id=\"T_d946e_row4_col3\" class=\"data row4 col3\" >0.317206</td>\n",
       "      <td id=\"T_d946e_row4_col4\" class=\"data row4 col4\" >0.298095</td>\n",
       "      <td id=\"T_d946e_row4_col5\" class=\"data row4 col5\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row5\" class=\"row_heading level0 row5\" >ppcor</th>\n",
       "      <td id=\"T_d946e_row5_col0\" class=\"data row5 col0\" >0.196689</td>\n",
       "      <td id=\"T_d946e_row5_col1\" class=\"data row5 col1\" >0.238486</td>\n",
       "      <td id=\"T_d946e_row5_col2\" class=\"data row5 col2\" >0.362190</td>\n",
       "      <td id=\"T_d946e_row5_col3\" class=\"data row5 col3\" >0.289743</td>\n",
       "      <td id=\"T_d946e_row5_col4\" class=\"data row5 col4\" >0.291190</td>\n",
       "      <td id=\"T_d946e_row5_col5\" class=\"data row5 col5\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row6\" class=\"row_heading level0 row6\" >grnboost2</th>\n",
       "      <td id=\"T_d946e_row6_col0\" class=\"data row6 col0\" >0.735540</td>\n",
       "      <td id=\"T_d946e_row6_col1\" class=\"data row6 col1\" >0.923076</td>\n",
       "      <td id=\"T_d946e_row6_col2\" class=\"data row6 col2\" >0.581265</td>\n",
       "      <td id=\"T_d946e_row6_col3\" class=\"data row6 col3\" >0.471491</td>\n",
       "      <td id=\"T_d946e_row6_col4\" class=\"data row6 col4\" >0.329940</td>\n",
       "      <td id=\"T_d946e_row6_col5\" class=\"data row6 col5\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row7\" class=\"row_heading level0 row7\" >scenic</th>\n",
       "      <td id=\"T_d946e_row7_col0\" class=\"data row7 col0\" >0.318873</td>\n",
       "      <td id=\"T_d946e_row7_col1\" class=\"data row7 col1\" >0.467008</td>\n",
       "      <td id=\"T_d946e_row7_col2\" class=\"data row7 col2\" >0.519103</td>\n",
       "      <td id=\"T_d946e_row7_col3\" class=\"data row7 col3\" >0.406525</td>\n",
       "      <td id=\"T_d946e_row7_col4\" class=\"data row7 col4\" >0.317489</td>\n",
       "      <td id=\"T_d946e_row7_col5\" class=\"data row7 col5\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row8\" class=\"row_heading level0 row8\" >granie</th>\n",
       "      <td id=\"T_d946e_row8_col0\" class=\"data row8 col0\" >0.147522</td>\n",
       "      <td id=\"T_d946e_row8_col1\" class=\"data row8 col1\" >0.193977</td>\n",
       "      <td id=\"T_d946e_row8_col2\" class=\"data row8 col2\" >0.165400</td>\n",
       "      <td id=\"T_d946e_row8_col3\" class=\"data row8 col3\" >0.211497</td>\n",
       "      <td id=\"T_d946e_row8_col4\" class=\"data row8 col4\" >0.283239</td>\n",
       "      <td id=\"T_d946e_row8_col5\" class=\"data row8 col5\" >21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row9\" class=\"row_heading level0 row9\" >scglue</th>\n",
       "      <td id=\"T_d946e_row9_col0\" class=\"data row9 col0\" >0.155642</td>\n",
       "      <td id=\"T_d946e_row9_col1\" class=\"data row9 col1\" >0.678908</td>\n",
       "      <td id=\"T_d946e_row9_col2\" class=\"data row9 col2\" >0.499250</td>\n",
       "      <td id=\"T_d946e_row9_col3\" class=\"data row9 col3\" >0.293863</td>\n",
       "      <td id=\"T_d946e_row9_col4\" class=\"data row9 col4\" >0.293939</td>\n",
       "      <td id=\"T_d946e_row9_col5\" class=\"data row9 col5\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row10\" class=\"row_heading level0 row10\" >celloracle</th>\n",
       "      <td id=\"T_d946e_row10_col0\" class=\"data row10 col0\" >0.466119</td>\n",
       "      <td id=\"T_d946e_row10_col1\" class=\"data row10 col1\" >0.762438</td>\n",
       "      <td id=\"T_d946e_row10_col2\" class=\"data row10 col2\" >0.577843</td>\n",
       "      <td id=\"T_d946e_row10_col3\" class=\"data row10 col3\" >0.412485</td>\n",
       "      <td id=\"T_d946e_row10_col4\" class=\"data row10 col4\" >0.309474</td>\n",
       "      <td id=\"T_d946e_row10_col5\" class=\"data row10 col5\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row11\" class=\"row_heading level0 row11\" >figr</th>\n",
       "      <td id=\"T_d946e_row11_col0\" class=\"data row11 col0\" >0.225030</td>\n",
       "      <td id=\"T_d946e_row11_col1\" class=\"data row11 col1\" >0.534416</td>\n",
       "      <td id=\"T_d946e_row11_col2\" class=\"data row11 col2\" >0.288675</td>\n",
       "      <td id=\"T_d946e_row11_col3\" class=\"data row11 col3\" >0.301267</td>\n",
       "      <td id=\"T_d946e_row11_col4\" class=\"data row11 col4\" >0.295626</td>\n",
       "      <td id=\"T_d946e_row11_col5\" class=\"data row11 col5\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row12\" class=\"row_heading level0 row12\" >scenicplus</th>\n",
       "      <td id=\"T_d946e_row12_col0\" class=\"data row12 col0\" >0.588578</td>\n",
       "      <td id=\"T_d946e_row12_col1\" class=\"data row12 col1\" >0.753705</td>\n",
       "      <td id=\"T_d946e_row12_col2\" class=\"data row12 col2\" >0.612103</td>\n",
       "      <td id=\"T_d946e_row12_col3\" class=\"data row12 col3\" >0.467226</td>\n",
       "      <td id=\"T_d946e_row12_col4\" class=\"data row12 col4\" >0.324725</td>\n",
       "      <td id=\"T_d946e_row12_col5\" class=\"data row12 col5\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row13\" class=\"row_heading level0 row13\" >ANANSE_tissue_networks_lung.parquet</th>\n",
       "      <td id=\"T_d946e_row13_col0\" class=\"data row13 col0\" >0.011492</td>\n",
       "      <td id=\"T_d946e_row13_col1\" class=\"data row13 col1\" >0.042744</td>\n",
       "      <td id=\"T_d946e_row13_col2\" class=\"data row13 col2\" >0.190339</td>\n",
       "      <td id=\"T_d946e_row13_col3\" class=\"data row13 col3\" >0.264441</td>\n",
       "      <td id=\"T_d946e_row13_col4\" class=\"data row13 col4\" >0.290627</td>\n",
       "      <td id=\"T_d946e_row13_col5\" class=\"data row13 col5\" >17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row14\" class=\"row_heading level0 row14\" >ANANSE_tissue_networks_stomach.parquet</th>\n",
       "      <td id=\"T_d946e_row14_col0\" class=\"data row14 col0\" >0.000624</td>\n",
       "      <td id=\"T_d946e_row14_col1\" class=\"data row14 col1\" >0.003118</td>\n",
       "      <td id=\"T_d946e_row14_col2\" class=\"data row14 col2\" >0.177599</td>\n",
       "      <td id=\"T_d946e_row14_col3\" class=\"data row14 col3\" >0.255215</td>\n",
       "      <td id=\"T_d946e_row14_col4\" class=\"data row14 col4\" >0.288802</td>\n",
       "      <td id=\"T_d946e_row14_col5\" class=\"data row14 col5\" >22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row15\" class=\"row_heading level0 row15\" >ANANSE_tissue_networks_heart.parquet</th>\n",
       "      <td id=\"T_d946e_row15_col0\" class=\"data row15 col0\" >0.002072</td>\n",
       "      <td id=\"T_d946e_row15_col1\" class=\"data row15 col1\" >0.010872</td>\n",
       "      <td id=\"T_d946e_row15_col2\" class=\"data row15 col2\" >0.185617</td>\n",
       "      <td id=\"T_d946e_row15_col3\" class=\"data row15 col3\" >0.260697</td>\n",
       "      <td id=\"T_d946e_row15_col4\" class=\"data row15 col4\" >0.289334</td>\n",
       "      <td id=\"T_d946e_row15_col5\" class=\"data row15 col5\" >20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row16\" class=\"row_heading level0 row16\" >ANANSE_tissue_networks_bone_marrow.parquet</th>\n",
       "      <td id=\"T_d946e_row16_col0\" class=\"data row16 col0\" >0.008027</td>\n",
       "      <td id=\"T_d946e_row16_col1\" class=\"data row16 col1\" >0.034896</td>\n",
       "      <td id=\"T_d946e_row16_col2\" class=\"data row16 col2\" >0.163763</td>\n",
       "      <td id=\"T_d946e_row16_col3\" class=\"data row16 col3\" >0.266824</td>\n",
       "      <td id=\"T_d946e_row16_col4\" class=\"data row16 col4\" >0.291476</td>\n",
       "      <td id=\"T_d946e_row16_col5\" class=\"data row16 col5\" >18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row17\" class=\"row_heading level0 row17\" >gtex_rna_networks_Whole_Blood.parquet</th>\n",
       "      <td id=\"T_d946e_row17_col0\" class=\"data row17 col0\" >0.099496</td>\n",
       "      <td id=\"T_d946e_row17_col1\" class=\"data row17 col1\" >0.379754</td>\n",
       "      <td id=\"T_d946e_row17_col2\" class=\"data row17 col2\" >0.223288</td>\n",
       "      <td id=\"T_d946e_row17_col3\" class=\"data row17 col3\" >0.269420</td>\n",
       "      <td id=\"T_d946e_row17_col4\" class=\"data row17 col4\" >0.290558</td>\n",
       "      <td id=\"T_d946e_row17_col5\" class=\"data row17 col5\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row18\" class=\"row_heading level0 row18\" >gtex_rna_networks_Brain_Amygdala.parquet</th>\n",
       "      <td id=\"T_d946e_row18_col0\" class=\"data row18 col0\" >0.029852</td>\n",
       "      <td id=\"T_d946e_row18_col1\" class=\"data row18 col1\" >0.143870</td>\n",
       "      <td id=\"T_d946e_row18_col2\" class=\"data row18 col2\" >0.217035</td>\n",
       "      <td id=\"T_d946e_row18_col3\" class=\"data row18 col3\" >0.254217</td>\n",
       "      <td id=\"T_d946e_row18_col4\" class=\"data row18 col4\" >0.287616</td>\n",
       "      <td id=\"T_d946e_row18_col5\" class=\"data row18 col5\" >16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row19\" class=\"row_heading level0 row19\" >gtex_rna_networks_Breast_Mammary_Tissue.parquet</th>\n",
       "      <td id=\"T_d946e_row19_col0\" class=\"data row19 col0\" >0.056548</td>\n",
       "      <td id=\"T_d946e_row19_col1\" class=\"data row19 col1\" >0.237685</td>\n",
       "      <td id=\"T_d946e_row19_col2\" class=\"data row19 col2\" >0.206502</td>\n",
       "      <td id=\"T_d946e_row19_col3\" class=\"data row19 col3\" >0.259042</td>\n",
       "      <td id=\"T_d946e_row19_col4\" class=\"data row19 col4\" >0.288375</td>\n",
       "      <td id=\"T_d946e_row19_col5\" class=\"data row19 col5\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row20\" class=\"row_heading level0 row20\" >gtex_rna_networks_Lung.parquet</th>\n",
       "      <td id=\"T_d946e_row20_col0\" class=\"data row20 col0\" >0.065444</td>\n",
       "      <td id=\"T_d946e_row20_col1\" class=\"data row20 col1\" >0.286007</td>\n",
       "      <td id=\"T_d946e_row20_col2\" class=\"data row20 col2\" >0.216973</td>\n",
       "      <td id=\"T_d946e_row20_col3\" class=\"data row20 col3\" >0.265278</td>\n",
       "      <td id=\"T_d946e_row20_col4\" class=\"data row20 col4\" >0.290077</td>\n",
       "      <td id=\"T_d946e_row20_col5\" class=\"data row20 col5\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d946e_level0_row21\" class=\"row_heading level0 row21\" >gtex_rna_networks_Stomach.parquet</th>\n",
       "      <td id=\"T_d946e_row21_col0\" class=\"data row21 col0\" >0.057147</td>\n",
       "      <td id=\"T_d946e_row21_col1\" class=\"data row21 col1\" >0.236925</td>\n",
       "      <td id=\"T_d946e_row21_col2\" class=\"data row21 col2\" >0.184944</td>\n",
       "      <td id=\"T_d946e_row21_col3\" class=\"data row21 col3\" >0.260095</td>\n",
       "      <td id=\"T_d946e_row21_col4\" class=\"data row21 col4\" >0.287619</td>\n",
       "      <td id=\"T_d946e_row21_col5\" class=\"data row21 col5\" >15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x149cc3f0e6e0>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores_gb = pd.read_csv(f\"resources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global-True.csv\", index_col=0)\n",
    "df_scores = pd.read_csv(f\"resources/scores/op/X_norm-50000-skeleton_False-binarize_True-ridge-global-False.csv\", index_col=0)\n",
    "\n",
    "df_scores = pd.concat([df_scores, df_scores_gb])\n",
    "# df_scores[df_scores<0] = 0\n",
    "df_all_n = (df_scores-df_scores.min(axis=0))/(df_scores.max(axis=0)-df_scores.min(axis=0))\n",
    "df_scores['rank'] = df_all_n.mean(axis=1).rank(ascending=False).astype(int)\n",
    "df_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robustness analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 7770049\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    !sbatch scripts/sbatch/robustness_analysis.sh # !python src/robustness_analysis/script_all.py\n",
    "base_dir = 'resources/results/robustness_analysis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_robustness_results(base_dir, noise_type='net'):\n",
    "    degrees = [0, 10, 20, 50, 100]\n",
    "    reg1_metric = 'S1'\n",
    "    reg2_metric = 'static-theta-0.5'\n",
    "    for i, degree in enumerate(degrees):\n",
    "        df = pd.read_csv(f'{base_dir}/{noise_type}-{degree}-scores.csv',index_col=0)\n",
    "        df_reg1 = df.loc[:, [reg1_metric]].rename(columns={reg1_metric:degree})\n",
    "        df_reg2 = df.loc[:, [reg2_metric]].rename(columns={reg2_metric:degree})\n",
    "        \n",
    "        if i == 0:\n",
    "            reg1_scores = df_reg1\n",
    "            reg2_scores = df_reg2\n",
    "        else:\n",
    "            reg1_scores = pd.concat([reg1_scores, df_reg1], axis=1)\n",
    "            reg2_scores = pd.concat([reg2_scores, df_reg2], axis=1)\n",
    "        \n",
    "    reg1_scores = reg1_scores.T\n",
    "    reg2_scores = reg2_scores.T\n",
    "    return reg1_scores, reg2_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permute net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net \n",
    "noise_type = 'net'\n",
    "reg1_scores, reg2_scores = format_robustness_results(base_dir, noise_type=noise_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_aca36_row0_col0, #T_aca36_row0_col2, #T_aca36_row0_col3, #T_aca36_row0_col4, #T_aca36_row0_col6, #T_aca36_row0_col7, #T_aca36_row0_col8, #T_aca36_row0_col9, #T_aca36_row0_col10, #T_aca36_row0_col11, #T_aca36_row0_col12, #T_aca36_row1_col1, #T_aca36_row3_col5 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row0_col1, #T_aca36_row1_col5 {\n",
       "  background-color: #1c7fb8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row0_col5 {\n",
       "  background-color: #0569a4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col0 {\n",
       "  background-color: #2383ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col2, #T_aca36_row1_col6 {\n",
       "  background-color: #03517e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col3, #T_aca36_row1_col10 {\n",
       "  background-color: #045280;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col4 {\n",
       "  background-color: #04588a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col7, #T_aca36_row1_col11 {\n",
       "  background-color: #045483;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col8 {\n",
       "  background-color: #04649e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col9 {\n",
       "  background-color: #034c78;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row1_col12 {\n",
       "  background-color: #03476f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col0 {\n",
       "  background-color: #69a5cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col1, #T_aca36_row4_col0, #T_aca36_row4_col2, #T_aca36_row4_col3, #T_aca36_row4_col4, #T_aca36_row4_col5, #T_aca36_row4_col6, #T_aca36_row4_col7, #T_aca36_row4_col8, #T_aca36_row4_col9, #T_aca36_row4_col10, #T_aca36_row4_col11, #T_aca36_row4_col12 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aca36_row2_col2 {\n",
       "  background-color: #04639b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col3 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col4 {\n",
       "  background-color: #056ba9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col5 {\n",
       "  background-color: #589ec8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col6, #T_aca36_row3_col3 {\n",
       "  background-color: #0566a0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col7 {\n",
       "  background-color: #0567a1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col8 {\n",
       "  background-color: #1e80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col9 {\n",
       "  background-color: #046198;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col10, #T_aca36_row2_col11 {\n",
       "  background-color: #046097;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row2_col12 {\n",
       "  background-color: #045687;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col0 {\n",
       "  background-color: #bfc9e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aca36_row3_col1 {\n",
       "  background-color: #034267;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col2 {\n",
       "  background-color: #2a88bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col4 {\n",
       "  background-color: #8eb3d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aca36_row3_col6 {\n",
       "  background-color: #80aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col7 {\n",
       "  background-color: #76aad0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col8 {\n",
       "  background-color: #ebe6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aca36_row3_col9 {\n",
       "  background-color: #cdd0e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aca36_row3_col10 {\n",
       "  background-color: #4094c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col11 {\n",
       "  background-color: #4697c4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row3_col12 {\n",
       "  background-color: #5ea0ca;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aca36_row4_col1 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_aca36\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_aca36_level0_col0\" class=\"col_heading level0 col0\" >collectri</th>\n",
       "      <th id=\"T_aca36_level0_col1\" class=\"col_heading level0 col1\" >negative_control</th>\n",
       "      <th id=\"T_aca36_level0_col2\" class=\"col_heading level0 col2\" >positive_control</th>\n",
       "      <th id=\"T_aca36_level0_col3\" class=\"col_heading level0 col3\" >pearson_corr</th>\n",
       "      <th id=\"T_aca36_level0_col4\" class=\"col_heading level0 col4\" >portia</th>\n",
       "      <th id=\"T_aca36_level0_col5\" class=\"col_heading level0 col5\" >ppcor</th>\n",
       "      <th id=\"T_aca36_level0_col6\" class=\"col_heading level0 col6\" >grnboost2</th>\n",
       "      <th id=\"T_aca36_level0_col7\" class=\"col_heading level0 col7\" >scenic</th>\n",
       "      <th id=\"T_aca36_level0_col8\" class=\"col_heading level0 col8\" >granie</th>\n",
       "      <th id=\"T_aca36_level0_col9\" class=\"col_heading level0 col9\" >scglue</th>\n",
       "      <th id=\"T_aca36_level0_col10\" class=\"col_heading level0 col10\" >celloracle</th>\n",
       "      <th id=\"T_aca36_level0_col11\" class=\"col_heading level0 col11\" >figr</th>\n",
       "      <th id=\"T_aca36_level0_col12\" class=\"col_heading level0 col12\" >scenicplus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_aca36_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_aca36_row0_col0\" class=\"data row0 col0\" >-0.052885</td>\n",
       "      <td id=\"T_aca36_row0_col1\" class=\"data row0 col1\" >-0.038053</td>\n",
       "      <td id=\"T_aca36_row0_col2\" class=\"data row0 col2\" >0.285482</td>\n",
       "      <td id=\"T_aca36_row0_col3\" class=\"data row0 col3\" >0.227900</td>\n",
       "      <td id=\"T_aca36_row0_col4\" class=\"data row0 col4\" >0.114365</td>\n",
       "      <td id=\"T_aca36_row0_col5\" class=\"data row0 col5\" >-0.009030</td>\n",
       "      <td id=\"T_aca36_row0_col6\" class=\"data row0 col6\" >0.277384</td>\n",
       "      <td id=\"T_aca36_row0_col7\" class=\"data row0 col7\" >0.132838</td>\n",
       "      <td id=\"T_aca36_row0_col8\" class=\"data row0 col8\" >0.065566</td>\n",
       "      <td id=\"T_aca36_row0_col9\" class=\"data row0 col9\" >0.054329</td>\n",
       "      <td id=\"T_aca36_row0_col10\" class=\"data row0 col10\" >0.177773</td>\n",
       "      <td id=\"T_aca36_row0_col11\" class=\"data row0 col11\" >0.097069</td>\n",
       "      <td id=\"T_aca36_row0_col12\" class=\"data row0 col12\" >0.275561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aca36_level0_row1\" class=\"row_heading level0 row1\" >10</th>\n",
       "      <td id=\"T_aca36_row1_col0\" class=\"data row1 col0\" >-0.063194</td>\n",
       "      <td id=\"T_aca36_row1_col1\" class=\"data row1 col1\" >-0.037703</td>\n",
       "      <td id=\"T_aca36_row1_col2\" class=\"data row1 col2\" >0.258300</td>\n",
       "      <td id=\"T_aca36_row1_col3\" class=\"data row1 col3\" >0.201382</td>\n",
       "      <td id=\"T_aca36_row1_col4\" class=\"data row1 col4\" >0.094161</td>\n",
       "      <td id=\"T_aca36_row1_col5\" class=\"data row1 col5\" >-0.016007</td>\n",
       "      <td id=\"T_aca36_row1_col6\" class=\"data row1 col6\" >0.246858</td>\n",
       "      <td id=\"T_aca36_row1_col7\" class=\"data row1 col7\" >0.117373</td>\n",
       "      <td id=\"T_aca36_row1_col8\" class=\"data row1 col8\" >0.052529</td>\n",
       "      <td id=\"T_aca36_row1_col9\" class=\"data row1 col9\" >0.046615</td>\n",
       "      <td id=\"T_aca36_row1_col10\" class=\"data row1 col10\" >0.159076</td>\n",
       "      <td id=\"T_aca36_row1_col11\" class=\"data row1 col11\" >0.085768</td>\n",
       "      <td id=\"T_aca36_row1_col12\" class=\"data row1 col12\" >0.259247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aca36_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "      <td id=\"T_aca36_row2_col0\" class=\"data row2 col0\" >-0.068023</td>\n",
       "      <td id=\"T_aca36_row2_col1\" class=\"data row2 col1\" >-0.038826</td>\n",
       "      <td id=\"T_aca36_row2_col2\" class=\"data row2 col2\" >0.232850</td>\n",
       "      <td id=\"T_aca36_row2_col3\" class=\"data row2 col3\" >0.188608</td>\n",
       "      <td id=\"T_aca36_row2_col4\" class=\"data row2 col4\" >0.076579</td>\n",
       "      <td id=\"T_aca36_row2_col5\" class=\"data row2 col5\" >-0.025485</td>\n",
       "      <td id=\"T_aca36_row2_col6\" class=\"data row2 col6\" >0.213148</td>\n",
       "      <td id=\"T_aca36_row2_col7\" class=\"data row2 col7\" >0.103506</td>\n",
       "      <td id=\"T_aca36_row2_col8\" class=\"data row2 col8\" >0.043782</td>\n",
       "      <td id=\"T_aca36_row2_col9\" class=\"data row2 col9\" >0.037751</td>\n",
       "      <td id=\"T_aca36_row2_col10\" class=\"data row2 col10\" >0.145942</td>\n",
       "      <td id=\"T_aca36_row2_col11\" class=\"data row2 col11\" >0.079237</td>\n",
       "      <td id=\"T_aca36_row2_col12\" class=\"data row2 col12\" >0.243952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aca36_level0_row3\" class=\"row_heading level0 row3\" >50</th>\n",
       "      <td id=\"T_aca36_row3_col0\" class=\"data row3 col0\" >-0.075000</td>\n",
       "      <td id=\"T_aca36_row3_col1\" class=\"data row3 col1\" >-0.037746</td>\n",
       "      <td id=\"T_aca36_row3_col2\" class=\"data row3 col2\" >0.182995</td>\n",
       "      <td id=\"T_aca36_row3_col3\" class=\"data row3 col3\" >0.174772</td>\n",
       "      <td id=\"T_aca36_row3_col4\" class=\"data row3 col4\" >0.020020</td>\n",
       "      <td id=\"T_aca36_row3_col5\" class=\"data row3 col5\" >0.005844</td>\n",
       "      <td id=\"T_aca36_row3_col6\" class=\"data row3 col6\" >0.099969</td>\n",
       "      <td id=\"T_aca36_row3_col7\" class=\"data row3 col7\" >0.057258</td>\n",
       "      <td id=\"T_aca36_row3_col8\" class=\"data row3 col8\" >0.005112</td>\n",
       "      <td id=\"T_aca36_row3_col9\" class=\"data row3 col9\" >-0.019924</td>\n",
       "      <td id=\"T_aca36_row3_col10\" class=\"data row3 col10\" >0.099687</td>\n",
       "      <td id=\"T_aca36_row3_col11\" class=\"data row3 col11\" >0.052120</td>\n",
       "      <td id=\"T_aca36_row3_col12\" class=\"data row3 col12\" >0.147341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aca36_level0_row4\" class=\"row_heading level0 row4\" >100</th>\n",
       "      <td id=\"T_aca36_row4_col0\" class=\"data row4 col0\" >-0.084617</td>\n",
       "      <td id=\"T_aca36_row4_col1\" class=\"data row4 col1\" >-0.038591</td>\n",
       "      <td id=\"T_aca36_row4_col2\" class=\"data row4 col2\" >-0.009365</td>\n",
       "      <td id=\"T_aca36_row4_col3\" class=\"data row4 col3\" >-0.045844</td>\n",
       "      <td id=\"T_aca36_row4_col4\" class=\"data row4 col4\" >-0.052908</td>\n",
       "      <td id=\"T_aca36_row4_col5\" class=\"data row4 col5\" >-0.064681</td>\n",
       "      <td id=\"T_aca36_row4_col6\" class=\"data row4 col6\" >-0.057610</td>\n",
       "      <td id=\"T_aca36_row4_col7\" class=\"data row4 col7\" >-0.016075</td>\n",
       "      <td id=\"T_aca36_row4_col8\" class=\"data row4 col8\" >-0.004035</td>\n",
       "      <td id=\"T_aca36_row4_col9\" class=\"data row4 col9\" >-0.045894</td>\n",
       "      <td id=\"T_aca36_row4_col10\" class=\"data row4 col10\" >-0.019244</td>\n",
       "      <td id=\"T_aca36_row4_col11\" class=\"data row4 col11\" >-0.013468</td>\n",
       "      <td id=\"T_aca36_row4_col12\" class=\"data row4 col12\" >-0.006252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x151bf0237d90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_98d18_row0_col0, #T_98d18_row0_col2, #T_98d18_row0_col3, #T_98d18_row0_col4, #T_98d18_row0_col5, #T_98d18_row0_col6, #T_98d18_row0_col7, #T_98d18_row0_col9, #T_98d18_row0_col10, #T_98d18_row0_col12, #T_98d18_row1_col4, #T_98d18_row1_col11, #T_98d18_row4_col1, #T_98d18_row4_col8 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row0_col1 {\n",
       "  background-color: #f4edf6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_98d18_row0_col8, #T_98d18_row2_col8 {\n",
       "  background-color: #f1ebf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_98d18_row0_col11 {\n",
       "  background-color: #03446a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col0 {\n",
       "  background-color: #056ba9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col1 {\n",
       "  background-color: #63a2cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col2, #T_98d18_row1_col10 {\n",
       "  background-color: #034f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col3, #T_98d18_row2_col12 {\n",
       "  background-color: #034e7b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col5, #T_98d18_row2_col4 {\n",
       "  background-color: #034871;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col6, #T_98d18_row1_col12 {\n",
       "  background-color: #034369;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col7 {\n",
       "  background-color: #03476f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row1_col8, #T_98d18_row2_col1, #T_98d18_row4_col0, #T_98d18_row4_col2, #T_98d18_row4_col3, #T_98d18_row4_col4, #T_98d18_row4_col5, #T_98d18_row4_col6, #T_98d18_row4_col7, #T_98d18_row4_col9, #T_98d18_row4_col10, #T_98d18_row4_col11, #T_98d18_row4_col12 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_98d18_row1_col9 {\n",
       "  background-color: #034165;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col0 {\n",
       "  background-color: #045b8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col2 {\n",
       "  background-color: #046299;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col3 {\n",
       "  background-color: #0567a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col5 {\n",
       "  background-color: #167bb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col6 {\n",
       "  background-color: #045280;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col7 {\n",
       "  background-color: #045f95;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col9 {\n",
       "  background-color: #045687;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col10 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row2_col11 {\n",
       "  background-color: #04598c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col0 {\n",
       "  background-color: #6da6cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col1, #T_98d18_row3_col10 {\n",
       "  background-color: #3991c1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col2 {\n",
       "  background-color: #4e9ac6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col3 {\n",
       "  background-color: #4a98c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col4 {\n",
       "  background-color: #2786bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col5 {\n",
       "  background-color: #69a5cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col6 {\n",
       "  background-color: #1278b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col7 {\n",
       "  background-color: #358fc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col8 {\n",
       "  background-color: #81aed2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col9 {\n",
       "  background-color: #2685bb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col11 {\n",
       "  background-color: #1b7eb7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_98d18_row3_col12 {\n",
       "  background-color: #0a73b2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_98d18\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_98d18_level0_col0\" class=\"col_heading level0 col0\" >collectri</th>\n",
       "      <th id=\"T_98d18_level0_col1\" class=\"col_heading level0 col1\" >negative_control</th>\n",
       "      <th id=\"T_98d18_level0_col2\" class=\"col_heading level0 col2\" >positive_control</th>\n",
       "      <th id=\"T_98d18_level0_col3\" class=\"col_heading level0 col3\" >pearson_corr</th>\n",
       "      <th id=\"T_98d18_level0_col4\" class=\"col_heading level0 col4\" >portia</th>\n",
       "      <th id=\"T_98d18_level0_col5\" class=\"col_heading level0 col5\" >ppcor</th>\n",
       "      <th id=\"T_98d18_level0_col6\" class=\"col_heading level0 col6\" >grnboost2</th>\n",
       "      <th id=\"T_98d18_level0_col7\" class=\"col_heading level0 col7\" >scenic</th>\n",
       "      <th id=\"T_98d18_level0_col8\" class=\"col_heading level0 col8\" >granie</th>\n",
       "      <th id=\"T_98d18_level0_col9\" class=\"col_heading level0 col9\" >scglue</th>\n",
       "      <th id=\"T_98d18_level0_col10\" class=\"col_heading level0 col10\" >celloracle</th>\n",
       "      <th id=\"T_98d18_level0_col11\" class=\"col_heading level0 col11\" >figr</th>\n",
       "      <th id=\"T_98d18_level0_col12\" class=\"col_heading level0 col12\" >scenicplus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_98d18_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_98d18_row0_col0\" class=\"data row0 col0\" >0.314904</td>\n",
       "      <td id=\"T_98d18_row0_col1\" class=\"data row0 col1\" >0.298416</td>\n",
       "      <td id=\"T_98d18_row0_col2\" class=\"data row0 col2\" >0.438187</td>\n",
       "      <td id=\"T_98d18_row0_col3\" class=\"data row0 col3\" >0.434587</td>\n",
       "      <td id=\"T_98d18_row0_col4\" class=\"data row0 col4\" >0.337533</td>\n",
       "      <td id=\"T_98d18_row0_col5\" class=\"data row0 col5\" >0.316130</td>\n",
       "      <td id=\"T_98d18_row0_col6\" class=\"data row0 col6\" >0.501373</td>\n",
       "      <td id=\"T_98d18_row0_col7\" class=\"data row0 col7\" >0.447392</td>\n",
       "      <td id=\"T_98d18_row0_col8\" class=\"data row0 col8\" >0.268367</td>\n",
       "      <td id=\"T_98d18_row0_col9\" class=\"data row0 col9\" >0.335675</td>\n",
       "      <td id=\"T_98d18_row0_col10\" class=\"data row0 col10\" >0.467258</td>\n",
       "      <td id=\"T_98d18_row0_col11\" class=\"data row0 col11\" >0.354542</td>\n",
       "      <td id=\"T_98d18_row0_col12\" class=\"data row0 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_98d18_level0_row1\" class=\"row_heading level0 row1\" >10</th>\n",
       "      <td id=\"T_98d18_row1_col0\" class=\"data row1 col0\" >0.310607</td>\n",
       "      <td id=\"T_98d18_row1_col1\" class=\"data row1 col1\" >0.300799</td>\n",
       "      <td id=\"T_98d18_row1_col2\" class=\"data row1 col2\" >0.433080</td>\n",
       "      <td id=\"T_98d18_row1_col3\" class=\"data row1 col3\" >0.427072</td>\n",
       "      <td id=\"T_98d18_row1_col4\" class=\"data row1 col4\" >0.337533</td>\n",
       "      <td id=\"T_98d18_row1_col5\" class=\"data row1 col5\" >0.314340</td>\n",
       "      <td id=\"T_98d18_row1_col6\" class=\"data row1 col6\" >0.492975</td>\n",
       "      <td id=\"T_98d18_row1_col7\" class=\"data row1 col7\" >0.439982</td>\n",
       "      <td id=\"T_98d18_row1_col8\" class=\"data row1 col8\" >0.263060</td>\n",
       "      <td id=\"T_98d18_row1_col9\" class=\"data row1 col9\" >0.334209</td>\n",
       "      <td id=\"T_98d18_row1_col10\" class=\"data row1 col10\" >0.453522</td>\n",
       "      <td id=\"T_98d18_row1_col11\" class=\"data row1 col11\" >0.356273</td>\n",
       "      <td id=\"T_98d18_row1_col12\" class=\"data row1 col12\" >0.506758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_98d18_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "      <td id=\"T_98d18_row2_col0\" class=\"data row2 col0\" >0.312346</td>\n",
       "      <td id=\"T_98d18_row2_col1\" class=\"data row2 col1\" >0.298023</td>\n",
       "      <td id=\"T_98d18_row2_col2\" class=\"data row2 col2\" >0.428219</td>\n",
       "      <td id=\"T_98d18_row2_col3\" class=\"data row2 col3\" >0.416558</td>\n",
       "      <td id=\"T_98d18_row2_col4\" class=\"data row2 col4\" >0.334752</td>\n",
       "      <td id=\"T_98d18_row2_col5\" class=\"data row2 col5\" >0.307381</td>\n",
       "      <td id=\"T_98d18_row2_col6\" class=\"data row2 col6\" >0.481193</td>\n",
       "      <td id=\"T_98d18_row2_col7\" class=\"data row2 col7\" >0.427551</td>\n",
       "      <td id=\"T_98d18_row2_col8\" class=\"data row2 col8\" >0.268367</td>\n",
       "      <td id=\"T_98d18_row2_col9\" class=\"data row2 col9\" >0.330640</td>\n",
       "      <td id=\"T_98d18_row2_col10\" class=\"data row2 col10\" >0.444836</td>\n",
       "      <td id=\"T_98d18_row2_col11\" class=\"data row2 col11\" >0.351414</td>\n",
       "      <td id=\"T_98d18_row2_col12\" class=\"data row2 col12\" >0.498718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_98d18_level0_row3\" class=\"row_heading level0 row3\" >50</th>\n",
       "      <td id=\"T_98d18_row3_col0\" class=\"data row3 col0\" >0.305605</td>\n",
       "      <td id=\"T_98d18_row3_col1\" class=\"data row3 col1\" >0.301245</td>\n",
       "      <td id=\"T_98d18_row3_col2\" class=\"data row3 col2\" >0.413182</td>\n",
       "      <td id=\"T_98d18_row3_col3\" class=\"data row3 col3\" >0.397406</td>\n",
       "      <td id=\"T_98d18_row3_col4\" class=\"data row3 col4\" >0.322203</td>\n",
       "      <td id=\"T_98d18_row3_col5\" class=\"data row3 col5\" >0.301887</td>\n",
       "      <td id=\"T_98d18_row3_col6\" class=\"data row3 col6\" >0.442722</td>\n",
       "      <td id=\"T_98d18_row3_col7\" class=\"data row3 col7\" >0.399035</td>\n",
       "      <td id=\"T_98d18_row3_col8\" class=\"data row3 col8\" >0.288905</td>\n",
       "      <td id=\"T_98d18_row3_col9\" class=\"data row3 col9\" >0.320246</td>\n",
       "      <td id=\"T_98d18_row3_col10\" class=\"data row3 col10\" >0.407539</td>\n",
       "      <td id=\"T_98d18_row3_col11\" class=\"data row3 col11\" >0.344040</td>\n",
       "      <td id=\"T_98d18_row3_col12\" class=\"data row3 col12\" >0.464195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_98d18_level0_row4\" class=\"row_heading level0 row4\" >100</th>\n",
       "      <td id=\"T_98d18_row4_col0\" class=\"data row4 col0\" >0.295797</td>\n",
       "      <td id=\"T_98d18_row4_col1\" class=\"data row4 col1\" >0.303241</td>\n",
       "      <td id=\"T_98d18_row4_col2\" class=\"data row4 col2\" >0.379001</td>\n",
       "      <td id=\"T_98d18_row4_col3\" class=\"data row4 col3\" >0.345088</td>\n",
       "      <td id=\"T_98d18_row4_col4\" class=\"data row4 col4\" >0.291961</td>\n",
       "      <td id=\"T_98d18_row4_col5\" class=\"data row4 col5\" >0.286424</td>\n",
       "      <td id=\"T_98d18_row4_col6\" class=\"data row4 col6\" >0.293421</td>\n",
       "      <td id=\"T_98d18_row4_col7\" class=\"data row4 col7\" >0.317192</td>\n",
       "      <td id=\"T_98d18_row4_col8\" class=\"data row4 col8\" >0.318627</td>\n",
       "      <td id=\"T_98d18_row4_col9\" class=\"data row4 col9\" >0.289704</td>\n",
       "      <td id=\"T_98d18_row4_col10\" class=\"data row4 col10\" >0.310580</td>\n",
       "      <td id=\"T_98d18_row4_col11\" class=\"data row4 col11\" >0.316354</td>\n",
       "      <td id=\"T_98d18_row4_col12\" class=\"data row4 col12\" >0.321514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x151b2df92350>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permute sign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_type = 'sign'\n",
    "reg1_scores, reg2_scores = format_robustness_results(base_dir, noise_type=noise_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3792a_row0_col0, #T_3792a_row0_col2, #T_3792a_row0_col3, #T_3792a_row0_col4, #T_3792a_row0_col6, #T_3792a_row0_col7, #T_3792a_row0_col8, #T_3792a_row0_col9, #T_3792a_row0_col10, #T_3792a_row0_col11, #T_3792a_row0_col12, #T_3792a_row3_col1, #T_3792a_row3_col5, #T_3792a_row4_col0, #T_3792a_row4_col2, #T_3792a_row4_col3, #T_3792a_row4_col4, #T_3792a_row4_col6, #T_3792a_row4_col7, #T_3792a_row4_col8, #T_3792a_row4_col9, #T_3792a_row4_col10, #T_3792a_row4_col11, #T_3792a_row4_col12 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row0_col1, #T_3792a_row2_col5, #T_3792a_row3_col0, #T_3792a_row3_col2, #T_3792a_row3_col3, #T_3792a_row3_col4, #T_3792a_row3_col6, #T_3792a_row3_col7, #T_3792a_row3_col8, #T_3792a_row3_col9, #T_3792a_row3_col10, #T_3792a_row3_col11, #T_3792a_row3_col12, #T_3792a_row4_col1 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row0_col5, #T_3792a_row4_col5 {\n",
       "  background-color: #2182b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col0 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col1 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row1_col2 {\n",
       "  background-color: #045c90;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col3 {\n",
       "  background-color: #056caa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col4 {\n",
       "  background-color: #4094c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col5 {\n",
       "  background-color: #bbc7e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row1_col6 {\n",
       "  background-color: #045d92;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col7 {\n",
       "  background-color: #0567a1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col8 {\n",
       "  background-color: #2a88bc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col9 {\n",
       "  background-color: #0568a3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col10 {\n",
       "  background-color: #1278b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col11 {\n",
       "  background-color: #167bb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row1_col12 {\n",
       "  background-color: #045e94;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col0 {\n",
       "  background-color: #f6eff7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row2_col1 {\n",
       "  background-color: #d3d4e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row2_col2 {\n",
       "  background-color: #187cb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col3 {\n",
       "  background-color: #348ebf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col4 {\n",
       "  background-color: #c9cee4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row2_col6 {\n",
       "  background-color: #2081b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col7 {\n",
       "  background-color: #4496c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col8 {\n",
       "  background-color: #acc0dd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row2_col9 {\n",
       "  background-color: #5a9ec9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col10 {\n",
       "  background-color: #9ab8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3792a_row2_col11 {\n",
       "  background-color: #60a1ca;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3792a_row2_col12 {\n",
       "  background-color: #2383ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3792a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3792a_level0_col0\" class=\"col_heading level0 col0\" >collectri</th>\n",
       "      <th id=\"T_3792a_level0_col1\" class=\"col_heading level0 col1\" >negative_control</th>\n",
       "      <th id=\"T_3792a_level0_col2\" class=\"col_heading level0 col2\" >positive_control</th>\n",
       "      <th id=\"T_3792a_level0_col3\" class=\"col_heading level0 col3\" >pearson_corr</th>\n",
       "      <th id=\"T_3792a_level0_col4\" class=\"col_heading level0 col4\" >portia</th>\n",
       "      <th id=\"T_3792a_level0_col5\" class=\"col_heading level0 col5\" >ppcor</th>\n",
       "      <th id=\"T_3792a_level0_col6\" class=\"col_heading level0 col6\" >grnboost2</th>\n",
       "      <th id=\"T_3792a_level0_col7\" class=\"col_heading level0 col7\" >scenic</th>\n",
       "      <th id=\"T_3792a_level0_col8\" class=\"col_heading level0 col8\" >granie</th>\n",
       "      <th id=\"T_3792a_level0_col9\" class=\"col_heading level0 col9\" >scglue</th>\n",
       "      <th id=\"T_3792a_level0_col10\" class=\"col_heading level0 col10\" >celloracle</th>\n",
       "      <th id=\"T_3792a_level0_col11\" class=\"col_heading level0 col11\" >figr</th>\n",
       "      <th id=\"T_3792a_level0_col12\" class=\"col_heading level0 col12\" >scenicplus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3792a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_3792a_row0_col0\" class=\"data row0 col0\" >-0.052885</td>\n",
       "      <td id=\"T_3792a_row0_col1\" class=\"data row0 col1\" >-0.038053</td>\n",
       "      <td id=\"T_3792a_row0_col2\" class=\"data row0 col2\" >0.285482</td>\n",
       "      <td id=\"T_3792a_row0_col3\" class=\"data row0 col3\" >0.227900</td>\n",
       "      <td id=\"T_3792a_row0_col4\" class=\"data row0 col4\" >0.114365</td>\n",
       "      <td id=\"T_3792a_row0_col5\" class=\"data row0 col5\" >-0.009030</td>\n",
       "      <td id=\"T_3792a_row0_col6\" class=\"data row0 col6\" >0.277384</td>\n",
       "      <td id=\"T_3792a_row0_col7\" class=\"data row0 col7\" >0.132916</td>\n",
       "      <td id=\"T_3792a_row0_col8\" class=\"data row0 col8\" >0.065566</td>\n",
       "      <td id=\"T_3792a_row0_col9\" class=\"data row0 col9\" >0.054329</td>\n",
       "      <td id=\"T_3792a_row0_col10\" class=\"data row0 col10\" >0.177773</td>\n",
       "      <td id=\"T_3792a_row0_col11\" class=\"data row0 col11\" >0.097069</td>\n",
       "      <td id=\"T_3792a_row0_col12\" class=\"data row0 col12\" >0.275561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3792a_level0_row1\" class=\"row_heading level0 row1\" >10</th>\n",
       "      <td id=\"T_3792a_row1_col0\" class=\"data row1 col0\" >-0.063294</td>\n",
       "      <td id=\"T_3792a_row1_col1\" class=\"data row1 col1\" >-0.037943</td>\n",
       "      <td id=\"T_3792a_row1_col2\" class=\"data row1 col2\" >0.244253</td>\n",
       "      <td id=\"T_3792a_row1_col3\" class=\"data row1 col3\" >0.175927</td>\n",
       "      <td id=\"T_3792a_row1_col4\" class=\"data row1 col4\" >0.068840</td>\n",
       "      <td id=\"T_3792a_row1_col5\" class=\"data row1 col5\" >-0.026427</td>\n",
       "      <td id=\"T_3792a_row1_col6\" class=\"data row1 col6\" >0.225111</td>\n",
       "      <td id=\"T_3792a_row1_col7\" class=\"data row1 col7\" >0.097068</td>\n",
       "      <td id=\"T_3792a_row1_col8\" class=\"data row1 col8\" >0.043191</td>\n",
       "      <td id=\"T_3792a_row1_col9\" class=\"data row1 col9\" >0.039099</td>\n",
       "      <td id=\"T_3792a_row1_col10\" class=\"data row1 col10\" >0.130693</td>\n",
       "      <td id=\"T_3792a_row1_col11\" class=\"data row1 col11\" >0.066178</td>\n",
       "      <td id=\"T_3792a_row1_col12\" class=\"data row1 col12\" >0.232978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3792a_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "      <td id=\"T_3792a_row2_col0\" class=\"data row2 col0\" >-0.078692</td>\n",
       "      <td id=\"T_3792a_row2_col1\" class=\"data row2 col1\" >-0.037331</td>\n",
       "      <td id=\"T_3792a_row2_col2\" class=\"data row2 col2\" >0.195138</td>\n",
       "      <td id=\"T_3792a_row2_col3\" class=\"data row2 col3\" >0.143907</td>\n",
       "      <td id=\"T_3792a_row2_col4\" class=\"data row2 col4\" >0.030350</td>\n",
       "      <td id=\"T_3792a_row2_col5\" class=\"data row2 col5\" >-0.041580</td>\n",
       "      <td id=\"T_3792a_row2_col6\" class=\"data row2 col6\" >0.162380</td>\n",
       "      <td id=\"T_3792a_row2_col7\" class=\"data row2 col7\" >0.059698</td>\n",
       "      <td id=\"T_3792a_row2_col8\" class=\"data row2 col8\" >0.023835</td>\n",
       "      <td id=\"T_3792a_row2_col9\" class=\"data row2 col9\" >0.021276</td>\n",
       "      <td id=\"T_3792a_row2_col10\" class=\"data row2 col10\" >0.078184</td>\n",
       "      <td id=\"T_3792a_row2_col11\" class=\"data row2 col11\" >0.049052</td>\n",
       "      <td id=\"T_3792a_row2_col12\" class=\"data row2 col12\" >0.183118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3792a_level0_row3\" class=\"row_heading level0 row3\" >50</th>\n",
       "      <td id=\"T_3792a_row3_col0\" class=\"data row3 col0\" >-0.080401</td>\n",
       "      <td id=\"T_3792a_row3_col1\" class=\"data row3 col1\" >-0.034971</td>\n",
       "      <td id=\"T_3792a_row3_col2\" class=\"data row3 col2\" >-0.016077</td>\n",
       "      <td id=\"T_3792a_row3_col3\" class=\"data row3 col3\" >0.001196</td>\n",
       "      <td id=\"T_3792a_row3_col4\" class=\"data row3 col4\" >-0.000673</td>\n",
       "      <td id=\"T_3792a_row3_col5\" class=\"data row3 col5\" >0.006405</td>\n",
       "      <td id=\"T_3792a_row3_col6\" class=\"data row3 col6\" >-0.085877</td>\n",
       "      <td id=\"T_3792a_row3_col7\" class=\"data row3 col7\" >-0.047863</td>\n",
       "      <td id=\"T_3792a_row3_col8\" class=\"data row3 col8\" >0.000526</td>\n",
       "      <td id=\"T_3792a_row3_col9\" class=\"data row3 col9\" >-0.019741</td>\n",
       "      <td id=\"T_3792a_row3_col10\" class=\"data row3 col10\" >0.010997</td>\n",
       "      <td id=\"T_3792a_row3_col11\" class=\"data row3 col11\" >-0.007829</td>\n",
       "      <td id=\"T_3792a_row3_col12\" class=\"data row3 col12\" >-0.006821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3792a_level0_row4\" class=\"row_heading level0 row4\" >100</th>\n",
       "      <td id=\"T_3792a_row4_col0\" class=\"data row4 col0\" >-0.052885</td>\n",
       "      <td id=\"T_3792a_row4_col1\" class=\"data row4 col1\" >-0.038053</td>\n",
       "      <td id=\"T_3792a_row4_col2\" class=\"data row4 col2\" >0.285482</td>\n",
       "      <td id=\"T_3792a_row4_col3\" class=\"data row4 col3\" >0.227900</td>\n",
       "      <td id=\"T_3792a_row4_col4\" class=\"data row4 col4\" >0.114365</td>\n",
       "      <td id=\"T_3792a_row4_col5\" class=\"data row4 col5\" >-0.009030</td>\n",
       "      <td id=\"T_3792a_row4_col6\" class=\"data row4 col6\" >0.277384</td>\n",
       "      <td id=\"T_3792a_row4_col7\" class=\"data row4 col7\" >0.132916</td>\n",
       "      <td id=\"T_3792a_row4_col8\" class=\"data row4 col8\" >0.065566</td>\n",
       "      <td id=\"T_3792a_row4_col9\" class=\"data row4 col9\" >0.054329</td>\n",
       "      <td id=\"T_3792a_row4_col10\" class=\"data row4 col10\" >0.177773</td>\n",
       "      <td id=\"T_3792a_row4_col11\" class=\"data row4 col11\" >0.097069</td>\n",
       "      <td id=\"T_3792a_row4_col12\" class=\"data row4 col12\" >0.275561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x151bf027ff70>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f66ea_row0_col0 {\n",
       "  background-color: #a7bddb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f66ea_row0_col1, #T_f66ea_row0_col2, #T_f66ea_row0_col3, #T_f66ea_row0_col4, #T_f66ea_row0_col5, #T_f66ea_row0_col6, #T_f66ea_row0_col7, #T_f66ea_row0_col9, #T_f66ea_row0_col10, #T_f66ea_row0_col11, #T_f66ea_row0_col12, #T_f66ea_row1_col2, #T_f66ea_row1_col3, #T_f66ea_row1_col4, #T_f66ea_row1_col5, #T_f66ea_row1_col6, #T_f66ea_row1_col7, #T_f66ea_row1_col9, #T_f66ea_row1_col10, #T_f66ea_row1_col11, #T_f66ea_row1_col12, #T_f66ea_row2_col2, #T_f66ea_row2_col3, #T_f66ea_row2_col4, #T_f66ea_row2_col5, #T_f66ea_row2_col6, #T_f66ea_row2_col7, #T_f66ea_row2_col9, #T_f66ea_row2_col10, #T_f66ea_row2_col11, #T_f66ea_row2_col12, #T_f66ea_row3_col2, #T_f66ea_row3_col3, #T_f66ea_row3_col4, #T_f66ea_row3_col5, #T_f66ea_row3_col6, #T_f66ea_row3_col7, #T_f66ea_row3_col8, #T_f66ea_row3_col9, #T_f66ea_row3_col10, #T_f66ea_row3_col11, #T_f66ea_row3_col12, #T_f66ea_row4_col0, #T_f66ea_row4_col2, #T_f66ea_row4_col3, #T_f66ea_row4_col4, #T_f66ea_row4_col5, #T_f66ea_row4_col6, #T_f66ea_row4_col7, #T_f66ea_row4_col9, #T_f66ea_row4_col10, #T_f66ea_row4_col11, #T_f66ea_row4_col12 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f66ea_row0_col8 {\n",
       "  background-color: #6da6cd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f66ea_row1_col0 {\n",
       "  background-color: #3790c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f66ea_row1_col1 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f66ea_row1_col8 {\n",
       "  background-color: #1077b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f66ea_row2_col0 {\n",
       "  background-color: #4094c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f66ea_row2_col1 {\n",
       "  background-color: #a8bedc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f66ea_row2_col8 {\n",
       "  background-color: #d8d7e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_f66ea_row3_col0, #T_f66ea_row3_col1, #T_f66ea_row4_col8 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_f66ea_row4_col1 {\n",
       "  background-color: #c5cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f66ea\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f66ea_level0_col0\" class=\"col_heading level0 col0\" >collectri</th>\n",
       "      <th id=\"T_f66ea_level0_col1\" class=\"col_heading level0 col1\" >negative_control</th>\n",
       "      <th id=\"T_f66ea_level0_col2\" class=\"col_heading level0 col2\" >positive_control</th>\n",
       "      <th id=\"T_f66ea_level0_col3\" class=\"col_heading level0 col3\" >pearson_corr</th>\n",
       "      <th id=\"T_f66ea_level0_col4\" class=\"col_heading level0 col4\" >portia</th>\n",
       "      <th id=\"T_f66ea_level0_col5\" class=\"col_heading level0 col5\" >ppcor</th>\n",
       "      <th id=\"T_f66ea_level0_col6\" class=\"col_heading level0 col6\" >grnboost2</th>\n",
       "      <th id=\"T_f66ea_level0_col7\" class=\"col_heading level0 col7\" >scenic</th>\n",
       "      <th id=\"T_f66ea_level0_col8\" class=\"col_heading level0 col8\" >granie</th>\n",
       "      <th id=\"T_f66ea_level0_col9\" class=\"col_heading level0 col9\" >scglue</th>\n",
       "      <th id=\"T_f66ea_level0_col10\" class=\"col_heading level0 col10\" >celloracle</th>\n",
       "      <th id=\"T_f66ea_level0_col11\" class=\"col_heading level0 col11\" >figr</th>\n",
       "      <th id=\"T_f66ea_level0_col12\" class=\"col_heading level0 col12\" >scenicplus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f66ea_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f66ea_row0_col0\" class=\"data row0 col0\" >0.314904</td>\n",
       "      <td id=\"T_f66ea_row0_col1\" class=\"data row0 col1\" >0.298416</td>\n",
       "      <td id=\"T_f66ea_row0_col2\" class=\"data row0 col2\" >0.438187</td>\n",
       "      <td id=\"T_f66ea_row0_col3\" class=\"data row0 col3\" >0.434587</td>\n",
       "      <td id=\"T_f66ea_row0_col4\" class=\"data row0 col4\" >0.343306</td>\n",
       "      <td id=\"T_f66ea_row0_col5\" class=\"data row0 col5\" >0.316130</td>\n",
       "      <td id=\"T_f66ea_row0_col6\" class=\"data row0 col6\" >0.501373</td>\n",
       "      <td id=\"T_f66ea_row0_col7\" class=\"data row0 col7\" >0.447392</td>\n",
       "      <td id=\"T_f66ea_row0_col8\" class=\"data row0 col8\" >0.257013</td>\n",
       "      <td id=\"T_f66ea_row0_col9\" class=\"data row0 col9\" >0.335675</td>\n",
       "      <td id=\"T_f66ea_row0_col10\" class=\"data row0 col10\" >0.467258</td>\n",
       "      <td id=\"T_f66ea_row0_col11\" class=\"data row0 col11\" >0.354542</td>\n",
       "      <td id=\"T_f66ea_row0_col12\" class=\"data row0 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f66ea_level0_row1\" class=\"row_heading level0 row1\" >10</th>\n",
       "      <td id=\"T_f66ea_row1_col0\" class=\"data row1 col0\" >0.315233</td>\n",
       "      <td id=\"T_f66ea_row1_col1\" class=\"data row1 col1\" >0.298546</td>\n",
       "      <td id=\"T_f66ea_row1_col2\" class=\"data row1 col2\" >0.438187</td>\n",
       "      <td id=\"T_f66ea_row1_col3\" class=\"data row1 col3\" >0.434587</td>\n",
       "      <td id=\"T_f66ea_row1_col4\" class=\"data row1 col4\" >0.343306</td>\n",
       "      <td id=\"T_f66ea_row1_col5\" class=\"data row1 col5\" >0.316130</td>\n",
       "      <td id=\"T_f66ea_row1_col6\" class=\"data row1 col6\" >0.501373</td>\n",
       "      <td id=\"T_f66ea_row1_col7\" class=\"data row1 col7\" >0.447392</td>\n",
       "      <td id=\"T_f66ea_row1_col8\" class=\"data row1 col8\" >0.257063</td>\n",
       "      <td id=\"T_f66ea_row1_col9\" class=\"data row1 col9\" >0.335675</td>\n",
       "      <td id=\"T_f66ea_row1_col10\" class=\"data row1 col10\" >0.467258</td>\n",
       "      <td id=\"T_f66ea_row1_col11\" class=\"data row1 col11\" >0.354542</td>\n",
       "      <td id=\"T_f66ea_row1_col12\" class=\"data row1 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f66ea_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "      <td id=\"T_f66ea_row2_col0\" class=\"data row2 col0\" >0.315206</td>\n",
       "      <td id=\"T_f66ea_row2_col1\" class=\"data row2 col1\" >0.298645</td>\n",
       "      <td id=\"T_f66ea_row2_col2\" class=\"data row2 col2\" >0.438187</td>\n",
       "      <td id=\"T_f66ea_row2_col3\" class=\"data row2 col3\" >0.434587</td>\n",
       "      <td id=\"T_f66ea_row2_col4\" class=\"data row2 col4\" >0.343306</td>\n",
       "      <td id=\"T_f66ea_row2_col5\" class=\"data row2 col5\" >0.316130</td>\n",
       "      <td id=\"T_f66ea_row2_col6\" class=\"data row2 col6\" >0.501373</td>\n",
       "      <td id=\"T_f66ea_row2_col7\" class=\"data row2 col7\" >0.447392</td>\n",
       "      <td id=\"T_f66ea_row2_col8\" class=\"data row2 col8\" >0.256943</td>\n",
       "      <td id=\"T_f66ea_row2_col9\" class=\"data row2 col9\" >0.335675</td>\n",
       "      <td id=\"T_f66ea_row2_col10\" class=\"data row2 col10\" >0.467258</td>\n",
       "      <td id=\"T_f66ea_row2_col11\" class=\"data row2 col11\" >0.354542</td>\n",
       "      <td id=\"T_f66ea_row2_col12\" class=\"data row2 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f66ea_level0_row3\" class=\"row_heading level0 row3\" >50</th>\n",
       "      <td id=\"T_f66ea_row3_col0\" class=\"data row3 col0\" >0.315729</td>\n",
       "      <td id=\"T_f66ea_row3_col1\" class=\"data row3 col1\" >0.299033</td>\n",
       "      <td id=\"T_f66ea_row3_col2\" class=\"data row3 col2\" >0.438187</td>\n",
       "      <td id=\"T_f66ea_row3_col3\" class=\"data row3 col3\" >0.434587</td>\n",
       "      <td id=\"T_f66ea_row3_col4\" class=\"data row3 col4\" >0.343306</td>\n",
       "      <td id=\"T_f66ea_row3_col5\" class=\"data row3 col5\" >0.316130</td>\n",
       "      <td id=\"T_f66ea_row3_col6\" class=\"data row3 col6\" >0.501373</td>\n",
       "      <td id=\"T_f66ea_row3_col7\" class=\"data row3 col7\" >0.447392</td>\n",
       "      <td id=\"T_f66ea_row3_col8\" class=\"data row3 col8\" >0.256891</td>\n",
       "      <td id=\"T_f66ea_row3_col9\" class=\"data row3 col9\" >0.335675</td>\n",
       "      <td id=\"T_f66ea_row3_col10\" class=\"data row3 col10\" >0.467258</td>\n",
       "      <td id=\"T_f66ea_row3_col11\" class=\"data row3 col11\" >0.354542</td>\n",
       "      <td id=\"T_f66ea_row3_col12\" class=\"data row3 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f66ea_level0_row4\" class=\"row_heading level0 row4\" >100</th>\n",
       "      <td id=\"T_f66ea_row4_col0\" class=\"data row4 col0\" >0.314412</td>\n",
       "      <td id=\"T_f66ea_row4_col1\" class=\"data row4 col1\" >0.298592</td>\n",
       "      <td id=\"T_f66ea_row4_col2\" class=\"data row4 col2\" >0.438187</td>\n",
       "      <td id=\"T_f66ea_row4_col3\" class=\"data row4 col3\" >0.434587</td>\n",
       "      <td id=\"T_f66ea_row4_col4\" class=\"data row4 col4\" >0.343306</td>\n",
       "      <td id=\"T_f66ea_row4_col5\" class=\"data row4 col5\" >0.316130</td>\n",
       "      <td id=\"T_f66ea_row4_col6\" class=\"data row4 col6\" >0.501373</td>\n",
       "      <td id=\"T_f66ea_row4_col7\" class=\"data row4 col7\" >0.447392</td>\n",
       "      <td id=\"T_f66ea_row4_col8\" class=\"data row4 col8\" >0.257129</td>\n",
       "      <td id=\"T_f66ea_row4_col9\" class=\"data row4 col9\" >0.335675</td>\n",
       "      <td id=\"T_f66ea_row4_col10\" class=\"data row4 col10\" >0.467258</td>\n",
       "      <td id=\"T_f66ea_row4_col11\" class=\"data row4 col11\" >0.354542</td>\n",
       "      <td id=\"T_f66ea_row4_col12\" class=\"data row4 col12\" >0.514954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x151bf027fcd0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2_scores.style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permute weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_type = 'weight'\n",
    "reg1_scores, reg2_scores = format_robustness_results(base_dir, noise_type=noise_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of pseudobulking on performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.helper import analyse_meta_cells\n",
    "analyse_meta_cells(task_grn_inference_dir='./')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of imputation on performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.helper import analyse_imputation\n",
    "analyse_imputation(task_grn_inference_dir='./')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal versus correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convert to long dataframe\n",
      "TF subsetting\n",
      "(456, 25090)\n",
      "----cross validate for default----\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing groups: 100%|██████████| 5/5 [00:12<00:00,  2.41s/it]\n",
      "GRN preprocessing: 25090it [00:00, 25373.48it/s]\n",
      "ridge CV: 100%|██████████| 25090/25090 [00:02<00:00, 11765.42it/s]\n",
      "GRN preprocessing: 25090it [00:00, 25339.89it/s]\n",
      "ridge CV: 100%|██████████| 25090/25090 [01:55<00:00, 217.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Static approach (theta=1):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "GRN preprocessing: 25090it [00:00, 25640.46it/s]\n",
      "ridge CV: 100%|██████████| 25090/25090 [06:10<00:00, 67.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convert to long dataframe\n",
      "(456, 25090)\n",
      "----cross validate for default----\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing groups: 100%|██████████| 5/5 [00:02<00:00,  1.86it/s]\n",
      "GRN preprocessing: 25090it [00:11, 2215.38it/s]\n",
      "ridge CV: 100%|██████████| 25090/25090 [00:02<00:00, 9352.85it/s] \n",
      "GRN preprocessing: 25090it [00:11, 2211.47it/s]\n",
      "ridge CV: 100%|██████████| 25090/25090 [02:10<00:00, 191.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Static approach (theta=1):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GRN preprocessing: 25090it [00:11, 2197.01it/s]\n",
      "ridge CV:  21%|██        | 5145/25090 [01:17<05:00, 66.47it/s]"
     ]
    }
   ],
   "source": [
    "from src.helper import analyse_causal\n",
    "analyse_causal(task_grn_inference_dir='./')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ZNF354C', 'KLF12', 'ZNF143', ..., 'ZNF831', 'ZRSR2', 'ZSWIM1'],\n",
       "      dtype='<U14')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}