diff --git "a/software_defect_prediction_final.ipynb" "b/software_defect_prediction_final.ipynb" --- "a/software_defect_prediction_final.ipynb" +++ "b/software_defect_prediction_final.ipynb" @@ -1 +1,8451 @@ -PLACEHOLDER \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "56a937f6", + "metadata": {}, + "source": [ + "# Section 0 — Imports and Configuration\n", + "\n", + "All imports, seeds, and global configuration in one place." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "14b3703d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:15.648777Z", + "iopub.status.busy": "2026-05-07T14:55:15.648567Z", + "iopub.status.idle": "2026-05-07T14:55:18.479874Z", + "shell.execute_reply": "2026-05-07T14:55:18.478674Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuration loaded.\n", + "Phase 1: ['eclipse', 'mylyn']\n", + "Phase 2: ['equinox', 'lucene', 'pde']\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "import json\n", + "import random\n", + "import warnings\n", + "from collections import defaultdict\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "from tqdm.auto import tqdm\n", + "\n", + "# sklearn\n", + "from sklearn.model_selection import train_test_split, StratifiedKFold\n", + "from sklearn.preprocessing import FunctionTransformer, RobustScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.utils.class_weight import compute_sample_weight\n", + "\n", + "# models\n", + "from sklearn.ensemble import (\n", + " RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n", + ")\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "# xgboost / lightgbm\n", + "try:\n", + " from xgboost import XGBClassifier\n", + "except Exception:\n", + " XGBClassifier = None\n", + "try:\n", + " from lightgbm import LGBMClassifier\n", + "except Exception:\n", + " LGBMClassifier = None\n", + "\n", + "# imbalanced-learn\n", + "from imblearn.over_sampling import SMOTE\n", + "\n", + "# metrics\n", + "from sklearn.metrics import (\n", + " f1_score, roc_auc_score, average_precision_score,\n", + " classification_report, confusion_matrix, roc_curve, precision_recall_curve\n", + ")\n", + "\n", + "# SHAP / LIME\n", + "import shap\n", + "from lime.lime_tabular import LimeTabularExplainer\n", + "\n", + "# Seeds\n", + "SEED = 42\n", + "np.random.seed(SEED)\n", + "random.seed(SEED)\n", + "\n", + "# Matplotlib defaults\n", + "plt.rcParams['figure.dpi'] = 100\n", + "plt.rcParams['savefig.dpi'] = 150\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# ---------------- CONFIG ----------------\n", + "CONFIG = {\n", + " 'data_dir': 'data',\n", + " 'datasets': {\n", + " 'eclipse': 'eclipse.csv',\n", + " 'equinox': 'equinox.csv',\n", + " 'lucene': 'lucene.csv',\n", + " 'mylyn': 'mylyn.csv',\n", + " 'pde': 'pde.csv',\n", + " },\n", + " 'phase1': ['eclipse', 'mylyn'],\n", + " 'phase2': ['equinox', 'lucene', 'pde'],\n", + " 'random_state': 42,\n", + " 'cv_folds': 5,\n", + " 'test_size': 0.20,\n", + " 'smote_threshold': 2.0,\n", + " 'ZERO_VAR_FEATURES': {\n", + " 'lucene': [\n", + " 'numberOfNonTrivialBugsFoundUntil:',\n", + " 'numberOfMajorBugsFoundUntil:',\n", + " 'numberOfCriticalBugsFoundUntil:',\n", + " 'numberOfHighPriorityBugsFoundUntil:',\n", + " ],\n", + " },\n", + "}\n", + "\n", + "# Storage containers\n", + "DATASETS = {}\n", + "RESULTS_PHASE1 = {}\n", + "BEST_MODEL = {}\n", + "TOP2_MODELS = {}\n", + "SHAP_WEIGHTS = {}\n", + "AVERAGED_SHAP_WEIGHTS = None\n", + "ALL_FEATURES = []\n", + "\n", + "print(\"Configuration loaded.\")\n", + "print(\"Phase 1:\", CONFIG['phase1'])\n", + "print(\"Phase 2:\", CONFIG['phase2'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "746df5c7", + "metadata": {}, + "source": [ + "# Section 1 — Data Loading\n", + "\n", + "Load all 5 datasets, drop leakage columns, create binary target, drop zero-variance features per CONFIG, and report shapes + class distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f624d0f5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:18.483498Z", + "iopub.status.busy": "2026-05-07T14:55:18.482981Z", + "iopub.status.idle": "2026-05-07T14:55:18.525217Z", + "shell.execute_reply": "2026-05-07T14:55:18.523969Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset: eclipse | shape=(997, 5) | buggy=206 | clean=791 | imbalance_ratio=3.84\n", + "Dataset: equinox | shape=(324, 5) | buggy=129 | clean=195 | imbalance_ratio=1.51\n", + " [lucene] Dropping zero-variance: ['numberOfNonTrivialBugsFoundUntil:', 'numberOfMajorBugsFoundUntil:', 'numberOfCriticalBugsFoundUntil:', 'numberOfHighPriorityBugsFoundUntil:']\n", + "Dataset: lucene | shape=(691, 1) | buggy=64 | clean=627 | imbalance_ratio=9.80\n", + "Dataset: mylyn | shape=(1862, 5) | buggy=245 | clean=1617 | imbalance_ratio=6.60\n", + "Dataset: pde | shape=(1497, 5) | buggy=209 | clean=1288 | imbalance_ratio=6.16\n", + "\n", + "All datasets loaded.\n" + ] + } + ], + "source": [ + "def load_dataset(name, path):\n", + " df = pd.read_csv(path, sep=';', skipinitialspace=True)\n", + " df.columns = df.columns.str.strip()\n", + "\n", + " # Drop unnamed / empty columns\n", + " unnamed = [c for c in df.columns if c == '' or c.startswith('Unnamed')]\n", + " df = df.drop(columns=unnamed)\n", + "\n", + " # Drop leakage columns\n", + " leakage = ['nonTrivialBugs', 'majorBugs', 'criticalBugs', 'highPriorityBugs']\n", + " leakage = [c for c in leakage if c in df.columns]\n", + " df = df.drop(columns=leakage)\n", + "\n", + " # Binary target\n", + " y = (df['bugs'] > 0).astype(int)\n", + " df = df.drop(columns=['bugs'])\n", + "\n", + " # Drop identifier\n", + " df = df.drop(columns=['classname'])\n", + "\n", + " # Drop zero-variance per CONFIG\n", + " zv = CONFIG['ZERO_VAR_FEATURES'].get(name, [])\n", + " zv = [c for c in zv if c in df.columns]\n", + " if zv:\n", + " print(f\" [{name}] Dropping zero-variance: {zv}\")\n", + " df = df.drop(columns=zv)\n", + "\n", + " feature_names = [f.rstrip(':') for f in df.columns.tolist()]\n", + " df.columns = feature_names\n", + " return df, y, feature_names\n", + "\n", + "\n", + "for name, fname in CONFIG['datasets'].items():\n", + " path = os.path.join(CONFIG['data_dir'], fname)\n", + " X, y, fnames = load_dataset(name, path)\n", + " DATASETS[name] = {'X': X, 'y': y, 'feature_names': fnames}\n", + " n_buggy = int(y.sum())\n", + " n_clean = int((y == 0).sum())\n", + " ratio = n_clean / n_buggy if n_buggy > 0 else float('inf')\n", + " print(f\"Dataset: {name:8s} | shape={X.shape} | buggy={n_buggy} | clean={n_clean} | imbalance_ratio={ratio:.2f}\")\n", + "\n", + "print(\"\\nAll datasets loaded.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b1c7557c", + "metadata": {}, + "source": [ + "# Section 2 — EDA (Concise Per-Dataset)\n", + "\n", + "For each dataset: class balance bar chart, feature boxplots (log-scaled), correlation heatmap, and a printed summary table — all in a single figure using `gridspec`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9a73dd1f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:18.527156Z", + "iopub.status.busy": "2026-05-07T14:55:18.526946Z", + "iopub.status.idle": "2026-05-07T14:55:24.479169Z", + "shell.execute_reply": "2026-05-07T14:55:24.477582Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "EDA for dataset: eclipse\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved EDA figure: eda_eclipse.png\n", + "\n", + "--- Summary table for eclipse ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremeanmedianmaxskewness%zerosIQR_outliers
0numberOfBugsFoundUntil11.645.002144.9511.3111
1numberOfNonTrivialBugsFoundUntil10.154.002004.9414.4114
2numberOfMajorBugsFoundUntil1.140.00385.6164.7140
3numberOfCriticalBugsFoundUntil0.430.00155.3478.3216
4numberOfHighPriorityBugsFoundUntil0.460.00104.3272.640
\n", + "
" + ], + "text/plain": [ + " feature mean median max skewness %zeros \\\n", + "0 numberOfBugsFoundUntil 11.64 5.00 214 4.95 11.3 \n", + "1 numberOfNonTrivialBugsFoundUntil 10.15 4.00 200 4.94 14.4 \n", + "2 numberOfMajorBugsFoundUntil 1.14 0.00 38 5.61 64.7 \n", + "3 numberOfCriticalBugsFoundUntil 0.43 0.00 15 5.34 78.3 \n", + "4 numberOfHighPriorityBugsFoundUntil 0.46 0.00 10 4.32 72.6 \n", + "\n", + " IQR_outliers \n", + "0 111 \n", + "1 114 \n", + "2 140 \n", + "3 216 \n", + "4 40 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "EDA for dataset: equinox\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved EDA figure: eda_equinox.png\n", + "\n", + "--- Summary table for equinox ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremeanmedianmaxskewness%zerosIQR_outliers
0numberOfBugsFoundUntil4.592.00784.953.437
1numberOfNonTrivialBugsFoundUntil4.302.00714.944.331
2numberOfMajorBugsFoundUntil0.480.00114.9076.576
3numberOfCriticalBugsFoundUntil0.220.0043.5485.547
4numberOfHighPriorityBugsFoundUntil0.040.0049.6496.910
\n", + "
" + ], + "text/plain": [ + " feature mean median max skewness %zeros \\\n", + "0 numberOfBugsFoundUntil 4.59 2.00 78 4.95 3.4 \n", + "1 numberOfNonTrivialBugsFoundUntil 4.30 2.00 71 4.94 4.3 \n", + "2 numberOfMajorBugsFoundUntil 0.48 0.00 11 4.90 76.5 \n", + "3 numberOfCriticalBugsFoundUntil 0.22 0.00 4 3.54 85.5 \n", + "4 numberOfHighPriorityBugsFoundUntil 0.04 0.00 4 9.64 96.9 \n", + "\n", + " IQR_outliers \n", + "0 37 \n", + "1 31 \n", + "2 76 \n", + "3 47 \n", + "4 10 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "EDA for dataset: lucene\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved EDA figure: eda_lucene.png\n", + "\n", + "--- Summary table for lucene ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremeanmedianmaxskewness%zerosIQR_outliers
0numberOfBugsFoundUntil2.481.00818.1724.648
\n", + "
" + ], + "text/plain": [ + " feature mean median max skewness %zeros IQR_outliers\n", + "0 numberOfBugsFoundUntil 2.48 1.00 81 8.17 24.6 48" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "EDA for dataset: mylyn\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved EDA figure: eda_mylyn.png\n", + "\n", + "--- Summary table for mylyn ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremeanmedianmaxskewness%zerosIQR_outliers
0numberOfBugsFoundUntil7.834.001975.901.1153
1numberOfNonTrivialBugsFoundUntil3.662.00926.1916.5169
2numberOfMajorBugsFoundUntil0.320.00135.9879.0391
3numberOfCriticalBugsFoundUntil0.130.0055.2490.3180
4numberOfHighPriorityBugsFoundUntil4.302.00854.2927.0141
\n", + "
" + ], + "text/plain": [ + " feature mean median max skewness %zeros \\\n", + "0 numberOfBugsFoundUntil 7.83 4.00 197 5.90 1.1 \n", + "1 numberOfNonTrivialBugsFoundUntil 3.66 2.00 92 6.19 16.5 \n", + "2 numberOfMajorBugsFoundUntil 0.32 0.00 13 5.98 79.0 \n", + "3 numberOfCriticalBugsFoundUntil 0.13 0.00 5 5.24 90.3 \n", + "4 numberOfHighPriorityBugsFoundUntil 4.30 2.00 85 4.29 27.0 \n", + "\n", + " IQR_outliers \n", + "0 153 \n", + "1 169 \n", + "2 391 \n", + "3 180 \n", + "4 141 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "EDA for dataset: pde\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved EDA figure: eda_pde.png\n", + "\n", + "--- Summary table for pde ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremeanmedianmaxskewness%zerosIQR_outliers
0numberOfBugsFoundUntil3.882.0023217.2017.0149
1numberOfNonTrivialBugsFoundUntil2.801.0014312.4631.1133
2numberOfMajorBugsFoundUntil0.240.0084.9485.2221
3numberOfCriticalBugsFoundUntil0.070.0068.3895.568
4numberOfHighPriorityBugsFoundUntil0.060.0068.6395.765
\n", + "
" + ], + "text/plain": [ + " feature mean median max skewness %zeros \\\n", + "0 numberOfBugsFoundUntil 3.88 2.00 232 17.20 17.0 \n", + "1 numberOfNonTrivialBugsFoundUntil 2.80 1.00 143 12.46 31.1 \n", + "2 numberOfMajorBugsFoundUntil 0.24 0.00 8 4.94 85.2 \n", + "3 numberOfCriticalBugsFoundUntil 0.07 0.00 6 8.38 95.5 \n", + "4 numberOfHighPriorityBugsFoundUntil 0.06 0.00 6 8.63 95.7 \n", + "\n", + " IQR_outliers \n", + "0 149 \n", + "1 133 \n", + "2 221 \n", + "3 68 \n", + "4 65 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def eda_dataset(name, data_dict, save_prefix='eda'):\n", + " X = data_dict['X']\n", + " y = data_dict['y']\n", + " features = data_dict['feature_names']\n", + "\n", + " fig = plt.figure(figsize=(18, 14))\n", + " gs = fig.add_gridspec(3, 2, height_ratios=[1, 1.2, 1.2])\n", + "\n", + " # --- Class balance ---\n", + " ax1 = fig.add_subplot(gs[0, 0])\n", + " counts = y.value_counts().sort_index()\n", + " bars = ax1.bar(['clean (0)', 'buggy (1)'], counts.values, color=['steelblue', 'coral'])\n", + " ax1.set_title('Class Balance')\n", + " ax1.set_ylabel('Count')\n", + " for bar, val in zip(bars, counts.values):\n", + " ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.02*counts.max(),\n", + " str(val), ha='center', va='bottom', fontsize=11)\n", + "\n", + " # --- Feature boxplots (log1p scale) ---\n", + " ax2 = fig.add_subplot(gs[0, 1])\n", + " X_log1p = np.log1p(X)\n", + " vals = [X_log1p[c].dropna().values for c in features]\n", + " bp = ax2.boxplot(vals, labels=features, vert=False, patch_artist=True)\n", + " for patch in bp['boxes']:\n", + " patch.set_facecolor('lightsteelblue')\n", + " ax2.set_title('Feature Distributions (log1p scale)')\n", + " ax2.set_xlabel('log1p(value)')\n", + "\n", + " # --- Correlation heatmap ---\n", + " ax3 = fig.add_subplot(gs[1, :])\n", + " corr = X.corr(method='pearson')\n", + " sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', center=0, ax=ax3,\n", + " vmin=-1, vmax=1, square=True)\n", + " ax3.set_title('Pearson Correlation Heatmap (features only)')\n", + "\n", + " # --- Summary table ---\n", + " ax4 = fig.add_subplot(gs[2, :])\n", + " ax4.axis('off')\n", + " rows = []\n", + " for col in features:\n", + " s = X[col]\n", + " pct_zeros = (s == 0).mean() * 100\n", + " q1, q3 = s.quantile(0.25), s.quantile(0.75)\n", + " iqr = q3 - q1\n", + " low, high = q1 - 1.5*iqr, q3 + 1.5*iqr\n", + " outlier_count = ((s < low) | (s > high)).sum()\n", + " rows.append({\n", + " 'feature': col,\n", + " 'mean': f\"{s.mean():.2f}\",\n", + " 'median': f\"{s.median():.2f}\",\n", + " 'max': f\"{s.max():.0f}\",\n", + " 'skewness': f\"{s.skew():.2f}\",\n", + " '%zeros': f\"{pct_zeros:.1f}\",\n", + " 'IQR_outliers': outlier_count,\n", + " })\n", + " summary_df = pd.DataFrame(rows)\n", + " table = ax4.table(cellText=summary_df.values, colLabels=summary_df.columns,\n", + " loc='center', cellLoc='center')\n", + " table.auto_set_font_size(False)\n", + " table.set_fontsize(9)\n", + " table.scale(1.2, 1.5)\n", + " ax4.set_title('Feature Summary Table', y=0.95, pad=10)\n", + "\n", + " fig.suptitle(f'EDA: {name}', fontsize=16, fontweight='bold')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.96])\n", + " fname = f\"{save_prefix}_{name}.png\"\n", + " plt.savefig(fname)\n", + " plt.close(fig)\n", + " print(f\"Saved EDA figure: {fname}\")\n", + " print(f\"\\n--- Summary table for {name} ---\")\n", + " display(summary_df)\n", + " return summary_df\n", + "\n", + "\n", + "for name in DATASETS:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"EDA for dataset: {name}\")\n", + " print(f\"{'='*60}\")\n", + " eda_dataset(name, DATASETS[name])\n" + ] + }, + { + "cell_type": "markdown", + "id": "0a0befbb", + "metadata": {}, + "source": [ + "# Section 3 — Preprocessing Pipelines\n", + "\n", + "Three pipelines:\n", + "- **Pipeline A**: tree-compatible (no scaling)\n", + "- **Pipeline B**: linear/distance-compatible (log1p + RobustScaler + optional SMOTE)\n", + "- **Pipeline C**: interpretability-optimised (log1p only, no SMOTE)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3388aba9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:24.488412Z", + "iopub.status.busy": "2026-05-07T14:55:24.487683Z", + "iopub.status.idle": "2026-05-07T14:55:24.497158Z", + "shell.execute_reply": "2026-05-07T14:55:24.496173Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pipeline A (tree): Pipeline(steps=[('identity', 'passthrough')])\n", + "\n", + "Preproc B (log1p+RobustScaler): Pipeline(steps=[('log1p', FunctionTransformer(func=)),\n", + " ('scaler', RobustScaler())])\n", + "\n", + "Pipeline C (interpretability): Pipeline(steps=[('log1p', FunctionTransformer(func=))])\n" + ] + } + ], + "source": [ + "def make_pipeline_a():\n", + " return Pipeline([('identity', 'passthrough')])\n", + "\n", + "\n", + "def make_preproc_b():\n", + " \"\"\"Return the preprocessing part of Pipeline B (log1p + RobustScaler).\"\"\"\n", + " return Pipeline([\n", + " ('log1p', FunctionTransformer(np.log1p)),\n", + " ('scaler', RobustScaler()),\n", + " ])\n", + "\n", + "\n", + "def make_pipeline_c():\n", + " return Pipeline([\n", + " ('log1p', FunctionTransformer(np.log1p)),\n", + " ])\n", + "\n", + "\n", + "def imbalance_ratio(y):\n", + " counts = np.bincount(y)\n", + " if len(counts) < 2 or counts[1] == 0:\n", + " return float('inf')\n", + " return counts[0] / counts[1]\n", + "\n", + "\n", + "print(\"Pipeline A (tree):\", make_pipeline_a())\n", + "print(\"\\nPreproc B (log1p+RobustScaler):\", make_preproc_b())\n", + "print(\"\\nPipeline C (interpretability):\", make_pipeline_c())\n" + ] + }, + { + "cell_type": "markdown", + "id": "1e4d6d76", + "metadata": {}, + "source": [ + "# Section 4 — Model Definitions\n", + "\n", + "Define all 8 models with assigned pipelines. XGBoost `scale_pos_weight` is computed per dataset at training time." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4b9211dd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:24.499876Z", + "iopub.status.busy": "2026-05-07T14:55:24.499597Z", + "iopub.status.idle": "2026-05-07T14:55:24.508565Z", + "shell.execute_reply": "2026-05-07T14:55:24.507645Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RandomForest -> pipeline A | RandomForestClassifier\n", + "XGBoost -> pipeline A | XGBClassifier\n", + "LightGBM -> pipeline A | LGBMClassifier\n", + "GradientBoosting -> pipeline A | GradientBoostingClassifier\n", + "ExtraTrees -> pipeline A | ExtraTreesClassifier\n", + "LogisticRegression -> pipeline B | LogisticRegression\n", + "SVM -> pipeline B | SVC\n", + "KNN -> pipeline B | KNeighborsClassifier\n" + ] + } + ], + "source": [ + "def build_models(y_train=None):\n", + " spw = 1.0\n", + " if y_train is not None:\n", + " counts = np.bincount(y_train)\n", + " if len(counts) > 1 and counts[1] > 0:\n", + " spw = counts[0] / counts[1]\n", + "\n", + " models = {\n", + " 'RandomForest': {\n", + " 'model': RandomForestClassifier(\n", + " n_estimators=300, class_weight='balanced',\n", + " random_state=CONFIG['random_state'], n_jobs=-1\n", + " ),\n", + " 'pipeline': 'A',\n", + " },\n", + " 'XGBoost': {\n", + " 'model': XGBClassifier(\n", + " n_estimators=300,\n", + " eval_metric='logloss',\n", + " scale_pos_weight=spw,\n", + " random_state=CONFIG['random_state'],\n", + " n_jobs=-1,\n", + " ) if XGBClassifier is not None else None,\n", + " 'pipeline': 'A',\n", + " },\n", + " 'LightGBM': {\n", + " 'model': LGBMClassifier(\n", + " n_estimators=300, class_weight='balanced',\n", + " random_state=CONFIG['random_state'],\n", + " n_jobs=-1, verbose=-1,\n", + " ) if LGBMClassifier is not None else None,\n", + " 'pipeline': 'A',\n", + " },\n", + " 'GradientBoosting': {\n", + " 'model': GradientBoostingClassifier(\n", + " n_estimators=200, random_state=CONFIG['random_state']\n", + " ),\n", + " 'pipeline': 'A',\n", + " },\n", + " 'ExtraTrees': {\n", + " 'model': ExtraTreesClassifier(\n", + " n_estimators=300, class_weight='balanced',\n", + " random_state=CONFIG['random_state'], n_jobs=-1\n", + " ),\n", + " 'pipeline': 'A',\n", + " },\n", + " 'LogisticRegression': {\n", + " 'model': LogisticRegression(\n", + " C=1.0, class_weight='balanced', max_iter=1000,\n", + " random_state=CONFIG['random_state']\n", + " ),\n", + " 'pipeline': 'B',\n", + " },\n", + " 'SVM': {\n", + " 'model': SVC(\n", + " kernel='rbf', C=1.0, probability=True,\n", + " class_weight='balanced', random_state=CONFIG['random_state']\n", + " ),\n", + " 'pipeline': 'B',\n", + " },\n", + " 'KNN': {\n", + " 'model': KNeighborsClassifier(\n", + " n_neighbors=7, metric='euclidean', n_jobs=-1\n", + " ),\n", + " 'pipeline': 'B',\n", + " },\n", + " }\n", + " models = {k: v for k, v in models.items() if v['model'] is not None}\n", + " return models\n", + "\n", + "\n", + "MODELS_CHECK = build_models(np.array([0,0,0,1,1]))\n", + "for k, v in MODELS_CHECK.items():\n", + " print(f\"{k:20s} -> pipeline {v['pipeline']} | {type(v['model']).__name__}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4e7f4546", + "metadata": {}, + "source": [ + "# Section 5 — Training and Evaluation (Phase 1 datasets)\n", + "\n", + "Stratified split, 5-fold CV on train set, final test evaluation, model ranking, and comparative visualisation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a26d2aca", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:55:24.510686Z", + "iopub.status.busy": "2026-05-07T14:55:24.510460Z", + "iopub.status.idle": "2026-05-07T14:56:03.673975Z", + "shell.execute_reply": "2026-05-07T14:56:03.672496Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "PHASE 1 — Dataset: eclipse\n", + "======================================================================\n", + "Train: (797, 5) | Test: (200, 5)\n", + "Train dist: [632 165] | Test dist: [159 41]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36e19e3b212f4e2a8ff4962b1d019b69", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "CV eclipse: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelf1_macroroc_aucpr_auc
6SVM0.7166 ± 0.02950.7786 ± 0.03860.5303 ± 0.0404
7KNN0.6893 ± 0.02730.7593 ± 0.02540.4703 ± 0.0366
5LogisticRegression0.6842 ± 0.02190.8133 ± 0.02610.6371 ± 0.0397
0RandomForest0.6812 ± 0.03760.7411 ± 0.01700.5368 ± 0.0466
2LightGBM0.6771 ± 0.02400.7133 ± 0.03160.5389 ± 0.0427
3GradientBoosting0.6679 ± 0.04340.7073 ± 0.03920.5357 ± 0.0373
1XGBoost0.6621 ± 0.02990.6868 ± 0.03060.5158 ± 0.0455
4ExtraTrees0.6601 ± 0.01700.7150 ± 0.01970.4693 ± 0.0459
\n", + "" + ], + "text/plain": [ + " model f1_macro roc_auc pr_auc\n", + "6 SVM 0.7166 ± 0.0295 0.7786 ± 0.0386 0.5303 ± 0.0404\n", + "7 KNN 0.6893 ± 0.0273 0.7593 ± 0.0254 0.4703 ± 0.0366\n", + "5 LogisticRegression 0.6842 ± 0.0219 0.8133 ± 0.0261 0.6371 ± 0.0397\n", + "0 RandomForest 0.6812 ± 0.0376 0.7411 ± 0.0170 0.5368 ± 0.0466\n", + "2 LightGBM 0.6771 ± 0.0240 0.7133 ± 0.0316 0.5389 ± 0.0427\n", + "3 GradientBoosting 0.6679 ± 0.0434 0.7073 ± 0.0392 0.5357 ± 0.0373\n", + "1 XGBoost 0.6621 ± 0.0299 0.6868 ± 0.0306 0.5158 ± 0.0455\n", + "4 ExtraTrees 0.6601 ± 0.0170 0.7150 ± 0.0197 0.4693 ± 0.0459" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "247614267d7b45eb8ca13b12ee3e4125", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Test eclipse: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelf1_macroroc_aucpr_auc
3GradientBoosting0.7236230.7464340.522457
6SVM0.7086830.7718980.595115
7KNN0.7035570.7498850.572197
5LogisticRegression0.7011430.7843230.627092
4ExtraTrees0.6293230.7198960.388231
2LightGBM0.6230310.7309400.465996
0RandomForest0.6190480.7242680.471125
1XGBoost0.6129030.6830800.452841
\n", + "" + ], + "text/plain": [ + " model f1_macro roc_auc pr_auc\n", + "3 GradientBoosting 0.723623 0.746434 0.522457\n", + "6 SVM 0.708683 0.771898 0.595115\n", + "7 KNN 0.703557 0.749885 0.572197\n", + "5 LogisticRegression 0.701143 0.784323 0.627092\n", + "4 ExtraTrees 0.629323 0.719896 0.388231\n", + "2 LightGBM 0.623031 0.730940 0.465996\n", + "0 RandomForest 0.619048 0.724268 0.471125\n", + "1 XGBoost 0.612903 0.683080 0.452841" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-1 (SHAP): GradientBoosting\n", + "Top-2 (LIME): ['GradientBoosting', 'SVM']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model_comparison_eclipse.png\n", + "\n", + "======================================================================\n", + "PHASE 1 — Dataset: mylyn\n", + "======================================================================\n", + "Train: (1489, 5) | Test: (373, 5)\n", + "Train dist: [1293 196] | Test dist: [324 49]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6a9373f078d4b079ff1e6f0a095b629", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "CV mylyn: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelf1_macroroc_aucpr_auc
7KNN0.6001 ± 0.01600.6599 ± 0.01750.2451 ± 0.0282
0RandomForest0.5965 ± 0.02960.6723 ± 0.03570.3147 ± 0.0618
2LightGBM0.5952 ± 0.04280.6563 ± 0.04920.2923 ± 0.0665
6SVM0.5946 ± 0.03370.7372 ± 0.02910.3210 ± 0.0621
5LogisticRegression0.5851 ± 0.03370.7007 ± 0.04240.2914 ± 0.0353
4ExtraTrees0.5850 ± 0.02840.6538 ± 0.03590.2644 ± 0.0514
1XGBoost0.5824 ± 0.04270.6362 ± 0.06410.2881 ± 0.0653
3GradientBoosting0.5741 ± 0.03050.6809 ± 0.04390.3075 ± 0.0475
\n", + "" + ], + "text/plain": [ + " model f1_macro roc_auc pr_auc\n", + "7 KNN 0.6001 ± 0.0160 0.6599 ± 0.0175 0.2451 ± 0.0282\n", + "0 RandomForest 0.5965 ± 0.0296 0.6723 ± 0.0357 0.3147 ± 0.0618\n", + "2 LightGBM 0.5952 ± 0.0428 0.6563 ± 0.0492 0.2923 ± 0.0665\n", + "6 SVM 0.5946 ± 0.0337 0.7372 ± 0.0291 0.3210 ± 0.0621\n", + "5 LogisticRegression 0.5851 ± 0.0337 0.7007 ± 0.0424 0.2914 ± 0.0353\n", + "4 ExtraTrees 0.5850 ± 0.0284 0.6538 ± 0.0359 0.2644 ± 0.0514\n", + "1 XGBoost 0.5824 ± 0.0427 0.6362 ± 0.0641 0.2881 ± 0.0653\n", + "3 GradientBoosting 0.5741 ± 0.0305 0.6809 ± 0.0439 0.3075 ± 0.0475" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4f67f20befda43119e11318be4df03c5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Test mylyn: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelf1_macroroc_aucpr_auc
6SVM0.6199940.7598260.331761
7KNN0.6061850.6413770.224083
3GradientBoosting0.5977110.6608720.255290
5LogisticRegression0.5651060.7207730.306504
0RandomForest0.5528010.6059780.184380
1XGBoost0.5523190.5745150.220625
2LightGBM0.5515560.6109220.191383
4ExtraTrees0.5469880.5747350.154150
\n", + "" + ], + "text/plain": [ + " model f1_macro roc_auc pr_auc\n", + "6 SVM 0.619994 0.759826 0.331761\n", + "7 KNN 0.606185 0.641377 0.224083\n", + "3 GradientBoosting 0.597711 0.660872 0.255290\n", + "5 LogisticRegression 0.565106 0.720773 0.306504\n", + "0 RandomForest 0.552801 0.605978 0.184380\n", + "1 XGBoost 0.552319 0.574515 0.220625\n", + "2 LightGBM 0.551556 0.610922 0.191383\n", + "4 ExtraTrees 0.546988 0.574735 0.154150" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-1 (SHAP): SVM\n", + "Top-2 (LIME): ['SVM', 'KNN']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model_comparison_mylyn.png\n", + "\n", + "Phase 1 training complete.\n" + ] + } + ], + "source": [ + "def apply_pipeline_b(X_train, y_train, X_test, apply_smote=False):\n", + " pre = make_preproc_b()\n", + " X_train_t = pre.fit_transform(X_train)\n", + " X_test_t = pre.transform(X_test)\n", + " if apply_smote:\n", + " sm = SMOTE(random_state=CONFIG['random_state'])\n", + " X_train_t, y_train_t = sm.fit_resample(X_train_t, y_train)\n", + " else:\n", + " y_train_t = y_train\n", + " return X_train_t, y_train_t, X_test_t, pre\n", + "\n", + "\n", + "def evaluate_model(model, X_train, y_train, X_test, y_test, pipeline_key):\n", + " ir = imbalance_ratio(y_train)\n", + " apply_smote = (pipeline_key == 'B') and (ir > CONFIG['smote_threshold'])\n", + "\n", + " if pipeline_key == 'A':\n", + " pipe = make_pipeline_a()\n", + " X_train_t = pipe.fit_transform(X_train)\n", + " X_test_t = pipe.transform(X_test)\n", + " if isinstance(model, GradientBoostingClassifier):\n", + " sw = compute_sample_weight('balanced', y_train)\n", + " model.fit(X_train_t, y_train, sample_weight=sw)\n", + " else:\n", + " model.fit(X_train_t, y_train)\n", + " elif pipeline_key == 'B':\n", + " X_train_t, y_train_t, X_test_t, _ = apply_pipeline_b(\n", + " X_train, y_train, X_test, apply_smote=apply_smote\n", + " )\n", + " model.fit(X_train_t, y_train_t)\n", + " else:\n", + " raise ValueError(f\"Unknown pipeline key {pipeline_key}\")\n", + "\n", + " y_pred = model.predict(X_test_t)\n", + " y_prob = model.predict_proba(X_test_t)[:, 1]\n", + " return {\n", + " 'f1_macro': f1_score(y_test, y_pred, average='macro'),\n", + " 'roc_auc': roc_auc_score(y_test, y_prob),\n", + " 'pr_auc': average_precision_score(y_test, y_prob),\n", + " 'y_pred': y_pred,\n", + " 'y_prob': y_prob,\n", + " }\n", + "\n", + "\n", + "def cross_val_model(model, X, y, pipeline_key):\n", + " skf = StratifiedKFold(n_splits=CONFIG['cv_folds'], shuffle=True,\n", + " random_state=CONFIG['random_state'])\n", + " fold_f1, fold_roc, fold_pr = [], [], []\n", + "\n", + " for tr_idx, val_idx in skf.split(X, y):\n", + " X_tr, X_val = X.iloc[tr_idx], X.iloc[val_idx]\n", + " y_tr, y_val = y.iloc[tr_idx], y.iloc[val_idx]\n", + " ir = imbalance_ratio(y_tr)\n", + " apply_smote = (pipeline_key == 'B') and (ir > CONFIG['smote_threshold'])\n", + "\n", + " if pipeline_key == 'A':\n", + " pipe = make_pipeline_a()\n", + " X_tr_t = pipe.fit_transform(X_tr)\n", + " X_val_t = pipe.transform(X_val)\n", + " m = type(model)(**model.get_params())\n", + " if isinstance(m, GradientBoostingClassifier):\n", + " sw = compute_sample_weight('balanced', y_tr)\n", + " m.fit(X_tr_t, y_tr, sample_weight=sw)\n", + " else:\n", + " m.fit(X_tr_t, y_tr)\n", + " elif pipeline_key == 'B':\n", + " X_tr_t, y_tr_t, X_val_t, _ = apply_pipeline_b(\n", + " X_tr, y_tr, X_val, apply_smote=apply_smote\n", + " )\n", + " m = type(model)(**model.get_params())\n", + " m.fit(X_tr_t, y_tr_t)\n", + " else:\n", + " raise ValueError(f\"Unknown pipeline key {pipeline_key}\")\n", + "\n", + " y_pred = m.predict(X_val_t)\n", + " y_prob = m.predict_proba(X_val_t)[:, 1]\n", + " fold_f1.append(f1_score(y_val, y_pred, average='macro'))\n", + " fold_roc.append(roc_auc_score(y_val, y_prob))\n", + " fold_pr.append(average_precision_score(y_val, y_prob))\n", + "\n", + " return {\n", + " 'f1_macro_mean': np.mean(fold_f1),\n", + " 'f1_macro_std': np.std(fold_f1),\n", + " 'roc_auc_mean': np.mean(fold_roc),\n", + " 'roc_auc_std': np.std(fold_roc),\n", + " 'pr_auc_mean': np.mean(fold_pr),\n", + " 'pr_auc_std': np.std(fold_pr),\n", + " }\n", + "\n", + "\n", + "# --- Run Phase 1 ---\n", + "for ds_name in CONFIG['phase1']:\n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"PHASE 1 — Dataset: {ds_name}\")\n", + " print(f\"{'='*70}\")\n", + "\n", + " data = DATASETS[ds_name]\n", + " X, y = data['X'], data['y']\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=CONFIG['test_size'], stratify=y,\n", + " random_state=CONFIG['random_state']\n", + " )\n", + " print(f\"Train: {X_train.shape} | Test: {X_test.shape}\")\n", + " print(f\"Train dist: {np.bincount(y_train)} | Test dist: {np.bincount(y_test)}\")\n", + "\n", + " DATASETS[ds_name]['X_train'] = X_train\n", + " DATASETS[ds_name]['X_test'] = X_test\n", + " DATASETS[ds_name]['y_train'] = y_train\n", + " DATASETS[ds_name]['y_test'] = y_test\n", + "\n", + " # 5.2 Cross-validation\n", + " models = build_models(y_train.values)\n", + " cv_results = []\n", + " for mname, mdict in tqdm(models.items(), desc=f\"CV {ds_name}\"):\n", + " cv = cross_val_model(mdict['model'], X_train, y_train, mdict['pipeline'])\n", + " cv_results.append({\n", + " 'model': mname,\n", + " 'f1_macro': f\"{cv['f1_macro_mean']:.4f} ± {cv['f1_macro_std']:.4f}\",\n", + " 'roc_auc': f\"{cv['roc_auc_mean']:.4f} ± {cv['roc_auc_std']:.4f}\",\n", + " 'pr_auc': f\"{cv['pr_auc_mean']:.4f} ± {cv['pr_auc_std']:.4f}\",\n", + " 'f1_macro_raw': cv['f1_macro_mean'],\n", + " 'roc_auc_raw': cv['roc_auc_mean'],\n", + " })\n", + " cv_df = pd.DataFrame(cv_results).sort_values('f1_macro_raw', ascending=False)\n", + " print(f\"\\n--- CV Results ({ds_name}) ---\")\n", + " display(cv_df[['model', 'f1_macro', 'roc_auc', 'pr_auc']])\n", + "\n", + " # 5.3 Final evaluation on held-out test set\n", + " test_results = {}\n", + " fig, axes = plt.subplots(2, 4, figsize=(20, 10))\n", + " axes = axes.flatten()\n", + " ax_idx = 0\n", + "\n", + " for mname, mdict in tqdm(models.items(), desc=f\"Test {ds_name}\"):\n", + " res = evaluate_model(mdict['model'], X_train, y_train, X_test, y_test, mdict['pipeline'])\n", + " test_results[mname] = res\n", + " print(f\"\\n--- {mname} on {ds_name} test set ---\")\n", + " print(classification_report(y_test, res['y_pred'],\n", + " target_names=['clean', 'buggy'], digits=4))\n", + " cm = confusion_matrix(y_test, res['y_pred'])\n", + " if ax_idx < len(axes):\n", + " ax = axes[ax_idx]\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,\n", + " xticklabels=['clean', 'buggy'],\n", + " yticklabels=['clean', 'buggy'])\n", + " ax.set_title(f'{mname} — CM')\n", + " ax.set_ylabel('True')\n", + " ax.set_xlabel('Predicted')\n", + " ax_idx += 1\n", + " # ROC\n", + " fpr, tpr, _ = roc_curve(y_test, res['y_prob'])\n", + " plt.figure(figsize=(5, 4))\n", + " plt.plot(fpr, tpr, label=f'{mname} (AUC={res[\"roc_auc\"]:.3f})')\n", + " plt.plot([0, 1], [0, 1], 'k--')\n", + " plt.xlabel('FPR'); plt.ylabel('TPR')\n", + " plt.title(f'ROC: {mname} — {ds_name}')\n", + " plt.legend()\n", + " plt.savefig(f\"roc_{ds_name}_{mname}.png\")\n", + " plt.close()\n", + " # PR\n", + " prec, rec, _ = precision_recall_curve(y_test, res['y_prob'])\n", + " plt.figure(figsize=(5, 4))\n", + " plt.plot(rec, prec, label=f'{mname} (AP={res[\"pr_auc\"]:.3f})')\n", + " plt.xlabel('Recall'); plt.ylabel('Precision')\n", + " plt.title(f'PR: {mname} — {ds_name}')\n", + " plt.legend()\n", + " plt.savefig(f\"pr_{ds_name}_{mname}.png\")\n", + " plt.close()\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(f\"confusion_matrices_{ds_name}.png\")\n", + " plt.close()\n", + "\n", + " # 5.4 Model ranking\n", + " test_summary = []\n", + " for mname, res in test_results.items():\n", + " test_summary.append({\n", + " 'model': mname,\n", + " 'f1_macro': res['f1_macro'],\n", + " 'roc_auc': res['roc_auc'],\n", + " 'pr_auc': res['pr_auc'],\n", + " })\n", + " test_df = pd.DataFrame(test_summary).sort_values(\n", + " ['f1_macro', 'roc_auc'], ascending=[False, False]\n", + " )\n", + " print(f\"\\n--- Test Set Ranking ({ds_name}) ---\")\n", + " display(test_df)\n", + "\n", + " top1 = test_df.iloc[0]['model']\n", + " top2 = [test_df.iloc[0]['model'], test_df.iloc[1]['model']]\n", + " BEST_MODEL[ds_name] = top1\n", + " TOP2_MODELS[ds_name] = top2\n", + " RESULTS_PHASE1[ds_name] = test_results\n", + " print(f\"Top-1 (SHAP): {top1}\")\n", + " print(f\"Top-2 (LIME): {top2}\")\n", + "\n", + " # 5.5 Comparative visualisation\n", + " fig, ax = plt.subplots(figsize=(12, 6))\n", + " x = np.arange(len(test_df))\n", + " width = 0.25\n", + " color_map = {\n", + " 'RandomForest': 'teal', 'ExtraTrees': 'teal',\n", + " 'XGBoost': 'coral', 'LightGBM': 'coral', 'GradientBoosting': 'coral',\n", + " 'LogisticRegression': '#DAA520', 'SVM': '#DAA520', 'KNN': '#DAA520',\n", + " }\n", + " colors = [color_map.get(m, 'gray') for m in test_df['model']]\n", + " ax.bar(x - width, test_df['f1_macro'], width, label='F1-macro',\n", + " color=colors, alpha=0.8, edgecolor='black')\n", + " ax.bar(x, test_df['roc_auc'], width, label='ROC-AUC',\n", + " color=colors, alpha=0.6, edgecolor='black', hatch='//')\n", + " ax.bar(x + width, test_df['pr_auc'], width, label='PR-AUC',\n", + " color=colors, alpha=0.4, edgecolor='black', hatch='xx')\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(test_df['model'], rotation=45, ha='right')\n", + " ax.set_ylabel('Score')\n", + " ax.set_title(f'Model Comparison — {ds_name}')\n", + " ax.legend()\n", + " ax.set_ylim(0, 1.05)\n", + " plt.tight_layout()\n", + " plt.savefig(f\"model_comparison_{ds_name}.png\")\n", + " plt.close()\n", + " print(f\"Saved model_comparison_{ds_name}.png\")\n", + "\n", + "print(\"\\nPhase 1 training complete.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2e5fe03a", + "metadata": {}, + "source": [ + "# Section 6 — LIME (Top-2 Models, Phase 1 Datasets)\n", + "\n", + "LIME explanations for the top-2 models on each Phase 1 dataset, including stability checks." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d3dfe5af", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:56:03.677280Z", + "iopub.status.busy": "2026-05-07T14:56:03.677043Z", + "iopub.status.idle": "2026-05-07T14:56:09.833455Z", + "shell.execute_reply": "2026-05-07T14:56:09.832470Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "LIME — Dataset: eclipse\n", + "======================================================================\n", + "\n", + "--- LIME for GradientBoosting ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_GradientBoosting_confidently_buggy.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_GradientBoosting_borderline.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_GradientBoosting_confidently_clean.png\n", + "\n", + "--- LIME Stability (eclipse, GradientBoosting, confidently_buggy) ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_LIME_weightstdstable
0numberOfBugsFoundUntil0.1778900.005512True
1numberOfNonTrivialBugsFoundUntil0.0379330.001901True
2numberOfMajorBugsFoundUntil0.0974610.001851True
3numberOfCriticalBugsFoundUntil-0.0202490.002427False
4numberOfHighPriorityBugsFoundUntil0.0113790.001608False
\n", + "
" + ], + "text/plain": [ + " feature mean_LIME_weight std stable\n", + "0 numberOfBugsFoundUntil 0.177890 0.005512 True\n", + "1 numberOfNonTrivialBugsFoundUntil 0.037933 0.001901 True\n", + "2 numberOfMajorBugsFoundUntil 0.097461 0.001851 True\n", + "3 numberOfCriticalBugsFoundUntil -0.020249 0.002427 False\n", + "4 numberOfHighPriorityBugsFoundUntil 0.011379 0.001608 False" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Unstable features: ['numberOfCriticalBugsFoundUntil', 'numberOfHighPriorityBugsFoundUntil']\n", + "\n", + "--- LIME for SVM ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_SVM_confidently_buggy.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_SVM_borderline.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_eclipse_SVM_confidently_clean.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- LIME Stability (eclipse, SVM, confidently_buggy) ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_LIME_weightstdstable
0numberOfBugsFoundUntil0.1080200.000932True
1numberOfNonTrivialBugsFoundUntil0.0069680.001553False
2numberOfMajorBugsFoundUntil0.0097970.000285True
3numberOfCriticalBugsFoundUntil0.0037140.000333True
4numberOfHighPriorityBugsFoundUntil0.0193480.002188False
\n", + "
" + ], + "text/plain": [ + " feature mean_LIME_weight std stable\n", + "0 numberOfBugsFoundUntil 0.108020 0.000932 True\n", + "1 numberOfNonTrivialBugsFoundUntil 0.006968 0.001553 False\n", + "2 numberOfMajorBugsFoundUntil 0.009797 0.000285 True\n", + "3 numberOfCriticalBugsFoundUntil 0.003714 0.000333 True\n", + "4 numberOfHighPriorityBugsFoundUntil 0.019348 0.002188 False" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Unstable features: ['numberOfNonTrivialBugsFoundUntil', 'numberOfHighPriorityBugsFoundUntil']\n", + "\n", + "======================================================================\n", + "LIME — Dataset: mylyn\n", + "======================================================================\n", + "\n", + "--- LIME for SVM ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_SVM_confidently_buggy.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_SVM_borderline.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_SVM_confidently_clean.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- LIME Stability (mylyn, SVM, confidently_buggy) ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_LIME_weightstdstable
0numberOfBugsFoundUntil0.2054260.001041True
1numberOfNonTrivialBugsFoundUntil-0.1162400.001091True
2numberOfMajorBugsFoundUntil0.0460750.001564True
3numberOfCriticalBugsFoundUntil-0.0346080.002826True
4numberOfHighPriorityBugsFoundUntil0.0292250.001809True
\n", + "
" + ], + "text/plain": [ + " feature mean_LIME_weight std stable\n", + "0 numberOfBugsFoundUntil 0.205426 0.001041 True\n", + "1 numberOfNonTrivialBugsFoundUntil -0.116240 0.001091 True\n", + "2 numberOfMajorBugsFoundUntil 0.046075 0.001564 True\n", + "3 numberOfCriticalBugsFoundUntil -0.034608 0.002826 True\n", + "4 numberOfHighPriorityBugsFoundUntil 0.029225 0.001809 True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- LIME for KNN ---\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_KNN_confidently_buggy.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_KNN_borderline.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved LIME explanation: lime_mylyn_KNN_confidently_clean.png\n", + "\n", + "--- LIME Stability (mylyn, KNN, confidently_buggy) ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_LIME_weightstdstable
0numberOfBugsFoundUntil0.0826440.000496True
1numberOfNonTrivialBugsFoundUntil0.0632680.001292True
2numberOfMajorBugsFoundUntil0.0379950.001511True
3numberOfCriticalBugsFoundUntil-0.0092600.002500False
4numberOfHighPriorityBugsFoundUntil0.1053830.001307True
\n", + "
" + ], + "text/plain": [ + " feature mean_LIME_weight std stable\n", + "0 numberOfBugsFoundUntil 0.082644 0.000496 True\n", + "1 numberOfNonTrivialBugsFoundUntil 0.063268 0.001292 True\n", + "2 numberOfMajorBugsFoundUntil 0.037995 0.001511 True\n", + "3 numberOfCriticalBugsFoundUntil -0.009260 0.002500 False\n", + "4 numberOfHighPriorityBugsFoundUntil 0.105383 0.001307 True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Unstable features: ['numberOfCriticalBugsFoundUntil']\n" + ] + } + ], + "source": [ + "# PHASE 1 LIME\n", + "for ds_name in CONFIG['phase1']:\n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"LIME — Dataset: {ds_name}\")\n", + " print(f\"{'='*70}\")\n", + "\n", + " data = DATASETS[ds_name]\n", + " X_train = data['X_train']\n", + " X_test = data['X_test']\n", + " y_test = data['y_test']\n", + " feature_names = data['feature_names']\n", + "\n", + " pipe_c = make_pipeline_c()\n", + " X_train_c = pipe_c.fit_transform(X_train).values\n", + " X_test_c = pipe_c.transform(X_test).values\n", + "\n", + " for mname in TOP2_MODELS[ds_name]:\n", + " print(f\"\\n--- LIME for {mname} ---\")\n", + " models = build_models(data['y_train'].values)\n", + " model = models[mname]['model']\n", + " model.fit(X_train_c, data['y_train'])\n", + " probs = model.predict_proba(X_test_c)[:, 1]\n", + "\n", + " buggy_idx = np.where(y_test.values == 1)[0]\n", + " clean_idx = np.where(y_test.values == 0)[0]\n", + " conf_buggy_idx = int(buggy_idx[np.argmax(probs[buggy_idx])])\n", + " conf_clean_idx = int(clean_idx[np.argmin(probs[clean_idx])])\n", + " border_idx = int(np.argmin(np.abs(probs - 0.5)))\n", + " instances = {\n", + " 'confidently_buggy': conf_buggy_idx,\n", + " 'borderline': border_idx,\n", + " 'confidently_clean': conf_clean_idx,\n", + " }\n", + "\n", + " explainer = LimeTabularExplainer(\n", + " training_data=X_train_c,\n", + " feature_names=feature_names,\n", + " class_names=['clean', 'buggy'],\n", + " mode='classification',\n", + " discretize_continuous=False,\n", + " sample_around_instance=True,\n", + " random_state=CONFIG['random_state'],\n", + " )\n", + "\n", + " for inst_label, idx in instances.items():\n", + " x_inst = X_test_c[idx]\n", + " true_label = int(y_test.values[idx])\n", + " pred_prob = float(probs[idx])\n", + " exp = explainer.explain_instance(\n", + " x_inst, model.predict_proba,\n", + " num_features=len(feature_names), num_samples=5000,\n", + " )\n", + " fig = exp.as_pyplot_figure()\n", + " fig.suptitle(\n", + " f\"{ds_name} | {mname} | {inst_label} | idx={idx} | \"\n", + " f\"true={true_label} | prob={pred_prob:.3f}\"\n", + " )\n", + " plt.tight_layout()\n", + " fname = f\"lime_{ds_name}_{mname}_{inst_label}.png\"\n", + " plt.savefig(fname)\n", + " plt.close(fig)\n", + " print(f\"Saved LIME explanation: {fname}\")\n", + "\n", + " # 6.3 LIME stability check for confidently-buggy instance\n", + " idx = conf_buggy_idx\n", + " x_inst = X_test_c[idx]\n", + " weights_runs = []\n", + " for seed in [42, 123, 999]:\n", + " expl_s = LimeTabularExplainer(\n", + " training_data=X_train_c,\n", + " feature_names=feature_names,\n", + " class_names=['clean', 'buggy'],\n", + " mode='classification',\n", + " discretize_continuous=False,\n", + " sample_around_instance=True,\n", + " random_state=seed,\n", + " )\n", + " exp_s = expl_s.explain_instance(\n", + " x_inst, model.predict_proba,\n", + " num_features=len(feature_names), num_samples=5000,\n", + " )\n", + " w_dict = dict(exp_s.as_list())\n", + " weights_runs.append([w_dict.get(f, 0.0) for f in feature_names])\n", + "\n", + " weights_arr = np.array(weights_runs)\n", + " mean_w = weights_arr.mean(axis=0)\n", + " std_w = weights_arr.std(axis=0)\n", + " stab_df = pd.DataFrame({\n", + " 'feature': feature_names,\n", + " 'mean_LIME_weight': mean_w,\n", + " 'std': std_w,\n", + " 'stable': [std_w[i] < 0.1 * abs(mean_w[i]) if abs(mean_w[i]) > 1e-9 else False\n", + " for i in range(len(feature_names))],\n", + " })\n", + " print(f\"\\n--- LIME Stability ({ds_name}, {mname}, confidently_buggy) ---\")\n", + " display(stab_df)\n", + " unstable = stab_df[~stab_df['stable']]['feature'].tolist()\n", + " if unstable:\n", + " print(f\"WARNING: Unstable features: {unstable}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cbf7d0d9", + "metadata": {}, + "source": [ + "# Section 7 — SHAP (Best Model, Phase 1 Datasets)\n", + "\n", + "SHAP analysis for the best model on each Phase 1 dataset. Extract mean absolute SHAP values as weight vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7a6cc3dd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:56:09.835841Z", + "iopub.status.busy": "2026-05-07T14:56:09.835580Z", + "iopub.status.idle": "2026-05-07T14:57:04.451464Z", + "shell.execute_reply": "2026-05-07T14:57:04.450574Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SHAP — Dataset: eclipse | Best model: GradientBoosting\n", + "======================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2350/1849820119.py:53: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", + " shap.summary_plot(shap_values, X_test_c, feature_names=feature_names,\n", + "/tmp/ipykernel_2350/1849820119.py:63: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", + " shap.summary_plot(shap_values, X_test_c, feature_names=feature_names,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved shap_summary_eclipse.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- SHAP Weights (eclipse) ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_abs_SHAPweight
0numberOfBugsFoundUntil1.1247810.511651
1numberOfNonTrivialBugsFoundUntil0.5080460.231105
2numberOfMajorBugsFoundUntil0.3173380.144353
3numberOfCriticalBugsFoundUntil0.1750320.079620
4numberOfHighPriorityBugsFoundUntil0.0731410.033271
\n", + "
" + ], + "text/plain": [ + " feature mean_abs_SHAP weight\n", + "0 numberOfBugsFoundUntil 1.124781 0.511651\n", + "1 numberOfNonTrivialBugsFoundUntil 0.508046 0.231105\n", + "2 numberOfMajorBugsFoundUntil 0.317338 0.144353\n", + "3 numberOfCriticalBugsFoundUntil 0.175032 0.079620\n", + "4 numberOfHighPriorityBugsFoundUntil 0.073141 0.033271" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved shap_weights_eclipse.json\n", + "\n", + "======================================================================\n", + "SHAP — Dataset: mylyn | Best model: SVM\n", + "======================================================================\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bb77565a12f3457f98a236d99e9d795e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/373 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuremean_abs_SHAPweight
0numberOfBugsFoundUntil0.1206000.450861
4numberOfHighPriorityBugsFoundUntil0.0781520.292171
2numberOfMajorBugsFoundUntil0.0296330.110783
1numberOfNonTrivialBugsFoundUntil0.0260530.097398
3numberOfCriticalBugsFoundUntil0.0130500.048787
\n", + "" + ], + "text/plain": [ + " feature mean_abs_SHAP weight\n", + "0 numberOfBugsFoundUntil 0.120600 0.450861\n", + "4 numberOfHighPriorityBugsFoundUntil 0.078152 0.292171\n", + "2 numberOfMajorBugsFoundUntil 0.029633 0.110783\n", + "1 numberOfNonTrivialBugsFoundUntil 0.026053 0.097398\n", + "3 numberOfCriticalBugsFoundUntil 0.013050 0.048787" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved shap_weights_mylyn.json\n", + "\n", + "Saved shap_weights_phase1.json\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# PHASE 1 SHAP\n", + "for ds_name in CONFIG['phase1']:\n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"SHAP — Dataset: {ds_name} | Best model: {BEST_MODEL[ds_name]}\")\n", + " print(f\"{'='*70}\")\n", + "\n", + " data = DATASETS[ds_name]\n", + " X_train = data['X_train']\n", + " X_test = data['X_test']\n", + " y_train = data['y_train']\n", + " y_test = data['y_test']\n", + " feature_names = data['feature_names']\n", + "\n", + " pipe_c = make_pipeline_c()\n", + " X_train_c = pipe_c.fit_transform(X_train).values\n", + " X_test_c = pipe_c.transform(X_test).values\n", + "\n", + " models = build_models(y_train.values)\n", + " best_name = BEST_MODEL[ds_name]\n", + " model = models[best_name]['model']\n", + " model.fit(X_train_c, y_train)\n", + "\n", + " # 7.1 Determine explainer type\n", + " tree_types = (RandomForestClassifier, DecisionTreeClassifier,\n", + " ExtraTreesClassifier, GradientBoostingClassifier)\n", + " if isinstance(model, tree_types):\n", + " explainer = shap.TreeExplainer(model)\n", + " elif XGBClassifier is not None and isinstance(model, XGBClassifier):\n", + " explainer = shap.TreeExplainer(model)\n", + " elif LGBMClassifier is not None and isinstance(model, LGBMClassifier):\n", + " explainer = shap.TreeExplainer(model)\n", + " elif isinstance(model, LogisticRegression):\n", + " explainer = shap.LinearExplainer(model, X_train_c)\n", + " else:\n", + " background = shap.sample(X_train_c, 100, random_state=CONFIG['random_state'])\n", + " explainer = shap.KernelExplainer(model.predict_proba, background)\n", + "\n", + " # 7.2 Compute SHAP values\n", + " shap_vals = explainer.shap_values(X_test_c)\n", + "\n", + " # Handle different return shapes\n", + " if isinstance(shap_vals, list):\n", + " shap_values = shap_vals[1]\n", + " elif shap_vals.ndim == 3:\n", + " # (n_samples, n_features, n_classes) -> take positive class\n", + " shap_values = shap_vals[:, :, 1]\n", + " else:\n", + " shap_values = shap_vals\n", + "\n", + " # 7.3 SHAP plots\n", + " # Bar summary\n", + " plt.figure(figsize=(10, 6))\n", + " shap.summary_plot(shap_values, X_test_c, feature_names=feature_names,\n", + " plot_type='bar', show=False)\n", + " plt.title(f\"SHAP Bar Summary — {ds_name}\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"shap_summary_{ds_name}.png\")\n", + " plt.close()\n", + " print(f\"Saved shap_summary_{ds_name}.png\")\n", + "\n", + " # Beeswarm\n", + " plt.figure(figsize=(10, 6))\n", + " shap.summary_plot(shap_values, X_test_c, feature_names=feature_names,\n", + " plot_type='dot', show=False)\n", + " plt.title(f\"SHAP Beeswarm — {ds_name}\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"shap_beeswarm_{ds_name}.png\")\n", + " plt.close()\n", + "\n", + " # Dependence plots for top-3 features\n", + " mean_abs = np.abs(shap_values).mean(axis=0)\n", + " top3_idx = np.argsort(mean_abs)[-3:][::-1]\n", + " for idx in top3_idx:\n", + " plt.figure(figsize=(8, 5))\n", + " shap.dependence_plot(idx, shap_values, X_test_c,\n", + " feature_names=feature_names, show=False)\n", + " plt.title(f\"Dependence: {feature_names[idx]} — {ds_name}\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"shap_dependence_{ds_name}_{feature_names[idx]}.png\")\n", + " plt.close()\n", + "\n", + " # Waterfall plots for 3 representative instances\n", + " probs = model.predict_proba(X_test_c)[:, 1]\n", + " buggy_idx = np.where(y_test.values == 1)[0]\n", + " clean_idx = np.where(y_test.values == 0)[0]\n", + " rep_indices = [\n", + " int(buggy_idx[np.argmax(probs[buggy_idx])]),\n", + " int(np.argmin(np.abs(probs - 0.5))),\n", + " int(clean_idx[np.argmin(probs[clean_idx])]),\n", + " ]\n", + "\n", + " for ridx in rep_indices:\n", + " plt.figure(figsize=(10, 6))\n", + " base = explainer.expected_value\n", + " # Convert array expected_value to scalar for waterfall\n", + " if hasattr(base, '__len__') and not isinstance(base, str):\n", + " base = float(base[0])\n", + " shap.waterfall_plot(shap.Explanation(\n", + " values=shap_values[ridx],\n", + " base_values=base,\n", + " data=X_test_c[ridx],\n", + " feature_names=feature_names,\n", + " ), max_display=len(feature_names), show=False)\n", + " plt.title(f\"Waterfall — {ds_name} — idx={ridx} — true={int(y_test.values[ridx])}\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"shap_waterfall_{ds_name}_idx{ridx}.png\")\n", + " plt.close()\n", + "\n", + " # 7.4 Extract SHAP weight vector\n", + " mean_abs_shap = np.abs(shap_values).mean(axis=0)\n", + " shap_weights = mean_abs_shap / mean_abs_shap.sum()\n", + " SHAP_WEIGHTS[ds_name] = dict(zip(feature_names, shap_weights))\n", + "\n", + " weight_df = pd.DataFrame({\n", + " 'feature': feature_names,\n", + " 'mean_abs_SHAP': mean_abs_shap,\n", + " 'weight': shap_weights,\n", + " }).sort_values('weight', ascending=False)\n", + " print(f\"\\n--- SHAP Weights ({ds_name}) ---\")\n", + " display(weight_df)\n", + "\n", + " with open(f\"shap_weights_{ds_name}.json\", 'w') as f:\n", + " json.dump(SHAP_WEIGHTS[ds_name], f, indent=2)\n", + " print(f\"Saved shap_weights_{ds_name}.json\")\n", + "\n", + "# Save merged phase1 SHAP weights\n", + "with open(\"shap_weights_phase1.json\", 'w') as f:\n", + " json.dump(SHAP_WEIGHTS, f, indent=2)\n", + "print(\"\\nSaved shap_weights_phase1.json\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "23125db6", + "metadata": {}, + "source": [ + "# Section 8 — Cross-Dataset SHAP Weight Averaging\n", + "\n", + "Align feature spaces across Phase 1 datasets, handle missing features as NaN, and compute a canonical averaged weight vector." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1aed1920", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:57:04.454361Z", + "iopub.status.busy": "2026-05-07T14:57:04.454039Z", + "iopub.status.idle": "2026-05-07T14:57:04.707461Z", + "shell.execute_reply": "2026-05-07T14:57:04.706524Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unified feature space (5 features): ['numberOfBugsFoundUntil', 'numberOfCriticalBugsFoundUntil', 'numberOfHighPriorityBugsFoundUntil', 'numberOfMajorBugsFoundUntil', 'numberOfNonTrivialBugsFoundUntil']\n", + "Saved averaged_shap_weights.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved averaged_shap_weights.png\n", + "\n", + "--- Averaged SHAP Weight Comparison ---\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featureaveraged_weightweight_eclipseweight_mylynn_datasets_contributing
0numberOfBugsFoundUntil0.48130.51170.45092
1numberOfCriticalBugsFoundUntil0.06420.07960.04882
2numberOfHighPriorityBugsFoundUntil0.16270.03330.29222
3numberOfMajorBugsFoundUntil0.12760.14440.11082
4numberOfNonTrivialBugsFoundUntil0.16430.23110.09742
\n", + "
" + ], + "text/plain": [ + " feature averaged_weight weight_eclipse \\\n", + "0 numberOfBugsFoundUntil 0.4813 0.5117 \n", + "1 numberOfCriticalBugsFoundUntil 0.0642 0.0796 \n", + "2 numberOfHighPriorityBugsFoundUntil 0.1627 0.0333 \n", + "3 numberOfMajorBugsFoundUntil 0.1276 0.1444 \n", + "4 numberOfNonTrivialBugsFoundUntil 0.1643 0.2311 \n", + "\n", + " weight_mylyn n_datasets_contributing \n", + "0 0.4509 2 \n", + "1 0.0488 2 \n", + "2 0.2922 2 \n", + "3 0.1108 2 \n", + "4 0.0974 2 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 8.1 Build unified feature space\n", + "ALL_FEATURES = sorted(list(set().union(*[set(SHAP_WEIGHTS[ds].keys()) for ds in CONFIG['phase1']])))\n", + "print(f\"Unified feature space ({len(ALL_FEATURES)} features): {ALL_FEATURES}\")\n", + "\n", + "weight_matrix = []\n", + "for ds_name in CONFIG['phase1']:\n", + " vec = []\n", + " for f in ALL_FEATURES:\n", + " vec.append(SHAP_WEIGHTS[ds_name].get(f, np.nan))\n", + " weight_matrix.append(vec)\n", + "\n", + "weight_matrix = np.array(weight_matrix, dtype=float)\n", + "\n", + "# 8.2 Averaged weight vector (NaN-aware)\n", + "AVERAGED_SHAP_WEIGHTS = np.nanmean(weight_matrix, axis=0)\n", + "AVERAGED_SHAP_WEIGHTS = AVERAGED_SHAP_WEIGHTS / AVERAGED_SHAP_WEIGHTS.sum()\n", + "avg_dict = {f: float(w) for f, w in zip(ALL_FEATURES, AVERAGED_SHAP_WEIGHTS)}\n", + "\n", + "with open(\"averaged_shap_weights.json\", 'w') as f:\n", + " json.dump(avg_dict, f, indent=2)\n", + "print(\"Saved averaged_shap_weights.json\")\n", + "\n", + "# Visualise\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "y_pos = np.arange(len(ALL_FEATURES))\n", + "ax.barh(y_pos, AVERAGED_SHAP_WEIGHTS, color='seagreen')\n", + "ax.set_yticks(y_pos)\n", + "ax.set_yticklabels(ALL_FEATURES)\n", + "ax.set_xlabel('Averaged SHAP Weight')\n", + "ax.set_title('Averaged SHAP Weights Across Phase 1 Datasets')\n", + "for i, v in enumerate(AVERAGED_SHAP_WEIGHTS):\n", + " ax.text(v + 0.005, i, f\"{v:.3f}\", va='center', fontsize=9)\n", + "plt.tight_layout()\n", + "plt.savefig(\"averaged_shap_weights.png\")\n", + "plt.close()\n", + "print(\"Saved averaged_shap_weights.png\")\n", + "\n", + "# Table\n", + "comp_rows = []\n", + "for i, f in enumerate(ALL_FEATURES):\n", + " row = {'feature': f, 'averaged_weight': f\"{AVERAGED_SHAP_WEIGHTS[i]:.4f}\"}\n", + " for j, ds_name in enumerate(CONFIG['phase1']):\n", + " row[f'weight_{ds_name}'] = f\"{weight_matrix[j, i]:.4f}\" if not np.isnan(weight_matrix[j, i]) else \"NaN\"\n", + " row['n_datasets_contributing'] = int(np.sum(~np.isnan(weight_matrix[:, i])))\n", + " comp_rows.append(row)\n", + "\n", + "comp_df = pd.DataFrame(comp_rows)\n", + "print(\"\\n--- Averaged SHAP Weight Comparison ---\")\n", + "display(comp_df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4b2e7919", + "metadata": {}, + "source": [ + "# Section 9 — Phase 2: Apply Averaged SHAP Weights to Remaining Datasets\n", + "\n", + "For each Phase 2 dataset: load, align features, create original and SHAP-weighted variants, retrain all 8 models, and compare." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "49290102", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:57:04.709683Z", + "iopub.status.busy": "2026-05-07T14:57:04.709463Z", + "iopub.status.idle": "2026-05-07T14:57:18.321327Z", + "shell.execute_reply": "2026-05-07T14:57:18.320129Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "PHASE 2 — Dataset: equinox\n", + "======================================================================\n", + "Train: (259, 5) | Test: (65, 5)\n", + "Class dist: [156 103]\n", + "\n", + "--- Variant: original ---\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2bb2416ab15144c18b8b37104e7ef8a9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "equinox-original: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modeldelta_f1_strdelta_roc_str
0RandomForest+0.0000+0.0020
1XGBoost+0.0000+0.0000
2LightGBM+0.0000+0.0000
3GradientBoosting+0.0000+0.0000
4ExtraTrees+0.0000+0.0000
5LogisticRegression+0.0000-0.0030
6SVM+0.0424-0.0325
7KNN-0.0720-0.0725
\n", + "" + ], + "text/plain": [ + " model delta_f1_str delta_roc_str\n", + "0 RandomForest +0.0000 +0.0020\n", + "1 XGBoost +0.0000 +0.0000\n", + "2 LightGBM +0.0000 +0.0000\n", + "3 GradientBoosting +0.0000 +0.0000\n", + "4 ExtraTrees +0.0000 +0.0000\n", + "5 LogisticRegression +0.0000 -0.0030\n", + "6 SVM +0.0424 -0.0325\n", + "7 KNN -0.0720 -0.0725" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Models benefiting from SHAP weighting: ['SVM']\n", + "Models hurt by SHAP weighting: ['KNN']\n", + "\n", + "======================================================================\n", + "PHASE 2 — Dataset: lucene\n", + "======================================================================\n", + "Train: (552, 5) | Test: (139, 5)\n", + "Class dist: [501 51]\n", + "\n", + "--- Variant: original ---\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "83f83c850b5748f1a4ea8bd69078cccf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "lucene-original: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modeldelta_f1_strdelta_roc_str
0RandomForest+0.0000+0.0000
1XGBoost+0.0000+0.0000
2LightGBM+0.0000+0.0000
3GradientBoosting+0.0000+0.0000
4ExtraTrees+0.0000+0.0000
5LogisticRegression+0.0000+0.0000
6SVM+0.0000-0.0049
7KNN+0.0000+0.0000
\n", + "" + ], + "text/plain": [ + " model delta_f1_str delta_roc_str\n", + "0 RandomForest +0.0000 +0.0000\n", + "1 XGBoost +0.0000 +0.0000\n", + "2 LightGBM +0.0000 +0.0000\n", + "3 GradientBoosting +0.0000 +0.0000\n", + "4 ExtraTrees +0.0000 +0.0000\n", + "5 LogisticRegression +0.0000 +0.0000\n", + "6 SVM +0.0000 -0.0049\n", + "7 KNN +0.0000 +0.0000" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Models benefiting from SHAP weighting: []\n", + "Models hurt by SHAP weighting: []\n", + "\n", + "======================================================================\n", + "PHASE 2 — Dataset: pde\n", + "======================================================================\n", + "Train: (1197, 5) | Test: (300, 5)\n", + "Class dist: [1030 167]\n", + "\n", + "--- Variant: original ---\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d1434329ff284e2bb99208252de791f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "pde-original: 0%| | 0/8 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modeldelta_f1_strdelta_roc_str
0RandomForest+0.0025-0.0007
1XGBoost+0.0000+0.0000
2LightGBM+0.0000+0.0000
3GradientBoosting+0.0000+0.0014
4ExtraTrees+0.0000+0.0000
5LogisticRegression+0.0547-0.0008
6SVM+0.0240-0.0071
7KNN-0.0201-0.0672
\n", + "" + ], + "text/plain": [ + " model delta_f1_str delta_roc_str\n", + "0 RandomForest +0.0025 -0.0007\n", + "1 XGBoost +0.0000 +0.0000\n", + "2 LightGBM +0.0000 +0.0000\n", + "3 GradientBoosting +0.0000 +0.0014\n", + "4 ExtraTrees +0.0000 +0.0000\n", + "5 LogisticRegression +0.0547 -0.0008\n", + "6 SVM +0.0240 -0.0071\n", + "7 KNN -0.0201 -0.0672" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Models benefiting from SHAP weighting: ['RandomForest', 'LogisticRegression', 'SVM']\n", + "Models hurt by SHAP weighting: ['KNN']\n" + ] + } + ], + "source": [ + "# PHASE 2\n", + "RESULTS_PHASE2 = {}\n", + "\n", + "for ds_name in CONFIG['phase2']:\n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"PHASE 2 — Dataset: {ds_name}\")\n", + " print(f\"{'='*70}\")\n", + "\n", + " data = DATASETS[ds_name]\n", + " X = data['X']\n", + " y = data['y']\n", + " feature_names = data['feature_names']\n", + "\n", + " # 9.1 Align to ALL_FEATURES\n", + " X_aligned = pd.DataFrame(0.0, index=X.index, columns=ALL_FEATURES)\n", + " for f in feature_names:\n", + " if f in ALL_FEATURES:\n", + " X_aligned[f] = X[f].values\n", + "\n", + " # 9.2 Apply averaged SHAP weights\n", + " weight_vec = np.array([avg_dict.get(f, 0.0) for f in ALL_FEATURES])\n", + " X_weighted = X_aligned * weight_vec\n", + "\n", + " # Train/test split\n", + " X_train_o, X_test_o, y_train, y_test = train_test_split(\n", + " X_aligned, y, test_size=CONFIG['test_size'], stratify=y,\n", + " random_state=CONFIG['random_state']\n", + " )\n", + " X_train_w, X_test_w, _, _ = train_test_split(\n", + " X_weighted, y, test_size=CONFIG['test_size'], stratify=y,\n", + " random_state=CONFIG['random_state']\n", + " )\n", + " print(f\"Train: {X_train_o.shape} | Test: {X_test_o.shape}\")\n", + " print(f\"Class dist: {np.bincount(y_train)}\")\n", + "\n", + " RESULTS_PHASE2[ds_name] = {'original': {}, 'weighted': {}}\n", + "\n", + " for variant, X_tr, X_te in [('original', X_train_o, X_test_o),\n", + " ('weighted', X_train_w, X_test_w)]:\n", + " print(f\"\\n--- Variant: {variant} ---\")\n", + " models = build_models(y_train.values)\n", + " for mname, mdict in tqdm(models.items(), desc=f\"{ds_name}-{variant}\"):\n", + " res = evaluate_model(mdict['model'], X_tr, y_train, X_te, y_test, mdict['pipeline'])\n", + " RESULTS_PHASE2[ds_name][variant][mname] = res\n", + " print(f\"{mname:20s} | F1={res['f1_macro']:.4f} | ROC={res['roc_auc']:.4f} | PR={res['pr_auc']:.4f}\")\n", + "\n", + " # 9.4 Comparative analysis\n", + " orig_df = pd.DataFrame([\n", + " {'model': m, 'f1_macro': RESULTS_PHASE2[ds_name]['original'][m]['f1_macro'],\n", + " 'roc_auc': RESULTS_PHASE2[ds_name]['original'][m]['roc_auc'],\n", + " 'pr_auc': RESULTS_PHASE2[ds_name]['original'][m]['pr_auc']}\n", + " for m in RESULTS_PHASE2[ds_name]['original']\n", + " ])\n", + " wgt_df = pd.DataFrame([\n", + " {'model': m, 'f1_macro': RESULTS_PHASE2[ds_name]['weighted'][m]['f1_macro'],\n", + " 'roc_auc': RESULTS_PHASE2[ds_name]['weighted'][m]['roc_auc'],\n", + " 'pr_auc': RESULTS_PHASE2[ds_name]['weighted'][m]['pr_auc']}\n", + " for m in RESULTS_PHASE2[ds_name]['weighted']\n", + " ])\n", + " merged = orig_df.merge(wgt_df, on='model', suffixes=('_orig', '_wgt'))\n", + " merged['delta_f1'] = merged['f1_macro_wgt'] - merged['f1_macro_orig']\n", + " merged['delta_roc'] = merged['roc_auc_wgt'] - merged['roc_auc_orig']\n", + "\n", + " # Grouped bar chart\n", + " fig, ax = plt.subplots(figsize=(12, 6))\n", + " x = np.arange(len(merged))\n", + " width = 0.35\n", + " ax.bar(x - width/2, merged['f1_macro_orig'], width, label='Original', color='steelblue')\n", + " ax.bar(x + width/2, merged['f1_macro_wgt'], width, label='SHAP-weighted', color='coral')\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(merged['model'], rotation=45, ha='right')\n", + " ax.set_ylabel('F1-macro')\n", + " ax.set_title(f'Original vs SHAP-weighted — {ds_name}')\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.savefig(f\"phase2_comparison_{ds_name}.png\")\n", + " plt.close()\n", + " print(f\"Saved phase2_comparison_{ds_name}.png\")\n", + "\n", + " # Delta table\n", + " print(f\"\\n--- Delta Table ({ds_name}) ---\")\n", + " delta_df = merged[['model', 'delta_f1', 'delta_roc']].copy()\n", + " delta_df['delta_f1_str'] = delta_df['delta_f1'].apply(lambda v: f\"{v:+.4f}\")\n", + " delta_df['delta_roc_str'] = delta_df['delta_roc'].apply(lambda v: f\"{v:+.4f}\")\n", + " display(delta_df[['model', 'delta_f1_str', 'delta_roc_str']])\n", + " winners = delta_df[delta_df['delta_f1'] > 0]['model'].tolist()\n", + " losers = delta_df[delta_df['delta_f1'] < 0]['model'].tolist()\n", + " print(f\"Models benefiting from SHAP weighting: {winners}\")\n", + " print(f\"Models hurt by SHAP weighting: {losers}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb11bc98", + "metadata": {}, + "source": [ + "# Section 10 — Summary and Cross-Dataset Report\n", + "\n", + "Master results table, cross-dataset consistency plot, SHAP comparison, and final printed summary." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9e9283a1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-07T14:57:18.323682Z", + "iopub.status.busy": "2026-05-07T14:57:18.323425Z", + "iopub.status.idle": "2026-05-07T14:57:18.878584Z", + "shell.execute_reply": "2026-05-07T14:57:18.877327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved results_summary.csv\n", + "Master results shape: (64, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datasetmodelvariantf1_macroroc_aucpr_aucis_phase1
0eclipseRandomForestoriginal0.6190480.7242680.471125True
1eclipseXGBoostoriginal0.6129030.6830800.452841True
2eclipseLightGBMoriginal0.6230310.7309400.465996True
3eclipseGradientBoostingoriginal0.7236230.7464340.522457True
4eclipseExtraTreesoriginal0.6293230.7198960.388231True
5eclipseLogisticRegressionoriginal0.7011430.7843230.627092True
6eclipseSVMoriginal0.7086830.7718980.595115True
7eclipseKNNoriginal0.7035570.7498850.572197True
8mylynRandomForestoriginal0.5528010.6059780.184380True
9mylynXGBoostoriginal0.5523190.5745150.220625True
\n", + "
" + ], + "text/plain": [ + " dataset model variant f1_macro roc_auc pr_auc \\\n", + "0 eclipse RandomForest original 0.619048 0.724268 0.471125 \n", + "1 eclipse XGBoost original 0.612903 0.683080 0.452841 \n", + "2 eclipse LightGBM original 0.623031 0.730940 0.465996 \n", + "3 eclipse GradientBoosting original 0.723623 0.746434 0.522457 \n", + "4 eclipse ExtraTrees original 0.629323 0.719896 0.388231 \n", + "5 eclipse LogisticRegression original 0.701143 0.784323 0.627092 \n", + "6 eclipse SVM original 0.708683 0.771898 0.595115 \n", + "7 eclipse KNN original 0.703557 0.749885 0.572197 \n", + "8 mylyn RandomForest original 0.552801 0.605978 0.184380 \n", + "9 mylyn XGBoost original 0.552319 0.574515 0.220625 \n", + "\n", + " is_phase1 \n", + "0 True \n", + "1 True \n", + "2 True \n", + "3 True \n", + "4 True \n", + "5 True \n", + "6 True \n", + "7 True \n", + "8 True \n", + "9 True " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved cross_dataset_consistency.png\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved shap_comparison.png\n", + "\n", + "======================================================================\n", + "FINAL SUMMARY\n", + "======================================================================\n", + "Best model overall (highest mean F1-macro): SVM (mean F1=0.6320)\n", + "Most consistent model (lowest std F1-macro): LightGBM (std F1=0.0366)\n", + "Feature with highest averaged SHAP weight: numberOfBugsFoundUntil (weight=0.4813)\n", + "Mean Δ_F1 on Phase 2 (weighted - original): +0.0013\n", + "SHAP weighting IMPROVED performance on Phase 2 datasets on average.\n", + "======================================================================\n" + ] + } + ], + "source": [ + "# 10.1 Master results table\n", + "master_rows = []\n", + "\n", + "# Phase 1\n", + "for ds_name in CONFIG['phase1']:\n", + " for mname, res in RESULTS_PHASE1[ds_name].items():\n", + " master_rows.append({\n", + " 'dataset': ds_name,\n", + " 'model': mname,\n", + " 'variant': 'original',\n", + " 'f1_macro': res['f1_macro'],\n", + " 'roc_auc': res['roc_auc'],\n", + " 'pr_auc': res['pr_auc'],\n", + " 'is_phase1': True,\n", + " })\n", + "\n", + "# Phase 2\n", + "for ds_name in CONFIG['phase2']:\n", + " for variant in ['original', 'weighted']:\n", + " for mname, res in RESULTS_PHASE2[ds_name][variant].items():\n", + " master_rows.append({\n", + " 'dataset': ds_name,\n", + " 'model': mname,\n", + " 'variant': variant,\n", + " 'f1_macro': res['f1_macro'],\n", + " 'roc_auc': res['roc_auc'],\n", + " 'pr_auc': res['pr_auc'],\n", + " 'is_phase1': False,\n", + " })\n", + "\n", + "master_df = pd.DataFrame(master_rows)\n", + "master_df.to_csv(\"results_summary.csv\", index=False)\n", + "print(\"Saved results_summary.csv\")\n", + "print(f\"Master results shape: {master_df.shape}\")\n", + "display(master_df.head(10))\n", + "\n", + "# 10.2 Cross-dataset model consistency plot\n", + "consistency = defaultdict(dict)\n", + "for _, row in master_df.iterrows():\n", + " if row['is_phase1'] or (not row['is_phase1'] and row['variant'] == 'weighted'):\n", + " consistency[row['model']][row['dataset']] = row['f1_macro']\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "all_datasets = CONFIG['phase1'] + CONFIG['phase2']\n", + "x = np.arange(len(all_datasets))\n", + "for mname in sorted(consistency.keys()):\n", + " vals = [consistency[mname].get(ds, np.nan) for ds in all_datasets]\n", + " ax.plot(x, vals, marker='o', label=mname)\n", + "ax.set_xticks(x)\n", + "ax.set_xticklabels(all_datasets)\n", + "ax.set_ylabel('F1-macro')\n", + "ax.set_title('Cross-Dataset Model Consistency (Phase 1 original + Phase 2 weighted)')\n", + "ax.legend()\n", + "ax.set_ylim(0, 1.05)\n", + "plt.tight_layout()\n", + "plt.savefig(\"cross_dataset_consistency.png\")\n", + "plt.close()\n", + "print(\"Saved cross_dataset_consistency.png\")\n", + "\n", + "# 10.3 SHAP feature importance comparison\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "bar_width = 0.25\n", + "x = np.arange(len(ALL_FEATURES))\n", + "for i, ds_name in enumerate(CONFIG['phase1']):\n", + " weights = [SHAP_WEIGHTS[ds_name].get(f, 0.0) for f in ALL_FEATURES]\n", + " ax.bar(x + i*bar_width, weights, bar_width, label=ds_name)\n", + "ax.bar(x + len(CONFIG['phase1'])*bar_width, AVERAGED_SHAP_WEIGHTS,\n", + " bar_width, label='averaged', color='black')\n", + "ax.set_xticks(x + bar_width)\n", + "ax.set_xticklabels(ALL_FEATURES, rotation=45, ha='right')\n", + "ax.set_ylabel('SHAP Weight')\n", + "ax.set_title('SHAP Feature Importance Comparison')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.savefig(\"shap_comparison.png\")\n", + "plt.close()\n", + "print(\"Saved shap_comparison.png\")\n", + "\n", + "# 10.4 Final printed summary\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"FINAL SUMMARY\")\n", + "print(\"=\"*70)\n", + "\n", + "model_mean_f1 = {}\n", + "model_std_f1 = {}\n", + "for mname in sorted(consistency.keys()):\n", + " vals = [consistency[mname].get(ds, np.nan) for ds in all_datasets]\n", + " model_mean_f1[mname] = np.nanmean(vals)\n", + " model_std_f1[mname] = np.nanstd(vals)\n", + "\n", + "best_overall = max(model_mean_f1, key=model_mean_f1.get)\n", + "print(f\"Best model overall (highest mean F1-macro): {best_overall} (mean F1={model_mean_f1[best_overall]:.4f})\")\n", + "\n", + "most_consistent = min(model_std_f1, key=model_std_f1.get)\n", + "print(f\"Most consistent model (lowest std F1-macro): {most_consistent} (std F1={model_std_f1[most_consistent]:.4f})\")\n", + "\n", + "top_feature = ALL_FEATURES[np.argmax(AVERAGED_SHAP_WEIGHTS)]\n", + "print(f\"Feature with highest averaged SHAP weight: {top_feature} (weight={AVERAGED_SHAP_WEIGHTS.max():.4f})\")\n", + "\n", + "phase2_deltas = []\n", + "for ds_name in CONFIG['phase2']:\n", + " for mname in RESULTS_PHASE2[ds_name]['original']:\n", + " d = RESULTS_PHASE2[ds_name]['weighted'][mname]['f1_macro'] - RESULTS_PHASE2[ds_name]['original'][mname]['f1_macro']\n", + " phase2_deltas.append(d)\n", + "mean_delta = np.mean(phase2_deltas)\n", + "print(f\"Mean Δ_F1 on Phase 2 (weighted - original): {mean_delta:+.4f}\")\n", + "if mean_delta > 0:\n", + " print(\"SHAP weighting IMPROVED performance on Phase 2 datasets on average.\")\n", + "else:\n", + " print(\"SHAP weighting did NOT improve performance on Phase 2 datasets on average.\")\n", + "\n", + "print(\"=\"*70)\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0284da0f5b274e78838f38b3ef2dca55": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "02e2dc7a600a4c91a197341fa9d40e78": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "04ec49c98dd94569900101513328aa90": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "05235f0bc96547059ca6fe4457025389": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_10d60d1486b44fbc8bbd3faa54a6f915", + "placeholder": "​", + "style": "IPY_MODEL_564a613aa6d94ba39345122e9b745f8b", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:02<00:00,  2.81it/s]" + } + }, + "05d22bc8cd804154b2157d2a82a331ee": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0858affa4842445cac0f54e2a8f7253c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0879b02b60f64264b14d61f50ae2c36e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0919568854c74666ba410773c46d6531": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0d1ee3fa705d42be8e41ceec681c4ce3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5aadfc1b281a4e59abc5d87ae7c89ea4", + "placeholder": "​", + "style": "IPY_MODEL_57fbb52b97684b428f933a6e02fb2228", + "tabbable": null, + "tooltip": null, + "value": "equinox-original: 100%" + } + }, + "0dc89b8c52b24ff4b1e6a0b45351d27c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0ecba73843334c449c6adc370c54391b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "10d60d1486b44fbc8bbd3faa54a6f915": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "110576e094ef44fcaa43bdfe287d65b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31ba4fe654af4fad98c1e09f29de0494", + "placeholder": "​", + "style": "IPY_MODEL_cd03671898e64c7d8b47801776ce2b24", + "tabbable": null, + "tooltip": null, + "value": " 373/373 [00:49<00:00,  7.09it/s]" + } + }, + "1458302bf50f4a2186435d0e0f2d49ad": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "160da6fb6320427591dfed49754b016b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "17e39b92dabf4c81a81bc5ae88796ad8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1893bf392d76449daa021ddb69c0aa6a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1458302bf50f4a2186435d0e0f2d49ad", + "placeholder": "​", + "style": "IPY_MODEL_3c9899dbd3cb4695b21d2c4ea4686810", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:10<00:00,  1.10it/s]" + } + }, + "198f2b2bb8ac4a19815ab28440e28af1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_160da6fb6320427591dfed49754b016b", + "placeholder": "​", + "style": "IPY_MODEL_0ecba73843334c449c6adc370c54391b", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:05<00:00,  1.56it/s]" + } + }, + "1d2208887ff84ef28b759fb284cc1c8a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1ebd7a176f7a498ba91eff163d6477f3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2e6dcc8ce49349b3b01e560b91f2d5bc", + "placeholder": "​", + "style": "IPY_MODEL_daf841ec919145349c4fe0ac8baf41ca", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "1f4cb99e204a4b259e5a0bafbf45b1ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8eff8d8991ae483c9b9334c9049f4c4b", + "placeholder": "​", + "style": "IPY_MODEL_e0825ef7ec7240f2a010a6d67b62dca8", + "tabbable": null, + "tooltip": null, + "value": "lucene-weighted: 100%" + } + }, + "1fec56b1ec0f44bab464a367147f0d63": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2050bf4ed4754e5fa129740cc3440a00": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "20b779efcc864138a4a4d823fcb607b5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "22ad2409dd7f4e3e927b4ed51ab0b687": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2417bda2b35441aa813960c2d3853e62": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_de9024a569504bd4afd36143c315d361", + "placeholder": "​", + "style": "IPY_MODEL_17e39b92dabf4c81a81bc5ae88796ad8", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:06<00:00,  1.30it/s]" + } + }, + "247614267d7b45eb8ca13b12ee3e4125": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7140e4eee15d43b3b78e05505798ed43", + "IPY_MODEL_d606de86e26d45579dd670efbb6dca0f", + "IPY_MODEL_198f2b2bb8ac4a19815ab28440e28af1" + ], + "layout": "IPY_MODEL_a20759f9177f4340878ee83d8e199f65", + "tabbable": null, + "tooltip": null + } + }, + "26e7c61625fc41e4bfe28c54ae6d062e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "28bd53fb8811463096b598cb5ffe599d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_26e7c61625fc41e4bfe28c54ae6d062e", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_375fbe7d0d554fd0a72c890daf36f60b", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "2bb2416ab15144c18b8b37104e7ef8a9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0d1ee3fa705d42be8e41ceec681c4ce3", + "IPY_MODEL_919f2ce85b5e49e2b1c170467e254b17", + "IPY_MODEL_b8f77d7dbbc1491690614d207daa44ac" + ], + "layout": "IPY_MODEL_b73469d3def44411a13c5371dc15c5e5", + "tabbable": null, + "tooltip": null + } + }, + "2c297559b06c46b1b6f073a675d90d0e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2d415d65588b45fd9fc4d08239ae14fc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2e6dcc8ce49349b3b01e560b91f2d5bc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "31639c90e81442a983107b10fb9163c0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "31ba4fe654af4fad98c1e09f29de0494": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "346e868928b0421f80a3a70ae2bd40a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "35055e1b7389436b88600679dbbc22eb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6f4357e554ec4d98bc23bfddd3996007", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ad58d70ddeff4b48a4155b8c3200f6d0", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "36e19e3b212f4e2a8ff4962b1d019b69": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_39b91419e0e1422a87a5cd4c0a6e4986", + "IPY_MODEL_fd5beca9762c4386a116cb6ea0601b9d", + "IPY_MODEL_1893bf392d76449daa021ddb69c0aa6a" + ], + "layout": "IPY_MODEL_672370ebe3bb44a488573bf617fad3b7", + "tabbable": null, + "tooltip": null + } + }, + "375fbe7d0d554fd0a72c890daf36f60b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "38d1f02d5ce643888933e703e9d2d89b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "3933f5e4a7594d28849fa75577e9aa2b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1fec56b1ec0f44bab464a367147f0d63", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0879b02b60f64264b14d61f50ae2c36e", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "39b91419e0e1422a87a5cd4c0a6e4986": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_56bcb0086c664a2990b9a8e9f8669140", + "placeholder": "​", + "style": "IPY_MODEL_2c297559b06c46b1b6f073a675d90d0e", + "tabbable": null, + "tooltip": null, + "value": "CV eclipse: 100%" + } + }, + "3bdd59c4fbd04a10a952e98f3ef690ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3c9899dbd3cb4695b21d2c4ea4686810": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3d0781dbf4504a8dbb18872f4983879d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_38d1f02d5ce643888933e703e9d2d89b", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_04ec49c98dd94569900101513328aa90", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "442dc07ba2c04dfa8b082c16eefe8a51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8dc69776a361482c9ccf03de5aa2a6e5", + "placeholder": "​", + "style": "IPY_MODEL_85da01686e91495bbb563806acd71548", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:02<00:00,  2.96it/s]" + } + }, + "4a1d247bfa6c41f09a7e86e274e8356c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0dc89b8c52b24ff4b1e6a0b45351d27c", + "placeholder": "​", + "style": "IPY_MODEL_ef513f3203f34b0588b26f821bd4c7bc", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:13<00:00,  1.30s/it]" + } + }, + "4d2febe205f542f1a8351873555e2b2c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "4ef662a0223341eeaf94159b736f66bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4f67f20befda43119e11318be4df03c5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d4ab78cc3f874a9a83efd22c31587d45", + "IPY_MODEL_28bd53fb8811463096b598cb5ffe599d", + "IPY_MODEL_2417bda2b35441aa813960c2d3853e62" + ], + "layout": "IPY_MODEL_0919568854c74666ba410773c46d6531", + "tabbable": null, + "tooltip": null + } + }, + "51bc441a2ed34beca459f67f6f515d2b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "51d41c1f599546b892ad698782a365c0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "522cf0aca5074160a4d626771fb88607": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "564a613aa6d94ba39345122e9b745f8b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "56bcb0086c664a2990b9a8e9f8669140": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "57fbb52b97684b428f933a6e02fb2228": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5aadfc1b281a4e59abc5d87ae7c89ea4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "5ede1b85299748cfa21d04e2397c8aa6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_522cf0aca5074160a4d626771fb88607", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fd7e2dcd71d94de8b1f1ca6bd1777c7d", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "605d13e302c849b7a0022fad5b851159": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5abd23bb547493a8820c0ab496a7533", + "max": 373.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_961efe5a5c634a7d83dd4d378d0dd9ed", + "tabbable": null, + "tooltip": null, + "value": 373.0 + } + }, + "6083e74a95ca49cabe1652425a7f8e7e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c35082e5a14145cc8faee9a29f32b967", + "placeholder": "​", + "style": "IPY_MODEL_4ef662a0223341eeaf94159b736f66bb", + "tabbable": null, + "tooltip": null, + "value": "pde-weighted: 100%" + } + }, + "617e1b7eed9747e2988c9d572f209b0a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "672370ebe3bb44a488573bf617fad3b7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6b0138ae2305411a98ab3b1a1616b33b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6e08471824604272acf89241db17436f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6f4357e554ec4d98bc23bfddd3996007": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7140e4eee15d43b3b78e05505798ed43": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d88824b36690468685e3d539b71b1c08", + "placeholder": "​", + "style": "IPY_MODEL_3bdd59c4fbd04a10a952e98f3ef690ea", + "tabbable": null, + "tooltip": null, + "value": "Test eclipse: 100%" + } + }, + "79ab93a18efe40e380d1f663e9ed349b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "7af6cb2e27ee4eac9b706dc21c32dd50": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8152e391854d41418417b1f9008208f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "83f83c850b5748f1a4ea8bd69078cccf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_df37ae06dd9b4f84bd6390a1486ea9c0", + "IPY_MODEL_a4961208765a4615b9936322ef0141a5", + "IPY_MODEL_e1321c32fe604168a3114e4efd86e1f5" + ], + "layout": "IPY_MODEL_22ad2409dd7f4e3e927b4ed51ab0b687", + "tabbable": null, + "tooltip": null + } + }, + "85da01686e91495bbb563806acd71548": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8b829c3c176343c68badc2c60e955d5d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8dc69776a361482c9ccf03de5aa2a6e5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8eff8d8991ae483c9b9334c9049f4c4b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "919f2ce85b5e49e2b1c170467e254b17": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6b0138ae2305411a98ab3b1a1616b33b", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2050bf4ed4754e5fa129740cc3440a00", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "922a74d8bfe245b493776e230f19e35f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_93a382e07d484cedbf2bd4c82c5b511f", + "placeholder": "​", + "style": "IPY_MODEL_f94c9fbd8a2a48c088189d77428c43b6", + "tabbable": null, + "tooltip": null, + "value": "equinox-weighted: 100%" + } + }, + "93a382e07d484cedbf2bd4c82c5b511f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "961efe5a5c634a7d83dd4d378d0dd9ed": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9646791c601f422599eea54b25460718": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "9a2021e1e6cf4dd484127c057cdfd263": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6083e74a95ca49cabe1652425a7f8e7e", + "IPY_MODEL_35055e1b7389436b88600679dbbc22eb", + "IPY_MODEL_05235f0bc96547059ca6fe4457025389" + ], + "layout": "IPY_MODEL_fca5a24e30104db6b134da0596895588", + "tabbable": null, + "tooltip": null + } + }, + "9b852374f85146ea823f1ed5c3a06e5b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0858affa4842445cac0f54e2a8f7253c", + "placeholder": "​", + "style": "IPY_MODEL_6e08471824604272acf89241db17436f", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:01<00:00,  3.11it/s]" + } + }, + "a19d03918e994c6f8cab3f054c5a4108": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1f4cb99e204a4b259e5a0bafbf45b1ad", + "IPY_MODEL_5ede1b85299748cfa21d04e2397c8aa6", + "IPY_MODEL_ca3f9f0f93574948a68229d0a5141d5d" + ], + "layout": "IPY_MODEL_dc429b44001e4edcbdebd2fb3fd1c973", + "tabbable": null, + "tooltip": null + } + }, + "a20759f9177f4340878ee83d8e199f65": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "a4961208765a4615b9936322ef0141a5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_02e2dc7a600a4c91a197341fa9d40e78", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_346e868928b0421f80a3a70ae2bd40a3", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "a55c1e367ca54a74a222e7e18bfede16": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ad58d70ddeff4b48a4155b8c3200f6d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "b65cd0e13eeb4fefb899be3f0a7f316c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "b73469d3def44411a13c5371dc15c5e5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b8f77d7dbbc1491690614d207daa44ac": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_05d22bc8cd804154b2157d2a82a331ee", + "placeholder": "​", + "style": "IPY_MODEL_cc364820cffc48f1b34e0569b5708fbc", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:01<00:00,  3.20it/s]" + } + }, + "b99273bb58a44526825c247e3a902f3c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c2fd44f6263b414f9ddb886736d02d9d", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9646791c601f422599eea54b25460718", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "bb77565a12f3457f98a236d99e9d795e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1ebd7a176f7a498ba91eff163d6477f3", + "IPY_MODEL_605d13e302c849b7a0022fad5b851159", + "IPY_MODEL_110576e094ef44fcaa43bdfe287d65b0" + ], + "layout": "IPY_MODEL_1d2208887ff84ef28b759fb284cc1c8a", + "tabbable": null, + "tooltip": null + } + }, + "bfae16dd28224fbabb7e1905db995c9e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c2fd44f6263b414f9ddb886736d02d9d": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c35082e5a14145cc8faee9a29f32b967": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "c9899cc475b242b684fdef7203c75fbc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ca3f9f0f93574948a68229d0a5141d5d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_79ab93a18efe40e380d1f663e9ed349b", + "placeholder": "​", + "style": "IPY_MODEL_bfae16dd28224fbabb7e1905db995c9e", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:02<00:00,  4.17it/s]" + } + }, + "cc364820cffc48f1b34e0569b5708fbc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cd03671898e64c7d8b47801776ce2b24": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d1434329ff284e2bb99208252de791f9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f00ba37c0cc94e9d963f88a3e55a355c", + "IPY_MODEL_b99273bb58a44526825c247e3a902f3c", + "IPY_MODEL_442dc07ba2c04dfa8b082c16eefe8a51" + ], + "layout": "IPY_MODEL_ff61fd8b0bf44d4584cabd6680eef38e", + "tabbable": null, + "tooltip": null + } + }, + "d4ab78cc3f874a9a83efd22c31587d45": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8152e391854d41418417b1f9008208f6", + "placeholder": "​", + "style": "IPY_MODEL_51d41c1f599546b892ad698782a365c0", + "tabbable": null, + "tooltip": null, + "value": "Test mylyn: 100%" + } + }, + "d606de86e26d45579dd670efbb6dca0f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a55c1e367ca54a74a222e7e18bfede16", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e385ab9eb62d48d08d8276f721c0d980", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "d88824b36690468685e3d539b71b1c08": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "daf841ec919145349c4fe0ac8baf41ca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "dc429b44001e4edcbdebd2fb3fd1c973": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "de9024a569504bd4afd36143c315d361": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "df37ae06dd9b4f84bd6390a1486ea9c0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f6149532775647bba639b367269b4f34", + "placeholder": "​", + "style": "IPY_MODEL_0284da0f5b274e78838f38b3ef2dca55", + "tabbable": null, + "tooltip": null, + "value": "lucene-original: 100%" + } + }, + "e0825ef7ec7240f2a010a6d67b62dca8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "e1321c32fe604168a3114e4efd86e1f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7af6cb2e27ee4eac9b706dc21c32dd50", + "placeholder": "​", + "style": "IPY_MODEL_51bc441a2ed34beca459f67f6f515d2b", + "tabbable": null, + "tooltip": null, + "value": " 8/8 [00:02<00:00,  4.01it/s]" + } + }, + "e385ab9eb62d48d08d8276f721c0d980": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e574f804ba554d1f8e97c1256ba9fa1c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_617e1b7eed9747e2988c9d572f209b0a", + "placeholder": "​", + "style": "IPY_MODEL_8b829c3c176343c68badc2c60e955d5d", + "tabbable": null, + "tooltip": null, + "value": "CV mylyn: 100%" + } + }, + "e5abd23bb547493a8820c0ab496a7533": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e6a9373f078d4b079ff1e6f0a095b629": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e574f804ba554d1f8e97c1256ba9fa1c", + "IPY_MODEL_3d0781dbf4504a8dbb18872f4983879d", + "IPY_MODEL_4a1d247bfa6c41f09a7e86e274e8356c" + ], + "layout": "IPY_MODEL_20b779efcc864138a4a4d823fcb607b5", + "tabbable": null, + "tooltip": null + } + }, + "ef513f3203f34b0588b26f821bd4c7bc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f00ba37c0cc94e9d963f88a3e55a355c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2d415d65588b45fd9fc4d08239ae14fc", + "placeholder": "​", + "style": "IPY_MODEL_b65cd0e13eeb4fefb899be3f0a7f316c", + "tabbable": null, + "tooltip": null, + "value": "pde-original: 100%" + } + }, + "f6149532775647bba639b367269b4f34": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f94c9fbd8a2a48c088189d77428c43b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fc0f28da08a04721acc6065aa9bcb56c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_922a74d8bfe245b493776e230f19e35f", + "IPY_MODEL_3933f5e4a7594d28849fa75577e9aa2b", + "IPY_MODEL_9b852374f85146ea823f1ed5c3a06e5b" + ], + "layout": "IPY_MODEL_c9899cc475b242b684fdef7203c75fbc", + "tabbable": null, + "tooltip": null + } + }, + "fca5a24e30104db6b134da0596895588": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "fd5beca9762c4386a116cb6ea0601b9d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31639c90e81442a983107b10fb9163c0", + "max": 8.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d2febe205f542f1a8351873555e2b2c", + "tabbable": null, + "tooltip": null, + "value": 8.0 + } + }, + "fd7e2dcd71d94de8b1f1ca6bd1777c7d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "ff61fd8b0bf44d4584cabd6680eef38e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}