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"**Methodology:** CRISP-DM (Chapman et al., 2000)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8ZQwQurgu2mt" }, "source": [ "---\n", "## Section 0 — Setup\n", "Run every time you open the notebook. Requires T4 GPU for XLM-RoBERTa." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VCPbI_JOu2mt", "outputId": "8098d139-7a55-41f6-8c29-107053333b82" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU: Tesla T4, 15360 MiB\n" ] } ], "source": [ "# 0.1 GPU check\n", "import subprocess\n", "r = subprocess.run(['nvidia-smi','--query-gpu=name,memory.total','--format=csv,noheader'],\n", " capture_output=True, text=True)\n", "if r.returncode == 0:\n", " print(\"GPU:\", r.stdout.strip())\n", "else:\n", " print(\"WARNING: No GPU. Go to Runtime > Change runtime type > T4 GPU\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8I-BwnSPu2mt", "outputId": "b06443d1-95b9-4f5f-d53c-a2fba1040381" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Done.\n" ] } ], "source": [ "# 0.2 Install dependencies\n", "!pip install -q transformers datasets accelerate scikit-learn xgboost\n", "!pip install -q matplotlib seaborn wordcloud scipy imbalanced-learn\n", "print(\"Done.\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fhV-W48Xu2mt", "outputId": "a4a377ba-ad91-4935-bbe3-6851d8004ef2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "PyTorch 2.10.0+cu128 | CUDA: True | Device: cuda\n" ] } ], "source": [ "# 0.3 Imports\n", "import pandas as pd\n", "import numpy as np\n", "import re, string, os, joblib, warnings, json\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from wordcloud import WordCloud\n", "from collections import Counter\n", "from scipy import stats\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import LinearSVC\n", "from sklearn.metrics import (accuracy_score, f1_score, cohen_kappa_score,\n", " classification_report, confusion_matrix, roc_auc_score)\n", "from xgboost import XGBClassifier\n", "\n", "import torch\n", "from transformers import (AutoTokenizer, AutoModelForSequenceClassification,\n", " TrainingArguments, Trainer)\n", "from datasets import Dataset as HFDataset\n", "\n", "warnings.filterwarnings('ignore')\n", "RANDOM_STATE = 42\n", "plt.rcParams['figure.dpi'] = 120\n", "\n", "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "print(f\"PyTorch {torch.__version__} | CUDA: {torch.cuda.is_available()} | Device: {DEVICE}\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yEgbyT6zu2mt", "outputId": "e4e0f016-ca96-4a1c-a698-925c291887e7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n", "Output directory: /content/drive/MyDrive/MindScan_SplitStudy\n" ] } ], "source": [ "# 0.4 Mount Google Drive\n", "from google.colab import drive\n", "drive.mount('/content/drive')\n", "DRIVE_DIR = '/content/drive/MyDrive/MindScan_SplitStudy'\n", "os.makedirs(DRIVE_DIR, exist_ok=True)\n", "print(f\"Output directory: {DRIVE_DIR}\")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 180 }, "id": "qQgMqwuju2mt", "outputId": "965fcefe-f28a-48a4-ef4c-d415c4efa55f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Please upload: DA_DB_3.csv | DA_DB_3_H1.csv | DA_DB_3_H2.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving DA_DB_3_H1.csv to DA_DB_3_H1.csv\n", "Saving DA_DB_3_H2.csv to DA_DB_3_H2.csv\n", "Saving DA_DB_3.csv to DA_DB_3.csv\n", "Uploaded: ['DA_DB_3_H1.csv', 'DA_DB_3_H2.csv', 'DA_DB_3.csv']\n" ] } ], "source": [ "# 0.5 Upload CSV files\n", "# Upload DA_DB_3.csv, DA_DB_3_H1.csv, DA_DB_3_H2.csv\n", "from google.colab import files\n", "print(\"Please upload: DA_DB_3.csv | DA_DB_3_H1.csv | DA_DB_3_H2.csv\")\n", "uploaded = files.upload()\n", "print(\"Uploaded:\", list(uploaded.keys()))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "K7I1HkW9u2mt" }, "source": [ "---\n", "## CRISP-DM Stage 1 — Business Understanding\n", "\n", "### Background\n", "Suicide risk detection in online text has significant public health implications. Our original MindScan experiment trained XLM-RoBERTa on 50,000 Reddit posts (25k/class), achieving **F1=0.9810**.\n", "\n", "A key operational question: **how sensitive is performance to training set size?** Labelling clinical text is expensive. If 50% of the data achieves equivalent performance to 100%, the labelling budget can be halved.\n", "\n", "### Research Question\n", "> *Does training data volume affect suicide risk classification performance when class distribution and text characteristics are held constant?*\n", "\n", "### Hypotheses\n", "- **H1 (Classical models):** Full dataset will outperform halves by a meaningful margin (>0.5% F1), as bag-of-words models continue to benefit from vocabulary coverage\n", "- **H2 (XLM-RoBERTa):** Transformer will show smaller sensitivity to data volume — pre-training on 2.5TB of multilingual text provides a strong prior that reduces dependency on fine-tuning volume\n", "- **H3 (Split consistency):** H1 and H2 will produce near-identical scores, confirming the dataset is uniformly shuffled with no systematic ordering bias\n", "\n", "### Success Criteria\n", "| Criterion | Threshold |\n", "|-----------|-----------|\n", "| Meaningful volume effect | Gap > 0.5% Macro F1 between Full and average of halves |\n", "| Split consistency | |H1 − H2| < 0.3% F1 |\n", "| Transformer advantage | XLM-RoBERTa F1 > best classical by > 1% on Full split |\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZOdR_UrHu2mt" }, "source": [ "---\n", "## CRISP-DM Stage 2 — Data Understanding & Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gFeK1jqAu2mt", "outputId": "86fda4a7-f42f-4c7c-a42e-585f57568197" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=======================================================\n", "\n", "D3-Full (232,074 rows):\n", " suicide : 116,037 (50.00%)\n", " non-suicide : 116,037 (50.00%)\n", "\n", "D3-H1 (116,037 rows):\n", " suicide : 58,071 (50.05%)\n", " non-suicide : 57,966 (49.95%)\n", "\n", "D3-H2 (116,037 rows):\n", " non-suicide : 58,071 (50.05%)\n", " suicide : 57,966 (49.95%)\n" ] } ], "source": [ "# 2.1 Load all three splits\n", "df_full = pd.read_csv('DA_DB_3.csv')\n", "df_h1 = pd.read_csv('DA_DB_3_H1.csv')\n", "df_h2 = pd.read_csv('DA_DB_3_H2.csv')\n", "\n", "# Standardise column names\n", "for df in [df_full, df_h1, df_h2]:\n", " df.rename(columns={c: c.lower().strip() for c in df.columns}, inplace=True)\n", " drop_cols = [c for c in df.columns if c.startswith('unnamed')]\n", " df.drop(columns=drop_cols, inplace=True)\n", "\n", "DATASETS = {'D3-Full': df_full, 'D3-H1': df_h1, 'D3-H2': df_h2}\n", "\n", "print(\"=\" * 55)\n", "for name, df in DATASETS.items():\n", " counts = df['class'].value_counts()\n", " print(f\"\\n{name} ({len(df):,} rows):\")\n", " for label, cnt in counts.items():\n", " print(f\" {label:<15}: {cnt:,} ({cnt/len(df)*100:.2f}%)\")\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dilZyiWHu2mu", "outputId": "495f68c5-dcbe-4da9-b48a-59fd0693b353" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=======================================================\n", " NULL / DUPLICATE / EMPTY CHECK\n", "=======================================================\n", "D3-Full: nulls=0 text_duplicates=0 empty=0\n", "D3-H1: nulls=0 text_duplicates=0 empty=0\n", "D3-H2: nulls=0 text_duplicates=0 empty=0\n" ] } ], "source": [ "# 2.2 Data quality checks\n", "print(\"=\" * 55)\n", "print(\" NULL / DUPLICATE / EMPTY CHECK\")\n", "print(\"=\" * 55)\n", "for name, df in DATASETS.items():\n", " nulls = df.isnull().sum().sum()\n", " dups = df.duplicated(subset=['text']).sum()\n", " empty = (df['text'].str.strip() == '').sum()\n", " print(f\"{name}: nulls={nulls} text_duplicates={dups} empty={empty}\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 517 }, "id": "o8ryL4QBu2mu", "outputId": "2cedf367-56d8-41a4-a743-10bb5d8d2b2b" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ], "source": [ "# 2.3 EDA Fig 1 — Class Distribution Across Splits\n", "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "fig.suptitle('Fig 1 — Class Distribution Across Dataset Splits', fontsize=14, fontweight='bold')\n", "colors = {'suicide': '#e05c5c', 'non-suicide': '#5c9ee0'}\n", "\n", "for ax, (name, df) in zip(axes, DATASETS.items()):\n", " counts = df['class'].value_counts()\n", " bars = ax.bar(counts.index, counts.values,\n", " color=[colors.get(l,'#888') for l in counts.index],\n", " edgecolor='white', linewidth=1.2, width=0.6)\n", " for bar, val in zip(bars, counts.values):\n", " ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+300,\n", " f\"{val:,}\\n({val/len(df)*100:.1f}%)\",\n", " ha='center', va='bottom', fontsize=9, fontweight='bold')\n", " ax.set_title(f\"{name}\\n(n={len(df):,})\", fontsize=11, fontweight='bold')\n", " ax.set_ylabel('Count'); ax.set_ylim(0, max(counts.values)*1.25)\n", " ax.grid(axis='y', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig1_class_distribution.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "dCUmeA8Fu2mu", "outputId": "6140fabf-21ff-4203-91b0-730a239be819" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Text Length Summary:\n", "Split Class Mean Median Std Max\n", "------------------------------------------------------------\n", "D3-Full suicide 212.1 134 264.4 9685\n", "D3-Full non-suicide 63.1 32 138.4 8221\n", "D3-H1 suicide 211.8 133 264.6 7481\n", "D3-H1 non-suicide 63.3 32 143.6 7758\n", "D3-H2 suicide 212.4 134 264.2 9685\n", "D3-H2 non-suicide 62.9 32 133.0 8221\n" ] } ], "source": [ "# 2.4 Text Length Analysis\n", "def word_count(text):\n", " return len(re.findall(r'\\w+', str(text)))\n", "\n", "for name, df in DATASETS.items():\n", " df['word_count'] = df['text'].apply(word_count)\n", "\n", "# EDA Fig 2 — Text Length by Class and Split\n", "fig, axes = plt.subplots(2, 3, figsize=(16, 10))\n", "fig.suptitle('Fig 2 — Text Length Distribution by Class and Split', fontsize=14, fontweight='bold')\n", "\n", "for col, (name, df) in enumerate(DATASETS.items()):\n", " # Box plot row\n", " data_bp = [df[df['class']==lbl]['word_count'].values for lbl in ['suicide','non-suicide']]\n", " bp = axes[0,col].boxplot(data_bp, labels=['Suicide','Non-Suicide'],\n", " patch_artist=True, notch=True,\n", " medianprops={'color':'white','linewidth':2})\n", " bp['boxes'][0].set_facecolor('#e05c5c')\n", " bp['boxes'][1].set_facecolor('#5c9ee0')\n", " axes[0,col].set_title(f\"{name} — Box Plot\", fontsize=10, fontweight='bold')\n", " axes[0,col].set_ylabel('Word Count'); axes[0,col].set_ylim(0, 600)\n", " axes[0,col].grid(axis='y', alpha=0.3)\n", "\n", " # Histogram row\n", " for lbl, col_hex in [('suicide','#e05c5c'),('non-suicide','#5c9ee0')]:\n", " subset = df[df['class']==lbl]['word_count'].clip(0,600)\n", " axes[1,col].hist(subset, bins=40, alpha=0.6, color=col_hex, label=lbl, edgecolor='none')\n", " axes[1,col].set_title(f\"{name} — Histogram (capped 600)\", fontsize=10, fontweight='bold')\n", " axes[1,col].set_xlabel('Word Count'); axes[1,col].set_ylabel('Frequency')\n", " axes[1,col].legend(fontsize=8); axes[1,col].grid(axis='y', alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig2_text_length.png'), bbox_inches='tight')\n", "plt.show()\n", "\n", "print(\"\\nText Length Summary:\")\n", "print(f\"{'Split':<12} {'Class':<16} {'Mean':>8} {'Median':>8} {'Std':>8} {'Max':>6}\")\n", "print(\"-\"*60)\n", "for name, df in DATASETS.items():\n", " for lbl in ['suicide','non-suicide']:\n", " wc = df[df['class']==lbl]['word_count']\n", " print(f\"{name:<12} {lbl:<16} {wc.mean():>8.1f} {wc.median():>8.0f} {wc.std():>8.1f} {wc.max():>6}\")\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 673 }, "id": "gZW4E_Tsu2mu", "outputId": "3c578d00-351b-419d-bfe6-91ac43ee369a" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ], "source": [ "# 2.5 Word Frequency Analysis\n", "STOPWORDS = set(['the','a','an','and','or','but','in','on','at','to','for','of','is',\n", " 'it','this','that','was','are','be','as','i','my','me','we','you',\n", " 'he','she','they','have','has','had','do','did','will','would','can',\n", " 'could','just','not','with','from','so','if','about','when','been',\n", " 'also','its','up','im','ive','dont','cant','wont','its','there'])\n", "\n", "def clean_for_freq(text):\n", " text = str(text).lower()\n", " text = re.sub(r'http\\S+|www\\S+', '', text)\n", " text = re.sub(r'@\\w+', '', text)\n", " text = text.translate(str.maketrans('','',string.punctuation))\n", " return text\n", "\n", "def top_words(texts, n=20):\n", " all_words = []\n", " for t in texts:\n", " all_words.extend([w for w in clean_for_freq(t).split()\n", " if w not in STOPWORDS and len(w) > 2])\n", " return Counter(all_words).most_common(n)\n", "\n", "df = df_full.copy()\n", "suicide_words = top_words(df[df['class']=='suicide']['text'])\n", "non_suicide_words = top_words(df[df['class']=='non-suicide']['text'])\n", "\n", "# Fig 3 — Top Word Frequencies\n", "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", "fig.suptitle('Fig 3 — Top 20 Words by Class (D3-Full, stopwords removed)',\n", " fontsize=14, fontweight='bold')\n", "\n", "for ax, (title, word_counts, color) in zip(axes, [\n", " ('Suicide Posts', suicide_words, '#e05c5c'),\n", " ('Non-Suicide Posts', non_suicide_words, '#5c9ee0')\n", "]):\n", " words, counts = zip(*word_counts)\n", " y_pos = range(len(words))\n", " bars = ax.barh(y_pos, counts, color=color, alpha=0.85, edgecolor='none')\n", " ax.set_yticks(y_pos); ax.set_yticklabels(words, fontsize=10)\n", " ax.invert_yaxis()\n", " ax.set_xlabel('Frequency')\n", " ax.set_title(title, fontsize=12, fontweight='bold')\n", " for bar, cnt in zip(bars, counts):\n", " ax.text(bar.get_width()+max(counts)*0.01, bar.get_y()+bar.get_height()/2,\n", " f\"{cnt:,}\", va='center', fontsize=8)\n", " ax.grid(axis='x', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig3_word_frequency.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EAnNtyZCu2mu", "outputId": "9f51402e-7f2f-4008-9b12-f01cb409812c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=================================================================\n", " KS TEST: Are H1 and H2 from the same distribution?\n", "=================================================================\n", "(p > 0.05 = cannot reject same-distribution hypothesis)\n", "\n", "Class: suicide\n", " KS(H1 vs H2) : stat=0.0049 p=0.4967 SAME\n", " KS(Full vs H1) : stat=0.0024 p=0.9758 SAME\n", " KS(Full vs H2) : stat=0.0024 p=0.9755 SAME\n", "\n", "Class: non-suicide\n", " KS(H1 vs H2) : stat=0.0037 p=0.8125 SAME\n", " KS(Full vs H1) : stat=0.0019 p=0.9992 SAME\n", " KS(Full vs H2) : stat=0.0019 p=0.9993 SAME\n", "\n", "=================================================================\n", " SHUFFLE VALIDATION (50% transitions = perfectly random)\n", "=================================================================\n", " D3-Full: 116,367/232,073 transitions (50.1%)\n", " D3-H1: 58,202/116,036 transitions (50.2%)\n", " D3-H2: 58,165/116,036 transitions (50.1%)\n" ] } ], "source": [ "# 2.6 Statistical Validation — KS Test + Transition Rate\n", "print(\"=\" * 65)\n", "print(\" KS TEST: Are H1 and H2 from the same distribution?\")\n", "print(\"=\" * 65)\n", "print(\"(p > 0.05 = cannot reject same-distribution hypothesis)\")\n", "print()\n", "\n", "for lbl in ['suicide','non-suicide']:\n", " h1_wc = df_h1[df_h1['class']==lbl]['word_count'].values\n", " h2_wc = df_h2[df_h2['class']==lbl]['word_count'].values\n", " full_wc = df_full[df_full['class']==lbl]['word_count'].values\n", "\n", " ks12, p12 = stats.ks_2samp(h1_wc, h2_wc)\n", " ks_fh1, pfh1 = stats.ks_2samp(full_wc, h1_wc)\n", " ks_fh2, pfh2 = stats.ks_2samp(full_wc, h2_wc)\n", "\n", " print(f\"Class: {lbl}\")\n", " print(f\" KS(H1 vs H2) : stat={ks12:.4f} p={p12:.4f} {'SAME' if p12>0.05 else 'DIFFERENT'}\")\n", " print(f\" KS(Full vs H1) : stat={ks_fh1:.4f} p={pfh1:.4f} {'SAME' if pfh1>0.05 else 'DIFFERENT'}\")\n", " print(f\" KS(Full vs H2) : stat={ks_fh2:.4f} p={pfh2:.4f} {'SAME' if pfh2>0.05 else 'DIFFERENT'}\")\n", " print()\n", "\n", "print(\"=\" * 65)\n", "print(\" SHUFFLE VALIDATION (50% transitions = perfectly random)\")\n", "print(\"=\" * 65)\n", "for name, df in DATASETS.items():\n", " labels = df['class'].tolist()\n", " transitions = sum(1 for i in range(1,len(labels)) if labels[i]!=labels[i-1])\n", " rate = transitions/(len(labels)-1)*100\n", " print(f\" {name}: {transitions:,}/{len(labels)-1:,} transitions ({rate:.1f}%)\")\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 696 }, "id": "74jn6wEcu2mu", "outputId": "e1d2c24e-aa4b-41d2-8dc7-fecfc88080c5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Vocabulary Summary:\n", " Split Class Unique Total Density\n", "D3-Full suicide 158943 16935124 0.94\n", "D3-Full non-suicide 121209 5196728 2.33\n", " D3-H1 suicide 103402 8465409 1.22\n", " D3-H1 non-suicide 81817 2608185 3.14\n", " D3-H2 suicide 102663 8469715 1.21\n", " D3-H2 non-suicide 75405 2588543 2.91\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ], "source": [ "# 2.7 EDA Fig 4 — Vocabulary Analysis\n", "vocab_data = []\n", "for name, df in DATASETS.items():\n", " for lbl in ['suicide','non-suicide']:\n", " texts = df[df['class']==lbl]['text'].apply(clean_for_freq)\n", " all_words = []\n", " for t in texts:\n", " all_words.extend([w for w in t.split() if len(w)>2])\n", " unique = len(set(all_words))\n", " total = len(all_words)\n", " vocab_data.append({'Split':name, 'Class':lbl,\n", " 'Unique':unique, 'Total':total,\n", " 'Density':round(unique/total*100,2)})\n", "\n", "vocab_df = pd.DataFrame(vocab_data)\n", "print(\"Vocabulary Summary:\")\n", "print(vocab_df.to_string(index=False))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "fig.suptitle('Fig 4 — Vocabulary Analysis Across Splits', fontsize=13, fontweight='bold')\n", "x = np.arange(3); w = 0.35\n", "split_names = list(DATASETS.keys())\n", "\n", "for ax, metric, title in [(axes[0],'Unique','Unique Vocabulary Size'),\n", " (axes[1],'Density','Lexical Density (%)')]:\n", " sv = [vocab_df[(vocab_df['Split']==s)&(vocab_df['Class']=='suicide')][metric].values[0]\n", " for s in split_names]\n", " nsv = [vocab_df[(vocab_df['Split']==s)&(vocab_df['Class']=='non-suicide')][metric].values[0]\n", " for s in split_names]\n", " ax.bar(x-w/2, sv, w, label='Suicide', color='#e05c5c', alpha=0.85)\n", " ax.bar(x+w/2, nsv, w, label='Non-Suicide', color='#5c9ee0', alpha=0.85)\n", " ax.set_xticks(x); ax.set_xticklabels(split_names)\n", " ax.set_title(title, fontsize=11, fontweight='bold')\n", " ax.legend(); ax.grid(axis='y', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig4_vocabulary.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "F7ZPU1aju2mu" }, "source": [ "---\n", "## CRISP-DM Stage 3 — Data Preparation & Feature Engineering\n", "\n", "### Preprocessing Pipeline\n", "1. **Lowercase** — normalise case variation\n", "2. **URL removal** — `http/https/www` patterns carry no semantic signal\n", "3. **@mention removal** — Reddit handles are noise\n", "4. **Punctuation removal** — reduces vocabulary fragmentation\n", "5. **Whitespace normalisation** — clean tokenisation\n", "\n", "### TF-IDF Design Choices\n", "| Parameter | Value | Justification |\n", "|-----------|-------|---------------|\n", "| `max_features` | 60,000 | Matches original D3 experiment; captures rich Reddit vocabulary |\n", "| `ngram_range` | (1, 2) | Bigrams capture key phrases: *\"want to die\"*, *\"feeling hopeless\"* |\n", "| `min_df` | 2 | Removes hapax legomena that won't generalise |\n", "| `sublinear_tf` | True | Log-scaled TF dampens high-frequency term dominance |\n", "\n", "### No SMOTE Required\n", "All three splits are 50/50 balanced. SMOTE would introduce synthetic noise on an already-balanced dataset.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dPVNMqT_u2mu", "outputId": "c5f34ae2-97eb-4833-ffd7-99a2c5172e2c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cleaning complete. Sample:\n", "ex wife threatening suiciderecently i left my wife for good because she has cheated on me twice and lied to me so much that i have decided to refuse t\n" ] } ], "source": [ "# 3.1 Text cleaning (identical to original MindScan notebooks)\n", "def clean_text(text):\n", " text = str(text).lower()\n", " text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n", " text = re.sub(r'@\\w+', '', text)\n", " text = re.sub(r'#', '', text)\n", " text = text.translate(str.maketrans('','',string.punctuation))\n", " text = re.sub(r'\\s+', ' ', text).strip()\n", " return text\n", "\n", "for name, df in DATASETS.items():\n", " df['clean'] = df['text'].apply(clean_text)\n", "\n", "print(\"Cleaning complete. Sample:\")\n", "print(df_full['clean'].iloc[0][:150])\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MDfU_k0Xu2mu", "outputId": "4694dddf-d9f8-4f5d-c45c-6b5c9c1395b5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Classes: [np.str_('non-suicide'), np.str_('suicide')] → [0, 1]\n", " 0 = non-suicide | 1 = suicide\n" ] } ], "source": [ "# 3.2 Label encoding\n", "le = LabelEncoder()\n", "le.fit(['non-suicide','suicide'])\n", "print(f\"Classes: {list(le.classes_)} → {list(range(len(le.classes_)))}\")\n", "print(\" 0 = non-suicide | 1 = suicide\")\n", "\n", "for name, df in DATASETS.items():\n", " df['label'] = le.transform(df['class'])\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "op7jHND1u2mu", "outputId": "f77b0658-6f00-4fe4-910e-faed074c3c50" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "D3-Full: 185,659 train | 46,415 test | suicide in test: 23,207 (50.0%)\n", "D3-H1: 92,829 train | 23,208 test | suicide in test: 11,615 (50.0%)\n", "D3-H2: 92,829 train | 23,208 test | suicide in test: 11,593 (50.0%)\n" ] } ], "source": [ "# 3.3 Stratified 80/20 train/test splits for all three datasets\n", "SPLITS = {}\n", "for name, df in DATASETS.items():\n", " X_tr, X_te, y_tr, y_te = train_test_split(\n", " df['clean'], df['label'],\n", " test_size=0.2, stratify=df['label'], random_state=RANDOM_STATE\n", " )\n", " SPLITS[name] = {'X_tr':X_tr, 'X_te':X_te, 'y_tr':y_tr, 'y_te':y_te}\n", " print(f\"{name}: {len(X_tr):,} train | {len(X_te):,} test \"\n", " f\"| suicide in test: {y_te.sum():,} ({y_te.mean()*100:.1f}%)\")\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LSy3lDW4u2mu", "outputId": "74c35b93-9c44-4731-f476-dd926e3bd2a3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "D3-Full: (185659, 60000) vocabulary=60,000\n", "D3-H1: (92829, 60000) vocabulary=60,000\n", "D3-H2: (92829, 60000) vocabulary=60,000\n" ] } ], "source": [ "# 3.4 TF-IDF vectorisation (fit on train only — no leakage)\n", "TFIDF = {}\n", "for name, split in SPLITS.items():\n", " tfidf = TfidfVectorizer(max_features=60000, ngram_range=(1,2),\n", " min_df=2, sublinear_tf=True)\n", " X_tr_t = tfidf.fit_transform(split['X_tr'])\n", " X_te_t = tfidf.transform(split['X_te'])\n", " TFIDF[name] = {'vec': tfidf, 'X_tr': X_tr_t, 'X_te': X_te_t}\n", " print(f\"{name}: {X_tr_t.shape} vocabulary={len(tfidf.vocabulary_):,}\")\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 671 }, "id": "z84G_YYeu2mu", "outputId": "89180c7b-915e-446f-a299-5106d9c44213" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ], "source": [ "# 3.5 Fig 5 — Top TF-IDF Discriminative Features (LR coefficients, D3-Full)\n", "lr_temp = LogisticRegression(max_iter=1000, C=1.0, random_state=RANDOM_STATE)\n", "lr_temp.fit(TFIDF['D3-Full']['X_tr'], SPLITS['D3-Full']['y_tr'])\n", "\n", "feature_names = np.array(TFIDF['D3-Full']['vec'].get_feature_names_out())\n", "coefs = lr_temp.coef_[0]\n", "top_suicide_idx = np.argsort(coefs)[-20:][::-1]\n", "top_nonsuicide_idx = np.argsort(coefs)[:20]\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", "fig.suptitle('Fig 5 — Top 20 Discriminative TF-IDF Features (D3-Full, LR coefficients)',\n", " fontsize=13, fontweight='bold')\n", "\n", "for ax, indices, title, color in [\n", " (axes[0], top_suicide_idx, 'Top features → Suicide', '#e05c5c'),\n", " (axes[1], top_nonsuicide_idx, 'Top features → Non-Suicide', '#5c9ee0')\n", "]:\n", " feats = feature_names[indices]\n", " values = np.abs(coefs[indices])\n", " yp = range(len(feats))\n", " ax.barh(yp, values, color=color, alpha=0.85, edgecolor='none')\n", " ax.set_yticks(yp); ax.set_yticklabels(feats, fontsize=9)\n", " ax.invert_yaxis()\n", " ax.set_xlabel('|LR Coefficient|')\n", " ax.set_title(title, fontsize=11, fontweight='bold')\n", " ax.grid(axis='x', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig5_tfidf_features.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "zJf0zstfu2mu" }, "source": [ "---\n", "## CRISP-DM Stage 4 — Modelling\n", "\n", "### Classical Models (LR · SVM · XGBoost)\n", "- Use **full training data** from each split — this is the core data-volume comparison\n", "- D3-Full trains on ~185k samples; D3-H1/H2 each train on ~93k samples\n", "\n", "### XLM-RoBERTa — Proportional Sampling\n", "To maintain a fair size comparison while staying within T4 GPU memory:\n", "- D3-Full: 25,000/class → **40,000 train / 10,000 test**\n", "- D3-H1 / D3-H2: 12,500/class → **20,000 train / 5,000 test**\n", "\n", "**Estimated GPU time: ~30 min (Full) + ~15 min (H1) + ~15 min (H2) ≈ 60 min total**\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qejtHa5yu2mu", "outputId": "1e6c3c11-401d-443a-d339-37d6c7a832a2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Evaluation function ready.\n" ] } ], "source": [ "# 4.0 Evaluation helper — stores results globally\n", "ALL_RESULTS = {}\n", "PREDICTIONS = {} # store y_true, y_pred per key for confusion matrices\n", "\n", "def evaluate(model_name, split_name, y_true, y_pred, proba=None):\n", " y_true = list(y_true); y_pred = list(y_pred)\n", " acc = accuracy_score(y_true, y_pred)\n", " macro = f1_score(y_true, y_pred, average='macro')\n", " kappa = cohen_kappa_score(y_true, y_pred)\n", " f1_cls = f1_score(y_true, y_pred, average=None)\n", " auc = roc_auc_score(y_true, proba) if proba is not None else None\n", "\n", " key = f\"{split_name} | {model_name}\"\n", " ALL_RESULTS[key] = {\n", " 'Split': split_name, 'Model': model_name,\n", " 'Accuracy': round(acc,4), 'Macro F1': round(macro,4),\n", " 'Kappa': round(kappa,4),\n", " 'F1 (non-suicide)': round(f1_cls[0],4),\n", " 'F1 (suicide)': round(f1_cls[1],4),\n", " 'AUC-ROC': round(auc,4) if auc else None\n", " }\n", " PREDICTIONS[key] = {'y_true': y_true, 'y_pred': y_pred}\n", "\n", " print(f\"\\n {model_name} [{split_name}]\")\n", " print(f\" Accuracy : {acc*100:.2f}%\")\n", " print(f\" Macro F1 : {macro:.4f}\")\n", " print(f\" Kappa : {kappa:.4f}\")\n", " if auc: print(f\" AUC-ROC : {auc:.4f}\")\n", " print(classification_report(y_true, y_pred, target_names=le.classes_, digits=3))\n", "\n", "print(\"Evaluation function ready.\")\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ysgypBMGu2mu", "outputId": "01400aa3-4cfb-4ba9-c349-42b724ead63f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================\n", " LOGISTIC REGRESSION\n", "============================================================\n", "\n", " Logistic Regression [D3-Full]\n", " Accuracy : 94.34%\n", " Macro F1 : 0.9434\n", " Kappa : 0.8868\n", " AUC-ROC : 0.9858\n", " precision recall f1-score support\n", "\n", " non-suicide 0.935 0.953 0.944 23208\n", " suicide 0.952 0.934 0.943 23207\n", "\n", " accuracy 0.943 46415\n", " macro avg 0.944 0.943 0.943 46415\n", "weighted avg 0.944 0.943 0.943 46415\n", "\n", "\n", " Logistic Regression [D3-H1]\n", " Accuracy : 93.84%\n", " Macro F1 : 0.9384\n", " Kappa : 0.8769\n", " AUC-ROC : 0.9824\n", " precision recall f1-score support\n", "\n", " non-suicide 0.930 0.948 0.939 11593\n", " suicide 0.947 0.928 0.938 11615\n", "\n", " accuracy 0.938 23208\n", " macro avg 0.939 0.938 0.938 23208\n", "weighted avg 0.939 0.938 0.938 23208\n", "\n", "\n", " Logistic Regression [D3-H2]\n", " Accuracy : 93.74%\n", " Macro F1 : 0.9374\n", " Kappa : 0.8748\n", " AUC-ROC : 0.9832\n", " precision recall f1-score support\n", "\n", " non-suicide 0.928 0.949 0.938 11615\n", " suicide 0.947 0.926 0.937 11593\n", "\n", " accuracy 0.937 23208\n", " macro avg 0.938 0.937 0.937 23208\n", "weighted avg 0.938 0.937 0.937 23208\n", "\n", "\n", "LR complete.\n" ] } ], "source": [ "# 4.1 Logistic Regression — all 3 splits\n", "print(\"=\" * 60)\n", "print(\" LOGISTIC REGRESSION\")\n", "print(\"=\" * 60)\n", "for name in DATASETS:\n", " lr = LogisticRegression(max_iter=1000, C=1.0, class_weight='balanced',\n", " random_state=RANDOM_STATE)\n", " lr.fit(TFIDF[name]['X_tr'], SPLITS[name]['y_tr'])\n", " preds = lr.predict(TFIDF[name]['X_te'])\n", " proba = lr.predict_proba(TFIDF[name]['X_te'])[:,1]\n", " evaluate('Logistic Regression', name, SPLITS[name]['y_te'], preds, proba)\n", "print(\"\\nLR complete.\")\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IUTFq0fDu2mu", "outputId": "75aad7cf-1569-439f-8456-b7b50e4c3d1f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================\n", " SVM (LinearSVC)\n", "============================================================\n", "\n", " SVM [D3-Full]\n", " Accuracy : 94.60%\n", " Macro F1 : 0.9460\n", " Kappa : 0.8919\n", " AUC-ROC : 0.9862\n", " precision recall f1-score support\n", "\n", " non-suicide 0.941 0.951 0.946 23208\n", " suicide 0.951 0.941 0.946 23207\n", "\n", " accuracy 0.946 46415\n", " macro avg 0.946 0.946 0.946 46415\n", "weighted avg 0.946 0.946 0.946 46415\n", "\n", "\n", " SVM [D3-H1]\n", " Accuracy : 94.18%\n", " Macro F1 : 0.9418\n", " Kappa : 0.8836\n", " AUC-ROC : 0.9835\n", " precision recall f1-score support\n", "\n", " non-suicide 0.937 0.947 0.942 11593\n", " suicide 0.946 0.937 0.942 11615\n", "\n", " accuracy 0.942 23208\n", " macro avg 0.942 0.942 0.942 23208\n", "weighted avg 0.942 0.942 0.942 23208\n", "\n", "\n", " SVM [D3-H2]\n", " Accuracy : 94.21%\n", " Macro F1 : 0.9421\n", " Kappa : 0.8842\n", " AUC-ROC : 0.9850\n", " precision recall f1-score support\n", "\n", " non-suicide 0.935 0.950 0.943 11615\n", " suicide 0.949 0.934 0.942 11593\n", "\n", " accuracy 0.942 23208\n", " macro avg 0.942 0.942 0.942 23208\n", "weighted avg 0.942 0.942 0.942 23208\n", "\n", "\n", "SVM complete.\n" ] } ], "source": [ "# 4.2 SVM (LinearSVC) — all 3 splits\n", "print(\"=\" * 60)\n", "print(\" SVM (LinearSVC)\")\n", "print(\"=\" * 60)\n", "for name in DATASETS:\n", " svm = LinearSVC(max_iter=2000, C=1.0, class_weight='balanced',\n", " random_state=RANDOM_STATE)\n", " svm.fit(TFIDF[name]['X_tr'], SPLITS[name]['y_tr'])\n", " preds = svm.predict(TFIDF[name]['X_te'])\n", " df_scores = svm.decision_function(TFIDF[name]['X_te'])\n", " proba = 1 / (1 + np.exp(-df_scores)) # sigmoid for AUC\n", " evaluate('SVM', name, SPLITS[name]['y_te'], preds, proba)\n", "print(\"\\nSVM complete.\")\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OiEU6JHnu2mu", "outputId": "5b9db350-06ab-4285-cc30-6bc5ce262344" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================\n", " XGBoost\n", "============================================================\n", "\n", " XGBoost [D3-Full]\n", " Accuracy : 70.52%\n", " Macro F1 : 0.6998\n", " Kappa : 0.4104\n", " AUC-ROC : 0.7064\n", " precision recall f1-score support\n", "\n", " non-suicide 0.780 0.571 0.660 23208\n", " suicide 0.662 0.839 0.740 23207\n", "\n", " accuracy 0.705 46415\n", " macro avg 0.721 0.705 0.700 46415\n", "weighted avg 0.721 0.705 0.700 46415\n", "\n", "\n", " XGBoost [D3-H1]\n", " Accuracy : 60.11%\n", " Macro F1 : 0.5521\n", " Kappa : 0.2017\n", " AUC-ROC : 0.6051\n", " precision recall f1-score support\n", "\n", " non-suicide 0.796 0.271 0.404 11593\n", " suicide 0.561 0.931 0.700 11615\n", "\n", " accuracy 0.601 23208\n", " macro avg 0.679 0.601 0.552 23208\n", "weighted avg 0.679 0.601 0.552 23208\n", "\n", "\n", " XGBoost [D3-H2]\n", " Accuracy : 71.00%\n", " Macro F1 : 0.7085\n", " Kappa : 0.4201\n", " AUC-ROC : 0.6805\n", " precision recall f1-score support\n", "\n", " non-suicide 0.747 0.637 0.687 11615\n", " suicide 0.683 0.784 0.730 11593\n", "\n", " accuracy 0.710 23208\n", " macro avg 0.715 0.710 0.708 23208\n", "weighted avg 0.715 0.710 0.708 23208\n", "\n", "\n", "XGBoost complete.\n" ] } ], "source": [ "# 4.3 XGBoost — all 3 splits\n", "print(\"=\" * 60)\n", "print(\" XGBoost\")\n", "print(\"=\" * 60)\n", "for name in DATASETS:\n", " xgb = XGBClassifier(n_estimators=300, learning_rate=0.1, max_depth=6,\n", " use_label_encoder=False, eval_metric='logloss',\n", " random_state=RANDOM_STATE, tree_method='hist',\n", " device='cuda' if DEVICE=='cuda' else 'cpu')\n", " xgb.fit(TFIDF[name]['X_tr'], SPLITS[name]['y_tr'])\n", " preds = xgb.predict(TFIDF[name]['X_te'])\n", " proba = xgb.predict_proba(TFIDF[name]['X_te'])[:,1]\n", " evaluate('XGBoost', name, SPLITS[name]['y_te'], preds, proba)\n", "print(\"\\nXGBoost complete.\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "7BtSJ8Biu2mu" }, "source": [ "---\n", "## XLM-RoBERTa Fine-Tuning\n", "**~60 min total on T4 GPU.** Saves each model to Google Drive after training." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 288, "referenced_widgets": [ "06ad06a153c9469cb9335b6e1eff3be3", "3b5ff7dcb06349c8ac9b358c9ac4a29f", "6588173933a54b81be7fc3cd0ede3228", "69fc18b3ce4847b2a1dfd24f805e2712", "6a60745478d345ffb0307f47a07e550c", "28c81ee3e14045b2b300ad61813c627a", "800d75e940b84b768654ef2a292398cd", "577a8176fd5a4c92bcb44df5e5c82bbf", "31f44a2ddf124f8482f14190a144ff3b", "0b99201e1e8a4e49ae35f6174a0b856a", "c0512fc27b81482b8ab19b0ece4449ff", "daae63a30d86424c90e1cba8a4a475b4", "7a9ed3530f524b588dbb49d496f7adde", "e2cc936f141a4cff913dc4203504f74d", "01c1206a6a894f13b49e834ca418cc57", "a500dab40b33487781a7a2e4604a2659", "3be69f5eef43444296a2f7b31cace888", "74e12c09c99c4a5bbd9555a105d79b6a", "1420376f690644a995c09ad0c3a4c59d", "204bbe2aee5a44b6b07c8d967c4c2f9e", "8573a5b079fd4e2aa4a7cee2106b9507", "ea81ad4396fe4dfcaf4d873c1ced7cea", "e3234c25a38c40afa2a8f9d37f4fb5d8", "351c820374a94a66bcb2720631695854", "5a12c8a782694fdf800abd248a6c3f96", "0f2b4f3fb8a9453db076f4c3fe1ed554", "1441317dd2a240e09fdbb561ce01ec6b", "9734e22584a94adc822ed30259c5280a", "ede8229cd67f4bff8025510a06132794", "444ae31df2044642abd75db13818bd2e", "9ce25d0c932d49bdb714a7399d734bed", "de93f26bff3a493baeb82416569e188a", "63be39423b484ca097a56e45000c9c95", "724160fe01484a42ac19314e2b136de6", "93e2e7b1eeab416b9fd3a17656c7b91c", "48a92dd381d54c2aa4ee9f2dc29cb391", "d7b39749fb8c4a2590bee337cfb0a6d5", "637f07490017442b9e8139d56faec9aa", "6e610c0f5d5a462081508ea819a2cdd0", "6f547e81a1644fc48e04335fa573de64", "b71b0a164f2b4cbb9f21571801bfe54d", "513f9c2e9721471b90cd55bd5d492075", "c2a5ef085c4d4d0b8cf251ecbadf967a", "de591f7bc7a64f9db5977de49a4aaf03" ] }, "id": "U7mn6IKTu2mv", "outputId": "6f52dd3f-17de-4fda-939a-93600c684756" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/615 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [7500/7500 47:07, Epoch 3/3]\n", "
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EpochTraining LossValidation Loss
10.2060530.122545
20.1589990.104046
30.0781500.108137

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", " XLM-RoBERTa [D3-Full]\n", " Accuracy : 98.02%\n", " Macro F1 : 0.9802\n", " Kappa : 0.9604\n", " precision recall f1-score support\n", "\n", " non-suicide 0.976 0.984 0.980 5000\n", " suicide 0.984 0.976 0.980 5000\n", "\n", " accuracy 0.980 10000\n", " macro avg 0.980 0.980 0.980 10000\n", "weighted avg 0.980 0.980 0.980 10000\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [ "\n", "

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EpochTraining LossValidation Loss
10.1960880.142673
20.1300330.118129
30.0805980.130365

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", " XLM-RoBERTa [D3-H1]\n", " Accuracy : 97.78%\n", " Macro F1 : 0.9778\n", " Kappa : 0.9556\n", " precision recall f1-score support\n", "\n", " non-suicide 0.977 0.978 0.978 2500\n", " suicide 0.978 0.977 0.978 2500\n", "\n", " accuracy 0.978 5000\n", " macro avg 0.978 0.978 0.978 5000\n", "weighted avg 0.978 0.978 0.978 5000\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [ "\n", "

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EpochTraining LossValidation Loss
10.2847510.098061
20.1934810.084265
30.0930020.093697

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", " XLM-RoBERTa [D3-H2]\n", " Accuracy : 98.02%\n", " Macro F1 : 0.9802\n", " Kappa : 0.9604\n", " precision recall f1-score support\n", "\n", " non-suicide 0.979 0.981 0.980 2500\n", " suicide 0.981 0.979 0.980 2500\n", "\n", " accuracy 0.980 5000\n", " macro avg 0.980 0.980 0.980 5000\n", "weighted avg 0.980 0.980 0.980 5000\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "image/png": 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}, "metadata": {} } ], "source": [ "# 5.2 Fig 6 — Macro F1 Comparison: All Models × All Splits\n", "models_order = ['Logistic Regression','SVM','XGBoost','XLM-RoBERTa']\n", "splits_order = ['D3-Full','D3-H1','D3-H2']\n", "split_colors = {'D3-Full':'#6c63ff','D3-H1':'#ff6b6b','D3-H2':'#4ecdc4'}\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n", "fig.suptitle('Fig 6 — Model Performance Across Dataset Splits',\n", " fontsize=14, fontweight='bold')\n", "\n", "x = np.arange(len(models_order))\n", "w = 0.28\n", "\n", "for ax, (metric, title) in zip(axes, [\n", " ('Macro F1', 'Macro F1 Score'),\n", " ('Kappa', \"Cohen's Kappa\"),\n", " ('AUC-ROC', 'AUC-ROC')\n", "]):\n", " for i, split in enumerate(splits_order):\n", " vals = [ALL_RESULTS.get(f\"{split} | {m}\",{}).get(metric, 0) or 0\n", " for m in models_order]\n", " bars = ax.bar(x+(i-1)*w, vals, w, label=split,\n", " color=split_colors[split], alpha=0.85, edgecolor='none')\n", " for bar, v in zip(bars, vals):\n", " if v > 0:\n", " ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.002,\n", " f\"{v:.3f}\", ha='center', va='bottom', fontsize=7,\n", " fontweight='bold', rotation=45)\n", " ax.set_title(title, fontsize=11, fontweight='bold')\n", " ax.set_xticks(x)\n", " ax.set_xticklabels([m.replace(' ','\\n') for m in models_order], fontsize=9)\n", " ax.set_ylim(0.8, 1.06)\n", " ax.axhline(0.81, color='red', linestyle='--', alpha=0.5, linewidth=1)\n", " ax.text(3.5, 0.812, 'Baseline\\n(0.81)', color='red', fontsize=7, ha='right')\n", " ax.legend(fontsize=9); ax.grid(axis='y', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig6_comparison.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "OrBCiwvQu2mv", "outputId": "88a75b1e-e643-465a-f040-93e3f5f237ce" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "

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}, "metadata": {} } ], "source": [ "# 5.3 Fig 7 — Heatmaps: F1 and Kappa\n", "pivot_f1 = results_df.pivot(index='Model', columns='Split', values='Macro F1')\n", "pivot_k = results_df.pivot(index='Model', columns='Split', values='Kappa')\n", "# Reorder columns\n", "for pv in [pivot_f1, pivot_k]:\n", " pv = pv.reindex(columns=splits_order)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "fig.suptitle(\"Fig 7 — Performance Heatmap\", fontsize=13, fontweight='bold')\n", "\n", "for ax, pivot, title, cmap in [\n", " (axes[0], pivot_f1, 'Macro F1', 'YlGn'),\n", " (axes[1], pivot_k, \"Cohen's κ\", 'YlOrRd')\n", "]:\n", " sns.heatmap(pivot, annot=True, fmt='.4f', cmap=cmap, ax=ax,\n", " linewidths=0.5, annot_kws={'size':11,'weight':'bold'},\n", " vmin=pivot.values.min()-0.01, vmax=pivot.values.max()+0.01)\n", " ax.set_title(title, fontsize=12, fontweight='bold')\n", " ax.tick_params(axis='x', rotation=0); ax.tick_params(axis='y', rotation=0)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig7_heatmap.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "OFMBqph0u2mv", "outputId": "49bf17a6-ce39-4b53-a9cb-b603e4b22c5d" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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4FovFnH0zNZGRkZo+fbr5Ofn1xjt5/PHHtXr1al28eFExMTG6cuWK3nrrLUnSkiVLrJ7qAuDhRnEUkAmrV6+WJDk5OWnUqFFyc3NTixYtVLt2bUmJRU1RUVHavn27uc2HH34ob29v1a5dO8NhqWTJkpKkf//9V6NHj9aSJUsUHx+vAQMGmI9jsdWff/5pTon54YcfqkGDBsqfP78qVKigHj16ZLifevXqycnJSYMHD1aePHnUr18/9enTx1z/+++/m+87duwoZ2dnlSpVSkeOHJEk7d69W5GRkdq5c6cSEhIkJRYWFS9eXBUqVFDXrl3T3X/Hjh3VpEkT5c+fX+XKldOaNWvM7/X111/Ly8tLnp6eWrx4saTEKdN37twpDw8Pubm5SUp8BN+nn36q7du3y9/fXwMGDJCkDLVJTWRkpFkMl/RzdnNz06hRo+Tk5CQp8fjfrmvXrnr66afl5+dn/hm622c/AwCA++/VV1/VTz/9pCeffNJqeVhYmIYMGWI+6rdKlSp67LHHJEnLly9XQkKCli1bZrZP7ZF6SZIKoZYuXapffvnFqmAKAAAgu5w8edI8vyMlPuIuNjY21bb3oqj77NmzVpkp6VxdqVKl9O6778rNzU3vvvuu/Pz8JN06H5PUTpI+/vhjeXp6qnbt2uYFuMDAQB0/ftw8JydJY8eO1ddff62jR4+qSZMmNt9cCAAAcgdb8k5G2dnZmYVQ3bp1082bN9WgQQOrLGKrefPmyc7OTpUrV9a1a9dUrVo1/fTTT+b67du3Kzw8XJL01ltvqWLFivL19dXw4cPNNqldy7pdbGysXnvtNV28eDHTY5US81v+/Pk1depU5cuXT5988omaN2+e4e0LFy6sjRs3qk6dOnJ1dVWBAgU0c+ZMSYlFYsePH7+r8QHIPSiOAjIhJCREUuJzhh0dHc3lxYoVkyTFx8fr6tWrCgoKMtcVLVo0Rbs7GTZsmGrWrKlr167p888/V5cuXVS1alU9/vjjCgwMzNTYg4ODzffJ79S7G4ZhKCIiIs39pLVNaGhopo9RhQoVbNqfJF29elX29vaaM2eOChYsqD179mjYsGFq2bKlihYtalaSZ6RNakJDQ81gXLx4cXO5o6OjvL290xxn0sVRSWYRVUxMzB2/DwAAyHlatGihPXv26MyZM5ozZ46eeeYZc90vv/xivm/Tpo0kKSgoSNu2bTOLo/z8/FStWrU0+y9VqpSef/55RUZGKjo6Wg0bNrTKHQAAAPfbjRs31Lt3b0m3Cp8OHz6siRMnmm1KlChhvk+ajV2Spk2bJsMw7ngBcMOGDTIMQwEBAfL395ckbdq0Sbt27ZJ061zd7eeTkj4nnY9Jand72+Tvg4OD9dRTT2no0KFydnbW8uXL9f777+vFF19UwYIFNWvWrHTHCgAAcp+M5J3M6ty5s+zs7BQaGipJaU4eEBgYmGI2ppEjR2Zo7EmTC0jWeSj5OaXb89DtRowYIcMwdO3aNXXo0EGSdOnSJc2bNy9F25IlS5qzaia9MlLwFB8frxs3btyxXXL/93//p3Hjxum///5TdHR0ivVJs4ECAMVRQCYkL3RJ/hdt0qNQ7O3t5enpqSJFipjrkhcBZXRWoIIFC2rLli06d+6cfv/9d3366adycXHRsWPH9PHHH2dq7D4+Pub7//77L1N9SIknpYKDg9W8eXPFx8dr5syZVo+MSzpGdnZ2unr1aooQlJCQoKJFi2b6GCUVEd2+P0n6+eefU91f+/btJUktW7bUhQsXtG/fPv34449q3769DMPQrFmztG3btgy3uZ2np6c5pWrSnwUp8fGDSWEz+TiT5MmTx3zPI3EAAHhwJd11JyWeXOrUqZPVnXZXr1413yef6WDo0KEKCAiQdKtoKj1dunRJ9T0AAEB2GD58uHkeZ/LkySpXrpwk6aOPPjIzzosvvmi2nzZtWqZnWfDz89NLL71kfj516pSkW+dbkp+PkaTz589brU9+XiZp3e3bJbUZM2aMQkJCtG3bNs2dO1fPPPOMoqKi1KdPH8XFxUniPA4AAA+LjOSdzCpRooTq168vSfLy8rJp1qTUdOzYUTExMZo7d64sFosOHz6sNm3amAVSyfNQ8gyUWh5Kjbu7u9q2bWt+TspjmRUQEKCzZ8/q2WefVXR0tEaPHm01w/qdLFmyRJLk7++vM2fOyDAM8/HKAJAcxVFAOnbv3q0//vjD6hUVFaWGDRtKSqw2HjVqlMLCwvTLL79o8+bNkqTnnntO+fLlU40aNcy+PvnkE129elVbtmzR8uXLM7T/GTNmaOHChYqJiVG9evX02muvycvLS5J11banp6ck6cSJE4qKikq3z4YNG8re3l5S4vTh69evV2RkpP79919NmzYtg0cmkbe3t2bNmmU+gm7MmDG6fv26JKlRo0aSpISEBPXo0UPnz59XdHS0Dhw4oKFDh6pfv36SpGeeecYsKPryyy914cIFHTx4ULNnz7ZpLC+88ILZz4cffqgDBw4oJiZGgYGB+vrrr/XCCy+YbXv37q0tW7aocOHCatasmTlW6dZxzUib2+XLl081a9aUlHj34s8//6zw8HCNGjXKrExP+rMDAAByn6ZNm6p79+7auHGjwsPDFRkZqe+++85cn3zWzooVK+rxxx+XJPOxvFL6j9RL0rJlS7Vq1Upt2rRRs2bN7uE3AAAAsM3+/fv11VdfSZJq1Kihnj17atq0abJYLIqKilKvXr0kJT5WuGnTppISL4C1aNFCBw8eVGxsrM6ePZvhO/pPnz6t33//3fxcqFAhSbfOtwQEBGjq1KkKDw/X1KlTzYuVSeuTn5f58MMPde3aNW3dutVqFs8yZcro4MGD+uijj3Ts2DGVL19erVu3VpUqVSRJUVFRZlF80jk5STp48KAthw4AADwgMpp3kouKikpxfXH37t1p7qN///5q1qyZhg8fbvXEmuT8/PxSTAyQ1sxRefPmVceOHc0ipv3792vhwoWSpGeffVYuLi6SpJkzZ+rgwYMKDg7WmDFjzO3Tu5Z1/fp1LVq0yPyclMfuRrFixTR79mzz+uWgQYOsZrtKT9KTWPLmzav8+fPrv//+09dff33XYwKQCxkArHTs2NGQlOYrICDACA0NNUqXLp3qemdnZ2P37t1mf7Vq1UrRplChQub7wMBAwzAMY86cOeayDRs23HEs33zzjbmPhg0bplh//PhxwzAM83PHjh3N9iNHjky1zzp16mT42CSN0TAMY+jQoeby4cOHG4ZhGDExMUbt2rXTHH/y8bzxxhvpHqONGzcahmEYGzZsMJfNmTMnxfgGDx6c5v5Klixptkurjbu7uxEUFJThNsmPR5Ldu3cbzs7OqW5bunRpIzQ0NN3vklqfAADgwVC9evU0M0T+/PmNo0ePWrUfNmyYVZtHH300RZ8BAQHm+q5du6a57+QZ4uzZs/f8uwEAANwuPj7ezD958uQxDhw4YK7r0qWLmU1+/PFHwzAM48qVK8bTTz+d7nm35HnoTufoKlSoYMTGxhqGkZiZvL29U23n7e1tBAQEGIaReL7q2WefTbWdvb298fPPPxuGYX3e5vZX9erVzTEuWrQoxfqhQ4dm9aEHAAD3ia15J73sknQNbsSIEeayLVu2pLnvpDb169e/4ziTZ5fk19+OHj1q2NvbmzkrKTtNnjw5zXG2bt3a3D75tcvUXi4uLsaJEydSjDm9728YhlGnTh1zeVJOMwzDaN++vbl89uzZVt8x+VhGjBhhLk/tGuOjjz5qvk9+PRPAw42Zo4BM8PDw0I4dO9SrVy+VKFFCefLkUYECBdSiRQvt3LlTTz/9tNl22bJlat26tfLly6dChQrpo48+UqtWrcz1ye8wu12rVq30yiuvqHjx4nJycpK7u7uqVKmiKVOmqEePHma7r776SnXr1pWrq2uGxj9ixAgtWbJEtWvXlqurqxwdHfXYY49ZzY5ki/fff9+cPeqLL75QaGio8ubNq9WrV2vUqFEqV66cHB0d5e7urgoVKui9997T+++/b24/bdo0vfXWW3Jzc5OXl5f69u2rPn36mOvTO0bJjR07VgsXLlSNGjXk4uIiZ2dnlS5dWh06dNDUqVPNdgMHDlT16tXl7e2tvHnzqlChQnrllVe0du1as8I9I21S8/TTT2vnzp1q2bKlChQooDx58qh48eLq2bOnduzYIQ8PD1sOLQAAeICMGTNGPXr0UIUKFeTt7a08efLIx8dHzZs31+bNm61mjpJSzhKVkUfqAQAA5BTTp0/Xrl27JEnvvfeeKlSoYK6bMGGCfHx8zHVhYWHy8vLS1q1bNWXKFD333HNyd3c3z6nVqFFDH374oVatWpXuPvPmzatSpUqpZ8+eWrdunfLkySMpcSaFv/76S2+++aYKFSqkPHnyqFChQnrjjTf0119/yc/Pz9x+7dq1GjJkiEqXLq28efPK3d1dDRs21Pr1681ZOUuXLq3u3burfPnycnd3l6Ojo/z8/NS9e3f9/PPP5nheffVV9e3bV4ULF75XhxUAAOQgtuadnKZs2bJ6/fXXJUknT57UvHnzJEnvvvuuli9frlq1asnFxUWOjo564okn9Mknn2jx4sXp9mlnZydfX1+1aNFCW7Zs0aOPPnrPxjts2DBz9qgxY8aYjzJOz5dffql27drJw8ND3t7eGjBggAYPHnzPxgQg97AYhmFk9yCA3Gz37t3y8/OTr6+vJOnQoUN6/vnnFRwcrCpVquiff/7J5hFmv4MHD8rd3V0lSpSQJJ05c0Yvvviijh49Kh8fHwUFBZlhCAAAAAAAAAAAAAAAIKOYOQrIYjNmzFChQoXk6+urggULqkKFCgoODpaDg4M+//zz7B5ejvDLL7+oZMmSKlCggIoUKaJSpUrp6NGjslgs+uKLLyiMAgAAAAAAAAAAAAAAmUJxFJDFGjRooKefflqxsbG6evWqChcurNdee027d+9WnTp1snt4OcIzzzyj2rVry2KxKDg4WF5eXmratKk2bNigdu3aZffwAAAAAAAAAAAAAADAA4rH6gEAAAAAAAAAAAAAAADIlZg5CgAAAAAAAAAAAAAAAECuRHEUAAAAAAAAAAAAAAAAgFyJ4ig8dDp16iSLxZLmK0lMTIz69++vGjVqyMnJyVy/devWezKOyMhIDRkyRH5+fnJ0dJSfn5+GDBmiqKioDG1/7tw5derUSYUKFZKjo6Mef/xxffrpp4qPj7dq98MPP6hevXoqVKiQHBwclD9/flWpUkWffPKJ4uLirNoGBgaqe/fuevTRR+Xs7KxChQqpQYMG+vPPP9Mcx6xZs6yO37lz52w/GPfB1atX1bNnTxUpUsQ8XhMmTEhxvNKydetWNW3aVMWKFZOTk5P8/PzUoUMHHT161Gwzd+7cdP9s+fn5pej3t99+0/PPPy93d3c5Ozvr0UcfVadOnZT8iafXrl1T7969Vbx4cTk4OKhUqVIaPHiwIiMj7/q4AACQUQ9bhkruTnln9OjRqlevnvLnz2+2WbhwYYp+IiIi9NFHH6l8+fJydXWVp6ennnzySX3zzTdKSEiw7UDcJ4cOHdIrr7wiT09P5cuXT88++6x++eWXDG8/f/58Pfvss/L29lb+/PlVtmxZDR48WKGhoVbtDh48qNdff10lS5aUk5OTihUrpmbNmmn37t1W7aZMmaIaNWrI19dXefPmlZubm5555hnNmDEj1f0fOXJEr7/+ugoWLChHR0cVKVJEzZo108mTJ20/GAAAZEJuyVDpfYdr166Z7UaOHJlmu/fee89sd+DAAfXs2VMVK1aUl5eXXFxcVLFiRX3yySeKjo622veaNWv02muvqXjx4mZfDRo0uBeHJcvcTYaaP3++WrRooVKlSilfvnwqWLCg6tevrw0bNqRoGx8frwkTJqhs2bJm1unZs6euXr1q1W7Pnj168803Vbp0afMYli5dOkV/devWTfdnPXLkyEwdDwAAbJVbMlRGzkMFBgam+10tFosCAwMzdFw6depk9hsXF6evvvpKVatWlaenp9zc3FShQgWNHz8+w+O/386cOaMOHTrIx8dHTk5OqlSpkr799tsMb79jxw69+OKLcnd3TzeDpZd5fv75Z7Pdxo0b02xXuXLlFP1evXpVH3zwgR555BE5OjrK29tbtWrV0u+//56ZwwHgXjOAh0zHjh0NSWm+koSGhqa6fsuWLXc9hoSEBKNBgwap9v/CCy8YCQkJ6W4fFBRkFC1aNNXtu3TpYtX27bffTvO79ujRw2x3/fp1o3Dhwqm2s1gsxm+//ZZiHFevXjW8vb2t2p49e/auj8+9duPGDaNChQqpfrdu3brdcfvt27cb9vb2qW7v4eFhXLhwwTAMw5gzZ066f7bKly9v1e+ECRPSbBsbG3vHsTdo0MCIj4+/9wcMAIBUPGwZKklG8o67u3uK/hYsWJCir8aNG6d5/AYMGJD5A5NFDh06ZLi5uaU63tS+3+0+/fTTNL9v9erVzXYBAQFG/vz5U23n4OBg7N2712z74osvptnn+PHjrfa/adMmI1++fKm2XbNmzT07TgAApCc3ZCjDMNL9DqGhoWa7ESNGpNmub9++Zrtx48al2a5Ro0ZW++7bt2+KNvXr17/r45JV7jZDlS1bNs1js3jxYqu2Xbt2TbVdhQoVjMjISLPd559/nqLNo48+mmLfderUSfdnPWHChLs/QAAAZEBuyFAZPQ8VEBCQ7ne1WCzG5cuXM3RcevXqZfbbs2fPNNu9+uqrd3187rX0jteYMWPuuP26deuMvHnzprr9vHnzrNqml3mWL19uttuwYUOa7SpVqpRi/KVLl0617dChQ+/FIQJwl5g5Cg+1DRs2yDAMq1eSvHnzqlevXvruu+/Uo0ePe7rf77//XmvXrpUk9erVSyEhIerVq5ekxLvhfvjhh3S3Hz9+vM6fPy9J+u677xQWFqa3335bkjR79myrivh69epp1apVunTpkm7cuKGZM2ea6xYtWmS+X7dunYKCgiRJzZo1U1hYmLneMAzNnTs3xTiGDh2qkJAQ5cuXz9ZDcF99+eWXOnjwoCRp3LhxCg4OVosWLSQlzgSxc+fOdLf//vvvzSr+cePGKSIiwvx5Xbt2TcuXL5ckc8an5K8VK1aY/bRr1858v3//fg0aNEhS4s9o//79ioyM1PHjxzVp0iTZ2SX+53nGjBnm2MePH6/w8HCNGTNGkrR27VotXrz47g4OAACZ8DBkqCQZyTudO3fW7Nmz072TPjQ0VKtWrZIk+fn56cyZMzp8+LDc3NwkSXPmzEl37Nnh/fffV1hYmBwcHLR27VoFBASoVKlSkqQ+ffrccRbLpNmz7O3ttWnTJoWEhKhatWqSpF27dunff/+VJC1fvlw3btyQJPXs2VMREREaP368pMQ7QJNn1pYtW2rjxo26cuWKrl+/bnXMk7e7efOm2rdvr8jISJUsWVKrV69WeHi4Lly4oIULF6p48eJ3eXQAALDdg5qh7vQdPDw8UrSrU6dOinZffPGFud5isahVq1batm2bIiMjtWnTJrm7u0uS/vjjD/31119m26eeekrjx4/X5s2bM/Ht77+7zVAeHh4aM2aMAgMDFRYWpiFDhpjrPvroI/P9jh07zJkUWrRooeDgYI0bN05S4qycX375pdm2bNmyGjVqlNasWaOiRYumue+NGzem+Lm1atVKUmKme+2112w8GgAA3L0HNUNl9DyUn59fiu8XEhIiR0dHSVKDBg3k4+MjKfEJJre3/eCDD8x9Jr8OlXRexs3NTYcPH9bZs2fNJ5wsXbpU4eHhd3V87rURI0aYx2vBggUKCgpS9erVJUmjRo3SmTNn0t3+/fffV2xsrDw8PHTgwAFdvHhRTz31lCSpb9++ioiISHWftx/P5s2bp9r/7e327dtntf7dd9/ViRMn5OjoqBkzZigkJERXr17Vn3/+qdq1a9t4NABkiXtebgXkcMmrqjds2JChbZLf9XYvqs2bNGli9nfp0iXDMAzj0qVL5rKmTZumu32lSpUMSYaPj4+5bP/+/eb277zzTrrbe3l5GZKMAgUKmMt+/fVXc/uZM2cahmEYMTEx5rImTZpY9fHPP/8YdnZ2Rvny5Y327dub7TI7c1TJkiUNSUadOnUMwzCMnTt3mrMwNGjQwOpuN1v5+/sbkgxXV1cjLi7OMAzD2LVrlznm3r17p7v9+++/b7Y9fvy4YRiGsWbNGnPZxIkT09z25ZdfNiQZefPmNS5evGguf+uttwxJhouLi3HlypU0t2/WrJm5nxs3bhiGkTjLV9Kyl156KcPHAQCAu/EwZihb807yWSRvnxUg+d/f7du3N5c/99xzZiawVfI7C0eMGGEYhmF8/fXXhsViueu70i5fvmzY2dmlyBuffPKJuc9ly5al20fVqlUNSUbRokXNZUOHDjW337Nnj2EYhvHVV1+Zy5JmdDp+/Li57N13301zH2FhYWa7J5980lz+3XffmctXr16dqWMAAMC9kBsylGEYGf4OSWNPOr+TlvDw8BTL3n33XXM/ixYtSnccdzNzVE7PULcfm4SEBHMmKgcHB3N58uO1a9cuwzAMIy4uznB1dTWkxNmjUpN0Di61maNuFxQUZOTJkyfDf04AALhXckOGuptrecmfPJJedrh586ZRoEABQ5JRsWJFq3VJ1wKfe+45c1ny81shISHpjj81Sdt27NjRMAzD+OWXXwwHBwdDkvHGG29k+mkn8fHx5jXBcuXKmct/+OEHc5+TJk1Kc/vkM4i1adPGXP7ll1+ay3/44QdzedLMUUlZMC3JZ45Kz9mzZ80MOHjw4Dt8WwDZhZmjgGzwzz//SJLc3d3l6+srSfL19TXvkNu7d2+629/pWcBpbR8REaGZM2fq6tWrkmRVRV+/fn3zLraVK1cqPDxcS5YsMdc3bNjQfG8Yhnr16qWEhARNmTJFefLkSXc8ttqxY4caNmyo69evq379+vr111/l7Oycqb5u3rxpzkrwyCOPyN7eXpJUpkwZs82djneHDh3k5OQkKbGaPjIy0pwtymKxqEGDBqlud+bMGfM5ws2bN1fBggXNdZs2bZIkFS9eXL169ZK3t7dcXV3VtGlT/ffff2a7zP6sAQDIje5nhrrXecfNzc28037btm06e/asjhw5Ys4QmTxrZdbXX3+tXr16yTAMDRkyxJxtMjP27dunhIQESda5yZYM9dZbb0mSgoKCtHnzZl25ckVr1qyRJBUpUkT+/v6SpFatWsnLy0vSrVmkli5davaT1rG5du2aJk2aZH5Onm2Tspa9vb3+/PNPlSxZUk5OTqpWrZqZzwAAeFjcbYZKrk2bNsqbN698fHzUpk0bHTlyJNV2f/31l9zc3OTk5KQKFSroiy++MLOFJLm4uKTY5ubNm+b79GY3utdyWoa6/djExMSYM5onPy5JP9fk/dvb2+uRRx6RJP3777+Kjo7OzNcwffvtt4qLi5Okez4bBwAAOV12XcszDEMzZsyQlHj+5JVXXkmzj6VLl+rKlSuSZM5KlSTpvMzBgwf177//6ty5c9q2bZskqWrVqipQoEC647uTn3/+Wa1bt1ZMTIw6dOiguXPnmk9FsdXJkyd1/fp1SZnLUHc61mlt/9VXX8nR0VGurq6qU6eOOet7agoWLKi8efPKz89Pffv21bVr18x1W7ZsMTNgSEiIypcvLycnJ5UuXVqfffaZ1WxnALIPxVF4qNWrV08Wi8V8pTVV4p3UrVvXqp/UXp06dTLbh4SESJL5GJUkSZ8vX76c7v4qVqwoSQoODtaiRYsUERGhqVOnmuuTglCSQ4cOyWKxyNXVVd27d5eUOL1j8pM9+fLl09atW+Xv769ffvlFbm5uat++vZydnTVkyBBzqlBJmjdvnnbs2KG2bduqbt26GTxKGXPy5Em9+OKLCgsL0/PPP68VK1akKIzy8/O74/FOesRKaGioGUiSH+/k7+90vKtUqaI///xTHh4eGjx4sPLnz6+pU6eqcOHCWrx4sSpVqpTqdjNnzjT3/c4771itO3v2rCTpyJEj+v7773XlyhVFRERo5cqVqlWrlvmIw6SftSRNnjxZN27c0OTJk81lt/+sAQC4Hx6GDJUVeWf+/Pl6/fXXFRgYqBIlSuiJJ55QWFiYWrZsqVmzZt1V3z/++KPeffddSdLgwYP18ccfW60PDAy847G2WCzauHGjpFvHWsp8hurRo4e++OILGYahOnXqyNvbW7t371aVKlX022+/mdPDFylSRFu2bFGxYsU0depUubi4aPDgwfLw8NCXX36ppk2bWvW7cuVKWSwWeXp6atSoUbJYLPrkk0/UrVs3s01S1oqPj9ekSZN05swZRUdH66+//lKTJk20evXqjB5aAADumQc1QyUXEhKiuLg4hYSEaOnSpXrmmWesbvJKEhkZqfDwcEVHR+vQoUPq169finMjyR0/ftx8RG6ZMmVUs2bNDI/pbuTEDHW7iRMnmo8g7tq1q7n8TvuKj483b5DMjISEBM2cOVNS4rm4Ro0aZbovAADuxoOaoWy9lpdk/fr1On78uCSpW7du6d6wN23aNEmJxdVvvPGG1bpx48bpgw8+UFhYmMqXL6/ixYsrMDBQdevW1U8//ZTu2O9k+/btevXVVxUbG6v27dtr3rx5KQqjMpKh5s6dK+nuM1ShQoXMArY1a9bo4MGDunTpkhYsWGC2Se14h4aGKiYmRhEREdq8ebNefvllM5Pe7vLly4qLi9Pp06f11VdfqU6dOmYhetJ5KCnx2mBSkfrJkyf1wQcfaNiwYWmOHcD9Q3EUkIMkVQ5bLJZ02w0cOFAODg6SpPbt28vV1VXTp0831+fNm/eO+5oyZYqGDh1qfo6IiFCLFi106NAhq3ZJJ7GSgsn169c1aNAgubq6auLEiRn7YjY4d+6c+ZzjHj16ZHrGqDtJXqV9p+N96NAhtW7d2qoKXEo8Fn/99Zd5B11ycXFx+vbbbyVJZcuWVb169azWx8bGmu/HjBmj8PBws1gtODhYU6ZMkZRYxObh4SFJGjRokFxcXPThhx+a22bkZw0AQG53rzNUVuWdfv366fvvv0+x/MSJEzpx4sRd9Z00a4ODg4N69ux5V32lx5YMtWjRIvXv3z/F3XEXL17U/v37zc8XLlxQ8+bNde7cOat2N27c0N69e82LgemNadCgQeZdlZJ11mrSpIlCQ0O1du1a2dnZKSEhQaNHj063TwAAHgYZzVCSNHToUP3999+KiIhQYGCgmjVrJkkKCwvTuHHjzHZPPvmkvv/+e507d04RERFavny5eW5n5syZOnXqVIq+z5w5oxdffFGRkZFydXXVDz/8kOlZB2yVEzNUcvPnz9fw4cMlJV4YHjBgQJbt63Z//PGHTp8+LUnq3r37ffuZAACQ02X1tbykNvb29ubsT6n5999/tXXrVklSu3bt5OrqarV+woQJVjNuJzl79qz5xJXMOn78uHnupW/fvlmWEzKaaywWi3nN89q1a6pYsaIKFSqkPXv2mG2SH+82bdpo7dq1unLliq5evapRo0aZ64YMGWK+L1iwoL766isdP35ckZGR+uuvv1S2bFlJ0oEDB7R48WJJ1ueh/Pz8dOrUKZ06dUolS5aUlFjsnnTtE0D24f9o8FDbsGGDDMMwXz///HOm+tm4caNVP6m9kqqfJcnb21tS4gmk5JL+YvTx8Ul3f0899ZTWrl2rWrVqycnJSYUKFdI777xj9lu8eHGr9v7+/jIMQ2FhYfrll1/MKT8/+eQTXbp0SZI0a9Ys7d69W1LiX/w3btzQzp075ezsrF9//dW8M23mzJm6dOmS2rZtq8uXL2vfvn1Wd6H9+++/OnPmjE3HLy39+/dP9WJYYGDgHY930sxRXl5eZihLfryTh5A7He8PP/xQly9flp2dnX799VdFRkZq2rRpioyM1KRJk/Tll1+m2ObXX381Z3+6fSpTSVbTlfbu3VsuLi7q3bu3uSzpgmHJkiW1efNmNWrUSPnz55e3t7fatm2rxx9/XFLKnzUAAPdDbs9QWZF39u3bZ94d2LBhQwUHB+vMmTOqVKmSDhw4oCZNmigyMtKmPlMTExOj9957L8VyPz+/Ox5rwzDMWbKSjomUuQyVkJCg3r17Ky4uTkWLFtW///6rsLAwdezYUUFBQercubN5gurTTz8174j85ptvFBkZqRUrVig+Pl5z587VoEGDrPpu0qSJDMNQaGio5syZo7x588owDA0YMMCctTN51nrrrbfk4eGh+vXrmzN+Ji/OAgDgfnlQM5SUeGNX1apVlT9/fpUsWVLffPONue6vv/4y3zdt2lSvvfaaihYtqvz586t58+bmLAaGYejvv/+26vfMmTOqW7euAgIC5OLiot9++02VK1e26XjcCzklQyU3b948de7cWQkJCapZs6Z++eUXq4t6d9qXvb29PD09M7Sv1CTNRJE3b16rGasAALjfHtQMZeu1PEm6dOmS+f1efvllFStWLM3+k/6ullI+/jY4ONi80b5SpUo6ffq0goOD1bBhQ508eVItW7ZMcZNaZvXq1cvq8clJMpKhkmbquhcZqk+fPpoxY4Yef/xxOTg4qHTp0urbt6+5Pvnx7tWrl+rXry8vLy95enpq+PDh5iP8ko6VJJUrV069e/dW6dKl5ezsrKeeekojRoww+0nKwcnPQ7Vo0UKlSpVSqVKl1LJlS0mJE1GkNtsqgPuL4ijgHrB1Ks6qVatKSpyVIGkayMuXL5vP061Spcod91mrVi1t3rxZUVFRCgoKUu/evc3ZnerUqZPqNq6urnrllVfMWYzi4+MVEBAgSTp69KjZrmPHjsqXL5+qV69uTvu5fv16SYkzTEnSjBkzVKVKFVWpUkUrVqwwt33xxRfNO9oyo3Tp0uYjUc6cOZPqXf22PFbP0dFRTzzxhCTp1KlTio+PlyQdO3bM7O9Oxzvp2BQsWFBNmzaVs7OzOnfubK5POjbJJYVSZ2dnq599RveZfMasChUq6Pfff1dERISCg4P11VdfmVN0pvWzBgDgQZBTM1RW5J3kWatVq1by9vZW8eLF1bhxY/N7HD582KY+k3v99dfNC4nLli3T77//brXe1kfCVK5c2SwwT56bMpqhLl++bBaU1a5dW+XKlZOrq6vatWsnKfEE2YYNGyRZH5suXbrI2dlZTZo0UcGCBSWlnrUkycPDQ506dVL58uUlWf+5sCVrAQDwoLnfGSq1i13J79xP/t6WtqdPnzYLo9zd3bVmzRrVqlUr3bHcazktQyWZO3euunTpooSEBD3//PP6448/UswGkfRzTd5/fHy8Tp48KUl64oknzMcY2+rs2bNatWqVJKlly5bmY2oAAHiQ5dTzUMl9++235ixE6T2SODIy0nxkXPXq1VOM5eTJk2Y/jRs3VokSJeTt7a1WrVpJkm7evKkdO3bccfzpfa8mTZpISiwQSj4jVhJbHqv36KOPmhM73E2Geuutt3TkyBFFR0fr+PHj5kQD0q3jnVpeTRrv7e8zmm0zMjbORQHZj+IoIB0hISEKCQmxuov/+vXrCgkJMS+aZUbSRSFJGj16tK5cuWJVBJR8fVJY8/PzsxrX3LlzdeHCBUVFRWn79u167bXXJCUWQCUV7kRERKhXr17aunWrrl69qsjISK1atcq8EJW838KFC5v9z5s3T5GRkdq1a5cOHDggSeaj3bJa0aJFNWnSJHM8n3/++V1dKJRuHc/w8HBNmDBBISEhGj9+fIr10q3Cq6Q7/qRbx+bSpUtauXKloqKiNGfOHHP97cfm5MmTWrt2rSTp1VdfTfUuvddff918P3nyZEVERGjy5MnmsuQhbcqUKQoMDNTNmzd14MABtWrVSjdu3JC9vX2WTvkOAEBmPegZylahoaEpvltERIRCQkLME2bJs9ayZcsUEhKic+fOmRedpLvLW2XLltW0adPMkzLvvvuubt68men+fHx81KBBA0nSunXrtG7dOgUGBpoF4F5eXmrUqJEk64uGSQXqnp6ecnJykiRt3rxZR48eVXh4uBYtWmTuI+n7Jj82s2fPVlRUlFauXGnOcJrU7sSJExo4cKD27NmjsLAwhYeHa+HChWZWdHNzM+80bN26tezt7SUlzgR2/fp1rV+/3pwxigJzAEBOlFMz1DfffKO3335bu3fv1s2bN3X69Gmri3XPPfec1fs5c+bo4sWLioyM1M8//6z58+dLSpzJqHr16pKsC6O8vLy0bt06PfPMM6mOPzIy0jw2SWJjY81laV3gyoiclqEkac6cOeratasSEhLUqFEj/fbbb8qfP3+KfSX/uY0fP14hISGaMGGC+Wcl+fro6OgUxyshIcFclvxRMFLiDPNJNxjePhMFAAA5TU7NULaeh0pISNDM/2fvvsObLPc/jn/SXQqUFlpo2QgVZFVZiggiiExxVRFRRIZHhohHFFB/DBVBhgs5gAMUFGUjCMoQEAVBsCAoCMpsqZZdSgcd+f1RmxJTaJ/QNGnyfl1XrpM8z/3cuRM98iH55nu//74kqWbNmrrrrruuuLYvvvhC586dk5T/n9WXf9ayatUqHT9+XKdPn9bixYstx6/lc6hatWrp3XffValSpSTl7EiTW0BmDy8vL8t7s2/fPn366af666+/NHXqVEk5nSxjYmIs4/MrZvvpp5+0evVqnTlzRklJSVqwYIFGjhwpKaeL1y233CIpZzu8Dh06aNWqVbpw4YLOnj2rcePGWTo7RUVFWT5f6tevn1599VUdPHhQ6enp2rlzp1Vuy83BzZs3V61atSRJS5cu1eHDh3X48GEtWbJEkhQeHm5VqAXAScyAh+ndu7dZklmSecOGDVcdmzsuv1vv3r3tXkN2dra5ffv2+c575513mrOzsy1j27RpY5Zkrl69uuXYwYMH873W29vb/Pnnn1vGnT179qqv4emnn7aMPXr0qDk4OPiKY1999dUrvp7L39Pjx4/b9Z5Ur17dLMncpk0bs9lsNs+fP98y52233Wb1nhiVkpJibtiwYb6vq1+/flddh9lsNi9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Qv7+/hgwZYrZJ6bOs28XExKhNmzY6e/ZsuscqJeQ3T09PTZgwQTlz5tSYMWPUokWLNB9foEABrV69WvXq1VOuXLmUL18+TZkyRVJCkdjhw4fvaXwAHAfFUUA6XLhwQVLCOsNubm7m9sKFC0uS4uLidOnSJZ05c8bcV6hQoWTt7mbw4MGqU6eOrly5os8++0ydO3dWlSpVVKZMGR09ejRdYw8LCzOfJ/2m3r0wDEPh4eGpnie1Yy5fvpzua1S+fHmbzidJly5dUo4cOTR9+nQ99NBD2rp1qwYPHqxnn31WhQoVMivJ09ImJZcvXzaDcUBAgLndzc1Nvr6+qY4z8cNRSWYRVXR09F3fDwAAyHpatmyprVu36vjx45o+fbpq1apl7luyZIn5vHXr1pKkM2fOaN26dWZxVFBQkGrUqJFq/0WLFtVjjz2miIgIRUVFqUmTJla5AwAAIKPduHFDr7/+uqRbhU979+7Vxx9/bLYJDAw0nyfOxi5JEydOlGEYd/0AcNWqVTIMQ6GhoQoODpYkrVmzRps2bZJ0617d7feTEl8n3o9JbHd726TPw8LCVK1aNQ0cOFAeHh5atGiR3nrrLT3++ON66KGHNHXq1DuOFQAAOJ605J306tSpk5ycnHT58mVJSnXygKNHjyabjWnYsGFpGnvi5AKSdR5Kek/p9jx0u6FDh8owDF25ckUvvfSSJOncuXOaOXNmsrZFihQxZ9VMfKSl4CkuLk43bty4a7uk3nnnHX344Yc6ePCgoqKiku1PnA0UACiOAtIhaaFL0l+0iUuh5MiRQz4+PipYsKC5L2kRUFpnBXrooYe0du1anTx5Ur///rs++ugjeXl56dChQ/rggw/SNXY/Pz/z+cGDB9PVh5RwUyosLEwtWrRQXFycpkyZYrVkXOI1cnJy0qVLl5KFoPj4eBUqVCjd1yixiOj280nS4sWLUzxfu3btJEnPPvusTp8+rR07dmj+/Plq166dDMPQ1KlTtW7dujS3uZ2Pj485pWri3wUpYfnBxLCZdJyJnJ2dzecsiQMAwIMr8Vt3UsLNpY4dO1p90+7SpUvm86QzHQwcOFChoaGSbhVN3Unnzp1TfA4AAJAZhgwZYt7H+fLLL1W2bFlJ0ogRI8yM8/jjj5vtJ06cmO5ZFoKCgtSsWTPz9b///ivp1v2WpPdjJOnUqVNW+5Pel0ncd/txiW1GjhypCxcuaN26dZoxY4Zq1aqlyMhI9e7dW7GxsZK4jwMAQHaRlryTXoGBgWrYsKEkKW/evDbNmpSSDh06KDo6WjNmzJDFYtHevXvVunVrs0AqaR5KmoFSykMp8fb21osvvmi+Tsxj6RUaGqoTJ06odu3aioqK0vvvv281w/rd/PDDD5Kk4OBgHT9+XIZhmMsrA0BSFEcBd7B582b973//s3pERkaqSZMmkhKqjYcPH65r165pyZIl+vPPPyVJjzzyiHLmzKmQkBCzrzFjxujSpUtau3atFi1alKbzT548WbNnz1Z0dLQaNGigNm3aKG/evJKsq7Z9fHwkSUeOHFFkZOQd+2zSpIly5MghKWH68JUrVyoiIkL79u3TxIkT03hlEvj6+mrq1KnmEnQjR47U1atXJUlNmzaVJMXHx6tbt246deqUoqKitGvXLg0cOFBvvvmmJKlWrVpmQdG4ceN0+vRp7d69W9OmTbNpLI0bNzb7GTRokHbt2qXo6GgdPXpUX331lRo3bmy2ff3117V27VoVKFBAzzzzjDlW6dZ1TUub2+XMmVN16tSRlPDtxcWLF+v69esaPny4WZme+HcHAAA4nubNm6tr165avXq1rl+/roiICM2ZM8fcn3TWzgoVKqhMmTKSZC7LK915Sb1Ezz77rJ577jm1bt1azzzzzH18BwAAALbZuXOnvvjiC0lSSEiIevTooYkTJ8pisSgyMlI9e/aUlLCscPPmzSUlfADWsmVL7d69WzExMTpx4kSav9F/7Ngx/f777+br/PnzS7p1vyU0NFQTJkzQ9evXNWHCBPPDysT9Se/LDBo0SFeuXNFff/1lNYtnqVKltHv3bo0YMUKHDh1SuXLl1KpVK1WuXFmSFBkZaRbFJ96Tk6Tdu3fbcukAAMADIq15J6nIyMhkny9u3rw51XP07dtXzzzzjIYMGWK1Yk1SQUFBySYGSG3mKBcXF3Xo0MEsYtq5c6dmz54tSapdu7a8vLwkSVOmTNHu3bsVFhamkSNHmsff6bOsq1evau7cuebrxDx2LwoXLqxp06aZn1++9957VrNd3UniSiwuLi7y9PTUwYMH9dVXX93zmAA4IAOAlQ4dOhiSUn2EhoYaly9fNkqUKJHifg8PD2Pz5s1mf48++miyNvnz5zefHz161DAMw5g+fbq5bdWqVXcdy9dff22eo0mTJsn2Hz582DAMw3zdoUMHs/2wYcNS7LNevXppvjaJYzQMwxg4cKC5fciQIYZhGEZ0dLRRt27dVMefdDwvv/zyHa/R6tWrDcMwjFWrVpnbpk+fnmx8/fv3T/V8RYoUMdul1sbb29s4c+ZMmtskvR6JNm/ebHh4eKR4bIkSJYzLly/f8b2k1CcAAHgw1KxZM9UM4enpaRw4cMCq/eDBg63aFC9ePFmfoaGh5v5XXnkl1XMnzRAnTpy47+8NAADgdnFxcWb+cXZ2Nnbt2mXu69y5s5lN5s+fbxiGYVy8eNGoXr36He+7Jc1Dd7tHV758eSMmJsYwjITM5Ovrm2I7X19fIzQ01DCMhPtVtWvXTrFdjhw5jMWLFxuGYX3f5vZHzZo1zTHOnTs32f6BAwfa+9IDAIAMYmveuVN2SfwMbujQoea2tWvXpnruxDYNGza86ziTZpekn78dOHDAyJEjh5mzErPTl19+meo4W7VqZR6f9LPLlB5eXl7GkSNHko35Tu/fMAyjXr165vbEnGYYhtGuXTtz+7Rp06zeY9KxDB061Nye0meMxYsXN58n/TwTQPbGzFFAOuTJk0cbNmxQz549FRgYKGdnZ+XLl08tW7bUxo0bVb16dbPtwoUL1apVK+XMmVP58+fXiBEj9Nxzz5n7k37D7HbPPfecnn76aQUEBMjd3V3e3t6qXLmyxo8fr27dupntvvjiC9WvX1+5cuVK0/iHDh2qH374QXXr1lWuXLnk5uamkiVLWs2OZIu33nrLnD3q888/1+XLl+Xi4qKlS5dq+PDhKlu2rNzc3OTt7a3y5cvrjTfe0FtvvWUeP3HiRL366qvKnTu38ubNqz59+qh3797m/jtdo6RGjRql2bNnKyQkRF5eXvLw8FCJEiX00ksvacKECWa7fv36qWbNmvL19ZWLi4vy58+vp59+WsuXLzcr3NPSJiXVq1fXxo0b9eyzzypfvnxydnZWQECAevTooQ0bNihPnjy2XFoAAPAAGTlypLp166by5cvL19dXzs7O8vPzU4sWLfTnn39azRwlJZ8lKi1L6gEAAGQVkyZN0qZNmyRJb7zxhsqXL2/uGzt2rPz8/Mx9165dU968efXXX39p/PjxeuSRR+Tt7W3eUwsJCdGgQYP022+/3fGcLi4uKlq0qHr06KEVK1bI2dlZUsJMClu2bFH79u2VP39+OTs7K3/+/Hr55Ze1ZcsWBQUFmccvX75cAwYMUIkSJeTi4iJvb281adJEK1euNGflLFGihLp27apy5crJ29tbbm5uCgoKUteuXbV48WJzPM8//7z69OmjAgUK3K/LCgAAshBb805WU7p0ab3wwguSpH/++UczZ86UJPXq1UuLFi3So48+Ki8vL7m5uenhhx/WmDFjNG/evDv26eTkJH9/f7Vs2VJr165V8eLF79t4Bw8ebM4eNXLkSHMp4zsZN26c2rZtqzx58sjX11fvvvuu+vfvf9/GBMBxWAzDMDJ7EIAj27x5s4KCguTv7y9J2rNnjx577DGFhYWpcuXK+vvvvzN5hJlv9+7d8vb2VmBgoCTp+PHjevzxx3XgwAH5+fnpzJkzZhgCAAAAAAAAAAAAAABIK2aOAuxs8uTJyp8/v/z9/fXQQw+pfPnyCgsLk6urqz777LPMHl6WsGTJEhUpUkT58uVTwYIFVbRoUR04cEAWi0Wff/45hVEAAAAAAAAAAAAAACBdKI4C7KxRo0aqXr26YmJidOnSJRUoUEBt2rTR5s2bVa9evcweXpZQq1Yt1a1bVxaLRWFhYcqbN6+aN2+uVatWqW3btpk9PAAAAAAAAAAAAAAA8IBiWT0AAAAAAAAAAAAAAAAADomZowAAAAAAAAAAAAAAAAA4JIqjkK107NhRFosl1Uei6Oho9e3bVyEhIXJ3dzf3//XXX/dlHBERERowYICCgoLk5uamoKAgDRgwQJGRkWk6/uTJk+rYsaPy588vNzc3lSlTRh999JHi4uKs2n3//fdq0KCB8ufPL1dXV3l6eqpy5coaM2aMYmNjzXbDhg2743WpX79+iuNYvny5Vbv7dX3ut3u93rt379YLL7ygIkWKyN3dXYULF9YzzzyjzZs3m21Wr159x2uY9O9XUonLCCa2GTlypNX+8PBwjRgxQuXKlVOuXLnk4+OjqlWr6uuvv1Z8fHz6LwoAADbKbjkqqalTp1q915MnT5r7du3apR49eqhChQrKmzevvLy8VKFCBY0ZM0ZRUVFW/Sxbtkxt2rRRQECA2VejRo3SdyEyyJ49e/T000/Lx8dHOXPmVO3atbVkyZI0H//tt9+qdu3a8vX1laenp0qXLq3+/fvr8uXLVu3SkrduN2jQIPM6Ojs7W+1bu3atOnfurLJly8rb21ve3t6qWrWqpkyZcsc/awAA7jdHyFB3ew8dO3a0ap/W/JDW3/93Ov/nn39+L5fFbo4fP66XXnpJfn5+cnd3V8WKFfXNN9/Y3M/+/fvl6upqvt/Zs2eb+y5evKhBgwYpJCRE+fPnl7u7u0qWLKlu3brp7Nmz6R7ThAkT1LRpU+XJkyfV+1UAANibI2QoKe33oWJjY/XFF1+oSpUq8vHxUe7cuVW+fHmNHj062bnSMqabN29q5MiRatCggQoVKiR3d3cFBQWpbdu2+ueff+79wtjJvd6H+vXXX/Xoo4/K09NTuXLl0mOPPZbq34WTJ0/q1VdfVeHCheXq6ip/f381adJEW7ZsuacxRUVFqUSJEubfxS5duqR5/ADszACykQ4dOhiSUn0kunz5cor7165de89jiI+PNxo1apRi/40bNzbi4+PvePyZM2eMQoUKpXh8586drdq+9tprqb7Xbt26me2GDh16x+vy5JNPJhtHdHS0UaZMmft+fe63e73eoaGhhqenZ4rHu7q6Gtu3bzcMwzBWrVp1x2vo6emZYv+dO3e2ajdixAir/U888USqfb777rv35RoBAJAW2S1HJbp06ZLh6+tr1fbEiRPm/g8//DDVa9K0aVOrvvr06ZOsTcOGDe/5utjLnj17jNy5c6f43mbNmnXX4z/66KNUr03NmjXNdmnNW0kdPnzYcHNzM9vlyJHDan9aczAAAPbmCBnqbu+hZ8+eZtu05gdbfv/f6fyfffbZPV+f++1OmXPkyJE29dWwYcNUr+GGDRtSvS6FChUyLly4kK4xVaxYMVmb2+9XAQBgb46QoWy5D9WjR49U3+vzzz9v85jOnDmTan+5c+c2Dh48eM/X53671/tQM2fONCwWS7JjXVxcjOXLl1u13bdvn+Hn55fiuaZMmXJPY3r//fet2r3yyiv3dmEA3DfMHIVsa9WqVTIMw+qRyMXFRT179tScOXPUrVu3+3re7777TsuXL5ck9ezZUxcuXFDPnj0lJcwm8P3339/x+NGjR+vUqVOSpDlz5ujatWt67bXXJEnTpk2zqoBu0KCBfvvtN507d043btzQlClTzH1z5841nw8bNizZtfjyyy/N/W3btk02js8++0wHDhxQzpw5bb0EGeper/eiRYt048YNSVKPHj0UHh6u0aNHS0r4RkLidaxfv36ya7h7926zn5Su4aZNmzR9+vRUr+Hly5f122+/SZKCgoJ0/Phx7d27V7lz55YkTZ8+Pc3XAQCA+yk75KhEAwcO1IULF1L9fW2xWPTcc89p3bp1ioiI0Jo1a+Tt7S1J+t///mf1bbNq1app9OjR+vPPP21855njrbfe0rVr1+Tq6qrly5crNDRURYsWlST17t1bERERdzw+cWaDHDlyaM2aNbpw4YJq1KghKSEH7du3T1La81ZSffr0UVRUVKp/Ljly5FCXLl20fft2RURE6McffzRnl5o0aZLOnTtn6+UAAOCePagZasaMGcnG/fbbb5v7k97zSGt+SM/v/w4dOiQbxxtvvJHOq2I/Q4cONTPnrFmzdObMGdWsWVOSNHz4cB0/fjxN/cyfP18rVqy44723KlWqaOHChbp27ZoOHz6sKlWqSJJOnTqladOmpWtMLVu21IQJE/T111/b8K4BALCfBzVD2XIfKvEeSu7cubV3716dOHFCQUFBkqQFCxbo+vXrNo+pZMmSmjFjhi5evKjTp0/r8ccflyRdu3ZN48aNS/+FsZN7uQ8VGxurt99+W4ZhqFixYgoNDdU///yjIkWKKCYmRt27d7f6e9O+fXuFhYXJx8dH8+fP15UrV3T+/HktXrxY5cuXT/eYjh07pg8//DDLf3YKZFv2rr4CspKklearVq1K0zFJZ1W6H5XmTz31lNnfuXPnDMMwjHPnzpnbmjdvfsfjE7+95efnZ27buXOneXz37t3veHzevHkNSUa+fPnu2C44ONiQZPj6+hpRUVFW+06ePGl4eXkZ/v7+VjMgpPf61KtXz5BkFClSxDCMhJkAChcubEgyKlSoYPVNN1vd6/X+4osvzLbLli0zx5e4rVevXqke27NnT7Pdtm3brPbFxcUZVatWNSwWi/HBBx+k+E28q1evmtvbtWtnbn/kkUcMSYaXl5fN1wMAgPTKjjnq77//NpycnIxy5coZ7dq1M9slnTnq+vXryc7Tq1cvs+3cuXNTHEvi/nuZOSo0NNTsZ+jQoYZhGMZXX31lfktu4MCB6e77/PnzhpOTkyHJaNasmbl9zJgx5jkXLlx4xz6qVKliSAkzFyQaOHCgefzWrVsNw7A9by1ZssSQZDRp0sTMkbfPHJXSn0vSvz/r16+37YIAAJBOjpChbnfz5k0jX7585n2bRLbkB1t+/ydeww4dOqT3EiSTeJ7EPpcsWWK4uroakoyXX37ZiIuLS1e/cXFxhre3tyHJKFu2rLn9+++/N8/5ySef3LWf8PBwIyAgwHB3dzcGDx6c4gwFN27cSDbOBQsWmG27du16T2NKOks6M0cBADKaI2QoW+5DJX5298gjj5jbkt6LSvycLK1jio6ONm7evGk1nq1bt5rtmjRpYvP1yMr3obZv3262e+edd8ztb775prl906ZNhmEYxrp168xtkyZNuq9jatmypSHJGDVqlNmGmaOArIOZo4AM9vfff0uSvL295e/vL0ny9/c3ZxjYvn37HY+/2zrGqR0fHh6uKVOm6NKlS5J0xwr6devWac+ePZKkTp06ydXV1Wp/3759FR4erjFjxihPnjx3HI+tDh8+rPr16+vkyZOqUKGCVqxYoXz58qW7v3u93s8995zy5s0r6da3GhcsWGDub9KkSYrHRUREmJX+1atXN7+5l2jKlCnatm2b2rdvr5CQkBT7yJ07t9q0aSMp4c/kxIkT2r9/vzkjVWrnBgDAUWVkjjIMQz179lR8fLzGjx9vzjp0Oy8vr2Tbbt68aT4vVKjQHc95P3311Vfq2bOnDMPQgAEDNHLkyHT3tWPHDsXHx0uSSpUqZW5P+vxu1/vVV1+VJJ05c0Z//vmnLl68qGXLlkmSChYsqODgYEm25a2bN2/qjTfekKurq9VMp7fLSn8uAABktnvNULdbsGCBLl68KEnm7AeSbfkhPfdbFi1apJw5cypnzpyqUaOGZs2aZdO4U7N48WK1atVK0dHReumllzRjxgw5OaXvtvk///yjq1evSkp/hpKkkSNH6sSJE+rXr5+KFSuWYpucOXMmG2dKeed+jQkAgOwmI+9DJd5D2b17t/bt26eTJ09q3bp1khJmikz8nCytY3JxcZGbm5vV+e73fZGsdB/qbtc66fFr1qwxt+3bt08lSpSQu7u7goODrfKlrWNaunSpFi1apPr16+vFF1+863gAZDyKo5BtNWjQQBaLxXy0aNEiXf3Ur1/fqp+UHh07djTbX7hwQZLMpdESJb4+f/78Hc9XoUIFSVJYWJjmzp2r8PBwTZgwwdyfeHMq0Z49e2SxWJQrVy517dpVktSrV687hpSJEydKSlgmJulNLklavXq1vvvuO4WEhKhDhw53HKutrl69qvr16+vUqVMqX768VqxYIV9fX6s2GX29CxYsqLVr16pw4cKaMGGCvLy81L9/f+XJk0fjxo1T8+bNUzxu3rx55o2n7t27W+27ePGiBgwYIG9vb3300Ud3PP+3336rF154QUePHlVgYKAefvhhXbt2Tc8++6ymTp16x2MBALCX7JCjZs6cqQ0bNujFF19U/fr10/yeDh8+bC4DU6pUKdWpUyfNx96L+fPnq1evXpKk/v3764MPPrDaf/To0btea4vFotWrV0u6da0l6+ud9Pndrne3bt30+eefyzAM1atXT76+vtq8ebMqV66sX3/91bxJZ0veGj16tEJDQ/X2229b3Yy6m7Vr12rlypWSpEaNGikwMDDNxwIAcL88qBnqdon3jby8vPTyyy8nO8/t50opP6Tnfsu1a9cUGRmpyMhIbdmyRe3bt9eHH35o09hvt379ej3//POKiYlRu3btNHPmzGQFR2nJUDNmzLD5GqTm0KFD+vTTT1W0aFG99957aX4vkZGRGjNmjCTJ1dVVL7300n0bEwAAmelBzVC23If68MMP9fbbb+vatWsqV66cAgICdPToUdWvX18//vjjPY8pPj5e77//vvm6c+fOdxz73WS1+1Bly5aVi4uLJGnhwoU6duyYQkNDra5d4vU+ceKEuW3cuHH6559/FBUVpb1796p9+/bmZ2+2jCk6Olq9e/eWs7Ozxo8fn+o4AWQuiqOALML4b61bi8Vyx3b9+vUzZ3Jq166dcuXKpUmTJpn7E3/538n48eM1cODAFPddunTJ/KZe48aNVbx4cXNfbGysXn/9deXIkUNfffXVXcdqqytXruj06dOSpJdffjlZYdT9lNbrffr0abVo0UInT5602n7jxg1t375dN27cSPG4xD+TPHny6IUXXrDaN3DgQF26dEkjRowwK/tT8+abb+q7775Ltv3IkSM6cuTIHY8FACC7uN856urVq3rvvfeUK1cuffzxx2kex/Hjx/X4448rIiJCuXLl0vfff5/uWQdstX//fkkJH4T16NHDbudJvNbS3a/33Llz1bdvX6tjJOns2bPauXOn+TqteSs0NFRjxoxRQECABg0alOYx79y5Uy1btlR8fLwKFiyoadOmpflYAAAcWVozVFL79u3TX3/9JUlq27atcuXKlebzJD2XLfdbGjVqpJ9//llnz57VtWvXNGXKFDNjjRgxQhEREWke/+0OHz6smJgYSVKfPn3slt1syVC9e/dWdHS0xo0bJ3d39zT1HxUVpVatWpmzjX/11VepzjiVnjEBAIBb7PF53tixY/XJJ58k6+PEiRPat2/fPY3JMAx169ZNf/zxhyRp0KBB9/xlvqx2HypPnjxmsda///6roKAgFStWTMeOHTPbJF7vxOwnSdWqVdO5c+e0Y8cOs+hp6NChNo/p008/1aFDh9S7d2+VK1curW8PQAajOArZ1qpVq2QYhvlYvHhxuvpZvXq1VT8pPRK/PSbJLPi5du2aVT/Xr1+XJPn5+d3xfNWqVdPy5cv16KOPyt3dXfnz51f37t3NfgMCAqzaBwcHyzAMXbt2TUuWLDGn1hwzZozOnTuXrP8ZM2aYU2vevvTe4sWLtWfPHjVr1kxSwpSSZ8+eNfffz4KdESNGmIVSSWX09f7oo490+PBhSdLXX3+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}, "metadata": {} } ], "source": [ "# 5.4 Fig 8 — Confusion Matrices (3 splits × 4 models = 12 panels)\n", "fig, axes = plt.subplots(3, 4, figsize=(20, 14))\n", "fig.suptitle('Fig 8 — Confusion Matrices: All 12 Model × Split Combinations',\n", " fontsize=14, fontweight='bold')\n", "\n", "for row_i, split in enumerate(splits_order):\n", " for col_i, model in enumerate(models_order):\n", " ax = axes[row_i][col_i]\n", " key = f\"{split} | {model}\"\n", " if key not in PREDICTIONS:\n", " ax.set_visible(False); continue\n", " cm = confusion_matrix(PREDICTIONS[key]['y_true'], PREDICTIONS[key]['y_pred'])\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,\n", " xticklabels=['Non-Suicide','Suicide'],\n", " yticklabels=['Non-Suicide','Suicide'],\n", " linewidths=0.5, annot_kws={'size':10,'weight':'bold'})\n", " f1 = ALL_RESULTS[key]['Macro F1']\n", " kap = ALL_RESULTS[key]['Kappa']\n", " ax.set_title(f\"{model}\\nF1={f1:.4f} κ={kap:.4f}\", fontsize=9, fontweight='bold')\n", " ax.set_xlabel('Predicted', fontsize=8); ax.set_ylabel('Actual', fontsize=8)\n", " ax.tick_params(labelsize=8)\n", " if row_i == 0:\n", " ax.set_title(f\"{model}\\n[{split}]\\nF1={f1:.4f}\", fontsize=9, fontweight='bold')\n", " if col_i == 0:\n", " ax.set_ylabel(f\"{split}\\nActual\", fontsize=9, fontweight='bold')\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig8_confusion_matrices.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 659 }, "id": "a4y5fppMu2mv", "outputId": "1082957b-4e89-425f-b77e-8a87eb18e168" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "# 5.5 Fig 9 — Data Volume Effect Line Chart\n", "classical_train = {\n", " 'D3-Full': int(0.8*len(df_full)),\n", " 'D3-H1': int(0.8*len(df_h1)),\n", " 'D3-H2': int(0.8*len(df_h2))\n", "}\n", "xlmr_train = {\n", " 'D3-Full': int(0.8*25000*2),\n", " 'D3-H1': int(0.8*12500*2),\n", " 'D3-H2': int(0.8*12500*2)\n", "}\n", "model_line_colors = {\n", " 'Logistic Regression':'#94a3b8','SVM':'#6c8fff',\n", " 'XGBoost':'#2dd4bf','XLM-RoBERTa':'#a78bfa'\n", "}\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", "fig.suptitle('Fig 9 — Effect of Training Data Volume on Model Performance',\n", " fontsize=13, fontweight='bold')\n", "\n", "for ax, metric in zip(axes, ['Macro F1', 'Kappa']):\n", " for model in models_order:\n", " pts = []\n", " for split in splits_order:\n", " key = f\"{split} | {model}\"\n", " if key in ALL_RESULTS:\n", " sz = (xlmr_train if model=='XLM-RoBERTa' else classical_train)[split]\n", " pts.append((sz, ALL_RESULTS[key][metric] or 0))\n", " if pts:\n", " pts_sorted = sorted(pts)\n", " szs, vals = zip(*pts_sorted)\n", " ax.plot(szs, vals, marker='o', linewidth=2.5, label=model,\n", " color=model_line_colors[model])\n", " for sz, v in pts_sorted:\n", " ax.annotate(f\"{v:.4f}\", (sz,v),\n", " textcoords='offset points', xytext=(0,8),\n", " ha='center', fontsize=8, color=model_line_colors[model])\n", " ax.set_xlabel('Training Set Size', fontsize=11)\n", " ax.set_ylabel(metric, fontsize=11)\n", " ax.set_title(f\"{metric} vs Training Size\", fontsize=11, fontweight='bold')\n", " ax.legend(fontsize=9); ax.grid(alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig9_size_effect.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "8CWhLwhYu2mv", "outputId": "97042d6b-6981-4fea-96c6-52da7065616f" }, "outputs": [ { "output_type": 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}, "metadata": {} } ], "source": [ "# 5.6 Fig 10 — Per-Class F1 (suicide vs non-suicide)\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n", "fig.suptitle('Fig 10 — Per-Class F1: Suicide vs Non-Suicide Across Models and Splits',\n", " fontsize=13, fontweight='bold')\n", "x = np.arange(len(models_order)); w = 0.35\n", "\n", "for ax, split in zip(axes, splits_order):\n", " f1_s = [ALL_RESULTS.get(f\"{split} | {m}\",{}).get('F1 (suicide)', 0) or 0 for m in models_order]\n", " f1_ns = [ALL_RESULTS.get(f\"{split} | {m}\",{}).get('F1 (non-suicide)',0) or 0 for m in models_order]\n", " b1 = ax.bar(x-w/2, f1_s, w, label='Suicide', color='#e05c5c', alpha=0.85)\n", " b2 = ax.bar(x+w/2, f1_ns, w, label='Non-Suicide', color='#5c9ee0', alpha=0.85)\n", " for bars in [b1,b2]:\n", " for bar in bars:\n", " h = bar.get_height()\n", " if h>0: ax.text(bar.get_x()+bar.get_width()/2, h+0.002,\n", " f\"{h:.3f}\", ha='center', va='bottom', fontsize=7, rotation=45)\n", " ax.set_title(split, fontsize=11, fontweight='bold')\n", " ax.set_xticks(x)\n", " ax.set_xticklabels([m.replace(' ','\\n') for m in models_order], fontsize=9)\n", " ax.set_ylim(0.80, 1.05); ax.set_ylabel('F1 Score')\n", " ax.legend(fontsize=9); ax.grid(axis='y', alpha=0.3); ax.set_axisbelow(True)\n", "\n", "plt.tight_layout()\n", "plt.savefig(os.path.join(DRIVE_DIR, 'fig10_perclass_f1.png'), bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tMhWVQw0u2mv", "outputId": "6f6ee892-48e9-44a7-b18c-f03713331cd7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", " DATA VOLUME EFFECT: Full vs Average(H1, H2) [Macro F1]\n", "======================================================================\n", "Model Full H1 H2 Avg Half Gap Verdict\n", "----------------------------------------------------------------------\n", "Logistic Regression 0.9434 0.9384 0.9374 0.9379 +0.0055 meaningful\n", "SVM 0.9460 0.9418 0.9421 0.9420 +0.0040 negligible\n", "XGBoost 0.6998 0.5521 0.7085 0.6303 +0.0695 meaningful\n", "XLM-RoBERTa 0.9802 0.9778 0.9802 0.9790 +0.0012 negligible\n", "\n", "======================================================================\n", " H1 vs H2 CONSISTENCY CHECK [Macro F1]\n", "======================================================================\n", " Logistic Regression : |H1-H2|=0.0010 → CONSISTENT\n", " SVM : |H1-H2|=0.0003 → CONSISTENT\n", " XGBoost : |H1-H2|=0.1564 → INCONSISTENT\n", " XLM-RoBERTa : |H1-H2|=0.0024 → CONSISTENT\n", "\n", "======================================================================\n", " KAPPA INTERPRETATION (Landis & Koch, 1977)\n", "======================================================================\n", " D3-Full Logistic Regression : κ=0.8868 → Almost perfect\n", " D3-H1 Logistic Regression : κ=0.8769 → Almost perfect\n", " D3-H2 Logistic Regression : κ=0.8748 → Almost perfect\n", " D3-Full SVM : κ=0.8919 → Almost perfect\n", " D3-H1 SVM : κ=0.8836 → Almost perfect\n", " D3-H2 SVM : κ=0.8842 → Almost perfect\n", " D3-Full XGBoost : κ=0.4104 → Moderate\n", " D3-H1 XGBoost : κ=0.2017 → Fair/Poor\n", " D3-H2 XGBoost : κ=0.4201 → Moderate\n", " D3-Full XLM-RoBERTa : κ=0.9604 → Almost perfect\n", " D3-H1 XLM-RoBERTa : κ=0.9556 → Almost perfect\n", " D3-H2 XLM-RoBERTa : κ=0.9604 → Almost perfect\n" ] } ], "source": [ "# 5.7 Quantitative Gap Analysis — Answer the Research Question\n", "print(\"=\" * 70)\n", "print(\" DATA VOLUME EFFECT: Full vs Average(H1, H2) [Macro F1]\")\n", "print(\"=\" * 70)\n", "print(f\"{'Model':<25} {'Full':>8} {'H1':>8} {'H2':>8} {'Avg Half':>10} {'Gap':>8} {'Verdict'}\")\n", "print(\"-\" * 70)\n", "\n", "for model in models_order:\n", " r = {sp: ALL_RESULTS.get(f\"{sp} | {model}\", {}) for sp in splits_order}\n", " full = r['D3-Full'].get('Macro F1') or 0\n", " h1 = r['D3-H1'].get('Macro F1') or 0\n", " h2 = r['D3-H2'].get('Macro F1') or 0\n", " if all([full,h1,h2]):\n", " avg = (h1+h2)/2\n", " gap = full - avg\n", " h_diff = abs(h1-h2)\n", " verdict = 'meaningful' if abs(gap)>0.005 else 'negligible'\n", " print(f\"{model:<25} {full:>8.4f} {h1:>8.4f} {h2:>8.4f} {avg:>10.4f} {gap:>+8.4f} {verdict}\")\n", "\n", "print()\n", "print(\"=\" * 70)\n", "print(\" H1 vs H2 CONSISTENCY CHECK [Macro F1]\")\n", "print(\"=\" * 70)\n", "for model in models_order:\n", " h1 = ALL_RESULTS.get(f\"D3-H1 | {model}\",{}).get('Macro F1') or 0\n", " h2 = ALL_RESULTS.get(f\"D3-H2 | {model}\",{}).get('Macro F1') or 0\n", " if h1 and h2:\n", " diff = abs(h1-h2)\n", " verdict = 'CONSISTENT' if diff < 0.003 else ('MINOR VARIANCE' if diff < 0.005 else 'INCONSISTENT')\n", " print(f\" {model:<25}: |H1-H2|={diff:.4f} → {verdict}\")\n", "\n", "print()\n", "print(\"=\" * 70)\n", "print(\" KAPPA INTERPRETATION (Landis & Koch, 1977)\")\n", "print(\"=\" * 70)\n", "for model in models_order:\n", " for split in splits_order:\n", " k = ALL_RESULTS.get(f\"{split} | {model}\",{}).get('Kappa') or 0\n", " if k:\n", " if k>=0.81: interp = 'Almost perfect'\n", " elif k>=0.61: interp = 'Substantial'\n", " elif k>=0.41: interp = 'Moderate'\n", " else: interp = 'Fair/Poor'\n", " print(f\" {split:<12} {model:<25}: κ={k:.4f} → {interp}\")\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DzK_ojmWu2mv", "outputId": "9bfe2c2d-09c8-432c-af2e-1ba593bd3645" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=======================================================\n", " ALL OUTPUTS SAVED\n", "=======================================================\n", " [OK] master_results.csv\n", " [OK] fig1_class_distribution.png\n", " [OK] fig2_text_length.png\n", " [OK] fig3_word_frequency.png\n", " [OK] fig4_vocabulary.png\n", " [OK] fig5_tfidf_features.png\n", " [OK] fig6_comparison.png\n", " [OK] fig7_heatmap.png\n", " [OK] fig8_confusion_matrices.png\n", " [OK] fig9_size_effect.png\n", " [OK] fig10_perclass_f1.png\n", " [OK] xlmr_full/\n", " [OK] xlmr_h1/\n", " [OK] xlmr_h2/\n", "\n", "Drive folder: /content/drive/MyDrive/MindScan_SplitStudy\n" ] } ], "source": [ "# 5.8 Save all outputs\n", "results_df.to_csv(os.path.join(DRIVE_DIR, 'master_results.csv'), index=False)\n", "print(\"=\" * 55)\n", "print(\" ALL OUTPUTS SAVED\")\n", "print(\"=\" * 55)\n", "outputs = [\n", " 'master_results.csv',\n", " 'fig1_class_distribution.png', 'fig2_text_length.png',\n", " 'fig3_word_frequency.png', 'fig4_vocabulary.png',\n", " 'fig5_tfidf_features.png', 'fig6_comparison.png',\n", " 'fig7_heatmap.png', 'fig8_confusion_matrices.png',\n", " 'fig9_size_effect.png', 'fig10_perclass_f1.png',\n", " 'xlmr_full/', 'xlmr_h1/', 'xlmr_h2/'\n", "]\n", "for f in outputs:\n", " path = os.path.join(DRIVE_DIR, f)\n", " status = 'OK' if os.path.exists(path) else 'PENDING'\n", " print(f\" [{status}] {f}\")\n", "print(f\"\\nDrive folder: {DRIVE_DIR}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Nh16bIK1u2mv" }, "source": [ "---\n", "## CRISP-DM Stage 6 — Conclusions & Future Work\n", "\n", "### Fill in after running the notebook\n", "\n", "**Research Question:** Does training data volume affect model performance when distribution is held constant?\n", "\n", "**Findings template** *(update with actual results)*:\n", "\n", "**Classical Models (LR / SVM / XGBoost):**\n", "- If gap Full vs Half > 0.5%: classical models continue to benefit from additional data at this scale → more training data is worth the labelling cost\n", "- If gap < 0.5%: performance has plateaued → halving the dataset is sufficient for deployment\n", "\n", "**XLM-RoBERTa:**\n", "- If gap is smaller than classical models: confirms H2 — transformer pre-training reduces data dependency\n", "- If gap is similar: at this scale, even transformers benefit from additional labelled data\n", "\n", "**H1 vs H2 Consistency:**\n", "- If |H1−H2| < 0.3%: confirms the dataset is uniformly shuffled — the split methodology is valid\n", "- If > 0.3%: suggests potential ordering effects in the original dataset — worth investigating\n", "\n", "---\n", "\n", "### Limitations\n", "1. XLM-RoBERTa uses proportional subsamples (not full data) — GPU memory constraint\n", "2. Single random seed (42) — bootstrapped confidence intervals would strengthen claims\n", "3. No k-fold cross-validation — results from single 80/20 split\n", "4. Classical models use full training data; transformer uses proportional cap — partially confounded\n", "\n", "### Future Work\n", "1. **Learning curve analysis** — train on 10%, 25%, 50%, 75%, 100% of each split to map the full scaling curve\n", "2. **k-fold cross-validation (k=5)** — compute 95% confidence intervals on all metrics\n", "3. **Generalisation study** — apply the same split methodology to D1 and D2 datasets\n", "4. **Active learning** — investigate whether strategic sample selection can match full-dataset performance with fewer labels\n", "5. **Ensemble** — combine classical best (SVM) and transformer (XLM-RoBERTa) predictions\n", "\n", "---\n", "\n", "### CRISP-DM Stage Mapping\n", "| Stage | This Study |\n", "|-------|-----------|\n", "| Business Understanding | RQ defined, hypotheses stated, success criteria set |\n", "| Data Understanding | EDA: KS tests, transition rate, vocabulary analysis, length distributions |\n", "| Data Preparation | Consistent cleaning, TF-IDF (60k features, bigrams), stratified 80/20 split |\n", "| Modelling | 4 models × 3 splits = 12 experimental runs |\n", "| Evaluation | 5 metrics, 10 figures, quantitative gap analysis |\n", "| Deployment | Models saved to Drive; results CSV for report; integration into MindScan UI |\n", "\n", "---\n", "*NCI H9DAI · Data Analytics for Artificial Intelligence · MSc AI 2026*\n" ] } ] }