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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding how Active Learning affects Random Forest\n",
"\n",
"This notebook explores how the active learning process affects the random forest model. The goal is to understand how the model changes as the active learning process progresses and new samples are used for training."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Import Dependencies\n",
"\n",
"First, we import the necessary dependencies."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# Base Dependencies \n",
"# -----------------\n",
"import numpy as np\n",
"\n",
"from joblib import dump, load\n",
"from pathlib import Path\n",
"from os.path import join as pjoin\n",
"from typing import List\n",
"\n",
"# Local Dependencies\n",
"# ------------------\n",
"from features import RandomForestFeaturesNegation\n",
"from models import Relation, RelationCollection\n",
"from ml_models.random_forest import RandomForestClassifierOneStage\n",
"\n",
"# 3rd-Party Dependencies\n",
"# ----------------------\n",
"import dtreeviz\n",
"import seaborn as sns\n",
"from sklearn.tree import export_text, DecisionTreeClassifier\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Load test data\n",
"\n",
"We load the desired test dataset. In this case, we choose the Duration-Drug sub-dataset of the n2c2 corpus."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading test dataset...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 41086/41086 [00:01<00:00, 28612.88it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test data size: 568\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"CORPUS = \"n2c2\"\n",
"REL_TYPE = \"Duration-Drug\"\n",
"\n",
"# load test data\n",
"print(\"Loading test dataset...\")\n",
"test_data_path: Path = Path(pjoin(\"..\", \"data\", CORPUS, \"test_datading\", \"relations.msgpack\"))\n",
"test_collection: RelationCollection = RelationCollection.from_datading(CORPUS, test_data_path)\n",
"test_collection: RelationCollection = test_collection.type_subcollection(REL_TYPE)\n",
"y_test: List[float] = test_collection.labels\n",
"print(f\"Test data size: {len(test_collection)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Load models\n",
"\n",
"We load the two Random Forest models obtained at two points of the Active Learning process. We first choose the two AL steps we want to analyse."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"INIT_STEP = 5 \n",
"FINAL_STEP = 6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We load the corresponding checkpoints."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"checkpoints_path = Path(pjoin(\"..\", \"data\", \"checkpoints\", \"al\", \"rf\", CORPUS, REL_TYPE))\n",
"\n",
"# load initial trained model, active set and feature encoder\n",
"init_checkpoint = load(Path(pjoin(checkpoints_path, \"model_\" + str(INIT_STEP) + \".joblib\")))\n",
"init_model: RandomForestClassifierOneStage = init_checkpoint[\"model\"]\n",
"init_fenc: RandomForestFeaturesNegation = init_checkpoint[\"f_encoder\"]\n",
"init_X_active: np.array = init_checkpoint[\"X_active\"]\n",
"init_y_active: np.array = init_checkpoint[\"y_active\"]\n",
"\n",
"# load final trained model, active_set and feature encoder\n",
"final_checkpoint = load(Path(pjoin(checkpoints_path, \"model_\" + str(FINAL_STEP) + \".joblib\")))\n",
"final_model: RandomForestClassifierOneStage = final_checkpoint[\"model\"]\n",
"final_fenc: RandomForestFeaturesNegation = final_checkpoint[\"f_encoder\"]\n",
"final_X_active: np.array = final_checkpoint[\"X_active\"]\n",
"final_y_active: np.array = final_checkpoint[\"y_active\"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"And we encode obtain the two representations of the test set. At each AL step, the different active sets give place to different values for the feature *bag of entities*, so it is necessary to obtain different test feature encodings."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train data size (init): 112\n",
"Train data size (end): 128\n"
]
}
],
"source": [
"# encode test data\n",
"init_X_test: np.array = init_fenc.transform(test_collection)\n",
"final_X_test: np.array = final_fenc.transform(test_collection)\n",
"\n",
"print(f\"Train data size (init): {len(init_X_active)}\")\n",
"print(f\"Train data size (end): {len(final_X_active)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Find test samples that from changes misclassified to correctly classified"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We measure the gain in accuracy after adding new annotated samples in the next AL step."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F1-Score at step 5: 0.7711267605633803\n",
"F1-Score at step 6: 0.8028169014084507\n"
]
}
],
"source": [
"init_score: float = init_model.clf.score(init_X_test, y_test)\n",
"final_score: float = final_model.clf.score(final_X_test, y_test)\n",
"\n",
"print(f\"F1-Score at step {INIT_STEP}: {init_score}\")\n",
"print(f\"F1-Score at step {FINAL_STEP}: {final_score}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we cannot visualize all the Decision Trees in the forest, we pick one of the trees and visualize the samples that are misclassified by the initial tree and correctly classified by the final tree."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"TREE_INDEX = 15\n",
"\n",
"init_tree: DecisionTreeClassifier = init_model.clf.estimators_[TREE_INDEX]\n",
"final_tree: DecisionTreeClassifier = final_model.clf.estimators_[TREE_INDEX]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We find the samples that are misclassified by the initial model and correctly classified by the final model."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of samples that are correctly classified after 1 steps: 147 (25.88%)\n"
]
}
],
"source": [
"init_y_test_pred: float = init_tree.predict(init_X_test)\n",
"final_y_test_pred: float = final_tree.predict(final_X_test)\n",
"\n",
"changed: np.array = (\n",
" (init_y_test_pred == 0) & (final_y_test_pred == 1) & (y_test == 1)\n",
") | ((init_y_test_pred == 1) & (final_y_test_pred == 0) & (y_test == 0))\n",
"indices: np.array = np.where(changed)\n",
"\n",
"print(\n",
" \"Number of samples that are correctly classified after {} steps: {} ({:.2f}%)\".format(\n",
" FINAL_STEP - INIT_STEP, np.sum(changed), np.sum(changed) / len(changed) * 100\n",
" )\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We select one of the samples whose classification changes."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sentence: 10. Clonazepam 0.5 mg PO QHS:PRN anxiety/insomnia 11. MetRONIDAZOLE (FLagyl) 500 mg PO Q8H Duration: 7 Days\n",
"Type: Duration-Drug\n",
"e1: MetRONIDAZOLE\n",
"e2: 7 Days\n",
"Label: 1\n",
"Initial prediction: 0\n",
"Final prediction: 1\n"
]
}
],
"source": [
"SAMPLE_INDEX = indices[0][0]\n",
"\n",
"relation: Relation = test_collection.relations[SAMPLE_INDEX]\n",
"\n",
"print(\"Sentence: \", relation.text)\n",
"print(\"Type: \", relation.type)\n",
"print(\"e1: {}\".format(relation.entity1.text))\n",
"print(\"e2: {}\".format(relation.entity2.text))\n",
"print(\"Label: \", relation.label)\n",
"print(\"Initial prediction: \", int(init_y_test_pred[SAMPLE_INDEX]))\n",
"print(\"Final prediction: \", int(final_y_test_pred[SAMPLE_INDEX]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Visualize a tree from the initial RF classifier"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# color setting\n",
"COLOR_PALETTE = \"Set2\"\n",
"colors = sns.color_palette(COLOR_PALETTE) # set color palette\n",
"colors_for_2_classes = [\n",
" None, # 0 classes\n",
" colors[0], # 1 class\n",
" ['#FEFEBB', '#a1dab4'], # 2 classes\n",
" colors[0:4], # 3 classes\n",
"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We first print a textual representation of the initial decision tree."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree at Step 5\n",
"\n",
"|--- count_Drug <= 1.500\n",
"| |--- punct_dist <= 1.500\n",
"| | |--- class: 1.0\n",
"| |--- punct_dist > 1.500\n",
"| | |--- class: 0.0\n",
"|--- count_Drug > 1.500\n",
"| |--- count_Reason <= 0.500\n",
"| | |--- class: 0.0\n",
"| |--- count_Reason > 0.500\n",
"| | |--- class: 0.0\n",
"\n"
]
}
],
"source": [
"print(\"Decision Tree at Step \", INIT_STEP)\n",
"print()\n",
"print(export_text(init_tree, spacing=3, decimals=3, feature_names=init_fenc.get_feature_names()))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"And a visualisation of the decision path of the chosen sample in the initial decision tree."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Path at Step 5\n"
]
},
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"text/plain": [
"<dtreeviz.utils.DTreeVizRender at 0x289dd4bc250>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"init_viz_model = dtreeviz.model(\n",
" init_tree,\n",
" X_train=init_X_active, \n",
" y_train=init_y_active,\n",
" feature_names=init_fenc.get_feature_names(),\n",
" target_name=f\"{REL_TYPE}?\",\n",
" class_names=['No', 'Yes']\n",
" )\n",
"print(\"Decision Path at Step \", INIT_STEP)\n",
"init_viz_model.view(x=init_X_test[SAMPLE_INDEX], colors={\"classes\": colors_for_2_classes}) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. Visualize a tree from the final RF classifier"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We repeat the process with the final decision tree."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree at Step 6\n",
"\n",
"|--- count_Drug <= 1.500\n",
"| |--- punct_dist <= 0.500\n",
"| | |--- class: 1.0\n",
"| |--- punct_dist > 0.500\n",
"| | |--- class: 1.0\n",
"|--- count_Drug > 1.500\n",
"| |--- char_dist <= 28.000\n",
"| | |--- class: 1.0\n",
"| |--- char_dist > 28.000\n",
"| | |--- class: 0.0\n",
"\n"
]
}
],
"source": [
"print(\"Decision Tree at Step \", FINAL_STEP)\n",
"print()\n",
"print(export_text(final_tree, \n",
" spacing=3, decimals=3,\n",
" feature_names=final_fenc.get_feature_names()))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Path at Step 6\n"
]
},
{
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"text/plain": [
"<dtreeviz.utils.DTreeVizRender at 0x289ddd45d50>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_viz_model = dtreeviz.model(\n",
" final_tree,\n",
" X_train=final_X_active,\n",
" y_train=final_y_active,\n",
" feature_names=final_fenc.get_feature_names(),\n",
" target_name=f\"{REL_TYPE}?\",\n",
" class_names=[\"No\", \"Yes\"],\n",
")\n",
"print(\"Decision Path at Step \", FINAL_STEP)\n",
"final_viz_model.view(x=final_X_test[SAMPLE_INDEX], colors={\"classes\": colors_for_2_classes}) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resources\n",
"\n",
"- [Sklearn's `export_tree`](https://scikit-learn.org/stable/modules/generated/sklearn.tree.export_text.html)\n",
"- [dtreeviz](https://github.com/parrt/dtreeviz)\n"
]
}
],
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"display_name": ".venv",
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