Andras Schmelczer
cf61b38b45
All checks were successful
Publish documentation to great-ai.schmelczer.dev / publish (push) Successful in 1m10s
Check / Lint, format & type checks (push) Successful in 1m55s
Check / Test on Python 3.12 (push) Successful in 3m8s
Check / Test on Python 3.13 (push) Successful in 2m32s
Check / Notebooks on Python 3.12 (push) Successful in 8m49s
Check / Notebooks on Python 3.13 (push) Successful in 7m53s
2314 lines
132 KiB
Text
2314 lines
132 KiB
Text
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-0",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Explore data and feasibility of approach\n",
|
|
"\n",
|
|
"The original annotations are proprietary. This notebook defaults to 60\n",
|
|
"clearly synthetic sentences with two simulated annotators so the entire\n",
|
|
"training/deployment workflow can run locally without credentials.\n",
|
|
"These data and their scores demonstrate execution only.\n",
|
|
"\n",
|
|
"To use your own JSON annotation files (each maps sentences to binary\n",
|
|
"labels), set `GREAT_AI_ANNOTATIONS_DIR` to their directory before starting\n",
|
|
"Jupyter. No private data or cloud configuration is included in this repo."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6da6bb8a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Local setup\n",
|
|
"\n",
|
|
"Use Python 3.12 or 3.13 and install this checkout with `uv pip install --editable '.[notebooks]'`.\n",
|
|
"See **Run notebooks locally** in the repository README for the kernel, execution order,\n",
|
|
"and full-data options. Run related notebooks from the same working directory\n",
|
|
"to share the default `.cache/` model store and `tracing_database.json` database.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "cell-3",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:59.684612Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:59.684455Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:59.694166Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:59.693485Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Using synthetic demo annotations; scores are not research results.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import json\n",
|
|
"import os\n",
|
|
"from pathlib import Path\n",
|
|
"\n",
|
|
"from docs.notebook_support import demo_annotations\n",
|
|
"\n",
|
|
"annotation_dir = os.environ.get(\"GREAT_AI_ANNOTATIONS_DIR\")\n",
|
|
"if annotation_dir:\n",
|
|
" annotations = [\n",
|
|
" json.loads(p.read_text(encoding=\"utf-8\"))\n",
|
|
" for p in sorted(Path(annotation_dir).expanduser().glob(\"*.json\"))\n",
|
|
" ]\n",
|
|
" if not annotations:\n",
|
|
" raise ValueError(f\"No annotation JSON files found in {annotation_dir}\")\n",
|
|
"else:\n",
|
|
" annotations = demo_annotations()\n",
|
|
" print(\"Using synthetic demo annotations; scores are not research results.\")\n",
|
|
"\n",
|
|
"evaluations = {\n",
|
|
" sentence: [\n",
|
|
" annotation[sentence] for annotation in annotations if sentence in annotation\n",
|
|
" ]\n",
|
|
" for sentence in sorted(\n",
|
|
" {sentence for annotation in annotations for sentence in annotation}\n",
|
|
" )\n",
|
|
"}\n",
|
|
"X = list(evaluations)\n",
|
|
"y = [int(sum(e) > 0) for e in evaluations.values()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-4",
|
|
"metadata": {},
|
|
"source": [
|
|
"Save the processed dataset using local versioned storage for the training notebook."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "cell-5",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:59.695771Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:59.695589Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.795320Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.795003Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of summary-train-dataset-small\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of summary-train-dataset-small\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of summary-train-dataset-small\u001b[0m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from great_ai.large_file import LargeFileLocal\n",
|
|
"\n",
|
|
"with LargeFileLocal(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f:\n",
|
|
" json.dump((X, y), f)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-6",
|
|
"metadata": {},
|
|
"source": [
|
|
"Filter out sentences which don't have enough annotations."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "cell-7",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.796420Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.796263Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.798035Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.797684Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"y1 = [e[0] for e in evaluations.values() if len(e) == 2]\n",
|
|
"y2 = [e[1] for e in evaluations.values() if len(e) == 2]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-8",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate [Cohen's kappa](https://en.wikipedia.org/wiki/Cohen%27s_kappa).\n",
|
|
"\n",
|
|
"It's a bit low but the task itself is pretty subjective so it's not all that surprising."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "cell-9",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.798711Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.798654Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.806309Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.806045Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.8\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import sklearn.metrics\n",
|
|
"\n",
|
|
"if y1:\n",
|
|
" print(sklearn.metrics.cohen_kappa_score(y1, y2))\n",
|
|
"else:\n",
|
|
" print(\"No sentences have exactly two annotations; kappa is unavailable.\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-10",
|
|
"metadata": {},
|
|
"source": [
|
|
"Can we train anything on this data?\n",
|
|
"\n",
|
|
"Let's try with a trivial SVM."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "cell-11",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.807045Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.806983Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.808468Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.808132Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = [s for s in evaluations.keys()]\n",
|
|
"y = [int(sum(e) > 0) for e in evaluations.values()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "cell-12",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.809082Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.809029Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.861903Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.861397Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([0.41666667, 0.25 , 0.16666667, 0.41666667, 0.5 ])"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"from sklearn.svm import LinearSVC\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
|
"from sklearn.model_selection import cross_val_score\n",
|
|
"\n",
|
|
"model = Pipeline(\n",
|
|
" steps=[\n",
|
|
" (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)),\n",
|
|
" (\"classifier\", LinearSVC(random_state=42)),\n",
|
|
" ]\n",
|
|
") # baseline model\n",
|
|
"\n",
|
|
"cross_val_score(model, X, y, cv=5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-13",
|
|
"metadata": {},
|
|
"source": [
|
|
"The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "cell-14",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.862626Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.862564Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.873305Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.872879Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style>.sk-global {\n",
|
|
" /* Definition of color scheme common for light and dark mode */\n",
|
|
" --sklearn-color-text: #000;\n",
|
|
" --sklearn-color-text-muted: #666;\n",
|
|
" --sklearn-color-line: gray;\n",
|
|
" /* Definition of color scheme for unfitted estimators */\n",
|
|
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
|
|
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
|
|
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
|
" --sklearn-color-unfitted-level-3: chocolate;\n",
|
|
" /* Definition of color scheme for fitted estimators */\n",
|
|
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
|
" --sklearn-color-fitted-level-1: #d4ebff;\n",
|
|
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
|
|
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global.light {\n",
|
|
" /* Specific color for light theme */\n",
|
|
" --sklearn-color-text-on-default-background: black;\n",
|
|
" --sklearn-color-background: white;\n",
|
|
" --sklearn-color-border-box: black;\n",
|
|
" --sklearn-color-icon: #696969;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global.dark {\n",
|
|
" --sklearn-color-text-on-default-background: white;\n",
|
|
" --sklearn-color-background: #111;\n",
|
|
" --sklearn-color-border-box: white;\n",
|
|
" --sklearn-color-icon: #878787;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global pre {\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-hidden--visually {\n",
|
|
" border: 0;\n",
|
|
" clip-path: inset(100%);\n",
|
|
" height: 1px;\n",
|
|
" margin: -1px;\n",
|
|
" overflow: hidden;\n",
|
|
" padding: 0;\n",
|
|
" position: absolute;\n",
|
|
" width: 1px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-dashed-wrapped {\n",
|
|
" border: 1px dashed var(--sklearn-color-line);\n",
|
|
" margin: 0 0.4em 0.5em 0.4em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" padding-bottom: 0.4em;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-container {\n",
|
|
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
|
|
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
|
" so we also need the `!important` here to be able to override the\n",
|
|
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
|
|
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
|
|
" display: inline-block !important;\n",
|
|
" position: relative;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-text-repr-fallback {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"div.sk-parallel-item,\n",
|
|
"div.sk-serial,\n",
|
|
"div.sk-item {\n",
|
|
" /* draw centered vertical line to link estimators */\n",
|
|
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
|
|
" background-size: 2px 100%;\n",
|
|
" background-repeat: no-repeat;\n",
|
|
" background-position: center center;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Parallel-specific style estimator block */\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item::after {\n",
|
|
" content: \"\";\n",
|
|
" width: 100%;\n",
|
|
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
|
|
" flex-grow: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel {\n",
|
|
" display: flex;\n",
|
|
" align-items: stretch;\n",
|
|
" justify-content: center;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
" position: relative;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item {\n",
|
|
" display: flex;\n",
|
|
" flex-direction: column;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:first-child::after {\n",
|
|
" align-self: flex-end;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:last-child::after {\n",
|
|
" align-self: flex-start;\n",
|
|
" width: 50%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-parallel-item:only-child::after {\n",
|
|
" width: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Serial-specific style estimator block */\n",
|
|
"\n",
|
|
".sk-global div.sk-serial {\n",
|
|
" display: flex;\n",
|
|
" flex-direction: column;\n",
|
|
" align-items: center;\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
" padding-right: 1em;\n",
|
|
" padding-left: 1em;\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
|
|
"clickable and can be expanded/collapsed.\n",
|
|
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
|
|
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
|
|
"*/\n",
|
|
"\n",
|
|
"/* Pipeline and ColumnTransformer style (default) */\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable {\n",
|
|
" /* Default theme specific background. It is overwritten whether we have a\n",
|
|
" specific estimator or a Pipeline/ColumnTransformer */\n",
|
|
" background-color: var(--sklearn-color-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Toggleable label */\n",
|
|
".sk-global label.sk-toggleable__label {\n",
|
|
" cursor: pointer;\n",
|
|
" display: flex;\n",
|
|
" width: 100%;\n",
|
|
" margin-bottom: 0;\n",
|
|
" padding: 0.5em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" text-align: center;\n",
|
|
" align-items: center;\n",
|
|
" justify-content: center;\n",
|
|
" gap: 0.5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label .caption {\n",
|
|
" font-size: 0.6rem;\n",
|
|
" font-weight: lighter;\n",
|
|
" color: var(--sklearn-color-text-muted);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label-arrow:before {\n",
|
|
" /* Arrow on the left of the label */\n",
|
|
" content: \"▸\";\n",
|
|
" float: left;\n",
|
|
" margin-right: 0.25em;\n",
|
|
" color: var(--sklearn-color-icon);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Toggleable content - dropdown */\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content {\n",
|
|
" display: none;\n",
|
|
" text-align: left;\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content pre {\n",
|
|
" margin: 0.2em;\n",
|
|
" border-radius: 0.25em;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-toggleable__content.fitted pre {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
|
|
" /* Expand drop-down */\n",
|
|
" display: block;\n",
|
|
" width: 100%;\n",
|
|
" overflow: visible;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
|
|
" content: \"▾\";\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Pipeline/ColumnTransformer-specific style */\n",
|
|
"\n",
|
|
".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator-specific style */\n",
|
|
"\n",
|
|
"/* Colorize estimator box */\n",
|
|
".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label label.sk-toggleable__label,\n",
|
|
".sk-global div.sk-label label {\n",
|
|
" /* The background is the default theme color */\n",
|
|
" color: var(--sklearn-color-text-on-default-background);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover, darken the color of the background */\n",
|
|
".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Label box, darken color on hover, fitted */\n",
|
|
".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator label */\n",
|
|
"\n",
|
|
".sk-global div.sk-label label {\n",
|
|
" font-family: monospace;\n",
|
|
" font-weight: bold;\n",
|
|
" line-height: 1.2em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-label-container {\n",
|
|
" text-align: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Estimator-specific */\n",
|
|
".sk-global div.sk-estimator {\n",
|
|
" font-family: monospace;\n",
|
|
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
|
" border-radius: 0.25em;\n",
|
|
" box-sizing: border-box;\n",
|
|
" margin-bottom: 0.5em;\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* on hover */\n",
|
|
".sk-global div.sk-estimator:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global div.sk-estimator.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
|
|
"\n",
|
|
"/* Common style for \"i\" and \"?\" */\n",
|
|
"\n",
|
|
".sk-estimator-doc-link,\n",
|
|
"a:link.sk-estimator-doc-link,\n",
|
|
"a:visited.sk-estimator-doc-link {\n",
|
|
" float: right;\n",
|
|
" font-size: smaller;\n",
|
|
" line-height: 1em;\n",
|
|
" font-family: monospace;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 1em;\n",
|
|
" height: 1em;\n",
|
|
" width: 1em;\n",
|
|
" text-decoration: none !important;\n",
|
|
" margin-left: 0.5em;\n",
|
|
" text-align: center;\n",
|
|
" /* unfitted */\n",
|
|
" border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
|
|
" color: var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link.fitted,\n",
|
|
"a:link.sk-estimator-doc-link.fitted,\n",
|
|
"a:visited.sk-estimator-doc-link.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover */\n",
|
|
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
|
|
".sk-estimator-doc-link:hover,\n",
|
|
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
|
|
".sk-estimator-doc-link:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
|
" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
|
|
" color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
|
|
".sk-estimator-doc-link.fitted:hover,\n",
|
|
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
|
|
".sk-estimator-doc-link.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
|
" border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-0);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Span, style for the box shown on hovering the info icon */\n",
|
|
".sk-estimator-doc-link span {\n",
|
|
" display: none;\n",
|
|
" z-index: 9999;\n",
|
|
" position: relative;\n",
|
|
" font-weight: normal;\n",
|
|
" right: .2ex;\n",
|
|
" padding: .5ex;\n",
|
|
" margin: .5ex;\n",
|
|
" width: min-content;\n",
|
|
" min-width: 20ex;\n",
|
|
" max-width: 50ex;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" box-shadow: 2pt 2pt 4pt #999;\n",
|
|
" /* unfitted */\n",
|
|
" background: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link.fitted span {\n",
|
|
" /* fitted */\n",
|
|
" background: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-estimator-doc-link:hover span {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link {\n",
|
|
" float: right;\n",
|
|
" font-size: 1rem;\n",
|
|
" line-height: 1em;\n",
|
|
" font-family: monospace;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 1rem;\n",
|
|
" height: 1rem;\n",
|
|
" width: 1rem;\n",
|
|
" text-decoration: none;\n",
|
|
" /* unfitted */\n",
|
|
" color: var(--sklearn-color-unfitted-level-1);\n",
|
|
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
|
|
" color: var(--sklearn-color-fitted-level-1);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* On hover */\n",
|
|
".sk-global a.estimator_doc_link:hover {\n",
|
|
" /* unfitted */\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-3);\n",
|
|
" color: var(--sklearn-color-background);\n",
|
|
" text-decoration: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-global a.estimator_doc_link.fitted:hover {\n",
|
|
" /* fitted */\n",
|
|
" background-color: var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".sk-top-container.sk-global {\n",
|
|
" /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
|
|
" We need to set it to !important and add tabindex=\"0\" in the HTML\n",
|
|
" to allow keyboard-only users to navigate the display. */\n",
|
|
" overflow-x: scroll !important;\n",
|
|
" max-width: 100%;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table {\n",
|
|
" font-family: monospace;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary {\n",
|
|
" padding: .5rem;\n",
|
|
" cursor: pointer;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table summary::marker {\n",
|
|
" font-size: 0.7rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table details[open] {\n",
|
|
" padding-left: 0.1rem;\n",
|
|
" padding-right: 0.1rem;\n",
|
|
" padding-bottom: 0.3rem;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table {\n",
|
|
" margin-left: auto !important;\n",
|
|
" margin-right: auto !important;\n",
|
|
" margin-top: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table .parameters-table tr:hover td {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".estimator-table table :is(td, th) {\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" `table td`is set in notebook with right text-align.\n",
|
|
" We need to overwrite it.\n",
|
|
"*/\n",
|
|
".estimator-table table td.param {\n",
|
|
" text-align: left;\n",
|
|
" position: relative;\n",
|
|
" padding: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td.value {\n",
|
|
" color:rgb(255, 94, 0);\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".default td, .estimator-table th {\n",
|
|
" color: black;\n",
|
|
" text-align: left !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".user-set td i,\n",
|
|
".default td i {\n",
|
|
" color: black;\n",
|
|
"}\n",
|
|
"\n",
|
|
"td.fitted-att-type {\n",
|
|
" white-space: preserve nowrap;\n",
|
|
"}\n",
|
|
"\n",
|
|
"/*\n",
|
|
" Styles for parameter documentation links\n",
|
|
" We need styling for visited so jupyter doesn't overwrite it\n",
|
|
"*/\n",
|
|
"a.param-doc-link,\n",
|
|
"a.param-doc-link:link,\n",
|
|
"a.param-doc-link:visited {\n",
|
|
" text-decoration: underline dashed;\n",
|
|
" text-underline-offset: .3em;\n",
|
|
" color: inherit;\n",
|
|
" display: block;\n",
|
|
" padding: .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(anchor-name: --doc-link) {\n",
|
|
" a.param-doc-link,\n",
|
|
" a.param-doc-link:link,\n",
|
|
" a.param-doc-link:visited {\n",
|
|
" anchor-name: --doc-link;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
|
|
"a.param-doc-link::before {\n",
|
|
" position: absolute;\n",
|
|
" content: \"\";\n",
|
|
" inset: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-description {\n",
|
|
" display: none;\n",
|
|
" position: absolute;\n",
|
|
" z-index: 9999;\n",
|
|
" left: 0;\n",
|
|
" padding: .5ex;\n",
|
|
" margin-left: 1.5em;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" box-shadow: .3em .3em .4em #999;\n",
|
|
" width: max-content;\n",
|
|
" text-align: left;\n",
|
|
" max-height: 10em;\n",
|
|
" overflow-y: auto;\n",
|
|
"\n",
|
|
" /* unfitted */\n",
|
|
" background: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-unfitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
"@supports(position-area: center right) {\n",
|
|
" .param-doc-description {\n",
|
|
" position-area: center right;\n",
|
|
" position: fixed;\n",
|
|
" margin-left: 0;\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"/* Fitted state for parameter tooltips */\n",
|
|
".fitted .param-doc-description {\n",
|
|
" /* fitted */\n",
|
|
" background: var(--sklearn-color-fitted-level-0);\n",
|
|
" border: thin solid var(--sklearn-color-fitted-level-3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".param-doc-link:hover .param-doc-description {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon {\n",
|
|
" background-image: url(data:image/svg+xml;base64,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);\n",
|
|
" background-repeat: no-repeat;\n",
|
|
" background-size: 14px 14px;\n",
|
|
" background-position: 0;\n",
|
|
" display: inline-block;\n",
|
|
" width: 14px;\n",
|
|
" height: 14px;\n",
|
|
" cursor: pointer;\n",
|
|
" border: none;\n",
|
|
" padding: 0;\n",
|
|
" background-color: transparent;\n",
|
|
"}\n",
|
|
"\n",
|
|
".copy-paste-icon:focus-visible {\n",
|
|
" outline: 1px solid currentColor;\n",
|
|
" outline-offset: 1px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features {\n",
|
|
" font-family: monospace;\n",
|
|
" cursor: pointer;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border: 1px dotted var(--sklearn-color-border-box);\n",
|
|
" border-radius: .20em;\n",
|
|
" margin-bottom: 0.5em;\n",
|
|
" font-size: inherit; /* Needed for jupyter */\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features summary {\n",
|
|
" cursor: pointer;\n",
|
|
" display: flex;\n",
|
|
" margin-bottom: 0;\n",
|
|
" text-align: center;\n",
|
|
" align-items: center;\n",
|
|
" justify-content: center;\n",
|
|
" gap: 0.5em;\n",
|
|
" padding: .25em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary {\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details[open] > summary {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
" border-radius: .20em 0 0 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details > summary .arrow::before {\n",
|
|
" content: \"▸\";\n",
|
|
" color: grey;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details[open] > summary .arrow::before {\n",
|
|
" content: \"▾\";\n",
|
|
"}\n",
|
|
"\n",
|
|
".features details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted details:hover > summary {\n",
|
|
" margin: 0;\n",
|
|
" background-color: var(--sklearn-color-fitted-level-2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container {\n",
|
|
" max-height: 10em;\n",
|
|
" overflow: auto;\n",
|
|
" scrollbar-width: thin;\n",
|
|
" padding: .25em 0.1rem;\n",
|
|
" background-color: var(--sklearn-color-unfitted-level-0);\n",
|
|
" border-radius: 0 0 .5em .5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features.fitted .features-container {\n",
|
|
" background-color: var(--sklearn-color-fitted-level-0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .image-container {\n",
|
|
" block-size: 1em;\n",
|
|
" inline-size: 1em;\n",
|
|
" padding: 0;\n",
|
|
" margin: 0%;\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" align-items: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .copy-paste-icon {\n",
|
|
" background-size: 1em 1em;\n",
|
|
" width: 1em;\n",
|
|
" height: 1em;\n",
|
|
" filter: grayscale(100%) opacity(60%);\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" width: 100%;\n",
|
|
" margin: 0.01em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(odd) {\n",
|
|
" background-color: #fff;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:nth-child(even) {\n",
|
|
" background-color: #f6f6f6;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table tr:hover {\n",
|
|
" background-color: #e0e0e0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table {\n",
|
|
" table-layout: inherit;\n",
|
|
"}\n",
|
|
"\n",
|
|
".features .features-container table td {\n",
|
|
" text-align: left;\n",
|
|
" padding: 0 0.5em;\n",
|
|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
|
" white-space: nowrap;\n",
|
|
" color: var(--sklearn-color-text);\n",
|
|
"}\n",
|
|
"\n",
|
|
".total_features {\n",
|
|
" display: flex;\n",
|
|
" justify-content: center;\n",
|
|
" margin-top: 0.5em;\n",
|
|
"}\n",
|
|
"</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('vectorizer',\n",
|
|
" TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)),\n",
|
|
" ('classifier', LinearSVC(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Parameters</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy steps to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('steps',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-steps;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
|
|
" steps\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-steps;\">\n",
|
|
" steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">[('vectorizer', ...), ('classifier', ...)]</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy transform_input to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('transform_input',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-transform_input;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
|
|
" transform_input\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-transform_input;\">\n",
|
|
" transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy memory to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('memory',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-memory;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
|
|
" memory\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-memory;\">\n",
|
|
" memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy verbose to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('verbose',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-verbose;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
|
|
" verbose\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-verbose;\">\n",
|
|
" verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">False</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" \n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Fitted attributes</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>Name</th>\n",
|
|
" <th>Type</th>\n",
|
|
" <th>Value</th>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-classes_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
|
|
" classes_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-classes_;\">\n",
|
|
" classes_: ndarray of shape (n_classes,)<br><br>The classes labels. Only exist if the last step of the pipeline is a<br>classifier.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
|
|
" <td>[0,1]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TfidfVectorizer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"vectorizer__\">\n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Parameters</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy max_df to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('max_df',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-max_df;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=max_df,-float%20or%20int%2C%20default%3D1.0\">\n",
|
|
" max_df\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-max_df;\">\n",
|
|
" max_df: float or int, default=1.0<br><br>When building the vocabulary ignore terms that have a document<br>frequency strictly higher than the given threshold (corpus-specific<br>stop words).<br>If float in range [0.0, 1.0], the parameter represents a proportion of<br>documents, integer absolute counts.<br>This parameter is ignored if vocabulary is not None.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0.3</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy min_df to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('min_df',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-min_df;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=min_df,-float%20or%20int%2C%20default%3D1\">\n",
|
|
" min_df\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-min_df;\">\n",
|
|
" min_df: float or int, default=1<br><br>When building the vocabulary ignore terms that have a document<br>frequency strictly lower than the given threshold. This value is also<br>called cut-off in the literature.<br>If float in range of [0.0, 1.0], the parameter represents a proportion<br>of documents, integer absolute counts.<br>This parameter is ignored if vocabulary is not None.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">3</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy sublinear_tf to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('sublinear_tf',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-sublinear_tf;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=sublinear_tf,-bool%2C%20default%3DFalse\">\n",
|
|
" sublinear_tf\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-sublinear_tf;\">\n",
|
|
" sublinear_tf: bool, default=False<br><br>Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy input to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('input',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-input;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=input,-%7B%27filename%27%2C%20%27file%27%2C%20%27content%27%7D%2C%20default%3D%27content%27\">\n",
|
|
" input\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-input;\">\n",
|
|
" input: {'filename', 'file', 'content'}, default='content'<br><br>- If `'filename'`, the sequence passed as an argument to fit is<br> expected to be a list of filenames that need reading to fetch<br> the raw content to analyze.<br><br>- If `'file'`, the sequence items must have a 'read' method (file-like<br> object) that is called to fetch the bytes in memory.<br><br>- If `'content'`, the input is expected to be a sequence of items that<br> can be of type string or byte.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'content'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy encoding to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('encoding',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-encoding;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=encoding,-str%2C%20default%3D%27utf-8%27\">\n",
|
|
" encoding\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-encoding;\">\n",
|
|
" encoding: str, default='utf-8'<br><br>If bytes or files are given to analyze, this encoding is used to<br>decode.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'utf-8'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy decode_error to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('decode_error',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-decode_error;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=decode_error,-%7B%27strict%27%2C%20%27ignore%27%2C%20%27replace%27%7D%2C%20default%3D%27strict%27\">\n",
|
|
" decode_error\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-decode_error;\">\n",
|
|
" decode_error: {'strict', 'ignore', 'replace'}, default='strict'<br><br>Instruction on what to do if a byte sequence is given to analyze that<br>contains characters not of the given `encoding`. By default, it is<br>'strict', meaning that a UnicodeDecodeError will be raised. Other<br>values are 'ignore' and 'replace'.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'strict'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy strip_accents to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('strip_accents',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-strip_accents;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=strip_accents,-%7B%27ascii%27%2C%20%27unicode%27%7D%20or%20callable%2C%20default%3DNone\">\n",
|
|
" strip_accents\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-strip_accents;\">\n",
|
|
" strip_accents: {'ascii', 'unicode'} or callable, default=None<br><br>Remove accents and perform other character normalization<br>during the preprocessing step.<br>'ascii' is a fast method that only works on characters that have<br>a direct ASCII mapping.<br>'unicode' is a slightly slower method that works on any characters.<br>None (default) means no character normalization is performed.<br><br>Both 'ascii' and 'unicode' use NFKD normalization from<br>:func:`unicodedata.normalize`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy lowercase to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('lowercase',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-lowercase;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=lowercase,-bool%2C%20default%3DTrue\">\n",
|
|
" lowercase\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-lowercase;\">\n",
|
|
" lowercase: bool, default=True<br><br>Convert all characters to lowercase before tokenizing.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy preprocessor to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('preprocessor',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-preprocessor;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=preprocessor,-callable%2C%20default%3DNone\">\n",
|
|
" preprocessor\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-preprocessor;\">\n",
|
|
" preprocessor: callable, default=None<br><br>Override the preprocessing (string transformation) stage while<br>preserving the tokenizing and n-grams generation steps.<br>Only applies if ``analyzer`` is not callable.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy tokenizer to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('tokenizer',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-tokenizer;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=tokenizer,-callable%2C%20default%3DNone\">\n",
|
|
" tokenizer\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-tokenizer;\">\n",
|
|
" tokenizer: callable, default=None<br><br>Override the string tokenization step while preserving the<br>preprocessing and n-grams generation steps.<br>Only applies if ``analyzer == 'word'``.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy analyzer to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('analyzer',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-analyzer;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=analyzer,-%7B%27word%27%2C%20%27char%27%2C%20%27char_wb%27%7D%20or%20callable%2C%20default%3D%27word%27\">\n",
|
|
" analyzer\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-analyzer;\">\n",
|
|
" analyzer: {'word', 'char', 'char_wb'} or callable, default='word'<br><br>Whether the feature should be made of word or character n-grams.<br>Option 'char_wb' creates character n-grams only from text inside<br>word boundaries; n-grams at the edges of words are padded with space.<br><br>If a callable is passed it is used to extract the sequence of features<br>out of the raw, unprocessed input.<br><br>.. versionchanged:: 0.21<br> Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data<br> is first read from the file and then passed to the given callable<br> analyzer.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'word'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy stop_words to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('stop_words',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-stop_words;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=stop_words,-%7B%27english%27%7D%2C%20list%2C%20default%3DNone\">\n",
|
|
" stop_words\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-stop_words;\">\n",
|
|
" stop_words: {'english'}, list, default=None<br><br>If a string, it is passed to _check_stop_list and the appropriate stop<br>list is returned. 'english' is currently the only supported string<br>value.<br>There are several known issues with 'english' and you should<br>consider an alternative (see :ref:`stop_words`).<br><br>If a list, that list is assumed to contain stop words, all of which<br>will be removed from the resulting tokens.<br>Only applies if ``analyzer == 'word'``.<br><br>If None, no stop words will be used. In this case, setting `max_df`<br>to a higher value, such as in the range (0.7, 1.0), can automatically detect<br>and filter stop words based on intra corpus document frequency of terms.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy token_pattern to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('token_pattern',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-token_pattern;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=token_pattern,-str%2C%20default%3Dr%22%28%3Fu%29%5C%5Cb%5C%5Cw%5C%5Cw%2B%5C%5Cb%22\">\n",
|
|
" token_pattern\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-token_pattern;\">\n",
|
|
" token_pattern: str, default=r"(?u)\\\\b\\\\w\\\\w+\\\\b"<br><br>Regular expression denoting what constitutes a "token", only used<br>if ``analyzer == 'word'``. The default regexp selects tokens of 2<br>or more alphanumeric characters (punctuation is completely ignored<br>and always treated as a token separator).<br><br>If there is a capturing group in token_pattern then the<br>captured group content, not the entire match, becomes the token.<br>At most one capturing group is permitted.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'(?u)\\\\b\\\\w\\\\w+\\\\b'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy ngram_range to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('ngram_range',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-ngram_range;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=ngram_range,-tuple%20%28min_n%2C%20max_n%29%2C%20default%3D%281%2C%201%29\">\n",
|
|
" ngram_range\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-ngram_range;\">\n",
|
|
" ngram_range: tuple (min_n, max_n), default=(1, 1)<br><br>The lower and upper boundary of the range of n-values for different<br>n-grams to be extracted. All values of n such that min_n <= n <= max_n<br>will be used. For example an ``ngram_range`` of ``(1, 1)`` means only<br>unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means<br>only bigrams.<br>Only applies if ``analyzer`` is not callable.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">(1, ...)</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy max_features to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('max_features',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-max_features;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=max_features,-int%2C%20default%3DNone\">\n",
|
|
" max_features\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-max_features;\">\n",
|
|
" max_features: int, default=None<br><br>If not None, build a vocabulary that only consider the top<br>`max_features` ordered by term frequency across the corpus.<br>Otherwise, all features are used.<br><br>This parameter is ignored if vocabulary is not None.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy vocabulary to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('vocabulary',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-vocabulary;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=vocabulary,-Mapping%20or%20iterable%2C%20default%3DNone\">\n",
|
|
" vocabulary\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-vocabulary;\">\n",
|
|
" vocabulary: Mapping or iterable, default=None<br><br>Either a Mapping (e.g., a dict) where keys are terms and values are<br>indices in the feature matrix, or an iterable over terms. If not<br>given, a vocabulary is determined from the input documents.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy binary to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('binary',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-binary;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=binary,-bool%2C%20default%3DFalse\">\n",
|
|
" binary\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-binary;\">\n",
|
|
" binary: bool, default=False<br><br>If True, all non-zero term counts are set to 1. This does not mean<br>outputs will have only 0/1 values, only that the tf term in tf-idf<br>is binary. (Set `binary` to True, `use_idf` to False and<br>`norm` to None to get 0/1 outputs).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">False</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy dtype to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('dtype',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-dtype;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=dtype,-dtype%2C%20default%3Dfloat64\">\n",
|
|
" dtype\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-dtype;\">\n",
|
|
" dtype: dtype, default=float64<br><br>Type of the matrix returned by fit_transform() or transform().</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\"><class 'numpy.float64'></td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy norm to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('norm',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-norm;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=norm,-%7B%27l1%27%2C%20%27l2%27%7D%20or%20None%2C%20default%3D%27l2%27\">\n",
|
|
" norm\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-norm;\">\n",
|
|
" norm: {'l1', 'l2'} or None, default='l2'<br><br>Each output row will have unit norm, either:<br><br>- 'l2': Sum of squares of vector elements is 1. The cosine<br> similarity between two vectors is their dot product when l2 norm has<br> been applied.<br>- 'l1': Sum of absolute values of vector elements is 1.<br> See :func:`~sklearn.preprocessing.normalize`.<br>- None: No normalization.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'l2'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy use_idf to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('use_idf',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-use_idf;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=use_idf,-bool%2C%20default%3DTrue\">\n",
|
|
" use_idf\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-use_idf;\">\n",
|
|
" use_idf: bool, default=True<br><br>Enable inverse-document-frequency reweighting. If False, idf(t) = 1.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy smooth_idf to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('smooth_idf',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-smooth_idf;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=smooth_idf,-bool%2C%20default%3DTrue\">\n",
|
|
" smooth_idf\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-smooth_idf;\">\n",
|
|
" smooth_idf: bool, default=True<br><br>Smooth idf weights by adding one to document frequencies, as if an<br>extra document was seen containing every term in the collection<br>exactly once. Prevents zero divisions.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" \n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Fitted attributes</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>Name</th>\n",
|
|
" <th>Type</th>\n",
|
|
" <th>Value</th>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-fixed_vocabulary_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=fixed_vocabulary_,-bool\">\n",
|
|
" fixed_vocabulary_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-fixed_vocabulary_;\">\n",
|
|
" fixed_vocabulary_: bool<br><br>True if a fixed vocabulary of term to indices mapping<br>is provided by the user.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">bool</td>\n",
|
|
" <td>False</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-idf_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=idf_,-array%20of%20shape%20%28n_features%2C%29\">\n",
|
|
" idf_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-idf_;\">\n",
|
|
" idf_: array of shape (n_features,)<br><br>The inverse document frequency (IDF) vector; only defined<br>if ``use_idf`` is True.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](6,)</td>\n",
|
|
" <td>[2.49,2.81,2.69,2.81,2.69,2.69]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-vocabulary_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#:~:text=vocabulary_,-dict\">\n",
|
|
" vocabulary_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-vocabulary_;\">\n",
|
|
" vocabulary_: dict<br><br>A mapping of terms to feature indices.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">dict</td>\n",
|
|
" <td>{'cancer': 0, 'genome': 1, 'neural': 2, 'protein': 3, ...}</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" </div></div></div>\n",
|
|
" <div class=\"features fitted\">\n",
|
|
" <details>\n",
|
|
" <summary>\n",
|
|
" <div class=\"arrow\"></div>\n",
|
|
" <div>6 features</div>\n",
|
|
" <div class=\"image-container\">\n",
|
|
" <button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" title=\"Copy output features (max 100)\"\n",
|
|
" aria-label=\"Copy output features (max 100)\"\n",
|
|
" onclick=\"\n",
|
|
" event.stopPropagation();\n",
|
|
" event.preventDefault();\n",
|
|
" copyFeatureNamesToClipboard(this);\n",
|
|
" \"\n",
|
|
" >\n",
|
|
" </button>\n",
|
|
" </div>\n",
|
|
" </summary>\n",
|
|
" <div class=\"features-container\">\n",
|
|
" <table class=\"features-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>cancer</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>genome</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>neural</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>protein</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>quantum</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>solar</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </div>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"classifier__\">\n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Parameters</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy random_state to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('random_state',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-random_state;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
|
|
" random_state\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-random_state;\">\n",
|
|
" random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">42</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy penalty to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('penalty',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-penalty;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
|
|
" penalty\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-penalty;\">\n",
|
|
" penalty: {'l1', 'l2'}, default='l2'<br><br>Specifies the norm used in the penalization. The 'l2'<br>penalty is the standard used in SVC. The 'l1' leads to ``coef_``<br>vectors that are sparse.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'l2'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy loss to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('loss',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-loss;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
|
|
" loss\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-loss;\">\n",
|
|
" loss: {'hinge', 'squared_hinge'}, default='squared_hinge'<br><br>Specifies the loss function. 'hinge' is the standard SVM loss<br>(used e.g. by the SVC class) while 'squared_hinge' is the<br>square of the hinge loss. The combination of ``penalty='l1'``<br>and ``loss='hinge'`` is not supported.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'squared_hinge'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy dual to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('dual',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-dual;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
|
|
" dual\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-dual;\">\n",
|
|
" dual: "auto" or bool, default="auto"<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples > n_features.<br>`dual="auto"` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` < `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br> The `"auto"` option is added in version 1.3 and will be the default<br> in version 1.5.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'auto'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy tol to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('tol',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-tol;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
|
|
" tol\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-tol;\">\n",
|
|
" tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0.0001</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy C to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('C',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-C;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
|
|
" C\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-C;\">\n",
|
|
" C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1.0</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy multi_class to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('multi_class',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-multi_class;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
|
|
" multi_class\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-multi_class;\">\n",
|
|
" multi_class: {'ovr', 'crammer_singer'}, default='ovr'<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``"ovr"`` trains n_classes one-vs-rest classifiers, while<br>``"crammer_singer"`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``"crammer_singer"`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">'ovr'</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy fit_intercept to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('fit_intercept',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-fit_intercept;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
|
|
" fit_intercept\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-fit_intercept;\">\n",
|
|
" fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">True</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy intercept_scaling to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('intercept_scaling',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
|
|
" intercept_scaling\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
|
|
" intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a "synthetic" feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy class_weight to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('class_weight',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-class_weight;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
|
|
" class_weight\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-class_weight;\">\n",
|
|
" class_weight: dict or 'balanced', default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy verbose to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('verbose',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-verbose;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
|
|
" verbose\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-verbose;\">\n",
|
|
" verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy max_iter to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('max_iter',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-max_iter;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
|
|
" max_iter\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-max_iter;\">\n",
|
|
" max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">1000</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" \n",
|
|
" <div class=\"estimator-table\">\n",
|
|
" <details>\n",
|
|
" <summary>Fitted attributes</summary>\n",
|
|
" <table class=\"parameters-table\">\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>Name</th>\n",
|
|
" <th>Type</th>\n",
|
|
" <th>Value</th>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-classes_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
|
|
" classes_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-classes_;\">\n",
|
|
" classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
|
|
" <td>[0,1]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-coef_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
|
|
" coef_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-coef_;\">\n",
|
|
" coef_: ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](1, 6)</td>\n",
|
|
" <td>[[-0.07, 0.06, 0.16, 0.06,-0.07,-0.07]]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-intercept_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
|
|
" intercept_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-intercept_;\">\n",
|
|
" intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
|
|
" <td>[0.08]</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_features_in_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
|
|
" n_features_in_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_features_in_;\">\n",
|
|
" n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int</td>\n",
|
|
" <td>6</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"default\">\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-n_iter_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
|
|
" n_iter_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-n_iter_;\">\n",
|
|
" n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">int</td>\n",
|
|
" <td>4</td>\n",
|
|
"\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </details>\n",
|
|
" </div>\n",
|
|
" </div></div></div></div></div></div></div><script>/* Authors: The scikit-learn developers\n",
|
|
" SPDX-License-Identifier: BSD-3-Clause\n",
|
|
"*/\n",
|
|
"\n",
|
|
"function copyToClipboard(text, element) {\n",
|
|
" // Get the parameter prefix from the closest toggleable content\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
|
|
"\n",
|
|
" const originalStyle = element.style;\n",
|
|
" const computedStyle = window.getComputedStyle(element);\n",
|
|
" const originalWidth = computedStyle.width;\n",
|
|
" const originalHTML = element.innerHTML.replace('Copied!', '');\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(fullParamName)\n",
|
|
" .then(() => {\n",
|
|
" element.style.width = originalWidth;\n",
|
|
" element.style.color = 'green';\n",
|
|
" element.innerHTML = \"Copied!\";\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'red';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 2000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"\n",
|
|
"document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
|
|
" const toggleableContent = element.closest('.sk-toggleable__content');\n",
|
|
" const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
|
|
"\n",
|
|
" const parent = element.parentElement;\n",
|
|
" if (!parent || !parent.nextElementSibling) {\n",
|
|
" console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
|
|
" return;\n",
|
|
" }\n",
|
|
"\n",
|
|
" const paramName = element.parentElement.nextElementSibling\n",
|
|
" .textContent.trim().split(' ')[0];\n",
|
|
" const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
|
|
"\n",
|
|
" element.setAttribute('title', fullParamName);\n",
|
|
"});\n",
|
|
"\n",
|
|
"/**\n",
|
|
" * Copy the list of feature names formatted as a Python list.\n",
|
|
" *\n",
|
|
" * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
|
|
" * contain a `details` element and a table containing feature named.\n",
|
|
" * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
|
|
" */\n",
|
|
"function copyFeatureNamesToClipboard(element) {\n",
|
|
" var detailsElem = element.closest('.features').querySelector('details');\n",
|
|
" var wasOpen = detailsElem.open;\n",
|
|
" detailsElem.open = true;\n",
|
|
" var content = element.closest('.features').querySelector('tbody')\n",
|
|
" .innerText.trim();\n",
|
|
" if (!wasOpen) detailsElem.open = false;\n",
|
|
" const rows = content.split('\\n').map(row => ` \"${row}\"`);\n",
|
|
" const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
|
|
" const originalHTML = element.innerHTML.replace('✔', '');\n",
|
|
" const originalStyle = element.style;\n",
|
|
" const copyMark = document.createElement('span');\n",
|
|
" copyMark.innerHTML = '✔';\n",
|
|
" copyMark.style.color = 'blue';\n",
|
|
" copyMark.style.fontSize = '1em';\n",
|
|
"\n",
|
|
" navigator.clipboard.writeText(formattedText)\n",
|
|
" .then(() => {\n",
|
|
" element.style.display = 'none';\n",
|
|
" element.parentElement.appendChild(copyMark);\n",
|
|
"\n",
|
|
" setTimeout(() => {\n",
|
|
" copyMark.remove();\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" })\n",
|
|
" .catch(err => {\n",
|
|
" console.error('Failed to copy:', err);\n",
|
|
" element.style.color = 'orange';\n",
|
|
" element.innerHTML = \"Failed!\";\n",
|
|
" setTimeout(() => {\n",
|
|
" element.innerHTML = originalHTML;\n",
|
|
" element.style = originalStyle;\n",
|
|
" }, 1000);\n",
|
|
" });\n",
|
|
" return false;\n",
|
|
"}\n",
|
|
"/**\n",
|
|
" * Adapted from Skrub\n",
|
|
" * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
|
|
" * @returns \"light\" or \"dark\"\n",
|
|
" */\n",
|
|
"function detectTheme(element) {\n",
|
|
" const body = document.querySelector('body');\n",
|
|
"\n",
|
|
" // Check VSCode theme\n",
|
|
" const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
|
|
" const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
|
|
"\n",
|
|
" if (themeKindAttr && themeNameAttr) {\n",
|
|
" const themeKind = themeKindAttr.toLowerCase();\n",
|
|
" const themeName = themeNameAttr.toLowerCase();\n",
|
|
"\n",
|
|
" if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
|
|
" return \"dark\";\n",
|
|
" }\n",
|
|
" if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
|
|
" return \"light\";\n",
|
|
" }\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Check Jupyter theme\n",
|
|
" if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
|
|
" return 'dark';\n",
|
|
" } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Guess based on a parent element's color\n",
|
|
" const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
|
|
" const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
|
|
" if (match) {\n",
|
|
" const [r, g, b] = [\n",
|
|
" parseFloat(match[1]),\n",
|
|
" parseFloat(match[2]),\n",
|
|
" parseFloat(match[3])\n",
|
|
" ];\n",
|
|
"\n",
|
|
" // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
|
|
" const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
|
|
"\n",
|
|
" if (luma > 180) {\n",
|
|
" // If the text is very bright we have a dark theme\n",
|
|
" return 'dark';\n",
|
|
" }\n",
|
|
" if (luma < 75) {\n",
|
|
" // If the text is very dark we have a light theme\n",
|
|
" return 'light';\n",
|
|
" }\n",
|
|
" // Otherwise fall back to the next heuristic.\n",
|
|
" }\n",
|
|
"\n",
|
|
" // Fallback to system preference\n",
|
|
" return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
|
|
"}\n",
|
|
"\n",
|
|
"\n",
|
|
"function forceTheme(elementId) {\n",
|
|
" const estimatorElement = document.querySelector(`#${elementId}`);\n",
|
|
" if (estimatorElement === null) {\n",
|
|
" console.error(`Element with id ${elementId} not found.`);\n",
|
|
" } else {\n",
|
|
" const theme = detectTheme(estimatorElement);\n",
|
|
" estimatorElement.classList.add(theme);\n",
|
|
" }\n",
|
|
"}\n",
|
|
"\n",
|
|
"forceTheme('sk-container-id-1');</script></body>"
|
|
],
|
|
"text/plain": [
|
|
"Pipeline(steps=[('vectorizer',\n",
|
|
" TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)),\n",
|
|
" ('classifier', LinearSVC(random_state=42))])"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X, y, test_size=0.2, random_state=42, stratify=y\n",
|
|
")\n",
|
|
"model.fit(X_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "cell-15",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:48:00.874007Z",
|
|
"iopub.status.busy": "2026-10-28T22:48:00.873948Z",
|
|
"iopub.status.idle": "2026-10-28T22:48:00.920993Z",
|
|
"shell.execute_reply": "2026-10-28T22:48:00.920537Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" False 0.00 0.00 0.00 5\n",
|
|
" True 0.58 1.00 0.74 7\n",
|
|
"\n",
|
|
" accuracy 0.58 12\n",
|
|
" macro avg 0.29 0.50 0.37 12\n",
|
|
"weighted avg 0.34 0.58 0.43 12\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"y_predicted = model.predict(X_test)\n",
|
|
"print(\n",
|
|
" sklearn.metrics.classification_report(\n",
|
|
" [y > 0 for y in y_test], [y > 0 for y in y_predicted], zero_division=0\n",
|
|
" )\n",
|
|
")\n",
|
|
"sklearn.metrics.ConfusionMatrixDisplay.from_predictions(\n",
|
|
" [y > 0 for y in y_test],\n",
|
|
" [y > 0 for y in y_predicted],\n",
|
|
" xticks_rotation=\"vertical\",\n",
|
|
" values_format=\".2f\",\n",
|
|
")\n",
|
|
"None"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-16",
|
|
"metadata": {},
|
|
"source": [
|
|
"The report above describes the active dataset. The synthetic demo cannot\n",
|
|
"establish the feasibility or quality of a real summarisation system.\n",
|
|
"\n",
|
|
"Next: [Part 2](train.ipynb)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv (3.13.5)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|