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
2932 lines
155 KiB
Text
2932 lines
155 KiB
Text
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-0",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Optimise and train a model\n",
|
|
"\n",
|
|
"\n",
|
|
"> The blue boxes show the steps implemented in this notebook.\n",
|
|
"\n",
|
|
"Run `data.ipynb` first. We read its ground truth from the shared local TinyDB database. The default uses a compact parameter search suitable for the bundled sample; `GREAT_AI_NOTEBOOK_FULL=1` enables the original larger search on the full MAG training data. \n",
|
|
"\n",
|
|
"In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using `great_ai.save_model`.\n",
|
|
"\n",
|
|
"## Load data that has been extracted in [part 1](https://great-ai.schmelczer.dev/examples/simple/data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d3495327",
|
|
"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": "d8ae0655",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:33.469672Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:33.469359Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:33.475106Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:33.473998Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from docs.notebook_support import FULL_RUN"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "cell-3",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:33.476993Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:33.476853Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:34.629711Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:34.629235Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226mEnvironment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mGreatAI (v0.1.11): configured ✅\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 is_production: False\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 should_log_exception_stack: True\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 prediction_cache_size: 512\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39m 🔩 dashboard_table_size: 50\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from great_ai import query_ground_truth\n",
|
|
"\n",
|
|
"data = query_ground_truth([\"train\", \"local-domain-example\"])\n",
|
|
"if not data:\n",
|
|
" raise ValueError(\"Run data.ipynb first in the same local/full mode.\")\n",
|
|
"\n",
|
|
"X = [d.input for d in data for domain in d.feedback]\n",
|
|
"y = [domain for d in data for domain in d.feedback]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "cell-4",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:34.630652Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:34.630494Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:34.675524Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:34.675167Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 2000x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from collections import Counter\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"domains, counts = zip(*Counter(y).most_common())\n",
|
|
"\n",
|
|
"# Configure matplotlib to have nice, high-resolution charts\n",
|
|
"%matplotlib inline\n",
|
|
"plt.rcParams[\"figure.figsize\"] = (20, 5)\n",
|
|
"plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
|
|
"plt.rcParams[\"font.size\"] = 12\n",
|
|
"\n",
|
|
"plt.xticks(rotation=90)\n",
|
|
"plt.bar(domains, counts)\n",
|
|
"None"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-5",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Optimise and train Multinomial Naive Bayes classifier\n",
|
|
"\n",
|
|
"Each paper may have several accepted labels. We expand it into one training row per label for this single-label classifier, but keep all rows with the same paper text in one cross-validation fold using `GroupKFold`. The final train/test split was already made by paper in Part 1, before expansion.\n",
|
|
"\n",
|
|
"The search uses macro F1 on the expanded single-label validation rows as a training surrogate. This differs from the top-1 hit rate and fixed-target multilabel metrics used to evaluate the deployed label sets in [Part 3](https://great-ai.schmelczer.dev/examples/simple/deploy). Rerun the search to obtain results for these grouped folds; the previous ungrouped outputs have been removed."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "cell-6",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:34.676373Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:34.676316Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:34.683015Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:34.682660Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.naive_bayes import MultinomialNB\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
|
"\n",
|
|
"\n",
|
|
"def create_pipeline() -> Pipeline:\n",
|
|
" return Pipeline(\n",
|
|
" steps=[\n",
|
|
" (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)),\n",
|
|
" (\"classifier\", MultinomialNB()),\n",
|
|
" ]\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "cell-7",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:34.683793Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:34.683737Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:35.666188Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:35.665816Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>mean_fit_time</th>\n",
|
|
" <th>std_fit_time</th>\n",
|
|
" <th>mean_score_time</th>\n",
|
|
" <th>std_score_time</th>\n",
|
|
" <th>param_classifier__alpha</th>\n",
|
|
" <th>param_classifier__fit_prior</th>\n",
|
|
" <th>param_vectorizer__max_df</th>\n",
|
|
" <th>param_vectorizer__min_df</th>\n",
|
|
" <th>params</th>\n",
|
|
" <th>split0_test_score</th>\n",
|
|
" <th>split1_test_score</th>\n",
|
|
" <th>split2_test_score</th>\n",
|
|
" <th>mean_test_score</th>\n",
|
|
" <th>std_test_score</th>\n",
|
|
" <th>rank_test_score</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.002220</td>\n",
|
|
" <td>0.000173</td>\n",
|
|
" <td>0.001228</td>\n",
|
|
" <td>0.000017</td>\n",
|
|
" <td>0.5</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
|
|
" <td>0.455431</td>\n",
|
|
" <td>0.434571</td>\n",
|
|
" <td>0.423547</td>\n",
|
|
" <td>0.437850</td>\n",
|
|
" <td>0.013221</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>0.002214</td>\n",
|
|
" <td>0.000084</td>\n",
|
|
" <td>0.001206</td>\n",
|
|
" <td>0.000011</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
|
|
" <td>0.458154</td>\n",
|
|
" <td>0.415951</td>\n",
|
|
" <td>0.406097</td>\n",
|
|
" <td>0.426734</td>\n",
|
|
" <td>0.022578</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0.008167</td>\n",
|
|
" <td>0.004108</td>\n",
|
|
" <td>0.001306</td>\n",
|
|
" <td>0.000036</td>\n",
|
|
" <td>0.5</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
|
|
" <td>0.422790</td>\n",
|
|
" <td>0.389784</td>\n",
|
|
" <td>0.352330</td>\n",
|
|
" <td>0.388301</td>\n",
|
|
" <td>0.028784</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0.002201</td>\n",
|
|
" <td>0.000114</td>\n",
|
|
" <td>0.001167</td>\n",
|
|
" <td>0.000094</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
|
|
" <td>0.381607</td>\n",
|
|
" <td>0.382587</td>\n",
|
|
" <td>0.285005</td>\n",
|
|
" <td>0.349733</td>\n",
|
|
" <td>0.045772</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0.001697</td>\n",
|
|
" <td>0.000039</td>\n",
|
|
" <td>0.001383</td>\n",
|
|
" <td>0.000154</td>\n",
|
|
" <td>0.5</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
|
|
" <td>0.348260</td>\n",
|
|
" <td>0.358595</td>\n",
|
|
" <td>0.329416</td>\n",
|
|
" <td>0.345424</td>\n",
|
|
" <td>0.012080</td>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>0.001766</td>\n",
|
|
" <td>0.000113</td>\n",
|
|
" <td>0.001158</td>\n",
|
|
" <td>0.000053</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>False</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
|
|
" <td>0.344794</td>\n",
|
|
" <td>0.332797</td>\n",
|
|
" <td>0.323581</td>\n",
|
|
" <td>0.333724</td>\n",
|
|
" <td>0.008685</td>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.001895</td>\n",
|
|
" <td>0.000174</td>\n",
|
|
" <td>0.001272</td>\n",
|
|
" <td>0.000105</td>\n",
|
|
" <td>0.5</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
|
|
" <td>0.349248</td>\n",
|
|
" <td>0.328047</td>\n",
|
|
" <td>0.277855</td>\n",
|
|
" <td>0.318383</td>\n",
|
|
" <td>0.029937</td>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>0.001901</td>\n",
|
|
" <td>0.000075</td>\n",
|
|
" <td>0.001156</td>\n",
|
|
" <td>0.000107</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>True</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
|
|
" <td>0.337362</td>\n",
|
|
" <td>0.306776</td>\n",
|
|
" <td>0.292058</td>\n",
|
|
" <td>0.312065</td>\n",
|
|
" <td>0.018870</td>\n",
|
|
" <td>8</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" mean_fit_time std_fit_time mean_score_time std_score_time \\\n",
|
|
"2 0.002220 0.000173 0.001228 0.000017 \n",
|
|
"6 0.002214 0.000084 0.001206 0.000011 \n",
|
|
"0 0.008167 0.004108 0.001306 0.000036 \n",
|
|
"4 0.002201 0.000114 0.001167 0.000094 \n",
|
|
"3 0.001697 0.000039 0.001383 0.000154 \n",
|
|
"7 0.001766 0.000113 0.001158 0.000053 \n",
|
|
"1 0.001895 0.000174 0.001272 0.000105 \n",
|
|
"5 0.001901 0.000075 0.001156 0.000107 \n",
|
|
"\n",
|
|
" param_classifier__alpha param_classifier__fit_prior \\\n",
|
|
"2 0.5 False \n",
|
|
"6 1.0 False \n",
|
|
"0 0.5 True \n",
|
|
"4 1.0 True \n",
|
|
"3 0.5 False \n",
|
|
"7 1.0 False \n",
|
|
"1 0.5 True \n",
|
|
"5 1.0 True \n",
|
|
"\n",
|
|
" param_vectorizer__max_df param_vectorizer__min_df \\\n",
|
|
"2 0.9 1 \n",
|
|
"6 0.9 1 \n",
|
|
"0 0.9 1 \n",
|
|
"4 0.9 1 \n",
|
|
"3 0.9 3 \n",
|
|
"7 0.9 3 \n",
|
|
"1 0.9 3 \n",
|
|
"5 0.9 3 \n",
|
|
"\n",
|
|
" params split0_test_score \\\n",
|
|
"2 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.455431 \n",
|
|
"6 {'classifier__alpha': 1, 'classifier__fit_prio... 0.458154 \n",
|
|
"0 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.422790 \n",
|
|
"4 {'classifier__alpha': 1, 'classifier__fit_prio... 0.381607 \n",
|
|
"3 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.348260 \n",
|
|
"7 {'classifier__alpha': 1, 'classifier__fit_prio... 0.344794 \n",
|
|
"1 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.349248 \n",
|
|
"5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.337362 \n",
|
|
"\n",
|
|
" split1_test_score split2_test_score mean_test_score std_test_score \\\n",
|
|
"2 0.434571 0.423547 0.437850 0.013221 \n",
|
|
"6 0.415951 0.406097 0.426734 0.022578 \n",
|
|
"0 0.389784 0.352330 0.388301 0.028784 \n",
|
|
"4 0.382587 0.285005 0.349733 0.045772 \n",
|
|
"3 0.358595 0.329416 0.345424 0.012080 \n",
|
|
"7 0.332797 0.323581 0.333724 0.008685 \n",
|
|
"1 0.328047 0.277855 0.318383 0.029937 \n",
|
|
"5 0.306776 0.292058 0.312065 0.018870 \n",
|
|
"\n",
|
|
" rank_test_score \n",
|
|
"2 1 \n",
|
|
"6 2 \n",
|
|
"0 3 \n",
|
|
"4 4 \n",
|
|
"3 5 \n",
|
|
"7 6 \n",
|
|
"1 7 \n",
|
|
"5 8 "
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.model_selection import GridSearchCV, GroupKFold\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"optimisation_pipeline = GridSearchCV(\n",
|
|
" create_pipeline(),\n",
|
|
" {\n",
|
|
" \"vectorizer__min_df\": ([5, 20, 100] if FULL_RUN else [1, 3]),\n",
|
|
" \"vectorizer__max_df\": ([0.05, 0.1] if FULL_RUN else [0.9]),\n",
|
|
" \"classifier__alpha\": [0.5, 1],\n",
|
|
" \"classifier__fit_prior\": [True, False],\n",
|
|
" },\n",
|
|
" scoring=\"f1_macro\",\n",
|
|
" cv=GroupKFold(n_splits=3),\n",
|
|
" n_jobs=2,\n",
|
|
" verbose=1,\n",
|
|
")\n",
|
|
"# Group by paper text so that expanded labels and duplicate texts stay together.\n",
|
|
"optimisation_pipeline.fit(X, y, groups=X)\n",
|
|
"\n",
|
|
"results = pd.DataFrame(optimisation_pipeline.cv_results_)\n",
|
|
"results.sort_values(\"rank_test_score\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "cell-8",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:35.667075Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:35.667011Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:35.717496Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:35.716936Z"
|
|
}
|
|
},
|
|
"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', TfidfVectorizer(max_df=0.9, sublinear_tf=True)),\n",
|
|
" ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))])</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[<U10](7,)</td>\n",
|
|
" <td>['business','economics','geography',...,'politics','psychology',\n",
|
|
" 'sociology']</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\">0.9</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 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\">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](2093,)</td>\n",
|
|
" <td>[5.43,5.72,6.13,...,6.13,6.13,6.13]</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>{'00': 0, '10': 1, '1258': 2, '14...71': 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>100 of 2,093 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>00</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>10</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1258</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1472454871</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>15</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>16</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1750</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1769</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1782</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1810</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1821</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1846</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1850</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1872</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1880</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1881</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>18th</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1919</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1924</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1933</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1970</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1972</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1986</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1988</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1990</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1991</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1992</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1st</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2002</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2005</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2006</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2009</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>200pp</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2011</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2013</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2014</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2015</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2017</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>20th</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>234</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>251</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>267</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>28332</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>29</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>299</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>30</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>300</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>302</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>31st</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>338</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>369</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>3d</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>3rd</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>42</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>45488</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>569</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>58</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>669</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>7th</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>90</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>99</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>abilities</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>abolitionism</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>about</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>abuse</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>accessibilite</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>accident</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>accommodation</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>aconseguir</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>acquired</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>acreage</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>across</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>acting</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>action</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>activation</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>activity</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adaptacion</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>added</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>address</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adhd</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adjustment</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>administration</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adopting</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adoption</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adrenoceptor</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>adults</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>advanced</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>advantages</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>advertising</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>advices</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>aesthetics</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>affect</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>affecting</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>affordability</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>affordable</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>africa</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>african</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>after</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>again</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>against</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>MultinomialNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</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 alpha to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('alpha',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-alpha;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=alpha,-float%20or%20array-like%20of%20shape%20%28n_features%2C%29%2C%20default%3D1.0\">\n",
|
|
" alpha\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-alpha;\">\n",
|
|
" alpha: float or array-like of shape (n_features,), default=1.0<br><br>Additive (Laplace/Lidstone) smoothing parameter<br>(set alpha=0 and force_alpha=True, for no smoothing).</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">0.5</td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
"\n",
|
|
" <tr class=\"user-set\">\n",
|
|
" <td><button type=\"button\" class=\"copy-paste-icon\"\n",
|
|
" aria-label=\"Copy fit_prior to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('fit_prior',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-fit_prior;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=fit_prior,-bool%2C%20default%3DTrue\">\n",
|
|
" fit_prior\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-fit_prior;\">\n",
|
|
" fit_prior: bool, default=True<br><br>Whether to learn class prior probabilities or not.<br>If false, a uniform prior will be used.</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 force_alpha to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('force_alpha',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-force_alpha;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=force_alpha,-bool%2C%20default%3DTrue\">\n",
|
|
" force_alpha\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-force_alpha;\">\n",
|
|
" force_alpha: bool, default=True<br><br>If False and alpha is less than 1e-10, it will set alpha to<br>1e-10. If True, alpha will remain unchanged. This may cause<br>numerical errors if alpha is too close to 0.<br><br>.. versionadded:: 1.2<br>.. versionchanged:: 1.4<br> The default value of `force_alpha` changed to `True`.</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 class_prior to clipboard\"\n",
|
|
" onclick=\"copyToClipboard('class_prior',\n",
|
|
" this.parentElement.nextElementSibling)\"\n",
|
|
" ></button></td>\n",
|
|
" <td class=\"param\">\n",
|
|
" <a class=\"param-doc-link\"\n",
|
|
" style=\"anchor-name: --doc-link-class_prior;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=class_prior,-array-like%20of%20shape%20%28n_classes%2C%29%2C%20default%3DNone\">\n",
|
|
" class_prior\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-class_prior;\">\n",
|
|
" class_prior: array-like of shape (n_classes,), default=None<br><br>Prior probabilities of the classes. If specified, the priors are not<br>adjusted according to the data.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"value\">None</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-class_count_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=class_count_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
|
|
" class_count_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-class_count_;\">\n",
|
|
" class_count_: ndarray of shape (n_classes,)<br><br>Number of samples encountered for each class during fitting. This<br>value is weighted by the sample weight when provided.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](7,)</td>\n",
|
|
" <td>[51.,49.,46.,...,49.,44.,51.]</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-class_log_prior_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=class_log_prior_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
|
|
" class_log_prior_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-class_log_prior_;\">\n",
|
|
" class_log_prior_: ndarray of shape (n_classes,)<br><br>Smoothed empirical log probability for each class.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](7,)</td>\n",
|
|
" <td>[-1.95,-1.95,-1.95,...,-1.95,-1.95,-1.95]</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-classes_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.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>Class labels known to the classifier</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[<U10](7,)</td>\n",
|
|
" <td>['business','economics','geography',...,'politics','psychology',\n",
|
|
" 'sociology']</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-feature_count_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=feature_count_,-ndarray%20of%20shape%20%28n_classes%2C%20n_features%29\">\n",
|
|
" feature_count_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-feature_count_;\">\n",
|
|
" feature_count_: ndarray of shape (n_classes, n_features)<br><br>Number of samples encountered for each (class, feature)<br>during fitting. This value is weighted by the sample weight when<br>provided.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](7, 2093)</td>\n",
|
|
" <td>[[0.21,0. ,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0. ,0.31,0. ,...,0.39,0. ,0.29],\n",
|
|
" [0. ,0. ,0. ,...,0. ,0. ,0. ],\n",
|
|
" ...,\n",
|
|
" [0. ,0.18,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0. ,0. ,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0.37,0. ,0.41,...,0. ,0.24,0. ]]</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-feature_log_prob_;\"\n",
|
|
" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.naive_bayes.MultinomialNB.html#:~:text=feature_log_prob_,-ndarray%20of%20shape%20%28n_classes%2C%20n_features%29\">\n",
|
|
" feature_log_prob_\n",
|
|
" <span class=\"param-doc-description\"\n",
|
|
" style=\"position-anchor: --doc-link-feature_log_prob_;\">\n",
|
|
" feature_log_prob_: ndarray of shape (n_classes, n_features)<br><br>Empirical log probability of features<br>given a class, ``P(x_i|y)``.</span>\n",
|
|
" </a>\n",
|
|
" </td>\n",
|
|
" <td class=\"fitted-att-type\">ndarray[float64](7, 2093)</td>\n",
|
|
" <td>[[-7.43,-7.78,-7.78,...,-7.78,-7.78,-7.78],\n",
|
|
" [-7.77,-7.29,-7.77,...,-7.19,-7.77,-7.32],\n",
|
|
" [-7.78,-7.78,-7.78,...,-7.78,-7.78,-7.78],\n",
|
|
" ...,\n",
|
|
" [-7.78,-7.48,-7.78,...,-7.78,-7.78,-7.78],\n",
|
|
" [-7.77,-7.77,-7.77,...,-7.77,-7.77,-7.77],\n",
|
|
" [-7.24,-7.79,-7.19,...,-7.79,-7.4 ,-7.79]]</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.naive_bayes.MultinomialNB.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>2093</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', TfidfVectorizer(max_df=0.9, sublinear_tf=True)),\n",
|
|
" ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))])"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn import set_config\n",
|
|
"\n",
|
|
"set_config(display=\"diagram\")\n",
|
|
"\n",
|
|
"classifier = create_pipeline()\n",
|
|
"classifier.set_params(**optimisation_pipeline.best_params_)\n",
|
|
"classifier.fit(X, y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-9",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Export the model using GreatAI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "cell-10",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:35.718404Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:35.718242Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:35.738575Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:35.738292Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of small-domain-prediction\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of small-domain-prediction\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mFetching cached versions of small-domain-prediction\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[38;5;39mModel small-domain-prediction uploaded with version 0\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'small-domain-prediction:0'"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from great_ai import save_model\n",
|
|
"\n",
|
|
"save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cell-11",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Next: [Part 3](https://great-ai.schmelczer.dev/examples/simple/deploy)"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|