Andras Schmelczer
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3042 lines
363 KiB
Text
3042 lines
363 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "cell-0",
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"metadata": {},
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"source": [
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"# Train a field-of-study classifier on the bundled MAG sentences\n",
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"\n",
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"By default this notebook uses 100 training and 30 test samples per class\n",
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"and a small Naive Bayes parameter search for a quick local run. Set\n",
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"`GREAT_AI_NOTEBOOK_FULL=1` before launching Jupyter to use all bundled data\n",
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"and the larger search. Generated figures are saved in the working directory.\n",
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"Local subset scores do not reproduce the historical\n",
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"full-dataset comparison with the 2019 SciBERT baseline (macro F1 0.6571)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "190cd341",
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"metadata": {},
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"source": [
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"## Local setup\n",
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"\n",
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"Use Python 3.12 or 3.13 and install this checkout with `uv pip install --editable '.[notebooks]'`.\n",
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"See **Run notebooks locally** in the repository README for the kernel, execution order,\n",
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"and full-data options. Generated figures are saved in the working directory.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4a11c71a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-10-28T22:47:48.995471Z",
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"iopub.status.busy": "2026-10-28T22:47:48.995236Z",
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"iopub.status.idle": "2026-10-28T22:47:49.001740Z",
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"shell.execute_reply": "2026-10-28T22:47:49.000980Z"
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}
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},
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"outputs": [],
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"source": [
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"from docs.notebook_support import DATA_DIR, FULL_RUN, mag_lines"
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]
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},
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{
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"cell_type": "code",
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"iopub.status.idle": "2026-10-28T22:47:52.423523Z",
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"shell.execute_reply": "2026-10-28T22:47:52.422483Z"
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}
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"\n"
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}
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],
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"source": [
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"import json\n",
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"from typing import Tuple\n",
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"from great_ai.utilities import clean, simple_parallel_map\n",
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"\n",
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"\n",
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"def preprocess(line: str) -> Tuple[str, str]:\n",
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" data_point = json.loads(line)\n",
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"\n",
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" return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"])\n",
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"\n",
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"\n",
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"training_lines = mag_lines(DATA_DIR / \"train.txt\", None if FULL_RUN else 100)\n",
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"training_data = simple_parallel_map(preprocess, training_lines, concurrency=2)\n",
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"X_train = [d[0] for d in training_data]\n",
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"y_train = [d[1] for d in training_data]\n",
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"\n",
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"test_lines = mag_lines(DATA_DIR / \"test.txt\", None if FULL_RUN else 30)\n",
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"test_data = simple_parallel_map(preprocess, test_lines, concurrency=2)\n",
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"X_test = [d[0] for d in test_data]\n",
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"y_test = [d[1] for d in test_data]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "cell-4",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-10-28T22:47:52.426201Z",
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"iopub.status.busy": "2026-10-28T22:47:52.425143Z",
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"iopub.status.idle": "2026-10-28T22:47:52.585612Z",
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"shell.execute_reply": "2026-10-28T22:47:52.585283Z"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 800x500 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_train).most_common())\n",
|
|
"\n",
|
|
"# Configure matplotlib to have nice, high-resolution charts\n",
|
|
"%matplotlib inline\n",
|
|
"plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
|
|
"plt.rcParams[\"font.size\"] = 18\n",
|
|
"plt.rcParams[\"figure.figsize\"] = (8, 5)\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"\n",
|
|
"plt.xticks(rotation=90)\n",
|
|
"ax.bar(domains, counts)\n",
|
|
"ax.set_ylabel(\"Count\")\n",
|
|
"fig.tight_layout()\n",
|
|
"fig.savefig(\"mag-distribution.png\", dpi=500)\n",
|
|
"None"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "cell-5",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:52.586376Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:52.586316Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:52.593735Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:52.593353Z"
|
|
}
|
|
},
|
|
"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",
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"execution_count": 5,
|
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"id": "cell-6",
|
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"metadata": {
|
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"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:52.594535Z",
|
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"shell.execute_reply": "2026-10-28T22:47:53.535480Z"
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}
|
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},
|
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"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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"text": [
|
|
"Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
|
|
]
|
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},
|
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{
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"data": {
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|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>mean_fit_time</th>\n",
|
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" <th>std_fit_time</th>\n",
|
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" <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",
|
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" <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",
|
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" <th>split2_test_score</th>\n",
|
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" <th>mean_test_score</th>\n",
|
|
" <th>std_test_score</th>\n",
|
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" <th>rank_test_score</th>\n",
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|
" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0.003533</td>\n",
|
|
" <td>0.000225</td>\n",
|
|
" <td>0.001693</td>\n",
|
|
" <td>0.000093</td>\n",
|
|
" <td>1.0</td>\n",
|
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" <td>True</td>\n",
|
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" <td>0.9</td>\n",
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" <td>1</td>\n",
|
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" <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
|
|
" <td>0.432466</td>\n",
|
|
" <td>0.452735</td>\n",
|
|
" <td>0.515415</td>\n",
|
|
" <td>0.466872</td>\n",
|
|
" <td>0.035308</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>0.003541</td>\n",
|
|
" <td>0.000153</td>\n",
|
|
" <td>0.001560</td>\n",
|
|
" <td>0.000040</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>0.434901</td>\n",
|
|
" <td>0.447344</td>\n",
|
|
" <td>0.515260</td>\n",
|
|
" <td>0.465835</td>\n",
|
|
" <td>0.035316</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0.008523</td>\n",
|
|
" <td>0.003302</td>\n",
|
|
" <td>0.002370</td>\n",
|
|
" <td>0.000126</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>0.439486</td>\n",
|
|
" <td>0.455269</td>\n",
|
|
" <td>0.487305</td>\n",
|
|
" <td>0.460687</td>\n",
|
|
" <td>0.019894</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.003354</td>\n",
|
|
" <td>0.000213</td>\n",
|
|
" <td>0.001738</td>\n",
|
|
" <td>0.000117</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>0.438540</td>\n",
|
|
" <td>0.451386</td>\n",
|
|
" <td>0.488106</td>\n",
|
|
" <td>0.459344</td>\n",
|
|
" <td>0.021003</td>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.003001</td>\n",
|
|
" <td>0.000492</td>\n",
|
|
" <td>0.001699</td>\n",
|
|
" <td>0.000205</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>0.369420</td>\n",
|
|
" <td>0.389690</td>\n",
|
|
" <td>0.453996</td>\n",
|
|
" <td>0.404369</td>\n",
|
|
" <td>0.036054</td>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0.002694</td>\n",
|
|
" <td>0.000210</td>\n",
|
|
" <td>0.001664</td>\n",
|
|
" <td>0.000100</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>0.372802</td>\n",
|
|
" <td>0.385098</td>\n",
|
|
" <td>0.450138</td>\n",
|
|
" <td>0.402679</td>\n",
|
|
" <td>0.033932</td>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>0.002624</td>\n",
|
|
" <td>0.000035</td>\n",
|
|
" <td>0.002088</td>\n",
|
|
" <td>0.000339</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>0.366778</td>\n",
|
|
" <td>0.391056</td>\n",
|
|
" <td>0.444378</td>\n",
|
|
" <td>0.400737</td>\n",
|
|
" <td>0.032411</td>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>0.002653</td>\n",
|
|
" <td>0.000031</td>\n",
|
|
" <td>0.001487</td>\n",
|
|
" <td>0.000070</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>0.367218</td>\n",
|
|
" <td>0.386159</td>\n",
|
|
" <td>0.444473</td>\n",
|
|
" <td>0.399283</td>\n",
|
|
" <td>0.032876</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",
|
|
"4 0.003533 0.000225 0.001693 0.000093 \n",
|
|
"6 0.003541 0.000153 0.001560 0.000040 \n",
|
|
"0 0.008523 0.003302 0.002370 0.000126 \n",
|
|
"2 0.003354 0.000213 0.001738 0.000117 \n",
|
|
"1 0.003001 0.000492 0.001699 0.000205 \n",
|
|
"3 0.002694 0.000210 0.001664 0.000100 \n",
|
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"5 0.002624 0.000035 0.002088 0.000339 \n",
|
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"7 0.002653 0.000031 0.001487 0.000070 \n",
|
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"\n",
|
|
" param_classifier__alpha param_classifier__fit_prior \\\n",
|
|
"4 1.0 True \n",
|
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"6 1.0 False \n",
|
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"0 0.5 True \n",
|
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"2 0.5 False \n",
|
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"1 0.5 True \n",
|
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"3 0.5 False \n",
|
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"5 1.0 True \n",
|
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"7 1.0 False \n",
|
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"\n",
|
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" param_vectorizer__max_df param_vectorizer__min_df \\\n",
|
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"4 0.9 1 \n",
|
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"6 0.9 1 \n",
|
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"0 0.9 1 \n",
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"2 0.9 1 \n",
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"1 0.9 3 \n",
|
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"3 0.9 3 \n",
|
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"5 0.9 3 \n",
|
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"7 0.9 3 \n",
|
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"\n",
|
|
" params split0_test_score \\\n",
|
|
"4 {'classifier__alpha': 1, 'classifier__fit_prio... 0.432466 \n",
|
|
"6 {'classifier__alpha': 1, 'classifier__fit_prio... 0.434901 \n",
|
|
"0 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.439486 \n",
|
|
"2 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.438540 \n",
|
|
"1 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369420 \n",
|
|
"3 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.372802 \n",
|
|
"5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.366778 \n",
|
|
"7 {'classifier__alpha': 1, 'classifier__fit_prio... 0.367218 \n",
|
|
"\n",
|
|
" split1_test_score split2_test_score mean_test_score std_test_score \\\n",
|
|
"4 0.452735 0.515415 0.466872 0.035308 \n",
|
|
"6 0.447344 0.515260 0.465835 0.035316 \n",
|
|
"0 0.455269 0.487305 0.460687 0.019894 \n",
|
|
"2 0.451386 0.488106 0.459344 0.021003 \n",
|
|
"1 0.389690 0.453996 0.404369 0.036054 \n",
|
|
"3 0.385098 0.450138 0.402679 0.033932 \n",
|
|
"5 0.391056 0.444378 0.400737 0.032411 \n",
|
|
"7 0.386159 0.444473 0.399283 0.032876 \n",
|
|
"\n",
|
|
" rank_test_score \n",
|
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"4 1 \n",
|
|
"6 2 \n",
|
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"0 3 \n",
|
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"2 4 \n",
|
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"1 5 \n",
|
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"3 6 \n",
|
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"5 7 \n",
|
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"7 8 "
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.model_selection import GridSearchCV\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=3,\n",
|
|
" n_jobs=2,\n",
|
|
" verbose=1,\n",
|
|
")\n",
|
|
"optimisation_pipeline.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"results = pd.DataFrame(optimisation_pipeline.cv_results_)\n",
|
|
"results.sort_values(\"rank_test_score\")"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
|
|
"execution_count": 6,
|
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"id": "cell-7",
|
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"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:53.536912Z",
|
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"iopub.status.busy": "2026-10-28T22:47:53.536844Z",
|
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"iopub.status.idle": "2026-10-28T22:47:53.592995Z",
|
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"shell.execute_reply": "2026-10-28T22:47:53.592634Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>.sk-global {\n",
|
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" /* Definition of color scheme common for light and dark mode */\n",
|
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" --sklearn-color-text: #000;\n",
|
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" --sklearn-color-text-muted: #666;\n",
|
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" --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",
|
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" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
|
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" --sklearn-color-unfitted-level-3: chocolate;\n",
|
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" /* Definition of color scheme for fitted estimators */\n",
|
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" --sklearn-color-fitted-level-0: #f0f8ff;\n",
|
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" --sklearn-color-fitted-level-1: #d4ebff;\n",
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" --sklearn-color-fitted-level-2: #b3dbfd;\n",
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" --sklearn-color-fitted-level-3: cornflowerblue;\n",
|
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"}\n",
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"\n",
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".sk-global.light {\n",
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" /* Specific color for light theme */\n",
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" --sklearn-color-text-on-default-background: black;\n",
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" --sklearn-color-background: white;\n",
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" --sklearn-color-border-box: black;\n",
|
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" --sklearn-color-icon: #696969;\n",
|
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"}\n",
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"\n",
|
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".sk-global.dark {\n",
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" --sklearn-color-text-on-default-background: white;\n",
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" --sklearn-color-background: #111;\n",
|
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" --sklearn-color-border-box: white;\n",
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" --sklearn-color-icon: #878787;\n",
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"}\n",
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"\n",
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".sk-global {\n",
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" color: var(--sklearn-color-text);\n",
|
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"}\n",
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"\n",
|
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".sk-global pre {\n",
|
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" padding: 0;\n",
|
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"}\n",
|
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"\n",
|
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".sk-global input.sk-hidden--visually {\n",
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" border: 0;\n",
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" clip-path: inset(100%);\n",
|
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" height: 1px;\n",
|
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" margin: -1px;\n",
|
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" overflow: hidden;\n",
|
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" padding: 0;\n",
|
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" position: absolute;\n",
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" width: 1px;\n",
|
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"}\n",
|
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"\n",
|
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".sk-global div.sk-dashed-wrapped {\n",
|
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" border: 1px dashed var(--sklearn-color-line);\n",
|
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" margin: 0 0.4em 0.5em 0.4em;\n",
|
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" box-sizing: border-box;\n",
|
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" padding-bottom: 0.4em;\n",
|
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" background-color: var(--sklearn-color-background);\n",
|
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"}\n",
|
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"\n",
|
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".sk-global div.sk-container {\n",
|
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" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
|
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" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
|
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" so we also need the `!important` here to be able to override the\n",
|
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" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
|
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" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
|
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" display: inline-block !important;\n",
|
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" position: relative;\n",
|
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"}\n",
|
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"\n",
|
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".sk-global div.sk-text-repr-fallback {\n",
|
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" display: none;\n",
|
|
"}\n",
|
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"\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",
|
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" background-size: 2px 100%;\n",
|
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" 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",
|
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" flex-grow: 1;\n",
|
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"}\n",
|
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"\n",
|
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".sk-global div.sk-parallel {\n",
|
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" display: flex;\n",
|
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" 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",
|
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" left: 0;\n",
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" padding: .5ex;\n",
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" margin-left: 1.5em;\n",
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" color: var(--sklearn-color-text);\n",
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" box-shadow: .3em .3em .4em #999;\n",
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" width: max-content;\n",
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|
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".features {\n",
|
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" font-family: monospace;\n",
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|
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|
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|
|
"\n",
|
|
".features summary {\n",
|
|
" cursor: pointer;\n",
|
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|
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|
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"\n",
|
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|
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|
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|
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|
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"\n",
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".features.fitted details:hover > summary {\n",
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|
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|
|
".features .copy-paste-icon {\n",
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|
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|
|
"\n",
|
|
".features .features-container table {\n",
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|
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|
"\n",
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|
".features .features-container table tr:nth-child(odd) {\n",
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|
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|
|
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|
|
".features .features-container table tr:hover {\n",
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|
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"\n",
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".features .features-container table {\n",
|
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|
"}\n",
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|
"\n",
|
|
".features .features-container table td {\n",
|
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|
" border: 1px solid rgba(106, 105, 104, 0.232);\n",
|
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" white-space: nowrap;\n",
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|
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|
"}\n",
|
|
"\n",
|
|
".total_features {\n",
|
|
" display: flex;\n",
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" justify-content: center;\n",
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|
" 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=1))])</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](3655,)</td>\n",
|
|
" <td>[5.25,6.86,6.86,...,6.86,6.86,6.86]</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, '0864': 1, '0973': 2, '10': 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 3,655 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>0864</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>0973</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>10</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>100</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1120</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>12</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1258</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>14</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>14614</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>1500</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1538</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1544</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1550</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1558</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>16</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>161pp</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1727</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>18</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>1870</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>1890</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>18th</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>19</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1907</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1909</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1919</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1920</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>1940</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1950s</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1963</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1970</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1971</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1972</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1980</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1984</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>1989</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>1997</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1999</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>1st</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>20</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2001</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2002</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2003</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>2007</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2008</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>2016</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>2017</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>203</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>204</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>20th</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>21</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>22</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>234</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>24</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>242</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>247</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>25</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>269p</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>275</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>278</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>28</td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <td>283</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>295</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",
|
|
" </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=\"default\">\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\">1</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 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\">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>[100.,100.,100.,...,100.,100.,100.]</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, 3655)</td>\n",
|
|
" <td>[[0.38,0. ,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0.19,0. ,0. ,...,0. ,0. ,0.29],\n",
|
|
" [0. ,0. ,0. ,...,0. ,0.6 ,0. ],\n",
|
|
" ...,\n",
|
|
" [0.51,0.16,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0. ,0. ,0. ,...,0. ,0. ,0. ],\n",
|
|
" [0.69,0. ,0.27,...,0.24,0. ,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, 3655)</td>\n",
|
|
" <td>[[-7.96,-8.28,-8.28,...,-8.28,-8.28,-8.28],\n",
|
|
" [-8.11,-8.28,-8.28,...,-8.28,-8.28,-8.03],\n",
|
|
" [-8.29,-8.29,-8.29,...,-8.29,-7.82,-8.29],\n",
|
|
" ...,\n",
|
|
" [-7.87,-8.14,-8.28,...,-8.28,-8.28,-8.28],\n",
|
|
" [-8.28,-8.28,-8.28,...,-8.28,-8.28,-8.28],\n",
|
|
" [-7.76,-8.29,-8.05,...,-8.07,-8.29,-8.29]]</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>3655</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=1))])"
|
|
]
|
|
},
|
|
"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_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "cell-8",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-10-28T22:47:53.594117Z",
|
|
"iopub.status.busy": "2026-10-28T22:47:53.594030Z",
|
|
"iopub.status.idle": "2026-10-28T22:47:56.661828Z",
|
|
"shell.execute_reply": "2026-10-28T22:47:56.661325Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" business 0.4286 0.6000 0.5000 30\n",
|
|
" economics 0.3878 0.6333 0.4810 30\n",
|
|
" geography 0.4615 0.2000 0.2791 30\n",
|
|
" medicine 0.6800 0.5667 0.6182 30\n",
|
|
" politics 0.5152 0.5667 0.5397 30\n",
|
|
" psychology 0.4167 0.3333 0.3704 30\n",
|
|
" sociology 0.3333 0.2667 0.2963 30\n",
|
|
"\n",
|
|
" accuracy 0.4524 210\n",
|
|
" macro avg 0.4604 0.4524 0.4407 210\n",
|
|
"weighted avg 0.4604 0.4524 0.4407 210\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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f+xni/6MjQ8hucYH4fZIpZWeES8lVMSJ+lECDVZDTMUZl4z7alylwnAIG1WHsx/pod8vmaHngnmHywKNLJCqqWK69cbVcdd1qLYNSlYuNiy8UX1/rtzcnx1+efeIEWbva/XjOaMLHSYXV+10ucBrjMiig4ZWEDfA3yUWnrZEurVPl7ldOk9Iy96WZ0bQEhzjGBc7PrfrSX0Ge0290sMnr2u3a+4iccvYBbf7tpztJcRGfddTgc+lU8ju3uOq/D3lOyzi/tz60jc2Um4ct1eYLSozyyrzaXdt0poaEGdtlm/Roflgu++LsagdAAcAbNKmA5aeffiq33nqrZGeXvwj01VdfycMPPywvvviiXH311VpArrpBQbW+b775Riwuig5PmTJFkpOTtf+edtppla5rwYIFcu2112oBLVfU9qkA2vvvvy/Nmzc/LvuZkZFhX2bEiBGye/duufDCC7VAojO1j85SUlK0Zd358MMPte1TQc6HHnpIfFT9nWq0v2PHDhk/frysXLmywrIvvfSSnHPOOfLxxx9LeHi4VIcKoqoArgoSH+vtt9+Wk046SX766SeJjCxfRiE4OFh77ocfftAez5kzR04++eRK29q2bZu8++672ufkhBNOIFhZAwEBjpOW4uKqT0rU+EnqgpbtYxUYWCb5+bW8kOxBjWU/oK+AAMcF3uKj2SaVKS52/JkPdLqI5+1CQ4rlqcmzJTxUjWEm8t7n/WTH7qpL4KIJUSVdj7L4VzNw51wCtrBug0X4v5chhhyzmJsZpfSi2pdbMhQ47Ue0r/h/lCl+roKVtuXNIn5Tc8RwqEyKH0lQ9Q9r3TYaj5oeYzhf5A2sQ8CyvtrdsC5Wrpx4mtx8+0oZOXqP+PqKxCeUrzaxaEFzefn5/lLodIMXEOjvdJxUWr1LHaq8qk2Q03GWNygp85V/1iTJmi3N5FBGqKRnBWvnCDGRBdKjwyEZM2irln2p9Op0SG6+cIm8/PkQvTcbXiIg0PEbXVJc9c1dRU6/0dbAoTpWMnhFu0ajWW5+aJN2bjz/zwT5d6H3ZULDm1kkyM/p70NZ1X8fipz+hgT71d95dExIgbxx3jQJPvr366mZw2RvVuVVhcwWg1YydtnuFrLnSISk5oVIfrG/RAUXStdmqTKu22Z7OVv135fPniGXfnF2rb7PAKCHJhOw/OCDD7QgnTsq4/CWW27RMgirY/v27TJq1CgtK68yKgvxjDPOkK+//loLtrny22+/yXnnnadtQ2WmTZsmAwcO1DICVVDweOynTVZWlpYxqoKGxzo2WFtZsNKmqKhIHn30US0o+frrr1erfRUUrKy/VXBx//79Mm/ePAkMdJQVcUVlr6r1rVtnLSflisoQVQFaldF5rBtuuMEesHzvvfeqDFiqZWz9dPPNN1e6LMorK3McVBmNVV9g9vEx24N8Smmpd2QlNpb9gL7KnLJs1Yl7VfyMjgsGDeWO++CgEi1YmZxkDdr8Mr2TTP2zs96bBW9jdDrhNlUz+FjmtFwdYhw+qwrFb7a1NFTxzbEi/j4eORI3ZJvE+JP1c182JMRa/jXBqJWE9dlWIn6/54jPPusFE+PSAjH9miNlZ1EmGcf+baj6+2A8WjKzrscX9dXukGH75Job/5PYWPelAU8cekC6dPtTPvmgm8z6s3WNthuNV7nPpG/1znedj5VUgNCbXPHQuVJY7PoP1sJVKfL5773k/qvmy6Aee7XnTh+6Wb6b2V32HeZvA8qff/pW4zfaz/k3usRQ6+BGfbR7/qRdktw2X3JzjPLe8x1qtV1oygza+I5+R/8uGH2qcR7t9DekxFQ/fxsiAotkyvm/SYsIa3DxvcV95ff1Hat8391TT5HCUjd/G3a0kg+X9JE7TloiE/ut1Z7r2eKwnNxhh8ze0tbDewAA9aNJBCxVdpsK0tmoDDyVzTh48GAJCgrSgo9ffPGFljl4zz33VLm+wsJCLZvPFjxTpU5VwFEFrVS5VVVWdP369Vq2n8qYVI8nTZqklUJV5Uad7d27Vy655BJ7sFKVj73sssu0daksPlX29Pvvv5c///zTvvwFF1wgixcvrpCZ6On9PDbjUJVAVdt05ZVXautU5VBVedPExIqDQXfr1k0rOasCqwkJCdqyqpSqKp87depULaNUeeONN2TixIla1mF12g8JCdECskOHDtXm1T6p8qpLl1rLJ6j/qizS559/vtL1vfnmm1qfq1K5an9U+2FhYVr/fvLJJ/btU/2uAqAqw9OZyr5UpXpVydtffvlFy7Z1V+JWbbfKelVUX7gLXMO1QqeSlirLsCqBTnd0mkwGKSnxjp+5xrIf0P9zFBluvZM+sBpZAM7ZL0VFxgYRrHzmgb+kS4c07fEff7WXtz4eoPdmwRsFOY6BDEWWmmdlOr2/RkrMEvCG9fNZNjJUzH2qHrevMpZAnwplbovuiRPTyPJj65i7BUnZ2DAJeCpVC1Yqfj9kEbCEptCp1Gp1jjHK/W0oNHpVu+dftFEun7Rem1f3Vy77p5ksWtBCGyPTYjZomZYnDD4gg4fs10rG3n73CmmZnKuNfQkUFDkdb1czWzLAKSuz0Cnb0hu4C1ba5OYHyiNvnSwfPf6TJCXkaOWSh/bZJV9P73ncthHeqzDf6Tc6qOoSrwGBZpe/73q326JVvhawVD55rZ0cyWA8PtRcQYmfRARZz6OD/Eolu6jyJIdAp6xK9V5PiwoqlHcv+E06xGdqjz9Z2lPeWlC98153wUqbMrOvPD97iCRF5sjwdtaEktEdCViiflksBm1CzfsNFXnXEXk9efbZZ7Ugo6LGO1QlPI8dP1BlzD344IPy1FNPVbm+d955R9asWaPNt27dWhtPsXPn8tkfqnzrbbfdJtddd5189NFHWkbfCy+8oAXKjt02W+lWFcxS5Um7d+9ebhkVUFPruOqqq7QsPRWUU0EyFTStz/08NujWtWtXbfuaNWtW6bIq6JeUlOT29TvvvFMrC6v2x9afVQUsVfuq9Kzap7Zt21bYJ1Ve9rnnntMeq4xNFZBVwUh3VLBSjXmpsltjYmLKvXbFFVdomZVqDFBFZVIeG7C0jUmpsiXVulSQ8+6773bZ1s8//6yVn1WuueYaLcDtTnFxsTbZ5OTkSFOnxieyiYmxfr4rExvrKBmWm+s9JVQby35AXzm5AdIswZrZFRtdIDv3VF4mNTYmv9x7vVlYSLE888As6djOOi7g9Dnt5NX31PgdHMChIku4U6AvoxoXpU0WMRxxXDSzhNfuTmm/L7PE50CZWMJ8pPia8scPtWGJKL8dZYOCKwQr7fx9pPj2OPG9eLcYykR80k1i2F0illb8jWjqcp2PMWKrcYwRV+jy+ETvdlPaZMmlV1iDlSUlPvLw5CGy5r9jxqdcKzLnr1bSoWOmPP3i3xIcXCbnTdgi/yxurpWSRdOWm+841gkPKdaCkcVV3PQXF+V0rJRX+QVsb1RSZpRpCzrKNedZx/1un5yu9ybBS+RkO4IaMfGOawzuxCY4stpznd6rb7sWufmhjeIfYJb1qyJk+g8tar1daNqyCgPtAcv4sHw5lOvmePuohDDH34aqgpu1KQP73oRfpV3cEe3x58t7yCvzBounfbOymz1g2TnBesMlADQEjb7GYFlZmT3wpHz++eduxw984oknZMiQqsd8UFmBisouVMGoY4OVNn5+ftr4lSqoqRw71qXKvFQZjzZqfMpjg5XOQUsV7LJR2Zv1vZ/O1L6qsrZVBSuVyoKVNirjtEcP653QqsRtdagsxWODlTbPPPOMnHjiidq8Cvipvq6MyjhVgchjg5W2fX388cftj12NmamoTNjQ0FD7v11l5WAVo9GoZbxWRu1HRESEfXL3b9iU7N/vGJM0KanqAG6LFtZyGsrevdUbz/R4aCz7AX3tPego8ZXUvOrPUVIzxzJ79ntvebCIsCJ54ZEZ9mDljLlt5ZV31UkbwUq4Zk5yXNAypJaVy550xXC4TAvyKSrYKJG1C1gaZx79bbaIBN5/UAJv2Ody8l3nCMyocSltz/t9n1V+hZG+1u05ytS3iozNSF8xt3FckPdR+44mb99ex0W3pJaO4wd3WiTlunyv3u2OHLXHXg5/+u9tKgYrnWzZHC2//Nje8d7RVQ9JgcZPZSSmZTp+R1smuB8TWPExmKV5nONzueeQ9x4rVWZfquNcwTamJbB/l+O7kJTiCL6406KV44bZvTuDvaLdxKRC6dHfeuwU36xI3vh2qbzx7T8up6AQxzHRk1NW2Z/vdYL1/AJN2+5Mx5jzKdHHHI+70MppmZ0ZtR+v/lgJYXny8UW/2IOVX/7bXV6cY72W6WlqfEubyGD3ZfYBwNs0+oClyoTMy7Nmo/Tt21cbA9IdFahS2XqVUSVIbaVgVdCvZ8/Ky62ooOW4ceO0eTVeoyqJ6rxttgy6Nm3aaGNdVkZlJtosWrSoXPDT0/t5LJWN6C6Y6s6KFSu0rMdLL71U/ve//2llVFWmom1SmZiK6s+qxtRUJWZdZTk679NNN91Urn8qc9ppp0nz5s3dvt6hQwet/K1iy448liohq8r5KurfVY156erzorJCFZURq0oGV2by5Mlaxq1tsvVRU7Z7d4RWEkxp2TJXoqIqv4O/e3fHv9euXd5z0aGx7Af0tWuP42SpR5dDVY6DaiutquzcEyXeKCaqQF567E9pm2I9aZs5r628NOVESmOgcqG+Yo61Bh0NJhGfDZVfoPVd4/jNNafUPSPRkGcW3x0lbidbeVdbUNH+vItsUHNrp+0JqEaQ3nmZ0mqWw0Wjtmun4zihW4+qs6u6Oy2za0eE17TbIsl6LqNs21r1xcFtW6JcvhdN2479juoTPTserHTZjinpEnS0nHF2XoBkZIVIQxQaZB1eRinysrK20M+urdabq5W2nXMlMKjym5y69zvi8r16tmtwOuSJSyyWtp3y3E6+TveitWqbb38+JIybuyCyNc3xt6Ff8oFKu0SVjO2c4Dhm2ZpW96oqSouIHC1Y2SraejPNV/9210q31pewgJJql5EFAG/S6I9md+zYYZ+vquyoUlmgT1FjFjqPGTlq1Cj7Y1sA0TmQqOZ373bc8XvgwAEtGKbs3LnT/rwtO7Ay7du318rGHj58WI4cOSJZWVkSFRVVL/t5LBUEra5Vq1Zp2aD//vtvtZZXfaRK5qqMwsoCplVxXkYFCitj+zeojNoe1cf5+e7vClSBX1XS1pZJqYKyzlTmpe3z4BxQdUeNYaomOOTl+cumTTHSpYv1zsiRI3fJjz+6zmo2Gk0yfPge++Ply90HpY+3xrIf0NeyVS3kqonWrO/+vQ5omYnZua5L1Aw5YY+EBFvH3khND5Fde70vYBkXkycvPDJTWiRasxtmzm8rL75DsBLVY+ofLD7TrZ8d4+xcKekd5HZZ41955d7nTcoGBIvvGutdzz67HBcWXDJbxGePYxlLVO0yRdG4rFkdJ0VFvtr41ypw16lLhmza4PriWstWOdKh0xH7GNkrVyR4TbtlpY57aSMjq84Si4p2ZAuUljT6+3BRTUvXJskJ3fdp86cM2iY//tXN7bJjTnTcTLx0bcOtbDO4l+N6w8H02mdNo3FJPRgke7aHSHLbfPHzs8jw0w7LjJ9c30CtgoqDTnLc6PjvwtgG1y5QmYU7kmXSoFXa/Entd2pBSXdBvLFdtoqfr9ke6DycW/sAvk2rqCx5/8Jf7aVmVbDyuXoMVioj2juuOe/P4m8DgIaj0QcsbeNDKpWNaVjdZVSWpM3Bgwe1qSacxyRUwTCbuLi4ar3fFrBUVNDSFrD09H4ey5ZtWJXVq1fL8OHDtQBkTagxKitTnf6Jj4932beu1CQo6ByAdpX5OWzYMK2srSoPnJ6ebu9btU9qbEtFlb8dOnRotdtEeQsWJNsDfePHb5I5c1LkyJGKF6bPO2+TRB+9eKXGffzvv9pfhKsPjWU/oB81ZuWe/eGS3CJHAvxNMumilfKyVjq1vMCAUrl8gvWETPl7SSvxNonxufLCwzMkMT7fEax8m2Alqq9saIj42QKW8/Kk7IxwMXesGMD3XZwvvuusv6kWH5GyIbXPoCl6ppkYTFVnNfq/k2Fvs+TKaDH1DXI5ZqViGhoilo8yxWAWMc7IldKzIsQS5/oQ3fh7jhiyrRdQLIEGMbflJieIFBcZ5d9liTJk2H6tOyZds0buvWOEmM3HZuxa5OrrVtsf/bcyXnJzArym3QMHHN/NU0/fKb//2lZrwxV/f5OcPs5xg+Khg3W/mIjGYcHK1nLjhKXi62vRMihHD9wqs/5xlA+2ad0iU047cYv98dzl1mFcGhq1f0N6O93ouK7q4VnQdCyYGS8Tr7cGLS68ZocsmhUvebkVgzQTr98hoeHWTMS0QwGycXWEV7SbdjBQbjq/6hvylec//leCQ6zjlT9wXW/JzrRWsDi03/0NbWg6Vu1rJqm5Idr4leGBJXL9kOXy8tyK59FhAcVy9eAV9sczN7kelqom2sZmyrsTfpO40ILjFqzs1uywXNbfcey1eGfDvSkHQNPT6AOWzoEpNbZhVapaRo1DWBfOpU9rum1KUZHjTuLAwMB6289j+dgGlKnCrbfeag9WqpKpZ511lvTr108rv6oe+/v7a+VblXvuuUeWL19erfVWZ3vd9U19U1mWKmCptlGNs2kr3Tt16lR7cPnmm28+btvTGE2f3lbGj9+oBfEiIorl2WfnyiuvDJBNm6zB4cDAUjnnnM1y0UXr7e/57rvOUlrqOvOkc+d0ufHGf+2Zj/fdN7JB7geapi9/7CmTb1mgzZ928lYpKfWRT7/rJbl51t+95BZZcsd1i6Xl0TEuC4uM8v1vXd2u77rLlkmvrtbystNmd5BfZ3Sq931okZgjzz88Q+JjrSdtBCtRG+Y+wWLqHCC+G4u18SkDHz4kxbfFiWlgsLWGWJlFy7z0f9txs1nZSaFiae6mJFKWSRuX0qbo2WYi4eV/fy0p/mr4yipZgh3HTeZ4Y6WBRUuCn5SNCdOCr6rUbODdB6Tkhlgx9QtSA6xZF8o1id/UHPH72lE2rWxUmIg/47zC6psvOsvgIfu1MSC7ds+Qh59YJG+/3ltSD1uDgDGxhVrQsN8A67Gp8vXnris9KP87c5uMGWu92Lzmvzh5/51e9d7ukoUtZMJFm7V5lbH54mtz5ctPu8qyf5rZg6AGg0X6DTgkF1++Xtq0ddywuWQR1ShglXYkRKYv6iD/G2b9LN112UIJCiyVaQs6SplJ/aZbZEC3fXLPFQu0m7+UzbtiZMnqZLdd+Pq9v0lwoPUG25c+GyIbd7ofX9VT7rhkoew7HC7/bmghuw9GicnkUyHges7J6+X0odb9VLbvjZJlBCzhZOpXyXLmxL1aUDC+WbE888EKeeWRLrJjk3Xc05CwUjl/0i459zJH0Pvr91q7HZqhz+AMueJWa2Zy2qFAefzWXvXablmZj+zYXL3MMLPJ8V6V4ZmRevyuCcH7WcQgHyzpI/efYj2PvmzAau3YQj2XV2I9Tu8UnyaPjZ0nzcKtlVmyCwPk6xXuh8Z65NS50iXRmiH8+fKe8vv6jhWWaR+XIe9N+FWiQ6zXK79e0a1OwcqrB63Qhn5ZuitJNh6Ok+Ky8teoE8Ny5eyeG7X9C/Kz3gyQU+QvP67uUus2gepQ58gMVlJz9FkTDVg6ZxKqEq5VcR5j0l2Go40ak7CmgSiVkecqa3DzZseJhjsqIGYbP1MFEGNiYuptP2sjLS1NC9wpzZo1k2XLlklSUlKtMhdrs71btmypdQZpXajPQWJiohw6dEgrAWsLWKoSsYrKgp04ceJx257GqLjYKG+/3Vfuv3+RdkEsOTlHXnnlL8nMDJSCAj+JiyuQgADrBQdly5YomTrVfdnf4OBSadvWmoWbnV35WGa33rpM2rfPLPdc8+aO0oIq8Km2wdkbb/SXzZtj6n0/0DTNWdhaRg3brpWEVc48dbOMHbVVUtNCxM/PZA8C2nz4VR/JzHJfAjMxPs8+fmR0ZPn3OkuIy5PH7raOyWvjZ3TchBMTXShTnv+13OuqFO3Dz59cYV03XLGs3Ha2S8mUd577Taqy5N+W8ul3vatcDk1HyY2xEnjXATEUWbTMw8DHDosl1EcrlarGi3QeS9Ic7SulVzjGrzmWodSijTNpf1xmOW4nECWXRYvvqkLxOVSmTSr4agk2iCXWqI1TaThcpmVg2pgTjVJyifeVeYZ+dmyPlKk/tZezz7MeMw8YeEj69p8uaanB2gXg+IQCLePMZtrvrWXDevfHy+rmqrbtrAFBW/Cxvtvdsjla/p6bJMNOspbzVO0//MRiKSszSHpakLa+2LgCrcSgs+VLE2VVHUrbovH58Oe+0r/rPkmIydeCkndcsliuG79c0jKDJTKsSCLCHDfDFpf4yitfqOFZ3N8A0ibpiIQGW/8+2AKXrgzvu1Mu+Z+jwoUSHupoa3CvPfLBIz+Ve/3vlSny2W99Kqyrb+cDMm7EJvvj3Hx/ycoNFJPZR2IiCiQspHwJ8fxCP3n2o+GMAY5y8nL85P2X2svtj23UHqsxHd/8dpmkpwZIcaGvxDcrFD9/x2/q2n8jZeYv7m8ACQ0v1dahBAU7zlvru13AE35c3VlO67JVeidZb9a9YuB/MrHfGjmYE6YF91T2pbPnZ58oucXubzpsGZUjnRKsN0ZGhxS6XOahMfPtwUqzRaR30kH59vLvqtzWaRvay6fLKp73dkxIl9Edd8gNQ6w34OcX+8mRwkAtcBkZVCQxx2yHyWyQJ2YMl6xCMo0BNByNPmDZp4/j4H/27NlSUFAgwcHuL9z+/vvvla5vwIAB4uvrKyaTSQuijRgxwiPbtmDBAi3gV1np099++01KSkrsgU/nrEpP72dtqDE5bUFIFcSrLFip9lWVj62uOXPmaJmbKkvTnV9//bVWY27WlZ+fn1x99dXyxBNPaIHn+fPnS3Jysvz111/a65MmTZKgIA4O6mrRopbyzjt95ZprVomfn9l+Mc1WOtVmx45IefTRYR7LSmzePNce3HSlWbOKY5wGBZV63X6gMTHIEy+PkCfunS09ux62Bw5bNCtfilsl9H/9c3eZ+qf7DJqa8Pcz2QObrqhtOPb1wEDrXZ3HMh4dE8SmTSv363W2fZf7YBOaJnO7ACl+IEECnk0VQ771c6WyFNVUbrlYXyl+NNEaAPRGkb5S9FQzLVDps9/6N0QFWw17Kv49Mbfyk6JHEkVclJdF0/bBlB4SGloio0+1jmfn6yuS2KzijSgqKPj2a729st2Xn+8vRj+zDB5ivSlHMRotLten/LssQZ55fGCd9wGNy5GcYLnvtTHyzK0zJTEmzx5obNXckZWrFBT5yVPvj5BNHsqYjAgtknbJ5W90dBYeUqxNzjbvqt6NtipAeWyQ0kZlYj7x3kmydQ/j/6GiWb+0kMjoUrn0pm3a77MSG1+xgtWmNeHyxO09xXxMNm9Daxdwp8zsK7f+eJq8Pf4P6dY8VXvO32iWVtHl/zaUmQ3y+vwTXGZM1pTRx3FOogqn2AKcVfl3b/UC+CEBpdrkSlZhgDz+53CZvaXuZW0B4Hhq9EcEalzDXr162cc1fPLJJysNuL355puVri8iIkJGjRqlza9du1Zefvnlam3H3r175fPPPy/3nMoC7N+/vz170paZ54oao/K+++6zPz7ttNPqdT9rw7lsrC0T1BUV1LzllluqHLfSWV5enjz44INuX9++fbu88cYbbvunvl177bX2csEqs1JlWqr9VH2iSsbCM37/vb3cdttomT+/pRQVlb9Qu3dvmHz0UQ+59dbRLseF9CaNZT+gn8IiP7n78THy2vsDZeuO8kG80jIf+WdFkvb6J99WvGMfaGxM/YOlcEqSlJ4WJpaw8oe25hhfKTk3QgrfSdKCm97M0sJPCt9uISWXRok5qXzmvhp709TWX4qvi5HC11u4L2uLJk1lIL7yQn954uFBsnpVnJhM5W9iWb82Rp598gR59smBYjb7eGW7JSW+8uQjg+WR+0/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|
|
"text/plain": [
|
|
"<Figure size 2000x1800 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn import metrics\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"y_predicted = classifier.predict(X_test)\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"plt.rcParams[\"figure.figsize\"] = (20, 18)\n",
|
|
"plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
|
|
"plt.rcParams[\"font.size\"] = 22\n",
|
|
"plt.rcParams[\"axes.xmargin\"] = 0\n",
|
|
"\n",
|
|
"print(metrics.classification_report(y_test, y_predicted, digits=4))\n",
|
|
"metrics.ConfusionMatrixDisplay.from_predictions(\n",
|
|
" y_true=y_test,\n",
|
|
" y_pred=y_predicted,\n",
|
|
" xticks_rotation=\"vertical\",\n",
|
|
" normalize=\"pred\",\n",
|
|
" values_format=\".2f\",\n",
|
|
")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.savefig(\"mag-confusion.png\", dpi=600)"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|