272 lines
7.9 KiB
Text
272 lines
7.9 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Financial Sentiment Analysis\n",
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"\n",
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"[Dataset source](https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!rm -f tracing_database.json\n",
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"%pip install --upgrade great-ai > /dev/null"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"data = pd.read_csv('data/data.csv')\n",
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"\n",
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"pd.set_option(\"display.max_rows\", 30)\n",
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"pd.set_option(\"display.max_columns\", 30)\n",
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"data.head(30)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from great_ai import add_ground_truth, delete_ground_truth\n",
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"\n",
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"X = data['Sentence'].to_list()\n",
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"y = data['Sentiment'].to_list()\n",
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"\n",
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"add_ground_truth(X, y, train_split_ratio=0.85, test_split_ratio=0.15)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from great_ai import query_ground_truth\n",
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"\n",
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"train_split = query_ground_truth('train')\n",
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"test_split = query_ground_truth('test')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from great_ai.utilities import clean, simple_parallel_map\n",
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"import re\n",
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"from great_ai import Trace\n",
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"\n",
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"def normalize(text: str) -> str:\n",
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" cleaned = clean(text, convert_to_ascii=True).lower()\n",
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" return re.sub(r\"[^a-z]+\", \" \", cleaned)\n",
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"\n",
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"X_train = simple_parallel_map(normalize, [t.input for t in train_split])\n",
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"X_test = simple_parallel_map(normalize, [t.input for t in test_split])\n",
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"\n",
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"y_train = [t.output for t in train_split]\n",
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"y_test = [t.output for t in test_split]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import Pipeline, make_pipeline\n",
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"from sklearn.linear_model import SGDClassifier\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"\n",
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"\n",
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"def create_pipeline() -> Pipeline:\n",
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" return make_pipeline(\n",
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" TfidfVectorizer(min_df=5, max_df=0.3, ngram_range=(1, 3), sublinear_tf=True),\n",
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" SGDClassifier(max_iter=10000, tol=1e-4, penalty=\"elasticnet\")\n",
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" )"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import RandomizedSearchCV\n",
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"import scipy.stats\n",
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"\n",
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"optimisation_pipeline = RandomizedSearchCV(\n",
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" create_pipeline(),\n",
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" {\n",
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" \"sgdclassifier__alpha\": scipy.stats.uniform(0.00005, 0.01),\n",
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" \"sgdclassifier__l1_ratio\": scipy.stats.uniform(0.5, 0.4),\n",
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" },\n",
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" cv=4,\n",
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" n_iter=150,\n",
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" verbose=1,\n",
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" scoring='f1_macro',\n",
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" n_jobs=-1\n",
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")\n",
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"\n",
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"optimisation_pipeline.fit(X_train, y_train)\n",
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"results = pd.DataFrame(optimisation_pipeline.cv_results_)\n",
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"results.sort_values(\"rank_test_score\").head(20)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = create_pipeline()\n",
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"model.set_params(\n",
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" **optimisation_pipeline.best_params_,\n",
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" sgdclassifier__max_iter=100000,\n",
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" sgdclassifier__tol=1e-5,\n",
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")\n",
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"\n",
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"model.fit(X_train, y_train)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from sklearn import metrics\n",
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"\n",
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"%matplotlib inline\n",
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"plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
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"plt.rcParams[\"font.size\"] = 16\n",
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"\n",
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"y_predicted = model.predict(X_test)\n",
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"\n",
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"print(metrics.classification_report(y_test, y_predicted))\n",
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"metrics.ConfusionMatrixDisplay.from_predictions(\n",
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" y_true=y_test,\n",
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" y_pred=y_predicted,\n",
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" xticks_rotation=\"vertical\",\n",
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" normalize=\"pred\",\n",
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" values_format=\".2f\",\n",
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")\n",
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"None"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"features = model.named_steps[\"tfidfvectorizer\"].get_feature_names_out()\n",
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"\n",
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"for i, name in enumerate(model.named_steps[\"sgdclassifier\"].classes_):\n",
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" weight = model.named_steps[\"sgdclassifier\"].coef_[i]\n",
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"\n",
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" print(f'There are {len([w for w in weight if w != 0])} features for the`{name}` class.')\n",
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"\n",
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" for w, f in sorted(zip(weight, features), reverse=True)[:15]:\n",
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" if w == 0:\n",
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" break\n",
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" print(f\" {f}: {w:.4f}\")\n",
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"\n",
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" print()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict(text: str):\n",
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" text = normalize(text)\n",
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" features = model.named_steps[\"tfidfvectorizer\"].transform([text])\n",
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" prediction = model.named_steps[\"sgdclassifier\"].predict(features)[0]\n",
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"\n",
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" explanation = [\n",
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" (feature_name, weight)\n",
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" for weight, feature_name in sorted(\n",
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" (\n",
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" (feature_weight * feature, feature_name)\n",
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" for feature_name, feature_weight, feature in zip(\n",
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" model.named_steps[\"tfidfvectorizer\"].get_feature_names_out(),\n",
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" model.named_steps[\"sgdclassifier\"].coef_[list(model.named_steps[\"sgdclassifier\"].classes_).index(prediction)],\n",
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" features.toarray()[0],\n",
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" )\n",
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" if feature * feature_weight != 0\n",
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" ),\n",
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" reverse=True,\n",
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" )\n",
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" ][:10]\n",
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"\n",
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" return prediction, explanation\n",
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"\n",
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"predict('''\n",
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" The last 12 months for Tesla shares have been fairly but positively volatile. \n",
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" The stock is up in the past year, as it was trading at just under $700 per share back in early August 2021. \n",
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" The share price spent much of late 2021 and early 2022 over the $1,000 mark. \n",
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" Prices dipped below $1,000—and stayed there—starting in late April.\n",
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"\n",
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" I have been bearish on Tesla lately, owing to its elevated share price and its growing competition in the electric vehicle field.\n",
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" The competition part isn't changing much. \n",
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" That's really only gotten worse thanks to most of the major automakers looking to get in on the market.\n",
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"\n",
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" However, Tesla's move to make its shares more reasonably priced should catch some attention. Thus, I'm moving to neutral on Tesla stock.\n",
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"''')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# todo: export the model"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.10.4 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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