250 lines
11 KiB
Plaintext
250 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "7174c11a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import polars as pl\n",
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"from utils import QualtricsSurvey, calculate_weighted_ranking_scores"
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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": "d9d11d52",
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"metadata": {},
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"outputs": [],
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"source": [
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"SPEECH_DATA = 'data/speech_data/JPMC Speech data ab samples (Final Speech Data AB samples).csv'\n",
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"RESULTS_FILE = 'data/exports/2-4-26/JPMC_Chase Brand Personality_Quant Round 1_February 4, 2026_Labels.csv'\n",
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"QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'"
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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": 23,
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"id": "c8f06ff8",
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"metadata": {},
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"outputs": [],
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"source": [
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"speech_df = pl.read_csv(SPEECH_DATA, separator=';')\n",
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"\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": 12,
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"id": "8da85898",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert comma decimal separator to period and cast to float\n",
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"speech_df = speech_df.with_columns([\n",
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" pl.col('dur (s)').str.replace(',', '.').cast(pl.Float64),\n",
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" pl.col('phonationtime (s)').str.replace(',', '.').cast(pl.Float64),\n",
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" pl.col('articulation rate (nsyll / phonationtime)').str.replace(',', '.').cast(pl.Float64),\n",
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" pl.col('speech rate words per minute').str.replace(',', '.').cast(pl.Float64),\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": 22,
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"id": "e7ccd8ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert specified columns to float, handling percent signs, commas and spaces\n",
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"cols_to_convert = []\n",
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"\n",
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"if \"Standard deviation pitch\" in speech_df.columns:\n",
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" cols_to_convert.append(\n",
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" pl.col(\"Standard deviation pitch\")\n",
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" .cast(pl.Utf8)\n",
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" .str.replace(\",\", \".\")\n",
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" .str.replace(\" \", \"\")\n",
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" # strict=False converts unparseable strings (like empty ones) to Null\n",
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" .cast(pl.Float64, strict=False)\n",
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" )\n",
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"\n",
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"for col_name in [\"Jitter (local)\", \"Shimmer (local)\"]:\n",
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" if col_name in speech_df.columns:\n",
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" cols_to_convert.append(\n",
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" pl.col(col_name)\n",
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" .cast(pl.Utf8)\n",
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" .str.replace(\"%\", \"\")\n",
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" .str.replace(\" \", \"\")\n",
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" .str.replace(\",\", \".\")\n",
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" .cast(pl.Float64, strict=False)\n",
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" )\n",
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"\n",
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"if cols_to_convert:\n",
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" speech_df = speech_df.with_columns(cols_to_convert)\n",
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"\n",
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"# speech_df\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": 33,
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"id": "450d1d29",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: (5, 22)\n",
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"┌───────┬────────┬─────┬────────────┬───┬──────────────┬──────────────┬──────────────┬─────────────┐\n",
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"│ Voice ┆ Gender ┆ Age ┆ Mean pitch ┆ … ┆ phonationtim ┆ speechrate ┆ articulation ┆ speech rate │\n",
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"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e (s) ┆ (nsyll/dur) ┆ rate (nsyll ┆ words per │\n",
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"│ str ┆ str ┆ i64 ┆ f64 ┆ ┆ --- ┆ --- ┆ / pho… ┆ minute │\n",
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"│ ┆ ┆ ┆ ┆ ┆ str ┆ str ┆ --- ┆ --- │\n",
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"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ str ┆ str │\n",
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"╞═══════╪════════╪═════╪════════════╪═══╪══════════════╪══════════════╪══════════════╪═════════════╡\n",
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"│ VVV74 ┆ male ┆ 41 ┆ 95.25 ┆ … ┆ 11,17 ┆ 4,32468397 ┆ 5,81915846 ┆ 185,3436 │\n",
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"│ VVV54 ┆ male ┆ 36 ┆ 126.93 ┆ … ┆ 10,83 ┆ 4,38596491 ┆ 6,00184672 ┆ 187,9699 │\n",
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"│ VVV48 ┆ female ┆ 35 ┆ 193.296 ┆ … ┆ 11,92 ┆ 4,02227723 ┆ 5,45302013 ┆ 172,3833 │\n",
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"│ VVV14 ┆ female ┆ 50 ┆ 169.214 ┆ … ┆ 12,75 ┆ 3,97553517 ┆ 5,09803922 ┆ 170,3801 │\n",
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"│ VVV4 ┆ female ┆ 28 ┆ 208.079 ┆ … ┆ 12,22 ┆ 4,10094637 ┆ 5,31914894 ┆ 175,7548 │\n",
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"└───────┴────────┴─────┴────────────┴───┴──────────────┴──────────────┴──────────────┴─────────────┘\n"
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]
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}
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],
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"source": [
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"# Convert Voice ints to strings like \"V81\"\n",
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"# Convert Voice ints to strings like \"V81\"\n",
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"speech_df = speech_df.with_columns(\n",
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" pl.when(pl.col(\"Voice\").is_not_null())\n",
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" .then(pl.concat_str([pl.lit(\"V\"), pl.col(\"Voice\").cast(pl.Utf8)]))\n",
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" .otherwise(None)\n",
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" .alias(\"Voice\")\n",
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")\n",
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"\n",
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"print(speech_df.head())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fb615fe",
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"metadata": {},
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"source": [
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"# Get survey 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": null,
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"id": "bb4200ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"S = QualtricsSurvey(RESULTS_FILE, QSF_FILE, figures_dir=None)\n",
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"data_all = S.load_data()\n",
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"data = S.filter_data(data_all)"
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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": 34,
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"id": "57243afd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: (5, 2)\n",
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"┌───────────┬────────────────┐\n",
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"│ Character ┆ Weighted Score │\n",
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"│ --- ┆ --- │\n",
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"│ str ┆ i64 │\n",
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"╞═══════════╪════════════════╡\n",
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"│ V14 ┆ 209 │\n",
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"│ V04 ┆ 209 │\n",
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"│ V08 ┆ 180 │\n",
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"│ V82 ┆ 172 │\n",
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"│ V77 ┆ 158 │\n",
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"└───────────┴────────────────┘\n"
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]
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}
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],
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"source": [
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"top3_voices = S.get_top_3_voices(data)[0]\n",
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"voices_weighted_rank = calculate_weighted_ranking_scores(top3_voices)\n",
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"print(voices_weighted_rank.head())"
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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": 35,
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"id": "b38d21fc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape: (5, 18)\n",
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"┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
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"│ _recordId ┆ Voice_Sca ┆ Voice_Sca ┆ Voice_Sca ┆ … ┆ Voice_Sca ┆ Voice_Sca ┆ Voice_Sca ┆ Voice_Sc │\n",
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"│ --- ┆ le_1_10__ ┆ le_1_10__ ┆ le_1_10__ ┆ ┆ le_1_10__ ┆ le_1_10__ ┆ le_1_10__ ┆ ale_1_10 │\n",
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"│ str ┆ V14 ┆ V04 ┆ V08 ┆ ┆ V74 ┆ V81 ┆ V86 ┆ __V88 │\n",
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"│ ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
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"│ ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n",
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"╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
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"│ R_59pdrC3 ┆ null ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 5.5 ┆ null │\n",
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"│ urLmZnbP ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ R_3fJSKy5 ┆ 6.0 ┆ 5.0 ┆ null ┆ … ┆ null ┆ null ┆ 6.0 ┆ null │\n",
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"│ SVxmNdBC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ R_3g11G0u ┆ 9.5 ┆ null ┆ 5.0 ┆ … ┆ null ┆ null ┆ null ┆ 9.5 │\n",
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"│ pJ7iGt8Q ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ R_3i3dGL7 ┆ 6.0 ┆ 9.0 ┆ 8.0 ┆ … ┆ null ┆ 2.0 ┆ 3.0 ┆ null │\n",
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"│ cfLOTgxb ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ R_3BBF1fR ┆ null ┆ null ┆ null ┆ … ┆ 6.0 ┆ null ┆ 8.5 ┆ null │\n",
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"│ WGGeButr ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n"
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]
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}
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],
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"source": [
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"voice_1_10 = S.get_voice_scale_1_10(data)[0].collect()\n",
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"print(voice_1_10.head())\n",
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"\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": null,
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"id": "5b3e6ad0",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "phase-3-quant",
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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": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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