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For the strange fruit triplets
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"center int64\n", | |
"left int64\n", | |
"right int64\n", | |
"answer int64\n", | |
"dtype: object" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"responses = pd.read_csv('./strange-fruit-triplet/responses.csv')\n", | |
"cols = ['Center', 'Left', 'Right', 'Answer']\n", | |
"df = responses[cols].copy()\n", | |
"df.columns = [c.lower() for c in df.columns]\n", | |
"df.head()\n", | |
"\n", | |
"col = \"center\"\n", | |
"for col in cols:\n", | |
" df[col.lower()] = df[col.lower()].apply(lambda x: int(x.strip('i')))\n", | |
"df.dtypes" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"center 356\n", | |
"left 234\n", | |
"right 248\n", | |
"answer 234\n", | |
"correct_answer 248\n", | |
"correct False\n", | |
"Name: 9, dtype: object" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"def correct(row):\n", | |
" l, r, c, ans = row.left, row.right, row.center, row.answer\n", | |
" return l if abs(l - c) < abs(r - c) else r\n", | |
"\n", | |
" \n", | |
"df[\"correct_answer\"] = df.apply(correct, axis=1)\n", | |
"df[\"correct\"] = df.apply(lambda row: row.correct_answer == row.answer, axis=1)\n", | |
"df.iloc[9]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def dist_ratio(row):\n", | |
" l, r, center = row.left, row.right, row.center\n", | |
" if abs(l - center) < abs(r - center):\n", | |
" far, close = r, l\n", | |
" else:\n", | |
" far, close = l, r\n", | |
" \n", | |
" return abs(far - center) / (abs(far - center) + abs(close - center))\n", | |
"\n", | |
"df[\"dist_ratio\"] = df.apply(dist_ratio, axis=1)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"df[\"dist_ratio (binned)\"] = ((df.dist_ratio*1000) // 10) / 100" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"show = df.pivot_table(index=\"dist_ratio (binned)\", values=\"correct\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([-12.12273934, 5.7937731 ])" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"from scipy.optimize import curve_fit\n", | |
"import numpy as np\n", | |
"\n", | |
"def sigmoid(x, a, b):\n", | |
" return 1 / (1 + np.exp(a * x + b))\n", | |
"\n", | |
"popt, pcov = curve_fit(sigmoid, show.index.values, show.values.flat[:])\n", | |
"popt" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x10236fb5f8>" | |
] | |
}, | |
"execution_count": 9, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"height": 264, | |
"width": 387 | |
} | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import matplotlib.pyplot as plt\n", | |
"fig, ax = plt.subplots()\n", | |
"show.plot(style='o', ax=ax)\n", | |
"d = np.linspace(0.5, 1)\n", | |
"ax.plot(d, sigmoid(d, *popt), label='sigmoid({:0.2f}, {:0.2f})'.format(*popt))\n", | |
"ax.set_ylabel('P(correct)')\n", | |
"ax.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"!open ." | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"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": "3.6.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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