diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 95bfcb9..ec750d5 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -31,10 +31,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.6" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/2\n", + "PB = (30+20)/80\n", + "PBdA = 30/40\n", + "PAdB = (PBdA*PA)/PB\n", + "PAdB" + ] }, { "cell_type": "markdown", @@ -45,10 +62,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.4" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/2\n", + "PB = (30+20)/80\n", + "PBdA = 20/40\n", + "PAdB = (PBdA*PA)/PB\n", + "PAdB" + ] }, { "cell_type": "markdown", @@ -59,10 +93,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3333333333333333 bowl 1\n", + "0.6666666666666666 bowl 2\n" + ] + } + ], + "source": [ + "PA = 1/2\n", + "PB = (10+20)/80\n", + "PBdA_bowl1 = 10/40\n", + "PAdB_bowl1= (PBdA_bowl1*PA)/PB\n", + "print(f\"{PAdB_bowl1} bowl 1\")\n", + "\n", + "PBdA_bowl2 = 20/40\n", + "PAdB_bowl2= (PBdA_bowl2*PA)/PB\n", + "print(f\"{PAdB_bowl2} bowl 2\")" + ] }, { "cell_type": "markdown", @@ -95,10 +148,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7407407407407408 bag 1\n" + ] + } + ], + "source": [ + "PA = 1/2\n", + "PBdA_bag1_1 = 0.20*0.20\n", + "PBdA_bag1_2 = 0.14*0.10\n", + "PAdB_bag1= (PBdA_bag1_1*PA)/(PBdA_bag1_1*PA+PBdA_bag1_2*PA)\n", + "print(f\"{PAdB_bag1} bag 1\")" + ] }, { "cell_type": "markdown", @@ -109,10 +176,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25925925925925924 bag 2\n" + ] + } + ], + "source": [ + "PA = 1/2\n", + "PBdA_bag2_1 = 0.10*0.14\n", + "PBdA_bag2_2 = 0.20*0.20\n", + "PAdB_bag2= (PBdA_bag2_1*PA)/(PBdA_bag2_1*PA+PBdA_bag2_2*PA)\n", + "print(f\"{PAdB_bag2} bag 2\")" + ] }, { "cell_type": "markdown", @@ -123,10 +204,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25925925925925924 bag 1\n", + "0.7407407407407408 bag 2\n" + ] + } + ], + "source": [ + "PA = 1/2\n", + "PBdA_bag1_1 = 0.10*0.14\n", + "PBdA_bag1_2 = 0.20*0.20\n", + "PAdB_bag1= (PBdA_bag1_1*PA)/(PBdA_bag1_1*PA+PBdA_bag1_2*PA)\n", + "print(f\"{PAdB_bag1} bag 1\")\n", + "\n", + "PA = 1/2\n", + "PBdA_bag2_1 = 0.20*0.20\n", + "PBdA_bag2_2 = 0.10*0.14\n", + "PAdB_bag2= (PBdA_bag2_1*PA)/(PBdA_bag2_1*PA+PBdA_bag2_2*PA)\n", + "print(f\"{PAdB_bag2} bag 2\")" + ] }, { "cell_type": "markdown", @@ -141,10 +243,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.3333333333333333" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/3\n", + "PB = (1/6) + (1/3)\n", + "PBdA_doorA = 1/2\n", + "PAdB_doorA = (PBdA_doorA*PA)/PB\n", + "PAdB_doorA" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6666666666666666" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PA = 1/3\n", + "PB = (1/6) + (1/3)\n", + "PBdA_doorC = 1\n", + "PAdB_doorC = (PBdA_doorC*PA)/PB\n", + "PAdB_doorC" + ] }, { "cell_type": "markdown", @@ -157,10 +300,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "n_draws = 10000\n", + "prior = pd.Series(np.random.uniform(0, 100, size=n_draws))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 34.911464\n", + "1 87.324585\n", + "2 11.134602\n", + "3 8.434977\n", + "4 94.261854\n", + "dtype: float64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prior.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def generative_model(param):\n", + " result = np.random.poisson(param)\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "compraron = list()\n", + "for equipo in prior:\n", + " compraron.append(generative_model(equipo))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "posteriori = prior[list(map(lambda x: x==14,compraron))]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "posteriori.hist()" + ] }, { "cell_type": "markdown", @@ -171,10 +405,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "count 113.000000\n", + "mean 14.155657\n", + "std 3.676406\n", + "min 6.626519\n", + "25% 11.987084\n", + "50% 13.769305\n", + "75% 15.996066\n", + "max 26.621789\n", + "dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "posteriori.describe()" + ] }, { "cell_type": "markdown", @@ -185,10 +440,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "(8.65605585974109, 21.04532297403454)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "posteriori.quantile(0.05), posteriori.quantile(0.95)" + ] }, { "cell_type": "markdown", @@ -199,10 +467,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import statsmodels as sm\n", + "from statsmodels.api import Poisson" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "ename": "AxisError", + "evalue": "axis 1 is out of bounds for array of dimension 0", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAxisError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m poisson_reg = Poisson(posteriori, 'const',\n\u001b[0m\u001b[0;32m 2\u001b[0m missing='drop')\n\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoisson_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\oscar\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, offset, exposure, missing, check_rank, **kwargs)\u001b[0m\n\u001b[0;32m 772\u001b[0m def __init__(self, endog, exog, offset=None, exposure=None, missing='none',\n\u001b[0;32m 773\u001b[0m check_rank=True, **kwargs):\n\u001b[1;32m--> 774\u001b[1;33m super().__init__(endog, exog, check_rank, missing=missing,\n\u001b[0m\u001b[0;32m 775\u001b[0m offset=offset, exposure=exposure, **kwargs)\n\u001b[0;32m 776\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexposure\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m 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"\u001b[1;32mc:\\users\\oscar\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\statsmodels\\base\\data.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformula\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'formula'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'none'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m arrays, nan_idx = self.handle_missing(endog, exog, missing,\n\u001b[0m\u001b[0;32m 72\u001b[0m **kwargs)\n\u001b[0;32m 73\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissing_row_idx\u001b[0m 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return np.logical_or(_asarray_2d_null_rows(x),\n\u001b[1;32m---> 48\u001b[1;33m (x_is_boolean_array | _asarray_2d_null_rows(y)))\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_nan_row_maybe_two_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\oscar\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\statsmodels\\base\\data.py\u001b[0m in \u001b[0;36m_asarray_2d_null_rows\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36many\u001b[1;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36many\u001b[1;34m(a, axis, out, keepdims, where)\u001b[0m\n\u001b[0;32m 2356\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2357\u001b[0m \"\"\"\n\u001b[1;32m-> 2358\u001b[1;33m return _wrapreduction(a, np.logical_or, 'any', axis, None, out,\n\u001b[0m\u001b[0;32m 2359\u001b[0m keepdims=keepdims, where=where)\n\u001b[0;32m 2360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 0" + ] + } + ], + "source": [ + "poisson_reg = Poisson(posteriori, 'const',\n", + " missing='drop')\n", + "print(poisson_reg.summary())" + ] } ], 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