diff --git a/0_7_CA_segment.py b/0_7_CA_segment.py index d4319b6..9d8e7fe 100644 --- a/0_7_CA_segment.py +++ b/0_7_CA_segment.py @@ -25,6 +25,7 @@ from sklearn.naive_bayes import GaussianNB from scipy.optimize import fsolve import pickle import warnings +import io # define type of activity type_of_activity = "sport" @@ -42,19 +43,20 @@ X_test_segment["score_adjusted"] = score_adjusted_train # plot adjusted scores and save (to be tested) -plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted") +plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted", type_of_activity = type_of_activity) image_buffer = io.BytesIO() - plt.savefig(image_buffer, format='png') - image_buffer.seek(0) - file_name = "hist_score_adjusted" - FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".png" - with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file: - s3_file.write(image_buffer.read()) - plt.close() +plt.savefig(image_buffer, format='png') +image_buffer.seek(0) +file_name = "hist_score_adjusted" +FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".png" +with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file: + s3_file.write(image_buffer.read()) +plt.close() # comparison between score and adjusted score -X_test_table_adjusted_scores = X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean().reset_index().round(2) +X_test_table_adjusted_scores = (100 * X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean()).round(2).reset_index() +X_test_table_adjusted_scores = X_test_table_adjusted_scores.rename(columns = {col : f"{col} (%)" for col in X_test_table_adjusted_scores.columns if col in ["score","score_adjusted", "has_purchased"]}) file_name = "table_adjusted_score" FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".csv" diff --git a/Sport/Modelization/CA_segment_sport.ipynb b/Sport/Modelization/CA_segment_sport.ipynb index ad83d55..346e68f 100644 --- a/Sport/Modelization/CA_segment_sport.ipynb +++ b/Sport/Modelization/CA_segment_sport.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 107, "id": "9771bf29-d08e-4674-8c23-9a2672fbef8f", "metadata": {}, "outputs": [], @@ -47,6 +47,7 @@ "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", "from sklearn.naive_bayes import GaussianNB\n", "from scipy.optimize import fsolve\n", + "import io\n", "\n", "import pickle\n", "import warnings" @@ -1817,9 +1818,45 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 105, "id": "6f9396db-e213-408c-a596-eaeec3bc79f3", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3d3fptYWFBWxsbNCkSRPY29tL7c7OzgCgMRwLAJ988onsdd++faGjoyMbZv/rr7/g7+8POzs7aGtrQ1dXF97e3gCA+Ph4jWW+//77r9zWd955R1rfTz/9hL///lsj5sCBA3BxcUGLFi1k7QMGDIAQAgcOHJC1d+vWDdra2tLrRo0aASh8uwtTsC9atWqFmjVrSn0RHR2NBw8eoH///nj27Jn0l5eXh86dOyMmJkbjFGZBRkZG8PT0lE45RUZGokqVKpgwYQKys7Nx5MgRAM9PzXbs2FGa77fffoOrqyuaNGkiW7evr6/szsLffvsNKpUKn376qSzOzs4OjRs31rjT0c7OTqN/GzVq9Mo+K877t3fvXtSvX1+2HQUdOHAAxsbG6NOnj6w9/zNQcJ9v3749zM3NpddPnz7F/v378d5778HIyEi2zV27dsXTp09feer8zJkz6NGjBywtLaX9u1+/fsjNzcWVK1dkscXpr4MHD6Jhw4Zo3LixLM7f37/IPPI9e/YMs2fPhouLC/T09KCjowM9PT1cvXpV9nl73fUUV4sWLbBu3TrMmjULx48f1zidXJTffvsNVapUQffu3WXvTZMmTWBnZyftj/mfsZd9HxUnx7S0NHz88cf49ddf8c8//2jE7N27F+3atZO+DwtT0u+cHj16QFdXV3p97do1/Pnnn9J2FNwfk5KScPny5VduT1nn9TLF+Rzn8/f3l50Or1mzJlq1aiW9d5cvX8adO3cQEBAALa3/O4ybmJjg/fffx/HjxzVOwRXnWFGU6OhoZGRkICgoSJbbi0qTl5+fn+y1s7MzVCoVunTpIrXp6Oigbt26hX5XvqqvAODRo0eYNGkS6tatCx0dHejo6MDExASZmZmFHldfvLwKKN7xrbifvyZNmkBPTw9Dhw7F+vXrC71cqDjemuLNysoKRkZGSEhIKFb8/fv3ATwvygqyt7eXpuezsLDQiNPT09No19PTA/D8QFeQnZ2d7LWOjg4sLS2ldT169AitW7fGiRMnMGvWLBw6dAgxMTHYtm0bAODJkyey+Y2MjGBmZlbkdgJAmzZtsGPHDjx79gz9+vVD9erV4erqih9//FGKuX///kv7In/6iywtLWWv868xLJjjyxTsi/y2/PXkXxfTp08f6Orqyv7mzp0LIUSxHvvSsWNHHD9+HJmZmdi3bx/at28PS0tLuLu7Y9++fUhISEBCQoKs6Ll79y7OnTunsV5TU1MIIaSD1d27dyGEgK2trUbs8ePHNQ5qBfssv99e1WfFef/u3buH6tWrF7mc+/fvw87OTuOL18bGBjo6OhrvccH94f79+3j27BmWLVumsb1du3YFgEIP5Plu3bqF1q1b4++//8bSpUtx+PBhxMTESNfdFOyH4vRX/jYVVFhbYcaNG4epU6eiV69e2LVrF06cOIGYmBg0bty4TNdTXFu2bEH//v3x3XffwdPTExYWFujXrx+Sk5NfOe/du3eRlpYGPT09jfcnOTlZem/y3+eXfR+9SkBAANasWYObN2/i/fffh42NDTw8PBAZGSnFFHd/LMl3TsHY/O+I8ePHa2xvUFAQgKL3x/LK62WK8znO96rvx1cdv/Ly8pCamlqqPF8m/7qtot7X0uRV2DHUyMgIBgYGGu3FOa7mt734Pvn7+2P58uUYMmQIfv/9d5w8eRIxMTGwtrYu9Pu3NMe34n7+6tSpg3379sHGxgYjR45EnTp1UKdOHSxduvSlyy7MW3O3qba2Njp06IC9e/fi9u3br/ziyH9zkpKSNGLv3LkDKyurMs8xOTkZ1apVk14/e/YM9+/fl3I5cOAA7ty5g0OHDkmjbQBe+giMl/3rpzA9e/ZEz549kZWVhePHj2POnDnw9/dHrVq14OnpCUtLSyQlJWnMl3+RZln3R2EHo+TkZNStW1e2vmXLlqFly5aFLsPW1vaV6+nQoQOmTp2KP/74A/v378e0adOk9oiICDg6Okqv81lZWcHQ0BBr1qwpdJn5uVlZWUGlUuHw4cOF3iBTlnecver9s7a21rjRpiBLS0ucOHECQgjZvpOSkoJnz55pvMcF9y9zc3Noa2sjICAAI0eOLHQd+f1ZmB07diAzMxPbtm1DzZo1pfa4uLgi8y6KpaXlS/el4ti4cSP69euH2bNny9r/+ecfVKlSpUzWk38QKnhTSMEiAHi+Ty1ZsgRLlizBrVu3sHPnTnz22WdISUl55Z3y+RdXvyzO1NRU2pb83Av7PiqOgQMHYuDAgcjMzMQff/yBadOmwc/PD1euXEHNmjWLvT+W5Dun4P6YP33y5Mno3bt3oetwcnIq1vaUZV5FedXnON/L9rX89+7F41dheWppaclGzUuaZ2Gsra0BoMj3tTR5va5X9VV6ejp+++03TJs2DZ999pkUk5WVVabPfS3u5w8AWrdujdatWyM3NxenTp3CsmXLEBISAltbW3z00UfFWt9bM/IGPP8QCyEQGBiI7Oxsjek5OTnYtWsXgOenhIDnX94viomJQXx8vOxgXlY2bdoke/3TTz/h2bNn0h0r+R+uggf9VatWlVkO+vr68Pb2xty5cwFAuiunQ4cOuHTpEk6fPi2L/+GHH6BSqdCuXbsyywHQ7Ivo6GjcvHlT6gsvLy9UqVIFly5dQvPmzQv9yx/lLOpfRS1atICZmRmWLFmC5ORkdOrUCcDzEbkzZ87gp59+gouLi+zUt5+fH65fvw5LS8tC15t/J6Gfnx+EEPj7778LjXNzcyvTPsvf1sLevy5duuDKlSsap3Re1KFDBzx69Ag7duyQtf/www/S9KIYGRmhXbt2OHPmDBo1alToNhc1clPY/i2EwOrVq4tcb1HatWuHixcv4uzZs7L2zZs3F2t+lUql8XnbvXu3ximt11mPra0tDAwMcO7cOVn7r7/+WuR8NWrUwKhRo9CpUyfZ5/Jlo7V+fn64f/8+cnNzC31v8guZ/M/Yy76PSsLY2BhdunTBlClTkJ2djYsXLwJ4vj8ePHiwyNOWr/ud4+TkhHr16uHs2bMv/Y548YBZXBXxXfiyz3G+H3/8EUII6fXNmzcRHR0tvXdOTk6oVq0aNm/eLIvLzMzE1q1bpTs9i5MHULwzJq1atYJarcbKlStl63xRWeVVEq/qK5VKBSGExuf8u+++kz2J4HUV9/P3Im1tbXh4eEhnHwruc0V5a0beAMDT0xMrVqxAUFAQ3N3dMWLECDRs2BA5OTk4c+YMvv32W7i6uqJ79+5wcnLC0KFDsWzZMmhpaaFLly64ceMGpk6dCgcHh3J5WOO2bdugo6ODTp064eLFi5g6dSoaN26Mvn37Anj+4TA3N8fw4cMxbdo06OrqYtOmTRoHjJL6/PPPcfv2bXTo0AHVq1dHWloali5dKruebuzYsfjhhx/QrVs3zJw5EzVr1sTu3bvxzTffYMSIEahfv/5rb/+LTp06hSFDhuCDDz5AYmIipkyZgmrVqkmnO0xMTLBs2TL0798fDx48QJ8+fWBjY4N79+7h7NmzuHfvHlasWAEAUpG0dOlS9O/fH7q6unBycoKpqSm0tbXh7e2NXbt2wdHRUXoGl5eXF/T19bF//34EBwfLcgsJCcHWrVvRpk0bjB07Fo0aNUJeXh5u3bqFiIgIhIaGwsPDA15eXhg6dCgGDhyIU6dOoU2bNjA2NkZSUhKOHDkCNzc3jBgx4rX7qjjvX0hICLZs2YKePXvis88+Q4sWLfDkyRNERUXBz88P7dq1Q79+/fD111+jf//+uHHjBtzc3HDkyBHMnj0bXbt2LfJ6uXxLly7Fu+++i9atW2PEiBGoVasWHj58iGvXrmHXrl1FFo+dOnWCnp4ePv74Y0ycOBFPnz7FihUrNE6jlERISAjWrFmDbt26YdasWbC1tcWmTZtkj0cpip+fH9atW4cGDRqgUaNGiI2Nxfz58zVG419nPfnXRa5ZswZ16tRB48aNcfLkSY3CLz09He3atYO/vz8aNGgAU1NTxMTEIDw8XDay5Obmhm3btmHFihVwd3eHlpYWmjdvjo8++gibNm1C165dMWbMGLRo0QK6urq4ffs2Dh48iJ49e+K9996Ds7MzPv30UyxZsgS6urro2LEjLly4gAULFhTrMozAwEAYGhrCy8sLVatWRXJyMubMmQO1Wi1d1zVz5kzs3bsXbdq0wX/+8x+4ubkhLS0N4eHhGDduHBo0aFAm3zmrVq1Cly5d4OvriwEDBqBatWp48OAB4uPjcfr0afz888+vXEZB5fVdWJzPcb6UlBS89957CAwMRHp6OqZNmwYDAwNMnjwZAKClpYV58+bhk08+gZ+fH4YNG4asrCzMnz8faWlp+PLLL4uVU/5359y5c9GlSxdoa2ujUaNG0j+MX2RiYoKFCxdiyJAh6NixIwIDA2Fra4tr167h7NmzWL58eZnlVRKv6iszMzO0adMG8+fPh5WVFWrVqoWoqCh8//33stH111Xcz9/KlStx4MABdOvWDTVq1MDTp0+lszzF+Q6WvNbtDm+ouLg40b9/f1GjRg2hp6cnPZLj888/FykpKVJcbm6umDt3rqhfv77Q1dUVVlZW4tNPPxWJiYmy5Xl7e4uGDRtqrKewO5WEeH4H2ciRI6XX+XfVxMbGiu7duwsTExNhamoqPv74Y3H37l3ZvNHR0cLT01MYGRkJa2trMWTIEHH69GmNu9X69+8vjI2NC93+gne7/fbbb6JLly6iWrVqQk9PT9jY2IiuXbvKbtsWQoibN28Kf39/YWlpKXR1dYWTk5OYP3++7K6h/Dus5s+fX+h2v3jnXGHy7+aJiIgQAQEBokqVKsLQ0FB07dpVXL16VSM+KipKdOvWTVhYWAhdXV1RrVo10a1bN/Hzzz/L4iZPnizs7e2FlpaWxt1/S5cuFQBEYGCgbJ78OwV37typsd5Hjx6J//73v8LJyUno6elJj8cYO3as7BEgQgixZs0a4eHhIYyNjYWhoaGoU6eO6Nevn+zu5JftQwXfq8IU9/1LTU0VY8aMETVq1BC6urrCxsZGdOvWTfbIi/v374vhw4eLqlWrCh0dHVGzZk0xefJkjccDFNyHX5SQkCAGDRokqlWrJnR1dYW1tbVo1aqVdKdwUXbt2iUaN24sDAwMRLVq1cSECRPE3r17Nd6zkvTXpUuXRKdOnYSBgYGwsLAQgwcPFr/++mux7jZNTU0VgwcPFjY2NsLIyEi8++674vDhwxp3kpVkPf379xe1atWSzZueni6GDBkibG1thbGxsejevbu4ceOG7DPz9OlTMXz4cNGoUSNhZmYmDA0NhZOTk5g2bZrIzMyUlvXgwQPRp08fUaVKFaFSqWR3C+bk5IgFCxZIfWxiYiIaNGgghg0bJvt8ZWVlidDQUGFjYyMMDAxEy5YtxbFjx0TNmjVfebfp+vXrRbt27YStra3Q09MT9vb2om/fvuLcuXOyuMTERDFo0CBhZ2cndHV1pbgXv/Ne9ztHCCHOnj0r+vbtK2xsbISurq6ws7MT7du3FytXrixyO4R4+Xd4WeRVUHE+x/l3UG7YsEEEBwcLa2troa+vL1q3bi37Psm3Y8cO4eHhIQwMDISxsbHo0KGDOHr0qCwm//jz4iMx8mVlZYkhQ4YIa2traV962aNO8u3Zs0d4e3sLY2NjYWRkJFxcXMTcuXPLLK+XHdsKfieUpK9u374t3n//fWFubi5MTU1F586dxYULFzT29/zjU8EnDeSvq+B3VMHviOJ8/o4dOybee+89UbNmTaGvry8sLS2Ft7d3ocehoqiEeMn4J5WZ6dOnY8aMGbh37165XEtHRG+O9957D4mJiWX6+73073Do0CG0a9cOP//8s8Zd4ST3b++rt+qaNyKiynLr1i2EhYXh4MGDsovPiYjKGos3IqIysGbNGgwfPhzt27eX7momIioPPG1KREREpCAceSMiIiJSEBZvRERERArC4o2IiIhIQd6qh/RWtry8PNy5cwempqav/VMkREREVDGEEHj48CHs7e2hpfXmj2uxeCtDd+7cgYODQ2WnQURERKWQmJj4yt9GfxOweCtD+b+jl5iYWKyfmSEiIqLKl5GRAQcHh1L9Hm5lYPFWhvJPlZqZmbF4IyIiUhilXPL05p/YJSIiIiIJizciIiIiBWHxRkRERKQgvOaNiIjeOLm5ucjJyansNOgtoaurC21t7cpOo8yweCMiojeGEALJyclIS0ur7FToLVOlShXY2dkp5qaEorB4IyKiN0Z+4WZjYwMjI6O34kBLlUsIgcePHyMlJQUAULVq1UrO6PWxeCMiojdCbm6uVLhZWlpWdjr0FjE0NAQApKSkwMbGRvGnUHnDAhERvRHyr3EzMjKq5EzobZS/X70N11KyeCMiojcKT5VSeXib9isWb0REREQKwuKNiIiISEF4wwIREb3RJm87X6Hrm9PbrULXR1RSHHkjIiJSiLfhYnt6fSzeiIiIXtMvv/wCNzc3GBoawtLSEh07dkRmZiYAYM2aNWjYsCH09fVRtWpVjBo1Sprv1q1b6NmzJ0xMTGBmZoa+ffvi7t270vTp06ejSZMmWLNmDWrXrg19fX0IIZCeno6hQ4fCxsYGZmZmaN++Pc6ePVvh202Vg8UbERHRa0hKSsLHH3+MQYMGIT4+HocOHULv3r0hhMCKFSswcuRIDB06FOfPn8fOnTtRt25dAM8fHturVy88ePAAUVFRiIyMxPXr1/Hhhx/Kln/t2jX89NNP2Lp1K+Li4gAA3bp1Q3JyMvbs2YPY2Fg0a9YMHTp0wIMHDyp686kS8Jo3pdg1prIzKLnuSys7AyKicpeUlIRnz56hd+/eqFmzJgDAze35dXOzZs1CaGgoxoz5v+/wd955BwCwb98+nDt3DgkJCXBwcAAAbNiwAQ0bNkRMTIwUl52djQ0bNsDa2hoAcODAAZw/fx4pKSnQ19cHACxYsAA7duzAL7/8gqFDh1bMhlOlYfFGRET0Gho3bowOHTrAzc0Nvr6+8PHxQZ8+fZCTk4M7d+6gQ4cOhc4XHx8PBwcHqXADABcXF1SpUgXx8fFS8VazZk2pcAOA2NhYPHr0SONXKJ48eYLr16+XwxbSm4bFGxER0WvQ1tZGZGQkoqOjERERgWXLlmHKlCnYv39/kfMJIQp9cGzBdmNjY9n0vLw8VK1aFYcOHdKYt0qVKqXaBlIWFm9ERESvSaVSwcvLC15eXvj8889Rs2ZNREZGolatWti/fz/atWunMY+Liwtu3bqFxMREafTt0qVLSE9Ph7Oz80vX1axZMyQnJ0NHRwe1atUqr02iNxiLNyIiotdw4sQJ7N+/Hz4+PrCxscGJEydw7949ODs7Y/r06Rg+fDhsbGzQpUsXPHz4EEePHsXo0aPRsWNHNGrUCJ988gmWLFmCZ8+eISgoCN7e3mjevPlL19exY0d4enqiV69emDt3LpycnHDnzh3s2bMHvXr1KnJeejuweCMiInoNZmZm+OOPP7BkyRJkZGSgZs2aWLhwIbp06QIAePr0KRYvXozx48fDysoKffr0AfB8tG7Hjh0YPXo02rRpAy0tLXTu3BnLli0rcn0qlQp79uzBlClTMGjQINy7dw92dnZo06YNbG1ty317qfKphBCiMhP4+++/MWnSJOzduxdPnjxB/fr18f3338Pd3R3A83P/M2bMwLfffovU1FR4eHjg66+/RsOGDaVlZGVlYfz48fjxxx/x5MkTdOjQAd988w2qV68uxaSmpiI4OBg7d+4EAPTo0QPLli2TXR9w69YtjBw5EgcOHIChoSH8/f2xYMEC6OnpFWtbMjIyoFarkZ6eDjMzszLonRfwblMiess9ffoUCQkJcHR0hIGBQWWnQ2+Zovavcj1+l4NKfc5bamoqvLy8oKuri7179+LSpUtYuHChrKCaN28eFi1ahOXLlyMmJgZ2dnbo1KkTHj58KMWEhIRg+/btCAsLw5EjR/Do0SP4+fkhNzdXivH390dcXBzCw8MRHh6OuLg4BAQESNNzc3PRrVs3ZGZm4siRIwgLC8PWrVsRGhpaIX1BREREVByVOvL22Wef4ejRozh8+HCh04UQsLe3R0hICCZNmgTg+Sibra0t5s6di2HDhiE9PR3W1tbYsGGD9GDDO3fuwMHBAXv27IGvry/i4+Ph4uKC48ePw8PDAwBw/PhxeHp64s8//4STkxP27t0LPz8/JCYmwt7eHgAQFhaGAQMGICUlpViVOEfeCuDIGxGVAEfeqDxx5K2M7Ny5E82bN8cHH3wAGxsbNG3aFKtXr5amJyQkIDk5GT4+PlKbvr4+vL29ER0dDeD5825ycnJkMfb29nB1dZVijh07BrVaLRVuANCyZUuo1WpZjKurq1S4AYCvry+ysrIQGxtbaP5ZWVnIyMiQ/RERERGVp0ot3v766y+sWLEC9erVw++//47hw4cjODgYP/zwAwAgOTkZADQuwLS1tZWmJScnQ09PD+bm5kXG2NjYaKzfxsZGFlNwPebm5tDT05NiCpozZw7UarX09+KDFomIiIjKQ6UWb3l5eWjWrBlmz56Npk2bYtiwYQgMDMSKFStkcQUfYviyBxsWFVOcByEWJ+ZFkydPRnp6uvSXmJhYZE5EREREr6tSi7eqVavCxcVF1ubs7Ixbt24BAOzs7ABAY+QrJSVFGiWzs7NDdnY2UlNTi4y5e/euxvrv3bsniym4ntTUVOTk5Lz01mt9fX2YmZnJ/oiIiIjKU6UWb15eXrh8+bKs7cqVK9IP+zo6OsLOzg6RkZHS9OzsbERFRaFVq1YAAHd3d+jq6spikpKScOHCBSnG09MT6enpOHnypBRz4sQJpKeny2IuXLiApKQkKSYiIgL6+vrSY0uIiIiIKlulPqR37NixaNWqFWbPno2+ffvi5MmT+Pbbb/Htt98CeH4aMyQkBLNnz0a9evVQr149zJ49G0ZGRvD39wcAqNVqDB48GKGhobC0tISFhQXGjx8PNzc3dOzYEcDz0bzOnTsjMDAQq1atAgAMHToUfn5+cHJyAgD4+PjAxcUFAQEBmD9/Ph48eIDx48cjMDCQI2pERET0xqjU4u2dd97B9u3bMXnyZMycOROOjo5YsmQJPvnkEylm4sSJePLkCYKCgqSH9EZERMDU1FSKWbx4MXR0dNC3b1/pIb3r1q2Dtra2FLNp0yYEBwdLd6X26NEDy5cvl6Zra2tj9+7dCAoKgpeXl+whvURERERvikr/hYW3CZ/zVgCf80ZEJfBves7bjRs34OjoiDNnzqBJkyYar98k69atQ0hICNLS0io7ldfyNj3njb9tSkREb7aK/sdrJfzD08HBAUlJSbCysiqT5b0tBRcVrlJvWCAiIqLnl+7Y2dlBR4djKq+SnZ1d2SlUOhZvREREryE8PBzvvvsuqlSpAktLS/j5+eH69euymJMnT6Jp06YwMDBA8+bNcebMGdn0GzduQKVSIS4uDsDzkbMXf+cbAHbs2CF77ujZs2fRrl07mJqawszMDO7u7jh16hQOHTqEgQMHIj09HSqVCiqVCtOnTwfwvPCZOHEiqlWrBmNjY3h4eODQoUOy9axbtw41atSAkZER3nvvPdy/f7/I7c/OzsaoUaNQtWpVGBgYoFatWpgzZ440PS0tDUOHDoWtrS0MDAzg6uqK3377TZq+detWNGzYEPr6+qhVqxYWLlwoW36tWrUwa9YsDBgwAGq1GoGBgQCA6OhotGnTBoaGhnBwcEBwcDAyMzOLzPVtweKNiIjoNWRmZmLcuHGIiYnB/v37oaWlhffeew95eXnS9PynG8TGxmL69OkYP378a6/3k08+QfXq1RETE4PY2Fh89tln0NXVRatWrbBkyRKYmZkhKSkJSUlJ0voGDhyIo0ePIiwsDOfOncMHH3yAzp074+rVqwCeP0Zr0KBBCAoKQlxcHNq1a4dZs2YVmcdXX32FnTt34qeffsLly5exceNG1KpVC8Dzh/F36dIF0dHR2LhxIy5duoQvv/xSuqEwNjYWffv2xUcffYTz589j+vTpmDp1KtatWydbx/z58+Hq6orY2FhMnToV58+fh6+vL3r37o1z585hy5YtOHLkCEaNGvXa/aoEHJ8lIiJ6De+//77s9ffffw8bGxtcunQJrq6u2LRpE3Jzc7FmzRoYGRmhYcOGuH37NkaMGPFa67116xYmTJiABg0aAADq1asnTVOr1VCpVNLD7gHg+vXr+PHHH3H79m3pd7zHjx+P8PBwrF27FrNnz8bSpUvh6+uLzz77DABQv359REdHIzw8vMg86tWrh3fffRcqlUp6VisA7Nu3DydPnkR8fDzq168PAKhdu7Y0fdGiRejQoQOmTp0qre/SpUuYP38+BgwYIMW1b99eVvD269cP/v7+CAkJkbb9q6++gre3N1asWPHW3/DCkTciIqLXcP36dfj7+6N27dowMzODo6MjAEi/FhQfH4/GjRvDyMhImsfT0/O11ztu3DgMGTIEHTt2xJdffqlxqrag06dPQwiB+vXrw8TERPqLioqS5o2Pj9fI7VW5DhgwAHFxcXByckJwcDAiIiKkaXFxcahevbpUuBUUHx8PLy8vWZuXlxeuXr2K3Nxcqa158+aymNjYWKxbt062Hb6+vsjLy0NCQkKR+b4NOPJGRET0Grp37w4HBwesXr0a9vb2yMvLg6urq3RhfWmeyKWlpaUxX05Ojuz19OnT4e/vj927d2Pv3r2YNm0awsLC8N577xW6zLy8PGhrayM2Nlb2HFQAMDExKXWuzZo1Q0JCAvbu3Yt9+/ahb9++6NixI3755RcYGhoWOW9hvx9eWA7GxsYa2zJs2DAEBwdrxNaoUaPE26A0LN6IiIhK6f79+4iPj8eqVavQunVrAMCRI0dkMS4uLtiwYQOePHkiFTPHjx8vcrnW1tZ4+PAhMjMzpcIl/2aGF9WvXx/169fH2LFj8fHHH2Pt2rV47733oKenJxu5AoCmTZsiNzcXKSkpUq4Fubi4aOT2qlwBwMzMDB9++CE+/PBD9OnTB507d8aDBw/QqFEj3L59G1euXCl09M3FxUWjv6Kjo1G/fn2NAvNFzZo1w8WLF1G3bt1X5vY24mlTIiKiUjI3N4elpSW+/fZbXLt2DQcOHMC4ceNkMf7+/tDS0sLgwYNx6dIl7Nmz55W/3uPh4QEjIyP85z//wbVr17B582bZRfxPnjzBqFGjcOjQIdy8eRNHjx5FTEwMnJ2dATy/Q/PRo0fYv38//vnnHzx+/Bj169fHJ598gn79+mHbtm1ISEhATEwM5s6diz179gAAgoODER4ejnnz5uHKlStYvnx5kde7Ac9/5SgsLAx//vknrly5gp9//hl2dnaoUqUKvL290aZNG7z//vuIjIyURujylxkaGor9+/fjiy++wJUrV7B+/XosX778lTd0TJo0CceOHcPIkSMRFxeHq1evYufOnRg9enSR870tWLwRERGVkpaWFsLCwhAbGwtXV1eMHTsW8+fPl8WYmJhg165duHTpEpo2bYopU6Zg7ty5RS7XwsICGzduxJ49e+Dm5oYff/xRetwH8Py5cPfv30e/fv1Qv3599O3bF126dMGMGTMAAK1atcLw4cPx4YcfwtraGvPmzQMArF27Fv369UNoaCicnJzQo0cPnDhxAg4ODgCAli1b4rvvvsOyZcvQpEkTRERE4L///W+RuZqYmGDu3Llo3rw53nnnHdy4cQN79uyBltbzEmPr1q1455138PHHH8PFxQUTJ06URgWbNWuGn376CWFhYXB1dcXnn3+OmTNnym5WKEyjRo0QFRWFq1evonXr1mjatCmmTp2KqlWrFjnf24I/j1WG+PNYBfDnsYioBP5NP49V0OXLl9GgQQNcvXr1X3sqsLy9TT+PxZE3IiKiSvTgwQP88ssvMDMzk0bAiIrCGxaIiIgq0eDBgxEbG4sVK1ZAX1+/stMhBWDxRkREVIm2b99e2SmQwvC0KREREZGCsHgjIqI3Cu+jo/LwNu1XLN6IiOiNoKurCwB4/PhxJWdCb6P8/Sp/P1MyXvNGRERvBG1tbVSpUgUpKSkAACMjI42fTiIqKSEEHj9+jJSUFFSpUqXIX25QChZvRET0xrCzswMAqYAjKitVqlSR9i+lY/FGRERvDJVKhapVq8LGxkbjh9iJSktXV/etGHHLx+KNiIjeONra2m/VwZaoLPGGBSIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFqdTibfr06VCpVLI/Ozs7aboQAtOnT4e9vT0MDQ3Rtm1bXLx4UbaMrKwsjB49GlZWVjA2NkaPHj1w+/ZtWUxqaioCAgKgVquhVqsREBCAtLQ0WcytW7fQvXt3GBsbw8rKCsHBwcjOzi63bSciIiIqjUofeWvYsCGSkpKkv/Pnz0vT5s2bh0WLFmH58uWIiYmBnZ0dOnXqhIcPH0oxISEh2L59O8LCwnDkyBE8evQIfn5+yM3NlWL8/f0RFxeH8PBwhIeHIy4uDgEBAdL03NxcdOvWDZmZmThy5AjCwsKwdetWhIaGVkwnEBERERWTTqUnoKMjG23LJ4TAkiVLMGXKFPTu3RsAsH79etja2mLz5s0YNmwY0tPT8f3332PDhg3o2LEjAGDjxo1wcHDAvn374Ovri/j4eISHh+P48ePw8PAAAKxevRqenp64fPkynJycEBERgUuXLiExMRH29vYAgIULF2LAgAH43//+BzMzswrqDSIiIqKiVfrI29WrV2Fvbw9HR0d89NFH+OuvvwAACQkJSE5Oho+PjxSrr68Pb29vREdHAwBiY2ORk5Mji7G3t4erq6sUc+zYMajVaqlwA4CWLVtCrVbLYlxdXaXCDQB8fX2RlZWF2NjYl+aelZWFjIwM2R8RERFRearU4s3DwwM//PADfv/9d6xevRrJyclo1aoV7t+/j+TkZACAra2tbB5bW1tpWnJyMvT09GBubl5kjI2Njca6bWxsZDEF12Nubg49PT0ppjBz5syRrqNTq9VwcHAoYQ8QERERlUylFm9dunTB+++/Dzc3N3Ts2BG7d+8G8Pz0aD6VSiWbRwih0VZQwZjC4ksTU9DkyZORnp4u/SUmJhaZFxEREdHrqvTTpi8yNjaGm5sbrl69Kl0HV3DkKyUlRRols7OzQ3Z2NlJTU4uMuXv3rsa67t27J4spuJ7U1FTk5ORojMi9SF9fH2ZmZrI/IiIiovL0RhVvWVlZiI+PR9WqVeHo6Ag7OztERkZK07OzsxEVFYVWrVoBANzd3aGrqyuLSUpKwoULF6QYT09PpKen4+TJk1LMiRMnkJ6eLou5cOECkpKSpJiIiAjo6+vD3d29XLeZiIiIqCQq9W7T8ePHo3v37qhRowZSUlIwa9YsZGRkoH///lCpVAgJCcHs2bNRr1491KtXD7Nnz4aRkRH8/f0BAGq1GoMHD0ZoaCgsLS1hYWGB8ePHS6dhAcDZ2RmdO3dGYGAgVq1aBQAYOnQo/Pz84OTkBADw8fGBi4sLAgICMH/+fDx48ADjx49HYGAgR9OIiIjojVKpxdvt27fx8ccf459//oG1tTVatmyJ48ePo2bNmgCAiRMn4smTJwgKCkJqaio8PDwQEREBU1NTaRmLFy+Gjo4O+vbtiydPnqBDhw5Yt24dtLW1pZhNmzYhODhYuiu1R48eWL58uTRdW1sbu3fvRlBQELy8vGBoaAh/f38sWLCggnqCiIiIqHhUQghR2Um8LTIyMqBWq5Genl72I3a7xpTt8ipC96WVnQEREdErlevxuxy8Ude8EREREVHRWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrwxxducOXOgUqkQEhIitQkhMH36dNjb28PQ0BBt27bFxYsXZfNlZWVh9OjRsLKygrGxMXr06IHbt2/LYlJTUxEQEAC1Wg21Wo2AgACkpaXJYm7duoXu3bvD2NgYVlZWCA4ORnZ2dnltLhEREVGpvBHFW0xMDL799ls0atRI1j5v3jwsWrQIy5cvR0xMDOzs7NCpUyc8fPhQigkJCcH27dsRFhaGI0eO4NGjR/Dz80Nubq4U4+/vj7i4OISHhyM8PBxxcXEICAiQpufm5qJbt27IzMzEkSNHEBYWhq1btyI0NLT8N56IiIioBCq9eHv06BE++eQTrF69Gubm5lK7EAJLlizBlClT0Lt3b7i6umL9+vV4/PgxNm/eDABIT0/H999/j4ULF6Jjx45o2rQpNm7ciPPnz2Pfvn0AgPj4eISHh+O7776Dp6cnPD09sXr1avz222+4fPkyACAiIgKXLl3Cxo0b0bRpU3Ts2BELFy7E6tWrkZGRUfGdQkRERPQSlV68jRw5Et26dUPHjh1l7QkJCUhOToaPj4/Upq+vD29vb0RHRwMAYmNjkZOTI4uxt7eHq6urFHPs2DGo1Wp4eHhIMS1btoRarZbFuLq6wt7eXorx9fVFVlYWYmNjX5p7VlYWMjIyZH9ERERE5UmnMlceFhaG06dPIyYmRmNacnIyAMDW1lbWbmtri5s3b0oxenp6shG7/Jj8+ZOTk2FjY6OxfBsbG1lMwfWYm5tDT09PiinMnDlzMGPGjFdtJhEREVGZqbSRt8TERIwZMwYbN26EgYHBS+NUKpXstRBCo62ggjGFxZcmpqDJkycjPT1d+ktMTCwyLyIiIqLXVWnFW2xsLFJSUuDu7g4dHR3o6OggKioKX331FXR0dKSRsIIjXykpKdI0Ozs7ZGdnIzU1tciYu3fvaqz/3r17spiC60lNTUVOTo7GiNyL9PX1YWZmJvsjIiIiKk+VVrx16NAB58+fR1xcnPTXvHlzfPLJJ4iLi0Pt2rVhZ2eHyMhIaZ7s7GxERUWhVatWAAB3d3fo6urKYpKSknDhwgUpxtPTE+np6Th58qQUc+LECaSnp8tiLly4gKSkJCkmIiIC+vr6cHd3L9d+ICIiIiqJSrvmzdTUFK6urrI2Y2NjWFpaSu0hISGYPXs26tWrh3r16mH27NkwMjKCv78/AECtVmPw4MEIDQ2FpaUlLCwsMH78eLi5uUk3QDg7O6Nz584IDAzEqlWrAABDhw6Fn58fnJycAAA+Pj5wcXFBQEAA5s+fjwcPHmD8+PEIDAzkaBoRERG9USr1hoVXmThxIp48eYKgoCCkpqbCw8MDERERMDU1lWIWL14MHR0d9O3bF0+ePEGHDh2wbt06aGtrSzGbNm1CcHCwdFdqjx49sHz5cmm6trY2du/ejaCgIHh5ecHQ0BD+/v5YsGBBxW0sERERUTGohBCispN4W2RkZECtViM9Pb3sR+x2jSnb5VWE7ksrOwMiIqJXKtfjdzmo9Oe8EREREVHxsXgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERApSquKtffv2SEtL02jPyMhA+/btXzcnIiIiInqJUhVvhw4dQnZ2tkb706dPcfjw4ddOioiIiIgKp1OS4HPnzkn/f+nSJSQnJ0uvc3NzER4ejmrVqpVddkREREQkU6LirUmTJlCpVFCpVIWeHjU0NMSyZcvKLDkiIiIikitR8ZaQkAAhBGrXro2TJ0/C2tpamqanpwcbGxtoa2uXeZJERERE9FyJireaNWsCAPLy8solGSIiIiIqWomKtxdduXIFhw4dQkpKikYx9/nnn792YkRERESkqVTF2+rVqzFixAhYWVnBzs4OKpVKmqZSqVi8EREREZWTUhVvs2bNwv/+9z9MmjSprPMhIiIioiKU6jlvqamp+OCDD8o6FyIiIiJ6hVIVbx988AEiIiLKOhciIiIieoVSnTatW7cupk6diuPHj8PNzQ26urqy6cHBwWWSHBERERHJqYQQoqQzOTo6vnyBKhX++uuv10pKqTIyMqBWq5Geng4zM7OyXfiuMWW7vIrQfWllZ0BERPRK5Xr8LgelGnlLSEgo6zyIiIiIqBhKdc0bEREREVWOUo28DRo0qMjpa9asKVUyRERERFS0UhVvqampstc5OTm4cOEC0tLSCv3BeiIiIiIqG6Uq3rZv367RlpeXh6CgINSuXfu1kyIiIiKiwpXZNW9aWloYO3YsFi9eXFaLJCIiIqICyvSGhevXr+PZs2dluUgiIiIiekGpTpuOGzdO9loIgaSkJOzevRv9+/cvk8SIiIiISFOpirczZ87IXmtpacHa2hoLFy585Z2oRERERFR6pSreDh48WNZ5EBEREVExlKp4y3fv3j1cvnwZKpUK9evXh7W1dVnlRURERESFKNUNC5mZmRg0aBCqVq2KNm3aoHXr1rC3t8fgwYPx+PHjss6RiIiIiP6/UhVv48aNQ1RUFHbt2oW0tDSkpaXh119/RVRUFEJDQ8s6RyIiIiL6/0p12nTr1q345Zdf0LZtW6mta9euMDQ0RN++fbFixYqyyo+IiIiIXlCqkbfHjx/D1tZWo93GxqZEp01XrFiBRo0awczMDGZmZvD09MTevXul6UIITJ8+Hfb29jA0NETbtm1x8eJF2TKysrIwevRoWFlZwdjYGD169MDt27dlMampqQgICIBarYZarUZAQADS0tJkMbdu3UL37t1hbGwMKysrBAcHIzs7u9jbQkRERFQRSlW8eXp6Ytq0aXj69KnU9uTJE8yYMQOenp7FXk716tXx5Zdf4tSpUzh16hTat2+Pnj17SgXavHnzsGjRIixfvhwxMTGws7NDp06d8PDhQ2kZISEh2L59O8LCwnDkyBE8evQIfn5+yM3NlWL8/f0RFxeH8PBwhIeHIy4uDgEBAdL03NxcdOvWDZmZmThy5AjCwsKwdetWngImIiKiN45KCCFKOtP58+fRpUsXPH36FI0bN4ZKpUJcXBz09fURERGBhg0bljohCwsLzJ8/H4MGDYK9vT1CQkIwadIkAM9H2WxtbTF37lwMGzYM6enpsLa2xoYNG/Dhhx8CAO7cuQMHBwfs2bMHvr6+iI+Ph4uLC44fPw4PDw8AwPHjx+Hp6Yk///wTTk5O2Lt3L/z8/JCYmAh7e3sAQFhYGAYMGICUlBSYmZkVK/eMjAyo1Wqkp6cXe55i2zWmbJdXEbovrewMiIiIXqlcj9/loFQjb25ubrh69SrmzJmDJk2aoFGjRvjyyy9x7dq1Uhduubm5CAsLQ2ZmJjw9PZGQkIDk5GT4+PhIMfr6+vD29kZ0dDQAIDY2Fjk5ObIYe3t7uLq6SjHHjh2DWq2WCjcAaNmyJdRqtSzG1dVVKtwAwNfXF1lZWYiNjX1pzllZWcjIyJD9EREREZWnUt2wMGfOHNja2iIwMFDWvmbNGty7d08aKSuO8+fPw9PTE0+fPoWJiQm2b98OFxcXqbAqeG2dra0tbt68CQBITk6Gnp4ezM3NNWKSk5OlGBsbG4312tjYyGIKrsfc3Bx6enpSTGHmzJmDGTNmFHtbiYiIiF5XqUbeVq1ahQYNGmi0N2zYECtXrizRspycnBAXF4fjx49jxIgR6N+/Py5duiRNV6lUsnghhEZbQQVjCosvTUxBkydPRnp6uvSXmJhYZF5EREREr6tUxVtycjKqVq2q0W5tbY2kpKQSLUtPTw9169ZF8+bNMWfOHDRu3BhLly6FnZ2dtK4XpaSkSKNkdnZ2yM7ORmpqapExd+/e1VjvvXv3ZDEF15OamoqcnJxC76rNp6+vL90pm/9HREREVJ5KVbw5ODjg6NGjGu1Hjx6VXTdWGkIIZGVlwdHREXZ2doiMjJSmZWdnIyoqCq1atQIAuLu7Q1dXVxaTlJSECxcuSDGenp5IT0/HyZMnpZgTJ04gPT1dFnPhwgVZ4RkREQF9fX24u7u/1vYQERERlaVSXfM2ZMgQhISEICcnB+3btwcA7N+/HxMnTizR4zX+85//oEuXLnBwcMDDhw8RFhaGQ4cOITw8HCqVCiEhIZg9ezbq1auHevXqYfbs2TAyMoK/vz8AQK1WY/DgwQgNDYWlpSUsLCwwfvx4uLm5oWPHjgAAZ2dndO7cGYGBgVi1ahUAYOjQofDz84OTkxMAwMfHBy4uLggICMD8+fPx4MEDjB8/HoGBgRxNIyIiojdKqYq3iRMn4sGDBwgKCpIeZGtgYIBJkyZh8uTJxV7O3bt3ERAQgKSkJKjVajRq1Ajh4eHo1KmTtJ4nT54gKCgIqamp8PDwQEREBExNTaVlLF68GDo6Oujbty+ePHmCDh06YN26ddDW1pZiNm3ahODgYOmu1B49emD58uXSdG1tbezevRtBQUHw8vKCoaEh/P39sWDBgtJ0DxEREVG5KdVz3vI9evQI8fHxMDQ0RL169aCvr1+WuSkOn/NWAJ/zRkRECqC057yVauQtn4mJCd55552yyoWIiIiIXqFUNywQERERUeVg8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECvJajwqht8+JhAdltqwd286X2bIKM6e3W7kun4iI6E3EkTciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9EREREClKpxducOXPwzjvvwNTUFDY2NujVqxcuX74sixFCYPr06bC3t4ehoSHatm2LixcvymKysrIwevRoWFlZwdjYGD169MDt27dlMampqQgICIBarYZarUZAQADS0tJkMbdu3UL37t1hbGwMKysrBAcHIzs7u1y2nYiIiKg0KrV4i4qKwsiRI3H8+HFERkbi2bNn8PHxQWZmphQzb948LFq0CMuXL0dMTAzs7OzQqVMnPHz4UIoJCQnB9u3bERYWhiNHjuDRo0fw8/NDbm6uFOPv74+4uDiEh4cjPDwccXFxCAgIkKbn5uaiW7duyMzMxJEjRxAWFoatW7ciNDS0YjqDiIiIqBhUQghR2Unku3fvHmxsbBAVFYU2bdpACAF7e3uEhIRg0qRJAJ6Pstna2mLu3LkYNmwY0tPTYW1tjQ0bNuDDDz8EANy5cwcODg7Ys2cPfH19ER8fDxcXFxw/fhweHh4AgOPHj8PT0xN//vknnJycsHfvXvj5+SExMRH29vYAgLCwMAwYMAApKSkwMzN7Zf4ZGRlQq9VIT08vVnyJ7BpTtst7iRMJD8psWTuqTyyzZRVmTm+3cl0+ERH9O5Tr8bscvFHXvKWnpwMALCwsAAAJCQlITk6Gj4+PFKOvrw9vb29ER0cDAGJjY5GTkyOLsbe3h6urqxRz7NgxqNVqqXADgJYtW0KtVstiXF1dpcINAHx9fZGVlYXY2NhC883KykJGRobsj4iIiKg8vTHFmxAC48aNw7vvvgtXV1cAQHJyMgDA1tZWFmtraytNS05Ohp6eHszNzYuMsbGx0VinjY2NLKbgeszNzaGnpyfFFDRnzhzpGjq1Wg0HB4eSbjYRERFRibwxxduoUaNw7tw5/PjjjxrTVCqV7LUQQqOtoIIxhcWXJuZFkydPRnp6uvSXmJhYZE5EREREr+uNKN5Gjx6NnTt34uDBg6hevbrUbmdnBwAaI18pKSnSKJmdnR2ys7ORmppaZMzdu3c11nvv3j1ZTMH1pKamIicnR2NELp++vj7MzMxkf0RERETlqVKLNyEERo0ahW3btuHAgQNwdHSUTXd0dISdnR0iIyOltuzsbERFRaFVq1YAAHd3d+jq6spikpKScOHCBSnG09MT6enpOHnypBRz4sQJpKeny2IuXLiApKQkKSYiIgL6+vpwd3cv+40nIiIiKgWdylz5yJEjsXnzZvz6668wNTWVRr7UajUMDQ2hUqkQEhKC2bNno169eqhXrx5mz54NIyMj+Pv7S7GDBw9GaGgoLC0tYWFhgfHjx8PNzQ0dO3YEADg7O6Nz584IDAzEqlWrAABDhw6Fn58fnJycAAA+Pj5wcXFBQEAA5s+fjwcPHmD8+PEIDAzkiBoRERG9MSq1eFuxYgUAoG3btrL2tWvXYsCAAQCAiRMn4smTJwgKCkJqaio8PDwQEREBU1NTKX7x4sXQ0dFB37598eTJE3To0AHr1q2Dtra2FLNp0yYEBwdLd6X26NEDy5cvl6Zra2tj9+7dCAoKgpeXFwwNDeHv748FCxaU09YTERERldwb9Zw3peNz3uT4nDciIlICPueNiIiIiMoNizciIiIiBWHxRkRERKQgLN6IiIiIFKRS7zYl+jeYvO18ZadQbLwJhIjozceRNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBdCo7AXp79bo9r3xXsMui7JfZfWnZL5OIiKgMceSNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhMUbERERkYKweCMiIiJSEBZvRERERApSqcXbH3/8ge7du8Pe3h4qlQo7duyQTRdCYPr06bC3t4ehoSHatm2LixcvymKysrIwevRoWFlZwdjYGD169MDt27dlMampqQgICIBarYZarUZAQADS0tJkMbdu3UL37t1hbGwMKysrBAcHIzs7uzw2m4iIiKjUKrV4y8zMROPGjbF8+fJCp8+bNw+LFi3C8uXLERMTAzs7O3Tq1AkPHz6UYkJCQrB9+3aEhYXhyJEjePToEfz8/JCbmyvF+Pv7Iy4uDuHh4QgPD0dcXBwCAgKk6bm5uejWrRsyMzNx5MgRhIWFYevWrQgNDS2/jSciIiIqBZ3KXHmXLl3QpUuXQqcJIbBkyRJMmTIFvXv3BgCsX78etra22Lx5M4YNG4b09HR8//332LBhAzp27AgA2LhxIxwcHLBv3z74+voiPj4e4eHhOH78ODw8PAAAq1evhqenJy5fvgwnJydERETg0qVLSExMhL29PQBg4cKFGDBgAP73v//BzMysAnqDiIiI6NUqtXgrSkJCApKTk+Hj4yO16evrw9vbG9HR0Rg2bBhiY2ORk5Mji7G3t4erqyuio6Ph6+uLY8eOQa1WS4UbALRs2RJqtRrR0dFwcnLCsWPH4OrqKhVuAODr64usrCzExsaiXbt2FbPRVCInEh6U+TJ3bDtf5sskIiIqS29s8ZacnAwAsLW1lbXb2tri5s2bUoyenh7Mzc01YvLnT05Oho2NjcbybWxsZDEF12Nubg49PT0ppjBZWVnIysqSXmdkZBR38+gN1ev2vMpOocR2VJ9Y2SkQEVEFeuPvNlWpVLLXQgiNtoIKxhQWX5qYgubMmSPdBKFWq+Hg4FBkXkRERESv640t3uzs7ABAY+QrJSVFGiWzs7NDdnY2UlNTi4y5e/euxvLv3bsniym4ntTUVOTk5GiMyL1o8uTJSE9Pl/4SExNLuJVEREREJfPGFm+Ojo6ws7NDZGSk1JadnY2oqCi0atUKAODu7g5dXV1ZTFJSEi5cuCDFeHp6Ij09HSdPnpRiTpw4gfT0dFnMhQsXkJSUJMVERERAX18f7u7uL81RX18fZmZmsj8iIiKi8lSp17w9evQI165dk14nJCQgLi4OFhYWqFGjBkJCQjB79mzUq1cP9erVw+zZs2FkZAR/f38AgFqtxuDBgxEaGgpLS0tYWFhg/PjxcHNzk+4+dXZ2RufOnREYGIhVq1YBAIYOHQo/Pz84OTkBAHx8fODi4oKAgADMnz8fDx48wPjx4xEYGMiCjIiIiN4olVq8nTp1SnYn57hx4wAA/fv3x7p16zBx4kQ8efIEQUFBSE1NhYeHByIiImBqairNs3jxYujo6KBv37548uQJOnTogHXr1kFbW1uK2bRpE4KDg6W7Unv06CF7tpy2tjZ2796NoKAgeHl5wdDQEP7+/liwYEF5dwERERFRiaiEEKKyk3hbZGRkQK1WIz09vexH7HaNKdvlvUR5PH6DyldZ3m06p7dbmS2LiEgpyvX4XQ7e2GveiIiIiEjTG/ucNyIiqjiTFfSAao4Q078dR96IiIiIFITFGxEREZGCsHgjIiIiUhAWb0REREQKwuKNiIiISEFYvBEREREpCB8VQkRUjpT0CA4iUgaOvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSENywQkeLwJgAi+jdj8UZEEhZFRERvPp42JSIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhM95IyIi+pdTyjMe5/R2q+wU3ggceSMiIiJSEBZvRERERArC4o2IiIhIQVi8ERERESkIizciIiIiBWHxRkRERKQgLN6IiIiIFITFGxEREZGC8CG9RESkKHygLP3bceSNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkILxhgUjhet2eV9kplMqO6hMrOwUiIkVi8UZERFQOlHJXLCkPT5sSERERKQiLNyIiIiIFYfFGREREpCAs3oiIiIgUhDcsEBER/Qsp8071DZWdwBuBI29ERERECsKRNyKqFMr8Vz8RUeXjyBsRERGRgrB4K+Cbb76Bo6MjDAwM4O7ujsOHD1d2SkREREQSFm8v2LJlC0JCQjBlyhScOXMGrVu3RpcuXXDr1q3KTo2IiIgIAIs3mUWLFmHw4MEYMmQInJ2dsWTJEjg4OGDFihWVnRoRERERABZvkuzsbMTGxsLHx0fW7uPjg+jo6ErKioiIiEiOd5v+f//88w9yc3Nha2sra7e1tUVycnKh82RlZSErK0t6nZ6eDgDIyMgo+wQfZ706pgxkPs2ukPUQERGVVLkcX19YrhCiXJZf1li8FaBSqWSvhRAabfnmzJmDGTNmaLQ7ODiUS25ERET/apN+KtfFP3z4EGq1ulzXURZYvP1/VlZW0NbW1hhlS0lJ0RiNyzd58mSMGzdOep2Xl4cHDx7A0tLypQXfq2RkZMDBwQGJiYkwMzMr1TKoeNjXFYd9XbHY3xWHfV1xyrOvhRB4+PAh7O3ty3S55YXF2/+np6cHd3d3REZG4r333pPaIyMj0bNnz0Ln0dfXh76+vqytSpUqZZKPmZkZvwgqCPu64rCvKxb7u+KwrytOefW1Ekbc8rF4e8G4ceMQEBCA5s2bw9PTE99++y1u3bqF4cOHV3ZqRERERABYvMl8+OGHuH//PmbOnImkpCS4urpiz549qFmzZmWnRkRERASAxZuGoKAgBAUFVdr69fX1MW3aNI3TsVT22NcVh31dsdjfFYd9XXHY1/9HJZRyXywRERER8SG9RERERErC4o2IiIhIQVi8ERERESkIizciIiIiBWHxVgm++eYbODo6wsDAAO7u7jh8+HCR8VFRUXB3d4eBgQFq166NlStXVlCmyleSvt62bRs6deoEa2trmJmZwdPTE7///nsFZqtsJd2v8x09ehQ6Ojpo0qRJ+Sb4FilpX2dlZWHKlCmoWbMm9PX1UadOHaxZs6aCslW+kvb3pk2b0LhxYxgZGaFq1aoYOHAg7t+/X0HZKtcff/yB7t27w97eHiqVCjt27HjlPP/a46OgChUWFiZ0dXXF6tWrxaVLl8SYMWOEsbGxuHnzZqHxf/31lzAyMhJjxowRly5dEqtXrxa6urril19+qeDMlaekfT1mzBgxd+5ccfLkSXHlyhUxefJkoaurK06fPl3BmStPSfs6X1pamqhdu7bw8fERjRs3rphkFa40fd2jRw/h4eEhIiMjRUJCgjhx4oQ4evRoBWatXCXt78OHDwstLS2xdOlS8ddff4nDhw+Lhg0bil69elVw5sqzZ88eMWXKFLF161YBQGzfvr3I+H/z8ZHFWwVr0aKFGD58uKytQYMG4rPPPis0fuLEiaJBgwaytmHDhomWLVuWW45vi5L2dWFcXFzEjBkzyjq1t05p+/rDDz8U//3vf8W0adNYvBVTSft67969Qq1Wi/v371dEem+dkvb3/PnzRe3atWVtX331lahevXq55fg2Kk7x9m8+PvK0aQXKzs5GbGwsfHx8ZO0+Pj6Ijo4udJ5jx45pxPv6+uLUqVPIyckpt1yVrjR9XVBeXh4ePnwICwuL8kjxrVHavl67di2uX7+OadOmlXeKb43S9PXOnTvRvHlzzJs3D9WqVUP9+vUxfvx4PHnypCJSVrTS9HerVq1w+/Zt7NmzB0II3L17F7/88gu6detWESn/q/ybj4/8hYUK9M8//yA3Nxe2traydltbWyQnJxc6T3JycqHxz549wz///IOqVauWW75KVpq+LmjhwoXIzMxE3759yyPFt0Zp+vrq1av47LPPcPjwYejo8GuouErT13/99ReOHDkCAwMDbN++Hf/88w+CgoLw4MEDXvf2CqXp71atWmHTpk348MMP8fTpUzx79gw9evTAsmXLKiLlf5V/8/GRI2+VQKVSyV4LITTaXhVfWDtpKmlf5/vxxx8xffp0bNmyBTY2NuWV3luluH2dm5sLf39/zJgxA/Xr16+o9N4qJdmv8/LyoFKpsGnTJrRo0QJdu3bFokWLsG7dOo6+FVNJ+vvSpUsIDg7G559/jtjYWISHhyMhIQHDhw+viFT/df6tx0f+k7cCWVlZQVtbW+NfbCkpKRr/eshnZ2dXaLyOjg4sLS3LLVelK01f59uyZQsGDx6Mn3/+GR07dizPNN8KJe3rhw8f4tSpUzhz5gxGjRoF4HmBIYSAjo4OIiIi0L59+wrJXWlKs19XrVoV1apVg1qtltqcnZ0hhMDt27dRr169cs1ZyUrT33PmzIGXlxcmTJgAAGjUqBGMjY3RunVrzJo1660eDapo/+bjI0feKpCenh7c3d0RGRkpa4+MjESrVq0KncfT01MjPiIiAs2bN4eurm655ap0pelr4PmI24ABA7B582Zeo1JMJe1rMzMznD9/HnFxcdLf8OHD4eTkhLi4OHh4eFRU6opTmv3ay8sLd+7cwaNHj6S2K1euQEtLC9WrVy/XfJWuNP39+PFjaGnJD63a2toA/m9UiMrGv/r4WEk3Svxr5d92/v3334tLly6JkJAQYWxsLG7cuCGEEOKzzz4TAQEBUnz+rdBjx44Vly5dEt9///2/5lbo11XSvt68ebPQ0dERX3/9tUhKSpL+0tLSKmsTFKOkfV0Q7zYtvpL29cOHD0X16tVFnz59xMWLF0VUVJSoV6+eGDJkSGVtgqKUtL/Xrl0rdHR0xDfffCOuX78ujhw5Ipo3by5atGhRWZugGA8fPhRnzpwRZ86cEQDEokWLxJkzZ6THsvD4+H9YvFWCr7/+WtSsWVPo6emJZs2aiaioKGla//79hbe3tyz+0KFDomnTpkJPT0/UqlVLrFixooIzVq6S9LW3t7cAoPHXv3//ik9cgUq6X7+IxVvJlLSv4+PjRceOHYWhoaGoXr26GDdunHj8+HEFZ61cJe3vr776Sri4uAhDQ0NRtWpV8cknn4jbt29XcNbKc/DgwSK/g3l8/D8qITiOS0RERKQUvOaNiIiISEFYvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9Eb6FatWphyZIlr7WMdevWoUqVKkXGTJ8+HU2aNJFeDxgwAL169ZJet23bFiEhIa+VR2kdPXoUbm5u0NXVleX0uh4/foz3338fZmZmUKlUSEtLK7Nlv60qcz8gehuxeCOiUhs/fjz279//0unbtm3DF198Ib0ui6KyuMaNG4cmTZogISEB69atK7Plrl+/HocPH0Z0dDSSkpJkP/j+Mjdu3IBKpUJcXFyZ5UFE/146lZ0AERVfdnY29PT0KjsNiYmJCUxMTF463cLCogKzkbt+/TqGDx9e5j++fv36dTg7O8PV1bVMl1tcb9o+8KblQ/RvwJE3okrStm1bjBo1CqNGjUKVKlVgaWmJ//73v3jxF+tq1aqFWbNmYcCAAVCr1QgMDAQAbN26FQ0bNoS+vj5q1aqFhQsXaiz/4cOH8Pf3h4mJCezt7bFs2TLZ9EWLFsHNzQ3GxsZwcHBAUFAQHj16pLGcHTt2oH79+jAwMECnTp2QmJgoTSt42rSwbcw/Xda2bVvcvHkTY8eOhUqlgkqlQmZmJszMzPDLL7/I5tu1axeMjY3x8OHDQpeblZWF4OBg2NjYwMDAAO+++y5iYmIA/N8o1/379zFo0CCoVKqXjrxt3LgRzZs3h6mpKezs7ODv74+UlJQit2fhwoX4448/oFKp0LZtWwCASqXCjh07ZLFVqlSR1uvo6AgAaNq0qWy+wk4n9urVCwMGDJBev2wfiI6ORps2bWBoaAgHBwcEBwcjMzPzpbnnv1erVq2Cg4MDjIyM8MEHH8hO+75OPkePHoW3tzeMjIxgbm4OX19fpKamSvPl5eVh4sSJsLCwgJ2dHaZPny5bz6v2x5s3b6J79+4wNzeHsbExGjZsiD179kjTL126hK5du8LExAS2trYICAjAP//889L+IFIyFm9ElWj9+vXQ0dHBiRMn8NVXX2Hx4sX47rvvZDHz58+Hq6srYmNjMXXqVMTGxqJv37746KOPcP78eUyfPh1Tp07VKFDmz5+PRo0a4fTp05g8eTLGjh2LyMhIabqWlha++uorXLhwAevXr8eBAwcwceJE2TIeP36M//3vf1i/fj2OHj2KjIwMfPTRR6Xa1m3btqF69eqYOXMmkpKSkJSUBGNjY3z00UdYu3atLHbt2rXo06cPTE1NC13WxIkTsXXrVqxfvx6nT59G3bp14evriwcPHsDBwQFJSUkwMzPDkiVLkJSUhA8//LDQ5WRnZ+OLL77A2bNnsWPHDiQkJMgKlcK2ITAwEJ6enkhKSsK2bduKte0nT54EAOzbt69E8+UruA+cP38evr6+6N27N86dO4ctW7bgyJEjGDVqVJHLuXbtGn766Sfs2rUL4eHhiIuLw8iRI0uUS2H5xMXFoUOHDmjYsCGOHTuGI0eOoHv37sjNzZXmWb9+PYyNjXHixAnMmzcPM2fOLNH+OHLkSGRlZeGPP/7A+fPnMXfuXGnUNykpCd7e3mjSpAlOnTqF8PBw3L17F3379i3xthEpQjn/8D0RvYS3t7dwdnYWeXl5UtukSZOEs7Oz9LpmzZqiV69esvn8/f1Fp06dZG0TJkwQLi4usvk6d+4si/nwww9Fly5dXprPTz/9JCwtLaXXa9euFQDE8ePHpbb4+HgBQJw4cUIIIcS0adNE48aNpen9+/cXPXv2lG3jmDFjZHktXrxYtt4TJ04IbW1t8ffffwshhLh3757Q1dUVhw4dKjTPR48eCV1dXbFp0yapLTs7W9jb24t58+ZJbWq1Wqxdu/al21uYkydPCgDi4cOHL40ZM2aM8Pb2lrUBENu3b5e1vbj+hIQEAUCcOXNGFlOwf4QQomfPnqJ///7S68L2gYCAADF06FBZ2+HDh4WWlpZ48uRJoXlPmzZNaGtri8TERKlt7969QktLSyQlJb1WPh9//LHw8vIqdL35y3333Xdlbe+8846YNGnSS+cpuD+6ubmJ6dOnFxo7depU4ePjI2tLTEwUAMTly5dfug4ipeLIG1ElatmyJVQqlfTa09MTV69elY1YNG/eXDZPfHw8vLy8ZG1eXl4a83l6espiPD09ER8fL70+ePAgOnXqhGrVqsHU1BT9+vXD/fv3ZafedHR0ZOtv0KABqlSpIlvO62rRogUaNmyIH374AQCwYcMG1KhRA23atCk0/vr168jJyZH1ga6uLlq0aFHivM6cOYOePXuiZs2aMDU1lU5n3rp1q3QbU04K7gOxsbFYt26ddM2hiYkJfH19kZeXh4SEhJcup0aNGrJrAD09PZGXl4fLly+/Vj75I29FadSokex11apVZaeoX7U/BgcHY9asWfDy8sK0adNw7tw5ad7Y2FgcPHhQ1h8NGjQA8Hx/IXrbsHgjesMZGxvLXgshZAVffltx5M938+ZNdO3aFa6urti6dStiY2Px9ddfAwBycnIKnedVba9jyJAh0qnTtWvXYuDAgS9dR/62FtYHJckrMzMTPj4+MDExwcaNGxETE4Pt27cDeH46tSRUKpXGe1CwHwujpaVVrPkK7gN5eXkYNmwY4uLipL+zZ8/i6tWrqFOnTonyfvG/pc3H0NDwlevS1dXVWHdeXh6A4u2PQ4YMwV9//YWAgACcP38ezZs3l67jzMvLQ/fu3WX9ERcXh6tXr770HwFESsbijagSHT9+XON1vXr1oK2t/dJ5XFxccOTIEVlbdHQ06tevL5uvsGXnj0acOnUKz549w8KFC9GyZUvUr18fd+7c0VjXs2fPcOrUKen15cuXkZaWJi2npPT09GSjg/k+/fRT3Lp1C1999RUuXryI/v37v3QZdevWhZ6enqwPcnJycOrUKTg7Oxc7lz///BP//PMPvvzyS7Ru3RoNGjQo8maFolhbWyMpKUl6ffXqVTx+/Fh6nX83ZsFtLzhfbm4uLly48Mr1NWvWDBcvXkTdunU1/oq68/PWrVuy9/nYsWPQ0tJC/fr1XyufRo0aFfnImFcp7v7o4OCA4cOHY9u2bQgNDcXq1asB/F9/1KpVS6M/ChaaRG8DFm9ElSgxMRHjxo3D5cuX8eOPP2LZsmUYM2ZMkfOEhoZi//79+OKLL3DlyhWsX78ey5cvx/jx42VxR48exbx583DlyhV8/fXX+Pnnn6Vl16lTB8+ePcOyZcvw119/YcOGDVi5cqXGunR1dTF69GicOHECp0+fxsCBA9GyZUu0aNGiVNtbq1Yt/PHHH/j7779ldwKam5ujd+/emDBhAnx8fIp8vIexsTFGjBiBCRMmIDw8HJcuXUJgYCAeP36MwYMHFzuXGjVqQE9PT+qDnTt3yp5JVxLt27fH8uXLcfr0aZw6dQrDhw+XjTTZ2NjA0NBQupA+PT1dmm/37t3YvXs3/vzzTwQFBRXrob+TJk3CsWPHMHLkSGmEaefOnRg9enSR8xkYGKB///44e/YsDh8+jODgYPTt2xd2dnavlc/kyZMRExODoKAgnDt3Dn/++SdWrFhR7Ls9i7M/hoSE4Pfff0dCQgJOnz6NAwcOSMX6yJEj8eDBA3z88cc4efIk/vrrL0RERGDQoEGF/mOBSOlYvBFVon79+uHJkydo0aIFRo4cidGjR2Po0KFFztOsWTP89NNPCAsLg6urKz7//HPMnDlT4y7J0NBQxMbGomnTpvjiiy+wcOFC+Pr6AgCaNGmCRYsWYe7cuXB1dcWmTZswZ84cjXUZGRlh0qRJ8Pf3h6enJwwNDREWFlbq7Z05cyZu3LiBOnXqwNraWjZt8ODByM7OxqBBg165nC+//BLvv/8+AgIC0KxZM1y7dg2///47zM3Ni52LtbU11q1bh59//hkuLi748ssvsWDBghJvEwAsXLgQDg4OaNOmDfz9/TF+/HgYGRlJ03V0dPDVV19h1apVsLe3R8+ePQEAgwYNQv/+/dGvXz94e3vD0dER7dq1e+X6GjVqhKioKFy9ehWtW7dG06ZNMXXqVFStWrXI+erWrYvevXuja9eu8PHxgaurK7755htpemnzqV+/PiIiInD27Fm0aNECnp6e+PXXX6GjU7xHiRZnf8zNzcXIkSPh7OyMzp07w8nJScrd3t4eR48eRW5uLnx9feHq6ooxY8ZArVZDS4uHOXr7qERxL5YhojLVtm1bNGnSpMJ+ceBNt2nTJowZMwZ37tzhQ1/LwfTp07Fjxw7+ygPRW4C/sEBElerx48dISEjAnDlzMGzYMBZuRESvwPFkIqpU8+bNQ5MmTWBra4vJkydXdjpERG88njYlIiIiUhCOvBEREREpCIs3IiIiIgVh8UZERESkICzeiIiIiBSExRsRERGRgrB4IyIiIlIQFm9ERERECsLijYiIiEhBWLwRERERKcj/AyRD7hy3DLvhAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualization\n", + "\n", + "# histogramme des probas et des probas ajustées\n", + "\n", + "def plot_hist_scores(df, score, score_adjusted, type_of_activity) :\n", + "\n", + " plt.figure()\n", + " plt.hist(df[score], label = \"score\", alpha=0.6)\n", + " plt.hist(df[score_adjusted], label=\"adjusted score\", alpha=0.6)\n", + " plt.legend()\n", + " plt.xlabel(\"probability of a future purchase\")\n", + " plt.ylabel(\"count\")\n", + " plt.title(f\"Comparison between score and adjusted score for {type_of_activity} companies\")\n", + " plt.show()\n", + "\n", + "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\", type_of_activity=\"sport\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "b478d40d-9677-4204-87bd-16fb0bc1fe9a", + "metadata": {}, "outputs": [ { "data": { @@ -1833,24 +1870,102 @@ } ], "source": [ - "# visualization\n", - "\n", - "# histogramme des probas et des probas ajustées\n", - "\n", - "def plot_hist_scores(df, score, score_adjusted) :\n", - "\n", - " plt.figure()\n", - " plt.hist(df[score], label = \"score\", alpha=0.6)\n", - " plt.hist(df[score_adjusted], label=\"adjusted score\", alpha=0.6)\n", - " plt.legend()\n", - " plt.xlabel(\"probability of a future purchase\")\n", - " plt.ylabel(\"count\")\n", - " plt.title(\"Comparison between score and adjusted score\")\n", - " plt.show()\n", - "\n", "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\")" ] }, + { + "cell_type": "code", + "execution_count": 103, + "id": "add631d7-0757-45a5-bb5b-f7f4b4baa961", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "projet-bdc2324-team1/Output_expected_CA/sport/\n" + ] + } + ], + "source": [ + "# define path so save graphics\n", + "\n", + "# define type of activity \n", + "type_of_activity = \"sport\"\n", + "PATH = f\"projet-bdc2324-team1/Output_expected_CA/{type_of_activity}/\"\n", + "print(PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3a5b5bd9-e033-4436-8c56-bf5fb61df87f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ClientError", + "evalue": "An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[108], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhist_score_adjusted\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 10\u001b[0m FILE_PATH_OUT_S3 \u001b[38;5;241m=\u001b[39m PATH \u001b[38;5;241m+\u001b[39m file_name \u001b[38;5;241m+\u001b[39m type_of_activity \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mFILE_PATH_OUT_S3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms3_file\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_buffer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39mclose()\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1963\u001b[0m, in \u001b[0;36mAbstractBufferedFile.__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m-> 1963\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1930\u001b[0m, in \u001b[0;36mAbstractBufferedFile.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1928\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1929\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforced:\n\u001b[0;32m-> 1930\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflush\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39minvalidate_cache(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath)\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1801\u001b[0m, in \u001b[0;36mAbstractBufferedFile.flush\u001b[0;34m(self, force)\u001b[0m\n\u001b[1;32m 1798\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclosed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1799\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m-> 1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_upload_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 1802\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 1803\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1252\u001b[0m, in \u001b[0;36mS3File._upload_chunk\u001b[0;34m(self, final)\u001b[0m\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparts\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPartNumber\u001b[39m\u001b[38;5;124m'\u001b[39m: part, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m]})\n\u001b[1;32m 1251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautocommit \u001b[38;5;129;01mand\u001b[39;00m final:\n\u001b[0;32m-> 1252\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m final\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1267\u001b[0m, in \u001b[0;36mS3File.commit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 1266\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m-> 1267\u001b[0m write_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1268\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mput_object\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1269\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 1270\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1271\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39mversion_aware:\n\u001b[1;32m 1272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion_id \u001b[38;5;241m=\u001b[39m write_result\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1130\u001b[0m, in \u001b[0;36mS3File._call_s3\u001b[0;34m(self, method, *kwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call_s3\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, \u001b[38;5;241m*\u001b[39mkwarglist, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwarglist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1131\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 197\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCALL: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, akwarglist, kw2))\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1005\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1006\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1007\u001b[0m )\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", + "\u001b[0;31mClientError\u001b[0m: An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records." + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# export png \n", + "\n", + "# plot adjusted scores and save (to be tested)\n", + "plot_hist_scores(X_test_segment, score = \"score\", score_adjusted = \"score_adjusted\", type_of_activity = type_of_activity)\n", + "\n", + "image_buffer = io.BytesIO()\n", + "plt.savefig(image_buffer, format='png')\n", + "image_buffer.seek(0)\n", + "file_name = \"hist_score_adjusted\"\n", + "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".png\"\n", + "with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n", + " s3_file.write(image_buffer.read())\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19881d5a-e2cc-45eb-af56-43441b3a16d3", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "e6fae260-fab8-4f51-90dc-9b6d7314c77b", @@ -1861,330 +1976,143 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "c618cebc-c295-47f7-bd76-b7e18778a17c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxtime_between_purchasenb_tickets_internetfidelity...gender_femalegender_malegender_othernb_campaignsnb_campaigns_openedhas_purchasedhas_purchased_estimscorequartilescore_adjusted
04.01.0100.01.00.05.1771875.1771870.0000000.01...1000.00.00.01.00.65767130.240397
11.01.055.01.00.0426.265613426.2656130.0000000.02...0100.00.01.00.00.26653820.056482
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34.01.0120.01.00.05.1964125.1964120.0000000.01...1000.00.00.01.00.65777030.240478
434.02.0416.01.00.0478.693148115.631470363.0616780.04...1000.00.01.01.00.89417340.581920
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5 rows × 22 columns

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" - ], - "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers vente_internet_max \\\n", - "0 4.0 1.0 100.0 1.0 0.0 \n", - "1 1.0 1.0 55.0 1.0 0.0 \n", - "2 17.0 1.0 80.0 1.0 0.0 \n", - "3 4.0 1.0 120.0 1.0 0.0 \n", - "4 34.0 2.0 416.0 1.0 0.0 \n", - "\n", - " purchase_date_min purchase_date_max time_between_purchase \\\n", - "0 5.177187 5.177187 0.000000 \n", - "1 426.265613 426.265613 0.000000 \n", - "2 436.033437 436.033437 0.000000 \n", - "3 5.196412 5.196412 0.000000 \n", - "4 478.693148 115.631470 363.061678 \n", - "\n", - " nb_tickets_internet fidelity ... gender_female gender_male \\\n", - "0 0.0 1 ... 1 0 \n", - "1 0.0 2 ... 0 1 \n", - "2 0.0 2 ... 1 0 \n", - "3 0.0 1 ... 1 0 \n", - "4 0.0 4 ... 1 0 \n", - "\n", - " gender_other nb_campaigns nb_campaigns_opened has_purchased \\\n", - "0 0 0.0 0.0 0.0 \n", - "1 0 0.0 0.0 1.0 \n", - "2 0 0.0 0.0 0.0 \n", - "3 0 0.0 0.0 0.0 \n", - "4 0 0.0 0.0 1.0 \n", - "\n", - " has_purchased_estim score quartile score_adjusted \n", - "0 1.0 0.657671 3 0.240397 \n", - "1 0.0 0.266538 2 0.056482 \n", - "2 0.0 0.214668 1 0.043089 \n", - "3 1.0 0.657770 3 0.240478 \n", - "4 1.0 0.894173 4 0.581920 \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "29633dd2-8b4b-48dc-be02-52f4015e686d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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quartilescorescore_adjustedhas_purchased
010.1324570.0251050.015691
120.3389140.0799900.098486
230.6306470.2257570.214729
340.9052160.6619970.650133
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" - ], - "text/plain": [ - " quartile score score_adjusted has_purchased\n", - "0 1 0.132457 0.025105 0.015691\n", - "1 2 0.338914 0.079990 0.098486\n", - "2 3 0.630647 0.225757 0.214729\n", - "3 4 0.905216 0.661997 0.650133" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_segment.groupby(\"quartile\")[[\"score\",\"score_adjusted\", \"has_purchased\"]].mean().reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 100, + "execution_count": 161, "id": "90c4c2b5-0ede-4001-889f-749cfbd9df04", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
quartilescore (%)score adjusted (%)has purchased (%)
0113.252.511.57
1233.898.009.85
2363.0622.5821.47
3490.5266.2065.01
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" + ], "text/plain": [ - "'\\\\begin{tabular}{rrr}\\n\\\\toprule\\nscore & score_adjusted & has_purchased \\\\\\\\\\n\\\\midrule\\n0.130000 & 0.030000 & 0.020000 \\\\\\\\\\n0.340000 & 0.080000 & 0.100000 \\\\\\\\\\n0.630000 & 0.230000 & 0.210000 \\\\\\\\\\n0.910000 & 0.660000 & 0.650000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" + " quartile score (%) score adjusted (%) has purchased (%)\n", + "0 1 13.25 2.51 1.57\n", + "1 2 33.89 8.00 9.85\n", + "2 3 63.06 22.58 21.47\n", + "3 4 90.52 66.20 65.01" ] }, - "execution_count": 100, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X_test_segment.groupby(\"quartile\")[[\"score\",\"score_adjusted\", \"has_purchased\"]].mean().round(2).to_latex(index=False)" + "X_test_table_adjusted_scores = (100 * X_test_segment.groupby(\"quartile\")[[\"score\",\"score_adjusted\", \"has_purchased\"]].mean()).round(2).reset_index()\n", + "X_test_table_adjusted_scores = X_test_table_adjusted_scores.rename(columns = {col : f\"{col.replace('_', ' ')} (%)\" for col in X_test_table_adjusted_scores.columns if col in [\"score\",\"score_adjusted\", \"has_purchased\"]})\n", + "X_test_table_adjusted_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "d0b8740c-cf48-4a3e-83cb-23d95059f62f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\\\begin{tabular}{lrrr}\\n\\\\toprule\\nquartile & score (%) & score adjusted (%) & has purchased (%) \\\\\\\\\\n\\\\midrule\\n1 & 13.250000 & 2.510000 & 1.570000 \\\\\\\\\\n2 & 33.890000 & 8.000000 & 9.850000 \\\\\\\\\\n3 & 63.060000 & 22.580000 & 21.470000 \\\\\\\\\\n4 & 90.520000 & 66.200000 & 65.010000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_table_adjusted_scores.to_latex(index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "d6a04d3e-c454-43e4-ae4c-0746e928575b", + "metadata": {}, + "outputs": [ + { + "ename": "ClientError", + "evalue": "An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[130], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtable_adjusted_score\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m FILE_PATH_OUT_S3 \u001b[38;5;241m=\u001b[39m PATH \u001b[38;5;241m+\u001b[39m file_name \u001b[38;5;241m+\u001b[39m type_of_activity \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mFILE_PATH_OUT_S3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfile_out\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_test_table_adjusted_scores\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1930\u001b[0m, in \u001b[0;36mAbstractBufferedFile.close\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1928\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1929\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforced:\n\u001b[0;32m-> 1930\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflush\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs\u001b[38;5;241m.\u001b[39minvalidate_cache(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath)\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1801\u001b[0m, in \u001b[0;36mAbstractBufferedFile.flush\u001b[0;34m(self, force)\u001b[0m\n\u001b[1;32m 1798\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclosed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1799\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m-> 1801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_upload_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 1802\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 1803\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1252\u001b[0m, in \u001b[0;36mS3File._upload_chunk\u001b[0;34m(self, final)\u001b[0m\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparts\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPartNumber\u001b[39m\u001b[38;5;124m'\u001b[39m: part, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m]})\n\u001b[1;32m 1251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautocommit \u001b[38;5;129;01mand\u001b[39;00m final:\n\u001b[0;32m-> 1252\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m final\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1267\u001b[0m, in \u001b[0;36mS3File.commit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 1266\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m-> 1267\u001b[0m write_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1268\u001b[0m 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- \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, akwarglist, kw2))\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 550\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1005\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1006\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1007\u001b[0m )\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", + "\u001b[0;31mClientError\u001b[0m: An error occurred (InvalidAccessKeyId) when calling the PutObject operation: The Access Key Id you provided does not exist in our records." + ] + } + ], + "source": [ + "# comparison between score and adjusted score - export csv associated\n", + "\n", + "file_name = \"table_adjusted_score\"\n", + "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".csv\"\n", + "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", + " X_test_table_adjusted_scores.to_csv(file_out, index = False)" ] }, { @@ -2211,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 143, "id": "dd8a52e1-d06e-4790-8687-8e58e3e6b84e", "metadata": {}, "outputs": [ @@ -2611,7 +2539,7 @@ "[96096 rows x 26 columns]" ] }, - "execution_count": 79, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -2623,7 +2551,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 144, "id": "f58f9151-2f91-45df-abb7-1ddcf0652adc", "metadata": {}, "outputs": [], @@ -2645,7 +2573,7 @@ " # compute share of CA recovered\n", " duration_ratio=duration_ref/duration_projection\n", " \n", - " df_expected_CA[\"perct_revenue_recovered\"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \\\n", + " df_expected_CA[\"revenue_recovered_perct\"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \\\n", " df.groupby(segment)[total_amount].sum().values\n", " \n", " return df_expected_CA" @@ -2653,7 +2581,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 145, "id": "c8df6c80-43e8-4f00-9cd3-eb9022744313", "metadata": {}, "outputs": [ @@ -2683,7 +2611,7 @@ " size_perct\n", " nb_tickets_expected\n", " total_amount_expected\n", - " perct_revenue_recovered\n", + " revenue_recovered_perct\n", " \n", " \n", " \n", @@ -2734,14 +2662,14 @@ "2 3 20137 20.96 10876.79 344286.66 \n", "3 4 9032 9.40 215194.83 9899417.81 \n", "\n", - " perct_revenue_recovered \n", + " revenue_recovered_perct \n", "0 4.38 \n", "1 9.85 \n", "2 22.84 \n", "3 90.11 " ] }, - "execution_count": 65, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -2755,23 +2683,41 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 164, "id": "ac706ed7-defa-4df1-82e1-06f12fc1b6ad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'\\\\begin{tabular}{lrrrrr}\\n\\\\toprule\\nquartile & size & size_perct & nb_tickets_expected & total_amount_expected & perct_revenue_recovered \\\\\\\\\\n\\\\midrule\\n1 & 37410 & 38.930000 & 84.760000 & 1867.190000 & 4.380000 \\\\\\\\\\n2 & 29517 & 30.720000 & 2899.290000 & 74461.020000 & 9.850000 \\\\\\\\\\n3 & 20137 & 20.960000 & 10876.790000 & 344286.660000 & 22.840000 \\\\\\\\\\n4 & 9032 & 9.400000 & 215194.830000 & 9899417.810000 & 90.110000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" + "'\\\\begin{tabular}{lrrrrr}\\n\\\\toprule\\nquartile & size & size (%) & nb tickets expected & total amount expected & revenue recovered (%) \\\\\\\\\\n\\\\midrule\\n1 & 37410 & 38.930000 & 84.760000 & 1867.190000 & 4.380000 \\\\\\\\\\n2 & 29517 & 30.720000 & 2899.290000 & 74461.020000 & 9.850000 \\\\\\\\\\n3 & 20137 & 20.960000 & 10876.790000 & 344286.660000 & 22.840000 \\\\\\\\\\n4 & 9032 & 9.400000 & 215194.830000 & 9899417.810000 & 90.110000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'" ] }, - "execution_count": 92, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X_test_expected_CA.to_latex(index=False)" + "# Création du dictionnaire de mapping pour les noms de colonnes\n", + "mapping_dict = {col: col.replace(\"perct\", \"(%)\").replace(\"_\", \" \") for col in X_test_expected_CA.columns}\n", + "\n", + "X_test_expected_CA.rename(columns=mapping_dict).to_latex(index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "771da0cf-c49f-4e7e-b52f-ebcfb0fb2df3", + "metadata": {}, + "outputs": [], + "source": [ + "# export summary table to the MinIO storage\n", + "\n", + "file_name = \"table_expected_CA\"\n", + "FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + \".csv\"\n", + "with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:\n", + " X_test_expected_CA.to_csv(file_out, index = False)" ] }, { diff --git a/utils_CA_segment.py b/utils_CA_segment.py index f7fd82e..587194b 100644 --- a/utils_CA_segment.py +++ b/utils_CA_segment.py @@ -61,7 +61,7 @@ def find_bias(odd_ratios, y_objective, initial_guess=6) : return bias_estimated[0] -def plot_hist_scores(df, score, score_adjusted) : +def plot_hist_scores(df, score, score_adjusted, type_of_activity) : """ Plot a histogram comparing scores and adjusted scores. @@ -69,6 +69,7 @@ def plot_hist_scores(df, score, score_adjusted) : - df (DataFrame): DataFrame containing score data. - score (str): Name of the column in df representing the original scores. - score_adjusted (str): Name of the column in df representing the adjusted scores. + - type_of_activity (str) : type of activity of the companies considered. Returns: None @@ -80,9 +81,10 @@ def plot_hist_scores(df, score, score_adjusted) : plt.legend() plt.xlabel("probability of a future purchase") plt.ylabel("count") - plt.title("Comparison between score and adjusted score") + plt.title(f"Comparison between score and adjusted score for {type_of_activity} companies") plt.show() + def project_tickets_CA (df, nb_tickets, total_amount, score_adjusted, duration_ref, duration_projection) : """ Project ticket counts and total amount for a given duration and adjust based on a score. @@ -140,7 +142,7 @@ def summary_expected_CA(df, segment, nb_tickets_expected, total_amount_expected, # compute share of CA recovered duration_ratio=duration_ref/duration_projection - df_expected_CA["perct_revenue_recovered"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \ + df_expected_CA["revenue_recovered_perct"] = 100 * duration_ratio * df_expected_CA[total_amount_expected] / \ df.groupby(segment)[total_amount].sum().values return df_expected_CA