diff --git a/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb b/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb index 167171a..6897d8e 100644 --- a/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb +++ b/M2/Machine Learning/TP_4/2025_M2_ISF_TP_4.ipynb @@ -45,17 +45,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "97d58527", "metadata": {}, "outputs": [], "source": [ "# Données\n", - "import pandas as pd\n", "import numpy as np\n", + "import pandas as pd\n", "\n", - "# Graphiques \n", + "# Graphiques\n", "import seaborn as sns\n", + "\n", "sns.set()\n", "import plotly.express as px\n", "import plotly.graph_objects as gp\n", @@ -87,13 +88,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c67db932", "metadata": {}, "outputs": [], "source": [ "def cramers_V(var1,var2) :\n", - " crosstab =np.array(pd.crosstab(var1,var2, rownames=None, colnames=None)) # Cross table building\n", + " crosstab = np.array(pd.crosstab(var1,var2, rownames=None, colnames=None)) # Cross table building\n", " stat = chi2_contingency(crosstab)[0] # Keeping of the test statistic of the Chi2 test\n", " obs = np.sum(crosstab) # Number of observations\n", " mini = min(crosstab.shape)-1 # Take the minimum value between the columns and the rows of the cross table\n", @@ -110,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "id": "c9597b48", "metadata": {}, "outputs": [], @@ -129,30 +130,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "id": "8051b5f4", "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: './1_inputs/base_retraitee.csv'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m path =input_path + \u001b[33m'\u001b[39m\u001b[33m/base_retraitee.csv\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m data_retraitee = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43msep\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m,\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdecimal\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m.\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \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[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/pandas/io/common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: './1_inputs/base_retraitee.csv'" - ] - } - ], + "outputs": [], "source": [ - "path =input_path + '/base_retraitee.csv'\n", - "data_retraitee = pd.read_csv(path,sep=\",\",decimal=\".\")" + "path = input_path + '/base_retraitee.csv'\n", + "data_retraitee = pd.read_csv(path, sep=\",\", decimal=\".\")" ] }, { @@ -175,12 +159,1443 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "b9b98d36", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "ANNEE_CTR", + "rawType": "int64", + "type": "integer" + }, + { + "name": "CONTRAT_ANCIENNETE", + "rawType": "object", + "type": "string" + }, + { + "name": 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14236 rows × 17 columns

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" + ], + "text/plain": [ + " ANNEE_CTR CONTRAT_ANCIENNETE FREQUENCE_PAIEMENT_COTISATION \\\n", + "0 2019 (-1,0] ANNUEL \n", + "1 2019 (-1,0] ANNUEL \n", + "2 2021 (1,2] ANNUEL \n", + "3 2021 (2,5] ANNUEL \n", + "4 2018 (2,5] MENSUEL \n", + "... ... ... ... \n", + "14231 2021 (2,5] MENSUEL \n", + "14232 2019 (2,5] MENSUEL \n", + "14233 2017 (-1,0] ANNUEL \n", + "14234 2018 (-1,0] TRIMESTRIEL \n", + "14235 2017 (-1,0] ANNUEL \n", + "\n", + " GROUPE_KM ZONE_RISQUE AGE_ASSURE_PRINCIPAL GENRE \\\n", + "0 [20000;40000[ B 54 M \n", + "1 [20000;40000[ B 88 F \n", + "2 [0;20000[ D 35 F \n", + "3 [0;20000[ C 46 M \n", + "4 [20000;40000[ A 46 F \n", + "... ... ... ... ... \n", + "14231 [0;20000[ D 55 M \n", + "14232 [20000;40000[ A 33 M \n", + "14233 [0;20000[ A 62 M \n", + "14234 [20000;40000[ D 20 M \n", + "14235 [0;20000[ C 73 F \n", + "\n", + " DEUXIEME_CONDUCTEUR ANCIENNETE_PERMIS ANNEE_CONSTRUCTION ENERGIE \\\n", + "0 False 47 2016.0 ESSENCE \n", + "1 True 55 2018.0 DIESEL \n", + "2 True 16 2017.0 ESSENCE \n", + "3 False 44 2018.0 ESSENCE \n", + "4 False 31 2009.0 DIESEL \n", + "... ... ... ... ... \n", + "14231 False 49 2017.0 ESSENCE \n", + "14232 False 14 2017.0 ESSENCE \n", + "14233 False 58 2017.0 ESSENCE \n", + "14234 False 7 2016.0 DIESEL \n", + "14235 False 41 2017.0 ESSENCE \n", + "\n", + " EQUIPEMENT_SECURITE VALEUR_DU_BIEN NB CHARGE EXPO sinistré \n", + "0 FAUX [10000;15000[ 0 0.0 245.327869 0 \n", + "1 VRAI [20000;25000[ 0 0.0 230.368852 0 \n", + "2 FAUX [15000;20000[ 0 0.0 300.000000 0 \n", + "3 VRAI [35000;99999[ 0 0.0 304.000000 0 \n", + "4 FAUX [10000;15000[ 0 0.0 365.000000 0 \n", + "... ... ... .. ... ... ... \n", + "14231 FAUX [20000;25000[ 0 0.0 181.000000 0 \n", + "14232 FAUX [10000;15000[ 0 0.0 364.669399 0 \n", + "14233 VRAI [10000;15000[ 0 0.0 182.000000 0 \n", + "14234 FAUX [25000;35000[ 0 0.0 9.000000 0 \n", + "14235 FAUX [10000;15000[ 0 0.0 52.000000 0 \n", + "\n", + "[14236 rows x 17 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#Calculez la variable \"sinistré\" qui est vraie si la personne a eu un ou plusieurs sinistres ou faux le cas échéant \n" + "# Calculez la variable \"sinistré\" qui est 1 si la personne a eu un ou plusieurs sinistres, 0 sinon\n", + "data_retraitee[\"sinistré\"] = data_retraitee[\"NB\"] > 0\n", + "data_retraitee[\"sinistré\"] = data_retraitee[\"sinistré\"].astype(int)\n", + "data_retraitee" ] }, { @@ -193,11 +1608,855 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "47cf4b69", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "bingroup": "x", + "hovertemplate": "sinistré=%{x}
count=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "", + "orientation": "v", + "showlegend": false, + "type": "histogram", + "x": { + "bdata": "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ", 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+ "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Distribution de la variable 'sinistré'" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "sinistré" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Observation de la distribution\n", + "fig = px.histogram(data_retraitee, x=\"sinistré\", title=\"Distribution de la variable 'sinistré'\")\n", + "fig.show()" + ] }, { "cell_type": "markdown", @@ -209,11 +2468,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "a0bc6278", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(14236, 16)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_set = data_retraitee.drop(\"sinistré\", axis=1)\n", + "data_set.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "73d31ea4", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Séparation en variables qualitatives ou catégorielles\n", + "variables_na = []\n", + "variables_numeriques = []\n", + "variables_01 = []\n", + "variables_categorielles = []\n", + "for colu in data_set.columns:\n", + " if True in data_set[colu].isna().unique():\n", + " variables_na.append(data_set[colu])\n", + " else:\n", + " if str(data_set[colu].dtypes) in [\"int32\", \"int64\", \"float64\"]:\n", + " if len(data_set[colu].unique()) == 2:\n", + " variables_categorielles.append(data_set[colu])\n", + " else:\n", + " variables_numeriques.append(data_set[colu])\n", + " else:\n", + " if len(data_set[colu].unique()) == 2:\n", + " variables_categorielles.append(data_set[colu])\n", + " else:\n", + " variables_categorielles.append(data_set[colu])\n" + ] }, { "cell_type": "markdown", @@ -229,7 +2530,423 @@ "id": "30df8bd5", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "vars_categorielles = pd.DataFrame(variables_categorielles).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "be7a7d00", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "object", + "type": "string" + }, + { + "name": "CONTRAT_ANCIENNETE", + "rawType": "float64", + "type": "float" + }, + { + "name": "FREQUENCE_PAIEMENT_COTISATION", + "rawType": "float64", + "type": "float" + }, + { + "name": "GROUPE_KM", + "rawType": "float64", + "type": "float" + }, + { + "name": "ZONE_RISQUE", + "rawType": "float64", + "type": "float" + }, + { + "name": "GENRE", + "rawType": "float64", + "type": "float" + }, + { + "name": "DEUXIEME_CONDUCTEUR", + "rawType": "float64", + "type": "float" + }, + { + "name": "ENERGIE", + "rawType": "float64", + "type": "float" + }, + { + "name": "EQUIPEMENT_SECURITE", + "rawType": "float64", + "type": "float" + }, + { + "name": "VALEUR_DU_BIEN", + "rawType": "float64", + "type": "float" + } + ], + "ref": "cdaf33f1-78b7-4df1-9a7c-93b778e94756", + "rows": [ + [ + "CONTRAT_ANCIENNETE", + "1.0", + "0.0", + "0.01", + "0.02", + "0.0", + "0.0", + "0.0", + "0.01", + "0.0" + ], + [ + "FREQUENCE_PAIEMENT_COTISATION", + "0.0", + "1.0", + "0.0", + "0.0", + "0.01", + "0.0", + "0.0", + "0.01", + "0.02" + ], + [ + "GROUPE_KM", + "0.01", + "0.0", + "1.0", + "0.01", + "0.01", + "0.0", + "0.04", + "0.01", + "0.02" + ], + [ + "ZONE_RISQUE", + "0.02", + "0.0", + "0.01", + "1.0", + "0.0", + "0.0", + "0.01", + "0.03", + "0.0" + ], + [ + "GENRE", + "0.0", + "0.01", + "0.01", + "0.0", + "1.0", + "0.0", + "0.02", + "0.01", + "0.07" + ], + [ + "DEUXIEME_CONDUCTEUR", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0", + "0.0" + ], + [ + "ENERGIE", + "0.0", + "0.0", + "0.04", + "0.01", + "0.02", + "0.0", + "1.0", + "0.02", + "0.08" + ], + [ + "EQUIPEMENT_SECURITE", + "0.01", + "0.01", + "0.01", + "0.03", + "0.01", + "0.0", + "0.02", + "1.0", + "0.07" + ], + [ + "VALEUR_DU_BIEN", + "0.0", + "0.02", + "0.02", + "0.0", + "0.07", + "0.0", + "0.08", + "0.07", + "1.0" + ] + ], + "shape": { + "columns": 9, + "rows": 9 + } + }, + "text/html": [ + "
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"execution_count": null, + "execution_count": 32, "id": "d1fa12fc", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "vars_numeriques = pd.DataFrame(variables_numeriques).transpose()" + ] }, { "cell_type": "markdown", @@ -255,6 +2974,290 @@ "**Question :** quels sont vos commentaires ?" ] }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c70946b4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "object", + "type": "string" + }, + { + "name": "ANNEE_CTR", + "rawType": "float64", + "type": "float" + }, + { + "name": "AGE_ASSURE_PRINCIPAL", + "rawType": "float64", + "type": "float" + }, + { + "name": "ANCIENNETE_PERMIS", + "rawType": "float64", + "type": "float" + }, + { + "name": "ANNEE_CONSTRUCTION", + "rawType": "float64", + "type": "float" + }, + { + "name": "NB", + "rawType": "float64", + "type": "float" + }, + { + "name": "CHARGE", + "rawType": "float64", + "type": "float" + }, + { + "name": "EXPO", + "rawType": "float64", + "type": "float" + } + ], + "ref": "5ae1d96a-bfa4-47eb-bc85-b1de1b32bf1e", + "rows": [ + [ + "ANNEE_CTR", + "1.0", + "0.048023234802924315", + "0.043983174120495815", + "0.3615499864845018", + "-0.05775190894636334", + "-0.028901069139582642", + "-0.04770515515535773" + ], + [ + "AGE_ASSURE_PRINCIPAL", + "0.048023234802924315", + "1.0", + "0.4987430846753776", + "-0.0591835157827114", + "-0.012425345899111317", + "-0.020907992524227155", + "0.06096340138959582" + ], + [ + "ANCIENNETE_PERMIS", + "0.043983174120495815", + "0.4987430846753776", + "1.0", + "-0.0298138263902136", + "-0.008703999957333864", + "-0.011347002839350888", + "0.0324606537737922" + ], + [ + "ANNEE_CONSTRUCTION", + "0.3615499864845018", + "-0.0591835157827114", + "-0.0298138263902136", + "1.0", + "-0.01437673371578632", + "-0.0012301736578250726", + "-0.07395284013392618" + ], + [ + "NB", + "-0.05775190894636334", + "-0.012425345899111317", + "-0.008703999957333864", + "-0.01437673371578632", + "1.0", + "0.5071071150738479", + "0.0507022890091039" + ], + [ + "CHARGE", + "-0.028901069139582642", + "-0.020907992524227155", + "-0.011347002839350888", + "-0.0012301736578250726", + "0.5071071150738479", + "1.0", + "-0.021418687122216843" + ], + [ + "EXPO", + "-0.04770515515535773", + "0.06096340138959582", + "0.0324606537737922", + "-0.07395284013392618", + "0.0507022890091039", + "-0.021418687122216843", + "1.0" + ] + ], + "shape": { + "columns": 7, + "rows": 7 + } + }, + "text/html": [ + "
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ANNEE_CTRAGE_ASSURE_PRINCIPALANCIENNETE_PERMISANNEE_CONSTRUCTIONNBCHARGEEXPO
ANNEE_CTR1.0000000.0480230.0439830.361550-0.057752-0.028901-0.047705
AGE_ASSURE_PRINCIPAL0.0480231.0000000.498743-0.059184-0.012425-0.0209080.060963
ANCIENNETE_PERMIS0.0439830.4987431.000000-0.029814-0.008704-0.0113470.032461
ANNEE_CONSTRUCTION0.361550-0.059184-0.0298141.000000-0.014377-0.001230-0.073953
NB-0.057752-0.012425-0.008704-0.0143771.0000000.5071070.050702
CHARGE-0.028901-0.020908-0.011347-0.0012300.5071071.000000-0.021419
EXPO-0.0477050.0609630.032461-0.0739530.050702-0.0214191.000000
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" + ], + "text/plain": [ + " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS \\\n", + "ANNEE_CTR 1.000000 0.048023 0.043983 \n", + "AGE_ASSURE_PRINCIPAL 0.048023 1.000000 0.498743 \n", + "ANCIENNETE_PERMIS 0.043983 0.498743 1.000000 \n", + "ANNEE_CONSTRUCTION 0.361550 -0.059184 -0.029814 \n", + "NB -0.057752 -0.012425 -0.008704 \n", + "CHARGE -0.028901 -0.020908 -0.011347 \n", + "EXPO -0.047705 0.060963 0.032461 \n", + "\n", + " ANNEE_CONSTRUCTION NB CHARGE EXPO \n", + "ANNEE_CTR 0.361550 -0.057752 -0.028901 -0.047705 \n", + "AGE_ASSURE_PRINCIPAL -0.059184 -0.012425 -0.020908 0.060963 \n", + "ANCIENNETE_PERMIS -0.029814 -0.008704 -0.011347 0.032461 \n", + "ANNEE_CONSTRUCTION 1.000000 -0.014377 -0.001230 -0.073953 \n", + "NB -0.014377 1.000000 0.507107 0.050702 \n", + "CHARGE -0.001230 0.507107 1.000000 -0.021419 \n", + "EXPO -0.073953 0.050702 -0.021419 1.000000 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Corrélation de Pearson\n", + "correlations_num = vars_numeriques.corr(method=\"pearson\")\n", + "correlations_num" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "4c29f1f0", + "metadata": {}, + "outputs": [], + "source": [ + "# On repère les variables trop corrélées\n", + "nb_variables = correlations_num.shape[0]\n", + "for i in range(nb_variables):\n", + " for j in range(i + 1, nb_variables):\n", + " if abs(correlations_num.iloc[i, j]) > 0.7:\n", + " print(\n", + " correlations_num.index.to_numpy()[i]\n", + " + \" et \"\n", + " + correlations_num.columns[j]\n", + " + \" sont trop dépendantes, corr = \"\n", + " + str(correlations_num.iloc[i, j])\n", + " )" + ] + }, { "cell_type": "markdown", "id": "212209ec", @@ -284,11 +3287,647 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "b8530717", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { 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" + ], + "text/plain": [ + " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS ANNEE_CONSTRUCTION \\\n", + "0 0.139356 0.658287 0.563588 0.174011 \n", + "1 0.139356 3.151628 0.987434 0.744207 \n", + "2 1.347192 -0.735051 -1.078814 0.459109 \n", + "3 1.347192 0.071618 0.404646 0.744207 \n", + "4 -0.464562 0.071618 -0.284103 -1.821676 \n", + "\n", + " NB CHARGE EXPO \n", + "0 -0.242029 -0.181254 -0.289146 \n", + "1 -0.242029 -0.181254 -0.427093 \n", + "2 -0.242029 -0.181254 0.215021 \n", + "3 -0.242029 -0.181254 0.251907 \n", + "4 -0.242029 -0.181254 0.814427 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalisation des varibales numériques\n", + "preproc_scale = preproc.StandardScaler(with_mean=True, with_std=True)\n", + "preproc_scale.fit(vars_numeriques)\n", + "\n", + "vars_numeriques_scaled = preproc_scale.transform(vars_numeriques)\n", + "vars_numeriques_scaled = pd.DataFrame(\n", + " vars_numeriques_scaled, columns=vars_numeriques.columns\n", + ")\n", + "vars_numeriques_scaled.head()" + ] }, { "cell_type": "markdown", @@ -347,11 +4203,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "d9342ad6", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X_global = vars_numeriques_scaled.merge(\n", + " variables_categorielles_ohe, left_index=True, right_index=True\n", + ")\n", + "\n", + "# Réorganisation des données\n", + "X = X_global.to_numpy()\n", + "Y = data_retraitee[\"sinistré\"]\n", + "\n", + "# Sampling en 80% train et 20% test\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, Y, test_size=0.2, random_state=42\n", + ")" + ] }, { "cell_type": "markdown", @@ -367,7 +4236,135 @@ "id": "cb60fe19", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Définir la grille d'hyperparamètres à rechercher\n", + "param_grid = {\n", + " \"n_estimators\": [60, 65, 70, 75],\n", + " \"max_depth\": [None, 1, 2, 3],\n", + " \"min_samples_split\": [5, 8, 10, 11, 13, 14, 15],\n", + "}\n", + "# Nombre de folds pour la validation croisée\n", + "num_folds = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "b976720e", + "metadata": {}, + "outputs": [ + { + "ename": "InvalidParameterError", + "evalue": "The 'scoring' parameter of GridSearchCV must be a str among {'average_precision', 'adjusted_rand_score', 'roc_auc', 'top_k_accuracy', 'recall', 'neg_negative_likelihood_ratio', 'neg_mean_squared_error', 'positive_likelihood_ratio', 'precision', 'neg_mean_squared_log_error', 'precision_micro', 'neg_mean_poisson_deviance', 'completeness_score', 'accuracy', 'adjusted_mutual_info_score', 'precision_macro', 'neg_max_error', 'mutual_info_score', 'jaccard_samples', 'recall_samples', 'neg_mean_absolute_percentage_error', 'fowlkes_mallows_score', 'neg_brier_score', 'f1_samples', 'jaccard_weighted', 'recall_micro', 'd2_absolute_error_score', 'homogeneity_score', 'matthews_corrcoef', 'f1_micro', 'f1_macro', 'neg_root_mean_squared_error', 'precision_samples', 'neg_root_mean_squared_log_error', 'neg_mean_gamma_deviance', 'jaccard', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'roc_auc_ovr', 'jaccard_micro', 'jaccard_macro', 'roc_auc_ovo', 'neg_log_loss', 'normalized_mutual_info_score', 'balanced_accuracy', 'f1_weighted', 'r2', 'recall_macro', 'rand_score', 'v_measure_score', 'explained_variance', 'roc_auc_ovo_weighted', 'precision_weighted', 'roc_auc_ovr_weighted', 'f1', 'recall_weighted'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got '' instead.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mInvalidParameterError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m 5\u001b[39m grid_search = GridSearchCV(\n\u001b[32m 6\u001b[39m estimator = rf,\n\u001b[32m 7\u001b[39m param_grid = param_grid,\n\u001b[32m (...)\u001b[39m\u001b[32m 12\u001b[39m n_jobs = -\u001b[32m1\u001b[39m, \u001b[38;5;66;03m# Utiliser tous les cœurs du processeur\u001b[39;00m\n\u001b[32m 13\u001b[39m )\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# Exécution de la recherche sur grille\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# Afficher les meilleurs hyperparamètres\u001b[39;00m\n\u001b[32m 19\u001b[39m best_params = grid_search.best_params_\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/base.py:1382\u001b[39m, in \u001b[36m_fit_context..decorator..wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m 1377\u001b[39m partial_fit_and_fitted = (\n\u001b[32m 1378\u001b[39m fit_method.\u001b[34m__name__\u001b[39m == \u001b[33m\"\u001b[39m\u001b[33mpartial_fit\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[32m 1379\u001b[39m )\n\u001b[32m 1381\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[32m-> \u001b[39m\u001b[32m1382\u001b[39m \u001b[43mestimator\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1384\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m 1385\u001b[39m skip_parameter_validation=(\n\u001b[32m 1386\u001b[39m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 1387\u001b[39m )\n\u001b[32m 1388\u001b[39m ):\n\u001b[32m 1389\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, *args, **kwargs)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/base.py:436\u001b[39m, in \u001b[36mBaseEstimator._validate_params\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 428\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 429\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[32m 430\u001b[39m \n\u001b[32m 431\u001b[39m \u001b[33;03m The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 434\u001b[39m \u001b[33;03m accepted constraints.\u001b[39;00m\n\u001b[32m 435\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m436\u001b[39m \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 437\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parameter_constraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[34;43m__class__\u001b[39;49m\u001b[43m.\u001b[49m\u001b[34;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/utils/_param_validation.py:98\u001b[39m, in \u001b[36mvalidate_parameter_constraints\u001b[39m\u001b[34m(parameter_constraints, params, caller_name)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 93\u001b[39m constraints_str = (\n\u001b[32m 94\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m.join([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:-\u001b[32m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m or\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[-\u001b[32m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 96\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m98\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[32m 99\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m must be\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 100\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m instead.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 101\u001b[39m )\n", + "\u001b[31mInvalidParameterError\u001b[39m: The 'scoring' parameter of GridSearchCV must be a str among {'average_precision', 'adjusted_rand_score', 'roc_auc', 'top_k_accuracy', 'recall', 'neg_negative_likelihood_ratio', 'neg_mean_squared_error', 'positive_likelihood_ratio', 'precision', 'neg_mean_squared_log_error', 'precision_micro', 'neg_mean_poisson_deviance', 'completeness_score', 'accuracy', 'adjusted_mutual_info_score', 'precision_macro', 'neg_max_error', 'mutual_info_score', 'jaccard_samples', 'recall_samples', 'neg_mean_absolute_percentage_error', 'fowlkes_mallows_score', 'neg_brier_score', 'f1_samples', 'jaccard_weighted', 'recall_micro', 'd2_absolute_error_score', 'homogeneity_score', 'matthews_corrcoef', 'f1_micro', 'f1_macro', 'neg_root_mean_squared_error', 'precision_samples', 'neg_root_mean_squared_log_error', 'neg_mean_gamma_deviance', 'jaccard', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'roc_auc_ovr', 'jaccard_micro', 'jaccard_macro', 'roc_auc_ovo', 'neg_log_loss', 'normalized_mutual_info_score', 'balanced_accuracy', 'f1_weighted', 'r2', 'recall_macro', 'rand_score', 'v_measure_score', 'explained_variance', 'roc_auc_ovo_weighted', 'precision_weighted', 'roc_auc_ovr_weighted', 'f1', 'recall_weighted'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got '' instead." + ] + } + ], + "source": [ + "# Initialisation du modèle GradientBoostingClassifier\n", + "rf = GradientBoostingClassifier(random_state=42)\n", + "\n", + "# Création de l'objet GridSearchCV pour la recherche sur grille avec validation croisée\n", + "grid_search = GridSearchCV(\n", + " estimator = rf,\n", + " param_grid = param_grid,\n", + " cv = StratifiedKFold(\n", + " n_splits = num_folds, shuffle = True, random_state = 42\n", + " ), # Validation croisée avec 5 folds\n", + " scoring = \"\", # Métrique d'évaluation (moins c'est mieux)\n", + " n_jobs = -1, # Utiliser tous les cœurs du processeur\n", + ")\n", + "\n", + "# Exécution de la recherche sur grille\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "# Afficher les meilleurs hyperparamètres\n", + "best_params = grid_search.best_params_\n", + "print(\"Meilleurs hyperparamètres : \", best_params)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "0a35a4bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialiser le modèle final avec les meilleurs hyperparamètres\n", + "best_rf = GradientBoostingClassifier(random_state=42, **best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a7f59ea7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE pour le fold 1: -0.0\n", + "RMSE pour le fold 2: -0.0\n", + "RMSE pour le fold 3: -0.0\n", + "RMSE pour le fold 4: -0.0\n", + "RMSE pour le fold 5: -0.0\n", + "\n", + "\n", + "MSE pour le fold 1: -0.0\n", + "MSE pour le fold 2: -0.0\n", + "MSE pour le fold 3: -0.0\n", + "MSE pour le fold 4: -0.0\n", + "MSE pour le fold 5: -0.0\n", + "\n", + "\n", + "MAE pour le fold 1: -0.0\n", + "MAE pour le fold 2: -0.0\n", + "MAE pour le fold 3: -0.0\n", + "MAE pour le fold 4: -0.0\n", + "MAE pour le fold 5: -0.0\n" + ] + } + ], + "source": [ + "# Cross validation\n", + "# RMSE de chaque fold\n", + "rmse_scores = cross_val_score(\n", + " best_rf, X_train, y_train, cv=num_folds, scoring=\"neg_root_mean_squared_error\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "for i, score in enumerate(rmse_scores):\n", + " print(f\"RMSE pour le fold {i + 1}: {score}\")\n", + "\n", + "# MSE de chaque fold\n", + "mse_scores = cross_val_score(\n", + " best_rf, X_train, y_train, cv=num_folds, scoring=\"neg_mean_squared_error\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "print(\"\\n\")\n", + "for i, score in enumerate(mse_scores):\n", + " print(f\"MSE pour le fold {i + 1}: {score}\")\n", + "\n", + "# MAE de chaque fold\n", + "mae_scores = cross_val_score(\n", + " best_rf, X_train, y_train, cv=num_folds, scoring=\"neg_mean_absolute_error\"\n", + ")\n", + "\n", + "# Afficher les scores pour chaque fold\n", + "print(\"\\n\")\n", + "for i, score in enumerate(mae_scores):\n", + " print(f\"MAE pour le fold {i + 1}: {score}\")" + ] }, { "cell_type": "markdown",