{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-10-20T03:46:20.254485Z","iopub.execute_input":"2022-10-20T03:46:20.255165Z","iopub.status.idle":"2022-10-20T03:47:18.510204Z","shell.execute_reply.started":"2022-10-20T03:46:20.255112Z","shell.execute_reply":"2022-10-20T03:47:18.508826Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> CONTEXTO****\n\nEl objetivo de esta competencia es comprender la relación entre el código y los comentarios en los cuadernos de Python. Usted tiene el desafío de reconstruir el orden de las celdas de descuento en un cuaderno determinado en función del orden de las celdas de código, lo que demuestra la comprensión de qué lenguaje natural hace referencia a qué código.\n\nContexto\nLos equipos de investigación de Google y Alphabet están explorando nuevas formas en que el aprendizaje automático puede ayudar a los desarrolladores de software y quieren reunir a más miembros de la comunidad de desarrolladores para ayudar a explorar esta área también. Los cuadernos de Python brindan una oportunidad de aprendizaje única porque, a diferencia de muchos códigos fuente estándar, los cuadernos suelen seguir un formato narrativo, con celdas de comentarios implementadas en Markdown que explican las intenciones de un programador para las celdas de código correspondientes. Una comprensión de las relaciones entre el código y las rebajas podría dar lugar a nuevas mejoras en muchos aspectos del desarrollo asistido por IA, como la construcción de mejores canales de preprocesamiento y filtrado de datos para el entrenamiento de modelos, o evaluaciones automáticas de la legibilidad de un portátil.\n\nHemos reunido un conjunto de datos de aproximadamente 160 000 cuadernos Python públicos de Kaggle y nos hemos asociado con X, la fábrica de lanzamientos a la luna para diseñar una competencia que desafía a los participantes a usar este conjunto de datos de cuadernos publicados para crear técnicas creativas destinadas a comprender mejor la relación entre las celdas de comentarios. y codificar celdas.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\ndf = pd.read_csv(\"../input/AI4Code/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.513488Z","iopub.execute_input":"2022-10-20T03:47:18.513965Z","iopub.status.idle":"2022-10-20T03:47:18.523939Z","shell.execute_reply.started":"2022-10-20T03:47:18.513926Z","shell.execute_reply":"2022-10-20T03:47:18.522475Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.525573Z","iopub.execute_input":"2022-10-20T03:47:18.526017Z","iopub.status.idle":"2022-10-20T03:47:18.540979Z","shell.execute_reply.started":"2022-10-20T03:47:18.525980Z","shell.execute_reply":"2022-10-20T03:47:18.539506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.dropna()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.542961Z","iopub.execute_input":"2022-10-20T03:47:18.544005Z","iopub.status.idle":"2022-10-20T03:47:18.560182Z","shell.execute_reply.started":"2022-10-20T03:47:18.543962Z","shell.execute_reply":"2022-10-20T03:47:18.558855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corre_p = df.corr()\ncorr_k = df.corr(method = \"kendall\")\ncorr_s = df.corr(method = \"spearman\")\ncorr_s\n","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.563256Z","iopub.execute_input":"2022-10-20T03:47:18.564943Z","iopub.status.idle":"2022-10-20T03:47:18.577945Z","shell.execute_reply.started":"2022-10-20T03:47:18.564891Z","shell.execute_reply":"2022-10-20T03:47:18.576533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr_k","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.579885Z","iopub.execute_input":"2022-10-20T03:47:18.580830Z","iopub.status.idle":"2022-10-20T03:47:18.598032Z","shell.execute_reply.started":"2022-10-20T03:47:18.580782Z","shell.execute_reply":"2022-10-20T03:47:18.596881Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_0 = pd.read_csv(\"../input/AI4Code/train_orders.csv\")\ndf_0","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:18.599592Z","iopub.execute_input":"2022-10-20T03:47:18.600000Z","iopub.status.idle":"2022-10-20T03:47:19.464921Z","shell.execute_reply.started":"2022-10-20T03:47:18.599964Z","shell.execute_reply":"2022-10-20T03:47:19.463198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1 = pd.read_csv(\"../input/AI4Code/train_ancestors.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.466159Z","iopub.execute_input":"2022-10-20T03:47:19.466648Z","iopub.status.idle":"2022-10-20T03:47:19.689640Z","shell.execute_reply.started":"2022-10-20T03:47:19.466612Z","shell.execute_reply":"2022-10-20T03:47:19.688089Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.691588Z","iopub.execute_input":"2022-10-20T03:47:19.692155Z","iopub.status.idle":"2022-10-20T03:47:19.709516Z","shell.execute_reply.started":"2022-10-20T03:47:19.692105Z","shell.execute_reply":"2022-10-20T03:47:19.708231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1.notna","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.712051Z","iopub.execute_input":"2022-10-20T03:47:19.712708Z","iopub.status.idle":"2022-10-20T03:47:19.729565Z","shell.execute_reply.started":"2022-10-20T03:47:19.712649Z","shell.execute_reply":"2022-10-20T03:47:19.728661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df1.dropna()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.730906Z","iopub.execute_input":"2022-10-20T03:47:19.731546Z","iopub.status.idle":"2022-10-20T03:47:19.779198Z","shell.execute_reply.started":"2022-10-20T03:47:19.731508Z","shell.execute_reply":"2022-10-20T03:47:19.778326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_1 = df.to_numpy()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.780702Z","iopub.execute_input":"2022-10-20T03:47:19.781521Z","iopub.status.idle":"2022-10-20T03:47:19.786213Z","shell.execute_reply.started":"2022-10-20T03:47:19.781478Z","shell.execute_reply":"2022-10-20T03:47:19.785157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_1","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.787626Z","iopub.execute_input":"2022-10-20T03:47:19.788246Z","iopub.status.idle":"2022-10-20T03:47:19.800896Z","shell.execute_reply.started":"2022-10-20T03:47:19.788188Z","shell.execute_reply":"2022-10-20T03:47:19.799363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndf_02 = df_1\n                                                           ","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.807925Z","iopub.execute_input":"2022-10-20T03:47:19.809046Z","iopub.status.idle":"2022-10-20T03:47:19.814659Z","shell.execute_reply.started":"2022-10-20T03:47:19.808983Z","shell.execute_reply":"2022-10-20T03:47:19.813438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"coeficiente de correlación de kendall\nEl coeficiente de correlación de Kendall, también conocido como coeficiente de correlación de rango de Kendall, también es un coeficiente de correlación de rango, pero el objeto que calcula es una variable categórica.\nLas variables categóricas 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"},"9b4030c8-39cd-4c48-a0a2-31f34ff7fb5d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"df_0","metadata":{}},{"cell_type":"code","source":"df_0","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.816231Z","iopub.execute_input":"2022-10-20T03:47:19.816631Z","iopub.status.idle":"2022-10-20T03:47:19.840170Z","shell.execute_reply.started":"2022-10-20T03:47:19.816597Z","shell.execute_reply":"2022-10-20T03:47:19.839179Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_0.to_numpy","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.841649Z","iopub.execute_input":"2022-10-20T03:47:19.842041Z","iopub.status.idle":"2022-10-20T03:47:19.854921Z","shell.execute_reply.started":"2022-10-20T03:47:19.842005Z","shell.execute_reply":"2022-10-20T03:47:19.853694Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"en estas celdas quiero mostrar las funciones de correlacion que pueden ayudar, a mostar relaciones entre codigo y celdas de markdown, pero al tratarse de muchos datos es que mas adelante mostare las mimas funciones abarcando todos los datos.....Manos a la obra!!!!!! ","metadata":{}},{"cell_type":"code","source":"df_01 = df_0.to_numpy()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.856475Z","iopub.execute_input":"2022-10-20T03:47:19.857738Z","iopub.status.idle":"2022-10-20T03:47:19.863970Z","shell.execute_reply.started":"2022-10-20T03:47:19.857694Z","shell.execute_reply":"2022-10-20T03:47:19.862640Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n \ndf = pd.DataFrame(({'A':[5 , 90 , 93,91,15,27,7,88],'B':[7,88,93,13,27,5,64,3]}))\n \nprint(df.corr())\n \nprint(df.corr('spearman'))\n \nprint(df.corr('kendall'))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.865678Z","iopub.execute_input":"2022-10-20T03:47:19.866279Z","iopub.status.idle":"2022-10-20T03:47:19.889686Z","shell.execute_reply.started":"2022-10-20T03:47:19.866241Z","shell.execute_reply":"2022-10-20T03:47:19.888164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nx = [\"00001756c60be8\" ,\"1862f0a6\", \"448eb224\" ,\"2a9e43d6\", \"7e2f170a\", \"038b763d\"]","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.891566Z","iopub.execute_input":"2022-10-20T03:47:19.891934Z","iopub.status.idle":"2022-10-20T03:47:19.900217Z","shell.execute_reply.started":"2022-10-20T03:47:19.891902Z","shell.execute_reply":"2022-10-20T03:47:19.897302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"en la variable X  y  Y se adjuntan dos   filas del dataframe para probar los datos y su relacion.  ","metadata":{}},{"cell_type":"code","source":"y = [\"0009d135ece78d\",\"ddfd239c\", \"c6cd22db\",\"1372ae9b\", \"90ed07ab\",\"7f388a41\"]\n","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.903004Z","iopub.execute_input":"2022-10-20T03:47:19.903551Z","iopub.status.idle":"2022-10-20T03:47:19.915521Z","shell.execute_reply.started":"2022-10-20T03:47:19.903498Z","shell.execute_reply":"2022-10-20T03:47:19.914041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create a function that takes in x's and y's\ndef spearmans_rank_correlation(xs, ys):\n    \n    # Calculate the rank of x's\n    xranks = pd.Series(xs).rank()\n    \n    # Caclulate the ranking of the y's\n    yranks = pd.Series(ys).rank()\n    \n    # Calculate Pearson's correlation coefficient on the ranked versions of the data\n    return scipy.stats.pearsonr(xranks, yranks)\n# Run the function\nspearmans_rank_correlation(x, y)[0]\n","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.917852Z","iopub.execute_input":"2022-10-20T03:47:19.918277Z","iopub.status.idle":"2022-10-20T03:47:19.935703Z","shell.execute_reply.started":"2022-10-20T03:47:19.918242Z","shell.execute_reply":"2022-10-20T03:47:19.934222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"en esta funcion llamada spearman se visualiza una relacion menor al 50% ahora veremos como las funciones KENDALL entra en accion tomando los dataframe ","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nprint(df.corr('kendall'))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.937657Z","iopub.execute_input":"2022-10-20T03:47:19.938182Z","iopub.status.idle":"2022-10-20T03:47:19.951616Z","shell.execute_reply.started":"2022-10-20T03:47:19.938131Z","shell.execute_reply":"2022-10-20T03:47:19.950304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(df.corr('spearman'))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.953607Z","iopub.execute_input":"2022-10-20T03:47:19.954014Z","iopub.status.idle":"2022-10-20T03:47:19.965288Z","shell.execute_reply.started":"2022-10-20T03:47:19.953980Z","shell.execute_reply":"2022-10-20T03:47:19.964166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":">CONVERSION DE LOS DATOS A BINARIOS PARA VISUALIZARLOS Y GRAFICAR SU RELACION **********","metadata":{}},{"cell_type":"code","source":"string_1 = \"00001756c60be81862f0a6448eb2242a9e43d67e2f170a038b763d\"\nbinary_converted = ' '.join(format(ord(c), 'b') for c in string_1)\nprint(\"The Binary Representation is:\", binary_converted)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.966369Z","iopub.execute_input":"2022-10-20T03:47:19.966757Z","iopub.status.idle":"2022-10-20T03:47:19.975210Z","shell.execute_reply.started":"2022-10-20T03:47:19.966724Z","shell.execute_reply":"2022-10-20T03:47:19.973960Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"string_2 = \"0009d135ece78dddfd239cc6cd22db1372ae9b90ed07ab7f388a41\"\nbinary_converted = ' '.join(format(ord(c), 'b') for c in string_2)\nprint(\"The Binary Representation is:\", binary_converted)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.976815Z","iopub.execute_input":"2022-10-20T03:47:19.977168Z","iopub.status.idle":"2022-10-20T03:47:19.988928Z","shell.execute_reply.started":"2022-10-20T03:47:19.977134Z","shell.execute_reply":"2022-10-20T03:47:19.987929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"matricula = np.array([110000, 110000, 110000, 110000, 110001, 110111, 110101, 110110, 1100011, 110110, 110000, 1100010, 1100101, 111000, 110001, 111000, 110110, 110010, 1100110, 110000, 1100001, 110110, 110100, 110100, 111000, 1100101, 1100010, 110010, 110010, 110100 ,110010, 1100001, 111001, 1100101, 110100, 110011, 1100100, 110110, 110111, 1100101, 110010, 1100110, 110001, 110111, 110000, 1100001, 110000, 110011, 111000, 1100010, 110111, 110110, 110011, 1100100])\n\nmatematicas = np.array([110000, 110000, 110000, 111001, 1100100, 110001, 110011, 110101, 1100101, 1100011, 1100101, 110111, 111000, 1100100, 1100100, 1100100, 1100110, 1100100, 110010, 110011, 111001, 1100011, 1100011, 110110, 1100011, 1100100, 110010, 110010, 1100100, 1100010, 110001, 110011, 110111, 110010, 1100001, 1100101, 111001, 1100010, 111001, 110000, 1100101, 1100100, 110000, 110111, 1100001, 1100010, 110111, 1100110, 110011, 111000, 111000, 1100001, 110100, 110001])\n\ndatos = pd.DataFrame({'ancestor': matricula, 'order': matematicas})\ndatos.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:19.990271Z","iopub.execute_input":"2022-10-20T03:47:19.990621Z","iopub.status.idle":"2022-10-20T03:47:20.011433Z","shell.execute_reply.started":"2022-10-20T03:47:19.990588Z","shell.execute_reply":"2022-10-20T03:47:20.010184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datos.value_counts().sort_index()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.013384Z","iopub.execute_input":"2022-10-20T03:47:20.013742Z","iopub.status.idle":"2022-10-20T03:47:20.032398Z","shell.execute_reply.started":"2022-10-20T03:47:20.013710Z","shell.execute_reply":"2022-10-20T03:47:20.031040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.DataFrame(datos, columns=['ancestor'])\n\nprint(df)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.034437Z","iopub.execute_input":"2022-10-20T03:47:20.034822Z","iopub.status.idle":"2022-10-20T03:47:20.044750Z","shell.execute_reply.started":"2022-10-20T03:47:20.034762Z","shell.execute_reply":"2022-10-20T03:47:20.043476Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.notna()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.046628Z","iopub.execute_input":"2022-10-20T03:47:20.047028Z","iopub.status.idle":"2022-10-20T03:47:20.066432Z","shell.execute_reply.started":"2022-10-20T03:47:20.046992Z","shell.execute_reply":"2022-10-20T03:47:20.065385Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nprint(datos.corr('kendall'))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.068491Z","iopub.execute_input":"2022-10-20T03:47:20.069106Z","iopub.status.idle":"2022-10-20T03:47:20.081877Z","shell.execute_reply.started":"2022-10-20T03:47:20.069053Z","shell.execute_reply":"2022-10-20T03:47:20.080501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"relacion exponencial entre un segmento de los datos, exite una relacion positiva entre las celdas de markdown y codigo para lo cual lo desarrolladore solo necesitan especificar mas las celdas de markdown en realcion con el codigo","metadata":{}},{"cell_type":"code","source":"import pandas as pd \n\n\ndf.hist(figsize=(15,13))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.083471Z","iopub.execute_input":"2022-10-20T03:47:20.083869Z","iopub.status.idle":"2022-10-20T03:47:20.453738Z","shell.execute_reply.started":"2022-10-20T03:47:20.083834Z","shell.execute_reply":"2022-10-20T03:47:20.452524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\ndf_matrix = pd.read_csv(\"../input/AI4Code/train_ancestors.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.455362Z","iopub.execute_input":"2022-10-20T03:47:20.455807Z","iopub.status.idle":"2022-10-20T03:47:20.653748Z","shell.execute_reply.started":"2022-10-20T03:47:20.455747Z","shell.execute_reply":"2022-10-20T03:47:20.652840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_matrix","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.655021Z","iopub.execute_input":"2022-10-20T03:47:20.655898Z","iopub.status.idle":"2022-10-20T03:47:20.670583Z","shell.execute_reply.started":"2022-10-20T03:47:20.655860Z","shell.execute_reply":"2022-10-20T03:47:20.669248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.hist(figsize=(15,13))","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:20.672813Z","iopub.execute_input":"2022-10-20T03:47:20.675253Z","iopub.status.idle":"2022-10-20T03:47:21.081010Z","shell.execute_reply.started":"2022-10-20T03:47:20.675193Z","shell.execute_reply":"2022-10-20T03:47:21.079581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.notna()\ndf_matrix.info()\n","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.082909Z","iopub.execute_input":"2022-10-20T03:47:21.083428Z","iopub.status.idle":"2022-10-20T03:47:21.121427Z","shell.execute_reply.started":"2022-10-20T03:47:21.083375Z","shell.execute_reply":"2022-10-20T03:47:21.119739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"se realizan pruebas de similitud con solo un segmento de datos y luego con los datos de entrenamiento y en ambos grafico existe una similitud.ademas el segmento de datos mas corto se convierten a binarios y asi aplicar la funcion KENDALL Y DE IGUAL MANERA EXISTE SIMILITUD.","metadata":{}},{"cell_type":"code","source":"matricula = np.array([110000, 110000, 110000, 110000, 110001, 110111, 110101, 110110, 1100011, 110110, 110000, 1100010, 1100101, 111000, 110001, 111000, 110110, 110010, 1100110, 110000, 1100001, 110110, 110100, 110100, 111000, 1100101, 1100010, 110010, 110010, 110100 ,110010, 1100001, 111001, 1100101, 110100, 110011, 1100100, 110110, 110111, 1100101, 110010, 1100110, 110001, 110111, 110000, 1100001, 110000, 110011, 111000, 1100010, 110111, 110110, 110011, 1100100])\n\nmatematicas = np.array([110000, 110000, 110000, 111001, 1100100, 110001, 110011, 110101, 1100101, 1100011, 1100101, 110111, 111000, 1100100, 1100100, 1100100, 1100110, 1100100, 110010, 110011, 111001, 1100011, 1100011, 110110, 1100011, 1100100, 110010, 110010, 1100100, 1100010, 110001, 110011, 110111, 110010, 1100001, 1100101, 111001, 1100010, 111001, 110000, 1100101, 1100100, 110000, 110111, 1100001, 1100010, 110111, 1100110, 110011, 111000, 111000, 1100001, 110100, 110001])\n\ndatos = pd.DataFrame({'ancestor': matricula, 'order': matematicas})\ndatos.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.123750Z","iopub.execute_input":"2022-10-20T03:47:21.124672Z","iopub.status.idle":"2022-10-20T03:47:21.145532Z","shell.execute_reply.started":"2022-10-20T03:47:21.124620Z","shell.execute_reply":"2022-10-20T03:47:21.144541Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"se debe convertir  los datos a float y las columnas tambien","metadata":{}},{"cell_type":"code","source":"import pandas as pd\ndf_train = pd.read_csv(\"../input/AI4Code/train_ancestors.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.147107Z","iopub.execute_input":"2022-10-20T03:47:21.147525Z","iopub.status.idle":"2022-10-20T03:47:21.366779Z","shell.execute_reply.started":"2022-10-20T03:47:21.147483Z","shell.execute_reply":"2022-10-20T03:47:21.365448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd  \n    \ndf = pd.read_csv(\"../input/AI4Code/train_ancestors.csv\")  \n   \nprint(df)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.368230Z","iopub.execute_input":"2022-10-20T03:47:21.368568Z","iopub.status.idle":"2022-10-20T03:47:21.597088Z","shell.execute_reply.started":"2022-10-20T03:47:21.368537Z","shell.execute_reply":"2022-10-20T03:47:21.595711Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom pylab import rcParams\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom matplotlib import rc\nimport unittest\n\n%matplotlib inline\n\nsns.set(style='whitegrid', palette='muted', font_scale=1.5)\n\nrcParams['figure.figsize'] = 14, 8\n\nRANDOM_SEED = 42\n\nnp.random.seed(RANDOM_SEED)\n\ndef run_tests():\n  unittest.main(argv=[''], verbosity=1, exit=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.599245Z","iopub.execute_input":"2022-10-20T03:47:21.599700Z","iopub.status.idle":"2022-10-20T03:47:21.611525Z","shell.execute_reply.started":"2022-10-20T03:47:21.599661Z","shell.execute_reply":"2022-10-20T03:47:21.610059Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df_train \ndf","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.612992Z","iopub.execute_input":"2022-10-20T03:47:21.613425Z","iopub.status.idle":"2022-10-20T03:47:21.643895Z","shell.execute_reply.started":"2022-10-20T03:47:21.613387Z","shell.execute_reply":"2022-10-20T03:47:21.642410Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.describe()","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.651109Z","iopub.execute_input":"2022-10-20T03:47:21.651621Z","iopub.status.idle":"2022-10-20T03:47:21.924778Z","shell.execute_reply.started":"2022-10-20T03:47:21.651579Z","shell.execute_reply":"2022-10-20T03:47:21.923563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datos.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-10-20T03:47:21.926536Z","iopub.execute_input":"2022-10-20T03:47:21.926933Z","iopub.status.idle":"2022-10-20T03:47:21.936000Z","shell.execute_reply.started":"2022-10-20T03:47:21.926898Z","shell.execute_reply":"2022-10-20T03:47:21.934452Z"},"trusted":true},"execution_count":null,"outputs":[]}]}