M7_C4_abiertaUGR.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "colab": {
      "name": "M7_C4_abiertaUGR.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4E-m14iq0P4"
      },
      "source": [
        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)\n",
        "## **Módulo 7 - Big Data**\n",
        "### **7.4 - Clasificación, Regresión, Clustering y Reglas de Asociación en Spark**\n",
        "\n",
        "**Autor**: \n",
        "\n",
        "*Por* **Fco. Javier García Castellano**.\n",
        "\n",
        "*Profesor Titular de Universidad. Departamento de Ciencias de Computación e Inteligencia Artificial (DECSAI). Universidad de Granada.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8Y_zJaSHFjR"
      },
      "source": [
        "\n",
        "**Recordatorio: Introducción a NoteBook**\n",
        "\n",
        "Dentro de este cuaderno (NoteBook), se le guiará paso a paso desde la carga de un conjunto de datos hasta el análisis descriptivo de su contenido.\n",
        "\n",
        "El cuaderno de Jupyter es un enfoque para ejecutar celdas precodificadas y ver los resultados directamente sobre la celda ejecutada. Cada celda de este cuaderno debe ser ejecutada secuencialmente. Si te saltas alguna, puede que el programa lance un error, así que empieza desde el principio en caso de duda.\n",
        "\n",
        "Antes de nada \n",
        "\n",
        "Es muy muy importante que al comienzo selecciones \"Abrir en modo de ensayo\" (draft mode), arriba a la izquierda. En caso contrario no te dejará ejecutar ningún bloque de código. Cuando ejecutes el primero de los bloques, aparecerá el siguiente mensaje: \"Advertencia: Este cuaderno no lo ha creado Google.\". Tranquilos, debéis confiar en el contenido del cuaderno (NoteBook) y pulsar en \"Ejecutar de todos modos\".\n",
        "\n",
        "¡Ánimo!\n",
        "\n",
        "¡Vamos a por todas!\n",
        "Haz clic en el botón \"play\" en la parte izquierda de cada celda de código. Las líneas que comienzan con un hashtag (#) son comentarios y no afectan a la ejecución del programa.\n",
        "\n",
        "También puedes pinchar sobre cada celda y hacer \"ctrl+enter\" (cmd+enter en Mac).\n",
        "\n",
        "Cada vez que ejecutes algo, verás la salida justo debajo. La información suele ser siempre la relativa a la última instrucción, junto con todos los print() que haya en el código."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "phaga9kDY6AF"
      },
      "source": [
        "## **ÍNDICE**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ny9tgvVapCqc"
      },
      "source": [
        "En este *notebook*: \n",
        "1. Vamos a aprender distintos algoritmos de aprendizaje supervisado que hay en Apache Spark, tanto para clasificación como regresión.\n",
        "2. También trabajaremos con distintos métodos de aprendizaje no supervisado en Spark, como Clustering y Reglas de Asociación.\n",
        "\n",
        "Contenidos:\n",
        "1. Introducción\n",
        "\n",
        "2. Clasificación en Spark.\n",
        "\n",
        "3. Regresión en Spark.  \n",
        "\n",
        "4. Clustering en Spark. \n",
        "\n",
        "5. Reglas de Asociación en Spark.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGeuySu9X9xU"
      },
      "source": [
        "##**1. INTRODUCCIÓN.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aosumELxpCqc"
      },
      "source": [
        "Ya hemos visto la metodología para trabajar con Big Data usando Apache Spark. \n",
        "\n",
        "Ahora vamos a ver distintas herramientas que ya hemos visto en otros apartados del curso, por ejemplo, veremos como usar un regresor o un clasificador en Spark. Se hace necesario remarcar que los distintos algoritmos que se van a ver aquí están programados para ser ejecutados de forma distribuida, con tolerancia a fallos y escalables, es decir, están pensados para ser ejecutados en muchos ordenadores simultáneamente, cuantos más ordenadores tengamos, mejor irá, y además, no hay problema si falla uno o varios ordenadores. \n",
        "\n",
        "En este cuaderno (notebook) vamos a seguir trabajando con Apache Spark en Google Colaboratory, por lo que los prolegómenos son los mismos, como se muestran a continuación."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Wf76hHtnAgS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7771ff61-35a5-4940-bb34-0409c04b3250"
      },
      "source": [
        "#Primero instalamos Java y Apache Spark\n",
        "!apt-get install openjdk-8-jdk-headless -qq  #Instalamos Java\n",
        "\n",
        "#Nos descargamos Apache Spark con Hadoop\n",
        "!wget -q https://downloads.apache.org/spark/spark-2.4.7/spark-2.4.7-bin-hadoop2.7.tgz\n",
        "!tar xf spark-2.4.7-bin-hadoop2.7.tgz #Instalamos Apache Spark\n",
        "\n",
        "\n",
        "#Instalamos los paquetes de Python para trabajar con Spark\n",
        "!pip install findspark #Instalamos FindSpark\n",
        "!pip install pyspark   #Instalamos Spark\n",
        "\n",
        "#Indicamos a PySpark donde está Spark\n",
        "import findspark\n",
        "findspark.init(\"spark-2.4.7-bin-hadoop2.7\")#SPARK_HOME\n",
        "\n",
        "\n",
        "#Inicializamos las variables de entorno\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-2.4.7-bin-hadoop2.7\"\n",
        "\n",
        "#Creamos una sesión de Spark para poder trabajar\n",
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder \\\n",
        "    .master(\"local[*]\") \\\n",
        "    .appName(\"Ejemplos de Aprendizaje Supervisado y no supervisado en PySpark\") \\\n",
        "    .config(\"spark.sql.execution.arrow.enabled\", \"true\") \\\n",
        "    .getOrCreate()\n",
        "\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package openjdk-8-jre-headless:amd64.\n",
            "(Reading database ... 160975 files and directories currently installed.)\n",
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            "Setting up openjdk-8-jre-headless:amd64 (8u282-b08-0ubuntu1~18.04) ...\n",
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            "update-alternatives: using /usr/lib/jvm/java-8-openjdk-amd64/bin/jhat to provide /usr/bin/jhat (jhat) in auto mode\n",
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            "  Created wheel for pyspark: filename=pyspark-3.1.1-py2.py3-none-any.whl size=212767604 sha256=0dc89b18eaeaadb7eb0455c5c79d24b97bb629d837df4411b40f36b0546616d3\n",
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          ],
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNoxOlfPKtLu"
      },
      "source": [
        "##**2. CLASIFICACIÓN EN APACHE SPARK.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZp4x101r72b"
      },
      "source": [
        "Como vimos en las primeras etapas del curso,  el aprendizaje supervisado es aquella parte del Machine Learningg donde tenemos los datos correctamente etiquetados o clasificados. A los algoritmos de aprendizaje supervisado también se les denomina clasificadores pues dado un conjunto de variables de entrada predicen a que valor de la variable de salida pertenecen. En un clasificador, tenemos por tanto, variables de entrada y la variable de salida.\n",
        "\n",
        "Lo primero que vamos a hacer es descargarnos los datos con los que vamos a trabajar y leerlos en un DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ox04GuFQpCqi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c13db5b-8436-4478-e200-61e105bff294"
      },
      "source": [
        "#Nos descargamos los ficheros de datos en Google Colaboratory \n",
        "#!wget -nv -c --no-check-certificate 'https://docs.google.com/uc?export=download&id=1J3jo-YRKGX1lBjA0-B8U1ZJibHYEAbmL' -O inmune_X.csv\n",
        "!wget -nv  --no-check-certificate 'https://docs.google.com/uc?export=download&id=1PYzEIdmnfjOnBpPDIFBE9hL1Lkj_OBCk' -O inmune_X.csv\n",
        "!wget -nv  --no-check-certificate 'https://docs.google.com/uc?export=download&id=1hHQfcvrFa5Jds-9tW_X4sHjKpYKdii9s' -O inmune_Y.csv \n",
        "\n",
        "#Leemos el fichero con las variables de entrada\n",
        "dfX = spark.read.csv('inmune_X.csv',inferSchema=True, header=True)\n",
        "#Leemos el fichero con la variable de salida\n",
        "dfY = spark.read.csv('inmune_Y.csv',inferSchema=True, header=True)\n",
        "\n",
        "#Unimos los dos DataFrame\n",
        "df=dfX.join(dfY, dfX._c0 == dfY._c0).drop('_c0')\n",
        "\n",
        "#Eliminamos la columna que indica el número de instancia\n",
        "dfX=dfX.drop('_c0')\n",
        "dfY=dfY.drop('_c0')\n",
        "\n",
        "#Partimos los datos (sin preprocesar)\n",
        "train, test = df.randomSplit([0.7, 0.3], seed = 2020)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-09 11:11:26 URL:https://doc-14-90-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/dfs90rdesh5mnbo3aj3oqfcs08li0s35/1615288275000/05131985187150583765/*/1PYzEIdmnfjOnBpPDIFBE9hL1Lkj_OBCk?e=download [314913/314913] -> \"inmune_X.csv\" [1]\n",
            "2021-03-09 11:11:27 URL:https://doc-0c-90-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/cng2bhpafippo1fiathm4so6fibsubl6/1615288275000/05131985187150583765/*/1hHQfcvrFa5Jds-9tW_X4sHjKpYKdii9s?e=download [3949/3949] -> \"inmune_Y.csv\" [1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlNoGt4dhqCD"
      },
      "source": [
        "Ahora preparamos las distintas etapas para preprocesar los datos, es decir prepararlos para que podamos aprender modelos con ellos. Además los partiremos en dos conjuntos, uno de entrenamiento y otro de test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcRUOd7hGop8"
      },
      "source": [
        "#Preprocesamos los datos para usarlos en un algoritmo de aprendizaje supervisado\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.feature import StringIndexer\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "\n",
        "#Indexamos la variable a clasificar\n",
        "indexador = StringIndexer(inputCol=\"RNASEQ-CLUSTER_CONSENHIER\", outputCol=\"clase\")\n",
        "\n",
        "#Unimos los atributos con VectorAssembler\n",
        "assembler = VectorAssembler(inputCols=dfX.columns,outputCol=\"atributos\")\n",
        "\n",
        "#Guardamos las etapas del preprocesamiento para qeu se usen en una tubería\n",
        "etapas=[indexador, assembler]\n",
        "\n",
        "#Partimos los datos (sin preprocesar)\n",
        "train, test = df.randomSplit([0.7, 0.3], seed = 2020)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7IeGUNXuiZb"
      },
      "source": [
        "En Apache Spark encontramos distintos clasificadores ya implementados. Distinguiremos aquellos que son para clasificación binaria, es decir, la variable de salida sólo tiene dos estados, de los que son para clasificación multiclase, es decir, aquellos en los que la variable de salida tiene más de dos estados.\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "<th><p>Tipo de Problema</p>\n",
        "</th><th><p> Métodos implementados en Spark</p>\n",
        "</th></tr>\n",
        "\n",
        "<tr><td><p>Clasificación Binaria</p>\n",
        "</td><td><p>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#binomial-logistic-regression\">Regresión Binaria Logística</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-support-vector-machine\"> Máquinas de Vectores de Soporte</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier\">Árboles de Decisión</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier\">Random Forest</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier\">Potenciación del Gradiente (Gradient-boosted tree)</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier\">Perceptrón Multicapa</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#naive-bayes\">Naïve Bayes</a></p>\n",
        "</td></tr>\n",
        "\n",
        "<tr><td><p>Clasificación Múltiple</p>\n",
        "</td><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#multinomial-logistic-regression\">Regresión Logística Multinomial</a> <br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier\">Árboles de Decisión</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier\">Random Forest</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier\">Potenciación del Gradiente (Gradient-boosted tree)</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier\">Perceptrón Multicapa</a><br/>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#naive-bayes\">Naïve Bayes</a><br />\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#one-vs-rest-classifier-aka-one-vs-all\">One-vs-Rest  (Uno-contra-todos)</a></p>\n",
        "</td></tr>\n",
        "\n",
        "</table>\n",
        "\n",
        "\n",
        "Como se puede observar en la tabla todos los clasificadores que nos permiten trabajar con problemas multiclase también nos permiten trabajar con problemas binarios. Lo cual tiene sentido, es decir,  si un algoritmo es capaz de discriminar entre 4 ó 10 valores de la variable de salida, también debería ser capaz de distinguir entre dos.\n",
        "\n",
        "Para el caso de la Regresión Logística, tenemos una versión binaria y otra multiclase. Las máquinas de Vectores de Soporte, en cambio, sólo nos permiten clasificación binaria. \n",
        "\n",
        "No obstante, en el apartado de clasificación múltiple hay una entrada que no aparece en el apartado de clasificación binaria, y es el metaclasificador Uno-contra-todos. En realidad no es un clasificador en sí mismo, pues necesita de un clasificador base. Es un esquema que nos permite utilizar clasificadores binarios en problemas multiclase. Lo que nos permite es contruir un clasificador base para cada valor de la variable de salida y dicho clasificador distingue el valor i-ésimo de la variable de salida del resto.  \n",
        "\n",
        "Veamos ahora un ejemplo de un clasificador:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKZQUkv3OZaW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f33d5b69-6251-4786-d2c8-cb95875db7ae"
      },
      "source": [
        "from pyspark.ml.classification import LinearSVC\n",
        "\n",
        "#Definimos el SVM\n",
        "svm = LinearSVC(featuresCol = 'atributos', labelCol = 'clase',maxIter=10)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el SVM\n",
        "tuberia=Pipeline().setStages(etapas+[svm] ) \n",
        "\n",
        "#Construimos el modelo con los datos de entrenamiento\n",
        "modeloSVM = tuberia.fit(train)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido (preprocesamiento + SVM)\n",
        "predicciones = modeloSVM.transform(test)\n",
        "\n",
        "#Ya podemos mostrar la bondad del modelo\n",
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
        "evaluador = MulticlassClassificationEvaluator(labelCol=\"clase\", metricName=\"accuracy\")\n",
        "print('Accuracy:', evaluador.evaluate(predicciones))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7319587628865979\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLlP7GtBhuPr"
      },
      "source": [
        "En los ejemplos de la cápsula anterior, usamos [Regresión Logística](https://spark.apache.org/docs/latest/ml-classification-regression.html#logistic-regression). En el anterior ejemplo hemos usado otro modelo,  que ha sido una [Máquina de Vectores de Soporte Lineal](https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-support-vector-machine) (*Lineal Support Vector Machines* en inglés, o abreviadamente, SVM), donde hemos aprendido un clasificador binario a partir de los datos generados anteriormente.\n",
        "\n",
        "\n",
        "Hay tres parámetros que nos aparecieron cuando usamos regresion lógistica y que también nos aparecen ahora, que creemos necesario describir:\n",
        "\n",
        "\n",
        "*   **`featuresCol`**: nos indica que variable del *DataFrame* contiene todas las variables de entrada unidas con `VectorAssembler`, en nuestros ejemplos se suelen llamar `atributos`, pero, por defecto si no se indica nada, se utiliza **`features`**.\n",
        "*   **`labelCol`**: nos indica qué variable del *DataFrame* es la variable de salida, en nuestros ejemplos se suele llamar `clase` pero, por defecto, si no se indica nada, se utiliza **`label`**.\n",
        "*   **`maxIter`**: nos indica el número máximo de iteraciones que realiza el algoritmo. En muchos algoritmos de Machine Learning son optimizados mediante procesos iterativos. Por tanto, es el número máximo de iteraciones para ejecutar el algoritmo de optimización. Un valor demasiado pequeño puede empeorar los resultados y uno demasiado grande, que tarde demasiado o se sobreajuste.\n",
        "\n",
        "En la interfaz de programación de aplicaciones, conocida también por la sigla API, en inglés, *application programming interface*, ​se puede encontrar más información de los parámetros usados en la [regresión logística](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.classification.LogisticRegression),  o en las [máquinas de vectores de soporte](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.classification.LinearSVC).\n",
        "\n",
        "Vamos a usar ahora un arbol de decisión."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNJm_yBAEz_Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c38ec774-6409-426e-92bc-715b91df8ce5"
      },
      "source": [
        "from pyspark.ml.classification import DecisionTreeClassifier\n",
        "\n",
        "#Definimos el árbol de decisión y sus parámetros\n",
        "dt = DecisionTreeClassifier(featuresCol = 'atributos', labelCol = 'clase')\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el árbol\n",
        "tuberia=Pipeline().setStages(etapas+[dt] ) \n",
        "\n",
        "#Construimos el modelo con los datos de entrenamiento\n",
        "modeloDT = tuberia.fit(train)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido (preprocesamiento + árbol)\n",
        "predicciones = modeloDT.transform(test)\n",
        "\n",
        "#Ya podemos mostrar la bondad del modelo\n",
        "print('Accuracy:', evaluador.evaluate(predicciones))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6804123711340206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDNnMjQt0DaI"
      },
      "source": [
        "Los [árboles de decisión](https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier) son modelos interpretables, de hecho, podemos verlo como un conjunto compactado de reglas del tipo ***Si ... Entonces ...*** . \n",
        "\n",
        "De entre los [parámetros del árbol de decisión](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier), podemos destacar: \n",
        "\n",
        "\n",
        "*   **`maxDepth`**: Profundidad máxima del árbol en número de nodos. Por defecto, 5.\n",
        "*   **`minInstancesPerNode`**: Número mínimo de instancias para seguir ramificando un nodo del árbol. Por defecto 1. \n",
        "*   **`minInfoGain`**: umbral mínimo del criterio usado de división para seguir ramificando. Por defecto, 0.\n",
        "*   **`maxBins`**: Número de intervalos usados para discretizar variables continuas. Por defecto, 32. \n",
        "*   **`impurity`**: Criterio seguido para seguir dividiendo los nodos, puede tomar dos valores:`entropy` o `gini`(valor por defecto).\n",
        "\n",
        "Los parámetros `maxDepth`, `minInstancesPerNode`, `minInfoGain` se usan para limitar el crecimiento del árbol, lo que se denomina prepoda. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vh1G4ZqhYTuF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93d01730-8081-4587-ddad-ff8df53dcfc3"
      },
      "source": [
        "from pyspark.ml.classification import RandomForestClassifier\n",
        "\n",
        "# Creamos un RandomForest.\n",
        "rf = RandomForestClassifier(featuresCol = 'atributos', labelCol = 'clase', numTrees=10)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y la RF\n",
        "tuberia=Pipeline().setStages(etapas+[rf] ) \n",
        "\n",
        "#Construimos el modelo y lo probamos\n",
        "predicciones = tuberia.fit(train).transform(test)\n",
        "\n",
        "#Ya podemos mostrar la bondad del modelo\n",
        "print('Accuracy:', evaluador.evaluate(predicciones))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8350515463917526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEIQAoFyDNxj"
      },
      "source": [
        "El siguiente clasificador que vamos a ver es el [**Perceptrón Multicapa**](https://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier) que es un tipo de **red neuronal artificial** bastante popular. \n",
        "\n",
        "El principal problema al que nos enfrentaremos al usar un perceptrón multicapa, es que tenemos que indicarle la arquitectura de la red neuronal, esto es, el número de capas y el número de neuronas artificiales por capa. \n",
        "\n",
        "La capa de entrada tiene que tener tantas neuronas como variables de entrada haya. La capa de salida tiene que tener tantas neuronas como valores tome la variable de salida. Además, podemos indicar tantas capas ocultas como queramos.\n",
        "\n",
        "Para especificar la arquitectura de la red neuronal usaremos una lista, donde cada elemento de la lista representa una capa y será un número, dicho número nos indica la cantidad de neuronas usadas en cada capa. Para especificar la arquitectura del perceptrón multicapa, se usa el argumento `layers`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AMzqivKUqPx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5ddc83e-aa4a-4792-a699-5adc5c9a259b"
      },
      "source": [
        "from pyspark.ml.classification import MultilayerPerceptronClassifier\n",
        "\n",
        "# Especificamos la arquitectura de la red neuronal.\n",
        "layers = [len(dfX.columns) , int(len(dfX.columns)/2), int(len(dfX.columns)/4), 2]\n",
        "\n",
        "# definimos la red neuronal \n",
        "rna = MultilayerPerceptronClassifier(featuresCol = 'atributos', labelCol = 'clase',\n",
        "                                     maxIter=100, layers=layers)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y la RNA\n",
        "tuberia=Pipeline().setStages(etapas+[rna] ) \n",
        "\n",
        "#Construimos el modelo con los datos de entrenamiento\n",
        "modeloRNA = tuberia.fit(train)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido (preprocesamiento + RNA)\n",
        "predicciones = modeloRNA.transform(test)\n",
        "\n",
        "#Ya podemos mostrar la bondad del modelo\n",
        "print('Accuracy:', evaluador.evaluate(predicciones))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8350515463917526\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ysYWNAXFld7"
      },
      "source": [
        "Cuando vimos los modelo tipo *Ensemble*, esto es, conjuntos de clasificadores que funcionan como uno solo, distinguimos dos enfoques: *Bagging* y *Boosting*. En dicho módulo, se presentó el clasificador **Random Forest** como ejemplo de *Bagging*. No obstante, no vimos ningún ejemplo de *Boosting*. \n",
        "\n",
        "En el siguiente ejemplo veremos un *ensemble* de tipo *boosting*, concretamente el [árbol de potenciación del gradiente](https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier) (en inglés, ***Gradiente Boosted Tree*** o GBT). Que en lugar de aprender muchos árboles de decisión a la vez, como hace el *random forest*, va construyendo árboles de decisión escalonadamente, buscando mejorar los resultados del árbol inmediatamente anterior. En Apache Spark se implementa con la clase `GBTClassifier` y el número de iteraciones también nos indica el número de árboles de decisión que se generan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0upQ9XiXmRQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cf1cef7-792f-4056-ce34-1b96fb28d028"
      },
      "source": [
        "from pyspark.ml.classification import GBTClassifier\n",
        "\n",
        "# Train a GBT model.\n",
        "gbt = GBTClassifier(featuresCol = 'atributos', labelCol = 'clase', maxIter=100)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y la gbt\n",
        "tuberia=Pipeline().setStages(etapas+[gbt] ) \n",
        "\n",
        "#Construimos el modelo con los datos de entrenamiento\n",
        "modeloGBT = tuberia.fit(train)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido (preprocesamiento + gbt)\n",
        "predicciones = modeloGBT.transform(test)\n",
        "\n",
        "#Ya podemos mostrar la bondad del modelo\n",
        "print('Accuracy:', evaluador.evaluate(predicciones))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7422680412371134\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OxNwkrDLL6n"
      },
      "source": [
        "##**3. REGRESIÓN EN APACHE SPARK.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHQrAPCwVunj"
      },
      "source": [
        "Hasta ahora hemos trabajado con un problema de clasificación, es decir, un problema de aprendizaje supervisado donde la variable de salida es una variable discreta. Pero ahora vamos a ver como trabajar con **Regresión** que es cuando la variable de salida es continua. Conceptualmente no hay mucha diferencia de aprender un clasificador o un regresor pero sí que tendremos modelos distintos y la forma de evaluar los resultados también son distintos.\n",
        "\n",
        "\n",
        "Para empezar, vamos a descargarnos el conjunto de datos **HOMA** y lo vamos a guardar en un *DataFrame* para empezar a trabajar con él."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JH_6DM5yS921",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39d5042f-d429-4b89-ffaa-2c05a954f549"
      },
      "source": [
        "#Nos descargamos el conjunto de datos en Google Colaboratory \n",
        "!wget -nv --no-check-certificate 'https://docs.google.com/uc?export=download&id=1GO2NBxYw54K6HkN-YgXbNadrLo5O6-0u' -O HOMA.csv \n",
        "\n",
        "#Leemos el conjunto de datos con todas las variables \n",
        "dfR = spark.read.csv('HOMA.csv',inferSchema=True, header=True)\n",
        "dfR.show(5)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-09 11:12:46 URL:https://doc-00-24-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/s5vp7vn59qdpidsdf0u5vuc0t8kas3oo/1615288350000/17606232221483619752/*/1GO2NBxYw54K6HkN-YgXbNadrLo5O6-0u?e=download [29228/29228] -> \"HOMA.csv\" [1]\n",
            "+---+----+-------+-------+-----+----+--------+---------+---------+-----+----+----------+----------+---------+---------+-----+\n",
            "|Sex| Age| Tanner| Height|  BMI|  WC| TAGmgDL| HDLCmgDL| LDLCmgDL|  SBP| DBP| Sedentary|     Light| Moderate| Vigorous| HOMA|\n",
            "+---+----+-------+-------+-----+----+--------+---------+---------+-----+----+----------+----------+---------+---------+-----+\n",
            "|  1| 9.5|    0.0|   1.55|11.34|60.0|    55.0|     51.0|     93.0| 97.0|60.0|411.089286|321.580357|22.133929| 3.982143| 1.98|\n",
            "|  1| 8.0|    0.0|   1.15| 12.4|46.3|    51.0|     70.0|     59.0| 90.0|55.0|435.607143| 316.97619|48.059524| 14.27381| 0.87|\n",
            "|  0|10.5|    0.0|   1.42|12.99|67.5|    65.0|     60.0|     96.0| 96.0|54.0|483.904762|337.785714|33.309524| 7.988095| 1.46|\n",
            "|  0| 8.1|    0.0|   1.27|13.43|53.1|    41.0|     78.0|    100.0|108.0|46.0|429.297619| 241.97619|39.678571|11.821429| 1.07|\n",
            "|  1|10.4|    0.0|   1.32|13.72|51.9|    39.0|    100.0|    120.0|107.0|69.0|512.071429|216.035714|     9.75| 2.410714|  0.8|\n",
            "+---+----+-------+-------+-----+----+--------+---------+---------+-----+----+----------+----------+---------+---------+-----+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aaBFbKxgQp_"
      },
      "source": [
        "Ahora vamos preparar el conjunto de datos para trabajar con él en Apache Spark. Lo primero que vamos a hacer cambiar el nombre de la variable de salida a `label` que es valor por defecto que usa Spark.\n",
        "\n",
        "Después vamos a indexar la variable discreta `Sex`. Posteriormente, vamos a unir las variables de entrada con `VectorAssembler`. \n",
        "\n",
        "Dejaremos las distintas etapas de preprocesamiento preparadas para añadir a la tubería junto con el regresor. Y, finalmente, partiremos los datos de entrada en los conjuntos de entrenamiento y test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XncijLCzgFM2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59a2ad04-5677-42a9-ec50-f37bc621e6e3"
      },
      "source": [
        "#Cambiamos el nombre a la variable de salida\n",
        "dfR=dfR.withColumnRenamed(\" HOMA\",\"label\") \n",
        "\n",
        "#Indexamos las variables discretas\n",
        "indexador = StringIndexer(inputCol=\"Sex\", outputCol=\"SexI\")\n",
        "\n",
        "#Unimos las variables de entrada con VectorAssembler\n",
        "varsEntrada=dfR.columns\n",
        "varsEntrada.append('SexI')\n",
        "varsEntrada.remove('Sex')\n",
        "varsEntrada.remove('label')\n",
        "assembler = VectorAssembler(inputCols=varsEntrada, outputCol=\"features\")\n",
        "\n",
        "#Guardamos las etapas del preprocesamiento para que se usen en una tubería\n",
        "etapasR=[indexador, assembler]\n",
        "\n",
        "#Partimos los datos (sin preprocesar)\n",
        "trainR, testR = dfR.randomSplit([0.7, 0.3], seed = 2020)\n",
        "print (\"Entrenamiento \", trainR.count(),' instancias.')\n",
        "print (\"Test \", testR.count(),' instancias.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entrenamiento  204  instancias.\n",
            "Test  88  instancias.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jh-4jTSKjDhz"
      },
      "source": [
        "En el siguiente ejemplo vamos a usar una [regresión lineal](https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression), que ya vimos con detenimiento en el módulo de regresión, para hacer nuestras predicciones sobre el conjunto de datos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fewWCtE9Y9PU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccc17a8f-cb34-4e89-8bc2-d61d0a9755d2"
      },
      "source": [
        "from pyspark.ml.regression import LinearRegression\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "\n",
        "#Definimos el regresor lineal\n",
        "lr = LinearRegression()\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el regresor\n",
        "tuberia=Pipeline().setStages(etapasR+[lr] ) \n",
        "\n",
        "#Construimos el modelo con los datos de entrenamiento\n",
        "regresor = tuberia.fit(trainR)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido (preprocesamiento + regresión)\n",
        "predicciones = regresor.transform(testR)\n",
        "\n",
        "#Ya podemos evaluar el regresor\n",
        "evaluadorR = RegressionEvaluator()\n",
        "print('rmse:', evaluadorR.evaluate(predicciones))\n",
        "print('r2:', evaluadorR.evaluate(predicciones, {evaluadorR.metricName: \"r2\"}))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rmse: 0.7027609135217638\n",
            "r2: 0.40487607713115203\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQoLn3YgTpmN"
      },
      "source": [
        "También podemos usar árboles de decisión en problemas de regresión, los parámetros que se utilizan son los mismos que en clasificación.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZM3XG2Ktd_38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9a07e47-b504-435e-c4c7-a083f4adbcf1"
      },
      "source": [
        "from pyspark.ml.regression import DecisionTreeRegressor\n",
        "\n",
        "#Definimos un árbol de decisión para regresión\n",
        "dtR = DecisionTreeRegressor()\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el regresor\n",
        "tuberia=Pipeline().setStages(etapasR+[dtR] ) \n",
        "\n",
        "#Construimos el regresor y lo probamos\n",
        "predicciones = tuberia.fit(trainR).transform(testR)\n",
        "print('rmse:', evaluadorR.evaluate(predicciones))\n",
        "print('r2:', evaluadorR.evaluate(predicciones, {evaluadorR.metricName: \"r2\"}))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rmse: 0.9413814574832902\n",
            "r2: -0.06788255136336407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S15FukVqj9RJ"
      },
      "source": [
        "Como hemos visto, podemos usar árboles de decisión como regresores. Pues al igual que vimos en aprendizaje supervisado, podemos usar un conjunto de árboles de decisión en un *ensemble*. Para ello podemos seguir una estrategia *bagging* y generar distintos árboles usando subconjuntos diferentes de entrenamiento. De esta forma, podemos usar conjuntos de regresores en un [*random forest*](https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression), para esto, en Apache Spark, usaremos la clase [`RandomForestRegressor`](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor). \n",
        "\n",
        "En el siguiente ejemplo veremos un regresor *random forest*, cuyo parámetro más interesante es `numTrees`que nos indica el número de árboles a usar:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Rn0hCkeaeI2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80ea9e65-91d0-4470-e7f1-db9e4509d012"
      },
      "source": [
        "from pyspark.ml.regression import RandomForestRegressor\n",
        "\n",
        "#Definimos un rnadom forest para regresión \n",
        "rfR = RandomForestRegressor(numTrees=10)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el regresor\n",
        "tuberia=Pipeline().setStages(etapasR+[rfR] ) \n",
        "\n",
        "#Construimos el regresor y lo evaluamos\n",
        "predicciones = tuberia.fit(trainR).transform(testR)\n",
        "print('rmse:', evaluadorR.evaluate(predicciones))\n",
        "print('r2:', evaluadorR.evaluate(predicciones, {evaluadorR.metricName: \"r2\"}))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rmse: 0.6610349858469294\n",
            "r2: 0.47344819962305507\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4jejAEYi7bM"
      },
      "source": [
        "\n",
        "Los ejemplos vistos son sólo un subconjunto de los regresores que están implementados en Apache SparK y que se muestran en la siguiente tabla.\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "<th><p> Métodos de Regresión Implementados en Apache Spark</p>\n",
        "</th></tr>\n",
        "\n",
        "<tr><td><p>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression\">Regresión Lineal </a></p><br/></td></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#generalized-linear-regression\">Modelos Lineales Generalizados </a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-regression\"> Árboles de Decisión</a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression\">Random forest</a></p><br/></td></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression\">Árbol de Potenciación del Gradiente</a></p><br/></td></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#survival-regression\">Análisis de Supervivencia</a></p><br/></td></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#isotonic-regression\">Regresión Isotónica</a></p><br/></td></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-classification-regression.html#factorization-machines-regressor\">Máquinas de Factorización</a></p><br/></td></tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QF9i1zQsLXbC"
      },
      "source": [
        "##**4. CLUSTERING EN APACHE SPARK.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUMjZQRUlJP_"
      },
      "source": [
        "Hasta ahora hemos trabajado en Spark con algoritmos de aprendizaje supervisados, es decir, tenemos variables de entrada y una variable de salida que queríamos predecir. \n",
        "\n",
        "En este apartado vamos a trabajar con Aprendizaje No Supervisado. Como vimos, el Clustering nos permite descubrir grupos de variables en los datos. Dichos grupos (o clusters) son un conjunto de instancias que se parecen entre sí. \n",
        " \n",
        "Para trabajar con los distintos algoritmos de Clustering en Spark, vamos a cargar en primer lugar la matriz de expresión de 1500 genes que obtuvimos en el Módulo 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CtRDDtJ37m5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09b6742-e14c-4efa-c641-0a2323429864"
      },
      "source": [
        "#Nos descargamos el conjunto de datos para clustering en Google Colaboratory \n",
        "!wget -nv --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rQd3EHPQvDSVsjwMZG3oM7khRCR_EPwi' -O clustering.csv \n",
        "#Leemos el conjunto de datos con todas las variables \n",
        "dfC = spark.read.csv('clustering.csv', inferSchema=True, header=True)\n",
        "dfC.show(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-09 11:12:54 URL:https://doc-08-bc-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/germss8va06ng16beri09ieonbbi5g1o/1615288350000/11180625338828972622/*/1rQd3EHPQvDSVsjwMZG3oM7khRCR_EPwi?e=download [12254970] -> \"clustering.csv\" [1]\n",
            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TCGA_ER_A19C_06|  TCGA_D3_A3C6_06|   TCGA_ER_A2NC_06|  TCGA_D3_A3C7_06|  TCGA_BF_A3DJ_01|  TCGA_D3_A2J6_06|   TCGA_FS_A4F5_06|  TCGA_EE_A3JB_06|  TCGA_D9_A1X3_06|  TCGA_RP_A690_06|   TCGA_D3_A8GO_06|  TCGA_D9_A4Z3_01|   TCGA_EE_A3J7_06|  TCGA_EB_A41B_01|  TCGA_FS_A1ZM_06|   TCGA_EE_A3JA_06|  TCGA_EE_A29B_06|  TCGA_BF_AAP6_01|   TCGA_RP_A6K9_06|  TCGA_D3_A1Q5_06|  TCGA_ER_A2NF_01|  TCGA_EE_A2GJ_06|   TCGA_D3_A3C8_06|  TCGA_GN_A266_06|   TCGA_DA_A1I4_06|  TCGA_FR_A7U9_06|  TCGA_ER_A19E_06|   TCGA_EE_A3AD_06|  TCGA_W3_AA1Q_06|  TCGA_D3_A5GT_01|  TCGA_YD_A89C_06|    TCGA_EE_A17Z_06|  TCGA_XV_A9W2_01|  TCGA_EB_A430_01|   TCGA_D3_A3MU_06|  TCGA_EE_A2GO_06|  TCGA_FR_A8YC_06|   TCGA_DA_A1I2_06| TCGA_D3_A2JK_06|\n",
            "+------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+------------------+------------------+----------------+------------------+-----------------+-----------------+------------------+----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-------------------+-----------------+------------------+-------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+----------------+----------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-------------------+-----------------+-----------------+-----------------+-----------------+-----------------+----------------+------------------+-----------------+-----------------+------------------+-----------------+----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------------+-----------------+-----------------+-----------------+-----------------+-------------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+----------------+------------------+-----------------+----------------+------------------+------------------+-----------------+-------------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+--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            "| TYRP1|  7.9415452107038| 2.75046277351166|  -5.5660300159965|-4.90726289047265|  5.54681507588922| 7.03679481902785| 1.29666714274317| 8.45333252240379|-4.29507770339333|-2.82273506668406|   5.34314482098534| -7.71861420024055|-7.90814773330712|  4.4859293487625| 4.13933978456928| -1.0573521108461|-0.652171597669941| 6.94320218627926|-5.95827301470496| 2.54638464156844|-8.21461118931393| 4.51204356840374| 1.60722159747721| -9.3022776907826| -2.8148942477461| 5.69648046190246| 6.63847962515683|-5.92677945108471|-8.30408218762935| 4.19928213421245| 7.71182297075097|  5.78359040546003| -4.1078202389564|0.373896788239009|-3.79281936209561| 5.09025209524491|    5.792210866874| -3.8581175377964|-7.23877474847644|-2.38418011963761|8.00225188882767|-2.78969523198122| 1.10990449211328|   6.396218868278|-3.22049495946715|-1.70589484264298|-8.52054030290427| 6.06827360906143|-8.13434177308029|  6.73830857515187| -8.36057038210164|-6.52785958667679| -9.3022776907826| -4.9140950433041|-5.14925064731778|  6.91060535424662| 6.02464355888676| -1.65323856266884|-8.77630906974951| -4.47308774146276|  6.37944768566214|4.82685053407029|  3.88471564374407| 5.59544996016667|  1.9059108127849|  1.03217170208574|2.76418797107789| 5.25292892823033| 6.78564541261921| -7.60463729555733|  6.2534332705198| 4.66441618778947| -2.69889317510366|  -1.0153928475713| 3.08205122036089| -4.95320970241035|-4.92860164189611|-7.04720000877715|-0.657894292311955|-7.40511472742377|  4.20300759028285| 4.67967989363991| -8.78253451736443|-4.26367909216004| 5.24747016342985|-0.979166620258052|-4.47483970989006| 6.97361382479141| -1.30810361186758| 3.72041755258483| -9.3022776907826| 4.68714067045887|   6.0823197654907|-1.33419558110573| 5.00621729815038| 5.82544809825511|   6.61764503009207|-6.10964162971239|-0.841296007986641|-0.0453205992910135|  -5.984886785445|0.151096871153008|0.878650129397743|-2.58354482052776| 2.87476745165992| 3.59764675679486|-0.678979247819836|  4.5269760624708| 4.67032784323297|  6.42015047482801| -4.4800727182352| 5.21787218503842|-5.60679603625469| 5.75605688929206|  5.41874011968067|  6.46763073916831|  -8.1695692513743|-2.22432868762424|-1.86527222687107|-3.83416361919897|  1.4736428224662|0.111782981845542| 5.72185186081771| 5.60526159603236|-0.197079445759048|-1.14352008026655| -8.49146444895259|-6.52173661102468|-2.30612624921929| 6.09957210369328| -3.17184921662654|-7.71112490098938|-2.16822210353672| 5.46765105691586| 5.68109814032381|-6.74614260778599| 4.63326753586587|-3.31865471374825|0.739714446810272| 6.23689950838911| -7.22746401917779|-3.73627237769955| 5.67324630602963|  -5.91172005480847| 7.15584884279634| 9.31454430807502|  6.59010874321219| 2.40231856458549| -9.3022776907826| 0.168100343844912|-8.00191963884216| -8.8889002455149|  5.21422803069581| -4.63594715443298| -7.93860196897534| -7.0286424517223| 6.32739553719755|-7.37472251934547|-5.53359578821107|-1.58227700939721| 4.26366864085407|  4.95859411234191|-8.37559505216741| 6.16139068298529| 5.96542799336837| 2.98590931506186|-8.79862365563705| 5.52227155534011| -7.37165388123065|-6.40472745792881| 2.57148264951188|  5.5704991603787| 5.98683774135722|  6.54830028858687|  7.31363304290415| -4.97720074609789| 1.47218274916998|-8.59537145655105|-7.41393382540783|  4.59002323977697|-4.24162066375554|-6.62955818216776|-2.65928882138334|3.43379976679774|7.13166463763306| 2.77323388221015| 3.78067369308341|  4.92406125898756|               0.0| 6.01808156450084| -9.3022776907826|-4.23329739590442|-3.59174398321102|-8.89400060562956|-0.450698535148382|   3.56177249822553|-4.32481457954314|  6.6404783156406|-6.75603760676182|-4.74374064817425|-7.02983638983893|2.44185602595783|  2.65008703811579| 3.75204886299263| 5.97671460345399| 0.261773837455776|-3.20372548892532|6.19496963844312| 2.86732739638941|0.968372950499376|  2.86727594422129| -7.8215989520278|-2.18417133921243| 5.15422228927549| -5.83071301951314| 7.92521019309877|  7.28450592548596|-8.71069400993907| 5.15741123077911| 7.59041432059475| -1.6277181737064|-2.03608052706978|    -8.025661922109|-1.87928199271773|-3.21512039377616| 5.23146162918558| 8.18462129947201|   -8.5150124080309| -7.05043743202788| -9.3022776907826| -7.86430429044458|-4.68142739683624|-5.27206522682955|   2.8177880380128| -9.3022776907826| 1.33624601314589| 2.54048866400944| 0.356672983775923|-7.72459509159427|  3.22570881138517|-1.80038080938957|-7.58261233701078|-1.23838759384985|-0.985060464623482|2.56466269417668|  5.30960103253442|-4.36196996721619|2.11487624506347| -3.70730821754208| 0.374169210916406| -5.8028399608011|  -2.47507219599161|-5.66246671306583| -2.49859342555178| 0.100221733381666|-1.55220921536291| 1.57327854616834|-7.95483880201446|  3.63840235208379| 7.44375585222413|  5.18425036098492| -9.3022776907826|-1.45421128106266| -8.72058901055396| 3.83629823380222| 6.45082638785185|  2.29979775819099| -8.4699142298343|-3.75779230454651| 1.80692541788863| 5.17151423623963|-7.51919826090899|-8.14426388419283| 1.26252991442013|-5.46387043979896|-3.43488279871769|-0.213500845294661|  6.16695362043016|-0.338231746353598| 0.28219932149169| 4.77840409488338|  6.06388929219194|4.78111577036979|-6.76239926294149|-0.841585084940972|4.08509766780557| -2.36700890054637|  4.75000131659597| 4.82387778732082| 5.06025462028957| 7.12096320034619| 1.59549736785114|-2.74487525955192| -7.82180574555513|   3.866445628954| 5.18961077117953|-5.73993756353914|-4.89488414771104| 6.45910435943961| 8.23684891489569| 3.33408476045915|-6.63477154661736|-8.51903059340449| 2.28936082405902| 6.76071503147548| 3.43890184454735|-9.45437340224231E-4| 3.67292034501441|2.77286825661901|-3.41735991180264|-6.77706072137428|  -3.7527409682509|-3.29573056090128|-8.06074254898689|  5.10245652723832|-8.56606249315767|-4.43889918602689|-2.40832944352267|-6.38286132773777| 6.03719717027502| 4.72653476653741| -7.91983176969003| 6.68617796239757|-3.23387558344675|  7.04883696161603|  2.8838187161951|-7.46340657989603|-1.22229698244697| 6.50165513028735|  5.41253572796266|-3.80063791986653|-8.42508314365636|  4.0787846650395|-5.32576740989411|-8.44836186296726|  5.7793351812755|  4.09195027883015| 2.31207704839856|0.829428820520288| -8.0634297013448|-8.46014361067698|-7.82506665958407|-3.40372588034085| 2.27483820466718|-4.32857458527418|  6.18576878009045| 9.27873303604822| -9.3022776907826| -7.20419506172802| 5.20004035377768|  7.70783122831122| 4.77034316426494| -4.49844621322143|-8.79780983807724|-7.70472268040601|-1.53119302500659|0.609197590444802|-8.51309098640056|-8.50933906838223| -7.92871299532631|0.927974566548929| 2.45566841158989| 5.42314974507583| -7.41615680664999|-7.11365521967011|-6.73101348506732|   7.08429405425438| 2.24346190987841| 4.75114508528745|-3.73879004838435| 3.99618339647435|-4.85610087748476| 3.98898684399184| 7.09554126794931| 1.42637642921221| 8.99842195528054|-3.34742068429907|-6.79567574859159|-7.23773969280066|-8.17352238452557|0.557859205821584| 6.53026253965154|  2.79082485630033|-4.50111386003878|-2.42277040673032| 4.19855473008018| 4.88650779680442|  4.09551199758711| 7.28464995450301| 5.13547182665792| 7.61555101560915| -4.9483898544977| -8.55777211215399| -6.52594204765278| 7.98660118692055| 7.34251696831484| 3.69873703405904|  2.26171394219635|0.620000004434992| 6.80583752782443|-1.34748697289153|-6.69913207697998| -3.64533519487147|-3.41735747041131|-1.5728391223452| 5.30740080105301|  2.7450712669317| 6.53975818048107|    3.52838603642|  6.99249320479798| 4.70483161041028|-8.08535726492573| 6.78731236258891|0.876057132439408|    1.655093488446|   4.8574767139138| -3.48334790242428| 4.69881035693358|  -7.97608552628849|-2.10719983549883| 3.90165254974245| 4.50807327547177| -2.25836673657076| -5.9419977026823|0.409548254370803| -6.06223818667896|  3.41851884636703| 7.05042621077986| -6.6780129957977| -6.85013036760403|6.62119195150396|-7.38809589578033| -9.3022776907826|-6.15978256241079| 1.36823047534952| -4.0140151145636|  -9.3022776907826| -8.7940518918845|-2.39654171311558|-3.67465334432423|-0.311008125817862| -9.3022776907826| 5.00779640783858| 5.48683056643862|-0.413154947346049| -9.3022776907826|-0.419783503440557| 4.82205470155706|-6.91878074662724|-0.841927875879156| 7.22144511845788| 3.01459231221796|-0.538085487446752|-7.58664785024594| 9.13929793492145| 6.54679600675269|0.0170548262937142| 3.82276063990791|  3.92820566043619|-6.53079350347031|-2.36661522556412| -4.54910106346048|-6.50524395489377|-5.38127941467304|-6.23723968196576|   7.91583980683873| 3.77992789605496| 6.73471791638516|  6.53429116047175|-4.48440888905462|-3.30296764430419|-0.236018670662407|3.22522394644627|\n",
            "|RPS4Y1| 3.47587808323183|-9.25208847558951| -8.78199418294173|-1.86658582313499| -7.54416618111721|0.983460294336822| 3.23024813384959| 1.55601030531249|-8.68707629469128|-8.21618309184738|   3.06441128263618|  2.58019740219451|  2.1953961381078|-1.71628977946674| 2.12787611092333| 2.77470349346505| -8.79970389956005|0.504180947256865| 3.33737813271685|-6.19129744803721| 1.86842896973977|-8.31481942196202| 3.70893520273684| 2.68939598252276| 1.99869034610241| 3.41413663697866|-8.17826824229784| 3.15094344617167| 3.38613581554031|-7.05397154643705|-1.20327359264999|-0.694577291246851| 2.65717419119279|-8.00726229095873|-9.25208847558951|-8.85702567607193| -8.25028623295552|-5.84268396120944|-9.25208847558951| 2.52385766794379|2.05559208936131|-2.14811043716342| 2.39060425263463| 3.73925615824602| 2.68637750957992|-4.35992091469869|  3.9404604253481|-4.85129099354269| 3.59581483512373|  2.73086361389798|  2.83867155553748|-7.28086683373501|-9.25208847558951| 3.13705894465307| 2.88946782671392| -8.00058144823358|-6.61257845241431|-0.536779877351963|-7.57415182947732|  2.70279618658287|  1.24634012453002|3.81784029246396|-0.716339537196074| 2.69977400318554|-9.25208847558951|  1.06463745093115|3.19550595901468| 4.49436999042005| 2.26600303098397|  -8.7937064714335|-6.29107643129804|-9.25208847558951| -8.87479811105273|  2.59832576789062| 3.21355671923565|  2.57323089362253| 3.76261369540825| 3.68618546068406| -9.25208847558951|-9.25208847558951|  2.86296278480099|-1.40904178343534|  3.54058751272455|-6.60959483315696| 3.35017208684073|  -7.4755633241676| 2.97179063670025|-7.66712597486835|-0.409106187872078|-6.51416380807767|-6.38494319906746| 3.59878974824455| -2.56912759028774|-4.88281045497899|-8.10302425787397|0.702386021242573|   3.09625289823198|-9.25208847558951| -9.25208847558951|  -8.43790723241911|  2.2775750091235|  2.9649832857603|-9.25208847558951|-6.48214751517502| 3.15148733019296| 4.76967885540927|  2.94501217667055| 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13.2470384835186| -0.15096667912267| 1.30887911178182|-1.55957824864986| 5.32330617810091|-1.46832003033073| 4.44766205314096|-2.82423548372214|-0.362650324320476|0.954367139578131| -2.1167807948519|-2.86884019753099|-3.65367916508873|-0.924191651822988|-0.205386070661251|-0.906700031772425|-3.65367916508873|0.538814872810432| 1.48491006987468|  5.28096589008907|-3.65367916508873|-2.73064411134853|  3.6501481503413|12.5965805938765|3.20474949352113| 11.4760160559527|-2.63205730723721|-0.798088057378279|  11.9385921798181|-3.65367916508873|-2.03934623748157|-2.32996323657857| -1.1611850134093|-3.65367916508873| -1.79299440820683|-0.0111508656748938| 11.0474017985768| 3.01116771641866|-1.30978815640983| 2.41353144630038| -1.8241137081886|13.0164717992109| 0.474820461894663| 1.93159588569958| 6.05357141563802| 0.469366188023178|0.873797840971665|8.97517524778287| 12.3412410237799| 8.15452618914215|-0.288784613553001|-3.21198322765416| 11.7985997319201| -3.0980593084206|-0.834889944572179| 1.90215845848938|-0.774287062258247|-3.65367916508873|-3.23446390502078|0.316051547266192| 6.19293183203101| 14.5906393258556|  -3.65367916508873|-3.65367916508873| 2.69451429632523|0.811570505845429|-1.94205210922027|0.00305744395345986|-0.921540237386628| 2.77406275676445| 0.152799568967195| 3.05475502991258|  3.9740925347168|-0.222872046737821|0.843990677274757|-2.48535804934901| 1.05179011076266|  1.79582278379093| 2.53325513421001|  13.6269805337608|0.784540485161723|-3.65367916508873|-3.01102379719923| 0.259823036683525|14.8501648529365|  1.31114454203846| 4.82550773426823|11.0814929810668| -3.02691297671049| 0.642418441404694|-1.03201406902379|   1.94469497367225| 1.24717315550506| -1.20016158587323|  3.57312893174496|-3.23360104910936| 15.5177794004829| 3.32492605494146|  11.3616082214334| 5.41040096625804| -1.02567552367394| 4.19001455355478| 1.77626814576734| -1.41339536930651|-1.10543984551347|              0.0| -2.62765819437436|-2.82131570414043| 8.33857776505454| 12.8445282617801| 2.39155628101379| 2.10166511818449|-0.58514592427602|-2.76171566771332|0.619739745989292|-2.22066632565244|  18.0394733092014|-0.597789406892448| -3.65367916508873| 7.80012898362985| 4.01320805862165|  -1.7845974113888|10.5167368811713| 1.57887031571828|   1.1217085630017|1.77815770944734|  0.39145796943967|-0.747308033204014|   9.028311654902|-3.12831183402304|-1.21530646175043|-3.65367916508873| 1.52305848230781|-0.757349183334092| 2.09658431054929| 7.35291326491794|0.785930675678622| 4.88815777918006|-3.65367916508873| 2.70894852076034| 13.1902490829779|-1.40657194303645|0.957014992093539| 1.03117322747725|-2.04194020758979|-2.22929449355063|   -3.65367916508873|-3.65367916508873|2.63358071434017| 3.26729768101997| 3.22523863731385| 0.250737493070941| 1.61332844699831| -2.2177306002282|  -1.0643710856183|0.219001870667584| 4.18517178107692| 5.71031121320128|-3.65367916508873| 11.1512042385532|0.956075797083713| 0.996286866506412| 6.18269410284966|-2.66330512903289|  10.8758832619996| -2.5238027042556|-1.03201406902379|-3.18483522211409| 9.58507481523842|  5.09758201770601| 8.13603708743402| 4.77756017259668|0.255008870166033|  11.445480282079|0.226643926894666|-2.59667953470317|  7.26447044725169| 8.66318046036166|-1.25513725083348|-3.65367916508873|-2.81154508498311| -2.1764681338902|0.537520239514763| 5.16969985851126|-3.65367916508873| -2.32300604237957|-3.30510679492909| 1.20325674139697| -3.65367916508873|0.060148597535238|  2.23497885880044|-2.40575165164515| -3.25248561402547|-3.65367916508873| 1.97952533484158|-3.65367916508873| 3.33255312888173|-3.65367916508873|-2.58411446999537|-0.254973090578322|-3.65367916508873|-2.54927540473954| 16.6323413641805| -3.65367916508873|-3.12280936715732| 0.45767771881731|   4.09210337798856| 7.41202393166527|-2.87690137466068|0.128687460419727|0.127712025075259|-3.65367916508873| 13.2305349013794| 8.56859657598329|-3.12751016974409|0.674855748959221| 1.50007754000076| -1.2594119871255|-3.65367916508873|-2.81283327752666|-1.07003974132468| 6.23158296184874|-0.715431017402019|-3.65367916508873|-2.79490241978054| 4.47993675931843| 14.0233802022226|-0.617949696168112|-2.74840529346053| 1.55500195708882| 5.02486041299662| 0.98467275517706| -2.22682403276452|  5.33778230761299| 4.63637471171353| 2.51362857898532| 9.66543007646929|-0.321973948589839| 10.2629422230376|-3.65367916508873| 2.45155470620005|-2.59176475184368|  4.21797230837246|-3.65367916508873|10.6210819839154|-3.65367916508873|-2.10253232775212| 6.24563983938265|-2.51322768813507|  12.7107697265726| 4.94011156156464|0.244267911139368|-1.59432388747231|-1.88399204386569|-0.301513882517388| -3.65367916508873|-0.992431221558659|-3.04288462700089|-0.0502367847206564| 2.80587376598553|-3.65367916508873| 11.7958580229789|  -1.3333966807243| 5.01982920595442| 13.2506326649623|-0.892330057075616|  0.21944499663281|-1.77911824222739|-1.88361130182498|-0.554619659473341|2.54778807086328| -2.0794323046199|-1.78144139144627|-3.10711802940348|   1.522017080153|-2.13127182697321|  -2.2604277279317| 3.62765558391419|  10.974053765878| 3.21413083342517|  2.59360088860606|-1.65184090055637| 2.08254415189504|  1.7361285346082| -3.65367916508873|-3.13806762559669| -3.65367916508873|-0.51265517386773|  5.6127806243887|  -1.7182571352939|-1.99083564759067| 3.08574637989979| -0.30777189019113|-3.00190415446767| 7.93689576875676|-3.65367916508873|   6.4303223632182|-2.42323018888122|-0.673379614294872|-3.65367916508873| 5.56004429602024|  5.04738084791154|-3.65367916508873| 2.55520870923825|-3.65367916508873|   5.73839370874269| 15.2339028028123|-2.46072754701888|-0.156846154671505| 2.65669367757009|-3.65367916508873|  -3.1003186617534|5.01866378532805|\n",
            "+------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+------------------+------------------+----------------+------------------+-----------------+-----------------+------------------+----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-------------------+-----------------+------------------+-------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+----------------+----------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-------------------+-----------------+-----------------+-----------------+-----------------+-----------------+----------------+------------------+-----------------+-----------------+------------------+-----------------+----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-------------------+-----------------+-----------------+-----------------+-----------------+-------------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+----------------+------------------+-----------------+----------------+------------------+------------------+-----------------+-------------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-----------------+------------------+----------------+-----------------+------------------+----------------+------------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+--------------------+-----------------+----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-------------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-----------------+------------------+------------------+------------------+-----------------+-------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+------------------+-----------------+-----------------+------------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+------------------+-----------------+------------------+-----------------+-----------------+------------------+-----------------+-----------------+-----------------+-------------------+-----------------+-----------------+------------------+-----------------+-----------------+------------------+----------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeyL_Jxez2b3"
      },
      "source": [
        "Ahora vamos aplicar el algoritmo[ K-medias](https://spark.apache.org/docs/latest/ml-clustering.html#k-means) a los datos que es uno de los algoritmos de clustering más simple y popular del aprendizaje no supervisado de datos. Veamos antes algunos de sus [parámetros](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.clustering.KMeans) más relevantes:\n",
        "\n",
        "*   **`k`**:  indica el número de centroides a usar, es decir, el número de clusters que va a detectar. \n",
        "*   **`maxIter`**: nos indica el número máximo de iteraciones que realiza el algoritmo.\n",
        "*   **`distanceMeasure`**: especifica al algoritmo de distancia a usar, que puede tomar los valores `cosine` y `euclidean` (por defecto).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMRzjlCEMPfU"
      },
      "source": [
        "from pyspark.ml.clustering import KMeans\n",
        "\n",
        "#Unimos las variables de entrada con VectorAssembler\n",
        "varsEntrada=dfC.columns\n",
        "varsEntrada.remove('_c0')\n",
        "assembler = VectorAssembler(inputCols=varsEntrada, outputCol=\"features\")\n",
        "\n",
        "# Definimos un K-medias para clustering, usando 2 clústers\n",
        "kmeans = KMeans(k=2)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el K-medias\n",
        "tuberia=Pipeline().setStages([assembler,kmeans] ) \n",
        "\n",
        "#Construimos el modelo con los datos\n",
        "modeloKmedias = tuberia.fit(dfC)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcR5SyLLDnYz"
      },
      "source": [
        "\n",
        "La evaluación de los modelos de aprendizaje no supervisado es difícil debido a la naturaleza del problema del Clustering: los clúster no se conocen de antemano y no es fácil separar los clústers buenos de los malos. Para ello  podemos calcular el índice Silueta (*Silhouette*) que, como vimos, es una estimación de la cohesión de una instancia con su clúster y la separación con los otros clústers. Toma valores entre [-1, 1], cuanto mayor, mejor. Para calcular dicho índice en Spark, tendremos que usar la clase `ClusteringEvaluator` que nos recuerda mucho a los evaluadores usados en regresión y clasificación."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdHiqj0jHCmq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30e14a67-d81e-42f7-f770-6b1c87f18be1"
      },
      "source": [
        "from pyspark.ml.evaluation import ClusteringEvaluator\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido\n",
        "predicciones = modeloKmedias.transform(dfC)\n",
        "\n",
        "#Ya podemos evaluar el clustering usando el índice Silhouette \n",
        "evaluador = ClusteringEvaluator()\n",
        "silueta = evaluador.evaluate(predicciones)\n",
        "print(\"El índice silueta es = \" + str(silueta)+\" (usando k=2)\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "El índice silueta es = 0.17492723894069465 (usando k=2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-avDu-1FJFIf"
      },
      "source": [
        "\n",
        "\n",
        "Otro modelo es el [bisecting-K-means](https://spark.apache.org/docs/latest/ml-clustering.html#bisecting-k-means), que es una variante jerárquica del K-medias. En cada paso, este algoritmo parte cada clúster en dos usando K-medias.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELSJk_AnEEla",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16cdfde1-9881-4baa-ed16-e5048a114eb0"
      },
      "source": [
        "from pyspark.ml.clustering import BisectingKMeans\n",
        "\n",
        "# Definimos un K-medias para clustering, usando 2 clústers\n",
        "bkmeans = BisectingKMeans().setK(2)\n",
        "\n",
        "#Creamos una tubería, con el preprocesamiento y el bisecting-K-means\n",
        "tuberia=Pipeline().setStages([assembler,bkmeans] ) \n",
        "\n",
        "#Construimos el modelo con los datos\n",
        "modeloBKmedias = tuberia.fit(dfC)\n",
        "\n",
        "#Hacemos predicciones con el modelo aprendido\n",
        "prediccionesB = modeloBKmedias.transform(dfC)\n",
        "\n",
        "#Ya podemos evaluar el clustering usando el índice Silhouette \n",
        "silueta = evaluador.evaluate(prediccionesB)\n",
        "print(\"EL índice silueta es = \" + str(silueta)+\" (usando k=2)\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "EL índice silueta es = 0.17492723894069465 (usando k=2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLMFukZCEfaM"
      },
      "source": [
        "Existen más métodos de Clustering en Spark. En la siguiente tabla se muestran los que hay. \n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "<th><p> Métodos de Clustering implementados en Spark</p>\n",
        "</th></tr>\n",
        "\n",
        "<tr><td><p>\n",
        "<a href=\"https://spark.apache.org/docs/latest/ml-clustering.html#k-means\">K-medias </a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-clustering.html#latent-dirichlet-allocation-lda\">Asignación Latente de Dirichlet </a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-clustering.html#bisecting-k-means\">Bisecting K-means</a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-clustering.html#gaussian-mixture-model-gmm\"> Mixtura de Gaussianas </a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-clustering.html#power-iteration-clustering-pic\">Power Iteration Clustering</a></p><br/></td></tr>\n",
        "\n",
        "\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTK8RlnOLjP0"
      },
      "source": [
        "##**5. REGLAS DE ASOCIACIÓN EN APACHE SPARK.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVakU5xJQSVW"
      },
      "source": [
        "Las reglas de asociación son otra herramienta del aprendizaje no supervisado que nos van a permitir descubrir relaciones entre variables en conjuntos de datos grandes. En Apache Spark, podemos encontrar el algoritmo [FP-growth](https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html#fp-growth) que ya hemos visto en el Módulo 6. \n",
        "\n",
        "El problema aquí está en el preprocesamiento de los datos, pues el algoritmo FP-Ggrowth de Spark es algo distinto a lo que hemos visto en el formato que acepta los datos.\n",
        "\n",
        "Tenemos que tener en cuenta que los datos transaccionales que utilza deben ser numéricos, deben ser únicos, es decir, no permite elementos repetidos y todos los itemsets deben ir en un `array`. Además, en Spark, no tenemos el método `get_dummies()` de `pandas`, por lo que tendremos nosotros que crear una columna para cada estado de cada variable. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2gPsrt8Ja7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "374a6f1c-6628-4573-fc22-f9592d4b6004"
      },
      "source": [
        "from pyspark.sql import functions as F\n",
        "\n",
        "#Nos descargamos el conjunto de datos para Reglas de Asociación en Google Colaboratory \n",
        "!wget  -nv --no-check-certificate 'https://docs.google.com/uc?export=download&id=1j7bPKMcMo1jHU-zERXo1lmUtbKw7AHlI' -O datos_entrada_reglas.csv\n",
        "#Leemos el conjunto de datos con todas las variables \n",
        "dfAR = spark.read.csv('datos_entrada_reglas.csv', inferSchema=True, header=True)\n",
        "print(\"Datos originales:\")\n",
        "dfAR.show(5)\n",
        "\n",
        "#Añadimos una nueva columna para cada posible estado de cada variable\n",
        "nuevas=[]\n",
        "contador=1\n",
        "variables=dfAR.columns\n",
        "#Recorremos las variables\n",
        "for variable in variables:\n",
        "  #Recorremos cada estado de la variable\n",
        "  for estado in dfAR.select(variable).distinct().collect():\n",
        "    if estado[variable] != '_': #Ignoramos valores nulos\n",
        "      #Si está dicho estado, añadiremos un identificador único, si no está 0\n",
        "      dfAR = dfAR.withColumn(variable+\"=\"+estado[variable], \n",
        "        F.when(dfAR[variable] == estado[variable], contador).otherwise(0))\n",
        "      #Incrementamos el número identificador de cada estado\n",
        "      contador=contador+1\n",
        "      #Guardamos en una lista las columnas ggeneradas\n",
        "      nuevas.append(variable+\"=\"+estado[variable])\n",
        "print(\"Datos con columnas por cada estado de cada variable:\")\n",
        "dfAR.select(nuevas).show(5)\n",
        "\n",
        "#Mostremos lo que significa cada identificador numérico\n",
        "print(\"Identificadores usados para cada estado de cada variable:\")\n",
        "for i in range(len(nuevas)):\n",
        "  print(i+1,\"=\",nuevas[i])\n",
        "\n",
        "#Unimos los atributos con la función array, pues VectorAssembler devuelve un \n",
        "#Vector y FPGrowth quiere los itemsets en array\n",
        "datosAR = dfAR.withColumn('itemsConCeros', F.array(nuevas))\n",
        "datosAR=datosAR.withColumn(\"items\", F.array_remove(\"itemsConCeros\", 0))\n",
        "\n",
        "print(\"\\nDatos procesador para FPGrowth:\")\n",
        "datosAR = datosAR.withColumn(\"id\", F.monotonically_increasing_id())\n",
        "datosAR.select(\"id\",\"items\").show(5,False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-09 11:13:26 URL:https://doc-0g-bc-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/ij8de6md11ffumtcto8sujaiah4m8nan/1615288350000/11180625338828972622/*/1j7bPKMcMo1jHU-zERXo1lmUtbKw7AHlI?e=download [22613/22613] -> \"datos_entrada_reglas.csv\" [1]\n",
            "Datos originales:\n",
            "+--------------------+------------+-------------------------+--------------------+----------+----------------+\n",
            "|    MUTATIONSUBTYPES|UV_signature|RNASEQ_CLUSTER_CONSENHIER|    MethTypes_201408|MIRCluster|LYMPHOCYTE_SCORE|\n",
            "+--------------------+------------+-------------------------+--------------------+----------+----------------+\n",
            "|BRAF_Hotspot_Mutants|UV_signature|                  keratin|         normal_like|MIR_type_3|               2|\n",
            "| RAS_Hotspot_Mutants|UV_signature|                  keratin|CpG_island_methyl...|MIR_type_2|               4|\n",
            "|BRAF_Hotspot_Mutants|UV_signature|                  keratin|         normal_like|MIR_type_1|               5|\n",
            "| RAS_Hotspot_Mutants|UV_signature|                  keratin|     hypo_methylated|MIR_type_2|               2|\n",
            "|           Triple_WT|      not_UV|                   immune|CpG_island_methyl...|MIR_type_2|               6|\n",
            "+--------------------+------------+-------------------------+--------------------+----------+----------------+\n",
            "only showing top 5 rows\n",
            "\n",
            "Datos con columnas por cada estado de cada variable:\n",
            "+------------------------------------+--------------------------------+--------------------------+-------------------------------------+-------------------------+-------------------+---------------------------------+--------------------------------+----------------------------------+--------------------------------+---------------------------------+----------------------------+--------------------------------------+---------------------+---------------------+---------------------+---------------------+------------------+------------------+------------------+------------------+------------------+------------------+\n",
            "|MUTATIONSUBTYPES=RAS_Hotspot_Mutants|MUTATIONSUBTYPES=NF1_Any_Mutants|MUTATIONSUBTYPES=Triple_WT|MUTATIONSUBTYPES=BRAF_Hotspot_Mutants|UV_signature=UV_signature|UV_signature=not_UV|RNASEQ_CLUSTER_CONSENHIER=keratin|RNASEQ_CLUSTER_CONSENHIER=immune|RNASEQ_CLUSTER_CONSENHIER=MITF_low|MethTypes_201408=hypo_methylated|MethTypes_201408=hyper_methylated|MethTypes_201408=normal_like|MethTypes_201408=CpG_island_methylated|MIRCluster=MIR_type_2|MIRCluster=MIR_type_1|MIRCluster=MIR_type_3|MIRCluster=MIR_type_4|LYMPHOCYTE_SCORE=3|LYMPHOCYTE_SCORE=0|LYMPHOCYTE_SCORE=5|LYMPHOCYTE_SCORE=6|LYMPHOCYTE_SCORE=4|LYMPHOCYTE_SCORE=2|\n",
            "+------------------------------------+--------------------------------+--------------------------+-------------------------------------+-------------------------+-------------------+---------------------------------+--------------------------------+----------------------------------+--------------------------------+---------------------------------+----------------------------+--------------------------------------+---------------------+---------------------+---------------------+---------------------+------------------+------------------+------------------+------------------+------------------+------------------+\n",
            "|                                   0|                               0|                         0|                                    4|                        5|                  0|                                7|                               0|                                 0|                               0|                                0|                          12|                                     0|                    0|                    0|                   16|                    0|                 0|                 0|                 0|                 0|                 0|                23|\n",
            "|                                   1|                               0|                         0|                                    0|                        5|                  0|                                7|                               0|                                 0|                               0|                                0|                           0|                                    13|                   14|                    0|                    0|                    0|                 0|                 0|                 0|                 0|                22|                 0|\n",
            "|                                   0|                               0|                         0|                                    4|                        5|                  0|                                7|                               0|                                 0|                               0|                                0|                          12|                                     0|                    0|                   15|                    0|                    0|                 0|                 0|                20|                 0|                 0|                 0|\n",
            "|                                   1|                               0|                         0|                                    0|                        5|                  0|                                7|                               0|                                 0|                              10|                                0|                           0|                                     0|                   14|                    0|                    0|                    0|                 0|                 0|                 0|                 0|                 0|                23|\n",
            "|                                   0|                               0|                         3|                                    0|                        0|                  6|                                0|                               8|                                 0|                               0|                                0|                           0|                                    13|                   14|                    0|                    0|                    0|                 0|                 0|                 0|                21|                 0|                 0|\n",
            "+------------------------------------+--------------------------------+--------------------------+-------------------------------------+-------------------------+-------------------+---------------------------------+--------------------------------+----------------------------------+--------------------------------+---------------------------------+----------------------------+--------------------------------------+---------------------+---------------------+---------------------+---------------------+------------------+------------------+------------------+------------------+------------------+------------------+\n",
            "only showing top 5 rows\n",
            "\n",
            "Identificadores usados para cada estado de cada variable:\n",
            "1 = MUTATIONSUBTYPES=RAS_Hotspot_Mutants\n",
            "2 = MUTATIONSUBTYPES=NF1_Any_Mutants\n",
            "3 = MUTATIONSUBTYPES=Triple_WT\n",
            "4 = MUTATIONSUBTYPES=BRAF_Hotspot_Mutants\n",
            "5 = UV_signature=UV_signature\n",
            "6 = UV_signature=not_UV\n",
            "7 = RNASEQ_CLUSTER_CONSENHIER=keratin\n",
            "8 = RNASEQ_CLUSTER_CONSENHIER=immune\n",
            "9 = RNASEQ_CLUSTER_CONSENHIER=MITF_low\n",
            "10 = MethTypes_201408=hypo_methylated\n",
            "11 = MethTypes_201408=hyper_methylated\n",
            "12 = MethTypes_201408=normal_like\n",
            "13 = MethTypes_201408=CpG_island_methylated\n",
            "14 = MIRCluster=MIR_type_2\n",
            "15 = MIRCluster=MIR_type_1\n",
            "16 = MIRCluster=MIR_type_3\n",
            "17 = MIRCluster=MIR_type_4\n",
            "18 = LYMPHOCYTE_SCORE=3\n",
            "19 = LYMPHOCYTE_SCORE=0\n",
            "20 = LYMPHOCYTE_SCORE=5\n",
            "21 = LYMPHOCYTE_SCORE=6\n",
            "22 = LYMPHOCYTE_SCORE=4\n",
            "23 = LYMPHOCYTE_SCORE=2\n",
            "\n",
            "Datos procesador para FPGrowth:\n",
            "+---+---------------------+\n",
            "|id |items                |\n",
            "+---+---------------------+\n",
            "|0  |[4, 5, 7, 12, 16, 23]|\n",
            "|1  |[1, 5, 7, 13, 14, 22]|\n",
            "|2  |[4, 5, 7, 12, 15, 20]|\n",
            "|3  |[1, 5, 7, 10, 14, 23]|\n",
            "|4  |[3, 6, 8, 13, 14, 21]|\n",
            "+---+---------------------+\n",
            "only showing top 5 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCQJV2bq3clu"
      },
      "source": [
        "\n",
        "La implementación de FP-growth en Spark, toma los siguientes parámetros:\n",
        "\n",
        "*  **`minSupport`**: el soporte mínimo para que un *itemset* se identifique como frecuente.\n",
        "*  **`minConfidence`**: confianza mínima para generar una regla de asociación. El parámetro no afectará a la extracción de *itemsets*  frecuentes, pero determinará la confianza mínima para generar reglas de asociación a partir de conjuntos de artículos frecuentes.\n",
        "\n",
        "Una vez que hemos aprendido el modelo, podemos usar el método `freqItemsets` para obtener los *itemsets* frecuentes, o se puede usar el método `associationRules` con el que obtenemos las reglas de asociación."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfZoX3KavPuq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b03d06f-fc88-473f-a710-78890e626e24"
      },
      "source": [
        "from pyspark.ml.fpm import FPGrowth\n",
        "\n",
        "#Definimos el algoritmo FPGrowth\n",
        "fpGrowth = FPGrowth(itemsCol=\"items\", minSupport=0.25, minConfidence=0.2)\n",
        "\n",
        "#Construimos el modelo con los datos\n",
        "modeloFPgrowth = fpGrowth.fit(datosAR)\n",
        "\n",
        "# Mostramos los elementos frecuentes.\n",
        "modeloFPgrowth.freqItemsets.show()\n",
        "\n",
        "# Mostramos la reglas de asociación generadas.\n",
        "modeloFPgrowth.associationRules.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+------+----+\n",
            "| items|freq|\n",
            "+------+----+\n",
            "|  [14]|  85|\n",
            "|   [4]| 150|\n",
            "|[4, 5]| 136|\n",
            "|   [1]|  92|\n",
            "|[1, 5]|  86|\n",
            "|  [11]|  93|\n",
            "|  [13]|  85|\n",
            "|   [8]| 168|\n",
            "|[8, 5]| 142|\n",
            "|  [10]|  84|\n",
            "|   [7]| 102|\n",
            "|  [19]|  91|\n",
            "|   [5]| 265|\n",
            "+------+----+\n",
            "\n",
            "+----------+----------+-------------------+------------------+\n",
            "|antecedent|consequent|         confidence|              lift|\n",
            "+----------+----------+-------------------+------------------+\n",
            "|       [8]|       [5]| 0.8452380952380952|1.0621293800539082|\n",
            "|       [4]|       [5]| 0.9066666666666666| 1.139320754716981|\n",
            "|       [5]|       [4]| 0.5132075471698113| 1.139320754716981|\n",
            "|       [5]|       [1]|0.32452830188679244|1.1746513535684988|\n",
            "|       [5]|       [8]| 0.5358490566037736|1.0621293800539084|\n",
            "|       [1]|       [5]| 0.9347826086956522|1.1746513535684988|\n",
            "+----------+----------+-------------------+------------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKwc7qf9qkgx"
      },
      "source": [
        "En Apache Spark no está implmentado el algoritmo *a priori*, no obstante, como hemos visto, sí que está implementado el algoritmo *Fp-Growth*.\n",
        "<table>\n",
        "<tr>\n",
        "<th><p> Algoritmos de Reglas de Asociación en Apache Spark</p>\n",
        "</th></tr>\n",
        "\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html#fp-growth\">FP-Growth </a></p><br/></td></tr>\n",
        "<tr><td><p><a href=\"https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html#prefixspan\"> PrefixSpan </a></p><br/></td></tr>\n",
        "\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHtaTrToin7P"
      },
      "source": [
        "## **REFERENCIAS BIBLIOGRÁFICAS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8J4zaNfgZ-L"
      },
      "source": [
        "* The Apache Software Foundation.\"Classification and regression\". (2020). [Acceso 23 de junio de 2020]. Disponible en: https://spark.apache.org/docs/latest/ml-classification-regression.html#classification-and-regression\n",
        "\n",
        "* The Apache Software Foundation.\"Clustering\". (2020). [Acceso 23 de junio de 2020]. Disponible en: https://spark.apache.org/docs/latest/ml-clustering.html#clustering\n",
        "\n",
        "* The Apache Software Foundation.\"Frequent Pattern Mining\". (2020). [Acceso 23 de junio de 2020]. Disponible en: https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html#frequent-pattern-mining"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5_VU5syYO-s"
      },
      "source": [
        "<div class=\"alert alert-block alert-success\">\n",
        "\n",
        "MOOC Machine Learning y Big Data para la Bioinformática (1ª edición)   \n",
        "http://abierta.ugr.es     \n",
        "    \n",
        "![CC](https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by-nc-nd.png)\n",
        "</div>    "
      ]
    }
  ]
}