GravityML_Experiment3_TODO.ipynb

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      "name": "GravityML-Experiment3-TODO.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/juanBIMMUNE/GravityML/blob/master/GravityML_Experiment3_TODO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuuzs4idILhE",
        "colab_type": "text"
      },
      "source": [
        "# **EXPERIMENTO 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvyBnrJipKgF",
        "colab_type": "text"
      },
      "source": [
        "**DESCRIPCIÓN:**\n",
        "\n",
        "Vamos a realizar un último experimento que nos permitirá entender mejor otros factores que afectan al movimiento vertical. En él, además de desactivar el vacío, desactivaremos el control de temperatura de la cámara, de tal forma que podremos encontrar **diferentes densidades del aire** en diferentes lanzamientos de objetos.\n",
        "\n",
        "* Solicitamos a nuestros compañeros que ejecuten un set de tests con la **cámara de vacío apagada** y el **control de temperatura apagado**. \n",
        "\n",
        "* Todos los objetos se dejarán caer desde la misma altura: **200m**.\n",
        "\n",
        "* En este experimento **todos los objetos tendrán la misma forma** (mismo coeficiente de drag).\n",
        "\n",
        "Veamos que sucede..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6k_iBndGJADw",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "![texto alternativo](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e5/NASA_logo.svg/200px-NASA_logo.svg.png)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faxgBpITCrAb",
        "colab_type": "text"
      },
      "source": [
        "**INSTRUCCIONES:**\n",
        "\n",
        "* Para completar el reto, debes completar cada una de las tareas abajo indicadas. \n",
        "* Sólo debes modificar aquellas líneas donde veas una interrogación (**?**) en el código.\n",
        "* Antes de empezar, navega a la sección **ANEXOS > GENERADOR DATASET** y ejecuta su celda de código. Esto te permitirá generar durante el reto nuevas observaciones.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxRV0eeoutSU",
        "colab_type": "text"
      },
      "source": [
        "## **PREPARA TUS HERRAMIENTAS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbIOt-63qGoV",
        "colab_type": "text"
      },
      "source": [
        "Hemos importado ya las librerías necesarias para ti, ejecuta la siguiente celda para poder utilizarlas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7vcv8OSvHcb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#MANIPULACIÓN DE DATOS\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "#VISUALIZACIÓN\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cm\n",
        "import seaborn as sns\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "#ALGORITMOS ML\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.model_selection import train_test_split  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOgDBOFlTdgZ",
        "colab_type": "text"
      },
      "source": [
        "## **PREPARA TU DATASET**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvERqfO0qN6b",
        "colab_type": "text"
      },
      "source": [
        "Nunca generar nuevas observaciones fue tan fácil, es una de las ventajas de trabajar para la NASA! A continuación generamos un dataset adaptado a las condiciones del experimento.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lvz-VrsHFuXD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#DATASET GENERADO\n",
        "samples = dataset_generator(vacuum=0, population_size=50, checkpoints=20,temperature_control=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_p8SDe8CUy6L",
        "colab_type": "text"
      },
      "source": [
        "## **EXPLORAMOS LOS DATOS**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfmV5YPbHFTy",
        "colab_type": "text"
      },
      "source": [
        "Vamos a ver qué aspecto tienen los datos recabados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IDnRcbbUquP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "6c95be8f-fca1-4c3b-a2f5-42746fe21048"
      },
      "source": [
        "samples.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Z</th>\n",
              "      <th>T</th>\n",
              "      <th>DRAG</th>\n",
              "      <th>AIR_DENSITY</th>\n",
              "      <th>MASS</th>\n",
              "      <th>AREA</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Object0</td>\n",
              "      <td>168.342554</td>\n",
              "      <td>2.755058</td>\n",
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              "      <td>135.203759</td>\n",
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              "      <td>183.973773</td>\n",
              "      <td>1.960236</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.157053</td>\n",
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              "    <tr>\n",
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              "      <td>Object0</td>\n",
              "      <td>123.200680</td>\n",
              "      <td>4.291128</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.157053</td>\n",
              "      <td>5</td>\n",
              "      <td>0.125</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Object0</td>\n",
              "      <td>185.172445</td>\n",
              "      <td>1.885504</td>\n",
              "      <td>1.05</td>\n",
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              "      <td>5</td>\n",
              "      <td>0.125</td>\n",
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              "      <td>4.017100</td>\n",
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              "    <tr>\n",
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              "      <td>196.765900</td>\n",
              "      <td>0.880581</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.157053</td>\n",
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            "text/plain": [
              "        ID           Z         T  DRAG  AIR_DENSITY  MASS   AREA\n",
              "0  Object0  168.342554  2.755058  1.05     1.157053     5  0.125\n",
              "1  Object0  135.203759  3.941556  1.05     1.157053     5  0.125\n",
              "2  Object0  199.935086  0.124756  1.05     1.157053     5  0.125\n",
              "3  Object0  183.973773  1.960236  1.05     1.157053     5  0.125\n",
              "4  Object0  123.200680  4.291128  1.05     1.157053     5  0.125\n",
              "5  Object0  185.172445  1.885504  1.05     1.157053     5  0.125\n",
              "6  Object0  132.696169  4.017100  1.05     1.157053     5  0.125\n",
              "7  Object0  167.604305  2.786997  1.05     1.157053     5  0.125\n",
              "8  Object0  196.765900  0.880581  1.05     1.157053     5  0.125\n",
              "9  Object0  184.321800  1.938835  1.05     1.157053     5  0.125"
            ]
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          "metadata": {
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          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmA-zwRHH1y0",
        "colab_type": "text"
      },
      "source": [
        "**ID** = identificador del objeto lanzado\n",
        "\n",
        "**Z** = altura del objeto en un determinado tiempo\n",
        "\n",
        "**T** = tiempo que ha pasado desde que se ha dejado caer el objeto y se ha \n",
        "realizado la medición\n",
        "\n",
        "**DRAG** = coeficiente de rozamiento del objeto\n",
        "\n",
        "**AIR_DENSITY** = densidad del aire\n",
        "\n",
        "**MASS** = masa del objeto\n",
        "\n",
        "**AREA** = área del objeto (perpendicular al movimiento)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56MNFqRXKd-0",
        "colab_type": "text"
      },
      "source": [
        "Ponemos foco en la relación altura-tiempo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HXxlPadIxn0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2b45f1b3-8469-4212-8874-6d52f2f971ae"
      },
      "source": [
        "sns.scatterplot('T','Z',data=samples);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEnH4LevB5zr",
        "colab_type": "text"
      },
      "source": [
        "Podemos ver de nuevo que el tiempo por sí solo no parece explicar el comportamiento de los objetos: vemos cierta variación en la curva generada."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OdXclLtESeU",
        "colab_type": "text"
      },
      "source": [
        "## **GENERAMOS EL MODELO**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCjb9gksv9cf",
        "colab_type": "text"
      },
      "source": [
        "### **TAREA 1:** GENERA LAS COLUMNAS NECESARIAS PARA ENTRENAR TU MODELO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6mD3PYbZUnh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A) COLUMNA T^2 (T AL CUADRADO)\n",
        "samples['T2'] = ?\n",
        "\n",
        "# B) COLUMNA T^2 * AIR_DENSITY (T AL CUADRADO POR DENSIDAD DEL AIRE)\n",
        "samples['T2-AIR_DENSITY'] = ?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oJNzQILwN41",
        "colab_type": "text"
      },
      "source": [
        "### **TAREA 2:** DEFINE Y ENTRENA TU MODELO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LypQWJ3LwNaO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d9bb4e3-1783-4b16-8b67-e48a121f166d"
      },
      "source": [
        "# INICIALIZAMOS UN NUEVO MODELO DE REGRESIÓN\n",
        "model = LinearRegression()\n",
        "\n",
        "# DEFINIMOS LAS VARIABLES DE ENTRADA Y SALIDA\n",
        "X = ? # ENTRADA\n",
        "y = ? # SALIDA\n",
        "\n",
        "# ENTRENAMOS EL MODELO\n",
        "model.fit(X, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkqIgarqxOEO",
        "colab_type": "text"
      },
      "source": [
        "### **TAREA 3:** PON A PRUEBA TU MODELO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpkDrnILGKZ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = ?\n",
        "plt.scatter(samples[['T']],y)\n",
        "plt.scatter(samples[['T']],predictions);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHiNL9TyD9hz",
        "colab_type": "text"
      },
      "source": [
        "Deberías ver cómo las predicciones y las observaciones originales quedan solapadas en el gráfico al adaptarse el modelo perfectamente al fenómeno estudiado."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm22yiq-FOnt",
        "colab_type": "text"
      },
      "source": [
        "Otro punto de vista (3 dimensiones):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpRAz0AeElJ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "e33f04b9-33df-412c-a08e-d8737446c34e"
      },
      "source": [
        "# 3D Plotting\n",
        "fig = plt.figure(figsize=[8, 6.5])\n",
        "ax = fig.gca(projection='3d')\n",
        "\n",
        "t_surf=np.arange(0, 6.5, 0.1)                # generate mesh\n",
        "d_surf=np.arange(1, 1.55, 0.1)\n",
        "t_surf, d_surf = np.meshgrid(t_surf, d_surf)\n",
        "\n",
        "z_surf = model.intercept_ + model.coef_[0]*t_surf + model.coef_[1]*t_surf**2 + model.coef_[2]*t_surf**2*d_surf\n",
        "ax.plot_surface(t_surf, d_surf, z_surf, cmap=cm.hot, alpha=0.6);\n",
        "ax.set_xlabel('t')\n",
        "ax.set_ylabel('p')\n",
        "ax.set_zlabel('z')\n",
        "ax.plot(samples['T'],samples['AIR_DENSITY'],samples['Z'],'ro');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x468 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOg7PtDGv3Df",
        "colab_type": "text"
      },
      "source": [
        "## **ANEXOS**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxqVLDnUTIEv",
        "colab_type": "text"
      },
      "source": [
        "### **GENERADOR DATASET**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8FN_vNAyPdU",
        "colab_type": "text"
      },
      "source": [
        "Esta funcionalidad nos pemitirá generar nuevos datasets bajo demanda."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUYrziAOv8pw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "#CONSTANTS\n",
        "Z_0 = 200 #posición cuando t=0 -> siempre dejamos caer los objetos desde esta altura\n",
        "V_0 = 0 #velocidad cuando t=0 -> los objetos parten a velocidad cero\n",
        "GRAVITY = -9.8 #m/s2\n",
        "AIR_DENSITY_VACUUM_ON = 0 #kg/m3\n",
        "AIR_DENSITY_VACUUM_OFF = 1.2041 #kg/m3\n",
        "MASS = 5 #kg\n",
        "DRAG_COEFFICIENT = 1.05 #cube coefficient\n",
        "AREA = 0.125 #m2\n",
        "np.random.seed(0)\n",
        "default_population_size = 50 #cantidad de objetos lanzados\n",
        "default_checkpoints = 10 #cantidad de puntos de control durante la caída\n",
        "\n",
        "\n",
        "#F(X) REAL DEL MOVIMIENTO VERTICAL\n",
        "def object_vertical_location(air_density,drag_coeff,t):\n",
        "  return Z_0 + t*V_0 + t**2*GRAVITY/2 + t**2*(air_density*AREA*drag_coeff*GRAVITY**2/(2*MASS))/2\n",
        "\n",
        "\n",
        "#GENERADOR DATASET\n",
        "def dataset_generator(vacuum=1, population_size=default_population_size, checkpoints=default_checkpoints,temperature_control=1):\n",
        "  #Initialize dataset\n",
        "  dataset = pd.DataFrame(columns=['ID','Z','T','DRAG'])  \n",
        "  \n",
        "  #Generamos un coefficiente de rozamiento para cada objeto\n",
        "  drag_coeffs = np.random.normal(DRAG_COEFFICIENT_AVG, 0.34, population_size)\n",
        "\n",
        "  #Generamos una densidad del aire para cada objeto (densidad cuando se llevó a cabo el lanzamiento)\n",
        "  air_densities = np.random.normal(1.2466, 0.10, population_size)\n",
        "\n",
        "    \n",
        "  #Vertical locations\n",
        "  if vacuum == 1: # Con vacío\n",
        "    for i in range(0,population_size): #Para cada objeto calculamos su altura en cada checkpoint\n",
        "      t = np.random.uniform( low=0,high=6.5,size=checkpoints)\n",
        "      dataset = dataset.append(pd.DataFrame(\n",
        "          { \"ID\": np.full((checkpoints,), i, dtype=int),\n",
        "            \"Z\": object_vertical_location(AIR_DENSITY_VACUUM_ON,drag_coeffs[i],t), \n",
        "            \"T\":t,\n",
        "            \"AIR_DENSITY\":np.full((checkpoints,), air_densities[i])\n",
        "            }\n",
        "          )\n",
        "      )\n",
        "  else: # Sin vacío\n",
        "    if temperature_control==1: #Temperatura constante, densidad del aire no cambia\n",
        "      for i in range(0,population_size): #Para cada objeto calculamos su altura en cada checkpoint\n",
        "        t = np.random.uniform( low=0,high=6.5,size=checkpoints)\n",
        "        dataset = dataset.append(pd.DataFrame(\n",
        "            { \"ID\": np.full((checkpoints,), i, dtype=int),\n",
        "              \"Z\": object_vertical_location(AIR_DENSITY_VACUUM_OFF,DRAG_COEFFICIENT,t), \n",
        "              \"T\":t,\n",
        "              \"AIR_DENSITY\":np.full((checkpoints,), air_densities[i])\n",
        "              }\n",
        "            )\n",
        "        )\n",
        "    else:\n",
        "      for i in range(0,population_size): #Para cada objeto calculamos su altura en cada checkpoint\n",
        "        t = np.random.uniform( low=0,high=6.5,size=checkpoints)\n",
        "        dataset = dataset.append(pd.DataFrame(\n",
        "            { \"ID\": np.full((checkpoints,), i, dtype=int),\n",
        "              \"Z\": object_vertical_location(air_densities[i],DRAG_COEFFICIENT,t), \n",
        "              \"T\":t,\n",
        "              \"AIR_DENSITY\":np.full((checkpoints,), air_densities[i])\n",
        "              }\n",
        "            )\n",
        "        )\n",
        "  dataset['ID'] = 'Object' + dataset['ID'].astype(str)\n",
        "  dataset['DRAG'] = DRAG_COEFFICIENT\n",
        "  dataset['MASS'] = MASS\n",
        "  dataset['AREA'] = AREA\n",
        "  return dataset\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyH00V_BAL8k",
        "colab_type": "text"
      },
      "source": [
        "\\* La ecuación física utilizada para construir este generador de datasets no sigue la ecuación física real, sino que se ha utilizado una ecuación simplificada con fines educativos."
      ]
    }
  ]
}