Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.rand(100)*10\n",
    "y = np.random.rand(100)*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'binned_model_errors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-bd3e842c1bf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbinned_model_errors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRMS_abs_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'blue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myfit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'binned_model_errors' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.set_title('axes title')\n",
    "\n",
    "ax.set_xlabel('xlabel')\n",
    "ax.set_ylabel('ylabel')\n",
    "\n",
    "ax.plot(x,y,'o')\n",
    "plt.plot(binned_model_errors, RMS_abs_res, 'o', color='blue')\n",
    "plt.plot(xfit, yfit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[100, 0.5, 0.3], [200, 0.6, 0.1], [500, 0.55, 0.05]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, err = [], [], []\n",
    "for i in range(0, len(a)):\n",
    "    k = a[i]\n",
    "    x.append(k[0])\n",
    "    y.append(k[1])\n",
    "    err.append(k[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 500]\n",
      "[0.5, 0.6, 0.55]\n",
      "[0.3, 0.1, 0.05]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)\n",
    "print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "#ax.scatter(x,y)\n",
    "ax.errorbar(x,y,yerr=err, fmt='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_direct_unscaled = np.load('friedman_500_data/a_direct_unscaled.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.00000000e+01 4.86652313e-01 3.50850321e-01]\n",
      " [1.00000000e+02 7.99439960e-01 1.84902044e-01]\n",
      " [1.50000000e+02 6.54844432e-01 2.17419419e-01]\n",
      " [2.00000000e+02 6.77556249e-01 1.53167019e-01]\n",
      " [2.50000000e+02 5.19110472e-01 3.17360182e-01]\n",
      " [3.00000000e+02 6.56400178e-01 1.94336486e-01]\n",
      " [3.50000000e+02 6.54510456e-01 2.12784308e-01]\n",
      " [4.00000000e+02 6.58927197e-01 1.81292749e-01]\n",
      " [4.50000000e+02 7.22427016e-01 7.50414371e-03]\n",
      " [5.00000000e+02 6.64863075e-01 1.71805853e-01]\n",
      " [5.50000000e+02 7.22524803e-01 7.15001394e-03]\n",
      " [6.00000000e+02 6.60339221e-01 1.76649599e-01]\n",
      " [6.50000000e+02 6.63368337e-01 1.87802778e-01]\n",
      " [7.00000000e+02 7.21646222e-01 9.15402886e-03]\n",
      " [7.50000000e+02 6.52592864e-01 1.97674247e-01]\n",
      " [8.00000000e+02 6.57184820e-01 1.91410894e-01]\n",
      " [8.50000000e+02 7.16853644e-01 4.45452981e-03]\n",
      " [9.00000000e+02 7.22387158e-01 8.60322335e-03]\n",
      " [9.50000000e+02 6.47716771e-01 2.30027745e-01]\n",
      " [1.00000000e+03 7.21483426e-01 8.37626562e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(a_direct_unscaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from package import CVData as cvd\n",
    "from package import CorrectionFactors as cf\n",
    "from package import MakePlot as mp\n",
    "from package import TestData as td\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = np.load('perovskite_data/all_x_values.npy')\n",
    "#y = np.load('perovskite_data/all_y_values.npy')\n",
    "X = np.load('friedman_500_data/training_x_values.npy')\n",
    "y = np.load('friedman_500_data/training_y_values.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standarize features\n",
    "scaler = StandardScaler()\n",
    "X_std = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ridge regression with three possible alpha values\n",
    "regr_cv = RidgeCV(alphas=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the linear regression\n",
    "model_cv = regr_cv.fit(X_std, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View alpha\n",
    "model_cv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load('data_for_paper_plots/Friedman_500/LR/Test/a.npy')\n",
    "b = np.load('data_for_paper_plots/Friedman_500/LR/Test/b.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.21369092]\n",
      "[-0.06079379]\n",
      "mean of a:\n",
      "8.213690915943891\n",
      "stdev of a:\n",
      "0.0\n",
      "mean of b:\n",
      "-0.06079379433812552\n",
      "stdev of b:\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)\n",
    "print(\"mean of a:\")\n",
    "print(np.mean(a))\n",
    "print(\"stdev of a:\")\n",
    "print(np.std(a))\n",
    "print(\"mean of b:\")\n",
    "print(np.mean(b))\n",
    "print(\"stdev of b:\")\n",
    "print(np.std(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pv = np.load('perovskite_data/all_x_values.npy')\n",
    "y_train_pv = np.load('perovskite_data/all_y_values.npy')\n",
    "X_train_diff = np.load('diffusion_data/all_x_values.npy')\n",
    "y_train_diff = np.load('diffusion_data/all_y_values.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1918\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train_pv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "408\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train_diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train_pv[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "287\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train_diff[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.53312500e+01  3.00000000e+02  0.00000000e+00  4.05000000e+02\n",
      "  2.67893750e+03  2.25000000e+01  5.00000000e+00  1.60000000e-01\n",
      "  0.00000000e+00  2.70625000e+01  2.30000000e+02  7.00000000e+00\n",
      " -5.32394920e+00  1.63000000e+00  5.00000000e+00  2.22625000e+01\n",
      "  7.00000000e+00  7.00000000e+00 -1.68281360e+00  7.00000000e+00\n",
      "  2.92006250e+02  1.70787500e+03  5.00000000e+00  1.27758750e+01\n",
      "  2.20437500e+00  1.70000000e-01  0.00000000e+00  2.91250000e+02\n",
      "  5.00000000e+00  2.07800000e+03  1.92000000e+00  7.00000000e+00\n",
      "  1.00000000e+00  3.67500000e+02  7.00000000e+00  3.00000000e+00\n",
      "  2.62850136e-01  0.00000000e+00  4.46700000e+02 -1.67793991e+00\n",
      "  1.06800000e+01  1.76630435e-01  8.00000000e+00  3.00000000e+00\n",
      "  1.30100000e+01  0.00000000e+00  0.00000000e+00  2.13223900e-03\n",
      "  8.01000000e+00 -5.32608144e+00  6.45502198e+00  1.73987500e+00\n",
      "  0.00000000e+00  1.26000000e+00  8.00000000e+00  4.07586269e+00\n",
      "  7.00000000e+00 -5.17500000e+01 -7.31187500e+00  3.07000000e+01\n",
      "  8.40000000e+00  4.72500000e+00  6.18731250e+00  6.80000000e+00\n",
      " -3.43750000e-01  3.00000000e+00 -8.93926069e+00  8.00000000e+00\n",
      "  0.00000000e+00  1.84000000e+01]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_pv[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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