Perovskite learning curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning curve calculation and plotting.\n",
    "## Author: Zhi Li\n",
    "## Date: 11/10/2019\n",
    "### After removing fixed ratio data points, we now have 4967 reactions to model. Based on the overal data, PUFK_SVM model performs the best. In this script, I will accomplish below task for Methylammonium, Ethylammonium, n-Butylammonium,  Formamidinium, Acetamidinium, Cyclohexylmethylammonium (with all 4 metrics) : 1) PUFK_SVM learning curve with N-1 amine 2) PUFK_SVM learning curve with only target amine 3) 1NN learning curve with only target amine 4) single feature (formic acid, organic, inorganic) learning curve with only target amine.\n",
    "### I fixed learn_rate == 1 end point bug (target amine and N-1 amine with same learning curve end point)\n",
    "### I fixed N-1 learning curves: in version 3, N-1 learning curves only used 3 concentrations as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import os, fnmatch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pprint\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.spatial.distance import pdist, cdist, squareform\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data input\n",
    "file = \"0042.perovskitedata_RAPID.csv\"\n",
    "df_all = pd.read_csv(file)\n",
    "df = pd.DataFrame(columns = df_all.columns)\n",
    "\n",
    "Inchi = pd.read_csv(\"Organic Inchikey.csv\")\n",
    "Inchidict = dict(zip(Inchi['Chemical Name'], Inchi['InChI Key (ID)']))\n",
    "\n",
    "lst = [\"LLWRXQXPJMPHLR-UHFFFAOYSA-N\",\"XFYICZOIWSBQSK-UHFFFAOYSA-N\",\"CALQKRVFTWDYDG-UHFFFAOYSA-N\",\\\n",
    "       \"QHJPGANWSLEMTI-UHFFFAOYSA-N\",\"GGYGJCFIYJVWIP-UHFFFAOYSA-N\",\"FCTHQYIDLRRROX-UHFFFAOYSA-N\",\\\n",
    "       \"JMXLWMIFDJCGBV-UHFFFAOYSA-N\",\"PXWSKGXEHZHFJA-UHFFFAOYSA-N\",\"DMFMZFFIQRMJQZ-UHFFFAOYSA-N\",\\\n",
    "       \"NOHLSFNWSBZSBW-UHFFFAOYSA-N\",\"NLJDBTZLVTWXRG-UHFFFAOYSA-N\",\"LCTUISCIGMWMAT-UHFFFAOYSA-N\",\\\n",
    "       \"WGYRINYTHSORGH-UHFFFAOYSA-N\",\"VAWHFUNJDMQUSB-UHFFFAOYSA-N\",\"HBPSMMXRESDUSG-UHFFFAOYSA-N\",\\\n",
    "       \"VNAAUNTYIONOHR-UHFFFAOYSA-N\",\"ZEVRFFCPALTVDN-UHFFFAOYSA-N\",\"NXRUEVJQMBGVAT-UHFFFAOYSA-N\",\\\n",
    "       \"JERSPYRKVMAEJY-UHFFFAOYSA-N\"]\n",
    "for x in lst:\n",
    "    df = df.append(df_all[(df_all._rxn_organic_inchikey == x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Methylammonium iodide', 'Ethylammonium Iodide', 'n-Butylammonium iodide', 'Formamidinium Iodide', 'Acetamidinium iodide', 'iso-Butylammonium iodide', 'Dimethylammonium iodide', 'n-Dodecylammonium iodide', 'Pyrrolidinium Iodide', '4-Fluoro-Phenethylammonium iodide', 't-Butylammonium Iodide', '4-Fluoro-Benzylammonium iodide', 'Cyclohexylammonium iodide', 'Morpholinium Iodide', 'Piperidinium Iodide', 'n-Hexylammonium iodide', 'Cyclohexylmethylammonium iodide', 'N,N-Diethylpropane-1,3-diammonium iodide', 'N,N-dimethylpropane- 1,3-diammonium iodide']\n"
     ]
    }
   ],
   "source": [
    "Amine_done = [] # actually it is amine for analysis\n",
    "for i in df['_rxn_organic_inchikey']:\n",
    "    if i not in Amine_done: \n",
    "        Amine_done.append(i)\n",
    "\n",
    "for i in range(len(Amine_done)):\n",
    "    Amine_done[i] = dict(zip(Inchi['InChI Key (ID)'],Inchi['Chemical Name']))[Amine_done[i]]\n",
    "print (Amine_done)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate input, output and reference dataframe\n",
    "X = df.drop(['RunID_vial', '_out_crystalscore', '_rxn_organic_inchikey'], axis =1)\n",
    "y = df.filter(['_out_crystalscore'], axis =1)\n",
    "inchikey = df.filter(['RunID_vial', '_rxn_organic_inchikey'])\n",
    "\n",
    "# Standarize the input\n",
    "x = StandardScaler().fit_transform(X) # dataframe turned into array and it is reformed as dataframe in the below line\n",
    "X = pd.DataFrame(x, index = X.index, columns = X.columns)\n",
    "\n",
    "y = (y.replace([1,2,3],0)).replace(4,1) # turned into binary classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate lmetric of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM_PUFK_metrics (X_train,y_train,X_test,y_test, pos_label=1, feature = '_rxn_M_acid'):\n",
    "    \n",
    "    def PearsonVII_kernel(X1,X2, sigma=1.0, omega=1.0):\n",
    "        if X1 is X2 :\n",
    "            kernel = squareform(pdist(X1, 'euclidean'))\n",
    "        else:\n",
    "            kernel = cdist(X1, X2, 'euclidean')\n",
    "        kernel = (1 + (kernel * 4 * np.sqrt(2**(1.0/omega)-1)) / sigma**2) ** omega\n",
    "        kernel = 1/kernel\n",
    "        return kernel\n",
    "    \n",
    "    clf = SVC(C=10,cache_size=6000,max_iter=-1,kernel=PearsonVII_kernel,\\\n",
    "              decision_function_shape='ovr', probability=False, class_weight='balanced')\n",
    "    X_train = X_train.filter(['_rxn_M_acid', '_rxn_M_inorganic', '_rxn_M_organic'], axis = 1)\n",
    "    X_test = X_test.filter(['_rxn_M_acid', '_rxn_M_inorganic', '_rxn_M_organic'], axis = 1)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_predict = clf.predict(X_test)\n",
    "    accuracy = clf.score(X_test,y_test)\n",
    "    precision = precision_score(y_test,y_predict, pos_label=pos_label) \n",
    "    recall = recall_score(y_test,y_predict, pos_label=pos_label)\n",
    "    f1 = f1_score (y_test,y_predict, pos_label=pos_label) \n",
    "    return {'accuracy': accuracy, 'precision':precision, 'recall':recall, 'f1':f1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM_PUFK_metrics_fullfeature (X_train,y_train,X_test,y_test, pos_label=1, feature = '_rxn_M_acid'):\n",
    "    \n",
    "    def PearsonVII_kernel(X1,X2, sigma=1.0, omega=1.0):\n",
    "        if X1 is X2 :\n",
    "            kernel = squareform(pdist(X1, 'euclidean'))\n",
    "        else:\n",
    "            kernel = cdist(X1, X2, 'euclidean')\n",
    "        kernel = (1 + (kernel * 4 * np.sqrt(2**(1.0/omega)-1)) / sigma**2) ** omega\n",
    "        kernel = 1/kernel\n",
    "        return kernel\n",
    "    \n",
    "    clf = SVC(C=10,cache_size=6000,max_iter=-1,kernel=PearsonVII_kernel,\\\n",
    "              decision_function_shape='ovr', probability=False, class_weight='balanced')\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_predict = clf.predict(X_test)\n",
    "    accuracy = clf.score(X_test,y_test)\n",
    "    precision = precision_score(y_test,y_predict, pos_label=pos_label) \n",
    "    recall = recall_score(y_test,y_predict, pos_label=pos_label)\n",
    "    f1 = f1_score (y_test,y_predict, pos_label=pos_label) \n",
    "    return {'accuracy': accuracy, 'precision':precision, 'recall':recall, 'f1':f1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KNN_metrics (X_train,y_train,X_test,y_test, pos_label=1, feature = '_rxn_M_acid'):\n",
    "    clf = KNeighborsClassifier(n_neighbors = 1, weights = \"uniform\", p = 2)\n",
    "    X_train = X_train.filter(['_rxn_M_acid', '_rxn_M_inorganic', '_rxn_M_organic'], axis = 1)\n",
    "    X_test = X_test.filter(['_rxn_M_acid', '_rxn_M_inorganic', '_rxn_M_organic'], axis = 1)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_predict = clf.predict(X_test)\n",
    "    accuracy = clf.score(X_test,y_test)\n",
    "    precision = precision_score(y_test,y_predict, pos_label=pos_label) \n",
    "    recall = recall_score(y_test,y_predict, pos_label=pos_label)\n",
    "    f1 = f1_score (y_test,y_predict, pos_label=pos_label) \n",
    "    return {'accuracy': accuracy, 'precision':precision, 'recall':recall, 'f1':f1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Human prediction: based on the feacture with highest priority\n",
    "def onefeaturekNN (X_train,y_train,X_test,y_test, pos_label=1, feature = '_rxn_M_acid', k=1):\n",
    "    clf = KNeighborsClassifier(n_neighbors = k, weights = \"uniform\", p = 2)\n",
    "    X_train = X_train.filter([feature], axis = 1)\n",
    "    X_test = X_test.filter([feature], axis = 1)\n",
    "    clf.fit(X_train,y_train)   \n",
    "    y_predict = clf.predict(X_test)\n",
    "    accuracy = clf.score(X_test,y_test)                       \n",
    "    precision = precision_score(y_test,y_predict, pos_label=pos_label)                      \n",
    "    recall = recall_score(y_test,y_predict, pos_label=pos_label)                  \n",
    "    f1 = f1_score(y_test,y_predict, pos_label=pos_label)\n",
    "    return {'accuracy': accuracy, 'precision':precision, 'recall':recall, 'f1':f1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_size (X_ammonium_train, node):\n",
    "    n = len(X_ammonium_train[0].index)\n",
    "    split = list(map(lambda x: int(x*n), node))\n",
    "    return split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to save any obj in 'obj' folder\n",
    "def save_obj(obj, name ):\n",
    "    with open('obj/'+ name + '.pkl', 'wb') as file:\n",
    "        pickle.dump(obj, file, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "def load_obj(name ):\n",
    "    with open('obj/' + name + '.pkl', 'rb') as file:\n",
    "        return pickle.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate learning metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn_score (learn_rate, n_splits,\\\n",
    "                 X_ammonium_train, y_ammonium_train, X_ammonium_test, y_ammonium_test,\\\n",
    "                 X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                 f = SVM_PUFK_metrics, metric = 'accuracy', feature = '_rxn_M_acid', rest_ammonium = 0):\n",
    "    learn_metric = np.zeros((len(learn_rate), n_splits, n_splits))\n",
    "    for j in range(len(learn_rate)):\n",
    "        if (learn_rate[j]>0) & (learn_rate[j]<1):\n",
    "            for i in range(n_splits):               \n",
    "                score_list = []\n",
    "                learn_split = StratifiedShuffleSplit(n_splits=n_splits , test_size=1-learn_rate[j], random_state=42)\n",
    "                for train_index, test_index in learn_split.split(X_ammonium_train[i], y_ammonium_train[i]):\n",
    "                    if rest_ammonium == 0:\n",
    "                        score_list.append(f(X_ammonium_train[i].iloc[train_index], y_ammonium_train[i].iloc[train_index],\\\n",
    "                                            X_ammonium_test[i], y_ammonium_test[i], feature = feature)[metric])\n",
    "                    elif rest_ammonium == 1:\n",
    "                        score_list.append(f(pd.concat([X_ammonium_train[i].iloc[train_index], X_rest_ammonium]),\\\n",
    "                                            pd.concat([y_ammonium_train[i].iloc[train_index], y_rest_ammonium]),\\\n",
    "                                            X_ammonium_test[i], y_ammonium_test[i])[metric])#stop here\n",
    "                learn_metric[j][i] = score_list\n",
    "                print('finished train/test split number: ', (i+1))\n",
    "        elif learn_rate[j] == 1:\n",
    "            for i in range(n_splits):\n",
    "                if rest_ammonium == 0:\n",
    "                    learn_metric[j][i] = [f(X_ammonium_train[i],y_ammonium_train[i],\\\n",
    "                                            X_ammonium_test[i], y_ammonium_test[i], feature = feature)[metric]]*n_splits\n",
    "                elif rest_ammonium == 1:\n",
    "                    learn_metric[j][i] = [f(pd.concat([X_ammonium_train[i], X_rest_ammonium]),\\\n",
    "                                            pd.concat([y_ammonium_train[i], y_rest_ammonium]),\\\n",
    "                                            X_ammonium_test[i], y_ammonium_test[i], feature = feature)[metric]]*n_splits\n",
    "                print('finished train/test split number: ', (i+1))\n",
    "        else:\n",
    "            raise IndexError()\n",
    "        print('finished learning rate: ', learn_rate[j])\n",
    "        \n",
    "    if rest_ammonium == 1:\n",
    "        learn_0 = np.zeros((n_splits, n_splits))\n",
    "        for i in range(n_splits):\n",
    "            learn_0[i] = [f(X_rest_ammonium, y_rest_ammonium, X_ammonium_test[i], y_ammonium_test[i])[metric]]*n_splits\n",
    "        learn_metric = np.insert(learn_metric, obj = [0], values = learn_0, axis = 0)\n",
    "        print('Added prediction metric with only N-1 ammonium')\n",
    "    return learn_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fuction to plot learning curve for different metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn_plot (learn_1, learn_2, learn_3, learn_4, learn_5, \\\n",
    "                X_ammonium_train, learn_rate, percentage_0_test, \\\n",
    "                name, metrics_list = ['accuracy', 'precision', 'recall', 'f1'], ymin = 0, ymax = 1):\n",
    "    \n",
    "    #building x axis (# of trainingsets in target ammonium)\n",
    "    x_axis = train_size (X_ammonium_train, node = learn_rate)\n",
    "    y_axis_1_mean = {}\n",
    "    y_axis_1_std = {}\n",
    "    y_axis_2_mean = {}\n",
    "    y_axis_2_std = {}\n",
    "    y_axis_3_mean = {}\n",
    "    y_axis_3_std = {}\n",
    "    y_axis_4_mean = {}\n",
    "    y_axis_4_std = {}\n",
    "    y_axis_5_mean = {}\n",
    "    y_axis_5_std = {}\n",
    "    \n",
    "    #Calculation of mean and std for metric curves\n",
    "    for metric in metrics_list:\n",
    "        y_axis_1_mean[metric] = []\n",
    "        y_axis_1_std[metric] = []\n",
    "        y_axis_2_mean[metric] = []\n",
    "        y_axis_2_std[metric] = []\n",
    "        y_axis_3_mean[metric] = []\n",
    "        y_axis_3_std[metric] = []\n",
    "        y_axis_4_mean[metric] = []\n",
    "        y_axis_4_std[metric] = []\n",
    "        y_axis_5_mean[metric] = []\n",
    "        y_axis_5_std[metric] = []\n",
    "        \n",
    "        for i in range(len(learn_rate)):\n",
    "            y_axis_1_mean[metric].append(np.mean(learn_1[metric][i].flatten()))\n",
    "            y_axis_1_std[metric].append(np.std(learn_1[metric][i].flatten()))\n",
    "        for i in range(len(learn_rate)):\n",
    "            y_axis_2_mean[metric].append(np.mean(learn_2[metric][i].flatten()))\n",
    "            y_axis_2_std[metric].append(np.std(learn_2[metric][i].flatten()))\n",
    "        for i in range(len(learn_rate)):\n",
    "            y_axis_3_mean[metric].append(np.mean(learn_3[metric][i].flatten()))\n",
    "            y_axis_3_std[metric].append(np.std(learn_3[metric][i].flatten()))\n",
    "        for i in range(len(learn_rate)):\n",
    "            y_axis_4_mean[metric].append(np.mean(learn_4[metric][i].flatten()))\n",
    "            y_axis_4_std[metric].append(np.std(learn_4[metric][i].flatten()))\n",
    "        for i in range(len(learn_rate)):\n",
    "            y_axis_5_mean[metric].append(np.mean(learn_5[metric][i].flatten()))\n",
    "            y_axis_5_std[metric].append(np.std(learn_5[metric][i].flatten()))\n",
    "        \n",
    "    # Metrics for random guess\n",
    "    y_axis_monotone = {'accuracy': [max(percentage_0_test, (1-percentage_0_test))]*(len(learn_rate)), \\\n",
    "                       'precision': [(1-percentage_0_test)]*(len(learn_rate)), \\\n",
    "                       'recall': [1]*(len(learn_rate)), \\\n",
    "                       'f1':[(2*(1-percentage_0_test)/(1+(1-percentage_0_test)))]*(len(learn_rate))}\n",
    "    \n",
    "    # Plotting\n",
    "    for metric in metrics_list:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(x_axis, y_axis_1_mean[metric], 'o-', color = \"r\", label = \"PUFK-SVM prediction\")\n",
    "        ax.plot(x_axis, y_axis_2_mean[metric], '^-', color = \"green\", label = \"Formic acid based prediction\")\n",
    "        ax.plot(x_axis, y_axis_3_mean[metric], 's-', color = \"blue\", label = \"Inorganic based prediction\")\n",
    "        ax.plot(x_axis, y_axis_4_mean[metric], 'd-', color = \"purple\", label = \"Organic based prediction\")\n",
    "        ax.plot(x_axis, y_axis_5_mean[metric], 'H-', color='black', label = \"kNN prediction\")\n",
    "        ax.plot(x_axis, y_axis_monotone[metric], color = 'black', label = \"Majority voting\" if metric == 'accuracy' else 'All positive voting', linestyle='dashed')\n",
    "        \n",
    "\n",
    "        ax.fill_between (x_axis, np.array(y_axis_1_mean[metric]) - np.array(y_axis_1_std[metric]), \\\n",
    "                         np.array(y_axis_1_mean[metric]) + np.array(y_axis_1_std[metric]), \\\n",
    "                         color=\"r\", alpha = 0.2)\n",
    "        ax.fill_between (x_axis, np.array(y_axis_5_mean[metric]) - np.array(y_axis_5_std[metric]), \\\n",
    "                         np.array(y_axis_5_mean[metric]) + np.array(y_axis_5_std[metric]), \\\n",
    "                         color=\"gray\", alpha = 0.2)\n",
    "        ax.fill_between (x_axis, np.array(y_axis_2_mean[metric]) - np.array(y_axis_2_std[metric]), \\\n",
    "                         np.array(y_axis_2_mean[metric]) + np.array(y_axis_2_std[metric]), \\\n",
    "                         color=\"green\", alpha = 0.2)\n",
    "        ax.fill_between (x_axis, np.array(y_axis_3_mean[metric]) - np.array(y_axis_3_std[metric]), \\\n",
    "                         np.array(y_axis_3_mean[metric]) + np.array(y_axis_3_std[metric]), \\\n",
    "                         color=\"blue\", alpha = 0.1)\n",
    "        ax.fill_between (x_axis, np.array(y_axis_4_mean[metric]) - np.array(y_axis_4_std[metric]), \\\n",
    "                         np.array(y_axis_4_mean[metric]) + np.array(y_axis_4_std[metric]), \\\n",
    "                         color=\"purple\", alpha = 0.1)\\\n",
    "\n",
    "        \n",
    "        ax.set_ylim(ymin, ymax)\n",
    "        ax.legend()          \n",
    "        ax.set_xlabel('Number of training experiments')\n",
    "        ax.set_ylabel('Prediction ' + metric)\n",
    "        ax.set_title('SVM prediction on ' + name)\n",
    "        plt.savefig('Learning_Curves/Model comparison on '+ name + '_' + metric + '.svg', format = 'svg', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User input parameters start here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test splitting options\n",
    "n_splits = 5\n",
    "sss = StratifiedShuffleSplit(n_splits=n_splits , test_size=0.2, random_state=42)\n",
    "\n",
    "# Learning rate/step for different model metrics\n",
    "learn_rate_1 = [0.1,0.33,0.55,0.78,1.0]\n",
    "learn_rate_2 = [0.02,0.06,0.1,0.2,0.33,0.55,0.78,1.0]\n",
    "learn_rate_3 = [0.2,0.4,0.6,0.8,1.0]\n",
    "\n",
    "metrics_list = ['accuracy', 'precision', 'recall', 'f1']\n",
    "feature_list = ['_rxn_M_acid', '_rxn_M_inorganic', '_rxn_M_organic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "amine_dict = {'Methylammonium iodide':'MAPbI3','Ethylammonium Iodide':'EtAPbI3', 'n-Butylammonium iodide':'nBuA2PbI4',\\\n",
    "               'Formamidinium Iodide':'FAPbI3', 'Acetamidinium iodide':'AcetPbI3', \\\n",
    "              'Cyclohexylmethylammonium iodide': 'CHMA2PbI4'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning curve generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Methylammonium iodide',\n",
       " 'Ethylammonium Iodide',\n",
       " 'n-Butylammonium iodide',\n",
       " 'Formamidinium Iodide',\n",
       " 'Acetamidinium iodide',\n",
       " 'iso-Butylammonium iodide',\n",
       " 'Dimethylammonium iodide',\n",
       " 'n-Dodecylammonium iodide',\n",
       " 'Pyrrolidinium Iodide',\n",
       " '4-Fluoro-Phenethylammonium iodide',\n",
       " 't-Butylammonium Iodide',\n",
       " '4-Fluoro-Benzylammonium iodide',\n",
       " 'Cyclohexylammonium iodide',\n",
       " 'Morpholinium Iodide',\n",
       " 'Piperidinium Iodide',\n",
       " 'n-Hexylammonium iodide',\n",
       " 'Cyclohexylmethylammonium iodide',\n",
       " 'N,N-Diethylpropane-1,3-diammonium iodide',\n",
       " 'N,N-dimethylpropane- 1,3-diammonium iodide']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amine_done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 25, 41, 59, 76]\n",
      "0.85\n"
     ]
    }
   ],
   "source": [
    "ammonium = 'N,N-dimethylpropane- 1,3-diammonium iodide'\n",
    "\n",
    "##Split \"unknown\" amine and \"known\" amine\n",
    "\n",
    "# Find ammonium index in original dataframe\n",
    "inchi = Inchidict[ammonium]\n",
    "ammonium_index = inchikey.index[inchikey['_rxn_organic_inchikey'] == inchi].tolist()\n",
    "\n",
    "# pick target ammonium\n",
    "X_ammonium = X.filter(ammonium_index, axis = 0)\n",
    "y_ammonium = y.filter(ammonium_index, axis = 0)\n",
    "\n",
    "# pick N-1 ammonium\n",
    "X_rest_ammonium = X.drop(ammonium_index, axis = 0)\n",
    "y_rest_ammonium = y.drop(ammonium_index, axis = 0)\n",
    "\n",
    "\n",
    "## Split trainsets and testsets\n",
    "# Generate train and test list, number of elements (dataframe) is n_splits in Train-test splitting options\n",
    "X_ammonium_train = []\n",
    "y_ammonium_train = []\n",
    "X_ammonium_test = []\n",
    "y_ammonium_test = []\n",
    "\n",
    "for train_index, test_index in sss.split(X_ammonium, y_ammonium):\n",
    "    X_ammonium_train.append(X_ammonium.iloc[train_index])\n",
    "    y_ammonium_train.append(y_ammonium.iloc[train_index])\n",
    "    X_ammonium_test.append(X_ammonium.iloc[test_index])\n",
    "    y_ammonium_test.append(y_ammonium.iloc[test_index])\n",
    "\n",
    "print(train_size(X_ammonium_train, (learn_rate_1)))\n",
    "\n",
    "# percentage of 0 and 1 in y_ammonium_test\n",
    "percentage_0_test = list(y_ammonium_test[0]['_out_crystalscore']).count(0)\\\n",
    "    /len(y_ammonium_test[0].index)\n",
    "print (percentage_0_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16 entries, 2494 to 2570\n",
      "Data columns (total 1 columns):\n",
      "_out_crystalscore    16 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 256.0 bytes\n"
     ]
    }
   ],
   "source": [
    "percentage_0_test\n",
    "count_1 = y_ammonium[y_ammonium['_out_crystalscore']==1]\n",
    "count_1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single feature prediction learning curve generation,  one feature nearest neighbor score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  f1\n",
      "finished predicion feature:  _rxn_M_organic\n"
     ]
    }
   ],
   "source": [
    "for feature in feature_list:\n",
    "    learn_ammonium_only_1feature = {}\n",
    "    for metric in metrics_list:\n",
    "        learn_ammonium_only_1feature[metric] = learn_score (learn_rate = learn_rate_1, n_splits=n_splits,\\\n",
    "                                                             X_ammonium_train = X_ammonium_train,\\\n",
    "                                                             y_ammonium_train = y_ammonium_train,\\\n",
    "                                                             X_ammonium_test = X_ammonium_test,\\\n",
    "                                                             y_ammonium_test = y_ammonium_test,\\\n",
    "                                                             X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                                             f = onefeaturekNN, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "        print('finished predicion metric: ', metric)\n",
    "    print('finished predicion feature: ', feature)\n",
    "    save_obj(learn_ammonium_only_1feature, 'learn_ammonium_only_1feature_' + ammonium + feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM learning curve generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.2\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.4\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.6\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.8\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  accuracy\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.2\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.4\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.6\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.8\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  precision\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.2\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.4\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.6\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.8\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  recall\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.2\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.4\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.6\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.8\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  f1\n"
     ]
    }
   ],
   "source": [
    "# With only targe amine\n",
    "learn_ammonium_only = {}\n",
    "for metric in metrics_list:\n",
    "    learn_ammonium_only[metric] = learn_score (learn_rate = learn_rate_3, n_splits=n_splits,\\\n",
    "                                               X_ammonium_train = X_ammonium_train, \\\n",
    "                                               y_ammonium_train = y_ammonium_train,\\\n",
    "                                               X_ammonium_test = X_ammonium_test, \\\n",
    "                                               y_ammonium_test = y_ammonium_test,\\\n",
    "                                               X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                               f = SVM_PUFK_metrics, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "    print('finished predicion metric: ', metric)\n",
    "save_obj(learn_ammonium_only, 'learn_ammonium_only_PUFKSVM_3D_' + ammonium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished train/test split number:  1\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-260-c0c3fd055f55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m                                                \u001b[0my_ammonium_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_ammonium_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m                                                \u001b[0mX_rest_ammonium\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_rest_ammonium\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_rest_ammonium\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_rest_ammonium\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m                                                f = SVM_PUFK_metrics_fullfeature, feature = feature, metric = metric, rest_ammonium = 1)\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'finished predicion metric: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0msave_obj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn_ammonium_rest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'learn_ammonium_rest_PUFKSVM_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mammonium\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-251-ba2f6ea1c22f>\u001b[0m in \u001b[0;36mlearn_score\u001b[0;34m(learn_rate, n_splits, X_ammonium_train, y_ammonium_train, X_ammonium_test, y_ammonium_test, X_rest_ammonium, y_rest_ammonium, f, metric, feature, rest_ammonium)\u001b[0m\n\u001b[1;32m     16\u001b[0m                         score_list.append(f(pd.concat([X_ammonium_train[i].iloc[train_index], X_rest_ammonium]),\\\n\u001b[1;32m     17\u001b[0m                                             \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_ammonium_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_rest_ammonium\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                                             X_ammonium_test[i], y_ammonium_test[i])[metric])#stop here\n\u001b[0m\u001b[1;32m     19\u001b[0m                 \u001b[0mlearn_metric\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'finished train/test split number: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\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;32m<ipython-input-249-468d97504f09>\u001b[0m in \u001b[0;36mSVM_PUFK_metrics_fullfeature\u001b[0;34m(X_train, y_train, X_test, y_test, pos_label, feature)\u001b[0m\n\u001b[1;32m     12\u001b[0m     clf = SVC(C=10,cache_size=6000,max_iter=-1,kernel=PearsonVII_kernel,\\\n\u001b[1;32m     13\u001b[0m               decision_function_shape='ovr', probability=False, class_weight='balanced')\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\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     15\u001b[0m     \u001b[0my_predict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mfit\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[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\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    188\u001b[0m         \u001b[0;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[0;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;31m# TODO: add keyword copy to copy on demand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__Xfit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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;32m/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_compute_kernel\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    361\u001b[0m             \u001b[0;31m# in the case of precomputed kernel given as a function, we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0;31m# have to compute explicitly the kernel matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__Xfit\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    364\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\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[1;32m    365\u001b[0m                 \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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;32m<ipython-input-249-468d97504f09>\u001b[0m in \u001b[0;36mPearsonVII_kernel\u001b[0;34m(X1, X2, sigma, omega)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mPearsonVII_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0momega\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\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[1;32m      4\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mX2\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msquareform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'euclidean'\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[0m\u001b[1;32m      6\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcdist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'euclidean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36mpdist\u001b[0;34m(X, metric, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1717\u001b[0m             pdist_fn = getattr(_distance_wrap,\n\u001b[1;32m   1718\u001b[0m                                \"pdist_%s_%s_wrap\" % (metric_name, typ))\n\u001b[0;32m-> 1719\u001b[0;31m             \u001b[0mpdist_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1720\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# With N-1 + target amine\n",
    "learn_ammonium_rest = {}\n",
    "for metric in metrics_list:\n",
    "    learn_ammonium_rest[metric] = learn_score (learn_rate = learn_rate_3, n_splits=n_splits,\\\n",
    "                                               X_ammonium_train = X_ammonium_train, \\\n",
    "                                               y_ammonium_train = y_ammonium_train,\\\n",
    "                                               X_ammonium_test = X_ammonium_test, \\\n",
    "                                               y_ammonium_test = y_ammonium_test,\\\n",
    "                                               X_rest_ammonium = X_rest_ammonium, y_rest_ammonium = y_rest_ammonium,\\\n",
    "                                               f = SVM_PUFK_metrics_fullfeature, feature = feature, metric = metric, rest_ammonium = 1)\n",
    "    print('finished predicion metric: ', metric)\n",
    "save_obj(learn_ammonium_rest, 'learn_ammonium_rest_PUFKSVM_' + ammonium)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kNN learning curve generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.33\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.55\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  accuracy\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.33\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.55\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  precision\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.33\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.55\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  recall\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.33\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.55\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "finished predicion metric:  f1\n"
     ]
    }
   ],
   "source": [
    "learn_ammonium_only_kNN = {}\n",
    "for metric in metrics_list:\n",
    "    learn_ammonium_only_kNN[metric] = learn_score (learn_rate = learn_rate_1, n_splits=n_splits,\\\n",
    "                                                   X_ammonium_train = X_ammonium_train, \\\n",
    "                                                   y_ammonium_train = y_ammonium_train,\\\n",
    "                                                   X_ammonium_test = X_ammonium_test, \\\n",
    "                                                   y_ammonium_test = y_ammonium_test,\\\n",
    "                                                   X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                                   f = KNN_metrics, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "    print('finished predicion metric: ', metric)\n",
    "save_obj(learn_ammonium_only_kNN, 'learn_ammonium_only_kNN_3D_' + ammonium)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning curve generation (loop out all 19 ammoniums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Methylammonium iodide',\n",
       " 'Ethylammonium Iodide',\n",
       " 'n-Butylammonium iodide',\n",
       " 'Formamidinium Iodide',\n",
       " 'Acetamidinium iodide',\n",
       " 'iso-Butylammonium iodide',\n",
       " 'Dimethylammonium iodide',\n",
       " 'n-Dodecylammonium iodide',\n",
       " 'Pyrrolidinium Iodide',\n",
       " '4-Fluoro-Phenethylammonium iodide',\n",
       " 't-Butylammonium Iodide',\n",
       " '4-Fluoro-Benzylammonium iodide',\n",
       " 'Cyclohexylammonium iodide',\n",
       " 'Morpholinium Iodide',\n",
       " 'Piperidinium Iodide',\n",
       " 'n-Hexylammonium iodide',\n",
       " 'Cyclohexylmethylammonium iodide',\n",
       " 'N,N-Diethylpropane-1,3-diammonium iodide',\n",
       " 'N,N-dimethylpropane- 1,3-diammonium iodide']"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Amine_done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StartMethylammonium iodide\n",
      "Learning rate is\n",
      "[12, 40, 67, 95, 123]\n",
      "************************************************\n",
      "Start single feature\n",
      "finished train/test split number:  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
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     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \"\"\"\n"
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartEthylammonium Iodide\n",
      "Learning rate is\n",
      "[27, 81, 135, 270, 446, 744, 1056, 1354]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Startn-Butylammonium iodide\n",
      "Learning rate is\n",
      "[26, 86, 144, 204, 262]\n",
      "************************************************\n",
      "Start single feature\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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     ]
    },
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "StartFormamidinium Iodide\n",
      "Learning rate is\n",
      "[17, 56, 94, 134, 172]\n",
      "************************************************\n",
      "Start single feature\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartAcetamidinium iodide\n",
      "Learning rate is\n",
      "[56, 184, 308, 436, 560]\n",
      "************************************************\n",
      "Start single feature\n",
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     "text": [
      "finished train/test split number:  4\n",
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      "finished learning rate:  1.0\n",
      "finished predicion metric:  f1\n",
      "SVM N-1 amine\n",
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  precision\n",
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  recall\n",
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Startiso-Butylammonium iodide\n",
      "Learning rate is\n",
      "[19, 63, 105, 149, 192]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Start kNN 3D\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartDimethylammonium iodide\n",
      "Learning rate is\n",
      "[23, 76, 127, 180, 232]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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     ]
    },
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Startn-Dodecylammonium iodide\n",
      "Learning rate is\n",
      "[11, 37, 63, 89, 115]\n",
      "************************************************\n",
      "Start single feature\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartPyrrolidinium Iodide\n",
      "Learning rate is\n",
      "[15, 50, 84, 119, 153]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Start4-Fluoro-Phenethylammonium iodide\n",
      "Learning rate is\n",
      "[11, 37, 63, 89, 115]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Start kNN 3D\n",
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      "SVM only target amine\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Startt-Butylammonium Iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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     ]
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Start4-Fluoro-Benzylammonium iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartCyclohexylammonium iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  precision\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "StartMorpholinium Iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Start kNN 3D\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "StartPiperidinium Iodide\n",
      "Learning rate is\n",
      "[15, 30, 45, 60, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  recall\n",
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     ]
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "Startn-Hexylammonium iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "SVM N-1 amine\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "StartCyclohexylmethylammonium iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "finished learning rate:  0.78\n",
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      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  recall\n",
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      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n",
      "StartN,N-Diethylpropane-1,3-diammonium iodide\n",
      "Learning rate is\n",
      "[15, 30, 45, 60, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "Start kNN 3D\n",
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      "SVM N-1 amine\n",
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      "StartN,N-dimethylpropane- 1,3-diammonium iodide\n",
      "Learning rate is\n",
      "[7, 25, 41, 59, 76]\n",
      "************************************************\n",
      "Start single feature\n",
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      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.1\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.33\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.55\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  0.78\n",
      "finished train/test split number:  1\n",
      "finished train/test split number:  2\n",
      "finished train/test split number:  3\n",
      "finished train/test split number:  4\n",
      "finished train/test split number:  5\n",
      "finished learning rate:  1.0\n",
      "Added prediction metric with only N-1 ammonium\n",
      "finished predicion metric:  f1\n"
     ]
    }
   ],
   "source": [
    "for ammonium in Amine_done:\n",
    "    \n",
    "    ##Split \"unknown\" amine and \"known\" amine\n",
    "    # Find ammonium index in original dataframe\n",
    "    inchi = Inchidict[ammonium]\n",
    "    ammonium_index = inchikey.index[inchikey['_rxn_organic_inchikey'] == inchi].tolist()\n",
    "    \n",
    "    if ammonium == 'Ethylammonium Iodide':\n",
    "        learn_rate_loop = learn_rate_2\n",
    "    elif (ammonium == 'Piperidinium Iodide') or (ammonium =='N,N-Diethylpropane-1,3-diammonium iodide'):\n",
    "        learn_rate_loop = learn_rate_3   \n",
    "    else:\n",
    "        learn_rate_loop = learn_rate_1\n",
    "    \n",
    "\n",
    "    # pick target ammonium\n",
    "    X_ammonium = X.filter(ammonium_index, axis = 0)\n",
    "    y_ammonium = y.filter(ammonium_index, axis = 0)\n",
    "\n",
    "    # pick N-1 ammonium\n",
    "    X_rest_ammonium = X.drop(ammonium_index, axis = 0)\n",
    "    y_rest_ammonium = y.drop(ammonium_index, axis = 0)\n",
    "\n",
    "\n",
    "    ## Split trainsets and testsets\n",
    "    # Generate train and test list, number of elements (dataframe) is n_splits in Train-test splitting options\n",
    "    X_ammonium_train = []\n",
    "    y_ammonium_train = []\n",
    "    X_ammonium_test = []\n",
    "    y_ammonium_test = []\n",
    "\n",
    "    for train_index, test_index in sss.split(X_ammonium, y_ammonium):\n",
    "        X_ammonium_train.append(X_ammonium.iloc[train_index])\n",
    "        y_ammonium_train.append(y_ammonium.iloc[train_index])\n",
    "        X_ammonium_test.append(X_ammonium.iloc[test_index])\n",
    "        y_ammonium_test.append(y_ammonium.iloc[test_index])\n",
    "\n",
    "    print('Start' + ammonium)\n",
    "    print('Learning rate is')\n",
    "    print(train_size(X_ammonium_train, (learn_rate_loop)))\n",
    "    print('************************************************')\n",
    "    \n",
    "    print('Start single feature')\n",
    "    ## Single feature prediction learning curve generation,  one feature nearest neighbor score\n",
    "    for feature in feature_list:\n",
    "        learn_ammonium_only_1feature = {}\n",
    "        for metric in metrics_list:\n",
    "            learn_ammonium_only_1feature[metric] = learn_score (learn_rate = learn_rate_loop, n_splits=n_splits,\\\n",
    "                                                                 X_ammonium_train = X_ammonium_train,\\\n",
    "                                                                 y_ammonium_train = y_ammonium_train,\\\n",
    "                                                                 X_ammonium_test = X_ammonium_test,\\\n",
    "                                                                 y_ammonium_test = y_ammonium_test,\\\n",
    "                                                                 X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                                                 f = onefeaturekNN, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "            print('finished predicion metric: ', metric)\n",
    "        print('finished predicion feature: ', feature)\n",
    "        save_obj(learn_ammonium_only_1feature, 'learn_ammonium_only_1feature_' + ammonium + feature)\n",
    "    \n",
    "    print('Start kNN 3D')\n",
    "    ## kNN learning curve generation\n",
    "    learn_ammonium_only_kNN = {}\n",
    "    for metric in metrics_list:\n",
    "        learn_ammonium_only_kNN[metric] = learn_score (learn_rate = learn_rate_loop, n_splits=n_splits,\\\n",
    "                                                       X_ammonium_train = X_ammonium_train, \\\n",
    "                                                       y_ammonium_train = y_ammonium_train,\\\n",
    "                                                       X_ammonium_test = X_ammonium_test, \\\n",
    "                                                       y_ammonium_test = y_ammonium_test,\\\n",
    "                                                       X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                                       f = KNN_metrics, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "        print('finished predicion metric: ', metric)\n",
    "    save_obj(learn_ammonium_only_kNN, 'learn_ammonium_only_kNN_3D_' + ammonium)\n",
    "    \n",
    "    ## SVM learning curve generation\n",
    "    \n",
    "    print('SVM only target amine')\n",
    "    # With only target amine\n",
    "    learn_ammonium_only = {}\n",
    "    for metric in metrics_list:\n",
    "        learn_ammonium_only[metric] = learn_score (learn_rate = learn_rate_loop, n_splits=n_splits,\\\n",
    "                                                   X_ammonium_train = X_ammonium_train, \\\n",
    "                                                   y_ammonium_train = y_ammonium_train,\\\n",
    "                                                   X_ammonium_test = X_ammonium_test, \\\n",
    "                                                   y_ammonium_test = y_ammonium_test,\\\n",
    "                                                   X_rest_ammonium = 0, y_rest_ammonium = 0,\\\n",
    "                                                   f = SVM_PUFK_metrics, feature = feature, metric = metric, rest_ammonium = 0)\n",
    "        print('finished predicion metric: ', metric)\n",
    "    save_obj(learn_ammonium_only, 'learn_ammonium_only_PUFKSVM_3D_' + ammonium)\n",
    "    \n",
    "    print('SVM N-1 amine')\n",
    "    # With N-1 + target amine\n",
    "    learn_ammonium_rest = {}\n",
    "    for metric in metrics_list:\n",
    "        learn_ammonium_rest[metric] = learn_score (learn_rate = learn_rate_loop, n_splits=n_splits,\\\n",
    "                                                   X_ammonium_train = X_ammonium_train, \\\n",
    "                                                   y_ammonium_train = y_ammonium_train,\\\n",
    "                                                   X_ammonium_test = X_ammonium_test, \\\n",
    "                                                   y_ammonium_test = y_ammonium_test,\\\n",
    "                                                   X_rest_ammonium = X_rest_ammonium, y_rest_ammonium = y_rest_ammonium,\\\n",
    "                                                   f = SVM_PUFK_metrics_fullfeature, feature = feature, metric = metric, rest_ammonium = 1)\n",
    "        print('finished predicion metric: ', metric)\n",
    "    save_obj(learn_ammonium_rest, 'learn_ammonium_rest_PUFKSVM_' + ammonium)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Methylammonium iodide': [12, 40, 67, 95, 123],\n",
       " 'Ethylammonium Iodide': [27, 81, 135, 270, 446, 744, 1056, 1354],\n",
       " 'n-Butylammonium iodide': [26, 86, 144, 204, 262],\n",
       " 'Formamidinium Iodide': [17, 56, 94, 134, 172],\n",
       " 'Acetamidinium iodide': [56, 184, 308, 436, 560],\n",
       " 'iso-Butylammonium iodide': [19, 63, 105, 149, 192],\n",
       " 'Dimethylammonium iodide': [23, 76, 127, 180, 232],\n",
       " 'n-Dodecylammonium iodide': [11, 37, 63, 89, 115],\n",
       " 'Pyrrolidinium Iodide': [15, 50, 84, 119, 153],\n",
       " '4-Fluoro-Phenethylammonium iodide': [11, 37, 63, 89, 115],\n",
       " 't-Butylammonium Iodide': [7, 25, 41, 59, 76],\n",
       " '4-Fluoro-Benzylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Cyclohexylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Morpholinium Iodide': [7, 25, 41, 59, 76],\n",
       " 'Piperidinium Iodide': [15, 30, 45, 60, 76],\n",
       " 'n-Hexylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Cyclohexylmethylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'N,N-Diethylpropane-1,3-diammonium iodide': [15, 30, 45, 60, 76],\n",
       " 'N,N-dimethylpropane- 1,3-diammonium iodide': [7, 25, 41, 59, 76]}"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn_rate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "amine_dict = {'Methylammonium iodide': 'MAPbI3','Ethylammonium Iodide': 'EtAPbI3','n-Butylammonium iodide': 'nBuA2PbI4',\\\n",
    "              'Formamidinium Iodide': 'FAPbI3','Acetamidinium iodide': 'AcetPbI3','Cyclohexylmethylammonium iodide': 'CHMA2PbI4',\\\n",
    "             'iso-Butylammonium iodide': 'iBuA2PbI4','Dimethylammonium iodide': 'DiMAPbI3', 'n-Dodecylammonium iodide': 'nDodA2PbI4',\\\n",
    "             'Pyrrolidinium Iodide': 'PyrroPbI3', '4-Fluoro-Phenethylammonium iodide': '4FPhenEtAPbI3', 't-Butylammonium Iodide': 'tBuA2PbI4',\\\n",
    "             '4-Fluoro-Benzylammonium iodide':'4FBenAPbI3', 'Cyclohexylammonium iodide': 'CHA2PbI4', 'Morpholinium Iodide':'MorphPbI3',\\\n",
    "             'Piperidinium Iodide': 'PiperPbI3', 'n-Hexylammonium iodide':'nHexPbI3','Cyclohexylmethylammonium iodide':'CHMAPbI3',\\\n",
    "             'N,N-Diethylpropane-1,3-diammonium iodide': 'NNDiEtPAPbI3', 'N,N-dimethylpropane- 1,3-diammonium iodide':'NNDiMPAPbI3'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Methylammonium iodide': 'MAPbI3',\n",
       " 'Ethylammonium Iodide': 'EtAPbI3',\n",
       " 'n-Butylammonium iodide': 'nBuA2PbI4',\n",
       " 'Formamidinium Iodide': 'FAPbI3',\n",
       " 'Acetamidinium iodide': 'AcetPbI3',\n",
       " 'Cyclohexylmethylammonium iodide': 'CHMAPbI3',\n",
       " 'iso-Butylammonium iodide': 'iBuA2PbI4',\n",
       " 'Dimethylammonium iodide': 'DiMAPbI3',\n",
       " 'n-Dodecylammonium iodide': 'nDodA2PbI4',\n",
       " 'Pyrrolidinium Iodide': 'PyrroPbI3',\n",
       " '4-Fluoro-Phenethylammonium iodide': '4FPhenEtAPbI3',\n",
       " 't-Butylammonium Iodide': 'tBuA2PbI4',\n",
       " '4-Fluoro-Benzylammonium iodide': '4FBenAPbI3',\n",
       " 'Cyclohexylammonium iodide': 'CHA2PbI4',\n",
       " 'Morpholinium Iodide': 'MorphPbI3',\n",
       " 'Piperidinium Iodide': 'PiperPbI3',\n",
       " 'n-Hexylammonium iodide': 'nHexPbI3',\n",
       " 'N,N-Diethylpropane-1,3-diammonium iodide': 'NNDiEtPAPbI3',\n",
       " 'N,N-dimethylpropane- 1,3-diammonium iodide': 'NNDiMPAPbI3'}"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amine_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Learning Curves for each amine (kNN, onefeaturekNN, SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 25, 41, 59, 76]\n"
     ]
    },
    {
     "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": [
    "learn_svm = load_obj('learn_ammonium_only_PUFKSVM_3D_' + ammonium)\n",
    "learn_svm_rest = load_obj('learn_ammonium_rest_PUFKSVM_' + ammonium)\n",
    "learn_kNN = load_obj('learn_ammonium_only_kNN_3D_' + ammonium)\n",
    "learn_acid = load_obj('learn_ammonium_only_1feature_' + ammonium + '_rxn_M_acid')\n",
    "learn_inorganic = load_obj('learn_ammonium_only_1feature_' + ammonium + '_rxn_M_inorganic')\n",
    "learn_organic = load_obj('learn_ammonium_only_1feature_' + ammonium + '_rxn_M_organic')\n",
    "\n",
    "['accuracy', 'precision', 'recall', 'f1']\n",
    "\n",
    "print (learn_rate_list[ammonium])\n",
    "\n",
    "learn_plot (learn_1 = learn_svm, learn_2 = learn_acid, learn_3 = learn_inorganic, \\\n",
    "            learn_4 = learn_organic, learn_5 = learn_kNN,\\\n",
    "            X_ammonium_train = X_ammonium_train, learn_rate = learn_rate_1, \\\n",
    "            percentage_0_test = percentage_0_test, \\\n",
    "            name = amine_dict[ammonium], metrics_list = ['f1'], ymin = 0.0, ymax = 1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### N-1 vs 1 prediction comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "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": [
    "['accuracy', 'precision', 'recall', 'f1']\n",
    "\n",
    "metric = 'f1' # the metric we would like to look at\n",
    "\n",
    "#building x axis (# of trainingsets in target ammonium)\n",
    "x_axis = learn_rate_list[ammonium]\n",
    "x_axis_0 = x_axis.copy()\n",
    "x_axis_0.insert(0,0)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "y_axis_mean_only = []\n",
    "y_axis_std_only = []\n",
    "y_axis_mean_rest = []\n",
    "y_axis_std_rest = []\n",
    "\n",
    "for i in range(len(x_axis)):\n",
    "    y_axis_mean_only.append(np.mean(learn_svm[metric][i].flatten()))\n",
    "    y_axis_std_only.append(np.std(learn_svm[metric][i].flatten()))\n",
    "for i in range(len(x_axis)+1):\n",
    "    y_axis_mean_rest.append(np.mean(learn_svm_rest[metric][i].flatten()))\n",
    "    y_axis_std_rest.append(np.std(learn_svm_rest[metric][i].flatten()))\n",
    "        \n",
    "# Plotting\n",
    "ax.plot (x_axis, y_axis_mean_only, marker = 'o', color = 'red', label = 'Without N-1 perovskite')\n",
    "ax.plot (x_axis_0, y_axis_mean_rest, marker = '^', color = 'green', label = 'With N-1 perovskite')\n",
    "ax.fill_between (x_axis, np.array(y_axis_mean_only) - np.array(y_axis_std_only), \\\n",
    "                        np.array(y_axis_mean_only) + np.array(y_axis_std_only), \\\n",
    "                        color = 'red', alpha = 0.2)\n",
    "ax.fill_between (x_axis_0, np.array(y_axis_mean_rest) - np.array(y_axis_std_rest), \\\n",
    "                        np.array(y_axis_mean_rest) + np.array(y_axis_std_rest), \\\n",
    "                        color = 'green', alpha = 0.2)\n",
    "\n",
    "ax.set_ylim(0.0,1.0)\n",
    "ax.legend()          \n",
    "ax.set_xlabel('Number of target perovskite training sets')\n",
    "ax.set_ylabel('Prediction ' + metric)\n",
    "ax.set_title(ammonium)\n",
    "plt.savefig('Learning_Curves/N vs 1' + '_' + ammonium + '_' + metric + '.svg', format = 'svg', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy prediction for all 6 perovskites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Methylammonium iodide': [12, 40, 67, 95, 123],\n",
       " 'Ethylammonium Iodide': [27, 81, 135, 270, 446, 744, 1056, 1354],\n",
       " 'n-Butylammonium iodide': [26, 86, 144, 204, 262],\n",
       " 'Formamidinium Iodide': [17, 56, 94, 134, 172],\n",
       " 'Acetamidinium iodide': [56, 184, 308, 436, 560],\n",
       " 'iso-Butylammonium iodide': [19, 63, 105, 149, 192],\n",
       " 'Dimethylammonium iodide': [23, 76, 127, 180, 232],\n",
       " 'n-Dodecylammonium iodide': [11, 37, 63, 89, 115],\n",
       " 'Pyrrolidinium Iodide': [15, 50, 84, 119, 153],\n",
       " '4-Fluoro-Phenethylammonium iodide': [11, 37, 63, 89, 115],\n",
       " 't-Butylammonium Iodide': [7, 25, 41, 59, 76],\n",
       " '4-Fluoro-Benzylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Cyclohexylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Morpholinium Iodide': [7, 25, 41, 59, 76],\n",
       " 'Piperidinium Iodide': [15, 30, 45, 60, 76],\n",
       " 'n-Hexylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'Cyclohexylmethylammonium iodide': [7, 25, 41, 59, 76],\n",
       " 'N,N-Diethylpropane-1,3-diammonium iodide': [15, 30, 45, 60, 76],\n",
       " 'N,N-dimethylpropane- 1,3-diammonium iodide': [7, 25, 41, 59, 76]}"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn_rate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Methylammonium iodide': 'MAPbI3',\n",
       " 'Ethylammonium Iodide': 'EtAPbI3',\n",
       " 'n-Butylammonium iodide': 'nBuA2PbI4',\n",
       " 'Formamidinium Iodide': 'FAPbI3',\n",
       " 'Acetamidinium iodide': 'AcetPbI3',\n",
       " 'Cyclohexylmethylammonium iodide': 'CHMA2PbI4'}"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amine_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_svm_list = {}\n",
    "for key,value in amine_dict.items(): \n",
    "    learn_svm_list[value] = load_obj('learn_ammonium_only_PUFKSVM_3D_' + key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "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": [
    "# Training data number for learning curve.\n",
    "x_MA = learn_rate_list['Methylammonium iodide']\n",
    "x_EtA = learn_rate_list['Ethylammonium Iodide']\n",
    "x_nBuA = learn_rate_list['n-Butylammonium iodide']\n",
    "x_FA = learn_rate_list['Formamidinium Iodide']\n",
    "x_Acet = learn_rate_list['Acetamidinium iodide']\n",
    "x_CHMA = learn_rate_list['Cyclohexylmethylammonium iodide']\n",
    "\n",
    "#building x axis (# of trainingsets in target ammonium)\n",
    "x_axis = [x_MA, x_EtA, x_nBuA, x_FA, x_Acet,x_CHMA]\n",
    "metric = 'accuracy'\n",
    "color_type = ['blue', 'red', 'green','orange', 'purple','black']\n",
    "marker_type = ['^', 'o', 's', 'p', 'd','h']\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "for j in range(len(learn_svm_list)):\n",
    "    y_axis_mean = []\n",
    "    y_axis_std = []\n",
    "    if (list(learn_svm_list.keys())[j] == 'EtAPbI3'):\n",
    "        learn_rate_plot = learn_rate_2\n",
    "    elif (list(learn_svm_list.keys())[j] == 'PiperPbI3') or (list(learn_svm_list.keys())[j] =='NNDiEtPAPbI3'):\n",
    "        learn_rate_plot = learn_rate_3   \n",
    "    else:\n",
    "        learn_rate_plot = learn_rate_1\n",
    "        \n",
    "    for i in range(len(learn_rate_plot)):\n",
    "        y_axis_mean.append(np.mean(learn_svm_list[list(learn_svm_list.keys())[j]][metric][i].flatten()))\n",
    "        y_axis_std.append(np.std(learn_svm_list[list(learn_svm_list.keys())[j]][metric][i].flatten()))\n",
    "        \n",
    "    # Plotting\n",
    "    ax.plot (x_axis[j], y_axis_mean, marker = marker_type[j], markerfacecolor = 'None', color = color_type[j], label = list(learn_svm_list.keys())[j])\n",
    "    ax.fill_between (x_axis[j], np.array(y_axis_mean) - np.array(y_axis_std), \\\n",
    "                         np.array(y_axis_mean) + np.array(y_axis_std), \\\n",
    "                         color = color_type[j], alpha = 0.2)\n",
    "\n",
    "    #ax.set_xlim(0, len(X_ammonium.index))\n",
    "    ax.legend()          \n",
    "    ax.set_xlabel('Number of training experiments')\n",
    "    ax.set_ylabel('Prediction ' + metric)\n",
    "    ax.set_title('All Prediction Accuracy')\n",
    "plt.savefig('Learning_Curves/PUFK_SVM_Prediction_all 6 perovskite' + '_' + metric + '.svg', format = 'svg', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(learn_svm_list['EtAPbI3']['accuracy'][3].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_kNN_EtA = load_obj('learn_ammonium_only_kNN_3D_' + 'Ethylammonium Iodide')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean((learn_kNN_EtA['accuracy'][2]).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean((learn_kNN_EtA['accuracy'][3]).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12, 40, 67, 95, 123]\n",
      "[27, 81, 135, 270, 446, 744, 1056, 1354]\n",
      "[26, 86, 144, 204, 262]\n",
      "[17, 56, 94, 134, 172]\n",
      "[56, 184, 308, 436, 560]\n",
      "[19, 63, 105, 149, 192]\n",
      "[23, 76, 127, 180, 232]\n",
      "[11, 37, 63, 89, 115]\n",
      "[15, 50, 84, 119, 153]\n",
      "[11, 37, 63, 89, 115]\n",
      "[7, 25, 41, 59, 76]\n",
      "[7, 25, 41, 59, 76]\n",
      "[7, 25, 41, 59, 76]\n",
      "[7, 25, 41, 59, 76]\n",
      "[15, 30, 45, 60, 76]\n",
      "[7, 25, 41, 59, 76]\n",
      "[7, 25, 41, 59, 76]\n",
      "[15, 30, 45, 60, 76]\n",
      "[7, 25, 41, 59, 76]\n"
     ]
    }
   ],
   "source": [
    "learn_rate_list = {}\n",
    "\n",
    "for ammonium in Amine_done:\n",
    "    \n",
    "    if ammonium == \"Ethylammonium Iodide\":\n",
    "        learn_rate = learn_rate_2\n",
    "    elif ammonium == \"Piperidinium Iodide\":\n",
    "        learn_rate = learn_rate_3\n",
    "    elif ammonium == \"N,N-Diethylpropane-1,3-diammonium iodide\":\n",
    "        learn_rate = learn_rate_3\n",
    "    else:\n",
    "        learn_rate = learn_rate_1\n",
    "\n",
    "    ##Split \"unknown\" amine and \"known\" amine\n",
    "\n",
    "    # Find ammonium index in original dataframe\n",
    "    inchi = Inchidict[ammonium]\n",
    "    ammonium_index = inchikey.index[inchikey['_rxn_organic_inchikey'] == inchi].tolist()\n",
    "\n",
    "    # pick target ammonium\n",
    "    X_ammonium = X.filter(ammonium_index, axis = 0)\n",
    "    y_ammonium = y.filter(ammonium_index, axis = 0)\n",
    "\n",
    "    # pick N-1 ammonium\n",
    "    X_rest_ammonium = X.drop(ammonium_index, axis = 0)\n",
    "    y_rest_ammonium = y.drop(ammonium_index, axis = 0)\n",
    "\n",
    "\n",
    "    ## Split trainsets and testsets\n",
    "    # Generate train and test list, number of elements (dataframe) is n_splits in Train-test splitting options\n",
    "    X_ammonium_train = []\n",
    "    y_ammonium_train = []\n",
    "    X_ammonium_test = []\n",
    "    y_ammonium_test = []\n",
    "\n",
    "    for train_index, test_index in sss.split(X_ammonium, y_ammonium):\n",
    "        X_ammonium_train.append(X_ammonium.iloc[train_index])\n",
    "        y_ammonium_train.append(y_ammonium.iloc[train_index])\n",
    "        X_ammonium_test.append(X_ammonium.iloc[test_index])\n",
    "        y_ammonium_test.append(y_ammonium.iloc[test_index])\n",
    "\n",
    "    print(train_size(X_ammonium_train, (learn_rate)))\n",
    "    learn_rate_list[ammonium] = train_size(X_ammonium_train, (learn_rate))\n",
    "\n",
    "    # percentage of 0 and 1 in y_ammonium_test\n",
    "    percentage_0_test = list(y_ammonium_test[0]['_out_crystalscore']).count(0)\\\n",
    "        /len(y_ammonium_test[0].index)\n",
    "    \n",
    "save_obj(learn_rate_list, 'learn_rate_list')"
   ]
  }
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