pre_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UrajMxg_5d2-"
   },
   "source": [
    "# Testing the different types of scalers with logistic regression\n",
    "\n",
    "- Do Postgresql instead of pickling\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1718,
     "status": "ok",
     "timestamp": 1603378654499,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "Z1hdVobyCcLI"
   },
   "outputs": [],
   "source": [
    "import io, requests\n",
    "\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from time import time\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import xgboost as xgb\n",
    "\n",
    "from sklearn.preprocessing import power_transform\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.svm import NuSVR\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "###############################################################################\n",
    "# Undersampling did not show any appreciable improvement in model performance #\n",
    "###############################################################################\n",
    "\n",
    "# from imblearn.under_sampling import TomekLinks # removed 5ish samples and only made a marginal improvement to the logloss values\n",
    "# from imblearn.under_sampling import ClusterCentroids # wasn't useful\n",
    "# from imblearn.under_sampling import NeighbourhoodCleaningRule # made model marginally worse and took significantly longer\n",
    "# from imblearn.under_sampling import InstanceHardnessThreshold #kept getting errors\n",
    "# from imblearn.under_sampling import EditedNearestNeighbours#did not remove any samples\n",
    "# from imblearn.under_sampling import AllKNN#did not remove any samples\n",
    "# from imblearn.under_sampling import CondensedNearestNeighbour#crashed after using all ram\n",
    "# from imblearn.under_sampling import NearMiss# marginally worse after running NearMiss(version=2, n_jobs=2, sampling_strategy=sampling_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7a0LBSJd1vJ7"
   },
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 2313,
     "status": "ok",
     "timestamp": 1603378657832,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "LINf-Doscml1"
   },
   "outputs": [],
   "source": [
    "def dummy_vars(df):\n",
    "    df.loc[:, 'cp_type'] = df.loc[:, 'cp_type'].map({'trt_cp': 0, 'ctl_vehicle': 1})\n",
    "    # trt_cp is a control and has no MOA, ctl_vehicle indicates a real test\n",
    "    df.loc[:, 'cp_dose'] = df.loc[:, 'cp_dose'].map({'D1': 0, 'D2': 1})\n",
    "    # create columns ['cp_type_24', 'cp_type_48', 'cp_type_72'] which are one hot encoded based on cp_time\n",
    "    df['cp_type_24'] = df['cp_time'].astype(str) == '24'\n",
    "    df['cp_type_48'] = df['cp_time'].astype(str) == '48'\n",
    "    df['cp_type_72'] = df['cp_time'].astype(str) == '72'\n",
    "    df.drop(columns=['sig_id','cp_time'], inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "executionInfo": {
     "elapsed": 6428,
     "status": "ok",
     "timestamp": 1603378662286,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "Gffd7zyYFOF7",
    "outputId": "7207fcc3-2982-4e46-94f7-4379d419396b"
   },
   "outputs": [],
   "source": [
    "def load_files():\n",
    "    # Train Features\n",
    "    train_features = pd.read_csv('train_features.csv')\n",
    "    train_features = dummy_vars(train_features)\n",
    "    print('train_features loaded')\n",
    "\n",
    "    # Train Targets\n",
    "    train_targets = pd.read_csv('train_targets_scored.csv')\n",
    "    train_targets.drop(columns=['sig_id'], inplace = True)\n",
    "    print('train_targets loaded')\n",
    "\n",
    "    # Test Features\n",
    "    test_features = pd.read_csv('test_features.csv')\n",
    "    test_features = dummy_vars(test_features)\n",
    "    #test_features = pd.DataFrame(power_transform(test_features, method='yeo-johnson'))\n",
    "    print('test_features loaded')\n",
    "\n",
    "    # Sample Submission DF (Targets)\n",
    "    ss_lr = pd.read_csv('sample_submission.csv')\n",
    "    ss_lr.drop(columns=['sig_id'], inplace = True)\n",
    "    print('ss_lr loaded')\n",
    "\n",
    "    print('done!')\n",
    "\n",
    "    return train_features, train_targets, test_features, ss_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fahpRhhW3TJq"
   },
   "source": [
    "## Data Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 5791,
     "status": "ok",
     "timestamp": 1603378662287,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "snbrfwC4izZk"
   },
   "outputs": [],
   "source": [
    "def log_loss_metric(y_true, y_pred):\n",
    "    metrics = []\n",
    "    for _target in train_targets.columns:\n",
    "        metrics.append(log_loss(y_true.loc[:, _target], y_pred.loc[:, _target].astype(float), labels = [0,1]))\n",
    "    return np.mean(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 5457,
     "status": "ok",
     "timestamp": 1603378662288,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "rQIRw5YvAaIR"
   },
   "outputs": [],
   "source": [
    "def k_fold_fit_pred(model_train_num, column, x_test, target_vals, N_SPLITS, res, ss):\n",
    "    for rand_state in range(model_train_num):\n",
    "        # train the model for each one of the random states \n",
    "        skf = StratifiedKFold(n_splits = N_SPLITS, random_state = rand_state, shuffle = True)\n",
    "\n",
    "        for (train, val) in skf.split(target_vals, target_vals):\n",
    "        # for each of the train/validate splits...\n",
    "            scaler = StandardScaler()\n",
    "\n",
    "            x_train = X[train, column].reshape(-1, 1).astype(float)\n",
    "            y_train = target_vals[train].astype(float)\n",
    "\n",
    "            x_train = scaler.fit_transform(x_train)\n",
    "\n",
    "            x_val = X[val, column].reshape(-1, 1).astype(float)\n",
    "            y_val = target_vals[val].astype(float)\n",
    "\n",
    "            x_val = scaler.transform(x_val)\n",
    "\n",
    "            model.fit(x_train, y_train)\n",
    "\n",
    "            try:\n",
    "                ss_pred_vals = model.predict_proba(x_test[:, column].reshape(-1,1))[:,1]\n",
    "            except: # For the tree based models\n",
    "                ss_pred_vals = model.predict(x_test[:, column].reshape(-1,1))\n",
    "            ss.loc[:, train_targets.columns[column]] += (\n",
    "                ss_pred_vals / (N_SPLITS * model_train_num))\n",
    "\n",
    "            try:\n",
    "                res_pred_vals = model.predict_proba(x_val)[:,1]\n",
    "            except: # For the tree based models\n",
    "                res_pred_vals = model.predict(x_val)\n",
    "            res.loc[val, train_targets.columns[column]] += (\n",
    "                res_pred_vals / model_train_num)\n",
    "\n",
    "    return res, ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 632,
     "status": "ok",
     "timestamp": 1603378797207,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "mjpia2xNk77Y"
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   "outputs": [],
   "source": [
    "# cols = [c for c in ss_lr.columns.values]\n",
    "def run_score(train_targets, X, x_test, res, ss, logloss_vals):\n",
    "# train_targets is the training targets\n",
    "# X is the train features\n",
    "# x_test is the holdout set of features\n",
    "# ss_ is blank since it's the empty sample submission predictions\n",
    "\n",
    "    N_SPLITS = 5 # number of k-fold splits\n",
    "    model_train_num = 2 # number of random states the model is trained on for each target\n",
    "\n",
    "    for column in tqdm(range(train_targets.shape[1])):\n",
    "    # create a model for each of the targets\n",
    "        start_time = time()\n",
    "        target_vals = train_targets.values[:, column]\n",
    "\n",
    "        if target_vals.sum() >= 5:\n",
    "        # split if there's enough positive target values\n",
    "            res, ss = k_fold_fit_pred(model_train_num, column, x_test, target_vals, N_SPLITS, res, ss)\n",
    "\n",
    "        score = log_loss(\n",
    "            train_targets.loc[:, train_targets.columns[column]],\n",
    "            res.loc[:, train_targets.columns[column]])\n",
    "        feat_time = str(datetime.timedelta(seconds = time() - start_time))[2:7]\n",
    "        #print(f'[{feat_time}] Target {column+1}/{train_targets.shape[1]} Log Loss:', score)\n",
    "        target_counts.append(np.sum(target_vals))\n",
    "        logloss_vals.append(score)\n",
    "\n",
    "    print(f'Log Loss: {log_loss_metric(train_targets, res)}')\n",
    "\n",
    "    # these lines automatically set the target values equal to zero if it cp_type = 1 AKA control group  \n",
    "    res.loc[train_features['cp_type'] == 1, train_targets.columns] = 0\n",
    "    ss.loc[test_features['cp_type'] == 1, train_targets.columns] = 0\n",
    "    print(f'After manually eliminating control groups: {log_loss_metric(train_targets, res)}\\n\\n')\n",
    "    \n",
    "    return res, ss, logloss_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_features loaded\n",
      "train_targets loaded\n",
      "test_features loaded\n",
      "ss_lr loaded\n",
      "done!\n"
     ]
    }
   ],
   "source": [
    "train_features, train_targets, test_features, ss_lr = load_files()\n",
    "\n",
    "# g_tags = [g for g in list(train_features.columns) if g[0]+g[1] == 'g-']\n",
    "# c_tags = [c for c in list(train_features.columns) if c[0]+c[1] == 'c-']\n",
    "\n",
    "# train_features[c_tags] = scaler.fit_transform(train_features[c_tags])\n",
    "# test_features[c_tags] = scaler.fit_transform(test_features[c_tags])\n",
    "\n",
    "# train_features[g_tags] = scaler.fit_transform(train_features[g_tags])\n",
    "# test_features[g_tags] = scaler.fit_transform(test_features[g_tags])\n",
    "\n",
    "\n",
    "X = train_features.values[:,:]\n",
    "x_test = test_features.values[:,:]\n",
    "\n",
    "#This is for the sample_submission.csv df\n",
    "ss_lr.loc[:, train_targets.columns] = 0\n",
    "\n",
    "#These are the target values which are being predicted and tested\n",
    "res_lr = train_targets.copy()\n",
    "res_lr.loc[:, train_targets.columns] = 0\n",
    "\n",
    "cols = ss_lr.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running and Evaluating the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Log Loss: 0.020385199315341\n",
      "After manually eliminating control groups: 0.020100039338947443\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#model = LogisticRegression(C = 128, max_iter = 100000) #128 is the ideal value for cuml using standard scaler\n",
    "try:\n",
    "    res_lr = pd.from_pickle('pickles/res_lr.p')\n",
    "    ss_lr = pd.from_pickle('pickles/ss_lr.p')\n",
    "except:\n",
    "    from cuml.linear_model import LogisticRegression\n",
    "    for i in [256]: # left for further hyperparameter tuning\n",
    "        model = LogisticRegression(C = 128, max_iter = 100000)\n",
    "        target_counts = []\n",
    "        logistic_regr_loss_vals = []\n",
    "        ss_lr.loc[:, train_targets.columns] = 0\n",
    "        res_lr.loc[:, train_targets.columns] = 0\n",
    "        res_lr, ss_lr, logistic_regr_loss_vals = run_score(train_targets, X, x_test, res_lr, ss_lr, logistic_regr_loss_vals)\n",
    "    plt.scatter(target_counts,logistic_regr_loss_vals)\n",
    "    plt.xlabel('target_counts')\n",
    "    plt.ylabel('log loss values')\n",
    "    plt.title('cuML Logistic Regression')\n",
    "    plt.show()\n",
    "    res_lr.to_pickle('pickles/res_cuml_lr.p')\n",
    "    ss_lr.to_pickle('pickles/ss_cuml_lr.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "259ba841852240529c05cb6774efcc3f",
      "0b03083b658f4e478de3b57791f42da8",
      "ba3f897e98e84be5a180ef100aa6d934",
      "6887ebab40d248c2b345cb7417ee2a9a",
      "1abb228a8d164b2a892055bc77aa56fd",
      "5c177f172ae8443a8b71a76cdff14220",
      "7299f41bbc7c493fac12c6500da94599",
      "1f77a084b822458699718400bf5a291b",
      "25286e32af134ccfa3f3770f82a36c16",
      "1e29afe4e9fe43fb8d854cff56fde3af",
      "4baa80741801474695d594eb4f4bb215"
     ]
    },
    "executionInfo": {
     "elapsed": 4020984,
     "status": "ok",
     "timestamp": 1603344676747,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "PkmwTfogsdbl",
    "outputId": "204ab49d-d1ed-484a-aa9c-03a9920e7b54",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45de43b8b56b47f0b032a576a9335fbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=206.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Log Loss: 0.02032292674082123\n",
      "After manually eliminating control groups: 0.020062833012380658\n",
      "\n",
      "\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": [
    "#model = SVR(kernel='poly', gamma='auto', C=200, cache_size=14000) #had estimated finish time of 12 hrs\n",
    "#model = SVR(kernel='rbf', gamma='scale', C=10, epsilon=0.1)# slooowwwww\n",
    "#model = NuSVR(C=1, nu=0.1) # The default kernel is RBF\n",
    "#model = LogisticRegression(C = 128, max_iter = 100000) #128 is the ideal value for cuml using standard scaler\n",
    "#model = LogisticRegression(C = 128, solver = 'lbfgs', warm_start=True, n_jobs=-1 max_iter = 10000)\n",
    "try:\n",
    "    res_lr = pd.from_pickle('pickles/res_lr.p')\n",
    "    ss_lr = pd.from_pickle('pickles/ss_lr.p')\n",
    "except:\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    for i in [256]: # left for further hyperparameter tuning\n",
    "        model = LogisticRegression(C = i, solver = 'saga', warm_start=True, n_jobs=-1, max_iter = 100000)\n",
    "        target_counts = []\n",
    "        logistic_regr_loss_vals = []\n",
    "        ss_lr.loc[:, train_targets.columns] = 0\n",
    "        res_lr.loc[:, train_targets.columns] = 0\n",
    "        res_lr, ss_lr, logistic_regr_loss_vals = run_score(train_targets, X, x_test, res_lr, ss_lr, logistic_regr_loss_vals)\n",
    "    plt.scatter(target_counts,logistic_regr_loss_vals)\n",
    "    plt.xlabel('target_counts')\n",
    "    plt.ylabel('log loss values')\n",
    "    plt.title('SKLearn Logistic Regression')\n",
    "    plt.show()\n",
    "    res_lr.to_pickle('pickles/res_lr.p')\n",
    "    ss_lr.to_pickle('pickles/ss_lr.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2717,
     "status": "ok",
     "timestamp": 1603378666806,
     "user": {
      "displayName": "ryan lewis",
      "photoUrl": "",
      "userId": "09680394787948119906"
     },
     "user_tz": 420
    },
    "id": "oTO2xR4ViYKX",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4ee841598824d8e886eff2550fec29c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=206.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    res_rfr = pd.from_pickle('pickles/res_rfr.p')\n",
    "    ss_rfr = pd.from_pickle('pickles/ss_rfr.p')\n",
    "except:\n",
    "    from sklearn.ensemble import RandomForestRegressor\n",
    "    for i in [1]: # left for further hyperparameter tuning\n",
    "        model = RandomForestRegressor(max_depth=3, random_state=42)\n",
    "        target_counts = []\n",
    "        rfr_loss_vals = []\n",
    "        ss_rfr = ss_lr.copy()\n",
    "        res_rfr = res_lr.copy()\n",
    "        ss_rfr.loc[:, train_targets.columns] = 0\n",
    "        res_rfr.loc[:, train_targets.columns] = 0\n",
    "        res_rfr, ss_rfr, rfr_loss_vals = run_score(train_targets, X, x_test, res_rfr, ss_rfr, rfr_loss_vals)\n",
    "    plt.scatter(target_counts, rfr_loss_vals)\n",
    "    plt.xlabel('target_counts')\n",
    "    plt.ylabel('log loss values')\n",
    "    plt.title('Random Forest Regression')\n",
    "    plt.show()\n",
    "    res_rfr.to_pickle('pickles/res_rfr.p')\n",
    "    ss_rfr.to_pickle('pickles/ss_rfr.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    res_linsvr = pd.read_pickle('pickles/res_linsvr')\n",
    "    ss_linsvr = pd.read_pickle('pickles/ss_linsvr')\n",
    "except:\n",
    "    from sklearn.svm import LinearSVR\n",
    "    for i in [1]: # left for further hyperparameter tuning\n",
    "        model = LinearSVR(random_state = 42, C=16, dual=False, loss='squared_epsilon_insensitive', tol=1e-5)\n",
    "        target_counts = []\n",
    "        linsvr_logloss_vals = []\n",
    "        ss_linsvr = ss_lr.copy()\n",
    "        res_linsvr = res_lr.copy()\n",
    "        ss_linsvr.loc[:, train_targets.columns] = 0\n",
    "        res_linsvr.loc[:, train_targets.columns] = 0\n",
    "        print(f'intercept_scaling = {i}')\n",
    "        res_linsvr, ss_linsvr, linsvr_loss_vals = run_score(\n",
    "            train_targets, X, x_test, res_linsvr, ss_linsvr, linsvr_logloss_vals)\n",
    "    plt.scatter(target_counts,linsvr_logloss_vals)\n",
    "    plt.xlabel('target_counts')\n",
    "    plt.ylabel('log loss values')\n",
    "    plt.title('LinearSVR')\n",
    "    plt.show()\n",
    "    res_linsvr.to_pickle('pickles/res_linsvr')\n",
    "    ss_linsvr.to_pickle('pickles/ss_linsvr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    res_nusvr = pd.read_pickle('pickles/res_nusvr')\n",
    "    ss_nusvr = pd.read_pickle('pickles/ss_nusvr')\n",
    "except:\n",
    "    from sklearn.svm import NuSVR\n",
    "    for i in [1]: # left for further hyperparameter tuning\n",
    "        model = NuSVR(kernel='poly', degree=2, C=1, tol=1e-3, cache_size=2024, max_iter=10000)\n",
    "        target_counts = []\n",
    "        nusvr_logloss_vals = []\n",
    "        ss_nusvr = ss_lr.copy()\n",
    "        res_nusvr = res_lr.copy()\n",
    "        ss_nusvr.loc[:, train_targets.columns] = 0\n",
    "        res_nusvr.loc[:, train_targets.columns] = 0\n",
    "        print(f'intercept_scaling = {i}')\n",
    "        res_nusvr, ss_nusvr, nusvr_loss_vals = run_score(\n",
    "            train_targets, X, x_test, res_nusvr, ss_nusvr, nusvr_logloss_vals)\n",
    "    plt.scatter(target_counts,nusvr_logloss_vals)\n",
    "    plt.xlabel('target_counts')\n",
    "    plt.ylabel('log loss values')\n",
    "    plt.title('nuSVR')\n",
    "    plt.show()\n",
    "    res_nusvr.to_pickle('pickles/res_nusvr')\n",
    "    ss_nusvr.to_pickle('pickles/ss_nusvr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the Baseline Model Score\n",
    "___\n",
    "This was used to benchmark the models and gain a sense of each model'd overall performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros_target_counts = []\n",
    "zeros_loss_vals = []\n",
    "res_zeros = res_lr.copy()\n",
    "res_zeros.loc[:, train_targets.columns] = 0\n",
    "for i in range(res_zeros.shape[1]):\n",
    "    loss = log_loss(train_targets.iloc[:,i], res_zeros.iloc[:,i])\n",
    "    zeros_loss_vals.append(loss)\n",
    "    zeros_target_counts.append(np.sum(train_targets.iloc[:,i]))\n",
    "# log loss score is the same for all zeros or the average values per column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded graph data\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import os\n",
    "# Try loading the graph data. If it isn't found generate it\n",
    "try:\n",
    "    graph_data = pickle.load(open('pickles/graph_data.p','rb'))\n",
    "    target_counts = graph_data[0]\n",
    "    nusvr_logloss_vals = graph_data[1]\n",
    "    linsvr_loss_vals = graph_data[2]\n",
    "    logistic_regr_loss_vals = graph_data[3]\n",
    "    rfr_loss_vals = graph_data[4]\n",
    "    print('loaded graph data')\n",
    "except:\n",
    "    graph_data = [target_counts,nusvr_logloss_vals,linsvr_loss_vals,logistic_regr_loss_vals,rfr_loss_vals]\n",
    "    pickle.dump(graph_data,open('pickles/graph_data.p','wb'))\n",
    "    print('saved graph data')\n",
    "\n",
    "if not os.path.exists(\"figures\"):\n",
    "    os.mkdir(\"figures\")"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
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       "                            \n",
       "var gd = document.getElementById('9cbc5aaf-d2da-49b8-955d-a623ea4a0114');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly\n",
    "import os\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=zeros_target_counts,\n",
    "    y=zeros_loss_vals,\n",
    "    name='all zeros',\n",
    "    mode='lines',\n",
    "    hovertemplate=['' for i in range(len(train_targets.columns))])\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=target_counts,\n",
    "    y=nusvr_logloss_vals,\n",
    "    name='NuSVR',\n",
    "    mode='markers',\n",
    "    hovertemplate = train_targets.columns)\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=target_counts,\n",
    "    y=linsvr_loss_vals,\n",
    "    name='linSVR',\n",
    "    mode='markers',\n",
    "    hovertemplate = train_targets.columns)\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=target_counts,\n",
    "    y=logistic_regr_loss_vals,\n",
    "    name='Logistic Regression',\n",
    "    mode='markers',\n",
    "    hovertemplate = train_targets.columns)\n",
    ")\n",
    "# fig.add_trace(go.Scatter(\n",
    "#     x=target_counts,\n",
    "#     y=xgb_loss_vals,\n",
    "#     name='XGBoost',\n",
    "#     mode='markers',\n",
    "#     hovertemplate = train_targets.columns)\n",
    "# )\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=target_counts,\n",
    "    y=rfr_loss_vals,\n",
    "    name='Random Forest Regression',\n",
    "    mode='markers',\n",
    "    hovertemplate = train_targets.columns)\n",
    ")\n",
    "fig.update_layout(\n",
    "    title='<b>Model Evaluation for all Targets</b>',\n",
    "    title_x=0.5,\n",
    "    xaxis_title='Size of Minority Class (out of 20,000)',\n",
    "    yaxis_title='Log Loss Scores',\n",
    "    xaxis_showgrid = False,\n",
    "    yaxis_showgrid = False,\n",
    "    yaxis_range=[0,0.2],\n",
    "    xaxis_range=[0,850],\n",
    "    hoverlabel_align = 'right',\n",
    "    paper_bgcolor='rgba(154,186,211,0.8)',\n",
    "    plot_bgcolor='rgba(232,246,252,0.2)'\n",
    ")\n",
    "fig.update_xaxes(showline=True, linewidth=2, linecolor='rgba(232,246,252,0.2)')\n",
    "fig.update_yaxes(showline=True, linewidth=2, linecolor='rgba(232,246,252,0.2)')\n",
    "fig.show()\n",
    "    \n",
    "the_date = str(datetime.datetime.now())[:10]\n",
    "html_path = 'figures/Plotly_all_logloss_' + the_date + '.html'\n",
    "plotly.io.write_html(fig, html_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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    "baryvals = ['NuSVR','LinSVR','Logistic Regression','Rand Forest Regression']\n",
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    "fig = make_subplots(rows=4, cols=1,\n",
    "    shared_xaxes=True,\n",
    "    subplot_titles=['VEGFR Inhibitor','Vitamin B','Vitamin D Receptor Agonist','WNT Inhibitor'])\n",
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    "    ), row=1, col=1)\n",
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    "    marker_color = colors,\n",
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    "    ), row=2, col=1)\n",
    "fig.append_trace(\n",
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    "    hoverinfo = \"y\",\n",
    "    marker_color = colors,\n",
    "    orientation='h'\n",
    "    ), row=3, col=1)\n",
    "fig.append_trace(\n",
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    "    x=barxvals[3],\n",
    "    hoverinfo = \"y\",\n",
    "    marker_color = colors,\n",
    "    orientation='h'), row=4, col=1)\n",
    "# Change the bar mode\n",
    "#fig.update_xaxes(autorange='reversed')\n",
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    "fig.update_layout(\n",
    "    title='<b>Log Loss Scores for Different MOAs</b>',\n",
    "    title_x=0.5,\n",
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    "    paper_bgcolor='rgba(154,186,211,0.8)',\n",
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