{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "import math\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt # for plotting \n",
    "import seaborn as sns # for plotting\n",
    "       \n",
    "# Feature and Model Selection:\n",
    "import lightgbm as lgb\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier  # linear classifiers\n",
    "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.model_selection import StratifiedKFold  # train/test splitting tool for cross-validation\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc  # scoring metrics\n",
    "from sklearn.impute import SimpleImputer, MissingIndicator\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "from sklearn.pipeline import FeatureUnion, make_pipeline, Pipeline\n",
    "\n",
    "# My own packages/modules\n",
    "import rek_ml.dsets_t as dst\n",
    "import rek_ml.ml_misc as rml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up some notebook display defaults\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "%matplotlib inline\n",
    "plt.style.use('default')\n",
    "sns.set()\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the datasets currently of interest\n",
    "k_trn = pd.read_csv(dst.oma_trn_5, dtype=dst.d_types_5)\n",
    "y_trn = k_trn[\"Survived\"]\n",
    "_ = k_trn.drop(\"Survived\", axis=1, inplace=True)\n",
    "# _ = k_trn.drop(\"Cabin\", axis=1, inplace=True)\n",
    "\n",
    "k_tst = pd.read_csv(dst.oma_tst_5, dtype=dst.d_types_5)\n",
    "y_tst = k_tst[\"Survived\"]\n",
    "_ = k_tst.drop(\"Survived\", axis=1, inplace=True)\n",
    "# _ = k_tst.drop(\"Cabin\", axis=1, inplace=True)\n",
    "\n",
    "k_all = k_trn\n",
    "k_all = pd.concat([k_all, k_tst], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's try the full feature set\n",
    "X_trn = k_trn[dst.full_trn_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_preds = {}\n",
    "for ds_nm, ds_cl in dst.rdc_ds.items():\n",
    "  X_dtrn = k_trn[ds_cl]\n",
    "  X_dtst = k_tst[ds_cl]\n",
    "  tmp_pred = rml.run_classifiers_test(rml.classifier_list, X_dtrn, y_trn, X_dtst, y_tst)\n",
    "  clf_preds[ds_nm] = list(tmp_pred.values())\n",
    "print(clf_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tst_acc = {\n",
    "  'PCA(12)': [0.7751196172248804, 0.7751196172248804, 0.7559808612440191, 0.7416267942583732, 0.6961722488038278, 0.7464114832535885]\n",
    "}\n",
    "\n",
    "clf_preds['PCA(12)'] = tst_acc['PCA(12)']\n",
    "df_cols = ['LogRegression', 'SGD', 'GBoosting', 'RandomForest', 'DecisionTree', 'ExtraTrees']\n",
    "df_preds = pd.DataFrame.from_dict(clf_preds, orient='index', columns=df_cols)\n",
    "pd.options.display.float_format = '{:,.5f}'.format\n",
    "df_preds"
   ]
  }
 ],
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