Random Forest - Apr 8 2022 - RQ.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74758055",
   "metadata": {},
   "source": [
    "### 1.   Prepare input data\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ac683f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function: determine PHA-L read cut-offs for binary classification \n",
    "def categorize_lectin(data_all, quantile_high, quantile_low, ref_col_loc):\n",
    "    cutoff = np.quantile(data_all.iloc[:,ref_col_loc], [quantile_high, quantile_low], interpolation=\"nearest\").tolist()\n",
    "    print(f\"Cut-off for PHA-L high: {cutoff[0]}; Cut-off for PHA-L low: {cutoff[1]}\")\n",
    "    \n",
    "    high_indices = np.array(data_all.iloc[:,ref_col_loc]>=cutoff[0])\n",
    "    low_indices = np.array(data_all.iloc[:,ref_col_loc]<cutoff[1])\n",
    "    high_low_indices = np.logical_or(high_indices, low_indices)\n",
    "\n",
    "    high_count = high_indices.sum()\n",
    "    low_count = low_indices.sum()\n",
    "    \n",
    "    return cutoff, [high_indices, low_indices, high_low_indices], [high_count, low_count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7128392",
   "metadata": {
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   "outputs": [],
   "source": [
    "# Load input file\n",
    "input_df = pd.read_csv('TIL_transformed_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6be21275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process data: binary classification\n",
    "quantile_high, quantile_low = 0.75, 0.25\n",
    "cutoff, indices, count = categorize_lectin(input_df, quantile_high, quantile_low, -1)\n",
    "\n",
    "input_df.loc[indices[0], \"PHA-L\"] = 1\n",
    "input_df.loc[indices[1], \"PHA-L\"] = 0\n",
    "\n",
    "input_df = input_df.loc[indices[2], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c1f84f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y: class array\n",
    "y = input_df['PHA-L'].values \n",
    "#X: transcript data array\n",
    "X = input_df.iloc[:, 1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4596357",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training, validation and test set\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
    "    X, y, test_size=0.1, random_state=342, stratify=y)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=0.2, random_state=2, stratify=y_train_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff194672",
   "metadata": {},
   "source": [
    "### 2.   Model training\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ded72e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters for grid search\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.arange(200, 800, step=100)]\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.arange(10, 50, step=10)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3b205ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use RandomSearchCV to optimize hyperparameters\n",
    "model = RandomForestClassifier()\n",
    "\n",
    "model_random = RandomizedSearchCV(estimator = model, param_distributions = random_grid, n_iter = 20, cv = 5, verbose=5, random_state=42, n_jobs = -1)\n",
    "\n",
    "model_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95485f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return the best estimator\n",
    "model = model_random.best_estimator_\n",
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb39046",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_evaluation(model, X, y):\n",
    "    print(f\"Accuracy for 'PHA-L high' class: {100*(model.score(X[y==1], y[y==1])):>4f}%\")\n",
    "    print(f\"Accuracy for 'PHA-L low' class: {100*(model.score(X[y==0], y[y==0])):>4f}%\")\n",
    "    print(f\"Overall accuracy: {100*(model.score(X, y)):>4f}%\")\n",
    "\n",
    "    model_predict = model.predict(X)\n",
    "    model_predict_prob = model.predict_proba(X)\n",
    "\n",
    "    print(f\"Average loss: {log_loss(y, model_predict_prob):>4f}\")\n",
    "    print(f\"ROC Curve AUC: {roc_auc_score(y, model_predict):>4f}\")\n",
    "    print(f\"F1 score: {f1_score(y, model_predict):>4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9213ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_evaluation(model, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb09d354",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_evaluation(model, X_val, y_val)"
   ]
  }
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