{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing random forest classifier performance" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import preprocessing\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import TimeSeriesSplit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# import data\n", "df = pd.read_csv(\"data/SCADA_downtime_merged.csv\", skip_blank_lines=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# list of turbines to plot\n", "list1 = list(df[\"turbine_id\"].unique())\n", "# sort turbines in ascending order\n", "list1 = sorted(list1, key=int)\n", "# list of categories\n", "list2 = list(df[\"TurbineCategory_id\"].unique())\n", "# remove NaN from list\n", "list2 = [g for g in list2 if g >= 0]\n", "# sort categories in ascending order\n", "list2 = sorted(list2, key=int)\n", "# categories to remove\n", "list2 = [m for m in list2 if m not in (1, 12, 13, 14, 15, 17, 21, 22)]\n", "# empty list to hold optimal n values for all turbines\n", "num = []\n", "# empty list to hold minimum error readings for all turbines\n", "err = []" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 1 is 29\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 2 is 17\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 3 is 29\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 4 is 27\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 5 is 11\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 6 is 3\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 7 is 27\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 8 is 29\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 9 is 25\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 10 is 29\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 11 is 29\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The optimal number of trees in the forest for turbine 12 is 21\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filter only data for turbine x\n", "for x in list1:\n", " dfx = df[(df[\"turbine_id\"] == x)].copy()\n", "\n", " # copying fault to new column (mins) (fault when turbine category id is y)\n", " for y in list2:\n", "\n", " def f(c):\n", " if c[\"TurbineCategory_id\"] == y:\n", " return 0\n", " else:\n", " return 1\n", "\n", " dfx[\"mins\"] = dfx.apply(f, axis=1)\n", "\n", " # sort values by timestamp in descending order\n", " dfx = dfx.sort_values(by=\"timestamp\", ascending=False)\n", " # reset index\n", " dfx.reset_index(drop=True, inplace=True)\n", "\n", " # assigning value to first cell if it's not 0 with a large number\n", " if dfx.loc[0, \"mins\"] == 0:\n", " dfx.set_value(0, \"mins\", 0)\n", " else:\n", " # to allow the following loop to work\n", " dfx.set_value(0, \"mins\", 999999999)\n", "\n", " # using previous value's row to evaluate time\n", " for i, e in enumerate(dfx[\"mins\"]):\n", " if e == 1:\n", " dfx.at[i, \"mins\"] = dfx.at[i - 1, \"mins\"] + 10\n", "\n", " # sort in ascending order\n", " dfx = dfx.sort_values(by=\"timestamp\")\n", " # reset index\n", " dfx.reset_index(drop=True, inplace=True)\n", " # convert to hours, then round to nearest hour\n", " dfx[\"hours\"] = dfx[\"mins\"].astype(np.int64)\n", " dfx[\"hours\"] = dfx[\"hours\"] / 60\n", " # round to integer\n", " dfx[\"hours\"] = round(dfx[\"hours\"]).astype(np.int64)\n", "\n", " # > 48 hours - label as normal (999)\n", " def f1(c):\n", " if c[\"hours\"] > 48:\n", " return 999\n", " else:\n", " return c[\"hours\"]\n", "\n", " dfx[\"hours\"] = dfx.apply(f1, axis=1)\n", "\n", " # filter out curtailment - curtailed when turbine is pitching outside\n", " # 0deg <= normal <= 3.5deg\n", " def f2(c):\n", " if (\n", " 0 <= c[\"pitch\"] <= 3.5\n", " or c[\"hours\"] != 999\n", " or (\n", " (c[\"pitch\"] > 3.5 or c[\"pitch\"] < 0)\n", " and (\n", " c[\"ap_av\"] <= (0.1 * dfx[\"ap_av\"].max())\n", " or c[\"ap_av\"] >= (0.9 * dfx[\"ap_av\"].max())\n", " )\n", " )\n", " ):\n", " return \"normal\"\n", " else:\n", " return \"curtailed\"\n", "\n", " dfx[\"curtailment\"] = dfx.apply(f2, axis=1)\n", "\n", " # filter unusual readings, i.e., for normal operation, power <= 0 in\n", " # operating wind speeds, power > 100 ...\n", " def f3(c):\n", " # before cut-in, runtime < 600 and other downtime categories\n", " if c[\"hours\"] == 999 and (\n", " (\n", " 3 < c[\"ws_av\"] < 25\n", " and (\n", " c[\"ap_av\"] <= 0\n", " or c[\"runtime\"] < 600\n", " or c[\"EnvironmentalCategory_id\"] > 1\n", " or c[\"GridCategory_id\"] > 1\n", " or c[\"InfrastructureCategory_id\"] > 1\n", " or c[\"AvailabilityCategory_id\"] == 2\n", " or 12 <= c[\"TurbineCategory_id\"] <= 15\n", " or 21 <= c[\"TurbineCategory_id\"] <= 22\n", " )\n", " )\n", " or (c[\"ws_av\"] < 3 and c[\"ap_av\"] > 100)\n", " ):\n", " return \"unusual\"\n", " else:\n", " return \"normal\"\n", "\n", " dfx[\"unusual\"] = dfx.apply(f3, axis=1)\n", "\n", " # round to 6 hour intervals to reduce number of classes\n", " def f4(c):\n", " if 1 <= c[\"hours\"] <= 6:\n", " return 6\n", " elif 7 <= c[\"hours\"] <= 12:\n", " return 12\n", " elif 13 <= c[\"hours\"] <= 18:\n", " return 18\n", " elif 19 <= c[\"hours\"] <= 24:\n", " return 24\n", " elif 25 <= c[\"hours\"] <= 30:\n", " return 30\n", " elif 31 <= c[\"hours\"] <= 36:\n", " return 36\n", " elif 37 <= c[\"hours\"] <= 42:\n", " return 42\n", " elif 43 <= c[\"hours\"] <= 48:\n", " return 48\n", " else:\n", " return c[\"hours\"]\n", "\n", " dfx[\"hours6\"] = dfx.apply(f4, axis=1)\n", "\n", " # change label for unusual and curtailed data (9999), if originally\n", " # labelled as normal\n", " def f5(c):\n", " if c[\"unusual\"] == \"unusual\" or c[\"curtailment\"] == \"curtailed\":\n", " return 9999\n", " else:\n", " return c[\"hours6\"]\n", "\n", " # apply to new column specific to each fault\n", " dfx[\"hours_%s\" % y] = dfx.apply(f5, axis=1)\n", "\n", " # drop unnecessary columns\n", " dfx = dfx.drop(\"hours6\", axis=1)\n", " dfx = dfx.drop(\"hours\", axis=1)\n", " dfx = dfx.drop(\"mins\", axis=1)\n", " dfx = dfx.drop(\"curtailment\", axis=1)\n", " dfx = dfx.drop(\"unusual\", axis=1)\n", "\n", " features = [\n", " \"ap_av\",\n", " \"ws_av\",\n", " \"wd_av\",\n", " \"pitch\",\n", " \"ap_max\",\n", " \"ap_dev\",\n", " \"reactive_power\",\n", " \"rs_av\",\n", " \"gen_sp\",\n", " \"nac_pos\",\n", " ]\n", " # separate features from classes for classification\n", " classes = [col for col in dfx.columns if \"hours\" in col]\n", " # list of columns to copy into new df\n", " list3 = features + classes + [\"timestamp\"]\n", " df2 = dfx[list3].copy()\n", " # drop NaNs\n", " df2 = df2.dropna()\n", " X = df2[features]\n", " # normalise features to values b/w 0 and 1\n", " X = preprocessing.normalize(X)\n", " Y = df2[classes]\n", " # convert from pd dataframe to np array\n", " Y = Y.as_matrix()\n", "\n", " # evaluating optimal number of trees\n", " # creating odd list of n\n", " myList = list(range(1, 30))\n", " # subsetting just the odd ones\n", " estimators = list(filter(lambda x: x % 2 != 0, myList))\n", " # empty list that will hold average cross validation scores for each n\n", " scores = []\n", " # cross validation using time series split\n", " tscv = TimeSeriesSplit(n_splits=5)\n", "\n", " # looping for each value of n and defining random forest classifier\n", " for n in estimators:\n", " rf = RandomForestClassifier(\n", " criterion=\"entropy\",\n", " class_weight=\"balanced_subsample\",\n", " random_state=42,\n", " n_estimators=n,\n", " n_jobs=-1,\n", " )\n", " # empty list to hold score for each cross validation fold\n", " p1 = []\n", " # looping for each cross validation fold\n", " for train_index, test_index in tscv.split(X):\n", " # split train and test sets\n", " X_train, X_test = X[train_index], X[test_index]\n", " Y_train, Y_test = Y[train_index], Y[test_index]\n", " # fit to classifier and predict\n", " rf1 = rf.fit(X_train, Y_train)\n", " pred = rf1.predict(X_test)\n", " # accuracy score\n", " p2 = (\n", " np.sum(np.equal(np.array(Y_test), pred))\n", " / np.array(Y_test).size\n", " )\n", " # add to list\n", " p1.append(p2)\n", " # average score across all cross validation folds\n", " p = sum(p1) / len(p1)\n", " scores.append(p)\n", " # changing to misclassification error\n", " MSE = [1 - x for x in scores]\n", " # determining best n\n", " optimal_n = estimators[MSE.index(min(MSE))]\n", " num.append(optimal_n)\n", " err.append(min(MSE))\n", "\n", " print(\n", " \"The optimal number of trees in the forest for turbine %s\" % x\n", " + \" is %d\" % optimal_n\n", " )\n", " # plot misclassification error vs n\n", " plt.plot(estimators, MSE)\n", " plt.xlabel(\"Number of Trees in the Forest\")\n", " plt.ylabel(\"Misclassification Error\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = pd.DataFrame(num, columns=\"number of estimators\")\n", "d[\"error\"] = err\n", "d[\"turbine\"] = list1" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.77976540313618969,\n", " 0.81880479071490309,\n", " 0.83739062435691647,\n", " 0.84063958513396708,\n", " 0.84513149771576734,\n", " 0.84991562744371729,\n", " 0.85255875210931387,\n", " 0.85299748940198383,\n", " 0.85286825534016553,\n", " 0.85693953986088833,\n", " 0.85595094044532249,\n", " 0.85549738650862239,\n", " 0.85559287154792774,\n", " 0.85676503272008886,\n", " 0.85697411203029183,\n", " 0.85644235913898825,\n", " 0.85688768160678275,\n", " 0.85690332139770342,\n", " 0.85704490266288003,\n", " 0.85644071284520729,\n", " 0.85609169856360867,\n", " 0.85617236695888388,\n", " 0.85644647487344128]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, 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