{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Top 10 Statistics Mistakes Made by Data Scientists - Examples Code\n", "\n", "Blog post available at https://github.com/d6t/d6t-python/blob/master/blogs/top10-mistakes-statistics.md" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# calc\n", "import pandas as pd\n", "import numpy as np\n", "import scipy.stats\n", "\n", "# viz\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "\n", "# ML\n", "import sklearn.model_selection\n", "import sklearn.metrics\n", "from sklearn.metrics import mean_squared_error as sklmse" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# helpers\n", "def print2(a,b,ta=None,tb=None):\n", " print(ta,np.round(a,3),tb,np.round(b,3))\n", "\n", "def CVTestMean(model, dfg):\n", " return -sklearn.model_selection.cross_validate(model,dfg.iloc[:,:-1],dfg.iloc[:,-1], cv=10, scoring='neg_mean_squared_error')['test_score'].mean()\n", "\n", "def mse(model, dfg):\n", " return sklearn.metrics.mean_squared_error(model.predict(dfg.iloc[:,:-1]),dfg.iloc[:,-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data\n", "\n", "The code uses workflow management library [d6tflow](https://github.com/d6t/d6tflow) and data is shared with dataset management library [d6tpipe](https://github.com/d6t/d6tpipe)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Welcome to d6tflow!\n", "Welcome to d6tpipe!\n", "Connected to https://pipe.databolt.tech as citynorman\n", "Successfully connected to pipe top10-mistakes-stats. \n", "pulling: 0.00MB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import d6tflow.pipes\n", "d6tflow.pipes.init('top10-mistakes-stats')\n", "d6tflow.pipes.get_pipe().pull()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import cfg, tasks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 0.571 accuracy 0.4\n" ] } ], "source": [ "# objective function\n", "from sklearn.metrics import f1_score, accuracy_score\n", "y_true = [0, 0, 0, 0, 0]\n", "y_pred = [0, 1, 0, 1, 0]\n", "y_true = np.tile(y_true,10)+[1]\n", "y_pred = np.tile(y_pred,10)+[0]\n", "\n", "print2(f1_score(y_true,y_pred), accuracy_score(y_true,y_pred), 'f1', 'accuracy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df = tasks.DataRegression().outputLoad()\n", "dfp = df.melt(value_vars=['x1','x2'],id_vars='y')\n", "sns.lmplot(x=\"value\", y=\"y\", col=\"variable\", data=dfp)\n", "plt.savefig('reports/top10-stats-example2.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "slope x1 w/o outlier 0.906 slope w/ outlier -0.375\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df = tasks.DataOutliers().outputLoad()\n", "m1, m2 = tasks.ModelOutliers().outputLoad()\n", "dfp = df.melt(value_vars=['x1','x2'],id_vars='y')\n", "sns.lmplot(x=\"value\", y=\"y\", col=\"variable\", data=dfp)\n", "plt.savefig('reports/top10-stats-example3.png')\n", "print2(m1.coef_[0],m2.coef_[0], 'slope x1 w/o outlier', 'slope w/ outlier')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 4" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ols CV mse 0.215\n", "rf CV mse 0.428\n", "last out-sample mse 0.003\n" ] } ], "source": [ "df_ts = tasks.DataTS().outputLoad()\n", "m1, m2 = tasks.ModelTS().outputLoad()\n", "print('ols CV mse',round(CVTestMean(m1,df_ts),3))\n", "print('rf CV mse', round(CVTestMean(m2,df_ts),3))\n", "print('last out-sample mse', round(sklmse(df_ts.iloc[1:,-1],df_ts['y'].shift().dropna()),3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 5" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "in-sample\n", "rf mse 0.04 ols mse 0.183\n", "out-sample\n", "rf mse 0.261 ols mse 0.187\n" ] } ], "source": [ "df = tasks.DataRegression().outputLoad()\n", "m1, m2 = tasks.OLSvsRF().outputLoad()\n", "print('in-sample')\n", "print2(mse(m2, df),mse(m1, df),'rf mse','ols mse')\n", "print('out-sample')\n", "print2(CVTestMean(m2,df),CVTestMean(m1,df),'rf mse','ols mse')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 6" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mixed out-sample CV mse 0.187 true out-sample CV mse 0.181\n" ] } ], "source": [ "df = tasks.DataRegression().outputLoad()\n", "m1, m2 = tasks.OLSvsRF().outputLoad()\n", "\n", "# mixing training and test data\n", "df_insample = pd.DataFrame(scipy.stats.zscore(df.copy())) # everything just became training data!\n", "\n", "# better\n", "X=df.iloc[:,:-1]; y=df.iloc[:,-1];test_error=[]\n", "for train_index, test_index in sklearn.model_selection.KFold(n_splits=10).split(df):\n", " X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n", " X_train, X_test = scipy.stats.zscore(X.iloc[train_index]), scipy.stats.zscore(X.iloc[test_index])\n", " y_train, y_test = scipy.stats.zscore(y.iloc[train_index]), scipy.stats.zscore(y.iloc[test_index])\n", " m1.fit(X_train,y_train)\n", " test_error.append(sklmse(y_test,m1.predict(X_test)))\n", " # => distributional properties haven't changed\n", "\n", "print2(CVTestMean(m1,df_insample),np.mean(test_error),'mixed out-sample CV mse','true out-sample CV mse')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 7" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "normal CV\n", "ols 0.203 rf 0.049\n", "true out-sample error\n", "ols 0.166 rf 0.237\n" ] } ], "source": [ "df_ts = tasks.DataTS().outputLoad().reset_index(drop=True).sort_index()\n", "df_ts = pd.concat([df_ts,df_ts]) # data for two entities which are highly correlated, in this case identical\n", "\n", "# default CV\n", "m1 = sklearn.linear_model.LinearRegression()\n", "m2 = sklearn.ensemble.RandomForestRegressor(n_estimators=10)\n", "print('normal CV')\n", "print2(CVTestMean(m1,df_ts),CVTestMean(m2,df_ts),'ols','rf')\n", "\n", "# roll-forward testing\n", "mroll = sklearn.linear_model.LinearRegression()\n", "mroll2 = sklearn.ensemble.RandomForestRegressor(n_estimators=10)\n", "pred_ols = []; pred_rf = [];\n", "for i in range(cfg.nobs//3,df.shape[0]):\n", " x_train = df.iloc[:i-1,:-1]\n", " y_train = df.iloc[:i-1,-1]\n", " x_test = df.iloc[i,:-1]\n", " mroll.fit(x_train,y_train)\n", " mroll2.fit(x_train,y_train)\n", " pred_ols.append(mroll.predict([x_test])[0])\n", " pred_rf.append(mroll2.predict([x_test])[0])\n", "\n", "y_os_true = df.iloc[cfg.nobs//3:,-1]\n", "\n", "print('true out-sample error')\n", "print2(sklmse(y_os_true,np.array(pred_ols)),sklmse(y_os_true,np.array(pred_rf)),'ols','rf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 8" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first dataset\n", "rf mse 0.261 ols mse 0.187\n", "new dataset\n", "rf mse 0.681 ols mse 0.495\n" ] } ], "source": [ "df1 = tasks.DataRegression().outputLoad()\n", "m1, m2 = tasks.OLSvsRF().outputLoad()\n", "\n", "print('first dataset')\n", "print2(CVTestMean(m2,df1),CVTestMean(m1,df1),'rf mse','ols mse')\n", "\n", "print('new dataset')\n", "params = {'random_state':10, 'noise':20}\n", "tasks.DataRegression(**params).run()\n", "df2 = tasks.DataRegression(**params).outputLoad()\n", "print2(CVTestMean(m2,df2),CVTestMean(m1,df2),'rf mse','ols mse')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }