{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Baseline model and assessment - Uses 100 days rather than cumsum versus unadjusted popularity" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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track_idpopularityall_streamslog_all_streams
000kzys67XYXiB31cSP5jfo4024837.817223
100mc2RHScEYMEFlc7FRGaK286606.492240
204HzRAn3BJaIvmhpvc1GVT5156668.642239
304qrVtScdD4IBGSL5q6yEv49148329.604542
409aaq7feVx9Jykdw0f00QU5288539.088512
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" ], "text/plain": [ " track_id popularity all_streams log_all_streams\n", "0 00kzys67XYXiB31cSP5jfo 40 2483 7.817223\n", "1 00mc2RHScEYMEFlc7FRGaK 28 660 6.492240\n", "2 04HzRAn3BJaIvmhpvc1GVT 51 5666 8.642239\n", "3 04qrVtScdD4IBGSL5q6yEv 49 14832 9.604542\n", "4 09aaq7feVx9Jykdw0f00QU 52 8853 9.088512" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_feather('../data/basemodel.feather')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5,\n", " error_score=,\n", " estimator=Pipeline(memory=None,\n", " steps=[('polynomialfeatures', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('linearregression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))]),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'polynomialfeatures__degree': array([0, 1, 2]), 'linearregression__normalize': [True, False]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import r2_score\n", "from sklearn.metrics import mean_squared_error\n", "model = LinearRegression(fit_intercept = True)\n", "x = df[['popularity']]\n", "y = df[['log_all_streams']]\n", "\n", "def PolynomialRegression(degree=2, **kwargs):\n", " return make_pipeline(PolynomialFeatures(degree), LinearRegression(**kwargs))\n", "\n", "param_grid = {'polynomialfeatures__degree': np.arange(3), 'linearregression__normalize': [True, False]}\n", "\n", "grid = GridSearchCV(PolynomialRegression(), param_grid, cv = 5, error_score=mean_squared_error)\n", "grid.fit(x,y)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: log_all_streams 8.756124\n", "dtype: float64\n", "Rsq: 0.3292778744281144\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = grid.best_estimator_\n", "y_pred = model.predict(x)\n", "\n", "plt.scatter(y_pred, y_pred - y)\n", "plt.hlines(y = 0, xmin = 6, xmax = 10)\n", "\n", "rmse = np.sqrt(np.sum((y_pred - y)**2))\n", "print(f'RMSE: {rmse}')\n", "print(f'Rsq: {r2_score(y,y_pred)}')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x,y)\n", "lim = plt.axis()\n", "plt.scatter(x,y_pred, c = 'red')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next iteration\n", "* Evaluate outlier at 0 popularity. It seems to skew the results. It looks like linear would be a good fit here actually" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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track_idpopularityall_streamslog_all_streams
60A488iaPeDAUP5q7Jm3paF031758.063063
411ekNZULmcBHp3WRNKft7ou81014.615121
110DPXH5sPDVwjsqfgvmi1yt263585.880533
371cinSNWNJeGVxMgUSNCHRT2715437.341484
100mc2RHScEYMEFlc7FRGaK286606.492240
260uc7dcChlOHGqfcgmQtD1k2910526.958448
250U1pfb8oRmhHazPuIOndaM307946.677083
361ceX0RJWaTob6mJuENTUVG321575.056246
3417oEEVCQy3VGZuYIv4fk7g341154.744932
535FqevirebpKNGF5FQk60ey352215510.005818
310YjlkuZpp05Z9aZsoeTL3S3617187.448916
511TDS5WmnoAMCksGScA62rp387996.683361
150iHqFwZn8z4HvbjHJctOua3922137.702104
90D19xrl4GXZWih4NX5Yue8405916.381816
000kzys67XYXiB31cSP5jfo4024837.817223
491qLi7TR7RUGedFwV9b8sot4124907.820038
381dlip6LqrFE3cBdyMqQExf416116.415097
120e3y2auTf9hpAMlK9l9BWm411755.164786
210Qq6DG8zjPu8nl8AXi49lw4238688.260493
501QwgOsR7M0vVYO1mBZs9uP4414377.270313
421fNSjDh98pwR0HAkvIcDj344153429.638349
140iHA83002w19QxhOIixKDy442215510.005818
270UvtpHJIvFGuCgJ9rV95Qe4459568.692154
280VUJYazoMnaZIu5HTQIuB64428297.947679
170lvN2fIgifvC63C70WQZDz454546.118097
320ySovENVdl0e50eJDdQMML455806.363028
230rIZGjN9oQTyAwByKpeGBf4638068.244334
100D6I50oBN4opIAaPp0MRrb4784469.041448
80bPSRn4crnh5f1JhELPlyL4786099.060563
391Ea4QAgJoNCFHqNj7oCOon475806.363028
33122lxLiuCUAdpaGOk2KSbe4821697.682022
50A1xaq9kcUeVZwXHDJAtH84822637.724447
290WYZBUsTyhUkaH2eQ6HUlU485236.259581
160iUC7NOX8g5dXVRX61LmJW4920017.601402
471ONgRO7Mp49j33EAAMoK3i4919557.578145
304qrVtScdD4IBGSL5q6yEv49148329.604542
451KVQsZPeztZ7GnpVfqAnFX502793910.237779
200OmgaIr7HdNJpanSQbyKov5021657.680176
70A500BtVTgViTjhWS07yEr50110599.311000
204HzRAn3BJaIvmhpvc1GVT5156668.642239
409aaq7feVx9Jykdw0f00QU5288539.088512
300xhY4rD6Gj3djZRyT7T08b5314347.268223
441imBH4y5b85vL1mFDxBp9N543417810.439337
481OZWyPMwub90x2V6TzRIsz5493639.144521
180NsZlandaYpsQZ98GjymqZ5440638.309677
220rEwD7QVscNnz05ooEb0qB5531058.040769
431ie1M25b3I0Kvl1DQY6lbI5518247.508787
461lLuhJSggOEPVfSgfa9r2n5514537.281386
240Tt81ZbYRH0Aj04CJBRYWK5889029.094031
35184HI8TB2GGFMkgOQvVwYW5898239.192482
190Nw0Z2VXKuiFSikzWvgqR659118399.379154
401EDkPryAg1Sv2H2WNoKRLJ6031168.044305
524n8df1lKaP8on42bdQJHcz60128229.458918
130gOloXVox0hwdtty5VsCfS623983310.592451
\n", "
" ], "text/plain": [ " track_id popularity all_streams log_all_streams\n", "6 0A488iaPeDAUP5q7Jm3paF 0 3175 8.063063\n", "41 1ekNZULmcBHp3WRNKft7ou 8 101 4.615121\n", "11 0DPXH5sPDVwjsqfgvmi1yt 26 358 5.880533\n", "37 1cinSNWNJeGVxMgUSNCHRT 27 1543 7.341484\n", "1 00mc2RHScEYMEFlc7FRGaK 28 660 6.492240\n", "26 0uc7dcChlOHGqfcgmQtD1k 29 1052 6.958448\n", "25 0U1pfb8oRmhHazPuIOndaM 30 794 6.677083\n", "36 1ceX0RJWaTob6mJuENTUVG 32 157 5.056246\n", "34 17oEEVCQy3VGZuYIv4fk7g 34 115 4.744932\n", "53 5FqevirebpKNGF5FQk60ey 35 22155 10.005818\n", "31 0YjlkuZpp05Z9aZsoeTL3S 36 1718 7.448916\n", "51 1TDS5WmnoAMCksGScA62rp 38 799 6.683361\n", "15 0iHqFwZn8z4HvbjHJctOua 39 2213 7.702104\n", "9 0D19xrl4GXZWih4NX5Yue8 40 591 6.381816\n", "0 00kzys67XYXiB31cSP5jfo 40 2483 7.817223\n", "49 1qLi7TR7RUGedFwV9b8sot 41 2490 7.820038\n", "38 1dlip6LqrFE3cBdyMqQExf 41 611 6.415097\n", "12 0e3y2auTf9hpAMlK9l9BWm 41 175 5.164786\n", "21 0Qq6DG8zjPu8nl8AXi49lw 42 3868 8.260493\n", "50 1QwgOsR7M0vVYO1mBZs9uP 44 1437 7.270313\n", "42 1fNSjDh98pwR0HAkvIcDj3 44 15342 9.638349\n", "14 0iHA83002w19QxhOIixKDy 44 22155 10.005818\n", "27 0UvtpHJIvFGuCgJ9rV95Qe 44 5956 8.692154\n", "28 0VUJYazoMnaZIu5HTQIuB6 44 2829 7.947679\n", "17 0lvN2fIgifvC63C70WQZDz 45 454 6.118097\n", "32 0ySovENVdl0e50eJDdQMML 45 580 6.363028\n", "23 0rIZGjN9oQTyAwByKpeGBf 46 3806 8.244334\n", "10 0D6I50oBN4opIAaPp0MRrb 47 8446 9.041448\n", "8 0bPSRn4crnh5f1JhELPlyL 47 8609 9.060563\n", "39 1Ea4QAgJoNCFHqNj7oCOon 47 580 6.363028\n", "33 122lxLiuCUAdpaGOk2KSbe 48 2169 7.682022\n", "5 0A1xaq9kcUeVZwXHDJAtH8 48 2263 7.724447\n", "29 0WYZBUsTyhUkaH2eQ6HUlU 48 523 6.259581\n", "16 0iUC7NOX8g5dXVRX61LmJW 49 2001 7.601402\n", "47 1ONgRO7Mp49j33EAAMoK3i 49 1955 7.578145\n", "3 04qrVtScdD4IBGSL5q6yEv 49 14832 9.604542\n", "45 1KVQsZPeztZ7GnpVfqAnFX 50 27939 10.237779\n", "20 0OmgaIr7HdNJpanSQbyKov 50 2165 7.680176\n", "7 0A500BtVTgViTjhWS07yEr 50 11059 9.311000\n", "2 04HzRAn3BJaIvmhpvc1GVT 51 5666 8.642239\n", "4 09aaq7feVx9Jykdw0f00QU 52 8853 9.088512\n", "30 0xhY4rD6Gj3djZRyT7T08b 53 1434 7.268223\n", "44 1imBH4y5b85vL1mFDxBp9N 54 34178 10.439337\n", "48 1OZWyPMwub90x2V6TzRIsz 54 9363 9.144521\n", "18 0NsZlandaYpsQZ98GjymqZ 54 4063 8.309677\n", "22 0rEwD7QVscNnz05ooEb0qB 55 3105 8.040769\n", "43 1ie1M25b3I0Kvl1DQY6lbI 55 1824 7.508787\n", "46 1lLuhJSggOEPVfSgfa9r2n 55 1453 7.281386\n", "24 0Tt81ZbYRH0Aj04CJBRYWK 58 8902 9.094031\n", "35 184HI8TB2GGFMkgOQvVwYW 58 9823 9.192482\n", "19 0Nw0Z2VXKuiFSikzWvgqR6 59 11839 9.379154\n", "40 1EDkPryAg1Sv2H2WNoKRLJ 60 3116 8.044305\n", "52 4n8df1lKaP8on42bdQJHcz 60 12822 9.458918\n", "13 0gOloXVox0hwdtty5VsCfS 62 39833 10.592451" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values('popularity')" ] } ], "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": "3.6.6" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }