diff --git a/doc/HandWrittenNotes/NotesOctober29.pdf b/doc/HandWrittenNotes/NotesOctober29.pdf new file mode 100644 index 000000000..6f2951a15 Binary files /dev/null and b/doc/HandWrittenNotes/NotesOctober29.pdf differ diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index e74f98c5b..3afaa4d3c 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -660,9 +660,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -678,9 +676,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "n = 10\n", @@ -699,9 +695,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -720,9 +714,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -746,9 +738,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -770,9 +760,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -790,9 +778,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -810,9 +796,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -834,9 +818,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -854,9 +836,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -875,9 +855,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -896,9 +874,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -918,9 +894,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -940,9 +914,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1026,9 +998,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1051,9 +1021,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -1100,9 +1068,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -1129,9 +1095,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n", @@ -1148,9 +1112,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "display(data_pandas.loc['Aragorn'])" @@ -1166,9 +1128,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "new_hobbit = {'First Name': [\"Peregrin\"],\n", @@ -1191,9 +1151,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1221,9 +1179,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", @@ -1256,9 +1212,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "b = np.arange(16).reshape((4,4))\n", @@ -1382,9 +1336,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1513,9 +1465,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1557,9 +1507,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", @@ -1729,9 +1677,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -1934,10 +1880,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "# Common imports\n", @@ -1984,10 +1928,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "from pylab import plt, mpl\n", @@ -2019,11 +1961,20 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' \\nThis is taken from the data file of the mass 2016 evaluation. \\nAll files are 3436 lines long with 124 character per line. \\n Headers are 39 lines long. \\n col 1 : Fortran character control: 1 = page feed 0 = line feed \\n format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5 \\n These formats are reflected in the pandas widths variable below, see the statement \\n widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), \\n Pandas has also a variable header, with length 39 in this case. \\n'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\" \n", "This is taken from the data file of the mass 2016 evaluation. \n", @@ -2049,10 +2000,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# Read the experimental data with Pandas\n", @@ -2093,11 +2042,31 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N Z A Element Ebinding\n", + "A \n", + "1 0 0 1 1 H 0.000000\n", + "2 1 1 1 2 H 1.112283\n", + "3 2 2 1 3 H 2.827265\n", + "4 6 2 2 4 He 7.073915\n", + "5 9 3 2 5 He 5.512132\n", + "... ... ... ... ... ...\n", + "264 3304 156 108 264 Hs 7.298375\n", + "265 3310 157 108 265 Hs 7.296247\n", + "266 3317 158 108 266 Hs 7.298273\n", + "269 3338 159 110 269 Ds 7.250154\n", + "270 3344 160 110 270 Ds 7.253775\n", + "\n", + "[267 rows x 5 columns]\n" + ] + } + ], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", @@ -2117,10 +2086,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# Now we set up the design matrix X\n", @@ -2141,10 +2108,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "clf = skl.LinearRegression().fit(X, Energies)\n", @@ -2161,11 +2126,31 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 0.04\n", + "Variance score: 0.95\n", + "Mean absolute error: 0.05\n", + "[ 0.00000000e+00 7.06492086e-03 -1.73091052e-01 -1.66020213e+01\n", + " 1.17385778e+00] 15.212327334149494\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2200,18 +2185,49 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N Z A Element Ebinding Eapprox\n", + "A \n", + "1 0 0 1 1 H 0.000000 0.000000\n", + "2 1 1 1 2 H 1.112283 1.112283\n", + "3 2 2 1 3 H 2.827265 2.827265\n", + "4 6 2 2 4 He 7.073915 7.073915\n", + "5 9 3 2 5 He 5.512132 5.512132\n", + "... ... ... ... ... ... ...\n", + "264 3304 156 108 264 Hs 7.298375 7.298375\n", + "265 3310 157 108 265 Hs 7.296247 7.296247\n", + "266 3317 158 108 266 Hs 7.298273 7.298273\n", + "269 3338 159 110 269 Ds 7.250154 7.250154\n", + "270 3344 160 110 270 Ds 7.253775 7.253775\n", + "\n", + "[267 rows x 6 columns]\n", + "0.009883615646716182\n" + ] + } + ], "source": [ "\n", "#Decision Tree Regression\n", "from sklearn.tree import DecisionTreeRegressor\n", "regr_1=DecisionTreeRegressor(max_depth=5)\n", "regr_2=DecisionTreeRegressor(max_depth=7)\n", - "regr_3=DecisionTreeRegressor(max_depth=9)\n", + "regr_3=DecisionTreeRegressor(max_depth=11)\n", "regr_1.fit(X, Energies)\n", "regr_2.fit(X, Energies)\n", "regr_3.fit(X, Energies)\n", @@ -2251,9 +2267,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", @@ -2305,7 +2319,25 @@ ] } ], - "metadata": {}, + "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": "3.6.8" + } + }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb index 04536e6ab..20df52fdc 100644 --- a/doc/pub/week43/ipynb/week43.ipynb +++ b/doc/pub/week43/ipynb/week43.ipynb @@ -673,9 +673,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -840,9 +838,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -1096,9 +1092,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -1358,9 +1352,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Assume that all function definitions from the example program using Autograd\n", @@ -1561,9 +1553,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -1871,9 +1861,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -2296,9 +2284,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def sigmoid(z):\n", @@ -2397,9 +2383,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Set up the trial function:\n", @@ -2466,9 +2450,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -2826,9 +2808,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -3356,9 +3336,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -3389,9 +3367,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -3435,9 +3411,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -3467,9 +3441,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Common imports\n", @@ -3764,10 +3736,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -3776,7 +3746,7 @@ "from IPython.display import display\n", "n = 10000\n", "mean = (-1, 2)\n", - "cov = [[4, 2], [2, 2]]\n", + "cov = [[10, 0.01], [0.01, 2]]\n", "X = np.random.multivariate_normal(mean, cov, n)" ] }, @@ -3828,10 +3798,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(X)\n", @@ -3879,11 +3847,21 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1\n", + "0 10.067769 0.008068\n", + "1 0.008068 2.024183\n", + "[[1.00677691e+01 8.06773313e-03]\n", + " [8.06773313e-03 2.02418284e+00]]\n" + ] + } + ], "source": [ "print(df.cov())\n", "print(np.cov(X_centered.T))" @@ -3899,11 +3877,31 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Centered covariance using own code\n", + "[[1.00677691e+01 8.06773313e-03]\n", + " [8.06773313e-03 2.02418284e+00]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# extract the relevant columns from the centered design matrix of dim n x 2\n", "x = X_centered[:,0]\n", @@ -3969,11 +3967,25 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues of Covariance matrix\n", + "10.06777716248222\n", + "2.024174751235422\n", + "First eigenvector\n", + "[0.9999995 0.001003 ]\n", + "Second eigenvector\n", + "[-0.001003 0.9999995]\n", + "Eigenvector of largest eigenvalue\n", + "[0.9999995 0.001003 ]\n" + ] + } + ], "source": [ "# diagonalize and obtain eigenvalues, not necessarily sorted\n", "EigValues, EigVectors = np.linalg.eig(Cov)\n", @@ -4143,9 +4155,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -4190,9 +4200,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "W2 = V.T[:, :2]\n", @@ -4214,9 +4222,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "#thereafter we do a PCA with Scikit-learn\n", @@ -4238,9 +4244,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca.components_.T[:, 0]" @@ -4262,9 +4266,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -4316,9 +4318,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA()\n", @@ -4339,9 +4339,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=0.95)\n", @@ -4386,9 +4384,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import KernelPCA\n", @@ -4426,7 +4422,25 @@ ] } ], - "metadata": {}, + "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": "3.6.8" + } + }, "nbformat": 4, "nbformat_minor": 4 } diff --git a/doc/pub/week44/ipynb/Datafiles/cancer.dot b/doc/pub/week44/ipynb/Datafiles/cancer.dot index 5b4b48a9b..5777bf8e2 100644 --- a/doc/pub/week44/ipynb/Datafiles/cancer.dot +++ b/doc/pub/week44/ipynb/Datafiles/cancer.dot @@ -1,57 +1,57 @@ digraph Tree { node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ; edge [fontname=helvetica] ; -0 [label="worst perimeter <= 106.05\ngini = 0.465\nsamples = 426\nvalue = [[269, 157]\n[157, 269]]", fillcolor="#e5813908"] ; -1 [label="worst concave points <= 0.159\ngini = 0.067\nsamples = 259\nvalue = [[250, 9]\n[9, 250]]", fillcolor="#e58139db"] ; +0 [label="worst perimeter <= 106.05\ngini = 0.465\nsamples = 426\nvalue = [[269, 157]\n[157, 269]]", fillcolor="#fefbf9"] ; +1 [label="worst concave points <= 0.159\ngini = 0.067\nsamples = 259\nvalue = [[250, 9]\n[9, 250]]", fillcolor="#e99355"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; -2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e58139ee"] ; +2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ; +3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; -4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139ff"] ; +4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="worst symmetry <= 0.208\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#e5813913"] ; +5 [label="concavity error <= 0.017\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; -6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139ff"] ; +6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; -7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139ff"] ; +7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; 5 -> 7 ; -8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#e581392c"] ; +8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; 2 -> 8 ; -9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139ff"] ; +9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ; 8 -> 9 ; -10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139ff"] ; +10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#e581396b"] ; +11 [label="worst texture <= 24.785\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; -12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; +12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; -13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139ff"] ; +13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139"] ; 11 -> 13 ; -14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#e5813994"] ; +14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ; +15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; -16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139ff"] ; +16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="mean texture <= 13.745\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ; +17 [label="mean concavity <= 0.06\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; -18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; +18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; -19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139ff"] ; +19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139"] ; 17 -> 19 ; -20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#e58139d0"] ; +20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ; 14 -> 20 ; -21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#e5813900"] ; +21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; 20 -> 21 ; -22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139ff"] ; +22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ; 21 -> 22 ; -23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139ff"] ; +23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ; +24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; 20 -> 24 ; -25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; +25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 24 -> 25 ; -26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ; +26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; 24 -> 26 ; } \ No newline at end of file diff --git a/doc/pub/week44/ipynb/Datafiles/ride.dot b/doc/pub/week44/ipynb/Datafiles/ride.dot index 50aaa7638..f440c3da1 100644 --- a/doc/pub/week44/ipynb/Datafiles/ride.dot +++ b/doc/pub/week44/ipynb/Datafiles/ride.dot @@ -1,13 +1,13 @@ digraph Tree { node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ; edge [fontname=helvetica] ; -0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ; -1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ; +0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#93f1ba"] ; +1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#88efb3"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; -2 [label="gini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#39e58155"] ; +2 [label="gini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#bdf6d5"] ; 1 -> 2 ; -3 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4, 0]", fillcolor="#39e581ff"] ; +3 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4, 0]", fillcolor="#39e581"] ; 1 -> 3 ; -4 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5ff"] ; +4 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5"] ; 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; } \ No newline at end of file diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb index 3461de514..917256cc0 100644 --- a/doc/pub/week44/ipynb/week44.ipynb +++ b/doc/pub/week44/ipynb/week44.ipynb @@ -135,11 +135,44 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2nd degree coefficients:\n", + "zero power: -2.188618732996508\n", + "first power: 0.09515893290237344\n", + "second power: -0.0004760754513086032\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -208,7 +241,7 @@ "from sklearn.tree import DecisionTreeRegressor\n", "regr_1=DecisionTreeRegressor(max_depth=2)\n", "regr_2=DecisionTreeRegressor(max_depth=5)\n", - "regr_3=DecisionTreeRegressor(max_depth=7)\n", + "regr_3=DecisionTreeRegressor(max_depth=11)\n", "regr_1.fit(X, distance_list)\n", "regr_2.fit(X, distance_list)\n", "regr_3.fit(X, distance_list)\n", @@ -551,10 +584,118 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + ".. ... ... ... ... ... \n", + "564 21.56 22.39 142.00 1479.0 0.11100 \n", + "565 20.13 28.25 131.20 1261.0 0.09780 \n", + "566 16.60 28.08 108.30 858.1 0.08455 \n", + "567 20.60 29.33 140.10 1265.0 0.11780 \n", + "568 7.76 24.54 47.92 181.0 0.05263 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.30010 0.14710 0.2419 \n", + "1 0.07864 0.08690 0.07017 0.1812 \n", + "2 0.15990 0.19740 0.12790 0.2069 \n", + "3 0.28390 0.24140 0.10520 0.2597 \n", + "4 0.13280 0.19800 0.10430 0.1809 \n", + ".. ... ... ... ... \n", + "564 0.11590 0.24390 0.13890 0.1726 \n", + "565 0.10340 0.14400 0.09791 0.1752 \n", + "566 0.10230 0.09251 0.05302 0.1590 \n", + "567 0.27700 0.35140 0.15200 0.2397 \n", + "568 0.04362 0.00000 0.00000 0.1587 \n", + "\n", + " mean fractal dimension ... worst radius worst texture \\\n", + "0 0.07871 ... 25.380 17.33 \n", + "1 0.05667 ... 24.990 23.41 \n", + "2 0.05999 ... 23.570 25.53 \n", + "3 0.09744 ... 14.910 26.50 \n", + "4 0.05883 ... 22.540 16.67 \n", + ".. ... ... ... ... \n", + "564 0.05623 ... 25.450 26.40 \n", + "565 0.05533 ... 23.690 38.25 \n", + "566 0.05648 ... 18.980 34.12 \n", + "567 0.07016 ... 25.740 39.42 \n", + "568 0.05884 ... 9.456 30.37 \n", + "\n", + " worst perimeter worst area worst smoothness worst compactness \\\n", + "0 184.60 2019.0 0.16220 0.66560 \n", + "1 158.80 1956.0 0.12380 0.18660 \n", + "2 152.50 1709.0 0.14440 0.42450 \n", + "3 98.87 567.7 0.20980 0.86630 \n", + "4 152.20 1575.0 0.13740 0.20500 \n", + ".. ... ... ... ... \n", + "564 166.10 2027.0 0.14100 0.21130 \n", + "565 155.00 1731.0 0.11660 0.19220 \n", + "566 126.70 1124.0 0.11390 0.30940 \n", + "567 184.60 1821.0 0.16500 0.86810 \n", + "568 59.16 268.6 0.08996 0.06444 \n", + "\n", + " worst concavity worst concave points worst symmetry \\\n", + "0 0.7119 0.2654 0.4601 \n", + "1 0.2416 0.1860 0.2750 \n", + "2 0.4504 0.2430 0.3613 \n", + "3 0.6869 0.2575 0.6638 \n", + "4 0.4000 0.1625 0.2364 \n", + ".. ... ... ... \n", + "564 0.4107 0.2216 0.2060 \n", + "565 0.3215 0.1628 0.2572 \n", + "566 0.3403 0.1418 0.2218 \n", + "567 0.9387 0.2650 0.4087 \n", + "568 0.0000 0.0000 0.2871 \n", + "\n", + " worst fractal dimension \n", + "0 0.11890 \n", + "1 0.08902 \n", + "2 0.08758 \n", + "3 0.17300 \n", + "4 0.07678 \n", + ".. ... \n", + "564 0.07115 \n", + "565 0.06637 \n", + "566 0.07820 \n", + "567 0.12400 \n", + "568 0.07039 \n", + "\n", + "[569 rows x 30 columns]\n", + " malignant benign\n", + "0 1 0\n", + "1 1 0\n", + "2 1 0\n", + "3 1 0\n", + "4 1 0\n", + ".. ... ...\n", + "564 1 0\n", + "565 1 0\n", + "566 1 0\n", + "567 1 0\n", + "568 0 1\n", + "\n", + "[569 rows x 2 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "32512" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -601,10 +742,19 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32512" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -779,10 +929,77 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 0)\t1.0\n", + " (0, 7)\t1.0\n", + " (0, 9)\t1.0\n", + " (0, 13)\t1.0\n", + " (1, 3)\t1.0\n", + " (1, 5)\t1.0\n", + " (1, 8)\t1.0\n", + " (1, 12)\t1.0\n", + " (2, 3)\t1.0\n", + " (2, 5)\t1.0\n", + " (2, 8)\t1.0\n", + " (2, 11)\t1.0\n", + " (3, 1)\t1.0\n", + " (3, 5)\t1.0\n", + " (3, 8)\t1.0\n", + " (3, 12)\t1.0\n", + " (4, 2)\t1.0\n", + " (4, 6)\t1.0\n", + " (4, 8)\t1.0\n", + " (4, 12)\t1.0\n", + " (5, 2)\t1.0\n", + " (5, 4)\t1.0\n", + " (5, 10)\t1.0\n", + " (5, 12)\t1.0\n", + " (6, 2)\t1.0\n", + " :\t:\n", + " (8, 12)\t1.0\n", + " (9, 3)\t1.0\n", + " (9, 4)\t1.0\n", + " (9, 10)\t1.0\n", + " (9, 12)\t1.0\n", + " (10, 2)\t1.0\n", + " (10, 6)\t1.0\n", + " (10, 10)\t1.0\n", + " (10, 12)\t1.0\n", + " (11, 3)\t1.0\n", + " (11, 6)\t1.0\n", + " (11, 10)\t1.0\n", + " (11, 11)\t1.0\n", + " (12, 1)\t1.0\n", + " (12, 6)\t1.0\n", + " (12, 8)\t1.0\n", + " (12, 11)\t1.0\n", + " (13, 1)\t1.0\n", + " (13, 5)\t1.0\n", + " (13, 10)\t1.0\n", + " (13, 12)\t1.0\n", + " (14, 2)\t1.0\n", + " (14, 6)\t1.0\n", + " (14, 8)\t1.0\n", + " (14, 11)\t1.0\n", + "Train set accuracy with Decision Tree: 0.73\n" + ] + }, + { + "data": { + "text/plain": [ + "32512" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -870,10 +1087,72 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 < 0.000 Gini=0.408\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 1.000 Gini=0.407\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "Split: [X3 < 1.000]\n" + ] + } + ], "source": [ "# Split a dataset based on an attribute and an attribute value\n", "def test_split(index, value, dataset):\n", @@ -979,10 +1258,37 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.95\n", + "Test set accuracy with SVM: 0.63\n", + "Test set accuracy with Decision Trees: 0.90\n", + "Test set accuracy Logistic Regression with scaled data: 0.96\n", + "Test set accuracy SVM with scaled data: 0.96\n", + "Test set accuracy with Decision Trees and scaled data: 0.90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1037,10 +1343,21 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "np.random.seed(6)\n", "Xs = np.random.rand(100, 2) - 0.5\n", @@ -1155,9 +1483,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1171,10 +1497,19 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeRegressor(max_depth=2, random_state=42)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1192,10 +1527,21 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1240,10 +1586,21 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n", @@ -1398,10 +1755,21 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "heads_proba = 0.51\n", "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n", @@ -1428,10 +1796,23 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.896\n", + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.912\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -1488,10 +1869,21 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", + " ('rf', RandomForestClassifier(random_state=42)),\n", + " ('svc', SVC(random_state=42))])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -1516,10 +1908,19 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.896\n", + "SVC 0.896\n", + "VotingClassifier 0.912\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -1532,10 +1933,22 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", + " ('rf', RandomForestClassifier(random_state=42)),\n", + " ('svc', SVC(probability=True, random_state=42))],\n", + " voting='soft')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "log_clf = LogisticRegression(random_state=42)\n", "rnd_clf = RandomForestClassifier(random_state=42)\n", @@ -1550,10 +1963,19 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.896\n", + "SVC 0.896\n", + "VotingClassifier 0.92\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -1573,9 +1995,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", @@ -1591,10 +2011,16 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.904\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "print(accuracy_score(y_test, y_pred))" @@ -1603,10 +2029,16 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.856\n" + ] + } + ], "source": [ "tree_clf = DecisionTreeClassifier(random_state=42)\n", "tree_clf.fit(X_train, y_train)\n", @@ -1617,10 +2049,21 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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