diff --git a/ml1/2_0_0_Intro_ML.ipynb b/ml1/2_0_0_Intro_ML.ipynb index 92194ca..e199900 100644 --- a/ml1/2_0_0_Intro_ML.ipynb +++ b/ml1/2_0_0_Intro_ML.ipynb @@ -102,7 +102,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_0_1_Objectives.ipynb b/ml1/2_0_1_Objectives.ipynb index f73fc14..fcb8d83 100644 --- a/ml1/2_0_1_Objectives.ipynb +++ b/ml1/2_0_1_Objectives.ipynb @@ -95,7 +95,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_1_Intro_ScikitLearn.ipynb b/ml1/2_1_Intro_ScikitLearn.ipynb index b0664bc..c04cd58 100644 --- a/ml1/2_1_Intro_ScikitLearn.ipynb +++ b/ml1/2_1_Intro_ScikitLearn.ipynb @@ -178,7 +178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_3_0_Visualisation.ipynb b/ml1/2_3_0_Visualisation.ipynb index 03ce4fd..b208255 100644 --- a/ml1/2_3_0_Visualisation.ipynb +++ b/ml1/2_3_0_Visualisation.ipynb @@ -161,9 +161,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# Plot the distribution of the dataset\n", @@ -192,9 +190,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "x_index = 0\n", diff --git a/ml1/2_3_1_Advanced_Visualisation.ipynb b/ml1/2_3_1_Advanced_Visualisation.ipynb index 1a2218c..f113745 100644 --- a/ml1/2_3_1_Advanced_Visualisation.ipynb +++ b/ml1/2_3_1_Advanced_Visualisation.ipynb @@ -93,90 +93,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
\n", - "
" - ], - "text/plain": [ - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", - "0 5.1 3.5 1.4 0.2\n", - "1 4.9 3.0 1.4 0.2\n", - "2 4.7 3.2 1.3 0.2\n", - "3 4.6 3.1 1.5 0.2\n", - "4 5.0 3.6 1.4 0.2" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from pandas import DataFrame\n", "from sklearn import datasets\n", @@ -194,103 +113,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)species
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
\n", - "
" - ], - "text/plain": [ - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", - "0 5.1 3.5 1.4 0.2 \n", - "1 4.9 3.0 1.4 0.2 \n", - "2 4.7 3.2 1.3 0.2 \n", - "3 4.6 3.1 1.5 0.2 \n", - "4 5.0 3.6 1.4 0.2 \n", - "\n", - " species \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "iris_df['species'] = iris.target\n", "iris_df.head()" @@ -328,33 +153,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "ename": "AttributeError", - "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", @@ -383,23 +184,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# PairGrid\n", "g = sns.PairGrid(iris_df)\n", @@ -417,38 +204,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'numpy.int64' object is not iterable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPairGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"species\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_offdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#names = {i: name for i,name in enumerate(iris.target_names)}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#g.add_legend(legend_data=names)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1397\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfixed_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1401\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 2956\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2957\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2958\u001b[0;31m stacked=stacked, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 2959\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2960\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1810\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1811\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1812\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1813\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1814\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m 6216\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6217\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6218\u001b[0;31m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlab\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlab\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6220\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlbl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip_longest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'numpy.int64' object is not iterable" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "g = sns.PairGrid(iris_df, hue=\"species\")\n", "g.map_diag(plt.hist)\n", @@ -485,9 +243,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "g = sns.PairGrid(iris_df)\n", @@ -688,7 +444,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_5_1_kNN_Model.ipynb b/ml1/2_5_1_kNN_Model.ipynb index 623b645..ec2d9dc 100644 --- a/ml1/2_5_1_kNN_Model.ipynb +++ b/ml1/2_5_1_kNN_Model.ipynb @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -111,9 +111,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "## Train classifier" ] @@ -135,22 +133,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=15, p=2,\n", - " weights='uniform')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "import numpy as np\n", @@ -164,24 +149,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 1 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", - " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", - " 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 2 0 0 0 2 1 2 1 2 1 2\n", - " 0]\n", - "Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", - " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", - " 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", - " 0]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"Prediction \", model.predict(x_train))\n", "print(\"Expected \", y_train)" @@ -189,17 +159,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy in training 0.964285714286\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Evaluate Accuracy in training\n", "\n", @@ -210,17 +172,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy in testing 0.921052631579\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Now we evaluate error in testing\n", "y_test_pred = model.predict(x_test)\n", @@ -238,28 +192,12 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'run'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'util_knn.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot_classification_iris\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/GoogleDrive/cursos/sitc/sitc/ml1/util_knn.py\u001b[0m in \u001b[0;36mplot_classification_iris\u001b[0;34m()\u001b[0m\n\u001b[1;32m 49\u001b[0m % (n_neighbors, weights))\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 242\u001b[0m \"\"\"\n\u001b[1;32m 243\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%run util_knn.py\n", + "\n", "plot_classification_iris()" ] }, @@ -290,24 +228,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " setosa 1.00 1.00 1.00 8\n", - " versicolor 0.79 1.00 0.88 11\n", - " virginica 1.00 0.84 0.91 19\n", - "\n", - "avg / total 0.94 0.92 0.92 38\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(metrics.classification_report(y_test, y_test_pred, target_names=iris.target_names))" ] @@ -328,19 +251,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 8 0 0]\n", - " [ 0 11 0]\n", - " [ 0 3 16]]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(metrics.confusion_matrix(y_test, y_test_pred))" ] @@ -368,18 +281,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.93333333 0.8 1. 0.93333333 0.93333333 0.93333333\n", - " 1. 1. 0.86666667 1. ]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.pipeline import Pipeline\n", @@ -401,26 +305,16 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean score: 0.940 (+/- 0.021)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from scipy.stats import sem\n", "def mean_score(scores):\n", @@ -451,30 +345,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "k_range = range(1, 21)\n", "accuracy = []\n", @@ -537,7 +410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_5_2_Decision_Tree_Model.ipynb b/ml1/2_5_2_Decision_Tree_Model.ipynb index c50122a..1600258 100644 --- a/ml1/2_5_2_Decision_Tree_Model.ipynb +++ b/ml1/2_5_2_Decision_Tree_Model.ipynb @@ -74,18 +74,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n", - " _nan_object_mask = _nan_object_array != _nan_object_array\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# library for displaying plots\n", "import matplotlib.pyplot as plt\n", @@ -114,9 +105,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "## Train classifier" ] @@ -135,25 +124,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n", - " splitter='best')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "import numpy as np\n", @@ -172,24 +145,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", - " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", - " 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", - " 0]\n", - "Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n", - " 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n", - " 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n", - " 0]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(\"Prediction \", model.predict(x_train))\n", "print(\"Expected \", y_train)" @@ -204,26 +162,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicted probabilities [[ 0. 0.97368421 0.02631579]\n", - " [ 1. 0. 0. ]\n", - " [ 0. 0.97368421 0.02631579]\n", - " [ 0. 0.97368421 0.02631579]\n", - " [ 0. 0.97368421 0.02631579]\n", - " [ 1. 0. 0. ]\n", - " [ 1. 0. 0. ]\n", - " [ 0. 0.97368421 0.02631579]\n", - " [ 1. 0. 0. ]\n", - " [ 0. 0. 1. ]]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Print the \n", "print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))" @@ -231,17 +172,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy in training 0.982142857143\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Evaluate Accuracy in training\n", "\n", @@ -252,17 +185,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy in testing 0.921052631579\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Now we evaluate error in testing\n", "y_test_pred = model.predict(x_test)\n", @@ -284,21 +209,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import Image \n", "from sklearn.externals.six import StringIO\n", @@ -328,26 +241,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# display plots in the notebook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplot_tree_iris\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/GoogleDrive/cursos/sitc/sitc/ml1/util_ds.py\u001b[0m in \u001b[0;36mplot_tree_iris\u001b[0;34m()\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Decision surface of a decision tree using paired features\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 242\u001b[0m \"\"\"\n\u001b[1;32m 243\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n", - "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%run util_ds\n", "\n", @@ -503,9 +399,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure" ] @@ -574,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_6_Model_Tuning.ipynb b/ml1/2_6_Model_Tuning.ipynb index 8a57a31..f115de0 100644 --- a/ml1/2_6_Model_Tuning.ipynb +++ b/ml1/2_6_Model_Tuning.ipynb @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -101,9 +101,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "## Train classifier" ] @@ -117,17 +115,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean score: 0.940 (+/- 0.021)\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.pipeline import Pipeline\n", @@ -179,51 +169,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ds': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'),\n", - " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True)}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.named_steps" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)),\n", - " ('ds',\n", - " DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'))]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.steps" ] @@ -237,20 +194,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['steps', 'scaler', 'ds', 'scaler__copy', 'scaler__with_mean', 'scaler__with_std', 'ds__class_weight', 'ds__criterion', 'ds__max_depth', 'ds__max_features', 'ds__max_leaf_nodes', 'ds__min_impurity_split', 'ds__min_samples_leaf', 'ds__min_samples_split', 'ds__min_weight_fraction_leaf', 'ds__presort', 'ds__random_state', 'ds__splitter'])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.get_params().keys()" ] @@ -264,24 +210,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", - " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'))])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.set_params(ds__class_weight='balanced')" ] @@ -295,24 +226,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", - " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'))])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model = Pipeline([\n", " ('scaler', StandardScaler()),\n", @@ -330,17 +246,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.01834862 0.01910853 0.05728223 0.90526062]\n" - ] - } - ], + "outputs": [], "source": [ "# Fit the model\n", "model.fit(x_train, y_train) \n", @@ -351,17 +259,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.01834862 0.01910853 0.05728223 0.90526062]\n" - ] - } - ], + "outputs": [], "source": [ "#Using steps, we take the last step (-1) or the second step (1)\n", "#name, my_desision_tree = model.steps[1]\n", @@ -389,47 +289,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ds': DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", - " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'),\n", - " 'ds__class_weight': 'balanced',\n", - " 'ds__criterion': 'gini',\n", - " 'ds__max_depth': None,\n", - " 'ds__max_features': None,\n", - " 'ds__max_leaf_nodes': None,\n", - " 'ds__min_impurity_split': 1e-07,\n", - " 'ds__min_samples_leaf': 1,\n", - " 'ds__min_samples_split': 2,\n", - " 'ds__min_weight_fraction_leaf': 0.0,\n", - " 'ds__presort': False,\n", - " 'ds__random_state': None,\n", - " 'ds__splitter': 'best',\n", - " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True),\n", - " 'scaler__copy': True,\n", - " 'scaler__with_mean': True,\n", - " 'scaler__with_std': True,\n", - " 'steps': [('scaler',\n", - " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", - " ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", - " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_impurity_split=1e-07, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " presort=False, random_state=None, splitter='best'))]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.get_params()" ] @@ -466,18 +328,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best score: 0.946428571429\n", - "Best params: {'max_depth': 3}\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -496,32 +349,16 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "Now we are going to show the results of grid search" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.946 (+/-0.075) for {'max_depth': 3}\n", - "0.929 (+/-0.024) for {'max_depth': 4}\n", - "0.946 (+/-0.075) for {'max_depth': 5}\n", - "0.929 (+/-0.024) for {'max_depth': 6}\n", - "0.946 (+/-0.075) for {'max_depth': 7}\n", - "0.946 (+/-0.075) for {'max_depth': 8}\n", - "0.929 (+/-0.024) for {'max_depth': 9}\n" - ] - } - ], + "outputs": [], "source": [ "# We print the score for each value of max_depth\n", "for i, max_depth in enumerate(gs.cv_results_['params']):\n", @@ -539,17 +376,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean score: 0.953 (+/- 0.020)\n" - ] - } - ], + "outputs": [], "source": [ "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", "model = Pipeline([\n", @@ -581,550 +410,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Tuning hyper-parameters for precision\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters set found on development set:\n", - "\n", - "{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "\n", - "Grid scores on development set:\n", - "\n", - "0.964 (+/-0.092) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.943 (+/-0.084) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.936 (+/-0.122) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.973 (+/-0.068) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.968 (+/-0.132) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.943 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.919 (+/-0.251) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.975 (+/-0.079) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.951 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.943 (+/-0.113) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.948 (+/-0.108) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.961 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.928 (+/-0.165) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.949 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.953 (+/-0.134) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.942 (+/-0.067) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.980 (+/-0.062) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.945 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.949 (+/-0.095) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.961 (+/-0.114) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.972 (+/-0.069) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.946 (+/-0.125) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.142) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.956 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.968 (+/-0.082) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.956 (+/-0.097) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - 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"0.933 (+/-0.125) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.939 (+/-0.117) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.918 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.945 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.931 (+/-0.153) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.944 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.972 (+/-0.069) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.955 (+/-0.111) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "\n", - "Detailed classification report:\n", - "\n", - "The model is trained on the full development set.\n", - "The scores are computed on the full evaluation set.\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 8\n", - " 1 0.92 1.00 0.96 11\n", - " 2 1.00 0.95 0.97 19\n", - "\n", - "avg / total 0.98 0.97 0.97 38\n", - "\n", - "\n", - "# Tuning hyper-parameters for recall\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters set found on development set:\n", - "\n", - "{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "\n", - "Grid scores on development set:\n", - "\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.893 (+/-0.215) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.955 (+/-0.092) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.138) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.920 (+/-0.241) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.866 (+/-0.268) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.884 (+/-0.218) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.911 (+/-0.179) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.973 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.893 (+/-0.177) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.920 (+/-0.162) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.187) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.929 (+/-0.104) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.911 (+/-0.191) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.938 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.114) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.911 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.902 (+/-0.148) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.158) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.946 (+/-0.113) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.147) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.893 (+/-0.255) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.117) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.938 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.955 (+/-0.115) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.893 (+/-0.139) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.168) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.911 (+/-0.179) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.946 (+/-0.146) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.911 (+/-0.179) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.955 (+/-0.115) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.938 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.955 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.119) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.109) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.938 (+/-0.183) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.168) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.183) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.955 (+/-0.147) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.955 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.158) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.168) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.866 (+/-0.202) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.154) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.920 (+/-0.151) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.964 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.955 (+/-0.115) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.140) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.131) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.920 (+/-0.181) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.204) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - 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"0.920 (+/-0.168) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.131) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.964 (+/-0.119) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.938 (+/-0.110) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.110) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.159) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.920 (+/-0.147) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.920 (+/-0.127) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.955 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.893 (+/-0.213) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.875 (+/-0.216) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.196) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.911 (+/-0.173) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.163) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.131) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.920 (+/-0.127) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.938 (+/-0.159) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.902 (+/-0.179) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.175) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.902 (+/-0.148) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.955 (+/-0.146) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.955 (+/-0.169) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.964 (+/-0.121) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.929 (+/-0.136) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", - "0.920 (+/-0.147) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", - "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", - "0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", - "0.929 (+/-0.168) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", - "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", - "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", - "\n", - "Detailed classification report:\n", - "\n", - "The model is trained on the full development set.\n", - "The scores are computed on the full evaluation set.\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 8\n", - " 1 1.00 0.64 0.78 11\n", - " 2 0.83 1.00 0.90 19\n", - "\n", - "avg / total 0.91 0.89 0.89 38\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Set the parameters by cross-validation\n", "\n", @@ -1156,8 +444,11 @@ " print()\n", " print(\"Grid scores on development set:\")\n", " print()\n", - " for params, mean_score, scores in gs.grid_scores_:\n", - " print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, scores.std() * 2, params))\n", + " means = gs.cv_results_['mean_test_score']\n", + " stds = gs.cv_results_['std_test_score']\n", + "\n", + " for mean_score, std_score, params in zip(means, stds, gs.cv_results_['params']):\n", + " print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, std_score * 2, params))\n", " print()\n", "\n", " print(\"Detailed classification report:\")\n", @@ -1172,26 +463,16 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "Let's evaluate the resulting tuning." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean score: 0.907 (+/- 0.015)\n" - ] - } - ], + "outputs": [], "source": [ "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n", "model = Pipeline([\n", @@ -1271,7 +552,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_7_Model_Persistence.ipynb b/ml1/2_7_Model_Persistence.ipynb index 9184324..5e9a5ff 100644 --- a/ml1/2_7_Model_Persistence.ipynb +++ b/ml1/2_7_Model_Persistence.ipynb @@ -55,22 +55,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('KNN', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", - " weights='uniform'))])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# load iris\n", "from sklearn import datasets\n", @@ -106,20 +93,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pickle\n", "s = pickle.dumps(model)\n", @@ -136,10 +112,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# save model\n", @@ -192,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/2_8_Conclusions.ipynb b/ml1/2_8_Conclusions.ipynb index 9ee877f..f5fbfec 100644 --- a/ml1/2_8_Conclusions.ipynb +++ b/ml1/2_8_Conclusions.ipynb @@ -61,20 +61,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Qns/OSD0i5xXZ9hvEsXovlrQ6sQfhr4ljouaXGQ/cSExbKmKw7/uSfh9CeHMpy5r1GfH4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\nBVkaCN181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l/aQFiw73ktCCJmUyZFFev2/LtXkvhw8SrWbqkkNsrM1eeOolv83sXmvJJy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\nKopxy66/miKJdn5a+ggWw8vAOOAgMsVAbpj6PFqf6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BpBAK0bHQd0R1VJrC1JEmuWryBl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\nDY9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tVzsDpSiyMHoQt/YciIAj5m2MQJ0zXyBQBrjiWG91wDKtU22Mm1FVzjN7t+IjSeTK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W9X/1NZllny6Ax0hZ8CY4Bkflzeiy79+tGyXSe7537z+DFkpX1J0sUjIBvpObgVEd0btgrq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\nG0qpo5VSzQo7BkN7sOrplpB3GxH5PxH5KJ52hSMi54jIZ62c87GI1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E3PoB2xsP5/xvq5\ntf77fLR4vEz0dhzHtGCfwdATCZ0Du9nZDxajU4WH1o1CrL5KdArE70PagXHsWufC+8VdUEo1KKVu\nU0rtix4HvISOhg9ug/Uq2vFpP7Tgk4RuT8J5DR2xfjFmX2ZD72am0lFhh6AzOIX3u3dYfWQysA9w\np4gcEXJ8nnU8y/o/osOQ5cR7iVJqBLodcLNrhOrTwEmis0mchc4qUx5yPOjA/yxaiD2LnX2uwdAT\naLauich+bVijaq5fzWf3dZwgA9m93wyfu4avWbe0hh2JPUPq/6UhNrV5/YhW1vDQwQTHAOtFpzue\n3IpNfQYjsIYhIvuiX8Dgfo7PodMfFCidquwRdnYokYTPW9HpdcdZ55/JrhGuzyulDka/tKBTqYFe\nIDrKqgjBTjFFKbW5mfuEswGd1qgthJZXjvY+au3aEuu8nBD7MpVSE6zn2qqU+rVSqgCdjvFBsfbI\nMhiiQUjd/ByoQy/iBumy/O6WV8+L6IHkmew60fsA+EU7imu2fbG8mv6qlBqHTnd6HJYnoVLqfaXU\n4ejnXo724m8vJcBvwtqcVLUzuh36+CblhnZxE9oTPnSgVoKOIAl9x9KVUhdbx1vqX4OosPKeCSsv\nTSl1O7RYL9r7Ht+JXujZO+zea8LunaGUOi7C9ZvRC1QAiEgSOwWmthIaSV6MjhDE+j84fkBEUqyy\nQwfFuzyvUup+pdQ+6FTfo2g+JaTB0G7Cxs3l6EnmuJC6kql0iiXQ9ajVsWobx5TlWJGuIZ8Vo1N9\nx4LwdqQEmBFh3L7VOvZkhLbqrhjZZjB0lJsI67tF7xd8JXCi9e5moSNKm8uI8h+0Q8FkIEEp9bH1\neXNjgIvaYphS6nN0GsNgatCH0Cm/h1ljhuvCbPoXMFNEJqAXy4KpClvsv5XOPnW6UqofcDvwstVv\nGwy9nRLglghzwResyJZHgQtD2oHF7Frn2jy+tpwxbgVS0KkTg45PL6NTKJ4JPK909Hv4tfVoZ63f\nYkQcQ+8muA70GVrkbHbcqHSWtS/QQkeHUTrK7gF0qs/gZ5+jgxqOR0eWR9xb1bJzIJCnlPqiM3YY\nDF1MS3Xt37S+RtUcm9l9HSdIadgx0E6A0Zy7RrKzvetHLa7hKaW+U0oFt5mbw86tNfr82rERWC1E\nJE1EjkVPEp9VOzfwTgV2KKW8lnfd6SGXbUOLqaGLRWnonN01IlJAyEKmiIwUkemWJ1Aj2gMhuNfp\nI+g9KgZZ5/aTnXm+I90nnCeAvwZTO4jIHtKGvZ6UUgp4ErhbRAaKiE1EJsvOvTeCDU8ZOg/3PdZ3\nJSIyVESmWvc70XpegErL3kj7uBoM7SJC3VyM3nP0lyKSZL3z/9fFZj2L3r/mOHYVWO8G0kXk6ZC6\nXCAidwU98MNotn0RkWkiMt6KaqtFLyQHREfJ/1x0bnyvdawjde1h4FrR+2UhIhkicmIHyjEYghka\nXkDvJRHkLWCkiJwpIg4RcYrIPiGRpS31r7D7APFfwHEicrjVVyWKyCEikt9KvdgCFIb0a609SxVa\nZP1TyMffoPv1P1n3tYvIOBHZJ0IRL1t2BvvSm9pw2/BnvchqO7LR+3AE98n4D3CeiEwQkQT0YtVX\nSqmIUQTW972f6D0/6tGOUqZvNnSaSONma0z5GDBLdDRrsA8MCiRPoN/f6dY4Ml9ERkYou9UxpdKp\n7V8EbhGRVNFZJf5I10W3PAL8XazUqFYbFHS4eBb4hYj8LKStmiY7s9YYDN2CZvruVHQ/ul1EXCJy\nI3p+2xxvoxdubrbKCtLcGKDVPVgBROQAYAx6mw8sG6qVUm6rjN+FPcsm9LYBzwKvqJ0pS1vsv0Xk\nDBEJRiZUoReITD9p6Am4RCQh5J+9ndc/ht5bbj/Qi64icrS1+JqCrgflVj92HiECTFsQkeutOu+0\nxqyXAjvQTpBBnkFnoPklzYg4FtcAhzQ33jUYeiGzgBkiskekg1Y/eBA7+8g2ISKZInKTiAyzxuK5\n6EwO88JOfRYdDJSBTi3eHMei929sukV77DEYugHhda29a1ShvAhcY9WzQnTmhSBfA25rPOoQkWno\n+vOfdtjakfrVrvUjWhi/Wz+fLiLpSm+vV4Pepgv0mluOiKR3wMZegRFYdXqvKnQE6DXoRdXzQ45f\niBYuq4DrCZk4Wt50twBfyM4c939BR70E97h4JaSsBPReVNvQXgT92JmWaTZa/X/PuteXWHtcNHOf\ncO5GV+bg9Y+j06xA654EVwA/At+iQ8VvY+e7EXrt2eg9ZJegPZpeYmfk4L7A16LzmL+OTvG0rpX7\nGgwt0VLdvAe9+FMGPMXOdGRBwt/59nrTtHi90vvBBYAFoR2T0vseT7Fs+9qy/310e7AqQlnNti/o\nuvUyerFnMfAReqBrAy5DezqVA1MJW2Rqy3MopV5H1/XnRae++IFd94fr8x5IhlYJf0duRkeWK2jy\nVj8cnTqw1Pp3G7ovhJbf/93KV0ptRE/grkX3o+vR/ZeNluvFh+g6VCYioam/W3qWewFfyLME0APg\nSeg9a7aiF6Z2G0BaDlq/t56nFB35s5WW96VRYT//G+3UtAq959UtVtlzgRvQadU2oaMATm2mHCz7\nHkP32WvR380dLdhhMLRGa+Pmq9Dv7VdW3/IeMBKa9kk8Dz2RrQI+ZqdHbei729KYMvS8S9ARs2uA\nT4F/KaWeasH2tvRrbe377kJH1My1vo/P0enaUHo/2F+g6+o2YB26fTLzLkN3oMW+G709xf+AFeh+\nw00LqUCV3ifqVeAwQvZoa2EM4GrBtvtFpNqq+08D1ym9hzHo/v4M69gj7HQ8CuVptAjUFOXWhv77\nSPT+kNXo+cUpKvJ+kgZDd+O/6PpZb/3/5wjnNNunKb3FzQXoeleBrvPnWMeWovu5r9Dz7XHofq49\nKPQ8fRt6zHoYcLTS23oEbfgUPR4oUTu33NnNdqX3O/8y0jGDoZcQPu8tR/dpN4Z8/Cerj6wB3kXv\nq9jeTGaNwGD0GlUVeg3Igx6fh/IMOtrueaX3bYxoq1JqqdVeRHwOg6Eb0lpdu4h2rFGF/f4X9Bx5\nLbqOho5HvegAnaPRazL3A2cppVY2U26rtrflWHvXj9owfj8LWGvN83+NjnJHKbUcLeausXSrPudY\nLEqZ9s9gMBjai4jMBZ5TSj0Zb1sMBkP3xYoEqASGW8KLwWAwGAyGKCM6vfGzSqnB8bbFYDAYDAaD\nwWAw9A2MJ7XBYDC0E9F7zu3J7t5MBoPBgIgcKzqFeQo6AuAHI64aDAaDwRAbRKfk/wM6OtVgMBgM\nBoPBYDAYugQjsBoMBkM7EJF/olMe/kEpVRdncwwGQ/dkJjqdykb0/umntny6wWAwGAyGjmDtRbcD\n6I/edsdgMBgMBoPBYDAYugSTIthgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBjaiIlgNRgMBoPBYDAY\nDAaDwWAwGAwGg8FgMBgMhjbiaOmgiJjwVoOhGZRSEm8bImHqrcHQPN213oKpuwZDc5h6azD0PEy9\nNRh6Jt217pp6azA0T3ett2DqrsHQHKbeGgw9j+bqbYsCq3Vh9K0xGHo4It22HwTAu219vE0wGLod\nzn7F8TahVV6f/st4m2CIAdMumQFA8uBhuNet5uN734+zRT2H4z96Nd4mtIqptwbDrvSEevvOYSfG\n2wSDodtx1NyX421Ci7x0SOf62+IcDwBZo4ZFw5xuS+7o/gAkFgyKsyWxxT5kfNPPkp4TR0tiTxlp\nAIjDtduxwqzUrjan3dR/8Ey8TTC0E8+mDR26rre3O9Ei6Wdnx9uEVultc9whxZA5snf3/11N5YrV\nrI0gP4R/182d19NoaY7bqsBqMBgMBoPBYOgYQyxdP3mwGcwbDAaDwWAwdDV9TViF3i9yBMXVviys\nGgzRojkxtXzZlnaXldvM5729TTIYDH0bI7AaDAaDwWAwxIAhxVA8cwaOxIRdPp92yQwTxWowGAwG\ng8EQY/qauNrbRQwTtWowdJ5IgmpHxNRIRCond3T/Xe7Z29spg8HQ9zACq8FgMBgMBkMMCIqrrgGF\nTZ8F0wQbDAaDwWAwGGJDXxNWofeLFiZq1WDoOOGiarQE1bYQfq/QKNfe3m4ZDIa+gRFYDQaDwWAw\nGKJMcN/VUHHVYDAYDAaDwRBb+pq42tsFir4mrIIRVw3RIZ6iaksE7TCRrQaDobdgBFaDwWAwGAyG\nKBIUV1vad3VIMaxd31UWGQwGg8FgMPRu+pqwCr1fkOhr4qoRVg2dpbuKqpEItS1UbO3t7ZrBYOh9\nGIHVYDAYDAaDIUoMKdb/tySuJg8eRvFMWGv2YTUYDAaDwWDoNH1NXO3tAkRfE1bBiKuGjhHL/VS7\nkkhRrb29nTMYDL0HI7AaDAaDwWAwRBFHYkK8TTAYDAaDwWDo9fQ1YRV6v+jQ18RVI6wa2kNvEVSb\nwwitBoOhJ2IEVoPBYDAYDIYoMKQYimfOaPO+qyZNsMFgMBgMBkPH6Gviam8XGfqasApGXDXsTiQB\nNZTeJKa2RLjQ2tvbP4PB0LMxAmsv48MPP2TevHkUFBRwxhln4HQ6422SwWBohaUrVvLf/80lISGB\n006YSW5OdrxNMhgMrVDjbeSTLSU0+n3smzuQopR0oO3RqybK1WDoevyBAJ9uLaHcU8/I9GwmZufF\n2ySDwdAGFmzfwvLqCvISk5nWvwi7zRZvkwxxpK8Jq9AzxdUl6zby9lcLSU5wcephU8hOT2323L4m\nrhphte/RmnAaSjxF1Ap3PW8uXkGD38dhI4YwLM5rU01Cq/V7T2wLDYZY4/X7eX3hUsqqa9hrUD4H\nDiuOt0l9DiOw9iJm33UXs268kVMaGvhnYiIvPvUUb374IXa7Pd6mGQyGZvji62858aSzOM3rZYfd\nzr2z7ufzj9+lf16/eJtmMBiaoaqxgWu/nctBvkb6BxQ3rlvG5ROmMO2S09scvWowGLqWgFLc8cMX\nSFUFBwX8PGGzc1jxaGYOHhVv0wwGQwu8snYp769bxskBP5/b7MwrW8+1kw7GJhJv0wxdTFBYhb4j\nrvZUMeHThUs547q7OMPnY6vdzgP/eYtPHruF3Iy0Xc7ra8IqGHG1t9IbIk+31dZx8hP/4ZCGRrKV\n4ozPv+HBU49nr8KB8TbNCK0GQzP4AwEueOJFApu2sL/Px9UOO+fPOIjzDto33qb1KYzA2kvwer1c\nc801LPV6KQb8dXXsu2AB77//PkceeWS8zTMYDM1w03V/YXZ9PacD+HxcvMPPfQ8+xt9uujbephkM\nhmZ4Z+MqjvY28KhSABwU8HP3jjVc2s6o1OKZM1h77/uxMNFgMITxU+U2qqor+CHgxwH8IeBn+Lol\nHD1oOE6bcUY0GLojjX4//1q7hFVKUQB4A34mVlXw445tJgK9j9JXhFXo2QLCn+9/locbGjkBwB/g\ngqoaHnn9Pa4754Smc/qauGqE1d5Dc2JqTxBRW+Lpr7/nFx4P9wb0HHf/QIDZ73/K0+edEmfLdlK+\nbIvZn9VgCOHTleuoLN3Ct14vduBCr48x737KWQfsjcNuMr50FUZg7SXU19cjShHsWuzAUBEqKyuj\nUr7X6+XTTz+lvr6eAw88kKysrKiUazD0dXbsqGREyO+jfD4WV1RErfz5C39gU+lmJo4fy+BBRVEr\n12Doy9R5G9nHElcBRgJVde52Ra+6BhRC2caI+7CW1dexrraSfonJDEsz/a3BEA1qvV4GI02Tn3zA\niVDv90VFYK32NrCsqoJEu51xGbkmhanBEAXcfh8uhHx0n+sEBgO1Pm9UyvcGAvxUuQ1vIMDYjInK\nGR0AACAASURBVBxSnUYA6c70FXG1NwgGlbV1u85x/X7WV9UCnRdWlVJ8+/0iNpdtYc8J4xlUWNBZ\nc2OCiVrtHUQSU3u6kNoc1e56pgR2znFHANUeT/MXtJP1FZUs37adwsx0xvbveMa28P1ZoXe0mwZD\nR6iq9zAErQMBBFd9G3w+HPbO9z0VdW4WbCgl2eVi/yGFZo7bDEZg7SWkp6czadw4rl68mCt8PuYB\nnyrFPQce2Omy3W43Rxx0EHUrV5Jjs/E7p5O5X37JyJEjO2+4wdDHOfKYI7j2n//iyXoPFcA9SUnc\ndfQRUSn7iiuv4/UXX2EPu52vfD4efmg2M48xEe0GQ2eZlDuQWWUbmB7w0x+4yuXkyMMOjkrZn28p\n4YllC9hfhB+UYkr+EM4cMSEqZRsMfZnRGdk8BrwKHAzcAxQkpZAWhUXPkrpqbl7wCeOVYisKV0o6\n10yaists02EwdIoMp4v8pBSud9dwKfAZ8DVwZkbn94Sr9/m4Yf6HOD1uMoCHbXZu2Xc6A5Oa3yfS\nYIgFvSVqNZTDp+zFVe9+xuONjWwB7ktwcd/kPaMirv7h0qt45/U3GG+381ufnycef4CjDz8sitZ3\nHhO12rMJF1V7q6AaztRRw7h9+SoO9vrIBq52ODh4ZHQcW+b8uJTb3v2Y/W02vg8EOGGfiVwyfUqn\nyjRCq8EA+w4u5GYFbwAHALfbbIzvn0NKQuf7n6Wbt3L2Y88zUSk2K0XuwDwe+79TcDnMHDccIzv3\nIl55911+mjKFUcnJXDd4MK+8/TZFRZ2PWLtv9mzyli5lfm0t71dXc/mOHfzxgguiYLHBYPjzDVcz\n6sRfsHdKCsdlZXLZn6/huCNndLrced9+xxsvvsIidz1v1tTybr2HX/3uD/j9/ihYbTD0bfbOGcDM\n4RM4xuFiosNB0cH7cPNlv+50ud6An0eWfcfHAT/v+H38FPDzZelaVtfsiILVBkPfJjshiasmHshV\nickMt9n5ICOHP006CInCPo5PLf2Om3xePvT7WOT3M6C2indL10bBaoOhbyMiXL/XVN7PyGG4zc5V\niSlcv+fBZCckdbrs19YvZ5y7lgV+Hx/7fVzibeCpZQuiYLXB0HZCo1Z7kzBw829PZ9D0/dkzKZFf\nZqRy3Y1/4qhTTkfSczqVEvjTL7/ig9ff5EdrjvtmfT3n//r3qJDMMvGkjDQjrvZAPJs27PKvfNmW\nXf71FX42cijnTD+QYxIT2cvlpGj8KH4/7YBOl1vv9fKXdz7iE5+PtxobWeTz8dL8RazYtj0KVrPL\n36m1vXANht5GQWY6D517AldlpjPS6WDBoHweOvekqJR940tvc6ungfcaGlnY6MVZuoUX5v8QlbJ7\nGyaCtRcxYMAA/vvJJ1Evd/3KlUzzeJrU+GmBAE+sWxf1+xgMfRGn08msu29j1t23RbXcDRs3sbfN\nRob1+95AwOenqrqG7KzMqN7LYOiLzCgYwoyCIUy7ZAbJgzvm2esaULjLPqw13kZcQDBeNQvYQ4Rt\nnnqTKthgiAKjM3K484DoZ3LY1uDmUOtnOzA9EOCL+tqo38dg6EkM3zuj9ZPaUg4Z/HvK8bt8tuq7\nqk6XW+6u4XgVIOhicSjwdH1dp8s1GNpCb0oHHIkEl5PZV17A/Q/ObvosGnutri/ZxH4CwTjz/YFa\njwe3u56UlOROl98ZjLDas+irUaqtccbeEzhj7+hmT9peV0+6TRhr+frnAuPsNjZX1zCyX/T2YG6K\naLV+763tq8EQzv5Divjfn34T9XI3VdXsMsed6vVRuqPzY/DeiBFYDa2y70EH8dALL3CW200aMMvp\npF9uLnddfTWDxo9n5skn43KZQaTB0J2YOH4sl/n9LAHGAk8CaUmJPH3/I2QOyOPnJxxPTrYRbAyG\nzjCkOLrlZbgScdodvBBo5BRgETDP76dfxRbeq6tmXF4BBclprRVjMBi6mGFp2dxXUcZ9SlEJPC42\nhjc28O6axRRl5TE2MzcqkbIGQ3eiNQE1I0ppBcOpWrG6xXu3VXwdltWPJ8s3c2rATzJwD0Kaw8Wb\nq34kJzWD/fIKcZh9pgwxoLeLq0E6mw44EntOGM+1gQArgJHAw8CA9DQevetesvIHMvPE48nKjI5z\nR1sxe632LEKFVSOqdg3901Lw2+285vXxC+A7YL7Xxx6r17NpyzYOHD2C4uzoBQEYodVgiA4TCwdw\n3+oN3BUIUA780+FgSlUNT73/GeOHD2G4UoCZ44IRWA1t4NzzzmPRN99Q8OSTOEUozszk2uxsJqxZ\nw+fff8+jJSVcdPXVZuHIYOhGjB4xnDvuvJXJl11Nogguu4MLR47g6HXrWbNkKXf/uJhr/35z3D19\nDYaeTPHMGTgSEzpdzpBiWLse7CJcOfFALl30BRf5fbgDAY5NSeN0bwOexnperq7gkBET6Z+UEgXr\nDQZDtDh/zF7csfBzsutq8KgA+yQkcm7AT5q7hjerd+APBJiQ07/1ggyGbkpzgmasRNSWaO2ew1kd\n8fNw4fWogqGsr65gQNkG7AqKnC4ucTgZ6a7ly5odfNBQz5HFo6Jmt8HQ14RViK64CrDH2NH87da/\nsPdVN5BkE5IcTi4aPoyj129g1dJl3L14Cdfe+heSkhKjet/mMFGrPQMjqsYXp93O/af8nIteeIPf\n+HzUBxQn5WZxYm0dddU1vFi6lTNmTCU/I7qOxEZoNRg6x19POppfP/kSOeUV1PsDHJSeytleLylb\nt/NqyWaqhw0im37xNrNbYARWQ6uICLMefpibb7+dH3/8kc9vvZVTCgsBGJmRwRWffUbNRReRnp4e\nZ0sNBkMop598AifMPJYtW8u544pruDx/IE6bjTGZGawt3czSlavYZ1J0078YDH0N14DCTl2fPHgY\nxTNpShM8LC2LBw48hqpGD5+tX84Z3gaGOZwAVHvq+alymxFYDYZuRrozgZv3OZQqbwOLyssYu20T\n+yboxd0UEZ4sLzUCq6FHES6oxkNI7SiRbI0U9brquyouHLsv54ycxOqaSirWLeOYRO14OMTh5NqK\nbXgKh5FoN0smhs7T18TVaAuroZx75qmceuLxbN6ylVl/up5L8wfisOa4q0s3s2L1GiaOHxuz+4OJ\nWu0pGGG1+zAxfwAf/eFXlNe5eeWLbznb08CQBF13KqtrWbSxlPyM2Dg1lS/bQu7o/k3vQ29vhw2G\naJGbmsIrvz+H7XVuPl6yiuLFK9g/Va9FJdpsPLOhlGkDjMAKRmDtNQQCAaqrq0lOTo5Zut709HRy\ncnLwK4VSChEhAASsnw0GQ/tQSlFbV4fdZic5OSkm90hISCB/YH/EZsMXCOC0Up35lMJm6q3B0CEa\n/X4m/+YQ7Amx6W/tImQnJOGw2fEr1fS5DxBMukKDoSP4AgE8fh/JDmdM+j8RIdOVSILDsVu9xfS3\nhm5OdxVUA4EANZ4Gkl0unA57h8sJf55wwXXbJ262iTTNcf2AEoWYtGeGTtLXhFUA0rKprqnBYXfE\nbI6bmJhI/oD+IIJfKRzoubWvC9amTNRq9ycopHW1qFqxfNcMCtmjukdf2lY8Xh/eQIBUlzMm9chu\ns9E/LRWH3Y4vdKzcBfW2KZrVCK2GXobX78fd6CUtIQGbLTZz3NzUFJJcTvwhn/uVMlPcEIzA2gso\nKyvjgZtuon7DBhqdTk689FKmTp8ek3sNHz4cNXYs//rpJ0YnJTHP7WaPY44hLc3sCWcwtAePx8Nj\nDz7Gum/mEwD2OmoGZ5x1OrYY7PXkcDiYctwxPPDaHA5JTmaNx0NFcRFjR42I+r0Mht6MUopvtm5k\nZdkGPr1rFQP2GMfvLjidtBil2h7Zr4D/rFvCsSpAXQA+sNuZkW08BA2G9rK6egdfrV9OYsCP15XA\n9MFjYhYJPjozl3e3biLRU0+aTXg7oBhd0LMW2Ax9g+4qqgYpq6zmqTkf4KuspsFu57jDD2L/kUOi\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ngRUgKyuLrKxdo05qamp4/MYb+ZUIo4qKmL9tGw/ccAN/e/zxpkFud6KoqIi5SnFUYyMZ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1PeID8vj0kL/gbqTpSsDubMFOy2TsAp4A1AwW7z4fzff9N22Mhy+1Qkuu7YfIy8uKNkHNyCoig0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\nnSImUsXGreMAtxAgihPZtvOWS46vJrNW16xczeczt2A0DsXhKGbn9k956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LR0D6AFFjFm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\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2m9mwYRWzZ19OefmFhIYmcdpp4Vx66e8pKCggJSWlRXtJknA4HOh0ul6xUZX5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\nQKIDg6ucf6FerOVLBVt7sVZXKrgy6+ehPf1Y9+fkOaxcsOatOJM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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# display plots in the notebook \n", "#%matplotlib inline\n", @@ -127,7 +116,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml1/filename.pkl b/ml1/filename.pkl new file mode 100644 index 0000000..25ecbad Binary files /dev/null and b/ml1/filename.pkl differ diff --git a/ml1/iris-tree.png b/ml1/iris-tree.png index 27de8b1..59f713c 100644 Binary files a/ml1/iris-tree.png and b/ml1/iris-tree.png differ diff --git a/ml1/plotml.py b/ml1/plotml.py index 5ba3d1b..aac6474 100644 --- a/ml1/plotml.py +++ b/ml1/plotml.py @@ -1,7 +1,7 @@ import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap -from sklearn.cross_validation import train_test_split +from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.neighbors import KNeighborsClassifier