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@fyr91
Created October 5, 2020 15:24
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import pickle\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Load model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18:42:11] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
]
}
],
"source": [
"filename = 'model.sav'\n",
"model = pickle.load(open(filename, 'rb'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Load data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"2017.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### PDP"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Country', 'Happiness.Rank', 'Happiness.Score', 'Whisker.high',\n",
" 'Whisker.low', 'Economy..GDP.per.Capita.', 'Family',\n",
" 'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
" 'Trust..Government.Corruption.', 'Dystopia.Residual'],\n",
" dtype='object')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px\n",
"from plotly.subplots import make_subplots\n",
"import plotly.graph_objects as go"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"features = ['Economy..GDP.per.Capita.', 'Family',\n",
" 'Health..Life.Expectancy.', 'Freedom', 'Generosity',\n",
" 'Trust..Government.Corruption.', 'Dystopia.Residual']"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"def pdp(feature, df, x_labels, y_label, i, fig):\n",
" df_copy = df.copy()\n",
" unique_vals = np.unique(df_copy[feature].values)\n",
" y = []\n",
" for val in unique_vals:\n",
" df_copy[feature] = val\n",
" X = df_copy[x_labels]\n",
" y.append(np.average(model.predict(X)))\n",
" \n",
" fig.add_trace(\n",
" go.Line(x=unique_vals,y=y,\n",
" name = feature), \n",
" row=i//3+1, \n",
" col=i%3+1,\n",
" )\n",
" return unique_vals\n",
"# plt.ylim(2, 8)\n",
"# g = sns.lineplot(x=unique_vals, y=y)\n",
"# g.set(xticks=unique_vals)\n",
"# g.set(xticklabels=[])\n",
"# g.set(xlabel = feature)\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
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},
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}
],
"source": [
"fig = make_subplots(rows=3, cols=3, shared_yaxes=True)\n",
"vals = []\n",
"for i, feature in enumerate(features):\n",
" vals.append(pdp(feature, df, features, 'Happiness.Score', i, fig))\n",
" \n",
"fig['layout']['yaxis1'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis2'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis3'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis4'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis5'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis6'].update(range=[2, 8], autorange=False)\n",
"fig['layout']['yaxis7'].update(range=[2, 8], autorange=False)\n",
"\n",
"fig['layout']['xaxis1'].update(tickmode = 'array', tickvals = vals[0], ticks='inside', ticktext = ['']*len(vals[0]))\n",
"fig['layout']['xaxis2'].update(tickmode = 'array', tickvals = vals[1], ticks='inside', ticktext = ['']*len(vals[1]))\n",
"fig['layout']['xaxis3'].update(tickmode = 'array', tickvals = vals[2], ticks='inside', ticktext = ['']*len(vals[2]))\n",
"fig['layout']['xaxis4'].update(tickmode = 'array', tickvals = vals[3], ticks='inside', ticktext = ['']*len(vals[3]))\n",
"fig['layout']['xaxis5'].update(tickmode = 'array', tickvals = vals[4], ticks='inside', ticktext = ['']*len(vals[4]))\n",
"fig['layout']['xaxis6'].update(tickmode = 'array', tickvals = vals[5], ticks='inside', ticktext = ['']*len(vals[5]))\n",
"fig['layout']['xaxis7'].update(tickmode = 'array', tickvals = vals[6], ticks='inside', ticktext = ['']*len(vals[6]))\n",
"\n",
"fig.update_layout(height=900, width=900, title_text=\"PDP\")\n",
"fig.update_layout(legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"bottom\",\n",
" y=-0.2,\n",
" xanchor=\"left\",\n",
" x=0\n",
"))\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
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"metadata": {},
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"source": []
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"language": "python",
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},
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}
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