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devin-petersohn/pandas_on_ray_spark_summit_demo_2018.ipynb

Last active June 5, 2018 15:16
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Pandas on Ray demo for Spark Summit 2018
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pandas_on_ray_spark_summit_demo_2018.ipynb
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pandas_on_ray_spark_summit_scikitlearn_demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Process STDOUT and STDERR is being redirected to /tmp/raylogs/.\n",
"Waiting for redis server at 127.0.0.1:44667 to respond...\n",
"Waiting for redis server at 127.0.0.1:40325 to respond...\n",
"Starting local scheduler with the following resources: {'CPU': 4, 'GPU': 0}.\n",
"\n",
"======================================================================\n",
"View the web UI at http://localhost:8889/notebooks/ray_ui82805.ipynb?token=b8a5181da8fd40b073e7457e426ddaa2e44c9e8dfe6c53fe\n",
"======================================================================\n",
"\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import ray.dataframe as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>CRIM</th>\n",
" <th>ZN</th>\n",
" <th>INDUS</th>\n",
" <th>CHAS</th>\n",
" <th>NOX</th>\n",
" <th>RM</th>\n",
" <th>AGE</th>\n",
" <th>DIS</th>\n",
" <th>RAD</th>\n",
" <th>TAX</th>\n",
" <th>PTRATIO</th>\n",
" <th>B</th>\n",
" <th>LSTAT</th>\n",
" <th>PRICE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0.0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1.0</td>\n",
" <td>296.0</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0.0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2.0</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0.0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2.0</td>\n",
" <td>242.0</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0.0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3.0</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0.0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3.0</td>\n",
" <td>222.0</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 CRIM ZN INDUS CHAS NOX RM AGE DIS RAD \\\n",
"0 0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 \n",
"1 1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 \n",
"2 2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 \n",
"3 3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 \n",
"4 4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 \n",
"\n",
" TAX PTRATIO B LSTAT PRICE \n",
"0 296.0 15.3 396.90 4.98 24.0 \n",
"1 242.0 17.8 396.90 9.14 21.6 \n",
"2 242.0 17.8 392.83 4.03 34.7 \n",
"3 222.0 18.7 394.63 2.94 33.4 \n",
"4 222.0 18.7 396.90 5.33 36.2 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"/Users/kunalgosar/research/ray/boston_housing.csv\")\n",
"\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ray.dataframe.dataframe.DataFrame"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = data.drop(\"PRICE\", axis=1)\n",
"labels = data[\"PRICE\"]\n",
"\n",
"type(features)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kunalgosar/dev/anaconda3/envs/ray-dev/lib/python3.5/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
" linalg.lstsq(X, y)\n"
]
},
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"lm = LinearRegression()\n",
"lm.fit(features, labels)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(data[\"RM\"], labels)\n",
"plt.xlabel(\"Average number of rooms per dwelling\")\n",
"plt.ylabel(\"Housing Price\")\n",
"plt.title(\"Relationship between Rooms and Price\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"predicted_prices = lm.predict(features)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(labels, predicted_prices)\n",
"plt.xlabel(\"Prices\")\n",
"plt.ylabel(\"Predicted Prices\")\n",
"plt.title(\"Prices versus Predicted Prices\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21.831934375295628"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"training_error = \\\n",
" (labels - predicted_prices).apply(lambda x: x ** 2).mean()\n",
"\n",
"training_error"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Citation: http://bigdata-madesimple.com/how-to-run-linear-regression-in-python-scikit-learn/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:ray-dev]",
"language": "python",
"name": "conda-env-ray-dev-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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