Python assignment. Look at the zip folder, that has both the Python assignment and data set for it.

{
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"## Module A _(55 pts)_ Optimizing for predicted separation\n",
"__Data.__ For this module we'll be working with the basic baseball player heights and weights from the lecture notes. These are packaged in the following location:\n",
"\n",
"- `\"./data
aseball_heightweight.csv\"`\n",
"\n",
"__Overview.__ The purpose of this module is to provide experience with optimization and to view/investigate a different optimizable functions (from e
or). In the __Chapter 5.__ lecture notes we investigated the sum of squared e
ors as a function of (linear) model parameters. This made the goal of optimization (executed through gradient descent) into a 'fitting' procedure, i.e., describing a pattern that data follow.\n",
"\n",
"Another way we can use a line is as a 'separator': supposing some data $x_1, \\cdots, x_n$ have labels $y_1, \\cdots, y_n$ falling into two classes (coded as $\\pm 1$), which set of line-defining $w$ and $b$ parameters separate the points the best. Check out the picture below for an intution (note: their $b$ is our $-b$).\n",
"\n",
"Note: even though we're talking about 'lines', and actually working with true, 2-dimensional lines throughout this module, it's important to think/code throughout this module with the expectation that each data point $x_i$ will have $m$ (potentially greater than 2) dimensions. When $m>3$, a 'line' technically refers to a plane, but after we leave the comfort of 3-dimensionality our 'lines' are technically _hyperplanes_, for which we have little experiential intuition. Hence, we'll adopt the terminology 'line' throughout, since this provides the strongest grounding of intuition."
]
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""
]
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"__A1.__ _(7 pts_) To get things started: \n",
"\n",
"1. load the baseball player data with pandas into a dataframe called `
all`;\n",
"2. filter `
all` to contain only those rows that co
espond to shortstops and catchers;\n",
"3. create an `.a
ay()` called `y_
all` of labels, containing a `1` for each catcher and a `-1` for each shortstop;\n",
"4. filter the remaining rows of `
all` to only the `Height` and `Weight` columns; and\n",
"5. standardize the columns of `
all` and exhibit the first 10 rows."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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{
"ename": "FileNotFoundE
or",
"evalue": "File b'./data
aseball_heightweight.csv' does not exist",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundE
or\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 4\u001b[0m \u001b[0ma
\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[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0m
all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./data
aseball_heightweight.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0m
all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m
all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m
all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Position'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Shortstop'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m
all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Position'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Catcher'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\user\\PycharmProjects\\Lastname-Assn7\\venv\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, ve
ose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, e
or_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 676\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\user\\PycharmProjects\\Lastname-Assn7\\venv\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\user\\PycharmProjects\\Lastname-Assn7\\venv\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\user\\PycharmProjects\\Lastname-Assn7\\venv\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1012\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1015\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1016\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\user\\PycharmProjects\\Lastname-Assn7\\venv\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1706\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1710\u001b[0m \u001b[0mpassed_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundE
or\u001b[0m: File b'./data
aseball_heightweight.csv' does not exist"
],
"output_type": "e
or"
}
],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"\n",
"a
= []\n",
"\n",
"
all = pd.read_csv(\"./data
aseball_heightweight.csv\") \n",
"
all =
all.loc[(
all['Position'] == 'Shortstop') | (
all['Position'] == 'Catcher')]\n",
"\n",
"for x in
all['Position']:\n",
" if x == 'Catcher':\n",
" a
.append(1)\n",
" else:\n",
" a
.append(-1)\n",
"\n",
"
all =
all[['Height','Weight']]\n",
"\n",
"## pre-processing: standardize the heights and weights\n",
"
all_height =
all[\"Height\"] - np.mean(
all[\"Height\"])\n",
"
all[\"Height\"] =
all_height / np.std(
all[\"Height\"])\n",
"\n",
"
all_weight =
all[\"Weight\"] - np.mean(
all[\"Weight\"])\n",
"
all[\"Weight\"] =
all_weight / np.std(
all[\"Weight\"])\n",
"\n",
"print(
all.head(10))"
]
},
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"metadata": {},
"source": [
"__A2.__ _(5 pts)_ Since our goal with this problem will be to draw a line that separates the catchers from the shortstops, write and execute a function called \n",
"\n",
"- `plot_data_boundary(data, y)` \n",
"\n",
"that creates a scatter plot of the standardized heights and weights where the points are color coded with shortstops as black circles and catchers as red squares. In the response box below, disucss how well you think a line can separate these data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Response._ \n",
"\n",
"A line wouldn't be necessary as group separation is evident as is (via color segmentation)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png":...