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Done ch-08
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@qpsy
qpsy committed Jul 31, 2024
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32 changes: 5 additions & 27 deletions 04_geocentric_models-2.ipynb
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"from collections import namedtuple"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import rethinking"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from rethinking.data import RethinkingDataset\n",
"from rethinking.data import dataframe_to_tensors\n",
"# from rethinking.mcmc import sample_posterior"
]
},
{
"cell_type": "code",
"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d.head()"
]
},
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d2 = d[d.age > 18]"
]
},
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d2 = d[d.age > 18]"
]
},
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d2 = d[d.age > 18]\n",
"x_bar = d2.weight.mean()"
]
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}
],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d.describe()"
]
},
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18 changes: 4 additions & 14 deletions 05_the_many_variables_and_the_spurious_waffles-2.ipynb
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Expand Up @@ -24,7 +24,6 @@
"import arviz as az\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import daft\n",
"from collections import namedtuple\n",
"# from causalgraphicalmodels import CausalGraphicalModel\n",
"\n",
Expand All @@ -36,14 +35,6 @@
"tfd = tfp.distributions\n",
"tfb = tfp.bijectors\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"import rethinking\n",
"\n",
"from rethinking.data import RethinkingDataset\n",
"from rethinking.data import dataframe_to_tensors\n",
"# from rethinking.mcmc import sample_posterior\n",
"\n",
"plt.rcParams['figure.figsize'] = [6, 4]\n",
"\n",
"def a_key():\n",
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.WaffleDivorce.get_dataset()\n",
"\n",
"d = pd.read_csv(\"./data/WaffleDivorce.csv\", sep=\";\")\n",
"# standardize variables\n",
"d[\"A\"] = d.MedianAgeMarriage.pipe(lambda x: (x - x.mean()) / x.std())\n",
"d[\"D\"] = d.Divorce.pipe(lambda x: (x - x.mean()) / x.std())"
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}
],
"source": [
"d = RethinkingDataset.Milk.get_dataset()\n",
"d = pd.read_csv(\"./data/milk.csv\", sep=\";\")\n",
"d.head()"
]
},
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}
],
"source": [
"d = RethinkingDataset.Howell1.get_dataset()\n",
"d = pd.read_csv(\"./data/Howell1.csv\", sep=\";\")\n",
"d.head()"
]
},
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}
],
"source": [
"d = RethinkingDataset.Milk.get_dataset()\n",
"d = pd.read_csv(\"./data/milk.csv\", sep=\";\")\n",
"d.clade.unique()"
]
},
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10 changes: 2 additions & 8 deletions 06_the_haunted_dag_and_the_causal_terror-2.ipynb
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Expand Up @@ -24,20 +24,16 @@
"import arviz as az\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import daft\n",
"from collections import namedtuple\n",
"from statsmodels.regression import linear_model\n",
"\n",
"import random\n",
"import jax\n",
"import jax.numpy as jnp\n",
"\n",
"from tensorflow_probability.substrates import jax as tfp\n",
"tfd = tfp.distributions\n",
"tfb = tfp.bijectors\n",
"\n",
"from rethinking.data import RethinkingDataset\n",
"\n",
"plt.rcParams['figure.figsize'] = [6, 4]"
]
},
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"metadata": {},
"outputs": [],
"source": [
"d = RethinkingDataset.Milk.get_dataset()\n",
"\n",
"d = pd.read_csv(\"./data/milk.csv\", sep=\";\")\n",
"d[\"K\"] = d[\"kcal.per.g\"].pipe(lambda x: (x - x.mean()) / x.std())\n",
"d[\"F\"] = d[\"perc.fat\"].pipe(lambda x: (x - x.mean()) / x.std())\n",
"d[\"L\"] = d[\"perc.lactose\"].pipe(lambda x: (x - x.mean()) / x.std())"
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}
],
"source": [
"d = RethinkingDataset.Milk.get_dataset()\n",
"\n",
"d = pd.read_csv(\"./data/milk.csv\", sep=\";\")\n",
"\n",
"def simcoll(r=0.9):\n",
"\n",
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93 changes: 15 additions & 78 deletions 07_ulysses_compass-2.ipynb
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Expand Up @@ -20,7 +20,6 @@
"metadata": {},
"outputs": [],
"source": [
"from io import StringIO\n",
"import numpy as np\n",
"import arviz as az\n",
"import pandas as pd\n",
Expand All @@ -38,8 +37,6 @@
"tfd = tfp.distributions\n",
"tfb = tfp.bijectors\n",
"\n",
"from rethinking.data import RethinkingDataset\n",
"\n",
"plt.rcParams['figure.figsize'] = [6, 4]"
]
},
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},
{
"cell_type": "code",
"execution_count": 120,
"execution_count": 2,
"metadata": {},
"outputs": [
{
Expand All @@ -2512,7 +2509,7 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>rownames</th>\n",
" <th>speed</th>\n",
" <th>dist</th>\n",
" </tr>\n",
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"</div>"
],
"text/plain": [
" Unnamed: 0 speed dist\n",
"0 1 4 2\n",
"1 2 4 10\n",
"2 3 7 4\n",
"3 4 7 22\n",
"4 5 8 16"
" rownames speed dist\n",
"0 1 4 2\n",
"1 2 4 10\n",
"2 3 7 4\n",
"3 4 7 22\n",
"4 5 8 16"
]
},
"execution_count": 120,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# There is no CSV file for cars dataset in author's repo\n",
"# hence I have inlined the data. In R this dataset much be bundled in\n",
"# and this is why his code snippets do not need the csv file\n",
"\n",
"cars_data = \"\"\"\n",
"\"\",\"speed\",\"dist\"\n",
"\"1\",4,2\n",
"\"2\",4,10\n",
"\"3\",7,4\n",
"\"4\",7,22\n",
"\"5\",8,16\n",
"\"6\",9,10\n",
"\"7\",10,18\n",
"\"8\",10,26\n",
"\"9\",10,34\n",
"\"10\",11,17\n",
"\"11\",11,28\n",
"\"12\",12,14\n",
"\"13\",12,20\n",
"\"14\",12,24\n",
"\"15\",12,28\n",
"\"16\",13,26\n",
"\"17\",13,34\n",
"\"18\",13,34\n",
"\"19\",13,46\n",
"\"20\",14,26\n",
"\"21\",14,36\n",
"\"22\",14,60\n",
"\"23\",14,80\n",
"\"24\",15,20\n",
"\"25\",15,26\n",
"\"26\",15,54\n",
"\"27\",16,32\n",
"\"28\",16,40\n",
"\"29\",17,32\n",
"\"30\",17,40\n",
"\"31\",17,50\n",
"\"32\",18,42\n",
"\"33\",18,56\n",
"\"34\",18,76\n",
"\"35\",18,84\n",
"\"36\",19,36\n",
"\"37\",19,46\n",
"\"38\",19,68\n",
"\"39\",20,32\n",
"\"40\",20,48\n",
"\"41\",20,52\n",
"\"42\",20,56\n",
"\"43\",20,64\n",
"\"44\",22,66\n",
"\"45\",23,54\n",
"\"46\",24,70\n",
"\"47\",24,92\n",
"\"48\",24,93\n",
"\"49\",24,120\n",
"\"50\",25,85\n",
"\"\"\"\n",
"\n",
"buffer = StringIO(cars_data)\n",
"d = pd.read_csv(buffer, sep=\",\")\n",
"\n",
"d = pd.read_csv(\"./data/cars.csv\")\n",
"d.head()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 float32\n",
"speed float32\n",
"dist float32\n",
"rownames float32\n",
"speed float32\n",
"dist float32\n",
"dtype: object"
]
},
"execution_count": 121,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
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