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Description: Use cached datasets in tests and examples
Also remove a download that isn't actually used in that example.
This allows the tests to pass, and at least some of the examples to
be built, in an offline environment such as a Debian buildd.
The cached data is extracted from R packages by debian/datasets/*.
Author: Diane Trout <[email protected]>, Rebecca N. Palmer <[email protected]>
Forwarded: not-needed
--- a/docs/source/contingency_tables.rst
b/docs/source/contingency_tables.rst
@@ -49,7 49,7 @@ contingency table cell counts:
import pandas as pd
import statsmodels.api as sm
- df = sm.datasets.get_rdataset("Arthritis", "vcd").data
df = sm.datasets.get_rdataset("Arthritis", "vcd", cache=True).data
tab = pd.crosstab(df['Treatment'], df['Improved'])
tab = tab.loc[:, ["None", "Some", "Marked"]]
@@ -184,7 184,7 @@ contingency table.
.. ipython:: python
- df = sm.datasets.get_rdataset("VisualAcuity", "vcd").data
df = sm.datasets.get_rdataset("VisualAcuity", "vcd", cache=True).data
df = df.loc[df.gender == "female", :]
tab = df.set_index(['left', 'right'])
del tab["gender"]
--- a/docs/source/duration.rst
b/docs/source/duration.rst
@@ -41,7 41,7 @@ We fit the survival distribution only fo
import statsmodels.api as sm
- data = sm.datasets.get_rdataset("flchain", "survival").data
data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
df = data.loc[data.sex == "F", :]
sf = sm.SurvfuncRight(df["futime"], df["death"])
@@ -152,7 152,7 @@ depending on the value of the covariates
import statsmodels.api as sm
import statsmodels.formula.api as smf
- data = sm.datasets.get_rdataset("flchain", "survival").data
data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
del data["chapter"]
data = data.dropna()
data["lam"] = data["lambda"]
--- a/docs/source/example_formulas.rst
b/docs/source/example_formulas.rst
@@ -47,7 47,7 @@ and list-wise delete to remove missing o
.. ipython:: python
- df = sm.datasets.get_rdataset("Guerry", "HistData").data
df = sm.datasets.get_rdataset("Guerry", "HistData", cache=True).data
df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()
df.head()
--- a/docs/source/gee.rst
b/docs/source/gee.rst
@@ -24,7 24,7 @@ within clusters using data on epilepsy s
import statsmodels.api as sm
import statsmodels.formula.api as smf
- data = sm.datasets.get_rdataset('epil', package='MASS').data
data = sm.datasets.get_rdataset('epil', package='MASS', cache=True).data
fam = sm.families.Poisson()
ind = sm.cov_struct.Exchangeable()
--- a/docs/source/gettingstarted.rst
b/docs/source/gettingstarted.rst
@@ -50,7 50,7 @@ We could download the file locally and t
.. ipython:: python
- df = sm.datasets.get_rdataset("Guerry", "HistData").data
df = sm.datasets.get_rdataset("Guerry", "HistData", cache=True).data
The `Input/Output doc page <iolib.html>`_ shows how to import from various
other formats.
--- a/docs/source/index.rst
b/docs/source/index.rst
@@ -23,7 23,7 @@ Here is a simple example using ordinary
import statsmodels.formula.api as smf
# Load data
- dat = sm.datasets.get_rdataset("Guerry", "HistData").data
dat = sm.datasets.get_rdataset("Guerry", "HistData", cache=True).data
# Fit regression model (using the natural log of one of the regressors)
results = smf.ols('Lottery ~ Literacy np.log(Pop1831)', data=dat).fit()
--- a/docs/source/mixed_linear.rst
b/docs/source/mixed_linear.rst
@@ -83,7 83,7 @@ Examples
import statsmodels.api as sm
import statsmodels.formula.api as smf
- data = sm.datasets.get_rdataset("dietox", "geepack").data
data = sm.datasets.get_rdataset("dietox", "geepack", cache=True).data
md = smf.mixedlm("Weight ~ Time", data, groups=data["Pig"])
mdf = md.fit()
--- a/docs/source/release/version0.6.rst
b/docs/source/release/version0.6.rst
@@ -41,7 41,7 @@ covariates.
import statsmodels.api as sm
import statsmodels.formula.api as smf
- data = sm.datasets.get_rdataset("epil", "MASS").data
data = sm.datasets.get_rdataset("epil", "MASS", cache=True).data
md = smf.gee("y ~ age trt base", "subject", data,
cov_struct=sm.cov_struct.Independence(),
--- a/docs/source/datasets/index.rst
b/docs/source/datasets/index.rst
@@ -30,7 30,7 @@ The `Rdatasets project <https://vincenta
.. ipython:: python
import statsmodels.api as sm
- duncan_prestige = sm.datasets.get_rdataset("Duncan", "carData")
duncan_prestige = sm.datasets.get_rdataset("Duncan", "carData", cache=True)
print(duncan_prestige.__doc__)
duncan_prestige.data.head(5)
--- a/examples/notebooks/markov_regression.ipynb
b/examples/notebooks/markov_regression.ipynb
@@ -28,11 28,7 @@
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
- "\n",
- "# NBER recessions\n",
- "from pandas_datareader.data import DataReader\n",
- "from datetime import datetime\n",
- "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))"
"from datetime import datetime\n"
]
},
{
--- a/examples/notebooks/mixed_lm_example.ipynb
b/examples/notebooks/mixed_lm_example.ipynb
@@ -86,7 86,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data = sm.datasets.get_rdataset('dietox', 'geepack').data\n",
"data = sm.datasets.get_rdataset('dietox', 'geepack', cache=True).data\n",
"md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"])\n",
"mdf = md.fit(method=[\"lbfgs\"])\n",
"print(mdf.summary())"
@@ -318,7 318,7 @@
"metadata": {},
"outputs": [],
"source": [
- "data = sm.datasets.get_rdataset(\"Sitka\", \"MASS\").data\n",
"data = sm.datasets.get_rdataset(\"Sitka\", \"MASS\", cache=True).data\n",
"endog = data[\"size\"]\n",
"data[\"Intercept\"] = 1\n",
"exog = data[[\"Intercept\", \"Time\"]]"
--- a/examples/notebooks/regression_diagnostics.ipynb
b/examples/notebooks/regression_diagnostics.ipynb
@@ -47,8 47,8 @@
"import matplotlib.pyplot as plt\n",
"\n",
"# Load data\n",
- "url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Guerry.csv'\n",
- "dat = pd.read_csv(url)\n",
"import statsmodels.datasets\n",
"dat = statsmodels.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True).data\n",
"\n",
"# Fit regression model (using the natural log of one of the regressors)\n",
"results = smf.ols('Lottery ~ Literacy np.log(Pop1831)', data=dat).fit()\n",
--- a/statsmodels/stats/tests/test_dist_dependant_measures.py
b/statsmodels/stats/tests/test_dist_dependant_measures.py
@@ -137,7 137,7 @@ class TestDistDependenceMeasures(object)
0.1025087
"""
try:
- iris = get_rdataset("iris").data.values[:, :4]
iris = get_rdataset("iris", cache=True).data.values[:, :4]
except IGNORED_EXCEPTIONS:
pytest.skip('Failed with HTTPError or URLError, these are random')
@@ -177,7 177,7 @@ class TestDistDependenceMeasures(object)
30.01526
"""
try:
- quakes = get_rdataset("quakes").data.values[:, :3]
quakes = get_rdataset("quakes", cache=True).data.values[:, :3]
except IGNORED_EXCEPTIONS:
pytest.skip('Failed with HTTPError or URLError, these are random')
|
