correct typo

CHANGELOG.md

@@ -13,6 +13,7 @@
 ##### API Changes
 
  - To align values across models, the column names for the confidence intervals in parametric univariate models `summary` have changed.
+ - Fixed typo in `ParametricUnivariateFitter` name.
 
 
 #### 0.22.10 - 2019-11-08

docs/jupyter_notebooks/Modelling time-lagged conversion rates.ipynb

@@ -196,7 +196,7 @@
     "\n",
     "where $c$ is the (unknown) horizontal asymptote, and $\\theta$ is a vector of (unknown) parameters for the CDF, $F$. \n",
     "\n",
-    "We can create a custom cumulative hazard model using `ParametericUnivariateFitter` (for a tutorial on how to create custom models, see [this here](Piecewise Exponential Models and Creating Custom Models.ipynb)). Let's choose the Normal CDF for our model. \n",
+    "We can create a custom cumulative hazard model using `ParametricUnivariateFitter` (for a tutorial on how to create custom models, see [this here](Piecewise Exponential Models and Creating Custom Models.ipynb)). Let's choose the Normal CDF for our model. \n",
     "\n",
     "Remember we **must** use the imports from `autograd` for this, i.e. `from autograd.scipy.stats import norm`."
    ]
@@ -208,10 +208,10 @@
    "outputs": [],
    "source": [
     "from autograd.scipy.stats import norm\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
     "\n",
-    "class UpperAsymptoteFitter(ParametericUnivariateFitter):\n",
+    "class UpperAsymptoteFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"c_\", \"mu_\", \"sigma_\"]\n",
     "\n",
@@ -489,9 +489,9 @@
    "outputs": [],
    "source": [
     "from autograd.scipy.stats import norm\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
-    "class SimpleUpperAsymptoteFitter(ParametericUnivariateFitter):\n",
+    "class SimpleUpperAsymptoteFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"c_\", \"lambda_\"]\n",
     "\n",

docs/jupyter_notebooks/Piecewise Exponential Models and Creating Custom Models.ipynb

@@ -242,7 +242,7 @@
     "I mentioned that the `PiecewiseExponentialFitter` was implemented using only its cumulative hazard function. This is not a lie. _lifelines_ has very general semantics for univariate fitters. For example, this is how the entire `ExponentialFitter` is implemented:\n",
     "\n",
     "```python\n",
-    "class ExponentialFitter(ParametericUnivariateFitter):\n",
+    "class ExponentialFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"lambda_\"]\n",
     "\n",
@@ -272,11 +272,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
     "import autograd.numpy as np\n",
     "\n",
-    "class InverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class InverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    # we tell the model what we want the names of the unknown parameters to be\n",
     "    _fitted_parameter_names = ['alpha_']\n",
@@ -368,7 +368,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class TwoParamInverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class TwoParamInverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    _fitted_parameter_names = ['alpha_', 'beta_']\n",
     "    \n",
@@ -458,9 +458,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
-    "class ThreeParamInverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class ThreeParamInverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    _fitted_parameter_names = ['alpha_', 'beta_', 'gamma_']\n",
     "    _bounds = [(0, None), (75.0001, None), (0, None)]\n",
@@ -615,7 +615,7 @@
     "# I'm shifting this to exaggerate the effect \n",
     "T = T + 10\n",
     "\n",
-    "class ThreeParameterWeibullFitter(ParametericUnivariateFitter):\n",
+    "class ThreeParameterWeibullFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"lambda_\", \"rho_\", \"theta_\"]\n",
     "    _bounds = [(0, None), (0, None), (0, T.min()-0.001)]\n",
@@ -690,7 +690,7 @@
     "from autograd.scipy.stats import norm\n",
     "\n",
     "\n",
-    "class InverseGaussianFitter(ParametericUnivariateFitter):\n",
+    "class InverseGaussianFitter(ParametricUnivariateFitter):\n",
     "    _fitted_parameter_names = ['lambda_', 'mu_']\n",
     "    \n",
     "    def _cumulative_density(self, params, times):\n",
@@ -791,7 +791,7 @@
    "source": [
     "from autograd_gamma import betainc, betaincln\n",
     "\n",
-    "class BetaFitter(ParametericUnivariateFitter):\n",
+    "class BetaFitter(ParametricUnivariateFitter):\n",
     "    _fitted_parameter_names = ['alpha_', 'beta_', \"m_\"]\n",
     "    _bounds = [(0, None), (0, None), (T.max(), None)]\n",
     "    _scipy_fit_method = \"SLSQP\"\n",
@@ -876,7 +876,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class GompertzFitter(ParametericUnivariateFitter):\n",
+    "class GompertzFitter(ParametricUnivariateFitter):\n",
     "    # this parameterization is slightly different than wikipedia. \n",
     "    _fitted_parameter_names = ['nu_', 'b_']\n",
     "\n",

examples/Mixture of Exponentials and Binning.ipynb

@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "from numpy.random import exponential\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "import autograd.numpy as np\n",
     "np.random.seed(10)"
    ]
@@ -123,7 +123,7 @@
    ],
    "source": [
     "\n",
-    "class MixtureExponential(ParametericUnivariateFitter):\n",
+    "class MixtureExponential(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = ['lambda1', 'lambda2', 'p']\n",
     "    _bounds = [(0, mean_obs), (mean_obs, None), (0, 1)]\n",

examples/Modelling time-lagged conversion rates.ipynb

@@ -196,7 +196,7 @@
     "\n",
     "where $c$ is the (unknown) horizontal asymptote, and $\\theta$ is a vector of (unknown) parameters for the CDF, $F$. \n",
     "\n",
-    "We can create a custom cumulative hazard model using `ParametericUnivariateFitter` (for a tutorial on how to create custom models, see [this here](Piecewise Exponential Models and Creating Custom Models.ipynb)). Let's choose the Normal CDF for our model. \n",
+    "We can create a custom cumulative hazard model using `ParametricUnivariateFitter` (for a tutorial on how to create custom models, see [this here](Piecewise Exponential Models and Creating Custom Models.ipynb)). Let's choose the Normal CDF for our model. \n",
     "\n",
     "Remember we **must** use the imports from `autograd` for this, i.e. `from autograd.scipy.stats import norm`."
    ]
@@ -208,10 +208,10 @@
    "outputs": [],
    "source": [
     "from autograd.scipy.stats import norm\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
     "\n",
-    "class UpperAsymptoteFitter(ParametericUnivariateFitter):\n",
+    "class UpperAsymptoteFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"c_\", \"mu_\", \"sigma_\"]\n",
     "\n",
@@ -489,9 +489,9 @@
    "outputs": [],
    "source": [
     "from autograd.scipy.stats import norm\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
-    "class SimpleUpperAsymptoteFitter(ParametericUnivariateFitter):\n",
+    "class SimpleUpperAsymptoteFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"c_\", \"lambda_\"]\n",
     "\n",
@@ -586,9 +586,9 @@
    "outputs": [],
    "source": [
     "from autograd import numpy as np\n",
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
-    "class CureFitter(ParametericUnivariateFitter):\n",
+    "class CureFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"p_\", \"lambda_\", \"rho_\"]\n",
     "\n",

examples/Piecewise Exponential Models and Creating Custom Models.ipynb

@@ -242,7 +242,7 @@
     "I mentioned that the `PiecewiseExponentialFitter` was implemented using only its cumulative hazard function. This is not a lie. _lifelines_ has very general semantics for univariate fitters. For example, this is how the entire `ExponentialFitter` is implemented:\n",
     "\n",
     "```python\n",
-    "class ExponentialFitter(ParametericUnivariateFitter):\n",
+    "class ExponentialFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"lambda_\"]\n",
     "\n",
@@ -272,11 +272,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
     "import autograd.numpy as np\n",
     "\n",
-    "class InverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class InverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    # we tell the model what we want the names of the unknown parameters to be\n",
     "    _fitted_parameter_names = ['alpha_']\n",
@@ -368,7 +368,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class TwoParamInverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class TwoParamInverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    _fitted_parameter_names = ['alpha_', 'beta_']\n",
     "    \n",
@@ -458,9 +458,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from lifelines.fitters import ParametericUnivariateFitter\n",
+    "from lifelines.fitters import ParametricUnivariateFitter\n",
     "\n",
-    "class ThreeParamInverseTimeHazardFitter(ParametericUnivariateFitter):\n",
+    "class ThreeParamInverseTimeHazardFitter(ParametricUnivariateFitter):\n",
     "    \n",
     "    _fitted_parameter_names = ['alpha_', 'beta_', 'gamma_']\n",
     "    _bounds = [(0, None), (75.0001, None), (0, None)]\n",
@@ -615,7 +615,7 @@
     "# I'm shifting this to exaggerate the effect \n",
     "T = T + 10\n",
     "\n",
-    "class ThreeParameterWeibullFitter(ParametericUnivariateFitter):\n",
+    "class ThreeParameterWeibullFitter(ParametricUnivariateFitter):\n",
     "\n",
     "    _fitted_parameter_names = [\"lambda_\", \"rho_\", \"theta_\"]\n",
     "    _bounds = [(0, None), (0, None), (0, T.min()-0.001)]\n",
@@ -690,7 +690,7 @@
     "from autograd.scipy.stats import norm\n",
     "\n",
     "\n",
-    "class InverseGaussianFitter(ParametericUnivariateFitter):\n",
+    "class InverseGaussianFitter(ParametricUnivariateFitter):\n",
     "    _fitted_parameter_names = ['lambda_', 'mu_']\n",
     "    \n",
     "    def _cumulative_density(self, params, times):\n",
@@ -791,7 +791,7 @@
    "source": [
     "from autograd_gamma import betainc\n",
     "\n",
-    "class BetaFitter(ParametericUnivariateFitter):\n",
+    "class BetaFitter(ParametricUnivariateFitter):\n",
     "    _fitted_parameter_names = ['alpha_', 'beta_', \"m_\"]\n",
     "    _bounds = [(0, None), (0, None), (T.max(), None)]\n",
     "    \n",
@@ -875,7 +875,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class GompertzFitter(ParametericUnivariateFitter):\n",
+    "class GompertzFitter(ParametricUnivariateFitter):\n",
     "    # this parameterization is slightly different than wikipedia. \n",
     "    _fitted_parameter_names = ['nu_', 'b_']\n",
     "\n",

lifelines/fitters/__init__.py

@@ -294,7 +294,7 @@ def percentile(self, p: float):
         return utils.qth_survival_times(p, self.survival_function_)
 
 
-class ParametericUnivariateFitter(UnivariateFitter):
+class ParametricUnivariateFitter(UnivariateFitter):
     """
     Without overriding anything, assumes all parameters must be greater than 0.
     """
@@ -305,7 +305,7 @@ class ParametericUnivariateFitter(UnivariateFitter):
     _scipy_fit_options: Dict[str, Any] = dict()
 
     def __init__(self, *args, **kwargs):
-        super(ParametericUnivariateFitter, self).__init__(*args, **kwargs)
+        super(ParametricUnivariateFitter, self).__init__(*args, **kwargs)
         self._estimate_name = "cumulative_hazard_"
         if not hasattr(self, "_bounds"):
             self._bounds = [(0.0, None)] * len(self._fitted_parameter_names)
@@ -1165,7 +1165,7 @@ def _conditional_time_to_event_(self):
         )
 
 
-class KnownModelParametericUnivariateFitter(ParametericUnivariateFitter):
+class KnownModelParametricUnivariateFitter(ParametricUnivariateFitter):
 
     _KNOWN_MODEL = True
 
@@ -2984,7 +2984,7 @@ def print(self):
             from IPython.core.getipython import get_ipython
 
             ip = get_ipython()
-            if ip.has_trait("kernel"):
+            if ip and ip.has_trait("kernel"):
                 self.html_print()
             else:
                 self.console_print()

lifelines/fitters/exponential_fitter.py

@@ -1,9 +1,9 @@
 # -*- coding: utf-8 -*-
 import numpy as np
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 
 
-class ExponentialFitter(KnownModelParametericUnivariateFitter):
+class ExponentialFitter(KnownModelParametricUnivariateFitter):
     r"""
     This class implements an Exponential model for univariate data. The model has parameterized
     form:

lifelines/fitters/generalized_gamma_fitter.py

@@ -3,12 +3,12 @@
 from autograd.numpy import exp, log
 from scipy.special import gammainccinv, gammaincinv
 from autograd_gamma import gammaincc, gammainc, gammaln, gammainccln, gammaincln
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 from lifelines.utils import CensoringType
 from lifelines.utils.safe_exp import safe_exp
 
 
-class GeneralizedGammaFitter(KnownModelParametericUnivariateFitter):
+class GeneralizedGammaFitter(KnownModelParametricUnivariateFitter):
     r"""
 
     This class implements a Generalized Gamma model for univariate data. The model has parameterized

lifelines/fitters/log_logistic_fitter.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 import autograd.numpy as np
 
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 from lifelines.utils import CensoringType
 
 
-class LogLogisticFitter(KnownModelParametericUnivariateFitter):
+class LogLogisticFitter(KnownModelParametricUnivariateFitter):
 
     r"""
     This class implements a Log-Logistic model for univariate data. The model has parameterized

lifelines/fitters/log_normal_fitter.py

@@ -3,11 +3,11 @@
 from scipy.special import erfinv
 import autograd.numpy as np
 from autograd.scipy.stats import norm
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 from lifelines.utils import CensoringType
 
 
-class LogNormalFitter(KnownModelParametericUnivariateFitter):
+class LogNormalFitter(KnownModelParametricUnivariateFitter):
     r"""
     This class implements an Log Normal model for univariate data. The model has parameterized
     form:

lifelines/fitters/piecewise_exponential_fitter.py

@@ -1,9 +1,9 @@
 # -*- coding: utf-8 -*-
 import autograd.numpy as np
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 
 
-class PiecewiseExponentialFitter(KnownModelParametericUnivariateFitter):
+class PiecewiseExponentialFitter(KnownModelParametricUnivariateFitter):
     r"""
     This class implements an Piecewise Exponential model for univariate data. The model has parameterized
     hazard rate:

lifelines/fitters/weibull_fitter.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 import autograd.numpy as np
-from lifelines.fitters import KnownModelParametericUnivariateFitter
+from lifelines.fitters import KnownModelParametricUnivariateFitter
 from lifelines.utils.safe_exp import safe_exp
 from lifelines import utils
 
 
-class WeibullFitter(KnownModelParametericUnivariateFitter):
+class WeibullFitter(KnownModelParametricUnivariateFitter):
 
     r"""
 

lifelines/plotting.py

@@ -15,9 +15,9 @@ def get_distribution_name_of_lifelines_model(model):
 
 
 def create_scipy_stats_model_from_lifelines_model(model):
-    from lifelines.fitters import KnownModelParametericUnivariateFitter
+    from lifelines.fitters import KnownModelParametricUnivariateFitter
 
-    is_univariate_model = isinstance(model, KnownModelParametericUnivariateFitter)
+    is_univariate_model = isinstance(model, KnownModelParametricUnivariateFitter)
     dist = get_distribution_name_of_lifelines_model(model)
 
     if not (is_univariate_model):