Better Quickstart

README.md

@@ -33,47 +33,25 @@ You can install StepMix with pip, preferably in a virtual environment:
 pip install stepmix
 ``` 
 # Quickstart
-A simple StepMix mixture using the continuous variables of the Iris Dataset:
+A StepMix mixture using categorical variables on a preloaded data matrix. StepMix accepts either `numpy.array`or 
+`pandas.DataFrame`. Categories should be integer-encoded and 0-indexed.
 
 ```python
-import pandas as pd
-from sklearn.datasets import load_iris
-from sklearn.metrics import rand_score
-
 from stepmix.stepmix import StepMix
 
-# Load dataset in a Dataframe
-data_continuous, target = load_iris(return_X_y=True, as_frame=True)
-
-# Continuous StepMix Model with 3 latent classes
-model = StepMix(n_components=3, measurement="continuous", verbose=0, random_state=123)
-
-# Fit model and predict clusters
-model.fit(data_continuous)
-pred_continuous = model.predict(data_continuous)
+# Categorical StepMix Model with 3 latent classes
+model = StepMix(n_components=3, measurement="categorical")
+model.fit(data)
 
-# A Rand score close to 1 indicates good alignment between clusters and flower types
-print(rand_score(pred_continuous, target))
+# Allow missing values
+model_nan = StepMix(n_components=3, measurement="categorical_nan")
+model_nan.fit(data_nan)
 ```
-StepMix also provides support for categorical mixtures:
+For binary data you can also use `measurement="binary"` or `measurement="binary_nan"`. For continuous data, you can fit a Gaussian Mixture with diagonal covariances using `measurement="continuous"` or `measurement="continuous_nan"`.
 
-```python
-# Create categorical data based on the Iris Dataset quantiles
-data_categorical = data_continuous.copy()
-for col in data_categorical:
-   data_categorical[col] = pd.qcut(data_continuous[col], q=3).cat.codes
+Set `verbose=1` for a detailed output.
 
-# Categorical StepMix Model with 3 latent classes
-model = StepMix(n_components=3, measurement="categorical", verbose=0, random_state=123)
-
-# Fit model and predict clusters
-model.fit(data_categorical)
-pred_categorical = model.predict(data_categorical)
-
-# A Rand score close to 1 indicates good alignment between clusters and flower types
-print(rand_score(pred_categorical, target))
-```
-Please refer to the StepMix tutorials to learn how to handle missing values and combine continuous and categorical data in the same model.
+Please refer to the StepMix tutorials to learn how to combine continuous and categorical data in the same model.
 # Tutorials
 Detailed tutorials are available in notebooks: 
 1. [Generalized Mixture Models with StepMix](https://colab.research.google.com/drive/1KAxcvxjL_vB2lAG9e47we7hrf_2fR1eK?usp=sharing): 

docs/source/tutorials.rst

@@ -2,54 +2,23 @@ Tutorials
 =========
 Quickstart
 ----------
-The following shows a simple StepMix mixture using the continuous variables of the Iris Dataset. ``n_components`` controls
-the number of latent classes.::
-
-    import pandas as pd
-    from sklearn.datasets import load_iris
-    from sklearn.metrics import rand_score
+A StepMix mixture using categorical variables on a preloaded data matrix. StepMix accepts either ``numpy.array`` or ``pandas.DataFrame``. Categories should be integer-encoded and 0-indexed. ::
 
     from stepmix.stepmix import StepMix
 
-    # Load dataset in a Dataframe
-    data_continuous, target = load_iris(return_X_y=True, as_frame=True)
-
-    # Continuous StepMix Model with 3 latent classes
-    model = StepMix(n_components=3, measurement="continuous", verbose=1, random_state=123)
-
-    # Fit model and predict clusters
-    model.fit(data_continuous)
-    pred_continuous = model.predict(data_continuous)
-
-    # A Rand score close to 1 indicates good alignment between clusters and flower types
-    print(rand_score(pred_continuous, target))
-
-The API allows to easily predict class memberships or probabilities::
-
-    class_ids = model.predict(X, Y)
-    class_probs = model.predict_proba(X, Y)
-
-StepMix also provides support for categorical mixtures::
-
-    # Create categorical data based on the Iris Dataset quantiles
-    data_categorical = data_continuous.copy()
-    for col in data_categorical:
-       data_categorical[col] = pd.qcut(data_continuous[col], q=3).cat.codes
-
     # Categorical StepMix Model with 3 latent classes
-    model = StepMix(n_components=3, measurement="categorical", verbose=0, random_state=123)
+    model = StepMix(n_components=3, measurement="categorical")
+    model.fit(data)
 
-    # Fit model and predict clusters
-    model.fit(data_categorical)
-    pred_categorical = model.predict(data_categorical)
+    # Allow missing values
+    model_nan = StepMix(n_components=3, measurement="categorical_nan")
+    model_nan.fit(data_nan)
 
-    # A Rand score close to 1 indicates good alignment between clusters and flower types
-    print(rand_score(pred_categorical, target))
+For binary data you can also use ``measurement="binary"`` or ``measurement="binary_nan"``. For continuous data, you can fit a Gaussian Mixture with diagonal covariances using ``measurement="continuous"`` or ``measurement="continuous_nan"``.
 
-Input Data
-----------
-StepMix accepts the ``numpy.array`` and ``pandas.DataFrame`` data types. Additionally, emission models suffixed with
-``_nan`` support missing values denoted by ``np.NaN``.
+Set ``verbose=1`` for a detailed output.
+
+Please refer to the StepMix tutorials to learn how to combine continuous and categorical data in the same model.
 
 Advanced Usage
 --------------