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adds fuller test suite for segmentation

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This commit is contained in:
Andy Eschbacher
2018-03-15 11:41:47 -04:00
parent bc9cae1a63
commit 6260a080a6
5 changed files with 207 additions and 44 deletions
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16
src/py/crankshaft/crankshaft/segmentation/segmentation.py
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@@ -29,26 +29,25 @@ class Segmentation(object):
straight form the SQL calling the function.
Input:
@param target: The 1D array of lenth NSamples containing the
@param target: The 1D array of length NSamples containing the
target variable we want the model to predict
@param features: The 2D array of size NSamples * NFeatures that
form the imput to the model
form the input to the model
@param target_ids: A 1D array of target_ids that will be used
to associate the results of the prediction with the rows which
they come from
@param model_parameters: A dictionary containing parameters for
the model.
"""
clean_target = replace_nan_with_mean(target)
clean_features = replace_nan_with_mean(features)
target_features = replace_nan_with_mean(target_features)
clean_target, _ = replace_nan_with_mean(target)
clean_features, _ = replace_nan_with_mean(features)
target_features, _ = replace_nan_with_mean(target_features)
model, accuracy = train_model(clean_target, clean_features,
model_parameters, 0.2)
prediction = model.predict(target_features)
accuracy_array = [accuracy] * prediction.shape[0]
return zip(target_ids, prediction,
np.full(prediction.shape, accuracy_array))
return zip(target_ids, prediction, accuracy_array)
def create_and_predict_segment(self, query, variable, feature_columns,
target_query, model_params,
@@ -65,7 +64,6 @@ class Segmentation(object):
scikit learn page for [GradientBoostingRegressor]
(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
"""
params = {"subquery": target_query,
"id_col": id_col}
@@ -198,7 +196,7 @@ def train_model(target, features, model_params, test_split):
Input:
@param target: 1D Array of the variable that the model is to be
trained to predict
@param features: 2D Array NSamples *NFeatures to use in trining
@param features: 2D Array NSamples *NFeatures to use in training
the model
@param model_params: A dictionary of model parameters, the full
specification can be found on the