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adding inline documentation

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This commit is contained in:
Stuart Lynn 2016-06-28 16:02:06 +00:00
parent f6f9d6e9c8
commit 7d6148456e
1 changed files with 19 additions and 5 deletions
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src/py/crankshaft/crankshaft/segmentation/segmentation.py
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@ -63,7 +63,14 @@ def get_data(variable, feature_columns, query):
def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters): def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
""" """
Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
the function.
Input:
@param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
@param features: Thw 2D array of size NSamples * NFeatures that form the imput 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_target = replace_nan_with_mean(target)
@ -100,7 +107,13 @@ def create_and_predict_segment(query, variable, target_query, model_params):
def train_model(target, features, model_params, test_split): def train_model(target, features, model_params, test_split):
""" """
Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
Input:
@param target: 1D Array of the variable that the model is to be trianed to predict
@param features: 2D Array NSamples * NFeatures to use in trining the model
@param model_params: A dictionary of model parameters, the full specification can be found on the
scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
@parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
""" """
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split) features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
model = GradientBoostingRegressor(**model_params) model = GradientBoostingRegressor(**model_params)
@ -123,10 +136,11 @@ def calculate_model_accuracy(model, features, target):
def predict_segment(model, features, target_query): def predict_segment(model, features, target_query):
""" """
predict a segment with machine learning Use the provided model to predict the values for the new feature set
Stuart Lynn Input:
@param model: The pretrained model
description of params? @features: A list of features to use in the model prediction (list of column names)
@target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
""" """
batch_size = 1000 batch_size = 1000