def battery(labels, unscaled_features, features_list, my_data):
#SET UP GRID SEARCH PARAMETERS AND MODELS
#Sets up ranges of C, Gamma, and Kernel to test with SVM in GridsearchCV
scaler = preprocessing.MinMaxScaler()
scaled_features = scaler.fit_transform(unscaled_features)
C_range = np.logspace(-2, 5, 8)
gamma_range = np.logspace(-5, 2, 8)
parametersSVM = {
'kernel': ('linear', 'rbf'),
'C': C_range,
'gamma': gamma_range
}
#Sets up range of max depth, min sample split, and criterion to test with GridsearchCV
max_depth = range(2, 20, 2)
min_samples_split = range(2, 10, 2)
parametersDT = {
'criterion': ('gini', 'entropy'),
'max_depth': max_depth,
'min_samples_split': min_samples_split
}
#Set up range of number of estimators to use in addition to previously defined ranges in random forest with GridsearchCV
n_estimators = range(10, 100, 10)
parametersRF = {
'n_estimators': n_estimators,
'criterion': ('gini', 'entropy')
}
print "CURRENT FEATURES: ", features_list
dt = tree.DecisionTreeClassifier()
rf = RandomForestClassifier()
svr = svm.SVC()
print "SVM"
SVM = run_algorithm(svr, parametersSVM, scaled_features, labels)
print "DT"
DT = run_algorithm(dt, parametersDT, unscaled_features, labels)
print "RF"
RF = run_algorithm(rf, parametersRF, unscaled_features, labels)
scaling = preprocessing.MinMaxScaler()
estimators_SVM = [('scaling', scaling), ('algorithm', SVM)]
estimators_DT = [('algorithm', DT)]
estimators_RF = [('algorithm', RF)]
print "CURRENT FEATURES: ", features_list
print type(my_data)
SVM_score = run_test(estimators_SVM, my_data, features_list)
DT_score = run_test(estimators_DT, my_data, features_list)
RF_score = run_test(estimators_RF, my_data, features_list)
return SVM_score, DT_score, RF_score
def battery(labels, unscaled_features, features_list, my_data):
#SET UP GRID SEARCH PARAMETERS AND MODELS
#Sets up ranges of C, Gamma, and Kernel to test with SVM in GridsearchCV
scaler = preprocessing.MinMaxScaler()
scaled_features = scaler.fit_transform(unscaled_features)
C_range = np.logspace(-2,5,8)
gamma_range = np.logspace(-5,2,8)
parametersSVM = {'kernel':('linear','rbf'),'C':C_range, 'gamma':gamma_range}
#Sets up range of max depth, min sample split, and criterion to test with GridsearchCV
max_depth = range(2,20,2)
min_samples_split = range(2,10,2)
parametersDT ={'criterion':('gini','entropy'),'max_depth':max_depth, 'min_samples_split':min_samples_split}
#Set up range of number of estimators to use in addition to previously defined ranges in random forest with GridsearchCV
n_estimators = range(10,100,10)
parametersRF = {'n_estimators': n_estimators, 'criterion':('gini','entropy')}
print "CURRENT FEATURES: ", features_list
dt=tree.DecisionTreeClassifier()
rf=RandomForestClassifier()
svr = svm.SVC()
print "SVM"
SVM=run_algorithm(svr, parametersSVM, scaled_features, labels)
print "DT"
DT=run_algorithm(dt, parametersDT, unscaled_features, labels)
print "RF"
RF=run_algorithm(rf, parametersRF, unscaled_features, labels)
scaling = preprocessing.MinMaxScaler()
estimators_SVM = [('scaling', scaling), ('algorithm', SVM)]
estimators_DT = [('algorithm', DT)]
estimators_RF = [('algorithm', RF)]
print "CURRENT FEATURES: ", features_list
print type(my_data)
SVM_score = run_test(estimators_SVM, my_data, features_list)
DT_score = run_test(estimators_DT, my_data, features_list)
RF_score = run_test(estimators_RF, my_data, features_list)
return SVM_score, DT_score, RF_score
#SCALE REDUCED DATA
#Scales the data sets that have the reduced numbers of features created above.
scaler2 = preprocessing.MinMaxScaler()
reduced_features = scaler2.fit_transform(reduced_unscaled_features)
#SET UP GRID SEARCH PARAMETERS AND MODELS
#Set up range of number of estimators to use in addition to previously defined ranges in random forest with GridsearchCV
n_estimators = range(10,100,10)
parametersRF = {'n_estimators': n_estimators, 'criterion':('gini','entropy')}
#Creates the decision tree, random forest, and SVM classifiers
rf=RandomForestClassifier()
#Runs GridsearchCV with the selected model and features
print "RF"
RF=run_algorithm(rf, parametersRF, reduced_unscaled_features, reduced_labels)
#Set up parameters for pipeline so that the entire pipeline can be passed to grader
scaling = preprocessing.MinMaxScaler()
estimators_RF = [('algorithm', RF)]
print "Reduced RF"
RRF = run_test(estimators_RF, my_dataset, reduced_features_list)
#Pickles model, data, and selected features
dump_classifier_and_data(RF, data_dict ,reduced_features_list)