def return_best_rf_regressor(df, target, num_trees_hyperparameter, num_trees_final_clf, num_iterations):
print "entering return best rf regressor function"
if df.shape[0] < 10000:
num_samples = df.shape[0]
else:
num_samples = int(df.shape[0]*0.7)
print "Sample dataframe"
#use
X, y, column_list_for_sampled = sample_data_frame_return_x_y_column_name(df, True, target, num_samples)
# figure out a vary this some how
"""
param_dist = {"max_depth": [5, None],
"max_features": sp_randint(1, df.shape[1]),
"min_samples_split": sp_randint(1, 15),
"min_samples_leaf": sp_randint(1, 15),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
"""
param_dist = {"max_depth": [5, None], "max_features": sp_randint(1, df.shape[1]), "min_samples_split": sp_randint(1, 15), "min_samples_leaf": sp_randint(1, 15), "bootstrap": [True]}
clf = RandomForestRegressor(n_estimators=num_trees_hyperparameter)
print "starting hyperparameter search"
clf_best, best_params = hyperparameter_search_random(X, y, clf, param_dist, num_iterations)
print "sample data for fitting model"
#train new classifier on the entire dataset
X, y, column_list_for_sampled = sample_data_frame_return_x_y_column_name(df, True, target, num_samples=df.shape[0])
clf_final = RandomForestRegressor(n_estimators=num_trees_final_clf, max_depth = best_params["max_depth"], min_samples_leaf = best_params["min_samples_leaf"], min_samples_split = best_params["min_samples_split"], bootstrap = best_params["bootstrap"], max_features = best_params["max_features"])
print "Fitting Random Forest Regressor"
clf_final.fit(X,y)
return clf_final, column_list_for_sampled
def run_lasso_on_input(df, target): X_part, y_part, _ = sample_data_frame_return_x_y_column_name(df, True, target, int(0.7*df.shape[0])) X_part, _ = scale_input_data(X_part) print "#######################################" print "Starting LARS CV" print "#######################################" lars_cv = LassoLarsCV(cv=10).fit(X_part, y_part) print "#######################################" print "Done with LARS CV" print "#######################################" #alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6) X, y, column_list_for_sampled = sample_data_frame_return_x_y_column_name(df, True, target, df.shape[0]) X, _ = scale_input_data(X) print "#######################################" print "Starting main lasso" print "#######################################" clf = RandomizedLasso(alpha= lars_cv.alphas_, random_state=12, n_resampling= 400, normalize=True).fit(X, y) print "#######################################" print "Done with main lasso" print "#######################################" return clf, column_list_for_sampled
def orthogonal_variable_selection_cannot_query_black_box(df, target, non_linear, no_bootstrap_iter, num_samples):
master_dictionary = {}
for name in list(df.columns):
if name != target:
master_dictionary[name] = []
for iteration in range(no_bootstrap_iter):
print "going through iteration " + str(iteration) + " of orthogonal feature selection"
X, y, column_list_for_sampled = sample_data_frame_return_x_y_column_name(df, True, target, num_samples)
master_dictionary = perform_orthogonal_variable_selection(X, y, column_list_for_sampled, non_linear, master_dictionary)
return master_dictionary
def orthogonal_variable_selection_cannot_query_black_box(
df, target, non_linear, no_bootstrap_iter, num_samples):
master_dictionary = {}
for name in list(df.columns):
if name != target:
master_dictionary[name] = []
for iteration in range(no_bootstrap_iter):
print "going through iteration " + str(
iteration) + " of orthogonal feature selection"
X, y, column_list_for_sampled = sample_data_frame_return_x_y_column_name(
df, True, target, num_samples)
master_dictionary = perform_orthogonal_variable_selection(
X, y, column_list_for_sampled, non_linear, master_dictionary)
return master_dictionary
