def train(output, models=['linear', 'tree', 'forest', 'svr', 'cat']):
data = get_predictions(output)
print('Primary predictions loaded.')
[X, y, X_train, y_train, X_test, y_test, X_scaled, y_scaled,
X_train_scaled, y_train_scaled, X_test_scaled, y_scaler] \
= pre.split_pipeline(data, output)
print('Data preprocessed.')
regressors = tra.build(X_train, y_train, X_train_scaled, y_train_scaled,
X_train, y_train, models)
best_regressor = tra.evaluate(regressors, X_train, y_train, X_train_scaled,
y_train_scaled, X_test, y_test,
X_test_scaled, y_scaler, X_train, y_train,
X_test, y_test)
print('Regressors evaluated. Best regressor is:\n' + str(best_regressor))
if 'SVR' in str(best_regressor):
best_regressor.fit(X_scaled, y_scaled)
else:
best_regressor.fit(X, y)
print('Regressor fit.')
tra.print_results(best_regressor, X, X_scaled, y, y_scaler, X)
tra.save(best_regressor, X, output + '_transfer')
print('Regressor saved.')
tra.upload(output + '_transfer')
print('Regressor uploaded.')
def calculate(output, model):
"""Determine feature importance for specified model"""
data = pd.read_csv('campaigns.csv')
print('Data loaded.')
data = tra.trim(data, output)
print('Data trimmed.')
# Add random column to data
np.random.seed(seed=0)
data['random'] = np.random.random(size=len(data))
data, data_cat = pre.data_pipeline(data, output)
[_, _, X_train, y_train, _, _, _, _,
X_train_scaled, y_train_scaled, _, y_scaler] \
= pre.split_pipeline(data, output, encoded=True)
[_, _, X_train_cat, y_train_cat, _, _] = \
pre.split_pipeline(data_cat, output, encoded=False)
print('Data preprocessed.')
regressor = tra.build(X_train, y_train, X_train_scaled, y_train_scaled,
X_train_cat, y_train_cat, [model])[0]
model_clone = clone(regressor)
# Set random_state for comparability
model_clone.random_state = 0
# Train and score the benchmark model
if 'SVR' in str(regressor):
model_clone.fit(X_train_scaled, y_train_scaled)
benchmark_score = hel.mean_relative_accuracy(
y_scaler.inverse_transform(model_clone.predict(X_train_scaled)),
y_train)
else:
model_clone.fit(X_train, y_train)
benchmark_score = \
hel.mean_relative_accuracy(model_clone.predict(X_train), y_train)
# Calculate and store feature importance benchmark deviation
importances = []
columns = X_train.columns
i = 1
for column in columns:
model_clone = clone(regressor)
model_clone.random_state = 0
if 'SVR' in str(regressor):
model_clone.fit(X_train_scaled.drop(column, axis=1),
y_train_scaled)
drop_col_score = hel.mean_relative_accuracy(
model_clone.predict(X_train_scaled.drop(column, axis=1)),
y_train_scaled)
else:
model_clone.fit(X_train.drop(column, axis=1), y_train)
drop_col_score = hel.mean_relative_accuracy(
model_clone.predict(X_train.drop(column, axis=1)), y_train)
importances.append(benchmark_score - drop_col_score)
i += 1
importances_df = \
pd.DataFrame({'column': X_train.columns, 'value': importances}) \
.sort_values('value', ascending=False).reset_index(drop=True)
print('Importances:')
for i in range(0, len(importances_df)):
print(
str(importances_df.iloc[i].column) + ': ' +
str(importances_df.iloc[i].value))