def train(self, df, targets, conditions):
# Obtain the targets column.
if len(targets) != 1:
raise BLE(
ValueError('MultipleRegression requires at least one column '
'in targets. Received {}'.format(targets)))
if targets[0][1].lower() != 'numerical':
raise BLE(
ValueError('MultipleRegression can only regress NUMERICAL '
'columns. Received {}'.format(targets)))
self.targets = [targets[0][0]]
# Obtain the condition columns.
if len(conditions) < 1:
raise BLE(
ValueError(
'MultipleRegression requires at least one '
'column in conditions. Received {}'.format(conditions)))
self.conditions_categorical = []
self.conditions_numerical = []
for c in conditions:
if c[1].lower() == 'categorical':
self.conditions_categorical.append(c[0])
else:
self.conditions_numerical.append(c[0])
self.conditions = self.conditions_numerical + \
self.conditions_categorical
# The dataset.
self.dataset = pd.DataFrame()
# Lookup for categoricals to code.
self.categories_to_val_map = dict()
# Training set (regressors and labels)
self.X_numerical = np.ndarray(0)
self.X_categorical = np.ndarray(0)
self.Y = np.ndarray(0)
# Linear regressors.
self.mr_partial = LinearRegression()
self.mr_full = LinearRegression()
# Preprocess the data.
self.dataset = utils.extract_sklearn_dataset(self.conditions,
self.targets, df)
self.categories_to_val_map = utils.build_categorical_to_value_map(
self.conditions_categorical, self.dataset)
self.X_categorical = utils.extract_sklearn_features_categorical(
self.conditions_categorical, self.categories_to_val_map,
self.dataset)
self.X_numerical = utils.extract_sklearn_features_numerical(
self.conditions_numerical, self.dataset)
self.Y = utils.extract_sklearn_univariate_target(
self.targets, self.dataset)
# Train the multiple regression.
self._train_mr()
def train(self, df, targets, conditions):
# Obtain the targets column.
if len(targets) != 1:
raise BLE(ValueError(
'MultipleRegression requires at least one column '
'in targets. Received {}'.format(targets)))
if targets[0][1].lower() != 'numerical':
raise BLE(ValueError(
'MultipleRegression can only regress NUMERICAL '
'columns. Received {}'.format(targets)))
self.targets = [targets[0][0]]
# Obtain the condition columns.
if len(conditions) < 1:
raise BLE(ValueError('MultipleRegression requires at least one '
'column in conditions. Received {}'.format(conditions)))
self.conditions_categorical = []
self.conditions_numerical = []
for c in conditions:
if c[1].lower() == 'categorical':
self.conditions_categorical.append(c[0])
else:
self.conditions_numerical.append(c[0])
self.conditions = self.conditions_numerical + \
self.conditions_categorical
# The dataset.
self.dataset = pd.DataFrame()
# Lookup for categoricals to code.
self.categories_to_val_map = dict()
# Training set (regressors and labels)
self.X_numerical = np.ndarray(0)
self.X_categorical = np.ndarray(0)
self.Y = np.ndarray(0)
# Linear regressors.
self.mr_partial = LinearRegression()
self.mr_full = LinearRegression()
# Preprocess the data.
self.dataset = utils.extract_sklearn_dataset(self.conditions,
self.targets, df)
self.categories_to_val_map = utils.build_categorical_to_value_map(
self.conditions_categorical, self.dataset)
self.X_categorical = utils.extract_sklearn_features_categorical(
self.conditions_categorical, self.categories_to_val_map,
self.dataset)
self.X_numerical = utils.extract_sklearn_features_numerical(
self.conditions_numerical, self.dataset)
self.Y = utils.extract_sklearn_univariate_target(self.targets,
self.dataset)
# Train the multiple regression.
self._train_mr()
def train(self, df, targets, conditions):
# Obtain the targets column.
if len(targets) != 1:
raise BLE(
ValueError('RandomForest requires exactly one column in '
'targets. Received {}'.format(targets)))
if targets[0][1].lower() != 'categorical':
raise BLE(
ValueError('RandomForest can only classify CATEGORICAL '
'columns. Received {}'.format(targets)))
self.targets = [targets[0][0]]
# Obtain the condition columns.
if len(conditions) < 1:
raise BLE(
ValueError('RandomForest requires at least one column in '
'conditions. Received {}'.format(conditions)))
self.conditions_categorical = []
self.conditions_numerical = []
for c in conditions:
if c[1].lower() == 'categorical':
self.conditions_categorical.append(c[0])
else:
self.conditions_numerical.append(c[0])
self.conditions = self.conditions_numerical + \
self.conditions_categorical
# The dataset.
self.dataset = pd.DataFrame()
# Lookup for categoricals to code.
self.categories_to_val_map = dict()
# Training set (regressors and labels)
self.X_numerical = np.ndarray(0)
self.X_categorical = np.ndarray(0)
self.Y = np.ndarray(0)
# Random Forests.
self.rf_partial = RandomForestClassifier(n_estimators=100)
self.rf_full = RandomForestClassifier(n_estimators=100)
# Preprocess the data.
self.dataset = utils.extract_sklearn_dataset(self.conditions,
self.targets, df)
self.categories_to_val_map = utils.build_categorical_to_value_map(
self.conditions_categorical, self.dataset)
self.X_categorical = utils.extract_sklearn_features_categorical(
self.conditions_categorical, self.categories_to_val_map,
self.dataset)
self.X_numerical = utils.extract_sklearn_features_numerical(
self.conditions_numerical, self.dataset)
self.Y = utils.extract_sklearn_univariate_target(
self.targets, self.dataset)
# Train the random forest.
self._train_rf()
def test_extract_sklearn_features_numerical():
dataset = pd.DataFrame({
'A': [1, 2, None, 4, 10],
'B': [0, -2, 3, None, 1],
'C': ['1', '2', '3', '4', '5'],
'D': [1, None, 3, 4, 5],
})
columns = ['A', 'B']
matrix = sku.extract_sklearn_features_numerical(columns, dataset)
# Test correct imputation.
mean_A = (1 + 2 + 4 + 10) / 4.
mean_B = (0 - 2 + 3 + 1) / 4.
expected = np.asarray([[1, 2, mean_A, 4, 10], [0, -2, 3, mean_B, 1]]).T
assert np.array_equal(matrix, expected)
def test_extract_sklearn_features_numerical():
dataset = pd.DataFrame({
'A':[1, 2, None, 4, 10],
'B':[0, -2, 3, None, 1],
'C':['1', '2', '3', '4', '5'],
'D':[1, None, 3, 4, 5],
})
columns = ['A', 'B']
matrix = sku.extract_sklearn_features_numerical(columns, dataset)
# Test correct imputation.
mean_A = (1 + 2 + 4 + 10) / 4.
mean_B = (0 -2 + 3 + 1) / 4.
expected = np.asarray([
[1, 2, mean_A, 4, 10],
[0, -2, 3, mean_B, 1]]).T
assert np.array_equal(matrix, expected)
def train(self, df, targets, conditions):
# Obtain the targets column.
if len(targets) != 1:
raise BLE(ValueError('RandomForest requires exactly one column in '
'targets. Received {}'.format(targets)))
if targets[0][1].lower() != 'categorical':
raise BLE(ValueError('RandomForest can only classify CATEGORICAL '
'columns. Received {}'.format(targets)))
self.targets = [targets[0][0]]
# Obtain the condition columns.
if len(conditions) < 1:
raise BLE(ValueError('RandomForest requires at least one column in '
'conditions. Received {}'.format(conditions)))
self.conditions_categorical = []
self.conditions_numerical = []
for c in conditions:
if c[1].lower() == 'categorical':
self.conditions_categorical.append(c[0])
else:
self.conditions_numerical.append(c[0])
self.conditions = self.conditions_numerical + \
self.conditions_categorical
# The dataset.
self.dataset = pd.DataFrame()
# Lookup for categoricals to code.
self.categories_to_val_map = dict()
# Training set (regressors and labels)
self.X_numerical = np.ndarray(0)
self.X_categorical = np.ndarray(0)
self.Y = np.ndarray(0)
# Random Forests.
self.rf_partial = RandomForestClassifier(n_estimators=100)
self.rf_full = RandomForestClassifier(n_estimators=100)
# Preprocess the data.
self.dataset = utils.extract_sklearn_dataset(self.conditions,
self.targets, df)
self.categories_to_val_map = utils.build_categorical_to_value_map(
self.conditions_categorical, self.dataset)
self.X_categorical = utils.extract_sklearn_features_categorical(
self.conditions_categorical, self.categories_to_val_map,
self.dataset)
self.X_numerical = utils.extract_sklearn_features_numerical(
self.conditions_numerical, self.dataset)
self.Y = utils.extract_sklearn_univariate_target(self.targets,
self.dataset)
# Train the random forest.
self._train_rf()
