def train_models(ModelClass,
invoices,
observation_end_dates,
rfe=False,
**kwargs):
train_results = dict(
models=[],
observation_end_dates=observation_end_dates,
X_train=[],
y_train=[],
X_test=[],
y_test=[],
)
X_trains, y_trains = [], [],
for observation_end_date in observation_end_dates:
X_train, y_train, X_test, y_test = get_train_test_data(
invoices, observation_end_date)
X_trains.append(X_train)
y_trains.append(y_train)
X_train, y_train = pd.concat(
X_trains[-lag:]).reset_index(drop=True), pd.concat(
y_trains[-lag:]).reset_index(drop=True).astype(
int) # Last "lag" months used as training data
X_test, y_test = X_test.reset_index(drop=True), y_test.reset_index(
drop=True).astype(int)
# Encode "MostBoughtItem" feature
rare_encoder = RareLabelCategoricalEncoder(
tol=0.02 if len(X_train) < 100 else 0.01,
variables=['MostBoughtItem']).fit(X_train)
X_train = rare_encoder.transform(X_train)
X_test = rare_encoder.transform(X_test)
mean_enc = MeanCategoricalEncoder(variables=['MostBoughtItem']).fit(
X_train, y_train)
X_train = mean_enc.transform(X_train)
X_test = mean_enc.transform(X_test)
if rfe:
sel_ = RFE(ModelClass(**kwargs), n_features_to_select=8)
sel_.fit(X_train, y_train)
selected_feats = X_train.columns[(sel_.get_support())]
model = ModelClass(**kwargs).fit(X_train[selected_feats], y_train)
train_results['X_train'].append(X_train[selected_feats])
train_results['X_test'].append(X_test[selected_feats])
else:
model = ModelClass(**kwargs)
model.fit(X_train, y_train)
train_results['X_train'].append(X_train)
train_results['X_test'].append(X_test)
train_results['models'].append(model)
train_results['y_train'].append(y_train)
train_results['y_test'].append(y_test)
return train_results
def test_MeanCategoricalEncoder():
# test dataframe
df = {
'category': ['A'] * 10 + ['B'] * 6 + ['C'] * 4,
'target': [
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
0,
0,
0,
0,
1,
1,
0,
0,
]
}
df = pd.DataFrame(df)
# transformed dataframe
transf_df = {
'category': [
0.200000, 0.200000, 0.200000, 0.200000, 0.200000, 0.200000,
0.200000, 0.200000, 0.200000, 0.200000, 0.333333, 0.333333,
0.333333, 0.333333, 0.333333, 0.333333, 0.500000, 0.500000,
0.500000, 0.500000
],
'target': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]
}
transf_df = pd.DataFrame(transf_df)
encoder = MeanCategoricalEncoder(variables=['category'])
encoder.fit(df['category'].to_frame(), df['target'])
X = encoder.transform(df['category'].to_frame())
pd.testing.assert_frame_equal(X, transf_df['category'].to_frame())
assert encoder.variables == ['category']
assert encoder.encoder_dict_ == {
'category': {
'A': 0.20000000000000001,
'B': 0.33333333333333331,
'C': 0.5
}
}
assert encoder.input_shape_ == (20, 1)
def test_MeanCategoricalEncoder(dataframe_enc, dataframe_enc_rare,
dataframe_enc_na):
# test case 1: 1 variable
encoder = MeanCategoricalEncoder(variables=['var_A'])
encoder.fit(dataframe_enc[['var_A', 'var_B']], dataframe_enc['target'])
X = encoder.transform(dataframe_enc[['var_A', 'var_B']])
# transformed dataframe
transf_df = dataframe_enc.copy()
transf_df['var_A'] = [
0.3333333333333333, 0.3333333333333333, 0.3333333333333333,
0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5, 0.5, 0.5, 0.5
]
# init params
assert encoder.variables == ['var_A']
# fit params
assert encoder.encoder_dict_ == {
'var_A': {
'A': 0.3333333333333333,
'B': 0.2,
'C': 0.5
}
}
assert encoder.input_shape_ == (20, 2)
# transform params
pd.testing.assert_frame_equal(X, transf_df[['var_A', 'var_B']])
# test case 2: automatically select variables
encoder = MeanCategoricalEncoder(variables=None)
encoder.fit(dataframe_enc[['var_A', 'var_B']], dataframe_enc['target'])
X = encoder.transform(dataframe_enc[['var_A', 'var_B']])
# transformed dataframe
transf_df['var_A'] = [
0.3333333333333333, 0.3333333333333333, 0.3333333333333333,
0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5, 0.5, 0.5, 0.5
]
transf_df['var_B'] = [
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3333333333333333,
0.3333333333333333, 0.3333333333333333, 0.3333333333333333,
0.3333333333333333, 0.3333333333333333, 0.5, 0.5, 0.5, 0.5
]
# init params
assert encoder.variables == ['var_A', 'var_B']
# fit params
assert encoder.encoder_dict_ == {
'var_A': {
'A': 0.3333333333333333,
'B': 0.2,
'C': 0.5
},
'var_B': {
'A': 0.2,
'B': 0.3333333333333333,
'C': 0.5
}
}
assert encoder.input_shape_ == (20, 2)
# transform params
pd.testing.assert_frame_equal(X, transf_df[['var_A', 'var_B']])
# test case 3: raises error if target is not passed
with pytest.raises(TypeError):
encoder = MeanCategoricalEncoder()
encoder.fit(dataframe_enc)
# test case 4: when dataset to be transformed contains categories not present in training dataset
with pytest.warns(UserWarning):
encoder = MeanCategoricalEncoder()
encoder.fit(dataframe_enc[['var_A', 'var_B']], dataframe_enc['target'])
encoder.transform(dataframe_enc_rare[['var_A', 'var_B']])
# test case 4: when dataset contains na, fit method
with pytest.raises(ValueError):
encoder = MeanCategoricalEncoder()
encoder.fit(dataframe_enc_na[['var_A', 'var_B']],
dataframe_enc_na['target'])
# test case 4: when dataset contains na, transform method
with pytest.raises(ValueError):
encoder = MeanCategoricalEncoder()
encoder.fit(dataframe_enc[['var_A', 'var_B']], dataframe_enc['target'])
encoder.transform(dataframe_enc_na)
with pytest.raises(NotFittedError):
imputer = OrdinalCategoricalEncoder()
imputer.transform(dataframe_enc)
homes.to_csv('Data/houses.csv', index=False)
#Create a copy of the homes dataframe
neighborhoods = homes.copy()
#We are going to create another column that stores the mean target value for each neighborhood
#First we need to save the neighborhood name into another column
neighborhoods['neighborhood_name'] = neighborhoods['Neighborhood']
mean_enc = MeanCategoricalEncoder(variables=['Neighborhood'])
#Fit the encoder
mean_enc.fit(neighborhoods, neighborhoods['SalePrice'])
#Transform the neighborhoods dataframe
neighborhoods = mean_enc.transform(neighborhoods)
#Load a dataframe that has the geocoordinates of each neighborhood
hoods = gpd.read_file('Los Angeles Neighborhood Map.geojson')
hoods = hoods.rename(columns={'name': 'neighborhood_name'})
hoods[['longitude', 'latitude']] = hoods[['longitude',
'latitude']].astype(float)
#Create a dataframe that averages out every attribute by neighborhood
avg = neighborhoods.groupby('neighborhood_name')[[
'HomeSize', 'LotSize', 'Bedrooms', 'Bathrooms', 'SexOffenders',
'EnviornmentalHazards', 'Age', 'SalePrice', 'Neighborhood', 'YearBuilt'
]].mean()
#Do the same thing as above but for only categorical variables
cat_avg = neighborhoods.groupby('neighborhood_name')[[
def integer_encode(train, test, variable, ordinal_mapping):
X_train[variable] = X_train[variable].map(ordinal_mapping)
X_test[variable] = X_test[variable].map(ordinal_mapping)
for variable in ['sex', 'embarked']:
mappings = find_category_mappings(X_train, variable, 'survived')
integer_encode(X_train, X_test, variable, mappings)
-- #With Feature-Engine
from feature_engine.categorical_encoders import MeanCategoricalEncoder
mean_enc = MeanCategoricalEncoder(
variables=['cabin', 'sex', 'embarked'])
mean_enc.fit(X_train, y_train)
X_train = mean_enc.transform(X_train)
X_test = mean_enc.transform(X_test)
mean_enc.encoder_dict_
mean_enc.variables
-
---------Probability Ration Encoding
# Only for binary classification problems
#Replacing categorical labels with this code and method will generate missing values
#for categories present in the test set that were not seen in the training set.
#Therefore it is extremely important to handle rare labels before-hand
ratio_enc = WoERatioCategoricalEncoder(
encoding_method = 'ratio',