This is a follow up attempt at Kaggle's Predicting Red Hat Business Value competition.
See my notebooks section for links to the first attempt and other kaggle competitions.
The focus of this iteration is exploring whether we can bring back the previously ignored categorical columns that have hundreds if not thousands of unique values, making it impractical to use one-hot encoding.
Two approaches are taken on categorical variables with a large amount of unique values:
The end results: reincluding the columns boosted performance on the training set by only 0.5%, and surprisingly the binary / one-hot combo did hardly any better than the ordinal encoding.
In [1]:
import pandas as pd
people = pd.read_csv('people.csv.zip')
people.head(3)
Out[1]:
In [2]:
actions = pd.read_csv('act_train.csv.zip')
actions.head(3)
Out[2]:
In [3]:
training_data_full = pd.merge(actions, people, how='inner', on='people_id', suffixes=['_action', '_person'], sort=False)
training_data_full.head(5)
Out[3]:
In [4]:
(actions.shape, people.shape, training_data_full.shape)
Out[4]:
Notice the new OmniEncoder transformer and read more about its development in my learning log.
In [15]:
# %load "preprocessing_transforms.py"
from sklearn.base import TransformerMixin, BaseEstimator
import pandas as pd
import heapq
import numpy as np
class BaseTransformer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
return self
class ColumnSelector(BaseTransformer):
"""Selects columns from Pandas Dataframe"""
def __init__(self, columns, c_type=None):
self.columns = columns
self.c_type = c_type
def transform(self, X, **transform_params):
cs = X[self.columns]
if self.c_type is None:
return cs
else:
return cs.astype(self.c_type)
class OmniEncoder(BaseTransformer):
"""
Encodes a categorical variable using no more than k columns. As many values as possible
are one-hot encoded, the remaining are fit within a binary encoded set of columns.
If necessary some are dropped (e.g if (#unique_values) > 2^k).
In deciding which values to one-hot encode, those that appear more frequently are
preferred.
"""
def __init__(self, max_cols=20):
self.column_infos = {}
self.max_cols = max_cols
if max_cols < 3 or max_cols > 100:
raise ValueError("max_cols {} not within range(3, 100)".format(max_cols))
def fit(self, X, y=None, **fit_params):
self.column_infos = {col: self._column_info(X[col], self.max_cols) for col in X.columns}
return self
def transform(self, X, **transform_params):
return pd.concat(
[self._encode_column(X[col], self.max_cols, *self.column_infos[col]) for col in X.columns],
axis=1
)
@staticmethod
def _encode_column(col, max_cols, one_hot_vals, binary_encoded_vals):
num_one_hot = len(one_hot_vals)
num_bits = max_cols - num_one_hot if len(binary_encoded_vals) > 0 else 0
# http://stackoverflow.com/a/29091970/231589
zero_base = ord('0')
def i_to_bit_array(i):
return np.fromstring(
np.binary_repr(i, width=num_bits),
'u1'
) - zero_base
binary_val_to_bit_array = {val: i_to_bit_array(idx + 1) for idx, val in enumerate(binary_encoded_vals)}
bit_cols = [np.binary_repr(2 ** i, width=num_bits) for i in reversed(range(num_bits))]
col_names = ["{}_{}".format(col.name, val) for val in one_hot_vals] + ["{}_{}".format(col.name, bit_col) for bit_col in bit_cols]
zero_bits = np.zeros(num_bits, dtype=np.int)
def splat(v):
v_one_hot = [1 if v == ohv else 0 for ohv in one_hot_vals]
v_bits = binary_val_to_bit_array.get(v, zero_bits)
return pd.Series(np.concatenate([v_one_hot, v_bits]))
df = col.apply(splat)
df.columns = col_names
return df
@staticmethod
def _column_info(col, max_cols):
"""
:param col: pd.Series
:return: {'val': 44, 'val2': 4, ...}
"""
val_counts = dict(col.value_counts())
num_one_hot = OmniEncoder._num_onehot(len(val_counts), max_cols)
return OmniEncoder._partition_one_hot(val_counts, num_one_hot)
@staticmethod
def _partition_one_hot(val_counts, num_one_hot):
"""
Paritions the values in val counts into a list of values that should be
one-hot encoded and a list of values that should be binary encoded.
The `num_one_hot` most popular values are chosen to be one-hot encoded.
:param val_counts: {'val': 433}
:param num_one_hot: the number of elements to be one-hot encoded
:return: ['val1', 'val2'], ['val55', 'val59']
"""
one_hot_vals = [k for (k, count) in heapq.nlargest(num_one_hot, val_counts.items(), key=lambda t: t[1])]
one_hot_vals_lookup = set(one_hot_vals)
bin_encoded_vals = [val for val in val_counts if val not in one_hot_vals_lookup]
return sorted(one_hot_vals), sorted(bin_encoded_vals)
@staticmethod
def _num_onehot(n, k):
"""
Determines the number of onehot columns we can have to encode n values
in no more than k columns, assuming we will binary encode the rest.
:param n: The number of unique values to encode
:param k: The maximum number of columns we have
:return: The number of one-hot columns to use
"""
num_one_hot = min(n, k)
def num_bin_vals(num):
if num == 0:
return 0
return 2 ** num - 1
def capacity(oh):
"""
Capacity given we are using `oh` one hot columns.
"""
return oh + num_bin_vals(k - oh)
while capacity(num_one_hot) < n and num_one_hot > 0:
num_one_hot -= 1
return num_one_hot
class EncodeCategorical(BaseTransformer):
def __init__(self):
self.categorical_vals = {}
def fit(self, X, y=None, **fit_params):
self.categorical_vals = {col: {label: idx + 1 for idx, label in enumerate(sorted(X[col].dropna().unique()))} for
col in X.columns}
return self
def transform(self, X, **transform_params):
return pd.concat(
[X[col].map(self.categorical_vals[col]) for col in X.columns],
axis=1
)
class SpreadBinary(BaseTransformer):
def transform(self, X, **transform_params):
return X.applymap(lambda x: 1 if x == 1 else -1)
class DfTransformerAdapter(BaseTransformer):
"""Adapts a scikit-learn Transformer to return a pandas DataFrame"""
def __init__(self, transformer):
self.transformer = transformer
def fit(self, X, y=None, **fit_params):
self.transformer.fit(X, y=y, **fit_params)
return self
def transform(self, X, **transform_params):
raw_result = self.transformer.transform(X, **transform_params)
return pd.DataFrame(raw_result, columns=X.columns, index=X.index)
class DfOneHot(BaseTransformer):
"""
Wraps helper method `get_dummies` making sure all columns get one-hot encoded.
"""
def __init__(self):
self.dummy_columns = []
def fit(self, X, y=None, **fit_params):
self.dummy_columns = pd.get_dummies(
X,
prefix=[c for c in X.columns],
columns=X.columns).columns
return self
def transform(self, X, **transform_params):
return pd.get_dummies(
X,
prefix=[c for c in X.columns],
columns=X.columns).reindex(columns=self.dummy_columns, fill_value=0)
class DfFeatureUnion(BaseTransformer):
"""A dataframe friendly implementation of `FeatureUnion`"""
def __init__(self, transformers):
self.transformers = transformers
def fit(self, X, y=None, **fit_params):
for l, t in self.transformers:
t.fit(X, y=y, **fit_params)
return self
def transform(self, X, **transform_params):
transform_results = [t.transform(X, **transform_params) for l, t in self.transformers]
return pd.concat(transform_results, axis=1)
In [7]:
for col in training_data_full.columns:
print("in {} there are {} unique values".format(col, len(training_data_full[col].unique())))
None
Notice that char_10_action, group_1 and others have a ton of unique values; one-hot encoding will result in a dataframe with thousands of columns.
Let's explore 3 approaches to dealing with categorical columns with a lot of unique values and compare performance:
In [16]:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer, StandardScaler
cat_columns = ['activity_category',
'char_1_action', 'char_2_action', 'char_3_action', 'char_4_action',
'char_5_action', 'char_6_action', 'char_7_action', 'char_8_action',
'char_9_action', 'char_1_person',
'char_2_person', 'char_3_person',
'char_4_person', 'char_5_person', 'char_6_person', 'char_7_person',
'char_8_person', 'char_9_person', 'char_10_person', 'char_11',
'char_12', 'char_13', 'char_14', 'char_15', 'char_16', 'char_17',
'char_18', 'char_19', 'char_20', 'char_21', 'char_22', 'char_23',
'char_24', 'char_25', 'char_26', 'char_27', 'char_28', 'char_29',
'char_30', 'char_31', 'char_32', 'char_33', 'char_34', 'char_35',
'char_36', 'char_37']
high_dim_cat_columns = ['date_action', 'char_10_action', 'group_1', 'date_person']
q_columns = ['char_38']
preprocessor_ignore = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
('impute-missing', DfTransformerAdapter(Imputer(strategy='median'))),
('scale', DfTransformerAdapter(StandardScaler()))
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns)),
('apply-onehot', DfOneHot()),
('spread-binary', SpreadBinary())
])),
]))
])
preprocessor_lexico = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('combine-q', DfFeatureUnion([
('highd', Pipeline([
('select-highd', ColumnSelector(high_dim_cat_columns)),
('encode-highd', EncodeCategorical())
])),
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
])),
('impute-missing', DfTransformerAdapter(Imputer(strategy='median'))),
('scale', DfTransformerAdapter(StandardScaler()))
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns)),
('apply-onehot', DfOneHot()),
('spread-binary', SpreadBinary())
])),
]))
])
preprocessor_omni_20 = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
('impute-missing', DfTransformerAdapter(Imputer(strategy='median'))),
('scale', DfTransformerAdapter(StandardScaler()))
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns + high_dim_cat_columns)),
('apply-onehot', OmniEncoder(max_cols=20)),
('spread-binary', SpreadBinary())
])),
]))
])
preprocessor_omni_50 = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
('impute-missing', DfTransformerAdapter(Imputer(strategy='median'))),
('scale', DfTransformerAdapter(StandardScaler()))
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns + high_dim_cat_columns)),
('apply-onehot', OmniEncoder(max_cols=50)),
('spread-binary', SpreadBinary())
])),
]))
])
If we train models based on the entire test dataset provided it exhausts the memory on my laptop. Again, in the spirit of getting something quick and dirty working, we'll sample the dataset and train on that. We'll then evaluate our model by testing the accuracy on a larger sample.
In [17]:
from sklearn.cross_validation import train_test_split
training_frac = 0.01
test_frac = 0.05
training_data, the_rest = train_test_split(training_data_full, train_size=training_frac, random_state=0)
test_data = the_rest.sample(frac=test_frac / (1-training_frac))
In [18]:
training_data.shape
Out[18]:
In [19]:
test_data.shape
Out[19]:
In [21]:
import time
import subprocess
class time_and_log():
def __init__(self, label, *, prefix='', say=False):
self.label = label
self.prefix = prefix
self.say = say
def __enter__(self):
msg = 'Starting {}'.format(self.label)
print('{}{}'.format(self.prefix, msg))
if self.say:
cmd_say(msg)
self.start = time.process_time()
return self
def __exit__(self, *exc):
self.interval = time.process_time() - self.start
msg = 'Finished {} in {:.2f} seconds'.format(self.label, self.interval)
print('{}{}'.format(self.prefix, msg))
if self.say:
cmd_say(msg)
return False
def cmd_say(msg):
subprocess.call("say '{}'".format(msg), shell=True)
In [22]:
with time_and_log('wrangling training data', say=True, prefix=" _"):
wrangled = preprocessor_omni_20.fit_transform(training_data)
In [23]:
wrangled.head()
Out[23]:
In [24]:
from sklearn.ensemble import RandomForestClassifier
pipe_rf_ignore = Pipeline([
('wrangle', preprocessor_ignore),
('rf', RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0))
])
pipe_rf_lexico = Pipeline([
('wrangle', preprocessor_lexico),
('rf', RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0))
])
pipe_rf_omni_20 = Pipeline([
('wrangle', preprocessor_omni_20),
('rf', RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0))
])
pipe_rf_omni_50 = Pipeline([
('wrangle', preprocessor_omni_50),
('rf', RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0))
])
In [25]:
feature_columns = cat_columns + q_columns + high_dim_cat_columns
In [26]:
def extract_X_y(df):
return df[feature_columns], df['outcome']
X_train, y_train = extract_X_y(training_data)
X_test, y_test = extract_X_y(test_data)
In [27]:
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import cross_val_score
import numpy as np
models = [
('random forest ignore', pipe_rf_ignore),
('random forest ordinal', pipe_rf_lexico),
('random forest omni 20', pipe_rf_omni_20),
('random forest omni 50', pipe_rf_omni_50),
]
for label, model in models:
print('Evaluating {}'.format(label))
cmd_say('Evaluating {}'.format(label))
# with time_and_log('cross validating', say=True, prefix=" _"):
# scores = cross_val_score(estimator=model,
# X=X_train,
# y=y_train,
# cv=5,
# n_jobs=1)
# print(' CV accuracy: {:.3f} +/- {:.3f}'.format(np.mean(scores), np.std(scores)))
with time_and_log('fitting full training set', say=True, prefix=" _"):
model.fit(X_train, y_train)
with time_and_log('evaluating on full test set', say=True, prefix=" _"):
print(" Full test accuracy ({:.2f} of dataset): {:.3f}".format(
test_frac,
accuracy_score(y_test, model.predict(X_test))))