Kaggle's Predicting Red Hat Business Value¶

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:

• encoding the values ordinally; sorting the values lexicographically and assigning a sequence of numbers, and then treating them quantitatively from there
• encoding the most frequently occuring values using one-hot and then binary encoding the rest. As part of this I developed a new scikit-learn transformer

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


Out[1]:
people_id char_1 group_1 char_2 date char_3 char_4 char_5 char_6 char_7 ... char_29 char_30 char_31 char_32 char_33 char_34 char_35 char_36 char_37 char_38
0 ppl_100 type 2 group 17304 type 2 2021-06-29 type 5 type 5 type 5 type 3 type 11 ... False True True False False True True True False 36
1 ppl_100002 type 2 group 8688 type 3 2021-01-06 type 28 type 9 type 5 type 3 type 11 ... False True True True True True True True False 76
2 ppl_100003 type 2 group 33592 type 3 2022-06-10 type 4 type 8 type 5 type 2 type 5 ... False False True True True True False True True 99

3 rows × 41 columns

In [2]:
actions = pd.read_csv('act_train.csv.zip')

Out[2]:
people_id activity_id date activity_category char_1 char_2 char_3 char_4 char_5 char_6 char_7 char_8 char_9 char_10 outcome
0 ppl_100 act2_1734928 2023-08-26 type 4 NaN NaN NaN NaN NaN NaN NaN NaN NaN type 76 0
1 ppl_100 act2_2434093 2022-09-27 type 2 NaN NaN NaN NaN NaN NaN NaN NaN NaN type 1 0
2 ppl_100 act2_3404049 2022-09-27 type 2 NaN NaN NaN NaN NaN NaN NaN NaN NaN type 1 0

Joining together to get dataset¶

In [3]:
training_data_full = pd.merge(actions, people, how='inner', on='people_id', suffixes=['_action', '_person'], sort=False)

Out[3]:
people_id activity_id date_action activity_category char_1_action char_2_action char_3_action char_4_action char_5_action char_6_action ... char_29 char_30 char_31 char_32 char_33 char_34 char_35 char_36 char_37 char_38
0 ppl_100 act2_1734928 2023-08-26 type 4 NaN NaN NaN NaN NaN NaN ... False True True False False True True True False 36
1 ppl_100 act2_2434093 2022-09-27 type 2 NaN NaN NaN NaN NaN NaN ... False True True False False True True True False 36
2 ppl_100 act2_3404049 2022-09-27 type 2 NaN NaN NaN NaN NaN NaN ... False True True False False True True True False 36
3 ppl_100 act2_3651215 2023-08-04 type 2 NaN NaN NaN NaN NaN NaN ... False True True False False True True True False 36
4 ppl_100 act2_4109017 2023-08-26 type 2 NaN NaN NaN NaN NaN NaN ... False True True False False True True True False 36

5 rows × 55 columns

In [4]:
(actions.shape, people.shape, training_data_full.shape)

Out[4]:
((2197291, 15), (189118, 41), (2197291, 55))

Building a preprocessing pipeline¶

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
)

def transform(self, X, **transform_params):
return X.applymap(lambda x: 1 if x == 1 else -1)

"""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

in people_id there are 151295 unique values
in activity_id there are 2197291 unique values
in date_action there are 411 unique values
in activity_category there are 7 unique values
in char_1_action there are 52 unique values
in char_2_action there are 33 unique values
in char_3_action there are 12 unique values
in char_4_action there are 8 unique values
in char_5_action there are 8 unique values
in char_6_action there are 6 unique values
in char_7_action there are 9 unique values
in char_8_action there are 19 unique values
in char_9_action there are 20 unique values
in char_10_action there are 6516 unique values
in outcome there are 2 unique values
in char_1_person there are 2 unique values
in group_1 there are 29899 unique values
in char_2_person there are 3 unique values
in date_person there are 1196 unique values
in char_3_person there are 43 unique values
in char_4_person there are 25 unique values
in char_5_person there are 9 unique values
in char_6_person there are 7 unique values
in char_7_person there are 25 unique values
in char_8_person there are 8 unique values
in char_9_person there are 9 unique values
in char_10_person there are 2 unique values
in char_11 there are 2 unique values
in char_12 there are 2 unique values
in char_13 there are 2 unique values
in char_14 there are 2 unique values
in char_15 there are 2 unique values
in char_16 there are 2 unique values
in char_17 there are 2 unique values
in char_18 there are 2 unique values
in char_19 there are 2 unique values
in char_20 there are 2 unique values
in char_21 there are 2 unique values
in char_22 there are 2 unique values
in char_23 there are 2 unique values
in char_24 there are 2 unique values
in char_25 there are 2 unique values
in char_26 there are 2 unique values
in char_27 there are 2 unique values
in char_28 there are 2 unique values
in char_29 there are 2 unique values
in char_30 there are 2 unique values
in char_31 there are 2 unique values
in char_32 there are 2 unique values
in char_33 there are 2 unique values
in char_34 there are 2 unique values
in char_35 there are 2 unique values
in char_36 there are 2 unique values
in char_37 there are 2 unique values
in char_38 there are 101 unique values


Potential trouble with high dimensionality¶

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:

• ignore them
• encode them ordinally, mapping every unique value to a different integer (assuming some ordered value that probably doesn't exist, at least not by our default lexicographical sorting)
• encode them with a combo of one-hot and binary
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')),
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns)),
('apply-onehot', DfOneHot()),
])),
]))
])

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')),
])),
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns)),
('apply-onehot', DfOneHot()),
])),
]))
])

preprocessor_omni_20 = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns + high_dim_cat_columns)),
('apply-onehot', OmniEncoder(max_cols=20)),
])),
]))
])

preprocessor_omni_50 = Pipeline([
('features', DfFeatureUnion([
('quantitative', Pipeline([
('select-quantitative', ColumnSelector(q_columns, c_type='float')),
])),
('categorical', Pipeline([
('select-categorical', ColumnSelector(cat_columns + high_dim_cat_columns)),
('apply-onehot', OmniEncoder(max_cols=50)),
])),
]))
])


Sampling to reduce runtime in training large dataset¶

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]:
(21972, 55)
In [19]:
test_data.shape

Out[19]:
(109865, 55)

Reporting utilities¶

Some utilities to make reporting progress easier

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)

 _Starting wrangling training data
_Finished wrangling training data in 383.88 seconds

In [23]:
wrangled.head()

Out[23]:
char_38 activity_category_type 1 activity_category_type 2 activity_category_type 3 activity_category_type 4 activity_category_type 5 activity_category_type 6 activity_category_type 7 char_1_action_type 1 char_1_action_type 10 ... date_person_01000000000 date_person_00100000000 date_person_00010000000 date_person_00001000000 date_person_00000100000 date_person_00000010000 date_person_00000001000 date_person_00000000100 date_person_00000000010 date_person_00000000001
1119692 -0.413876 -1 -1 -1 -1 1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
331126 0.332410 -1 1 -1 -1 -1 -1 -1 -1 -1 ... 1 -1 -1 1 -1 -1 1 1 1 -1
424011 -0.192754 -1 -1 -1 -1 1 -1 -1 -1 -1 ... 1 1 1 -1 -1 -1 -1 -1 1 1
341796 0.000727 -1 1 -1 -1 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
22692 0.885214 -1 -1 1 -1 -1 -1 -1 -1 -1 ... 1 1 -1 -1 -1 1 1 1 -1 -1

5 rows × 354 columns

Putting together classifiers¶

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)


Cross validation and full test set accuracy¶

We'll cross validate within the training set, and then train on the full training set and see how well it performs on the full test set.

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))))

Evaluating random forest ignore
_Starting fitting full training set
_Finished fitting full training set in 3.86 seconds
_Starting evaluating on full test set
Full test accuracy (0.05 of dataset): 0.880
_Finished evaluating on full test set in 16.32 seconds
Evaluating random forest ordinal
_Starting fitting full training set
_Finished fitting full training set in 4.26 seconds
_Starting evaluating on full test set
Full test accuracy (0.05 of dataset): 0.885
_Finished evaluating on full test set in 16.10 seconds
Evaluating random forest omni 20
_Starting fitting full training set
_Finished fitting full training set in 376.31 seconds
_Starting evaluating on full test set
Full test accuracy (0.05 of dataset): 0.885
_Finished evaluating on full test set in 1050.23 seconds
Evaluating random forest omni 50
_Starting fitting full training set
_Finished fitting full training set in 417.19 seconds
_Starting evaluating on full test set
Full test accuracy (0.05 of dataset): 0.886
_Finished evaluating on full test set in 1102.41 seconds