def test_make_union_kwargs():
pca = PCA(svd_solver='full')
mock = Transf()
fu = make_union(pca, mock, n_jobs=3)
assert_equal(fu.transformer_list, make_union(pca, mock).transformer_list)
assert_equal(3, fu.n_jobs)
# invalid keyword parameters should raise an error message
assert_raise_message(
TypeError,
'Unknown keyword arguments: "transformer_weights"',
make_union, pca, mock, transformer_weights={'pca': 10, 'Transf': 1}
)
def get_results(dataset):
X_full, y_full = dataset.data, dataset.target
n_samples = X_full.shape[0]
n_features = X_full.shape[1]
# Estimate the score on the entire dataset, with no missing values
estimator = RandomForestRegressor(random_state=0, n_estimators=100)
full_scores = cross_val_score(estimator, X_full, y_full,
scoring='neg_mean_squared_error')
# Add missing values in 75% of the lines
missing_rate = 0.75
n_missing_samples = int(np.floor(n_samples * missing_rate))
missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
dtype=np.bool),
np.ones(n_missing_samples,
dtype=np.bool)))
rng.shuffle(missing_samples)
missing_features = rng.randint(0, n_features, n_missing_samples)
# Estimate the score after replacing missing values by 0
X_missing = X_full.copy()
X_missing[np.where(missing_samples)[0], missing_features] = 0
y_missing = y_full.copy()
estimator = RandomForestRegressor(random_state=0, n_estimators=100)
zero_impute_scores = cross_val_score(estimator, X_missing, y_missing,
scoring='neg_mean_squared_error')
# Estimate the score after imputation (mean strategy) of the missing values
X_missing = X_full.copy()
X_missing[np.where(missing_samples)[0], missing_features] = 0
y_missing = y_full.copy()
estimator = make_pipeline(
make_union(SimpleImputer(missing_values=0, strategy="mean"),
MissingIndicator(missing_values=0)),
RandomForestRegressor(random_state=0, n_estimators=100))
mean_impute_scores = cross_val_score(estimator, X_missing, y_missing,
scoring='neg_mean_squared_error')
# Estimate the score after chained imputation of the missing values
estimator = make_pipeline(
make_union(ChainedImputer(missing_values=0, random_state=0),
MissingIndicator(missing_values=0)),
RandomForestRegressor(random_state=0, n_estimators=100))
chained_impute_scores = cross_val_score(estimator, X_missing, y_missing,
scoring='neg_mean_squared_error')
return ((full_scores.mean(), full_scores.std()),
(zero_impute_scores.mean(), zero_impute_scores.std()),
(mean_impute_scores.mean(), mean_impute_scores.std()),
(chained_impute_scores.mean(), chained_impute_scores.std()))
def __init__(self, training_values=None, training_targets=None):
self.vectorizer = make_union(TfidfVectorizer(), PostTransformer())
# Set using parameter_search. TODO: review after updating
# corpus.
self.classifier = svm.LinearSVC(C=1, loss='squared_hinge', multi_class='ovr', class_weight='balanced', tol=1e-6)
if training_values is not None and training_targets is not None:
self.fit(training_values, training_targets)
def PipelineTelstra(Classifier):
pipeline = make_pipeline(
make_union(
make_pipeline(
DataSpliterTrans(cols='location',transp=True),
preprocessing.OneHotEncoder(handle_unknown='ignore')
),
make_pipeline(
DataSpliterTrans(cols='event_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='severity_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='resource_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='volume',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='log_feature',matrix=True),
DictVectorizer()
)
),
Classifier()
)
print('pipeline done.')
return pipeline
def __init__(self, transforms):
self.transforms = transforms
union = make_union(*[t() for t in transforms])
pipeline = [union]
self.pipeline = make_pipeline(*pipeline)
self.classifier = LogisticRegression(penalty="l1", class_weight="auto")
def test_make_union():
pca = PCA()
mock = TransfT()
fu = make_union(pca, mock)
names, transformers = zip(*fu.transformer_list)
assert_equal(names, ("pca", "transft"))
assert_equal(transformers, (pca, mock))
def PipelineTelstra(Classifier):
pipeline = make_pipeline(
make_union(
make_pipeline(
DataSpliterTrans(cols='event_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='severity_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='resource_type',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='volume',matrix=True),
DictVectorizer()
),
make_pipeline(
DataSpliterTrans(cols='log_feature',matrix=True),
DictVectorizer()
)
),
Classifier()
)
print('pipeline done.')
return pipeline
def __init__(self, classifier="sgd", classifier_args=None, lowercase=True,
text_replacements=None, map_to_synsets=False, binary=False,
min_df=0, ngram=1, stopwords=None, limit_train=None,
map_to_lex=False, duplicates=False):
self.limit_train = limit_train
self.duplicates = duplicates
pipeline = [ExtractText(lowercase)]
if text_replacements:
pipeline.append(ReplaceText(text_replacements))
ext = [build_text_extraction(binary=binary, min_df=min_df,
ngram=ngram, stopwords=stopwords)]
if map_to_synsets:
ext.append(build_synset_extraction(binary=binary, min_df=min_df,
ngram=ngram))
if map_to_lex:
ext.append(build_lex_extraction(binary=binary, min_df=min_df,
ngram=ngram))
ext = make_union(*ext)
pipeline.append(ext)
#Building classifier
if classifier_args is None:
classifier_args={}
classifier = _valid_classifiers[classifier](**classifier_args)
self.pipeline = make_pipeline(*pipeline)
self.classifier = classifier
def get_extra_features(args):
forest = ExtraTreesClassifier(n_estimators=2000,
criterion='entropy',
max_features='sqrt',
max_depth=6,
min_samples_split=8,
n_jobs=-1,
bootstrap=True,
oob_score=True,
verbose=1,
class_weight='balanced')
pca = PCA(n_components=200)
ica = FastICA(n_components=200, max_iter=1000)
kmeans = KMeans(n_clusters=200, n_init=20, max_iter=1000)
pipeline = make_pipeline(selectKFromModel(forest, k=1000),
StandardScaler(),
make_union(pca, ica, kmeans))
X_train = np.load('feature/1_100/X_train.npy')
y_train = np.load('feature/1_100/y_train.npy')
X_test = np.load('feature/1_100/X_test.npy')
pipeline.fit(X_train, y_train[:, args.yix])
sel_ixs = pipeline.steps[0][1].indices[:500]
X_train_ext = np.hstack((pipeline.transform(X_train), X_train[:, sel_ixs]))
X_test_ext = np.hstack((pipeline.transform(X_test), X_test[:, sel_ixs]))
with open(path.join(save_dir, 'pipe.pkl'), 'wb') as f_pipe:
pickle.dump(pipeline, f_pipe)
np.save(path.join(save_dir, 'selix.npy'), sel_ixs)
return X_train_ext, X_test_ext
def test_missing_indicator_with_imputer(X, missing_values, X_trans_exp):
trans = make_union(
SimpleImputer(missing_values=missing_values, strategy='most_frequent'),
MissingIndicator(missing_values=missing_values)
)
X_trans = trans.fit_transform(X)
assert_array_equal(X_trans, X_trans_exp)
def get_pipeline(fsmethods, clfmethod):
"""Returns an instance of a sklearn Pipeline given the parameters
fsmethod1 and fsmethod2 will be joined in a FeatureUnion, then it will joined
in a Pipeline with clfmethod
Parameters
----------
fsmethods: list of estimators
All estimators in a pipeline, must be transformers (i.e. must have a transform method).
clfmethod: classifier
The last estimator may be any type (transformer, classifier, etc.).
Returns
-------
pipe
"""
feat_union = None
if not isinstance(fsmethods, list):
if hasattr(fsmethods, 'transform'):
feat_union = fsmethods
else:
raise ValueError('fsmethods expected to be either a list or a transformer method')
else:
feat_union = make_union(*fsmethods)
if feat_union is None:
pipe = make_pipeline(clfmethod)
else:
pipe = make_pipeline(feat_union, clfmethod)
return pipe
def preprocess(self,any_set,is_train):
if is_train:
dico_pattern={'match_lowercase_only':'\\b[a-z]+\\b',
'match_word':'\\w{2,}',
'match_word1': '(?u)\\b\\w+\\b',
'match_word_punct': '\w+|[,.?!;]',
'match_NNP': '\\b[A-Z][a-z]+\\b|\\b[A-Z]+\\b',
'match_punct': "[,.?!;'-]"
}
tfv_title = TfidfVectorizer(lowercase=True, stop_words='english', token_pattern=dico_pattern["match_word1"],
ngram_range=(1, 2), max_df=1.0, min_df=2, max_features=None,
vocabulary=None, binary=True, norm=u'l2',
use_idf=True, smooth_idf=True, sublinear_tf=True)
tfv_desc = TfidfVectorizer(lowercase=True, stop_words='english', token_pattern=dico_pattern["match_word1"],
ngram_range=(1, 2), max_df=1.0, min_df=2, max_features=None,
vocabulary=None, binary=True, norm=u'l2',
use_idf=True, smooth_idf=True, sublinear_tf=True)
title_pipe = make_pipeline(ColumnSelector(key='title'), tfv_title)
desc_pipe = make_pipeline(ColumnSelector(key='description'), tfv_desc)
self.pipeline = make_union(title_pipe, desc_pipe)
return self.pipeline.fit_transform(any_set)
else:
return self.pipeline.transform(any_set)
def pca_kpca(train_data, labels):
estimators = make_union(PCA(), TruncatedSVD(), KernelPCA())
# estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())]
combined = FeatureUnion(estimators)
combined.fit(train_data, labels) # combined.fit_tranform(tain_data, labels)
return combined
def __init__(self, **config):
# Validate options are present
for option in _configuration_options:
if option not in config:
raise ValueError("Missing configuration "
"option {!r}".format(option))
# Feature extraction
sparse_features = parse_features(config["sparse_features"])
densifier = make_pipeline(Vectorizer(sparse_features, sparse=True),
ClassifierAsFeature())
dense_features = parse_features(config["dense_features"])
vectorization = make_union(densifier,
Vectorizer(dense_features, sparse=False))
# Classifier
try:
classifier = _valid_classifiers[config["classifier"]]
except KeyError:
raise ValueError("Unknown classification algorithm "
"{!r}".format(config["classifier"]))
classifier = classifier(**config["classifier_args"])
self.pipeline = make_pipeline(vectorization, StandardScaler())
self.classifier = classifier
def test_make_union():
pca = PCA(svd_solver='full')
mock = Transf()
fu = make_union(pca, mock)
names, transformers = zip(*fu.transformer_list)
assert_equal(names, ("pca", "transf"))
assert_equal(transformers, (pca, mock))
def get_scores_for_imputer(imputer, X_missing, y_missing):
estimator = make_pipeline(
make_union(imputer, MissingIndicator(missing_values=0)),
REGRESSOR)
impute_scores = cross_val_score(estimator, X_missing, y_missing,
scoring='neg_mean_squared_error',
cv=N_SPLITS)
return impute_scores
def fit(self, X, y):
# Filthy hack
sids = X[:, -1]
all_pipelines = [make_pipeline(LogisticRegressionCV()).fit(X_s, y_s) for
X_s, y_s in subject_splitter(X[:, :-1], y, sids)]
f_union = make_union(*[FeatureUnionWrapper(p) for p in all_pipelines])
self.clf_ = make_pipeline(f_union, LogisticRegressionCV()).fit(X[:, :-1], y)
return self
def _create_feature_union(features):
"""
Create a FeatureUnion.
Each "feature" is a 3-tuple: (name, feature_extractor, vectorizer).
"""
return make_union(*[
make_pipeline(fe, vec)
for name, fe, vec in features
])
def make_pipe(classifier):
language_featurizer = make_union(CountVectorizer(),
FunctionFeaturizer(longest_run_of_capital_letters_feature,
percent_character_feature,
percent_character_combinations,
longest_run_of_character_feature,
character_combinations_binary
))
return make_pipeline(language_featurizer, classifier)
def __init__(self, transforms, n_estimators=2000, criterion='gini', min_samples_leaf=2, n_jobs=-1):
self.transforms = transforms
self.n_estimators = n_estimators
self.criterion = criterion
self.min_samples_leaf = min_samples_leaf
self.n_jobs = n_jobs
union = make_union(*[t() for t in transforms])
pipeline = [union]
self.pipeline = make_pipeline(*pipeline)
self.classifier = RandomForestClassifier(n_estimators, criterion, min_samples_leaf=min_samples_leaf, n_jobs=-1)
def create_input_transformer(fields, vec_name):
"""Create a pipeline of input transformations, allowing to use scaling of input fields."""
pipeline = []
for field in fields:
field_name = field['name']
field_scale = field['scale']
field_type = processed_db.get_field_type(field_name)
pipeline.append(
make_pipeline(ItemSelector(field_name), # select the correct column
Vectorizer(vec_name, field_type), # vectorize (depending on str/numeric input)
Scaler(field_scale)) # scale column based on user input
)
return make_union(*pipeline)
def build_prediction():
p_age = make_pipeline(
make_union(
OneHotTransformer(lambda x: x[1]['phone_brand'].lower()),
OneHotTransformer(lambda x: x[1]['device_model'].lower()),
TfidfVectorizer(preprocessor=lambda x: ' '.join(x[1]['app_id']))
),
LogisticRegression()
)
x_train = [(x, y) for x, y in PERSONS.items()]
x_test = [(x, y) for x, y in PERSONS_TESTS.items()]
y_train_age = [y.get('group') for y in PERSONS.values()]
print "fit age predictor"
p_age.fit(x_train, y_train_age)
print "predicting age"
classes = p_age.classes_
age_prediction = p_age.predict_proba(x_test)
return classes, age_prediction
def preprocess(self,any_set,is_train):
if is_train:
tfv_text = TfidfVectorizer(lowercase=True, max_features=2500)
tfv_topics = TfidfVectorizer(lowercase=True, max_features=20)
clf = MultinomialNB(alpha=0.05, fit_prior=True, class_prior=None)
title_pipe = make_pipeline(ColumnSelector(key=u'title'), tfv_text)
topics_pipe = make_pipeline(ColumnSelector(key=u'topicIds'), tfv_topics)
rel_topic_pipe = make_pipeline(ColumnSelector(key=u'relevantTopicIds'), tfv_topics)
text_pipe = make_pipeline(ColumnSelector(key=u'description'), tfv_text)
self.pipeline = make_union(title_pipe, topics_pipe,rel_topic_pipe,text_pipe)
return self.pipeline.fit_transform(any_set)
else:
return self.pipeline.transform(any_set)
def PipelineBNP(Classifier):
pipeline = make_pipeline(
NulltoNanTrans(),
make_union(
make_pipeline(
DataSpliterTrans(dtype=np.float64),
Imputer(strategy='median')
),
make_pipeline(
DataSpliterTrans(dtype=np.int),
Imputer(strategy='most_frequent'),
preprocessing.OneHotEncoder(handle_unknown='ignore')
),
make_pipeline(
DataSpliterTrans(dtype=np.object),
ObjtoCatStrtoIntTrans(),
Imputer(strategy='most_frequent'),
preprocessing.OneHotEncoder(handle_unknown='ignore')
)
),
Classifier()
)
print('pipeline done.')
return pipeline
def run_lr(train, test, y_train, num_models):
feature_extractor = make_union(create_basic_feature_extractor(),
create_BoW_feature_extractor())
X_train = feature_extractor.fit_transform(train)
X_test = feature_extractor.transform(test)
return [lr_predict(X_train, y_train, X_test) for _ in range(num_models)]
def __init__(self,
classifier="sgd",
classifier_args=None,
lowercase=True,
text_replacements=None,
map_to_synsets=False,
binary=False,
min_df=0,
ngram=1,
stopwords=None,
limit_train=None,
map_to_lex=False,
duplicates=False):
"""
Parameter description:
- `classifier`: The type of classifier used as main classifier,
valid values are "sgd", "knn", "svc", "randomforest".
- `classifier_args`: A dict to be passed as arguments to the main
classifier.
- `lowercase`: wheter or not all words are lowercased at the start of
the pipeline.
- `text_replacements`: A list of tuples `(from, to)` specifying
string replacements to be made at the start of the pipeline (after
lowercasing).
- `map_to_synsets`: Whether or not to use the Wordnet synsets
feature set.
- `binary`: Whether or not to count words in the bag-of-words
representation as 0 or 1.
- `min_df`: Minumim frequency a word needs to have to be included
in the bag-of-word representation.
- `ngram`: The maximum size of ngrams to be considered in the
bag-of-words representation.
- `stopwords`: A list of words to filter out of the bag-of-words
representation. Can also be the string "english", in which case
a default list of english stopwords will be used.
- `limit_train`: The maximum amount of training samples to give to
the main classifier. This can be useful for some slow main
classifiers (ex: svc) that converge with less samples to an
optimum.
- `max_to_lex`: Whether or not to use the Harvard Inquirer lexicon
features.
- `duplicates`: Whether or not to check for identical phrases between
train and prediction.
"""
self.limit_train = limit_train
self.duplicates = duplicates
# Build pre-processing common to every extraction
pipeline = [ExtractText(lowercase)]
if text_replacements:
pipeline.append(ReplaceText(text_replacements))
# Build feature extraction schemes
ext = [
build_text_extraction(binary=binary,
min_df=min_df,
ngram=ngram,
stopwords=stopwords)
]
if map_to_synsets:
ext.append(
build_synset_extraction(binary=binary,
min_df=min_df,
ngram=ngram))
if map_to_lex:
ext.append(
build_lex_extraction(binary=binary, min_df=min_df,
ngram=ngram))
ext = make_union(*ext)
pipeline.append(ext)
# Build classifier and put everything togheter
if classifier_args is None:
classifier_args = {}
classifier = _valid_classifiers[classifier](**classifier_args)
self.pipeline = make_pipeline(*pipeline)
self.classifier = classifier
def __init__(self, classifier="sgd", classifier_args=None, lowercase=True,
text_replacements=None, map_to_synsets=False, binary=False,
min_df=0, ngram=1, stopwords=None, limit_train=None,
map_to_lex=False, duplicates=False):
"""
Parameter description:
- `classifier`: The type of classifier used as main classifier,
valid values are "sgd", "knn", "svc", "randomforest".
- `classifier_args`: A dict to be passed as arguments to the main
classifier.
- `lowercase`: wheter or not all words are lowercased at the start of
the pipeline.
- `text_replacements`: A list of tuples `(from, to)` specifying
string replacements to be made at the start of the pipeline (after
lowercasing).
- `map_to_synsets`: Whether or not to use the Wordnet synsets
feature set.
- `binary`: Whether or not to count words in the bag-of-words
representation as 0 or 1.
- `min_df`: Minumim frequency a word needs to have to be included
in the bag-of-word representation.
- `ngram`: The maximum size of ngrams to be considered in the
bag-of-words representation.
- `stopwords`: A list of words to filter out of the bag-of-words
representation. Can also be the string "english", in which case
a default list of english stopwords will be used.
- `limit_train`: The maximum amount of training samples to give to
the main classifier. This can be useful for some slow main
classifiers (ex: svc) that converge with less samples to an
optimum.
- `max_to_lex`: Whether or not to use the Harvard Inquirer lexicon
features.
- `duplicates`: Whether or not to check for identical phrases between
train and prediction.
"""
self.limit_train = limit_train
self.duplicates = duplicates
# Build pre-processing common to every extraction
pipeline = [ExtractText(lowercase)]
if text_replacements:
pipeline.append(ReplaceText(text_replacements))
# Build feature extraction schemes
ext = [build_text_extraction(binary=binary, min_df=min_df,
ngram=ngram, stopwords=stopwords)]
if map_to_synsets:
ext.append(build_synset_extraction(binary=binary, min_df=min_df,
ngram=ngram))
if map_to_lex:
ext.append(build_lex_extraction(binary=binary, min_df=min_df,
ngram=ngram))
ext = make_union(*ext)
pipeline.append(ext)
# Build classifier and put everything togheter
if classifier_args is None:
classifier_args = {}
classifier = _valid_classifiers[classifier](**classifier_args)
self.pipeline = make_pipeline(*pipeline)
self.classifier = classifier
# In[13]:
# full pipeline for data engineering
full_pipeline = Pipeline(steps=[
(
"features",
make_union(
make_pipeline(DataFrameSelector(["Embarked"]), MostFrequentImputer(
), CategoricalEncoder(encoding='onehot-dense')),
make_pipeline(DataFrameSelector(["Pclass", "Sex"]),
CategoricalEncoder(encoding='onehot-dense')),
make_pipeline(DataFrameSelector(["Age", "Fare"]),
Imputer(strategy="median"), StandardScaler()),
make_pipeline(DataFrameSelector(["Name"]), ExtractTitle(),
CategoricalEncoder(encoding='onehot-dense')),
#make_pipeline(DataFrameSelector(["Cabin"]), FillMissingCabin(), ExtractCabin(), CategoricalEncoder(encoding='onehot-dense')),
make_pipeline(DataFrameSelector(["Cabin"]), HasCabin()),
make_pipeline(DataFrameSelector(["SibSp", "Parch"]),
CreateFamilySize(),
CategoricalEncoder(encoding='onehot-dense')),
)),
("poly", PolynomialFeatures()),
#("PCA", PCA(n_components=0.95)),
#("best", SelectKBest(k=20)),
("clf", RandomForestClassifier(random_state=42))
])
# **STEP 6 - Splitting the training dataset**
#
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE',
delimiter='COLUMN_SEPARATOR',
dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'),
axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
make_union(FunctionTransformer(lambda X: X),
FunctionTransformer(lambda X: X)),
RandomForestClassifier(n_estimators=500))
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.decomposition import RandomizedPCA
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
from sklearn.svm import LinearSVC
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
RandomizedPCA(iterated_power=10),
make_union(VotingClassifier([("est", LinearSVC(C=0.59, dual=False, penalty="l1"))]), FunctionTransformer(lambda X: X)),
RandomForestClassifier(n_estimators=500)
)
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
TOKENIZER = re.compile('([{}“”¨«»®´·º½¾¿¡§£₤‘’])'.format(string.punctuation))
def tokenize(s):
return TOKENIZER.sub(r' \1 ', s).split()
vectorizer = make_union(
on_field(
'question_text',
TfidfVectorizer(max_features=13000,
token_pattern='\w+',
strip_accents='unicode',
tokenizer=tokenize,
sublinear_tf=True)),
on_field('question_text',
TfidfVectorizer(ngram_range=(3, 3), analyzer='char', min_df=25)),
make_pipeline(
PandasSelector(columns=[
'num_words',
'num_singletons',
'caps_vs_length',
],
return_vector=False), MaxAbsScaler()),
)
with timer('process train'):
# df_train = pd.read_csv(os.path.join(INPUT_PATH, "train.csv"))
df_train = joblib.load('train.pkl')
df_test = pd.read_csv(os.path.join(INPUT_PATH, "test.csv"))
# df_test = joblib.load('valid_for_emsemble.pkl')
train_count = len(df_train)
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import MinMaxScaler, Normalizer
from tpot.builtins import StackingEstimator
from xgboost import XGBClassifier
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Average CV score on the training set was:0.839915792130982
exported_pipeline = make_pipeline(
make_union(
MinMaxScaler(),
FunctionTransformer(copy)
),
Normalizer(norm="max"),
XGBClassifier(learning_rate=0.01, max_depth=3, min_child_weight=7, n_estimators=600, nthread=1, subsample=0.8)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
from tpot.export_utils import set_param_recursive
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=42)
# Average CV score on the training set was: -5.853055578955521
exported_pipeline = make_pipeline(
make_union(
StackingEstimator(estimator=RidgeCV()),
make_pipeline(
FeatureAgglomeration(affinity="manhattan", linkage="complete"),
Nystroem(gamma=0.30000000000000004,
kernel="sigmoid",
n_components=5))),
FeatureAgglomeration(affinity="cosine", linkage="average"),
SGDRegressor(alpha=0.0,
eta0=0.01,
fit_intercept=False,
l1_ratio=1.0,
learning_rate="invscaling",
loss="epsilon_insensitive",
penalty="elasticnet",
power_t=100.0))
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 42)
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=None)
# Average CV score on the training set was: 0.8326392221287445
exported_pipeline = make_pipeline(
make_union(
make_pipeline(
make_union(
make_union(
FunctionTransformer(copy),
StackingEstimator(estimator=RandomForestClassifier(bootstrap=True, criterion="entropy", max_features=0.35000000000000003, min_samples_leaf=1, min_samples_split=7, n_estimators=100))
),
make_union(
FunctionTransformer(copy),
FunctionTransformer(copy)
)
),
SelectPercentile(score_func=f_classif, percentile=58)
),
FunctionTransformer(copy)
),
MultinomialNB(alpha=0.1, fit_prior=True)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
})],
input_df=True,
df_out=True,
default=False)
engineered_feature_pipeline4 = skp.DataFrameMapper(
[(['c1', 'c2', 'c3', 'c4', 'c5'], uf.Straight(), {
'alias': 'has_straight'
})],
input_df=True,
df_out=True,
default=False)
# here we lose feature names
features_pipeline = ppl.make_union(engineered_feature_pipeline1,
engineered_feature_pipeline2,
engineered_feature_pipeline3,
engineered_feature_pipeline4)
temp = d_in[d_in['hand'] == '8']
#features_pipeline.fit_transform(temp).head()
a = features_pipeline.fit_transform(temp)
a[0:10, ]
# modelling complete pipeline
pipe = ppl.Pipeline([
('prep', features_pipeline), ('encoding', ppr.OneHotEncoder()),
('clf',
LogisticRegression(multi_class='multinomial',
penalty='l2',
random_state=9546,
solver="lbfgs"))
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator, ZeroCount
from xgboost import XGBClassifier
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Score on the training set was:0.504247990815155
exported_pipeline = make_pipeline(
make_union(FunctionTransformer(copy), ZeroCount()),
XGBClassifier(learning_rate=0.001,
max_depth=3,
min_child_weight=3,
n_estimators=100,
nthread=1,
subsample=0.1))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
def __init__(self, classifier="sgd", classifier_args=None, lowercase=True,
text_replacements=None, map_to_synsets=True, binary=False,
min_df=0, ngram=1, stopwords=None, limit_train=None,
map_to_lex=True, duplicates=True, svm_features=False
, preprocessor=False, useLemmatization = True, stemming = False,
useStopWords = True, word2vecFeatures = False, splitModel= False,
useTfIdf= False):
"""
Parameter description:
- `classifier`: The type of classifier used as main classifier,
valid values are "sgd", "knn", "svc", "randomforest".
- `classifier_args`: A dict to be passed as arguments to the main
classifier.
- `lowercase`: wheter or not all words are lowercased at the start of
the pipeline.
- `text_replacements`: A list of tuples `(from, to)` specifying
string replacements to be made at the start of the pipeline (after
lowercasing).
- `map_to_synsets`: Whether or not to use the Wordnet synsets
feature set.
- `binary`: Whether or not to count words in the bag-of-words
representation as 0 or 1.
- `min_df`: Minumim frequency a word needs to have to be included
in the bag-of-word representation.
- `ngram`: The maximum size of ngrams to be considered in the
bag-of-words representation.
- `stopwords`: A list of words to filter out of the bag-of-words
representation. Can also be the string "english", in which case
a default list of english stopwords will be used.
- `limit_train`: The maximum amount of training samples to give to
the main classifier. This can be useful for some slow main
classifiers (ex: svc) that converge with less samples to an
optimum.
- `max_to_lex`: Whether or not to use the Harvard Inquirer lexicon
features.
- `duplicates`: Whether or not to check for identical phrases between
train and prediction.
- `svm_features`: Whether or not to include features from an SVM classifier
"""
self.limit_train = limit_train
self.duplicates = duplicates
print("Using tfidf: ", useTfIdf)
# Build pre-processing common to every extraction
pipeline = [Preprocessor(removeStopWords=useStopWords, lemmatize=useLemmatization, stem=stemming)] if preprocessor else [ExtractText(lowercase)]
if text_replacements:
pipeline.append(ReplaceText(text_replacements))
# Build feature extraction schemes
ext = [build_text_extraction(binary=binary, min_df=min_df,
ngram=ngram, stopwords=stopwords, useTfIdf=useTfIdf)]
if map_to_synsets:
ext.append(build_synset_extraction(binary=binary, min_df=min_df,
ngram=ngram, useTfIdf=useTfIdf))
if map_to_lex:
ext.append(build_lex_extraction(binary=binary, min_df=min_df,
ngram=ngram))
if svm_features:
ext.append(build_svm_features())
if word2vecFeatures:
ext.append(build_word2vec_features())
ext = make_union(*ext)
pipeline.append(ext)
# Build classifier and put everything togheter
if classifier_args is None:
classifier_args = {}
if classifier == "ensemble":
classifier_args = {"classifiers": [SGDClassifier(), RandomForestClassifier(), SVC(),KNeighborsClassifier(), RandomForestClassifier(n_estimators= 100, min_samples_leaf=10, n_jobs=-1)]}
#Classifier constructor E.g. SGDClassifier(args)
classifier = _valid_classifiers[classifier](**classifier_args)
self.pipeline = make_pipeline(*pipeline)
self.classifier = classifier
self.splitModel = splitModel
self.splitSize = 1
def get_feature_pipeline(tr_artifact,
hist_artifacts,
all_data,
cachedir='data/'):
"""Define feature transformation pipeline."""
return make_pipeline(
make_union(
identity(input_cols=[
FieldNames.customer_id,
FieldNames.coupon_id,
FieldNames.rented,
FieldNames.age_range,
FieldNames.marital_status,
FieldNames.no_of_children,
FieldNames.family_size,
FieldNames.income_bracket,
]),
make_pipeline(SelectCols(cols=[FieldNames.campaign_type]),
OrdinalEncoder()),
# make_pipeline(
# SelectCols(cols=[FieldNames.cust_cohort]),
# OneHotEncoder(handle_unknown='ignore')
# ),
make_pipeline(
GroupCatCatNUnique(FieldNames.campaign_id,
FieldNames.customer_id)),
make_pipeline(
GroupCatCatNUnique(FieldNames.campaign_id,
FieldNames.coupon_id)),
# make_pipeline(
# SelectCols(cols=[FieldNames.campaign_id]),
# GroupCatCountEncoder()
# ),
make_pipeline(
ExpandingMean(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.target,
hist_artifact=tr_artifact,
), ),
make_pipeline(
ExpandingCount(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.target,
hist_artifact=tr_artifact,
), ),
# make_pipeline(
# ExpandingMedian(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.transaction_day,
# hist_artifact=hist_artifacts[0]
# )
# ),
# make_pipeline(
# ExpandingSum(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.target,
# hist_artifact=tr_artifact,
# )
# ),
# make_pipeline(
# ExpandingCount(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.coupon_discount,
# hist_artifact=hist_artifacts[0],
# )
# ),
# make_pipeline(
# ExpandingMean(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.selling_price,
# hist_artifact=hist_artifacts[0],
# )
# ),
# make_pipeline(
# ExpandingMean(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.coupon_discount,
# hist_artifact=hist_artifacts[1],
# )
# ),
# make_pipeline(
# ExpandingSum(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.selling_price,
# hist_artifact=hist_artifacts[1],
# )
# ),
# make_pipeline(
# ExpandingMax(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.pct_discount,
# hist_artifact=hist_artifacts[0],
# )
# ),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
hist_artifact=hist_artifacts[0],
),
SelectCols(cols=[FieldNames.item_set]),
), CountCommon()),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
hist_artifact=hist_artifacts[0],
),
SelectCols(cols=[FieldNames.item_set]),
), Jaccard()),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
hist_artifact=hist_artifacts[1],
),
SelectCols(cols=[FieldNames.item_set]),
),
CountCommon(),
),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
hist_artifact=hist_artifacts[1],
),
SelectCols(cols=[FieldNames.item_set]),
), Jaccard(),
QuantileTransformer(output_distribution='normal')),
# make_pipeline(
# make_union(
# SetAggregation(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_set,
# hist_artifact=hist_artifacts[2],
# ),
# SelectCols(cols=[FieldNames.item_set]),
# ),
# Jaccard(),
# ),
# make_pipeline(
# make_union(
# SetAggregation(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_brand,
# hist_artifact=hist_artifacts[0],
# ),
# SelectCols(cols=[FieldNames.item_brand]),
# ),
# CountCommon(),
# ),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_brand,
hist_artifact=hist_artifacts[0],
),
SelectCols(cols=[FieldNames.item_brand]),
),
Jaccard(),
),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_brand,
hist_artifact=hist_artifacts[1],
),
SelectCols(cols=[FieldNames.item_brand]),
),
Jaccard(),
),
# make_pipeline(
# CouponItemMean(coupon_col=FieldNames.coupon_id,
# target_col=FieldNames.target)
# )
# make_pipeline(
# make_union(
# SetAggregation(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_category,
# hist_artifact=hist_artifacts[0],
# ),
# SelectCols(cols=[FieldNames.item_category]),
# ),
# Jaccard(),
# ),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_category,
hist_artifact=hist_artifacts[1],
),
SelectCols(cols=[FieldNames.item_category]),
),
Jaccard(),
),
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_category,
hist_artifact=hist_artifacts[2],
),
SelectCols(cols=[FieldNames.item_category]),
),
Jaccard(),
),
make_pipeline(
SetLen(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_brand,
hist_artifact=hist_artifacts[0],
), ),
make_pipeline(
SelectCols(cols=[
FieldNames.campaign_start_date,
FieldNames.campaign_end_date
]),
FunctionTransfomer(lambda x:
(x.iloc[:, 1] - x.iloc[:, 0]).dt.days)),
# make_pipeline(
# FunctionTransfomer(lambda x: x[FieldNames.item_set].apply(len).values.reshape(-1, 1))
# ),
make_pipeline(
FunctionTransfomer(lambda x: x[FieldNames.item_brand].apply(
len).values.reshape(-1, 1))),
make_pipeline(
FunctionTransfomer(lambda x: x[FieldNames.item_category].apply(
len).values.reshape(-1, 1))),
make_pipeline(
ZeroPct(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.coupon_discount,
hist_artifact=hist_artifacts[0],
)),
make_pipeline(
AllCountEncoder(
cols=[FieldNames.customer_id, FieldNames.coupon_id],
data=all_data)),
# make_pipeline(
# SetMean(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.selling_price,
# hist_artifact=hist_artifacts[0],
# )
# ),
# make_pipeline(
# ZeroPct(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.other_discount,
# hist_artifact=hist_artifacts[0],
# )
# )
# make_pipeline(
# VectorMapper(FieldNames.coupon_id, 'data/coupon_vectors_lda.npy')
# ),
# make_pipeline(
# VectorMapper(FieldNames.coupon_id, 'data/coupon_vectors_svd.npy')
# ),
# make_pipeline(
# SetLen(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_set,
# hist_artifact=hist_artifacts[0],
# ),
# ),
# make_pipeline(
# SetLen(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_set,
# hist_artifact=hist_artifacts[1],
# ),
# ),
# make_pipeline(
# make_union(
# SetAggregationLast3(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_set,
# hist_artifact=hist_artifacts[1],
# ),
# SelectCols(cols=[FieldNames.item_set]),
# ),
# Jaccard(),
# ),
# make_pipeline(
# make_union(
# SetAggregation(
# date_col=FieldNames.campaign_start_date,
# user_col=FieldNames.customer_id,
# key_col=FieldNames.item_set,
# hist_artifact=hist_artifacts[2],
# ),
# SelectCols(cols=[FieldNames.item_set]),
# ),
# Jaccard(),
# ),
),
make_union(
FunctionTransfomer(lambda x: x),
FunctionTransfomer(lambda x: x[:, 13] / (1e-4 + x[:, 15])),
FunctionTransfomer(lambda x: x[:, 14] / (1e-4 + x[:, 16])),
FunctionTransfomer(lambda x: x[:, 17] / (1e-4 + x[:, 18])),
FunctionTransfomer(lambda x: x[:, 19] / (1e-4 + x[:, 20])),
# FunctionTransfomer(lambda x: x[:, 17]/(1e-4 + x[:, 14])),
),
)
return len(sample_text)
def transform(self, X, y=None):
"""The workhorse of this feature extractor"""
result_series = X['review'].apply(self.text_length)
return result_series.to_frame(name=self.get_feature_names()[0])
def fit(self, df, y=None):
"""Returns `self` unless something different happens in train and test"""
return self
if __name__ == '__main__':
from sklearn.pipeline import make_pipeline, make_union
base_path = '../data/stanford_imdb'
df = pd.read_csv(f'{base_path}/imdb_df.csv.gzip', compression='gzip')
X = df.drop(['sentiment'], axis=1)
t1 = AverageWordLengthExtractor()
t2 = TextLengthExtractor()
# I expect the make_union to produce 2 additional columns
pipe = make_union(t1, t2)
n = pipe.transform(X)
print(n.shape)
print(type(n)) #numpy.ndarray
import pandas as pd
from sklearn.decomposition import FastICA
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator, ZeroCount
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Score on the training set was:0.8453186610518303
exported_pipeline = make_pipeline(
make_union(make_pipeline(ZeroCount(), FastICA(tol=0.2)),
FunctionTransformer(copy)),
ExtraTreesClassifier(bootstrap=False,
criterion="entropy",
max_features=0.2,
min_samples_leaf=1,
min_samples_split=4,
n_estimators=100))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
def main():
vectorizer = make_union(
on_field('title', Tfidf(max_features=100000, token_pattern='\w+')),
on_field('text', Tfidf(max_features=100000, token_pattern='\w+', ngram_range=(1, 2))),
on_field(['price', 'user_type', 'image_top_1'],
FunctionTransformer(to_records, validate=False), DictVectorizer()),
n_jobs=8)
y_scaler = StandardScaler()
with timer('process train'):
print('read train data ...')
train = pd.read_csv('../input/train.csv', parse_dates = ["activation_date"])
# cv = KFold(n_splits=10, shuffle=True, random_state=42)
# train_ids, valid_ids = next(cv.split(train))
# train, valid = train.iloc[train_ids], train.iloc[valid_ids]
train, valid = train_test_split(train, test_size=0.10, random_state=23)
y_train = y_scaler.fit_transform(train['deal_probability'].values.reshape(-1, 1))
X_train = vectorizer.fit_transform(preprocess(train))
print('X_train: {} of {}'.format(X_train.shape,X_train.dtype))
del train; gc.collect()
with timer('process valid'):
X_valid = vectorizer.transform(preprocess(valid))
with timer('process test'):
# TODO
print('read test data ...')
test = pd.read_csv('../input/test.csv', parse_dates = ["activation_date"])
X_test = vectorizer.transform(preprocess(test))
del test; gc.collect()
with ThreadPool(processes=8) as pool:
# Xb_train, Xb_valid = [x.astype(np.bool) for x in [X_train, X_valid]]
Xb_train, Xb_valid, Xb_test = [x.astype(np.bool) for x in [X_train, X_valid, X_test]]
xs = [[Xb_train, Xb_valid], [X_train, X_valid]] * 2
del X_valid; gc.collect()
# TODO
xs_test = [[Xb_train, Xb_test], [X_train, Xb_test]] * 2
del X_train, X_test; gc.collect()
y_pred = np.mean(pool.map(partial(fit_predict, y_train=y_train), xs), axis=0)
# TODO
y_pred_test = np.mean(pool.map(partial(fit_predict, y_train=y_train), xs_test), axis=0)
y_pred = y_scaler.inverse_transform(y_pred.reshape(-1, 1))[:, 0]
# TODO
y_pred_test = y_scaler.inverse_transform(y_pred_test.reshape(-1, 1))[:, 0]
print('Valid RMSLE: {:.4f}'.format(np.sqrt(mean_squared_log_error(valid['deal_probability'], y_pred))))
del valid; gc.collect()
sub = pd.read_csv('../input/sample_submission.csv')
sub['deal_probability'] = y_pred_test
sub.to_csv('sub.csv', index=False)
print('all done!')
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator
from xgboost import XGBClassifier
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=None)
# Average CV score on the training set was: 0.9100211739663182
exported_pipeline = make_pipeline(
make_union(StackingEstimator(estimator=GaussianNB()),
FunctionTransformer(copy)),
XGBClassifier(learning_rate=0.1,
max_depth=6,
min_child_weight=2,
n_estimators=100,
nthread=1,
subsample=0.6000000000000001))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Average CV score on the training set was:0.84550605863897
exported_pipeline = make_pipeline(
make_union(
make_pipeline(
OneHotEncoder(minimum_fraction=0.25, sparse=False, threshold=10),
RFE(estimator=ExtraTreesClassifier(criterion="gini",
max_features=0.5,
n_estimators=100),
step=0.2), ZeroCount(), MinMaxScaler()),
FunctionTransformer(copy)), Normalizer(norm="max"),
XGBClassifier(learning_rate=0.01,
max_depth=6,
min_child_weight=7,
n_estimators=600,
nthread=1,
subsample=0.9500000000000001))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
def get_feature_pipeline(tr_artifact, hist_artifacts, all_data):
"""Feature generation pipeline."""
hist_n = 3 # len(hist_artifacts)
tr_artifact_kws = {
"date_col": FieldNames.campaign_start_date,
"user_col": FieldNames.customer_id,
"key_col": FieldNames.target,
"hist_artifact": tr_artifact,
}
hist_cols = [
FieldNames.item_set, FieldNames.item_brand, FieldNames.item_category
]
hist_cols2 = [
FieldNames.coupon_discount,
FieldNames.other_discount,
FieldNames.pct_discount,
FieldNames.quantity,
FieldNames.selling_price,
]
return make_pipeline(
make_union(
# Numerical features directly available
make_pipeline(
SelectCols(cols=[
FieldNames.customer_id,
FieldNames.coupon_id,
FieldNames.age_range,
FieldNames.marital_status,
FieldNames.family_size,
FieldNames.no_of_children,
FieldNames.income_bracket,
FieldNames.campaign_type,
]),
FunctionTransfomer(lambda x: x),
),
# coupon-no. of unique item attributes
make_union(*[
make_pipeline(
SelectCols(cols=[col]),
FunctionTransfomer(
lambda X:
[len(set(x)) for x in X.values.flatten().tolist()]),
) for col in [
FieldNames.item_set,
FieldNames.item_brand,
FieldNames.item_brand_type,
FieldNames.item_category,
]
],
verbose=True),
# Campaign id features
make_union(*[
GroupCatCatNUnique(FieldNames.campaign_id, col2)
for col2 in [FieldNames.customer_id, FieldNames.coupon_id]
],
verbose=True),
# Customer id expanding mean, count, sum
make_pipeline(ExpandingMean(**tr_artifact_kws)),
make_pipeline(ExpandingCount(**tr_artifact_kws)),
make_pipeline(ExpandingSum(**tr_artifact_kws)),
# Count items common between current coupon and historical customer transactions
make_union(*[
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=col,
hist_artifact=hist_artifacts[i],
),
SelectCols(cols=[col]),
),
CountCommon(),
) for col, i in itertools.product(hist_cols, range(hist_n))
]),
make_union(*[
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=col,
hist_artifact=hist_artifacts[i],
),
SelectCols(cols=[col]),
),
Jaccard(),
) for col, i in itertools.product(hist_cols, range(hist_n))
],
verbose=True),
make_union(*[
make_pipeline(
make_union(
SetAggregation(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=col,
hist_artifact=hist_artifacts[i],
),
SelectCols(cols=[col]),
),
CountCommonRepeats(),
) for col, i in itertools.product(hist_cols, range(hist_n))
]),
# campaign length
make_pipeline(
SelectCols(cols=[
FieldNames.campaign_start_date,
FieldNames.campaign_end_date
]),
FunctionTransfomer(lambda x:
(x.iloc[:, 1] - x.iloc[:, 0]).dt.days),
),
# coupon discount, other dicount, selling price and quantity aggregations
make_union(*[
ExpandingMean(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=col,
hist_artifact=hist_artifacts[i],
) for col, i in itertools.product(hist_cols2, range(hist_n))
]),
make_pipeline(
GroupCatCountEncoder(
cols=[FieldNames.customer_id, FieldNames.campaign_id])),
make_pipeline(
AllCountEncoder(
cols=[FieldNames.customer_id, FieldNames.coupon_id],
data=all_data)),
make_pipeline(
make_union(*[
SetLen(
date_col=FieldNames.campaign_start_date,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
hist_artifact=hist_artifacts[0],
) for i in range(hist_n)
])),
make_pipeline(
make_union(
VectorMapper(col=FieldNames.coupon_id,
vector_file=FileNames.coupon_vectors),
HistVectorMean(
vector_file=FileNames.item_vectors,
user_col=FieldNames.customer_id,
key_col=FieldNames.item_set,
date_col=FieldNames.campaign_start_date,
hist_artifact=hist_artifacts[0],
),
),
CosineSimilarity(),
),
),
make_union(
FunctionTransfomer(lambda x: x),
# Ratios
make_pipeline(
make_union(*[
FunctionTransfomer(lambda x: x[:, i] / x[:, j])
for (i,
j) in itertools.product(range(16, 34), range(16, 34))
],
verbose=True)),
),
)
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import RFE, VarianceThreshold
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from tpot.builtins import StackingEstimator
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Score on the training set was:0.8172868435911914
exported_pipeline = make_pipeline(
make_union(
StandardScaler(),
RFE(estimator=ExtraTreesClassifier(criterion="gini",
max_features=0.2,
n_estimators=100),
step=0.1)), VarianceThreshold(threshold=0.25), StandardScaler(),
StandardScaler(), MinMaxScaler(), StandardScaler(),
LogisticRegression(C=0.01, dual=False, penalty="l2"))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
s = str(i.month) + str(i.day) + str(i.hour) + str(i.minute)
model_name = "GM_export/main_new/" + "GM" + s + ".py"
tpo.export(model_name)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator
from xgboost import XGBClassifier
from sklearn.preprocessing import FunctionTransformer
from copy import copy
exported_pipeline = make_pipeline(
make_union(FunctionTransformer(copy), FunctionTransformer(copy)),
StackingEstimator(estimator=XGBClassifier(learning_rate=0.1,
max_depth=5,
min_child_weight=6,
n_estimators=21,
scale_pos_weight=4.16,
subsample=0.85)),
RandomForestClassifier(class_weight={1: 4.16},
criterion="gini",
max_depth=8,
max_features=6,
n_estimators=23))
exported_pipeline.fit(x_train, y_train)
evalution_model(exported_pipeline, x_train, y_train)
evalution_model(exported_pipeline, x_test, y_test)
data,label = data[idx_row,:],label[idx_row]
features = data
tpot_data=pd.DataFrame({'class':label},columns=['class'])
# train the machine learning model
kf = KFold(n_splits=10,random_state=556,shuffle=True)
results,auc=[],[]
cnt = 0
print('machine learning model best ML model and cross validation by 10 folds')
fp,tp=[],[]
for train_index, test_index in kf.split(features):
training_features, testing_features = features[train_index],features[test_index]
training_classes, testing_classes = tpot_data['class'].values[train_index],tpot_data['class'].values[test_index]
exported_pipeline = make_pipeline(
make_union(VotingClassifier([("est", DecisionTreeClassifier())]), FunctionTransformer(lambda X: X)),
GradientBoostingClassifier(learning_rate=0.24, max_features=0.24, n_estimators=500)
)
exported_pipeline.fit(training_features, training_classes)
results.append(exported_pipeline.predict_proba(testing_features)[:,1])
fpr, tpr, thresholds = metrics.roc_curve(testing_classes,exported_pipeline.predict_proba(testing_features)[:,1])
auc.append(metrics.roc_auc_score(testing_classes,exported_pipeline.predict_proba(testing_features)[:,1]))
#ax.plot(fpr,tpr,label='%s,Area under the curve: %.3f'%(type_,auc[cnt]))
fp.append(fpr);tp.append(tpr)
print('get one done')
cnt += 1
print('done')
#from sklearn.externals import joblib
#pickle.dump(exported_pipeline, open('%smy_model.pkl'%folder,'wb'))
#exported_pipeline = joblib.load('%smy_model.pkl'%folder)
pickle.dump([results,auc,fp,tp],open("%slong process.p"%folder,"wb"))
X_test_post_hoc = df_test
df = df.drop(columns=['eid', '21022-0.0'], axis=1)
df_test = df_test.drop(columns=['eid', '21022-0.0'], axis=1)
# Learning curves: train sizes
train_sizes = [100, 500, 1000, 1500, 2000, 2500, 3000, 3500, 3700]
# Model
estimator = RandomForestRegressor(n_estimators=250,
criterion='mse',
n_jobs=10,
verbose=1,
random_state=0)
pipeline = Pipeline([('imputation',
make_union(SimpleImputer(strategy="median"),
MissingIndicator(error_on_new=False))),
('estimator', estimator)])
cv = ShuffleSplit(n_splits=100, test_size=0.1, random_state=0)
param_grid = {
'estimator__max_depth': [5, 10, 20, 40, None],
'estimator__max_features': [1, 5, 'log2', 'sqrt', 'auto', None]
}
grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5, verbose=2)
metrics = []
train_sizes, train_scores, validation_scores = learning_curve(
estimator=grid_search,
X=df,
y=y_train,
def _make_preprocessor(self):
def lexicon_pipeline(lexicon):
return make_pipeline(LexiconFeatures(lexicon), DictVectorizer())
unigram_lexicons_features = make_union(
lexicon_pipeline(NRCEmotionLexicon()),
lexicon_pipeline(NRCHashtagEmotionLexicon()),
lexicon_pipeline(MaxDiffTwitterLexicon()),
lexicon_pipeline(NRCHashtagSentimentWithContextUnigrams()),
lexicon_pipeline(NRCHashtagSentimentLexiconUnigrams()),
lexicon_pipeline(Sentiment140WithContextUnigrams()),
lexicon_pipeline(Sentiment140LexiconUnigrams()),
lexicon_pipeline(YelpReviewsLexiconUnigrams()),
lexicon_pipeline(AmazonLaptopsReviewsLexiconUnigrams()),
lexicon_pipeline(MPQAEffectLexicon()),
lexicon_pipeline(MPQASubjectivityLexicon()),
lexicon_pipeline(HarvardInquirerLexicon()),
lexicon_pipeline(BingLiuLexicon()),
lexicon_pipeline(AFINN111Lexicon()),
lexicon_pipeline(SentiWordNetLexicon()),
lexicon_pipeline(LoughranMcDonaldLexicon()),
)
bigram_lexicons_features = make_union(
lexicon_pipeline(NRCHashtagSentimentWithContextBigrams()),
lexicon_pipeline(NRCHashtagSentimentLexiconBigrams()),
lexicon_pipeline(Sentiment140WithContextBigrams()),
lexicon_pipeline(Sentiment140LexiconBigrams()),
lexicon_pipeline(YelpReviewsLexiconBigrams()),
lexicon_pipeline(AmazonLaptopsReviewsLexiconBigrams()),
lexicon_pipeline(MPQAEffectLexicon()),
)
preprocessor = make_pipeline(
BasicTokenizer(),
make_union(
make_pipeline(
CMUArkTweetPOSTagger(),
ListCountVectorizer(lowercase=False,
binary=True)), # POS features
# make_pipeline(W2Vembedding()),
make_pipeline(
CharNGramTransformer([1, 2, 3]),
ListCountVectorizer(lowercase=True,
max_features=10000,
binary=True)), # Character n-grams
make_pipeline(
LowercaseTransformer(), CMUArkTweetBrownClusters(),
ListCountVectorizer(lowercase=False,
binary=True)), # brown clusters
make_pipeline(
Negater(),
make_union(
make_pipeline(
NGramTransformer([3, 4]),
ListCountVectorizer(lowercase=True,
max_features=10000,
binary=True)), # ngram word
make_pipeline(
CountingFeatures(), DictVectorizer()
), # allcaps, punctuations, lengthening, emoticons, etc. counting feature
make_pipeline(LowercaseTransformer(),
unigram_lexicons_features
), # unigram lexicon features
ListCountVectorizer(lowercase=True,
max_features=10000,
binary=True), # ngram word
make_pipeline(
LowercaseTransformer(),
NGramTransformer(2),
make_union(
bigram_lexicons_features, # bigram lexicon features
ListCountVectorizer(lowercase=True,
max_features=10000,
binary=True), # ngram word
),
),
),
),
make_pipeline(LowercaseTransformer(), SSWEFeatures()),
make_pipeline(
NormalizedTokens(),
CollapsedTokens(),
PorterStemmer(),
Negater(),
make_union(
ListCountVectorizer(lowercase=False,
max_features=10000,
binary=True), # processed word
make_pipeline(
ClusterFeaturesWithNegation(),
ListCountVectorizer(
lowercase=False,
binary=True)), # processed cluster features
),
)),
)
return preprocessor
DATA_DIR_PORTABLE = "C:\\Users\\T149900\\ml_mercari\\"
DATA_DIR_BASEMENT = "D:\\mercari\\"
DATA_DIR = DATA_DIR_PORTABLE
df = pd.read_table(DATA_DIR + "train.tsv");
q = df[:10]
q_test = df[10:13]
q.price.isnull().sum()
q_test.price.isnull().sum()
vectorizer = make_union(
on_field(['shipping', 'item_condition_id'], PlussOneStage() ),
n_jobs=1)
p = on_field('item_condition_id', PlussOneStage())
p.fit(q)
X = p.transform(q)
X_test = p.transform(q_test)
X_train = vectorizer.fit_transform(preprocess(q)).astype(np.float32)
X_test = vectorizer.transform(preprocess(q_test)).astype(np.float32)
def get_feature_union():
return make_union(WaveletApprx(), SpatialFt(), DepthFt(),)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
imputer = Imputer(strategy="median")
imputer.fit(training_features)
training_features = imputer.transform(training_features)
testing_features = imputer.transform(testing_features)
# Score on the training set was:0.9576386406262041
exported_pipeline = make_pipeline(
make_union(
StackingEstimator(estimator=make_pipeline(
MaxAbsScaler(),
RandomForestClassifier(bootstrap=False,
criterion="entropy",
max_features=0.3,
min_samples_leaf=5,
min_samples_split=11,
n_estimators=100))),
FunctionTransformer(copy)),
OneHotEncoder(minimum_fraction=0.25, sparse=False),
ExtraTreesClassifier(bootstrap=True,
criterion="entropy",
max_features=0.6500000000000001,
min_samples_leaf=1,
min_samples_split=5,
n_estimators=100))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import StandardScaler
from tpot.builtins import StackingEstimator
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
make_union(
FunctionTransformer(copy),
FunctionTransformer(copy)
),
StandardScaler(),
GradientBoostingClassifier(learning_rate=0.1, max_depth=10, min_samples_leaf=15, min_samples_split=15, n_estimators=100, subsample=0.4)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
def test_missing_indicator_with_imputer(X, missing_values, X_trans_exp):
trans = make_union(
SimpleImputer(missing_values=missing_values, strategy='most_frequent'),
MissingIndicator(missing_values=missing_values))
X_trans = trans.fit_transform(X)
assert_array_equal(X_trans, X_trans_exp)
return [tok.strip().lower() for tok in re.findall(r'\w', re.sub(r'\d', ' ', text))]
train_data = []
train_lbls = []
for line in open("./data/data_set.json", "r"):
data = json.loads(line)
train_data.append(data["data"])
train_lbls.append(int(data["label"] == "EN")) # Класс 1 - Английский, класс 0 - Тагальский
#Создаём 3 извлекателя признаков (по словам, по парам букв, по буквам)
word_vectoriser = TfidfVectorizer(tokenizer=word_tokenizer)
ends_vectoriser = TfidfVectorizer(tokenizer=ending_tokenizer)
char_vectorizer = TfidfVectorizer(tokenizer=char_tokenizer)
#Группируем наши feature extractor-ы и создаём конвейер
feature_extractor = make_union(word_vectoriser, ends_vectoriser, char_vectorizer)
pipeline = make_pipeline(feature_extractor, LinearSVC(C=2))
#pipeline.fit(train_data[::2], train_lbls[::2])
#print(f1_score(train_lbls[1::2], pipeline.predict(train_data[1::2])))
scores = cross_validation.cross_val_score(pipeline, train_data, train_lbls, cv=5, scoring='f1_macro')
print(mean(scores))
def predict_with_best_parameters(train, test, class_names):
submission = pd.DataFrame.from_dict({"id": test["id"]})
scores = []
#assuming best parameters are already found by grid_search
best_parameters = {
"toxic": {
'clf': {
'C': 10.0
},
'vect': {
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter,
'use_idf': False
},
},
"severe_toxic": {
'clf': {
'C': 1.0
},
'vect': {
'max_features': 30000,
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter
},
},
"obscene": {
'clf': {
'C': 10.0
},
'vect': {
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter,
'use_idf': False
},
},
"threat": {
'clf': {
'C': 10.0
},
'vect': {
'max_features': 30000,
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter
},
},
"insult": {
'clf': {
'C': 1.0
},
'vect': {
'max_features': 30000,
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter
},
},
"identity_hate": {
'clf': {
'C': 1.0
},
'vect': {
'max_features': 30000,
'ngram_range': (1, 1),
'stop_words': None,
'tokenizer': tokenizer_porter
}
}
}
for target_class in class_names:
print("\nWorking with target_class: ", target_class)
train_target = train[target_class]
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents="unicode",
analyzer="word",
**best_parameters[target_class]["vect"])
char_vectorizer = TfidfVectorizer(sublinear_tf=True,
strip_accents="unicode",
analyzer="char",
ngram_range=(1, 4),
max_features=30000)
vectorizer = make_union(word_vectorizer, char_vectorizer, n_jobs=2)
classifier = LogisticRegression(solver="sag",
n_jobs=4,
**best_parameters[target_class]["clf"])
lr_vectorizer = Pipeline([('vect', vectorizer), ('clf', classifier)])
start_time = time.time()
cv_score = np.mean(
cross_val_score(lr_vectorizer,
train_text,
train_target,
cv=3,
scoring="roc_auc",
n_jobs=3))
end_time = time.time()
print("CV time:", end_time - start_time)
scores.append(cv_score)
print("cv_score for class {} : {}".format(target_class, cv_score))
start_time = time.time()
lr_vectorizer.fit(train_text, train_target)
end_time = time.time()
print("fitting time: ", end_time - start_time)
start_time = time.time()
submission[target_class] = lr_vectorizer.predict_proba(test_text)[:, 1]
#predict_proba returns two columns. The first is the probability that
# the sample is of class 0, the second is the probability that the
# sample is of class 1. So we only need to slice the second column.
end_time = time.time()
print("Prediction time: ", end_time - start_time)
print("total CV score is: {}".format(np.mean(scores)))
submission.to_csv("submission_gs_best_est_union_and_piped.csv",
index=False)
pass
from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import FunctionTransformer
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE',
delimiter='COLUMN_SEPARATOR',
dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'),
axis=1)
training_features, testing_features, training_classes, testing_classes = \
train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
make_union(
VotingClassifier([("est",
LogisticRegression(C=0.0001,
dual=False,
penalty="l1"))]),
FunctionTransformer(lambda X: X)),
GradientBoostingClassifier(learning_rate=1.0,
max_features=1.0,
n_estimators=500))
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
#56
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from tpot.builtins import StackingEstimator
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Score on the training set was:0.8194243156199679
exported_pipeline = make_pipeline(
make_union(
FunctionTransformer(copy),
make_union(
make_pipeline(
SelectPercentile(score_func=f_classif, percentile=83),
StandardScaler()
),
FunctionTransformer(copy)
)
),
PCA(iterated_power=5, svd_solver="randomized"),
LinearSVC(C=0.001, dual=True, loss="hinge", penalty="l2", tol=0.001)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import MaxAbsScaler
from tpot.builtins import StackingEstimator
from xgboost import XGBRegressor
from sklearn.preprocessing import FunctionTransformer
from copy import copy
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
# Score on the training set was:-0.00023017383030155843
exported_pipeline = make_pipeline(
make_union(MaxAbsScaler(), FunctionTransformer(copy)),
XGBRegressor(learning_rate=0.1,
max_depth=10,
min_child_weight=1,
n_estimators=100,
nthread=1,
subsample=0.8))
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.feature_selection import VarianceThreshold
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=None)
# Average CV score on the training set was: 0.6814117647058824
exported_pipeline = make_pipeline(
make_union(
StackingEstimator(estimator=KNeighborsClassifier(n_neighbors=23, p=1, weights="distance")),
make_pipeline(
StackingEstimator(estimator=ExtraTreesClassifier(bootstrap=False, criterion="gini", max_features=0.9500000000000001, min_samples_leaf=12, min_samples_split=16, n_estimators=100)),
StackingEstimator(estimator=MultinomialNB(alpha=10.0,True)),
VarianceThreshold(threshold=0.01)
)
),
StackingEstimator(estimator=MultinomialNB(alpha=0.1,False)),
RandomForestClassifier(bootstrap=True, criterion="gini", max_features=0.1, min_samples_leaf=4, min_samples_split=14, n_estimators=100)
)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
