def test_explorer():
print(pd.datetime.now())
n_rows = 200
n_cluster = 10
n_simplequestions = 200
n_hardquestions = 200
Xst = getXst(nrows=n_rows)
y_true = getytrue(Xst=Xst)
print(pd.datetime.now(), 'data loaded')
connector = DfConnector(scorer=Pipeline(
steps=[('scores', FeatureUnion(_score_list)
), ('imputer',
SimpleImputer(strategy='constant', fill_value=0))]))
explorer = Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster),
n_simple=n_simplequestions,
n_hard=n_hardquestions)
connector.fit(X=Xst)
# Xsm is the transformed output from the connector, i.e. the score matrix
Xsm = connector.transform(X=Xst)
print(pd.datetime.now(), 'score ok')
# ixc is the index corresponding to the score matrix
ixc = Xsm.index
ix_simple = explorer.ask_simple(X=pd.DataFrame(data=Xsm, index=ixc),
fit_cluster=True)
print(pd.datetime.now(),
'length of ix_simple {}'.format(ix_simple.shape[0]))
sbs_simple = connector.getsbs(X=Xst, on_ix=ix_simple)
print('***** SBS SIMPLE ******')
print(sbs_simple.sample(5))
print('*****')
y_simple = y_true.loc[ix_simple]
ix_hard = explorer.ask_hard(X=pd.DataFrame(data=Xsm, index=ixc),
y=y_simple)
print(pd.datetime.now(), 'length of ix_hard {}'.format(ix_hard.shape[0]))
sbs_hard = connector.getsbs(X=Xst, on_ix=ix_hard)
print(sbs_hard.sample(5))
print('*****')
y_train = y_true.loc[ix_simple.union(ix_hard)]
print('length of y_train: {}'.format(y_train.shape[0]))
explorer.fit(X=pd.DataFrame(data=Xsm, index=ixc),
y=y_train,
fit_cluster=True)
print('results of pred:\n',
pd.Series(explorer.predict(X=Xsm)).value_counts())
print('****')
def test_pruning():
print('start', pd.datetime.now())
n_rows = 200
n_cluster = 10
n_simplequestions = 200
n_hardquestions = 200
Xst = getXst(nrows=n_rows)
y_true = getytrue(Xst=Xst)
print(pd.datetime.now(), 'data loaded')
connector = DfConnector(scorer=Pipeline(
steps=[('scores', FeatureUnion(_score_list)
), ('imputer',
SimpleImputer(strategy='constant', fill_value=0))]))
explorer = Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster),
n_simple=n_simplequestions,
n_hard=n_hardquestions)
connector.fit(X=Xst)
# Xst is the transformed output from the connector, i.e. the score matrix
Xsm = connector.transform(X=Xst)
print(pd.datetime.now(), 'score ok')
# ixc is the index corresponding to the score matrix
ixc = Xsm.index
y_true = y_true.loc[ixc]
ix_simple = explorer.ask_simple(X=pd.DataFrame(data=Xsm, index=ixc),
fit_cluster=True)
ix_hard = explorer.ask_hard(X=pd.DataFrame(data=Xsm, index=ixc),
y=y_true.loc[ix_simple])
ix_train = ix_simple.union(ix_hard)
print('number of training samples:{}'.format(ix_train.shape[0]))
X_train = pd.DataFrame(data=Xsm, index=ixc).loc[ix_train]
y_train = y_true.loc[ix_train]
explorer.fit(X=X_train, y=y_train, fit_cluster=True)
y_pruning = explorer.predict(X=Xsm)
y_pruning = pd.Series(data=y_pruning, name='y_pruning', index=ixc)
y_pred = (y_pruning > 0).astype(int)
precision = precision_score(y_true=y_true, y_pred=y_pred)
recall = recall_score(y_true=y_true, y_pred=y_pred)
accuracy = balanced_accuracy_score(y_true=y_true, y_pred=y_pred)
print('***\npruning scores:\n')
print('precision score:{}\n recall score:{}\n balanced accuracy score:{}'.
format(precision, recall, accuracy))
('scores', FeatureUnion(_lr_score_list)),
('imputer', SimpleImputer(strategy='constant', fill_value=0))]
)
)
### Fit the cluster non-supervizes
explorer = Explorer(clustermixin=KBinsCluster(n_clusters=n_cluster), n_simple=n_simplequestions, n_hard=n_pointedquestions)
Xst = connector.fit_transform(X=Xst)
explorer.fit_cluster(X=Xst)
### Ask simple questions
ix_simple = explorer.ask_simple(X=Xst)
Sbs_simple = connector.getsbs(X=Xst, on_ix=ix_simple)
y_simple = y_true.loc[ix_simple]
### Fit the cluser with supervized data
explorer.fit(X=Xst, y=y_simple, fit_cluster=False)
### Ask hard (pointed) questions
ix_hard = explorer.ask_hard(X=Xst, y=y_simple)
Sbs_hard = connector.getsbs(X=Xst, on_ix=ix_hard)
y_hard = y_true.loc[ix_hard]
### Obtain the results of the labels
y_questions = y_true.loc[ix_hard.union(ix_simple)]
## Define the pruning pipe
pipe = PruningPipe(
connector=connector,
pruningclf=explorer,
sbsmodel=FeatureUnion(transformer_list=_sbs_score_list),