'clf__penalty': ['l2'],
'clf__tol': uniform(0.0001, 0.01),
'clf__verbose': [0],
}
param_grid.update(text_grid)
cv_kwargs = dict(n_iter=30,
scoring=None,
fit_params=None,
n_jobs=4,
iid=True,
refit=True,
cv=None,
verbose=3,
pre_dispatch='2*n_jobs',
error_score=0)
if __name__ == "__main__":
for level, (X, y), n_classes_ in iterate_heirarchy():
gridsearch = CustomGridSearch(pipeline,
param_grid,
n_classes_,
random=True,
**cv_kwargs)
gridsearch \
.set_data(X, y) \
.fit() \
.generate_report(name="LinearSVC", level=level, notes="") \
.write_to_mongo()
}
param_grid.update(text_grid)
cv_kwargs = dict(
n_iter=50,
scoring=None,
fit_params=None,
n_jobs=4,
iid=True,
refit=True,
cv=None,
verbose=3,
pre_dispatch='2*n_jobs',
error_score=0
)
if __name__ == "__main__":
print(param_grid)
for level, (X, y), n_classes_ in iterate_heirarchy():
gridsearch = CustomGridSearch(pipeline, param_grid, n_classes_, random=True, **cv_kwargs)
gridsearch.set_data(X, y)\
.fit()\
.generate_report(
name="LogisticRegression_LDA_NORM",
level=level,
notes="added 1300 + topics")\
.write_to_mongo()
