Merge pull request #111 from EpistasisLab/dev

Dev

tpot2/_version.py

@@ -1 +1 @@
-__version__ = '0.1.3-alpha'
+__version__ = '0.1.4-alpha'

tpot2/config/autoqtl_builtins.py

@@ -1,6 +1,7 @@
 from tpot2.builtin_modules import genetic_encoders
 from tpot2.builtin_modules import feature_encoding_frequency_selector
 import sklearn
+import numpy as np
 
 def params_FeatureEncodingFrequencySelector(trial, name=None):
  return {
@@ -21,4 +22,3 @@ def make_genetic_encoders_config_dictionary():
  genetic_encoders.UnderDominanceEncoder : {},
  genetic_encoders.OverDominanceEncoder : {},
  }
-

tpot2/config/classifiers.py

@@ -14,9 +14,11 @@
 
 from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
 
+import numpy as np
 
 
-def params_LogisticRegression(trial, name=None):
+
+def params_LogisticRegression(trial, random_state=None, name=None):
  params = {}
  params['solver'] = trial.suggest_categorical(name=f'solver_{name}',
  choices=[f'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'])
@@ -40,22 +42,22 @@ def params_LogisticRegression(trial, name=None):
  'C': params['C'],
  'n_jobs': 1,
  'max_iter': 1000,
+ 'random_state': random_state
  }
  return param_grid
 
 
 def params_KNeighborsClassifier(trial, name=None, n_samples=10):
  return {
- #'n_neighbors': trial.suggest_int(f'n_neighbors_{name}', 1, 20 ), #TODO: set as a function of the number of samples
- 'n_neighbors': trial.suggest_int(f'n_neighbors_{name}', 1, n_samples, log=True ), #TODO: set as a function of the number of samples
+ 'n_neighbors': trial.suggest_int(f'n_neighbors_{name}', 1, n_samples, log=True ),
  'weights': trial.suggest_categorical(f'weights_{name}', ['uniform', 'distance']),
  'p': trial.suggest_int('p', 1, 3),
- 'metric': trial.suggest_categorical(f'metric_{name}', ['euclidean', 'minkowski']),
+ 'metric': str(trial.suggest_categorical(f'metric_{name}', ['euclidean', 'minkowski'])),
  'n_jobs': 1,
  }
 
 
-def params_DecisionTreeClassifier(trial, name=None):
+def params_DecisionTreeClassifier(trial, random_state=None, name=None):
  return {
  'criterion': trial.suggest_categorical(f'criterion_{name}', ['gini', 'entropy']),
  'max_depth': trial.suggest_int(f'max_depth_{name}', 1, 11),
@@ -65,10 +67,11 @@ def params_DecisionTreeClassifier(trial, name=None):
  'min_weight_fraction_leaf': 0.0,
  'max_features': trial.suggest_categorical(f'max_features_{name}', [ 'sqrt', 'log2']),
  'max_leaf_nodes': None,
+ 'random_state': random_state
  }
 
 
-def params_SVC(trial, name=None):
+def params_SVC(trial, random_state=None, name=None):
  return {
  'kernel': trial.suggest_categorical(name=f'kernel_{name}', choices=['poly', 'rbf', 'linear', 'sigmoid']),
  'C': trial.suggest_float(f'C_{name}', 1e-4, 25, log=True),
@@ -79,16 +82,18 @@ def params_SVC(trial, name=None):
  'max_iter': 3000,
  'tol': 0.005,
  'probability': True,
+ 'random_state': random_state
  }
 
 
-def params_LinearSVC(trial, name=None):
+def params_LinearSVC(trial, random_state=None, name=None):
+
  penalty = trial.suggest_categorical(name=f'penalty_{name}', choices=['l1', 'l2'])
  if penalty == 'l1':
  loss = 'squared_hinge'
  else:
  loss = trial.suggest_categorical(name=f'loss_{name}', choices=['hinge', 'squared_hinge'])
- 
+
  if loss == 'hinge' and penalty == 'l2':
  dual = True
  else:
@@ -97,12 +102,13 @@ def params_LinearSVC(trial, name=None):
  return {
  'penalty': penalty,
  'loss': loss,
- 'dual': dual, 
+ 'dual': dual,
  'C': trial.suggest_float(f'C_{name}', 1e-4, 25, log=True),
+ 'random_state': random_state
  }
 
 
-def params_RandomForestClassifier(trial, name=None):
+def params_RandomForestClassifier(trial, random_state=None, name=None):
  params = {
  'n_estimators': 100,
  'criterion': trial.suggest_categorical(name=f'criterion_{name}', choices=['gini', 'entropy']),
@@ -111,16 +117,18 @@ def params_RandomForestClassifier(trial, name=None):
  'min_samples_split': trial.suggest_int(f'min_samples_split_{name}', 2, 20),
  'min_samples_leaf': trial.suggest_int(f'min_samples_leaf_{name}', 1, 20),
  'n_jobs': 1,
+ 'random_state': random_state
  }
  return params
 
 
-def params_GradientBoostingClassifier(trial,n_classes=None, name=None):
+def params_GradientBoostingClassifier(trial, random_state=None, n_classes=None, name=None):
+
  if n_classes is not None and n_classes > 2:
  loss = 'log_loss'
  else:
  loss = trial.suggest_categorical(name=f'loss_{name}', choices=['log_loss', 'exponential'])
- 
+
  params = {
  'n_estimators': 100,
  'loss': loss,
@@ -131,11 +139,12 @@ def params_GradientBoostingClassifier(trial,n_classes=None, name=None):
  'max_features': trial.suggest_float(f'max_features_{name}', 0.1, 1.0),
  'max_depth': trial.suggest_int(f'max_depth_{name}', 1, 10),
  'tol': 1e-4,
+ 'random_state': random_state
  }
  return params
 
 
-def params_XGBClassifier(trial, name=None):
+def params_XGBClassifier(trial, random_state=None, name=None):
  return {
  'learning_rate': trial.suggest_float(f'learning_rate_{name}', 1e-3, 1, log=True),
  'subsample': trial.suggest_float(f'subsample_{name}', 0.1, 1.0),
@@ -145,10 +154,11 @@ def params_XGBClassifier(trial, name=None):
  'max_depth': trial.suggest_int(f'max_depth_{name}', 1, 11),
  'n_jobs': 1,
  #'use_label_encoder' : True,
+ 'random_state': random_state
  }
 
 
-def params_LGBMClassifier(trial, name=None):
+def params_LGBMClassifier(trial, random_state=None, name=None):
  params = {
  'objective': 'binary',
  'metric': 'binary_logloss',
@@ -159,13 +169,15 @@ def params_LGBMClassifier(trial, name=None):
  'deterministic': True,
  'force_row_wise': True,
  'n_jobs': 1,
+ 'random_state': random_state
+
  }
  if 2 ** params['max_depth'] > params['num_leaves']:
  params['num_leaves'] = 2 ** params['max_depth']
  return params
 
 
-def params_ExtraTreesClassifier(trial, name=None):
+def params_ExtraTreesClassifier(trial, random_state=None, name=None):
  params = {
  'n_estimators': 100,
  'criterion': trial.suggest_categorical(name=f'criterion_{name}', choices=["gini", "entropy"]),
@@ -174,10 +186,11 @@ def params_ExtraTreesClassifier(trial, name=None):
  'min_samples_leaf': trial.suggest_int(f'min_samples_leaf_{name}', 1, 21, step=1),
  'bootstrap': trial.suggest_categorical(f'bootstrap_{name}', [True, False]),
  'n_jobs': 1,
+ 'random_state': random_state
  }
  return params
 
-def params_SGDClassifier(trial, name=None):
+def params_SGDClassifier(trial, random_state=None, name=None):
  params = {
  'loss': trial.suggest_categorical(f'loss_{name}', ['log_loss', 'modified_huber',]),
  'penalty': 'elasticnet',
@@ -188,20 +201,21 @@ def params_SGDClassifier(trial, name=None):
  'eta0': trial.suggest_float(f'eta0_{name}', 0.01, 1.0),
  'power_t': trial.suggest_float(f'power_t_{name}', 1e-5, 100.0, log=True),
  'n_jobs': 1,
+ 'random_state': random_state
  }
 
  return params
 
-def params_MLPClassifier_tpot(trial, name=None):
+def params_MLPClassifier_tpot(trial, random_state=None, name=None):
  params = {
  'alpha': trial.suggest_float(f'alpha_{name}', 1e-4, 1e-1, log=True),
- 'learning_rate_init': trial.suggest_float(f'learning_rate_init_{name}', 1e-3, 1., log=True)
+ 'learning_rate_init': trial.suggest_float(f'learning_rate_init_{name}', 1e-3, 1., log=True),
+ 'random_state': random_state
  }
 
  return params
 
 def params_MLPClassifier_large(trial, name=None):
-
  n_layers = trial.suggest_int(f'n_layers_{name}', 2, 3)
  layers = []
  for i in range(n_layers):
@@ -215,7 +229,8 @@ def params_MLPClassifier_large(trial, name=None):
  'max_iter' : 10000
  }
 
- return params 
+ return params
+
 
 def params_BernoulliNB(trial, name=None):
  params = {
@@ -233,24 +248,23 @@ def params_MultinomialNB(trial, name=None):
  return params
 
 
-def make_classifier_config_dictionary(n_samples=10, n_classes=None):
+def make_classifier_config_dictionary(random_state=None, n_samples=10, n_classes=None):
  n_samples = min(n_samples,100) #TODO optimize this
 
  return {
- LogisticRegression: params_LogisticRegression,
- DecisionTreeClassifier: params_DecisionTreeClassifier,
+ LogisticRegression: partial(params_LogisticRegression, random_state=random_state),
+ DecisionTreeClassifier: partial(params_DecisionTreeClassifier, random_state=random_state),
  KNeighborsClassifier: partial(params_KNeighborsClassifier,n_samples=n_samples),
- GradientBoostingClassifier: partial(params_GradientBoostingClassifier, n_classes=n_classes),
- ExtraTreesClassifier:params_ExtraTreesClassifier,
- RandomForestClassifier: params_RandomForestClassifier,
- SGDClassifier:params_SGDClassifier,
+ GradientBoostingClassifier: partial(params_GradientBoostingClassifier, random_state=random_state, n_classes=n_classes),
+ ExtraTreesClassifier: partial(params_ExtraTreesClassifier, random_state=random_state),
+ RandomForestClassifier: partial(params_RandomForestClassifier, random_state=random_state),
+ SGDClassifier: partial(params_SGDClassifier, random_state=random_state),
  GaussianNB: {},
  BernoulliNB: params_BernoulliNB,
  MultinomialNB: params_MultinomialNB,
- XGBClassifier: params_XGBClassifier,
- #LinearSVC: params_LinearSVC,
- SVC: params_SVC,
+ XGBClassifier: partial(params_XGBClassifier, random_state=random_state),
+ #LinearSVC: partial(params_LinearSVC, random_state=random_state),
+ SVC: partial(params_SVC, random_state=random_state),
  #: params_LGBMClassifier, # logistic regression and SVM/SVC are just special cases of this one? remove?
- MLPClassifier: params_MLPClassifier_tpot,
+ MLPClassifier: partial(params_MLPClassifier_tpot, random_state=random_state),
  }
-

tpot2/config/classifiers_sklearnex.py

@@ -4,14 +4,18 @@
 from sklearnex.svm import NuSVC
 from sklearnex.linear_model import LogisticRegression
 
+import numpy as np
 
-def params_RandomForestClassifier(trial, name=None):
+from functools import partial
+
+def params_RandomForestClassifier(trial, random_state=None, name=None):
  return {
  'n_estimators': 100,
  'bootstrap': trial.suggest_categorical(name=f'bootstrap_{name}', choices=[True, False]),
  'min_samples_split': trial.suggest_int(f'min_samples_split_{name}', 2, 20),
  'min_samples_leaf': trial.suggest_int(f'min_samples_leaf_{name}', 1, 20),
  'n_jobs': 1,
+ 'random_state': random_state
  }
 
 def params_KNeighborsClassifier(trial, name=None, n_samples=10):
@@ -21,7 +25,7 @@ def params_KNeighborsClassifier(trial, name=None, n_samples=10):
  'weights': trial.suggest_categorical(f'weights_{name}', ['uniform', 'distance']),
  }
 
-def params_LogisticRegression(trial, name=None):
+def params_LogisticRegression(trial, random_state=None, name=None):
  params = {}
  params['dual'] = False
  params['penalty'] = 'l2'
@@ -38,9 +42,10 @@ def params_LogisticRegression(trial, name=None):
  'dual': params['dual'],
  'C': trial.suggest_float(f'C_{name}', 1e-4, 1e4, log=True),
  'max_iter': 1000,
+ 'random_state': random_state
  }
 
-def params_SVC(trial, name=None):
+def params_SVC(trial, random_state=None, name=None):
  return {
  'kernel': trial.suggest_categorical(name=f'kernel_{name}', choices=['poly', 'rbf', 'linear', 'sigmoid']),
  'C': trial.suggest_float(f'C_{name}', 1e-4, 25, log=True),
@@ -49,9 +54,10 @@ def params_SVC(trial, name=None):
  'max_iter': 3000,
  'tol': 0.005,
  'probability': True,
+ 'random_state': random_state
  }
 
-def params_NuSVC(trial, name=None):
+def params_NuSVC(trial, random_state=None, name=None):
  return {
  'nu': trial.suggest_float(f'subsample_{name}', 0.05, 1.0),
  'kernel': trial.suggest_categorical(name=f'kernel_{name}', choices=['poly', 'rbf', 'linear', 'sigmoid']),
@@ -61,13 +67,14 @@ def params_NuSVC(trial, name=None):
  'max_iter': 3000,
  'tol': 0.005,
  'probability': True,
+ 'random_state': random_state
  }
 
-def make_sklearnex_classifier_config_dictionary(n_samples=10, n_classes=None):
+def make_sklearnex_classifier_config_dictionary(random_state=None, n_samples=10, n_classes=None):
  return {
- RandomForestClassifier: params_RandomForestClassifier,
- KNeighborsClassifier: params_KNeighborsClassifier,
- LogisticRegression: params_LogisticRegression,
- SVC: params_SVC,
- NuSVC: params_NuSVC,
- }
+ RandomForestClassifier: partial(params_RandomForestClassifier, random_state=random_state),
+ KNeighborsClassifier: partial(params_KNeighborsClassifier, n_samples=n_samples),
+ LogisticRegression: partial(params_LogisticRegression, random_state=random_state),
+ SVC: partial(params_SVC, random_state=random_state),
+ NuSVC: partial(params_NuSVC, random_state=random_state),
+ }