[GRASS-SVN] r70076 - grass-addons/grass7/raster/r.randomforest

[svn_grass at osgeo.org]

Author: spawley
Date: 2016-12-14 09:58:33 -0800 (Wed, 14 Dec 2016)
New Revision: 70076

Removed:
   grass-addons/grass7/raster/r.randomforest/ml_classifiers.py
Log:
'r.randomforest refactoring'

Deleted: grass-addons/grass7/raster/r.randomforest/ml_classifiers.py
===================================================================
--- grass-addons/grass7/raster/r.randomforest/ml_classifiers.py	2016-12-14 17:56:10 UTC (rev 70075)
+++ grass-addons/grass7/raster/r.randomforest/ml_classifiers.py	2016-12-14 17:58:33 UTC (rev 70076)
@@ -1,109 +0,0 @@
-#!/usr/bin/env python
-
-from sklearn.linear_model import LogisticRegression
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
-from sklearn.naive_bayes import GaussianNB
-from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
-from sklearn.ensemble import GradientBoostingClassifier
-from sklearn.ensemble import GradientBoostingRegressor
-from sklearn.svm import SVC
-
-
-def model_classifiers(estimator, random_state, class_weight,
-                      C, max_depth, max_features,
-                      min_samples_split, min_samples_leaf,
-                      n_estimators, subsample, learning_rate):
-
-    classifiers = {
-        'SVC': SVC(C=C, probability=True, random_state=random_state),
-        'LogisticRegression': LogisticRegression(C=C, class_weight=class_weight,
-                                                 random_state=random_state),
-        'DecisionTreeClassifier': DecisionTreeClassifier(max_depth=max_depth,
-                                                         max_features=max_features,
-                                                         min_samples_split=min_samples_split,
-                                                         min_samples_leaf=min_samples_leaf,
-                                                         random_state=random_state,
-                                                         class_weight=class_weight),
-        'DecisionTreeRegressor': DecisionTreeRegressor(max_features=max_features,
-                                                       min_samples_split=min_samples_split,
-                                                       min_samples_leaf=min_samples_leaf,
-                                                       random_state=random_state),
-        'RandomForestClassifier': RandomForestClassifier(n_estimators=n_estimators,
-                                                         class_weight=class_weight,
-                                                         max_features=max_features,
-                                                         min_samples_split=min_samples_split,
-                                                         random_state=random_state,
-                                                         n_jobs=-1,
-                                                         oob_score=True),
-        'RandomForestRegressor': RandomForestRegressor(n_estimators=n_estimators,
-                                                       max_features=max_features,
-                                                       min_samples_split=min_samples_split,
-                                                       random_state=random_state,
-                                                       n_jobs=-1,
-                                                       oob_score=True),
-        'GradientBoostingClassifier': GradientBoostingClassifier(learning_rate=learning_rate,
-                                                                 n_estimators=n_estimators,
-                                                                 max_depth=max_depth,
-                                                                 min_samples_split=min_samples_split,
-                                                                 min_samples_leaf=min_samples_leaf,
-                                                                 subsample=subsample,
-                                                                 max_features=max_features,
-                                                                 random_state=random_state),
-        'GradientBoostingRegressor': GradientBoostingRegressor(learning_rate=learning_rate,
-                                                               n_estimators=n_estimators,
-                                                               max_depth=max_depth,
-                                                               min_samples_split=min_samples_split,
-                                                               min_samples_leaf=min_samples_leaf,
-                                                               subsample=subsample,
-                                                               max_features=max_features,
-                                                               random_state=random_state),
-        'GaussianNB': GaussianNB(),
-        'LinearDiscriminantAnalysis': LinearDiscriminantAnalysis(),
-        'QuadraticDiscriminantAnalysis': QuadraticDiscriminantAnalysis(),
-    }
-
-    SVCOpts = {'C': [1, 10, 100], 'shrinking': [True, False]}
-    LogisticRegressionOpts = {'C': [1, 10, 100, 1000]}
-    DecisionTreeOpts = {'max_depth': [2, 4, 6, 8, 10, 20],
-                        'max_features': ['sqrt', 'log2', None],
-                        'min_samples_split': [2, 0.01, 0.05, 0.1, 0.25]}
-    RandomForestOpts = {'max_features': ['sqrt', 'log2', None]}
-    GradientBoostingOpts = {'learning_rate': [0.01, 0.02, 0.05, 0.1],
-                            'max_depth': [3, 4, 6, 10],
-                            'max_features': ['sqrt', 'log2', None],
-                            'n_estimators': [50, 100, 150, 250, 500]}
-
-    param_grids = {
-        'SVC': SVCOpts,
-        'LogisticRegression': LogisticRegressionOpts,
-        'DecisionTreeClassifier': DecisionTreeOpts,
-        'DecisionTreeRegressor': DecisionTreeOpts,
-        'RandomForestClassifier': RandomForestOpts,
-        'RandomForestRegressor': RandomForestOpts,
-        'GradientBoostingClassifier': GradientBoostingOpts,
-        'GradientBoostingRegressor': GradientBoostingOpts,
-        'GaussianNB': {},
-        'LinearDiscriminantAnalysis': {},
-        'QuadraticDiscriminantAnalysis': {},
-    }
-    
-    # define classifier
-    clf = classifiers[estimator]
-    params = param_grids[estimator]
-    
-    # classification or regression
-    if estimator == 'LogisticRegression' \
-        or estimator == 'DecisionTreeClassifier' \
-        or estimator == 'RandomForestClassifier' \
-        or estimator == 'GradientBoostingClassifier' \
-        or estimator == 'GaussianNB' \
-        or estimator == 'LinearDiscriminantAnalysis' \
-        or estimator == 'QuadraticDiscriminantAnalysis' \
-            or estimator == 'SVC':
-            mode = 'classification'
-    else:
-        mode = 'regression'
-
-    return (clf, params, mode)